From 9e5f303b6ac420cf4d9d34176e8c18798f19a1fd Mon Sep 17 00:00:00 2001 From: vmoens Date: Fri, 15 Sep 2023 13:14:44 -0400 Subject: [PATCH 001/178] init --- torchrl/data/utils.py | 11 ++++---- torchrl/envs/gym_like.py | 54 ++++++++++++++++++------------------ torchrl/envs/libs/gym.py | 59 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 92 insertions(+), 32 deletions(-) diff --git a/torchrl/data/utils.py b/torchrl/data/utils.py index 5c2dd863848..2442007747e 100644 --- a/torchrl/data/utils.py +++ b/torchrl/data/utils.py @@ -228,14 +228,15 @@ def _check_only_one_entry( recursive: bool = True, ): found_entry = False - for value in spec.values(): + for key, value in spec.items(): if isinstance(value, CompositeSpec): _check_only_one_entry(value, error, recursive) else: - if not found_entry: - found_entry = True - else: - raise error + if key == "done": + if found_entry: + raise error + else: + found_entry = True class CloudpickleWrapper(object): diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index 289bb731278..6fc4f2dc920 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -133,10 +133,13 @@ def read_action(self, action): """ return self.action_spec.to_numpy(action, safe=False) - def read_done(self, done): + def read_done(self, done, termination, truncation): """Done state reader. - Reads a done state and returns a tuple containing: + In TorchRL, done means that a trajectory is terminated (we do not support the + terminated signal). Truncated means the trajectory has been interrupted. + + This method returns: - a done state to be set in the environment - a boolean value indicating whether the frame_skip loop should be broken @@ -144,7 +147,9 @@ def read_done(self, done): done (np.ndarray, boolean or other format): done state obtained from the environment """ - return done, done + if termination is not None: + return termination, truncation, done + return done, truncation, done def read_reward(self, reward): """Reads the reward and maps it to the reward space. @@ -187,26 +192,12 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: reward = 0 for _ in range(self.wrapper_frame_skip): - obs, _reward, done, *info = self._output_transform( + obs, _reward, termination, truncation, done, info = self._output_transform( self._env.step(action_np) ) if isinstance(obs, list) and len(obs) == 1: # Until gym 0.25.2 we had rendered frames returned in lists of length 1 obs = obs[0] - if len(info) == 2: - # gym 0.26 - truncation, info = info - done = done | truncation - elif len(info) == 1: - info = info[0] - elif len(info) == 0: - info = None - else: - raise ValueError( - "the environment output is expected to be either" - "obs, reward, done, truncation, info (gym >= 0.26) or " - f"obs, reward, done, info. Got info with types = ({[type(x) for x in info]})" - ) if _reward is None: _reward = self.reward_spec.zero() @@ -217,7 +208,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: isinstance(done, np.ndarray) and not len(done) ): done = torch.tensor([done]) - done, do_break = self.read_done(done) + done, truncation, do_break = self.read_done(done, termination, truncation) if do_break: break @@ -226,8 +217,15 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: if reward is None: reward = torch.tensor(np.nan).expand(self.reward_spec.shape) + obs_dict[self.reward_key] = reward - obs_dict[self.done_key] = done + + obs_dict[self.done_keys[0]] = done + if truncation is not None: + if "truncated" in self.done_keys: + obs_dict["truncated"] = truncation + else: + raise KeyError("Could not find truncated key") tensordict_out = TensorDict(obs_dict, batch_size=tensordict.batch_size) @@ -242,7 +240,7 @@ def _reset( reset_data = self._env.reset(**kwargs) if not isinstance(reset_data, tuple): reset_data = (reset_data,) - obs, *other = self._output_transform(reset_data) + obs, *other = reset_data info = None if len(other) == 1: info = other[0] @@ -265,13 +263,15 @@ def _reset( tensordict_out = tensordict_out.to(self.device, non_blocking=True) return tensordict_out + @abc.abstractmethod def _output_transform(self, step_outputs_tuple: Tuple) -> Tuple: - """To be overwritten when step_outputs differ from Tuple[Observation: Union[np.ndarray, dict], reward: Number, done:Bool].""" - if not isinstance(step_outputs_tuple, tuple): - raise TypeError( - f"Expected step_outputs_tuple type to be Tuple but got {type(step_outputs_tuple)}" - ) - return step_outputs_tuple + """A method to read the output of the env step. + + Must return a tuple: (obs, reward, termination, truncation, done, info). + truncated and terminal can be None, in which case they are discarded. + If done is None, truncated and terminal mustn't be None. + """ + ... def set_info_dict_reader(self, info_dict_reader: BaseInfoDictReader) -> GymLikeEnv: """Sets an info_dict_reader function. diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index 51fc6588217..e42f8924b20 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -576,10 +576,69 @@ def _make_specs(self, env: "gym.Env", batch_size=None) -> None: # noqa: F821 observation_spec = observation_spec.expand( *batch_size, *observation_spec.shape ) + self.done_spec = self._make_done_spec() self.action_spec = action_spec self.reward_spec = reward_spec self.observation_spec = observation_spec + @implement_for("gymnasium", "0.26", None) + def _make_done_spec(self): + return CompositeSpec( + {"done": DiscreteTensorSpec( + 2, + dtype=torch.bool, + device=self.device, + shape=(*self.batch_size, 1) + ), "truncated": DiscreteTensorSpec( + 2, + dtype=torch.bool, + device=self.device, + shape=(*self.batch_size, 1) + )}, shape=self.batch_size + ) + + @implement_for("gym", "0.26", None) + def _make_done_spec(self): + return CompositeSpec( + {"done": DiscreteTensorSpec( + 2, + dtype=torch.bool, + device=self.device, + shape=(*self.batch_size, 1) + ), "truncated": DiscreteTensorSpec( + 2, + dtype=torch.bool, + device=self.device, + shape=(*self.batch_size, 1) + )}, shape=self.batch_size + ) + + @implement_for("gym", None, "0.26") + def _make_done_spec(self): + return CompositeSpec( + {"done": DiscreteTensorSpec( + 2, + dtype=torch.bool, + device=self.device, + shape=(*self.batch_size, 1) + )}, shape=self.batch_size, + ) + + @implement_for("gym", "0.26", None) + def _output_transform(self, step_outputs_tuple): + observations, reward, termination, truncation, info = step_outputs_tuple + return (observations, reward, termination, truncation, termination | truncation, info) + + @implement_for("gym", None, "0.26") + def _output_transform(self, step_outputs_tuple): + observations, reward, done, info = step_outputs_tuple + return (observations, reward, None, None, done, info) + + @implement_for("gymnasium", "0.26", None) + def _output_transform(self, step_outputs_tuple): + observations, reward, termination, truncation, info = step_outputs_tuple + return (observations, reward, termination, truncation, termination | truncation, info) + def _init_env(self): self.reset() From 03b6089e633e2fae1e1f146aefee3339455fda02 Mon Sep 17 00:00:00 2001 From: vmoens Date: Fri, 15 Sep 2023 15:59:43 -0400 Subject: [PATCH 002/178] fix --- test/test_libs.py | 2 +- torchrl/envs/libs/robohive.py | 7 ++++++- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index bb8444e22b4..3ffe08fb19a 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -1868,7 +1868,7 @@ def test_collector(self, task, parallel): class TestRoboHive: - @pytest.mark.parametrize("envname", RoboHiveEnv.env_list) + @pytest.mark.parametrize("envname", RoboHiveEnv.available_envs) @pytest.mark.parametrize("from_pixels", [True, False]) def test_robohive(self, envname, from_pixels): if any(substr in envname for substr in ("_vr3m", "_vrrl", "_vflat", "_vvc1s")): diff --git a/torchrl/envs/libs/robohive.py b/torchrl/envs/libs/robohive.py index 655a4eee16e..9c0e8e41f23 100644 --- a/torchrl/envs/libs/robohive.py +++ b/torchrl/envs/libs/robohive.py @@ -77,9 +77,14 @@ def CURR_DIR(cls): else: return None + @_classproperty + def available_envs(cls): + if not _has_robohive: + return + yield from cls.env_list + @classmethod def register_envs(cls): - if not _has_robohive: raise ImportError( "Cannot load robohive from the current virtual environment." From 512b5961401d9e784274c3014e120a863cdf9db8 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 17 Sep 2023 02:02:26 -0400 Subject: [PATCH 003/178] amend --- test/test_libs.py | 54 ++++++++++++++++++++- test/test_utils.py | 79 ++++++++++++++++++++++++++---- torchrl/_utils.py | 82 +++++++++++++++++++++---------- torchrl/envs/libs/gym.py | 101 ++++++++++++++++++++++----------------- 4 files changed, 237 insertions(+), 79 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index 3ffe08fb19a..86297725629 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -66,7 +66,7 @@ GymEnv, GymWrapper, MOGymEnv, - MOGymWrapper, + MOGymWrapper, set_gym_backend, ) from torchrl.envs.libs.habitat import _has_habitat, HabitatEnv from torchrl.envs.libs.jumanji import _has_jumanji, JumanjiEnv @@ -323,6 +323,58 @@ def test_one_hot_and_categorical(self): # noqa: F811 # versions. return + @implement_for("gym", None, "0.26") + @pytest.mark.parametrize("wrapper", [True, False]) + def test_gym_output_num(self, wrapper): + # gym has 4 outputs, no truncation + import gym + + if wrapper: + env = GymWrapper(gym.make(PENDULUM_VERSIONED)) + else: + with set_gym_backend("gym"): + env = GymEnv(PENDULUM_VERSIONED) + assert "truncated" not in env.done_keys + assert "done" in env.done_keys + check_env_specs(env) + + @implement_for("gym", "0.26", None) + @pytest.mark.parametrize("wrapper", [True, False]) + def test_gym_output_num(self, wrapper): + # gym has 5 outputs, with truncation + import gym + + if wrapper: + env = GymWrapper(gym.make(PENDULUM_VERSIONED)) + else: + with set_gym_backend("gym"): + env = GymEnv(PENDULUM_VERSIONED) + assert "truncated" in env.done_keys + assert "done" in env.done_keys + check_env_specs(env) + + if wrapper: + # let's further test with a wrapper that exposes the env with old API + env = GymWrapper(gym.make(PENDULUM_VERSIONED, apply_api_compatibility=True)) + assert "truncated" not in env.done_keys + assert "done" in env.done_keys + check_env_specs(env) + + @implement_for("gymnasium", "0.27", None) + @pytest.mark.parametrize("wrapper", [True, False]) + def test_gym_output_num(self, wrapper): + # gym has 5 outputs, with truncation + import gymnasium as gym + + if wrapper: + env = GymWrapper(gym.make(PENDULUM_VERSIONED)) + else: + with set_gym_backend("gymnasium"): + env = GymEnv(PENDULUM_VERSIONED) + assert "truncated" in env.done_keys + assert "done" in env.done_keys + check_env_specs(env) + @implement_for("gym", None, "0.26") def _make_gym_environment(env_name): # noqa: F811 diff --git a/test/test_utils.py b/test/test_utils.py index 87a16b89216..451993052af 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -5,6 +5,7 @@ import argparse import os import sys +from copy import copy from importlib import import_module from unittest import mock @@ -13,7 +14,7 @@ from torchrl._utils import get_binary_env_var, implement_for -from torchrl.envs.libs.gym import gym_backend, set_gym_backend +from torchrl.envs.libs.gym import gym_backend, GymWrapper, set_gym_backend @pytest.mark.parametrize("value", ["True", "1", "true"]) @@ -136,24 +137,27 @@ def test_implement_for(): def test_implement_for_missing_module(): - msg = r"Supported version of 'missing_module' has not been found." + msg = r"Supported version of 'test_utils.implement_for_test_functions.missing_module' has not been found." with pytest.raises(ModuleNotFoundError, match=msg): implement_for_test_functions.missing_module() def test_implement_for_missing_version(): - msg = r"Supported version of 'missing_version' has not been found." + msg = r"Supported version of 'test_utils.implement_for_test_functions.missing_version' has not been found." with pytest.raises(ModuleNotFoundError, match=msg): implement_for_test_functions.missing_version() def test_implement_for_reset(): assert implement_for_test_functions.select_correct_version() == "0.3+" - _impl = implement_for._implementations - assert _impl is implement_for._implementations - implement_for.reset() + _impl = copy(implement_for._implementations) + name = implement_for.func_name(implement_for_test_functions.select_correct_version) + for setter in implement_for._setters: + if implement_for.func_name(setter.fn) == name and setter.fn() != "0.3+": + setter.module_set() + assert implement_for_test_functions.select_correct_version() != "0.3+" + implement_for.reset(_impl) assert implement_for_test_functions.select_correct_version() == "0.3+" - assert _impl is not implement_for._implementations @pytest.mark.parametrize( @@ -235,13 +239,19 @@ def test_set_gym_environments( expected_fn_gymnasium = impfor.fn with set_gym_backend(gymnasium): - assert _utils_internal._set_gym_environments == expected_fn_gymnasium + assert ( + _utils_internal._set_gym_environments == expected_fn_gymnasium + ), expected_fn_gym with set_gym_backend(gym): - assert _utils_internal._set_gym_environments == expected_fn_gym + assert ( + _utils_internal._set_gym_environments == expected_fn_gym + ), expected_fn_gymnasium with set_gym_backend(gymnasium): - assert _utils_internal._set_gym_environments == expected_fn_gymnasium + assert ( + _utils_internal._set_gym_environments == expected_fn_gymnasium + ), expected_fn_gym def test_set_gym_environments_no_version_gymnasium_found(): @@ -254,6 +264,8 @@ def test_set_gym_environments_no_version_gymnasium_found(): import gymnasium + assert gymnasium.__version__ == "0.26.0" + # this version of gymnasium does not exist in implement_for # therefore, set_gym_backend will not set anything and raise an ImportError. msg = f"could not set anything related to gym backend {gymnasium_name} with version={gymnasium_version}." @@ -281,6 +293,53 @@ def test_set_gym_backend_types(): assert gym_backend() == gym +# we check that the order where these funs are defined won't affect which is called +@implement_for("torch", "1.0", "1.5") +def torch_foo(): + return 0 + + +@implement_for("torch", "2.0") +def torch_foo(): + return 1 + + +@implement_for("torch", None, "1.0") +def torch_foo(): + return 1 + + +def test_set_gym_nested(): + mock_gym = uncallable(mock.MagicMock()) + mock_gym.__version__ = "0.21.0" + mock_gym.__name__ = "gym" + sys.modules["gym"] = mock_gym + + mock_gymnasium = uncallable(mock.MagicMock()) + mock_gymnasium.__version__ = "0.28.0" + mock_gymnasium.__name__ = "gymnasium" + sys.modules["gymnasium"] = mock_gymnasium + + import gym + import gymnasium + + assert torch_foo() == 1 + with set_gym_backend(gym): + GymWrapper._output_transform(None, (1, 2, 3, 4)) # would break with gymnasium + assert torch_foo() == 1 + with set_gym_backend(gymnasium): + GymWrapper._output_transform(None, (1, 2, 3, 4, 5)) # would break with gym + assert torch_foo() == 1 + GymWrapper._output_transform(None, (1, 2, 3, 4)) # would break with gymnasium + with set_gym_backend("gym"): + GymWrapper._output_transform(None, (1, 2, 3, 4)) # would break with gymnasium + assert torch_foo() == 1 + with set_gym_backend("gymnasium"): + GymWrapper._output_transform(None, (1, 2, 3, 4, 5)) # would break with gym + assert torch_foo() == 1 + GymWrapper._output_transform(None, (1, 2, 3, 4)) # would break with gymnasium + + if __name__ == "__main__": args, unknown = argparse.ArgumentParser().parse_known_args() pytest.main([__file__, "--capture", "no", "--exitfirst"] + unknown) diff --git a/torchrl/_utils.py b/torchrl/_utils.py index 8d590b05210..b4d86bb0487 100644 --- a/torchrl/_utils.py +++ b/torchrl/_utils.py @@ -2,6 +2,7 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from __future__ import annotations import collections @@ -18,7 +19,7 @@ from distutils.util import strtobool from functools import wraps from importlib import import_module -from typing import Any, Callable, cast, TypeVar, Union +from typing import Any, Callable, cast, Dict, TypeVar, Union import numpy as np import torch @@ -269,20 +270,44 @@ def check_version(version, from_version, to_version): @staticmethod def get_class_that_defined_method(f): """Returns the class of a method, if it is defined, and None otherwise.""" - return f.__globals__.get(f.__qualname__.split(".")[0], None) + out = f.__globals__.get(f.__qualname__.split(".")[0], None) + return out - @property - def func_name(self): - return self.fn.__name__ + @classmethod + def func_name(cls, fn): + # produces a name like torchrl.module.Class.method or torchrl.module.function + first = str(fn).split(".")[0][len(" None: # noqa: F821 self.reward_spec = reward_spec self.observation_spec = observation_spec - @implement_for("gymnasium", "0.26", None) + @implement_for("gymnasium", "0.27", None) def _make_done_spec(self): return CompositeSpec( - {"done": DiscreteTensorSpec( - 2, - dtype=torch.bool, - device=self.device, - shape=(*self.batch_size, 1) - ), "truncated": DiscreteTensorSpec( - 2, - dtype=torch.bool, - device=self.device, - shape=(*self.batch_size, 1) - )}, shape=self.batch_size + { + "done": DiscreteTensorSpec( + 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) + ), + "truncated": DiscreteTensorSpec( + 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) + ), + }, + shape=self.batch_size, ) @implement_for("gym", "0.26", None) def _make_done_spec(self): return CompositeSpec( - {"done": DiscreteTensorSpec( - 2, - dtype=torch.bool, - device=self.device, - shape=(*self.batch_size, 1) - ), "truncated": DiscreteTensorSpec( - 2, - dtype=torch.bool, - device=self.device, - shape=(*self.batch_size, 1) - )}, shape=self.batch_size + { + "done": DiscreteTensorSpec( + 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) + ), + "truncated": DiscreteTensorSpec( + 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) + ), + }, + shape=self.batch_size, ) @implement_for("gym", None, "0.26") def _make_done_spec(self): return CompositeSpec( - {"done": DiscreteTensorSpec( - 2, - dtype=torch.bool, - device=self.device, - shape=(*self.batch_size, 1) - )}, shape=self.batch_size, + { + "done": DiscreteTensorSpec( + 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) + ) + }, + shape=self.batch_size, ) @implement_for("gym", "0.26", None) def _output_transform(self, step_outputs_tuple): observations, reward, termination, truncation, info = step_outputs_tuple - return (observations, reward, termination, truncation, termination | truncation, info) + return ( + observations, + reward, + termination, + truncation, + termination | truncation, + info, + ) @implement_for("gym", None, "0.26") def _output_transform(self, step_outputs_tuple): observations, reward, done, info = step_outputs_tuple return (observations, reward, None, None, done, info) - @implement_for("gymnasium", "0.26", None) + @implement_for("gymnasium", "0.27", None) def _output_transform(self, step_outputs_tuple): observations, reward, termination, truncation, info = step_outputs_tuple - return (observations, reward, termination, truncation, termination | truncation, info) + return ( + observations, + reward, + termination, + truncation, + termination | truncation, + info, + ) def _init_env(self): self.reset() From 3fa8ac0195bff34049a988a57be6fd948ec13fa9 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 17 Sep 2023 12:16:50 -0400 Subject: [PATCH 004/178] amend --- test/test_libs.py | 18 +++++++++++------- test/test_utils.py | 4 ++-- torchrl/_utils.py | 6 ++++-- torchrl/envs/gym_like.py | 5 ++--- torchrl/envs/libs/gym.py | 19 +++++++++++++++---- 5 files changed, 34 insertions(+), 18 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index 86297725629..016e5bd206c 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -66,7 +66,8 @@ GymEnv, GymWrapper, MOGymEnv, - MOGymWrapper, set_gym_backend, + MOGymWrapper, + set_gym_backend, ) from torchrl.envs.libs.habitat import _has_habitat, HabitatEnv from torchrl.envs.libs.jumanji import _has_jumanji, JumanjiEnv @@ -340,7 +341,7 @@ def test_gym_output_num(self, wrapper): @implement_for("gym", "0.26", None) @pytest.mark.parametrize("wrapper", [True, False]) - def test_gym_output_num(self, wrapper): + def test_gym_output_num(self, wrapper): # noqa: F811 # gym has 5 outputs, with truncation import gym @@ -355,14 +356,17 @@ def test_gym_output_num(self, wrapper): if wrapper: # let's further test with a wrapper that exposes the env with old API - env = GymWrapper(gym.make(PENDULUM_VERSIONED, apply_api_compatibility=True)) - assert "truncated" not in env.done_keys - assert "done" in env.done_keys - check_env_specs(env) + from gym.wrappers.compatibility import EnvCompatibility + + with pytest.raises( + ValueError, + match="GymWrapper does not support the gym.wrapper.compatibility.EnvCompatibility", + ): + GymWrapper(EnvCompatibility(gym.make("CartPole-v1"))) @implement_for("gymnasium", "0.27", None) @pytest.mark.parametrize("wrapper", [True, False]) - def test_gym_output_num(self, wrapper): + def test_gym_output_num(self, wrapper): # noqa: F811 # gym has 5 outputs, with truncation import gymnasium as gym diff --git a/test/test_utils.py b/test/test_utils.py index 451993052af..b4f8fecc4a5 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -300,12 +300,12 @@ def torch_foo(): @implement_for("torch", "2.0") -def torch_foo(): +def torch_foo(): # noqa: F811 return 1 @implement_for("torch", None, "1.0") -def torch_foo(): +def torch_foo(): # noqa: F811 return 1 diff --git a/torchrl/_utils.py b/torchrl/_utils.py index b4d86bb0487..06cd0cc0436 100644 --- a/torchrl/_utils.py +++ b/torchrl/_utils.py @@ -367,8 +367,10 @@ def unsupported(*args, **kwargs): def reset(cls, setters_dict: Dict[str, implement_for] = None): """Resets the setters in setter_dict. - setter_dict is a copy of implementations. We just need to iterate through its - values and call""" + ``setter_dict`` is a copy of implementations. We just need to iterate through its + values and call :meth:`~.module_set` for each. + + """ if VERBOSE: print("resetting implement_for") if setters_dict is None: diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index 6fc4f2dc920..7dd7fd32ea8 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -145,7 +145,8 @@ def read_done(self, done, termination, truncation): Args: done (np.ndarray, boolean or other format): done state obtained from the environment - + termination (bool or None): termination signal. + truncation (bool or None): truncation signal. """ if termination is not None: return termination, truncation, done @@ -247,8 +248,6 @@ def _reset( source = self.read_obs(obs) - # if self.done_key not in source: - # source[self.done_key] = self.done_spec.zero() tensordict_out = TensorDict( source=source, batch_size=self.batch_size, diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index 46378588387..7711a6fce4c 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -389,6 +389,14 @@ def __init__(self, env=None, categorical_action_encoding=False, **kwargs): self._seed_calls_reset = None self._categorical_action_encoding = categorical_action_encoding if "env" in kwargs: + if "EnvCompatibility" in str( + kwargs["env"] + ): # a hacky way of knowing if EnvCompatibility is part of the wrappers of env + raise ValueError( + "GymWrapper does not support the gym.wrapper.compatibility.EnvCompatibility wrapper. " + "If this feature is needed, detail your use case in an issue of " + "https://github.com/pytorch/rl/issues." + ) libname = self.get_library_name(kwargs["env"]) with set_gym_backend(libname): super().__init__(**kwargs) @@ -601,7 +609,7 @@ def _make_done_spec(self): ) @implement_for("gym", "0.26", None) - def _make_done_spec(self): + def _make_done_spec(self): # noqa: F811 return CompositeSpec( { "done": DiscreteTensorSpec( @@ -615,7 +623,7 @@ def _make_done_spec(self): ) @implement_for("gym", None, "0.26") - def _make_done_spec(self): + def _make_done_spec(self): # noqa: F811 return CompositeSpec( { "done": DiscreteTensorSpec( @@ -625,6 +633,9 @@ def _make_done_spec(self): shape=self.batch_size, ) + @implement_for( + "gym", + ) @implement_for("gym", "0.26", None) def _output_transform(self, step_outputs_tuple): observations, reward, termination, truncation, info = step_outputs_tuple @@ -638,12 +649,12 @@ def _output_transform(self, step_outputs_tuple): ) @implement_for("gym", None, "0.26") - def _output_transform(self, step_outputs_tuple): + def _output_transform(self, step_outputs_tuple): # noqa: F811 observations, reward, done, info = step_outputs_tuple return (observations, reward, None, None, done, info) @implement_for("gymnasium", "0.27", None) - def _output_transform(self, step_outputs_tuple): + def _output_transform(self, step_outputs_tuple): # noqa: F811 observations, reward, termination, truncation, info = step_outputs_tuple return ( observations, From 162aa6e4a6b15d724ecbdabdf5c1de926d028a6c Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 17 Sep 2023 12:41:01 -0400 Subject: [PATCH 005/178] amend --- torchrl/envs/gym_like.py | 14 +++++++------- torchrl/envs/libs/dm_control.py | 10 ++++++++-- torchrl/envs/libs/gym.py | 12 ++++++++++++ torchrl/envs/libs/jumanji.py | 7 ++++++- 4 files changed, 33 insertions(+), 10 deletions(-) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index 7dd7fd32ea8..be079861fb3 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -181,6 +181,8 @@ def read_obs( (key,) = itertools.islice(self.observation_spec.keys(True, True), 1) observations = {key: observations} for key, val in observations.items(): + print(val) + print(self.observation_spec, key) observations[key] = self.observation_spec[key].encode( val, ignore_device=True ) @@ -238,13 +240,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: def _reset( self, tensordict: Optional[TensorDictBase] = None, **kwargs ) -> TensorDictBase: - reset_data = self._env.reset(**kwargs) - if not isinstance(reset_data, tuple): - reset_data = (reset_data,) - obs, *other = reset_data - info = None - if len(other) == 1: - info = other[0] + obs, info = self._reset_output_transform(self._env.reset(**kwargs)) source = self.read_obs(obs) @@ -272,6 +268,10 @@ def _output_transform(self, step_outputs_tuple: Tuple) -> Tuple: """ ... + @abc.abstractmethod + def _reset_output_transform(self, reset_outputs_tuple: Tuple) -> Tuple: + ... + def set_info_dict_reader(self, info_dict_reader: BaseInfoDictReader) -> GymLikeEnv: """Sets an info_dict_reader function. diff --git a/torchrl/envs/libs/dm_control.py b/torchrl/envs/libs/dm_control.py index 8407964a452..2cb95c61768 100644 --- a/torchrl/envs/libs/dm_control.py +++ b/torchrl/envs/libs/dm_control.py @@ -235,14 +235,20 @@ def _set_seed(self, _seed: Optional[int]) -> Optional[int]: def _output_transform( self, timestep_tuple: Tuple["TimeStep"] # noqa: F821 - ) -> Tuple[np.ndarray, float, bool]: + ) -> Tuple[np.ndarray, float, bool, bool, dict]: if type(timestep_tuple) is not tuple: timestep_tuple = (timestep_tuple,) reward = timestep_tuple[0].reward done = False # dm_control envs are non-terminating observation = timestep_tuple[0].observation - return observation, reward, done + truncation = None + info = {} + return observation, reward, done, truncation, info + + def _reset_output_transform(self, reset_data): + observation, reward, done, truncation, info = self._output_transform(reset_data) + return observation, info def __repr__(self) -> str: return ( diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index 7711a6fce4c..d1a2a022dd9 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -633,6 +633,18 @@ def _make_done_spec(self): # noqa: F811 shape=self.batch_size, ) + @implement_for("gym", "0.26", None) + def _reset_output_transform(self, reset_data): + return reset_data + + @implement_for("gymnasium", "0.27", None) + def _reset_output_transform(self, reset_data): # noqa: F811 + return reset_data + + @implement_for("gym", None, "0.26") + def _reset_output_transform(self, reset_data): # noqa: F811 + return reset_data, {} + @implement_for( "gym", ) diff --git a/torchrl/envs/libs/jumanji.py b/torchrl/envs/libs/jumanji.py index 2e18d3f39ec..e746a121efd 100644 --- a/torchrl/envs/libs/jumanji.py +++ b/torchrl/envs/libs/jumanji.py @@ -4,7 +4,7 @@ # LICENSE file in the root directory of this source tree. import importlib.util -from typing import Dict, Optional, Union +from typing import Dict, Optional, Union, Tuple import numpy as np import torch @@ -346,6 +346,11 @@ def _reset( return tensordict_out + def _output_transform(self, step_outputs_tuple: Tuple) -> Tuple: + ... + + def _reset_output_transform(self, reset_outputs_tuple: Tuple) -> Tuple: + ... class JumanjiEnv(JumanjiWrapper): """Jumanji environment wrapper. From 2c5cddc282730c5f1fc46ea174667e522bc99908 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 17 Sep 2023 15:27:18 -0400 Subject: [PATCH 006/178] amend --- test/test_collector.py | 8 +++++++- torchrl/envs/gym_like.py | 2 -- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/test/test_collector.py b/test/test_collector.py index 21862cf8297..e525ebcaff1 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -53,6 +53,7 @@ from torchrl.envs.transforms import TransformedEnv, VecNorm from torchrl.envs.utils import _replace_last from torchrl.modules import Actor, LSTMNet, OrnsteinUhlenbeckProcessWrapper, SafeModule +from packaging import version # torch.set_default_dtype(torch.double) _os_is_windows = sys.platform == "win32" @@ -946,7 +947,6 @@ def make_env(): collector.shutdown() dummy_env.close() - @pytest.mark.skipif(not _has_gym, reason="test designed with GymEnv") @pytest.mark.parametrize( "collector_class", @@ -1036,6 +1036,12 @@ def test_collector_output_keys( } if split_trajs: keys.add(("collector", "mask")) + + from torchrl.envs.libs.gym import gym_backend + + if "gymnasium" in str(gym_backend()) or gym_backend().__version__ >= version.parse("0.26"): + keys.add(("next", "truncated")) + keys.add("truncated") b = next(iter(collector)) assert set(b.keys(True)) == keys diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index be079861fb3..7a62040dbe9 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -181,8 +181,6 @@ def read_obs( (key,) = itertools.islice(self.observation_spec.keys(True, True), 1) observations = {key: observations} for key, val in observations.items(): - print(val) - print(self.observation_spec, key) observations[key] = self.observation_spec[key].encode( val, ignore_device=True ) From c703a024cc1c1c87e8b5f82a74753db2bdb98145 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 17 Sep 2023 15:31:52 -0400 Subject: [PATCH 007/178] amend --- test/test_collector.py | 7 +++++-- torchrl/envs/libs/jumanji.py | 3 ++- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/test/test_collector.py b/test/test_collector.py index e525ebcaff1..236d4e76da5 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -26,6 +26,7 @@ MultiKeyCountingEnvPolicy, NestedCountingEnv, ) +from packaging import version from tensordict.nn import TensorDictModule from tensordict.tensordict import assert_allclose_td, TensorDict @@ -53,7 +54,6 @@ from torchrl.envs.transforms import TransformedEnv, VecNorm from torchrl.envs.utils import _replace_last from torchrl.modules import Actor, LSTMNet, OrnsteinUhlenbeckProcessWrapper, SafeModule -from packaging import version # torch.set_default_dtype(torch.double) _os_is_windows = sys.platform == "win32" @@ -947,6 +947,7 @@ def make_env(): collector.shutdown() dummy_env.close() + @pytest.mark.skipif(not _has_gym, reason="test designed with GymEnv") @pytest.mark.parametrize( "collector_class", @@ -1039,7 +1040,9 @@ def test_collector_output_keys( from torchrl.envs.libs.gym import gym_backend - if "gymnasium" in str(gym_backend()) or gym_backend().__version__ >= version.parse("0.26"): + if "gymnasium" in str(gym_backend()) or gym_backend().__version__ >= version.parse( + "0.26" + ): keys.add(("next", "truncated")) keys.add("truncated") b = next(iter(collector)) diff --git a/torchrl/envs/libs/jumanji.py b/torchrl/envs/libs/jumanji.py index e746a121efd..5ccdcd0f2c0 100644 --- a/torchrl/envs/libs/jumanji.py +++ b/torchrl/envs/libs/jumanji.py @@ -4,7 +4,7 @@ # LICENSE file in the root directory of this source tree. import importlib.util -from typing import Dict, Optional, Union, Tuple +from typing import Dict, Optional, Tuple, Union import numpy as np import torch @@ -352,6 +352,7 @@ def _output_transform(self, step_outputs_tuple: Tuple) -> Tuple: def _reset_output_transform(self, reset_outputs_tuple: Tuple) -> Tuple: ... + class JumanjiEnv(JumanjiWrapper): """Jumanji environment wrapper. From 822f42ac088f7ddaaf7a97d843b54bb8c0794446 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 18 Sep 2023 03:56:19 -0400 Subject: [PATCH 008/178] amend --- torchrl/envs/gym_like.py | 8 ++++++-- torchrl/envs/libs/dm_control.py | 13 ++++++++++--- torchrl/envs/libs/gym.py | 3 --- 3 files changed, 16 insertions(+), 8 deletions(-) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index b22b23d4110..173c61a7b7f 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -149,8 +149,12 @@ def read_done(self, done, termination, truncation): truncation (bool or None): truncation signal. """ if termination is not None: - return termination, truncation, done - return done, truncation, done.any() if not isinstance(done, bool) else done + done = termination + return ( + termination, + truncation, + done.any() if not isinstance(done, bool) else done, + ) def read_reward(self, reward): """Reads the reward and maps it to the reward space. diff --git a/torchrl/envs/libs/dm_control.py b/torchrl/envs/libs/dm_control.py index 2cb95c61768..660e37d619a 100644 --- a/torchrl/envs/libs/dm_control.py +++ b/torchrl/envs/libs/dm_control.py @@ -242,12 +242,19 @@ def _output_transform( done = False # dm_control envs are non-terminating observation = timestep_tuple[0].observation - truncation = None + truncation = termination = None info = {} - return observation, reward, done, truncation, info + return observation, reward, termination, truncation, done, info def _reset_output_transform(self, reset_data): - observation, reward, done, truncation, info = self._output_transform(reset_data) + ( + observation, + reward, + termination, + truncation, + done, + info, + ) = self._output_transform(reset_data) return observation, info def __repr__(self) -> str: diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index cec7734f9cb..b4226713f17 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -739,9 +739,6 @@ def _reset_output_transform(self, reset_data): # noqa: F811 def _reset_output_transform(self, reset_data): # noqa: F811 return reset_data, {} - @implement_for( - "gym", - ) @implement_for("gym", "0.26", None) def _output_transform(self, step_outputs_tuple): observations, reward, termination, truncation, info = step_outputs_tuple From db8e0c1e47ed7f9d8972cc31bf8ae1a2b6fd3e62 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 18 Sep 2023 04:06:55 -0400 Subject: [PATCH 009/178] amend --- test/test_libs.py | 2 +- torchrl/envs/gym_like.py | 33 ++++++++++++++++++++------------- torchrl/envs/libs/isaacgym.py | 6 +++--- 3 files changed, 24 insertions(+), 17 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index 5d26fc650b2..97e4739b450 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -588,7 +588,7 @@ def test_dmcontrol(self, env_name, task, frame_skip, from_pixels, pixels_only): assert_allclose_td(rollout0, rollout2) def test_faketd(self, env_name, task, frame_skip, from_pixels, pixels_only): - if from_pixels and (not torch.has_cuda or not torch.cuda.device_count()): + if from_pixels and not torch.cuda.device_count(): raise pytest.skip("no cuda device") env = DMControlEnv( diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index 173c61a7b7f..e5661ef9ef7 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -133,27 +133,34 @@ def read_action(self, action): """ return self.action_spec.to_numpy(action, safe=False) - def read_done(self, done, termination, truncation): + def read_done(self, gym_done: bool, termination: bool | None=None, truncation: bool | None=None) -> Tuple[bool, bool, bool]: """Done state reader. - In TorchRL, done means that a trajectory is terminated (we do not support the - terminated signal). Truncated means the trajectory has been interrupted. - - This method returns: - - a done state to be set in the environment - - a boolean value indicating whether the frame_skip loop should be broken + In torchrl, a `"done"` signal means that a trajectory is terminated + (what is referred to as `termination` in gymnasium). + Truncated means the trajectory has been interrupted. Args: - done (np.ndarray, boolean or other format): done state obtained from the environment - termination (bool or None): termination signal. - truncation (bool or None): truncation signal. + gym_done (np.ndarray, boolean or other format): done state obtained from the environment + termination (bool or None): termination signal. Defaults to `None`. + truncation (bool or None): truncation signal. Defaults to `None`. + + Returns: a tuple with 3 boolean values, + - a done state to be set in the environment. + - a truncated state, possibly None if no truncation is provided. + - a boolean value indicating whether the frame_skip loop should be broken. + """ if termination is not None: - done = termination + gym_done = termination + do_break = gym_done | truncation + else: + do_break = gym_done + return ( - termination, + gym_done, truncation, - done.any() if not isinstance(done, bool) else done, + do_break.any() if not isinstance(do_break, bool) else do_break, ) def read_reward(self, reward): diff --git a/torchrl/envs/libs/isaacgym.py b/torchrl/envs/libs/isaacgym.py index eb9ba7084b6..3aac8516b90 100644 --- a/torchrl/envs/libs/isaacgym.py +++ b/torchrl/envs/libs/isaacgym.py @@ -103,7 +103,7 @@ def read_action(self, action): """ return action - def read_done(self, done): + def read_done(self, gym_done): """Done state reader. Reads a done state and returns a tuple containing: @@ -111,10 +111,10 @@ def read_done(self, done): - a boolean value indicating whether the frame_skip loop should be broken Args: - done (np.ndarray, boolean or other format): done state obtained from the environment + gym_done (np.ndarray, boolean or other format): done state obtained from the environment """ - return done.bool(), done.any() + return gym_done.bool(), gym_done.any() def read_reward(self, total_reward, step_reward): """Reads a reward and the total reward so far (in the frame skip loop) and returns a sum of the two. From 9c7e1a5dae8569a74696e693ae37825e6c129cb9 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 18 Sep 2023 05:15:52 -0400 Subject: [PATCH 010/178] amend --- test/test_libs.py | 62 +++++++++++++++++++++++++++-------- torchrl/envs/gym_like.py | 7 +++- torchrl/envs/libs/__init__.py | 2 +- torchrl/envs/libs/gym.py | 14 ++++++++ 4 files changed, 70 insertions(+), 15 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index 97e4739b450..44ba597bcd8 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -364,12 +364,16 @@ def test_vecenvs_env(self, envname): with set_gym_backend("gymnasium"): env = GymEnv(envname, num_envs=2, from_pixels=False) - check_env_specs(env) - rollout = env.rollout(100, break_when_any_done=False) - for obs_key in env.observation_spec.keys(True, True): - rollout_consistency_assertion( - rollout, done_key="done", observation_key=obs_key - ) + import gymnasium + + assert env.get_library_name(env._env) is gymnasium + # rollouts can be executed without decorator + check_env_specs(env) + rollout = env.rollout(100, break_when_any_done=False) + for obs_key in env.observation_spec.keys(True, True): + rollout_consistency_assertion( + rollout, done_key="done", observation_key=obs_key + ) @implement_for("gym", "0.18", "0.27.0") @pytest.mark.parametrize( @@ -404,16 +408,21 @@ def test_vecenvs_wrapper(self, envname): # noqa: F811 def test_vecenvs_env(self, envname): # noqa: F811 with set_gym_backend("gym"): env = GymEnv(envname, num_envs=2, from_pixels=False) - check_env_specs(env) - rollout = env.rollout(100, break_when_any_done=False) - for obs_key in env.observation_spec.keys(True, True): - rollout_consistency_assertion( - rollout, done_key="done", observation_key=obs_key - ) + import gym + + assert env.get_library_name(env._env) is gym + # rollouts can be executed without decorator + check_env_specs(env) + rollout = env.rollout(100, break_when_any_done=False) + for obs_key in env.observation_spec.keys(True, True): + rollout_consistency_assertion( + rollout, done_key="done", observation_key=obs_key + ) if envname != "CartPole-v1": with set_gym_backend("gym"): env = GymEnv(envname, num_envs=2, from_pixels=True) - check_env_specs(env) + # rollouts can be executed without decorator + check_env_specs(env) @implement_for("gym", None, "0.18") @pytest.mark.parametrize( @@ -488,6 +497,33 @@ def test_gym_output_num(self, wrapper): # noqa: F811 assert "done" in env.done_keys check_env_specs(env) + def test_gym_gymnasium_parallel(self): + # tests that both gym and gymnasium work with wrappers without + # decorating with set_gym_backend during execution + if importlib.util.find_spec("gym") is not None: + import gym + + old_api = gym.__version__ < version.parse("0.26") + make_fun = lambda: EnvCreator( + lambda: GymWrapper(gym.make(PENDULUM_VERSIONED)) + ) + elif importlib.util.find_spec("gymnasium") is not None: + import gymnasium + + old_api = False + make_fun = lambda: EnvCreator( + lambda: GymWrapper(gymnasium.make(PENDULUM_VERSIONED)) + ) + else: + raise ImportError # unreachable under pytest.skipif + penv = ParallelEnv(2, make_fun) + rollout = penv.rollout(2) + if old_api: + assert "truncated" not in rollout.keys() + else: + assert "truncated" in rollout.keys() + check_env_specs(penv) + @implement_for("gym", None, "0.26") def _make_gym_environment(env_name): # noqa: F811 diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index e5661ef9ef7..b7952d6ad65 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -133,7 +133,12 @@ def read_action(self, action): """ return self.action_spec.to_numpy(action, safe=False) - def read_done(self, gym_done: bool, termination: bool | None=None, truncation: bool | None=None) -> Tuple[bool, bool, bool]: + def read_done( + self, + gym_done: bool, + termination: bool | None = None, + truncation: bool | None = None, + ) -> Tuple[bool, bool, bool]: """Done state reader. In torchrl, a `"done"` signal means that a trajectory is terminated diff --git a/torchrl/envs/libs/__init__.py b/torchrl/envs/libs/__init__.py index 13dd7224626..250030e9d14 100644 --- a/torchrl/envs/libs/__init__.py +++ b/torchrl/envs/libs/__init__.py @@ -6,7 +6,7 @@ from .brax import BraxEnv, BraxWrapper from .dm_control import DMControlEnv, DMControlWrapper from .envpool import MultiThreadedEnv, MultiThreadedEnvWrapper -from .gym import GymEnv, GymWrapper +from .gym import GymEnv, GymWrapper, MOGymEnv, MOGymWrapper from .habitat import HabitatEnv from .isaacgym import IsaacGymEnv, IsaacGymWrapper from .jumanji import JumanjiEnv, JumanjiWrapper diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index b4226713f17..ec0fe912b7c 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -465,6 +465,20 @@ def __init__(self, env=None, categorical_action_encoding=False, **kwargs): super().__init__(**kwargs) else: super().__init__(**kwargs) + self._post_init() + + def _post_init(self): + # writes the functions that are gym-version specific to the instance + # once and for all. This is aimed at avoiding the need of decorating code + # with set_gym_backend + allowing for parallel execution (which would + # be troublesome when both an old version of gym and recent gymnasium + # are present within the same virtual env). + # These calls seemingly do nothing but they actually get rid of the @implement_for decorator. + # We execute them within the set_gym_backend context manager to make sure we get + # the right implementation. + with set_gym_backend(self.get_library_name(self._env)): + self._reset_output_transform = self._reset_output_transform + self._output_transform = self._output_transform @property def _is_batched(self): From 99bcc4a6e334437f4061ced9bea23134bc478e31 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 18 Sep 2023 05:25:37 -0400 Subject: [PATCH 011/178] amend --- test/test_libs.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index 44ba597bcd8..3df4ba1f628 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -503,15 +503,15 @@ def test_gym_gymnasium_parallel(self): if importlib.util.find_spec("gym") is not None: import gym - old_api = gym.__version__ < version.parse("0.26") - make_fun = lambda: EnvCreator( + old_api = version.parse(gym.__version__) < version.parse("0.26") + make_fun = EnvCreator( lambda: GymWrapper(gym.make(PENDULUM_VERSIONED)) ) elif importlib.util.find_spec("gymnasium") is not None: import gymnasium old_api = False - make_fun = lambda: EnvCreator( + make_fun = EnvCreator( lambda: GymWrapper(gymnasium.make(PENDULUM_VERSIONED)) ) else: From 9b0069e435bcc43acc7388a58f930a3a61313154 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 18 Sep 2023 05:27:16 -0400 Subject: [PATCH 012/178] lint --- test/test_libs.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index 3df4ba1f628..ffff59f4212 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -504,9 +504,7 @@ def test_gym_gymnasium_parallel(self): import gym old_api = version.parse(gym.__version__) < version.parse("0.26") - make_fun = EnvCreator( - lambda: GymWrapper(gym.make(PENDULUM_VERSIONED)) - ) + make_fun = EnvCreator(lambda: GymWrapper(gym.make(PENDULUM_VERSIONED))) elif importlib.util.find_spec("gymnasium") is not None: import gymnasium From ac43a7ed581ed40863a3e463e86c5f4515bdf81d Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 18 Sep 2023 10:34:39 -0400 Subject: [PATCH 013/178] fix step counter --- test/test_collector.py | 5 +- test/test_transforms.py | 21 +- torchrl/envs/libs/gym.py | 34 +-- torchrl/envs/transforms/transforms.py | 290 ++++++++++++++++++++------ torchrl/envs/utils.py | 71 +++++++ 5 files changed, 327 insertions(+), 94 deletions(-) diff --git a/test/test_collector.py b/test/test_collector.py index 236d4e76da5..595582c8c6f 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -50,7 +50,7 @@ SerialEnv, StepCounter, ) -from torchrl.envs.libs.gym import _has_gym, GymEnv +from torchrl.envs.libs.gym import _has_gym, gym_backend, GymEnv, set_gym_backend from torchrl.envs.transforms import TransformedEnv, VecNorm from torchrl.envs.utils import _replace_last from torchrl.modules import Actor, LSTMNet, OrnsteinUhlenbeckProcessWrapper, SafeModule @@ -329,7 +329,8 @@ def test_collector_env_reset(): torch.manual_seed(0) def make_env(): - return TransformedEnv(GymEnv(PONG_VERSIONED, frame_skip=4), StepCounter()) + with set_gym_backend(gym_backend()): + return TransformedEnv(GymEnv(PONG_VERSIONED, frame_skip=4), StepCounter()) env = SerialEnv(2, make_env) # env = SerialEnv(2, lambda: GymEnv("CartPole-v1", frame_skip=4)) diff --git a/test/test_transforms.py b/test/test_transforms.py index 4c979510793..a0818c1a39d 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1382,6 +1382,10 @@ def test_r3m_spec_against_real(self, model, tensor_pixels_key, device): class TestStepCounter(TransformBase): + def test_single_trans_env_check(self): + env = TransformedEnv(ContinuousActionVecMockEnv(), StepCounter(max_steps=10)) + check_env_specs(env) + def test_parallel_trans_env_check(self): def make_env(): return TransformedEnv(ContinuousActionVecMockEnv(), StepCounter(10)) @@ -1406,10 +1410,6 @@ def test_trans_serial_env_check(self): env = TransformedEnv(SerialEnv(2, ContinuousActionVecMockEnv), StepCounter(10)) check_env_specs(env) - def test_single_trans_env_check(self): - env = TransformedEnv(ContinuousActionVecMockEnv(), StepCounter(10)) - check_env_specs(env) - @pytest.mark.skipif(not _has_gym, reason="Gym not found") def test_transform_env(self): env = TransformedEnv(GymEnv(PENDULUM_VERSIONED), StepCounter(10)) @@ -1417,7 +1417,7 @@ def test_transform_env(self): assert td["step_count"].max() == 9 assert td.shape[-1] == 100 - @pytest.mark.parametrize("step_key", ["step_count", ("other", "key")]) + @pytest.mark.parametrize("step_key", ["step_count", "other-key"]) @pytest.mark.parametrize("max_steps", [None, 10]) @pytest.mark.parametrize("nested_done", [True, False]) def test_nested( @@ -1429,10 +1429,13 @@ def test_nested( policy = CountingEnvCountPolicy( action_spec=env.action_spec, action_key=env.action_key ) + if nested_done: + step_key = (*env.done_key[:-1], step_key) transformed_env = TransformedEnv( env, StepCounter( - max_steps=max_steps, step_count_key=step_key, truncated_key=env.done_key + max_steps=max_steps, + step_count_keys=[step_key], ), ) td = transformed_env.rollout( @@ -1450,10 +1453,14 @@ def test_nested( assert step[:max_steps].eq(torch.arange(max_steps)).all() assert step[max_steps:].eq(torch.arange(rollout_length - max_steps)).all() + if nested_done: + reset_key = (*env.done_key[:-1], "_reset") + else: + reset_key = "_reset" _reset = env.done_spec.rand() td_reset = transformed_env.reset( TensorDict( - {"_reset": _reset, step_key: last_step}, + {reset_key: _reset, step_key: last_step}, batch_size=env.batch_size, device=env.device, ) diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index ec0fe912b7c..0007854bc4c 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -702,16 +702,13 @@ def _make_specs(self, env: "gym.Env", batch_size=None) -> None: # noqa: F821 self.reward_spec = reward_spec self.observation_spec = observation_spec - @implement_for("gymnasium", "0.27", None) - def _make_done_spec(self): + @implement_for("gym", None, "0.26") + def _make_done_spec(self): # noqa: F811 return CompositeSpec( { "done": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) - ), - "truncated": DiscreteTensorSpec( - 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) - ), + ) }, shape=self.batch_size, ) @@ -730,19 +727,26 @@ def _make_done_spec(self): # noqa: F811 shape=self.batch_size, ) - @implement_for("gym", None, "0.26") + @implement_for("gymnasium", "0.27", None) def _make_done_spec(self): # noqa: F811 return CompositeSpec( { "done": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) - ) + ), + "truncated": DiscreteTensorSpec( + 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) + ), }, shape=self.batch_size, ) + @implement_for("gym", None, "0.26") + def _reset_output_transform(self, reset_data): # noqa: F811 + return reset_data, {} + @implement_for("gym", "0.26", None) - def _reset_output_transform(self, reset_data): + def _reset_output_transform(self, reset_data): # noqa: F811 return reset_data @implement_for("gymnasium", "0.27", None) @@ -750,11 +754,12 @@ def _reset_output_transform(self, reset_data): # noqa: F811 return reset_data @implement_for("gym", None, "0.26") - def _reset_output_transform(self, reset_data): # noqa: F811 - return reset_data, {} + def _output_transform(self, step_outputs_tuple): # noqa: F811 + observations, reward, done, info = step_outputs_tuple + return (observations, reward, None, None, done, info) @implement_for("gym", "0.26", None) - def _output_transform(self, step_outputs_tuple): + def _output_transform(self, step_outputs_tuple): # noqa: F811 observations, reward, termination, truncation, info = step_outputs_tuple return ( observations, @@ -765,11 +770,6 @@ def _output_transform(self, step_outputs_tuple): info, ) - @implement_for("gym", None, "0.26") - def _output_transform(self, step_outputs_tuple): # noqa: F811 - observations, reward, done, info = step_outputs_tuple - return (observations, reward, None, None, done, info) - @implement_for("gymnasium", "0.27", None) def _output_transform(self, step_outputs_tuple): # noqa: F811 observations, reward, termination, truncation, info = step_outputs_tuple diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index d491d3bfa03..58a6b45e148 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -41,7 +41,7 @@ from torchrl.envs.common import EnvBase, make_tensordict from torchrl.envs.transforms import functional as F from torchrl.envs.transforms.utils import check_finite -from torchrl.envs.utils import _sort_keys, step_mdp +from torchrl.envs.utils import _sort_keys, done_or_truncated, step_mdp from torchrl.objectives.value.functional import reward2go try: @@ -4236,20 +4236,32 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase: class StepCounter(Transform): - """Counts the steps from a reset and sets the done state to True after a certain number of steps. + """Counts the steps from a reset and optionally sets the truncated state to ``True`` after a certain number of steps. Args: max_steps (int, optional): a positive integer that indicates the maximum number of steps to take before setting the ``truncated_key`` entry to ``True``. - However, the step count will still be - incremented on each call to step() into the `step_count` attribute. - truncated_key (NestedKey, optional): the key where the truncated key should - be written. Defaults to ``"truncated"``, which is recognised by - data collectors as a reset signal. - step_count_key (NestedKey, optional): the key where the step_count key should - be written. Defaults to ``"step_count"``, which is recognised by - data collectors. + truncated_keys (list of NestedKey, optional): the keys where the truncated entries + should be written. Defaults to ``["truncated"]``, which is recognised by + data collectors as a reset signal. Must match the length of ``step_count_keys`` and ``done_keys``, + if provided. + If the transform is nested within a transformed environment, the + truncated entry will be part of the ``done_spec``. + step_count_keys (list of NestedKeys, optional): the key names of the step + count. Must match the length of ``truncated_keys`` and ``done_keys``, + if provided. If not, it will default to the ``"step_count"`` key name. + done_keys (list of NestedKeys, optional): the keys that are indicative of the + done signal. Must match the length of ``truncated_keys`` and ``step_count_keys``, + if provided. + + .. note:: To ensure compatibility with environments that have multiple + done_key(s), this transform will write a step_count entry for + every done entry within the tensordict. + + Examples: + TODO + """ invertible = False @@ -4257,59 +4269,153 @@ class StepCounter(Transform): def __init__( self, max_steps: Optional[int] = None, - truncated_key: Optional[NestedKey] = "truncated", - step_count_key: Optional[NestedKey] = "step_count", + truncated_keys: Optional[List[NestedKey]] = None, + step_count_keys: Optional[List[NestedKey]] = None, + done_keys: Optional[List[NestedKey]] = None, + *, + truncated_key: Optional[NestedKey] = None, + step_count_key: Optional[NestedKey] = None, ): if max_steps is not None and max_steps < 1: raise ValueError("max_steps should have a value greater or equal to one.") + if truncated_key is not None: + warnings.warn( + "truncated_key will be deprecated in torchrl v0.3. Use truncated_keys instead.", + category=DeprecationWarning, + ) + if truncated_keys: + if len(truncated_keys) > 1 or truncated_keys[0] != truncated_key: + raise ValueError("truncated_key and truncated_keys mismatch.") + truncated_keys = [truncated_key] + if isinstance(truncated_keys, (str, tuple)): + truncated_keys = [truncated_keys] + if isinstance(done_keys, (str, tuple)): + done_keys = [done_keys] + if step_count_key is not None: + warnings.warn( + "step_count_key will be deprecated in torchrl v0.3. Use step_count_keys instead.", + category=DeprecationWarning, + ) + if step_count_keys: + if len(step_count_keys) > 1 or step_count_keys[0] != step_count_key: + raise ValueError("step_count_key and step_count_keys mismatch.") + step_count_keys = [step_count_key] + if isinstance(step_count_keys, (str, tuple)): + step_count_keys = [step_count_keys] + self.max_steps = max_steps - self.truncated_key = truncated_key - self.step_count_key = step_count_key + self._truncated_keys = truncated_keys + self.truncated_key = ( + truncated_keys[0] if truncated_keys and len(truncated_keys) == 1 else None + ) + self._step_count_keys = step_count_keys + self.step_count_key = ( + step_count_keys[0] + if step_count_keys and len(step_count_keys) == 1 + else None + ) + self._done_keys = done_keys + self.done_key = done_keys[0] if done_keys and len(done_keys) == 1 else None + self._reset_keys = None super().__init__([]) - def _get_done(self, tensordict): - done_key = self.parent.done_key if self.parent else "done" - done = tensordict.get(done_key, None) - if done is None: - done = torch.ones( - self.parent.done_spec.shape, - dtype=self.parent.done_spec.dtype, - device=self.parent.done_spec.device, - ) - return done + @property + def truncated_keys(self): + truncated_keys = self._truncated_keys + if truncated_keys is None: + # make the default truncated keys + truncated_keys = [] + for done_key in self.done_keys: + if isinstance(done_key, str): + key = "truncated" + else: + key = (*done_key[:-1], "truncated") + if key not in truncated_keys: + truncated_keys.append(key) + truncated_keys = self._truncated_keys = truncated_keys + return truncated_keys + + @property + def done_keys(self): + done_keys = self._done_keys + if done_keys is None: + if self.parent: + self._done_keys = done_keys = self.parent.done_keys + else: + self._done_keys = done_keys = ["done"] + return done_keys + + @property + def step_count_keys(self): + step_count_keys = self._step_count_keys + if step_count_keys is None: + # make the default step_count keys + step_count_keys = [] + for done_key in self.done_keys: + if isinstance(done_key, str): + key = "step_count" + else: + key = (*done_key[:-1], "step_count") + if key not in step_count_keys: + step_count_keys.append(key) + step_count_keys = self._step_count_keys = step_count_keys + return step_count_keys + + @property + def reset_keys(self): + reset_keys = self._reset_keys + if reset_keys is None: + # make the default reset keys + reset_keys = [] + for done_key in self.done_keys: + if isinstance(done_key, str): + key = "_reset" + else: + key = (*done_key[:-1], "_reset") + if key not in reset_keys: + reset_keys.append(key) + reset_keys = self._reset_keys = reset_keys + return reset_keys + + @property + def full_done_spec(self): + return self.parent.output_spec["full_done_spec"] if self.parent else None def reset(self, tensordict: TensorDictBase) -> TensorDictBase: - done = None - _reset = tensordict.get( - "_reset", - # TODO: decide if using done here, or using a default `True` tensor - default=None, + # get reset signal + tensordict_reset = done_or_truncated( + tensordict.clone(False), full_done_spec=self.full_done_spec ) - if _reset is None: - done = self._get_done(tensordict) - _reset = torch.ones_like(done) - step_count = tensordict.get(self.step_count_key, default=None) - if step_count is None: - if done is None: - # avoid getting done if not needed - done = self._get_done(tensordict) - step_count = torch.zeros_like(done, dtype=torch.int64) - step_count = torch.where(~_reset, step_count, 0) - tensordict.set(self.step_count_key, step_count) - if self.max_steps is not None: - truncated = step_count >= self.max_steps - tensordict.set(self.truncated_key, truncated) + + for step_count_key, truncated_key, reset_key in zip( + self.step_count_keys, self.truncated_keys, self.reset_keys + ): + step_count = tensordict.get(step_count_key, default=None) + reset = tensordict_reset.get(reset_key) + if step_count is None: + step_count = torch.zeros_like(reset, dtype=torch.int64) + + # zero the step count if reset is needed + step_count = torch.where(~reset, step_count, 0) + tensordict.set(step_count_key, step_count) + if self.max_steps is not None: + truncated = step_count >= self.max_steps + tensordict.set(truncated_key, truncated) + return tensordict def _step( self, tensordict: TensorDictBase, next_tensordict: TensorDictBase ) -> TensorDictBase: - step_count = tensordict.get(self.step_count_key) - next_step_count = step_count + 1 - next_tensordict.set(self.step_count_key, next_step_count) - if self.max_steps is not None: - truncated = next_step_count >= self.max_steps - next_tensordict.set(self.truncated_key, truncated) + for step_count_key, truncated_key in zip( + self.step_count_keys, self.truncated_keys + ): + step_count = tensordict.get(step_count_key) + next_step_count = step_count + 1 + next_tensordict.set(step_count_key, next_step_count) + if self.max_steps is not None: + truncated = next_step_count >= self.max_steps + next_tensordict.set(truncated_key, truncated) return next_tensordict def transform_observation_spec( @@ -4319,20 +4425,52 @@ def transform_observation_spec( raise ValueError( f"observation_spec was expected to be of type CompositeSpec. Got {type(observation_spec)} instead." ) - observation_spec[self.step_count_key] = UnboundedDiscreteTensorSpec( - shape=self.parent.done_spec.shape - if self.parent - else observation_spec.shape, - dtype=torch.int64, - device=observation_spec.device, - ) - observation_spec[self.step_count_key].space.low = ( - observation_spec[self.step_count_key].space.low * 0 - ) - if self.max_steps is not None and self.truncated_key != self.parent.done_key: - observation_spec[self.truncated_key] = self.parent.done_spec.clone() + full_done_spec = self.parent.output_spec["full_done_spec"] + for step_count_key in self.step_count_keys: + step_count_key = unravel_key(step_count_key) + # find a matching done key (there might be more than one) + for done_key in self.done_keys: + if type(done_key) == type(step_count_key): + shape = full_done_spec[done_key].shape + break + else: + raise RuntimeError( + "Could not find a matching done_key to get the step_count shape. " + "Make sure the step_count entries are associated with a done entry." + ) + observation_spec[step_count_key] = BoundedTensorSpec( + shape=shape, + dtype=torch.int64, + device=observation_spec.device, + low=0, + high=torch.iinfo(torch.int64).max, + ) return observation_spec + def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: + if self.max_steps: + full_done_spec = self.parent.output_spec["full_done_spec"] + for truncated_key in self.truncated_keys: + truncated_key = unravel_key(truncated_key) + # find a matching done key (there might be more than one) + for done_key in self.done_keys: + if type(done_key) == type(truncated_key): + shape = full_done_spec[done_key].shape + break + else: + raise RuntimeError( + "Could not find a matching done_key to get the truncated shape. " + "Make sure the truncated entries are associated with a done entry." + ) + full_done_spec[truncated_key] = DiscreteTensorSpec( + 2, dtype=torch.bool, device=output_spec.device, shape=shape + ) + output_spec["full_done_spec"] = full_done_spec + output_spec["full_observation_spec"] = self.transform_observation_spec( + output_spec["full_observation_spec"] + ) + return output_spec + def transform_input_spec(self, input_spec: CompositeSpec) -> CompositeSpec: if not isinstance(input_spec, CompositeSpec): raise ValueError( @@ -4342,13 +4480,29 @@ def transform_input_spec(self, input_spec: CompositeSpec) -> CompositeSpec: input_spec["full_state_spec"] = CompositeSpec( shape=input_spec.shape, device=input_spec.device ) - step_spec = UnboundedDiscreteTensorSpec( - shape=self.parent.done_spec.shape if self.parent else input_spec.shape, - dtype=torch.int64, - device=input_spec.device, - ) - step_spec.space.low *= 0 - input_spec["full_state_spec", self.step_count_key] = step_spec + + full_done_spec = self.parent.output_spec["full_done_spec"] + for step_count_key in self.step_count_keys: + step_count_key = unravel_key(step_count_key) + # find a matching done key (there might be more than one) + for done_key in self.done_keys: + if type(done_key) == type(step_count_key): + shape = full_done_spec[done_key].shape + break + else: + raise RuntimeError( + "Could not find a matching done_key to get the step_count shape. " + "Make sure the step_count entries are associated with a done entry." + ) + input_spec[ + unravel_key(("full_state_spec", step_count_key)) + ] = BoundedTensorSpec( + shape=shape, + dtype=torch.int64, + device=input_spec.device, + low=0, + high=torch.iinfo(torch.int64).max, + ) return input_spec diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index ff50f9d7c4f..5c82163888e 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -728,3 +728,74 @@ def check_marl_grouping(group_map: Dict[str, List[str]], agent_names: List[str]) for agent_name, found in found_agents.items(): if not found: raise ValueError(f"Agent {agent_name} not found in any group") + + +def done_or_truncated( + data: TensorDictBase, full_done_spec=None, name="_reset" +) -> TensorDictBase: + """Reads the done / truncated keys within a tensordict, and writes a new tensor where the values of both signals are aggregated. + + The modification occurs in-place within the TensorDict instance provided. + This function can be used to compute the `"_reset"` signal in batched + or multiagent settings, hence the default name of the output key. + + Args: + data (TensorDictBase): the input data, generally resulting from a call to :meth:`~torchrl.envs.EnvBase.step`. + full_done_spec (TensorSpec, optional): the done_spec from the env, indicating where + the done leaves have to be found. If not provided, the default `"done"` and + `"truncated"` entries will be searched for in the data. + name (NestedKey, optional): where the aggregated result should be written. + + Examples: + >>> from torchrl.data.tensor_specs import DiscreteTensorSpec + >>> from tensordict import TensorDict + >>> spec = CompositeSpec( + ... done=DiscreteTensorSpec(2, dtype=torch.bool), + ... truncated=DiscreteTensorSpec(2, dtype=torch.bool), + ... nested=CompositeSpec( + ... done=DiscreteTensorSpec(2, dtype=torch.bool), + ... truncated=DiscreteTensorSpec(2, dtype=torch.bool), + ... ) + ... ) + >>> data = TensorDict({ + ... "done": True, "truncated": False, + ... "nested": {"done": False, "truncated": True}}, + ... batch_size=[] + ... ) + >>> data = done_or_truncated(data, spec) + >>> print(data["_reset"]) + tensor(True) + >>> print(data["nested", "_reset"]) + tensor(True) + """ + aggregate = None + has_entry = name in data.keys(isinstance(name, tuple)) + if full_done_spec is None: + for key, item in data.items(): + if not has_entry and key in ("done", "truncated"): + done = data.get(key, None) + if done is None: + done = torch.zeros( + (*data.shape, 1), dtype=torch.bool, device=data.device + ) + if aggregate is None: + aggregate = torch.tensor(False, device=done.device) + aggregate = aggregate | done + elif isinstance(item, TensorDictBase): + done_or_truncated(data=item, full_done_spec=None, name=name) + else: + for key, item in full_done_spec.items(): + if isinstance(item, CompositeSpec): + done_or_truncated(data=data.get(key), full_done_spec=item, name=name) + elif not has_entry: + done = data.get(key, None) + if done is None: + done = torch.zeros( + (*data.shape, 1), dtype=torch.bool, device=data.device + ) + if aggregate is None: + aggregate = torch.tensor(False, device=done.device) + aggregate = aggregate | done + if aggregate is not None: + data.setdefault(name, aggregate) + return data From a822407348bb6a0d95a5b17a9f279c1ded7f3922 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 18 Sep 2023 14:37:12 -0400 Subject: [PATCH 014/178] amend --- test/test_transforms.py | 4 +- torchrl/envs/transforms/transforms.py | 273 +++++++++++++++++++++----- 2 files changed, 227 insertions(+), 50 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index a0818c1a39d..d0cf6c12397 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1545,7 +1545,7 @@ def test_transform_no_env(self, max_steps, device, batch, reset_workers): td = TensorDict({"done": done, ("next", "done"): done}, batch, device=device) _reset = torch.zeros((), dtype=torch.bool, device=device) while not _reset.any() and reset_workers: - _reset = torch.randn(batch, device=device) < 0 + _reset = torch.rand(batch, device=device) < 0.5 td.set("_reset", _reset) td.set("done", _reset) td.set(("next", "done"), done) @@ -8153,7 +8153,7 @@ def test_transform_rb(self, rbclass): def test_transform_inverse(self): raise pytest.skip("No inverse for InitTracker") - @pytest.mark.parametrize("init_key", ["is_init", "loool", ("other", "key")]) + @pytest.mark.parametrize("init_key", ["is_init", "loool"]) @pytest.mark.parametrize("nested_done", [True, False]) @pytest.mark.parametrize("max_steps", [5]) def test_nested( diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 58a6b45e148..235af38bea4 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -36,7 +36,6 @@ OneHotDiscreteTensorSpec, TensorSpec, UnboundedContinuousTensorSpec, - UnboundedDiscreteTensorSpec, ) from torchrl.envs.common import EnvBase, make_tensordict from torchrl.envs.transforms import functional as F @@ -2341,6 +2340,10 @@ class CatFrames(ObservationTransform): >>> # let's check that our sample is the same as the batch collected during inference >>> assert (data.exclude("collector")==s.squeeze(0).exclude("index", "collector")).all() + .. note:: :class:`~CatFrames` currently only supports ``done`` signal at the root. Nested ``done``, + such as those found in MARL settings, are currently not supported. + If this feature is needed, please raise an issue on TorchRL repo. + """ inplace = False @@ -4073,6 +4076,11 @@ class RewardSum(Transform): If no in_keys are specified, this transform assumes ´reward´ to be the input key. However, multiple rewards (e.g. reward1 and reward2) can also be specified. If ´in_keys´ are not present in the provided tensordict, this transform hos no effect. + + .. note:: :class:`~RewardSum` currently only supports ``done`` signal at the root. + Nested ``done``, such as those found in MARL settings, are currently not supported. + If this feature is needed, please raise an issue on TorchRL repo. + """ def __init__( @@ -4260,7 +4268,38 @@ class StepCounter(Transform): every done entry within the tensordict. Examples: - TODO + >>> import gymnasium + >>> from torchrl.envs import GymWrapper + >>> base_env = GymWrapper(gymnasium.make("Pendulum-v1")) + >>> env = TransformedEnv(base_env, StepCounter(max_steps=5)) + >>> rollout = env.rollout(100) + >>> print(rollout) + TensorDict( + fields={ + action: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.float32, is_shared=False), + done: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False), + next: TensorDict( + fields={ + done: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False), + observation: Tensor(shape=torch.Size([5, 3]), device=cpu, dtype=torch.float32, is_shared=False), + reward: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.float32, is_shared=False), + step_count: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.int64, is_shared=False), + truncated: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, + batch_size=torch.Size([5]), + device=cpu, + is_shared=False), + observation: Tensor(shape=torch.Size([5, 3]), device=cpu, dtype=torch.float32, is_shared=False), + step_count: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.int64, is_shared=False), + truncated: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, + batch_size=torch.Size([5]), + device=cpu, + is_shared=False) + >>> print(rollout["next", "step_count"]) + tensor([[1], + [2], + [3], + [4], + [5]]) """ @@ -4317,6 +4356,7 @@ def __init__( self._done_keys = done_keys self.done_key = done_keys[0] if done_keys and len(done_keys) == 1 else None self._reset_keys = None + self._done_keys_sorted = None super().__init__([]) @property @@ -4377,21 +4417,51 @@ def reset_keys(self): reset_keys = self._reset_keys = reset_keys return reset_keys + @property + def done_keys_sorted(self): + """A list of done keys, grouped as the reset keys. + + This is a list of lists. The outer list has the length of reset keys, the + inner lists constain the done keys (eg, done and truncated) that can + be read to determine a reset when it is absent. + + """ + done_keys_sorted = self._done_keys_sorted + if done_keys_sorted is not None: + return done_keys_sorted + # done keys, sorted as reset keys + reset_keys = self.reset_keys + done_keys = [[] for _ in range(len(reset_keys))] + reset_keys = iter(reset_keys) + done_keys_iter = iter(done_keys) + curr_reset_key = next(reset_keys) + curr_done_key = next(done_keys_iter) + + for done_key in self.done_keys: + while type(done_key) != type(curr_reset_key) or (isinstance(done_key, tuple) and done_key[:-1] != curr_reset_key[:-1]): # if they are string, they are at the same level + curr_reset_key = next(curr_reset_key) + curr_done_key = next(done_keys_iter) + curr_done_key.append(done_key) + self._done_keys_sorted = done_keys + return done_keys + @property def full_done_spec(self): return self.parent.output_spec["full_done_spec"] if self.parent else None def reset(self, tensordict: TensorDictBase) -> TensorDictBase: # get reset signal - tensordict_reset = done_or_truncated( - tensordict.clone(False), full_done_spec=self.full_done_spec - ) - - for step_count_key, truncated_key, reset_key in zip( - self.step_count_keys, self.truncated_keys, self.reset_keys + for step_count_key, truncated_key, reset_key, done_list_sorted in zip( + self.step_count_keys, self.truncated_keys, self.reset_keys, self.done_keys_sorted ): step_count = tensordict.get(step_count_key, default=None) - reset = tensordict_reset.get(reset_key) + reset = tensordict.get(reset_key, None) + if reset is None: + # get done status + for entry in done_list_sorted: + done = tensordict.get(entry) + break + reset = torch.ones_like(done) if step_count is None: step_count = torch.zeros_like(reset, dtype=torch.int64) @@ -4486,14 +4556,21 @@ def transform_input_spec(self, input_spec: CompositeSpec) -> CompositeSpec: step_count_key = unravel_key(step_count_key) # find a matching done key (there might be more than one) for done_key in self.done_keys: - if type(done_key) == type(step_count_key): + # check root + if type(done_key) != type(step_count_key): + continue + if isinstance(done_key, tuple): + if done_key[:-1] == step_count_key: + shape = full_done_spec[done_key].shape + break + if isinstance(done_key, str): shape = full_done_spec[done_key].shape break else: - raise RuntimeError( - "Could not find a matching done_key to get the step_count shape. " - "Make sure the step_count entries are associated with a done entry." + raise KeyError( + f"Could not find root of step_count_key {step_count_key}." ) + input_spec[ unravel_key(("full_state_spec", step_count_key)) ] = BoundedTensorSpec( @@ -4626,6 +4703,30 @@ class TimeMaxPool(Transform): in_keys (sequence of NestedKey, optional): input keys on which the max pool will be applied. Defaults to "observation" if left empty. out_keys (sequence of NestedKey, optional): output keys where the output will be written. Defaults to `in_keys` if left empty. T (int, optional): Number of time steps over which to apply max pooling. + + Examples: + >>> from torchrl.envs import GymEnv + >>> base_env = GymEnv("Pendulum-v1") + >>> env = TransformedEnv(base_env, TimeMaxPool(in_keys=["observation"], T=10)) + >>> torch.manual_seed(0) + >>> env.set_seed(0) + >>> rollout = env.rollout(10) + >>> print(rollout["observation"]) # values should be increasing up until the 10th step + tensor([[ 0.0000, 0.0000, 0.0000], + [ 0.0000, 0.0000, 0.0000], + [ 0.0000, 0.0000, 0.0000], + [ 0.0000, 0.0000, 0.0000], + [ 0.0000, 0.0216, 0.0000], + [ 0.0000, 0.1149, 0.0000], + [ 0.0000, 0.1990, 0.0000], + [ 0.0000, 0.2749, 0.0000], + [ 0.0000, 0.3281, 0.0000], + [-0.9290, 0.3702, -0.8978]]) + + .. note:: :class:`~TimeMaxPool` currently only supports ``done`` signal at the root. + Nested ``done``, such as those found in MARL settings, are currently not supported. + If this feature is needed, please raise an issue on TorchRL repo. + """ invertible = False @@ -4659,7 +4760,6 @@ def __init__( ) def reset(self, tensordict: TensorDictBase) -> TensorDictBase: - """Resets _buffers.""" # Non-batched environments if len(tensordict.batch_size) < 1 or tensordict.batch_size[0] == 1: for in_key in self.in_keys: @@ -4855,46 +4955,94 @@ class InitTracker(Transform): """ def __init__(self, init_key: NestedKey = "is_init"): - super().__init__(in_keys=[], out_keys=[init_key]) + self.init_key = init_key + self.reset_key = "_reset" + super().__init__(in_keys=[], out_keys=[]) + + def set_container(self, container: Union[Transform, EnvBase]) -> None: + out = super().set_container(container) + self.out_keys = self.init_keys + return out + + @property + def init_keys(self): + init_keys = [] + for done_key in self.parent.done_keys: + if isinstance(done_key, str): + init_key = self.init_key + else: + init_key = unravel_key((*done_key[:-1], self.init_key)) + if init_key not in init_keys: + init_keys.append(init_key) + return init_keys + + @property + def reset_keys(self): + reset_keys = [] + for done_key in self.parent.done_keys: + if isinstance(done_key, str): + reset_key = self.reset_key + else: + reset_key = unravel_key((*done_key[:-1], self.reset_key)) + if reset_key not in reset_keys: + reset_keys.append(reset_key) + return reset_keys def _call(self, tensordict: TensorDictBase) -> TensorDictBase: - if self.out_keys[0] not in tensordict.keys(True, True): - device = tensordict.device - if device is None: - device = torch.device("cpu") - tensordict.set( - self.out_keys[0], - torch.zeros( - self.parent.done_spec.shape, device=device, dtype=torch.bool - ), - ) + for init_key in self.init_keys: + if init_key not in tensordict.keys(True, True): + device = tensordict.device + if device is None: + device = torch.device("cpu") + tensordict.set( + init_key, + torch.zeros( + self.parent.done_spec.shape, device=device, dtype=torch.bool + ), + ) return tensordict def reset(self, tensordict: TensorDictBase) -> TensorDictBase: device = tensordict.device if device is None: device = torch.device("cpu") - _reset = tensordict.get("_reset", None) - if _reset is None: - tensordict.set( - self.out_keys[0], - torch.ones( - self.parent.done_spec.shape, - device=device, - dtype=torch.bool, - ), - ) - else: - tensordict.set(self.out_keys[0], _reset.clone()) + for reset_key, init_key in zip(self.reset_keys, self.init_keys): + _reset = tensordict.get(reset_key, None) + if _reset is None: + tensordict.set( + init_key, + torch.ones( + self.parent.done_spec.shape, + device=device, + dtype=torch.bool, + ), + ) + else: + tensordict.set(init_key, _reset.clone()) return tensordict def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec: - observation_spec[self.out_keys[0]] = DiscreteTensorSpec( - 2, - dtype=torch.bool, - device=self.parent.device, - shape=self.parent.done_spec.shape, - ) + full_done_spec = self.parent.output_spec["full_done_spec"] + for init_key in self.init_keys: + for done_key in self.parent.done_keys: + # check root + if type(done_key) != type(init_key): + continue + if isinstance(done_key, tuple): + if done_key[:-1] == init_key: + shape = full_done_spec[done_key].shape + break + if isinstance(done_key, str): + shape = full_done_spec[done_key] + break + else: + raise KeyError(f"Could not find root of init_key {init_key}.") + observation_spec[init_key] = DiscreteTensorSpec( + 2, + dtype=torch.bool, + device=self.parent.device, + shape=shape, + ) return observation_spec def forward(self, tensordict: TensorDictBase) -> TensorDictBase: @@ -5071,11 +5219,14 @@ class Reward2GoTransform(Transform): and not to the collector. Args: + gamma (float or torch.Tensor): the discount factor. Defaults to 1.0. in_keys (sequence of NestedKey): the entries to rename. Defaults to ``("next", "reward")`` if none is provided. out_keys (sequence of NestedKey): the entries to rename. Defaults to the values of ``in_keys`` if none is provided. - gamma (float or torch.Tensor): the discount factor. Defaults to 1.0. + done_key (NestedKey): the done entry. Defaults to ``"done"``. + truncated_key (NestedKey): the truncated entry. Defaults to ``"truncated"``. + If no truncated entry is found, only the ``"done"`` will be used. Examples: >>> # Using this transform as part of a replay buffer @@ -5157,6 +5308,9 @@ class Reward2GoTransform(Transform): >>> t = Reward2GoTransform(gamma=0.99) >>> TransformedEnv(GymEnv("Pendulum-v1"), t) # crashes + .. note:: In settings where multiple done entries are present, one should build + a single :class:`~Reward2GoTransform` for each done-reward pair. + """ ENV_ERR = ( @@ -5169,6 +5323,8 @@ def __init__( gamma: Optional[Union[float, torch.Tensor]] = 1.0, in_keys: Optional[Sequence[NestedKey]] = None, out_keys: Optional[Sequence[NestedKey]] = None, + done_key: Optional[NestedKey] = "done", + truncated_key: Optional[NestedKey] = "truncated", ): if in_keys is None: in_keys = [("next", "reward")] @@ -5180,6 +5336,8 @@ def __init__( in_keys_inv=in_keys, out_keys_inv=out_keys, ) + self.done_key = done_key + self.truncated_key = truncated_key if not isinstance(gamma, torch.Tensor): gamma = torch.tensor(gamma) @@ -5187,13 +5345,13 @@ def __init__( self.register_buffer("gamma", gamma) def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase: - done_key = self.parent.done_key if self.parent else "done" - done = tensordict.get(("next", done_key)) - truncated = tensordict.get(("next", "truncated"), None) + done = tensordict.get(("next", self.done_key)) + truncated = tensordict.get(("next", self.truncated_key), None) if truncated is not None: done_or_truncated = done | truncated else: done_or_truncated = done + if not done_or_truncated.any(-2).all(): raise RuntimeError( "No episode ends found to calculate the reward to go. Make sure that the number of frames_per_batch is larger than number of steps per episode." @@ -5370,11 +5528,26 @@ def __init__(self, final_name="final"): super().__init__(in_keys=[]) self._memo = {} + def set_container(self, container: Union[Transform, EnvBase]) -> None: + out = super().set_container(container) + expected_done_keys = {"done", "truncated"} + for done_key in self.parent.done_keys: + if done_key not in expected_done_keys: + raise RuntimeError( + f"VecGymEnvTransform only supports the following " + f"done keys: {expected_done_keys}, but it got {done_key}." + ) + return out + def _step( self, tensordict: TensorDictBase, next_tensordict: TensorDictBase ) -> TensorDictBase: # save the final info - done = self._memo["done"] = next_tensordict.get("done") + done = next_tensordict.get("done") + truncated = next_tensordict.get("truncated", None) + if truncated is not None: + done = done | truncated + done = self._memo["done"] = done final = next_tensordict.pop("final") # if anything's done, we need to swap the final obs if done.any(): @@ -5409,6 +5582,10 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: ~reset, tensordict.select(*saved_done.keys(True, True)), saved_done ) tensordict.update(updated_td) + if "truncated" in tensordict.keys(): + tensordict.set( + "truncated", tensordict.get("truncated").clone().fill_(0) + ) tensordict.set("done", tensordict.get("done").clone().fill_(0)) tensordict.pop("final", None) return tensordict From 6cec6e1115adbef062c5c8f7f34f25715c6489b5 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 18 Sep 2023 16:04:08 -0400 Subject: [PATCH 015/178] amend --- torchrl/envs/transforms/transforms.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 235af38bea4..a90572b7428 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -5033,7 +5033,7 @@ def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec shape = full_done_spec[done_key].shape break if isinstance(done_key, str): - shape = full_done_spec[done_key] + shape = full_done_spec[done_key].shape break else: raise KeyError(f"Could not find root of init_key {init_key}.") From 0612e09e66dd4c5683d39a9d1026acae5ffcbf65 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 18 Sep 2023 16:12:12 -0400 Subject: [PATCH 016/178] amend --- .github/unittest/linux/scripts/run_all.sh | 4 ++-- torchrl/envs/transforms/transforms.py | 15 ++++++++++++++- 2 files changed, 16 insertions(+), 3 deletions(-) diff --git a/.github/unittest/linux/scripts/run_all.sh b/.github/unittest/linux/scripts/run_all.sh index 06da0c3b2f0..c3d6b5f5f4c 100755 --- a/.github/unittest/linux/scripts/run_all.sh +++ b/.github/unittest/linux/scripts/run_all.sh @@ -184,10 +184,10 @@ pytest test/smoke_test.py -v --durations 200 pytest test/smoke_test_deps.py -v --durations 200 -k 'test_gym or test_dm_control_pixels or test_dm_control or test_tb' if [ "${CU_VERSION:-}" != cpu ] ; then python .github/unittest/helpers/coverage_run_parallel.py -m pytest test \ - --instafail --durations 200 --ignore test/test_rlhf.py + --instafail --durations 200 -v --ignore test/test_rlhf.py else python .github/unittest/helpers/coverage_run_parallel.py -m pytest test \ - --instafail --durations 200 --ignore test/test_rlhf.py --ignore test/test_distributed.py + --instafail --durations 200 -v --ignore test/test_rlhf.py --ignore test/test_distributed.py fi coverage combine diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index a90572b7428..61b0cdf77e7 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -4956,17 +4956,25 @@ class InitTracker(Transform): def __init__(self, init_key: NestedKey = "is_init"): self.init_key = init_key + self._init_keys = None self.reset_key = "_reset" super().__init__(in_keys=[], out_keys=[]) def set_container(self, container: Union[Transform, EnvBase]) -> None: + self._init_keys = None out = super().set_container(container) self.out_keys = self.init_keys return out @property def init_keys(self): + if self._init_keys is not None: + return self._init_keys init_keys = [] + if self.parent is None: + raise NotImplementedError( + FORWARD_NOT_IMPLEMENTED.format(self.__class__.__name__) + ) for done_key in self.parent.done_keys: if isinstance(done_key, str): init_key = self.init_key @@ -4974,11 +4982,16 @@ def init_keys(self): init_key = unravel_key((*done_key[:-1], self.init_key)) if init_key not in init_keys: init_keys.append(init_key) - return init_keys + self._init_keys = init_keys + return self._init_keys @property def reset_keys(self): reset_keys = [] + if self.parent is None: + raise NotImplementedError( + FORWARD_NOT_IMPLEMENTED.format(self.__class__.__name__) + ) for done_key in self.parent.done_keys: if isinstance(done_key, str): reset_key = self.reset_key From 02902db5f972ced2e905cb8198b003b1cbfebffb Mon Sep 17 00:00:00 2001 From: Vincent Moens Date: Tue, 19 Sep 2023 07:52:09 +0100 Subject: [PATCH 017/178] Update torchrl/envs/gym_like.py Co-authored-by: Skander Moalla <37197319+skandermoalla@users.noreply.github.com> --- torchrl/envs/gym_like.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index b7952d6ad65..c2688fb838e 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -163,7 +163,7 @@ def read_done( do_break = gym_done return ( - gym_done, + done, truncation, do_break.any() if not isinstance(do_break, bool) else do_break, ) From 7e22c55b379e3b9ef02dbb5aa9a6c186cac0776c Mon Sep 17 00:00:00 2001 From: Vincent Moens Date: Tue, 19 Sep 2023 07:52:18 +0100 Subject: [PATCH 018/178] Update torchrl/envs/gym_like.py Co-authored-by: Skander Moalla <37197319+skandermoalla@users.noreply.github.com> --- torchrl/envs/gym_like.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index c2688fb838e..76c8327b39f 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -157,7 +157,7 @@ def read_done( """ if termination is not None: - gym_done = termination + done = termination do_break = gym_done | truncation else: do_break = gym_done From 53927bcfdbc4856856a71aee861ca6c38c737f4d Mon Sep 17 00:00:00 2001 From: Vincent Moens Date: Tue, 19 Sep 2023 07:52:34 +0100 Subject: [PATCH 019/178] Update torchrl/envs/gym_like.py Co-authored-by: Skander Moalla <37197319+skandermoalla@users.noreply.github.com> --- torchrl/envs/gym_like.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index 76c8327b39f..cdc837cc470 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -161,7 +161,7 @@ def read_done( do_break = gym_done | truncation else: do_break = gym_done - + done = gym_done return ( done, truncation, From 43ff66a846273696e1cb9e47553242b901502c48 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 03:01:07 -0400 Subject: [PATCH 020/178] amend --- torchrl/envs/transforms/transforms.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 61b0cdf77e7..ef5dbc34fda 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -4457,10 +4457,15 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: step_count = tensordict.get(step_count_key, default=None) reset = tensordict.get(reset_key, None) if reset is None: - # get done status + # get done status, just to inform the reset shape, dtype and device for entry in done_list_sorted: - done = tensordict.get(entry) - break + done = tensordict.get(entry, None) + if done is not None: + break + else: + # It may be the case that reset did not provide a done state, in which case + # we fall back on the spec + done = self.parent.output_spec['full_done_spec', entry].zero() reset = torch.ones_like(done) if step_count is None: step_count = torch.zeros_like(reset, dtype=torch.int64) From 77dcd097808869fc6e432163578c603b7a93bb1a Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 03:06:21 -0400 Subject: [PATCH 021/178] rollout --- torchrl/envs/common.py | 6 +++++- torchrl/envs/utils.py | 13 +++++++++---- 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index 771c529933c..e8fff5b6021 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -28,7 +28,7 @@ _replace_last, DONE_AFTER_RESET_ERROR, get_available_libraries, - step_mdp, + step_mdp, done_or_truncated, ) LIBRARIES = get_available_libraries() @@ -1587,6 +1587,10 @@ def policy(td): any_done = False _reset_map = {} + # done and truncated are in done_keys + # To read the done status, we assess whether any of the done entries + # at a given level is True. This is written in the _reset key. + any_done = done_or_truncated(tensordict.get("next"), full_done_spec=self.output_spec['full_done_spec']) for done_key in self.done_keys: done = tensordict.get(("next", done_key)) truncated = tensordict.get( diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 5c82163888e..492021bd307 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -732,7 +732,7 @@ def check_marl_grouping(group_map: Dict[str, List[str]], agent_names: List[str]) def done_or_truncated( data: TensorDictBase, full_done_spec=None, name="_reset" -) -> TensorDictBase: +) -> bool: """Reads the done / truncated keys within a tensordict, and writes a new tensor where the values of both signals are aggregated. The modification occurs in-place within the TensorDict instance provided. @@ -746,6 +746,9 @@ def done_or_truncated( `"truncated"` entries will be searched for in the data. name (NestedKey, optional): where the aggregated result should be written. + Returns: a boolean value indicating whether any of the done states found in the data + contained a ``True``. + Examples: >>> from torchrl.data.tensor_specs import DiscreteTensorSpec >>> from tensordict import TensorDict @@ -768,6 +771,7 @@ def done_or_truncated( >>> print(data["nested", "_reset"]) tensor(True) """ + any_done = False aggregate = None has_entry = name in data.keys(isinstance(name, tuple)) if full_done_spec is None: @@ -782,11 +786,11 @@ def done_or_truncated( aggregate = torch.tensor(False, device=done.device) aggregate = aggregate | done elif isinstance(item, TensorDictBase): - done_or_truncated(data=item, full_done_spec=None, name=name) + any_done = any_done | done_or_truncated(data=item, full_done_spec=None, name=name) else: for key, item in full_done_spec.items(): if isinstance(item, CompositeSpec): - done_or_truncated(data=data.get(key), full_done_spec=item, name=name) + any_done = any_done | done_or_truncated(data=data.get(key), full_done_spec=item, name=name) elif not has_entry: done = data.get(key, None) if done is None: @@ -798,4 +802,5 @@ def done_or_truncated( aggregate = aggregate | done if aggregate is not None: data.setdefault(name, aggregate) - return data + any_done = any_done | aggregate.any() + return any_done From f404245a55480d9ca761cb70fc5724e79831910f Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 04:49:34 -0400 Subject: [PATCH 022/178] fix --- test/test_env.py | 11 +--- torchrl/envs/common.py | 82 ++++++++++++--------------- torchrl/envs/transforms/transforms.py | 10 ++-- torchrl/envs/utils.py | 23 +++++--- 4 files changed, 57 insertions(+), 69 deletions(-) diff --git a/test/test_env.py b/test/test_env.py index ffa336331f3..57ce420b0f9 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -918,15 +918,7 @@ def env_fn2(seed): env1.close() env2.close() - @pytest.mark.parametrize( - "batch_size", - [ - (32, 5), - (4,), - (1,), - (), - ], - ) + @pytest.mark.parametrize("batch_size",[(32, 5),(4,),(1,),()]) @pytest.mark.parametrize("n_workers", [2, 1]) def test_parallel_env_reset_flag(self, batch_size, n_workers, max_steps=3): torch.manual_seed(1) @@ -955,6 +947,7 @@ def test_parallel_env_reset_flag(self, batch_size, n_workers, max_steps=3): while not _reset.any(): _reset = env.done_spec.rand() + env._allow_done_after_reset = True td_reset = env.reset( TensorDict({"_reset": _reset}, batch_size=env.batch_size, device=env.device) ) diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index e8fff5b6021..0ed3266b66b 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -836,10 +836,6 @@ def _get_done_keys(self): self.__dict__["_done_keys"] = keys return keys - @property - def reset_keys(self) -> List[NestedKey]: - return [_replace_last(done_key, "_reset") for done_key in self.done_keys] - @property def done_keys(self) -> List[NestedKey]: """The done keys of an environment. @@ -1264,24 +1260,6 @@ def reset( """ if tensordict is not None: self._assert_tensordict_shape(tensordict) - _reset_map = {} - for done_key in self.done_keys: - _reset_key = _replace_last(done_key, "_reset") - _reset = tensordict.get(_reset_key, default=None) - if _reset is None: - continue - if ( - _reset.shape[ - -len(self.output_spec["full_done_spec"][done_key].shape) : - ] - != self.output_spec["full_done_spec"][done_key].shape - ): - raise RuntimeError( - "_reset flag in tensordict should follow env.done_spec" - ) - _reset_map.update({done_key: _reset}) - else: - _reset_map = {} tensordict_reset = self._reset(tensordict, **kwargs) # We assume that this is done properly @@ -1313,12 +1291,8 @@ def reset( if not self._allow_done_after_reset: for done_key in self.done_keys: - if done_key not in _reset_map: - if tensordict_reset.get(done_key).any(): - raise DONE_AFTER_RESET_ERROR - else: - if tensordict_reset.get(done_key)[_reset_map[done_key]].any(): - raise DONE_AFTER_RESET_ERROR + if tensordict_reset.get(done_key).any(): + raise DONE_AFTER_RESET_ERROR if tensordict is not None: tensordict.update(tensordict_reset) @@ -1585,24 +1559,10 @@ def policy(td): tensordict = self.step(tensordict) tensordicts.append(tensordict.clone(False)) - any_done = False - _reset_map = {} # done and truncated are in done_keys # To read the done status, we assess whether any of the done entries # at a given level is True. This is written in the _reset key. - any_done = done_or_truncated(tensordict.get("next"), full_done_spec=self.output_spec['full_done_spec']) - for done_key in self.done_keys: - done = tensordict.get(("next", done_key)) - truncated = tensordict.get( - ("next", _replace_last(done_key, "truncated")), - default=torch.zeros((), device=done.device, dtype=torch.bool), - ) - done = done | truncated - any_sub_done = done.any().item() - if any_sub_done and not break_when_any_done: - # Add this done to the map, we will need it to reset - _reset_map.update({_replace_last(done_key, "_reset"): done}) - any_done += any_sub_done + any_done = done_or_truncated(tensordict.get("next"), full_done_spec=self.output_spec['full_done_spec'], key="_reset" if not break_when_any_done else None) if (break_when_any_done and any_done) or i == max_steps - 1: break @@ -1617,11 +1577,8 @@ def policy(td): ) if not break_when_any_done and any_done: - for _reset_key, done in _reset_map.items(): - tensordict.set(_reset_key, done.clone()) self.reset(tensordict) - for _reset_key in _reset_map.keys(): - del tensordict[_reset_key] + tensordict.exclude(*self.reset_keys, inplace=True) if callback is not None: callback(self, tensordict) @@ -1633,6 +1590,37 @@ def policy(td): out_td.refine_names(..., "time") return out_td + @property + def reset_keys(self) -> List[NestedKey]: + """Returns a list of reset keys. + + Reset keys are keys that indicate partial reset, in batched, multitask or multiagent + settings. They are structured as ``(*prefix, "_reset")`` where ``prefix`` is + a (possibly empty) tuple of strings pointing to a tensordict location + where a done state can be found. + """ + reset_keys = self.__dict__.get("_reset_keys", None) + if reset_keys is not None: + return reset_keys + prefixes = set() + reset_keys = [] + def prefix(key: NestedKey): + if isinstance(key, str): + return None + return key[:-1] + def combine(prefix_key: tuple | None, key: str): + if prefix_key is None: + return key + return (*prefix_key, key) + for done_key in self.done_keys: + prefix_key = prefix(done_key) + if prefix_key in prefixes: + continue + prefixes.add(prefix_key) + reset_keys.append(combine(prefix_key, "_reset")) + self.__dict__['_reset_keys'] = reset_keys + return reset_keys + def _select_observation_keys(self, tensordict: TensorDictBase) -> Iterator[str]: for key in tensordict.keys(): if key.rfind("observation") >= 0: diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index ef5dbc34fda..cbe6988d8d0 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -66,7 +66,7 @@ def interpolation_fn(interpolation): # noqa: D103 IMAGE_KEYS = ["pixels"] _MAX_NOOPS_TRIALS = 10 -FORWARD_NOT_IMPLEMENTED = "class {} cannot be executed without a parent" "environment." +FORWARD_NOT_IMPLEMENTED = "class {} cannot be executed without a parent environment." def _apply_to_composite(function): @@ -4967,9 +4967,11 @@ def __init__(self, init_key: NestedKey = "is_init"): def set_container(self, container: Union[Transform, EnvBase]) -> None: self._init_keys = None - out = super().set_container(container) - self.out_keys = self.init_keys - return out + return super().set_container(container) + + @property + def out_keys(self): + return self.init_keys @property def init_keys(self): diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 492021bd307..27335e42ffa 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -45,7 +45,7 @@ from torchrl.data.utils import check_no_exclusive_keys DONE_AFTER_RESET_ERROR = RuntimeError( - "Env was done after reset on specified '_reset' dimensions. This is (currently) not allowed." + "Env was done after reset on specified '_reset' dimensions. This is not allowed." ) ACTION_MASK_ERROR = RuntimeError( "An out-of-bounds actions has been provided to an env with an 'action_mask' output." @@ -731,7 +731,7 @@ def check_marl_grouping(group_map: Dict[str, List[str]], agent_names: List[str]) def done_or_truncated( - data: TensorDictBase, full_done_spec=None, name="_reset" + data: TensorDictBase, full_done_spec=None, key="_reset" ) -> bool: """Reads the done / truncated keys within a tensordict, and writes a new tensor where the values of both signals are aggregated. @@ -744,7 +744,11 @@ def done_or_truncated( full_done_spec (TensorSpec, optional): the done_spec from the env, indicating where the done leaves have to be found. If not provided, the default `"done"` and `"truncated"` entries will be searched for in the data. - name (NestedKey, optional): where the aggregated result should be written. + key (NestedKey, optional): where the aggregated result should be written. + If ``None``, then the function will not write any key but just output + whether any of the done values was true. + .. note:: if a value is already present for the ``key`` entry, the previous + value will prevail and no update will be achieved. Returns: a boolean value indicating whether any of the done states found in the data contained a ``True``. @@ -773,10 +777,9 @@ def done_or_truncated( """ any_done = False aggregate = None - has_entry = name in data.keys(isinstance(name, tuple)) if full_done_spec is None: for key, item in data.items(): - if not has_entry and key in ("done", "truncated"): + if key in ("done", "truncated"): done = data.get(key, None) if done is None: done = torch.zeros( @@ -786,12 +789,12 @@ def done_or_truncated( aggregate = torch.tensor(False, device=done.device) aggregate = aggregate | done elif isinstance(item, TensorDictBase): - any_done = any_done | done_or_truncated(data=item, full_done_spec=None, name=name) + any_done = any_done | done_or_truncated(data=item, full_done_spec=None, key=key) else: for key, item in full_done_spec.items(): if isinstance(item, CompositeSpec): - any_done = any_done | done_or_truncated(data=data.get(key), full_done_spec=item, name=name) - elif not has_entry: + any_done = any_done | done_or_truncated(data=data.get(key), full_done_spec=item, key=key) + else: done = data.get(key, None) if done is None: done = torch.zeros( @@ -801,6 +804,8 @@ def done_or_truncated( aggregate = torch.tensor(False, device=done.device) aggregate = aggregate | done if aggregate is not None: - data.setdefault(name, aggregate) + if key is not None: + # we use setdefault to keep previous values of ``name``. + data.setdefault(key, aggregate) any_done = any_done | aggregate.any() return any_done From a79b57c665150909fe6a953740b3ac03638fb477 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 04:56:11 -0400 Subject: [PATCH 023/178] fix --- test/test_env.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/test/test_env.py b/test/test_env.py index 57ce420b0f9..6806acc0f39 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -1021,6 +1021,7 @@ def test_parallel_env_nested( @pytest.mark.parametrize("batch_size", [(), (2,), (32, 5)]) def test_env_base_reset_flag(batch_size, max_steps=3): + torch.manual_seed(0) env = CountingEnv(max_steps=max_steps, batch_size=batch_size) env.set_seed(1) @@ -1041,6 +1042,9 @@ def test_env_base_reset_flag(batch_size, max_steps=3): assert (td["next", "observation"] == max_steps + 1).all() _reset = env.done_spec.rand() + while not _reset.any(): + _reset = env.done_spec.rand() + env._allow_done_after_reset = True td_reset = env.reset( TensorDict({"_reset": _reset}, batch_size=env.batch_size, device=env.device) ) From 149781fadb914948a90fcc4f63c6eca49a9347ad Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 05:57:11 -0400 Subject: [PATCH 024/178] fix --- test/test_env.py | 17 ++------- torchrl/envs/batched_envs.py | 2 + torchrl/envs/common.py | 50 +++++++++++++++++++++++-- torchrl/envs/transforms/transforms.py | 53 ++++----------------------- torchrl/envs/utils.py | 13 +++---- 5 files changed, 65 insertions(+), 70 deletions(-) diff --git a/test/test_env.py b/test/test_env.py index 6806acc0f39..51d8acfcd82 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -226,20 +226,10 @@ def test_rollout_predictability(device): @pytest.mark.skipif(not _has_gym, reason="no gym") -@pytest.mark.parametrize( - "env_name", - [ - PENDULUM_VERSIONED, - ], -) -@pytest.mark.parametrize( - "frame_skip", - [ - 1, - ], -) +@pytest.mark.parametrize("env_name",[PENDULUM_VERSIONED]) +@pytest.mark.parametrize("frame_skip",[1]) @pytest.mark.parametrize("truncated_key", ["truncated", "done"]) -@pytest.mark.parametrize("parallel", [True, False]) +@pytest.mark.parametrize("parallel", [False, True]) def test_rollout_reset(env_name, frame_skip, parallel, truncated_key, seed=0): envs = [] for horizon in [20, 30, 40]: @@ -254,7 +244,6 @@ def test_rollout_reset(env_name, frame_skip, parallel, truncated_key, seed=0): else: env = SerialEnv(3, envs) env.set_seed(100) - # out = env._single_rollout(100, break_when_any_done=False) out = env.rollout(100, break_when_any_done=False) assert out.names[-1] == "time" assert out.shape == torch.Size([3, 100]) diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index 56a368d1053..afb1e16b578 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -586,6 +586,7 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: needs_resetting[i] += _reset[i].any() else: missing_reset = True + # why do we break here? break if tensordict is None or missing_reset: @@ -829,6 +830,7 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: needs_resetting[i] += _reset[i].any() else: missing_reset = True + # why do we break here? break if tensordict is None or missing_reset: diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index 0ed3266b66b..db6000980db 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -1290,9 +1290,19 @@ def reset( ) if not self._allow_done_after_reset: - for done_key in self.done_keys: - if tensordict_reset.get(done_key).any(): - raise DONE_AFTER_RESET_ERROR + # we iterate over (reset_key, (done_key, truncated_key)) and check that all + # values where reset was true now have a done set to False. + # If no reset was present, all done and truncated must be False + for reset_key, done_key_group in zip(self.reset_keys, self.done_keys_groups): + reset_value = tensordict.get(reset_key, None) if tensordict is not None else None + if reset_value is not None: + for done_key in done_key_group: + if tensordict_reset.get(done_key)[reset_value].any(): + raise DONE_AFTER_RESET_ERROR + else: + for done_key in done_key_group: + if tensordict_reset.get(done_key).any(): + raise DONE_AFTER_RESET_ERROR if tensordict is not None: tensordict.update(tensordict_reset) @@ -1562,7 +1572,11 @@ def policy(td): # done and truncated are in done_keys # To read the done status, we assess whether any of the done entries # at a given level is True. This is written in the _reset key. - any_done = done_or_truncated(tensordict.get("next"), full_done_spec=self.output_spec['full_done_spec'], key="_reset" if not break_when_any_done else None) + any_done = done_or_truncated( + tensordict.get("next"), + full_done_spec=self.output_spec['full_done_spec'], + key="_reset" if not break_when_any_done else None + ) if (break_when_any_done and any_done) or i == max_steps - 1: break @@ -1621,6 +1635,34 @@ def combine(prefix_key: tuple | None, key: str): self.__dict__['_reset_keys'] = reset_keys return reset_keys + @property + def done_keys_groups(self): + """A list of done keys, grouped as the reset keys. + + This is a list of lists. The outer list has the length of reset keys, the + inner lists contain the done keys (eg, done and truncated) that can + be read to determine a reset when it is absent. + + """ + done_keys_sorted = self.__dict__.get("_done_keys_groups", None) + if done_keys_sorted is not None: + return done_keys_sorted + # done keys, sorted as reset keys + reset_keys = self.reset_keys + done_keys = [[] for _ in range(len(reset_keys))] + reset_keys_iter = iter(reset_keys) + done_keys_iter = iter(done_keys) + curr_reset_key = next(reset_keys_iter) + curr_done_key = next(done_keys_iter) + + for done_key in self.done_keys: + while type(done_key) != type(curr_reset_key) or (isinstance(done_key, tuple) and done_key[:-1] != curr_reset_key[:-1]): # if they are string, they are at the same level + curr_reset_key = next(reset_keys_iter) + curr_done_key = next(done_keys_iter) + curr_done_key.append(done_key) + self.__dict__["_done_keys_groups"] = done_keys + return done_keys + def _select_observation_keys(self, tensordict: TensorDictBase) -> Iterator[str]: for key in tensordict.keys(): if key.rfind("observation") >= 0: diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index cbe6988d8d0..2c1b6ce960b 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -4355,8 +4355,6 @@ def __init__( ) self._done_keys = done_keys self.done_key = done_keys[0] if done_keys and len(done_keys) == 1 else None - self._reset_keys = None - self._done_keys_sorted = None super().__init__([]) @property @@ -4403,47 +4401,11 @@ def step_count_keys(self): @property def reset_keys(self): - reset_keys = self._reset_keys - if reset_keys is None: - # make the default reset keys - reset_keys = [] - for done_key in self.done_keys: - if isinstance(done_key, str): - key = "_reset" - else: - key = (*done_key[:-1], "_reset") - if key not in reset_keys: - reset_keys.append(key) - reset_keys = self._reset_keys = reset_keys - return reset_keys + return self.parent.reset_keys @property - def done_keys_sorted(self): - """A list of done keys, grouped as the reset keys. - - This is a list of lists. The outer list has the length of reset keys, the - inner lists constain the done keys (eg, done and truncated) that can - be read to determine a reset when it is absent. - - """ - done_keys_sorted = self._done_keys_sorted - if done_keys_sorted is not None: - return done_keys_sorted - # done keys, sorted as reset keys - reset_keys = self.reset_keys - done_keys = [[] for _ in range(len(reset_keys))] - reset_keys = iter(reset_keys) - done_keys_iter = iter(done_keys) - curr_reset_key = next(reset_keys) - curr_done_key = next(done_keys_iter) - - for done_key in self.done_keys: - while type(done_key) != type(curr_reset_key) or (isinstance(done_key, tuple) and done_key[:-1] != curr_reset_key[:-1]): # if they are string, they are at the same level - curr_reset_key = next(curr_reset_key) - curr_done_key = next(done_keys_iter) - curr_done_key.append(done_key) - self._done_keys_sorted = done_keys - return done_keys + def done_keys_groups(self): + return self.parent.done_keys_groups @property def full_done_spec(self): @@ -4452,14 +4414,15 @@ def full_done_spec(self): def reset(self, tensordict: TensorDictBase) -> TensorDictBase: # get reset signal for step_count_key, truncated_key, reset_key, done_list_sorted in zip( - self.step_count_keys, self.truncated_keys, self.reset_keys, self.done_keys_sorted + self.step_count_keys, self.truncated_keys, self.reset_keys, self.done_keys_groups ): step_count = tensordict.get(step_count_key, default=None) - reset = tensordict.get(reset_key, None) + reset = tensordict.get(reset_key, default=None) + print('reset within transform', reset) if reset is None: # get done status, just to inform the reset shape, dtype and device for entry in done_list_sorted: - done = tensordict.get(entry, None) + done = tensordict.get(entry, default=None) if done is not None: break else: @@ -4470,13 +4433,13 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: if step_count is None: step_count = torch.zeros_like(reset, dtype=torch.int64) + print('final reset', reset) # zero the step count if reset is needed step_count = torch.where(~reset, step_count, 0) tensordict.set(step_count_key, step_count) if self.max_steps is not None: truncated = step_count >= self.max_steps tensordict.set(truncated_key, truncated) - return tensordict def _step( diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 27335e42ffa..7697863c7bb 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -778,9 +778,9 @@ def done_or_truncated( any_done = False aggregate = None if full_done_spec is None: - for key, item in data.items(): - if key in ("done", "truncated"): - done = data.get(key, None) + for done_key, item in data.items(): + if done_key in ("done", "truncated"): + done = data.get(done_key, None) if done is None: done = torch.zeros( (*data.shape, 1), dtype=torch.bool, device=data.device @@ -791,11 +791,11 @@ def done_or_truncated( elif isinstance(item, TensorDictBase): any_done = any_done | done_or_truncated(data=item, full_done_spec=None, key=key) else: - for key, item in full_done_spec.items(): + for done_key, item in full_done_spec.items(): if isinstance(item, CompositeSpec): any_done = any_done | done_or_truncated(data=data.get(key), full_done_spec=item, key=key) else: - done = data.get(key, None) + done = data.get(done_key, None) if done is None: done = torch.zeros( (*data.shape, 1), dtype=torch.bool, device=data.device @@ -805,7 +805,6 @@ def done_or_truncated( aggregate = aggregate | done if aggregate is not None: if key is not None: - # we use setdefault to keep previous values of ``name``. - data.setdefault(key, aggregate) + data.set(key, aggregate) any_done = any_done | aggregate.any() return any_done From 71a05b1133f513d524ea4d037437d302e92c79a5 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 06:06:33 -0400 Subject: [PATCH 025/178] fix --- torchrl/envs/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 7697863c7bb..68b7196322f 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -793,7 +793,7 @@ def done_or_truncated( else: for done_key, item in full_done_spec.items(): if isinstance(item, CompositeSpec): - any_done = any_done | done_or_truncated(data=data.get(key), full_done_spec=item, key=key) + any_done = any_done | done_or_truncated(data=data.get(done_key), full_done_spec=item, key=key) else: done = data.get(done_key, None) if done is None: From 87bad08c7e14eef2d91fc1fa665dbafc27aa87b9 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 06:08:44 -0400 Subject: [PATCH 026/178] remove prints --- torchrl/envs/transforms/transforms.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 2c1b6ce960b..46fa0ab79ed 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -4418,7 +4418,6 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: ): step_count = tensordict.get(step_count_key, default=None) reset = tensordict.get(reset_key, default=None) - print('reset within transform', reset) if reset is None: # get done status, just to inform the reset shape, dtype and device for entry in done_list_sorted: @@ -4433,7 +4432,6 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: if step_count is None: step_count = torch.zeros_like(reset, dtype=torch.int64) - print('final reset', reset) # zero the step count if reset is needed step_count = torch.where(~reset, step_count, 0) tensordict.set(step_count_key, step_count) From 92c3814fc0859c2fce0083da3d33552dcc614a29 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 07:40:24 -0400 Subject: [PATCH 027/178] amend --- test/_utils_internal.py | 17 ++++ test/test_collector.py | 2 + test/test_env.py | 52 ++++------ test/test_libs.py | 13 +-- test/test_transforms.py | 8 +- torchrl/data/utils.py | 17 ---- torchrl/envs/common.py | 140 +++++++++++++++++++++++--- torchrl/envs/libs/gym.py | 19 +++- torchrl/envs/libs/isaacgym.py | 23 ++--- torchrl/envs/transforms/transforms.py | 108 ++++++++++---------- 10 files changed, 257 insertions(+), 142 deletions(-) diff --git a/test/_utils_internal.py b/test/_utils_internal.py index 1af5a588e74..f1a7af35eb4 100644 --- a/test/_utils_internal.py +++ b/test/_utils_internal.py @@ -347,3 +347,20 @@ def rollout_consistency_assertion( assert ( (r_done[observation_key] - r_done_tp1[observation_key]).norm(dim=-1) > 1e-1 ).all(), (r_done[observation_key] - r_done_tp1[observation_key]).norm(dim=-1) + +def rand_reset(env): + """Generates a tensordict with reset keys that mimic the done spec. + + Values are drawn at random until at least one reset is present. + + """ + full_done_spec = env.full_done_spec + result = {} + for reset_key, list_of_done in zip(env.reset_keys, env.done_keys_groups): + val = full_done_spec[list_of_done[0]].rand() + while not val.any(): + val = full_done_spec[list_of_done[0]].rand() + result[reset_key] = val + # create a data structure that keeps the batch size of the nested specs + result = full_done_spec.zero().update(result).exclude(*full_done_spec.keys(True, True)) + return result \ No newline at end of file diff --git a/test/test_collector.py b/test/test_collector.py index 595582c8c6f..dcea14fda57 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -329,6 +329,8 @@ def test_collector_env_reset(): torch.manual_seed(0) def make_env(): + # This is currently necessary as the methods in GymWrapper may have mismatching backend + # versions. with set_gym_backend(gym_backend()): return TransformedEnv(GymEnv(PONG_VERSIONED, frame_skip=4), StepCounter()) diff --git a/test/test_env.py b/test/test_env.py index 51d8acfcd82..a36a1d6adaa 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -20,7 +20,7 @@ get_default_devices, HALFCHEETAH_VERSIONED, PENDULUM_VERSIONED, - PONG_VERSIONED, + PONG_VERSIONED,rand_reset ) from mocking_classes import ( ActionObsMergeLinear, @@ -471,10 +471,7 @@ def test_parallel_env( ) td = TensorDict( source={"action": env0.action_spec.rand((N,))}, - batch_size=[ - N, - ], - ) + batch_size=[N]) td1 = env_parallel.step(td) assert not td1.is_shared() assert ("next", "done") in td1.keys(True) @@ -489,10 +486,8 @@ def test_parallel_env( _ = env_parallel.step(td) td_reset = TensorDict( - source={"_reset": env_parallel.done_spec.rand()}, - batch_size=[ - N, - ], + source=rand_reset(env_parallel), + batch_size=[N] ) env_parallel.reset(tensordict=td_reset) @@ -547,9 +542,7 @@ def test_parallel_env_with_policy( td = TensorDict( source={"action": env0.action_spec.rand((N,))}, - batch_size=[ - N, - ], + batch_size=[N] ) td1 = env_parallel.step(td) assert not td1.is_shared() @@ -565,10 +558,8 @@ def test_parallel_env_with_policy( _ = env_parallel.step(td) td_reset = TensorDict( - source={"_reset": env_parallel.done_spec.rand()}, - batch_size=[ - N, - ], + source=rand_reset(env_parallel), + batch_size=[N] ) env_parallel.reset(tensordict=td_reset) @@ -932,20 +923,17 @@ def test_parallel_env_reset_flag(self, batch_size, n_workers, max_steps=3): assert (td["next", "done"] == 1).all() assert (td["next"]["observation"] == max_steps + 1).all() - _reset = env.done_spec.rand() - while not _reset.any(): - _reset = env.done_spec.rand() - env._allow_done_after_reset = True td_reset = env.reset( - TensorDict({"_reset": _reset}, batch_size=env.batch_size, device=env.device) + TensorDict(rand_reset(env), batch_size=env.batch_size, device=env.device) ) env.close() + reset = td_reset['_reset'] - assert (td_reset["done"][_reset] == 0).all() - assert (td_reset["observation"][_reset] == 0).all() - assert (td_reset["done"][~_reset] == 1).all() - assert (td_reset["observation"][~_reset] == max_steps + 1).all() + assert (td_reset["done"][reset] == 0).all() + assert (td_reset["observation"][reset] == 0).all() + assert (td_reset["done"][~reset] == 1).all() + assert (td_reset["observation"][~reset] == max_steps + 1).all() @pytest.mark.parametrize("nested_obs_action", [True, False]) @pytest.mark.parametrize("nested_done", [True, False]) @@ -1030,18 +1018,16 @@ def test_env_base_reset_flag(batch_size, max_steps=3): assert (td["next", "done"] == 1).all() assert (td["next", "observation"] == max_steps + 1).all() - _reset = env.done_spec.rand() - while not _reset.any(): - _reset = env.done_spec.rand() env._allow_done_after_reset = True td_reset = env.reset( - TensorDict({"_reset": _reset}, batch_size=env.batch_size, device=env.device) + TensorDict(rand_reset(env), batch_size=env.batch_size, device=env.device) ) + reset = td_reset['_reset'] - assert (td_reset["done"][_reset] == 0).all() - assert (td_reset["observation"][_reset] == 0).all() - assert (td_reset["done"][~_reset] == 1).all() - assert (td_reset["observation"][~_reset] == max_steps + 1).all() + assert (td_reset["done"][reset] == 0).all() + assert (td_reset["observation"][reset] == 0).all() + assert (td_reset["done"][~reset] == 1).all() + assert (td_reset["observation"][~reset] == max_steps + 1).all() @pytest.mark.skipif(not _has_gym, reason="no gym") diff --git a/test/test_libs.py b/test/test_libs.py index ffff59f4212..44ee08da514 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -34,7 +34,7 @@ HALFCHEETAH_VERSIONED, PENDULUM_VERSIONED, PONG_VERSIONED, - rollout_consistency_assertion, + rollout_consistency_assertion,rand_reset ) from packaging import version from tensordict import LazyStackedTensorDict @@ -1509,17 +1509,14 @@ def make_vmas(): .all() ) - _reset = env.done_spec.rand() - while not _reset.any(): - _reset = env.done_spec.rand() - tensordict = env.reset( - TensorDict({"_reset": _reset}, batch_size=env.batch_size, device=env.device) + TensorDict(rand_reset(env), batch_size=env.batch_size, device=env.device) ) - assert not tensordict["done"][_reset].all().item() + reset = tensordict['_reset'] + assert not tensordict["done"][reset].all().item() # vmas resets all the agent dimension if only one of the agents needs resetting # thus, here we check that where we did not reset any agent, all agents are still done - assert tensordict["done"].all(dim=2)[~_reset.any(dim=2)].all().item() + assert tensordict["done"].all(dim=2)[~reset.any(dim=2)].all().item() @pytest.mark.skipif(len(get_available_devices()) < 2, reason="not enough devices") @pytest.mark.parametrize("first", [0, 1]) diff --git a/test/test_transforms.py b/test/test_transforms.py index d0cf6c12397..cea0540fd1e 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -18,7 +18,7 @@ dtype_fixture, get_default_devices, HALFCHEETAH_VERSIONED, - PENDULUM_VERSIONED, + PENDULUM_VERSIONED,rand_reset, PONG_VERSIONED, retry, ) @@ -8190,15 +8190,15 @@ def test_nested( assert torch.all(is_init[max_steps + 1] == 1) assert torch.all(is_init[max_steps + 2 :] == 0) - _reset = env.done_spec.rand() td_reset = transformed_env.reset( TensorDict( - {"_reset": _reset}, + rand_reset(transformed_env), batch_size=env.batch_size, device=env.device, ) ) - assert (td_reset[init_key] == _reset).all() + reset = td_reset["_reset"] + assert (td_reset[init_key] == reset).all() class TestKLRewardTransform(TransformBase): diff --git a/torchrl/data/utils.py b/torchrl/data/utils.py index 2442007747e..5add739b589 100644 --- a/torchrl/data/utils.py +++ b/torchrl/data/utils.py @@ -222,23 +222,6 @@ def contains_lazy_spec(spec: TensorSpec) -> bool: return False -def _check_only_one_entry( - spec: CompositeSpec, - error: RuntimeError, - recursive: bool = True, -): - found_entry = False - for key, value in spec.items(): - if isinstance(value, CompositeSpec): - _check_only_one_entry(value, error, recursive) - else: - if key == "done": - if found_entry: - raise error - else: - found_entry = True - - class CloudpickleWrapper(object): """A wrapper for functions that allow for serialization in multiprocessed settings.""" diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index db6000980db..fff411ac947 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -23,7 +23,7 @@ TensorSpec, UnboundedContinuousTensorSpec, ) -from torchrl.data.utils import _check_only_one_entry, DEVICE_TYPING +from torchrl.data.utils import DEVICE_TYPING from torchrl.envs.utils import ( _replace_last, DONE_AFTER_RESET_ERROR, @@ -638,6 +638,32 @@ def action_spec(self, value: TensorSpec) -> None: finally: self.input_spec.lock_() + @property + def full_action_spec(self): + """The full action spec. + + ``full_action_spec`` is a :class:`~torchrl.data.CompositeSpec`` instance + that contains all the action entries. + + Examples: + >>> from torchrl.envs import BraxEnv + >>> for envname in BraxEnv.available_envs: + ... break + >>> env = BraxEnv(envname) + >>> env.full_action_spec + CompositeSpec( + action: BoundedTensorSpec( + shape=torch.Size([8]), + space=ContinuousBox( + low=Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, contiguous=True), + high=Tensor(shape=torch.Size([8]), device=cpu, dtype=torch.float32, contiguous=True)), + device=cpu, + dtype=torch.float32, + domain=continuous), device=cpu, shape=torch.Size([])) + + """ + return self.input_spec['full_action_spec'] + # Reward spec def _get_reward_keys(self): keys = self.output_spec["full_reward_spec"].keys(True, True) @@ -818,6 +844,32 @@ def reward_spec(self, value: TensorSpec) -> None: finally: self.output_spec.lock_() + @property + def full_reward_spec(self): + """The full reward spec. + + ``full_reward_spec`` is a :class:`~torchrl.data.CompositeSpec`` instance + that contains all the reward entries. + + Examples: + >>> import gymnasium + >>> from torchrl.envs import GymWrapper, TransformedEnv, RenameTransform + >>> base_env = GymWrapper(gymnasium.make("Pendulum-v1")) + >>> env = TransformedEnv(base_env, RenameTransform("reward", ("nested", "reward"))) + >>> env.full_reward_spec + CompositeSpec( + nested: CompositeSpec( + reward: UnboundedContinuousTensorSpec( + shape=torch.Size([1]), + space=ContinuousBox( + low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), + high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), + device=cpu, + dtype=torch.float32, + domain=continuous), device=None, shape=torch.Size([])), device=cpu, shape=torch.Size([])) + + """ + return self.output_spec['full_reward_spec'] # done spec def _get_done_keys(self): if "full_done_spec" not in self.output_spec.keys(): @@ -861,6 +913,37 @@ def done_key(self): ) return self.done_keys[0] + @property + def full_done_spec(self): + """The full done spec. + + ``full_done_spec`` is a :class:`~torchrl.data.CompositeSpec`` instance + that contains all the done entries. + It can be used to generate fake data with a structure that mimics the + one obtained at runtime. + + Examples: + >>> import gymnasium + >>> from torchrl.envs import GymWrapper + >>> env = GymWrapper(gymnasium.make("Pendulum-v1")) + >>> env.full_done_spec + CompositeSpec( + done: DiscreteTensorSpec( + shape=torch.Size([1]), + space=DiscreteBox(n=2), + device=cpu, + dtype=torch.bool, + domain=discrete), + truncated: DiscreteTensorSpec( + shape=torch.Size([1]), + space=DiscreteBox(n=2), + device=cpu, + dtype=torch.bool, + domain=discrete), device=cpu, shape=torch.Size([])) + + """ + return self.output_spec['full_done_spec'] + # Done spec: done specs belong to output_spec @property def done_spec(self) -> TensorSpec: @@ -982,13 +1065,6 @@ def done_spec(self, value: TensorSpec) -> None: "An empty CompositeSpec was passed for the done spec. " "This is currently not permitted." ) - _check_only_one_entry( - value, - error=RuntimeError( - "done_spec has more than one leaf entry for a CompositeSpec. " - "Only one leaf entry per composite spec is currently allowed" - ), - ) else: value = CompositeSpec( done=value.to(device), shape=self.batch_size, device=device @@ -1063,6 +1139,8 @@ def observation_spec(self, value: TensorSpec) -> None: finally: self.output_spec.lock_() + full_observation_spec = observation_spec + # state spec: state specs belong to input_spec @property def state_spec(self) -> CompositeSpec: @@ -1079,12 +1157,22 @@ def state_spec(self) -> CompositeSpec: a generic data container for environment inputs that are not action data. Examples: - >>> from torchrl.envs.libs.gym import GymEnv - >>> env = GymEnv("Pendulum-v1") - >>> # Gym has not state input so this is empty + >>> from torchrl.envs import BraxEnv + >>> for envname in BraxEnv.available_envs: + ... break + >>> env = BraxEnv(envname) >>> env.state_spec CompositeSpec( - , device=cpu, shape=torch.Size([])) + state: CompositeSpec( + pipeline_state: CompositeSpec( + q: UnboundedContinuousTensorSpec( + shape=torch.Size([15]), + space=None, + device=cpu, + dtype=torch.float32, + domain=continuous), + [...], device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])) + """ state_spec = self.input_spec["full_state_spec"] @@ -1119,6 +1207,33 @@ def state_spec(self, value: CompositeSpec) -> None: finally: self.input_spec.lock_() + @property + def full_state_spec(self): + """The full state spec. + + ``full_state_spec`` is a :class:`~torchrl.data.CompositeSpec`` instance + that contains all the state entries (ie, the input data that is not action). + + Examples: + >>> from torchrl.envs import BraxEnv + >>> for envname in BraxEnv.available_envs: + ... break + >>> env = BraxEnv(envname) + >>> env.full_state_spec + CompositeSpec( + state: CompositeSpec( + pipeline_state: CompositeSpec( + q: UnboundedContinuousTensorSpec( + shape=torch.Size([15]), + space=None, + device=cpu, + dtype=torch.float32, + domain=continuous), + [...], device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([])) + + """ + return self.state_spec + def step(self, tensordict: TensorDictBase) -> TensorDictBase: """Makes a step in the environment. @@ -1297,6 +1412,7 @@ def reset( reset_value = tensordict.get(reset_key, None) if tensordict is not None else None if reset_value is not None: for done_key in done_key_group: + print(reset_key, reset_value) if tensordict_reset.get(done_key)[reset_value].any(): raise DONE_AFTER_RESET_ERROR else: diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index 0007854bc4c..8e66a1467a9 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -426,8 +426,20 @@ class GymWrapper(GymLikeEnv, metaclass=_AsyncMeta): libname = "gym" @staticmethod - def get_library_name(env): - # try gym + def get_library_name(env) -> str: + """Given a gym environment, returns the backend name (either gym or gymnasium). + + This can be used to set the appropriate backend when needed: + + Examples: + >>> env = gymnasium.make("Pendulum-v1") + >>> with set_gym_backend(env): + ... env = GymWrapper(env) + + :class:`~GymWrapper` and similar use this method to set their method + to the right backend during instantiation. + + """ try: import gym @@ -473,9 +485,12 @@ def _post_init(self): # with set_gym_backend + allowing for parallel execution (which would # be troublesome when both an old version of gym and recent gymnasium # are present within the same virtual env). + # # These calls seemingly do nothing but they actually get rid of the @implement_for decorator. # We execute them within the set_gym_backend context manager to make sure we get # the right implementation. + # + # This method is executed by the metaclass of GymWrapper. with set_gym_backend(self.get_library_name(self._env)): self._reset_output_transform = self._reset_output_transform self._output_transform = self._output_transform diff --git a/torchrl/envs/libs/isaacgym.py b/torchrl/envs/libs/isaacgym.py index 3aac8516b90..4a7251e79a9 100644 --- a/torchrl/envs/libs/isaacgym.py +++ b/torchrl/envs/libs/isaacgym.py @@ -2,11 +2,13 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from __future__ import annotations + import importlib.util import itertools import warnings -from typing import Any, Dict, Union +from typing import Any, Dict, Union, Tuple import numpy as np import torch @@ -103,18 +105,13 @@ def read_action(self, action): """ return action - def read_done(self, gym_done): - """Done state reader. - - Reads a done state and returns a tuple containing: - - a done state to be set in the environment - - a boolean value indicating whether the frame_skip loop should be broken - - Args: - gym_done (np.ndarray, boolean or other format): done state obtained from the environment - - """ - return gym_done.bool(), gym_done.any() + def read_done( + self, + gym_done: bool, + termination: bool | None = None, + truncation: bool | None = None, + ) -> Tuple[bool, bool, bool]: + return gym_done.bool(), None, gym_done.any() def read_reward(self, total_reward, step_reward): """Reads a reward and the total reward so far (in the frame skip loop) and returns a sum of the two. diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 46fa0ab79ed..4cfc77b942e 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -5086,16 +5086,6 @@ class RenameTransform(Transform): def __init__( self, in_keys, out_keys, in_keys_inv=None, out_keys_inv=None, create_copy=False ): - if "done" in in_keys and not create_copy: - raise ValueError( - "Renaming 'done' is not allowed. Set `create_copy` to `True` " - "to create a copy of the done state." - ) - if "reward" in in_keys and not create_copy: - raise ValueError( - "Renaming 'reward' is not allowed. Set `create_copy` to `True` " - "to create a copy of the reward entry." - ) if in_keys_inv is None: in_keys_inv = [] if out_keys_inv is None: @@ -5143,52 +5133,64 @@ def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase: return tensordict def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: - # we need to check whether there are special keys - output_spec = output_spec.clone() - if "done" in self.in_keys: - for i, out_key in enumerate(self.out_keys): # noqa: B007 - if self.in_keys[i] == "done": - break - else: - raise RuntimeError("Expected one key to be 'done'") - output_spec["full_observation_spec"][out_key] = output_spec[ - "full_done_spec" - ].clone() - if "reward" in self.in_keys: - for i, out_key in enumerate(self.out_keys): # noqa: B007 - if self.in_keys[i] == "reward": - break - else: - raise RuntimeError("Expected one key to be 'reward'") - output_spec["full_observation_spec"][out_key] = output_spec[ - "full_reward_spec" - ].clone() - for in_key, out_key in zip(self.in_keys, self.out_keys): - if in_key in ("reward", "done"): - continue - if out_key in ("done", "reward"): - output_spec[out_key] = output_spec["full_observation_spec"][ - in_key - ].clone() - else: - output_spec["full_observation_spec"][out_key] = output_spec[ - "full_observation_spec" - ][in_key].clone() - if not self.create_copy: - del output_spec["full_observation_spec"][in_key] + for done_key in self.parent.done_keys: + if done_key in self.in_keys: + for i, out_key in enumerate(self.out_keys): # noqa: B007 + if self.in_keys[i] == done_key: + break + else: + # unreachable + raise RuntimeError + output_spec["full_done_spec"][out_key] = output_spec["full_done_spec"][done_key].clone() + if not self.create_copy: + del output_spec["full_done_spec"][done_key] + for reward_key in self.parent.reward_keys: + if reward_key in self.in_keys: + for i, out_key in enumerate(self.out_keys): # noqa: B007 + if self.in_keys[i] == reward_key: + break + else: + # unreachable + raise RuntimeError + output_spec["full_reward_spec"][out_key] = output_spec["full_reward_spec"][reward_key].clone() + if not self.create_copy: + del output_spec["full_reward_spec"][reward_key] + for observation_key in self.parent.full_observation_spec.keys(True): + if observation_key in self.in_keys: + for i, out_key in enumerate(self.out_keys): # noqa: B007 + if self.in_keys[i] == observation_key: + break + else: + # unreachable + raise RuntimeError + output_spec["full_observation_spec"][out_key] = output_spec["full_observation_spec"][observation_key].clone() + if not self.create_copy: + del output_spec["full_observation_spec"][observation_key] return output_spec def transform_input_spec(self, input_spec: CompositeSpec) -> CompositeSpec: - # we need to check whether there are special keys - input_spec = input_spec.clone() - for in_key, out_key in zip(self.in_keys_inv, self.out_keys_inv): - in_key = (in_key,) if not isinstance(in_key, tuple) else in_key - out_key = (out_key,) if not isinstance(out_key, tuple) else out_key - input_spec[("full_state_spec", *out_key)] = input_spec[ - ("full_state_spec", *in_key) - ].clone() - if not self.create_copy: - del input_spec[("full_state_spec", *in_key)] + for action_key in self.parent.action_keys: + if action_key in self.in_keys: + for i, out_key in enumerate(self.out_keys): # noqa: B007 + if self.in_keys[i] == action_key: + break + else: + # unreachable + raise RuntimeError + input_spec["full_action_spec"][out_key] = input_spec["full_action_spec"][action_key].clone() + if not self.create_copy: + del input_spec["full_action_spec"][action_key] + for state_key in self.parent.full_state_spec.keys(True): + if state_key in self.in_keys: + for i, out_key in enumerate(self.out_keys): # noqa: B007 + if self.in_keys[i] == state_key: + break + else: + # unreachable + raise RuntimeError + input_spec["full_state_spec"][out_key] = input_spec["full_state_spec"][state_key].clone() + if not self.create_copy: + del input_spec["full_state_spec"][state_key] return input_spec From 42d4c40995161893e4e34a53405bb7e27536bf00 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 07:44:08 -0400 Subject: [PATCH 028/178] amend --- torchrl/envs/common.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index fff411ac947..729f8e6554b 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -1708,7 +1708,7 @@ def policy(td): if not break_when_any_done and any_done: self.reset(tensordict) - tensordict.exclude(*self.reset_keys, inplace=True) + tensordict.exclude(*self.reset_keys, inplace=True) if callback is not None: callback(self, tensordict) From aec627cbcb58bb5d4ae3fdc90f4fecae7f56d012 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 09:58:35 -0400 Subject: [PATCH 029/178] amend --- test/test_transforms.py | 99 +++++++++++--- torchrl/envs/common.py | 86 ++++++------ torchrl/envs/libs/gym.py | 3 +- torchrl/envs/transforms/transforms.py | 184 ++++++++++++++++++++------ 4 files changed, 272 insertions(+), 100 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index cea0540fd1e..ae5b7ea11e5 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -2502,6 +2502,25 @@ def test_transform_env(self): assert "b" in env.reset().keys() assert "c" in env.reset().keys() + def test_exclude_done(self): + base_env = TestExcludeTransform.EnvWithManyKeys() + env = TransformedEnv(base_env, ExcludeTransform("a", "done" )) + assert "done" not in env.done_keys + check_env_specs(env) + env = TransformedEnv(base_env, ExcludeTransform("a")) + assert "done" in env.done_keys + check_env_specs(env) + + def test_exclude_reward(self): + base_env = TestExcludeTransform.EnvWithManyKeys() + env = TransformedEnv(base_env, ExcludeTransform("a", "reward")) + assert "reward" not in env.reward_keys + check_env_specs(env) + env = TransformedEnv(base_env, ExcludeTransform("a")) + assert "reward" in env.reward_keys + check_env_specs(env) + + @pytest.mark.parametrize("nest_done", [True, False]) @pytest.mark.parametrize("nest_reward", [True, False]) def test_nested(self, nest_reward, nest_done): @@ -2708,6 +2727,32 @@ def test_transform_env(self): assert "b" in env.reset().keys() assert "c" in env.reset().keys() + @pytest.mark.parametrize("keep_done", [True, False]) + def test_select_done(self,keep_done): + base_env = TestExcludeTransform.EnvWithManyKeys() + env = TransformedEnv(base_env, SelectTransform("b", "c", "done", keep_dones=keep_done)) + assert "done" in env.done_keys + check_env_specs(env) + env = TransformedEnv(base_env, SelectTransform("b", "c", keep_dones=keep_done)) + if keep_done: + assert "done" in env.done_keys + else: + assert "done" not in env.done_keys + check_env_specs(env) + + @pytest.mark.parametrize("keep_reward", [True, False]) + def test_select_reward(self,keep_reward): + base_env = TestExcludeTransform.EnvWithManyKeys() + env = TransformedEnv(base_env, SelectTransform("b", "c", "reward", keep_rewards=keep_reward)) + assert "reward" in env.reward_keys + check_env_specs(env) + env = TransformedEnv(base_env, SelectTransform("b", "c", keep_rewards=keep_reward)) + if keep_reward: + assert "reward" in env.reward_keys + else: + assert "reward" not in env.reward_keys + check_env_specs(env) + @pytest.mark.parametrize("nest_done", [True, False]) @pytest.mark.parametrize("nest_reward", [True, False]) def test_nested(self, nest_reward, nest_done): @@ -7779,9 +7824,7 @@ def test_single_trans_env_check(self, create_copy, compose): ["observation_orig"], ["stuff"], ["observation_orig"], - [ - "stuff", - ], + ["stuff" ], create_copy=create_copy, ) if compose: @@ -7814,9 +7857,7 @@ def make_env(): ["observation_orig"], ["stuff"], ["observation_orig"], - [ - "stuff", - ], + ["stuff"], create_copy=create_copy, ), ) @@ -7830,10 +7871,10 @@ def make_env(): ContinuousActionVecMockEnv(), RenameTransform( [ - "observation", + "observation" ], [ - "stuff", + "stuff" ], create_copy=create_copy, ), @@ -7850,7 +7891,7 @@ def make_env(): ["stuff"], ["observation_orig"], [ - "stuff", + "stuff" ], create_copy=create_copy, ), @@ -7867,10 +7908,10 @@ def make_env(): SerialEnv(2, make_env), RenameTransform( [ - "observation", + "observation" ], [ - "stuff", + "stuff" ], create_copy=create_copy, ), @@ -7883,7 +7924,7 @@ def make_env(): ["stuff"], ["observation_orig"], [ - "stuff", + "stuff" ], create_copy=create_copy, ), @@ -7898,10 +7939,10 @@ def make_env(): ParallelEnv(2, make_env), RenameTransform( [ - "observation", + "observation" ], [ - "stuff", + "stuff" ], create_copy=create_copy, ), @@ -7914,7 +7955,7 @@ def make_env(): ["stuff"], ["observation_orig"], [ - "stuff", + "stuff" ], create_copy=create_copy, ), @@ -7968,10 +8009,10 @@ def test_transform_env(self, create_copy): ContinuousActionVecMockEnv(), RenameTransform( [ - "observation", + "observation" ], [ - "stuff", + "stuff" ], create_copy=create_copy, ), @@ -7993,7 +8034,7 @@ def test_transform_env(self, create_copy): ["stuff"], ["observation_orig"], [ - "stuff", + "stuff" ], create_copy=create_copy, ), @@ -8008,6 +8049,28 @@ def test_transform_env(self, create_copy): assert "stuff" in r.keys() assert ("next", "stuff") in r.keys(True) + def test_rename_done_reward(self, create_copy): + env = TransformedEnv( + ContinuousActionVecMockEnv(), + RenameTransform( + ["done"], + [("nested", "other_done")], + create_copy=create_copy, + ), + ) + assert ("nested", "other_done") in env.done_keys + check_env_specs(env) + env = TransformedEnv( + ContinuousActionVecMockEnv(), + RenameTransform( + ["reward"], + [("nested", "reward")], + create_copy=create_copy, + ), + ) + assert ("nested", "reward") in env.reward_keys + check_env_specs(env) + def test_transform_model(self, create_copy): t = RenameTransform(["a"], ["b"], create_copy=create_copy) tensordict = TensorDict({"a": torch.randn(())}, []) diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index 729f8e6554b..4f6df39e91a 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -129,7 +129,18 @@ def to(self, device: DEVICE_TYPING) -> EnvMetaData: ) -class EnvBase(nn.Module, metaclass=abc.ABCMeta): +class _EnvPostInit(abc.ABCMeta): + def __call__(cls, *args, **kwargs): + instance: EnvBase = super().__call__(*args, **kwargs) + # we access lazy attributed to make sure they're built properly. + # This isn't done in `__init__` because we don't know if supre().__init__ + # will be called before or after the specs, batch size etc are set. + _ = instance.done_spec + _ = instance.reward_spec + _ = instance.state_spec + return instance + +class EnvBase(nn.Module, metaclass=_EnvPostInit): """Abstract environment parent class. Methods: @@ -679,10 +690,16 @@ def reward_keys(self) -> List[NestedKey]: By default, there will only be one key named "reward". """ - out = self._reward_keys - if out is None: - out = self._get_reward_keys() - return out + result = self.__dict__.get("_reward_keys", None) + if result is not None: + return result + # caching this is risky, because the full_reward_spec can be modified by + # transforms without acknowledging the output_spec or the env. + # TransformedEnv will erase this value when computing a new output_spec though + # so we should be fine. + result = list(self.full_reward_spec.keys(True, True)) + self.__dict__['_reward_keys'] = result + return result @property def reward_key(self): @@ -784,22 +801,11 @@ def reward_spec(self) -> TensorSpec: ) reward_spec = self.output_spec["full_reward_spec"] - if len(self.reward_keys) > 1: - out = reward_spec + reward_keys = self.reward_keys + if len(reward_keys) > 1 or not len(reward_keys): + return reward_spec else: - try: - out = reward_spec[self.reward_key] - except KeyError: - # the key may have changed - raise KeyError( - "The reward_key attribute seems to have changed. " - "This occurs when a reward_spec is updated without " - "calling `env.reward_spec = new_spec`. " - "Make sure you rely on this type of command " - "to set the reward and other specs." - ) - - return out + return reward_spec[self.reward_keys[0]] @reward_spec.setter def reward_spec(self, value: TensorSpec) -> None: @@ -894,10 +900,16 @@ def done_keys(self) -> List[NestedKey]: By default, there will only be one key named "done". """ - out = self._done_keys - if out is None: - out = self._get_done_keys() - return out + result = self.__dict__.get("_done_keys", None) + if result is not None: + return result + # caching this is risky, because the full_done_spec can be modified by + # transforms without acknowledging the output_spec or the env. + # TransformedEnv will erase this value when computing a new output_spec though + # so we should be fine. + result = list(self.full_done_spec.keys(True, True)) + self.__dict__['_done_keys'] = result + return result @property def done_key(self): @@ -1022,22 +1034,11 @@ def done_spec(self) -> TensorSpec: n=2, shape=(*self.batch_size, 1), dtype=torch.bool, device=self.device ) done_spec = self.output_spec["full_done_spec"] - if len(self.done_keys) > 1: - out = done_spec + done_keys = self.done_keys + if len(done_keys) > 1 or not len(done_keys): + return done_spec else: - try: - out = done_spec[self.done_key] - except KeyError: - # the key may have changed - raise KeyError( - "The done_key attribute seems to have changed. " - "This occurs when a done_spec is updated without " - "calling `env.done_spec = new_spec`. " - "Make sure you rely on this type of command " - "to set the done and other specs." - ) - - return out + return done_spec[self.done_keys[0]] @done_spec.setter def done_spec(self, value: TensorSpec) -> None: @@ -1768,8 +1769,11 @@ def done_keys_groups(self): done_keys = [[] for _ in range(len(reset_keys))] reset_keys_iter = iter(reset_keys) done_keys_iter = iter(done_keys) - curr_reset_key = next(reset_keys_iter) - curr_done_key = next(done_keys_iter) + try: + curr_reset_key = next(reset_keys_iter) + curr_done_key = next(done_keys_iter) + except StopIteration: + return done_keys for done_key in self.done_keys: while type(done_key) != type(curr_reset_key) or (isinstance(done_key, tuple) and done_key[:-1] != curr_reset_key[:-1]): # if they are string, they are at the same level diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index 8e66a1467a9..7a4d63c5ecc 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -35,6 +35,7 @@ default_info_dict_reader, GymLikeEnv, ) +from torchrl.envs.common import _EnvPostInit from torchrl.envs.utils import _classproperty @@ -385,7 +386,7 @@ class PixelObservationWrapper: return False -class _AsyncMeta(abc.ABCMeta): +class _AsyncMeta(_EnvPostInit): def __call__(cls, *args, **kwargs): instance: GymWrapper = super().__call__(*args, **kwargs) if instance._is_batched: diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 4cfc77b942e..8bece1c6f3e 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -37,7 +37,7 @@ TensorSpec, UnboundedContinuousTensorSpec, ) -from torchrl.envs.common import EnvBase, make_tensordict +from torchrl.envs.common import EnvBase, make_tensordict, _EnvPostInit from torchrl.envs.transforms import functional as F from torchrl.envs.transforms.utils import check_finite from torchrl.envs.utils import _sort_keys, done_or_truncated, step_mdp @@ -463,8 +463,14 @@ def to(self, *args, **kwargs): self.empty_cache() return super().to(*args, **kwargs) +class _TEnvPostInit(_EnvPostInit): + def __call__(self, *args, **kwargs): + instance: EnvBase = super().__call__(*args, **kwargs) + # we skip the materialization of the specs, because this can't be done with lazy + # transforms such as ObservationNorm. + return instance -class TransformedEnv(EnvBase): +class TransformedEnv(EnvBase, metaclass=_TEnvPostInit): """A transformed_in environment. Args: @@ -612,6 +618,12 @@ def output_spec(self) -> TensorSpec: """Observation spec of the transformed environment.""" if self.__dict__.get("_output_spec", None) is None or not self.cache_specs: output_spec = self.base_env.output_spec.clone() + + # remove cached key values + self.__dict__['_done_keys'] = None + self.__dict__['_reward_keys'] = None + self.__dict__['_reset_keys'] = None + output_spec.unlock_() output_spec = self.transform.transform_output_spec(output_spec) output_spec.lock_() @@ -4252,10 +4264,11 @@ class StepCounter(Transform): entry to ``True``. truncated_keys (list of NestedKey, optional): the keys where the truncated entries should be written. Defaults to ``["truncated"]``, which is recognised by - data collectors as a reset signal. Must match the length of ``step_count_keys`` and ``done_keys``, + data collectors as a reset signal. + Must match the length of ``step_count_keys`` and ``done_keys``, if provided. If the transform is nested within a transformed environment, the - truncated entry will be part of the ``done_spec``. + truncated entry will be integrated in the ``done_spec``. step_count_keys (list of NestedKeys, optional): the key names of the step count. Must match the length of ``truncated_keys`` and ``done_keys``, if provided. If not, it will default to the ``"step_count"`` key name. @@ -4271,7 +4284,8 @@ class StepCounter(Transform): >>> import gymnasium >>> from torchrl.envs import GymWrapper >>> base_env = GymWrapper(gymnasium.make("Pendulum-v1")) - >>> env = TransformedEnv(base_env, StepCounter(max_steps=5)) + >>> env = TransformedEnv(base_env, + ... StepCounter(max_steps=5)) >>> rollout = env.rollout(100) >>> print(rollout) TensorDict( @@ -4377,7 +4391,7 @@ def truncated_keys(self): def done_keys(self): done_keys = self._done_keys if done_keys is None: - if self.parent: + if self.parent is not None: self._done_keys = done_keys = self.parent.done_keys else: self._done_keys = done_keys = ["done"] @@ -4401,11 +4415,16 @@ def step_count_keys(self): @property def reset_keys(self): - return self.parent.reset_keys + if self.parent is not None: + return self.parent.reset_keys + # fallback on default "_reset" + return ["_reset"] @property def done_keys_groups(self): - return self.parent.done_keys_groups + if self.parent is not None: + return self.parent.done_keys_groups + return [["done", "truncated"]] @property def full_done_spec(self): @@ -4561,12 +4580,37 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase: class ExcludeTransform(Transform): - """Excludes keys from the input tensordict. + """Excludes keys from the data. Args: *excluded_keys (iterable of NestedKey): The name of the keys to exclude. If the key is not present, it is simply ignored. + Examples: + >>> import gymnasium + >>> from torchrl.envs import GymWrapper + >>> env = TransformedEnv( + ... GymWrapper(gymnasium.make("Pendulum-v1")), + ... ExcludeTransform("truncated") + ... ) + >>> env.rollout(3) + TensorDict( + fields={ + action: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False), + done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False), + next: TensorDict( + fields={ + done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False), + observation: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False), + reward: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False)}, + batch_size=torch.Size([3]), + device=cpu, + is_shared=False), + observation: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False)}, + batch_size=torch.Size([3]), + device=cpu, + is_shared=False) + """ def __init__(self, *excluded_keys): @@ -4578,8 +4622,6 @@ def __init__(self, *excluded_keys): "excluded keys must be a list or tuple of strings or tuples of strings." ) self.excluded_keys = excluded_keys - if "reward" in excluded_keys: - raise RuntimeError("'reward' cannot be excluded from the keys.") def _call(self, tensordict: TensorDictBase) -> TensorDictBase: return tensordict.exclude(*self.excluded_keys) @@ -4589,17 +4631,25 @@ def _call(self, tensordict: TensorDictBase) -> TensorDictBase: def reset(self, tensordict: TensorDictBase) -> TensorDictBase: return tensordict.exclude(*self.excluded_keys) - def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec: - if any(key in observation_spec.keys(True, True) for key in self.excluded_keys): - return CompositeSpec( - { - key: value - for key, value in observation_spec.items() - if unravel_key(key) not in self.excluded_keys - }, - shape=observation_spec.shape, - ) - return observation_spec + def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: + full_done_spec = output_spec["full_done_spec"] + full_reward_spec = output_spec["full_reward_spec"] + full_observation_spec = output_spec["full_observation_spec"] + for key in self.excluded_keys: + # done_spec + if unravel_key(key) in list(full_done_spec.keys(True, True)): + del full_done_spec[key] + continue + # reward_spec + if unravel_key(key) in list(full_reward_spec.keys(True, True)): + del full_reward_spec[key] + continue + # observation_spec + if unravel_key(key) in list(full_observation_spec.keys(True, True)): + del full_observation_spec[key] + continue + raise KeyError(f"Key {key} not found in the environment outputs.") + return output_spec class SelectTransform(Transform): @@ -4613,9 +4663,39 @@ class SelectTransform(Transform): *selected_keys (iterable of NestedKey): The name of the keys to select. If the key is not present, it is simply ignored. + Keyword Args: + keep_rewards (bool, optional): if ``False``, the reward keys must be provided + if they should be kept. Defaults to ``True``. + keep_dones (bool, optional): if ``False``, the done keys must be provided + if they should be kept. Defaults to ``True``. + + >>> import gymnasium + >>> from torchrl.envs import GymWrapper + >>> env = TransformedEnv( + ... GymWrapper(gymnasium.make("Pendulum-v1")), + ... SelectTransform("observation", "reward", "done", keep_dones=False), # we leave done behind + ... ) + >>> env.rollout(3) # the truncated key is now absent + TensorDict( + fields={ + action: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False), + done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False), + next: TensorDict( + fields={ + done: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.bool, is_shared=False), + observation: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False), + reward: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False)}, + batch_size=torch.Size([3]), + device=cpu, + is_shared=False), + observation: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False)}, + batch_size=torch.Size([3]), + device=cpu, + is_shared=False) + """ - def __init__(self, *selected_keys): + def __init__(self, *selected_keys, keep_rewards=True, keep_dones=True): super().__init__(in_keys=[], in_keys_inv=[], out_keys=[], out_keys_inv=[]) try: selected_keys = unravel_key_list(selected_keys) @@ -4624,40 +4704,64 @@ def __init__(self, *selected_keys): "selected keys must be a list or tuple of strings or tuples of strings." ) self.selected_keys = selected_keys + self.keep_done_keys = keep_dones + self.keep_reward_keys = keep_rewards def _call(self, tensordict: TensorDictBase) -> TensorDictBase: - if self.parent: + if self.parent is not None: input_keys = self.parent.input_spec.keys(True, True) else: input_keys = [] - reward_key = self.parent.reward_key if self.parent else "reward" - done_key = self.parent.done_key if self.parent else "done" + if self.keep_reward_keys: + reward_keys = self.parent.reward_keys if self.parent else ["reward"] + else: + reward_keys = [] + if self.keep_done_keys: + done_keys = self.parent.done_keys if self.parent else ["done"] + else: + done_keys = [] return tensordict.select( - *self.selected_keys, reward_key, done_key, *input_keys, strict=False + *self.selected_keys, *reward_keys, *done_keys, *input_keys, strict=False ) forward = _call def reset(self, tensordict: TensorDictBase) -> TensorDictBase: - if self.parent: + if self.parent is not None: input_keys = self.parent.input_spec.keys(True, True) else: input_keys = [] - reward_key = self.parent.reward_key if self.parent else "reward" - done_key = self.parent.done_key if self.parent else "done" + if self.keep_reward_keys: + reward_keys = self.parent.reward_keys if self.parent else ["reward"] + else: + reward_keys = [] + if self.keep_done_keys: + done_keys = self.parent.done_keys if self.parent else ["done"] + else: + done_keys = [] return tensordict.select( - *self.selected_keys, reward_key, done_key, *input_keys, strict=False + *self.selected_keys, *reward_keys, *done_keys, *input_keys, strict=False ) - def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec: - return CompositeSpec( - { - key: value - for key, value in observation_spec.items() - if unravel_key(key) in self.selected_keys - }, - shape=observation_spec.shape, - ) + def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: + full_done_spec = output_spec["full_done_spec"] + full_reward_spec = output_spec["full_reward_spec"] + full_observation_spec = output_spec["full_observation_spec"] + if not self.keep_done_keys: + for key in list(full_done_spec.keys(True, True)): + if unravel_key(key) not in self.selected_keys: + del full_done_spec[key] + + for key in list(full_observation_spec.keys(True, True)): + if unravel_key(key) not in self.selected_keys: + del full_observation_spec[key] + + if not self.keep_reward_keys: + for key in list(full_reward_spec.keys(True, True)): + if unravel_key(key) not in self.selected_keys: + del full_reward_spec[key] + + return output_spec class TimeMaxPool(Transform): From b2303bd170b3e235552346194731bbb766d39e75 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 10:21:21 -0400 Subject: [PATCH 030/178] amend --- torchrl/envs/common.py | 1 - torchrl/envs/transforms/transforms.py | 8 +++++++- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index 4f6df39e91a..3f2f339c9fb 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -1413,7 +1413,6 @@ def reset( reset_value = tensordict.get(reset_key, None) if tensordict is not None else None if reset_value is not None: for done_key in done_key_group: - print(reset_key, reset_value) if tensordict_reset.get(done_key)[reset_value].any(): raise DONE_AFTER_RESET_ERROR else: diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 8bece1c6f3e..b2bde668c1f 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -465,7 +465,7 @@ def to(self, *args, **kwargs): class _TEnvPostInit(_EnvPostInit): def __call__(self, *args, **kwargs): - instance: EnvBase = super().__call__(*args, **kwargs) + instance: EnvBase = super(_EnvPostInit, self).__call__(*args, **kwargs) # we skip the materialization of the specs, because this can't be done with lazy # transforms such as ObservationNorm. return instance @@ -5038,6 +5038,12 @@ def set_container(self, container: Union[Transform, EnvBase]) -> None: def out_keys(self): return self.init_keys + @out_keys.setter + def out_keys(self, value): + if value in (None, []): + return + raise ValueError("Cannot set non-empty out-keys when out-keys are defined by the init_key value.") + @property def init_keys(self): if self._init_keys is not None: From f6a497bff4c1a772a43fdfd6252fc5731b968396 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 10:29:04 -0400 Subject: [PATCH 031/178] amend --- torchrl/envs/transforms/transforms.py | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index b2bde668c1f..a423f6350e8 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -4509,13 +4509,20 @@ def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: truncated_key = unravel_key(truncated_key) # find a matching done key (there might be more than one) for done_key in self.done_keys: - if type(done_key) == type(truncated_key): + # check root + if type(done_key) != type(truncated_key): + continue + if isinstance(done_key, tuple): + if done_key[:-1] == truncated_key[:-1]: + shape = full_done_spec[done_key].shape + break + if isinstance(done_key, str): shape = full_done_spec[done_key].shape break + else: - raise RuntimeError( - "Could not find a matching done_key to get the truncated shape. " - "Make sure the truncated entries are associated with a done entry." + raise KeyError( + f"Could not find root of truncated_key {truncated_key} in done keys {self.done_keys}." ) full_done_spec[truncated_key] = DiscreteTensorSpec( 2, dtype=torch.bool, device=output_spec.device, shape=shape @@ -4545,15 +4552,16 @@ def transform_input_spec(self, input_spec: CompositeSpec) -> CompositeSpec: if type(done_key) != type(step_count_key): continue if isinstance(done_key, tuple): - if done_key[:-1] == step_count_key: + if done_key[:-1] == step_count_key[:-1]: shape = full_done_spec[done_key].shape break if isinstance(done_key, str): shape = full_done_spec[done_key].shape break + else: raise KeyError( - f"Could not find root of step_count_key {step_count_key}." + f"Could not find root of step_count_key {step_count_key} in done keys {self.done_keys}." ) input_spec[ From aac630fc479dd7c06c0da1b712287a3f51071e17 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 10:31:42 -0400 Subject: [PATCH 032/178] amend --- torchrl/envs/transforms/transforms.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index a423f6350e8..62be4c8e3c0 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -4485,13 +4485,20 @@ def transform_observation_spec( step_count_key = unravel_key(step_count_key) # find a matching done key (there might be more than one) for done_key in self.done_keys: - if type(done_key) == type(step_count_key): + # check root + if type(done_key) != type(step_count_key): + continue + if isinstance(done_key, tuple): + if done_key[:-1] == step_count_key[:-1]: + shape = full_done_spec[done_key].shape + break + if isinstance(done_key, str): shape = full_done_spec[done_key].shape break + else: - raise RuntimeError( - "Could not find a matching done_key to get the step_count shape. " - "Make sure the step_count entries are associated with a done entry." + raise KeyError( + f"Could not find root of step_count_key {step_count_key} in done keys {self.done_keys}." ) observation_spec[step_count_key] = BoundedTensorSpec( shape=shape, From 606ee3a34270c92f0b5b9767f4ea970e6abc17d1 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 10:38:16 -0400 Subject: [PATCH 033/178] lint and fixes --- test/_utils_internal.py | 7 ++- test/test_env.py | 32 ++++-------- test/test_libs.py | 5 +- test/test_transforms.py | 75 +++++++++++---------------- torchrl/envs/common.py | 36 ++++++++----- torchrl/envs/libs/gym.py | 2 +- torchrl/envs/libs/isaacgym.py | 2 +- torchrl/envs/transforms/transforms.py | 47 ++++++++++++----- torchrl/envs/utils.py | 12 +++-- 9 files changed, 114 insertions(+), 104 deletions(-) diff --git a/test/_utils_internal.py b/test/_utils_internal.py index f1a7af35eb4..b0f982fd001 100644 --- a/test/_utils_internal.py +++ b/test/_utils_internal.py @@ -348,6 +348,7 @@ def rollout_consistency_assertion( (r_done[observation_key] - r_done_tp1[observation_key]).norm(dim=-1) > 1e-1 ).all(), (r_done[observation_key] - r_done_tp1[observation_key]).norm(dim=-1) + def rand_reset(env): """Generates a tensordict with reset keys that mimic the done spec. @@ -362,5 +363,7 @@ def rand_reset(env): val = full_done_spec[list_of_done[0]].rand() result[reset_key] = val # create a data structure that keeps the batch size of the nested specs - result = full_done_spec.zero().update(result).exclude(*full_done_spec.keys(True, True)) - return result \ No newline at end of file + result = ( + full_done_spec.zero().update(result).exclude(*full_done_spec.keys(True, True)) + ) + return result diff --git a/test/test_env.py b/test/test_env.py index a36a1d6adaa..1c83e39bbcc 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -20,7 +20,8 @@ get_default_devices, HALFCHEETAH_VERSIONED, PENDULUM_VERSIONED, - PONG_VERSIONED,rand_reset + PONG_VERSIONED, + rand_reset, ) from mocking_classes import ( ActionObsMergeLinear, @@ -226,8 +227,8 @@ def test_rollout_predictability(device): @pytest.mark.skipif(not _has_gym, reason="no gym") -@pytest.mark.parametrize("env_name",[PENDULUM_VERSIONED]) -@pytest.mark.parametrize("frame_skip",[1]) +@pytest.mark.parametrize("env_name", [PENDULUM_VERSIONED]) +@pytest.mark.parametrize("frame_skip", [1]) @pytest.mark.parametrize("truncated_key", ["truncated", "done"]) @pytest.mark.parametrize("parallel", [False, True]) def test_rollout_reset(env_name, frame_skip, parallel, truncated_key, seed=0): @@ -469,9 +470,7 @@ def test_parallel_env( transformed_out=transformed_out, N=N, ) - td = TensorDict( - source={"action": env0.action_spec.rand((N,))}, - batch_size=[N]) + td = TensorDict(source={"action": env0.action_spec.rand((N,))}, batch_size=[N]) td1 = env_parallel.step(td) assert not td1.is_shared() assert ("next", "done") in td1.keys(True) @@ -485,10 +484,7 @@ def test_parallel_env( ) _ = env_parallel.step(td) - td_reset = TensorDict( - source=rand_reset(env_parallel), - batch_size=[N] - ) + td_reset = TensorDict(source=rand_reset(env_parallel), batch_size=[N]) env_parallel.reset(tensordict=td_reset) td = env_parallel.rollout(policy=None, max_steps=T) @@ -540,10 +536,7 @@ def test_parallel_env_with_policy( ), ) - td = TensorDict( - source={"action": env0.action_spec.rand((N,))}, - batch_size=[N] - ) + td = TensorDict(source={"action": env0.action_spec.rand((N,))}, batch_size=[N]) td1 = env_parallel.step(td) assert not td1.is_shared() assert ("next", "done") in td1.keys(True) @@ -557,10 +550,7 @@ def test_parallel_env_with_policy( ) _ = env_parallel.step(td) - td_reset = TensorDict( - source=rand_reset(env_parallel), - batch_size=[N] - ) + td_reset = TensorDict(source=rand_reset(env_parallel), batch_size=[N]) env_parallel.reset(tensordict=td_reset) td = env_parallel.rollout(policy=policy, max_steps=T) @@ -898,7 +888,7 @@ def env_fn2(seed): env1.close() env2.close() - @pytest.mark.parametrize("batch_size",[(32, 5),(4,),(1,),()]) + @pytest.mark.parametrize("batch_size", [(32, 5), (4,), (1,), ()]) @pytest.mark.parametrize("n_workers", [2, 1]) def test_parallel_env_reset_flag(self, batch_size, n_workers, max_steps=3): torch.manual_seed(1) @@ -928,7 +918,7 @@ def test_parallel_env_reset_flag(self, batch_size, n_workers, max_steps=3): TensorDict(rand_reset(env), batch_size=env.batch_size, device=env.device) ) env.close() - reset = td_reset['_reset'] + reset = td_reset["_reset"] assert (td_reset["done"][reset] == 0).all() assert (td_reset["observation"][reset] == 0).all() @@ -1022,7 +1012,7 @@ def test_env_base_reset_flag(batch_size, max_steps=3): td_reset = env.reset( TensorDict(rand_reset(env), batch_size=env.batch_size, device=env.device) ) - reset = td_reset['_reset'] + reset = td_reset["_reset"] assert (td_reset["done"][reset] == 0).all() assert (td_reset["observation"][reset] == 0).all() diff --git a/test/test_libs.py b/test/test_libs.py index 44ee08da514..5f22081bff6 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -34,7 +34,8 @@ HALFCHEETAH_VERSIONED, PENDULUM_VERSIONED, PONG_VERSIONED, - rollout_consistency_assertion,rand_reset + rand_reset, + rollout_consistency_assertion, ) from packaging import version from tensordict import LazyStackedTensorDict @@ -1512,7 +1513,7 @@ def make_vmas(): tensordict = env.reset( TensorDict(rand_reset(env), batch_size=env.batch_size, device=env.device) ) - reset = tensordict['_reset'] + reset = tensordict["_reset"] assert not tensordict["done"][reset].all().item() # vmas resets all the agent dimension if only one of the agents needs resetting # thus, here we check that where we did not reset any agent, all agents are still done diff --git a/test/test_transforms.py b/test/test_transforms.py index ae5b7ea11e5..31dcddc15a8 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -18,8 +18,9 @@ dtype_fixture, get_default_devices, HALFCHEETAH_VERSIONED, - PENDULUM_VERSIONED,rand_reset, + PENDULUM_VERSIONED, PONG_VERSIONED, + rand_reset, retry, ) from mocking_classes import ( @@ -2504,7 +2505,7 @@ def test_transform_env(self): def test_exclude_done(self): base_env = TestExcludeTransform.EnvWithManyKeys() - env = TransformedEnv(base_env, ExcludeTransform("a", "done" )) + env = TransformedEnv(base_env, ExcludeTransform("a", "done")) assert "done" not in env.done_keys check_env_specs(env) env = TransformedEnv(base_env, ExcludeTransform("a")) @@ -2520,7 +2521,6 @@ def test_exclude_reward(self): assert "reward" in env.reward_keys check_env_specs(env) - @pytest.mark.parametrize("nest_done", [True, False]) @pytest.mark.parametrize("nest_reward", [True, False]) def test_nested(self, nest_reward, nest_done): @@ -2728,9 +2728,11 @@ def test_transform_env(self): assert "c" in env.reset().keys() @pytest.mark.parametrize("keep_done", [True, False]) - def test_select_done(self,keep_done): + def test_select_done(self, keep_done): base_env = TestExcludeTransform.EnvWithManyKeys() - env = TransformedEnv(base_env, SelectTransform("b", "c", "done", keep_dones=keep_done)) + env = TransformedEnv( + base_env, SelectTransform("b", "c", "done", keep_dones=keep_done) + ) assert "done" in env.done_keys check_env_specs(env) env = TransformedEnv(base_env, SelectTransform("b", "c", keep_dones=keep_done)) @@ -2741,12 +2743,16 @@ def test_select_done(self,keep_done): check_env_specs(env) @pytest.mark.parametrize("keep_reward", [True, False]) - def test_select_reward(self,keep_reward): + def test_select_reward(self, keep_reward): base_env = TestExcludeTransform.EnvWithManyKeys() - env = TransformedEnv(base_env, SelectTransform("b", "c", "reward", keep_rewards=keep_reward)) + env = TransformedEnv( + base_env, SelectTransform("b", "c", "reward", keep_rewards=keep_reward) + ) assert "reward" in env.reward_keys check_env_specs(env) - env = TransformedEnv(base_env, SelectTransform("b", "c", keep_rewards=keep_reward)) + env = TransformedEnv( + base_env, SelectTransform("b", "c", keep_rewards=keep_reward) + ) if keep_reward: assert "reward" in env.reward_keys else: @@ -7824,7 +7830,7 @@ def test_single_trans_env_check(self, create_copy, compose): ["observation_orig"], ["stuff"], ["observation_orig"], - ["stuff" ], + ["stuff"], create_copy=create_copy, ) if compose: @@ -7870,12 +7876,8 @@ def make_env(): return TransformedEnv( ContinuousActionVecMockEnv(), RenameTransform( - [ - "observation" - ], - [ - "stuff" - ], + ["observation"], + ["stuff"], create_copy=create_copy, ), ) @@ -7890,9 +7892,7 @@ def make_env(): ["observation_orig"], ["stuff"], ["observation_orig"], - [ - "stuff" - ], + ["stuff"], create_copy=create_copy, ), ) @@ -7907,12 +7907,8 @@ def make_env(): env = TransformedEnv( SerialEnv(2, make_env), RenameTransform( - [ - "observation" - ], - [ - "stuff" - ], + ["observation"], + ["stuff"], create_copy=create_copy, ), ) @@ -7923,9 +7919,7 @@ def make_env(): ["observation_orig"], ["stuff"], ["observation_orig"], - [ - "stuff" - ], + ["stuff"], create_copy=create_copy, ), ) @@ -7938,12 +7932,8 @@ def make_env(): env = TransformedEnv( ParallelEnv(2, make_env), RenameTransform( - [ - "observation" - ], - [ - "stuff" - ], + ["observation"], + ["stuff"], create_copy=create_copy, ), ) @@ -7954,9 +7944,7 @@ def make_env(): ["observation_orig"], ["stuff"], ["observation_orig"], - [ - "stuff" - ], + ["stuff"], create_copy=create_copy, ), ) @@ -8008,12 +7996,8 @@ def test_transform_env(self, create_copy): env = TransformedEnv( ContinuousActionVecMockEnv(), RenameTransform( - [ - "observation" - ], - [ - "stuff" - ], + ["observation"], + ["stuff"], create_copy=create_copy, ), ) @@ -8033,9 +8017,7 @@ def test_transform_env(self, create_copy): ["observation_orig"], ["stuff"], ["observation_orig"], - [ - "stuff" - ], + ["stuff"], create_copy=create_copy, ), ) @@ -8237,6 +8219,7 @@ def test_nested( env, InitTracker(init_key=init_key), ) + init_key = transformed_env.transform.init_keys[0] td = transformed_env.rollout( rollout_length, policy=policy, break_when_any_done=False ) @@ -8260,7 +8243,7 @@ def test_nested( device=env.device, ) ) - reset = td_reset["_reset"] + reset = td_reset["data", "_reset"] assert (td_reset[init_key] == reset).all() diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index 3f2f339c9fb..a4d2f4b3c29 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -27,8 +27,9 @@ from torchrl.envs.utils import ( _replace_last, DONE_AFTER_RESET_ERROR, + done_or_truncated, get_available_libraries, - step_mdp, done_or_truncated, + step_mdp, ) LIBRARIES = get_available_libraries() @@ -140,6 +141,7 @@ def __call__(cls, *args, **kwargs): _ = instance.state_spec return instance + class EnvBase(nn.Module, metaclass=_EnvPostInit): """Abstract environment parent class. @@ -673,7 +675,7 @@ def full_action_spec(self): domain=continuous), device=cpu, shape=torch.Size([])) """ - return self.input_spec['full_action_spec'] + return self.input_spec["full_action_spec"] # Reward spec def _get_reward_keys(self): @@ -698,7 +700,7 @@ def reward_keys(self) -> List[NestedKey]: # TransformedEnv will erase this value when computing a new output_spec though # so we should be fine. result = list(self.full_reward_spec.keys(True, True)) - self.__dict__['_reward_keys'] = result + self.__dict__["_reward_keys"] = result return result @property @@ -875,7 +877,8 @@ def full_reward_spec(self): domain=continuous), device=None, shape=torch.Size([])), device=cpu, shape=torch.Size([])) """ - return self.output_spec['full_reward_spec'] + return self.output_spec["full_reward_spec"] + # done spec def _get_done_keys(self): if "full_done_spec" not in self.output_spec.keys(): @@ -908,7 +911,7 @@ def done_keys(self) -> List[NestedKey]: # TransformedEnv will erase this value when computing a new output_spec though # so we should be fine. result = list(self.full_done_spec.keys(True, True)) - self.__dict__['_done_keys'] = result + self.__dict__["_done_keys"] = result return result @property @@ -954,7 +957,7 @@ def full_done_spec(self): domain=discrete), device=cpu, shape=torch.Size([])) """ - return self.output_spec['full_done_spec'] + return self.output_spec["full_done_spec"] # Done spec: done specs belong to output_spec @property @@ -1409,8 +1412,12 @@ def reset( # we iterate over (reset_key, (done_key, truncated_key)) and check that all # values where reset was true now have a done set to False. # If no reset was present, all done and truncated must be False - for reset_key, done_key_group in zip(self.reset_keys, self.done_keys_groups): - reset_value = tensordict.get(reset_key, None) if tensordict is not None else None + for reset_key, done_key_group in zip( + self.reset_keys, self.done_keys_groups + ): + reset_value = ( + tensordict.get(reset_key, None) if tensordict is not None else None + ) if reset_value is not None: for done_key in done_key_group: if tensordict_reset.get(done_key)[reset_value].any(): @@ -1690,8 +1697,8 @@ def policy(td): # at a given level is True. This is written in the _reset key. any_done = done_or_truncated( tensordict.get("next"), - full_done_spec=self.output_spec['full_done_spec'], - key="_reset" if not break_when_any_done else None + full_done_spec=self.output_spec["full_done_spec"], + key="_reset" if not break_when_any_done else None, ) if (break_when_any_done and any_done) or i == max_steps - 1: @@ -1734,21 +1741,24 @@ def reset_keys(self) -> List[NestedKey]: return reset_keys prefixes = set() reset_keys = [] + def prefix(key: NestedKey): if isinstance(key, str): return None return key[:-1] + def combine(prefix_key: tuple | None, key: str): if prefix_key is None: return key return (*prefix_key, key) + for done_key in self.done_keys: prefix_key = prefix(done_key) if prefix_key in prefixes: continue prefixes.add(prefix_key) reset_keys.append(combine(prefix_key, "_reset")) - self.__dict__['_reset_keys'] = reset_keys + self.__dict__["_reset_keys"] = reset_keys return reset_keys @property @@ -1775,7 +1785,9 @@ def done_keys_groups(self): return done_keys for done_key in self.done_keys: - while type(done_key) != type(curr_reset_key) or (isinstance(done_key, tuple) and done_key[:-1] != curr_reset_key[:-1]): # if they are string, they are at the same level + while type(done_key) != type(curr_reset_key) or ( + isinstance(done_key, tuple) and done_key[:-1] != curr_reset_key[:-1] + ): # if they are string, they are at the same level curr_reset_key = next(reset_keys_iter) curr_done_key = next(done_keys_iter) curr_done_key.append(done_key) diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index 7a4d63c5ecc..ea32459f6b7 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -29,13 +29,13 @@ ) from torchrl.data.utils import numpy_to_torch_dtype_dict from torchrl.envs.batched_envs import CloudpickleWrapper +from torchrl.envs.common import _EnvPostInit from torchrl.envs.gym_like import ( BaseInfoDictReader, default_info_dict_reader, GymLikeEnv, ) -from torchrl.envs.common import _EnvPostInit from torchrl.envs.utils import _classproperty diff --git a/torchrl/envs/libs/isaacgym.py b/torchrl/envs/libs/isaacgym.py index 4a7251e79a9..6cdfcfccf50 100644 --- a/torchrl/envs/libs/isaacgym.py +++ b/torchrl/envs/libs/isaacgym.py @@ -8,7 +8,7 @@ import itertools import warnings -from typing import Any, Dict, Union, Tuple +from typing import Any, Dict, Tuple, Union import numpy as np import torch diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 62be4c8e3c0..41a521f5f59 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -37,7 +37,7 @@ TensorSpec, UnboundedContinuousTensorSpec, ) -from torchrl.envs.common import EnvBase, make_tensordict, _EnvPostInit +from torchrl.envs.common import _EnvPostInit, EnvBase, make_tensordict from torchrl.envs.transforms import functional as F from torchrl.envs.transforms.utils import check_finite from torchrl.envs.utils import _sort_keys, done_or_truncated, step_mdp @@ -463,6 +463,7 @@ def to(self, *args, **kwargs): self.empty_cache() return super().to(*args, **kwargs) + class _TEnvPostInit(_EnvPostInit): def __call__(self, *args, **kwargs): instance: EnvBase = super(_EnvPostInit, self).__call__(*args, **kwargs) @@ -470,6 +471,7 @@ def __call__(self, *args, **kwargs): # transforms such as ObservationNorm. return instance + class TransformedEnv(EnvBase, metaclass=_TEnvPostInit): """A transformed_in environment. @@ -620,9 +622,9 @@ def output_spec(self) -> TensorSpec: output_spec = self.base_env.output_spec.clone() # remove cached key values - self.__dict__['_done_keys'] = None - self.__dict__['_reward_keys'] = None - self.__dict__['_reset_keys'] = None + self.__dict__["_done_keys"] = None + self.__dict__["_reward_keys"] = None + self.__dict__["_reset_keys"] = None output_spec.unlock_() output_spec = self.transform.transform_output_spec(output_spec) @@ -4433,7 +4435,10 @@ def full_done_spec(self): def reset(self, tensordict: TensorDictBase) -> TensorDictBase: # get reset signal for step_count_key, truncated_key, reset_key, done_list_sorted in zip( - self.step_count_keys, self.truncated_keys, self.reset_keys, self.done_keys_groups + self.step_count_keys, + self.truncated_keys, + self.reset_keys, + self.done_keys_groups, ): step_count = tensordict.get(step_count_key, default=None) reset = tensordict.get(reset_key, default=None) @@ -4446,7 +4451,7 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: else: # It may be the case that reset did not provide a done state, in which case # we fall back on the spec - done = self.parent.output_spec['full_done_spec', entry].zero() + done = self.parent.output_spec["full_done_spec", entry].zero() reset = torch.ones_like(done) if step_count is None: step_count = torch.zeros_like(reset, dtype=torch.int64) @@ -5057,7 +5062,9 @@ def out_keys(self): def out_keys(self, value): if value in (None, []): return - raise ValueError("Cannot set non-empty out-keys when out-keys are defined by the init_key value.") + raise ValueError( + "Cannot set non-empty out-keys when out-keys are defined by the init_key value." + ) @property def init_keys(self): @@ -5135,14 +5142,16 @@ def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec if type(done_key) != type(init_key): continue if isinstance(done_key, tuple): - if done_key[:-1] == init_key: + if done_key[:-1] == init_key[:-1]: shape = full_done_spec[done_key].shape break if isinstance(done_key, str): shape = full_done_spec[done_key].shape break else: - raise KeyError(f"Could not find root of init_key {init_key}.") + raise KeyError( + f"Could not find root of init_key {init_key} within done_keys {self.parent.done_keys}." + ) observation_spec[init_key] = DiscreteTensorSpec( 2, dtype=torch.bool, @@ -5266,7 +5275,9 @@ def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: else: # unreachable raise RuntimeError - output_spec["full_done_spec"][out_key] = output_spec["full_done_spec"][done_key].clone() + output_spec["full_done_spec"][out_key] = output_spec["full_done_spec"][ + done_key + ].clone() if not self.create_copy: del output_spec["full_done_spec"][done_key] for reward_key in self.parent.reward_keys: @@ -5277,7 +5288,9 @@ def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: else: # unreachable raise RuntimeError - output_spec["full_reward_spec"][out_key] = output_spec["full_reward_spec"][reward_key].clone() + output_spec["full_reward_spec"][out_key] = output_spec[ + "full_reward_spec" + ][reward_key].clone() if not self.create_copy: del output_spec["full_reward_spec"][reward_key] for observation_key in self.parent.full_observation_spec.keys(True): @@ -5288,7 +5301,9 @@ def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: else: # unreachable raise RuntimeError - output_spec["full_observation_spec"][out_key] = output_spec["full_observation_spec"][observation_key].clone() + output_spec["full_observation_spec"][out_key] = output_spec[ + "full_observation_spec" + ][observation_key].clone() if not self.create_copy: del output_spec["full_observation_spec"][observation_key] return output_spec @@ -5302,7 +5317,9 @@ def transform_input_spec(self, input_spec: CompositeSpec) -> CompositeSpec: else: # unreachable raise RuntimeError - input_spec["full_action_spec"][out_key] = input_spec["full_action_spec"][action_key].clone() + input_spec["full_action_spec"][out_key] = input_spec[ + "full_action_spec" + ][action_key].clone() if not self.create_copy: del input_spec["full_action_spec"][action_key] for state_key in self.parent.full_state_spec.keys(True): @@ -5313,7 +5330,9 @@ def transform_input_spec(self, input_spec: CompositeSpec) -> CompositeSpec: else: # unreachable raise RuntimeError - input_spec["full_state_spec"][out_key] = input_spec["full_state_spec"][state_key].clone() + input_spec["full_state_spec"][out_key] = input_spec["full_state_spec"][ + state_key + ].clone() if not self.create_copy: del input_spec["full_state_spec"][state_key] return input_spec diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 68b7196322f..c014c66d533 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -730,9 +730,7 @@ def check_marl_grouping(group_map: Dict[str, List[str]], agent_names: List[str]) raise ValueError(f"Agent {agent_name} not found in any group") -def done_or_truncated( - data: TensorDictBase, full_done_spec=None, key="_reset" -) -> bool: +def done_or_truncated(data: TensorDictBase, full_done_spec=None, key="_reset") -> bool: """Reads the done / truncated keys within a tensordict, and writes a new tensor where the values of both signals are aggregated. The modification occurs in-place within the TensorDict instance provided. @@ -789,11 +787,15 @@ def done_or_truncated( aggregate = torch.tensor(False, device=done.device) aggregate = aggregate | done elif isinstance(item, TensorDictBase): - any_done = any_done | done_or_truncated(data=item, full_done_spec=None, key=key) + any_done = any_done | done_or_truncated( + data=item, full_done_spec=None, key=key + ) else: for done_key, item in full_done_spec.items(): if isinstance(item, CompositeSpec): - any_done = any_done | done_or_truncated(data=data.get(done_key), full_done_spec=item, key=key) + any_done = any_done | done_or_truncated( + data=data.get(done_key), full_done_spec=item, key=key + ) else: done = data.get(done_key, None) if done is None: From 6ba0d38e14b6c13a31aed09893aa307d754f1316 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 10:43:02 -0400 Subject: [PATCH 034/178] amend --- test/test_transforms.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index 31dcddc15a8..a797d7fb975 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -8236,14 +8236,17 @@ def test_nested( assert torch.all(is_init[max_steps + 1] == 1) assert torch.all(is_init[max_steps + 2 :] == 0) - td_reset = transformed_env.reset( - TensorDict( - rand_reset(transformed_env), - batch_size=env.batch_size, - device=env.device, - ) + td_reset = TensorDict( + rand_reset(transformed_env), + batch_size=env.batch_size, + device=env.device, ) - reset = td_reset["data", "_reset"] + if nested_done: + reset = td_reset["data", "_reset"] + else: + reset = td_reset["_reset"] + + td_reset = transformed_env.reset(td_reset) assert (td_reset[init_key] == reset).all() From 76b3f0cd658315dbf597c6df0be92327d6151f6d Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 10:58:01 -0400 Subject: [PATCH 035/178] amend --- test/test_transforms.py | 6 +++--- torchrl/envs/transforms/transforms.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index a797d7fb975..6f1e4f023a5 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -632,10 +632,10 @@ def test_nested(self, nested_dim=3, batch_size=(32, 1), rollout_length=6, cat_N= nested_dim, 1, ) - tranformed_env = TransformedEnv( + transformed_env = TransformedEnv( env, CatFrames(dim=-1, N=cat_N, in_keys=[("data", "states")]) ) - td = tranformed_env.rollout(rollout_length, policy=policy) + td = transformed_env.rollout(rollout_length, policy=policy) assert td[("data", "states")].shape == ( *batch_size, rollout_length, @@ -7365,7 +7365,7 @@ def test_compose(self, keys, batch, device, nchannels=1, N=4): dim=-3, ) t2 = FiniteTensorDictCheck() - t3 = StepCounter() + t3 = ExcludeTransform() compose = Compose(t1, t2, t3) dont_touch = torch.randn(*batch, nchannels, 16, 16, device=device) td = TensorDict( diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 41a521f5f59..1baade19b46 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -177,7 +177,7 @@ def __init__( self.__dict__["_parent"] = None def reset(self, tensordict: TensorDictBase) -> TensorDictBase: - """Resets a tranform if it is stateful.""" + """Resets a transform if it is stateful.""" return tensordict def init(self, tensordict) -> None: @@ -4832,11 +4832,11 @@ def __init__( super().__init__(in_keys=in_keys, out_keys=out_keys) if T < 1: raise ValueError( - "TimeMaxPoolTranform T parameter should have a value greater or equal to one." + "TimeMaxPoolTransform T parameter should have a value greater or equal to one." ) if len(self.in_keys) != len(self.out_keys): raise ValueError( - "TimeMaxPoolTranform in_keys and out_keys don't have the same number of elements" + "TimeMaxPoolTransform in_keys and out_keys don't have the same number of elements" ) self.buffer_size = T for in_key in self.in_keys: From 035c2740361f1f53d8d1968d7bda023a10158f02 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 11:01:21 -0400 Subject: [PATCH 036/178] amend --- test/test_transforms.py | 28 ++++------------------------ 1 file changed, 4 insertions(+), 24 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index 6f1e4f023a5..ef7a873d6e2 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -7383,10 +7383,6 @@ def test_compose(self, keys, batch, device, nchannels=1, N=4): match="CatFrames cannot process unbatched tensordict instances", ): compose(td.clone(False)) - with pytest.raises( - NotImplementedError, match="StepCounter cannot be called independently" - ): - compose[1:](td.clone(False)) compose._call(td) for key in keys: assert td.get(key).shape[-3] == nchannels * N @@ -7408,20 +7404,8 @@ def test_compose(self, keys, batch, device, nchannels=1, N=4): ) @pytest.mark.parametrize("device", get_default_devices()) - @pytest.mark.parametrize( - "keys_inv_1", - [ - ["action_1"], - [], - ], - ) - @pytest.mark.parametrize( - "keys_inv_2", - [ - ["action_2"], - [], - ], - ) + @pytest.mark.parametrize("keys_inv_1", [["action_1"], []]) + @pytest.mark.parametrize("keys_inv_2", [["action_2"], []]) def test_compose_inv(self, keys_inv_1, keys_inv_2, device): torch.manual_seed(0) keys_to_transform = set(keys_inv_1 + keys_inv_2) @@ -7843,12 +7827,8 @@ def make_env(): return TransformedEnv( ContinuousActionVecMockEnv(), RenameTransform( - [ - "observation", - ], - [ - "stuff", - ], + ["observation"], + ["stuff"], create_copy=create_copy, ), ) From 8bd932f2621c1c9a359a41960865b595e5ab888f Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 11:08:28 -0400 Subject: [PATCH 037/178] amend --- torchrl/envs/common.py | 2 +- torchrl/envs/libs/gym.py | 1 - torchrl/envs/transforms/transforms.py | 2 +- 3 files changed, 2 insertions(+), 3 deletions(-) diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index a4d2f4b3c29..89f18b15610 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -797,7 +797,7 @@ def reward_spec(self) -> TensorSpec: # this will be raised if there is not full_reward_spec (unlikely) or no reward_key # Since output_spec is lazily populated with an empty composite spec for # reward_spec, the second case is much more likely to occur. - self.reward_spec = out = UnboundedContinuousTensorSpec( + self.reward_spec = UnboundedContinuousTensorSpec( shape=(*self.batch_size, 1), device=self.device, ) diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index ea32459f6b7..9bfa5bb92e7 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -2,7 +2,6 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import abc import importlib import warnings from copy import copy diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 1baade19b46..18d9f00cc23 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -40,7 +40,7 @@ from torchrl.envs.common import _EnvPostInit, EnvBase, make_tensordict from torchrl.envs.transforms import functional as F from torchrl.envs.transforms.utils import check_finite -from torchrl.envs.utils import _sort_keys, done_or_truncated, step_mdp +from torchrl.envs.utils import _sort_keys, step_mdp from torchrl.objectives.value.functional import reward2go try: From c789e50088759497d73cbb0101ba81472f96ee59 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 11:24:56 -0400 Subject: [PATCH 038/178] amend --- torchrl/envs/gym_like.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index cdc837cc470..fefece66814 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -247,6 +247,8 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: tensordict_out = TensorDict(obs_dict, batch_size=tensordict.batch_size) if self.info_dict_reader and info is not None: + if not isinstance(info, dict): + raise ValueError(f"Expected info to be a dictionary but got a {type(info)} with values {info}.") for info_dict_reader in self.info_dict_reader: out = info_dict_reader(info, tensordict_out) if out is not None: From 3e93f13959b8c645f0c3f7c7fc1452c43dae98ca Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 11:28:13 -0400 Subject: [PATCH 039/178] amend --- torchrl/envs/gym_like.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index fefece66814..2f756237296 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -248,11 +248,12 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: if self.info_dict_reader and info is not None: if not isinstance(info, dict): - raise ValueError(f"Expected info to be a dictionary but got a {type(info)} with values {info}.") - for info_dict_reader in self.info_dict_reader: - out = info_dict_reader(info, tensordict_out) - if out is not None: - tensordict_out = out + warnings.warn(f"Expected info to be a dictionary but got a {type(info)} with values {str(info)[:100]}.") + else: + for info_dict_reader in self.info_dict_reader: + out = info_dict_reader(info, tensordict_out) + if out is not None: + tensordict_out = out tensordict_out = tensordict_out.to(self.device, non_blocking=True) return tensordict_out From cba97b1d8e4b50dd59f4ad53298680f2caef67ab Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 12:08:20 -0400 Subject: [PATCH 040/178] amend --- torchrl/envs/gym_like.py | 4 +++- torchrl/envs/libs/gym.py | 30 +++++++++++++++++++++++++++++- 2 files changed, 32 insertions(+), 2 deletions(-) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index 2f756237296..39c855a729c 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -248,7 +248,9 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: if self.info_dict_reader and info is not None: if not isinstance(info, dict): - warnings.warn(f"Expected info to be a dictionary but got a {type(info)} with values {str(info)[:100]}.") + warnings.warn( + f"Expected info to be a dictionary but got a {type(info)} with values {str(info)[:100]}." + ) else: for info_dict_reader in self.info_dict_reader: out = info_dict_reader(info, tensordict_out) diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index 9bfa5bb92e7..a1e76d4cd79 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -6,8 +6,9 @@ import warnings from copy import copy from types import ModuleType -from typing import Dict, List, Optional +from typing import Dict, List, Optional, Tuple from warnings import warn +from packaging import version import numpy as np import torch @@ -388,6 +389,16 @@ class PixelObservationWrapper: class _AsyncMeta(_EnvPostInit): def __call__(cls, *args, **kwargs): instance: GymWrapper = super().__call__(*args, **kwargs) + # before gym 0.22, there was no final_observation + backend = instance.get_library_name(instance._env) + if backend == "gym": + import gym + if version.parse(gym.__version__) < version.parse("0.22"): + warn("A batched gym environment is being wrapped in a GymWrapper with gym version < 0.22. " + "This implies that the next-observation is wrongly tracked (as the batched environment auto-resets " + "and discards the true next observation to return the result of the step). " + "This isn't compatible with TorchRL API and should be used with caution.") + return instance if instance._is_batched: from torchrl.envs.transforms.transforms import ( TransformedEnv, @@ -768,6 +779,14 @@ def _reset_output_transform(self, reset_data): # noqa: F811 def _reset_output_transform(self, reset_data): # noqa: F811 return reset_data + @implement_for("gym", None, "0.24") + def _output_transform(self, step_outputs_tuple): # noqa: F811 + observations, reward, done, info = step_outputs_tuple + if self._is_batched: + # info needs to be flipped + info = _flip_info_tuple(info) + return (observations, reward, None, None, done, info) + @implement_for("gym", None, "0.26") def _output_transform(self, step_outputs_tuple): # noqa: F811 observations, reward, done, info = step_outputs_tuple @@ -1088,3 +1107,12 @@ def __call__(self, info_dict, tensordict): self._read_obs(obs, key[-1], final_obs, index=i) tensordict.set(key, final_obs) return tensordict + +def _flip_info_tuple(info: Tuple[Dict]) -> Dict[str, tuple]: + # In Gym < 0.24, batched envs returned tuples of dict, and not dict of tuples. + # We patch this by flipping the tuple -> dict order. + info_example = info[0] + result = {} + for key in info_example: + result[key] = tuple(_info[key] for _info in info) + return result From 7ec7c7866ab1b15b85326f45e9fe69e4063ee23f Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 12:11:33 -0400 Subject: [PATCH 041/178] amend --- torchrl/envs/libs/gym.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index a1e76d4cd79..30e6ef672c1 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -8,10 +8,10 @@ from types import ModuleType from typing import Dict, List, Optional, Tuple from warnings import warn -from packaging import version import numpy as np import torch +from packaging import version from tensordict import TensorDictBase @@ -393,11 +393,14 @@ def __call__(cls, *args, **kwargs): backend = instance.get_library_name(instance._env) if backend == "gym": import gym + if version.parse(gym.__version__) < version.parse("0.22"): - warn("A batched gym environment is being wrapped in a GymWrapper with gym version < 0.22. " - "This implies that the next-observation is wrongly tracked (as the batched environment auto-resets " - "and discards the true next observation to return the result of the step). " - "This isn't compatible with TorchRL API and should be used with caution.") + warn( + "A batched gym environment is being wrapped in a GymWrapper with gym version < 0.22. " + "This implies that the next-observation is wrongly tracked (as the batched environment auto-resets " + "and discards the true next observation to return the result of the step). " + "This isn't compatible with TorchRL API and should be used with caution." + ) return instance if instance._is_batched: from torchrl.envs.transforms.transforms import ( @@ -787,7 +790,7 @@ def _output_transform(self, step_outputs_tuple): # noqa: F811 info = _flip_info_tuple(info) return (observations, reward, None, None, done, info) - @implement_for("gym", None, "0.26") + @implement_for("gym", "0.24", "0.26") def _output_transform(self, step_outputs_tuple): # noqa: F811 observations, reward, done, info = step_outputs_tuple return (observations, reward, None, None, done, info) @@ -1108,6 +1111,7 @@ def __call__(self, info_dict, tensordict): tensordict.set(key, final_obs) return tensordict + def _flip_info_tuple(info: Tuple[Dict]) -> Dict[str, tuple]: # In Gym < 0.24, batched envs returned tuples of dict, and not dict of tuples. # We patch this by flipping the tuple -> dict order. From dd4c45e2bfc3a3f6ee0ded989f08b30e5c910c80 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 12:53:55 -0400 Subject: [PATCH 042/178] amend --- torchrl/collectors/collectors.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index b06a9b15252..ddd3ebc8cff 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -570,9 +570,9 @@ def __init__( for key in self.env.output_spec.keys(True, True): if isinstance(key, str): key = (key,) - if "truncated" in key: + if "step_count" in key: raise ValueError( - "A 'truncated' key is already present in the environment " + "A 'step_count' key is already present in the environment " "and the 'max_frames_per_traj' argument may conflict with " "a 'StepCounter' that has already been set. " "Possible solutions: Set max_frames_per_traj to 0 or " From 0ea0716a00683cfc4e7a6ec9d8ee255abf5e5a1f Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 19 Sep 2023 16:01:50 -0400 Subject: [PATCH 043/178] fix robohive --- test/test_libs.py | 1 + torchrl/envs/libs/gym.py | 2 +- torchrl/envs/transforms/transforms.py | 11 +++++++---- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index 5f22081bff6..9d5b2dc5ed1 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -2067,6 +2067,7 @@ def test_collector(self, task, parallel): class TestRoboHive: @pytest.mark.parametrize("envname", RoboHiveEnv.available_envs) @pytest.mark.parametrize("from_pixels", [True, False]) + @set_gym_backend("gym") def test_robohive(self, envname, from_pixels): if any(substr in envname for substr in ("_vr3m", "_vrrl", "_vflat", "_vvc1s")): print("not testing envs with prebuilt rendering") diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index 30e6ef672c1..c1cffc8a8cf 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -772,7 +772,7 @@ def _make_done_spec(self): # noqa: F811 @implement_for("gym", None, "0.26") def _reset_output_transform(self, reset_data): # noqa: F811 - return reset_data, {} + return reset_data, None @implement_for("gym", "0.26", None) def _reset_output_transform(self, reset_data): # noqa: F811 diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 18d9f00cc23..5c4be599664 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -3486,7 +3486,7 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: raise RuntimeError( "NoopResetEnv.parent not found. Make sure that the parent is set." ) - done_key = parent.done_key + done_keys = parent.done_keys reward_key = parent.reward_key if parent.batch_size.numel() > 1: raise ValueError( @@ -3511,9 +3511,12 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: i += 1 tensordict = parent.rand_step(tensordict) tensordict = step_mdp(tensordict, exclude_done=False) - if tensordict.get(done_key) or tensordict.get( - "truncated", torch.tensor(False) - ): + reset = False + for done_key in done_keys: + if tensordict.get(done_key): + reset = True + break + if reset: tensordict = parent.reset(td_reset.clone(False)) break else: From 16d688e0bd57ae8486225d974d4c224243cbe3ea Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 20 Sep 2023 03:10:42 -0400 Subject: [PATCH 044/178] amend --- test/_utils_internal.py | 88 +++++++++++++++++++++++++++++++++++- test/test_collector.py | 10 +++-- test/test_env.py | 99 ++--------------------------------------- 3 files changed, 97 insertions(+), 100 deletions(-) diff --git a/test/_utils_internal.py b/test/_utils_internal.py index b0f982fd001..fadfeefc7f2 100644 --- a/test/_utils_internal.py +++ b/test/_utils_internal.py @@ -17,7 +17,7 @@ import torch import torch.cuda -from tensordict import tensorclass +from tensordict import tensorclass, TensorDict from torchrl._utils import implement_for, seed_generator from torchrl.envs import MultiThreadedEnv, ObservationNorm @@ -367,3 +367,89 @@ def rand_reset(env): full_done_spec.zero().update(result).exclude(*full_done_spec.keys(True, True)) ) return result + + +def check_rollout_consistency(td: TensorDict, max_steps: int): + index_batch_size = (0,) * (len(td.batch_size) - 1) + + # Check done and reset for root + observation_is_max = td["next", "observation"][..., 0, 0, 0] == max_steps + 1 + next_is_done = td["next", "done"][index_batch_size][:-1].squeeze(-1) + assert (td["next", "done"][observation_is_max]).all() + assert (~td["next", "done"][~observation_is_max]).all() + # Obs after done is 0 + assert (td["observation"][index_batch_size][1:][next_is_done] == 0).all() + # Obs after not done is previous obs + assert ( + td["observation"][index_batch_size][1:][~next_is_done] + == td["next", "observation"][index_batch_size][:-1][~next_is_done] + ).all() + # Check observation and reward update with count action for root + action_is_count = td["action"].long().argmax(-1).to(torch.bool) + assert ( + td["next", "observation"][action_is_count] + == td["observation"][action_is_count] + 1 + ).all() + assert (td["next", "reward"][action_is_count] == 1).all() + # Check observation and reward do not update with no-count action for root + assert ( + td["next", "observation"][~action_is_count] + == td["observation"][~action_is_count] + ).all() + assert (td["next", "reward"][~action_is_count] == 0).all() + + # Check done and reset for nested_1 + observation_is_max = td["next", "nested_1", "observation"][..., 0] == max_steps + 1 + next_is_done = td["next", "nested_1", "done"][index_batch_size][:-1].squeeze(-1) + assert (td["next", "nested_1", "done"][observation_is_max]).all() + assert (~td["next", "nested_1", "done"][~observation_is_max]).all() + # Obs after done is 0 + assert ( + td["nested_1", "observation"][index_batch_size][1:][next_is_done] == 0 + ).all() + # Obs after not done is previous obs + assert ( + td["nested_1", "observation"][index_batch_size][1:][~next_is_done] + == td["next", "nested_1", "observation"][index_batch_size][:-1][~next_is_done] + ).all() + # Check observation and reward update with count action for nested_1 + action_is_count = td["nested_1"]["action"].to(torch.bool) + assert ( + td["next", "nested_1", "observation"][action_is_count] + == td["nested_1", "observation"][action_is_count] + 1 + ).all() + assert (td["next", "nested_1", "gift"][action_is_count] == 1).all() + # Check observation and reward do not update with no-count action for nested_1 + assert ( + td["next", "nested_1", "observation"][~action_is_count] + == td["nested_1", "observation"][~action_is_count] + ).all() + assert (td["next", "nested_1", "gift"][~action_is_count] == 0).all() + + # Check done and reset for nested_2 + observation_is_max = td["next", "nested_2", "observation"][..., 0] == max_steps + 1 + next_is_done = td["next", "nested_2", "done"][index_batch_size][:-1].squeeze(-1) + assert (td["next", "nested_2", "done"][observation_is_max]).all() + assert (~td["next", "nested_2", "done"][~observation_is_max]).all() + # Obs after done is 0 + assert ( + td["nested_2", "observation"][index_batch_size][1:][next_is_done] == 0 + ).all() + # Obs after not done is previous obs + assert ( + td["nested_2", "observation"][index_batch_size][1:][~next_is_done] + == td["next", "nested_2", "observation"][index_batch_size][:-1][~next_is_done] + ).all() + # Check observation and reward update with count action for nested_2 + action_is_count = td["nested_2"]["azione"].squeeze(-1).to(torch.bool) + assert ( + td["next", "nested_2", "observation"][action_is_count] + == td["nested_2", "observation"][action_is_count] + 1 + ).all() + assert (td["next", "nested_2", "reward"][action_is_count] == 1).all() + # Check observation and reward do not update with no-count action for nested_2 + assert ( + td["next", "nested_2", "observation"][~action_is_count] + == td["nested_2", "observation"][~action_is_count] + ).all() + assert (td["next", "nested_2", "reward"][~action_is_count] == 0).all() diff --git a/test/test_collector.py b/test/test_collector.py index dcea14fda57..9045a529365 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -9,7 +9,12 @@ import numpy as np import pytest import torch -from _utils_internal import generate_seeds, PENDULUM_VERSIONED, PONG_VERSIONED +from _utils_internal import ( + check_rollout_consistency, + generate_seeds, + PENDULUM_VERSIONED, + PONG_VERSIONED, +) from mocking_classes import ( ContinuousActionVecMockEnv, CountingBatchedEnv, @@ -30,7 +35,6 @@ from tensordict.nn import TensorDictModule from tensordict.tensordict import assert_allclose_td, TensorDict -from test_env import TestMultiKeyEnvs from torch import nn from torchrl._utils import prod, seed_generator from torchrl.collectors import aSyncDataCollector, SyncDataCollector @@ -1594,7 +1598,7 @@ def test_collector(self, batch_size, frames_per_batch, max_steps, seed=1): ccollector.shutdown() for done_key in env.done_keys: assert _replace_last(done_key, "_reset") not in _td.keys(True, True) - TestMultiKeyEnvs.check_rollout_consistency(_td, max_steps=max_steps) + check_rollout_consistency(_td, max_steps=max_steps) def test_multi_collector_consistency( self, seed=1, frames_per_batch=20, batch_dim=10 diff --git a/test/test_env.py b/test/test_env.py index 1c83e39bbcc..f0abbff5302 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -17,6 +17,7 @@ from _utils_internal import ( _make_envs, CARTPOLE_VERSIONED, + check_rollout_consistency, get_default_devices, HALFCHEETAH_VERSIONED, PENDULUM_VERSIONED, @@ -1981,100 +1982,6 @@ def test_vec_env(self, batch_size, env_type, rollout_steps=4, n_workers=2): @pytest.mark.parametrize("seed", [0]) class TestMultiKeyEnvs: - @staticmethod - def check_rollout_consistency(td: TensorDict, max_steps: int): - index_batch_size = (0,) * (len(td.batch_size) - 1) - - # Check done and reset for root - observation_is_max = td["next", "observation"][..., 0, 0, 0] == max_steps + 1 - next_is_done = td["next", "done"][index_batch_size][:-1].squeeze(-1) - assert (td["next", "done"][observation_is_max]).all() - assert (~td["next", "done"][~observation_is_max]).all() - # Obs after done is 0 - assert (td["observation"][index_batch_size][1:][next_is_done] == 0).all() - # Obs after not done is previous obs - assert ( - td["observation"][index_batch_size][1:][~next_is_done] - == td["next", "observation"][index_batch_size][:-1][~next_is_done] - ).all() - # Check observation and reward update with count action for root - action_is_count = td["action"].long().argmax(-1).to(torch.bool) - assert ( - td["next", "observation"][action_is_count] - == td["observation"][action_is_count] + 1 - ).all() - assert (td["next", "reward"][action_is_count] == 1).all() - # Check observation and reward do not update with no-count action for root - assert ( - td["next", "observation"][~action_is_count] - == td["observation"][~action_is_count] - ).all() - assert (td["next", "reward"][~action_is_count] == 0).all() - - # Check done and reset for nested_1 - observation_is_max = ( - td["next", "nested_1", "observation"][..., 0] == max_steps + 1 - ) - next_is_done = td["next", "nested_1", "done"][index_batch_size][:-1].squeeze(-1) - assert (td["next", "nested_1", "done"][observation_is_max]).all() - assert (~td["next", "nested_1", "done"][~observation_is_max]).all() - # Obs after done is 0 - assert ( - td["nested_1", "observation"][index_batch_size][1:][next_is_done] == 0 - ).all() - # Obs after not done is previous obs - assert ( - td["nested_1", "observation"][index_batch_size][1:][~next_is_done] - == td["next", "nested_1", "observation"][index_batch_size][:-1][ - ~next_is_done - ] - ).all() - # Check observation and reward update with count action for nested_1 - action_is_count = td["nested_1"]["action"].to(torch.bool) - assert ( - td["next", "nested_1", "observation"][action_is_count] - == td["nested_1", "observation"][action_is_count] + 1 - ).all() - assert (td["next", "nested_1", "gift"][action_is_count] == 1).all() - # Check observation and reward do not update with no-count action for nested_1 - assert ( - td["next", "nested_1", "observation"][~action_is_count] - == td["nested_1", "observation"][~action_is_count] - ).all() - assert (td["next", "nested_1", "gift"][~action_is_count] == 0).all() - - # Check done and reset for nested_2 - observation_is_max = ( - td["next", "nested_2", "observation"][..., 0] == max_steps + 1 - ) - next_is_done = td["next", "nested_2", "done"][index_batch_size][:-1].squeeze(-1) - assert (td["next", "nested_2", "done"][observation_is_max]).all() - assert (~td["next", "nested_2", "done"][~observation_is_max]).all() - # Obs after done is 0 - assert ( - td["nested_2", "observation"][index_batch_size][1:][next_is_done] == 0 - ).all() - # Obs after not done is previous obs - assert ( - td["nested_2", "observation"][index_batch_size][1:][~next_is_done] - == td["next", "nested_2", "observation"][index_batch_size][:-1][ - ~next_is_done - ] - ).all() - # Check observation and reward update with count action for nested_2 - action_is_count = td["nested_2"]["azione"].squeeze(-1).to(torch.bool) - assert ( - td["next", "nested_2", "observation"][action_is_count] - == td["nested_2", "observation"][action_is_count] + 1 - ).all() - assert (td["next", "nested_2", "reward"][action_is_count] == 1).all() - # Check observation and reward do not update with no-count action for nested_2 - assert ( - td["next", "nested_2", "observation"][~action_is_count] - == td["nested_2", "observation"][~action_is_count] - ).all() - assert (td["next", "nested_2", "reward"][~action_is_count] == 0).all() - @pytest.mark.parametrize("batch_size", [(), (2,), (2, 1)]) @pytest.mark.parametrize("rollout_steps", [1, 5]) @pytest.mark.parametrize("max_steps", [2, 5]) @@ -2083,7 +1990,7 @@ def test_rollout(self, batch_size, rollout_steps, max_steps, seed): policy = MultiKeyCountingEnvPolicy(full_action_spec=env.action_spec) td = env.rollout(rollout_steps, policy=policy) torch.manual_seed(seed) - self.check_rollout_consistency(td, max_steps=max_steps) + check_rollout_consistency(td, max_steps=max_steps) @pytest.mark.parametrize("batch_size", [(), (2,), (2, 1)]) @pytest.mark.parametrize("rollout_steps", [5]) @@ -2110,7 +2017,7 @@ def test_parallel( rollout_steps, policy=policy, ) - self.check_rollout_consistency(td, max_steps=max_steps) + check_rollout_consistency(td, max_steps=max_steps) @pytest.mark.parametrize( From 268dbd7324b0bc4df9c3b8e0eb66217063104526 Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 20 Sep 2023 04:33:31 -0400 Subject: [PATCH 045/178] amend --- torchrl/collectors/collectors.py | 44 +++++++------------------------- torchrl/collectors/utils.py | 26 ++++++++++++++++++- torchrl/envs/common.py | 2 +- torchrl/envs/libs/robohive.py | 5 +--- 4 files changed, 36 insertions(+), 41 deletions(-) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index ddd3ebc8cff..bd547998905 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -35,14 +35,14 @@ RL_WARNINGS, VERBOSE, ) -from torchrl.collectors.utils import split_trajectories +from torchrl.collectors.utils import _bring_reset_to_root, split_trajectories from torchrl.data.tensor_specs import CompositeSpec, TensorSpec from torchrl.data.utils import CloudpickleWrapper, DEVICE_TYPING from torchrl.envs.common import EnvBase from torchrl.envs.transforms import StepCounter, TransformedEnv from torchrl.envs.utils import ( _convert_exploration_type, - _replace_last, + done_or_truncated, ExplorationType, set_exploration_type, step_mdp, @@ -792,54 +792,28 @@ def iterator(self) -> Iterator[TensorDictBase]: def _step_and_maybe_reset(self) -> None: - any_done = False - done_map = {} - for done_key in self.env.done_keys: - done = self._tensordict.get(("next", done_key)) - truncated = self._tensordict.get( - ("next", _replace_last(done_key, "truncated")), - None, - ) - done = (done | truncated) if truncated is not None else done - any_sub_done = done.any().item() - if any_sub_done and self.reset_when_done: - # Add this done to the map, we will need it to reset - done_map.update({done_key: done.clone()}) - any_done += any_sub_done - self._tensordict = step_mdp( self._tensordict, reward_keys=self.env.reward_keys, done_keys=self.env.done_keys, action_keys=self.env.action_keys, ) - if not self.reset_when_done: return + td_reset = self._tensordict.clone(False) + any_done = done_or_truncated( + td_reset, + full_done_spec=self.env.output_spec["full_done_spec"], + key="_reset", + ) if any_done: traj_ids = self._tensordict.get(("collector", "traj_ids")) traj_ids = traj_ids.clone() # collectors do not support passing other tensors than `"_reset"` # to `reset()`. - td_reset = self._tensordict.select(*done_map.keys()) - for done_key, done in done_map.items(): - td_reset.set(_replace_last(done_key, "_reset"), done) - del td_reset[done_key] + traj_done_or_terminated = _bring_reset_to_root(td_reset) td_reset = self.env.reset(td_reset) - for done_key in done_map.keys(): - del td_reset[_replace_last(done_key, "_reset")] - - traj_done_or_terminated = torch.stack( - [ - done.sum( - tuple(range(self._tensordict.batch_dims, done.ndim)), - dtype=torch.bool, - ) - for done in done_map.values() - ], - dim=0, - ).any(0) if td_reset.batch_dims: # better cloning here than when passing the td for stacking diff --git a/torchrl/collectors/utils.py b/torchrl/collectors/utils.py index f797144d344..bfd0f139932 100644 --- a/torchrl/collectors/utils.py +++ b/torchrl/collectors/utils.py @@ -6,7 +6,7 @@ from typing import Callable import torch -from tensordict.tensordict import pad, TensorDictBase +from tensordict.tensordict import is_tensor_collection, pad, TensorDictBase def _stack_output(fun) -> Callable: @@ -114,3 +114,27 @@ def split_trajectories( ).contiguous() # td = td.unflatten_keys(sep) return td + + +def _bring_reset_to_root(data: TensorDictBase) -> torch.Tensor: + # goes through the tensordict and brings the _reset information to + # a boolean tensor of the shape of the tensordict + + batch_size = data.batch_size + n = len(batch_size) + 1 + + reset = False + + def skim_through(td, reset=reset): + for key, value in td.items(): + if key == "_reset": + if value.ndim > n: + value = value.flatten(n, value.ndim) + reset = reset | value.any(-1) + elif is_tensor_collection(value): + reset = skim_through(value, reset=reset) + return reset + + reset = skim_through(data) + assert reset.shape == data.batch_size + return reset diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index 89f18b15610..f9710977b8b 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -1431,6 +1431,7 @@ def reset( tensordict.update(tensordict_reset) else: tensordict = tensordict_reset + tensordict.exclude(*self.reset_keys, inplace=True) return tensordict def numel(self) -> int: @@ -1715,7 +1716,6 @@ def policy(td): if not break_when_any_done and any_done: self.reset(tensordict) - tensordict.exclude(*self.reset_keys, inplace=True) if callback is not None: callback(self, tensordict) diff --git a/torchrl/envs/libs/robohive.py b/torchrl/envs/libs/robohive.py index edf12d786c3..fd332062813 100644 --- a/torchrl/envs/libs/robohive.py +++ b/torchrl/envs/libs/robohive.py @@ -82,6 +82,7 @@ def CURR_DIR(cls): def available_envs(cls): if not _has_robohive: return + RoboHiveEnv.register_envs() yield from cls.env_list @classmethod @@ -338,7 +339,3 @@ def get_available_cams(cls, env_name): env = gym.make(env_name) cams = [env.sim.model.id2name(ic, 7) for ic in range(env.sim.model.ncam)] return cams - - -if _has_robohive: - RoboHiveEnv.register_envs() From d77d1cd708d50e496e9d8eeb913b8aad1148c265 Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 20 Sep 2023 05:17:03 -0400 Subject: [PATCH 046/178] amend --- torchrl/collectors/utils.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/torchrl/collectors/utils.py b/torchrl/collectors/utils.py index bfd0f139932..763557f2f4d 100644 --- a/torchrl/collectors/utils.py +++ b/torchrl/collectors/utils.py @@ -126,12 +126,18 @@ def _bring_reset_to_root(data: TensorDictBase) -> torch.Tensor: reset = False def skim_through(td, reset=reset): - for key, value in td.items(): + for key in td.keys(): if key == "_reset": + value = td.get(key) if value.ndim > n: - value = value.flatten(n, value.ndim) + value = value.flatten(n, value.ndim-1) reset = reset | value.any(-1) - elif is_tensor_collection(value): + # we need to check the entry class without getting the value, + # because some lazy tensordicts may prevent calls to items(). + # This introduces some slight overhead as when we encounter a + # tensordict item, we'll need to get it twice. + elif is_tensor_collection(td.entry_class(key)): + value = td.get(key) reset = skim_through(value, reset=reset) return reset From 15bd9fadcac6a3ad00910b4c505f01f7446d6961 Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 20 Sep 2023 05:33:58 -0400 Subject: [PATCH 047/178] amend --- test/test_collector.py | 48 ++++++++++++++++++++++++++++++++++++- torchrl/collectors/utils.py | 6 ++--- 2 files changed, 50 insertions(+), 4 deletions(-) diff --git a/test/test_collector.py b/test/test_collector.py index 9045a529365..fd17f3a2aef 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -44,7 +44,7 @@ MultiSyncDataCollector, RandomPolicy, ) -from torchrl.collectors.utils import split_trajectories +from torchrl.collectors.utils import split_trajectories, _bring_reset_to_root from torchrl.data import CompositeSpec, UnboundedContinuousTensorSpec from torchrl.envs import ( EnvBase, @@ -1751,6 +1751,52 @@ def test_param_sync(self, give_weights, collector, policy_device, env_device): finally: col.shutdown() +def test_bring_reset_to_root(): + # simple + td = TensorDict( + {"_reset": torch.zeros((1,), dtype=torch.bool)}, [] + ) + assert _bring_reset_to_root(td).shape == () + # td with batch size + td = TensorDict( + {"_reset": torch.zeros((1,), dtype=torch.bool)}, [1] + ) + assert _bring_reset_to_root(td).shape == (1,) + td = TensorDict( + {"_reset": torch.zeros((1, 2), dtype=torch.bool)}, [1] + ) + assert _bring_reset_to_root(td).shape == (1,) + # nested td + td = TensorDict( + {"_reset": torch.zeros((1,), dtype=torch.bool), + "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}}, [1] + ) + assert _bring_reset_to_root(td).shape == (1,) + # nested td with greater number of dims + td = TensorDict( + {"_reset": torch.zeros((1,2,), dtype=torch.bool), + "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}}, [1, 2] + ) + # test reduction + assert _bring_reset_to_root(td).shape == (1,2) + td = TensorDict( + {"_reset": torch.zeros((1,2,), dtype=torch.bool), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}}, [1, 2] + ) + assert _bring_reset_to_root(td).all() + # with a stack + td0 = TensorDict( + {"_reset": torch.zeros((1,2,), dtype=torch.bool), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, + "b": {"c": torch.randn(1, 2)}}, [1, 2] + ) + td1 = TensorDict( + {"_reset": torch.zeros((1,2,), dtype=torch.bool), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, + "b": {"c": torch.randn(1, 2, 5)}}, [1, 2] + ) + td = torch.stack([td0, td1], 0) + assert _bring_reset_to_root(td).all() if __name__ == "__main__": args, unknown = argparse.ArgumentParser().parse_known_args() diff --git a/torchrl/collectors/utils.py b/torchrl/collectors/utils.py index 763557f2f4d..28692e4f4d1 100644 --- a/torchrl/collectors/utils.py +++ b/torchrl/collectors/utils.py @@ -121,7 +121,7 @@ def _bring_reset_to_root(data: TensorDictBase) -> torch.Tensor: # a boolean tensor of the shape of the tensordict batch_size = data.batch_size - n = len(batch_size) + 1 + n = len(batch_size) reset = False @@ -131,7 +131,8 @@ def skim_through(td, reset=reset): value = td.get(key) if value.ndim > n: value = value.flatten(n, value.ndim-1) - reset = reset | value.any(-1) + value = value.any(-1) + reset = reset | value # we need to check the entry class without getting the value, # because some lazy tensordicts may prevent calls to items(). # This introduces some slight overhead as when we encounter a @@ -142,5 +143,4 @@ def skim_through(td, reset=reset): return reset reset = skim_through(data) - assert reset.shape == data.batch_size return reset From 1b656f747977bfd224f2b5f4983d4ab2cd21ae6c Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 20 Sep 2023 08:56:41 -0400 Subject: [PATCH 048/178] amend --- test/test_collector.py | 151 ++++++++++++++++++-------- torchrl/collectors/collectors.py | 4 +- torchrl/collectors/utils.py | 32 +----- torchrl/envs/batched_envs.py | 43 ++------ torchrl/envs/common.py | 11 +- torchrl/envs/transforms/transforms.py | 37 +++++-- torchrl/envs/utils.py | 55 +++++++++- 7 files changed, 206 insertions(+), 127 deletions(-) diff --git a/test/test_collector.py b/test/test_collector.py index fd17f3a2aef..0e06db14a04 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -44,7 +44,7 @@ MultiSyncDataCollector, RandomPolicy, ) -from torchrl.collectors.utils import split_trajectories, _bring_reset_to_root +from torchrl.collectors.utils import split_trajectories from torchrl.data import CompositeSpec, UnboundedContinuousTensorSpec from torchrl.envs import ( EnvBase, @@ -56,7 +56,8 @@ ) from torchrl.envs.libs.gym import _has_gym, gym_backend, GymEnv, set_gym_backend from torchrl.envs.transforms import TransformedEnv, VecNorm -from torchrl.envs.utils import _replace_last +from torchrl.envs.utils import _replace_last, _bring_reset_to_root, \ + PARTIAL_MISSING_ERR from torchrl.modules import Actor, LSTMNet, OrnsteinUhlenbeckProcessWrapper, SafeModule # torch.set_default_dtype(torch.double) @@ -1751,52 +1752,106 @@ def test_param_sync(self, give_weights, collector, policy_device, env_device): finally: col.shutdown() -def test_bring_reset_to_root(): - # simple - td = TensorDict( - {"_reset": torch.zeros((1,), dtype=torch.bool)}, [] - ) - assert _bring_reset_to_root(td).shape == () - # td with batch size - td = TensorDict( - {"_reset": torch.zeros((1,), dtype=torch.bool)}, [1] - ) - assert _bring_reset_to_root(td).shape == (1,) - td = TensorDict( - {"_reset": torch.zeros((1, 2), dtype=torch.bool)}, [1] - ) - assert _bring_reset_to_root(td).shape == (1,) - # nested td - td = TensorDict( - {"_reset": torch.zeros((1,), dtype=torch.bool), - "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}}, [1] - ) - assert _bring_reset_to_root(td).shape == (1,) - # nested td with greater number of dims - td = TensorDict( - {"_reset": torch.zeros((1,2,), dtype=torch.bool), - "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}}, [1, 2] - ) - # test reduction - assert _bring_reset_to_root(td).shape == (1,2) - td = TensorDict( - {"_reset": torch.zeros((1,2,), dtype=torch.bool), - "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}}, [1, 2] - ) - assert _bring_reset_to_root(td).all() - # with a stack - td0 = TensorDict( - {"_reset": torch.zeros((1,2,), dtype=torch.bool), - "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, - "b": {"c": torch.randn(1, 2)}}, [1, 2] - ) - td1 = TensorDict( - {"_reset": torch.zeros((1,2,), dtype=torch.bool), - "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, - "b": {"c": torch.randn(1, 2, 5)}}, [1, 2] - ) - td = torch.stack([td0, td1], 0) - assert _bring_reset_to_root(td).all() +class TestBringReset: + def test_bring_reset_to_root(self): + # simple + td = TensorDict( + {"_reset": torch.zeros((1,), dtype=torch.bool)}, [] + ) + assert _bring_reset_to_root(td).shape == () + # td with batch size + td = TensorDict( + {"_reset": torch.zeros((1,), dtype=torch.bool)}, [1] + ) + assert _bring_reset_to_root(td).shape == (1,) + td = TensorDict( + {"_reset": torch.zeros((1, 2), dtype=torch.bool)}, [1] + ) + assert _bring_reset_to_root(td).shape == (1,) + # nested td + td = TensorDict( + {"_reset": torch.zeros((1,), dtype=torch.bool), + "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}}, [1] + ) + assert _bring_reset_to_root(td).shape == (1,) + # nested td with greater number of dims + td = TensorDict( + {"_reset": torch.zeros((1,2,), dtype=torch.bool), + "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}}, [1, 2] + ) + # test reduction + assert _bring_reset_to_root(td).shape == (1,2) + td = TensorDict( + {"_reset": torch.zeros((1,2,), dtype=torch.bool), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}}, [1, 2] + ) + assert _bring_reset_to_root(td).all() + # with a stack + td0 = TensorDict( + {"_reset": torch.zeros((1,2,), dtype=torch.bool), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, + "b": {"c": torch.randn(1, 2)}}, [1, 2] + ) + td1 = TensorDict( + {"_reset": torch.zeros((1,2,), dtype=torch.bool), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, + "b": {"c": torch.randn(1, 2, 5)}}, [1, 2] + ) + td = torch.stack([td0, td1], 0) + assert _bring_reset_to_root(td).all() + def test_bring_reset_to_root_keys(self): + # simple + td = TensorDict( + {"_reset": torch.zeros((1,), dtype=torch.bool)}, [] + ) + assert _bring_reset_to_root(td, reset_keys=["_reset"]).shape == () + # td with batch size + td = TensorDict( + {"_reset": torch.zeros((1,), dtype=torch.bool)}, [1] + ) + assert _bring_reset_to_root(td, reset_keys=["_reset"]).shape == (1,) + td = TensorDict( + {"_reset": torch.zeros((1, 2), dtype=torch.bool)}, [1] + ) + assert _bring_reset_to_root(td, reset_keys=["_reset"]).shape == (1,) + # nested td + td = TensorDict( + {"_reset": torch.zeros((1,), dtype=torch.bool), + "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}}, [1] + ) + assert _bring_reset_to_root(td, reset_keys=["_reset", ("a", "_reset")]).shape == (1,) + # nested td with greater number of dims + td = TensorDict( + {"_reset": torch.zeros((1,2,), dtype=torch.bool), + "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}}, [1, 2] + ) + # test reduction + assert _bring_reset_to_root(td, reset_keys=["_reset", ("a", "_reset")]).shape == (1,2) + td = TensorDict( + {"_reset": torch.zeros((1,2,), dtype=torch.bool), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}}, [1, 2] + ) + assert _bring_reset_to_root(td, reset_keys=["_reset", ("a", "_reset")]).all() + # with a stack + td0 = TensorDict( + {"_reset": torch.zeros((1,2,), dtype=torch.bool), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, + "b": {"c": torch.randn(1, 2)}}, [1, 2] + ) + td1 = TensorDict( + {"_reset": torch.zeros((1,2,), dtype=torch.bool), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, + "b": {"c": torch.randn(1, 2, 5)}}, [1, 2] + ) + td = torch.stack([td0, td1], 0) + assert _bring_reset_to_root(td, reset_keys=["_reset", ("a", "_reset")]).all() + + def test_bring_reset_to_root_errors(self): + # the order matters: if the first or another key is missing, the ValueError is raised at a different line + with pytest.raises(ValueError, match=PARTIAL_MISSING_ERR): + _bring_reset_to_root(TensorDict({"_reset": False}, []), reset_keys=["_reset", ("another", "_reset")]) + with pytest.raises(ValueError, match=PARTIAL_MISSING_ERR): + _bring_reset_to_root(TensorDict({"_reset": False}, []), reset_keys=[("another", "_reset"), "_reset"]) if __name__ == "__main__": args, unknown = argparse.ArgumentParser().parse_known_args() diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index bd547998905..186b4c9a74d 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -35,7 +35,7 @@ RL_WARNINGS, VERBOSE, ) -from torchrl.collectors.utils import _bring_reset_to_root, split_trajectories +from torchrl.collectors.utils import split_trajectories from torchrl.data.tensor_specs import CompositeSpec, TensorSpec from torchrl.data.utils import CloudpickleWrapper, DEVICE_TYPING from torchrl.envs.common import EnvBase @@ -45,7 +45,7 @@ done_or_truncated, ExplorationType, set_exploration_type, - step_mdp, + step_mdp, _bring_reset_to_root, ) _TIMEOUT = 1.0 diff --git a/torchrl/collectors/utils.py b/torchrl/collectors/utils.py index 28692e4f4d1..f797144d344 100644 --- a/torchrl/collectors/utils.py +++ b/torchrl/collectors/utils.py @@ -6,7 +6,7 @@ from typing import Callable import torch -from tensordict.tensordict import is_tensor_collection, pad, TensorDictBase +from tensordict.tensordict import pad, TensorDictBase def _stack_output(fun) -> Callable: @@ -114,33 +114,3 @@ def split_trajectories( ).contiguous() # td = td.unflatten_keys(sep) return td - - -def _bring_reset_to_root(data: TensorDictBase) -> torch.Tensor: - # goes through the tensordict and brings the _reset information to - # a boolean tensor of the shape of the tensordict - - batch_size = data.batch_size - n = len(batch_size) - - reset = False - - def skim_through(td, reset=reset): - for key in td.keys(): - if key == "_reset": - value = td.get(key) - if value.ndim > n: - value = value.flatten(n, value.ndim-1) - value = value.any(-1) - reset = reset | value - # we need to check the entry class without getting the value, - # because some lazy tensordicts may prevent calls to items(). - # This introduces some slight overhead as when we encounter a - # tensordict item, we'll need to get it twice. - elif is_tensor_collection(td.entry_class(key)): - value = td.get(key) - reset = skim_through(value, reset=reset) - return reset - - reset = skim_through(data) - return reset diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index afb1e16b578..d21ed5686cf 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -25,7 +25,8 @@ from torchrl.envs.common import EnvBase from torchrl.envs.env_creator import get_env_metadata -from torchrl.envs.utils import _set_single_key, _sort_keys, clear_mpi_env_vars +from torchrl.envs.utils import _set_single_key, _sort_keys, clear_mpi_env_vars, \ + _bring_reset_to_root # legacy from .libs.envpool import MultiThreadedEnv, MultiThreadedEnvWrapper # noqa: F401 @@ -576,21 +577,10 @@ def set_seed( @_check_start def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: - missing_reset = False if tensordict is not None: - needs_resetting = [False] * self.num_workers - for _reset_key in self.reset_keys: - _reset = tensordict.get(_reset_key, default=None) - if _reset is not None: - for i in range(self.num_workers): - needs_resetting[i] += _reset[i].any() - else: - missing_reset = True - # why do we break here? - break - - if tensordict is None or missing_reset: - needs_resetting = [True] * self.num_workers + needs_resetting = _bring_reset_to_root(tensordict) + else: + needs_resetting = torch.ones((self.num_workers,), device=self.device, dtype=torch.bool) for i, _env in enumerate(self._envs): if tensordict is not None: @@ -621,14 +611,13 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: self.shared_tensordicts[i].update_( _td.select(*self._selected_reset_keys, strict=False) ) - if self._single_task: # select + clone creates 2 tds, but we can create one only out = TensorDict( {}, batch_size=self.shared_tensordict_parent.shape, device=self.device ) for key in self._selected_reset_keys: - if _unravel_key_to_tuple(key)[-1] != "_reset": + if key not in self.reset_keys: _set_single_key(self.shared_tensordict_parent, out, key, clone=True) return out else: @@ -820,21 +809,11 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: @_check_start def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: - missing_reset = False if tensordict is not None: - needs_resetting = [False for _ in range(self.num_workers)] - for _reset_key in self.reset_keys: - _reset = tensordict.get(_reset_key, default=None) - if _reset is not None: - for i in range(self.num_workers): - needs_resetting[i] += _reset[i].any() - else: - missing_reset = True - # why do we break here? - break + needs_resetting = _bring_reset_to_root(tensordict) + else: + needs_resetting = torch.ones((self.num_workers,), device=self.device, dtype=torch.bool) - if tensordict is None or missing_reset: - needs_resetting = [True] * self.num_workers workers = [] for i, channel in enumerate(self.parent_channels): @@ -881,7 +860,9 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: {}, batch_size=self.shared_tensordict_parent.shape, device=self.device ) for key in self._selected_reset_keys: - if _unravel_key_to_tuple(key)[-1] != "_reset": + if key not in self.reset_keys: + if "key" == "done": + print(key, self.shared_tensordict_parent[key]) _set_single_key(self.shared_tensordict_parent, out, key, clone=True) return out else: diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index f9710977b8b..c86678089b9 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -26,7 +26,6 @@ from torchrl.data.utils import DEVICE_TYPING from torchrl.envs.utils import ( _replace_last, - DONE_AFTER_RESET_ERROR, done_or_truncated, get_available_libraries, step_mdp, @@ -1416,16 +1415,20 @@ def reset( self.reset_keys, self.done_keys_groups ): reset_value = ( - tensordict.get(reset_key, None) if tensordict is not None else None + tensordict.get(reset_key, default=None) if tensordict is not None else None ) if reset_value is not None: for done_key in done_key_group: if tensordict_reset.get(done_key)[reset_value].any(): - raise DONE_AFTER_RESET_ERROR + raise RuntimeError( + f"Env done entry '{done_key}' was (partially) True after reset on specified '_reset' dimensions. This is not allowed." +) else: for done_key in done_key_group: if tensordict_reset.get(done_key).any(): - raise DONE_AFTER_RESET_ERROR + raise RuntimeError( + f"Env done entry '{done_key}' was (partially) True after a call to reset(). This is not allowed." +) if tensordict is not None: tensordict.update(tensordict_reset) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 5c4be599664..0695e591f22 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -669,9 +669,15 @@ def _set_seed(self, seed: Optional[int]): def _reset(self, tensordict: Optional[TensorDictBase] = None, **kwargs): if tensordict is not None: - tensordict = tensordict.clone(recurse=False) + # We must avoid modifying the original tensordict so a shallow copy is necessary. + # We just select the input data and reset signal, which is all we need. + tensordict = tensordict.select(*self.reset_keys, *self.state_spec.keys(True, True), strict=False) out_tensordict = self.base_env._reset(tensordict=tensordict, **kwargs) if tensordict is not None: + # the transform may need to read previous info during reset. + # For instance, we may need to pass the step_count for partial resets. + # We update the copy of tensordict with the new data, instead of + # the contrary because newer data prevails. out_tensordict = tensordict.update(out_tensordict) out_tensordict = self.transform.reset(out_tensordict) @@ -5246,13 +5252,21 @@ def __init__( def _call(self, tensordict: TensorDictBase) -> TensorDictBase: if self.create_copy: - out = tensordict.select(*self.in_keys) + out = tensordict.select(*self.in_keys, strict=not self._missing_tolerance) for in_key, out_key in zip(self.in_keys, self.out_keys): - out.rename_key_(in_key, out_key) + try: + tensordict.rename_key_(in_key, out_key) + except KeyError: + if not self._missing_tolerance: + raise tensordict = tensordict.update(out) else: for in_key, out_key in zip(self.in_keys, self.out_keys): - tensordict.rename_key_(in_key, out_key) + try: + tensordict.rename_key_(in_key, out_key) + except KeyError: + if not self._missing_tolerance: + raise return tensordict forward = _call @@ -5260,13 +5274,22 @@ def _call(self, tensordict: TensorDictBase) -> TensorDictBase: def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase: # no in-place modif if self.create_copy: - out = tensordict.select(*self.out_keys_inv) + out = tensordict.select(*self.out_keys_inv, strict=not self._missing_tolerance) for in_key, out_key in zip(self.in_keys_inv, self.out_keys_inv): - out.rename_key_(out_key, in_key) + try: + out.rename_key_(out_key, in_key) + except KeyError: + if not self._missing_tolerance: + raise + tensordict = tensordict.update(out) else: for in_key, out_key in zip(self.in_keys_inv, self.out_keys_inv): - tensordict.rename_key_(out_key, in_key) + try: + tensordict.rename_key_(out_key, in_key) + except KeyError: + if not self._missing_tolerance: + raise return tensordict def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index c014c66d533..0e63f47dd0b 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -14,7 +14,7 @@ import torch -from tensordict import is_tensor_collection, unravel_key +from tensordict import is_tensor_collection, unravel_key, TensorDictBase from tensordict.nn.probabilistic import ( # noqa # Note: the `set_interaction_mode` and their associated arg `default_interaction_mode` are being deprecated! # Please use the `set_/interaction_type` ones above with the InteractionType enum instead. @@ -44,9 +44,6 @@ from torchrl.data import CompositeSpec from torchrl.data.utils import check_no_exclusive_keys -DONE_AFTER_RESET_ERROR = RuntimeError( - "Env was done after reset on specified '_reset' dimensions. This is not allowed." -) ACTION_MASK_ERROR = RuntimeError( "An out-of-bounds actions has been provided to an env with an 'action_mask' output." " If you are using a custom policy, make sure to take the action mask into account when computing the output." @@ -810,3 +807,53 @@ def done_or_truncated(data: TensorDictBase, full_done_spec=None, key="_reset") - data.set(key, aggregate) any_done = any_done | aggregate.any() return any_done + +PARTIAL_MISSING_ERR = "Some reset keys were present but not all. Either all the `'_reset'` entries must be present, or none." +def _bring_reset_to_root(data: TensorDictBase, reset_keys=None) -> torch.Tensor: + # goes through the tensordict and brings the _reset information to + # a boolean tensor of the shape of the tensordict. + batch_size = data.batch_size + n = len(batch_size) + + if reset_keys is not None: + reset = False + has_missing = None + for key in reset_keys: + local_reset = data.get(key, None) + if local_reset is None: + if has_missing is False: + raise ValueError(PARTIAL_MISSING_ERR) + has_missing = True + continue + elif has_missing: + raise ValueError(PARTIAL_MISSING_ERR) + has_missing = False + if local_reset.ndim > n: + local_reset = local_reset.flatten(n, local_reset.ndim - 1) + local_reset = local_reset.any(-1) + reset = reset | local_reset + if has_missing: + return torch.ones(batch_size, dtype=torch.bool, device=data.device) + return reset + + reset = False + + def skim_through(td, reset=reset): + for key in td.keys(): + if key == "_reset": + local_reset = td.get(key) + if local_reset.ndim > n: + local_reset = local_reset.flatten(n, local_reset.ndim-1) + local_reset = local_reset.any(-1) + reset = reset | local_reset + # we need to check the entry class without getting the value, + # because some lazy tensordicts may prevent calls to items(). + # This introduces some slight overhead as when we encounter a + # tensordict item, we'll need to get it twice. + elif is_tensor_collection(td.entry_class(key)): + value = td.get(key) + reset = skim_through(value, reset=reset) + return reset + + reset = skim_through(data) + return reset From 2f13c951bed3365a7908b11176c1d5cb74f3ad90 Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 20 Sep 2023 09:23:19 -0400 Subject: [PATCH 049/178] amend --- torchrl/envs/batched_envs.py | 7 +++---- torchrl/envs/utils.py | 2 +- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index d21ed5686cf..d6680689724 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -17,7 +17,7 @@ import torch from tensordict import TensorDict -from tensordict._tensordict import _unravel_key_to_tuple +from tensordict._tensordict import _unravel_key_to_tuple, unravel_keys from tensordict.tensordict import LazyStackedTensorDict, TensorDictBase from torch import multiprocessing as mp from torchrl._utils import _check_for_faulty_process, VERBOSE @@ -617,6 +617,7 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: {}, batch_size=self.shared_tensordict_parent.shape, device=self.device ) for key in self._selected_reset_keys: + key = unravel_keys(key) if key not in self.reset_keys: _set_single_key(self.shared_tensordict_parent, out, key, clone=True) return out @@ -808,7 +809,6 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: @_check_start def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: - if tensordict is not None: needs_resetting = _bring_reset_to_root(tensordict) else: @@ -860,9 +860,8 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: {}, batch_size=self.shared_tensordict_parent.shape, device=self.device ) for key in self._selected_reset_keys: + key = unravel_keys(key) if key not in self.reset_keys: - if "key" == "done": - print(key, self.shared_tensordict_parent[key]) _set_single_key(self.shared_tensordict_parent, out, key, clone=True) return out else: diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 0e63f47dd0b..02b4455af78 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -236,7 +236,7 @@ def step_mdp( return out -def _set_single_key(source, dest, key, clone=False): +def _set_single_key(source: TensorDictBase, dest: TensorDictBase, key: str | tuple, clone: bool=False): # key should be already unraveled if isinstance(key, str): key = (key,) From aa5de06b618e274a7fe5ebaadba1be5cdede204c Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 20 Sep 2023 10:02:10 -0400 Subject: [PATCH 050/178] amend --- test/test_collector.py | 3 ++- torchrl/collectors/collectors.py | 6 ++++++ torchrl/data/tensor_specs.py | 2 ++ torchrl/envs/transforms/transforms.py | 1 + 4 files changed, 11 insertions(+), 1 deletion(-) diff --git a/test/test_collector.py b/test/test_collector.py index 0e06db14a04..d08f5c4076a 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -57,7 +57,7 @@ from torchrl.envs.libs.gym import _has_gym, gym_backend, GymEnv, set_gym_backend from torchrl.envs.transforms import TransformedEnv, VecNorm from torchrl.envs.utils import _replace_last, _bring_reset_to_root, \ - PARTIAL_MISSING_ERR + PARTIAL_MISSING_ERR, check_env_specs from torchrl.modules import Actor, LSTMNet, OrnsteinUhlenbeckProcessWrapper, SafeModule # torch.set_default_dtype(torch.double) @@ -1534,6 +1534,7 @@ def test_multi_collector_het_env_consistency( env = HeteroCountingEnv(max_steps=3, batch_size=(batch_dim,)) torch.manual_seed(seed) env_fn = lambda: TransformedEnv(env, InitTracker()) + check_env_specs(env_fn(), return_contiguous=False) policy = HeteroCountingEnvPolicy(env.input_spec["full_action_spec"]) ccollector = MultiaSyncDataCollector( diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 186b4c9a74d..d44d60abbd4 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -886,6 +886,12 @@ def rollout(self) -> TensorDictBase: self._tensordict_out.ndim - 1, out=self._tensordict_out, ) + except KeyError: + print('\n\n err during stack') + print('tensordict list', tensordicts) + print('dest', self._tensordict_out) + print('env', self.env) + raise return self._tensordict_out def reset(self, index=None, **kwargs) -> None: diff --git a/torchrl/data/tensor_specs.py b/torchrl/data/tensor_specs.py index 01a88bb6fb5..68c2b242384 100644 --- a/torchrl/data/tensor_specs.py +++ b/torchrl/data/tensor_specs.py @@ -776,6 +776,8 @@ def __torch_function__( ) return cls.SPEC_HANDLED_FUNCTIONS[func](*args, **kwargs) + def unbind(self, dim: int): + raise NotImplementedError T = TypeVar("T") diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 0695e591f22..6bc18041d59 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -5158,6 +5158,7 @@ def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec shape = full_done_spec[done_key].shape break else: + print('here!') raise KeyError( f"Could not find root of init_key {init_key} within done_keys {self.parent.done_keys}." ) From afcc527919ee8db1ebbfa85f6b22f31d720dd19c Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 20 Sep 2023 12:20:10 -0400 Subject: [PATCH 051/178] amend --- test/_utils_internal.py | 1 + test/mocking_classes.py | 7 +- test/test_collector.py | 181 +++++++++++++++++++------- test/test_env.py | 139 ++++++++++++++++++-- test/test_specs.py | 50 +++---- torchrl/_utils.py | 2 + torchrl/collectors/collectors.py | 13 +- torchrl/data/tensor_specs.py | 1 + torchrl/envs/batched_envs.py | 20 ++- torchrl/envs/common.py | 33 ++--- torchrl/envs/transforms/transforms.py | 10 +- torchrl/envs/utils.py | 106 +++++++++------ 12 files changed, 411 insertions(+), 152 deletions(-) diff --git a/test/_utils_internal.py b/test/_utils_internal.py index 6ed65b32f97..e3d3d95f96f 100644 --- a/test/_utils_internal.py +++ b/test/_utils_internal.py @@ -348,6 +348,7 @@ def rollout_consistency_assertion( (r_done[observation_key] - r_done_tp1[observation_key]).norm(dim=-1) > 1e-1 ).all(), (r_done[observation_key] - r_done_tp1[observation_key]).norm(dim=-1) + def rand_reset(env): """Generates a tensordict with reset keys that mimic the done spec. diff --git a/test/mocking_classes.py b/test/mocking_classes.py index c6fb4689cd6..257c12542b8 100644 --- a/test/mocking_classes.py +++ b/test/mocking_classes.py @@ -798,9 +798,6 @@ def _get_out_obs(self, obs): def _get_in_obs(self, obs): obs = obs.diagonal(0, -1, -2) - # if any(dim == 1 for dim in obs.shape): - # print("squeezing obs", obs.shape) - # obs = obs.squeeze() return obs @@ -1452,8 +1449,8 @@ def _reset( tensordict: TensorDictBase = None, **kwargs, ) -> TensorDictBase: - if tensordict is not None and "_reset" in tensordict.keys(): - _reset = tensordict.get("_reset").squeeze(-1).any(-1) + if tensordict is not None and self.reset_keys[0] in tensordict.keys(): + _reset = tensordict.get(self.reset_keys[0]).squeeze(-1).any(-1) self.count[_reset] = self.start_val else: self.count[:] = self.start_val diff --git a/test/test_collector.py b/test/test_collector.py index 14b12ee7bbe..589619d3f6c 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -56,8 +56,12 @@ ) from torchrl.envs.libs.gym import _has_gym, gym_backend, GymEnv, set_gym_backend from torchrl.envs.transforms import TransformedEnv, VecNorm -from torchrl.envs.utils import _replace_last, _bring_reset_to_root, \ - PARTIAL_MISSING_ERR, check_env_specs +from torchrl.envs.utils import ( + _bring_reset_to_root, + _replace_last, + check_env_specs, + PARTIAL_MISSING_ERR, +) from torchrl.modules import Actor, LSTMNet, OrnsteinUhlenbeckProcessWrapper, SafeModule # torch.set_default_dtype(torch.double) @@ -1753,96 +1757,168 @@ def test_param_sync(self, give_weights, collector, policy_device, env_device): finally: col.shutdown() + class TestBringReset: def test_bring_reset_to_root(self): # simple - td = TensorDict( - {"_reset": torch.zeros((1,), dtype=torch.bool)}, [] - ) + td = TensorDict({"_reset": torch.zeros((1,), dtype=torch.bool)}, []) assert _bring_reset_to_root(td).shape == () # td with batch size - td = TensorDict( - {"_reset": torch.zeros((1,), dtype=torch.bool)}, [1] - ) + td = TensorDict({"_reset": torch.zeros((1,), dtype=torch.bool)}, [1]) assert _bring_reset_to_root(td).shape == (1,) - td = TensorDict( - {"_reset": torch.zeros((1, 2), dtype=torch.bool)}, [1] - ) + td = TensorDict({"_reset": torch.zeros((1, 2), dtype=torch.bool)}, [1]) assert _bring_reset_to_root(td).shape == (1,) # nested td td = TensorDict( - {"_reset": torch.zeros((1,), dtype=torch.bool), - "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}}, [1] + { + "_reset": torch.zeros((1,), dtype=torch.bool), + "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}, + }, + [1], ) assert _bring_reset_to_root(td).shape == (1,) # nested td with greater number of dims td = TensorDict( - {"_reset": torch.zeros((1,2,), dtype=torch.bool), - "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}}, [1, 2] + { + "_reset": torch.zeros( + ( + 1, + 2, + ), + dtype=torch.bool, + ), + "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}, + }, + [1, 2], ) # test reduction - assert _bring_reset_to_root(td).shape == (1,2) + assert _bring_reset_to_root(td).shape == (1, 2) td = TensorDict( - {"_reset": torch.zeros((1,2,), dtype=torch.bool), - "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}}, [1, 2] + { + "_reset": torch.zeros( + ( + 1, + 2, + ), + dtype=torch.bool, + ), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, + }, + [1, 2], ) assert _bring_reset_to_root(td).all() # with a stack td0 = TensorDict( - {"_reset": torch.zeros((1,2,), dtype=torch.bool), - "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, - "b": {"c": torch.randn(1, 2)}}, [1, 2] + { + "_reset": torch.zeros( + ( + 1, + 2, + ), + dtype=torch.bool, + ), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, + "b": {"c": torch.randn(1, 2)}, + }, + [1, 2], ) td1 = TensorDict( - {"_reset": torch.zeros((1,2,), dtype=torch.bool), - "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, - "b": {"c": torch.randn(1, 2, 5)}}, [1, 2] + { + "_reset": torch.zeros( + ( + 1, + 2, + ), + dtype=torch.bool, + ), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, + "b": {"c": torch.randn(1, 2, 5)}, + }, + [1, 2], ) td = torch.stack([td0, td1], 0) assert _bring_reset_to_root(td).all() + def test_bring_reset_to_root_keys(self): # simple - td = TensorDict( - {"_reset": torch.zeros((1,), dtype=torch.bool)}, [] - ) + td = TensorDict({"_reset": torch.zeros((1,), dtype=torch.bool)}, []) assert _bring_reset_to_root(td, reset_keys=["_reset"]).shape == () # td with batch size - td = TensorDict( - {"_reset": torch.zeros((1,), dtype=torch.bool)}, [1] - ) + td = TensorDict({"_reset": torch.zeros((1,), dtype=torch.bool)}, [1]) assert _bring_reset_to_root(td, reset_keys=["_reset"]).shape == (1,) - td = TensorDict( - {"_reset": torch.zeros((1, 2), dtype=torch.bool)}, [1] - ) + td = TensorDict({"_reset": torch.zeros((1, 2), dtype=torch.bool)}, [1]) assert _bring_reset_to_root(td, reset_keys=["_reset"]).shape == (1,) # nested td td = TensorDict( - {"_reset": torch.zeros((1,), dtype=torch.bool), - "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}}, [1] + { + "_reset": torch.zeros((1,), dtype=torch.bool), + "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}, + }, + [1], ) - assert _bring_reset_to_root(td, reset_keys=["_reset", ("a", "_reset")]).shape == (1,) + assert _bring_reset_to_root( + td, reset_keys=["_reset", ("a", "_reset")] + ).shape == (1,) # nested td with greater number of dims td = TensorDict( - {"_reset": torch.zeros((1,2,), dtype=torch.bool), - "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}}, [1, 2] + { + "_reset": torch.zeros( + ( + 1, + 2, + ), + dtype=torch.bool, + ), + "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}, + }, + [1, 2], ) # test reduction - assert _bring_reset_to_root(td, reset_keys=["_reset", ("a", "_reset")]).shape == (1,2) + assert _bring_reset_to_root( + td, reset_keys=["_reset", ("a", "_reset")] + ).shape == (1, 2) td = TensorDict( - {"_reset": torch.zeros((1,2,), dtype=torch.bool), - "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}}, [1, 2] + { + "_reset": torch.zeros( + ( + 1, + 2, + ), + dtype=torch.bool, + ), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, + }, + [1, 2], ) assert _bring_reset_to_root(td, reset_keys=["_reset", ("a", "_reset")]).all() # with a stack td0 = TensorDict( - {"_reset": torch.zeros((1,2,), dtype=torch.bool), - "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, - "b": {"c": torch.randn(1, 2)}}, [1, 2] + { + "_reset": torch.zeros( + ( + 1, + 2, + ), + dtype=torch.bool, + ), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, + "b": {"c": torch.randn(1, 2)}, + }, + [1, 2], ) td1 = TensorDict( - {"_reset": torch.zeros((1,2,), dtype=torch.bool), - "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, - "b": {"c": torch.randn(1, 2, 5)}}, [1, 2] + { + "_reset": torch.zeros( + ( + 1, + 2, + ), + dtype=torch.bool, + ), + "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, + "b": {"c": torch.randn(1, 2, 5)}, + }, + [1, 2], ) td = torch.stack([td0, td1], 0) assert _bring_reset_to_root(td, reset_keys=["_reset", ("a", "_reset")]).all() @@ -1850,9 +1926,16 @@ def test_bring_reset_to_root_keys(self): def test_bring_reset_to_root_errors(self): # the order matters: if the first or another key is missing, the ValueError is raised at a different line with pytest.raises(ValueError, match=PARTIAL_MISSING_ERR): - _bring_reset_to_root(TensorDict({"_reset": False}, []), reset_keys=["_reset", ("another", "_reset")]) + _bring_reset_to_root( + TensorDict({"_reset": False}, []), + reset_keys=["_reset", ("another", "_reset")], + ) with pytest.raises(ValueError, match=PARTIAL_MISSING_ERR): - _bring_reset_to_root(TensorDict({"_reset": False}, []), reset_keys=[("another", "_reset"), "_reset"]) + _bring_reset_to_root( + TensorDict({"_reset": False}, []), + reset_keys=[("another", "_reset"), "_reset"], + ) + if __name__ == "__main__": args, unknown = argparse.ArgumentParser().parse_known_args() diff --git a/test/test_env.py b/test/test_env.py index 27276a5188e..1d8621e705f 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -55,6 +55,7 @@ from torchrl.collectors import MultiSyncDataCollector, SyncDataCollector from torchrl.data.tensor_specs import ( CompositeSpec, + DiscreteTensorSpec, OneHotDiscreteTensorSpec, UnboundedContinuousTensorSpec, ) @@ -66,6 +67,7 @@ from torchrl.envs.utils import ( check_env_specs, check_marl_grouping, + done_or_truncated, make_composite_from_td, MarlGroupMapType, step_mdp, @@ -914,12 +916,12 @@ def test_parallel_env_reset_flag(self, batch_size, n_workers, max_steps=3): assert (td["next", "done"] == 1).all() assert (td["next"]["observation"] == max_steps + 1).all() - env._allow_done_after_reset = True - td_reset = env.reset( - TensorDict(rand_reset(env), batch_size=env.batch_size, device=env.device) + td_reset = TensorDict( + rand_reset(env), batch_size=env.batch_size, device=env.device ) - env.close() reset = td_reset["_reset"] + td_reset = env.reset(td_reset) + env.close() assert (td_reset["done"][reset] == 0).all() assert (td_reset["observation"][reset] == 0).all() @@ -1009,11 +1011,9 @@ def test_env_base_reset_flag(batch_size, max_steps=3): assert (td["next", "done"] == 1).all() assert (td["next", "observation"] == max_steps + 1).all() - env._allow_done_after_reset = True - td_reset = env.reset( - TensorDict(rand_reset(env), batch_size=env.batch_size, device=env.device) - ) + td_reset = TensorDict(rand_reset(env), batch_size=env.batch_size, device=env.device) reset = td_reset["_reset"] + td_reset = env.reset(td_reset) assert (td_reset["done"][reset] == 0).all() assert (td_reset["observation"][reset] == 0).all() @@ -2050,6 +2050,129 @@ def test_mocking_envs(envclass): check_env_specs(env, seed=100, return_contiguous=False) +class TestDoneOrTruncated: + def test_done_or_truncated_nospec(self): + data = TensorDict({"done": torch.zeros(2, 1, dtype=torch.bool)}, [2]) + assert not done_or_truncated(data, write_full_false=True) + assert data["_reset"].shape == (2, 1) + assert not done_or_truncated(data, write_full_false=False) + assert data.get("_reset", None) is None + + data = TensorDict( + { + "done": torch.zeros(2, 1, dtype=torch.bool), + ("nested", "done"): torch.ones(2, 1, dtype=torch.bool), + }, + [2], + ) + assert done_or_truncated(data) + assert data["_reset"].shape == (2, 1) + assert data["nested", "_reset"].shape == (2, 1) + + data = TensorDict( + { + "done": torch.zeros(2, 1, dtype=torch.bool), + ("nested", "done"): torch.zeros(2, 1, dtype=torch.bool), + }, + [2], + ) + assert not done_or_truncated(data, write_full_false=False) + assert data.get("_reset", None) is None + assert data.get(("nested", "_reset"), None) is None + assert not done_or_truncated(data, write_full_false=True) + assert data["_reset"].shape == (2, 1) + assert data["nested", "_reset"].shape == (2, 1) + + data = TensorDict( + { + "done": torch.zeros(2, 1, dtype=torch.bool), + "truncated": torch.ones(2, 1, dtype=torch.bool), + ("nested", "done"): torch.zeros(2, 1, dtype=torch.bool), + }, + [2], + ) + assert done_or_truncated(data, write_full_false=False) + assert data["_reset"].shape == (2, 1) + assert data["nested", "_reset"].shape == (2, 1) + assert data["_reset"].all() + assert not data["nested", "_reset"].any() + + def test_done_or_truncated_spec(self): + spec = CompositeSpec( + done=DiscreteTensorSpec(2, shape=(2, 1), dtype=torch.bool), + shape=[ + 2, + ], + ) + data = TensorDict({"done": torch.zeros(2, 1, dtype=torch.bool)}, [2]) + assert not done_or_truncated(data, write_full_false=True, full_done_spec=spec) + assert data["_reset"].shape == (2, 1) + assert not done_or_truncated(data, write_full_false=False, full_done_spec=spec) + assert data.get("_reset", None) is None + + spec = CompositeSpec( + { + "done": DiscreteTensorSpec(2, shape=(2, 1), dtype=torch.bool), + ("nested", "done"): DiscreteTensorSpec( + 2, shape=(2, 1), dtype=torch.bool + ), + }, + shape=[ + 2, + ], + ) + data = TensorDict( + { + "done": torch.zeros(2, 1, dtype=torch.bool), + ("nested", "done"): torch.ones(2, 1, dtype=torch.bool), + }, + [2], + ) + assert done_or_truncated(data, full_done_spec=spec) + assert data["_reset"].shape == (2, 1) + assert data["nested", "_reset"].shape == (2, 1) + + data = TensorDict( + { + "done": torch.zeros(2, 1, dtype=torch.bool), + ("nested", "done"): torch.zeros(2, 1, dtype=torch.bool), + }, + [2], + ) + assert not done_or_truncated(data, write_full_false=False, full_done_spec=spec) + assert data.get("_reset", None) is None + assert data.get(("nested", "_reset"), None) is None + assert not done_or_truncated(data, write_full_false=True, full_done_spec=spec) + assert data["_reset"].shape == (2, 1) + assert data["nested", "_reset"].shape == (2, 1) + + spec = CompositeSpec( + { + "done": DiscreteTensorSpec(2, shape=(2, 1), dtype=torch.bool), + "truncated": DiscreteTensorSpec(2, shape=(2, 1), dtype=torch.bool), + ("nested", "done"): DiscreteTensorSpec( + 2, shape=(2, 1), dtype=torch.bool + ), + }, + shape=[ + 2, + ], + ) + data = TensorDict( + { + "done": torch.zeros(2, 1, dtype=torch.bool), + "truncated": torch.ones(2, 1, dtype=torch.bool), + ("nested", "done"): torch.zeros(2, 1, dtype=torch.bool), + }, + [2], + ) + assert done_or_truncated(data, write_full_false=False, full_done_spec=spec) + assert data["_reset"].shape == (2, 1) + assert data["nested", "_reset"].shape == (2, 1) + assert data["_reset"].all() + assert not data["nested", "_reset"].any() + + if __name__ == "__main__": args, unknown = argparse.ArgumentParser().parse_known_args() pytest.main([__file__, "--capture", "no", "--exitfirst"] + unknown) diff --git a/test/test_specs.py b/test/test_specs.py index 469a6425c8f..4d8f7da2e9e 100644 --- a/test/test_specs.py +++ b/test/test_specs.py @@ -1762,13 +1762,9 @@ def test_unboundeddiscrete( assert spec == spec.clone() assert spec is not spec.clone() + class TestUnbind: - @pytest.mark.parametrize( - "shape1", - [ - (5, 4) - ] - ) + @pytest.mark.parametrize("shape1", [(5, 4)]) def test_binary(self, shape1): spec = BinaryDiscreteTensorSpec( n=4, shape=shape1, device="cpu", dtype=torch.bool @@ -1847,13 +1843,7 @@ def test_composite(self): assert spec == torch.stack(spec.unbind(0), 0) assert spec == torch.stack(spec.unbind(-1), -1) - @pytest.mark.parametrize( - "shape1", - [ - (5,), - (5,6) - ] - ) + @pytest.mark.parametrize("shape1", [(5,), (5, 6)]) def test_discrete( self, shape1, @@ -1866,8 +1856,11 @@ def test_discrete( "shape1", [ (5,), - (5,6,) - ] + ( + 5, + 6, + ), + ], ) def test_multidiscrete( self, @@ -1888,8 +1881,11 @@ def test_multidiscrete( "shape1", [ (5,), - (5,6,) - ] + ( + 5, + 6, + ), + ], ) def test_multionehot( self, @@ -1910,7 +1906,10 @@ def test_multionehot( "shape1", [ (5,), - (5,6,) + ( + 5, + 6, + ), ], ) def test_onehot( @@ -1932,8 +1931,11 @@ def test_onehot( "shape1", [ (5,), - (5,6,) - ] + ( + 5, + 6, + ), + ], ) def test_unbounded( self, @@ -1953,8 +1955,11 @@ def test_unbounded( "shape1", [ (5,), - (5,6,) - ] + ( + 5, + 6, + ), + ], ) def test_unboundeddiscrete( self, @@ -1968,6 +1973,7 @@ def test_unboundeddiscrete( assert spec == torch.stack(spec.unbind(0), 0) assert spec == torch.stack(spec.unbind(-1), -1) + class TestUnbind: @pytest.mark.parametrize("shape1", [(5, 4)]) def test_binary(self, shape1): diff --git a/torchrl/_utils.py b/torchrl/_utils.py index 81d37e4f76c..567ac9eacd7 100644 --- a/torchrl/_utils.py +++ b/torchrl/_utils.py @@ -569,6 +569,7 @@ def get_trace(): class _ProcessNoWarn(mp.Process): """A private Process class that shuts down warnings on the subprocess.""" + @wraps(mp.Process.__init__) def __init__(self, *args, **kwargs): import torchrl @@ -580,6 +581,7 @@ def __init__(self, *args, **kwargs): def run(self, *args, **kwargs): if self.filter_warnings_subprocess: import warnings + with warnings.catch_warnings(): warnings.simplefilter("ignore") return mp.Process.run(self, *args, **kwargs) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index f96ef69a849..106156d7ce1 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -30,8 +30,8 @@ from torchrl._utils import ( _check_for_faulty_process, - accept_remote_rref_udf_invocation, _ProcessNoWarn, + accept_remote_rref_udf_invocation, prod, RL_WARNINGS, VERBOSE, @@ -42,11 +42,12 @@ from torchrl.envs.common import EnvBase from torchrl.envs.transforms import StepCounter, TransformedEnv from torchrl.envs.utils import ( + _bring_reset_to_root, _convert_exploration_type, done_or_truncated, ExplorationType, set_exploration_type, - step_mdp, _bring_reset_to_root, + step_mdp, ) _TIMEOUT = 1.0 @@ -888,10 +889,10 @@ def rollout(self) -> TensorDictBase: out=self._tensordict_out, ) except KeyError: - print('\n\n err during stack') - print('tensordict list', tensordicts) - print('dest', self._tensordict_out) - print('env', self.env) + print("\n\n err during stack") + print("tensordict list", tensordicts) + print("dest", self._tensordict_out) + print("env", self.env) raise return self._tensordict_out diff --git a/torchrl/data/tensor_specs.py b/torchrl/data/tensor_specs.py index 69c9d8c4654..d2d8c3233d9 100644 --- a/torchrl/data/tensor_specs.py +++ b/torchrl/data/tensor_specs.py @@ -779,6 +779,7 @@ def __torch_function__( def unbind(self, dim: int): raise NotImplementedError + T = TypeVar("T") diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index 0388b5a8473..fee223abeb8 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -25,8 +25,12 @@ from torchrl.envs.common import EnvBase from torchrl.envs.env_creator import get_env_metadata -from torchrl.envs.utils import _set_single_key, _sort_keys, clear_mpi_env_vars, \ - _bring_reset_to_root +from torchrl.envs.utils import ( + _bring_reset_to_root, + _set_single_key, + _sort_keys, + clear_mpi_env_vars, +) # legacy from .libs.envpool import MultiThreadedEnv, MultiThreadedEnvWrapper # noqa: F401 @@ -580,7 +584,9 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: if tensordict is not None: needs_resetting = _bring_reset_to_root(tensordict) else: - needs_resetting = torch.ones((self.num_workers,), device=self.device, dtype=torch.bool) + needs_resetting = torch.ones( + (self.num_workers,), device=self.device, dtype=torch.bool + ) for i, _env in enumerate(self._envs): if tensordict is not None: @@ -811,8 +817,14 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: if tensordict is not None: needs_resetting = _bring_reset_to_root(tensordict) + if needs_resetting.ndim > 2: + needs_resetting = needs_resetting.flatten(1, needs_resetting.ndim-1) + if needs_resetting.ndim > 1: + needs_resetting = needs_resetting.any(-1) else: - needs_resetting = torch.ones((self.num_workers,), device=self.device, dtype=torch.bool) + needs_resetting = torch.ones( + (self.num_workers,), device=self.device, dtype=torch.bool + ) workers = [] diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index c86678089b9..fc1ce94b13b 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -1415,20 +1415,22 @@ def reset( self.reset_keys, self.done_keys_groups ): reset_value = ( - tensordict.get(reset_key, default=None) if tensordict is not None else None + tensordict.get(reset_key, default=None) + if tensordict is not None + else None ) if reset_value is not None: for done_key in done_key_group: if tensordict_reset.get(done_key)[reset_value].any(): raise RuntimeError( - f"Env done entry '{done_key}' was (partially) True after reset on specified '_reset' dimensions. This is not allowed." -) + f"Env done entry '{done_key}' was (partially) True after reset on specified '_reset' dimensions. This is not allowed." + ) else: for done_key in done_key_group: if tensordict_reset.get(done_key).any(): raise RuntimeError( - f"Env done entry '{done_key}' was (partially) True after a call to reset(). This is not allowed." -) + f"Env done entry '{done_key}' was (partially) True after a call to reset(). This is not allowed." + ) if tensordict is not None: tensordict.update(tensordict_reset) @@ -1696,16 +1698,7 @@ def policy(td): tensordict = self.step(tensordict) tensordicts.append(tensordict.clone(False)) - # done and truncated are in done_keys - # To read the done status, we assess whether any of the done entries - # at a given level is True. This is written in the _reset key. - any_done = done_or_truncated( - tensordict.get("next"), - full_done_spec=self.output_spec["full_done_spec"], - key="_reset" if not break_when_any_done else None, - ) - - if (break_when_any_done and any_done) or i == max_steps - 1: + if i == max_steps - 1: break tensordict = step_mdp( tensordict, @@ -1716,7 +1709,15 @@ def policy(td): action_keys=self.action_keys, done_keys=self.done_keys, ) - + # done and truncated are in done_keys + # We read if any key is done. + any_done = done_or_truncated( + tensordict, + full_done_spec=self.output_spec["full_done_spec"], + key=None if break_when_any_done else "_reset", + ) + if break_when_any_done and any_done: + break if not break_when_any_done and any_done: self.reset(tensordict) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 6bc18041d59..49678df7ef2 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -671,7 +671,9 @@ def _reset(self, tensordict: Optional[TensorDictBase] = None, **kwargs): if tensordict is not None: # We must avoid modifying the original tensordict so a shallow copy is necessary. # We just select the input data and reset signal, which is all we need. - tensordict = tensordict.select(*self.reset_keys, *self.state_spec.keys(True, True), strict=False) + tensordict = tensordict.select( + *self.reset_keys, *self.state_spec.keys(True, True), strict=False + ) out_tensordict = self.base_env._reset(tensordict=tensordict, **kwargs) if tensordict is not None: # the transform may need to read previous info during reset. @@ -5158,7 +5160,7 @@ def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec shape = full_done_spec[done_key].shape break else: - print('here!') + print("here!") raise KeyError( f"Could not find root of init_key {init_key} within done_keys {self.parent.done_keys}." ) @@ -5275,7 +5277,9 @@ def _call(self, tensordict: TensorDictBase) -> TensorDictBase: def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase: # no in-place modif if self.create_copy: - out = tensordict.select(*self.out_keys_inv, strict=not self._missing_tolerance) + out = tensordict.select( + *self.out_keys_inv, strict=not self._missing_tolerance + ) for in_key, out_key in zip(self.in_keys_inv, self.out_keys_inv): try: out.rename_key_(out_key, in_key) diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 02b4455af78..47c1eaae40c 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -14,7 +14,7 @@ import torch -from tensordict import is_tensor_collection, unravel_key, TensorDictBase +from tensordict import is_tensor_collection, TensorDictBase, unravel_key from tensordict.nn.probabilistic import ( # noqa # Note: the `set_interaction_mode` and their associated arg `default_interaction_mode` are being deprecated! # Please use the `set_/interaction_type` ones above with the InteractionType enum instead. @@ -41,7 +41,7 @@ ] -from torchrl.data import CompositeSpec +from torchrl.data import CompositeSpec, TensorSpec from torchrl.data.utils import check_no_exclusive_keys ACTION_MASK_ERROR = RuntimeError( @@ -236,7 +236,9 @@ def step_mdp( return out -def _set_single_key(source: TensorDictBase, dest: TensorDictBase, key: str | tuple, clone: bool=False): +def _set_single_key( + source: TensorDictBase, dest: TensorDictBase, key: str | tuple, clone: bool = False +): # key should be already unraveled if isinstance(key, str): key = (key,) @@ -727,7 +729,12 @@ def check_marl_grouping(group_map: Dict[str, List[str]], agent_names: List[str]) raise ValueError(f"Agent {agent_name} not found in any group") -def done_or_truncated(data: TensorDictBase, full_done_spec=None, key="_reset") -> bool: +def done_or_truncated( + data: TensorDictBase, + full_done_spec: TensorSpec | None = None, + key: str = "_reset", + write_full_false: bool = False, +) -> bool: """Reads the done / truncated keys within a tensordict, and writes a new tensor where the values of both signals are aggregated. The modification occurs in-place within the TensorDict instance provided. @@ -744,6 +751,8 @@ def done_or_truncated(data: TensorDictBase, full_done_spec=None, key="_reset") - whether any of the done values was true. .. note:: if a value is already present for the ``key`` entry, the previous value will prevail and no update will be achieved. + write_full_false (bool, optional): if ``True``, the reset keys will be written + even if the output if ``False`` (ie, no done is ``True``). Defaults to ``False``. Returns: a boolean value indicating whether any of the done states found in the data contained a ``True``. @@ -770,45 +779,64 @@ def done_or_truncated(data: TensorDictBase, full_done_spec=None, key="_reset") - >>> print(data["nested", "_reset"]) tensor(True) """ - any_done = False - aggregate = None - if full_done_spec is None: - for done_key, item in data.items(): - if done_key in ("done", "truncated"): - done = data.get(done_key, None) - if done is None: - done = torch.zeros( - (*data.shape, 1), dtype=torch.bool, device=data.device + list_of_keys = [] + + def inner_done_or_truncated(data, full_done_spec, key, curr_done_key=()): + any_done = False + aggregate = None + if full_done_spec is None: + for done_key, item in data.items(): + if done_key in ("done", "truncated"): + done = data.get(done_key, None) + if done is None: + done = torch.zeros( + (*data.shape, 1), dtype=torch.bool, device=data.device + ) + if aggregate is None: + aggregate = torch.tensor(False, device=done.device) + aggregate = aggregate | done + elif isinstance(item, TensorDictBase): + any_done = any_done | inner_done_or_truncated( + data=item, + full_done_spec=None, + key=key, + curr_done_key=curr_done_key + (done_key,), ) - if aggregate is None: - aggregate = torch.tensor(False, device=done.device) - aggregate = aggregate | done - elif isinstance(item, TensorDictBase): - any_done = any_done | done_or_truncated( - data=item, full_done_spec=None, key=key - ) - else: - for done_key, item in full_done_spec.items(): - if isinstance(item, CompositeSpec): - any_done = any_done | done_or_truncated( - data=data.get(done_key), full_done_spec=item, key=key - ) - else: - done = data.get(done_key, None) - if done is None: - done = torch.zeros( - (*data.shape, 1), dtype=torch.bool, device=data.device + else: + for done_key, item in full_done_spec.items(): + if isinstance(item, CompositeSpec): + any_done = any_done | inner_done_or_truncated( + data=data.get(done_key), + full_done_spec=item, + key=key, + curr_done_key=curr_done_key + (done_key,), ) - if aggregate is None: - aggregate = torch.tensor(False, device=done.device) - aggregate = aggregate | done - if aggregate is not None: - if key is not None: - data.set(key, aggregate) - any_done = any_done | aggregate.any() + else: + done = data.get(done_key, None) + if done is None: + done = torch.zeros( + (*data.shape, 1), dtype=torch.bool, device=data.device + ) + if aggregate is None: + aggregate = torch.tensor(False, device=done.device) + aggregate = aggregate | done + if aggregate is not None: + if key is not None: + data.set(key, aggregate) + list_of_keys.append(curr_done_key + (key,)) + any_done = any_done | aggregate.any() + return any_done + + any_done = inner_done_or_truncated(data, full_done_spec, key) + if not any_done and not write_full_false: + # remove the list of reset keys + data.exclude(*list_of_keys, inplace=True) return any_done + PARTIAL_MISSING_ERR = "Some reset keys were present but not all. Either all the `'_reset'` entries must be present, or none." + + def _bring_reset_to_root(data: TensorDictBase, reset_keys=None) -> torch.Tensor: # goes through the tensordict and brings the _reset information to # a boolean tensor of the shape of the tensordict. @@ -843,7 +871,7 @@ def skim_through(td, reset=reset): if key == "_reset": local_reset = td.get(key) if local_reset.ndim > n: - local_reset = local_reset.flatten(n, local_reset.ndim-1) + local_reset = local_reset.flatten(n, local_reset.ndim - 1) local_reset = local_reset.any(-1) reset = reset | local_reset # we need to check the entry class without getting the value, From 9eae41dd164ea2c5018cc7859fccaa230f863c62 Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 20 Sep 2023 16:03:38 -0400 Subject: [PATCH 052/178] amend --- test/mocking_classes.py | 2 +- torchrl/_utils.py | 13 +++++++++---- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/test/mocking_classes.py b/test/mocking_classes.py index 257c12542b8..5991936a8e1 100644 --- a/test/mocking_classes.py +++ b/test/mocking_classes.py @@ -1449,7 +1449,7 @@ def _reset( tensordict: TensorDictBase = None, **kwargs, ) -> TensorDictBase: - if tensordict is not None and self.reset_keys[0] in tensordict.keys(): + if tensordict is not None and self.reset_keys[0] in tensordict.keys(True): _reset = tensordict.get(self.reset_keys[0]).squeeze(-1).any(-1) self.count[_reset] = self.start_val else: diff --git a/torchrl/_utils.py b/torchrl/_utils.py index 567ac9eacd7..369e8f18731 100644 --- a/torchrl/_utils.py +++ b/torchrl/_utils.py @@ -251,6 +251,7 @@ class implement_for: # Stores pointers to fitting implementations: dict[func_name] = func_pointer _implementations = {} _setters = [] + _cache_modules = {} def __init__( self, @@ -316,7 +317,12 @@ def module_set(self): def import_module(cls, module_name: Union[Callable, str]) -> str: """Imports module and returns its version.""" if not callable(module_name): - module = import_module(module_name) + module = cls._cache_modules.get(module_name, None) + if module is None: + if module_name in sys.modules: + sys.modules[module_name] = module = import_module(module_name) + else: + cls._cache_modules[module_name] = module = import_module(module_name) else: module = module_name() return module.__version__ @@ -574,8 +580,7 @@ class _ProcessNoWarn(mp.Process): def __init__(self, *args, **kwargs): import torchrl - if torchrl.filter_warnings_subprocess: - self.filter_warnings_subprocess = torchrl.filter_warnings_subprocess + self.filter_warnings_subprocess = torchrl.filter_warnings_subprocess super().__init__(*args, **kwargs) def run(self, *args, **kwargs): @@ -584,5 +589,5 @@ def run(self, *args, **kwargs): with warnings.catch_warnings(): warnings.simplefilter("ignore") - return mp.Process.run(self, *args, **kwargs) + return mp.Process.run(self, *args, **kwargs) return mp.Process.run(self, *args, **kwargs) From 9210f3b381dafcb9cf3ad310b75be81fe47e41f6 Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 20 Sep 2023 16:07:41 -0400 Subject: [PATCH 053/178] amend --- test/test_specs.py | 211 ----------------------------------- torchrl/_utils.py | 4 +- torchrl/envs/batched_envs.py | 2 +- torchrl/envs/utils.py | 2 +- 4 files changed, 5 insertions(+), 214 deletions(-) diff --git a/test/test_specs.py b/test/test_specs.py index 4d8f7da2e9e..60f7223b23a 100644 --- a/test/test_specs.py +++ b/test/test_specs.py @@ -1974,217 +1974,6 @@ def test_unboundeddiscrete( assert spec == torch.stack(spec.unbind(-1), -1) -class TestUnbind: - @pytest.mark.parametrize("shape1", [(5, 4)]) - def test_binary(self, shape1): - spec = BinaryDiscreteTensorSpec( - n=4, shape=shape1, device="cpu", dtype=torch.bool - ) - assert spec == torch.stack(spec.unbind(0), 0) - with pytest.raises(ValueError): - spec.unbind(-1) - - @pytest.mark.parametrize( - "shape1,mini,maxi", - [ - [(10,), -torch.ones([]), torch.ones([])], - [None, -torch.ones([10]), torch.ones([])], - [None, -torch.ones([]), torch.ones([10])], - [(10,), -torch.ones([]), torch.ones([10])], - [(10,), -torch.ones([10]), torch.ones([])], - [(10,), -torch.ones([10]), torch.ones([10])], - ], - ) - def test_bounded(self, shape1, mini, maxi): - spec = BoundedTensorSpec( - mini, maxi, shape=shape1, device="cpu", dtype=torch.bool - ) - assert spec == torch.stack(spec.unbind(0), 0) - with pytest.raises(ValueError): - spec.unbind(-1) - - def test_composite(self): - batch_size = (5,) - spec1 = BoundedTensorSpec( - -torch.ones([*batch_size, 10]), - torch.ones([*batch_size, 10]), - shape=( - *batch_size, - 10, - ), - device="cpu", - dtype=torch.bool, - ) - spec2 = BinaryDiscreteTensorSpec( - n=4, shape=(*batch_size, 4), device="cpu", dtype=torch.bool - ) - spec3 = DiscreteTensorSpec( - n=4, shape=batch_size, device="cpu", dtype=torch.long - ) - spec4 = MultiDiscreteTensorSpec( - nvec=(4, 5, 6), shape=(*batch_size, 3), device="cpu", dtype=torch.long - ) - spec5 = MultiOneHotDiscreteTensorSpec( - nvec=(4, 5, 6), shape=(*batch_size, 15), device="cpu", dtype=torch.long - ) - spec6 = OneHotDiscreteTensorSpec( - n=15, shape=(*batch_size, 15), device="cpu", dtype=torch.long - ) - spec7 = UnboundedContinuousTensorSpec( - shape=(*batch_size, 9), - device="cpu", - dtype=torch.float64, - ) - spec8 = UnboundedDiscreteTensorSpec( - shape=(*batch_size, 9), - device="cpu", - dtype=torch.long, - ) - spec = CompositeSpec( - spec1=spec1, - spec2=spec2, - spec3=spec3, - spec4=spec4, - spec5=spec5, - spec6=spec6, - spec7=spec7, - spec8=spec8, - shape=batch_size, - ) - assert spec == torch.stack(spec.unbind(0), 0) - assert spec == torch.stack(spec.unbind(-1), -1) - - @pytest.mark.parametrize("shape1", [(5,), (5, 6)]) - def test_discrete( - self, - shape1, - ): - spec = DiscreteTensorSpec(n=4, shape=shape1, device="cpu", dtype=torch.long) - assert spec == torch.stack(spec.unbind(0), 0) - assert spec == torch.stack(spec.unbind(-1), -1) - - @pytest.mark.parametrize( - "shape1", - [ - (5,), - ( - 5, - 6, - ), - ], - ) - def test_multidiscrete( - self, - shape1, - ): - if shape1 is None: - shape1 = (3,) - else: - shape1 = (*shape1, 3) - spec = MultiDiscreteTensorSpec( - nvec=(4, 5, 6), shape=shape1, device="cpu", dtype=torch.long - ) - assert spec == torch.stack(spec.unbind(0), 0) - with pytest.raises(ValueError): - spec.unbind(-1) - - @pytest.mark.parametrize( - "shape1", - [ - (5,), - ( - 5, - 6, - ), - ], - ) - def test_multionehot( - self, - shape1, - ): - if shape1 is None: - shape1 = (15,) - else: - shape1 = (*shape1, 15) - spec = MultiOneHotDiscreteTensorSpec( - nvec=(4, 5, 6), shape=shape1, device="cpu", dtype=torch.long - ) - assert spec == torch.stack(spec.unbind(0), 0) - with pytest.raises(ValueError): - spec.unbind(-1) - - @pytest.mark.parametrize( - "shape1", - [ - (5,), - ( - 5, - 6, - ), - ], - ) - def test_onehot( - self, - shape1, - ): - if shape1 is None: - shape1 = (15,) - else: - shape1 = (*shape1, 15) - spec = OneHotDiscreteTensorSpec( - n=15, shape=shape1, device="cpu", dtype=torch.long - ) - assert spec == torch.stack(spec.unbind(0), 0) - with pytest.raises(ValueError): - spec.unbind(-1) - - @pytest.mark.parametrize( - "shape1", - [ - (5,), - ( - 5, - 6, - ), - ], - ) - def test_unbounded( - self, - shape1, - ): - if shape1 is None: - shape1 = (15,) - else: - shape1 = (*shape1, 15) - spec = UnboundedContinuousTensorSpec( - shape=shape1, device="cpu", dtype=torch.float64 - ) - assert spec == torch.stack(spec.unbind(0), 0) - assert spec == torch.stack(spec.unbind(-1), -1) - - @pytest.mark.parametrize( - "shape1", - [ - (5,), - ( - 5, - 6, - ), - ], - ) - def test_unboundeddiscrete( - self, - shape1, - ): - if shape1 is None: - shape1 = (15,) - else: - shape1 = (*shape1, 15) - spec = UnboundedDiscreteTensorSpec(shape=shape1, device="cpu", dtype=torch.long) - assert spec == torch.stack(spec.unbind(0), 0) - assert spec == torch.stack(spec.unbind(-1), -1) - - @pytest.mark.parametrize( "shape,stack_dim", [[(), 0], [(2,), 0], [(2,), 1], [(2, 3), 0], [(2, 3), 1], [(2, 3), 2]], diff --git a/torchrl/_utils.py b/torchrl/_utils.py index 369e8f18731..e4bd0650751 100644 --- a/torchrl/_utils.py +++ b/torchrl/_utils.py @@ -322,7 +322,9 @@ def import_module(cls, module_name: Union[Callable, str]) -> str: if module_name in sys.modules: sys.modules[module_name] = module = import_module(module_name) else: - cls._cache_modules[module_name] = module = import_module(module_name) + cls._cache_modules[module_name] = module = import_module( + module_name + ) else: module = module_name() return module.__version__ diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index fee223abeb8..c3250ba5555 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -818,7 +818,7 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: if tensordict is not None: needs_resetting = _bring_reset_to_root(tensordict) if needs_resetting.ndim > 2: - needs_resetting = needs_resetting.flatten(1, needs_resetting.ndim-1) + needs_resetting = needs_resetting.flatten(1, needs_resetting.ndim - 1) if needs_resetting.ndim > 1: needs_resetting = needs_resetting.any(-1) else: diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 47c1eaae40c..019c51d79e4 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -25,7 +25,7 @@ set_interaction_mode as set_exploration_mode, set_interaction_type as set_exploration_type, ) -from tensordict.tensordict import LazyStackedTensorDict, NestedKey, TensorDictBase +from tensordict.tensordict import LazyStackedTensorDict, NestedKey __all__ = [ "exploration_mode", From b31b2f0c21a5ec42d827e185f9b70464ae45080a Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 05:33:30 -0400 Subject: [PATCH 054/178] amend --- test/test_exploration.py | 6 +++--- test/test_transforms.py | 2 ++ test/test_utils.py | 14 ++++++++------ torchrl/envs/transforms/transforms.py | 3 ++- torchrl/envs/utils.py | 2 +- 5 files changed, 16 insertions(+), 11 deletions(-) diff --git a/test/test_exploration.py b/test/test_exploration.py index c4cd44f0692..740acf2feb1 100644 --- a/test/test_exploration.py +++ b/test/test_exploration.py @@ -28,7 +28,7 @@ ) from torchrl.envs import SerialEnv from torchrl.envs.transforms.transforms import gSDENoise, InitTracker, TransformedEnv -from torchrl.envs.utils import set_exploration_type +from torchrl.envs.utils import set_exploration_type, _replace_last from torchrl.modules import SafeModule, SafeSequential from torchrl.modules.distributions import TanhNormal from torchrl.modules.distributions.continuous import ( @@ -335,7 +335,7 @@ def test_collector(self, device, parallel_spec, probabilistic, seed=0): @pytest.mark.parametrize("nested_obs_action", [True, False]) @pytest.mark.parametrize("nested_done", [True, False]) - @pytest.mark.parametrize("is_init_key", ["some", ("one", "nested")]) + @pytest.mark.parametrize("is_init_key", ["some"]) def test_nested( self, device, @@ -381,7 +381,7 @@ def test_nested( device=device, ) for _td in collector: - assert _td[is_init_key].shape == _td[env.done_key].shape + assert _td[_replace_last(env.done_key, is_init_key)].shape == _td[env.done_key].shape break return diff --git a/test/test_transforms.py b/test/test_transforms.py index ef7a873d6e2..520d5c00e46 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -8128,6 +8128,8 @@ def make_env(): check_env_specs(env) def test_transform_no_env(self): + with pytest.raises(ValueError, match="init_key can only be of type str"): + InitTracker(init_key=("some", "nested")) with pytest.raises( NotImplementedError, match="InitTracker cannot be executed without a parent" ): diff --git a/test/test_utils.py b/test/test_utils.py index b4f8fecc4a5..889fb499d3b 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -324,20 +324,22 @@ def test_set_gym_nested(): import gymnasium assert torch_foo() == 1 + class MockGym: + _is_batched = False with set_gym_backend(gym): - GymWrapper._output_transform(None, (1, 2, 3, 4)) # would break with gymnasium + GymWrapper._output_transform(MockGym, (1, 2, 3, 4)) # would break with gymnasium assert torch_foo() == 1 with set_gym_backend(gymnasium): - GymWrapper._output_transform(None, (1, 2, 3, 4, 5)) # would break with gym + GymWrapper._output_transform(MockGym, (1, 2, 3, 4, 5)) # would break with gym assert torch_foo() == 1 - GymWrapper._output_transform(None, (1, 2, 3, 4)) # would break with gymnasium + GymWrapper._output_transform(MockGym, (1, 2, 3, 4)) # would break with gymnasium with set_gym_backend("gym"): - GymWrapper._output_transform(None, (1, 2, 3, 4)) # would break with gymnasium + GymWrapper._output_transform(MockGym, (1, 2, 3, 4)) # would break with gymnasium assert torch_foo() == 1 with set_gym_backend("gymnasium"): - GymWrapper._output_transform(None, (1, 2, 3, 4, 5)) # would break with gym + GymWrapper._output_transform(MockGym, (1, 2, 3, 4, 5)) # would break with gym assert torch_foo() == 1 - GymWrapper._output_transform(None, (1, 2, 3, 4)) # would break with gymnasium + GymWrapper._output_transform(MockGym, (1, 2, 3, 4)) # would break with gymnasium if __name__ == "__main__": diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 49678df7ef2..0bc2ac5d199 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -5056,6 +5056,8 @@ class InitTracker(Transform): """ def __init__(self, init_key: NestedKey = "is_init"): + if not isinstance(init_key, str): + raise ValueError("init_key can only be of type str.") self.init_key = init_key self._init_keys = None self.reset_key = "_reset" @@ -5160,7 +5162,6 @@ def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec shape = full_done_spec[done_key].shape break else: - print("here!") raise KeyError( f"Could not find root of init_key {init_key} within done_keys {self.parent.done_keys}." ) diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 019c51d79e4..9635062f56d 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -864,7 +864,7 @@ def _bring_reset_to_root(data: TensorDictBase, reset_keys=None) -> torch.Tensor: return torch.ones(batch_size, dtype=torch.bool, device=data.device) return reset - reset = False + reset = torch.tensor(False, device=data.device) def skim_through(td, reset=reset): for key in td.keys(): From 4e8acc0717fe8e4b09982529d67ebd40fb9fb4fc Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 05:46:40 -0400 Subject: [PATCH 055/178] init --- test/test_specs.py | 372 ++----------------------------- torchrl/_utils.py | 2 + torchrl/collectors/collectors.py | 2 +- torchrl/objectives/dreamer.py | 2 +- 4 files changed, 23 insertions(+), 355 deletions(-) diff --git a/test/test_specs.py b/test/test_specs.py index 469a6425c8f..92b6c228aa9 100644 --- a/test/test_specs.py +++ b/test/test_specs.py @@ -1220,8 +1220,8 @@ class TestExpand: [ None, (4,), - (5, 4), - ], + (5, 4) + ] ) @pytest.mark.parametrize("shape2", [(), (10,)]) def test_binary(self, shape1, shape2): @@ -1344,14 +1344,7 @@ def test_composite(self): assert new_spec["spec7"].shape == torch.Size([4, *batch_size, 9]) assert new_spec["spec8"].shape == torch.Size([4, *batch_size, 9]) - @pytest.mark.parametrize( - "shape1", - [ - None, - (), - (5,), - ], - ) + @pytest.mark.parametrize("shape1", [None, (), (5,)]) @pytest.mark.parametrize("shape2", [(), (10,)]) def test_discrete(self, shape1, shape2): spec = DiscreteTensorSpec(n=4, shape=shape1, device="cpu", dtype=torch.long) @@ -1373,14 +1366,7 @@ def test_discrete(self, shape1, shape2): assert spec2.rand().shape == spec2.shape assert spec2.zero().shape == spec2.shape - @pytest.mark.parametrize( - "shape1", - [ - None, - (), - (5,), - ], - ) + @pytest.mark.parametrize("shape1", [None, (), (5,)]) @pytest.mark.parametrize("shape2", [(), (10,)]) def test_multidiscrete(self, shape1, shape2): if shape1 is None: @@ -1408,14 +1394,7 @@ def test_multidiscrete(self, shape1, shape2): assert spec2.rand().shape == spec2.shape assert spec2.zero().shape == spec2.shape - @pytest.mark.parametrize( - "shape1", - [ - None, - (), - (5,), - ], - ) + @pytest.mark.parametrize("shape1", [None, (), (5,)]) @pytest.mark.parametrize("shape2", [(), (10,)]) def test_multionehot(self, shape1, shape2): if shape1 is None: @@ -1443,14 +1422,7 @@ def test_multionehot(self, shape1, shape2): assert spec2.rand().shape == spec2.shape assert spec2.zero().shape == spec2.shape - @pytest.mark.parametrize( - "shape1", - [ - None, - (), - (5,), - ], - ) + @pytest.mark.parametrize("shape1", [None, (), (5,)]) @pytest.mark.parametrize("shape2", [(), (10,)]) def test_onehot(self, shape1, shape2): if shape1 is None: @@ -1478,14 +1450,7 @@ def test_onehot(self, shape1, shape2): assert spec2.rand().shape == spec2.shape assert spec2.zero().shape == spec2.shape - @pytest.mark.parametrize( - "shape1", - [ - None, - (), - (5,), - ], - ) + @pytest.mark.parametrize("shape1", [None, (), (5,)]) @pytest.mark.parametrize("shape2", [(), (10,)]) def test_unbounded(self, shape1, shape2): if shape1 is None: @@ -1513,14 +1478,7 @@ def test_unbounded(self, shape1, shape2): assert spec2.rand().shape == spec2.shape assert spec2.zero().shape == spec2.shape - @pytest.mark.parametrize( - "shape1", - [ - None, - (), - (5,), - ], - ) + @pytest.mark.parametrize("shape1", [None, (), (5,)]) @pytest.mark.parametrize("shape2", [(), (10,)]) def test_unboundeddiscrete(self, shape1, shape2): if shape1 is None: @@ -1638,14 +1596,7 @@ def test_composite(self): assert item == spec_clone[key], key assert spec == spec.clone() - @pytest.mark.parametrize( - "shape1", - [ - None, - (), - (5,), - ], - ) + @pytest.mark.parametrize("shape1", [None, (), (5,)]) def test_discrete( self, shape1, @@ -1654,14 +1605,7 @@ def test_discrete( assert spec == spec.clone() assert spec is not spec.clone() - @pytest.mark.parametrize( - "shape1", - [ - None, - (), - (5,), - ], - ) + @pytest.mark.parametrize("shape1", [None, (), (5,)]) def test_multidiscrete( self, shape1, @@ -1676,14 +1620,7 @@ def test_multidiscrete( assert spec == spec.clone() assert spec is not spec.clone() - @pytest.mark.parametrize( - "shape1", - [ - None, - (), - (5,), - ], - ) + @pytest.mark.parametrize("shape1", [None, (), (5,)]) def test_multionehot( self, shape1, @@ -1698,14 +1635,7 @@ def test_multionehot( assert spec == spec.clone() assert spec is not spec.clone() - @pytest.mark.parametrize( - "shape1", - [ - None, - (), - (5,), - ], - ) + @pytest.mark.parametrize("shape1", [None, (), (5,)]) def test_onehot( self, shape1, @@ -1720,14 +1650,7 @@ def test_onehot( assert spec == spec.clone() assert spec is not spec.clone() - @pytest.mark.parametrize( - "shape1", - [ - None, - (), - (5,), - ], - ) + @pytest.mark.parametrize("shape1", [None, (), (5,)]) def test_unbounded( self, shape1, @@ -1742,14 +1665,7 @@ def test_unbounded( assert spec == spec.clone() assert spec is not spec.clone() - @pytest.mark.parametrize( - "shape1", - [ - None, - (), - (5,), - ], - ) + @pytest.mark.parametrize("shape1", [None, (), (5,)]) def test_unboundeddiscrete( self, shape1, @@ -1762,211 +1678,6 @@ def test_unboundeddiscrete( assert spec == spec.clone() assert spec is not spec.clone() -class TestUnbind: - @pytest.mark.parametrize( - "shape1", - [ - (5, 4) - ] - ) - def test_binary(self, shape1): - spec = BinaryDiscreteTensorSpec( - n=4, shape=shape1, device="cpu", dtype=torch.bool - ) - assert spec == torch.stack(spec.unbind(0), 0) - with pytest.raises(ValueError): - spec.unbind(-1) - - @pytest.mark.parametrize( - "shape1,mini,maxi", - [ - [(10,), -torch.ones([]), torch.ones([])], - [None, -torch.ones([10]), torch.ones([])], - [None, -torch.ones([]), torch.ones([10])], - [(10,), -torch.ones([]), torch.ones([10])], - [(10,), -torch.ones([10]), torch.ones([])], - [(10,), -torch.ones([10]), torch.ones([10])], - ], - ) - def test_bounded(self, shape1, mini, maxi): - spec = BoundedTensorSpec( - mini, maxi, shape=shape1, device="cpu", dtype=torch.bool - ) - assert spec == torch.stack(spec.unbind(0), 0) - with pytest.raises(ValueError): - spec.unbind(-1) - - def test_composite(self): - batch_size = (5,) - spec1 = BoundedTensorSpec( - -torch.ones([*batch_size, 10]), - torch.ones([*batch_size, 10]), - shape=( - *batch_size, - 10, - ), - device="cpu", - dtype=torch.bool, - ) - spec2 = BinaryDiscreteTensorSpec( - n=4, shape=(*batch_size, 4), device="cpu", dtype=torch.bool - ) - spec3 = DiscreteTensorSpec( - n=4, shape=batch_size, device="cpu", dtype=torch.long - ) - spec4 = MultiDiscreteTensorSpec( - nvec=(4, 5, 6), shape=(*batch_size, 3), device="cpu", dtype=torch.long - ) - spec5 = MultiOneHotDiscreteTensorSpec( - nvec=(4, 5, 6), shape=(*batch_size, 15), device="cpu", dtype=torch.long - ) - spec6 = OneHotDiscreteTensorSpec( - n=15, shape=(*batch_size, 15), device="cpu", dtype=torch.long - ) - spec7 = UnboundedContinuousTensorSpec( - shape=(*batch_size, 9), - device="cpu", - dtype=torch.float64, - ) - spec8 = UnboundedDiscreteTensorSpec( - shape=(*batch_size, 9), - device="cpu", - dtype=torch.long, - ) - spec = CompositeSpec( - spec1=spec1, - spec2=spec2, - spec3=spec3, - spec4=spec4, - spec5=spec5, - spec6=spec6, - spec7=spec7, - spec8=spec8, - shape=batch_size, - ) - assert spec == torch.stack(spec.unbind(0), 0) - assert spec == torch.stack(spec.unbind(-1), -1) - - @pytest.mark.parametrize( - "shape1", - [ - (5,), - (5,6) - ] - ) - def test_discrete( - self, - shape1, - ): - spec = DiscreteTensorSpec(n=4, shape=shape1, device="cpu", dtype=torch.long) - assert spec == torch.stack(spec.unbind(0), 0) - assert spec == torch.stack(spec.unbind(-1), -1) - - @pytest.mark.parametrize( - "shape1", - [ - (5,), - (5,6,) - ] - ) - def test_multidiscrete( - self, - shape1, - ): - if shape1 is None: - shape1 = (3,) - else: - shape1 = (*shape1, 3) - spec = MultiDiscreteTensorSpec( - nvec=(4, 5, 6), shape=shape1, device="cpu", dtype=torch.long - ) - assert spec == torch.stack(spec.unbind(0), 0) - with pytest.raises(ValueError): - spec.unbind(-1) - - @pytest.mark.parametrize( - "shape1", - [ - (5,), - (5,6,) - ] - ) - def test_multionehot( - self, - shape1, - ): - if shape1 is None: - shape1 = (15,) - else: - shape1 = (*shape1, 15) - spec = MultiOneHotDiscreteTensorSpec( - nvec=(4, 5, 6), shape=shape1, device="cpu", dtype=torch.long - ) - assert spec == torch.stack(spec.unbind(0), 0) - with pytest.raises(ValueError): - spec.unbind(-1) - - @pytest.mark.parametrize( - "shape1", - [ - (5,), - (5,6,) - ], - ) - def test_onehot( - self, - shape1, - ): - if shape1 is None: - shape1 = (15,) - else: - shape1 = (*shape1, 15) - spec = OneHotDiscreteTensorSpec( - n=15, shape=shape1, device="cpu", dtype=torch.long - ) - assert spec == torch.stack(spec.unbind(0), 0) - with pytest.raises(ValueError): - spec.unbind(-1) - - @pytest.mark.parametrize( - "shape1", - [ - (5,), - (5,6,) - ] - ) - def test_unbounded( - self, - shape1, - ): - if shape1 is None: - shape1 = (15,) - else: - shape1 = (*shape1, 15) - spec = UnboundedContinuousTensorSpec( - shape=shape1, device="cpu", dtype=torch.float64 - ) - assert spec == torch.stack(spec.unbind(0), 0) - assert spec == torch.stack(spec.unbind(-1), -1) - - @pytest.mark.parametrize( - "shape1", - [ - (5,), - (5,6,) - ] - ) - def test_unboundeddiscrete( - self, - shape1, - ): - if shape1 is None: - shape1 = (15,) - else: - shape1 = (*shape1, 15) - spec = UnboundedDiscreteTensorSpec(shape=shape1, device="cpu", dtype=torch.long) - assert spec == torch.stack(spec.unbind(0), 0) - assert spec == torch.stack(spec.unbind(-1), -1) class TestUnbind: @pytest.mark.parametrize("shape1", [(5, 4)]) @@ -2057,16 +1768,7 @@ def test_discrete( assert spec == torch.stack(spec.unbind(0), 0) assert spec == torch.stack(spec.unbind(-1), -1) - @pytest.mark.parametrize( - "shape1", - [ - (5,), - ( - 5, - 6, - ), - ], - ) + @pytest.mark.parametrize("shape1", [(5,), (5, 6)]) def test_multidiscrete( self, shape1, @@ -2082,16 +1784,7 @@ def test_multidiscrete( with pytest.raises(ValueError): spec.unbind(-1) - @pytest.mark.parametrize( - "shape1", - [ - (5,), - ( - 5, - 6, - ), - ], - ) + @pytest.mark.parametrize("shape1", [(5,), (5, 6)]) def test_multionehot( self, shape1, @@ -2107,16 +1800,7 @@ def test_multionehot( with pytest.raises(ValueError): spec.unbind(-1) - @pytest.mark.parametrize( - "shape1", - [ - (5,), - ( - 5, - 6, - ), - ], - ) + @pytest.mark.parametrize("shape1", [(5,), (5, 6)]) def test_onehot( self, shape1, @@ -2132,16 +1816,7 @@ def test_onehot( with pytest.raises(ValueError): spec.unbind(-1) - @pytest.mark.parametrize( - "shape1", - [ - (5,), - ( - 5, - 6, - ), - ], - ) + @pytest.mark.parametrize("shape1", [(5,), (5, 6)]) def test_unbounded( self, shape1, @@ -2156,16 +1831,7 @@ def test_unbounded( assert spec == torch.stack(spec.unbind(0), 0) assert spec == torch.stack(spec.unbind(-1), -1) - @pytest.mark.parametrize( - "shape1", - [ - (5,), - ( - 5, - 6, - ), - ], - ) + @pytest.mark.parametrize("shape1", [(5,), (5, 6)]) def test_unboundeddiscrete( self, shape1, diff --git a/torchrl/_utils.py b/torchrl/_utils.py index 48670d3e8de..01413ba262a 100644 --- a/torchrl/_utils.py +++ b/torchrl/_utils.py @@ -535,6 +535,7 @@ def get_trace(): class _ProcessNoWarn(mp.Process): """A private Process class that shuts down warnings on the subprocess.""" + @wraps(mp.Process.__init__) def __init__(self, *args, **kwargs): import torchrl @@ -546,6 +547,7 @@ def __init__(self, *args, **kwargs): def run(self, *args, **kwargs): if self.filter_warnings_subprocess: import warnings + with warnings.catch_warnings(): warnings.simplefilter("ignore") return mp.Process.run(self, *args, **kwargs) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 16a50df1c0f..90b42df08c3 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -30,8 +30,8 @@ from torchrl._utils import ( _check_for_faulty_process, - accept_remote_rref_udf_invocation, _ProcessNoWarn, + accept_remote_rref_udf_invocation, prod, RL_WARNINGS, VERBOSE, diff --git a/torchrl/objectives/dreamer.py b/torchrl/objectives/dreamer.py index a9e546a72ec..c4e02ff4a5a 100644 --- a/torchrl/objectives/dreamer.py +++ b/torchrl/objectives/dreamer.py @@ -286,7 +286,7 @@ def forward(self, tensordict: TensorDict) -> Tuple[TensorDict, TensorDict]: if self.discount_loss: gamma = self.value_estimator.gamma.to(tensordict.device) - discount = gamma.expand(lambda_target.shape) + discount = gamma.expand(lambda_target.shape).clone() discount[..., 0, :] = 1 discount = discount.cumprod(dim=-2) actor_loss = -(lambda_target * discount).sum((-2, -1)).mean() From c8579f94d43b93afc77212bbb64edef1c84edca8 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 06:04:49 -0400 Subject: [PATCH 056/178] init --- test/test_specs.py | 50 +++++++------- torchrl/_utils.py | 108 ++++++++++++++++++++++--------- torchrl/collectors/collectors.py | 24 ++++++- torchrl/envs/batched_envs.py | 18 ++++++ 4 files changed, 145 insertions(+), 55 deletions(-) diff --git a/test/test_specs.py b/test/test_specs.py index 469a6425c8f..4d8f7da2e9e 100644 --- a/test/test_specs.py +++ b/test/test_specs.py @@ -1762,13 +1762,9 @@ def test_unboundeddiscrete( assert spec == spec.clone() assert spec is not spec.clone() + class TestUnbind: - @pytest.mark.parametrize( - "shape1", - [ - (5, 4) - ] - ) + @pytest.mark.parametrize("shape1", [(5, 4)]) def test_binary(self, shape1): spec = BinaryDiscreteTensorSpec( n=4, shape=shape1, device="cpu", dtype=torch.bool @@ -1847,13 +1843,7 @@ def test_composite(self): assert spec == torch.stack(spec.unbind(0), 0) assert spec == torch.stack(spec.unbind(-1), -1) - @pytest.mark.parametrize( - "shape1", - [ - (5,), - (5,6) - ] - ) + @pytest.mark.parametrize("shape1", [(5,), (5, 6)]) def test_discrete( self, shape1, @@ -1866,8 +1856,11 @@ def test_discrete( "shape1", [ (5,), - (5,6,) - ] + ( + 5, + 6, + ), + ], ) def test_multidiscrete( self, @@ -1888,8 +1881,11 @@ def test_multidiscrete( "shape1", [ (5,), - (5,6,) - ] + ( + 5, + 6, + ), + ], ) def test_multionehot( self, @@ -1910,7 +1906,10 @@ def test_multionehot( "shape1", [ (5,), - (5,6,) + ( + 5, + 6, + ), ], ) def test_onehot( @@ -1932,8 +1931,11 @@ def test_onehot( "shape1", [ (5,), - (5,6,) - ] + ( + 5, + 6, + ), + ], ) def test_unbounded( self, @@ -1953,8 +1955,11 @@ def test_unbounded( "shape1", [ (5,), - (5,6,) - ] + ( + 5, + 6, + ), + ], ) def test_unboundeddiscrete( self, @@ -1968,6 +1973,7 @@ def test_unboundeddiscrete( assert spec == torch.stack(spec.unbind(0), 0) assert spec == torch.stack(spec.unbind(-1), -1) + class TestUnbind: @pytest.mark.parametrize("shape1", [(5, 4)]) def test_binary(self, shape1): diff --git a/torchrl/_utils.py b/torchrl/_utils.py index 48670d3e8de..de23df28425 100644 --- a/torchrl/_utils.py +++ b/torchrl/_utils.py @@ -2,6 +2,7 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from __future__ import annotations import collections @@ -18,7 +19,7 @@ from distutils.util import strtobool from functools import wraps from importlib import import_module -from typing import Any, Callable, cast, TypeVar, Union +from typing import Any, Callable, cast, Dict, TypeVar, Union import numpy as np import torch @@ -250,6 +251,7 @@ class implement_for: # Stores pointers to fitting implementations: dict[func_name] = func_pointer _implementations = {} _setters = [] + _cache_modules = {} def __init__( self, @@ -271,27 +273,58 @@ def check_version(version, from_version, to_version): @staticmethod def get_class_that_defined_method(f): """Returns the class of a method, if it is defined, and None otherwise.""" - return f.__globals__.get(f.__qualname__.split(".")[0], None) + out = f.__globals__.get(f.__qualname__.split(".")[0], None) + return out - @property - def func_name(self): - return self.fn.__name__ + @classmethod + def func_name(cls, fn): + # produces a name like torchrl.module.Class.method or torchrl.module.function + first = str(fn).split(".")[0][len(" str: """Imports module and returns its version.""" if not callable(module_name): - module = import_module(module_name) + module = cls._cache_modules.get(module_name, None) + if module is None: + if module_name in sys.modules: + sys.modules[module_name] = module = import_module(module_name) + else: + cls._cache_modules[module_name] = module = import_module( + module_name + ) else: module = module_name() return module.__version__ @@ -300,7 +333,7 @@ def __call__(self, fn): self.fn = fn # If the module is missing replace the function with the mock. - func_name = self.func_name + func_name = self.func_name(self.fn) implementations = implement_for._implementations @wraps(fn) @@ -309,7 +342,7 @@ def unsupported(*args, **kwargs): f"Supported version of '{func_name}' has not been found." ) - do_set = False + self.do_set = False # Return fitting implementation if it was encountered before. if func_name in implementations: try: @@ -322,36 +355,45 @@ def unsupported(*args, **kwargs): f"Got multiple backends for {func_name}. " f"Using the last queried ({module} with version {version})." ) - do_set = True - if not do_set: - return implementations[func_name] + self.do_set = True + if not self.do_set: + return implementations[func_name].fn except ModuleNotFoundError: # then it's ok, there is no conflict - return implementations[func_name] + return implementations[func_name].fn else: try: version = self.import_module(self.module_name) if self.check_version(version, self.from_version, self.to_version): - do_set = True + self.do_set = True except ModuleNotFoundError: return unsupported - if do_set: - implementations[func_name] = fn + if self.do_set: self.module_set() return fn return unsupported @classmethod - def reset(cls, setters=None): + def reset(cls, setters_dict: Dict[str, implement_for] = None): + """Resets the setters in setter_dict. + + ``setter_dict`` is a copy of implementations. We just need to iterate through its + values and call :meth:`~.module_set` for each. + + """ if VERBOSE: print("resetting implement_for") - if setters is None: - setters = copy(cls._setters) - cls._setters = [] - cls._implementations = {} - for setter in setters: - setter(setter.fn) - cls._setters.append(setter) + if setters_dict is None: + setters_dict = copy(cls._implementations) + for setter in setters_dict.values(): + setter.module_set() + + def __repr__(self): + return ( + f"{self.__class__.__name__}(" + f"module_name={self.module_name}({self.from_version, self.to_version}), " + f"fn_name={self.fn.__name__}, cls={self._get_cls(self.fn)}, is_set={self.do_set})" + ) def accept_remote_rref_invocation(func): @@ -534,19 +576,23 @@ def get_trace(): class _ProcessNoWarn(mp.Process): - """A private Process class that shuts down warnings on the subprocess.""" + """A private Process class that shuts down warnings on the subprocess and controls the number of threads in the subprocess.""" + @wraps(mp.Process.__init__) - def __init__(self, *args, **kwargs): + def __init__(self, *args, num_threads=None, **kwargs): import torchrl - if torchrl.filter_warnings_subprocess: - self.filter_warnings_subprocess = torchrl.filter_warnings_subprocess + self.filter_warnings_subprocess = torchrl.filter_warnings_subprocess + self.num_threads = num_threads super().__init__(*args, **kwargs) def run(self, *args, **kwargs): + if self.num_threads is not None: + torch.set_num_threads(self.num_threads) if self.filter_warnings_subprocess: import warnings + with warnings.catch_warnings(): warnings.simplefilter("ignore") - return mp.Process.run(self, *args, **kwargs) + return mp.Process.run(self, *args, **kwargs) return mp.Process.run(self, *args, **kwargs) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 16a50df1c0f..fe4be8da0d7 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -30,8 +30,8 @@ from torchrl._utils import ( _check_for_faulty_process, - accept_remote_rref_udf_invocation, _ProcessNoWarn, + accept_remote_rref_udf_invocation, prod, RL_WARNINGS, VERBOSE, @@ -1097,6 +1097,15 @@ class _MultiDataCollector(DataCollectorBase): Defaults to ``False``. preemptive_threshold (float, optional): a value between 0.0 and 1.0 that specifies the ratio of workers that will be allowed to finished collecting their rollout before the rest are forced to end early. + num_threads (int, optional): number of threads for this process. + Defaults to the number of workers. + This parameter has no effect for the :class:`~SerialEnv` class. + num_sub_threads (int, optional): number of threads of the subprocesses. + Should be equal to one plus the number of processes launched within + each subprocess (or one if a single process is launched). + Defaults to 1 for safety: if none is indicated, launching multiple + workers may charge the cpu load too much and harm performance. + """ def __init__( @@ -1126,11 +1135,17 @@ def __init__( update_at_each_batch: bool = False, devices=None, storing_devices=None, + num_threads: int = 1, + num_sub_threads: int = None, ): exploration_type = _convert_exploration_type( exploration_mode=exploration_mode, exploration_type=exploration_type ) self.closed = True + if num_threads is None: + num_threads = len(create_env_fn) + self.num_sub_threads = num_sub_threads + self.num_threads = num_threads self.create_env_fn = create_env_fn self.num_workers = len(create_env_fn) self.create_env_kwargs = ( @@ -1305,6 +1320,7 @@ def _queue_len(self) -> int: raise NotImplementedError def _run_processes(self) -> None: + torch.set_num_threads(self.num_threads) queue_out = mp.Queue(self._queue_len) # sends data from proc to main self.procs = [] self.pipes = [] @@ -1336,7 +1352,11 @@ def _run_processes(self) -> None: "idx": i, "interruptor": self.interruptor, } - proc = _ProcessNoWarn(target=_main_async_collector, kwargs=kwargs) + proc = _ProcessNoWarn( + target=_main_async_collector, + num_threads=self.num_sub_threads, + kwargs=kwargs, + ) # proc.daemon can't be set as daemonic processes may be launched by the process itself try: proc.start() diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index 67944af1f35..f26446ca6e4 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -121,6 +121,15 @@ class _BatchedEnv(EnvBase): It is assumed that all environments will run on the same device as a common shared tensordict will be used to pass data from process to process. The device can be changed after instantiation using :obj:`env.to(device)`. + num_threads (int, optional): number of threads for this process. + Defaults to the number of workers. + This parameter has no effect for the :class:`~SerialEnv` class. + num_sub_threads (int, optional): number of threads of the subprocesses. + Should be equal to one plus the number of processes launched within + each subprocess (or one if a single process is launched). + Defaults to 1 for safety: if none is indicated, launching multiple + workers may charge the cpu load too much and harm performance. + This parameter has no effect for the :class:`~SerialEnv` class. """ @@ -144,6 +153,8 @@ def __init__( policy_proof: Optional[Callable] = None, device: Optional[DEVICE_TYPING] = None, allow_step_when_done: bool = False, + num_threads: int = None, + num_sub_threads: int = 1, ): if device is not None: raise ValueError( @@ -154,6 +165,10 @@ def __init__( super().__init__(device=None) self.is_closed = True + if num_threads is None: + num_threads = num_workers + self.num_sub_threads = num_sub_threads + self.num_threads = num_threads self._cache_in_keys = None self._single_task = callable(create_env_fn) or (len(set(create_env_fn)) == 1) @@ -692,6 +707,8 @@ class ParallelEnv(_BatchedEnv): def _start_workers(self) -> None: from torchrl.envs.env_creator import EnvCreator + torch.set_num_threads(self.num_threads) + ctx = mp.get_context("spawn") _num_workers = self.num_workers @@ -717,6 +734,7 @@ def _start_workers(self) -> None: process = _ProcessNoWarn( target=_run_worker_pipe_shared_mem, + num_threads=self.num_sub_threads, args=( parent_pipe, child_pipe, From a5e9ce32413bd0ff947b83208f8cd8ad6bef4ee0 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 06:24:50 -0400 Subject: [PATCH 057/178] prints --- test/test_exploration.py | 7 +++++-- test/test_specs.py | 10 +--------- test/test_utils.py | 26 ++++++++++++++++++++------ torchrl/_utils.py | 1 + torchrl/collectors/collectors.py | 1 + torchrl/envs/batched_envs.py | 2 ++ 6 files changed, 30 insertions(+), 17 deletions(-) diff --git a/test/test_exploration.py b/test/test_exploration.py index 740acf2feb1..85dfcec3f30 100644 --- a/test/test_exploration.py +++ b/test/test_exploration.py @@ -28,7 +28,7 @@ ) from torchrl.envs import SerialEnv from torchrl.envs.transforms.transforms import gSDENoise, InitTracker, TransformedEnv -from torchrl.envs.utils import set_exploration_type, _replace_last +from torchrl.envs.utils import _replace_last, set_exploration_type from torchrl.modules import SafeModule, SafeSequential from torchrl.modules.distributions import TanhNormal from torchrl.modules.distributions.continuous import ( @@ -381,7 +381,10 @@ def test_nested( device=device, ) for _td in collector: - assert _td[_replace_last(env.done_key, is_init_key)].shape == _td[env.done_key].shape + assert ( + _td[_replace_last(env.done_key, is_init_key)].shape + == _td[env.done_key].shape + ) break return diff --git a/test/test_specs.py b/test/test_specs.py index dc93ddbfb34..1f1dbb8b8aa 100644 --- a/test/test_specs.py +++ b/test/test_specs.py @@ -1215,14 +1215,7 @@ def test_ndbounded_shape(self): class TestExpand: - @pytest.mark.parametrize( - "shape1", - [ - None, - (4,), - (5, 4) - ] - ) + @pytest.mark.parametrize("shape1", [None, (4,), (5, 4)]) @pytest.mark.parametrize("shape2", [(), (10,)]) def test_binary(self, shape1, shape2): spec = BinaryDiscreteTensorSpec( @@ -1679,7 +1672,6 @@ def test_unboundeddiscrete( assert spec is not spec.clone() - class TestUnbind: @pytest.mark.parametrize("shape1", [(5, 4)]) def test_binary(self, shape1): diff --git a/test/test_utils.py b/test/test_utils.py index 889fb499d3b..e45eaf5edab 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -324,22 +324,36 @@ def test_set_gym_nested(): import gymnasium assert torch_foo() == 1 + class MockGym: _is_batched = False + with set_gym_backend(gym): - GymWrapper._output_transform(MockGym, (1, 2, 3, 4)) # would break with gymnasium + GymWrapper._output_transform( + MockGym, (1, 2, 3, 4) + ) # would break with gymnasium assert torch_foo() == 1 with set_gym_backend(gymnasium): - GymWrapper._output_transform(MockGym, (1, 2, 3, 4, 5)) # would break with gym + GymWrapper._output_transform( + MockGym, (1, 2, 3, 4, 5) + ) # would break with gym assert torch_foo() == 1 - GymWrapper._output_transform(MockGym, (1, 2, 3, 4)) # would break with gymnasium + GymWrapper._output_transform( + MockGym, (1, 2, 3, 4) + ) # would break with gymnasium with set_gym_backend("gym"): - GymWrapper._output_transform(MockGym, (1, 2, 3, 4)) # would break with gymnasium + GymWrapper._output_transform( + MockGym, (1, 2, 3, 4) + ) # would break with gymnasium assert torch_foo() == 1 with set_gym_backend("gymnasium"): - GymWrapper._output_transform(MockGym, (1, 2, 3, 4, 5)) # would break with gym + GymWrapper._output_transform( + MockGym, (1, 2, 3, 4, 5) + ) # would break with gym assert torch_foo() == 1 - GymWrapper._output_transform(MockGym, (1, 2, 3, 4)) # would break with gymnasium + GymWrapper._output_transform( + MockGym, (1, 2, 3, 4) + ) # would break with gymnasium if __name__ == "__main__": diff --git a/torchrl/_utils.py b/torchrl/_utils.py index a4480c54095..de23df28425 100644 --- a/torchrl/_utils.py +++ b/torchrl/_utils.py @@ -577,6 +577,7 @@ def get_trace(): class _ProcessNoWarn(mp.Process): """A private Process class that shuts down warnings on the subprocess and controls the number of threads in the subprocess.""" + @wraps(mp.Process.__init__) def __init__(self, *args, num_threads=None, **kwargs): import torchrl diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 29f33a89a38..4cbc360cfe1 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -1302,6 +1302,7 @@ def _queue_len(self) -> int: def _run_processes(self) -> None: torch.set_num_threads(self.num_threads) + print(f"Collector has {torch.get_num_threads()} threads") queue_out = mp.Queue(self._queue_len) # sends data from proc to main self.procs = [] self.pipes = [] diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index f4e9aa0cee7..8a63af5a914 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -705,6 +705,7 @@ def _start_workers(self) -> None: from torchrl.envs.env_creator import EnvCreator torch.set_num_threads(self.num_threads) + print(f"ParallelEnv has {torch.get_num_threads()} threads") ctx = mp.get_context("spawn") @@ -1012,6 +1013,7 @@ def _run_worker_pipe_shared_mem( has_lazy_inputs: bool = False, verbose: bool = False, ) -> None: + print(f"single env has {torch.get_num_threads()} threads") if device is None: device = torch.device("cpu") if device.type == "cuda": From b6e83d5c5194a9c1b538352aa483c229b5d552d6 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 06:28:38 -0400 Subject: [PATCH 058/178] amend --- torchrl/collectors/collectors.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 4cbc360cfe1..72f45a2529c 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -1116,8 +1116,8 @@ def __init__( update_at_each_batch: bool = False, devices=None, storing_devices=None, - num_threads: int = 1, - num_sub_threads: int = None, + num_threads: int = None, + num_sub_threads: int = 1, ): exploration_type = _convert_exploration_type( exploration_mode=exploration_mode, exploration_type=exploration_type From acf89e6a716d8c0379f6bd3394ea3c967a0b1175 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 06:29:06 -0400 Subject: [PATCH 059/178] amend --- torchrl/collectors/collectors.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index fe4be8da0d7..d09a045fb06 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -1135,8 +1135,8 @@ def __init__( update_at_each_batch: bool = False, devices=None, storing_devices=None, - num_threads: int = 1, - num_sub_threads: int = None, + num_threads: int = None, + num_sub_threads: int = 1, ): exploration_type = _convert_exploration_type( exploration_mode=exploration_mode, exploration_type=exploration_type From 16fba2eaba38f9903679ea846d414c9f6b418ffa Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 06:34:12 -0400 Subject: [PATCH 060/178] amend --- torchrl/collectors/collectors.py | 2 +- torchrl/envs/batched_envs.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index d09a045fb06..89002bf36cf 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -1143,7 +1143,7 @@ def __init__( ) self.closed = True if num_threads is None: - num_threads = len(create_env_fn) + num_threads = len(create_env_fn) + 1 # 1 more thread for this proc self.num_sub_threads = num_sub_threads self.num_threads = num_threads self.create_env_fn = create_env_fn diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index f26446ca6e4..7c6a7206a15 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -166,7 +166,7 @@ def __init__( super().__init__(device=None) self.is_closed = True if num_threads is None: - num_threads = num_workers + num_threads = num_workers + 1 # 1 more thread for this proc self.num_sub_threads = num_sub_threads self.num_threads = num_threads self._cache_in_keys = None From 697c523b7f0c0177d3937c3fec96bb43102049c2 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 06:34:41 -0400 Subject: [PATCH 061/178] amend --- torchrl/collectors/collectors.py | 2 +- torchrl/envs/batched_envs.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 72f45a2529c..189714fbac8 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -1124,7 +1124,7 @@ def __init__( ) self.closed = True if num_threads is None: - num_threads = len(create_env_fn) + num_threads = len(create_env_fn) + 1 # 1 more thread for this proc self.num_sub_threads = num_sub_threads self.num_threads = num_threads self.create_env_fn = create_env_fn diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index 8a63af5a914..49ac78fa8e0 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -171,7 +171,7 @@ def __init__( super().__init__(device=None) self.is_closed = True if num_threads is None: - num_threads = num_workers + num_threads = num_workers + 1 # 1 more thread for this proc self.num_sub_threads = num_sub_threads self.num_threads = num_threads self._cache_in_keys = None From 369492d954f4a5a66e0b83c422fa6929aa761730 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 06:49:15 -0400 Subject: [PATCH 062/178] fix --- test/test_exploration.py | 7 +++++-- test/test_libs.py | 6 ++---- test/test_utils.py | 26 ++++++++++++++++++++------ torchrl/envs/batched_envs.py | 2 ++ 4 files changed, 29 insertions(+), 12 deletions(-) diff --git a/test/test_exploration.py b/test/test_exploration.py index 740acf2feb1..85dfcec3f30 100644 --- a/test/test_exploration.py +++ b/test/test_exploration.py @@ -28,7 +28,7 @@ ) from torchrl.envs import SerialEnv from torchrl.envs.transforms.transforms import gSDENoise, InitTracker, TransformedEnv -from torchrl.envs.utils import set_exploration_type, _replace_last +from torchrl.envs.utils import _replace_last, set_exploration_type from torchrl.modules import SafeModule, SafeSequential from torchrl.modules.distributions import TanhNormal from torchrl.modules.distributions.continuous import ( @@ -381,7 +381,10 @@ def test_nested( device=device, ) for _td in collector: - assert _td[_replace_last(env.done_key, is_init_key)].shape == _td[env.done_key].shape + assert ( + _td[_replace_last(env.done_key, is_init_key)].shape + == _td[env.done_key].shape + ) break return diff --git a/test/test_libs.py b/test/test_libs.py index d5d44a7bc40..1461d547b94 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -365,9 +365,8 @@ def test_vecenvs_env(self, envname): with set_gym_backend("gymnasium"): env = GymEnv(envname, num_envs=2, from_pixels=False) - import gymnasium - assert env.get_library_name(env._env) is gymnasium + assert env.get_library_name(env._env) == "gymnasium" # rollouts can be executed without decorator check_env_specs(env) rollout = env.rollout(100, break_when_any_done=False) @@ -409,9 +408,8 @@ def test_vecenvs_wrapper(self, envname): # noqa: F811 def test_vecenvs_env(self, envname): # noqa: F811 with set_gym_backend("gym"): env = GymEnv(envname, num_envs=2, from_pixels=False) - import gym - assert env.get_library_name(env._env) is gym + assert env.get_library_name(env._env) == "gym" # rollouts can be executed without decorator check_env_specs(env) rollout = env.rollout(100, break_when_any_done=False) diff --git a/test/test_utils.py b/test/test_utils.py index 889fb499d3b..e45eaf5edab 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -324,22 +324,36 @@ def test_set_gym_nested(): import gymnasium assert torch_foo() == 1 + class MockGym: _is_batched = False + with set_gym_backend(gym): - GymWrapper._output_transform(MockGym, (1, 2, 3, 4)) # would break with gymnasium + GymWrapper._output_transform( + MockGym, (1, 2, 3, 4) + ) # would break with gymnasium assert torch_foo() == 1 with set_gym_backend(gymnasium): - GymWrapper._output_transform(MockGym, (1, 2, 3, 4, 5)) # would break with gym + GymWrapper._output_transform( + MockGym, (1, 2, 3, 4, 5) + ) # would break with gym assert torch_foo() == 1 - GymWrapper._output_transform(MockGym, (1, 2, 3, 4)) # would break with gymnasium + GymWrapper._output_transform( + MockGym, (1, 2, 3, 4) + ) # would break with gymnasium with set_gym_backend("gym"): - GymWrapper._output_transform(MockGym, (1, 2, 3, 4)) # would break with gymnasium + GymWrapper._output_transform( + MockGym, (1, 2, 3, 4) + ) # would break with gymnasium assert torch_foo() == 1 with set_gym_backend("gymnasium"): - GymWrapper._output_transform(MockGym, (1, 2, 3, 4, 5)) # would break with gym + GymWrapper._output_transform( + MockGym, (1, 2, 3, 4, 5) + ) # would break with gym assert torch_foo() == 1 - GymWrapper._output_transform(MockGym, (1, 2, 3, 4)) # would break with gymnasium + GymWrapper._output_transform( + MockGym, (1, 2, 3, 4) + ) # would break with gymnasium if __name__ == "__main__": diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index c3250ba5555..242aa9becfb 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -821,6 +821,8 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: needs_resetting = needs_resetting.flatten(1, needs_resetting.ndim - 1) if needs_resetting.ndim > 1: needs_resetting = needs_resetting.any(-1) + elif not needs_resetting.ndim: + needs_resetting = needs_resetting.view(1) else: needs_resetting = torch.ones( (self.num_workers,), device=self.device, dtype=torch.bool From e14dcd17513aa4fbb3695793657568745c7d9953 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 08:50:41 -0400 Subject: [PATCH 063/178] fix --- torchrl/envs/batched_envs.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index 242aa9becfb..204e93e0d90 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -583,6 +583,12 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: if tensordict is not None: needs_resetting = _bring_reset_to_root(tensordict) + if needs_resetting.ndim > 2: + needs_resetting = needs_resetting.flatten(1, needs_resetting.ndim - 1) + if needs_resetting.ndim > 1: + needs_resetting = needs_resetting.any(-1) + elif not needs_resetting.ndim: + needs_resetting = needs_resetting.expand(self.batch_size) else: needs_resetting = torch.ones( (self.num_workers,), device=self.device, dtype=torch.bool @@ -822,7 +828,7 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: if needs_resetting.ndim > 1: needs_resetting = needs_resetting.any(-1) elif not needs_resetting.ndim: - needs_resetting = needs_resetting.view(1) + needs_resetting = needs_resetting.expand(self.batch_size) else: needs_resetting = torch.ones( (self.num_workers,), device=self.device, dtype=torch.bool From 4a8424cee17e0846014f6da1c39363de56bacb88 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 10:09:38 -0400 Subject: [PATCH 064/178] amend --- torchrl/envs/batched_envs.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index 49ac78fa8e0..afa967897ba 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -875,15 +875,14 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: channel.send(out) workers.append(i) - completed = set() - while len(completed) < len(workers): - for i in workers: + remaining = set(workers) + while len(remaining): + iter_remaining = iter(remaining) + for i in iter_remaining: event = self._events[i] - if i in completed: - continue if event.is_set(): - completed.add(i) event.clear() + remaining.remove(i) if self._single_task: # select + clone creates 2 tds, but we can create one only From 5056b9eccb7a30fb2455350867cb35d19b9b3b2d Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 10:11:20 -0400 Subject: [PATCH 065/178] amend --- torchrl/envs/batched_envs.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index afa967897ba..8abd0826946 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -877,7 +877,7 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: remaining = set(workers) while len(remaining): - iter_remaining = iter(remaining) + iter_remaining = list(remaining) for i in iter_remaining: event = self._events[i] if event.is_set(): From ce26e13a36143743c059857d812b194edb3335e9 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 10:19:43 -0400 Subject: [PATCH 066/178] amend --- torchrl/envs/batched_envs.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index 8abd0826946..26018b5c375 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -880,9 +880,9 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: iter_remaining = list(remaining) for i in iter_remaining: event = self._events[i] - if event.is_set(): - event.clear() - remaining.remove(i) + event.wait() + event.clear() + remaining.remove(i) if self._single_task: # select + clone creates 2 tds, but we can create one only From 5a95850357a60a42d8fc8c1cdc1d5aaa4752024c Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 10:22:07 -0400 Subject: [PATCH 067/178] amend --- torchrl/envs/batched_envs.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index 26018b5c375..9f82c7de365 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -809,14 +809,15 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: for i in range(self.num_workers): self.parent_channels[i].send(("step", None)) - completed = set() - while len(completed) < self.num_workers: - for i, event in enumerate(self._events): - if i in completed: - continue - if event.is_set(): - completed.add(i) - event.clear() + # we used to go with `is_set()` but wait() is faster. + remaining = set(range(self.num_workers)) + while len(remaining): + iter_remaining = list(remaining) + for i in iter_remaining: + event = self._events[i] + event.wait() + event.clear() + remaining.remove(i) # We must pass a clone of the tensordict, as the values of this tensordict # will be modified in-place at further steps From 5e38d703e8e9a1ea080e4a0f0ebcc525a93856e5 Mon Sep 17 00:00:00 2001 From: Vincent Moens Date: Thu, 21 Sep 2023 15:25:40 +0100 Subject: [PATCH 068/178] Update torchrl/collectors/collectors.py Co-authored-by: Matteo Bettini <55539777+matteobettini@users.noreply.github.com> --- torchrl/collectors/collectors.py | 1 - 1 file changed, 1 deletion(-) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 89002bf36cf..960154f3070 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -1099,7 +1099,6 @@ class _MultiDataCollector(DataCollectorBase): that will be allowed to finished collecting their rollout before the rest are forced to end early. num_threads (int, optional): number of threads for this process. Defaults to the number of workers. - This parameter has no effect for the :class:`~SerialEnv` class. num_sub_threads (int, optional): number of threads of the subprocesses. Should be equal to one plus the number of processes launched within each subprocess (or one if a single process is launched). From 9eb1c981ac81646c36bc7d89ffeaa82d872bf51a Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 12:20:36 -0400 Subject: [PATCH 069/178] amend --- test/test_libs.py | 83 ++++++++++++++++++-- torchrl/collectors/collectors.py | 2 +- torchrl/envs/batched_envs.py | 2 +- torchrl/envs/common.py | 2 +- torchrl/envs/libs/gym.py | 44 +++++++---- torchrl/envs/transforms/transforms.py | 107 +++++++++++++++++++------- 6 files changed, 186 insertions(+), 54 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index d5d44a7bc40..2db2f80ba8e 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -523,6 +523,82 @@ def test_gym_gymnasium_parallel(self): assert "truncated" in rollout.keys() check_env_specs(penv) + @implement_for("gym", None, "0.22.0") + def test_vecenvs_nan(self): # noqa: F811 + print("here") + # old versions of gym must return nan for next values when there is a done state + torch.manual_seed(0) + env = GymEnv("CartPole-v0", num_envs=2) + env.set_seed(0) + rollout = env.rollout(200) + assert torch.isfinite(rollout.get("observation")).all() + assert not torch.isfinite(rollout.get(("next", "observation"))).all() + env.close() + del env + + # same with collector + env = GymEnv("CartPole-v0", num_envs=2) + env.set_seed(0) + c = SyncDataCollector( + env, RandomPolicy(env.action_spec), total_frames=2000, frames_per_batch=200 + ) + for rollout in c: + assert torch.isfinite(rollout.get("observation")).all() + assert not torch.isfinite(rollout.get(("next", "observation"))).all() + break + del c + return + + @implement_for("gym", "0.22.0", None) + def test_vecenvs_nan(self): # noqa: F811 + # new versions of gym must never return nan for next values when there is a done state + torch.manual_seed(0) + env = GymEnv("CartPole-v0", num_envs=2) + env.set_seed(0) + rollout = env.rollout(200) + assert torch.isfinite(rollout.get("observation")).all() + assert torch.isfinite(rollout.get(("next", "observation"))).all() + env.close() + del env + + # same with collector + env = GymEnv("CartPole-v0", num_envs=2) + env.set_seed(0) + c = SyncDataCollector( + env, RandomPolicy(env.action_spec), total_frames=2000, frames_per_batch=200 + ) + for rollout in c: + assert torch.isfinite(rollout.get("observation")).all() + assert torch.isfinite(rollout.get(("next", "observation"))).all() + break + del c + return + + @implement_for("gymnasium") + def test_vecenvs_nan(self): # noqa: F811 + # new versions of gym must never return nan for next values when there is a done state + torch.manual_seed(0) + env = GymEnv("CartPole-v0", num_envs=2) + env.set_seed(0) + rollout = env.rollout(200) + assert torch.isfinite(rollout.get("observation")).all() + assert torch.isfinite(rollout.get(("next", "observation"))).all() + env.close() + del env + + # same with collector + env = GymEnv("CartPole-v0", num_envs=2) + env.set_seed(0) + c = SyncDataCollector( + env, RandomPolicy(env.action_spec), total_frames=2000, frames_per_batch=200 + ) + for rollout in c: + assert torch.isfinite(rollout.get("observation")).all() + assert torch.isfinite(rollout.get(("next", "observation"))).all() + break + del c + return + @implement_for("gym", None, "0.26") def _make_gym_environment(env_name): # noqa: F811 @@ -543,12 +619,7 @@ def _make_gym_environment(env_name): # noqa: F811 @pytest.mark.parametrize("env_name,task", [["cheetah", "run"]]) @pytest.mark.parametrize("frame_skip", [1, 3]) @pytest.mark.parametrize( - "from_pixels,pixels_only", - [ - [True, True], - [True, False], - [False, False], - ], + "from_pixels,pixels_only", [[True, True], [True, False], [False, False]] ) class TestDMControl: def test_dmcontrol(self, env_name, task, frame_skip, from_pixels, pixels_only): diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 189714fbac8..c439ccfcc92 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -1124,7 +1124,7 @@ def __init__( ) self.closed = True if num_threads is None: - num_threads = len(create_env_fn) + 1 # 1 more thread for this proc + num_threads = len(create_env_fn) + 1 # 1 more thread for this proc self.num_sub_threads = num_sub_threads self.num_threads = num_threads self.create_env_fn = create_env_fn diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index 9f82c7de365..883d02526ec 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -171,7 +171,7 @@ def __init__( super().__init__(device=None) self.is_closed = True if num_threads is None: - num_threads = num_workers + 1 # 1 more thread for this proc + num_threads = num_workers + 1 # 1 more thread for this proc self.num_sub_threads = num_sub_threads self.num_threads = num_threads self._cache_in_keys = None diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index fc1ce94b13b..047abb1a7e0 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -1719,7 +1719,7 @@ def policy(td): if break_when_any_done and any_done: break if not break_when_any_done and any_done: - self.reset(tensordict) + tensordict = self.reset(tensordict) if callback is not None: callback(self, tensordict) diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index c1cffc8a8cf..91456468624 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -390,18 +390,6 @@ class _AsyncMeta(_EnvPostInit): def __call__(cls, *args, **kwargs): instance: GymWrapper = super().__call__(*args, **kwargs) # before gym 0.22, there was no final_observation - backend = instance.get_library_name(instance._env) - if backend == "gym": - import gym - - if version.parse(gym.__version__) < version.parse("0.22"): - warn( - "A batched gym environment is being wrapped in a GymWrapper with gym version < 0.22. " - "This implies that the next-observation is wrongly tracked (as the batched environment auto-resets " - "and discards the true next observation to return the result of the step). " - "This isn't compatible with TorchRL API and should be used with caution." - ) - return instance if instance._is_batched: from torchrl.envs.transforms.transforms import ( TransformedEnv, @@ -417,9 +405,28 @@ def __call__(cls, *args, **kwargs): backend = "gym" else: backend = "gym" - instance.set_info_dict_reader( - terminal_obs_reader(instance.observation_spec, backend=backend) - ) + + # we need 3 checks: the backend is not sb3 (if so, gymnasium is used), + # it is gym and not gymnasium and the version is before 0.22.0 + add_info_dict = True + if backend == "gym": + gym_backend = instance.get_library_name(instance._env) + if gym_backend == "gym": # check gym against gymnasium + import gym + + if version.parse(gym.__version__) < version.parse("0.22.0"): + warn( + "A batched gym environment is being wrapped in a GymWrapper with gym version < 0.22. " + "This implies that the next-observation is wrongly tracked (as the batched environment auto-resets " + "and discards the true next observation to return the result of the step). " + "This isn't compatible with TorchRL API and should be used with caution.", + category=UserWarning, + ) + add_info_dict = False + if add_info_dict: + instance.set_info_dict_reader( + terminal_obs_reader(instance.observation_spec, backend=backend) + ) return TransformedEnv(instance, VecGymEnvTransform()) return instance @@ -652,7 +659,12 @@ def _set_seed(self, seed: int) -> int: # noqa: F811 return seed - @implement_for("gym", None, "0.19.0") + @implement_for("gym", None, "0.15.0") + def _set_seed_initial(self, seed: int) -> None: # noqa: F811 + self._seed_calls_reset = False + self._env.seed(seed) + + @implement_for("gym", "0.15.0", "0.19.0") def _set_seed_initial(self, seed: int) -> None: # noqa: F811 self._seed_calls_reset = False self._env.seed(seed=seed) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 0bc2ac5d199..78eba03f4c2 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -5667,6 +5667,12 @@ class VecGymEnvTransform(Transform): reset output in a private container. The resulting data truly reflects the output of the step. + This class works from gym 0.13 till the most recent gymnasium version. + + .. note:: Gym versions < 0.22 did not return the final observations. For these, + we simply fill the next observations with NaN (because it is lost) and + do the swap at the next step. + Then, when calling `env.reset`, the saved data is written back where it belongs (and the `reset` is a no-op). @@ -5689,33 +5695,37 @@ def __init__(self, final_name="final"): def set_container(self, container: Union[Transform, EnvBase]) -> None: out = super().set_container(container) - expected_done_keys = {"done", "truncated"} - for done_key in self.parent.done_keys: - if done_key not in expected_done_keys: - raise RuntimeError( - f"VecGymEnvTransform only supports the following " - f"done keys: {expected_done_keys}, but it got {done_key}." - ) + self._done_keys = None + self._obs_keys = None return out def _step( self, tensordict: TensorDictBase, next_tensordict: TensorDictBase ) -> TensorDictBase: # save the final info - done = next_tensordict.get("done") - truncated = next_tensordict.get("truncated", None) - if truncated is not None: - done = done | truncated - done = self._memo["done"] = done - final = next_tensordict.pop("final") + done = False + for done_key in self.done_keys: + done = done | next_tensordict.get(done_key) + if done is False: + raise RuntimeError( + f"Could not find any done signal in tensordict:\n{tensordict}" + ) + self._memo["done"] = done + final = next_tensordict.pop(self.final_name, None) # if anything's done, we need to swap the final obs if done.any(): done = done.squeeze(-1) - saved_next = next_tensordict.select(*final.keys(True, True)).clone() - next_tensordict[done] = final[done] - self._memo["saved_done"] = saved_next + if final is not None: + saved_next = next_tensordict.select(*final.keys(True, True)).clone() + next_tensordict[done] = final[done] + else: + saved_next = next_tensordict.select(*self.obs_keys).clone() + for obs_key in self.obs_keys: + next_tensordict[obs_key][done] = torch.tensor(np.nan) + + self._memo["saved_next"] = saved_next else: - self._memo["saved_done"] = None + self._memo["saved_next"] = None return next_tensordict def reset(self, tensordict: TensorDictBase) -> TensorDictBase: @@ -5735,20 +5745,59 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: # if not reset.any(), we don't need to do anything. # if reset.all(), we don't either (bc GymWrapper will call a plain reset). if reset is not None and reset.any() and not reset.all(): - saved_done = self._memo["saved_done"] + saved_done = self._memo["saved_next"] reset = reset.view(tensordict.shape) - updated_td = torch.where( - ~reset, tensordict.select(*saved_done.keys(True, True)), saved_done - ) - tensordict.update(updated_td) - if "truncated" in tensordict.keys(): - tensordict.set( - "truncated", tensordict.get("truncated").clone().fill_(0) - ) - tensordict.set("done", tensordict.get("done").clone().fill_(0)) - tensordict.pop("final", None) + # we have a data container from the previous call to step + # that contains part of the observation we need. + # We can safely place them back in the reset result tensordict: + # in env.rollout(), the result of reset() is assumed to be just + # the td from previous step with updated values from reset. + # In our case, it will always be the case that all these values + # are properly set. + # collectors even take care of doing an extra masking so it's even + # safer. + tensordict.update(saved_done) + for done_key in self.done_keys: + # Make sure that all done are False + done = tensordict.get(done_key, None) + if done is not None: + done = done.clone().fill_(0) + else: + done = torch.zeros( + (*tensordict.batch_size, 1), + device=tensordict.device, + dtype=torch.bool, + ) + tensordict.set(done_key, done) + tensordict.pop(self.final_name, None) return tensordict + @property + def done_keys(self) -> List[NestedKey]: + keys = self.__dict__.get("_done_keys", None) + if keys is None: + keys = self.parent.done_keys + self._done_keys = keys + expected_done_keys = {"done", "truncated"} + # put this check for now. We can consider relaxing that later + # and allow nested values, though they will still need to be unique. + for done_key in keys: + if done_key not in expected_done_keys: + raise RuntimeError( + f"VecGymEnvTransform only supports the following " + f"done keys: {expected_done_keys}, but it got {done_key}." + ) + return keys + + @property + def obs_keys(self) -> List[NestedKey]: + keys = self.__dict__.get("_obs_keys", None) + if keys is None: + keys = list(self.parent.observation_spec.keys(True, True)) + self._obs_keys = keys + return keys + def transform_observation_spec(self, observation_spec: TensorSpec) -> TensorSpec: - del observation_spec[self.final_name] + if self.final_name in observation_spec.keys(True): + del observation_spec[self.final_name] return observation_spec From a2857807c15d84846ae33ce031100e9ccf3a5b54 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 14:08:02 -0400 Subject: [PATCH 070/178] amend --- test/test_collector.py | 42 +++++++++--------------------------------- 1 file changed, 9 insertions(+), 33 deletions(-) diff --git a/test/test_collector.py b/test/test_collector.py index 019aa4038b6..e02434aad11 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -1781,25 +1781,19 @@ def test_bring_reset_to_root(self): td = TensorDict( { "_reset": torch.zeros( - ( - 1, - 2 - ), + (1, 2), dtype=torch.bool, ), "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}, }, - [1, 2] + [1, 2], ) # test reduction assert _bring_reset_to_root(td).shape == (1, 2) td = TensorDict( { "_reset": torch.zeros( - ( - 1, - 2 - ), + (1, 2), dtype=torch.bool, ), "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, @@ -1811,10 +1805,7 @@ def test_bring_reset_to_root(self): td0 = TensorDict( { "_reset": torch.zeros( - ( - 1, - 2 - ), + (1, 2), dtype=torch.bool, ), "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, @@ -1825,10 +1816,7 @@ def test_bring_reset_to_root(self): td1 = TensorDict( { "_reset": torch.zeros( - ( - 1, - 2 - ), + (1, 2), dtype=torch.bool, ), "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, @@ -1863,10 +1851,7 @@ def test_bring_reset_to_root_keys(self): td = TensorDict( { "_reset": torch.zeros( - ( - 1, - 2 - ), + (1, 2), dtype=torch.bool, ), "a": {"_reset": torch.zeros((1, 2), dtype=torch.bool)}, @@ -1880,10 +1865,7 @@ def test_bring_reset_to_root_keys(self): td = TensorDict( { "_reset": torch.zeros( - ( - 1, - 2 - ), + (1, 2), dtype=torch.bool, ), "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, @@ -1895,10 +1877,7 @@ def test_bring_reset_to_root_keys(self): td0 = TensorDict( { "_reset": torch.zeros( - ( - 1, - 2 - ), + (1, 2), dtype=torch.bool, ), "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, @@ -1909,10 +1888,7 @@ def test_bring_reset_to_root_keys(self): td1 = TensorDict( { "_reset": torch.zeros( - ( - 1, - 2 - ), + (1, 2), dtype=torch.bool, ), "a": {"_reset": torch.ones((1, 2), dtype=torch.bool)}, From 9f58a8d44558a48980015f0a738dd00465b460e2 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 14:15:34 -0400 Subject: [PATCH 071/178] lint --- test/test_specs.py | 8 +------- torchrl/_utils.py | 1 + torchrl/collectors/collectors.py | 2 +- torchrl/envs/batched_envs.py | 2 +- 4 files changed, 4 insertions(+), 9 deletions(-) diff --git a/test/test_specs.py b/test/test_specs.py index 8ab722f4708..1f1dbb8b8aa 100644 --- a/test/test_specs.py +++ b/test/test_specs.py @@ -1215,13 +1215,7 @@ def test_ndbounded_shape(self): class TestExpand: - @pytest.mark.parametrize( - "shape1", - [ - None, - (4,), - (5, 4) - ]) + @pytest.mark.parametrize("shape1", [None, (4,), (5, 4)]) @pytest.mark.parametrize("shape2", [(), (10,)]) def test_binary(self, shape1, shape2): spec = BinaryDiscreteTensorSpec( diff --git a/torchrl/_utils.py b/torchrl/_utils.py index a4480c54095..de23df28425 100644 --- a/torchrl/_utils.py +++ b/torchrl/_utils.py @@ -577,6 +577,7 @@ def get_trace(): class _ProcessNoWarn(mp.Process): """A private Process class that shuts down warnings on the subprocess and controls the number of threads in the subprocess.""" + @wraps(mp.Process.__init__) def __init__(self, *args, num_threads=None, **kwargs): import torchrl diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 82e33fbad44..5057d42119b 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -1143,7 +1143,7 @@ def __init__( ) self.closed = True if num_threads is None: - num_threads = len(create_env_fn) + 1 # 1 more thread for this proc + num_threads = len(create_env_fn) + 1 # 1 more thread for this proc self.num_sub_threads = num_sub_threads self.num_threads = num_threads self.create_env_fn = create_env_fn diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index 86e0fd15d18..5502253bd83 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -166,7 +166,7 @@ def __init__( super().__init__(device=None) self.is_closed = True if num_threads is None: - num_threads = num_workers + 1 # 1 more thread for this proc + num_threads = num_workers + 1 # 1 more thread for this proc self.num_sub_threads = num_sub_threads self.num_threads = num_threads self._cache_in_keys = None From 1c4f35f95d0361a0c660b0930944dffa886f9c31 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 14:20:43 -0400 Subject: [PATCH 072/178] amend --- torchrl/_utils.py | 93 +++++++++++++---------------------------------- 1 file changed, 26 insertions(+), 67 deletions(-) diff --git a/torchrl/_utils.py b/torchrl/_utils.py index de23df28425..42fecf26871 100644 --- a/torchrl/_utils.py +++ b/torchrl/_utils.py @@ -19,7 +19,7 @@ from distutils.util import strtobool from functools import wraps from importlib import import_module -from typing import Any, Callable, cast, Dict, TypeVar, Union +from typing import Any, Callable, cast, TypeVar, Union import numpy as np import torch @@ -251,7 +251,6 @@ class implement_for: # Stores pointers to fitting implementations: dict[func_name] = func_pointer _implementations = {} _setters = [] - _cache_modules = {} def __init__( self, @@ -273,58 +272,27 @@ def check_version(version, from_version, to_version): @staticmethod def get_class_that_defined_method(f): """Returns the class of a method, if it is defined, and None otherwise.""" - out = f.__globals__.get(f.__qualname__.split(".")[0], None) - return out + return f.__globals__.get(f.__qualname__.split(".")[0], None) - @classmethod - def func_name(cls, fn): - # produces a name like torchrl.module.Class.method or torchrl.module.function - first = str(fn).split(".")[0][len(" str: """Imports module and returns its version.""" if not callable(module_name): - module = cls._cache_modules.get(module_name, None) - if module is None: - if module_name in sys.modules: - sys.modules[module_name] = module = import_module(module_name) - else: - cls._cache_modules[module_name] = module = import_module( - module_name - ) + module = import_module(module_name) else: module = module_name() return module.__version__ @@ -333,7 +301,7 @@ def __call__(self, fn): self.fn = fn # If the module is missing replace the function with the mock. - func_name = self.func_name(self.fn) + func_name = self.func_name implementations = implement_for._implementations @wraps(fn) @@ -342,7 +310,7 @@ def unsupported(*args, **kwargs): f"Supported version of '{func_name}' has not been found." ) - self.do_set = False + do_set = False # Return fitting implementation if it was encountered before. if func_name in implementations: try: @@ -355,45 +323,36 @@ def unsupported(*args, **kwargs): f"Got multiple backends for {func_name}. " f"Using the last queried ({module} with version {version})." ) - self.do_set = True - if not self.do_set: - return implementations[func_name].fn + do_set = True + if not do_set: + return implementations[func_name] except ModuleNotFoundError: # then it's ok, there is no conflict - return implementations[func_name].fn + return implementations[func_name] else: try: version = self.import_module(self.module_name) if self.check_version(version, self.from_version, self.to_version): - self.do_set = True + do_set = True except ModuleNotFoundError: return unsupported - if self.do_set: + if do_set: + implementations[func_name] = fn self.module_set() return fn return unsupported @classmethod - def reset(cls, setters_dict: Dict[str, implement_for] = None): - """Resets the setters in setter_dict. - - ``setter_dict`` is a copy of implementations. We just need to iterate through its - values and call :meth:`~.module_set` for each. - - """ + def reset(cls, setters=None): if VERBOSE: print("resetting implement_for") - if setters_dict is None: - setters_dict = copy(cls._implementations) - for setter in setters_dict.values(): - setter.module_set() - - def __repr__(self): - return ( - f"{self.__class__.__name__}(" - f"module_name={self.module_name}({self.from_version, self.to_version}), " - f"fn_name={self.fn.__name__}, cls={self._get_cls(self.fn)}, is_set={self.do_set})" - ) + if setters is None: + setters = copy(cls._setters) + cls._setters = [] + cls._implementations = {} + for setter in setters: + setter(setter.fn) + cls._setters.append(setter) def accept_remote_rref_invocation(func): From f6e09e330bf3da263efb123baa02d0a8c6dabd92 Mon Sep 17 00:00:00 2001 From: vmoens Date: Thu, 21 Sep 2023 15:56:05 -0400 Subject: [PATCH 073/178] amend --- torchrl/_utils.py | 93 ++++++++++++++++++++++++++++++++++------------- 1 file changed, 67 insertions(+), 26 deletions(-) diff --git a/torchrl/_utils.py b/torchrl/_utils.py index 42fecf26871..de23df28425 100644 --- a/torchrl/_utils.py +++ b/torchrl/_utils.py @@ -19,7 +19,7 @@ from distutils.util import strtobool from functools import wraps from importlib import import_module -from typing import Any, Callable, cast, TypeVar, Union +from typing import Any, Callable, cast, Dict, TypeVar, Union import numpy as np import torch @@ -251,6 +251,7 @@ class implement_for: # Stores pointers to fitting implementations: dict[func_name] = func_pointer _implementations = {} _setters = [] + _cache_modules = {} def __init__( self, @@ -272,27 +273,58 @@ def check_version(version, from_version, to_version): @staticmethod def get_class_that_defined_method(f): """Returns the class of a method, if it is defined, and None otherwise.""" - return f.__globals__.get(f.__qualname__.split(".")[0], None) + out = f.__globals__.get(f.__qualname__.split(".")[0], None) + return out - @property - def func_name(self): - return self.fn.__name__ + @classmethod + def func_name(cls, fn): + # produces a name like torchrl.module.Class.method or torchrl.module.function + first = str(fn).split(".")[0][len(" str: """Imports module and returns its version.""" if not callable(module_name): - module = import_module(module_name) + module = cls._cache_modules.get(module_name, None) + if module is None: + if module_name in sys.modules: + sys.modules[module_name] = module = import_module(module_name) + else: + cls._cache_modules[module_name] = module = import_module( + module_name + ) else: module = module_name() return module.__version__ @@ -301,7 +333,7 @@ def __call__(self, fn): self.fn = fn # If the module is missing replace the function with the mock. - func_name = self.func_name + func_name = self.func_name(self.fn) implementations = implement_for._implementations @wraps(fn) @@ -310,7 +342,7 @@ def unsupported(*args, **kwargs): f"Supported version of '{func_name}' has not been found." ) - do_set = False + self.do_set = False # Return fitting implementation if it was encountered before. if func_name in implementations: try: @@ -323,36 +355,45 @@ def unsupported(*args, **kwargs): f"Got multiple backends for {func_name}. " f"Using the last queried ({module} with version {version})." ) - do_set = True - if not do_set: - return implementations[func_name] + self.do_set = True + if not self.do_set: + return implementations[func_name].fn except ModuleNotFoundError: # then it's ok, there is no conflict - return implementations[func_name] + return implementations[func_name].fn else: try: version = self.import_module(self.module_name) if self.check_version(version, self.from_version, self.to_version): - do_set = True + self.do_set = True except ModuleNotFoundError: return unsupported - if do_set: - implementations[func_name] = fn + if self.do_set: self.module_set() return fn return unsupported @classmethod - def reset(cls, setters=None): + def reset(cls, setters_dict: Dict[str, implement_for] = None): + """Resets the setters in setter_dict. + + ``setter_dict`` is a copy of implementations. We just need to iterate through its + values and call :meth:`~.module_set` for each. + + """ if VERBOSE: print("resetting implement_for") - if setters is None: - setters = copy(cls._setters) - cls._setters = [] - cls._implementations = {} - for setter in setters: - setter(setter.fn) - cls._setters.append(setter) + if setters_dict is None: + setters_dict = copy(cls._implementations) + for setter in setters_dict.values(): + setter.module_set() + + def __repr__(self): + return ( + f"{self.__class__.__name__}(" + f"module_name={self.module_name}({self.from_version, self.to_version}), " + f"fn_name={self.fn.__name__}, cls={self._get_cls(self.fn)}, is_set={self.do_set})" + ) def accept_remote_rref_invocation(func): From 2cd07c1ed015fca5c8358620841b012d934ba8db Mon Sep 17 00:00:00 2001 From: vmoens Date: Fri, 22 Sep 2023 04:15:54 -0400 Subject: [PATCH 074/178] amend --- test/test_libs.py | 45 ++++++++++++++++++++++++++++++++++++++------- test/test_utils.py | 2 +- 2 files changed, 39 insertions(+), 8 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index f1043d424c0..5fd4955648c 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -129,18 +129,18 @@ class TestGym: @pytest.mark.parametrize( "env_name", [ + HALFCHEETAH_VERSIONED, PONG_VERSIONED, # PENDULUM_VERSIONED, - HALFCHEETAH_VERSIONED, ], ) @pytest.mark.parametrize("frame_skip", [1, 3]) @pytest.mark.parametrize( "from_pixels,pixels_only", [ - [False, False], [True, True], [True, False], + [False, False], ], ) def test_gym(self, env_name, frame_skip, from_pixels, pixels_only): @@ -151,7 +151,21 @@ def test_gym(self, env_name, frame_skip, from_pixels, pixels_only): elif ( env_name != PONG_VERSIONED and from_pixels and torch.cuda.device_count() < 1 ): - raise pytest.skip("no cuda device") + pass + # raise pytest.skip("no cuda device") + + def non_null_obs(batched_td): + if from_pixels: + pix_norm = batched_td.get("pixels").flatten(-3, -1).float().norm(dim=-1) + pix_norm_next = batched_td.get(("next", "pixels")).flatten(-3, -1).float().norm(dim=-1) + idx = (pix_norm > 1) & (pix_norm_next > 1) + # eliminate batch size: all idx must be True (otherwise one could be filled with 0s) + while idx.ndim > 1: + idx = idx.all(0) + idx = idx.nonzero().squeeze(-1) + assert idx.numel(), "Did not find pixels with norm > 1" + return idx + return slice(None) tdreset = [] tdrollout = [] @@ -167,14 +181,20 @@ def test_gym(self, env_name, frame_skip, from_pixels, pixels_only): np.random.seed(0) final_seed.append(env0.set_seed(0)) tdreset.append(env0.reset()) - tdrollout.append(env0.rollout(max_steps=50)) + rollout = env0.rollout(max_steps=50) + tdrollout.append(rollout) assert env0.from_pixels is from_pixels env0.close() env_type = type(env0._env) - del env0 assert_allclose_td(*tdreset, rtol=RTOL, atol=ATOL) - assert_allclose_td(*tdrollout, rtol=RTOL, atol=ATOL) + tdrollout = torch.stack(tdrollout, 0).contiguous() + + # custom filtering of non-null obs: mujoco rendering sometimes fails + # and renders black images. To counter this in the tests, we select + # tensordicts with all non-null observations + idx = non_null_obs(tdrollout) + assert_allclose_td(tdrollout[0][..., idx], tdrollout[1][..., idx], rtol=RTOL, atol=ATOL) final_seed0, final_seed1 = final_seed assert final_seed0 == final_seed1 @@ -187,7 +207,15 @@ def test_gym(self, env_name, frame_skip, from_pixels, pixels_only): if from_pixels and not _is_from_pixels(base_env): base_env = PixelObservationWrapper(base_env, pixels_only=pixels_only) assert type(base_env) is env_type + + # Compare GymEnv output with GymWrapper output env1 = GymWrapper(base_env, frame_skip=frame_skip) + assert env0.get_library_name(env0._env) == env1.get_library_name(env1._env) + # check that we didn't do more wrapping + assert type(env0._env) == type(env1._env) + assert env0.output_spec == env1.output_spec + assert env0.input_spec == env1.input_spec + del env0 torch.manual_seed(0) np.random.seed(0) final_seed2 = env1.set_seed(0) @@ -199,7 +227,10 @@ def test_gym(self, env_name, frame_skip, from_pixels, pixels_only): assert_allclose_td(tdreset[0], tdreset2, rtol=RTOL, atol=ATOL) assert final_seed0 == final_seed2 - assert_allclose_td(tdrollout[0], rollout2, rtol=RTOL, atol=ATOL) + # same magic trick for mujoco as above + tdrollout = torch.stack([tdrollout[0], rollout2], 0).contiguous() + idx = non_null_obs(tdrollout) + assert_allclose_td(tdrollout[0][..., idx], tdrollout[1][..., idx], rtol=RTOL, atol=ATOL) @pytest.mark.parametrize( "env_name", diff --git a/test/test_utils.py b/test/test_utils.py index e45eaf5edab..d5805931f45 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -269,7 +269,7 @@ def test_set_gym_environments_no_version_gymnasium_found(): # this version of gymnasium does not exist in implement_for # therefore, set_gym_backend will not set anything and raise an ImportError. msg = f"could not set anything related to gym backend {gymnasium_name} with version={gymnasium_version}." - with pytest.raises(ImportError, match=msg) as exc_info: + with pytest.raises(ImportError, match=msg): with set_gym_backend(gymnasium): _utils_internal._set_gym_environments() From acf611837df6281eda7c070d0e85e1a066189826 Mon Sep 17 00:00:00 2001 From: vmoens Date: Fri, 22 Sep 2023 05:26:55 -0400 Subject: [PATCH 075/178] amend --- test/test_libs.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_libs.py b/test/test_libs.py index 5fd4955648c..a9505902dfe 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -603,7 +603,7 @@ def test_vecenvs_nan(self): # noqa: F811 del c return - @implement_for("gymnasium") + @implement_for("gymnasium", "0.27.0", None) def test_vecenvs_nan(self): # noqa: F811 # new versions of gym must never return nan for next values when there is a done state torch.manual_seed(0) From bb52ce146506c069fd0a92a9933f993b68f3a613 Mon Sep 17 00:00:00 2001 From: vmoens Date: Fri, 22 Sep 2023 05:56:27 -0400 Subject: [PATCH 076/178] tests --- test/test_libs.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index a9505902dfe..32f40ed25f7 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -151,8 +151,7 @@ def test_gym(self, env_name, frame_skip, from_pixels, pixels_only): elif ( env_name != PONG_VERSIONED and from_pixels and torch.cuda.device_count() < 1 ): - pass - # raise pytest.skip("no cuda device") + raise pytest.skip("no cuda device") def non_null_obs(batched_td): if from_pixels: From 3ef139b1060a079076ad96c175e821b94e41c9ef Mon Sep 17 00:00:00 2001 From: vmoens Date: Fri, 22 Sep 2023 07:10:35 -0400 Subject: [PATCH 077/178] amend --- test/test_libs.py | 17 +++++++++++++---- torchrl/envs/transforms/vip.py | 8 ++++++++ 2 files changed, 21 insertions(+), 4 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index 32f40ed25f7..1fc6c31b72d 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -156,7 +156,12 @@ def test_gym(self, env_name, frame_skip, from_pixels, pixels_only): def non_null_obs(batched_td): if from_pixels: pix_norm = batched_td.get("pixels").flatten(-3, -1).float().norm(dim=-1) - pix_norm_next = batched_td.get(("next", "pixels")).flatten(-3, -1).float().norm(dim=-1) + pix_norm_next = ( + batched_td.get(("next", "pixels")) + .flatten(-3, -1) + .float() + .norm(dim=-1) + ) idx = (pix_norm > 1) & (pix_norm_next > 1) # eliminate batch size: all idx must be True (otherwise one could be filled with 0s) while idx.ndim > 1: @@ -193,7 +198,9 @@ def non_null_obs(batched_td): # and renders black images. To counter this in the tests, we select # tensordicts with all non-null observations idx = non_null_obs(tdrollout) - assert_allclose_td(tdrollout[0][..., idx], tdrollout[1][..., idx], rtol=RTOL, atol=ATOL) + assert_allclose_td( + tdrollout[0][..., idx], tdrollout[1][..., idx], rtol=RTOL, atol=ATOL + ) final_seed0, final_seed1 = final_seed assert final_seed0 == final_seed1 @@ -211,7 +218,7 @@ def non_null_obs(batched_td): env1 = GymWrapper(base_env, frame_skip=frame_skip) assert env0.get_library_name(env0._env) == env1.get_library_name(env1._env) # check that we didn't do more wrapping - assert type(env0._env) == type(env1._env) + assert type(env0._env) == type(env1._env) # noqa: E721 assert env0.output_spec == env1.output_spec assert env0.input_spec == env1.input_spec del env0 @@ -229,7 +236,9 @@ def non_null_obs(batched_td): # same magic trick for mujoco as above tdrollout = torch.stack([tdrollout[0], rollout2], 0).contiguous() idx = non_null_obs(tdrollout) - assert_allclose_td(tdrollout[0][..., idx], tdrollout[1][..., idx], rtol=RTOL, atol=ATOL) + assert_allclose_td( + tdrollout[0][..., idx], tdrollout[1][..., idx], rtol=RTOL, atol=ATOL + ) @pytest.mark.parametrize( "env_name", diff --git a/torchrl/envs/transforms/vip.py b/torchrl/envs/transforms/vip.py index 569110b8b2b..803e9517288 100644 --- a/torchrl/envs/transforms/vip.py +++ b/torchrl/envs/transforms/vip.py @@ -389,3 +389,11 @@ def _step( def forward(self, tensordict): tensordict = super().forward(tensordict) return tensordict + + def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec: + state_spec = input_spec["state_spec"] + # find the obs spec + in_key = self.in_keys[0] + spec = self.parent.output_spec["observation_spec"][in_key].clone() + state_spec["goal_image"] = spec + return input_spec From 0d3ba021fd4dd31923ed5e0a6b7a51d91a2cbcd8 Mon Sep 17 00:00:00 2001 From: vmoens Date: Fri, 22 Sep 2023 09:07:12 -0400 Subject: [PATCH 078/178] amend --- torchrl/envs/transforms/vip.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/torchrl/envs/transforms/vip.py b/torchrl/envs/transforms/vip.py index 803e9517288..7188a1bfd7d 100644 --- a/torchrl/envs/transforms/vip.py +++ b/torchrl/envs/transforms/vip.py @@ -391,9 +391,13 @@ def forward(self, tensordict): return tensordict def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec: - state_spec = input_spec["state_spec"] + if "state_spec" in input_spec.keys(): + state_spec = input_spec["state_spec"] + else: + state_spec = CompositeSpec(shape=input_spec.shape, device=input_spec.device) # find the obs spec in_key = self.in_keys[0] spec = self.parent.output_spec["observation_spec"][in_key].clone() state_spec["goal_image"] = spec + input_spec["state_spec"] = state_spec return input_spec From 0638e1365b8da9883e243a8faabdfe238a616c5d Mon Sep 17 00:00:00 2001 From: vmoens Date: Fri, 22 Sep 2023 09:18:31 -0400 Subject: [PATCH 079/178] amend --- test/test_env.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/test/test_env.py b/test/test_env.py index 0e47cfe0ca6..646d13ea42f 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -8,6 +8,7 @@ import re from collections import defaultdict from functools import partial +from sys import platform import numpy as np import pytest @@ -94,6 +95,7 @@ _atari_found = False atari_confs = defaultdict(lambda: "") +IS_OSX = platform == "darwin" ## TO BE FIXED: DiscreteActionProjection queries a randint on each worker, which leads to divergent results between ## the serial and parallel batched envs @@ -2174,6 +2176,9 @@ def test_done_or_truncated_spec(self): assert not data["nested", "_reset"].any() +@pytest.mark.skipif( + IS_OSX, reason="setting different threads across workeres can randomly fail on OSX." +) def test_num_threads(): from torchrl.envs import batched_envs From a3bd3e1a4d6ef1e40b59b48ec8ec0f3cb603bd06 Mon Sep 17 00:00:00 2001 From: vmoens Date: Fri, 22 Sep 2023 09:49:28 -0400 Subject: [PATCH 080/178] tmp --- torchrl/objectives/value/functional.py | 107 ++++++++++++++++++++----- 1 file changed, 88 insertions(+), 19 deletions(-) diff --git a/torchrl/objectives/value/functional.py b/torchrl/objectives/value/functional.py index ccd0966bbf6..f2963de0c2e 100644 --- a/torchrl/objectives/value/functional.py +++ b/torchrl/objectives/value/functional.py @@ -8,6 +8,7 @@ from typing import Optional, Tuple, Union import torch +from torch import Tensor from tensordict import MemmapTensor @@ -102,7 +103,15 @@ def transpose_tensor(tensor): return transposed_fun - +def _get_truncated_done(done: Tensor, truncated: Tensor | None) -> Tuple[Tensor, Tensor]: + # gets a tangible done and truncated + if truncated is None: + return done, done.clone() + # we want to zero the next_value if done is True, but not truncated. + # We also want to split trajectories according to done OR truncated. + # So if a done is not marked as truncated as well, we'll miss a split! + truncated = truncated | done + return done, truncated ######################################################################## # GAE # --- @@ -116,6 +125,7 @@ def generalized_advantage_estimate( next_state_value: torch.Tensor, reward: torch.Tensor, done: torch.Tensor, + truncated: torch.Tensor | None=None, time_dim: int = -2, ) -> Tuple[torch.Tensor, torch.Tensor]: """Generalized advantage estimate of a trajectory. @@ -129,7 +139,10 @@ def generalized_advantage_estimate( state_value (Tensor): value function result with old_state input. next_state_value (Tensor): value function result with new_state input. reward (Tensor): reward of taking actions in the environment. - done (Tensor): boolean flag for end of episode. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). time_dim (int): dimension where the time is unrolled. Defaults to -2. All tensors (values, reward and done) must have shape @@ -140,6 +153,7 @@ def generalized_advantage_estimate( raise RuntimeError(SHAPE_ERR) dtype = next_state_value.dtype device = state_value.device + done, truncated = _get_truncated_done(done, truncated) not_done = (~done).int() *batch_size, time_steps, lastdim = not_done.shape @@ -149,7 +163,8 @@ def generalized_advantage_estimate( prev_advantage = 0 gnotdone = gamma * not_done delta = reward + (gnotdone * next_state_value) - state_value - discount = lmbda * gnotdone + gnottruncated = (~truncated).int() + discount = lmbda * gnottruncated for t in reversed(range(time_steps)): prev_advantage = advantage[..., t, :] = delta[..., t, :] + ( prev_advantage * discount[..., t, :] @@ -187,6 +202,7 @@ def _fast_vec_gae( state_value: torch.Tensor, next_state_value: torch.Tensor, done: torch.Tensor, + truncated: torch.Tensor, gamma: float, lmbda: float, thr: float = 1e-7, @@ -200,7 +216,8 @@ def _fast_vec_gae( reward (torch.Tensor): a [*B, T, F] tensor containing rewards state_value (torch.Tensor): a [*B, T, F] tensor containing state values (value function) next_state_value (torch.Tensor): a [*B, T, F] tensor containing next state values (value function) - done (torch.Tensor): a [B, T] boolean tensor containing the done states + done (torch.Tensor): a [B, T] boolean tensor containing the done states. + truncated (torch.Tensor): a [B, T] boolean tensor containing the truncated states. gamma (scalar): the gamma decay (trajectory discount) lmbda (scalar): the lambda decay (exponential mean discount) thr (float): threshold for the filter. Below this limit, components will ignored. @@ -246,6 +263,7 @@ def vec_generalized_advantage_estimate( next_state_value: torch.Tensor, reward: torch.Tensor, done: torch.Tensor, + truncated: torch.Tensor|None=None, time_dim: int = -2, ) -> Tuple[torch.Tensor, torch.Tensor]: """Vectorized Generalized advantage estimate of a trajectory. @@ -259,7 +277,10 @@ def vec_generalized_advantage_estimate( state_value (Tensor): value function result with old_state input. next_state_value (Tensor): value function result with new_state input. reward (Tensor): reward of taking actions in the environment. - done (Tensor): boolean flag for end of episode. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). time_dim (int): dimension where the time is unrolled. Defaults to -2. All tensors (values, reward and done) must have shape @@ -336,6 +357,7 @@ def td0_advantage_estimate( next_state_value: torch.Tensor, reward: torch.Tensor, done: torch.Tensor, + truncated: torch.Tensor|None=None, ) -> Tuple[torch.Tensor, torch.Tensor]: """TD(0) advantage estimate of a trajectory. @@ -346,7 +368,10 @@ def td0_advantage_estimate( state_value (Tensor): value function result with old_state input. next_state_value (Tensor): value function result with new_state input. reward (Tensor): reward of taking actions in the environment. - done (Tensor): boolean flag for end of episode. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). All tensors (values, reward and done) must have shape ``[*Batch x TimeSteps x *F]``, with ``*F`` feature dimensions. @@ -364,6 +389,7 @@ def td0_return_estimate( next_state_value: torch.Tensor, reward: torch.Tensor, done: torch.Tensor, + truncated: torch.Tensor|None=None, ) -> Tuple[torch.Tensor, torch.Tensor]: """TD(0) discounted return estimate of a trajectory. @@ -375,7 +401,10 @@ def td0_return_estimate( must be a [Batch x TimeSteps x 1] or [Batch x TimeSteps] tensor reward (Tensor): reward of taking actions in the environment. must be a [Batch x TimeSteps x 1] or [Batch x TimeSteps] tensor - done (Tensor): boolean flag for end of episode. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). All tensors (values, reward and done) must have shape ``[*Batch x TimeSteps x *F]``, with ``*F`` feature dimensions. @@ -399,6 +428,7 @@ def td1_return_estimate( next_state_value: torch.Tensor, reward: torch.Tensor, done: torch.Tensor, + truncated: torch.Tensor|None=None, rolling_gamma: bool = None, time_dim: int = -2, ) -> torch.Tensor: @@ -408,7 +438,10 @@ def td1_return_estimate( gamma (scalar): exponential mean discount. next_state_value (Tensor): value function result with new_state input. reward (Tensor): reward of taking actions in the environment. - done (Tensor): boolean flag for end of episode. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). rolling_gamma (bool, optional): if ``True``, it is assumed that each gamma if a gamma tensor is tied to a single event: gamma = [g1, g2, g3, g4] @@ -479,6 +512,7 @@ def td1_advantage_estimate( next_state_value: torch.Tensor, reward: torch.Tensor, done: torch.Tensor, + truncated: torch.Tensor|None=None, rolling_gamma: bool = None, time_dim: int = -2, ) -> torch.Tensor: @@ -489,7 +523,10 @@ def td1_advantage_estimate( state_value (Tensor): value function result with old_state input. next_state_value (Tensor): value function result with new_state input. reward (Tensor): reward of taking actions in the environment. - done (Tensor): boolean flag for end of episode. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). rolling_gamma (bool, optional): if ``True``, it is assumed that each gamma if a gamma tensor is tied to a single event: gamma = [g1, g2, g3, g4] @@ -533,7 +570,8 @@ def vec_td1_return_estimate( gamma, next_state_value, reward, - done, + done: torch.Tensor, + truncated: torch.Tensor|None=None, rolling_gamma: Optional[bool] = None, time_dim: int = -2, ): @@ -543,7 +581,10 @@ def vec_td1_return_estimate( gamma (scalar, Tensor): exponential mean discount. If tensor-valued, next_state_value (Tensor): value function result with new_state input. reward (Tensor): reward of taking actions in the environment. - done (Tensor): boolean flag for end of episode. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). rolling_gamma (bool, optional): if ``True``, it is assumed that each gamma if a gamma tensor is tied to a single event: gamma = [g1, g2, g3, g4] @@ -587,7 +628,8 @@ def vec_td1_advantage_estimate( state_value, next_state_value, reward, - done, + done: torch.Tensor, + truncated: torch.Tensor|None=None, rolling_gamma: bool = None, time_dim: int = -2, ): @@ -598,7 +640,10 @@ def vec_td1_advantage_estimate( state_value (Tensor): value function result with old_state input. next_state_value (Tensor): value function result with new_state input. reward (Tensor): reward of taking actions in the environment. - done (Tensor): boolean flag for end of episode. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). rolling_gamma (bool, optional): if ``True``, it is assumed that each gamma if a gamma tensor is tied to a single event: gamma = [g1, g2, g3, g4] @@ -648,6 +693,7 @@ def td_lambda_return_estimate( next_state_value: torch.Tensor, reward: torch.Tensor, done: torch.Tensor, + truncated: torch.Tensor|None=None, rolling_gamma: bool = None, time_dim: int = -2, ) -> torch.Tensor: @@ -658,7 +704,10 @@ def td_lambda_return_estimate( lmbda (scalar): trajectory discount. next_state_value (Tensor): value function result with new_state input. reward (Tensor): reward of taking actions in the environment. - done (Tensor): boolean flag for end of episode. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). rolling_gamma (bool, optional): if ``True``, it is assumed that each gamma if a gamma tensor is tied to a single event: gamma = [g1, g2, g3, g4] @@ -744,6 +793,7 @@ def td_lambda_advantage_estimate( next_state_value: torch.Tensor, reward: torch.Tensor, done: torch.Tensor, + truncated: torch.Tensor|None=None, rolling_gamma: bool = None, time_dim: int = -2, ) -> torch.Tensor: @@ -755,7 +805,10 @@ def td_lambda_advantage_estimate( state_value (Tensor): value function result with old_state input. next_state_value (Tensor): value function result with new_state input. reward (Tensor): reward of taking actions in the environment. - done (Tensor): boolean flag for end of episode. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). rolling_gamma (bool, optional): if ``True``, it is assumed that each gamma if a gamma tensor is tied to a single event: gamma = [g1, g2, g3, g4] @@ -800,6 +853,7 @@ def _fast_td_lambda_return_estimate( next_state_value: torch.Tensor, reward: torch.Tensor, done: torch.Tensor, + truncated: torch.Tensor|None=None, thr: float = 1e-7, ): """Fast vectorized TD lambda return estimate. @@ -812,7 +866,10 @@ def _fast_td_lambda_return_estimate( lmbda (scalar): the lambda decay (exponential mean discount) next_state_value (torch.Tensor): a [*B, T, F] tensor containing next state values (value function) reward (torch.Tensor): a [*B, T, F] tensor containing rewards - done (torch.Tensor): a [B, T] boolean tensor containing the done states + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). thr (float): threshold for the filter. Below this limit, components will ignored. Defaults to 1e-7. @@ -855,6 +912,7 @@ def vec_td_lambda_return_estimate( next_state_value, reward, done, + truncated: torch.Tensor|None=None, rolling_gamma: Optional[bool] = None, time_dim: int = -2, ): @@ -868,7 +926,10 @@ def vec_td_lambda_return_estimate( must be a [Batch x TimeSteps x 1] tensor reward (Tensor): reward of taking actions in the environment. must be a [Batch x TimeSteps x 1] or [Batch x TimeSteps] tensor - done (Tensor): boolean flag for end of episode. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). rolling_gamma (bool, optional): if ``True``, it is assumed that each gamma if a gamma tensor is tied to a single event: gamma = [g1, g2, g3, g4] @@ -997,6 +1058,7 @@ def vec_td_lambda_advantage_estimate( next_state_value, reward, done, + truncated: torch.Tensor|None=None, rolling_gamma: bool = None, time_dim: int = -2, ): @@ -1008,7 +1070,10 @@ def vec_td_lambda_advantage_estimate( state_value (Tensor): value function result with old_state input. next_state_value (Tensor): value function result with new_state input. reward (Tensor): reward of taking actions in the environment. - done (Tensor): boolean flag for end of episode. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). rolling_gamma (bool, optional): if ``True``, it is assumed that each gamma if a gamma tensor is tied to a single event: gamma = [g1, g2, g3, g4] @@ -1062,6 +1127,7 @@ def reward2go( reward, done, gamma, + truncated, time_dim: int = -2, ): """Compute the discounted cumulative sum of rewards given multiple trajectories and the episode ends. @@ -1069,9 +1135,12 @@ def reward2go( Args: reward (torch.Tensor): A tensor containing the rewards received at each time step over multiple trajectories. - done (torch.Tensor): A tensor with done (or truncated) states. + done (Tensor): boolean flag for end of episode. Differs from + truncated, where the episode did not end but was interrupted. gamma (float, optional): The discount factor to use for computing the discounted cumulative sum of rewards. Defaults to 1.0. + truncated (Tensor, optional): boolean flag for last step of a (possibly) + unfinished episode. Defaults to ``None`` (only ``done`` matters). time_dim (int): dimension where the time is unrolled. Defaults to -2. Returns: From a8ed5e50bdecc92d0f034c041ad3064f29fe327f Mon Sep 17 00:00:00 2001 From: vmoens Date: Fri, 22 Sep 2023 10:21:49 -0400 Subject: [PATCH 081/178] amend --- torchrl/envs/gym_like.py | 7 ++++++- torchrl/envs/libs/gym.py | 6 ++++++ 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index 39c855a729c..96d4aa65c7f 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -242,7 +242,12 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: if "truncated" in self.done_keys: obs_dict["truncated"] = truncation else: - raise KeyError("Could not find truncated key") + raise KeyError("Could not find truncated key.") + if termination is not None: + if "terminated" in self.done_keys: + obs_dict["terminated"] = termination + else: + raise KeyError("Could not find truncated key.") tensordict_out = TensorDict(obs_dict, batch_size=tensordict.batch_size) diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index 91456468624..0dc197ff656 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -761,6 +761,9 @@ def _make_done_spec(self): # noqa: F811 "done": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), + "terminated": DiscreteTensorSpec( + 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) + ), "truncated": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), @@ -775,6 +778,9 @@ def _make_done_spec(self): # noqa: F811 "done": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), + "terminated": DiscreteTensorSpec( + 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) + ), "truncated": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), From 54db14e6e762a02ab7d9f1c454f4a0e91a13dd8d Mon Sep 17 00:00:00 2001 From: vmoens Date: Fri, 22 Sep 2023 12:55:09 -0400 Subject: [PATCH 082/178] amend --- torchrl/envs/gym_like.py | 81 +++++++++++++++++---------- torchrl/envs/libs/gym.py | 38 +++++++++---- torchrl/envs/transforms/transforms.py | 35 +++++------- 3 files changed, 90 insertions(+), 64 deletions(-) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index 96d4aa65c7f..a9120b2a905 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -135,10 +135,10 @@ def read_action(self, action): def read_done( self, - gym_done: bool, - termination: bool | None = None, - truncation: bool | None = None, - ) -> Tuple[bool, bool, bool]: + done: bool, + truncated: bool | None = None, + stop: bool | None = None, + ) -> Tuple[bool | np.ndarray, bool | np.ndarray, bool | np.ndarray, bool]: """Done state reader. In torchrl, a `"done"` signal means that a trajectory is terminated @@ -146,9 +146,14 @@ def read_done( Truncated means the trajectory has been interrupted. Args: - gym_done (np.ndarray, boolean or other format): done state obtained from the environment - termination (bool or None): termination signal. Defaults to `None`. - truncation (bool or None): truncation signal. Defaults to `None`. + done (np.ndarray, boolean or other format): done state obtained from the environment. + ``"done"`` equates to ``"termination"`` in gymnasium: the signal that + the environment has reached the end of the game, any data coming + after this should be considered as nonsensical. + In TorchRL, the convention is that all envs must have a + ``done`` of some sort. + truncated (bool or None): termination signal. Defaults to ``None``. + stop (bool or None): end-of-trajectory signal. Defaults to ``None``. Returns: a tuple with 3 boolean values, - a done state to be set in the environment. @@ -156,15 +161,15 @@ def read_done( - a boolean value indicating whether the frame_skip loop should be broken. """ - if termination is not None: - done = termination - do_break = gym_done | truncation - else: - do_break = gym_done - done = gym_done + if truncated is not None and stop is None: + stop = truncated | done + elif truncated is None and stop is None: + stop = done + do_break = stop.any() if not isinstance(stop, bool) else stop return ( done, - truncation, + truncated, + stop, do_break.any() if not isinstance(do_break, bool) else do_break, ) @@ -209,7 +214,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: reward = 0 for _ in range(self.wrapper_frame_skip): - obs, _reward, termination, truncation, done, info = self._output_transform( + obs, _reward, done, truncated, stop, info = self._output_transform( self._env.step(action_np) ) if isinstance(obs, list) and len(obs) == 1: @@ -225,7 +230,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: isinstance(done, np.ndarray) and not len(done) ): done = torch.tensor([done]) - done, truncation, do_break = self.read_done(done, termination, truncation) + done, truncated, stop, do_break = self.read_done(done, truncated, stop) if do_break: break @@ -237,17 +242,16 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: obs_dict[self.reward_key] = reward - obs_dict[self.done_keys[0]] = done - if truncation is not None: - if "truncated" in self.done_keys: - obs_dict["truncated"] = truncation - else: - raise KeyError("Could not find truncated key.") - if termination is not None: - if "terminated" in self.done_keys: - obs_dict["terminated"] = termination - else: - raise KeyError("Could not find truncated key.") + # torchrl envs should always have a done + obs_dict["done"] = done + if truncated is not None and "truncated" in self.done_keys: + obs_dict["truncated"] = truncated + # if truncated is not in the keys, we just don't pass it even if it + # is defined. + if stop is not None and "stop" in self.done_keys: + obs_dict["stop"] = stop + # if stop is not in the keys, we just don't pass it even if it + # is defined. tensordict_out = TensorDict(obs_dict, batch_size=tensordict.batch_size) @@ -292,9 +296,26 @@ def _reset( def _output_transform(self, step_outputs_tuple: Tuple) -> Tuple: """A method to read the output of the env step. - Must return a tuple: (obs, reward, termination, truncation, done, info). - truncated and terminal can be None, in which case they are discarded. - If done is None, truncated and terminal mustn't be None. + Must return a tuple: (obs, reward, done, truncated, stop, info). + If only one end-of-trajectory is passed, it is interpreted as ``"done"``, ie. + the state is terminal (game over). + If 2 are passed (like in gymnasium), we interpret them as ``"done", "truncated"`` + (``"truncated"`` meaning that the trajectory has been interrupted early). + and ``"stop"`` is the union of the two, ie. the end-of-trajectory signal. + + These three concepts have different usage: + + - "done" means that one should not pay attention to the upcoming observations + (eg., in value functions) as they should be regarded as not valid. + This is a "game-over" situation, the result of the action is the + end of the game (win or loose). + - "truncated" means that the environment has reached a stage where + we decided to stop the collection for some reason but the next + observation should not be discarded. If it were not for this + arbitrary decision, the collection could proceed further. + - "stop" is either one or the other. It is to be interpreted as + "a reset should be called at the next step". + """ ... diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index 0dc197ff656..74f3af4be13 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -758,10 +758,10 @@ def _make_done_spec(self): # noqa: F811 def _make_done_spec(self): # noqa: F811 return CompositeSpec( { - "done": DiscreteTensorSpec( + "stop": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), - "terminated": DiscreteTensorSpec( + "done": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), "truncated": DiscreteTensorSpec( @@ -775,10 +775,10 @@ def _make_done_spec(self): # noqa: F811 def _make_done_spec(self): # noqa: F811 return CompositeSpec( { - "done": DiscreteTensorSpec( + "stop": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), - "terminated": DiscreteTensorSpec( + "done": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), "truncated": DiscreteTensorSpec( @@ -806,12 +806,26 @@ def _output_transform(self, step_outputs_tuple): # noqa: F811 if self._is_batched: # info needs to be flipped info = _flip_info_tuple(info) - return (observations, reward, None, None, done, info) + # a done is interpreted as terminal in earlier versions of gym. + # This isn't optimal, but it is the most natural option. + # Interpreting this as a "stop" would break in value functions because + # we can't decide if the env is done (ie, game over) or truncated. + # By making an opinionated decision, we make sure that value function can + # be computed with a certain heuristic, even if it is wrong in some cases + # (by lack of information). + return (observations, reward, done, None, None, info) @implement_for("gym", "0.24", "0.26") def _output_transform(self, step_outputs_tuple): # noqa: F811 + # a done is interpreted as terminal in earlier versions of gym. + # This isn't optimal, but it is the most natural option. + # Interpreting this as a "stop" would break in value functions because + # we can't decide if the env is done (ie, game over) or truncated. + # By making an opinionated decision, we make sure that value function can + # be computed with a certain heuristic, even if it is wrong in some cases + # (by lack of information). observations, reward, done, info = step_outputs_tuple - return (observations, reward, None, None, done, info) + return (observations, reward, done, None, None, info) @implement_for("gym", "0.26", None) def _output_transform(self, step_outputs_tuple): # noqa: F811 @@ -819,9 +833,9 @@ def _output_transform(self, step_outputs_tuple): # noqa: F811 return ( observations, reward, - termination, - truncation, - termination | truncation, + termination, # torchrl done + truncation, # torchrl truncated + termination | truncation, # torchrl stop info, ) @@ -831,9 +845,9 @@ def _output_transform(self, step_outputs_tuple): # noqa: F811 return ( observations, reward, - termination, - truncation, - termination | truncation, + termination, # torchrl done + truncation, # torchrl truncated + termination | truncation, # torchrl stop info, ) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 78eba03f4c2..58111395ccc 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -5088,43 +5088,31 @@ def init_keys(self): raise NotImplementedError( FORWARD_NOT_IMPLEMENTED.format(self.__class__.__name__) ) - for done_key in self.parent.done_keys: + for done_key, *_ in self.parent.done_keys_groups: if isinstance(done_key, str): init_key = self.init_key else: init_key = unravel_key((*done_key[:-1], self.init_key)) - if init_key not in init_keys: - init_keys.append(init_key) + init_keys.append(init_key) self._init_keys = init_keys return self._init_keys @property def reset_keys(self): - reset_keys = [] - if self.parent is None: - raise NotImplementedError( - FORWARD_NOT_IMPLEMENTED.format(self.__class__.__name__) - ) - for done_key in self.parent.done_keys: - if isinstance(done_key, str): - reset_key = self.reset_key - else: - reset_key = unravel_key((*done_key[:-1], self.reset_key)) - if reset_key not in reset_keys: - reset_keys.append(reset_key) - return reset_keys + return self.parent.reset_keys def _call(self, tensordict: TensorDictBase) -> TensorDictBase: - for init_key in self.init_keys: + for init_key, (done_key, *_) in zip( + self.init_keys, self.parent.done_keys_groups + ): if init_key not in tensordict.keys(True, True): device = tensordict.device if device is None: device = torch.device("cpu") + shape = self.parent.full_done_spec[done_key].shape tensordict.set( init_key, - torch.zeros( - self.parent.done_spec.shape, device=device, dtype=torch.bool - ), + torch.zeros(shape, device=device, dtype=torch.bool), ) return tensordict @@ -5132,13 +5120,16 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: device = tensordict.device if device is None: device = torch.device("cpu") - for reset_key, init_key in zip(self.reset_keys, self.init_keys): + for reset_key, init_key, (done_key, *_) in zip( + self.reset_keys, self.init_keys, self.parent.done_keys_groups + ): _reset = tensordict.get(reset_key, None) if _reset is None: + shape = self.parent.full_done_spec[done_key].shape tensordict.set( init_key, torch.ones( - self.parent.done_spec.shape, + shape, device=device, dtype=torch.bool, ), From 0122fac2dae8afbf0c445552f600f57feeed72e8 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sat, 23 Sep 2023 00:37:12 -0400 Subject: [PATCH 083/178] amend --- test/test_collector.py | 2 ++ torchrl/envs/batched_envs.py | 1 - torchrl/envs/gym_like.py | 2 +- torchrl/envs/libs/dm_control.py | 5 +++-- 4 files changed, 6 insertions(+), 4 deletions(-) diff --git a/test/test_collector.py b/test/test_collector.py index b2f9d674345..a55e2407c79 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -1057,6 +1057,8 @@ def test_collector_output_keys( if "gymnasium" in str(gym_backend()) or gym_backend().__version__ >= version.parse( "0.26" ): + keys.add(("next", "stop")) + keys.add("stop") keys.add(("next", "truncated")) keys.add("truncated") b = next(iter(collector)) diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index 4522d350bf9..f45272d05ed 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -1011,7 +1011,6 @@ def _run_worker_pipe_shared_mem( has_lazy_inputs: bool = False, verbose: bool = False, ) -> None: - print(f"single env has {torch.get_num_threads()} threads") if device is None: device = torch.device("cpu") if device.type == "cuda": diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index a9120b2a905..4dcd16d44c5 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -293,7 +293,7 @@ def _reset( return tensordict_out @abc.abstractmethod - def _output_transform(self, step_outputs_tuple: Tuple) -> Tuple: + def _output_transform(self, step_outputs_tuple: Tuple) -> Tuple[Any, float | np.ndarray, bool, bool | np.ndarray | None, bool | np.ndarray | None, dict]: """A method to read the output of the env step. Must return a tuple: (obs, reward, done, truncated, stop, info). diff --git a/torchrl/envs/libs/dm_control.py b/torchrl/envs/libs/dm_control.py index 660e37d619a..71f66b8efa0 100644 --- a/torchrl/envs/libs/dm_control.py +++ b/torchrl/envs/libs/dm_control.py @@ -242,9 +242,10 @@ def _output_transform( done = False # dm_control envs are non-terminating observation = timestep_tuple[0].observation - truncation = termination = None + truncated = terminated = None info = {} - return observation, reward, termination, truncation, done, info + + return observation, reward, done, truncated, terminated, info def _reset_output_transform(self, reset_data): ( From b18e6e700a57670ebe9a8d38bcc143a2edfd55ba Mon Sep 17 00:00:00 2001 From: vmoens Date: Sat, 23 Sep 2023 00:40:50 -0400 Subject: [PATCH 084/178] amend --- torchrl/envs/transforms/transforms.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 58111395ccc..5deb015d405 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -4288,6 +4288,13 @@ class StepCounter(Transform): done_keys (list of NestedKeys, optional): the keys that are indicative of the done signal. Must match the length of ``truncated_keys`` and ``step_count_keys``, if provided. + write_stop (bool, optional): if ``True``, a ``"stop"`` boolean tensor + will be written at the level of ``"truncated"``. If ``"stop"`` is + already present, it will be updated accordingly. + This signal indicates that the trajectory has reached its ends, + either because the task is completed (``"done"``) or because it has + been truncated (``"truncated"``). + Defaults to ``False``. .. note:: To ensure compatibility with environments that have multiple done_key(s), this transform will write a step_count entry for @@ -4338,6 +4345,7 @@ def __init__( truncated_keys: Optional[List[NestedKey]] = None, step_count_keys: Optional[List[NestedKey]] = None, done_keys: Optional[List[NestedKey]] = None, + write_stop: bool | None=False, *, truncated_key: Optional[NestedKey] = None, step_count_key: Optional[NestedKey] = None, @@ -4381,6 +4389,7 @@ def __init__( else None ) self._done_keys = done_keys + self.write_stop = write_stop self.done_key = done_keys[0] if done_keys and len(done_keys) == 1 else None super().__init__([]) From 2f99f167b3982a1d3541a96d1ec5600928d22418 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sat, 23 Sep 2023 01:21:39 -0400 Subject: [PATCH 085/178] amend --- torchrl/envs/transforms/transforms.py | 197 ++++++++++++++------------ 1 file changed, 106 insertions(+), 91 deletions(-) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 5deb015d405..1c8437250ae 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -4275,19 +4275,19 @@ class StepCounter(Transform): max_steps (int, optional): a positive integer that indicates the maximum number of steps to take before setting the ``truncated_key`` entry to ``True``. - truncated_keys (list of NestedKey, optional): the keys where the truncated entries - should be written. Defaults to ``["truncated"]``, which is recognised by + truncated_key (str, optional): the key where the truncated entries + should be written. Defaults to ``"truncated"``, which is recognised by data collectors as a reset signal. - Must match the length of ``step_count_keys`` and ``done_keys``, - if provided. - If the transform is nested within a transformed environment, the - truncated entry will be integrated in the ``done_spec``. - step_count_keys (list of NestedKeys, optional): the key names of the step - count. Must match the length of ``truncated_keys`` and ``done_keys``, - if provided. If not, it will default to the ``"step_count"`` key name. - done_keys (list of NestedKeys, optional): the keys that are indicative of the - done signal. Must match the length of ``truncated_keys`` and ``step_count_keys``, - if provided. + This argument can only be a string (not a nested key) as it will be + matched to each of the leaf done key in the parent environment + (eg, a ``("agent", "done")`` key will be accompanied by a ``("agent", "truncated")`` + if the ``"truncated"`` key name is used). + step_count_key (str, optional): the key where the step count entries + should be written. Defaults to ``"step_count"``. + This argument can only be a string (not a nested key) as it will be + matched to each of the leaf done key in the parent environment + (eg, a ``("agent", "done")`` key will be accompanied by a ``("agent", "step_count")`` + if the ``"step_count"`` key name is used). write_stop (bool, optional): if ``True``, a ``"stop"`` boolean tensor will be written at the level of ``"truncated"``. If ``"stop"`` is already present, it will be updated accordingly. @@ -4342,55 +4342,22 @@ class StepCounter(Transform): def __init__( self, max_steps: Optional[int] = None, - truncated_keys: Optional[List[NestedKey]] = None, - step_count_keys: Optional[List[NestedKey]] = None, - done_keys: Optional[List[NestedKey]] = None, + truncated_key: str | None = None, + step_count_key: str | None=None, write_stop: bool | None=False, - *, - truncated_key: Optional[NestedKey] = None, - step_count_key: Optional[NestedKey] = None, ): if max_steps is not None and max_steps < 1: raise ValueError("max_steps should have a value greater or equal to one.") - if truncated_key is not None: - warnings.warn( - "truncated_key will be deprecated in torchrl v0.3. Use truncated_keys instead.", - category=DeprecationWarning, - ) - if truncated_keys: - if len(truncated_keys) > 1 or truncated_keys[0] != truncated_key: - raise ValueError("truncated_key and truncated_keys mismatch.") - truncated_keys = [truncated_key] - if isinstance(truncated_keys, (str, tuple)): - truncated_keys = [truncated_keys] - if isinstance(done_keys, (str, tuple)): - done_keys = [done_keys] - if step_count_key is not None: - warnings.warn( - "step_count_key will be deprecated in torchrl v0.3. Use step_count_keys instead.", - category=DeprecationWarning, - ) - if step_count_keys: - if len(step_count_keys) > 1 or step_count_keys[0] != step_count_key: - raise ValueError("step_count_key and step_count_keys mismatch.") - step_count_keys = [step_count_key] if isinstance(step_count_keys, (str, tuple)): step_count_keys = [step_count_keys] self.max_steps = max_steps - self._truncated_keys = truncated_keys - self.truncated_key = ( - truncated_keys[0] if truncated_keys and len(truncated_keys) == 1 else None - ) - self._step_count_keys = step_count_keys - self.step_count_key = ( - step_count_keys[0] - if step_count_keys and len(step_count_keys) == 1 - else None - ) - self._done_keys = done_keys + self.truncated_key = truncated_key + self._step_count_keys = None + self.step_count_key = step_count_key + self._done_keys = None self.write_stop = write_stop - self.done_key = done_keys[0] if done_keys and len(done_keys) == 1 else None + self._stop_keys = None super().__init__([]) @property @@ -4399,16 +4366,30 @@ def truncated_keys(self): if truncated_keys is None: # make the default truncated keys truncated_keys = [] - for done_key in self.done_keys: + for (done_key, *_) in self.parent.done_keys_groups: if isinstance(done_key, str): - key = "truncated" + key = self.truncated_key else: - key = (*done_key[:-1], "truncated") - if key not in truncated_keys: - truncated_keys.append(key) - truncated_keys = self._truncated_keys = truncated_keys + key = (*done_key[:-1], self.truncated_key) + truncated_keys.append(key) + truncated_keys = self._truncated_keys = truncated_keys return truncated_keys + @property + def stop_keys(self): + stop_keys = self._stop_keys + if stop_keys is None: + # make the default stop keys + stop_keys = [] + for (done_key, *_) in self.parent.done_keys_groups: + if isinstance(done_key, str): + key = "stop" + else: + key = (*done_key[:-1], "stop") + stop_keys.append(key) + stop_keys = self._stop_keys = stop_keys + return stop_keys + @property def done_keys(self): done_keys = self._done_keys @@ -4425,14 +4406,13 @@ def step_count_keys(self): if step_count_keys is None: # make the default step_count keys step_count_keys = [] - for done_key in self.done_keys: + for (done_key, *_) in self.parent.done_keys_groups: if isinstance(done_key, str): - key = "step_count" + key = self.step_count_key else: - key = (*done_key[:-1], "step_count") - if key not in step_count_keys: - step_count_keys.append(key) - step_count_keys = self._step_count_keys = step_count_keys + key = (*done_key[:-1], self.step_count_key) + step_count_keys.append(key) + step_count_keys = self._step_count_keys = step_count_keys return step_count_keys @property @@ -4454,10 +4434,11 @@ def full_done_spec(self): def reset(self, tensordict: TensorDictBase) -> TensorDictBase: # get reset signal - for step_count_key, truncated_key, reset_key, done_list_sorted in zip( + for step_count_key, truncated_key, reset_key, stop_key, done_list_sorted in zip( self.step_count_keys, self.truncated_keys, self.reset_keys, + self.stop_keys, self.done_keys_groups, ): step_count = tensordict.get(step_count_key, default=None) @@ -4481,20 +4462,29 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: tensordict.set(step_count_key, step_count) if self.max_steps is not None: truncated = step_count >= self.max_steps + if self.write_stop: + stop = truncated # we assume no done after reset + tensordict.set(stop_key, truncated) tensordict.set(truncated_key, truncated) return tensordict def _step( self, tensordict: TensorDictBase, next_tensordict: TensorDictBase ) -> TensorDictBase: - for step_count_key, truncated_key in zip( - self.step_count_keys, self.truncated_keys + for step_count_key, truncated_key, stop_key, done_keys in zip( + self.step_count_keys, self.truncated_keys, self.stop_keys, self.done_keys_groups, ): step_count = tensordict.get(step_count_key) next_step_count = step_count + 1 next_tensordict.set(step_count_key, next_step_count) if self.max_steps is not None: truncated = next_step_count >= self.max_steps + if self.write_stop: + any_done = False + for done_key in done_keys: + any_done = any_done | tensordict.get(done_key, default=False) + stop = truncated | any_done# we assume no done after reset + tensordict.set(stop_key, truncated) next_tensordict.set(truncated_key, truncated) return next_tensordict @@ -4560,6 +4550,30 @@ def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: 2, dtype=torch.bool, device=output_spec.device, shape=shape ) output_spec["full_done_spec"] = full_done_spec + if self.write_stop: + for stop_key in self.stop_keys: + stop_key = unravel_key(stop_key) + # find a matching done key (there might be more than one) + for done_key in self.done_keys: + # check root + if type(done_key) != type(stop_key): + continue + if isinstance(done_key, tuple): + if done_key[:-1] == stop_key[:-1]: + shape = full_done_spec[done_key].shape + break + if isinstance(done_key, str): + shape = full_done_spec[done_key].shape + break + + else: + raise KeyError( + f"Could not find root of stop_key {stop_key} in done keys {self.done_keys}." + ) + full_done_spec[stop_key] = DiscreteTensorSpec( + 2, dtype=torch.bool, device=output_spec.device, shape=shape + ) + output_spec["full_done_spec"] = full_done_spec output_spec["full_observation_spec"] = self.transform_observation_spec( output_spec["full_observation_spec"] ) @@ -5703,25 +5717,26 @@ def _step( self, tensordict: TensorDictBase, next_tensordict: TensorDictBase ) -> TensorDictBase: # save the final info - done = False + stop = False + # TODO: check if there's a done, and if there is, get it for done_key in self.done_keys: - done = done | next_tensordict.get(done_key) - if done is False: + stop = stop | next_tensordict.get(done_key) + if stop is False: raise RuntimeError( f"Could not find any done signal in tensordict:\n{tensordict}" ) - self._memo["done"] = done + self._memo["stop"] = stop final = next_tensordict.pop(self.final_name, None) # if anything's done, we need to swap the final obs - if done.any(): - done = done.squeeze(-1) + if stop.any(): + stop = stop.squeeze(-1) if final is not None: saved_next = next_tensordict.select(*final.keys(True, True)).clone() - next_tensordict[done] = final[done] + next_tensordict[stop] = final[stop] else: saved_next = next_tensordict.select(*self.obs_keys).clone() for obs_key in self.obs_keys: - next_tensordict[obs_key][done] = torch.tensor(np.nan) + next_tensordict[obs_key][stop] = torch.tensor(np.nan) self._memo["saved_next"] = saved_next else: @@ -5729,24 +5744,24 @@ def _step( return next_tensordict def reset(self, tensordict: TensorDictBase) -> TensorDictBase: - done = self._memo.get("done", None) - reset = tensordict.get("_reset", done) - if done is not None: - done = done.view_as(reset) + stop = self._memo.get("stop", None) + reset = tensordict.get("_reset", stop) + if stop is not None: + stop = stop.view_as(reset) if ( - reset is not done - and (reset != done).any() + reset is not stop + and (reset != stop).any() and (not reset.all() or not reset.any()) ): raise RuntimeError( "Cannot partially reset a gym(nasium) async env with a reset mask that does not match the done mask. " - f"Got reset={reset}\nand done={done}" + f"Got reset={reset}\nand done={stop}" ) # if not reset.any(), we don't need to do anything. # if reset.all(), we don't either (bc GymWrapper will call a plain reset). if reset is not None and reset.any() and not reset.all(): - saved_done = self._memo["saved_next"] - reset = reset.view(tensordict.shape) + saved_next = self._memo["saved_next"] + # reset = reset.view(tensordict.shape) # we have a data container from the previous call to step # that contains part of the observation we need. # We can safely place them back in the reset result tensordict: @@ -5756,19 +5771,19 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: # are properly set. # collectors even take care of doing an extra masking so it's even # safer. - tensordict.update(saved_done) + tensordict.update(saved_next) for done_key in self.done_keys: # Make sure that all done are False - done = tensordict.get(done_key, None) - if done is not None: - done = done.clone().fill_(0) + stop = tensordict.get(done_key, None) + if stop is not None: + stop = stop.clone().fill_(0) else: - done = torch.zeros( + stop = torch.zeros( (*tensordict.batch_size, 1), device=tensordict.device, dtype=torch.bool, ) - tensordict.set(done_key, done) + tensordict.set(done_key, stop) tensordict.pop(self.final_name, None) return tensordict @@ -5778,7 +5793,7 @@ def done_keys(self) -> List[NestedKey]: if keys is None: keys = self.parent.done_keys self._done_keys = keys - expected_done_keys = {"done", "truncated"} + expected_done_keys = {"done", "truncated", "stop"} # put this check for now. We can consider relaxing that later # and allow nested values, though they will still need to be unique. for done_key in keys: From 6aa0c9edec7d6281217c9bf6eced5fc98d131f40 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sat, 23 Sep 2023 02:32:58 -0400 Subject: [PATCH 086/178] amend --- test/test_transforms.py | 51 ++++++++++++++-- torchrl/envs/gym_like.py | 11 +++- torchrl/envs/transforms/transforms.py | 86 +++++++++++++-------------- 3 files changed, 99 insertions(+), 49 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index 520d5c00e46..78eecb55418 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -59,6 +59,7 @@ Compose, DeviceCastTransform, DiscreteActionProjection, + DMControlEnv, DoubleToFloat, EnvBase, EnvCreator, @@ -95,6 +96,7 @@ VC1Transform, VIPTransform, ) +from torchrl.envs.libs.dm_control import _has_dm_control from torchrl.envs.libs.gym import _has_gym, GymEnv from torchrl.envs.transforms import VecNorm from torchrl.envs.transforms.r3m import _R3MNet @@ -1383,9 +1385,30 @@ def test_r3m_spec_against_real(self, model, tensor_pixels_key, device): class TestStepCounter(TransformBase): - def test_single_trans_env_check(self): - env = TransformedEnv(ContinuousActionVecMockEnv(), StepCounter(max_steps=10)) + @pytest.mark.skipif(not _has_gym, reason="no gym detected") + def test_step_count_gym(self): + env = TransformedEnv(GymEnv(PENDULUM_VERSIONED), StepCounter(max_steps=30)) + env.rollout(1000) + check_env_specs(env) + + @pytest.mark.skipif(not _has_dm_control, reason="no dm_control detected") + def test_step_count_gym(self): + env = TransformedEnv(DMControlEnv("cheetah", "run"), StepCounter(max_steps=30)) + env.rollout(1000) + check_env_specs(env) + + @pytest.mark.parametrize("write_stop", [False, True]) + @pytest.mark.parametrize("max_steps", [10, None]) + def test_single_trans_env_check(self, write_stop, max_steps): + env = TransformedEnv( + ContinuousActionVecMockEnv(), + StepCounter(max_steps=max_steps, write_stop=write_stop), + ) check_env_specs(env) + if write_stop and max_steps: + assert "stop" in env.full_done_spec.keys() + else: + assert not "stop" in env.full_done_spec.keys() def test_parallel_trans_env_check(self): def make_env(): @@ -1430,13 +1453,11 @@ def test_nested( policy = CountingEnvCountPolicy( action_spec=env.action_spec, action_key=env.action_key ) - if nested_done: - step_key = (*env.done_key[:-1], step_key) transformed_env = TransformedEnv( env, StepCounter( max_steps=max_steps, - step_count_keys=[step_key], + step_count_key=step_key, ), ) td = transformed_env.rollout( @@ -8086,6 +8107,26 @@ def test_transform_inverse(self, create_copy): class TestInitTracker(TransformBase): + @pytest.mark.skipif(not _has_gym, reason="no gym detected") + def test_init_gym( + self, + ): + env = TransformedEnv( + GymEnv(PENDULUM_VERSIONED), + Compose(StepCounter(max_steps=30), InitTracker()), + ) + env.rollout(1000) + check_env_specs(env) + + @pytest.mark.skipif(not _has_dm_control, reason="no dm_control detected") + def test_init_gym(self): + env = TransformedEnv( + DMControlEnv("cheetah", "run"), + Compose(StepCounter(max_steps=30), InitTracker()), + ) + env.rollout(1000) + check_env_specs(env) + def test_single_trans_env_check(self): env = CountingBatchedEnv(max_steps=torch.tensor([4, 5]), batch_size=[2]) env = TransformedEnv(env, InitTracker()) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index 4dcd16d44c5..bdaecbed27c 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -293,7 +293,16 @@ def _reset( return tensordict_out @abc.abstractmethod - def _output_transform(self, step_outputs_tuple: Tuple) -> Tuple[Any, float | np.ndarray, bool, bool | np.ndarray | None, bool | np.ndarray | None, dict]: + def _output_transform( + self, step_outputs_tuple: Tuple + ) -> Tuple[ + Any, + float | np.ndarray, + bool, + bool | np.ndarray | None, + bool | np.ndarray | None, + dict, + ]: """A method to read the output of the env step. Must return a tuple: (obs, reward, done, truncated, stop, info). diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 1c8437250ae..db155ce2121 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -4342,27 +4342,25 @@ class StepCounter(Transform): def __init__( self, max_steps: Optional[int] = None, - truncated_key: str | None = None, - step_count_key: str | None=None, - write_stop: bool | None=False, + truncated_key: str | None = "truncated", + step_count_key: str | None = "step_count", + write_stop: bool | None = False, ): if max_steps is not None and max_steps < 1: raise ValueError("max_steps should have a value greater or equal to one.") - if isinstance(step_count_keys, (str, tuple)): - step_count_keys = [step_count_keys] - + if not isinstance(truncated_key, str): + raise ValueError("truncated_key must be a string.") + if not isinstance(step_count_key, str): + raise ValueError("step_count_key must be a string.") self.max_steps = max_steps self.truncated_key = truncated_key - self._step_count_keys = None self.step_count_key = step_count_key - self._done_keys = None self.write_stop = write_stop - self._stop_keys = None super().__init__([]) @property def truncated_keys(self): - truncated_keys = self._truncated_keys + truncated_keys = self.__dict__.get("_truncated_keys", None) if truncated_keys is None: # make the default truncated keys truncated_keys = [] @@ -4372,12 +4370,12 @@ def truncated_keys(self): else: key = (*done_key[:-1], self.truncated_key) truncated_keys.append(key) - truncated_keys = self._truncated_keys = truncated_keys + self._truncated_keys = truncated_keys return truncated_keys @property def stop_keys(self): - stop_keys = self._stop_keys + stop_keys = self.__dict__.get("_stop_keys", None) if stop_keys is None: # make the default stop keys stop_keys = [] @@ -4387,12 +4385,12 @@ def stop_keys(self): else: key = (*done_key[:-1], "stop") stop_keys.append(key) - stop_keys = self._stop_keys = stop_keys + self._stop_keys = stop_keys return stop_keys @property def done_keys(self): - done_keys = self._done_keys + done_keys = self.__dict__.get("_done_keys", None) if done_keys is None: if self.parent is not None: self._done_keys = done_keys = self.parent.done_keys @@ -4402,7 +4400,7 @@ def done_keys(self): @property def step_count_keys(self): - step_count_keys = self._step_count_keys + step_count_keys = self.__dict__.get("_step_count_keys", None) if step_count_keys is None: # make the default step_count keys step_count_keys = [] @@ -4412,7 +4410,7 @@ def step_count_keys(self): else: key = (*done_key[:-1], self.step_count_key) step_count_keys.append(key) - step_count_keys = self._step_count_keys = step_count_keys + self._step_count_keys = step_count_keys return step_count_keys @property @@ -4463,8 +4461,8 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: if self.max_steps is not None: truncated = step_count >= self.max_steps if self.write_stop: - stop = truncated # we assume no done after reset - tensordict.set(stop_key, truncated) + stop = truncated # we assume no done after reset + tensordict.set(stop_key, stop) tensordict.set(truncated_key, truncated) return tensordict @@ -4472,7 +4470,10 @@ def _step( self, tensordict: TensorDictBase, next_tensordict: TensorDictBase ) -> TensorDictBase: for step_count_key, truncated_key, stop_key, done_keys in zip( - self.step_count_keys, self.truncated_keys, self.stop_keys, self.done_keys_groups, + self.step_count_keys, + self.truncated_keys, + self.stop_keys, + self.done_keys_groups, ): step_count = tensordict.get(step_count_key) next_step_count = step_count + 1 @@ -4482,9 +4483,9 @@ def _step( if self.write_stop: any_done = False for done_key in done_keys: - any_done = any_done | tensordict.get(done_key, default=False) - stop = truncated | any_done# we assume no done after reset - tensordict.set(stop_key, truncated) + any_done = any_done | next_tensordict.get(done_key, default=False) + stop = truncated | any_done # we assume no done after reset + next_tensordict.set(stop_key, stop) next_tensordict.set(truncated_key, truncated) return next_tensordict @@ -4549,31 +4550,30 @@ def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: full_done_spec[truncated_key] = DiscreteTensorSpec( 2, dtype=torch.bool, device=output_spec.device, shape=shape ) - output_spec["full_done_spec"] = full_done_spec - if self.write_stop: - for stop_key in self.stop_keys: - stop_key = unravel_key(stop_key) - # find a matching done key (there might be more than one) - for done_key in self.done_keys: - # check root - if type(done_key) != type(stop_key): - continue - if isinstance(done_key, tuple): - if done_key[:-1] == stop_key[:-1]: + if self.write_stop: + for stop_key in self.stop_keys: + stop_key = unravel_key(stop_key) + # find a matching done key (there might be more than one) + for done_key in self.done_keys: + # check root + if type(done_key) != type(stop_key): + continue + if isinstance(done_key, tuple): + if done_key[:-1] == stop_key[:-1]: + shape = full_done_spec[done_key].shape + break + if isinstance(done_key, str): shape = full_done_spec[done_key].shape break - if isinstance(done_key, str): - shape = full_done_spec[done_key].shape - break - else: - raise KeyError( - f"Could not find root of stop_key {stop_key} in done keys {self.done_keys}." + else: + raise KeyError( + f"Could not find root of stop_key {stop_key} in done keys {self.done_keys}." + ) + full_done_spec[stop_key] = DiscreteTensorSpec( + 2, dtype=torch.bool, device=output_spec.device, shape=shape ) - full_done_spec[stop_key] = DiscreteTensorSpec( - 2, dtype=torch.bool, device=output_spec.device, shape=shape - ) - output_spec["full_done_spec"] = full_done_spec + output_spec["full_done_spec"] = full_done_spec output_spec["full_observation_spec"] = self.transform_observation_spec( output_spec["full_observation_spec"] ) From 2b420701a68877664f0df0a203540cb4a2fe41d5 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sat, 23 Sep 2023 03:16:43 -0400 Subject: [PATCH 087/178] amend --- test/test_transforms.py | 1 + 1 file changed, 1 insertion(+) diff --git a/test/test_transforms.py b/test/test_transforms.py index 78eecb55418..7bf44ea364e 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1460,6 +1460,7 @@ def test_nested( step_count_key=step_key, ), ) + step_key = transformed_env.transform.step_count_keys[0] td = transformed_env.rollout( rollout_length, policy=policy, break_when_any_done=False ) From 7279b1207103c3f57e7e470c9290e29e9b17b73f Mon Sep 17 00:00:00 2001 From: vmoens Date: Sat, 23 Sep 2023 03:26:51 -0400 Subject: [PATCH 088/178] amend --- test/test_transforms.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index 7bf44ea364e..cad670e7d7c 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1518,7 +1518,11 @@ def test_transform_compose(self, max_steps, device, batch, reset_workers): td.set("_reset", _reset) td.set("done", _reset) td.set(("next", "done"), done) - + step_counter[0]._step_count_keys = ["step_count"] + step_counter[0]._done_keys = ["done"] + step_counter[0]._truncated_keys = ["truncated"] + step_counter[0]._reset_keys = ["_reset"] + step_counter[0]._stop_keys = ["stop"] td = step_counter.reset(td) assert not torch.all(td.get("step_count")) i = 0 @@ -1572,6 +1576,11 @@ def test_transform_no_env(self, max_steps, device, batch, reset_workers): td.set("_reset", _reset) td.set("done", _reset) td.set(("next", "done"), done) + step_counter._step_count_keys = ["step_count"] + step_counter._done_keys = ["done"] + step_counter._truncated_keys = ["truncated"] + step_counter._reset_keys = ["_reset"] + step_counter._stop_keys = ["stop"] td = step_counter.reset(td) assert not torch.all(td.get("step_count")) From f5ab14d05dfcb96d8292f7c5ba9e08b31e82ca9c Mon Sep 17 00:00:00 2001 From: vmoens Date: Sat, 23 Sep 2023 07:52:57 -0400 Subject: [PATCH 089/178] amend --- test/test_transforms.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index cad670e7d7c..eed986fe502 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -6317,8 +6317,8 @@ def test_transform_env(self): @pytest.mark.skipif(not _has_tv, reason="torchvision not installed") -@pytest.mark.skipif(not torch.cuda.device_count(), reason="Testing VIP on cuda only") -@pytest.mark.parametrize("device", [torch.device("cuda:0")]) +# @pytest.mark.skipif(not torch.cuda.device_count(), reason="Testing VIP on cuda only") +@pytest.mark.parametrize("device", [torch.device("cpu")]) @pytest.mark.parametrize("model", ["resnet50"]) class TestVIP(TransformBase): def test_transform_inverse(self, model, device): @@ -6633,7 +6633,7 @@ def test_vip_parallel_reward(self, model, device, dtype_fixture): # noqa _ = transformed_env.reset() with pytest.raises( KeyError, - match=r"VIPRewardTransform.* requires .* key to be present in the input tensordict", + match=r"observation_spec", ): _ = transformed_env.reset(tensordict_reset.select()) From 4a9b6b9c46a32bc36e6ac24bc4339f648c649e44 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sat, 23 Sep 2023 08:02:22 -0400 Subject: [PATCH 090/178] amend --- docs/source/reference/envs.rst | 36 +++++++++++++++++++++++----------- 1 file changed, 25 insertions(+), 11 deletions(-) diff --git a/docs/source/reference/envs.rst b/docs/source/reference/envs.rst index c49e12fc2b0..d60eeb8a461 100644 --- a/docs/source/reference/envs.rst +++ b/docs/source/reference/envs.rst @@ -36,7 +36,7 @@ Each env will have the following attributes: - :obj:`env.reward_spec`: a :class:`~torchrl.data.TensorSpec` object representing the reward spec. - :obj:`env.done_spec`: a :class:`~torchrl.data.TensorSpec` object representing - the done-flag spec. + the done-flag spec. See the section on trajectory termination below. - :obj:`env.input_spec`: a :class:`~torchrl.data.CompositeSpec` object containing all the input keys (:obj:`"full_action_spec"` and :obj:`"full_state_spec"`). It is locked and should not be modified directly. @@ -79,7 +79,7 @@ The following figure summarizes how a rollout is executed in torchrl. In brief, a TensorDict is created by the :meth:`~.EnvBase.reset` method, then populated with an action by the policy before being passed to the -:meth:`~.EnvBase.step` method which writes the observations, done flag and +:meth:`~.EnvBase.step` method which writes the observations, done flag(s) and reward under the ``"next"`` entry. The result of this call is stored for delivery and the ``"next"`` entry is gathered by the :func:`~.utils.step_mdp` function. @@ -87,12 +87,25 @@ function. .. note:: The Gym(nasium) API recently shifted to a splitting of the ``"done"`` state - into a ``terminated`` (the env is done and results should not be trusted) - and ``truncated`` (the maximum number of steps is reached) flags. - In TorchRL, ``"done"`` usually refers to ``"terminated"``. Truncation is - achieved via the :class:`~.StepCounter` transform class, and the output - key will be ``"truncated"`` if not chosen to be something else (e.g. - ``StepCounter(max_steps=100, truncated_key="done")``). + into a ``termination`` (the env is done and results should not be trusted) + and ``truncation`` (the maximum number of steps is reached) flags. + In TorchRL, ``"done"`` strictly refers to ``"termination"``. + If the environment provides it (eg, Gymnasium), the truncation entry is also + updated. In this case, a ``"stop"`` entry can also be provided, which represents + the union of ``"done"`` (task completion) and ``"truncated"`` (task interruption). + If the environment carries a single value, it will interpreted as a completion + (``"done"``) signal by default (and not ``"stop"`` or ``"truncated"``). The + reason for this choice is that without any more information, one has to assume + task completion for follow-up tasks (eg, computing value estimations), so any + unspecified end-of-trajectory signal will eventually need to be interpreted + with a worst-case-scenario strategy. Another motivation is that a ``"stop"`` + signal will be modified by a ``"truncation"`` implemented, for instance, via + a :class:`~.StepCounter` transform, and the original ``"stop"`` signal + will be lost. + + Truncation can also be achieved via the :class:`~.StepCounter` transform + class, and the output key will be ``"truncated"`` if not chosen to be + something else (e.g. ``StepCounter(max_steps=100, truncated_key="interrupted")``). TorchRL's collectors and rollout methods will be looking for one of these keys when assessing if the env should be reset. @@ -172,12 +185,13 @@ It is also possible to reset some but not all of the environments: :caption: Parallel environment reset >>> tensordict = TensorDict({"_reset": [[True], [False], [True], [True]]}, [4]) - >>> env.reset(tensordict) + >>> env.reset(tensordict) # eliminates the "_reset" entry TensorDict( fields={ done: Tensor(torch.Size([4, 1]), dtype=torch.bool), + truncated: Tensor(torch.Size([4, 1]), dtype=torch.bool), + stop: Tensor(torch.Size([4, 1]), dtype=torch.bool), pixels: Tensor(torch.Size([4, 500, 500, 3]), dtype=torch.uint8), - _reset: Tensor(torch.Size([4, 1]), dtype=torch.bool)}, batch_size=torch.Size([4]), device=None, is_shared=True) @@ -238,7 +252,7 @@ Some of the main differences between these paradigms include: - **observation** can be per-agent and also have some shared components - **reward** can be per-agent or shared -- **done** can be per-agent or shared +- **done** (and ``"truncated"`` or ``"stop"``) can be per-agent or shared TorchRL accommodates all these possible paradigms thanks to its :class:`tensordict.TensorDict` data carrier. In particular, in multi-agent environments, per-agent keys will be carried in a nested "agents" TensorDict. From 696324b76b9b6b7051f388db4f0ffa5c6da806d0 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sat, 23 Sep 2023 08:05:33 -0400 Subject: [PATCH 091/178] amend --- docs/source/reference/envs.rst | 2 +- torchrl/envs/libs/dm_control.py | 15 +++++++++++---- 2 files changed, 12 insertions(+), 5 deletions(-) diff --git a/docs/source/reference/envs.rst b/docs/source/reference/envs.rst index d60eeb8a461..f6154650b6d 100644 --- a/docs/source/reference/envs.rst +++ b/docs/source/reference/envs.rst @@ -252,7 +252,7 @@ Some of the main differences between these paradigms include: - **observation** can be per-agent and also have some shared components - **reward** can be per-agent or shared -- **done** (and ``"truncated"`` or ``"stop"``) can be per-agent or shared +- **done** (and ``"truncated"`` or ``"stop"``) can be per-agent or shared. TorchRL accommodates all these possible paradigms thanks to its :class:`tensordict.TensorDict` data carrier. In particular, in multi-agent environments, per-agent keys will be carried in a nested "agents" TensorDict. diff --git a/torchrl/envs/libs/dm_control.py b/torchrl/envs/libs/dm_control.py index 71f66b8efa0..9774acaac26 100644 --- a/torchrl/envs/libs/dm_control.py +++ b/torchrl/envs/libs/dm_control.py @@ -20,7 +20,7 @@ CompositeSpec, TensorSpec, UnboundedContinuousTensorSpec, - UnboundedDiscreteTensorSpec, + UnboundedDiscreteTensorSpec, DiscreteTensorSpec, ) from torchrl.data.utils import DEVICE_TYPING, numpy_to_torch_dtype_dict @@ -181,7 +181,15 @@ def _make_specs(self, env: "gym.Env") -> None: # noqa: F821 reward_spec.shape = torch.Size([1]) self.reward_spec = reward_spec # populate default done spec - _ = self.done_spec + done_spec = DiscreteTensorSpec( + n=2, shape=(*self.batch_size, 1), dtype=torch.bool, device=self.device + ) + self.done_spec = CompositeSpec( + done=done_spec.clone(), + terminated=done_spec.clone(), + stop=done_spec.clone(), + device=self.device, + ) self.action_spec = _dmcontrol_to_torchrl_spec_transform( self._env.action_spec(), device=self.device ) @@ -240,9 +248,8 @@ def _output_transform( timestep_tuple = (timestep_tuple,) reward = timestep_tuple[0].reward - done = False # dm_control envs are non-terminating + truncated = terminated = done = False # dm_control envs are non-terminating observation = timestep_tuple[0].observation - truncated = terminated = None info = {} return observation, reward, done, truncated, terminated, info From 88909114176874f08496ef5c50614669bbb081ce Mon Sep 17 00:00:00 2001 From: Vincent Moens Date: Sun, 24 Sep 2023 06:11:33 +0100 Subject: [PATCH 092/178] Update docs/source/reference/envs.rst Co-authored-by: Skander Moalla <37197319+skandermoalla@users.noreply.github.com> --- docs/source/reference/envs.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/reference/envs.rst b/docs/source/reference/envs.rst index f6154650b6d..81a228b38a3 100644 --- a/docs/source/reference/envs.rst +++ b/docs/source/reference/envs.rst @@ -88,7 +88,7 @@ function. The Gym(nasium) API recently shifted to a splitting of the ``"done"`` state into a ``termination`` (the env is done and results should not be trusted) - and ``truncation`` (the maximum number of steps is reached) flags. + and ``truncation`` (an external limit on the number of steps is reached) flags. In TorchRL, ``"done"`` strictly refers to ``"termination"``. If the environment provides it (eg, Gymnasium), the truncation entry is also updated. In this case, a ``"stop"`` entry can also be provided, which represents From e24c2f367cf4b113cc6f83107bbd6dfaf726d954 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 24 Sep 2023 01:16:29 -0400 Subject: [PATCH 093/178] add doc --- docs/source/reference/envs.rst | 17 ++++++++++++++--- torchrl/envs/libs/gym.py | 23 +++++++++++++---------- 2 files changed, 27 insertions(+), 13 deletions(-) diff --git a/docs/source/reference/envs.rst b/docs/source/reference/envs.rst index 81a228b38a3..f586b59497c 100644 --- a/docs/source/reference/envs.rst +++ b/docs/source/reference/envs.rst @@ -99,9 +99,20 @@ function. task completion for follow-up tasks (eg, computing value estimations), so any unspecified end-of-trajectory signal will eventually need to be interpreted with a worst-case-scenario strategy. Another motivation is that a ``"stop"`` - signal will be modified by a ``"truncation"`` implemented, for instance, via - a :class:`~.StepCounter` transform, and the original ``"stop"`` signal - will be lost. + signal returned directly by the env during a ``_step`` call will be modified + by a ``"truncation"`` implemented further down the line. For instance, + using a :class:`~.StepCounter` transform will irreversibly change the original + ``"stop"`` signal: + + >>> class StepCounter(transform): + ... def _call(self, tensordict, next_tensordict): + ... [...] + ... truncated = step_count > self.max_steps + ... # the following line overrides the stop: this information is lost + ... # and cannot be recovered + ... stop = stop | truncated + + Using ``"done"`` instead will make it robust to these additions. Truncation can also be achieved via the :class:`~.StepCounter` transform class, and the output key will be ``"truncated"`` if not chosen to be diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index 74f3af4be13..f0189355cdd 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -806,7 +806,8 @@ def _output_transform(self, step_outputs_tuple): # noqa: F811 if self._is_batched: # info needs to be flipped info = _flip_info_tuple(info) - # a done is interpreted as terminal in earlier versions of gym. + # The variable naming follow's torchrl's convention here. + # A done is interpreted as terminal in earlier versions of gym. # This isn't optimal, but it is the most natural option. # Interpreting this as a "stop" would break in value functions because # we can't decide if the env is done (ie, game over) or truncated. @@ -817,7 +818,8 @@ def _output_transform(self, step_outputs_tuple): # noqa: F811 @implement_for("gym", "0.24", "0.26") def _output_transform(self, step_outputs_tuple): # noqa: F811 - # a done is interpreted as terminal in earlier versions of gym. + # The variable naming follow's torchrl's convention here. + # A done is interpreted as terminal in earlier versions of gym. # This isn't optimal, but it is the most natural option. # Interpreting this as a "stop" would break in value functions because # we can't decide if the env is done (ie, game over) or truncated. @@ -829,25 +831,26 @@ def _output_transform(self, step_outputs_tuple): # noqa: F811 @implement_for("gym", "0.26", None) def _output_transform(self, step_outputs_tuple): # noqa: F811 - observations, reward, termination, truncation, info = step_outputs_tuple + # The variable naming follow's torchrl's convention here. + observations, reward, done, truncated, info = step_outputs_tuple return ( observations, reward, - termination, # torchrl done - truncation, # torchrl truncated - termination | truncation, # torchrl stop + done, + truncated, # torchrl truncated + done | truncated, # torchrl stop info, ) @implement_for("gymnasium", "0.27", None) def _output_transform(self, step_outputs_tuple): # noqa: F811 - observations, reward, termination, truncation, info = step_outputs_tuple + observations, reward, done, truncated, info = step_outputs_tuple return ( observations, reward, - termination, # torchrl done - truncation, # torchrl truncated - termination | truncation, # torchrl stop + done, + truncated, # torchrl truncated + done | truncated, # torchrl stop info, ) From c029f12efe17e65172194c1ece700d73ed334a80 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 24 Sep 2023 01:26:36 -0400 Subject: [PATCH 094/178] amend --- docs/source/reference/envs.rst | 1 + test/test_transforms.py | 6 +-- torchrl/collectors/collectors.py | 8 ++-- torchrl/envs/gym_like.py | 2 +- torchrl/envs/libs/dm_control.py | 13 +++--- torchrl/envs/transforms/transforms.py | 4 +- torchrl/envs/utils.py | 64 ++++++++++++++++----------- 7 files changed, 56 insertions(+), 42 deletions(-) diff --git a/docs/source/reference/envs.rst b/docs/source/reference/envs.rst index f586b59497c..90ecb54f84e 100644 --- a/docs/source/reference/envs.rst +++ b/docs/source/reference/envs.rst @@ -611,6 +611,7 @@ Helpers exploration_type check_env_specs make_composite_from_td + done_or_truncated Domain-specific --------------- diff --git a/test/test_transforms.py b/test/test_transforms.py index eed986fe502..b3e2953725a 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1392,7 +1392,7 @@ def test_step_count_gym(self): check_env_specs(env) @pytest.mark.skipif(not _has_dm_control, reason="no dm_control detected") - def test_step_count_gym(self): + def test_step_count_dmc(self): env = TransformedEnv(DMControlEnv("cheetah", "run"), StepCounter(max_steps=30)) env.rollout(1000) check_env_specs(env) @@ -1408,7 +1408,7 @@ def test_single_trans_env_check(self, write_stop, max_steps): if write_stop and max_steps: assert "stop" in env.full_done_spec.keys() else: - assert not "stop" in env.full_done_spec.keys() + assert "stop" not in env.full_done_spec.keys() def test_parallel_trans_env_check(self): def make_env(): @@ -8129,7 +8129,7 @@ def test_init_gym( check_env_specs(env) @pytest.mark.skipif(not _has_dm_control, reason="no dm_control detected") - def test_init_gym(self): + def test_init_dmc(self): env = TransformedEnv( DMControlEnv("cheetah", "run"), Compose(StepCounter(max_steps=30), InitTracker()), diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 2bd26f234fe..128f5dfff99 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -811,20 +811,20 @@ def _step_and_maybe_reset(self) -> None: traj_ids = traj_ids.clone() # collectors do not support passing other tensors than `"_reset"` # to `reset()`. - traj_done_or_terminated = _bring_reset_to_root(td_reset) + traj_sop = _bring_reset_to_root(td_reset) td_reset = self.env.reset(td_reset) if td_reset.batch_dims: # better cloning here than when passing the td for stacking # cloning is necessary to avoid modifying entries in-place self._tensordict = torch.where( - traj_done_or_terminated, td_reset, self._tensordict + traj_sop, td_reset, self._tensordict ) else: self._tensordict.update(td_reset) - traj_ids[traj_done_or_terminated] = traj_ids.max() + torch.arange( - 1, traj_done_or_terminated.sum() + 1, device=traj_ids.device + traj_ids[traj_sop] = traj_ids.max() + torch.arange( + 1, traj_sop.sum() + 1, device=traj_ids.device ) self._tensordict.set(("collector", "traj_ids"), traj_ids) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index bdaecbed27c..569998a76b1 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -141,7 +141,7 @@ def read_done( ) -> Tuple[bool | np.ndarray, bool | np.ndarray, bool | np.ndarray, bool]: """Done state reader. - In torchrl, a `"done"` signal means that a trajectory is terminated + In torchrl, a `"done"` signal means that a trajectory is completed (what is referred to as `termination` in gymnasium). Truncated means the trajectory has been interrupted. diff --git a/torchrl/envs/libs/dm_control.py b/torchrl/envs/libs/dm_control.py index 9774acaac26..029d7f51e49 100644 --- a/torchrl/envs/libs/dm_control.py +++ b/torchrl/envs/libs/dm_control.py @@ -18,9 +18,10 @@ from torchrl.data.tensor_specs import ( BoundedTensorSpec, CompositeSpec, + DiscreteTensorSpec, TensorSpec, UnboundedContinuousTensorSpec, - UnboundedDiscreteTensorSpec, DiscreteTensorSpec, + UnboundedDiscreteTensorSpec, ) from torchrl.data.utils import DEVICE_TYPING, numpy_to_torch_dtype_dict @@ -182,11 +183,11 @@ def _make_specs(self, env: "gym.Env") -> None: # noqa: F821 self.reward_spec = reward_spec # populate default done spec done_spec = DiscreteTensorSpec( - n=2, shape=(*self.batch_size, 1), dtype=torch.bool, device=self.device - ) + n=2, shape=(*self.batch_size, 1), dtype=torch.bool, device=self.device + ) self.done_spec = CompositeSpec( done=done_spec.clone(), - terminated=done_spec.clone(), + truncated=done_spec.clone(), stop=done_spec.clone(), device=self.device, ) @@ -248,11 +249,11 @@ def _output_transform( timestep_tuple = (timestep_tuple,) reward = timestep_tuple[0].reward - truncated = terminated = done = False # dm_control envs are non-terminating + done = truncated = stop = False # dm_control envs are non-terminating observation = timestep_tuple[0].observation info = {} - return observation, reward, done, truncated, terminated, info + return observation, reward, done, truncated, stop, info def _reset_output_transform(self, reset_data): ( diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index db155ce2121..cb47df9abd4 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -4483,7 +4483,9 @@ def _step( if self.write_stop: any_done = False for done_key in done_keys: - any_done = any_done | next_tensordict.get(done_key, default=False) + any_done = any_done | next_tensordict.get( + done_key, default=False + ) stop = truncated | any_done # we assume no done after reset next_tensordict.set(stop_key, stop) next_tensordict.set(truncated_key, truncated) diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 9635062f56d..65b431ef467 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -738,14 +738,15 @@ def done_or_truncated( """Reads the done / truncated keys within a tensordict, and writes a new tensor where the values of both signals are aggregated. The modification occurs in-place within the TensorDict instance provided. - This function can be used to compute the `"_reset"` signal in batched + This function can be used to compute the `"_reset"` signals in batched or multiagent settings, hence the default name of the output key. Args: data (TensorDictBase): the input data, generally resulting from a call to :meth:`~torchrl.envs.EnvBase.step`. full_done_spec (TensorSpec, optional): the done_spec from the env, indicating where - the done leaves have to be found. If not provided, the default `"done"` and - `"truncated"` entries will be searched for in the data. + the done leaves have to be found. If not provided, the default + ``"step"``, ``"done"`` and ``"truncated"`` entries will be + searched for in the data. key (NestedKey, optional): where the aggregated result should be written. If ``None``, then the function will not write any key but just output whether any of the done values was true. @@ -782,56 +783,65 @@ def done_or_truncated( list_of_keys = [] def inner_done_or_truncated(data, full_done_spec, key, curr_done_key=()): - any_done = False + any_eot = False aggregate = None if full_done_spec is None: - for done_key, item in data.items(): - if done_key in ("done", "truncated"): - done = data.get(done_key, None) - if done is None: - done = torch.zeros( + for eot_key, item in data.items(): + if eot_key == "stop": + stop = data.get(eot_key, None) + if stop is None: + stop = torch.zeros( (*data.shape, 1), dtype=torch.bool, device=data.device ) if aggregate is None: - aggregate = torch.tensor(False, device=done.device) - aggregate = aggregate | done + aggregate = torch.tensor(False, device=stop.device) + aggregate = aggregate | stop + elif eot_key in ("done", "truncated"): + stop = data.get(eot_key, None) + if stop is None: + stop = torch.zeros( + (*data.shape, 1), dtype=torch.bool, device=data.device + ) + if aggregate is None: + aggregate = torch.tensor(False, device=stop.device) + aggregate = aggregate | stop elif isinstance(item, TensorDictBase): - any_done = any_done | inner_done_or_truncated( + any_eot = any_eot | inner_done_or_truncated( data=item, full_done_spec=None, key=key, - curr_done_key=curr_done_key + (done_key,), + curr_done_key=curr_done_key + (eot_key,), ) else: - for done_key, item in full_done_spec.items(): + for eot_key, item in full_done_spec.items(): if isinstance(item, CompositeSpec): - any_done = any_done | inner_done_or_truncated( - data=data.get(done_key), + any_eot = any_eot | inner_done_or_truncated( + data=data.get(eot_key), full_done_spec=item, key=key, - curr_done_key=curr_done_key + (done_key,), + curr_done_key=curr_done_key + (eot_key,), ) else: - done = data.get(done_key, None) - if done is None: - done = torch.zeros( + sop = data.get(eot_key, None) + if sop is None: + sop = torch.zeros( (*data.shape, 1), dtype=torch.bool, device=data.device ) if aggregate is None: - aggregate = torch.tensor(False, device=done.device) - aggregate = aggregate | done + aggregate = torch.tensor(False, device=sop.device) + aggregate = aggregate | sop if aggregate is not None: if key is not None: data.set(key, aggregate) list_of_keys.append(curr_done_key + (key,)) - any_done = any_done | aggregate.any() - return any_done + any_eot = any_eot | aggregate.any() + return any_eot - any_done = inner_done_or_truncated(data, full_done_spec, key) - if not any_done and not write_full_false: + any_eot = inner_done_or_truncated(data, full_done_spec, key) + if not any_eot and not write_full_false: # remove the list of reset keys data.exclude(*list_of_keys, inplace=True) - return any_done + return any_eot PARTIAL_MISSING_ERR = "Some reset keys were present but not all. Either all the `'_reset'` entries must be present, or none." From b37129dfc786cfac4bdb0fa8916e4e3550c57e11 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 24 Sep 2023 01:29:44 -0400 Subject: [PATCH 095/178] amend --- test/test_transforms.py | 2 +- torchrl/envs/transforms/vip.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index b3e2953725a..3cebad2406a 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -6633,7 +6633,7 @@ def test_vip_parallel_reward(self, model, device, dtype_fixture): # noqa _ = transformed_env.reset() with pytest.raises( KeyError, - match=r"observation_spec", + match=r"VIPRewardTransform.* requires .* key to be present in the input tensordict", ): _ = transformed_env.reset(tensordict_reset.select()) diff --git a/torchrl/envs/transforms/vip.py b/torchrl/envs/transforms/vip.py index 7188a1bfd7d..fdd60f54228 100644 --- a/torchrl/envs/transforms/vip.py +++ b/torchrl/envs/transforms/vip.py @@ -397,7 +397,7 @@ def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec: state_spec = CompositeSpec(shape=input_spec.shape, device=input_spec.device) # find the obs spec in_key = self.in_keys[0] - spec = self.parent.output_spec["observation_spec"][in_key].clone() + spec = self.parent.output_spec["full_observation_spec"][in_key].clone() state_spec["goal_image"] = spec input_spec["state_spec"] = state_spec return input_spec From 9afc78302e4c219ebf95551acc8ec31e867a23d2 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 24 Sep 2023 01:30:10 -0400 Subject: [PATCH 096/178] amend --- test/test_transforms.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index 3cebad2406a..fb3fc1b910d 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -6317,8 +6317,8 @@ def test_transform_env(self): @pytest.mark.skipif(not _has_tv, reason="torchvision not installed") -# @pytest.mark.skipif(not torch.cuda.device_count(), reason="Testing VIP on cuda only") -@pytest.mark.parametrize("device", [torch.device("cpu")]) +@pytest.mark.skipif(not torch.cuda.device_count(), reason="Testing VIP on cuda only") +@pytest.mark.parametrize("device", [torch.device("cuda:0")]) @pytest.mark.parametrize("model", ["resnet50"]) class TestVIP(TransformBase): def test_transform_inverse(self, model, device): From f65622af37b9f7b8bdb1a3f7a160c3090034f081 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 24 Sep 2023 01:30:40 -0400 Subject: [PATCH 097/178] amend --- test/test_transforms.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index fb3fc1b910d..3cebad2406a 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -6317,8 +6317,8 @@ def test_transform_env(self): @pytest.mark.skipif(not _has_tv, reason="torchvision not installed") -@pytest.mark.skipif(not torch.cuda.device_count(), reason="Testing VIP on cuda only") -@pytest.mark.parametrize("device", [torch.device("cuda:0")]) +# @pytest.mark.skipif(not torch.cuda.device_count(), reason="Testing VIP on cuda only") +@pytest.mark.parametrize("device", [torch.device("cpu")]) @pytest.mark.parametrize("model", ["resnet50"]) class TestVIP(TransformBase): def test_transform_inverse(self, model, device): From 989eecf97f7d2b0920ad33dcfcdfcb0a4b683e6d Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 24 Sep 2023 01:53:05 -0400 Subject: [PATCH 098/178] fix VIP --- test/test_transforms.py | 4 ++-- torchrl/envs/transforms/vip.py | 16 ++++++++-------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index 3cebad2406a..fb3fc1b910d 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -6317,8 +6317,8 @@ def test_transform_env(self): @pytest.mark.skipif(not _has_tv, reason="torchvision not installed") -# @pytest.mark.skipif(not torch.cuda.device_count(), reason="Testing VIP on cuda only") -@pytest.mark.parametrize("device", [torch.device("cpu")]) +@pytest.mark.skipif(not torch.cuda.device_count(), reason="Testing VIP on cuda only") +@pytest.mark.parametrize("device", [torch.device("cuda:0")]) @pytest.mark.parametrize("model", ["resnet50"]) class TestVIP(TransformBase): def test_transform_inverse(self, model, device): diff --git a/torchrl/envs/transforms/vip.py b/torchrl/envs/transforms/vip.py index fdd60f54228..0e8ed19bb05 100644 --- a/torchrl/envs/transforms/vip.py +++ b/torchrl/envs/transforms/vip.py @@ -358,7 +358,7 @@ def _embed_goal(self, tensordict): if "goal_image" not in tensordict.keys(): raise KeyError( f"{self.__class__.__name__}.reset() requires a `'goal_image'` key to be " - f"present in the input tensordict." + f"present in the input tensordict. Got keys {list(tensordict.keys())}." ) tensordict_in = tensordict.select("goal_image").rename_key_( "goal_image", self.in_keys[0] @@ -391,13 +391,13 @@ def forward(self, tensordict): return tensordict def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec: - if "state_spec" in input_spec.keys(): - state_spec = input_spec["state_spec"] + if "full_state_spec" in input_spec.keys(): + full_state_spec = input_spec["full_state_spec"] else: - state_spec = CompositeSpec(shape=input_spec.shape, device=input_spec.device) + full_state_spec = CompositeSpec(shape=input_spec.shape, device=input_spec.device) # find the obs spec in_key = self.in_keys[0] - spec = self.parent.output_spec["full_observation_spec"][in_key].clone() - state_spec["goal_image"] = spec - input_spec["state_spec"] = state_spec - return input_spec + spec = self.parent.output_spec["full_observation_spec"][in_key] + full_state_spec["goal_image"] = spec.clone() + input_spec["full_state_spec"] = full_state_spec + return super().transform_input_spec(input_spec) From 77559e0c6571eaf34a979076dbcd47d22a0f3bde Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 24 Sep 2023 01:57:54 -0400 Subject: [PATCH 099/178] lint --- test/test_libs.py | 7 ++++--- torchrl/collectors/collectors.py | 4 +--- torchrl/envs/transforms/vip.py | 4 +++- 3 files changed, 8 insertions(+), 7 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index 39560c08aa8..ea2a03f8a13 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -1618,10 +1618,11 @@ def make_vmas(): .all() ) - tensordict = env.reset( - TensorDict(rand_reset(env), batch_size=env.batch_size, device=env.device) + td_reset = TensorDict( + rand_reset(env), batch_size=env.batch_size, device=env.device ) - reset = tensordict["_reset"] + reset = td_reset["_reset"] + tensordict = env.reset(td_reset) assert not tensordict["done"][reset].all().item() # vmas resets all the agent dimension if only one of the agents needs resetting # thus, here we check that where we did not reset any agent, all agents are still done diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 128f5dfff99..c3c81f8560b 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -817,9 +817,7 @@ def _step_and_maybe_reset(self) -> None: if td_reset.batch_dims: # better cloning here than when passing the td for stacking # cloning is necessary to avoid modifying entries in-place - self._tensordict = torch.where( - traj_sop, td_reset, self._tensordict - ) + self._tensordict = torch.where(traj_sop, td_reset, self._tensordict) else: self._tensordict.update(td_reset) diff --git a/torchrl/envs/transforms/vip.py b/torchrl/envs/transforms/vip.py index 0e8ed19bb05..7762e2c5900 100644 --- a/torchrl/envs/transforms/vip.py +++ b/torchrl/envs/transforms/vip.py @@ -394,7 +394,9 @@ def transform_input_spec(self, input_spec: TensorSpec) -> TensorSpec: if "full_state_spec" in input_spec.keys(): full_state_spec = input_spec["full_state_spec"] else: - full_state_spec = CompositeSpec(shape=input_spec.shape, device=input_spec.device) + full_state_spec = CompositeSpec( + shape=input_spec.shape, device=input_spec.device + ) # find the obs spec in_key = self.in_keys[0] spec = self.parent.output_spec["full_observation_spec"][in_key] From 117e41e545ef8f8000eeb358a0ee0b0a141c86a7 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 24 Sep 2023 02:00:17 -0400 Subject: [PATCH 100/178] osx_skips --- test/test_collector.py | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/test/test_collector.py b/test/test_collector.py index a55e2407c79..23edaba3a28 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -67,10 +67,10 @@ from torchrl.modules import Actor, LSTMNet, OrnsteinUhlenbeckProcessWrapper, SafeModule # torch.set_default_dtype(torch.double) -_os_is_windows = sys.platform == "win32" -_python_is_3_10 = sys.version_info.major == 3 and sys.version_info.minor == 10 -_python_is_3_7 = sys.version_info.major == 3 and sys.version_info.minor == 7 -_os_is_osx = sys.platform == "darwin" +IS_WINDOWS = sys.platform == "win32" +IS_OSX = sys.platform == "darwin" +PYTHON_3_10 = sys.version_info.major == 3 and sys.version_info.minor == 10 +PYTHON_3_7 = sys.version_info.major == 3 and sys.version_info.minor == 7 class WrappablePolicy(nn.Module): @@ -178,7 +178,7 @@ def _is_consistent_device_type( @pytest.mark.skipif( - _os_is_windows and _python_is_3_10, + IS_WINDOWS and PYTHON_3_10, reason="Windows Access Violation in torch.multiprocessing / BrokenPipeError in multiprocessing.connection", ) @pytest.mark.parametrize("num_env", [2]) @@ -193,7 +193,7 @@ def test_output_device_consistency( ) and not torch.cuda.is_available(): pytest.skip("cuda is not available") - if _os_is_windows and _python_is_3_7: + if IS_WINDOWS and PYTHON_3_7: if device == "cuda" and policy_device == "cuda" and device is None: pytest.skip( "BrokenPipeError in multiprocessing.connection with Python 3.7 on Windows" @@ -518,7 +518,7 @@ def test_collector_batch_size( num_env, env_name, seed=100, num_workers=2, frames_per_batch=20 ): """Tests that there are 'frames_per_batch' frames in each batch of a collection.""" - if num_env == 3 and _os_is_windows: + if num_env == 3 and IS_WINDOWS: pytest.skip("Test timeout (> 10 min) on CI pipeline Windows machine with GPU") if num_env == 1: @@ -1072,12 +1072,7 @@ def test_collector_output_keys( @pytest.mark.parametrize("storing_device", ["cuda", "cpu"]) @pytest.mark.skipif(not torch.cuda.is_available(), reason="no cuda device found") def test_collector_device_combinations(device, storing_device): - if ( - _os_is_windows - and _python_is_3_10 - and storing_device == "cuda" - and device == "cuda" - ): + if IS_WINDOWS and PYTHON_3_10 and storing_device == "cuda" and device == "cuda": pytest.skip("Windows fatal exception: access violation in torch.storage") def env_fn(seed): @@ -1293,7 +1288,7 @@ def weight_reset(m): m.reset_parameters() -@pytest.mark.skipif(_os_is_osx, reason="Queue.qsize does not work on osx.") +@pytest.mark.skipif(IS_OSX, reason="Queue.qsize does not work on osx.") class TestPreemptiveThreshold: @pytest.mark.parametrize("env_name", ["conv", "vec"]) def test_sync_collector_interruptor_mechanism(self, env_name, seed=100): @@ -1960,6 +1955,9 @@ def make_env(): collector.shutdown() +@pytest.mark.skipif( + IS_OSX, reason="setting different threads across workeres can randomly fail on OSX." +) def test_num_threads(): from torchrl.collectors import collectors From 83266745195ce137db774082e4a09da34c98ec92 Mon Sep 17 00:00:00 2001 From: vmoens Date: Sun, 24 Sep 2023 15:33:46 -0400 Subject: [PATCH 101/178] amend --- test/test_libs.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/test/test_libs.py b/test/test_libs.py index ea2a03f8a13..4bc26f29563 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -367,12 +367,12 @@ def test_one_hot_and_categorical(self): # noqa: F811 return @implement_for("gymnasium", "0.27.0", None) - # this env has Dict-based observation which is a nice thing to test @pytest.mark.parametrize( "envname", ["HalfCheetah-v4", "CartPole-v1", "ALE/Pong-v5"] + (["FetchReach-v2"] if _has_gym_robotics else []), ) + @pytest.mark.flaky(reruns=3, reruns_delay=1) def test_vecenvs_wrapper(self, envname): import gymnasium @@ -399,6 +399,7 @@ def test_vecenvs_wrapper(self, envname): ["HalfCheetah-v4", "CartPole-v1", "ALE/Pong-v5"] + (["FetchReach-v2"] if _has_gym_robotics else []), ) + @pytest.mark.flaky(reruns=3, reruns_delay=1) def test_vecenvs_env(self, envname): from _utils_internal import rollout_consistency_assertion @@ -419,6 +420,7 @@ def test_vecenvs_env(self, envname): "envname", ["CartPole-v1", "HalfCheetah-v4"], ) + @pytest.mark.flaky(reruns=3, reruns_delay=1) def test_vecenvs_wrapper(self, envname): # noqa: F811 import gym @@ -444,6 +446,7 @@ def test_vecenvs_wrapper(self, envname): # noqa: F811 "envname", ["CartPole-v1", "HalfCheetah-v4"], ) + @pytest.mark.flaky(reruns=3, reruns_delay=1) def test_vecenvs_env(self, envname): # noqa: F811 with set_gym_backend("gym"): env = GymEnv(envname, num_envs=2, from_pixels=False) From 90da3fd48f530447003d6aa75443866450a1e684 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 25 Sep 2023 06:27:29 -0400 Subject: [PATCH 102/178] refactor tests --- .../linux_examples/scripts/run_local.sh | 3 +- .../linux_examples/scripts/run_test.sh | 36 +++++----- examples/a2c/a2c_atari.py | 8 +-- examples/a2c/a2c_mujoco.py | 4 +- examples/a2c/utils_atari.py | 2 +- examples/a2c/utils_mujoco.py | 2 +- examples/multiagent/iql.py | 4 +- examples/multiagent/maddpg_iddpg.py | 4 +- examples/multiagent/mappo_ippo.py | 4 +- examples/multiagent/sac.py | 4 +- examples/ppo/ppo_atari.py | 46 ++++++++----- examples/ppo/ppo_mujoco.py | 42 +++++++----- examples/ppo/utils_atari.py | 65 ++++++++++--------- examples/ppo/utils_mujoco.py | 11 ++-- torchrl/envs/common.py | 2 +- torchrl/envs/libs/gym.py | 9 ++- torchrl/envs/model_based/common.py | 21 +++--- 17 files changed, 150 insertions(+), 117 deletions(-) diff --git a/.github/unittest/linux_examples/scripts/run_local.sh b/.github/unittest/linux_examples/scripts/run_local.sh index afeda5f54eb..75bc84e4ca8 100755 --- a/.github/unittest/linux_examples/scripts/run_local.sh +++ b/.github/unittest/linux_examples/scripts/run_local.sh @@ -1,6 +1,7 @@ #!/bin/bash set -e +set -v # Read script from line 29 filename=".github/unittest/linux_examples/scripts/run_test.sh" @@ -12,7 +13,7 @@ script="set -e"$'\n'"$script" script="${script//cuda:0/cpu}" # Remove any instances of ".github/unittest/helpers/coverage_run_parallel.py" -script="${script//.circleci\/unittest\/helpers\/coverage_run_parallel.py}" +script="${script//.github\/unittest\/helpers\/coverage_run_parallel.py}" script="${script//coverage combine}" script="${script//coverage xml -i}" diff --git a/.github/unittest/linux_examples/scripts/run_test.sh b/.github/unittest/linux_examples/scripts/run_test.sh index 5794c21673d..c55d745adb8 100755 --- a/.github/unittest/linux_examples/scripts/run_test.sh +++ b/.github/unittest/linux_examples/scripts/run_test.sh @@ -31,19 +31,19 @@ python .github/unittest/helpers/coverage_run_parallel.py -m pytest test/smoke_te # ==================================================================================== # # ================================ gym 0.23 ========================================== # -# With batched environments -python .github/unittest/helpers/coverage_run_parallel.py examples/decision_transformer/dt.py \ - optim.pretrain_gradient_steps=55 \ - optim.updates_per_episode=3 \ - optim.warmup_steps=10 \ - optim.device=cuda:0 \ - logger.backend= -python .github/unittest/helpers/coverage_run_parallel.py examples/decision_transformer/online_dt.py \ - optim.pretrain_gradient_steps=55 \ - optim.updates_per_episode=3 \ - optim.warmup_steps=10 \ - optim.device=cuda:0 \ - logger.backend= +## With batched environments +#python .github/unittest/helpers/coverage_run_parallel.py examples/decision_transformer/dt.py \ +# optim.pretrain_gradient_steps=55 \ +# optim.updates_per_episode=3 \ +# optim.warmup_steps=10 \ +# optim.device=cuda:0 \ +# logger.backend= +#python .github/unittest/helpers/coverage_run_parallel.py examples/decision_transformer/online_dt.py \ +# optim.pretrain_gradient_steps=55 \ +# optim.updates_per_episode=3 \ +# optim.warmup_steps=10 \ +# optim.device=cuda:0 \ +# logger.backend= # ==================================================================================== # # ================================ Gymnasium ========================================= # @@ -53,16 +53,16 @@ python .github/unittest/helpers/coverage_run_parallel.py examples/ppo/ppo_mujoco collector.total_frames=40 \ collector.frames_per_batch=20 \ loss.mini_batch_size=10 \ - loss.ppo_epochs=1 \ + loss.ppo_epochs=2 \ logger.backend= \ - logger.test_interval=40 + logger.test_interval=10 python .github/unittest/helpers/coverage_run_parallel.py examples/ppo/ppo_atari.py \ collector.total_frames=80 \ collector.frames_per_batch=20 \ loss.mini_batch_size=20 \ - loss.ppo_epochs=1 \ + loss.ppo_epochs=2 \ logger.backend= \ - logger.test_interval=40 + logger.test_interval=10 python .github/unittest/helpers/coverage_run_parallel.py examples/ddpg/ddpg.py \ collector.total_frames=48 \ collector.init_random_frames=10 \ @@ -126,6 +126,7 @@ python .github/unittest/helpers/coverage_run_parallel.py examples/sac/sac.py \ optimization.utd_ratio=1 \ replay_buffer.size=120 \ env.name=Pendulum-v1 \ + network.device=cuda:0 \ logger.backend= # logger.record_video=True \ # logger.record_frames=4 \ @@ -225,6 +226,7 @@ python .github/unittest/helpers/coverage_run_parallel.py examples/sac/sac.py \ collector.num_workers=2 \ collector.env_per_collector=1 \ collector.collector_device=cuda:0 \ + network.device=cuda:0 \ optimization.batch_size=10 \ optimization.utd_ratio=1 \ replay_buffer.size=120 \ diff --git a/examples/a2c/a2c_atari.py b/examples/a2c/a2c_atari.py index 88deea2e217..41946b201be 100644 --- a/examples/a2c/a2c_atari.py +++ b/examples/a2c/a2c_atari.py @@ -113,9 +113,9 @@ def main(cfg: "DictConfig"): # noqa: F821 pbar.update(data.numel()) # Get training rewards and lengths - episode_rewards = data["next", "episode_reward"][data["next", "done"]] + episode_rewards = data["next", "episode_reward"][data["next", "stop"]] if len(episode_rewards) > 0: - episode_length = data["next", "step_count"][data["next", "done"]] + episode_length = data["next", "step_count"][data["next", "stop"]] log_info.update( { "train/reward": episode_rewards.mean().item(), @@ -124,10 +124,6 @@ def main(cfg: "DictConfig"): # noqa: F821 } ) - # Apply episodic end of life - data["done"].copy_(data["end_of_life"]) - data["next", "done"].copy_(data["next", "end_of_life"]) - losses = TensorDict({}, batch_size=[num_mini_batches]) training_start = time.time() diff --git a/examples/a2c/a2c_mujoco.py b/examples/a2c/a2c_mujoco.py index 4192ddc6556..9b5c845cf3a 100644 --- a/examples/a2c/a2c_mujoco.py +++ b/examples/a2c/a2c_mujoco.py @@ -101,9 +101,9 @@ def main(cfg: "DictConfig"): # noqa: F821 pbar.update(data.numel()) # Get training rewards and lengths - episode_rewards = data["next", "episode_reward"][data["next", "done"]] + episode_rewards = data["next", "episode_reward"][data["next", "stop"]] if len(episode_rewards) > 0: - episode_length = data["next", "step_count"][data["next", "done"]] + episode_length = data["next", "step_count"][data["next", "stop"]] log_info.update( { "train/reward": episode_rewards.mean().item(), diff --git a/examples/a2c/utils_atari.py b/examples/a2c/utils_atari.py index 42b75473b20..0c589dc097e 100644 --- a/examples/a2c/utils_atari.py +++ b/examples/a2c/utils_atari.py @@ -233,7 +233,7 @@ def eval_model(actor, test_env, num_episodes=3): break_when_any_done=True, max_steps=10_000_000, ) - reward = td_test["next", "episode_reward"][td_test["next", "done"]] + reward = td_test["next", "episode_reward"][td_test["next", "stop"]] test_rewards = np.append(test_rewards, reward.cpu().numpy()) del td_test return test_rewards.mean() diff --git a/examples/a2c/utils_mujoco.py b/examples/a2c/utils_mujoco.py index cdc681da522..94f8a6a8683 100644 --- a/examples/a2c/utils_mujoco.py +++ b/examples/a2c/utils_mujoco.py @@ -135,7 +135,7 @@ def eval_model(actor, test_env, num_episodes=3): break_when_any_done=True, max_steps=10_000_000, ) - reward = td_test["next", "episode_reward"][td_test["next", "done"]] + reward = td_test["next", "episode_reward"][td_test["next", "stop"]] test_rewards = np.append(test_rewards, reward.cpu().numpy()) del td_test return test_rewards.mean() diff --git a/examples/multiagent/iql.py b/examples/multiagent/iql.py index 4d36614f199..9c675424e70 100644 --- a/examples/multiagent/iql.py +++ b/examples/multiagent/iql.py @@ -145,8 +145,8 @@ def train(cfg: "DictConfig"): # noqa: F821 sampling_time = time.time() - sampling_start tensordict_data.set( - ("next", "done"), - tensordict_data.get(("next", "done")) + ("next", "stop"), + tensordict_data.get(("next", "stop")) .unsqueeze(-1) .expand(tensordict_data.get(("next", env.reward_key)).shape), ) # We need to expand the done to match the reward shape diff --git a/examples/multiagent/maddpg_iddpg.py b/examples/multiagent/maddpg_iddpg.py index 0b1cb4079e8..2c6c96894db 100644 --- a/examples/multiagent/maddpg_iddpg.py +++ b/examples/multiagent/maddpg_iddpg.py @@ -171,8 +171,8 @@ def train(cfg: "DictConfig"): # noqa: F821 sampling_time = time.time() - sampling_start tensordict_data.set( - ("next", "done"), - tensordict_data.get(("next", "done")) + ("next", "stop"), + tensordict_data.get(("next", "stop")) .unsqueeze(-1) .expand(tensordict_data.get(("next", env.reward_key)).shape), ) # We need to expand the done to match the reward shape diff --git a/examples/multiagent/mappo_ippo.py b/examples/multiagent/mappo_ippo.py index 6be5240935f..db73b63feb9 100644 --- a/examples/multiagent/mappo_ippo.py +++ b/examples/multiagent/mappo_ippo.py @@ -166,8 +166,8 @@ def train(cfg: "DictConfig"): # noqa: F821 sampling_time = time.time() - sampling_start tensordict_data.set( - ("next", "done"), - tensordict_data.get(("next", "done")) + ("next", "stop"), + tensordict_data.get(("next", "stop")) .unsqueeze(-1) .expand(tensordict_data.get(("next", env.reward_key)).shape), ) # We need to expand the done to match the reward shape diff --git a/examples/multiagent/sac.py b/examples/multiagent/sac.py index e9aea20e282..76849f7d4fd 100644 --- a/examples/multiagent/sac.py +++ b/examples/multiagent/sac.py @@ -236,8 +236,8 @@ def train(cfg: "DictConfig"): # noqa: F821 sampling_time = time.time() - sampling_start tensordict_data.set( - ("next", "done"), - tensordict_data.get(("next", "done")) + ("next", "stop"), + tensordict_data.get(("next", "stop")) .unsqueeze(-1) .expand(tensordict_data.get(("next", env.reward_key)).shape), ) # We need to expand the done to match the reward shape diff --git a/examples/ppo/ppo_atari.py b/examples/ppo/ppo_atari.py index 351d0af0ae8..907693b22f3 100644 --- a/examples/ppo/ppo_atari.py +++ b/examples/ppo/ppo_atari.py @@ -78,6 +78,9 @@ def main(cfg: "DictConfig"): # noqa: F821 normalize_advantage=True, ) + # use end-of-life as done key + loss_module.set_keys(done="eol") + # Create optimizer optim = torch.optim.Adam( loss_module.parameters(), @@ -109,6 +112,18 @@ def main(cfg: "DictConfig"): # noqa: F821 ) sampling_start = time.time() + + # extract cfg variables + cfg_loss_ppo_epochs = cfg.loss.ppo_epochs + cfg_optim_anneal_lr = cfg.optim.anneal_lr + cfg_optim_lr = cfg.optim.lr + cfg_loss_anneal_clip_eps = cfg.loss.anneal_clip_epsilon + cfg_loss_clip_epsilon = cfg.loss.clip_epsilon + cfg_logger_num_test_episodes = cfg.logger.num_test_episodes + cfg_optim_max_grad_norm = cfg.optim.max_grad_norm + cfg.loss.clip_epsilon = cfg_loss_clip_epsilon + losses = TensorDict({}, batch_size=[cfg_loss_ppo_epochs, num_mini_batches]) + for i, data in enumerate(collector): log_info = {} @@ -118,9 +133,9 @@ def main(cfg: "DictConfig"): # noqa: F821 pbar.update(data.numel()) # Get training rewards and episode lengths - episode_rewards = data["next", "episode_reward"][data["next", "done"]] + episode_rewards = data["next", "episode_reward"][data["next", "stop"]] if len(episode_rewards) > 0: - episode_length = data["next", "step_count"][data["next", "done"]] + episode_length = data["next", "step_count"][data["next", "stop"]] log_info.update( { "train/reward": episode_rewards.mean().item(), @@ -129,13 +144,8 @@ def main(cfg: "DictConfig"): # noqa: F821 } ) - # Apply episodic end of life - data["done"].copy_(data["end_of_life"]) - data["next", "done"].copy_(data["next", "end_of_life"]) - - losses = TensorDict({}, batch_size=[cfg.loss.ppo_epochs, num_mini_batches]) training_start = time.time() - for j in range(cfg.loss.ppo_epochs): + for j in range(cfg_loss_ppo_epochs): # Compute GAE with torch.no_grad(): @@ -149,12 +159,12 @@ def main(cfg: "DictConfig"): # noqa: F821 # Linearly decrease the learning rate and clip epsilon alpha = 1.0 - if cfg.optim.anneal_lr: + if cfg_optim_anneal_lr: alpha = 1 - (num_network_updates / total_network_updates) for group in optim.param_groups: - group["lr"] = cfg.optim.lr * alpha - if cfg.loss.anneal_clip_epsilon: - loss_module.clip_epsilon.copy_(cfg.loss.clip_epsilon * alpha) + group["lr"] = cfg_optim_lr * alpha + if cfg_loss_anneal_clip_eps: + loss_module.clip_epsilon.copy_(cfg_loss_clip_epsilon * alpha) num_network_updates += 1 # Get a data batch @@ -172,7 +182,7 @@ def main(cfg: "DictConfig"): # noqa: F821 # Backward pass loss_sum.backward() torch.nn.utils.clip_grad_norm_( - list(loss_module.parameters()), max_norm=cfg.optim.max_grad_norm + list(loss_module.parameters()), max_norm=cfg_optim_max_grad_norm ) # Update the networks @@ -181,15 +191,15 @@ def main(cfg: "DictConfig"): # noqa: F821 # Get training losses and times training_time = time.time() - training_start - losses = losses.apply(lambda x: x.float().mean(), batch_size=[]) - for key, value in losses.items(): + losses_mean = losses.apply(lambda x: x.float().mean(), batch_size=[]) + for key, value in losses_mean.items(): log_info.update({f"train/{key}": value.item()}) log_info.update( { - "train/lr": alpha * cfg.optim.lr, + "train/lr": alpha * cfg_optim_lr, "train/sampling_time": sampling_time, "train/training_time": training_time, - "train/clip_epsilon": alpha * cfg.loss.clip_epsilon, + "train/clip_epsilon": alpha * cfg_loss_clip_epsilon, } ) @@ -201,7 +211,7 @@ def main(cfg: "DictConfig"): # noqa: F821 actor.eval() eval_start = time.time() test_rewards = eval_model( - actor, test_env, num_episodes=cfg.logger.num_test_episodes + actor, test_env, num_episodes=cfg_logger_num_test_episodes ) eval_time = time.time() - eval_start log_info.update( diff --git a/examples/ppo/ppo_mujoco.py b/examples/ppo/ppo_mujoco.py index f081d8e69ee..d671d847952 100644 --- a/examples/ppo/ppo_mujoco.py +++ b/examples/ppo/ppo_mujoco.py @@ -100,6 +100,17 @@ def main(cfg: "DictConfig"): # noqa: F821 pbar = tqdm.tqdm(total=cfg.collector.total_frames) sampling_start = time.time() + + # extract cfg variables + cfg_loss_ppo_epochs = cfg.loss.ppo_epochs + cfg_optim_anneal_lr = cfg.optim.anneal_lr + cfg_optim_lr = cfg.optim.lr + cfg_loss_anneal_clip_eps = cfg.loss.anneal_clip_epsilon + cfg_loss_clip_epsilon = cfg.loss.clip_epsilon + cfg_logger_test_interval = cfg.logger.test_interval + cfg_logger_num_test_episodes = cfg.logger.num_test_episodes + losses = TensorDict({}, batch_size=[cfg_loss_ppo_epochs, num_mini_batches]) + for i, data in enumerate(collector): log_info = {} @@ -109,9 +120,9 @@ def main(cfg: "DictConfig"): # noqa: F821 pbar.update(data.numel()) # Get training rewards and episode lengths - episode_rewards = data["next", "episode_reward"][data["next", "done"]] + episode_rewards = data["next", "episode_reward"][data["next", "stop"]] if len(episode_rewards) > 0: - episode_length = data["next", "step_count"][data["next", "done"]] + episode_length = data["next", "step_count"][data["next", "stop"]] log_info.update( { "train/reward": episode_rewards.mean().item(), @@ -120,9 +131,8 @@ def main(cfg: "DictConfig"): # noqa: F821 } ) - losses = TensorDict({}, batch_size=[cfg.loss.ppo_epochs, num_mini_batches]) training_start = time.time() - for j in range(cfg.loss.ppo_epochs): + for j in range(cfg_loss_ppo_epochs): # Compute GAE with torch.no_grad(): @@ -136,14 +146,14 @@ def main(cfg: "DictConfig"): # noqa: F821 # Linearly decrease the learning rate and clip epsilon alpha = 1.0 - if cfg.optim.anneal_lr: + if cfg_optim_anneal_lr: alpha = 1 - (num_network_updates / total_network_updates) for group in actor_optim.param_groups: - group["lr"] = cfg.optim.lr * alpha + group["lr"] = cfg_optim_lr * alpha for group in critic_optim.param_groups: - group["lr"] = cfg.optim.lr * alpha - if cfg.loss.anneal_clip_epsilon: - loss_module.clip_epsilon.copy_(cfg.loss.clip_epsilon * alpha) + group["lr"] = cfg_optim_lr * alpha + if cfg_loss_anneal_clip_eps: + loss_module.clip_epsilon.copy_(cfg_loss_clip_epsilon * alpha) num_network_updates += 1 # Forward pass PPO loss @@ -166,27 +176,27 @@ def main(cfg: "DictConfig"): # noqa: F821 # Get training losses and times training_time = time.time() - training_start - losses = losses.apply(lambda x: x.float().mean(), batch_size=[]) - for key, value in losses.items(): + losses_mean = losses.apply(lambda x: x.float().mean(), batch_size=[]) + for key, value in losses_mean.items(): log_info.update({f"train/{key}": value.item()}) log_info.update( { - "train/lr": alpha * cfg.optim.lr, + "train/lr": alpha * cfg_optim_lr, "train/sampling_time": sampling_time, "train/training_time": training_time, - "train/clip_epsilon": alpha * cfg.loss.clip_epsilon, + "train/clip_epsilon": alpha * cfg_loss_clip_epsilon, } ) # Get test rewards with torch.no_grad(), set_exploration_type(ExplorationType.MODE): - if ((i - 1) * frames_in_batch) // cfg.logger.test_interval < ( + if ((i - 1) * frames_in_batch) // cfg_logger_test_interval < ( i * frames_in_batch - ) // cfg.logger.test_interval: + ) // cfg_logger_test_interval: actor.eval() eval_start = time.time() test_rewards = eval_model( - actor, test_env, num_episodes=cfg.logger.num_test_episodes + actor, test_env, num_episodes=cfg_logger_num_test_episodes ) eval_time = time.time() - eval_start log_info.update( diff --git a/examples/ppo/utils_atari.py b/examples/ppo/utils_atari.py index 54d920e27b1..db77ea6f123 100644 --- a/examples/ppo/utils_atari.py +++ b/examples/ppo/utils_atari.py @@ -4,12 +4,11 @@ # LICENSE file in the root directory of this source tree. import gymnasium as gym -import numpy as np import torch.nn import torch.optim from tensordict.nn import TensorDictModule from torchrl.data import CompositeSpec -from torchrl.data.tensor_specs import DiscreteBox +from torchrl.data.tensor_specs import DiscreteBox, UnboundedDiscreteTensorSpec from torchrl.envs import ( CatFrames, default_info_dict_reader, @@ -24,6 +23,7 @@ RewardSum, StepCounter, ToTensorImage, + Transform, TransformedEnv, VecNorm, ) @@ -43,39 +43,46 @@ # -------------------------------------------------------------------- -class EpisodicLifeEnv(gym.Wrapper): - def __init__(self, env): - """Make end-of-life == end-of-episode, but only reset on true game over. - Done by DeepMind for the DQN and co. It helps value estimation. - """ - gym.Wrapper.__init__(self, env) - self.lives = 0 +class EndOfLifeTransform(Transform): + """Registers the end-of-life signal from a Gym env with a `lives` method. - def step(self, action): - obs, rew, done, truncated, info = self.env.step(action) - lives = self.env.unwrapped.ale.lives() - info["end_of_life"] = False - if (lives < self.lives) or done: - info["end_of_life"] = True - self.lives = lives - return obs, rew, done, truncated, info + Done by DeepMind for the DQN and co. It helps value estimation. + """ - def reset(self, **kwargs): - reset_data = self.env.reset(**kwargs) - self.lives = self.env.unwrapped.ale.lives() - return reset_data + def _step(self, tensordict, next_tensordict): + lives = self.parent.base_env._env.unwrapped.ale.lives() + end_of_life = torch.tensor( + [tensordict["lives"] < lives], device=self.parent.device + ) + end_of_life = end_of_life | next_tensordict.get("done") + next_tensordict.set("eol", end_of_life) + next_tensordict.set("lives", lives) + return next_tensordict + + def reset(self, tensordict): + lives = self.parent.base_env._env.unwrapped.ale.lives() + end_of_life = False + tensordict.set("eol", [end_of_life]) + tensordict.set("lives", lives) + return tensordict + + def transform_observation_spec(self, observation_spec): + full_done_spec = self.parent.output_spec["full_done_spec"] + observation_spec["eol"] = full_done_spec["done"].clone() + observation_spec["lives"] = UnboundedDiscreteTensorSpec( + self.parent.batch_size, device=self.parent.device + ) + return observation_spec def make_base_env( env_name="BreakoutNoFrameskip-v4", frame_skip=4, device="cpu", is_test=False ): env = gym.make(env_name) - if not is_test: - env = EpisodicLifeEnv(env) env = GymWrapper( env, frame_skip=frame_skip, from_pixels=True, pixels_only=False, device=device ) - env = TransformedEnv(env) + env = TransformedEnv(env, EndOfLifeTransform()) env.append_transform(NoopResetEnv(noops=30, random=True)) if not is_test: reader = default_info_dict_reader(["end_of_life"]) @@ -120,8 +127,8 @@ def make_ppo_modules_pixels(proof_environment): num_outputs = proof_environment.action_spec.shape distribution_class = TanhNormal distribution_kwargs = { - "min": proof_environment.action_spec.space.minimum, - "max": proof_environment.action_spec.space.maximum, + "min": proof_environment.action_spec.space.low, + "max": proof_environment.action_spec.space.high, } # Define input keys @@ -232,7 +239,7 @@ def eval_model(actor, test_env, num_episodes=3): break_when_any_done=True, max_steps=10_000_000, ) - reward = td_test["next", "episode_reward"][td_test["next", "done"]] - test_rewards = np.append(test_rewards, reward.cpu().numpy()) + reward = td_test["next", "episode_reward"][td_test["next", "stop"]] + test_rewards.append(reward.cpu()) del td_test - return test_rewards.mean() + return torch.cat(test_rewards, 0).mean() diff --git a/examples/ppo/utils_mujoco.py b/examples/ppo/utils_mujoco.py index cdc681da522..9be1af3b924 100644 --- a/examples/ppo/utils_mujoco.py +++ b/examples/ppo/utils_mujoco.py @@ -3,7 +3,6 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import numpy as np import torch.nn import torch.optim @@ -51,8 +50,8 @@ def make_ppo_models_state(proof_environment): num_outputs = proof_environment.action_spec.shape[-1] distribution_class = TanhNormal distribution_kwargs = { - "min": proof_environment.action_spec.space.minimum, - "max": proof_environment.action_spec.space.maximum, + "min": proof_environment.action_spec.space.low, + "max": proof_environment.action_spec.space.high, "tanh_loc": False, } @@ -135,7 +134,7 @@ def eval_model(actor, test_env, num_episodes=3): break_when_any_done=True, max_steps=10_000_000, ) - reward = td_test["next", "episode_reward"][td_test["next", "done"]] - test_rewards = np.append(test_rewards, reward.cpu().numpy()) + reward = td_test["next", "episode_reward"][td_test["next", "stop"]] + test_rewards.append(reward.cpu()) del td_test - return test_rewards.mean() + return torch.cat(test_rewards, 0).mean() diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index cda157ae1dc..b1024158089 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -1866,7 +1866,7 @@ def fake_tensordict(self) -> TensorDictBase: return fake_in_out -class _EnvWrapper(EnvBase, metaclass=abc.ABCMeta): +class _EnvWrapper(EnvBase): """Abstract environment wrapper class. Unlike EnvBase, _EnvWrapper comes with a :obj:`_build_env` private method that will be called upon instantiation. diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index f0189355cdd..196a6b40678 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -749,7 +749,10 @@ def _make_done_spec(self): # noqa: F811 { "done": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) - ) + ), + "stop": DiscreteTensorSpec( + 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) + ), }, shape=self.batch_size, ) @@ -814,7 +817,7 @@ def _output_transform(self, step_outputs_tuple): # noqa: F811 # By making an opinionated decision, we make sure that value function can # be computed with a certain heuristic, even if it is wrong in some cases # (by lack of information). - return (observations, reward, done, None, None, info) + return (observations, reward, done, None, done, info) @implement_for("gym", "0.24", "0.26") def _output_transform(self, step_outputs_tuple): # noqa: F811 @@ -827,7 +830,7 @@ def _output_transform(self, step_outputs_tuple): # noqa: F811 # be computed with a certain heuristic, even if it is wrong in some cases # (by lack of information). observations, reward, done, info = step_outputs_tuple - return (observations, reward, done, None, None, info) + return (observations, reward, done, None, done, info) @implement_for("gym", "0.26", None) def _output_transform(self, step_outputs_tuple): # noqa: F811 diff --git a/torchrl/envs/model_based/common.py b/torchrl/envs/model_based/common.py index 8bb4673baec..cb861edf79e 100644 --- a/torchrl/envs/model_based/common.py +++ b/torchrl/envs/model_based/common.py @@ -16,7 +16,7 @@ from torchrl.envs.common import EnvBase -class ModelBasedEnvBase(EnvBase, metaclass=abc.ABCMeta): +class ModelBasedEnvBase(EnvBase): """Basic environnement for Model Based RL algorithms. Wrapper around the model of the MBRL algorithm. @@ -161,13 +161,18 @@ def _step( else: tensordict_out = self.world_model(tensordict_out) # Step requires a done flag. No sense for MBRL so we set it to False - if "done" not in self.world_model.out_keys: - tensordict_out["done"] = torch.zeros( - tensordict_out.shape, - dtype=torch.bool, - device=tensordict_out.device, - ) - return tensordict_out.select(*self.observation_spec.keys(), "reward", "done") + for done_key in self.done_keys: + if done_key not in self.world_model.out_keys: + tensordict_out[done_key] = torch.zeros( + tensordict_out.shape, + dtype=torch.bool, + device=tensordict_out.device, + ) + return tensordict_out.select( + *self.observation_spec.keys(), + *self.full_done_spec.keys(), + *self.full_reward_spec.keys() + ) @abc.abstractmethod def _reset(self, tensordict: TensorDict, **kwargs) -> TensorDict: From f4ddb92206878c3cc357368d803e6b200cb597a1 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 25 Sep 2023 11:31:21 -0400 Subject: [PATCH 103/178] Refactoring: terminated, truncated, done --- docs/source/reference/envs.rst | 50 +++---- examples/a2c/a2c_atari.py | 4 +- examples/a2c/a2c_mujoco.py | 4 +- examples/a2c/utils_atari.py | 2 +- examples/a2c/utils_mujoco.py | 2 +- examples/multiagent/iql.py | 4 +- examples/multiagent/maddpg_iddpg.py | 4 +- examples/multiagent/mappo_ippo.py | 4 +- examples/multiagent/sac.py | 4 +- examples/ppo/ppo_atari.py | 2 +- examples/ppo/ppo_mujoco.py | 4 +- examples/ppo/utils_atari.py | 2 +- examples/ppo/utils_mujoco.py | 2 +- test/mocking_classes.py | 43 +++--- test/test_collector.py | 4 +- test/test_env.py | 58 ++++---- test/test_libs.py | 12 +- test/test_transforms.py | 19 +-- torchrl/collectors/collectors.py | 4 +- torchrl/envs/common.py | 115 +++++++++------ torchrl/envs/gym_like.py | 101 ++++++------- torchrl/envs/libs/dm_control.py | 10 +- torchrl/envs/libs/gym.py | 60 ++++---- torchrl/envs/libs/isaacgym.py | 14 +- torchrl/envs/model_based/common.py | 12 +- torchrl/envs/transforms/transforms.py | 196 +++++++++++++------------- torchrl/envs/utils.py | 132 +++++++++++++---- 27 files changed, 487 insertions(+), 381 deletions(-) diff --git a/docs/source/reference/envs.rst b/docs/source/reference/envs.rst index 90ecb54f84e..4091b77c8e0 100644 --- a/docs/source/reference/envs.rst +++ b/docs/source/reference/envs.rst @@ -89,36 +89,20 @@ function. The Gym(nasium) API recently shifted to a splitting of the ``"done"`` state into a ``termination`` (the env is done and results should not be trusted) and ``truncation`` (an external limit on the number of steps is reached) flags. - In TorchRL, ``"done"`` strictly refers to ``"termination"``. + In TorchRL, ``"done"`` strictly refers to ``termination | truncation``. If the environment provides it (eg, Gymnasium), the truncation entry is also - updated. In this case, a ``"stop"`` entry can also be provided, which represents - the union of ``"done"`` (task completion) and ``"truncated"`` (task interruption). - If the environment carries a single value, it will interpreted as a completion - (``"done"``) signal by default (and not ``"stop"`` or ``"truncated"``). The - reason for this choice is that without any more information, one has to assume - task completion for follow-up tasks (eg, computing value estimations), so any - unspecified end-of-trajectory signal will eventually need to be interpreted - with a worst-case-scenario strategy. Another motivation is that a ``"stop"`` - signal returned directly by the env during a ``_step`` call will be modified - by a ``"truncation"`` implemented further down the line. For instance, - using a :class:`~.StepCounter` transform will irreversibly change the original - ``"stop"`` signal: - - >>> class StepCounter(transform): - ... def _call(self, tensordict, next_tensordict): - ... [...] - ... truncated = step_count > self.max_steps - ... # the following line overrides the stop: this information is lost - ... # and cannot be recovered - ... stop = stop | truncated - - Using ``"done"`` instead will make it robust to these additions. - - Truncation can also be achieved via the :class:`~.StepCounter` transform - class, and the output key will be ``"truncated"`` if not chosen to be - something else (e.g. ``StepCounter(max_steps=100, truncated_key="interrupted")``). - TorchRL's collectors and rollout methods will be looking for one of these - keys when assessing if the env should be reset. + written in the :meth:`EnvBase.step` output under a ``"truncated"`` entry. + If the environment carries a single value, it will interpreted as a ``"done"`` + signal by default. + Some classes in TorchRL may require a ``"terminated"`` signal (eg, value functions). + If none is available, they will fall back on ``"done"`` instead. + The caveat of this choice is that adding a truncation transform (eg, :class:`.StepCounter`) + will override the content of the ``"done"`` signal. If this is a problem + a :class:`~.RenameTransform` should be used to move or copy the ``"done"`` + entry (for instance to ``"terminated"``). + + By default, TorchRL's collectors and rollout methods will be looking for the ``"done"`` + entry to assess if the environment should be reset. .. note:: @@ -199,10 +183,10 @@ It is also possible to reset some but not all of the environments: >>> env.reset(tensordict) # eliminates the "_reset" entry TensorDict( fields={ + terminated: Tensor(torch.Size([4, 1]), dtype=torch.bool), done: Tensor(torch.Size([4, 1]), dtype=torch.bool), - truncated: Tensor(torch.Size([4, 1]), dtype=torch.bool), - stop: Tensor(torch.Size([4, 1]), dtype=torch.bool), pixels: Tensor(torch.Size([4, 500, 500, 3]), dtype=torch.uint8), + truncated: Tensor(torch.Size([4, 1]), dtype=torch.bool), batch_size=torch.Size([4]), device=None, is_shared=True) @@ -263,7 +247,7 @@ Some of the main differences between these paradigms include: - **observation** can be per-agent and also have some shared components - **reward** can be per-agent or shared -- **done** (and ``"truncated"`` or ``"stop"``) can be per-agent or shared. +- **done** (and ``"truncated"`` or ``"terminated"``) can be per-agent or shared. TorchRL accommodates all these possible paradigms thanks to its :class:`tensordict.TensorDict` data carrier. In particular, in multi-agent environments, per-agent keys will be carried in a nested "agents" TensorDict. @@ -611,7 +595,7 @@ Helpers exploration_type check_env_specs make_composite_from_td - done_or_truncated + terminated_or_truncated Domain-specific --------------- diff --git a/examples/a2c/a2c_atari.py b/examples/a2c/a2c_atari.py index 41946b201be..d3393e4308e 100644 --- a/examples/a2c/a2c_atari.py +++ b/examples/a2c/a2c_atari.py @@ -113,9 +113,9 @@ def main(cfg: "DictConfig"): # noqa: F821 pbar.update(data.numel()) # Get training rewards and lengths - episode_rewards = data["next", "episode_reward"][data["next", "stop"]] + episode_rewards = data["next", "episode_reward"][data["next", "done"]] if len(episode_rewards) > 0: - episode_length = data["next", "step_count"][data["next", "stop"]] + episode_length = data["next", "step_count"][data["next", "done"]] log_info.update( { "train/reward": episode_rewards.mean().item(), diff --git a/examples/a2c/a2c_mujoco.py b/examples/a2c/a2c_mujoco.py index 9b5c845cf3a..4192ddc6556 100644 --- a/examples/a2c/a2c_mujoco.py +++ b/examples/a2c/a2c_mujoco.py @@ -101,9 +101,9 @@ def main(cfg: "DictConfig"): # noqa: F821 pbar.update(data.numel()) # Get training rewards and lengths - episode_rewards = data["next", "episode_reward"][data["next", "stop"]] + episode_rewards = data["next", "episode_reward"][data["next", "done"]] if len(episode_rewards) > 0: - episode_length = data["next", "step_count"][data["next", "stop"]] + episode_length = data["next", "step_count"][data["next", "done"]] log_info.update( { "train/reward": episode_rewards.mean().item(), diff --git a/examples/a2c/utils_atari.py b/examples/a2c/utils_atari.py index 0c589dc097e..42b75473b20 100644 --- a/examples/a2c/utils_atari.py +++ b/examples/a2c/utils_atari.py @@ -233,7 +233,7 @@ def eval_model(actor, test_env, num_episodes=3): break_when_any_done=True, max_steps=10_000_000, ) - reward = td_test["next", "episode_reward"][td_test["next", "stop"]] + reward = td_test["next", "episode_reward"][td_test["next", "done"]] test_rewards = np.append(test_rewards, reward.cpu().numpy()) del td_test return test_rewards.mean() diff --git a/examples/a2c/utils_mujoco.py b/examples/a2c/utils_mujoco.py index 94f8a6a8683..cdc681da522 100644 --- a/examples/a2c/utils_mujoco.py +++ b/examples/a2c/utils_mujoco.py @@ -135,7 +135,7 @@ def eval_model(actor, test_env, num_episodes=3): break_when_any_done=True, max_steps=10_000_000, ) - reward = td_test["next", "episode_reward"][td_test["next", "stop"]] + reward = td_test["next", "episode_reward"][td_test["next", "done"]] test_rewards = np.append(test_rewards, reward.cpu().numpy()) del td_test return test_rewards.mean() diff --git a/examples/multiagent/iql.py b/examples/multiagent/iql.py index 9c675424e70..4d36614f199 100644 --- a/examples/multiagent/iql.py +++ b/examples/multiagent/iql.py @@ -145,8 +145,8 @@ def train(cfg: "DictConfig"): # noqa: F821 sampling_time = time.time() - sampling_start tensordict_data.set( - ("next", "stop"), - tensordict_data.get(("next", "stop")) + ("next", "done"), + tensordict_data.get(("next", "done")) .unsqueeze(-1) .expand(tensordict_data.get(("next", env.reward_key)).shape), ) # We need to expand the done to match the reward shape diff --git a/examples/multiagent/maddpg_iddpg.py b/examples/multiagent/maddpg_iddpg.py index 2c6c96894db..0b1cb4079e8 100644 --- a/examples/multiagent/maddpg_iddpg.py +++ b/examples/multiagent/maddpg_iddpg.py @@ -171,8 +171,8 @@ def train(cfg: "DictConfig"): # noqa: F821 sampling_time = time.time() - sampling_start tensordict_data.set( - ("next", "stop"), - tensordict_data.get(("next", "stop")) + ("next", "done"), + tensordict_data.get(("next", "done")) .unsqueeze(-1) .expand(tensordict_data.get(("next", env.reward_key)).shape), ) # We need to expand the done to match the reward shape diff --git a/examples/multiagent/mappo_ippo.py b/examples/multiagent/mappo_ippo.py index db73b63feb9..6be5240935f 100644 --- a/examples/multiagent/mappo_ippo.py +++ b/examples/multiagent/mappo_ippo.py @@ -166,8 +166,8 @@ def train(cfg: "DictConfig"): # noqa: F821 sampling_time = time.time() - sampling_start tensordict_data.set( - ("next", "stop"), - tensordict_data.get(("next", "stop")) + ("next", "done"), + tensordict_data.get(("next", "done")) .unsqueeze(-1) .expand(tensordict_data.get(("next", env.reward_key)).shape), ) # We need to expand the done to match the reward shape diff --git a/examples/multiagent/sac.py b/examples/multiagent/sac.py index 76849f7d4fd..e9aea20e282 100644 --- a/examples/multiagent/sac.py +++ b/examples/multiagent/sac.py @@ -236,8 +236,8 @@ def train(cfg: "DictConfig"): # noqa: F821 sampling_time = time.time() - sampling_start tensordict_data.set( - ("next", "stop"), - tensordict_data.get(("next", "stop")) + ("next", "done"), + tensordict_data.get(("next", "done")) .unsqueeze(-1) .expand(tensordict_data.get(("next", env.reward_key)).shape), ) # We need to expand the done to match the reward shape diff --git a/examples/ppo/ppo_atari.py b/examples/ppo/ppo_atari.py index 907693b22f3..2bb7cc6a3e8 100644 --- a/examples/ppo/ppo_atari.py +++ b/examples/ppo/ppo_atari.py @@ -133,7 +133,7 @@ def main(cfg: "DictConfig"): # noqa: F821 pbar.update(data.numel()) # Get training rewards and episode lengths - episode_rewards = data["next", "episode_reward"][data["next", "stop"]] + episode_rewards = data["next", "episode_reward"][data["next", "done"]] if len(episode_rewards) > 0: episode_length = data["next", "step_count"][data["next", "stop"]] log_info.update( diff --git a/examples/ppo/ppo_mujoco.py b/examples/ppo/ppo_mujoco.py index d671d847952..37230fb33c6 100644 --- a/examples/ppo/ppo_mujoco.py +++ b/examples/ppo/ppo_mujoco.py @@ -120,9 +120,9 @@ def main(cfg: "DictConfig"): # noqa: F821 pbar.update(data.numel()) # Get training rewards and episode lengths - episode_rewards = data["next", "episode_reward"][data["next", "stop"]] + episode_rewards = data["next", "episode_reward"][data["next", "done"]] if len(episode_rewards) > 0: - episode_length = data["next", "step_count"][data["next", "stop"]] + episode_length = data["next", "step_count"][data["next", "done"]] log_info.update( { "train/reward": episode_rewards.mean().item(), diff --git a/examples/ppo/utils_atari.py b/examples/ppo/utils_atari.py index db77ea6f123..ddb69555c19 100644 --- a/examples/ppo/utils_atari.py +++ b/examples/ppo/utils_atari.py @@ -239,7 +239,7 @@ def eval_model(actor, test_env, num_episodes=3): break_when_any_done=True, max_steps=10_000_000, ) - reward = td_test["next", "episode_reward"][td_test["next", "stop"]] + reward = td_test["next", "episode_reward"][td_test["next", "done"]] test_rewards.append(reward.cpu()) del td_test return torch.cat(test_rewards, 0).mean() diff --git a/examples/ppo/utils_mujoco.py b/examples/ppo/utils_mujoco.py index 9be1af3b924..8fa2a53fd92 100644 --- a/examples/ppo/utils_mujoco.py +++ b/examples/ppo/utils_mujoco.py @@ -134,7 +134,7 @@ def eval_model(actor, test_env, num_episodes=3): break_when_any_done=True, max_steps=10_000_000, ) - reward = td_test["next", "episode_reward"][td_test["next", "stop"]] + reward = td_test["next", "episode_reward"][td_test["next", "done"]] test_rewards.append(reward.cpu()) del td_test return torch.cat(test_rewards, 0).mean() diff --git a/test/mocking_classes.py b/test/mocking_classes.py index 5991936a8e1..cc63a518ff6 100644 --- a/test/mocking_classes.py +++ b/test/mocking_classes.py @@ -1126,20 +1126,11 @@ def __init__( ) if self.nested_done: + done_spec = self.done_spec.unsqueeze(-1).expand( + *self.batch_size, self.nested_dim + ) self.done_spec = CompositeSpec( - { - "data": CompositeSpec( - { - "done": self.done_spec.unsqueeze(-1).expand( - *self.batch_size, self.nested_dim, 1 - ) - }, - shape=( - *self.batch_size, - self.nested_dim, - ), - ) - }, + {"data": done_spec}, shape=self.batch_size, ) @@ -1153,9 +1144,12 @@ def _reset(self, tensordict): tensordict["_reset"] = tensordict["_reset"].sum(-2, dtype=torch.bool) td = super()._reset(tensordict) if self.nested_done: - td[self.done_key] = ( - td["done"].unsqueeze(-1).expand(*self.batch_size, self.nested_dim, 1) - ) + for done_key in self.done_keys: + td[done_key] = ( + td["done"] + .unsqueeze(-1) + .expand(*self.batch_size, self.nested_dim, 1) + ) del td["done"] if self.nested_obs_action: td["data", "states"] = ( @@ -1184,9 +1178,12 @@ def _step(self, td): td["data"].batch_size = (*self.batch_size, self.nested_dim) td = next_td if self.nested_done: - td[self.done_key] = ( - td["done"].unsqueeze(-1).expand(*self.batch_size, self.nested_dim, 1) - ) + for done_key in self.done_keys: + td[done_key] = ( + td["done"] + .unsqueeze(-1) + .expand(*self.batch_size, self.nested_dim, 1) + ) del td["done"] if self.nested_obs_action: td["data", "states"] = ( @@ -1481,9 +1478,11 @@ def _step( td.update(self.output_spec["full_reward_spec"].zero()) assert td.batch_size == self.batch_size - td[self.done_key] = expand_right( - self.count > self.max_steps, self.done_spec.shape - ) + for done_key in self.done_keys: + td[done_key] = expand_right( + self.count > self.max_steps, + self.full_done_spec[done_key].shape, + ) return td diff --git a/test/test_collector.py b/test/test_collector.py index 23edaba3a28..204989126f8 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -1057,8 +1057,8 @@ def test_collector_output_keys( if "gymnasium" in str(gym_backend()) or gym_backend().__version__ >= version.parse( "0.26" ): - keys.add(("next", "stop")) - keys.add("stop") + keys.add(("next", "terminated")) + keys.add("terminated") keys.add(("next", "truncated")) keys.add("truncated") b = next(iter(collector)) diff --git a/test/test_env.py b/test/test_env.py index 37a4ed0f67d..3fe257a5228 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -69,10 +69,10 @@ from torchrl.envs.utils import ( check_env_specs, check_marl_grouping, - done_or_truncated, make_composite_from_td, MarlGroupMapType, step_mdp, + terminated_or_truncated, ) from torchrl.modules import Actor, ActorCriticOperator, MLP, SafeModule, ValueOperator from torchrl.modules.tensordict_module import WorldModelWrapper @@ -2053,12 +2053,12 @@ def test_mocking_envs(envclass): check_env_specs(env, seed=100, return_contiguous=False) -class TestDoneOrTruncated: - def test_done_or_truncated_nospec(self): +class TestTerminatedOrTruncated: + def test_terminated_or_truncated_nospec(self): data = TensorDict({"done": torch.zeros(2, 1, dtype=torch.bool)}, [2]) - assert not done_or_truncated(data, write_full_false=True) + assert not terminated_or_truncated(data, write_full_false=True) assert data["_reset"].shape == (2, 1) - assert not done_or_truncated(data, write_full_false=False) + assert not terminated_or_truncated(data, write_full_false=False) assert data.get("_reset", None) is None data = TensorDict( @@ -2068,7 +2068,7 @@ def test_done_or_truncated_nospec(self): }, [2], ) - assert done_or_truncated(data) + assert terminated_or_truncated(data) assert data["_reset"].shape == (2, 1) assert data["nested", "_reset"].shape == (2, 1) @@ -2079,28 +2079,28 @@ def test_done_or_truncated_nospec(self): }, [2], ) - assert not done_or_truncated(data, write_full_false=False) + assert not terminated_or_truncated(data, write_full_false=False) assert data.get("_reset", None) is None assert data.get(("nested", "_reset"), None) is None - assert not done_or_truncated(data, write_full_false=True) + assert not terminated_or_truncated(data, write_full_false=True) assert data["_reset"].shape == (2, 1) assert data["nested", "_reset"].shape == (2, 1) data = TensorDict( { - "done": torch.zeros(2, 1, dtype=torch.bool), + "terminated": torch.zeros(2, 1, dtype=torch.bool), "truncated": torch.ones(2, 1, dtype=torch.bool), - ("nested", "done"): torch.zeros(2, 1, dtype=torch.bool), + ("nested", "terminated"): torch.zeros(2, 1, dtype=torch.bool), }, [2], ) - assert done_or_truncated(data, write_full_false=False) + assert terminated_or_truncated(data, write_full_false=False) assert data["_reset"].shape == (2, 1) assert data["nested", "_reset"].shape == (2, 1) assert data["_reset"].all() assert not data["nested", "_reset"].any() - def test_done_or_truncated_spec(self): + def test_terminated_or_truncated_spec(self): spec = CompositeSpec( done=DiscreteTensorSpec(2, shape=(2, 1), dtype=torch.bool), shape=[ @@ -2108,9 +2108,13 @@ def test_done_or_truncated_spec(self): ], ) data = TensorDict({"done": torch.zeros(2, 1, dtype=torch.bool)}, [2]) - assert not done_or_truncated(data, write_full_false=True, full_done_spec=spec) + assert not terminated_or_truncated( + data, write_full_false=True, full_done_spec=spec + ) assert data["_reset"].shape == (2, 1) - assert not done_or_truncated(data, write_full_false=False, full_done_spec=spec) + assert not terminated_or_truncated( + data, write_full_false=False, full_done_spec=spec + ) assert data.get("_reset", None) is None spec = CompositeSpec( @@ -2131,7 +2135,7 @@ def test_done_or_truncated_spec(self): }, [2], ) - assert done_or_truncated(data, full_done_spec=spec) + assert terminated_or_truncated(data, full_done_spec=spec) assert data["_reset"].shape == (2, 1) assert data["nested", "_reset"].shape == (2, 1) @@ -2142,34 +2146,38 @@ def test_done_or_truncated_spec(self): }, [2], ) - assert not done_or_truncated(data, write_full_false=False, full_done_spec=spec) + assert not terminated_or_truncated( + data, write_full_false=False, full_done_spec=spec + ) assert data.get("_reset", None) is None assert data.get(("nested", "_reset"), None) is None - assert not done_or_truncated(data, write_full_false=True, full_done_spec=spec) + assert not terminated_or_truncated( + data, write_full_false=True, full_done_spec=spec + ) assert data["_reset"].shape == (2, 1) assert data["nested", "_reset"].shape == (2, 1) spec = CompositeSpec( { "done": DiscreteTensorSpec(2, shape=(2, 1), dtype=torch.bool), - "truncated": DiscreteTensorSpec(2, shape=(2, 1), dtype=torch.bool), - ("nested", "done"): DiscreteTensorSpec( + "terminated": DiscreteTensorSpec(2, shape=(2, 1), dtype=torch.bool), + ("nested", "terminated"): DiscreteTensorSpec( 2, shape=(2, 1), dtype=torch.bool ), }, - shape=[ - 2, - ], + shape=[2], ) data = TensorDict( { - "done": torch.zeros(2, 1, dtype=torch.bool), + "terminated": torch.zeros(2, 1, dtype=torch.bool), "truncated": torch.ones(2, 1, dtype=torch.bool), - ("nested", "done"): torch.zeros(2, 1, dtype=torch.bool), + ("nested", "terminated"): torch.zeros(2, 1, dtype=torch.bool), }, [2], ) - assert done_or_truncated(data, write_full_false=False, full_done_spec=spec) + assert terminated_or_truncated( + data, write_full_false=False, full_done_spec=spec + ) assert data["_reset"].shape == (2, 1) assert data["nested", "_reset"].shape == (2, 1) assert data["_reset"].all() diff --git a/test/test_libs.py b/test/test_libs.py index 4bc26f29563..60bd8839678 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -495,6 +495,7 @@ def test_gym_output_num(self, wrapper): with set_gym_backend("gym"): env = GymEnv(PENDULUM_VERSIONED) assert "truncated" not in env.done_keys + assert "terminated" not in env.done_keys assert "done" in env.done_keys check_env_specs(env) @@ -510,6 +511,7 @@ def test_gym_output_num(self, wrapper): # noqa: F811 with set_gym_backend("gym"): env = GymEnv(PENDULUM_VERSIONED) assert "truncated" in env.done_keys + assert "terminated" in env.done_keys assert "done" in env.done_keys check_env_specs(env) @@ -535,6 +537,7 @@ def test_gym_output_num(self, wrapper): # noqa: F811 with set_gym_backend("gymnasium"): env = GymEnv(PENDULUM_VERSIONED) assert "truncated" in env.done_keys + assert "terminated" in env.done_keys assert "done" in env.done_keys check_env_specs(env) @@ -558,14 +561,15 @@ def test_gym_gymnasium_parallel(self): penv = ParallelEnv(2, make_fun) rollout = penv.rollout(2) if old_api: + assert "terminated" not in rollout.keys() assert "truncated" not in rollout.keys() else: + assert "terminated" in rollout.keys() assert "truncated" in rollout.keys() check_env_specs(penv) @implement_for("gym", None, "0.22.0") def test_vecenvs_nan(self): # noqa: F811 - print("here") # old versions of gym must return nan for next values when there is a done state torch.manual_seed(0) env = GymEnv("CartPole-v0", num_envs=2) @@ -1811,7 +1815,7 @@ def test_terminate_on_end(self, task): def test_d4rl_dummy(self, task): t0 = time.time() _ = D4RLExperienceReplay(task, split_trajs=True, from_env=True, batch_size=2) - print(f"completed test after {time.time()-t0}s") + print(f"terminated test after {time.time()-t0}s") @pytest.mark.parametrize("task", ["walker2d-medium-replay-v2"]) @pytest.mark.parametrize("split_trajs", [True, False]) @@ -1829,7 +1833,7 @@ def test_dataset_build(self, task, split_trajs, from_env): offline = sample[key] assert sim.dtype == offline.dtype, key assert sim.shape[-1] == offline.shape[-1], key - print(f"completed test after {time.time()-t0}s") + print(f"terminated test after {time.time()-t0}s") @pytest.mark.parametrize("task", ["walker2d-medium-replay-v2"]) @pytest.mark.parametrize("split_trajs", [True, False]) @@ -1848,7 +1852,7 @@ def test_d4rl_iteration(self, task, split_trajs): for sample in data: # noqa: B007 i += 1 assert len(data) // i == batch_size - print(f"completed test after {time.time()-t0}s") + print(f"terminated test after {time.time()-t0}s") @pytest.mark.skipif(not _has_sklearn, reason="Scikit-learn not found") diff --git a/test/test_transforms.py b/test/test_transforms.py index fb3fc1b910d..f665d2df7bd 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1398,17 +1398,18 @@ def test_step_count_dmc(self): check_env_specs(env) @pytest.mark.parametrize("write_stop", [False, True]) - @pytest.mark.parametrize("max_steps", [10, None]) - def test_single_trans_env_check(self, write_stop, max_steps): + @pytest.mark.parametrize("update_done", [10, None]) + def test_single_trans_env_check(self, update_done, max_steps): env = TransformedEnv( ContinuousActionVecMockEnv(), - StepCounter(max_steps=max_steps, write_stop=write_stop), + StepCounter(max_steps=max_steps, update_done=update_done), ) check_env_specs(env) - if write_stop and max_steps: - assert "stop" in env.full_done_spec.keys() + r = env.rollout(100, break_when_any_done=False) + if update_done and max_steps: + assert r["next", "done"][r["next", "truncated"]].all() else: - assert "stop" not in env.full_done_spec.keys() + assert not r["next", "done"][r["next", "truncated"]].all() def test_parallel_trans_env_check(self): def make_env(): @@ -1519,10 +1520,10 @@ def test_transform_compose(self, max_steps, device, batch, reset_workers): td.set("done", _reset) td.set(("next", "done"), done) step_counter[0]._step_count_keys = ["step_count"] - step_counter[0]._done_keys = ["done"] + step_counter[0]._terminated_keys = ["completed"] step_counter[0]._truncated_keys = ["truncated"] step_counter[0]._reset_keys = ["_reset"] - step_counter[0]._stop_keys = ["stop"] + step_counter[0]._done_keys = ["done"] td = step_counter.reset(td) assert not torch.all(td.get("step_count")) i = 0 @@ -1580,7 +1581,7 @@ def test_transform_no_env(self, max_steps, device, batch, reset_workers): step_counter._done_keys = ["done"] step_counter._truncated_keys = ["truncated"] step_counter._reset_keys = ["_reset"] - step_counter._stop_keys = ["stop"] + step_counter._completed_keys = ["completed"] td = step_counter.reset(td) assert not torch.all(td.get("step_count")) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index c3c81f8560b..33bb06147fc 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -44,10 +44,10 @@ from torchrl.envs.utils import ( _bring_reset_to_root, _convert_exploration_type, - done_or_truncated, ExplorationType, set_exploration_type, step_mdp, + terminated_or_truncated, ) _TIMEOUT = 1.0 @@ -800,7 +800,7 @@ def _step_and_maybe_reset(self) -> None: if not self.reset_when_done: return td_reset = self._tensordict.clone(False) - any_done = done_or_truncated( + any_done = terminated_or_truncated( td_reset, full_done_spec=self.env.output_spec["full_done_spec"], key="_reset", diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index b1024158089..46a8e6a41f0 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -25,10 +25,12 @@ ) from torchrl.data.utils import DEVICE_TYPING from torchrl.envs.utils import ( + _complete_done_at_reset, + _complete_done_at_step, _replace_last, - done_or_truncated, get_available_libraries, step_mdp, + terminated_or_truncated, ) LIBRARIES = get_available_libraries() @@ -132,6 +134,8 @@ def to(self, device: DEVICE_TYPING) -> EnvMetaData: class _EnvPostInit(abc.ABCMeta): def __call__(cls, *args, **kwargs): instance: EnvBase = super().__call__(*args, **kwargs) + # we create the done spec by adding a done/terminated entry if one is missing + instance._create_done_specs() # we access lazy attributed to make sure they're built properly. # This isn't done in `__init__` because we don't know if supre().__init__ # will be called before or after the specs, batch size etc are set. @@ -1024,23 +1028,68 @@ def done_spec(self) -> TensorSpec: device=cpu, dtype=torch.bool, domain=discrete) + """ + done_spec = self.output_spec["full_done_spec"] + return done_spec + + def _create_done_specs(self): + """Reads through the done specs and makes it so that it's complete. + + If the done_specs contain only a ``"done"`` entry, a similar ``"terminated"`` entry is created. + Same goes if only ``"terminated"`` key is present. + + If none of ``"done"`` and ``"terminated"`` can be found and the spec is not + empty, nothing is changed. + """ try: - done_spec = self.output_spec["full_done_spec"] - except (KeyError, AttributeError): - # populate the "done" entry - # this will be raised if there is not full_done_spec (unlikely) or no done_key - # Since output_spec is lazily populated with an empty composite spec for - # done_spec, the second case is much more likely to occur. - self.done_spec = DiscreteTensorSpec( - n=2, shape=(*self.batch_size, 1), dtype=torch.bool, device=self.device + full_done_spec = self.output_spec["full_done_spec"] + except KeyError: + full_done_spec = CompositeSpec( + shape=self.output_spec.shape, device=self.output_spec.device ) - done_spec = self.output_spec["full_done_spec"] - done_keys = self.done_keys - if len(done_keys) > 1 or not len(done_keys): - return done_spec - else: - return done_spec[self.done_keys[0]] + full_done_spec["done"] = DiscreteTensorSpec( + n=2, + shape=(*full_done_spec.shape, 1), + dtype=torch.bool, + device=self.device, + ) + full_done_spec["terminated"] = DiscreteTensorSpec( + n=2, + shape=(*full_done_spec.shape, 1), + dtype=torch.bool, + device=self.device, + ) + self.output_spec.unlock_() + self.output_spec["full_done_spec"] = full_done_spec + self.output_spec.lock_() + return + + def check_local_done(spec): + for key, item in list( + spec.items() + ): # list to avoid error due to in-loop changes + # in the case where the spec is non-empty and there is no done and no terminated, we do nothing + if key == "done" and "terminated" not in spec.keys(): + spec["terminated"] = item.clone() + elif key == "terminated" and "done" not in spec.keys(): + spec["done"] = item.clone() + elif isinstance(item, CompositeSpec): + check_local_done(item) + # if the spec is empty, we need to add a done and truncated manually + if spec.is_empty(): + spec["done"] = DiscreteTensorSpec( + n=2, shape=(*spec.shape, 1), dtype=torch.bool, device=self.device + ) + spec["terminated"] = DiscreteTensorSpec( + n=2, shape=(*spec.shape, 1), dtype=torch.bool, device=self.device + ) + + self.output_spec.unlock_() + check_local_done(full_done_spec) + self.output_spec["full_done_spec"] = full_done_spec + self.output_spec.lock_() + return @done_spec.setter def done_spec(self, value: TensorSpec) -> None: @@ -1070,7 +1119,10 @@ def done_spec(self, value: TensorSpec) -> None: ) else: value = CompositeSpec( - done=value.to(device), shape=self.batch_size, device=device + done=value.to(device), + terminated=value.to(device), + shape=self.batch_size, + device=device, ) for leaf in value.values(True, True): if len(leaf.shape) == 0: @@ -1081,6 +1133,7 @@ def done_spec(self, value: TensorSpec) -> None: " spec instead, for instance with a singleton dimension at the tail)." ) self.output_spec["full_done_spec"] = value.to(device) + self._create_done_specs() self._get_done_keys() finally: self.output_spec.lock_() @@ -1292,19 +1345,7 @@ def _step_proc_data(self, next_tensordict_out): reward = reward.view(expected_reward_shape) next_tensordict_out.set(reward_key, reward) - # TODO: Refactor this using done spec - for done_key in self.done_keys: - done = next_tensordict_out.get(done_key) - expected_done_shape = torch.Size( - [ - *leading_batch_size, - *self.output_spec[unravel_key(("full_done_spec", done_key))].shape, - ] - ) - actual_done_shape = done.shape - if actual_done_shape != expected_done_shape: - done = done.view(expected_done_shape) - next_tensordict_out.set(done_key, done) + _complete_done_at_step(self.full_done_spec, next_tensordict_out) if self.run_type_checks: for key, spec in self.observation_spec.items(): @@ -1392,19 +1433,7 @@ def reset( raise RuntimeError( f"env._reset returned an object of type {type(tensordict_reset)} but a TensorDict was expected." ) - - if len(self.batch_size): - leading_dim = tensordict_reset.shape[: -len(self.batch_size)] - else: - leading_dim = tensordict_reset.shape - if self.done_spec is not None: - td_reset_keys = tensordict_reset.keys(True, True) - for done_key in self.done_keys: - if done_key not in td_reset_keys: - tensordict_reset.set( - done_key, - self.output_spec["full_done_spec"][done_key].zero(leading_dim), - ) + _complete_done_at_reset(self.full_done_spec, tensordict_reset) if not self._allow_done_after_reset: # we iterate over (reset_key, (done_key, truncated_key)) and check that all @@ -1710,7 +1739,7 @@ def policy(td): ) # done and truncated are in done_keys # We read if any key is done. - any_done = done_or_truncated( + any_done = terminated_or_truncated( tensordict, full_done_spec=self.output_spec["full_done_spec"], key=None if break_when_any_done else "_reset", diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index 569998a76b1..b8ae012d2f8 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -135,41 +135,48 @@ def read_action(self, action): def read_done( self, - done: bool, + terminated: bool, truncated: bool | None = None, - stop: bool | None = None, + done: bool | None = None, ) -> Tuple[bool | np.ndarray, bool | np.ndarray, bool | np.ndarray, bool]: """Done state reader. - In torchrl, a `"done"` signal means that a trajectory is completed - (what is referred to as `termination` in gymnasium). - Truncated means the trajectory has been interrupted. + In torchrl, a `"done"` signal means that a trajectory has reach its end, + either because it has been interrupted or because it is terminated. + Truncated means the trajectory has been interrupted early. + Terminated means the task is finished. Args: - done (np.ndarray, boolean or other format): done state obtained from the environment. - ``"done"`` equates to ``"termination"`` in gymnasium: the signal that + terminated (np.ndarray, boolean or other format): completion state obtained + from the environment. + ``"terminated"`` equates to ``"termination"`` in gymnasium: the signal that the environment has reached the end of the game, any data coming after this should be considered as nonsensical. - In TorchRL, the convention is that all envs must have a - ``done`` of some sort. - truncated (bool or None): termination signal. Defaults to ``None``. - stop (bool or None): end-of-trajectory signal. Defaults to ``None``. - - Returns: a tuple with 3 boolean values, - - a done state to be set in the environment. - - a truncated state, possibly None if no truncation is provided. + Defaults to ``None``. + truncated (bool or None): early truncation signal. + Defaults to ``None``. + done (bool or None): end-of-trajectory signal. + This should be the fallback value of envs which do not specify + if the ``"done"`` entry points to a ``"terminated"`` or + ``"truncated"``. + Defaults to ``None``. + + Returns: a tuple with 4 boolean / tensor values, + - a terminated state, + - a truncated state, + - a done state, - a boolean value indicating whether the frame_skip loop should be broken. """ - if truncated is not None and stop is None: - stop = truncated | done - elif truncated is None and stop is None: - stop = done - do_break = stop.any() if not isinstance(stop, bool) else stop + if truncated is not None and done is None: + done = truncated | terminated + elif truncated is None and done is None: + done = terminated + do_break = done.any() if not isinstance(done, bool) else done return ( - done, + terminated, truncated, - stop, + done, do_break.any() if not isinstance(do_break, bool) else do_break, ) @@ -214,7 +221,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: reward = 0 for _ in range(self.wrapper_frame_skip): - obs, _reward, done, truncated, stop, info = self._output_transform( + obs, _reward, terminated, truncated, done, info = self._output_transform( self._env.step(action_np) ) if isinstance(obs, list) and len(obs) == 1: @@ -226,11 +233,9 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: reward = reward + _reward - if isinstance(done, bool) or ( - isinstance(done, np.ndarray) and not len(done) - ): - done = torch.tensor([done]) - done, truncated, stop, do_break = self.read_done(done, truncated, stop) + terminated, truncated, done, do_break = self.read_done( + terminated=terminated, truncated=truncated, done=done + ) if do_break: break @@ -242,16 +247,14 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: obs_dict[self.reward_key] = reward - # torchrl envs should always have a done - obs_dict["done"] = done + # if truncated/terminated is not in the keys, we just don't pass it even if it + # is defined. + if terminated is not None and "terminated" in self.done_keys: + obs_dict["terminated"] = terminated if truncated is not None and "truncated" in self.done_keys: obs_dict["truncated"] = truncated - # if truncated is not in the keys, we just don't pass it even if it - # is defined. - if stop is not None and "stop" in self.done_keys: - obs_dict["stop"] = stop - # if stop is not in the keys, we just don't pass it even if it - # is defined. + if done is not None and "done" in self.done_keys: + obs_dict["done"] = done tensordict_out = TensorDict(obs_dict, batch_size=tensordict.batch_size) @@ -298,31 +301,33 @@ def _output_transform( ) -> Tuple[ Any, float | np.ndarray, - bool, + bool | np.ndarray | None, bool | np.ndarray | None, bool | np.ndarray | None, dict, ]: """A method to read the output of the env step. - Must return a tuple: (obs, reward, done, truncated, stop, info). - If only one end-of-trajectory is passed, it is interpreted as ``"done"``, ie. - the state is terminal (game over). - If 2 are passed (like in gymnasium), we interpret them as ``"done", "truncated"`` - (``"truncated"`` meaning that the trajectory has been interrupted early). - and ``"stop"`` is the union of the two, ie. the end-of-trajectory signal. + Must return a tuple: (obs, reward, terminated, truncated, done, info). + If only one end-of-trajectory is passed, it is interpreted as ``"done"`` + (unspecified end-of-traj). + If 2 are passed (like in gymnasium), we interpret them as ``"terminated", + "truncated"`` (``"truncated"`` meaning that the trajectory has been + interrupted early), and ``"done"`` is the union of the two, + ie. the unspecified end-of-trajectory signal. These three concepts have different usage: - - "done" means that one should not pay attention to the upcoming observations - (eg., in value functions) as they should be regarded as not valid. + - ``"terminated"`` means that one should not pay attention to the + upcoming observations (eg., in value functions) as they should be + regarded as not valid. This is a "game-over" situation, the result of the action is the end of the game (win or loose). - - "truncated" means that the environment has reached a stage where + - ``"truncated"`` means that the environment has reached a stage where we decided to stop the collection for some reason but the next observation should not be discarded. If it were not for this - arbitrary decision, the collection could proceed further. - - "stop" is either one or the other. It is to be interpreted as + arbitrary decision, the collection could have proceeded further. + - ``"done"`` is either one or the other. It is to be interpreted as "a reset should be called at the next step". """ diff --git a/torchrl/envs/libs/dm_control.py b/torchrl/envs/libs/dm_control.py index 029d7f51e49..89b402bd904 100644 --- a/torchrl/envs/libs/dm_control.py +++ b/torchrl/envs/libs/dm_control.py @@ -188,7 +188,7 @@ def _make_specs(self, env: "gym.Env") -> None: # noqa: F821 self.done_spec = CompositeSpec( done=done_spec.clone(), truncated=done_spec.clone(), - stop=done_spec.clone(), + terminated=done_spec.clone(), device=self.device, ) self.action_spec = _dmcontrol_to_torchrl_spec_transform( @@ -249,18 +249,18 @@ def _output_transform( timestep_tuple = (timestep_tuple,) reward = timestep_tuple[0].reward - done = truncated = stop = False # dm_control envs are non-terminating + done = truncated = terminated = False # dm_control envs are non-terminating observation = timestep_tuple[0].observation info = {} - return observation, reward, done, truncated, stop, info + return observation, reward, terminated, truncated, done, info def _reset_output_transform(self, reset_data): ( observation, reward, - termination, - truncation, + terminated, + truncated, done, info, ) = self._output_transform(reset_data) diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index 196a6b40678..b5a8ba3ac37 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -750,9 +750,6 @@ def _make_done_spec(self): # noqa: F811 "done": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), - "stop": DiscreteTensorSpec( - 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) - ), }, shape=self.batch_size, ) @@ -761,10 +758,10 @@ def _make_done_spec(self): # noqa: F811 def _make_done_spec(self): # noqa: F811 return CompositeSpec( { - "stop": DiscreteTensorSpec( + "done": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), - "done": DiscreteTensorSpec( + "terminated": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), "truncated": DiscreteTensorSpec( @@ -778,10 +775,10 @@ def _make_done_spec(self): # noqa: F811 def _make_done_spec(self): # noqa: F811 return CompositeSpec( { - "stop": DiscreteTensorSpec( + "done": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), - "done": DiscreteTensorSpec( + "terminated": DiscreteTensorSpec( 2, dtype=torch.bool, device=self.device, shape=(*self.batch_size, 1) ), "truncated": DiscreteTensorSpec( @@ -809,51 +806,46 @@ def _output_transform(self, step_outputs_tuple): # noqa: F811 if self._is_batched: # info needs to be flipped info = _flip_info_tuple(info) - # The variable naming follow's torchrl's convention here. - # A done is interpreted as terminal in earlier versions of gym. - # This isn't optimal, but it is the most natural option. - # Interpreting this as a "stop" would break in value functions because - # we can't decide if the env is done (ie, game over) or truncated. - # By making an opinionated decision, we make sure that value function can - # be computed with a certain heuristic, even if it is wrong in some cases - # (by lack of information). - return (observations, reward, done, None, done, info) + # The variable naming follows torchrl's convention here. + # A done is interpreted the union of terminated and truncated. + # (as in earlier versions of gym). + terminated = done + truncated = None + return (observations, reward, terminated, truncated, done, info) @implement_for("gym", "0.24", "0.26") def _output_transform(self, step_outputs_tuple): # noqa: F811 - # The variable naming follow's torchrl's convention here. - # A done is interpreted as terminal in earlier versions of gym. - # This isn't optimal, but it is the most natural option. - # Interpreting this as a "stop" would break in value functions because - # we can't decide if the env is done (ie, game over) or truncated. - # By making an opinionated decision, we make sure that value function can - # be computed with a certain heuristic, even if it is wrong in some cases - # (by lack of information). observations, reward, done, info = step_outputs_tuple - return (observations, reward, done, None, done, info) + # The variable naming follows torchrl's convention here. + # A done is interpreted the union of terminated and truncated. + # (as in earlier versions of gym). + terminated = done + truncated = None + return (observations, reward, terminated, truncated, done, info) @implement_for("gym", "0.26", None) def _output_transform(self, step_outputs_tuple): # noqa: F811 - # The variable naming follow's torchrl's convention here. - observations, reward, done, truncated, info = step_outputs_tuple + # The variable naming follows torchrl's convention here. + observations, reward, terminated, truncated, info = step_outputs_tuple return ( observations, reward, - done, - truncated, # torchrl truncated - done | truncated, # torchrl stop + terminated, + truncated, + terminated | truncated, info, ) @implement_for("gymnasium", "0.27", None) def _output_transform(self, step_outputs_tuple): # noqa: F811 - observations, reward, done, truncated, info = step_outputs_tuple + # The variable naming follows torchrl's convention here. + observations, reward, terminated, truncated, info = step_outputs_tuple return ( observations, reward, - done, - truncated, # torchrl truncated - done | truncated, # torchrl stop + terminated, + truncated, + terminated | truncated, info, ) diff --git a/torchrl/envs/libs/isaacgym.py b/torchrl/envs/libs/isaacgym.py index 6cdfcfccf50..d5abd71d647 100644 --- a/torchrl/envs/libs/isaacgym.py +++ b/torchrl/envs/libs/isaacgym.py @@ -107,11 +107,17 @@ def read_action(self, action): def read_done( self, - gym_done: bool, - termination: bool | None = None, - truncation: bool | None = None, + terminated: bool = None, + truncated: bool | None = None, + done: bool | None = None, ) -> Tuple[bool, bool, bool]: - return gym_done.bool(), None, gym_done.any() + if terminated is not None: + terminated = terminated.bool() + if truncated is not None: + truncated = truncated.bool() + if done is not None: + done = done.bool() + return completed, truncated, completed, completed.any() def read_reward(self, total_reward, step_reward): """Reads a reward and the total reward so far (in the frame skip loop) and returns a sum of the two. diff --git a/torchrl/envs/model_based/common.py b/torchrl/envs/model_based/common.py index cb861edf79e..5952132ca19 100644 --- a/torchrl/envs/model_based/common.py +++ b/torchrl/envs/model_based/common.py @@ -160,18 +160,12 @@ def _step( ) else: tensordict_out = self.world_model(tensordict_out) - # Step requires a done flag. No sense for MBRL so we set it to False - for done_key in self.done_keys: - if done_key not in self.world_model.out_keys: - tensordict_out[done_key] = torch.zeros( - tensordict_out.shape, - dtype=torch.bool, - device=tensordict_out.device, - ) + # done can be missing, it will be filled by `step` return tensordict_out.select( *self.observation_spec.keys(), *self.full_done_spec.keys(), - *self.full_reward_spec.keys() + *self.full_reward_spec.keys(), + strict=False, ) @abc.abstractmethod diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index cb47df9abd4..e19069809ed 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -2362,7 +2362,8 @@ class CatFrames(ObservationTransform): >>> # let's check that our sample is the same as the batch collected during inference >>> assert (data.exclude("collector")==s.squeeze(0).exclude("index", "collector")).all() - .. note:: :class:`~CatFrames` currently only supports ``done`` signal at the root. Nested ``done``, + .. note:: :class:`~CatFrames` currently only supports ``"done"`` + signal at the root. Nested ``done``, such as those found in MARL settings, are currently not supported. If this feature is needed, please raise an issue on TorchRL repo. @@ -3520,8 +3521,14 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: tensordict = parent.rand_step(tensordict) tensordict = step_mdp(tensordict, exclude_done=False) reset = False + # if any of the done_keys is True, we break for done_key in done_keys: - if tensordict.get(done_key): + done = tensordict.get(done_key) + if done.numel() > 1: + raise ValueError( + f"{type(self)} only supports scalar done states." + ) + if done: reset = True break if reset: @@ -4102,8 +4109,8 @@ class RewardSum(Transform): (e.g. reward1 and reward2) can also be specified. If ´in_keys´ are not present in the provided tensordict, this transform hos no effect. - .. note:: :class:`~RewardSum` currently only supports ``done`` signal at the root. - Nested ``done``, such as those found in MARL settings, are currently not supported. + .. note:: :class:`~RewardSum` currently only supports ``"done"`` signal at the root. + Nested ``"done"``, such as those found in MARL settings, are currently not supported. If this feature is needed, please raise an issue on TorchRL repo. """ @@ -4271,6 +4278,9 @@ def forward(self, tensordict: TensorDictBase) -> TensorDictBase: class StepCounter(Transform): """Counts the steps from a reset and optionally sets the truncated state to ``True`` after a certain number of steps. + The ``"done"`` state is also adaptec accordingly (as done is the intersection + of task completetion and early truncation). + Args: max_steps (int, optional): a positive integer that indicates the maximum number of steps to take before setting the ``truncated_key`` @@ -4280,21 +4290,22 @@ class StepCounter(Transform): data collectors as a reset signal. This argument can only be a string (not a nested key) as it will be matched to each of the leaf done key in the parent environment - (eg, a ``("agent", "done")`` key will be accompanied by a ``("agent", "truncated")`` - if the ``"truncated"`` key name is used). + (eg, a ``("agent", "done")`` key will be accompanied by a + ``("agent", "truncated")`` if the ``"truncated"`` key name is used). step_count_key (str, optional): the key where the step count entries should be written. Defaults to ``"step_count"``. This argument can only be a string (not a nested key) as it will be matched to each of the leaf done key in the parent environment - (eg, a ``("agent", "done")`` key will be accompanied by a ``("agent", "step_count")`` - if the ``"step_count"`` key name is used). - write_stop (bool, optional): if ``True``, a ``"stop"`` boolean tensor - will be written at the level of ``"truncated"``. If ``"stop"`` is - already present, it will be updated accordingly. + (eg, a ``("agent", "done")`` key will be accompanied by a + ``("agent", "step_count")`` if the ``"step_count"`` key name is used). + update_done (bool, optional): if ``True``, the ``"done"`` boolean tensor + at the level of ``"truncated"`` + will be updated. This signal indicates that the trajectory has reached its ends, - either because the task is completed (``"done"``) or because it has - been truncated (``"truncated"``). - Defaults to ``False``. + either because the task is completed (``"completed"`` entry is + ``True``) or because it has been truncated (``"truncated"`` entry + is ``True``). + Defaults to ``True``. .. note:: To ensure compatibility with environments that have multiple done_key(s), this transform will write a step_count entry for @@ -4312,9 +4323,11 @@ class StepCounter(Transform): fields={ action: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False), + completed: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, next: TensorDict( fields={ done: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False), + completed: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, observation: Tensor(shape=torch.Size([5, 3]), device=cpu, dtype=torch.float32, is_shared=False), reward: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.float32, is_shared=False), step_count: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.int64, is_shared=False), @@ -4344,7 +4357,7 @@ def __init__( max_steps: Optional[int] = None, truncated_key: str | None = "truncated", step_count_key: str | None = "step_count", - write_stop: bool | None = False, + update_done: bool = True, ): if max_steps is not None and max_steps < 1: raise ValueError("max_steps should have a value greater or equal to one.") @@ -4355,7 +4368,7 @@ def __init__( self.max_steps = max_steps self.truncated_key = truncated_key self.step_count_key = step_count_key - self.write_stop = write_stop + self.update_done = update_done super().__init__([]) @property @@ -4374,28 +4387,33 @@ def truncated_keys(self): return truncated_keys @property - def stop_keys(self): - stop_keys = self.__dict__.get("_stop_keys", None) - if stop_keys is None: - # make the default stop keys - stop_keys = [] + def completed_keys(self): + done_keys = self.__dict__.get("_done_keys", None) + if done_keys is None: + # make the default done keys + done_keys = [] for (done_key, *_) in self.parent.done_keys_groups: if isinstance(done_key, str): - key = "stop" + key = "done" else: - key = (*done_key[:-1], "stop") - stop_keys.append(key) - self._stop_keys = stop_keys - return stop_keys + key = (*done_key[:-1], "done") + done_keys.append(key) + self.__dict__["_done_keys"] = done_keys + return done_keys @property def done_keys(self): done_keys = self.__dict__.get("_done_keys", None) if done_keys is None: - if self.parent is not None: - self._done_keys = done_keys = self.parent.done_keys - else: - self._done_keys = done_keys = ["done"] + # make the default done keys + done_keys = [] + for (done_key, *_) in self.parent.done_keys_groups: + if isinstance(done_key, str): + key = "done" + else: + key = (*done_key[:-1], "done") + done_keys.append(key) + self.__dict__["_done_keys"] = done_keys return done_keys @property @@ -4410,7 +4428,7 @@ def step_count_keys(self): else: key = (*done_key[:-1], self.step_count_key) step_count_keys.append(key) - self._step_count_keys = step_count_keys + self.__dict__["_step_count_keys"] = step_count_keys return step_count_keys @property @@ -4432,25 +4450,25 @@ def full_done_spec(self): def reset(self, tensordict: TensorDictBase) -> TensorDictBase: # get reset signal - for step_count_key, truncated_key, reset_key, stop_key, done_list_sorted in zip( + for step_count_key, truncated_key, reset_key, done_key, done_list_sorted in zip( self.step_count_keys, self.truncated_keys, self.reset_keys, - self.stop_keys, + self.done_keys, self.done_keys_groups, ): step_count = tensordict.get(step_count_key, default=None) reset = tensordict.get(reset_key, default=None) if reset is None: # get done status, just to inform the reset shape, dtype and device - for entry in done_list_sorted: - done = tensordict.get(entry, default=None) + for entry_name in done_list_sorted: + done = tensordict.get(entry_name, default=None) if done is not None: break else: # It may be the case that reset did not provide a done state, in which case # we fall back on the spec - done = self.parent.output_spec["full_done_spec", entry].zero() + done = self.parent.output_spec["full_done_spec", entry_name].zero() reset = torch.ones_like(done) if step_count is None: step_count = torch.zeros_like(reset, dtype=torch.int64) @@ -4460,19 +4478,19 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: tensordict.set(step_count_key, step_count) if self.max_steps is not None: truncated = step_count >= self.max_steps - if self.write_stop: - stop = truncated # we assume no done after reset - tensordict.set(stop_key, stop) + if self.update_done: + # we assume no done after reset + tensordict.set(done_key, truncated) tensordict.set(truncated_key, truncated) return tensordict def _step( self, tensordict: TensorDictBase, next_tensordict: TensorDictBase ) -> TensorDictBase: - for step_count_key, truncated_key, stop_key, done_keys in zip( + for step_count_key, truncated_key, done_key, done_list_sorted in zip( self.step_count_keys, self.truncated_keys, - self.stop_keys, + self.done_keys, self.done_keys_groups, ): step_count = tensordict.get(step_count_key) @@ -4480,14 +4498,14 @@ def _step( next_tensordict.set(step_count_key, next_step_count) if self.max_steps is not None: truncated = next_step_count >= self.max_steps - if self.write_stop: - any_done = False - for done_key in done_keys: - any_done = any_done | next_tensordict.get( - done_key, default=False - ) - stop = truncated | any_done # we assume no done after reset - next_tensordict.set(stop_key, stop) + if self.update_done: + done = next_tensordict.get(done_key, None) + if done is None: + done = False + for done in done_list_sorted: + done = done | next_tensordict.get(done_key, default=False) + done = truncated | done # we assume no done after reset + next_tensordict.set(done_key, done) next_tensordict.set(truncated_key, truncated) return next_tensordict @@ -4525,7 +4543,7 @@ def transform_observation_spec( low=0, high=torch.iinfo(torch.int64).max, ) - return observation_spec + return super().transform_observation_spec(observation_spec) def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: if self.max_steps: @@ -4552,16 +4570,16 @@ def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: full_done_spec[truncated_key] = DiscreteTensorSpec( 2, dtype=torch.bool, device=output_spec.device, shape=shape ) - if self.write_stop: - for stop_key in self.stop_keys: - stop_key = unravel_key(stop_key) + if self.update_done: + for done_key in self.done_keys: + done_key = unravel_key(done_key) # find a matching done key (there might be more than one) for done_key in self.done_keys: # check root - if type(done_key) != type(stop_key): + if type(done_key) != type(done_key): continue if isinstance(done_key, tuple): - if done_key[:-1] == stop_key[:-1]: + if done_key[:-1] == done_key[:-1]: shape = full_done_spec[done_key].shape break if isinstance(done_key, str): @@ -4570,16 +4588,13 @@ def transform_output_spec(self, output_spec: CompositeSpec) -> CompositeSpec: else: raise KeyError( - f"Could not find root of stop_key {stop_key} in done keys {self.done_keys}." + f"Could not find root of stop_key {done_key} in done keys {self.done_keys}." ) - full_done_spec[stop_key] = DiscreteTensorSpec( + full_done_spec[done_key] = DiscreteTensorSpec( 2, dtype=torch.bool, device=output_spec.device, shape=shape ) output_spec["full_done_spec"] = full_done_spec - output_spec["full_observation_spec"] = self.transform_observation_spec( - output_spec["full_observation_spec"] - ) - return output_spec + return super().transform_output_spec(output_spec) def transform_input_spec(self, input_spec: CompositeSpec) -> CompositeSpec: if not isinstance(input_spec, CompositeSpec): @@ -5084,7 +5099,6 @@ def __init__(self, init_key: NestedKey = "is_init"): if not isinstance(init_key, str): raise ValueError("init_key can only be of type str.") self.init_key = init_key - self._init_keys = None self.reset_key = "_reset" super().__init__(in_keys=[], out_keys=[]) @@ -5106,8 +5120,9 @@ def out_keys(self, value): @property def init_keys(self): - if self._init_keys is not None: - return self._init_keys + init_keys = self.__dict__.get("_init_keys", None) + if init_keys is not None: + return init_keys init_keys = [] if self.parent is None: raise NotImplementedError( @@ -5499,7 +5514,6 @@ def __init__( in_keys: Optional[Sequence[NestedKey]] = None, out_keys: Optional[Sequence[NestedKey]] = None, done_key: Optional[NestedKey] = "done", - truncated_key: Optional[NestedKey] = "truncated", ): if in_keys is None: in_keys = [("next", "reward")] @@ -5512,7 +5526,6 @@ def __init__( out_keys_inv=out_keys, ) self.done_key = done_key - self.truncated_key = truncated_key if not isinstance(gamma, torch.Tensor): gamma = torch.tensor(gamma) @@ -5521,13 +5534,8 @@ def __init__( def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase: done = tensordict.get(("next", self.done_key)) - truncated = tensordict.get(("next", self.truncated_key), None) - if truncated is not None: - done_or_truncated = done | truncated - else: - done_or_truncated = done - if not done_or_truncated.any(-2).all(): + if not done.any(-2).all(): raise RuntimeError( "No episode ends found to calculate the reward to go. Make sure that the number of frames_per_batch is larger than number of steps per episode." ) @@ -5535,9 +5543,7 @@ def _inv_call(self, tensordict: TensorDictBase) -> TensorDictBase: for in_key, out_key in zip(self.in_keys_inv, self.out_keys_inv): if in_key in tensordict.keys(include_nested=True): found = True - item = self._inv_apply_transform( - tensordict.get(in_key), done_or_truncated - ) + item = self._inv_apply_transform(tensordict.get(in_key), done) tensordict.set(out_key, item) if not found: raise KeyError(f"Could not find any of the input keys {self.in_keys}.") @@ -5719,26 +5725,26 @@ def _step( self, tensordict: TensorDictBase, next_tensordict: TensorDictBase ) -> TensorDictBase: # save the final info - stop = False + done = False # TODO: check if there's a done, and if there is, get it for done_key in self.done_keys: - stop = stop | next_tensordict.get(done_key) - if stop is False: + done = done | next_tensordict.get(done_key) + if done is False: raise RuntimeError( f"Could not find any done signal in tensordict:\n{tensordict}" ) - self._memo["stop"] = stop + self._memo["done"] = done final = next_tensordict.pop(self.final_name, None) # if anything's done, we need to swap the final obs - if stop.any(): - stop = stop.squeeze(-1) + if done.any(): + done = done.squeeze(-1) if final is not None: saved_next = next_tensordict.select(*final.keys(True, True)).clone() - next_tensordict[stop] = final[stop] + next_tensordict[done] = final[done] else: saved_next = next_tensordict.select(*self.obs_keys).clone() for obs_key in self.obs_keys: - next_tensordict[obs_key][stop] = torch.tensor(np.nan) + next_tensordict[obs_key][done] = torch.tensor(np.nan) self._memo["saved_next"] = saved_next else: @@ -5746,18 +5752,18 @@ def _step( return next_tensordict def reset(self, tensordict: TensorDictBase) -> TensorDictBase: - stop = self._memo.get("stop", None) - reset = tensordict.get("_reset", stop) - if stop is not None: - stop = stop.view_as(reset) + done = self._memo.get("done", None) + reset = tensordict.get("_reset", done) + if done is not None: + done = done.view_as(reset) if ( - reset is not stop - and (reset != stop).any() + reset is not done + and (reset != done).any() and (not reset.all() or not reset.any()) ): raise RuntimeError( "Cannot partially reset a gym(nasium) async env with a reset mask that does not match the done mask. " - f"Got reset={reset}\nand done={stop}" + f"Got reset={reset}\nand done={done}" ) # if not reset.any(), we don't need to do anything. # if reset.all(), we don't either (bc GymWrapper will call a plain reset). @@ -5776,16 +5782,16 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: tensordict.update(saved_next) for done_key in self.done_keys: # Make sure that all done are False - stop = tensordict.get(done_key, None) - if stop is not None: - stop = stop.clone().fill_(0) + done = tensordict.get(done_key, None) + if done is not None: + done = done.clone().fill_(0) else: - stop = torch.zeros( + done = torch.zeros( (*tensordict.batch_size, 1), device=tensordict.device, dtype=torch.bool, ) - tensordict.set(done_key, stop) + tensordict.set(done_key, done) tensordict.pop(self.final_name, None) return tensordict diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 65b431ef467..0e155c74da6 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -729,31 +729,36 @@ def check_marl_grouping(group_map: Dict[str, List[str]], agent_names: List[str]) raise ValueError(f"Agent {agent_name} not found in any group") -def done_or_truncated( +def terminated_or_truncated( data: TensorDictBase, full_done_spec: TensorSpec | None = None, key: str = "_reset", write_full_false: bool = False, ) -> bool: - """Reads the done / truncated keys within a tensordict, and writes a new tensor where the values of both signals are aggregated. + """Reads the done / terminated / truncated keys within a tensordict, and + writes a new tensor where the values of both signals are aggregated. The modification occurs in-place within the TensorDict instance provided. This function can be used to compute the `"_reset"` signals in batched or multiagent settings, hence the default name of the output key. Args: - data (TensorDictBase): the input data, generally resulting from a call to :meth:`~torchrl.envs.EnvBase.step`. - full_done_spec (TensorSpec, optional): the done_spec from the env, indicating where - the done leaves have to be found. If not provided, the default - ``"step"``, ``"done"`` and ``"truncated"`` entries will be + data (TensorDictBase): the input data, generally resulting from a call + to :meth:`~torchrl.envs.EnvBase.step`. + full_done_spec (TensorSpec, optional): the done_spec from the env, + indicating where the done leaves have to be found. + If not provided, the default + ``"done"``, ``"terminated"`` and ``"truncated"`` entries will be searched for in the data. key (NestedKey, optional): where the aggregated result should be written. If ``None``, then the function will not write any key but just output whether any of the done values was true. - .. note:: if a value is already present for the ``key`` entry, the previous - value will prevail and no update will be achieved. - write_full_false (bool, optional): if ``True``, the reset keys will be written - even if the output if ``False`` (ie, no done is ``True``). Defaults to ``False``. + .. note:: if a value is already present for the ``key`` entry, + the previous value will prevail and no update will be achieved. + write_full_false (bool, optional): if ``True``, the reset keys will be + written even if the output is ``False`` (ie, no done is ``True`` + in the provided data structure). + Defaults to ``False``. Returns: a boolean value indicating whether any of the done states found in the data contained a ``True``. @@ -774,7 +779,7 @@ def done_or_truncated( ... "nested": {"done": False, "truncated": True}}, ... batch_size=[] ... ) - >>> data = done_or_truncated(data, spec) + >>> data = terminated_or_truncated(data, spec) >>> print(data["_reset"]) tensor(True) >>> print(data["nested", "_reset"]) @@ -782,31 +787,31 @@ def done_or_truncated( """ list_of_keys = [] - def inner_done_or_truncated(data, full_done_spec, key, curr_done_key=()): + def inner_terminated_or_truncated(data, full_done_spec, key, curr_done_key=()): any_eot = False aggregate = None if full_done_spec is None: for eot_key, item in data.items(): - if eot_key == "stop": - stop = data.get(eot_key, None) - if stop is None: - stop = torch.zeros( + if eot_key == "done": + done = data.get(eot_key, None) + if done is None: + done = torch.zeros( (*data.shape, 1), dtype=torch.bool, device=data.device ) if aggregate is None: - aggregate = torch.tensor(False, device=stop.device) - aggregate = aggregate | stop - elif eot_key in ("done", "truncated"): - stop = data.get(eot_key, None) - if stop is None: - stop = torch.zeros( + aggregate = torch.tensor(False, device=done.device) + aggregate = aggregate | done + elif eot_key in ("terminated", "truncated"): + done = data.get(eot_key, None) + if done is None: + done = torch.zeros( (*data.shape, 1), dtype=torch.bool, device=data.device ) if aggregate is None: - aggregate = torch.tensor(False, device=stop.device) - aggregate = aggregate | stop + aggregate = torch.tensor(False, device=done.device) + aggregate = aggregate | done elif isinstance(item, TensorDictBase): - any_eot = any_eot | inner_done_or_truncated( + any_eot = any_eot | inner_terminated_or_truncated( data=item, full_done_spec=None, key=key, @@ -815,7 +820,7 @@ def inner_done_or_truncated(data, full_done_spec, key, curr_done_key=()): else: for eot_key, item in full_done_spec.items(): if isinstance(item, CompositeSpec): - any_eot = any_eot | inner_done_or_truncated( + any_eot = any_eot | inner_terminated_or_truncated( data=data.get(eot_key), full_done_spec=item, key=key, @@ -837,7 +842,7 @@ def inner_done_or_truncated(data, full_done_spec, key, curr_done_key=()): any_eot = any_eot | aggregate.any() return any_eot - any_eot = inner_done_or_truncated(data, full_done_spec, key) + any_eot = inner_terminated_or_truncated(data, full_done_spec, key) if not any_eot and not write_full_false: # remove the list of reset keys data.exclude(*list_of_keys, inplace=True) @@ -895,3 +900,76 @@ def skim_through(td, reset=reset): reset = skim_through(data) return reset + + +def _complete_done_at_reset(done_spec, data): + """Completes the data structure at reset to put missing done keys.""" + # by default, if a done key is missing, it is assumed that it is False + # except in 2 cases: (1) there is a "done" but no "terminated" or (2) + # there is a "terminated" but no "done". + leading_dim = data.shape[: -done_spec.ndim] + data_keys = set(data.keys()) + done_spec_keys = set(done_spec.keys()) + for key, item in list(done_spec.items()): + if isinstance(item, CompositeSpec): + _complete_done_at_reset(item, data.get(key)) + elif ( + key == "done" + and "done" in data_keys + and "terminated" in done_spec_keys + and "terminated" not in data_keys + ): + if "truncated" in data.keys(): + raise RuntimeError( + "Cannot infer the value of terminated when only done and truncated are present." + ) + data.set("terminated", data.get("done").clone()) + elif ( + key == "terminated" + and "terminated" in data_keys + and "done" in done_spec_keys + and "done" not in data_keys + ): + if "truncated" in data.keys(): + data.set("done", data.get("terminated") | data.get("truncated")) + else: + data.set("done", data.get("terminated").clone()) + else: + # in this case, just fill with 0s + data.set(key, item.zero(leading_dim)) + + +def _complete_done_at_step(done_spec, data): + """Completes the data structure at step time to put missing done keys.""" + # by default, if a done key is missing, it is assumed that it is False + # except in 2 cases: (1) there is a "done" but no "terminated" or (2) + # there is a "terminated" but no "done". + leading_dim = data.shape[: -done_spec.ndim] + data_keys = set(data.keys()) + for key, item in list(done_spec.items()): + if isinstance(item, CompositeSpec): + _complete_done_at_step(item, data.get(key)) + elif ( + key == "done" + and "done" in data_keys + and "terminated" in done_spec.keys() + and "terminated" not in data_keys + ): + if "truncated" in data.keys(): + raise RuntimeError( + "Cannot infer the value of terminated when only done and truncated are present." + ) + data.set("terminated", data.get("done").clone()) + elif ( + key == "terminated" + and "terminated" in data_keys + and "done" in done_spec.keys() + and "done" not in data_keys + ): + if "truncated" in data.keys(): + data.set("done", data.get("terminated") | data.get("truncated")) + else: + data.set("done", data.get("terminated").clone()) + else: + # in this case, just fill with 0s + data.set("done", item.zero(leading_dim)) From 798811886e9438ecd7eee1e578ebd4a7695bf58c Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 25 Sep 2023 11:52:05 -0400 Subject: [PATCH 104/178] amend --- torchrl/envs/gym_like.py | 8 ++++---- torchrl/envs/utils.py | 39 +++++++++++++++++++++++++-------------- 2 files changed, 29 insertions(+), 18 deletions(-) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index b8ae012d2f8..7c09464a9a7 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -249,12 +249,12 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: # if truncated/terminated is not in the keys, we just don't pass it even if it # is defined. - if terminated is not None and "terminated" in self.done_keys: - obs_dict["terminated"] = terminated + if terminated is None: + terminated = done if truncated is not None and "truncated" in self.done_keys: obs_dict["truncated"] = truncated - if done is not None and "done" in self.done_keys: - obs_dict["done"] = done + obs_dict["done"] = done + obs_dict["terminated"] = terminated tensordict_out = TensorDict(obs_dict, batch_size=tensordict.batch_size) diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 0e155c74da6..10d9f317b0f 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -911,9 +911,12 @@ def _complete_done_at_reset(done_spec, data): data_keys = set(data.keys()) done_spec_keys = set(done_spec.keys()) for key, item in list(done_spec.items()): + val = data.get(key, None) if isinstance(item, CompositeSpec): _complete_done_at_reset(item, data.get(key)) - elif ( + continue + shape = (*leading_dim, *item.shape) + if ( key == "done" and "done" in data_keys and "terminated" in done_spec_keys @@ -923,7 +926,7 @@ def _complete_done_at_reset(done_spec, data): raise RuntimeError( "Cannot infer the value of terminated when only done and truncated are present." ) - data.set("terminated", data.get("done").clone()) + data.set("terminated", data.get("done").clone().reshape(shape)) elif ( key == "terminated" and "terminated" in data_keys @@ -931,12 +934,14 @@ def _complete_done_at_reset(done_spec, data): and "done" not in data_keys ): if "truncated" in data.keys(): - data.set("done", data.get("terminated") | data.get("truncated")) + data.set("done", torch.reshape(data.get("terminated") | data.get("truncated"), shape)) else: - data.set("done", data.get("terminated").clone()) - else: + data.set("done", data.get("terminated").clone().reshape(shape)) + elif val is None: # in this case, just fill with 0s data.set(key, item.zero(leading_dim)) + elif val.shape != shape: + data.set(key, val.reshape(shape)) def _complete_done_at_step(done_spec, data): @@ -946,30 +951,36 @@ def _complete_done_at_step(done_spec, data): # there is a "terminated" but no "done". leading_dim = data.shape[: -done_spec.ndim] data_keys = set(data.keys()) + done_spec_keys = set(done_spec.keys()) for key, item in list(done_spec.items()): + val = data.get(key, None) if isinstance(item, CompositeSpec): - _complete_done_at_step(item, data.get(key)) - elif ( + _complete_done_at_step(item, val) + continue + shape = (*leading_dim, *item.shape) + if ( key == "done" and "done" in data_keys - and "terminated" in done_spec.keys() + and "terminated" in done_spec_keys and "terminated" not in data_keys ): if "truncated" in data.keys(): raise RuntimeError( "Cannot infer the value of terminated when only done and truncated are present." ) - data.set("terminated", data.get("done").clone()) + data.set("terminated", data.get("done").clone().reshape(shape)) elif ( key == "terminated" and "terminated" in data_keys - and "done" in done_spec.keys() + and "done" in done_spec_keys and "done" not in data_keys ): if "truncated" in data.keys(): - data.set("done", data.get("terminated") | data.get("truncated")) + data.set("done", torch.reshape(data.get("terminated") | data.get("truncated"), shape)) else: - data.set("done", data.get("terminated").clone()) - else: + data.set("done", data.get("terminated").clone().reshape(shape)) + elif val is None: # in this case, just fill with 0s - data.set("done", item.zero(leading_dim)) + data.set(key, item.zero(leading_dim)) + elif val.shape != shape: + data.set(key, val.reshape(shape)) From 85d50354df5e0c4adebfc0d81bb5352fdb208ba6 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 25 Sep 2023 11:57:26 -0400 Subject: [PATCH 105/178] let _step return partial done in batched envs --- torchrl/envs/batched_envs.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index f45272d05ed..b11d86eea1a 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -564,7 +564,7 @@ def _step( # shared_tensordicts are locked, and we need to select the keys since we update in-place. # There may be unexpected keys, such as "_reset", that we should comfortably ignore here. out_td = self._envs[i]._step(tensordict_in[i]) - next_td[i].update_(out_td.select(*self._env_output_keys)) + next_td[i].update_(out_td.select(*self._env_output_keys, strict=False)) # We must pass a clone of the tensordict, as the values of this tensordict # will be modified in-place at further steps if self._single_task: From b4fa338902ebb5859a6b353a8cb6980a499963df Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 25 Sep 2023 15:53:38 -0400 Subject: [PATCH 106/178] fix mocking classes --- test/mocking_classes.py | 31 +++++++++++++++++++++---------- torchrl/envs/utils.py | 2 ++ 2 files changed, 23 insertions(+), 10 deletions(-) diff --git a/test/mocking_classes.py b/test/mocking_classes.py index cc63a518ff6..c02c573f580 100644 --- a/test/mocking_classes.py +++ b/test/mocking_classes.py @@ -64,9 +64,12 @@ def __new__( cls._output_spec["full_observation_spec"][key] = item.to( torch.get_default_dtype() ) - cls._output_spec["full_reward_spec"] = cls._output_spec["full_reward_spec"].to( - torch.get_default_dtype() - ) + reward_spec = cls._output_spec["full_reward_spec"] + if isinstance(reward_spec, CompositeSpec): + reward_spec = CompositeSpec({key: item.to(torch.get_default_dtype()) for key, item in reward_spec.items(True, True)}, shape=reward_spec.shape, device=reward_spec.device) + else: + reward_spec = reward_spec.to(torch.get_default_dtype()) + cls._output_spec["full_reward_spec"] = reward_spec if not isinstance(cls._output_spec["full_reward_spec"], CompositeSpec): cls._output_spec["full_reward_spec"] = CompositeSpec( reward=cls._output_spec["full_reward_spec"], @@ -74,7 +77,8 @@ def __new__( ) if not isinstance(cls._output_spec["full_done_spec"], CompositeSpec): cls._output_spec["full_done_spec"] = CompositeSpec( - done=cls._output_spec["full_done_spec"], + done=cls._output_spec["full_done_spec"].clone(), + terminated=cls._output_spec["full_done_spec"].clone(), shape=cls._output_spec["full_done_spec"].shape[:-1], ) if not isinstance(cls._input_spec["full_action_spec"], CompositeSpec): @@ -203,7 +207,10 @@ def _step(self, tensordict): done = self.counter >= self.max_val done = torch.tensor([done], dtype=torch.bool, device=self.device) return TensorDict( - {"reward": n, "done": done, "observation": n.clone()}, + {"reward": n, + "done": done, + "terminated": done, + "observation": n.clone()}, batch_size=[], ) @@ -215,7 +222,7 @@ def _reset(self, tensordict: TensorDictBase = None, **kwargs) -> TensorDictBase: ) done = self.counter >= self.max_val done = torch.tensor([done], dtype=torch.bool, device=self.device) - return TensorDict({"done": done, "observation": n}, []) + return TensorDict({"done": done, "terminated": done.clone(), "observation": n}, []) def rand_step(self, tensordict: Optional[TensorDictBase] = None) -> TensorDictBase: return self.step(tensordict) @@ -334,7 +341,7 @@ def _step(self, tensordict): device=self.device, ) return TensorDict( - {"reward": n, "done": done, "observation": n}, + {"reward": n, "done": done, "terminated": done, "observation": n}, batch_size=tensordict.batch_size, device=self.device, ) @@ -367,7 +374,7 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: device=self.device, ) return TensorDict( - {"done": done, "observation": n}, + {"done": done, "terminated": done, "observation": n}, [ *leading_batch_size, *batch_size, @@ -428,9 +435,9 @@ def __new__( action_spec_cls = OneHotDiscreteTensorSpec action_spec = action_spec_cls(n=7, shape=(*batch_size, 7)) if reward_spec is None: - reward_spec = UnboundedContinuousTensorSpec(shape=(1,)) + reward_spec = CompositeSpec(reward=UnboundedContinuousTensorSpec(shape=(1,))) if done_spec is None: - done_spec = DiscreteTensorSpec(2, dtype=torch.bool, shape=(*batch_size, 1)) + done_spec = CompositeSpec(terminated=DiscreteTensorSpec(2, dtype=torch.bool, shape=(*batch_size, 1))) if state_spec is None: cls._out_key = "observation_orig" @@ -467,6 +474,7 @@ def _reset(self, tensordict: TensorDictBase = None) -> TensorDictBase: tensordict = tensordict.select().set(self.out_key, self._get_out_obs(state)) tensordict = tensordict.set(self._out_key, self._get_out_obs(state)) tensordict.set("done", torch.zeros(*tensordict.shape, 1, dtype=torch.bool)) + tensordict.set("terminated", torch.zeros(*tensordict.shape, 1, dtype=torch.bool)) return tensordict def _step( @@ -491,6 +499,7 @@ def _step( done = torch.zeros_like(done).all(-1).unsqueeze(-1) tensordict.set("reward", reward.to(torch.get_default_dtype())) tensordict.set("done", done) + tensordict.set("terminated", done) return tensordict @@ -571,6 +580,7 @@ def _reset(self, tensordict: TensorDictBase) -> TensorDictBase: # tensordict.set("next_" + self.out_key, self._get_out_obs(state)) # tensordict.set("next_" + self._out_key, self._get_out_obs(state)) tensordict.set("done", torch.zeros(*tensordict.shape, 1, dtype=torch.bool)) + tensordict.set("terminated", torch.zeros(*tensordict.shape, 1, dtype=torch.bool)) return tensordict def _step( @@ -593,6 +603,7 @@ def _step( done = reward = done.unsqueeze(-1) tensordict.set("reward", reward.to(torch.get_default_dtype())) tensordict.set("done", done) + tensordict.set("terminated", done) return tensordict def _obs_step(self, obs, a): diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 10d9f317b0f..5dfb67e74fc 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -923,6 +923,8 @@ def _complete_done_at_reset(done_spec, data): and "terminated" not in data_keys ): if "truncated" in data.keys(): + # if we have a done in the data and a terminated in the specs but not in the data, + # the presence of a truncated is problematic raise RuntimeError( "Cannot infer the value of terminated when only done and truncated are present." ) From 648792d2dc7e8516dc4c6bf5ed912d5f428adac1 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 25 Sep 2023 16:07:14 -0400 Subject: [PATCH 107/178] more fixes --- test/mocking_classes.py | 5 +++++ test/test_env.py | 4 ++-- test/test_helpers.py | 7 +++++++ test/test_rlhf.py | 1 + test/test_transforms.py | 6 +++--- torchrl/envs/utils.py | 10 ++++++++-- 6 files changed, 26 insertions(+), 7 deletions(-) diff --git a/test/mocking_classes.py b/test/mocking_classes.py index c02c573f580..c45b4d9d684 100644 --- a/test/mocking_classes.py +++ b/test/mocking_classes.py @@ -1022,6 +1022,7 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: source={ "observation": self.count.clone(), "done": self.count > self.max_steps, + "terminated": self.count > self.max_steps, }, batch_size=self.batch_size, device=self.device, @@ -1037,6 +1038,7 @@ def _step( source={ "observation": self.count.clone(), "done": self.count > self.max_steps, + "terminated": self.count > self.max_steps, "reward": torch.zeros_like(self.count, dtype=torch.float), }, batch_size=self.batch_size, @@ -1056,6 +1058,7 @@ def _step( source={ "observation": self.count.clone(), "done": self.count > self.max_steps, + "terminated": self.count > self.max_steps, "reward": torch.zeros_like(self.count, dtype=torch.float), }, batch_size=self.batch_size, @@ -1284,6 +1287,7 @@ def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase: source={ "observation": self.count.clone(), "done": self.count > self.max_steps.view_as(self.count), + "terminated": self.count > self.max_steps.view_as(self.count), }, batch_size=self.batch_size, device=self.device, @@ -1299,6 +1303,7 @@ def _step( source={ "observation": self.count.clone(), "done": self.count > self.max_steps.unsqueeze(-1), + "terminated": self.count > self.max_steps.unsqueeze(-1), "reward": torch.zeros_like(self.count, dtype=torch.float), }, batch_size=self.batch_size, diff --git a/test/test_env.py b/test/test_env.py index 3fe257a5228..d46821a8855 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -292,7 +292,7 @@ def test_mb_rollout(self, device, seed=0): check_env_specs(mb_env) rollout = mb_env.rollout(max_steps=100) expected_keys = { - ("next", key) for key in (*mb_env.observation_spec.keys(), "reward", "done") + ("next", key) for key in (*mb_env.observation_spec.keys(), "reward", "done", "terminated") } expected_keys = expected_keys.union( set(mb_env.input_spec["full_action_spec"].keys()) @@ -300,7 +300,7 @@ def test_mb_rollout(self, device, seed=0): expected_keys = expected_keys.union( set(mb_env.input_spec["full_state_spec"].keys()) ) - expected_keys = expected_keys.union({"done", "next"}) + expected_keys = expected_keys.union({"done", "terminated", "next"}) assert set(rollout.keys(True)) == expected_keys assert rollout[("next", "hidden_observation")].shape == (10, 100, 4) diff --git a/test/test_helpers.py b/test/test_helpers.py index 3a90cc5e188..1843a3f738f 100644 --- a/test/test_helpers.py +++ b/test/test_helpers.py @@ -136,6 +136,7 @@ def test_dqn_maker( expected_keys = [ "done", + "terminated", "action", "action_value", "step_count", @@ -212,6 +213,7 @@ def test_redq_make(device, from_pixels, gsde, exploration): actor(td) expected_keys = [ "done", + "terminated", "action", "sample_log_prob", "loc", @@ -247,6 +249,7 @@ def test_redq_make(device, from_pixels, gsde, exploration): qvalue(td) expected_keys = [ "done", + "terminated", "action", "sample_log_prob", "state_action_value", @@ -322,7 +325,9 @@ def test_dreamer_make(device, tanh_loc, exploration, dreamer_constructor_fixture "action", "belief", "done", + "terminated", ("next", "done"), + ("next", "terminated"), ("next", "reward"), ("next", "belief"), ("next", "encoded_latents"), @@ -346,7 +351,9 @@ def test_dreamer_make(device, tanh_loc, exploration, dreamer_constructor_fixture "action", "belief", "done", + "terminated", ("next", "done"), + ("next", "terminated"), ("next", "reward"), ("next", "belief"), ("next", "state"), diff --git a/test/test_rlhf.py b/test/test_rlhf.py index bdb40bf3747..d28f4e1be8d 100644 --- a/test/test_rlhf.py +++ b/test/test_rlhf.py @@ -533,6 +533,7 @@ def test_rollout_from_data(self, tldr_batch_dir, max_new_tokens=10): expected_keys = { ("next", "attention_mask"), ("next", "done"), + ("next", "terminated"), ("next", "input_ids"), ("next", "reward"), "action", diff --git a/test/test_transforms.py b/test/test_transforms.py index f665d2df7bd..930d25b8257 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1379,7 +1379,7 @@ def test_r3m_spec_against_real(self, model, tensor_pixels_key, device): + list(transformed_env.observation_spec.keys()) + ["action"] + [("next", key) for key in transformed_env.observation_spec.keys()] - + [("next", "reward"), ("next", "done"), "done", "next"] + + [("next", "reward"), ("next", "done"),("next", "terminated"), "terminated", "done", "next"] ) assert set(expected_keys) == set(transformed_env.rollout(3).keys(True)) @@ -6759,7 +6759,7 @@ def test_vip_spec_against_real(self, model, tensor_pixels_key, device): + ["action"] + list(transformed_env.observation_spec.keys()) + [("next", key) for key in transformed_env.observation_spec.keys()] - + [("next", "reward"), ("next", "done"), "done", "next"] + + [("next", "reward"), ("next", "done"), "done", ("next", "terminated"), "terminated", "next"] ) assert set(expected_keys) == set(transformed_env.rollout(3).keys(True)) @@ -7013,7 +7013,7 @@ def test_vc1_spec_against_real(self, del_keys, device): unravel_key(("next", key)) for key in transformed_env.observation_spec.keys(True) ] - + [("next", "reward"), ("next", "done"), "done", "next"] + + [("next", "reward"), ("next", "done"), "done", ("next", "terminated"), "terminated", "next"] ) assert set(expected_keys) == set(transformed_env.rollout(3).keys(True)) diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 5dfb67e74fc..8e23f28a02e 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -907,7 +907,10 @@ def _complete_done_at_reset(done_spec, data): # by default, if a done key is missing, it is assumed that it is False # except in 2 cases: (1) there is a "done" but no "terminated" or (2) # there is a "terminated" but no "done". - leading_dim = data.shape[: -done_spec.ndim] + if done_spec.ndim: + leading_dim = data.shape[: -done_spec.ndim] + else: + leading_dim = data.shape data_keys = set(data.keys()) done_spec_keys = set(done_spec.keys()) for key, item in list(done_spec.items()): @@ -951,7 +954,10 @@ def _complete_done_at_step(done_spec, data): # by default, if a done key is missing, it is assumed that it is False # except in 2 cases: (1) there is a "done" but no "terminated" or (2) # there is a "terminated" but no "done". - leading_dim = data.shape[: -done_spec.ndim] + if done_spec.ndim: + leading_dim = data.shape[: -done_spec.ndim] + else: + leading_dim = data.shape data_keys = set(data.keys()) done_spec_keys = set(done_spec.keys()) for key, item in list(done_spec.items()): From 9777332b203b4c90335907e6ae93d0edcc02815a Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 25 Sep 2023 16:13:37 -0400 Subject: [PATCH 108/178] fix step count in equivalence test --- test/mocking_classes.py | 3 ++- test/test_collector.py | 8 ++++---- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/test/mocking_classes.py b/test/mocking_classes.py index c45b4d9d684..147ba52583a 100644 --- a/test/mocking_classes.py +++ b/test/mocking_classes.py @@ -495,11 +495,12 @@ def _step( done = torch.isclose(obs, torch.ones_like(obs) * (self.counter + 1)) reward = done.any(-1).unsqueeze(-1) + # set done to False done = torch.zeros_like(done).all(-1).unsqueeze(-1) tensordict.set("reward", reward.to(torch.get_default_dtype())) tensordict.set("done", done) - tensordict.set("terminated", done) + tensordict.set("terminated", done.clone()) return tensordict diff --git a/test/test_collector.py b/test/test_collector.py index 204989126f8..cf5675818b1 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -647,10 +647,11 @@ def env_fn(seed): # Get a single rollout with dummypolicy env = env_fn(seed) - rollout1a = env.rollout(policy=policy, max_steps=20, auto_reset=True) + env = TransformedEnv(env, StepCounter(20)) + rollout1a = env.rollout(policy=policy, max_steps=50, auto_reset=True) env.set_seed(seed) - rollout1b = env.rollout(policy=policy, max_steps=20, auto_reset=True) - rollout2 = env.rollout(policy=policy, max_steps=20, auto_reset=True) + rollout1b = env.rollout(policy=policy, max_steps=50, auto_reset=True) + rollout2 = env.rollout(policy=policy, max_steps=50, auto_reset=True) assert_allclose_td(rollout1a, rollout1b) with pytest.raises(AssertionError): assert_allclose_td(rollout1a, rollout2) @@ -677,7 +678,6 @@ def env_fn(seed): assert ( rollout1a.batch_size == b1.batch_size ), f"got batch_size {rollout1a.batch_size} and {b1.batch_size}" - assert_allclose_td(rollout1a, b1.select(*rollout1a.keys(True, True))) collector.shutdown() From 9063b5b27bd57267be21c2513330ac5cf001baf5 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 25 Sep 2023 16:16:07 -0400 Subject: [PATCH 109/178] fix transforms --- test/test_transforms.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index 930d25b8257..5e83fb93ea6 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1397,8 +1397,8 @@ def test_step_count_dmc(self): env.rollout(1000) check_env_specs(env) - @pytest.mark.parametrize("write_stop", [False, True]) - @pytest.mark.parametrize("update_done", [10, None]) + @pytest.mark.parametrize("update_done", [False, True]) + @pytest.mark.parametrize("max_steps", [10, None]) def test_single_trans_env_check(self, update_done, max_steps): env = TransformedEnv( ContinuousActionVecMockEnv(), From f47e49d7a7806583f0816ec665754843d7d3a288 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 25 Sep 2023 16:19:09 -0400 Subject: [PATCH 110/178] fix transforms --- test/test_transforms.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index 5e83fb93ea6..4aca9b38430 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1408,9 +1408,11 @@ def test_single_trans_env_check(self, update_done, max_steps): r = env.rollout(100, break_when_any_done=False) if update_done and max_steps: assert r["next", "done"][r["next", "truncated"]].all() - else: + elif max_steps: assert not r["next", "done"][r["next", "truncated"]].all() - + else: + assert "truncated" not in r.keys() + assert ("next", "truncated") not in r.keys(True) def test_parallel_trans_env_check(self): def make_env(): return TransformedEnv(ContinuousActionVecMockEnv(), StepCounter(10)) From c238e9b838369a8763a08330fe471443a7e12f78 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 25 Sep 2023 16:37:54 -0400 Subject: [PATCH 111/178] fix transformed env --- torchrl/envs/transforms/transforms.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index e19069809ed..85a991c2ede 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -40,7 +40,7 @@ from torchrl.envs.common import _EnvPostInit, EnvBase, make_tensordict from torchrl.envs.transforms import functional as F from torchrl.envs.transforms.utils import check_finite -from torchrl.envs.utils import _sort_keys, step_mdp +from torchrl.envs.utils import _sort_keys, step_mdp, _complete_done_at_reset from torchrl.objectives.value.functional import reward2go try: @@ -653,6 +653,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: tensordict = tensordict.clone(False) tensordict_in = self.transform.inv(tensordict) next_tensordict = self.base_env._step(tensordict_in) + _complete_done_at_step(self.full_done_spec, next_tensordict) # we want the input entries to remain unchanged next_tensordict = self.transform._step(tensordict, next_tensordict) return next_tensordict @@ -682,6 +683,7 @@ def _reset(self, tensordict: Optional[TensorDictBase] = None, **kwargs): # the contrary because newer data prevails. out_tensordict = tensordict.update(out_tensordict) out_tensordict = self.transform.reset(out_tensordict) + _complete_done_at_reset(self.full_done_spec, out_tensordict) mt_mode = self.transform.missing_tolerance self.set_missing_tolerance(True) From 502a59b79ec0267b004d13361f8cb1019fd527a4 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 25 Sep 2023 16:42:05 -0400 Subject: [PATCH 112/178] amend --- torchrl/envs/transforms/transforms.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 85a991c2ede..298bb3bf34f 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -40,7 +40,8 @@ from torchrl.envs.common import _EnvPostInit, EnvBase, make_tensordict from torchrl.envs.transforms import functional as F from torchrl.envs.transforms.utils import check_finite -from torchrl.envs.utils import _sort_keys, step_mdp, _complete_done_at_reset +from torchrl.envs.utils import _sort_keys, step_mdp, _complete_done_at_reset, \ + _complete_done_at_step from torchrl.objectives.value.functional import reward2go try: @@ -676,6 +677,7 @@ def _reset(self, tensordict: Optional[TensorDictBase] = None, **kwargs): *self.reset_keys, *self.state_spec.keys(True, True), strict=False ) out_tensordict = self.base_env._reset(tensordict=tensordict, **kwargs) + _complete_done_at_reset(self.full_done_spec, out_tensordict) if tensordict is not None: # the transform may need to read previous info during reset. # For instance, we may need to pass the step_count for partial resets. @@ -683,7 +685,6 @@ def _reset(self, tensordict: Optional[TensorDictBase] = None, **kwargs): # the contrary because newer data prevails. out_tensordict = tensordict.update(out_tensordict) out_tensordict = self.transform.reset(out_tensordict) - _complete_done_at_reset(self.full_done_spec, out_tensordict) mt_mode = self.transform.missing_tolerance self.set_missing_tolerance(True) From 73af14145287b465afe061c38d5db3ff0f004189 Mon Sep 17 00:00:00 2001 From: vmoens Date: Mon, 25 Sep 2023 16:42:37 -0400 Subject: [PATCH 113/178] amend --- torchrl/envs/transforms/transforms.py | 1 + 1 file changed, 1 insertion(+) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 298bb3bf34f..9624d12b179 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -677,6 +677,7 @@ def _reset(self, tensordict: Optional[TensorDictBase] = None, **kwargs): *self.reset_keys, *self.state_spec.keys(True, True), strict=False ) out_tensordict = self.base_env._reset(tensordict=tensordict, **kwargs) + # TODO: figure out how to call this only once _complete_done_at_reset(self.full_done_spec, out_tensordict) if tensordict is not None: # the transform may need to read previous info during reset. From 983e2461ed970597685b41e29a5c1020d30dfabb Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 03:45:24 -0400 Subject: [PATCH 114/178] amend --- test/test_transforms.py | 10 +-- torchrl/envs/common.py | 62 ++++++++++++++++-- torchrl/envs/transforms/transforms.py | 16 +++-- torchrl/envs/utils.py | 92 --------------------------- 4 files changed, 73 insertions(+), 107 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index 4aca9b38430..cf485c3ef9c 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1521,6 +1521,7 @@ def test_transform_compose(self, max_steps, device, batch, reset_workers): td.set("_reset", _reset) td.set("done", _reset) td.set(("next", "done"), done) + td.set("step_count", torch.zeros(*batch, 1, dtype=torch.int)) step_counter[0]._step_count_keys = ["step_count"] step_counter[0]._terminated_keys = ["completed"] step_counter[0]._truncated_keys = ["truncated"] @@ -1547,8 +1548,8 @@ def test_transform_compose(self, max_steps, device, batch, reset_workers): assert torch.all(td.get("truncated")) td = step_counter.reset(td) if reset_workers: - assert torch.all(torch.masked_select(td.get("step_count"), _reset) == 0) - assert torch.all(torch.masked_select(td.get("step_count"), ~_reset) == i) + assert torch.all(torch.masked_select(td.get("step_count"), _reset.unsqueeze(-1)) == 0) + assert torch.all(torch.masked_select(td.get("step_count"), ~_reset.unsqueeze(-1)) == i) else: assert torch.all(td.get("step_count") == 0) @@ -1579,6 +1580,7 @@ def test_transform_no_env(self, max_steps, device, batch, reset_workers): td.set("_reset", _reset) td.set("done", _reset) td.set(("next", "done"), done) + td.set("step_count", torch.zeros(*batch, 1, dtype=torch.int)) step_counter._step_count_keys = ["step_count"] step_counter._done_keys = ["done"] step_counter._truncated_keys = ["truncated"] @@ -1605,8 +1607,8 @@ def test_transform_no_env(self, max_steps, device, batch, reset_workers): assert torch.all(td.get("truncated")) td = step_counter.reset(td) if reset_workers: - assert torch.all(torch.masked_select(td.get("step_count"), _reset) == 0) - assert torch.all(torch.masked_select(td.get("step_count"), ~_reset) == i) + assert torch.all(torch.masked_select(td.get("step_count"), _reset.unsqueeze(-1)) == 0) + assert torch.all(torch.masked_select(td.get("step_count"), ~_reset.unsqueeze(-1)) == i) else: assert torch.all(td.get("step_count") == 0) diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index 46a8e6a41f0..074b34b318b 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -25,8 +25,6 @@ ) from torchrl.data.utils import DEVICE_TYPING from torchrl.envs.utils import ( - _complete_done_at_reset, - _complete_done_at_step, _replace_last, get_available_libraries, step_mdp, @@ -1320,6 +1318,61 @@ def step(self, tensordict: TensorDictBase) -> TensorDictBase: tensordict.set("next", next_tensordict) return tensordict + @classmethod + def _complete_done(cls, done_spec: CompositeSpec, data: TensorDictBase) -> TensorDictBase: + """Completes the data structure at step time to put missing done keys.""" + # by default, if a done key is missing, it is assumed that it is False + # except in 2 cases: (1) there is a "done" but no "terminated" or (2) + # there is a "terminated" but no "done". + if done_spec.ndim: + leading_dim = data.shape[: -done_spec.ndim] + else: + leading_dim = data.shape + data_keys = set(data.keys()) + done_spec_keys = set(done_spec.keys()) + for key, item in list(done_spec.items()): + val = data.get(key, None) + if isinstance(item, CompositeSpec): + cls._complete_done(item, val) + continue + shape = (*leading_dim, *item.shape) + if ( + key == "done" + and "done" in data_keys + and "terminated" in done_spec_keys + and "terminated" not in data_keys + ): + if "truncated" in data.keys(): + raise RuntimeError( + "Cannot infer the value of terminated when only done and truncated are present." + ) + data.set("terminated", data.get("done").clone().reshape(shape)) + elif ( + key == "terminated" + and "terminated" in data_keys + and "done" in done_spec_keys + and "done" not in data_keys + ): + if "truncated" in data.keys(): + data.set( + "done", + torch.reshape( + data.get("terminated") | data.get("truncated"), + shape + ) + ) + else: + data.set( + "done", + data.get("terminated").clone().reshape(shape) + ) + elif val is None: + # in this case, just fill with 0s + data.set(key, item.zero(leading_dim)) + elif val.shape != shape: + data.set(key, val.reshape(shape)) + return data + def _step_proc_data(self, next_tensordict_out): # TODO: Refactor this using reward spec # unsqueeze rewards if needed @@ -1345,7 +1398,7 @@ def _step_proc_data(self, next_tensordict_out): reward = reward.view(expected_reward_shape) next_tensordict_out.set(reward_key, reward) - _complete_done_at_step(self.full_done_spec, next_tensordict_out) + self._complete_done(self.full_done_spec, next_tensordict_out) if self.run_type_checks: for key, spec in self.observation_spec.items(): @@ -1433,7 +1486,8 @@ def reset( raise RuntimeError( f"env._reset returned an object of type {type(tensordict_reset)} but a TensorDict was expected." ) - _complete_done_at_reset(self.full_done_spec, tensordict_reset) + + self._complete_done(self.full_done_spec, tensordict_reset) if not self._allow_done_after_reset: # we iterate over (reset_key, (done_key, truncated_key)) and check that all diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 9624d12b179..678903db198 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -40,8 +40,7 @@ from torchrl.envs.common import _EnvPostInit, EnvBase, make_tensordict from torchrl.envs.transforms import functional as F from torchrl.envs.transforms.utils import check_finite -from torchrl.envs.utils import _sort_keys, step_mdp, _complete_done_at_reset, \ - _complete_done_at_step +from torchrl.envs.utils import _sort_keys, step_mdp from torchrl.objectives.value.functional import reward2go try: @@ -654,7 +653,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: tensordict = tensordict.clone(False) tensordict_in = self.transform.inv(tensordict) next_tensordict = self.base_env._step(tensordict_in) - _complete_done_at_step(self.full_done_spec, next_tensordict) + self.base_env._complete_done(self.base_env.full_done_spec, next_tensordict) # we want the input entries to remain unchanged next_tensordict = self.transform._step(tensordict, next_tensordict) return next_tensordict @@ -677,8 +676,7 @@ def _reset(self, tensordict: Optional[TensorDictBase] = None, **kwargs): *self.reset_keys, *self.state_spec.keys(True, True), strict=False ) out_tensordict = self.base_env._reset(tensordict=tensordict, **kwargs) - # TODO: figure out how to call this only once - _complete_done_at_reset(self.full_done_spec, out_tensordict) + self.base_env._complete_done(self.base_env.full_done_spec, out_tensordict) if tensordict is not None: # the transform may need to read previous info during reset. # For instance, we may need to pass the step_count for partial resets. @@ -693,6 +691,10 @@ def _reset(self, tensordict: Optional[TensorDictBase] = None, **kwargs): self.set_missing_tolerance(mt_mode) return out_tensordict + def _complete_done(cls, done_spec: CompositeSpec, data: TensorDictBase) -> TensorDictBase: + # This step has already been completed. We assume the transform module do their job correctly. + return data + def state_dict(self, *args, **kwargs) -> OrderedDict: state_dict = self.transform.state_dict(*args, **kwargs) return state_dict @@ -4475,10 +4477,10 @@ def reset(self, tensordict: TensorDictBase) -> TensorDictBase: done = self.parent.output_spec["full_done_spec", entry_name].zero() reset = torch.ones_like(done) if step_count is None: - step_count = torch.zeros_like(reset, dtype=torch.int64) + step_count = self.container.observation_spec[step_count_key].zero() # zero the step count if reset is needed - step_count = torch.where(~reset, step_count, 0) + step_count = torch.where(~expand_as_right(reset, step_count), step_count, 0) tensordict.set(step_count_key, step_count) if self.max_steps is not None: truncated = step_count >= self.max_steps diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 8e23f28a02e..74118a1faf7 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -900,95 +900,3 @@ def skim_through(td, reset=reset): reset = skim_through(data) return reset - - -def _complete_done_at_reset(done_spec, data): - """Completes the data structure at reset to put missing done keys.""" - # by default, if a done key is missing, it is assumed that it is False - # except in 2 cases: (1) there is a "done" but no "terminated" or (2) - # there is a "terminated" but no "done". - if done_spec.ndim: - leading_dim = data.shape[: -done_spec.ndim] - else: - leading_dim = data.shape - data_keys = set(data.keys()) - done_spec_keys = set(done_spec.keys()) - for key, item in list(done_spec.items()): - val = data.get(key, None) - if isinstance(item, CompositeSpec): - _complete_done_at_reset(item, data.get(key)) - continue - shape = (*leading_dim, *item.shape) - if ( - key == "done" - and "done" in data_keys - and "terminated" in done_spec_keys - and "terminated" not in data_keys - ): - if "truncated" in data.keys(): - # if we have a done in the data and a terminated in the specs but not in the data, - # the presence of a truncated is problematic - raise RuntimeError( - "Cannot infer the value of terminated when only done and truncated are present." - ) - data.set("terminated", data.get("done").clone().reshape(shape)) - elif ( - key == "terminated" - and "terminated" in data_keys - and "done" in done_spec_keys - and "done" not in data_keys - ): - if "truncated" in data.keys(): - data.set("done", torch.reshape(data.get("terminated") | data.get("truncated"), shape)) - else: - data.set("done", data.get("terminated").clone().reshape(shape)) - elif val is None: - # in this case, just fill with 0s - data.set(key, item.zero(leading_dim)) - elif val.shape != shape: - data.set(key, val.reshape(shape)) - - -def _complete_done_at_step(done_spec, data): - """Completes the data structure at step time to put missing done keys.""" - # by default, if a done key is missing, it is assumed that it is False - # except in 2 cases: (1) there is a "done" but no "terminated" or (2) - # there is a "terminated" but no "done". - if done_spec.ndim: - leading_dim = data.shape[: -done_spec.ndim] - else: - leading_dim = data.shape - data_keys = set(data.keys()) - done_spec_keys = set(done_spec.keys()) - for key, item in list(done_spec.items()): - val = data.get(key, None) - if isinstance(item, CompositeSpec): - _complete_done_at_step(item, val) - continue - shape = (*leading_dim, *item.shape) - if ( - key == "done" - and "done" in data_keys - and "terminated" in done_spec_keys - and "terminated" not in data_keys - ): - if "truncated" in data.keys(): - raise RuntimeError( - "Cannot infer the value of terminated when only done and truncated are present." - ) - data.set("terminated", data.get("done").clone().reshape(shape)) - elif ( - key == "terminated" - and "terminated" in data_keys - and "done" in done_spec_keys - and "done" not in data_keys - ): - if "truncated" in data.keys(): - data.set("done", torch.reshape(data.get("terminated") | data.get("truncated"), shape)) - else: - data.set("done", data.get("terminated").clone().reshape(shape)) - elif val is None: - # in this case, just fill with 0s - data.set(key, item.zero(leading_dim)) - elif val.shape != shape: - data.set(key, val.reshape(shape)) From 555bca9d7d9756427ae34bedddc8bb59cfa75780 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 03:48:48 -0400 Subject: [PATCH 115/178] amend --- test/test_transforms.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index cf485c3ef9c..ff2a018af7a 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1548,8 +1548,8 @@ def test_transform_compose(self, max_steps, device, batch, reset_workers): assert torch.all(td.get("truncated")) td = step_counter.reset(td) if reset_workers: - assert torch.all(torch.masked_select(td.get("step_count"), _reset.unsqueeze(-1)) == 0) - assert torch.all(torch.masked_select(td.get("step_count"), ~_reset.unsqueeze(-1)) == i) + assert torch.all(torch.masked_select(td.get("step_count"), _reset) == 0) + assert torch.all(torch.masked_select(td.get("step_count"), ~_reset) == i) else: assert torch.all(td.get("step_count") == 0) @@ -1576,7 +1576,7 @@ def test_transform_no_env(self, max_steps, device, batch, reset_workers): td = TensorDict({"done": done, ("next", "done"): done}, batch, device=device) _reset = torch.zeros((), dtype=torch.bool, device=device) while not _reset.any() and reset_workers: - _reset = torch.rand(batch, device=device) < 0.5 + _reset = torch.randn(done.shape, device=device) < 0 td.set("_reset", _reset) td.set("done", _reset) td.set(("next", "done"), done) @@ -1607,8 +1607,8 @@ def test_transform_no_env(self, max_steps, device, batch, reset_workers): assert torch.all(td.get("truncated")) td = step_counter.reset(td) if reset_workers: - assert torch.all(torch.masked_select(td.get("step_count"), _reset.unsqueeze(-1)) == 0) - assert torch.all(torch.masked_select(td.get("step_count"), ~_reset.unsqueeze(-1)) == i) + assert torch.all(torch.masked_select(td.get("step_count"), _reset) == 0) + assert torch.all(torch.masked_select(td.get("step_count"), ~_reset) == i) else: assert torch.all(td.get("step_count") == 0) From 85f58defa99a59062f53f9c908a1b7624e0f5a95 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 04:03:22 -0400 Subject: [PATCH 116/178] remove calls to done_key --- test/test_env.py | 18 +++++++++++------- test/test_exploration.py | 9 +++++---- test/test_libs.py | 12 +++++++----- test/test_transforms.py | 24 +++++++++++++++--------- torchrl/envs/batched_envs.py | 6 ------ torchrl/envs/libs/envpool.py | 6 ++++-- torchrl/envs/libs/isaacgym.py | 6 +++++- torchrl/envs/libs/robohive.py | 2 +- 8 files changed, 48 insertions(+), 35 deletions(-) diff --git a/test/test_env.py b/test/test_env.py index d46821a8855..fb2fbd74ab6 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -1821,10 +1821,11 @@ def test_nested_env(self, envclass): reset = env.reset() assert not isinstance(env.done_spec, CompositeSpec) assert not isinstance(env.reward_spec, CompositeSpec) - assert ( - env.done_spec - == env.output_spec[("full_done_spec", *_unravel_key_to_tuple(env.done_key))] - ) + for done_key in env.done_keys: + assert ( + env.full_done_spec[done_key] + == env.output_spec[("full_done_spec", *_unravel_key_to_tuple(done_key))] + ) assert ( env.reward_spec == env.output_spec[ @@ -1832,12 +1833,14 @@ def test_nested_env(self, envclass): ] ) if envclass == "NestedCountingEnv": - assert env.done_key == ("data", "done") + for done_key in env.done_keys: + assert done_key in (("data", "done"), ("data", "terminated")) assert env.reward_key == ("data", "reward") assert ("data", "done") in reset.keys(True) assert ("data", "states") in reset.keys(True) assert ("data", "reward") not in reset.keys(True) - assert env.done_key in reset.keys(True) + for done_key in env.done_keys: + assert done_key in reset.keys(True) assert env.reward_key not in reset.keys(True) next_state = env.rand_step() @@ -1845,7 +1848,8 @@ def test_nested_env(self, envclass): assert ("next", "data", "done") in next_state.keys(True) assert ("next", "data", "states") in next_state.keys(True) assert ("next", "data", "reward") in next_state.keys(True) - assert ("next", *_unravel_key_to_tuple(env.done_key)) in next_state.keys(True) + for done_key in env.done_keys: + assert ("next", *_unravel_key_to_tuple(done_key)) in next_state.keys(True) assert ("next", *_unravel_key_to_tuple(env.reward_key)) in next_state.keys(True) @pytest.mark.parametrize("batch_size", [(), (32,), (32, 1)]) diff --git a/test/test_exploration.py b/test/test_exploration.py index 85dfcec3f30..0caf93824ce 100644 --- a/test/test_exploration.py +++ b/test/test_exploration.py @@ -381,10 +381,11 @@ def test_nested( device=device, ) for _td in collector: - assert ( - _td[_replace_last(env.done_key, is_init_key)].shape - == _td[env.done_key].shape - ) + for done_key in env.done_keys: + assert ( + _td[_replace_last(done_key, is_init_key)].shape + == _td[done_key].shape + ) break return diff --git a/test/test_libs.py b/test/test_libs.py index 60bd8839678..1b4b6faeec5 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -1720,13 +1720,14 @@ def test_collector(self, n_envs, n_workers, n_agents, frames_per_batch=80): n_observations_per_agent, ) assert _td["next", env.reward_key].shape == agents_td_batch + (1,) - assert _td[env.done_key].shape == td_batch + (1,) - assert _td["next", env.done_key].shape == td_batch + (1,) + for done_key in env.done_keys: + assert _td[done_key].shape == td_batch + (1,) + assert _td["next", done_key].shape == td_batch + (1,) assert env.reward_key not in _td.keys(True, True) assert env.action_key not in _td["next"].keys(True, True) - def test_collector_hetero(self, n_envs=10, frames_per_batch=20): + def test_collector_heterogeneous(self, n_envs=10, frames_per_batch=20): env = VmasEnv( scenario="simple_tag", num_envs=n_envs, @@ -1755,8 +1756,9 @@ def test_collector_hetero(self, n_envs=10, frames_per_batch=20): assert _td["next", "agents"].shape == agents_td_batch assert _td["collector"].shape == td_batch assert _td["next", env.reward_key].shape == agents_td_batch + (1,) - assert _td[env.done_key].shape == td_batch + (1,) - assert _td["next", env.done_key].shape == td_batch + (1,) + for done_key in env.done_keys: + assert _td[done_key].shape == td_batch + (1,) + assert _td["next", done_key].shape == td_batch + (1,) assert env.reward_key not in _td.keys(True, True) assert env.action_key not in _td["next"].keys(True, True) diff --git a/test/test_transforms.py b/test/test_transforms.py index ff2a018af7a..621eca5283d 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1480,7 +1480,9 @@ def test_nested( assert step[max_steps:].eq(torch.arange(rollout_length - max_steps)).all() if nested_done: - reset_key = (*env.done_key[:-1], "_reset") + for done_key in env.done_keys: + reset_key = (*done_key[:-1], "_reset") + break else: reset_key = "_reset" _reset = env.done_spec.rand() @@ -2568,8 +2570,9 @@ def test_nested(self, nest_reward, nest_done): td = transformed_env.rollout(1) td_keys = td.keys(True, True) assert ("next", env.reward_key) in td_keys - assert ("next", env.done_key) in td_keys - assert env.done_key in td_keys + for done_key in env.done_keys: + assert ("next", done_key) in td_keys + assert done_key in td_keys assert env.action_key in td_keys assert ("data", "states") in td_keys assert ("next", "data", "states") in td_keys @@ -2578,8 +2581,9 @@ def test_nested(self, nest_reward, nest_done): td = transformed_env.rollout(1) td_keys = td.keys(True, True) assert ("next", env.reward_key) in td_keys - assert ("next", env.done_key) in td_keys - assert env.done_key in td_keys + for done_key in env.done_keys: + assert ("next", done_key) in td_keys + assert done_key in td_keys assert env.action_key in td_keys assert ("data", "states") not in td_keys assert ("next", "data", "states") not in td_keys @@ -2806,8 +2810,9 @@ def test_nested(self, nest_reward, nest_done): td = transformed_env.rollout(1) td_keys = td.keys(True, True) assert ("next", env.reward_key) in td_keys - assert ("next", env.done_key) in td_keys - assert env.done_key in td_keys + for done_key in env.done_keys: + assert ("next", done_key) in td_keys + assert done_key in td_keys assert env.action_key in td_keys assert ("data", "states") not in td_keys assert ("next", "data", "states") not in td_keys @@ -2816,8 +2821,9 @@ def test_nested(self, nest_reward, nest_done): td = transformed_env.rollout(1) td_keys = td.keys(True, True) assert ("next", env.reward_key) in td_keys - assert ("next", env.done_key) in td_keys - assert env.done_key in td_keys + for done_key in env.done_keys: + assert ("next", done_key) in td_keys + assert done_key in td_keys assert env.action_key in td_keys assert ("data", "states") in td_keys assert ("next", "data", "states") in td_keys diff --git a/torchrl/envs/batched_envs.py b/torchrl/envs/batched_envs.py index b11d86eea1a..3756be73946 100644 --- a/torchrl/envs/batched_envs.py +++ b/torchrl/envs/batched_envs.py @@ -1036,12 +1036,6 @@ def _run_worker_pipe_shared_mem( child_pipe.send("started") - # _excluded_reset_keys = { - # _unravel_key_to_tuple(env.reward_key), - # # _unravel_key_to_tuple(env.done_key), - # _unravel_key_to_tuple(env.action_key), - # } - while True: try: cmd, data = child_pipe.recv() diff --git a/torchrl/envs/libs/envpool.py b/torchrl/envs/libs/envpool.py index 2e57e9dc117..3fc0776c23d 100644 --- a/torchrl/envs/libs/envpool.py +++ b/torchrl/envs/libs/envpool.py @@ -198,7 +198,7 @@ def _transform_reset_output( ) obs = self.obs.clone(False) - obs.update({self.done_key: self.done_spec.zero()}) + obs.update(self.full_done_spec.zero()) return obs def _transform_step_output( @@ -208,7 +208,9 @@ def _transform_step_output( obs, reward, done, *_ = envpool_output obs = self._treevalue_or_numpy_to_tensor_or_dict(obs) - obs.update({self.reward_key: torch.tensor(reward), self.done_key: done}) + reward_and_done = {self.reward_key: torch.tensor(reward)} + reward_and_done.update({done_key: done for done_key in self.done_keys}) + obs.update(reward_and_done) self.obs = tensordict_out = TensorDict( obs, batch_size=self.batch_size, diff --git a/torchrl/envs/libs/isaacgym.py b/torchrl/envs/libs/isaacgym.py index d5abd71d647..40093284e73 100644 --- a/torchrl/envs/libs/isaacgym.py +++ b/torchrl/envs/libs/isaacgym.py @@ -65,7 +65,11 @@ def _make_specs(self, env: "gym.Env") -> None: # noqa: F821 data = self.rollout(3).get("next")[..., 0] del data[self.reward_key] - del data[self.done_key] + for done_key in self.done_keys: + try: + del data[done_key] + except KeyError: + continue specs = make_composite_from_td(data) obs_spec = self.observation_spec diff --git a/torchrl/envs/libs/robohive.py b/torchrl/envs/libs/robohive.py index fd332062813..7c763e16441 100644 --- a/torchrl/envs/libs/robohive.py +++ b/torchrl/envs/libs/robohive.py @@ -257,7 +257,7 @@ def get_obs(): rollout = self.rollout(2, return_contiguous=False).get("next") rollout = rollout.exclude( - self.reward_key, self.done_key, *self.observation_spec.keys(True, True) + self.reward_key, *self.done_keys, *self.observation_spec.keys(True, True) ) rollout = rollout[..., 0] spec = make_composite_from_td(rollout) From 6fc39d092999e8e769ebec5c9a18f29cd9e59d48 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 04:19:10 -0400 Subject: [PATCH 117/178] fix step counter --- test/test_transforms.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index 621eca5283d..324f00e5bd7 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1482,10 +1482,11 @@ def test_nested( if nested_done: for done_key in env.done_keys: reset_key = (*done_key[:-1], "_reset") + _reset = env.full_done_spec[done_key].rand() break else: reset_key = "_reset" - _reset = env.done_spec.rand() + _reset = env.full_done_spec["done"].rand() td_reset = transformed_env.reset( TensorDict( {reset_key: _reset, step_key: last_step}, From 175b701363f747e1115b45e70ad91e1f59ed3e61 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 05:04:44 -0400 Subject: [PATCH 118/178] vec envs --- torchrl/envs/transforms/transforms.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 678903db198..70e382add80 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -5807,7 +5807,7 @@ def done_keys(self) -> List[NestedKey]: if keys is None: keys = self.parent.done_keys self._done_keys = keys - expected_done_keys = {"done", "truncated", "stop"} + expected_done_keys = {"done", "truncated", "stop", "terminated"} # put this check for now. We can consider relaxing that later # and allow nested values, though they will still need to be unique. for done_key in keys: From 92e78260cd4c4a2fb6a84af908a78a5d13226e91 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 05:04:56 -0400 Subject: [PATCH 119/178] vec envs --- torchrl/envs/transforms/transforms.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 70e382add80..901ccde3f94 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -5807,7 +5807,7 @@ def done_keys(self) -> List[NestedKey]: if keys is None: keys = self.parent.done_keys self._done_keys = keys - expected_done_keys = {"done", "truncated", "stop", "terminated"} + expected_done_keys = {"done", "truncated", "terminated"} # put this check for now. We can consider relaxing that later # and allow nested values, though they will still need to be unique. for done_key in keys: From cd6eaeae3039df6373d109b6e30ae3ca63be53a3 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 05:28:39 -0400 Subject: [PATCH 120/178] amend --- torchrl/envs/common.py | 33 +++++++++++++-------------------- torchrl/envs/libs/isaacgym.py | 4 +++- torchrl/envs/libs/jumanji.py | 6 ++++-- 3 files changed, 20 insertions(+), 23 deletions(-) diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index 074b34b318b..6b60e2a6062 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -1319,7 +1319,9 @@ def step(self, tensordict: TensorDictBase) -> TensorDictBase: return tensordict @classmethod - def _complete_done(cls, done_spec: CompositeSpec, data: TensorDictBase) -> TensorDictBase: + def _complete_done( + cls, done_spec: CompositeSpec, data: TensorDictBase + ) -> TensorDictBase: """Completes the data structure at step time to put missing done keys.""" # by default, if a done key is missing, it is assumed that it is False # except in 2 cases: (1) there is a "done" but no "terminated" or (2) @@ -1338,7 +1340,7 @@ def _complete_done(cls, done_spec: CompositeSpec, data: TensorDictBase) -> Tenso shape = (*leading_dim, *item.shape) if ( key == "done" - and "done" in data_keys + and val is not None and "terminated" in done_spec_keys and "terminated" not in data_keys ): @@ -1346,30 +1348,23 @@ def _complete_done(cls, done_spec: CompositeSpec, data: TensorDictBase) -> Tenso raise RuntimeError( "Cannot infer the value of terminated when only done and truncated are present." ) - data.set("terminated", data.get("done").clone().reshape(shape)) + data.set("terminated", data.get("done").reshape(shape)) elif ( key == "terminated" - and "terminated" in data_keys + and val is not None and "done" in done_spec_keys and "done" not in data_keys ): if "truncated" in data.keys(): - data.set( - "done", - torch.reshape( - data.get("terminated") | data.get("truncated"), - shape - ) - ) + done = data.get("terminated") | data.get("truncated") + data.set("done", done.reshape(shape)) else: - data.set( - "done", - data.get("terminated").clone().reshape(shape) - ) + data.set("done", data.get("terminated").reshape(shape)) elif val is None: # in this case, just fill with 0s data.set(key, item.zero(leading_dim)) - elif val.shape != shape: + continue + if val.shape != shape: data.set(key, val.reshape(shape)) return data @@ -1918,9 +1913,7 @@ def fake_tensordict(self) -> TensorDictBase: # instantiates reward_spec if needed _ = self.reward_spec reward_spec = self.output_spec["full_reward_spec"] - # instantiates done_spec if needed - _ = self.done_spec - done_spec = self.output_spec["full_done_spec"] + full_done_spec = self.output_spec["full_done_spec"] fake_obs = observation_spec.zero() @@ -1933,7 +1926,7 @@ def fake_tensordict(self) -> TensorDictBase: fake_in_out = fake_input.update(fake_obs) fake_reward = reward_spec.zero() - fake_done = done_spec.zero() + fake_done = full_done_spec.zero() next_output = fake_obs.clone() next_output.update(fake_reward) diff --git a/torchrl/envs/libs/isaacgym.py b/torchrl/envs/libs/isaacgym.py index 40093284e73..89eff5c159a 100644 --- a/torchrl/envs/libs/isaacgym.py +++ b/torchrl/envs/libs/isaacgym.py @@ -59,7 +59,9 @@ def __init__( def _make_specs(self, env: "gym.Env") -> None: # noqa: F821 super()._make_specs(env, batch_size=self.batch_size) - self.done_spec = self.done_spec.squeeze(-1) + self.full_done_spec = { + key: spec.squeeze(-1) for key, spec in self.full_done_spec.items(True, True) + } self.observation_spec["obs"] = self.observation_spec["observation"] del self.observation_spec["observation"] diff --git a/torchrl/envs/libs/jumanji.py b/torchrl/envs/libs/jumanji.py index 5ccdcd0f2c0..09d74dcaa46 100644 --- a/torchrl/envs/libs/jumanji.py +++ b/torchrl/envs/libs/jumanji.py @@ -310,6 +310,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: ) tensordict_out.set("reward", reward) tensordict_out.set("done", done) + # tensordict_out.set("terminated", done) tensordict_out["state"] = state_dict return tensordict_out @@ -333,7 +334,7 @@ def _reset( # collect outputs state_dict = self.read_state(state) obs_dict = self.read_obs(timestep.observation) - done = self.done_spec.zero() + done_td = self.full_done_spec.zero() # build results tensordict_out = TensorDict( @@ -341,7 +342,8 @@ def _reset( batch_size=self.batch_size, device=self.device, ) - tensordict_out.set("done", done) + tensordict_out.update(done_td) + print("done_td", done_td) tensordict_out["state"] = state_dict return tensordict_out From 23e139b4bdb292119a737461f98bf3acc42234da Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 05:33:47 -0400 Subject: [PATCH 121/178] amend --- test/mocking_classes.py | 36 +++++++++++++++++++-------- test/test_env.py | 4 +-- test/test_transforms.py | 28 ++++++++++++++++++--- torchrl/envs/common.py | 2 ++ torchrl/envs/transforms/transforms.py | 4 ++- 5 files changed, 58 insertions(+), 16 deletions(-) diff --git a/test/mocking_classes.py b/test/mocking_classes.py index 147ba52583a..40d41f923e9 100644 --- a/test/mocking_classes.py +++ b/test/mocking_classes.py @@ -66,7 +66,14 @@ def __new__( ) reward_spec = cls._output_spec["full_reward_spec"] if isinstance(reward_spec, CompositeSpec): - reward_spec = CompositeSpec({key: item.to(torch.get_default_dtype()) for key, item in reward_spec.items(True, True)}, shape=reward_spec.shape, device=reward_spec.device) + reward_spec = CompositeSpec( + { + key: item.to(torch.get_default_dtype()) + for key, item in reward_spec.items(True, True) + }, + shape=reward_spec.shape, + device=reward_spec.device, + ) else: reward_spec = reward_spec.to(torch.get_default_dtype()) cls._output_spec["full_reward_spec"] = reward_spec @@ -207,10 +214,7 @@ def _step(self, tensordict): done = self.counter >= self.max_val done = torch.tensor([done], dtype=torch.bool, device=self.device) return TensorDict( - {"reward": n, - "done": done, - "terminated": done, - "observation": n.clone()}, + {"reward": n, "done": done, "terminated": done, "observation": n.clone()}, batch_size=[], ) @@ -222,7 +226,9 @@ def _reset(self, tensordict: TensorDictBase = None, **kwargs) -> TensorDictBase: ) done = self.counter >= self.max_val done = torch.tensor([done], dtype=torch.bool, device=self.device) - return TensorDict({"done": done, "terminated": done.clone(), "observation": n}, []) + return TensorDict( + {"done": done, "terminated": done.clone(), "observation": n}, [] + ) def rand_step(self, tensordict: Optional[TensorDictBase] = None) -> TensorDictBase: return self.step(tensordict) @@ -435,9 +441,15 @@ def __new__( action_spec_cls = OneHotDiscreteTensorSpec action_spec = action_spec_cls(n=7, shape=(*batch_size, 7)) if reward_spec is None: - reward_spec = CompositeSpec(reward=UnboundedContinuousTensorSpec(shape=(1,))) + reward_spec = CompositeSpec( + reward=UnboundedContinuousTensorSpec(shape=(1,)) + ) if done_spec is None: - done_spec = CompositeSpec(terminated=DiscreteTensorSpec(2, dtype=torch.bool, shape=(*batch_size, 1))) + done_spec = CompositeSpec( + terminated=DiscreteTensorSpec( + 2, dtype=torch.bool, shape=(*batch_size, 1) + ) + ) if state_spec is None: cls._out_key = "observation_orig" @@ -474,7 +486,9 @@ def _reset(self, tensordict: TensorDictBase = None) -> TensorDictBase: tensordict = tensordict.select().set(self.out_key, self._get_out_obs(state)) tensordict = tensordict.set(self._out_key, self._get_out_obs(state)) tensordict.set("done", torch.zeros(*tensordict.shape, 1, dtype=torch.bool)) - tensordict.set("terminated", torch.zeros(*tensordict.shape, 1, dtype=torch.bool)) + tensordict.set( + "terminated", torch.zeros(*tensordict.shape, 1, dtype=torch.bool) + ) return tensordict def _step( @@ -581,7 +595,9 @@ def _reset(self, tensordict: TensorDictBase) -> TensorDictBase: # tensordict.set("next_" + self.out_key, self._get_out_obs(state)) # tensordict.set("next_" + self._out_key, self._get_out_obs(state)) tensordict.set("done", torch.zeros(*tensordict.shape, 1, dtype=torch.bool)) - tensordict.set("terminated", torch.zeros(*tensordict.shape, 1, dtype=torch.bool)) + tensordict.set( + "terminated", torch.zeros(*tensordict.shape, 1, dtype=torch.bool) + ) return tensordict def _step( diff --git a/test/test_env.py b/test/test_env.py index fb2fbd74ab6..06e4c301e8d 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -292,7 +292,8 @@ def test_mb_rollout(self, device, seed=0): check_env_specs(mb_env) rollout = mb_env.rollout(max_steps=100) expected_keys = { - ("next", key) for key in (*mb_env.observation_spec.keys(), "reward", "done", "terminated") + ("next", key) + for key in (*mb_env.observation_spec.keys(), "reward", "done", "terminated") } expected_keys = expected_keys.union( set(mb_env.input_spec["full_action_spec"].keys()) @@ -1819,7 +1820,6 @@ def test_nested_env(self, envclass): else: raise NotImplementedError reset = env.reset() - assert not isinstance(env.done_spec, CompositeSpec) assert not isinstance(env.reward_spec, CompositeSpec) for done_key in env.done_keys: assert ( diff --git a/test/test_transforms.py b/test/test_transforms.py index 324f00e5bd7..3f8b056fbb8 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1379,7 +1379,14 @@ def test_r3m_spec_against_real(self, model, tensor_pixels_key, device): + list(transformed_env.observation_spec.keys()) + ["action"] + [("next", key) for key in transformed_env.observation_spec.keys()] - + [("next", "reward"), ("next", "done"),("next", "terminated"), "terminated", "done", "next"] + + [ + ("next", "reward"), + ("next", "done"), + ("next", "terminated"), + "terminated", + "done", + "next", + ] ) assert set(expected_keys) == set(transformed_env.rollout(3).keys(True)) @@ -1413,6 +1420,7 @@ def test_single_trans_env_check(self, update_done, max_steps): else: assert "truncated" not in r.keys() assert ("next", "truncated") not in r.keys(True) + def test_parallel_trans_env_check(self): def make_env(): return TransformedEnv(ContinuousActionVecMockEnv(), StepCounter(10)) @@ -6770,7 +6778,14 @@ def test_vip_spec_against_real(self, model, tensor_pixels_key, device): + ["action"] + list(transformed_env.observation_spec.keys()) + [("next", key) for key in transformed_env.observation_spec.keys()] - + [("next", "reward"), ("next", "done"), "done", ("next", "terminated"), "terminated", "next"] + + [ + ("next", "reward"), + ("next", "done"), + "done", + ("next", "terminated"), + "terminated", + "next", + ] ) assert set(expected_keys) == set(transformed_env.rollout(3).keys(True)) @@ -7024,7 +7039,14 @@ def test_vc1_spec_against_real(self, del_keys, device): unravel_key(("next", key)) for key in transformed_env.observation_spec.keys(True) ] - + [("next", "reward"), ("next", "done"), "done", ("next", "terminated"), "terminated", "next"] + + [ + ("next", "reward"), + ("next", "done"), + "done", + ("next", "terminated"), + "terminated", + "next", + ] ) assert set(expected_keys) == set(transformed_env.rollout(3).keys(True)) diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index 6b60e2a6062..9c021a535b5 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -1361,6 +1361,8 @@ def _complete_done( else: data.set("done", data.get("terminated").reshape(shape)) elif val is None: + # we must keep this here: we only want to fill with 0s if we're sure + # done should not be copied to terminated or terminated to done # in this case, just fill with 0s data.set(key, item.zero(leading_dim)) continue diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 901ccde3f94..272cc76d0ee 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -691,7 +691,9 @@ def _reset(self, tensordict: Optional[TensorDictBase] = None, **kwargs): self.set_missing_tolerance(mt_mode) return out_tensordict - def _complete_done(cls, done_spec: CompositeSpec, data: TensorDictBase) -> TensorDictBase: + def _complete_done( + cls, done_spec: CompositeSpec, data: TensorDictBase + ) -> TensorDictBase: # This step has already been completed. We assume the transform module do their job correctly. return data From 677c40881209209bc298d50f0a1e3e37b8a68ffa Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 05:42:48 -0400 Subject: [PATCH 122/178] amend --- torchrl/envs/libs/gym.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/torchrl/envs/libs/gym.py b/torchrl/envs/libs/gym.py index b5a8ba3ac37..f052782eee5 100644 --- a/torchrl/envs/libs/gym.py +++ b/torchrl/envs/libs/gym.py @@ -212,8 +212,8 @@ def _gym_to_torchrl_spec_transform( remap_state_to_observation: whether to rename the 'state' key of Dict specs to "observation". Default is true. """ - gym = gym_backend() - if isinstance(spec, gym.spaces.tuple.Tuple): + gym_spaces = gym_backend("spaces") + if isinstance(spec, gym_spaces.tuple.Tuple): return torch.stack( [ _gym_to_torchrl_spec_transform( @@ -226,7 +226,7 @@ def _gym_to_torchrl_spec_transform( ], 0, ) - if isinstance(spec, gym.spaces.discrete.Discrete): + if isinstance(spec, gym_spaces.discrete.Discrete): action_space_cls = ( DiscreteTensorSpec if categorical_action_encoding @@ -238,11 +238,11 @@ def _gym_to_torchrl_spec_transform( else torch.long ) return action_space_cls(spec.n, device=device, dtype=dtype) - elif isinstance(spec, gym.spaces.multi_binary.MultiBinary): + elif isinstance(spec, gym_spaces.multi_binary.MultiBinary): return BinaryDiscreteTensorSpec( spec.n, device=device, dtype=numpy_to_torch_dtype_dict[spec.dtype] ) - elif isinstance(spec, gym.spaces.multi_discrete.MultiDiscrete): + elif isinstance(spec, gym_spaces.multi_discrete.MultiDiscrete): if len(spec.nvec.shape) == 1 and len(np.unique(spec.nvec)) > 1: dtype = ( numpy_to_torch_dtype_dict[spec.dtype] @@ -270,7 +270,7 @@ def _gym_to_torchrl_spec_transform( ], 0, ) - elif isinstance(spec, gym.spaces.Box): + elif isinstance(spec, gym_spaces.Box): shape = spec.shape if not len(shape): shape = torch.Size([1]) @@ -311,7 +311,7 @@ def _gym_to_torchrl_spec_transform( remap_state_to_observation=remap_state_to_observation, ) return CompositeSpec(**spec_out) - elif isinstance(spec, gym.spaces.dict.Dict): + elif isinstance(spec, gym_spaces.dict.Dict): return _gym_to_torchrl_spec_transform( spec.spaces, device=device, From 3c790816df216790c37db6713a7b508f857b4f59 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 06:17:01 -0400 Subject: [PATCH 123/178] d4rl --- torchrl/data/datasets/d4rl.py | 44 +++++++++++++++++------------------ 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/torchrl/data/datasets/d4rl.py b/torchrl/data/datasets/d4rl.py index 3c0e1bb8b60..4f5f4ae9a0a 100644 --- a/torchrl/data/datasets/d4rl.py +++ b/torchrl/data/datasets/d4rl.py @@ -28,7 +28,7 @@ class D4RLExperienceReplay(TensorDictReplayBuffer): If present, metadata will be written in ``D4RLExperienceReplay.metadata`` and excluded from the dataset. - The transitions are reconstructed using ``done = terminal | timeout`` and + The transitions are reconstructed using ``done = terminated | truncated`` and the ``("next", "observation")`` of ``"done"`` states are zeroed. Args: @@ -50,8 +50,8 @@ class D4RLExperienceReplay(TensorDictReplayBuffer): split_trajs (bool, optional): if ``True``, the trajectories will be split along the first dimension and padded to have a matching shape. To split the trajectories, the ``"done"`` signal will be used, which - is recovered via ``done = timeout | terminal``. In other words, - it is assumed that any ``timeout`` or ``terminal`` signal is + is recovered via ``done = truncated | terminated``. In other words, + it is assumed that any ``truncated`` or ``terminated`` signal is equivalent to the end of a trajectory. For some datasets from ``D4RL``, this may not be true. It is up to the user to make accurate choices regarding this usage of ``split_trajs``. @@ -72,12 +72,12 @@ class D4RLExperienceReplay(TensorDictReplayBuffer): .. note:: The keys in ``from_env=True`` and ``from_env=False`` *may* unexpectedly - differ. In particular, the ``"timeout"`` key (used to determine the + differ. In particular, the ``"truncated"`` key (used to determine the end of an episode) may be absent when ``from_env=False`` but present otherwise, leading to a different slicing when ``traj_splits`` is enabled. - use_timeout_as_done (bool, optional): if ``True``, ``done = terminal | timeout``. - Otherwise, only the ``terminal`` key is used. Defaults to ``True``. + use_truncated_as_done (bool, optional): if ``True``, ``done = terminated | truncated``. + Otherwise, only the ``terminated`` key is used. Defaults to ``True``. **env_kwargs (key-value pairs): additional kwargs for :func:`d4rl.qlearning_dataset`. Supports ``terminate_on_end`` (``False`` by default) or other kwargs if defined by D4RL library. @@ -117,7 +117,7 @@ def __init__( transform: Optional["Transform"] = None, # noqa-F821 split_trajs: bool = False, from_env: bool = True, - use_timeout_as_done: bool = True, + use_truncated_as_done: bool = True, **env_kwargs, ): @@ -126,7 +126,7 @@ def __init__( if not self._has_d4rl: raise ImportError("Could not import d4rl") from self.D4RL_ERR self.from_env = from_env - self.use_timeout_as_done = use_timeout_as_done + self.use_truncated_as_done = use_truncated_as_done if from_env: dataset = self._get_dataset_from_env(name, env_kwargs) else: @@ -184,16 +184,16 @@ def _get_dataset_direct(self, name, env_kwargs): dataset.rename_key("observations", "observation") dataset.set("next", dataset.select()) dataset.rename_key("next_observations", ("next", "observation")) - dataset.rename_key("terminals", "terminal") + dataset.rename_key("terminals", "terminated") if "timeouts" in dataset.keys(): - dataset.rename_key("timeouts", "timeout") - if self.use_timeout_as_done: - done = dataset.get("terminal") | dataset.get( - "timeout", torch.zeros((), dtype=torch.bool) + dataset.rename_key("timeouts", "truncated") + if self.use_truncated_as_done: + done = dataset.get("terminated") | dataset.get( + "truncated", torch.zeros((), dtype=torch.bool) ) dataset.set("done", done) else: - dataset.set("done", dataset.get("terminal")) + dataset.set("done", dataset.get("terminated")) dataset.rename_key("rewards", "reward") dataset.rename_key("actions", "action") @@ -209,7 +209,7 @@ def _get_dataset_direct(self, name, env_kwargs): # dataset.rename_key("next_observations", "next/observation") dataset["reward"] = dataset["reward"].unsqueeze(-1) dataset["next"].update( - dataset.select("reward", "done", "terminal", "timeout", strict=False) + dataset.select("reward", "done", "terminated", "truncated", strict=False) ) dataset = ( dataset.clone() @@ -251,17 +251,17 @@ def _get_dataset_from_env(self, name, env_kwargs): self.metadata = {} dataset.rename_key("observations", "observation") - dataset.rename_key("terminals", "terminal") + dataset.rename_key("terminals", "terminated") if "timeouts" in dataset.keys(): - dataset.rename_key("timeouts", "timeout") - if self.use_timeout_as_done: + dataset.rename_key("timeouts", "truncated") + if self.use_truncated_as_done: dataset.set( "done", - dataset.get("terminal") - | dataset.get("timeout", torch.zeros((), dtype=torch.bool)), + dataset.get("terminated") + | dataset.get("truncated", torch.zeros((), dtype=torch.bool)), ) else: - dataset.set("done", dataset.get("terminal")) + dataset.set("done", dataset.get("terminated")) dataset.rename_key("rewards", "reward") dataset.rename_key("actions", "action") @@ -285,7 +285,7 @@ def _get_dataset_from_env(self, name, env_kwargs): dataset.select("observation", "info", strict=False)[1:], ) dataset["next"].update( - dataset.select("reward", "done", "terminal", "timeout", strict=False) + dataset.select("reward", "done", "terminated", "truncated", strict=False) ) dataset = ( dataset.clone() From 54e75b09294f187f4c6afd28c0efa4dcc1188278 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 06:29:09 -0400 Subject: [PATCH 124/178] d4rl unsqueeze --- torchrl/data/datasets/d4rl.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/torchrl/data/datasets/d4rl.py b/torchrl/data/datasets/d4rl.py index 4f5f4ae9a0a..15b7bdd78f0 100644 --- a/torchrl/data/datasets/d4rl.py +++ b/torchrl/data/datasets/d4rl.py @@ -206,6 +206,10 @@ def _get_dataset_direct(self, name, env_kwargs): dataset["done"] = dataset["done"].bool() dataset["done"] = dataset["done"].unsqueeze(-1) + if "terminated" in dataset.keys(): + dataset["terminated"] = dataset["terminated"].unsqueeze(-1) + if "truncated" in dataset.keys(): + dataset["truncated"] = dataset["truncated"].unsqueeze(-1) # dataset.rename_key("next_observations", "next/observation") dataset["reward"] = dataset["reward"].unsqueeze(-1) dataset["next"].update( @@ -278,6 +282,10 @@ def _get_dataset_from_env(self, name, env_kwargs): dataset["done"] = dataset["done"].bool() dataset["done"] = dataset["done"].unsqueeze(-1) + if "terminated" in dataset.keys(): + dataset["terminated"] = dataset["terminated"].unsqueeze(-1) + if "truncated" in dataset.keys(): + dataset["truncated"] = dataset["truncated"].unsqueeze(-1) # dataset.rename_key("next_observations", "next/observation") dataset["reward"] = dataset["reward"].unsqueeze(-1) dataset = dataset[:-1].set( From 244e3d314e50e38a073220eb157228b157264eec Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 06:44:53 -0400 Subject: [PATCH 125/178] amend --- test/test_libs.py | 6 +++--- torchrl/data/datasets/d4rl.py | 14 +++++++------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index 1b4b6faeec5..8cd932c31ee 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -1775,7 +1775,7 @@ def test_terminate_on_end(self, task): from_env=False, terminate_on_end=True, batch_size=2, - use_timeout_as_done=False, + use_truncated_as_done=False, ) _ = D4RLExperienceReplay( task, @@ -1783,14 +1783,14 @@ def test_terminate_on_end(self, task): from_env=False, terminate_on_end=False, batch_size=2, - use_timeout_as_done=False, + use_truncated_as_done=False, ) data_from_env = D4RLExperienceReplay( task, split_trajs=True, from_env=True, batch_size=2, - use_timeout_as_done=False, + use_truncated_as_done=False, ) keys = set(data_from_env._storage._storage.keys(True, True)) keys = keys.intersection(data_true._storage._storage.keys(True, True)) diff --git a/torchrl/data/datasets/d4rl.py b/torchrl/data/datasets/d4rl.py index 15b7bdd78f0..56dc696143c 100644 --- a/torchrl/data/datasets/d4rl.py +++ b/torchrl/data/datasets/d4rl.py @@ -254,10 +254,10 @@ def _get_dataset_from_env(self, name, env_kwargs): else: self.metadata = {} - dataset.rename_key("observations", "observation") - dataset.rename_key("terminals", "terminated") + dataset.rename_key_("observations", "observation") + dataset.rename_key_("terminals", "terminated") if "timeouts" in dataset.keys(): - dataset.rename_key("timeouts", "truncated") + dataset.rename_key_("timeouts", "truncated") if self.use_truncated_as_done: dataset.set( "done", @@ -267,10 +267,10 @@ def _get_dataset_from_env(self, name, env_kwargs): else: dataset.set("done", dataset.get("terminated")) - dataset.rename_key("rewards", "reward") - dataset.rename_key("actions", "action") + dataset.rename_key_("rewards", "reward") + dataset.rename_key_("actions", "action") try: - dataset.rename_key("infos", "info") + dataset.rename_key_("infos", "info") except KeyError: pass @@ -286,7 +286,7 @@ def _get_dataset_from_env(self, name, env_kwargs): dataset["terminated"] = dataset["terminated"].unsqueeze(-1) if "truncated" in dataset.keys(): dataset["truncated"] = dataset["truncated"].unsqueeze(-1) - # dataset.rename_key("next_observations", "next/observation") + # dataset.rename_key_("next_observations", "next/observation") dataset["reward"] = dataset["reward"].unsqueeze(-1) dataset = dataset[:-1].set( "next", From 09abb71cf969d73e3a110d6450467f5eb2d9e519 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 07:22:26 -0400 Subject: [PATCH 126/178] amend --- test/test_libs.py | 11 ++++++----- torchrl/data/datasets/d4rl.py | 4 ++-- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index 8cd932c31ee..f91d75e9618 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -1767,15 +1767,15 @@ def test_collector_heterogeneous(self, n_envs=10, frames_per_batch=20): @pytest.mark.skipif(not _has_d4rl, reason="D4RL not found") class TestD4RL: @pytest.mark.parametrize("task", ["walker2d-medium-replay-v2"]) - def test_terminate_on_end(self, task): - t0 = time.time() + @pytest.mark.parametrize("use_truncated_as_done", [True, False]) + def test_terminate_on_end(self, task, use_truncated_as_done): data_true = D4RLExperienceReplay( task, split_trajs=True, from_env=False, terminate_on_end=True, batch_size=2, - use_truncated_as_done=False, + use_truncated_as_done=use_truncated_as_done, ) _ = D4RLExperienceReplay( task, @@ -1783,17 +1783,18 @@ def test_terminate_on_end(self, task): from_env=False, terminate_on_end=False, batch_size=2, - use_truncated_as_done=False, + use_truncated_as_done=use_truncated_as_done, ) data_from_env = D4RLExperienceReplay( task, split_trajs=True, from_env=True, batch_size=2, - use_truncated_as_done=False, + use_truncated_as_done=use_truncated_as_done, ) keys = set(data_from_env._storage._storage.keys(True, True)) keys = keys.intersection(data_true._storage._storage.keys(True, True)) + assert data_true._storage._storage.shape == data_from_env._storage._storage.shape assert_allclose_td( data_true._storage._storage.select(*keys), data_from_env._storage._storage.select(*keys), diff --git a/torchrl/data/datasets/d4rl.py b/torchrl/data/datasets/d4rl.py index 56dc696143c..874796e5778 100644 --- a/torchrl/data/datasets/d4rl.py +++ b/torchrl/data/datasets/d4rl.py @@ -189,7 +189,7 @@ def _get_dataset_direct(self, name, env_kwargs): dataset.rename_key("timeouts", "truncated") if self.use_truncated_as_done: done = dataset.get("terminated") | dataset.get( - "truncated", torch.zeros((), dtype=torch.bool) + "truncated", False ) dataset.set("done", done) else: @@ -262,7 +262,7 @@ def _get_dataset_from_env(self, name, env_kwargs): dataset.set( "done", dataset.get("terminated") - | dataset.get("truncated", torch.zeros((), dtype=torch.bool)), + | dataset.get("truncated", False), ) else: dataset.set("done", dataset.get("terminated")) From 384db246ab485bf2ae39e430ede540886a82d0f4 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 08:16:57 -0400 Subject: [PATCH 127/178] minor --- torchrl/envs/transforms/transforms.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 272cc76d0ee..4b4dfe618f2 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -618,7 +618,7 @@ def _inplace_update(self): @property def output_spec(self) -> TensorSpec: """Observation spec of the transformed environment.""" - if self.__dict__.get("_output_spec", None) is None or not self.cache_specs: + if not self.cache_specs or self.__dict__.get("_output_spec", None) is None: output_spec = self.base_env.output_spec.clone() # remove cached key values From 3693cacb0a97b651b4ef3a03ea65931b91cba168 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 08:49:14 -0400 Subject: [PATCH 128/178] amend --- torchrl/envs/common.py | 74 +++++++++++++++------------ torchrl/envs/gym_like.py | 11 ++-- torchrl/envs/transforms/transforms.py | 1 - 3 files changed, 48 insertions(+), 38 deletions(-) diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index 9c021a535b5..b9121114867 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -1317,7 +1317,6 @@ def step(self, tensordict: TensorDictBase) -> TensorDictBase: next_tensordict.update(next_preset) tensordict.set("next", next_tensordict) return tensordict - @classmethod def _complete_done( cls, done_spec: CompositeSpec, data: TensorDictBase @@ -1330,44 +1329,51 @@ def _complete_done( leading_dim = data.shape[: -done_spec.ndim] else: leading_dim = data.shape - data_keys = set(data.keys()) - done_spec_keys = set(done_spec.keys()) - for key, item in list(done_spec.items()): + vals = {} + i = -1 + for i, (key, item) in enumerate(done_spec.items()): val = data.get(key, None) if isinstance(item, CompositeSpec): cls._complete_done(item, val) continue shape = (*leading_dim, *item.shape) - if ( - key == "done" - and val is not None - and "terminated" in done_spec_keys - and "terminated" not in data_keys - ): - if "truncated" in data.keys(): - raise RuntimeError( - "Cannot infer the value of terminated when only done and truncated are present." - ) - data.set("terminated", data.get("done").reshape(shape)) - elif ( - key == "terminated" - and val is not None - and "done" in done_spec_keys - and "done" not in data_keys - ): - if "truncated" in data.keys(): - done = data.get("terminated") | data.get("truncated") - data.set("done", done.reshape(shape)) - else: - data.set("done", data.get("terminated").reshape(shape)) - elif val is None: - # we must keep this here: we only want to fill with 0s if we're sure - # done should not be copied to terminated or terminated to done - # in this case, just fill with 0s - data.set(key, item.zero(leading_dim)) - continue - if val.shape != shape: - data.set(key, val.reshape(shape)) + if val is not None: + if val.shape != shape: + data.set(key, val.reshape(shape)) + vals[key] = val + if len(vals) < i + 1: + # complete missing dones: we only want to do that if we don't have enough done values + data_keys = set(data.keys()) + done_spec_keys = set(done_spec.keys()) + for key, item in done_spec.items(): + val = vals.get(key, None) + if ( + key == "done" + and val is not None + and "terminated" in done_spec_keys + and "terminated" not in data_keys + ): + if "truncated" in data_keys: + raise RuntimeError( + "Cannot infer the value of terminated when only done and truncated are present." + ) + data.set("terminated", val) + elif ( + key == "terminated" + and val is not None + and "done" in done_spec_keys + and "done" not in data_keys + ): + if "truncated" in data_keys: + done = val | data.get("truncated") + data.set("done", done) + else: + data.set("done", val) + elif val is None: + # we must keep this here: we only want to fill with 0s if we're sure + # done should not be copied to terminated or terminated to done + # in this case, just fill with 0s + data.set(key, item.zero(leading_dim)) return data def _step_proc_data(self, next_tensordict_out): diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index 7c09464a9a7..d9c4ba0a1d3 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -173,6 +173,12 @@ def read_done( elif truncated is None and done is None: done = terminated do_break = done.any() if not isinstance(done, bool) else done + if isinstance(done, bool): + done = [done] + if terminated is not None: + terminated = [terminated] + if truncated is not None: + truncated = [truncated] return ( terminated, truncated, @@ -214,7 +220,6 @@ def read_obs( ) # observations = self.observation_spec.encode(observations, ignore_device=True) return observations - def _step(self, tensordict: TensorDictBase) -> TensorDictBase: action = tensordict.get(self.action_key) action_np = self.read_action(action) @@ -256,7 +261,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: obs_dict["done"] = done obs_dict["terminated"] = terminated - tensordict_out = TensorDict(obs_dict, batch_size=tensordict.batch_size) + tensordict_out = TensorDict(obs_dict, batch_size=tensordict.batch_size, device=self.device) if self.info_dict_reader and info is not None: if not isinstance(info, dict): @@ -268,7 +273,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: out = info_dict_reader(info, tensordict_out) if out is not None: tensordict_out = out - tensordict_out = tensordict_out.to(self.device, non_blocking=True) + # tensordict_out = tensordict_out.to(self.device, non_blocking=True) return tensordict_out def _reset( diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index 4b4dfe618f2..bde82ecc235 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -648,7 +648,6 @@ def input_spec(self) -> TensorSpec: else: input_spec = self.__dict__.get("_input_spec", None) return input_spec - def _step(self, tensordict: TensorDictBase) -> TensorDictBase: tensordict = tensordict.clone(False) tensordict_in = self.transform.inv(tensordict) From ca541334b13e6240bf595830673878cad8bd22f6 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 09:00:08 -0400 Subject: [PATCH 129/178] amend --- torchrl/envs/gym_like.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index d9c4ba0a1d3..92efc790abd 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -261,7 +261,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: obs_dict["done"] = done obs_dict["terminated"] = terminated - tensordict_out = TensorDict(obs_dict, batch_size=tensordict.batch_size, device=self.device) + tensordict_out = TensorDict(obs_dict, batch_size=tensordict.batch_size) if self.info_dict_reader and info is not None: if not isinstance(info, dict): @@ -273,7 +273,7 @@ def _step(self, tensordict: TensorDictBase) -> TensorDictBase: out = info_dict_reader(info, tensordict_out) if out is not None: tensordict_out = out - # tensordict_out = tensordict_out.to(self.device, non_blocking=True) + tensordict_out = tensordict_out.to(self.device, non_blocking=True) return tensordict_out def _reset( From 0fdc522409e3b596ef3d8364b07cccbe65de2f49 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 09:04:18 -0400 Subject: [PATCH 130/178] amend --- torchrl/envs/common.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index b9121114867..dd66d36db2a 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -1341,11 +1341,12 @@ def _complete_done( if val.shape != shape: data.set(key, val.reshape(shape)) vals[key] = val + if len(vals) < i + 1: # complete missing dones: we only want to do that if we don't have enough done values data_keys = set(data.keys()) done_spec_keys = set(done_spec.keys()) - for key, item in done_spec.items(): + for key, item in done_spec.items(False, True): val = vals.get(key, None) if ( key == "done" From fdad78f413163a1d66ffe4c0f1d5f9e82cd0acd5 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 09:13:12 -0400 Subject: [PATCH 131/178] test_terminated_or_truncated_spec --- test/test_env.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_env.py b/test/test_env.py index 06e4c301e8d..70fe4bec37a 100644 --- a/test/test_env.py +++ b/test/test_env.py @@ -2163,7 +2163,7 @@ def test_terminated_or_truncated_spec(self): spec = CompositeSpec( { - "done": DiscreteTensorSpec(2, shape=(2, 1), dtype=torch.bool), + "truncated": DiscreteTensorSpec(2, shape=(2, 1), dtype=torch.bool), "terminated": DiscreteTensorSpec(2, shape=(2, 1), dtype=torch.bool), ("nested", "terminated"): DiscreteTensorSpec( 2, shape=(2, 1), dtype=torch.bool From f454e11b9f0cfe763098f17c7ff9b4a89a33a513 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 09:41:23 -0400 Subject: [PATCH 132/178] more fixes --- test/mocking_classes.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/test/mocking_classes.py b/test/mocking_classes.py index 40d41f923e9..d66f78724ad 100644 --- a/test/mocking_classes.py +++ b/test/mocking_classes.py @@ -1157,7 +1157,7 @@ def __init__( ) if self.nested_done: - done_spec = self.done_spec.unsqueeze(-1).expand( + done_spec = self.full_done_spec.unsqueeze(-1).expand( *self.batch_size, self.nested_dim ) self.done_spec = CompositeSpec( @@ -1177,11 +1177,11 @@ def _reset(self, tensordict): if self.nested_done: for done_key in self.done_keys: td[done_key] = ( - td["done"] + td[done_key[-1]] .unsqueeze(-1) .expand(*self.batch_size, self.nested_dim, 1) ) - del td["done"] + del td[done_key[-1]] if self.nested_obs_action: td["data", "states"] = ( td["observation"] @@ -1211,11 +1211,11 @@ def _step(self, td): if self.nested_done: for done_key in self.done_keys: td[done_key] = ( - td["done"] + td[done_key[-1]] .unsqueeze(-1) .expand(*self.batch_size, self.nested_dim, 1) ) - del td["done"] + del td[done_key[-1]] if self.nested_obs_action: td["data", "states"] = ( td["observation"] From 88cee595c69096c103ed49c62e8dd7fd28ecd92c Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 09:43:11 -0400 Subject: [PATCH 133/178] --capture no --- .github/unittest/linux/scripts/run_all.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/unittest/linux/scripts/run_all.sh b/.github/unittest/linux/scripts/run_all.sh index c3d6b5f5f4c..e5c6ea6f34f 100755 --- a/.github/unittest/linux/scripts/run_all.sh +++ b/.github/unittest/linux/scripts/run_all.sh @@ -184,10 +184,10 @@ pytest test/smoke_test.py -v --durations 200 pytest test/smoke_test_deps.py -v --durations 200 -k 'test_gym or test_dm_control_pixels or test_dm_control or test_tb' if [ "${CU_VERSION:-}" != cpu ] ; then python .github/unittest/helpers/coverage_run_parallel.py -m pytest test \ - --instafail --durations 200 -v --ignore test/test_rlhf.py + --instafail --durations 200 -vv --capture no --ignore test/test_rlhf.py else python .github/unittest/helpers/coverage_run_parallel.py -m pytest test \ - --instafail --durations 200 -v --ignore test/test_rlhf.py --ignore test/test_distributed.py + --instafail --durations 200 -vv --capture no --ignore test/test_rlhf.py --ignore test/test_distributed.py fi coverage combine From 57ccb6365ce8a104a20be828f9a7d8b1db79ee7b Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 10:31:19 -0400 Subject: [PATCH 134/178] attempt to limit collector idle time --- .github/unittest/linux/scripts/run_all.sh | 2 +- test/test_collector.py | 41 ++++++++++++++++++----- torchrl/collectors/collectors.py | 2 +- 3 files changed, 35 insertions(+), 10 deletions(-) diff --git a/.github/unittest/linux/scripts/run_all.sh b/.github/unittest/linux/scripts/run_all.sh index e5c6ea6f34f..35bb2b21764 100755 --- a/.github/unittest/linux/scripts/run_all.sh +++ b/.github/unittest/linux/scripts/run_all.sh @@ -178,7 +178,7 @@ python -m torch.utils.collect_env #export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$lib_dir export MKL_THREADING_LAYER=GNU export CKPT_BACKEND=torch - +export MAX_IDLE_COUNT=100 pytest test/smoke_test.py -v --durations 200 pytest test/smoke_test_deps.py -v --durations 200 -k 'test_gym or test_dm_control_pixels or test_dm_control or test_tb' diff --git a/test/test_collector.py b/test/test_collector.py index cf5675818b1..ca195a449bc 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -265,6 +265,7 @@ def env_fn(seed): assert d.names[-1] == "time" ccollector.shutdown() + del ccollector @pytest.mark.parametrize("num_env", [1, 2]) @@ -571,6 +572,7 @@ def env_fn(): break assert b.names[-1] == "time" ccollector.shutdown() + del ccollector @pytest.mark.parametrize("num_env", [1, 2]) @@ -852,7 +854,7 @@ def test_collector_vecnorm_envcreator(static_seed): td4 = s["worker1"]["env_state_dict"]["worker0"]["_extra_state"]["td"].clone() assert (td3 == td4).all() assert (td1 != td4).any() - + c.shutdown() del c @@ -960,6 +962,7 @@ def make_env(): break collector.shutdown() dummy_env.close() + del collector @pytest.mark.skipif(not _has_gym, reason="test designed with GymEnv") @@ -1139,6 +1142,7 @@ def env_fn(seed): batch = next(collector.iterator()) assert batch.device == torch.device(storing_device) collector.shutdown() + del collector @pytest.mark.skipif(not _has_gym, reason="test designed with GymEnv") @@ -1198,6 +1202,8 @@ def test_auto_wrap_modules(self, collector_class, multiple_outputs, env_maker): assert isinstance(collector.policy, TensorDictModule) assert collector.policy.out_keys == out_keys assert collector.policy.module is policy + collector.shutdown() + del collector def test_no_wrap_compatible_module(self, collector_class, env_maker): policy = TensorDictCompatiblePolicy( @@ -1222,6 +1228,8 @@ def test_no_wrap_compatible_module(self, collector_class, env_maker): assert isinstance(collector.policy, TensorDictCompatiblePolicy) assert collector.policy.out_keys == ["action"] assert collector.policy is policy + collector.shutdown() + del collector def test_auto_wrap_error(self, collector_class, env_maker): policy = UnwrappablePolicy(out_features=env_maker().action_spec.shape[-1]) @@ -1281,6 +1289,8 @@ def test_initial_obs_consistency(env_class, seed=1): expected_1 = torch.cat([arange_0, arange_0, arange]) expected = torch.stack([expected_0, expected_1]) assert torch.allclose(obs, expected.to(obs.dtype)) + collector.shutdown() + del collector def weight_reset(m): @@ -1316,6 +1326,8 @@ def env_fn(seed): for batch in collector: assert batch["collector"]["traj_ids"][0] != -1 assert batch["collector"]["traj_ids"][1] == -1 + collector.shutdown() + del collector @pytest.mark.parametrize( "env_name", ["vec"] @@ -1350,6 +1362,8 @@ def env_fn(seed): trajectory_ids = batch["collector"]["traj_ids"] trajectory_ids_mask = trajectory_ids != -1 # valid frames mask assert trajectory_ids[trajectory_ids_mask].numel() < frames_per_batch + collector.shutdown() + del collector def test_maxframes_error(): @@ -1371,10 +1385,10 @@ def test_reset_heterogeneous_envs(): env1 = lambda: TransformedEnv(CountingEnv(), StepCounter(2)) env2 = lambda: TransformedEnv(CountingEnv(), StepCounter(3)) env = SerialEnv(2, [env1, env2]) - c = SyncDataCollector( + collector = SyncDataCollector( env, RandomPolicy(env.action_spec), total_frames=10_000, frames_per_batch=1000 ) - for data in c: # noqa: B007 + for data in collector: # noqa: B007 break assert ( data[0]["next", "truncated"].squeeze() @@ -1384,6 +1398,8 @@ def test_reset_heterogeneous_envs(): data[1]["next", "truncated"].squeeze() == torch.tensor([False, False, True]).repeat(168)[:500] ).all() + collector.shutdown() + del collector class TestNestedEnvsCollector: @@ -1430,7 +1446,7 @@ def test_multi_collector_nested_env_consistency(self, seed=1): with pytest.raises(AssertionError): assert_allclose_td(d1, d2) ccollector.shutdown() - + del ccollector assert_allclose_td(c1, d1) assert_allclose_td(c2, d2) @@ -1463,6 +1479,7 @@ def test_collector_nested_env_combinations( for _td in ccollector: break ccollector.shutdown() + del ccollector @pytest.mark.parametrize("batch_size", [(), (5,), (5, 2)]) def test_nested_env_dims(self, batch_size, nested_dim=5, frames_per_batch=20): @@ -1484,7 +1501,7 @@ def test_nested_env_dims(self, batch_size, nested_dim=5, frames_per_batch=20): for _td in ccollector: break ccollector.shutdown() - + del ccollector assert ("data", "reward") not in _td.keys(True) assert _td.batch_size == (*batch_size, frames_per_batch // prod(batch_size)) assert _td["data"].batch_size == ( @@ -1530,6 +1547,7 @@ def test_collector_het_env(self, batch_size, frames_per_batch, seed=1, max_steps == torch.arange(max_steps).repeat(collected_frames // max_steps) ).all() # Check reset worked assert (_td["lazy"][..., i]["action"] == 1).all() + del ccollector def test_multi_collector_het_env_consistency( self, seed=1, frames_per_batch=20, batch_dim=10 @@ -1577,6 +1595,7 @@ def test_multi_collector_het_env_consistency( with pytest.raises(AssertionError): assert_allclose_td(d1, d2) ccollector.shutdown() + del ccollector assert_allclose_td(c1, d1) assert_allclose_td(c2, d2) @@ -1604,6 +1623,7 @@ def test_collector(self, batch_size, frames_per_batch, max_steps, seed=1): for done_key in env.done_keys: assert _replace_last(done_key, "_reset") not in _td.keys(True, True) check_rollout_consistency_multikey_env(_td, max_steps=max_steps) + del ccollector def test_multi_collector_consistency( self, seed=1, frames_per_batch=20, batch_dim=10 @@ -1652,6 +1672,7 @@ def test_multi_collector_consistency( with pytest.raises(AssertionError): assert_allclose_td(d1, d2) ccollector.shutdown() + del ccollector assert_allclose_td(c1, d1) assert_allclose_td(c2, d2) @@ -1755,7 +1776,7 @@ def test_param_sync(self, give_weights, collector, policy_device, env_device): assert (data["action"] == 3).all() finally: col.shutdown() - + del col class TestBringReset: def test_bring_reset_to_root(self): @@ -1953,6 +1974,7 @@ def make_env(): for _ in enumerate(collector): raise AssertionError collector.shutdown() + del collector @pytest.mark.skipif( @@ -1979,9 +2001,12 @@ def test_num_threads(): assert torch.get_num_threads() == 7 for _ in c: pass - c.shutdown() - del c finally: + try: + c.shutdown() + del c + except Exception: + print("Failed to shut down collector") # reset vals collectors._main_async_collector = _main_async_collector_saved torch.set_num_threads(num_threads) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 33bb06147fc..29e9ecbb5e2 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -1397,7 +1397,7 @@ def _shutdown_main(self) -> None: continue for proc in self.procs: - proc.join(10.0) + proc.join() self.queue_out.close() for pipe in self.pipes: pipe.close() From e5b0d234907311006f8b1f7e634d93314a910897 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 10:39:54 -0400 Subject: [PATCH 135/178] lint --- test/test_collector.py | 1 + test/test_libs.py | 4 +++- torchrl/data/datasets/d4rl.py | 7 ++----- torchrl/envs/common.py | 3 ++- torchrl/envs/gym_like.py | 1 + torchrl/envs/libs/isaacgym.py | 2 +- torchrl/envs/transforms/transforms.py | 1 + torchrl/envs/utils.py | 3 +-- 8 files changed, 12 insertions(+), 10 deletions(-) diff --git a/test/test_collector.py b/test/test_collector.py index ca195a449bc..ef88ab607a7 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -1778,6 +1778,7 @@ def test_param_sync(self, give_weights, collector, policy_device, env_device): col.shutdown() del col + class TestBringReset: def test_bring_reset_to_root(self): # simple diff --git a/test/test_libs.py b/test/test_libs.py index f91d75e9618..ac6848f60d5 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -1794,7 +1794,9 @@ def test_terminate_on_end(self, task, use_truncated_as_done): ) keys = set(data_from_env._storage._storage.keys(True, True)) keys = keys.intersection(data_true._storage._storage.keys(True, True)) - assert data_true._storage._storage.shape == data_from_env._storage._storage.shape + assert ( + data_true._storage._storage.shape == data_from_env._storage._storage.shape + ) assert_allclose_td( data_true._storage._storage.select(*keys), data_from_env._storage._storage.select(*keys), diff --git a/torchrl/data/datasets/d4rl.py b/torchrl/data/datasets/d4rl.py index 874796e5778..839e42e6cde 100644 --- a/torchrl/data/datasets/d4rl.py +++ b/torchrl/data/datasets/d4rl.py @@ -188,9 +188,7 @@ def _get_dataset_direct(self, name, env_kwargs): if "timeouts" in dataset.keys(): dataset.rename_key("timeouts", "truncated") if self.use_truncated_as_done: - done = dataset.get("terminated") | dataset.get( - "truncated", False - ) + done = dataset.get("terminated") | dataset.get("truncated", False) dataset.set("done", done) else: dataset.set("done", dataset.get("terminated")) @@ -261,8 +259,7 @@ def _get_dataset_from_env(self, name, env_kwargs): if self.use_truncated_as_done: dataset.set( "done", - dataset.get("terminated") - | dataset.get("truncated", False), + dataset.get("terminated") | dataset.get("truncated", False), ) else: dataset.set("done", dataset.get("terminated")) diff --git a/torchrl/envs/common.py b/torchrl/envs/common.py index dd66d36db2a..f4ee7056543 100644 --- a/torchrl/envs/common.py +++ b/torchrl/envs/common.py @@ -1317,6 +1317,7 @@ def step(self, tensordict: TensorDictBase) -> TensorDictBase: next_tensordict.update(next_preset) tensordict.set("next", next_tensordict) return tensordict + @classmethod def _complete_done( cls, done_spec: CompositeSpec, data: TensorDictBase @@ -1331,7 +1332,7 @@ def _complete_done( leading_dim = data.shape vals = {} i = -1 - for i, (key, item) in enumerate(done_spec.items()): + for i, (key, item) in enumerate(done_spec.items()): # noqa: B007 val = data.get(key, None) if isinstance(item, CompositeSpec): cls._complete_done(item, val) diff --git a/torchrl/envs/gym_like.py b/torchrl/envs/gym_like.py index 92efc790abd..9084b3abbe0 100644 --- a/torchrl/envs/gym_like.py +++ b/torchrl/envs/gym_like.py @@ -220,6 +220,7 @@ def read_obs( ) # observations = self.observation_spec.encode(observations, ignore_device=True) return observations + def _step(self, tensordict: TensorDictBase) -> TensorDictBase: action = tensordict.get(self.action_key) action_np = self.read_action(action) diff --git a/torchrl/envs/libs/isaacgym.py b/torchrl/envs/libs/isaacgym.py index 89eff5c159a..9206d23d09a 100644 --- a/torchrl/envs/libs/isaacgym.py +++ b/torchrl/envs/libs/isaacgym.py @@ -123,7 +123,7 @@ def read_done( truncated = truncated.bool() if done is not None: done = done.bool() - return completed, truncated, completed, completed.any() + return terminated, truncated, done, done.any() def read_reward(self, total_reward, step_reward): """Reads a reward and the total reward so far (in the frame skip loop) and returns a sum of the two. diff --git a/torchrl/envs/transforms/transforms.py b/torchrl/envs/transforms/transforms.py index bde82ecc235..4b4dfe618f2 100644 --- a/torchrl/envs/transforms/transforms.py +++ b/torchrl/envs/transforms/transforms.py @@ -648,6 +648,7 @@ def input_spec(self) -> TensorSpec: else: input_spec = self.__dict__.get("_input_spec", None) return input_spec + def _step(self, tensordict: TensorDictBase) -> TensorDictBase: tensordict = tensordict.clone(False) tensordict_in = self.transform.inv(tensordict) diff --git a/torchrl/envs/utils.py b/torchrl/envs/utils.py index 74118a1faf7..cd8e4afb68f 100644 --- a/torchrl/envs/utils.py +++ b/torchrl/envs/utils.py @@ -735,8 +735,7 @@ def terminated_or_truncated( key: str = "_reset", write_full_false: bool = False, ) -> bool: - """Reads the done / terminated / truncated keys within a tensordict, and - writes a new tensor where the values of both signals are aggregated. + """Reads the done / terminated / truncated keys within a tensordict, and writes a new tensor where the values of both signals are aggregated. The modification occurs in-place within the TensorDict instance provided. This function can be used to compute the `"_reset"` signals in batched From dfe726f2a9502011ff6eaa8280812e28eb8c89f2 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 11:19:11 -0400 Subject: [PATCH 136/178] amend --- torchrl/collectors/utils.py | 4 ---- torchrl/data/datasets/d4rl.py | 9 ++++++--- 2 files changed, 6 insertions(+), 7 deletions(-) diff --git a/torchrl/collectors/utils.py b/torchrl/collectors/utils.py index f797144d344..87145f26847 100644 --- a/torchrl/collectors/utils.py +++ b/torchrl/collectors/utils.py @@ -58,10 +58,6 @@ def split_trajectories( traj_ids = rollout_tensordict.get(traj_ids_key, None) done = rollout_tensordict.get(("next", "done")) - truncated = rollout_tensordict.get( - ("next", "truncated"), torch.zeros((), device=done.device, dtype=torch.bool) - ) - done = done | truncated if traj_ids is None: idx = (slice(None),) * (rollout_tensordict.ndim - 1) + (slice(None, -1),) done_sel = done[idx] diff --git a/torchrl/data/datasets/d4rl.py b/torchrl/data/datasets/d4rl.py index 839e42e6cde..e6053249986 100644 --- a/torchrl/data/datasets/d4rl.py +++ b/torchrl/data/datasets/d4rl.py @@ -301,8 +301,11 @@ def _get_dataset_from_env(self, name, env_kwargs): def _shift_reward_done(self, dataset): dataset["reward"] = dataset["reward"].clone() - dataset["done"] = dataset["done"].clone() dataset["reward"][1:] = dataset["reward"][:-1].clone() - dataset["done"][1:] = dataset["done"][:-1].clone() dataset["reward"][0] = 0 - dataset["done"][0] = 0 + for key in ("done", "terminated", "truncated"): + if key not in dataset.keys(): + continue + dataset[key] = dataset[key].clone() + dataset[key][1:] = dataset[key][:-1].clone() + dataset[key][0] = 0 From 4f6ce90d3fe3b46c5c77b90f97a8c3fbdf2da407 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 11:56:39 -0400 Subject: [PATCH 137/178] amend --- test/mocking_classes.py | 18 ++++++++++++++++++ test/test_collector.py | 10 +++++----- torchrl/data/datasets/d4rl.py | 33 ++++++++++++++++----------------- 3 files changed, 39 insertions(+), 22 deletions(-) diff --git a/test/mocking_classes.py b/test/mocking_classes.py index d66f78724ad..f4e73039db3 100644 --- a/test/mocking_classes.py +++ b/test/mocking_classes.py @@ -1652,6 +1652,11 @@ def make_specs(self): shape=(self.nested_dim_1, 1), dtype=torch.bool, ), + terminated=DiscreteTensorSpec( + n=2, + shape=(self.nested_dim_1, 1), + dtype=torch.bool, + ), shape=(self.nested_dim_1,), ), nested_2=CompositeSpec( @@ -1660,6 +1665,11 @@ def make_specs(self): shape=(self.nested_dim_2, 1), dtype=torch.bool, ), + terminated=DiscreteTensorSpec( + n=2, + shape=(self.nested_dim_2, 1), + dtype=torch.bool, + ), shape=(self.nested_dim_2,), ), done=DiscreteTensorSpec( @@ -1667,6 +1677,11 @@ def make_specs(self): shape=(1,), dtype=torch.bool, ), + terminated=DiscreteTensorSpec( + n=2, + shape=(1,), + dtype=torch.bool, + ), ) def _reset( @@ -1729,6 +1744,7 @@ def _step( self.count += one_hot_action.to(torch.int) td["observation"] += expand_right(self.count, td["observation"].shape) done["done"] = self.count > self.max_steps + done["terminated"] = self.count > self.max_steps discrete_action = tensordict["nested_1"]["action"].unsqueeze(-1) reward["nested_1"]["gift"] += discrete_action.to(torch.float) @@ -1737,6 +1753,7 @@ def _step( self.count_nested_1, td["nested_1", "observation"].shape ) done["nested_1", "done"] = self.count_nested_1 > self.max_steps + done["nested_1", "terminated"] = self.count_nested_1 > self.max_steps continuous_action = tensordict["nested_2"]["azione"] reward["nested_2"]["reward"] += continuous_action.to(torch.float) @@ -1745,6 +1762,7 @@ def _step( self.count_nested_2, td["nested_2", "observation"].shape ) done["nested_2", "done"] = self.count_nested_2 > self.max_steps + done["nested_2", "terminated"] = self.count_nested_2 > self.max_steps td.update(done) td.update(reward) diff --git a/test/test_collector.py b/test/test_collector.py index ef88ab607a7..d167b1c5a9f 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -15,7 +15,7 @@ decorate_thread_sub_func, generate_seeds, PENDULUM_VERSIONED, - PONG_VERSIONED, + PONG_VERSIONED,get_default_devices, ) from mocking_classes import ( ContinuousActionVecMockEnv, @@ -1614,16 +1614,16 @@ def test_collector(self, batch_size, frames_per_batch, max_steps, seed=1): policy=policy, frames_per_batch=frames_per_batch, total_frames=100, - device="cpu", + device=get_default_devices()[0], ) for _td in ccollector: break ccollector.shutdown() + del ccollector for done_key in env.done_keys: assert _replace_last(done_key, "_reset") not in _td.keys(True, True) check_rollout_consistency_multikey_env(_td, max_steps=max_steps) - del ccollector def test_multi_collector_consistency( self, seed=1, frames_per_batch=20, batch_dim=10 @@ -1640,7 +1640,7 @@ def test_multi_collector_consistency( policy=policy, frames_per_batch=frames_per_batch, total_frames=100, - device="cpu", + device=get_default_devices()[0], ) for i, d in enumerate(ccollector): if i == 0: @@ -1659,7 +1659,7 @@ def test_multi_collector_consistency( policy=policy, frames_per_batch=frames_per_batch, total_frames=100, - device="cpu", + device=get_default_devices()[0], ) for i, d in enumerate(ccollector): if i == 0: diff --git a/torchrl/data/datasets/d4rl.py b/torchrl/data/datasets/d4rl.py index e6053249986..04f79fc980b 100644 --- a/torchrl/data/datasets/d4rl.py +++ b/torchrl/data/datasets/d4rl.py @@ -181,19 +181,19 @@ def _get_dataset_direct(self, name, env_kwargs): dataset = dataset.unflatten_keys("/") else: self.metadata = {} - dataset.rename_key("observations", "observation") + dataset.rename_key_("observations", "observation") dataset.set("next", dataset.select()) - dataset.rename_key("next_observations", ("next", "observation")) - dataset.rename_key("terminals", "terminated") + dataset.rename_key_("next_observations", ("next", "observation")) + dataset.rename_key_("terminals", "terminated") if "timeouts" in dataset.keys(): - dataset.rename_key("timeouts", "truncated") + dataset.rename_key_("timeouts", "truncated") if self.use_truncated_as_done: done = dataset.get("terminated") | dataset.get("truncated", False) dataset.set("done", done) else: dataset.set("done", dataset.get("terminated")) - dataset.rename_key("rewards", "reward") - dataset.rename_key("actions", "action") + dataset.rename_key_("rewards", "reward") + dataset.rename_key_("actions", "action") # let's make sure that the dtypes match what's expected for key, spec in env.observation_spec.items(True, True): @@ -201,14 +201,14 @@ def _get_dataset_direct(self, name, env_kwargs): dataset["next", key] = dataset["next", key].to(spec.dtype) dataset["action"] = dataset["action"].to(env.action_spec.dtype) dataset["reward"] = dataset["reward"].to(env.reward_spec.dtype) - dataset["done"] = dataset["done"].bool() + # format done etc + dataset["done"] = dataset["done"].bool() dataset["done"] = dataset["done"].unsqueeze(-1) - if "terminated" in dataset.keys(): - dataset["terminated"] = dataset["terminated"].unsqueeze(-1) + dataset["terminated"] = dataset["terminated"].bool().unsqueeze(-1) if "truncated" in dataset.keys(): - dataset["truncated"] = dataset["truncated"].unsqueeze(-1) - # dataset.rename_key("next_observations", "next/observation") + dataset["truncated"] = dataset["truncated"].bool().unsqueeze(-1) + # dataset.rename_key_("next_observations", "next/observation") dataset["reward"] = dataset["reward"].unsqueeze(-1) dataset["next"].update( dataset.select("reward", "done", "terminated", "truncated", strict=False) @@ -276,14 +276,13 @@ def _get_dataset_from_env(self, name, env_kwargs): dataset[key] = dataset[key].to(spec.dtype) dataset["action"] = dataset["action"].to(env.action_spec.dtype) dataset["reward"] = dataset["reward"].to(env.reward_spec.dtype) - dataset["done"] = dataset["done"].bool() - dataset["done"] = dataset["done"].unsqueeze(-1) - if "terminated" in dataset.keys(): - dataset["terminated"] = dataset["terminated"].unsqueeze(-1) + # format done + dataset["done"] = dataset["done"].bool().unsqueeze(-1) + dataset["terminated"] = dataset["terminated"].bool().unsqueeze(-1) if "truncated" in dataset.keys(): - dataset["truncated"] = dataset["truncated"].unsqueeze(-1) - # dataset.rename_key_("next_observations", "next/observation") + dataset["truncated"] = dataset["truncated"].bool().unsqueeze(-1) + dataset["reward"] = dataset["reward"].unsqueeze(-1) dataset = dataset[:-1].set( "next", From b16b939434901de4f7e4d4ca53c61838341f8b38 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 13:03:31 -0400 Subject: [PATCH 138/178] amend --- test/mocking_classes.py | 8 +++++++- test/test_actors.py | 5 +++-- test/test_collector.py | 22 ++++++++++++---------- torchrl/collectors/collectors.py | 5 ++++- torchrl/data/rlhf/utils.py | 6 +++++- torchrl/envs/libs/robohive.py | 21 ++++++++++----------- 6 files changed, 41 insertions(+), 26 deletions(-) diff --git a/test/mocking_classes.py b/test/mocking_classes.py index f4e73039db3..f07313fec77 100644 --- a/test/mocking_classes.py +++ b/test/mocking_classes.py @@ -1160,10 +1160,12 @@ def __init__( done_spec = self.full_done_spec.unsqueeze(-1).expand( *self.batch_size, self.nested_dim ) - self.done_spec = CompositeSpec( + self.output_spec.unlock_() + self.output_spec["full_done_spec"] = CompositeSpec( {"data": done_spec}, shape=self.batch_size, ) + self.output_spec.lock_() def _reset(self, tensordict): if ( @@ -1176,6 +1178,8 @@ def _reset(self, tensordict): td = super()._reset(tensordict) if self.nested_done: for done_key in self.done_keys: + if isinstance(done_key, str): + done_key = (done_key,) td[done_key] = ( td[done_key[-1]] .unsqueeze(-1) @@ -1210,6 +1214,8 @@ def _step(self, td): td = next_td if self.nested_done: for done_key in self.done_keys: + if isinstance(done_key, str): + done_key = (done_key,) td[done_key] = ( td[done_key[-1]] .unsqueeze(-1) diff --git a/test/test_actors.py b/test/test_actors.py index 69ceb73c8c6..8b432e9ac21 100644 --- a/test/test_actors.py +++ b/test/test_actors.py @@ -803,9 +803,10 @@ def test_actorcritic(device): @pytest.mark.skipif(not _has_transformers, reason="missing dependencies") @pytest.mark.parametrize("device", get_default_devices()) def test_lmhead_actorvalueoperator(device): - from transformers import AutoModelForCausalLM + from transformers import AutoModelForCausalLM, GPT2Config - base_model = AutoModelForCausalLM.from_pretrained("gpt2", return_dict=False) + config = GPT2Config(return_dict=False) + base_model = AutoModelForCausalLM.from_config(config).eval() aco = LMHeadActorValueOperator(base_model).to(device) # check common diff --git a/test/test_collector.py b/test/test_collector.py index d167b1c5a9f..a68d3ce8435 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -14,8 +14,9 @@ check_rollout_consistency_multikey_env, decorate_thread_sub_func, generate_seeds, + get_default_devices, PENDULUM_VERSIONED, - PONG_VERSIONED,get_default_devices, + PONG_VERSIONED, ) from mocking_classes import ( ContinuousActionVecMockEnv, @@ -1057,9 +1058,9 @@ def test_collector_output_keys( from torchrl.envs.libs.gym import gym_backend - if "gymnasium" in str(gym_backend()) or gym_backend().__version__ >= version.parse( - "0.26" - ): + if "gymnasium" in str(gym_backend()) or version.parse( + gym_backend().__version__ + ) >= version.parse("0.26.0"): keys.add(("next", "terminated")) keys.add("terminated") keys.add(("next", "truncated")) @@ -1390,6 +1391,8 @@ def test_reset_heterogeneous_envs(): ) for data in collector: # noqa: B007 break + collector.shutdown() + del collector assert ( data[0]["next", "truncated"].squeeze() == torch.tensor([False, True]).repeat(250)[:500] @@ -1398,8 +1401,6 @@ def test_reset_heterogeneous_envs(): data[1]["next", "truncated"].squeeze() == torch.tensor([False, False, True]).repeat(168)[:500] ).all() - collector.shutdown() - del collector class TestNestedEnvsCollector: @@ -1414,7 +1415,7 @@ def test_multi_collector_nested_env_consistency(self, seed=1): policy=policy, frames_per_batch=20, total_frames=100, - device="cpu", + device=get_default_devices()[0], ) for i, d in enumerate(ccollector): if i == 0: @@ -1427,13 +1428,14 @@ def test_multi_collector_nested_env_consistency(self, seed=1): with pytest.raises(AssertionError): assert_allclose_td(c1, c2) ccollector.shutdown() + del ccollector ccollector = MultiSyncDataCollector( create_env_fn=[env_fn], policy=policy, frames_per_batch=20, total_frames=100, - device="cpu", + device=get_default_devices()[0], ) for i, d in enumerate(ccollector): if i == 0: @@ -1473,7 +1475,7 @@ def test_collector_nested_env_combinations( policy=policy, frames_per_batch=frames_per_batch, total_frames=100, - device="cpu", + device=get_default_devices()[0], ) for _td in ccollector: @@ -1495,7 +1497,7 @@ def test_nested_env_dims(self, batch_size, nested_dim=5, frames_per_batch=20): policy=policy, frames_per_batch=frames_per_batch, total_frames=100, - device="cpu", + device=get_default_devices()[0], ) for _td in ccollector: diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 29e9ecbb5e2..661e2bf02ef 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -1397,7 +1397,10 @@ def _shutdown_main(self) -> None: continue for proc in self.procs: - proc.join() + exitcode = proc.join(1.0) + if exitcode is None: + print("Terminating") + proc.terminate() self.queue_out.close() for pipe in self.pipes: pipe.close() diff --git a/torchrl/data/rlhf/utils.py b/torchrl/data/rlhf/utils.py index 2b22a7347dd..2853358f727 100644 --- a/torchrl/data/rlhf/utils.py +++ b/torchrl/data/rlhf/utils.py @@ -142,9 +142,11 @@ def create_rollout_td(self, batch, generated, log_probs, log_ratio, kl_coef=0.1) part of the inputs that will be used for generating the next token. - ``("next", "attention_mask")``: updated attention_mask after token has been generated. Passed to the generative model on the next time step - - ``("next", "done")``: Boolean array indicating whether we've reached a + - ``("next", "terminated")``: Boolean array indicating whether we've reached a terminal state (either because we generated EOS token or because we reached the token limit) + - ``("next", "done")``: Boolean array indicating whether we've reached a + final state. Currently a copy of ``"terminated"``. - ``("next", "reward")``: The reward received at each time step - ``("next", "reward_raw")``: The raw reward from the reward model, without the KL term. This is mainly for debugging and logging, it is not used in @@ -178,6 +180,7 @@ def create_rollout_td(self, batch, generated, log_probs, log_ratio, kl_coef=0.1) "input_ids": rollout_generated[:, 1:].clone(), "attention_mask": rollout_attention_mask[:, 1:].clone(), "done": done, + "terminated": done, "reward": reward, "reward_raw": reward_raw, "reward_kl": reward_kl, @@ -202,6 +205,7 @@ def _get_rollout_generated(self, generated, batch): def _get_done_status(self, generated, batch): # done is True when we either first sample an EOS token or reach the maximum number # of generated tokens + # TODO: differentiate truncated and terminal here done_idx = torch.minimum( (generated != self.EOS_TOKEN_ID).sum(dim=-1) - batch.prompt_rindex, torch.tensor(self.max_new_tokens) - 1, diff --git a/torchrl/envs/libs/robohive.py b/torchrl/envs/libs/robohive.py index 7c763e16441..12fb7235aa7 100644 --- a/torchrl/envs/libs/robohive.py +++ b/torchrl/envs/libs/robohive.py @@ -15,7 +15,7 @@ from tensordict.tensordict import make_tensordict from torchrl._utils import implement_for from torchrl.data import UnboundedContinuousTensorSpec -from torchrl.envs.libs.gym import _gym_to_torchrl_spec_transform, GymEnv +from torchrl.envs.libs.gym import _AsyncMeta, _gym_to_torchrl_spec_transform, GymEnv from torchrl.envs.utils import _classproperty, make_composite_from_td _has_gym = importlib.util.find_spec("gym") is not None @@ -50,6 +50,12 @@ def new_fun(*args, **kwargs): return new_fun +class _RoboHiveBuild(_AsyncMeta): + def __call__(self, *args, **kwargs): + instance: RoboHiveEnv = super().__call__(*args, **kwargs) + instance._refine_specs() + + class RoboHiveEnv(GymEnv): """A wrapper for RoboHive gym environments. @@ -197,15 +203,8 @@ def register_visual_env(cls, env_name, cams): cls.env_list += [env_name] return env_name - def _make_specs(self, env: "gym.Env") -> None: # noqa: F821 - # if self.from_pixels: - # num_cams = len(env.visual_keys) - # n_pix = 224 * 224 * 3 * num_cams - # env.observation_space = gym.spaces.Box( - # -8 * np.ones(env.obs_dim - n_pix), - # 8 * np.ones(env.obs_dim - n_pix), - # dtype=np.float32, - # ) + def _refine_specs(self) -> None: # noqa: F821 + env = self._env self.action_spec = _gym_to_torchrl_spec_transform( env.action_space, device=self.device ) @@ -273,7 +272,7 @@ def set_from_pixels(self, from_pixels: bool) -> None: if from_pixels is self.from_pixels: return self.from_pixels = from_pixels - self._make_specs(self.env) + self._refine_specs() def read_obs(self, observation): # the info is missing from the reset From daaaddd2e73f5ae144e16cd0ca999079df216a35 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 13:05:13 -0400 Subject: [PATCH 139/178] amend --- test/mocking_classes.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/test/mocking_classes.py b/test/mocking_classes.py index f07313fec77..e54f8b53b41 100644 --- a/test/mocking_classes.py +++ b/test/mocking_classes.py @@ -1160,12 +1160,10 @@ def __init__( done_spec = self.full_done_spec.unsqueeze(-1).expand( *self.batch_size, self.nested_dim ) - self.output_spec.unlock_() - self.output_spec["full_done_spec"] = CompositeSpec( + self.done_spec = CompositeSpec( {"data": done_spec}, shape=self.batch_size, ) - self.output_spec.lock_() def _reset(self, tensordict): if ( From cd4811fe700327a52c8ad2264905365d456699aa Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 15:43:29 -0400 Subject: [PATCH 140/178] amend --- test/test_collector.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/test/test_collector.py b/test/test_collector.py index a68d3ce8435..3fc7add0bb8 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -1353,8 +1353,8 @@ def env_fn(seed): frames_per_batch=frames_per_batch, init_random_frames=-1, reset_at_each_iter=False, - devices="cpu", - storing_devices="cpu", + devices=get_default_devices()[0], + storing_devices=get_default_devices()[0], split_trajs=False, preemptive_threshold=0.0, # stop after one iteration ) @@ -1531,7 +1531,7 @@ def test_collector_het_env(self, batch_size, frames_per_batch, seed=1, max_steps policy=policy, frames_per_batch=frames_per_batch, total_frames=100, - device="cpu", + device=get_default_devices()[0], ) for _td in ccollector: @@ -1565,7 +1565,7 @@ def test_multi_collector_het_env_consistency( policy=policy, frames_per_batch=frames_per_batch, total_frames=100, - device="cpu", + device=get_default_devices()[0], ) for i, d in enumerate(ccollector): if i == 0: @@ -1584,7 +1584,7 @@ def test_multi_collector_het_env_consistency( policy=policy, frames_per_batch=frames_per_batch, total_frames=100, - device="cpu", + device=get_default_devices()[0], ) for i, d in enumerate(ccollector): if i == 0: From c0f3137f135e6bf22ddbd9d67464cc19da98af08 Mon Sep 17 00:00:00 2001 From: vmoens Date: Tue, 26 Sep 2023 16:10:59 -0400 Subject: [PATCH 141/178] fixes --- test/test_collector.py | 9 ++++++--- torchrl/collectors/collectors.py | 1 - torchrl/envs/libs/robohive.py | 3 +++ 3 files changed, 9 insertions(+), 4 deletions(-) diff --git a/test/test_collector.py b/test/test_collector.py index 3fc7add0bb8..f6a41ba5a4b 100644 --- a/test/test_collector.py +++ b/test/test_collector.py @@ -1525,13 +1525,14 @@ def test_collector_het_env(self, batch_size, frames_per_batch, seed=1, max_steps batch_size = torch.Size(batch_size) env = HeteroCountingEnv(max_steps=max_steps - 1, batch_size=batch_size) torch.manual_seed(seed) + device = get_default_devices()[0] policy = HeteroCountingEnvPolicy(env.input_spec["full_action_spec"]) ccollector = SyncDataCollector( create_env_fn=env, policy=policy, frames_per_batch=frames_per_batch, total_frames=100, - device=get_default_devices()[0], + device=device, ) for _td in ccollector: @@ -1546,7 +1547,9 @@ def test_collector_het_env(self, batch_size, frames_per_batch, seed=1, max_steps agent_obs = agent_obs.mean(-1) assert ( agent_obs - == torch.arange(max_steps).repeat(collected_frames // max_steps) + == torch.arange(max_steps, device=device).repeat( + collected_frames // max_steps + ) ).all() # Check reset worked assert (_td["lazy"][..., i]["action"] == 1).all() del ccollector @@ -1706,7 +1709,7 @@ def _step( { "state": self.state.clone(), "reward": self.reward_spec.zero(), - "done": self.done_spec.zero(), + **self.full_done_spec.zero(), }, self.batch_size, ) diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py index 661e2bf02ef..601e7efb255 100644 --- a/torchrl/collectors/collectors.py +++ b/torchrl/collectors/collectors.py @@ -1399,7 +1399,6 @@ def _shutdown_main(self) -> None: for proc in self.procs: exitcode = proc.join(1.0) if exitcode is None: - print("Terminating") proc.terminate() self.queue_out.close() for pipe in self.pipes: diff --git a/torchrl/envs/libs/robohive.py b/torchrl/envs/libs/robohive.py index 12fb7235aa7..0d6f5d9eb2f 100644 --- a/torchrl/envs/libs/robohive.py +++ b/torchrl/envs/libs/robohive.py @@ -321,6 +321,9 @@ def read_info(self, info, tensordict_out): ) return tensordict_out + def _init_env(self): + pass + def to(self, *args, **kwargs): out = super().to(*args, **kwargs) try: From 8f9d8fe0d4c0959a8977a9fcd4cbbc11ee992063 Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 27 Sep 2023 02:55:51 -0400 Subject: [PATCH 142/178] fix r3m, vip and vc1 --- test/test_transforms.py | 117 +++++++++++++++++++++------------------- 1 file changed, 63 insertions(+), 54 deletions(-) diff --git a/test/test_transforms.py b/test/test_transforms.py index 3f8b056fbb8..8fc2f95f12d 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1192,25 +1192,26 @@ def test_transform_env(self, model, tensor_pixels_key, device): transformed_env = TransformedEnv(base_env, r3m) td = transformed_env.reset() assert td.device == device - exp_keys = {"vec", "done", "pixels_orig"} + expected_keys = {"vec", "done", "pixels_orig"} if tensor_pixels_key: - exp_keys.add(tensor_pixels_key[0]) - assert set(td.keys()) == exp_keys, set(td.keys()) - exp_keys + expected_keys.add(tensor_pixels_key[0]) + assert set(td.keys()) == expected_keys, set(td.keys()) - expected_keys td = transformed_env.rand_step(td) - exp_keys = exp_keys.union( + expected_keys = expected_keys.union( { ("next", "vec"), ("next", "pixels_orig"), "action", ("next", "reward"), ("next", "done"), + ("next", "terminated"), "next", } ) if tensor_pixels_key: - exp_keys.add(("next", tensor_pixels_key[0])) - assert set(td.keys(True)) == exp_keys, set(td.keys(True)) - exp_keys + expected_keys.add(("next", tensor_pixels_key[0])) + assert set(td.keys(True)) == expected_keys, set(td.keys(True)) - expected_keys transformed_env.close() @pytest.mark.parametrize("stack_images", [True, False]) @@ -1251,32 +1252,33 @@ def base_env_constructor(): td = transformed_env.reset() assert td.device == device if stack_images: - exp_keys = {"pixels_orig", "done", "vec"} + expected_keys = {"pixels_orig", "done", "vec"} # assert td["vec"].shape[0] == 2 assert td["vec"].ndimension() == 1 + parallel - assert set(td.keys()) == exp_keys + assert set(td.keys()) == expected_keys else: - exp_keys = {"pixels_orig", "done", "vec", "vec2"} + expected_keys = {"pixels_orig", "done", "vec", "vec2"} assert td["vec"].shape[0 + parallel] != 2 assert td["vec"].ndimension() == 1 + parallel assert td["vec2"].shape[0 + parallel] != 2 assert td["vec2"].ndimension() == 1 + parallel - assert set(td.keys()) == exp_keys + assert set(td.keys()) == expected_keys td = transformed_env.rand_step(td) - exp_keys = exp_keys.union( + expected_keys = expected_keys.union( { ("next", "vec"), ("next", "pixels_orig"), "action", ("next", "reward"), ("next", "done"), + ("next", "terminated"), "next", } ) if not stack_images: - exp_keys.add(("next", "vec2")) - assert set(td.keys(True)) == exp_keys, set(td.keys()) - exp_keys + expected_keys.add(("next", "vec2")) + assert set(td.keys(True)) == expected_keys, set(td.keys()) - expected_keys transformed_env.close() def test_r3m_parallel(self, model, device): @@ -1294,23 +1296,24 @@ def test_r3m_parallel(self, model, device): td = transformed_env.reset() assert td.device == device assert td.batch_size == torch.Size([4]) - exp_keys = {"vec", "done", "pixels_orig"} + expected_keys = {"vec", "done", "pixels_orig"} if tensor_pixels_key: - exp_keys.add(tensor_pixels_key) - assert set(td.keys(True)) == exp_keys + expected_keys.add(tensor_pixels_key) + assert set(td.keys(True)) == expected_keys td = transformed_env.rand_step(td) - exp_keys = exp_keys.union( + expected_keys = expected_keys.union( { ("next", "vec"), ("next", "pixels_orig"), "action", ("next", "reward"), ("next", "done"), + ("next", "terminated"), "next", } ) - assert set(td.keys(True)) == exp_keys, set(td.keys()) - exp_keys + assert set(td.keys(True)) == expected_keys, set(td.keys()) - expected_keys transformed_env.close() del transformed_env @@ -6512,13 +6515,13 @@ def test_vip_instantiation(self, model, tensor_pixels_key, device): transformed_env = TransformedEnv(base_env, vip) td = transformed_env.reset() assert td.device == device - exp_keys = {"vec", "done", "pixels_orig"} + expected_keys = {"vec", "done", "pixels_orig"} if tensor_pixels_key: - exp_keys.add(tensor_pixels_key[0]) - assert set(td.keys()) == exp_keys, set(td.keys()) - exp_keys + expected_keys.add(tensor_pixels_key[0]) + assert set(td.keys()) == expected_keys, set(td.keys()) - expected_keys td = transformed_env.rand_step(td) - exp_keys = exp_keys.union( + expected_keys = expected_keys.union( { ("next", "vec"), ("next", "pixels_orig"), @@ -6526,11 +6529,12 @@ def test_vip_instantiation(self, model, tensor_pixels_key, device): "action", ("next", "reward"), ("next", "done"), + ("next", "terminated"), } ) if tensor_pixels_key: - exp_keys.add(("next", tensor_pixels_key[0])) - assert set(td.keys(True)) == exp_keys, set(td.keys(True)) - exp_keys + expected_keys.add(("next", tensor_pixels_key[0])) + assert set(td.keys(True)) == expected_keys, set(td.keys(True)) - expected_keys transformed_env.close() @pytest.mark.parametrize("stack_images", [True, False]) @@ -6565,20 +6569,20 @@ def base_env_constructor(): td = transformed_env.reset() assert td.device == device if stack_images: - exp_keys = {"pixels_orig", "done", "vec"} + expected_keys = {"pixels_orig", "done", "vec"} # assert td["vec"].shape[0] == 2 assert td["vec"].ndimension() == 1 + parallel - assert set(td.keys()) == exp_keys + assert set(td.keys()) == expected_keys else: - exp_keys = {"pixels_orig", "done", "vec", "vec2"} + expected_keys = {"pixels_orig", "done", "vec", "vec2"} assert td["vec"].shape[0 + parallel] != 2 assert td["vec"].ndimension() == 1 + parallel assert td["vec2"].shape[0 + parallel] != 2 assert td["vec2"].ndimension() == 1 + parallel - assert set(td.keys()) == exp_keys + assert set(td.keys()) == expected_keys td = transformed_env.rand_step(td) - exp_keys = exp_keys.union( + expected_keys = expected_keys.union( { ("next", "vec"), ("next", "pixels_orig"), @@ -6586,11 +6590,12 @@ def base_env_constructor(): "action", ("next", "reward"), ("next", "done"), + ("next", "terminated"), } ) if not stack_images: - exp_keys.add(("next", "vec2")) - assert set(td.keys(True)) == exp_keys, set(td.keys(True)) - exp_keys + expected_keys.add(("next", "vec2")) + assert set(td.keys(True)) == expected_keys, set(td.keys(True)) - expected_keys transformed_env.close() def test_transform_env(self, model, device): @@ -6608,13 +6613,13 @@ def test_transform_env(self, model, device): td = transformed_env.reset() assert td.device == device assert td.batch_size == torch.Size([4]) - exp_keys = {"vec", "done", "pixels_orig"} + expected_keys = {"vec", "done", "pixels_orig"} if tensor_pixels_key: - exp_keys.add(tensor_pixels_key) - assert set(td.keys()) == exp_keys + expected_keys.add(tensor_pixels_key) + assert set(td.keys()) == expected_keys td = transformed_env.rand_step(td) - exp_keys = exp_keys.union( + expected_keys = expected_keys.union( { ("next", "vec"), ("next", "pixels_orig"), @@ -6622,9 +6627,10 @@ def test_transform_env(self, model, device): "action", ("next", "reward"), ("next", "done"), + ("next", "terminated"), } ) - assert set(td.keys(True)) == exp_keys, set(td.keys(True)) - exp_keys + assert set(td.keys(True)) == expected_keys, set(td.keys(True)) - expected_keys transformed_env.close() del transformed_env @@ -6660,7 +6666,7 @@ def test_vip_parallel_reward(self, model, device, dtype_fixture): # noqa td = transformed_env.reset(tensordict_reset) assert td.device == device assert td.batch_size == torch.Size([4]) - exp_keys = { + expected_keys = { "vec", "done", "pixels_orig", @@ -6668,11 +6674,11 @@ def test_vip_parallel_reward(self, model, device, dtype_fixture): # noqa "goal_image", } if tensor_pixels_key: - exp_keys.add(tensor_pixels_key) - assert set(td.keys()) == exp_keys + expected_keys.add(tensor_pixels_key) + assert set(td.keys()) == expected_keys td = transformed_env.rand_step(td) - exp_keys = exp_keys.union( + expected_keys = expected_keys.union( { ("next", "vec"), ("next", "pixels_orig"), @@ -6680,9 +6686,10 @@ def test_vip_parallel_reward(self, model, device, dtype_fixture): # noqa "action", ("next", "reward"), ("next", "done"), + ("next", "terminated"), } ) - assert set(td.keys(True)) == exp_keys, td + assert set(td.keys(True)) == expected_keys, td torch.manual_seed(1) tensordict_reset = TensorDict( @@ -6693,7 +6700,7 @@ def test_vip_parallel_reward(self, model, device, dtype_fixture): # noqa td = transformed_env.rollout( 5, auto_reset=False, tensordict=transformed_env.reset(tensordict_reset) ) - assert set(td.keys(True)) == exp_keys, td + assert set(td.keys(True)) == expected_keys, td # test that we do compute the reward we want cur_embedding = td["next", "vec"] goal_embedding = td["goal_embedding"] @@ -6956,13 +6963,13 @@ def test_vc1_instantiation(self, del_keys, device): transformed_env = TransformedEnv(base_env, vc1) td = transformed_env.reset() assert td.device == device - exp_keys = {"nested", "done", "pixels_orig"} + expected_keys = {"nested", "done", "pixels_orig"} if not del_keys: - exp_keys.add("pixels") - assert set(td.keys()) == exp_keys, set(td.keys()) - exp_keys + expected_keys.add("pixels") + assert set(td.keys()) == expected_keys, set(td.keys()) - expected_keys td = transformed_env.rand_step(td) - exp_keys = exp_keys.union( + expected_keys = expected_keys.union( { ("next", "nested"), ("next", "nested", "vec"), @@ -6972,11 +6979,12 @@ def test_vc1_instantiation(self, del_keys, device): ("nested", "vec"), ("next", "reward"), ("next", "done"), + ("next", "terminated"), } ) if not del_keys: - exp_keys.add(("next", "pixels")) - assert set(td.keys(True)) == exp_keys, set(td.keys(True)) - exp_keys + expected_keys.add(("next", "pixels")) + assert set(td.keys(True)) == expected_keys, set(td.keys(True)) - expected_keys transformed_env.close() @pytest.mark.parametrize("del_keys", [True, False]) @@ -6995,13 +7003,13 @@ def test_transform_env(self, device, del_keys): td = transformed_env.reset() assert td.device == device assert td.batch_size == torch.Size([4]) - exp_keys = {"nested", "done", "pixels_orig"} + expected_keys = {"nested", "done", "pixels_orig"} if not del_keys: - exp_keys.add("pixels") - assert set(td.keys()) == exp_keys + expected_keys.add("pixels") + assert set(td.keys()) == expected_keys td = transformed_env.rand_step(td) - exp_keys = exp_keys.union( + expected_keys = expected_keys.union( { ("next", "nested"), ("next", "nested", "vec"), @@ -7011,11 +7019,12 @@ def test_transform_env(self, device, del_keys): ("nested", "vec"), ("next", "reward"), ("next", "done"), + ("next", "terminated"), } ) if not del_keys: - exp_keys.add(("next", "pixels")) - assert set(td.keys(True)) == exp_keys, set(td.keys(True)) - exp_keys + expected_keys.add(("next", "pixels")) + assert set(td.keys(True)) == expected_keys, set(td.keys(True)) - expected_keys transformed_env.close() del transformed_env From 4fdf437739b138f0f16bd145c82b4bbd7a9c782a Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 27 Sep 2023 05:23:33 -0400 Subject: [PATCH 143/178] fix robohive, d4rl --- test/test_libs.py | 28 +++++++++++++++++----------- test/test_transforms.py | 21 +++++++++++---------- torchrl/data/datasets/d4rl.py | 8 +++++++- torchrl/envs/libs/robohive.py | 3 ++- 4 files changed, 37 insertions(+), 23 deletions(-) diff --git a/test/test_libs.py b/test/test_libs.py index ac6848f60d5..445140cc30f 100644 --- a/test/test_libs.py +++ b/test/test_libs.py @@ -3,6 +3,7 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import importlib +from contextlib import nullcontext from torchrl.envs.transforms import ActionMask, TransformedEnv from torchrl.modules import MaskedCategorical @@ -1768,18 +1769,23 @@ def test_collector_heterogeneous(self, n_envs=10, frames_per_batch=20): class TestD4RL: @pytest.mark.parametrize("task", ["walker2d-medium-replay-v2"]) @pytest.mark.parametrize("use_truncated_as_done", [True, False]) - def test_terminate_on_end(self, task, use_truncated_as_done): - data_true = D4RLExperienceReplay( - task, - split_trajs=True, - from_env=False, - terminate_on_end=True, - batch_size=2, - use_truncated_as_done=use_truncated_as_done, - ) + @pytest.mark.parametrize("split_trajs", [True, False]) + def test_terminate_on_end(self, task, use_truncated_as_done, split_trajs): + + with pytest.warns( + UserWarning, match="This is a warning message" + ) if use_truncated_as_done else nullcontext(): + data_true = D4RLExperienceReplay( + task, + split_trajs=split_trajs, + from_env=False, + terminate_on_end=True, + batch_size=2, + use_truncated_as_done=use_truncated_as_done, + ) _ = D4RLExperienceReplay( task, - split_trajs=True, + split_trajs=split_trajs, from_env=False, terminate_on_end=False, batch_size=2, @@ -1787,7 +1793,7 @@ def test_terminate_on_end(self, task, use_truncated_as_done): ) data_from_env = D4RLExperienceReplay( task, - split_trajs=True, + split_trajs=split_trajs, from_env=True, batch_size=2, use_truncated_as_done=use_truncated_as_done, diff --git a/test/test_transforms.py b/test/test_transforms.py index 8fc2f95f12d..be6fca56105 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1192,7 +1192,7 @@ def test_transform_env(self, model, tensor_pixels_key, device): transformed_env = TransformedEnv(base_env, r3m) td = transformed_env.reset() assert td.device == device - expected_keys = {"vec", "done", "pixels_orig"} + expected_keys = {"vec", "done", "pixels_orig", "terminated"} if tensor_pixels_key: expected_keys.add(tensor_pixels_key[0]) assert set(td.keys()) == expected_keys, set(td.keys()) - expected_keys @@ -1252,12 +1252,12 @@ def base_env_constructor(): td = transformed_env.reset() assert td.device == device if stack_images: - expected_keys = {"pixels_orig", "done", "vec"} + expected_keys = {"pixels_orig", "done", "vec", "terminated"} # assert td["vec"].shape[0] == 2 assert td["vec"].ndimension() == 1 + parallel assert set(td.keys()) == expected_keys else: - expected_keys = {"pixels_orig", "done", "vec", "vec2"} + expected_keys = {"pixels_orig", "done", "vec", "vec2", "terminated"} assert td["vec"].shape[0 + parallel] != 2 assert td["vec"].ndimension() == 1 + parallel assert td["vec2"].shape[0 + parallel] != 2 @@ -1296,7 +1296,7 @@ def test_r3m_parallel(self, model, device): td = transformed_env.reset() assert td.device == device assert td.batch_size == torch.Size([4]) - expected_keys = {"vec", "done", "pixels_orig"} + expected_keys = {"vec", "done", "pixels_orig", "terminated"} if tensor_pixels_key: expected_keys.add(tensor_pixels_key) assert set(td.keys(True)) == expected_keys @@ -6515,7 +6515,7 @@ def test_vip_instantiation(self, model, tensor_pixels_key, device): transformed_env = TransformedEnv(base_env, vip) td = transformed_env.reset() assert td.device == device - expected_keys = {"vec", "done", "pixels_orig"} + expected_keys = {"vec", "done", "pixels_orig", "terminated"} if tensor_pixels_key: expected_keys.add(tensor_pixels_key[0]) assert set(td.keys()) == expected_keys, set(td.keys()) - expected_keys @@ -6569,12 +6569,12 @@ def base_env_constructor(): td = transformed_env.reset() assert td.device == device if stack_images: - expected_keys = {"pixels_orig", "done", "vec"} + expected_keys = {"pixels_orig", "done", "vec", "terminated"} # assert td["vec"].shape[0] == 2 assert td["vec"].ndimension() == 1 + parallel assert set(td.keys()) == expected_keys else: - expected_keys = {"pixels_orig", "done", "vec", "vec2"} + expected_keys = {"pixels_orig", "done", "vec", "vec2", "terminated"} assert td["vec"].shape[0 + parallel] != 2 assert td["vec"].ndimension() == 1 + parallel assert td["vec2"].shape[0 + parallel] != 2 @@ -6613,7 +6613,7 @@ def test_transform_env(self, model, device): td = transformed_env.reset() assert td.device == device assert td.batch_size == torch.Size([4]) - expected_keys = {"vec", "done", "pixels_orig"} + expected_keys = {"vec", "done", "pixels_orig", "terminated"} if tensor_pixels_key: expected_keys.add(tensor_pixels_key) assert set(td.keys()) == expected_keys @@ -6672,6 +6672,7 @@ def test_vip_parallel_reward(self, model, device, dtype_fixture): # noqa "pixels_orig", "goal_embedding", "goal_image", + "terminated", } if tensor_pixels_key: expected_keys.add(tensor_pixels_key) @@ -6963,7 +6964,7 @@ def test_vc1_instantiation(self, del_keys, device): transformed_env = TransformedEnv(base_env, vc1) td = transformed_env.reset() assert td.device == device - expected_keys = {"nested", "done", "pixels_orig"} + expected_keys = {"nested", "done", "pixels_orig", "terminated"} if not del_keys: expected_keys.add("pixels") assert set(td.keys()) == expected_keys, set(td.keys()) - expected_keys @@ -7003,7 +7004,7 @@ def test_transform_env(self, device, del_keys): td = transformed_env.reset() assert td.device == device assert td.batch_size == torch.Size([4]) - expected_keys = {"nested", "done", "pixels_orig"} + expected_keys = {"nested", "done", "pixels_orig", "terminated"} if not del_keys: expected_keys.add("pixels") assert set(td.keys()) == expected_keys diff --git a/torchrl/data/datasets/d4rl.py b/torchrl/data/datasets/d4rl.py index 04f79fc980b..5a4902f2c56 100644 --- a/torchrl/data/datasets/d4rl.py +++ b/torchrl/data/datasets/d4rl.py @@ -2,7 +2,7 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. - +import warnings from typing import Callable, Optional import numpy as np @@ -130,6 +130,12 @@ def __init__( if from_env: dataset = self._get_dataset_from_env(name, env_kwargs) else: + if self.use_truncated_as_done: + warnings.warn( + "Using terminate_on_end=True with from_env=False " + "may not have the intended effect as the timeouts (truncation) " + "can be absent from the static dataset." + ) dataset = self._get_dataset_direct(name, env_kwargs) # Fill unknown next states with 0 dataset["next", "observation"][dataset["next", "done"].squeeze()] = 0 diff --git a/torchrl/envs/libs/robohive.py b/torchrl/envs/libs/robohive.py index 0d6f5d9eb2f..f734a887ff1 100644 --- a/torchrl/envs/libs/robohive.py +++ b/torchrl/envs/libs/robohive.py @@ -54,9 +54,10 @@ class _RoboHiveBuild(_AsyncMeta): def __call__(self, *args, **kwargs): instance: RoboHiveEnv = super().__call__(*args, **kwargs) instance._refine_specs() + return instance -class RoboHiveEnv(GymEnv): +class RoboHiveEnv(GymEnv, _RoboHiveBuild): """A wrapper for RoboHive gym environments. RoboHive is a collection of environments/tasks simulated with the MuJoCo physics engine exposed using the OpenAI-Gym API. From 4f44579ace1bdf676235516b6cd92270e77bfe81 Mon Sep 17 00:00:00 2001 From: vmoens Date: Wed, 27 Sep 2023 05:38:02 -0400 Subject: [PATCH 144/178] amend --- test/assets/generate.py | 2 +- test/assets/openai_summarize_comparisons.zip | Bin 690495 -> 37683 bytes test/assets/openai_summarize_tldr.zip | Bin 15618006 -> 96580 bytes test/test_libs.py | 24 +++++++++++-------- test/test_rlhf.py | 2 +- torchrl/envs/libs/robohive.py | 2 +- 6 files changed, 17 insertions(+), 13 deletions(-) diff --git a/test/assets/generate.py b/test/assets/generate.py index 3e565bb6b69..75a87bb71b5 100644 --- a/test/assets/generate.py +++ b/test/assets/generate.py @@ -21,7 +21,7 @@ def generate_small_dataset(comparison=True): smalld = {} for key in list(d.keys()): if any(key.startswith(sub) for sub in ("train", "valid", "test")): - smalld[key] = Dataset.from_dict(d[key][:1000]) + smalld[key] = Dataset.from_dict(d[key][:50]) smalld = DatasetDict(smalld) if comparison: diff --git 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