fix style, typos, license (huggingface#103)

* fix style, typos, license * quality * minor updates, remove Mapping error for 3.10+ * fix style * update per feedback --------- Co-authored-by: leandro <leandro.vonwerra@spoud.io>
shizhediao · Jan 27, 2023 · ef5aaa7 · ef5aaa7
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diff --git a/Makefile b/Makefile
@@ -9,5 +9,5 @@ quality:
 	flake8  tests trl
 
 style:
-	black --line-length 119 --target-version py38 tests trl
+	black --line-length 119 --target-version py38 tests trl examples setup.py
 	isort tests trl
diff --git a/README.md b/README.md
@@ -45,6 +45,11 @@ cd trl/
 pip install .
 ```
 
+If you wish to develop TRL, you should install in editable mode:
+```bash
+pip install -e .
+```
+
 ## How to use
 
 ### Example

diff --git a/examples/scripts/ppo-sentiment-adam-8bit.py b/examples/scripts/ppo-sentiment-adam-8bit.py
@@ -0,0 +1,155 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+from tqdm import tqdm
+
+tqdm.pandas()
+
+from transformers import pipeline, AutoTokenizer
+from datasets import load_dataset
+
+import bitsandbytes as bnb
+
+from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead
+from trl.core import LengthSampler
+
+########################################################################
+# This is a fully working simple example to use trl with accelerate.
+#
+# This example fine-tunes a GPT2 model on the IMDB dataset using PPO
+# (proximal policy optimization).
+# in any of the following settings (with the same script):
+#   - single CPU or single GPU
+#   - multi GPUS (using PyTorch distributed mode)
+#   - multi GPUS (using DeepSpeed ZeRO-Offload stages 1 & 2)
+#   - fp16 (mixed-precision) or fp32 (normal precision)
+#
+# To run it in each of these various modes, first initialize the accelerate
+# configuration with `accelerate config`
+#
+########################################################################
+
+# We first define the configuration of the experiment, defining the model, the dataset,
+# the training parameters, and the PPO parameters.
+# Check the default arguments in the `PPOConfig` class for more details.
+config = PPOConfig(
+    model_name="lvwerra/gpt2-imdb",
+    learning_rate=1.41e-6,
+)
+
+# We then define the arguments to pass to the sentiment analysis pipeline.
+# We set `return_all_scores` to True to get the sentiment score for each token.
+sent_kwargs = {"return_all_scores": True, "function_to_apply": "none", "batch_size": config.forward_batch_size}
+
+# Below is an example function to build the dataset. In our case, we use the IMDB dataset
+# from the `datasets` library. One should customize this function to train the model on
+# its own dataset.
+def build_dataset(config, dataset_name="imdb", input_min_text_length=2, input_max_text_length=8):
+    """
+    Build dataset for training. This builds the dataset from `load_dataset`, one should
+    customize this function to train the model on its own dataset.
+
+    Args:
+        dataset_name (`str`):
+            The name of the dataset to be loaded.
+
+    Returns:
+        dataloader (`torch.utils.data.DataLoader`):
+            The dataloader for the dataset.
+    """
+    tokenizer = AutoTokenizer.from_pretrained(config.model_name)
+    tokenizer.pad_token = tokenizer.eos_token
+    # load imdb with datasets
+    ds = load_dataset(dataset_name, split="train")
+    ds = ds.rename_columns({"text": "review"})
+    ds = ds.filter(lambda x: len(x["review"]) > 200, batched=False)
+
+    input_size = LengthSampler(input_min_text_length, input_max_text_length)
+
+    def tokenize(sample):
+        sample["input_ids"] = tokenizer.encode(sample["review"])[: input_size()]
+        sample["query"] = tokenizer.decode(sample["input_ids"])
+        return sample
+
+    ds = ds.map(tokenize, batched=False)
+    ds.set_format(type="torch")
+    return ds
+
+
+# We retrieve the dataloader by calling the `build_dataset` function.
+dataset = build_dataset(config)
+
+
+def collator(data):
+    return dict((key, [d[key] for d in data]) for key in data[0])
+
+
+# Now let's build the model, the reference model, and the tokenizer.
+model = AutoModelForCausalLMWithValueHead.from_pretrained(config.model_name)
+ref_model = AutoModelForCausalLMWithValueHead.from_pretrained(config.model_name)
+tokenizer = AutoTokenizer.from_pretrained(config.model_name)
+optimizer = bnb.optim.Adam8bit(model.parameters(), lr=config.learning_rate)
+
+# GPT-2 tokenizer has a pad token, but it is not eos_token by default. We need to set it to eos_token.
+# only for this model.
+tokenizer.pad_token = tokenizer.eos_token
+
+# We then build the PPOTrainer, passing the model, the reference model, the tokenizer
+ppo_trainer = PPOTrainer(
+    config, model, ref_model, tokenizer, dataset=dataset, data_collator=collator, optimizer=optimizer
+)
+
+# We then build the sentiment analysis pipeline, passing the model name and the
+# sentiment analysis pipeline arguments. Let's also make sure to set the device
+# to the same device as the PPOTrainer.
+device = ppo_trainer.accelerator.device
+if ppo_trainer.accelerator.num_processes == 1:
+    device = 0 if torch.cuda.is_available() else "cpu"  # to avoid a `pipeline` bug
+sentiment_pipe = pipeline("sentiment-analysis", model="lvwerra/distilbert-imdb", device=device)
+
+# We then define the arguments to pass to the `generate` function. These arguments
+# are passed to the `generate` function of the PPOTrainer, which is a wrapper around
+# the `generate` function of the trained model.
+generation_kwargs = {
+    "min_length": -1,
+    "top_k": 0.0,
+    "top_p": 1.0,
+    "do_sample": True,
+    "pad_token_id": tokenizer.eos_token_id,
+}
+output_min_length = 4
+output_max_length = 16
+output_length_sampler = LengthSampler(output_min_length, output_max_length)
+
+for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
+    query_tensors = batch["input_ids"]
+
+    #### Get response from gpt2
+    response_tensors = []
+    for query in query_tensors:
+        gen_len = output_length_sampler()
+        generation_kwargs["max_new_tokens"] = gen_len
+        response = ppo_trainer.generate(query, **generation_kwargs)
+        response_tensors.append(response.squeeze()[-gen_len:])
+    batch["response"] = [tokenizer.decode(r.squeeze()) for r in response_tensors]
+
+    #### Compute sentiment score
+    texts = [q + r for q, r in zip(batch["query"], batch["response"])]
+    pipe_outputs = sentiment_pipe(texts, **sent_kwargs)
+    rewards = [torch.tensor(output[1]["score"]).to(device) for output in pipe_outputs]
+
+    #### Run PPO step
+    stats = ppo_trainer.step(query_tensors, response_tensors, rewards)
+    ppo_trainer.log_stats(stats, batch, rewards)
diff --git a/examples/sentiment/scripts/gpt2-sentiment.py b/examples/sentiment/scripts/gpt2-sentiment.py
@@ -14,6 +14,7 @@
 # limitations under the License.
 import torch
 from tqdm import tqdm
+
 tqdm.pandas()
 
 from transformers import pipeline, AutoTokenizer
@@ -25,7 +26,7 @@
 ########################################################################
 # This is a fully working simple example to use trl with accelerate.
 #
-# This example fine-tunes a GPT2 model on the IMDB dataset using PPO 
+# This example fine-tunes a GPT2 model on the IMDB dataset using PPO
 # (proximal policy optimization).
 # in any of the following settings (with the same script):
 #   - single CPU or single GPU
@@ -50,52 +51,51 @@
 
 # We then define the arguments to pass to the sentiment analysis pipeline.
 # We set `return_all_scores` to True to get the sentiment score for each token.
-sent_kwargs = {
-    "return_all_scores": True,
-    "function_to_apply": "none",
-    "batch_size": config.forward_batch_size
-}
+sent_kwargs = {"return_all_scores": True, "function_to_apply": "none", "batch_size": config.forward_batch_size}
 
 # Below is an example function to build the dataset. In our case, we use the IMDB dataset
 # from the `datasets` library. One should customize this function to train the model on
 # its own dataset.
 def build_dataset(config, dataset_name="imdb", input_min_text_length=2, input_max_text_length=8):
     """
-    Build dataset for training. This builds the dataset from `load_dataset`, one should 
+    Build dataset for training. This builds the dataset from `load_dataset`, one should
     customize this function to train the model on its own dataset.
-    
+
     Args:
-        dataset_name (`str`): 
+        dataset_name (`str`):
             The name of the dataset to be loaded.
-    
+
     Returns:
         dataloader (`torch.utils.data.DataLoader`):
             The dataloader for the dataset.
     """
     tokenizer = AutoTokenizer.from_pretrained(config.model_name)
     tokenizer.pad_token = tokenizer.eos_token
     # load imdb with datasets
-    ds = load_dataset(dataset_name, split='train')
-    ds = ds.rename_columns({'text': 'review'})
-    ds = ds.filter(lambda x: len(x["review"])>200, batched=False)
+    ds = load_dataset(dataset_name, split="train")
+    ds = ds.rename_columns({"text": "review"})
+    ds = ds.filter(lambda x: len(x["review"]) > 200, batched=False)
 
     input_size = LengthSampler(input_min_text_length, input_max_text_length)
 
     def tokenize(sample):
-        sample["input_ids"] = tokenizer.encode(sample["review"])[:input_size()]
+        sample["input_ids"] = tokenizer.encode(sample["review"])[: input_size()]
         sample["query"] = tokenizer.decode(sample["input_ids"])
         return sample
 
     ds = ds.map(tokenize, batched=False)
-    ds.set_format(type='torch')
+    ds.set_format(type="torch")
     return ds
 
+
 # We retrieve the dataloader by calling the `build_dataset` function.
 dataset = build_dataset(config)
 
+
 def collator(data):
     return dict((key, [d[key] for d in data]) for key in data[0])
 
+
 # Now let's build the model, the reference model, and the tokenizer.
 model = AutoModelForCausalLMWithValueHead.from_pretrained(config.model_name)
 ref_model = AutoModelForCausalLMWithValueHead.from_pretrained(config.model_name)
@@ -113,25 +113,25 @@ def collator(data):
 # to the same device as the PPOTrainer.
 device = ppo_trainer.accelerator.device
 if ppo_trainer.accelerator.num_processes == 1:
-   device = 0 if torch.cuda.is_available() else "cpu" # to avoid a `pipeline` bug
+    device = 0 if torch.cuda.is_available() else "cpu"  # to avoid a `pipeline` bug
 sentiment_pipe = pipeline("sentiment-analysis", model="lvwerra/distilbert-imdb", device=device)
 
 # We then define the arguments to pass to the `generate` function. These arguments
-# are passed to the `generate` function of the PPOTrainer, which is a wrapper around 
+# are passed to the `generate` function of the PPOTrainer, which is a wrapper around
 # the `generate` function of the trained model.
 generation_kwargs = {
-    "min_length":-1,
+    "min_length": -1,
     "top_k": 0.0,
     "top_p": 1.0,
     "do_sample": True,
-    "pad_token_id": tokenizer.eos_token_id
+    "pad_token_id": tokenizer.eos_token_id,
 }
 output_min_length = 4
 output_max_length = 16
 output_length_sampler = LengthSampler(output_min_length, output_max_length)
 
 for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
-    query_tensors = batch['input_ids']
+    query_tensors = batch["input_ids"]
 
     #### Get response from gpt2
     response_tensors = []
@@ -140,13 +140,13 @@ def collator(data):
         generation_kwargs["max_new_tokens"] = gen_len
         response = ppo_trainer.generate(query, **generation_kwargs)
         response_tensors.append(response.squeeze()[-gen_len:])
-    batch['response'] = [tokenizer.decode(r.squeeze()) for r in response_tensors]
+    batch["response"] = [tokenizer.decode(r.squeeze()) for r in response_tensors]
 
     #### Compute sentiment score
-    texts = [q + r for q,r in zip(batch['query'], batch['response'])]
+    texts = [q + r for q, r in zip(batch["query"], batch["response"])]
     pipe_outputs = sentiment_pipe(texts, **sent_kwargs)
     rewards = [torch.tensor(output[1]["score"]) for output in pipe_outputs]
 
-    #### Run PPO step 
+    #### Run PPO step
     stats = ppo_trainer.step(query_tensors, response_tensors, rewards)
-    ppo_trainer.log_stats(stats, batch, rewards)
+    ppo_trainer.log_stats(stats, batch, rewards)