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Enable Conv fusion optimizations in optimizeForIdeep #9255

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Enable Conv fusion optimizations in optimizeForIdeep #9255

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9eecae2
Enable Conv fusion optimizations in optimizeForIdeep
gujinghui May 29, 2018
1d801ed
fix test case of conv_fusion op
gujinghui May 31, 2018
90213c0
Refine code by clang-format
gujinghui Jul 12, 2018
aedefb8
Use CAFFE_ENFORCE, instead of assert
gujinghui Jul 15, 2018
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fix test case of conv_fusion op 
Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>
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@gujinghui
gujinghui committed Jul 12, 2018
commit 1d801edde353b8f14b89bfcd820f2d1ec50290ff
54 changes: 52 additions & 2 deletions caffe2/python/ideep/conv_op_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,9 @@
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from caffe2.python.transformations import optimizeForIDEEP
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu

Expand Down Expand Up @@ -63,12 +65,60 @@ def test_depthwise_convolution(self, batch_size, gc, dc):
pad=0,
kernel=1,
group=4,
device_option=dc[0]
)
op1 = core.CreateOperator(
"Conv",
["X", "w", "b"],
["Y"],
stride=1,
pad=0,
kernel=1,
group=4,
device_option=dc[1]
)
X = np.random.rand(batch_size, 544, 14, 14).astype(np.float32)
w = np.random.rand(544, 136, 1, 1).astype(np.float32)
b = np.random.rand(544).astype(np.float32)
inputs = [X, w, b]
self.assertDeviceChecks(dc, op, inputs, [0])

workspace.SwitchWorkspace("_device_check_", True)
workspace.FeedBlob('X', X, dc[0])
workspace.FeedBlob('w', w, dc[0])
workspace.FeedBlob('b', b, dc[0])
workspace.RunOperatorOnce(op)
Y0 = workspace.FetchBlob('Y')

workspace.ResetWorkspace()
workspace.FeedBlob('X', X, dc[1])
workspace.FeedBlob('w', w, dc[1])
workspace.FeedBlob('b', b, dc[1])
net = core.Net("net")
old_net = caffe2_pb2.NetDef()
old_net.op.extend([op1])
net.Proto().CopyFrom(old_net)
optimizeForIDEEP(net)
workspace.RunOperatorOnce(net.Proto().op[0])
Y1 = workspace.FetchBlob('Y')

if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01):
print(Y1.flatten())
print(Y0.flatten())
print(np.max(np.abs(Y1 - Y0)))
self.assertTrue(False)

workspace.ResetWorkspace()
workspace.FeedBlob('X', X, dc[1])
workspace.FeedBlob('w', w, dc[1])
workspace.FeedBlob('b', b, dc[1])
workspace.RunOperatorOnce(op1)
Y2 = workspace.FetchBlob('Y')

if not np.allclose(Y0, Y2, atol=0.01, rtol=0.01):
print(Y2.flatten())
print(Y0.flatten())
print(np.max(np.abs(Y2 - Y0)))
self.assertTrue(False)


if __name__ == "__main__":
unittest.main()
128 changes: 112 additions & 16 deletions caffe2/python/ideep/convfusion_op_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,7 @@ def test_convolution_relu_fusion(self, stride, pad, kernel, size,
device_option=dc[0]
)

# Manual fusion
# Manual fusion for Conv + ReLU
conv_fusion = core.CreateOperator(
"ConvFusion",
["X1", "w1", "b1"] if use_bias else ["X1", "w1"],
Expand All @@ -60,21 +60,6 @@ def test_convolution_relu_fusion(self, stride, pad, kernel, size,
device_option=dc[1]
)

# Auto fusion
old_net = caffe2_pb2.NetDef()
conv_old = caffe2_pb2.OperatorDef()
conv_old.CopyFrom(conv)
conv_old.device_option.CopyFrom(dc[1])
relu_old = caffe2_pb2.OperatorDef()
relu_old.CopyFrom(relu)
relu_old.device_option.CopyFrom(dc[1])
old_net.op.extend([conv_old, relu_old])
net = core.Net("net")
net.Proto().CopyFrom(old_net)
optimizeForIDEEP(net)
self.assertTrue(len(net.Proto().op) == 1)
self.assertTrue(net.Proto().op[0].type == "ConvFusion")

X = np.random.rand(
batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
w = np.random.rand(
Expand Down Expand Up @@ -103,10 +88,24 @@ def test_convolution_relu_fusion(self, stride, pad, kernel, size,
print(np.max(np.abs(Y1 - Y0)))
self.assertTrue(False)

# Auto fusion for Conv + ReLU
workspace.ResetWorkspace()
old_net = caffe2_pb2.NetDef()
conv_old = caffe2_pb2.OperatorDef()
conv_old.CopyFrom(conv)
conv_old.device_option.CopyFrom(dc[1])
relu_old = caffe2_pb2.OperatorDef()
relu_old.CopyFrom(relu)
relu_old.device_option.CopyFrom(dc[1])
old_net.op.extend([conv_old, relu_old])
workspace.FeedBlob('X0', X, dc[1])
workspace.FeedBlob('w0', w, dc[1])
workspace.FeedBlob('b0', b, dc[1])
net = core.Net("net")
net.Proto().CopyFrom(old_net)
optimizeForIDEEP(net)
self.assertTrue(len(net.Proto().op) == 1)
self.assertTrue(net.Proto().op[0].type == "ConvFusion")
workspace.RunOperatorOnce(net.Proto().op[0])
Y2 = workspace.FetchBlob('Y0')
if not np.allclose(Y0, Y2, atol=0.01, rtol=0.01):
Expand All @@ -130,6 +129,12 @@ def test_convolution_relu_fusion(self, stride, pad, kernel, size,
def test_convolution_sum_fusion(self, stride, pad, kernel, size,
input_channels, output_channels,
batch_size, use_bias, group, gc, dc):
relu_S0 = core.CreateOperator(
"Relu",
["S0"],
["S0"],
device_option=dc[0]
)
conv = core.CreateOperator(
"Conv",
["X0", "w0", "b0"] if use_bias else ["X0", "w0"],
Expand All @@ -146,6 +151,14 @@ def test_convolution_sum_fusion(self, stride, pad, kernel, size,
["S0"],
device_option=dc[0]
)

# Manual fusion for Conv + Sum
relu_S1 = core.CreateOperator(
"Relu",
["S1"],
["S1"],
device_option=dc[1]
)
conv_fusion = core.CreateOperator(
"ConvFusion",
["X1", "w1", "b1", "S1"] if use_bias else ["X1", "w1", "S1"],
Expand Down Expand Up @@ -173,6 +186,7 @@ def test_convolution_sum_fusion(self, stride, pad, kernel, size,
Y0 = workspace.FetchBlob('Y0')
S = np.random.rand(*Y0.shape).astype(np.float32) - 0.5
workspace.FeedBlob('S0', S, dc[0])
workspace.RunOperatorOnce(relu_S0)
workspace.RunOperatorOnce(sum)
S0 = workspace.FetchBlob('S0')

Expand All @@ -181,6 +195,7 @@ def test_convolution_sum_fusion(self, stride, pad, kernel, size,
workspace.FeedBlob('w1', w, dc[1])
workspace.FeedBlob('b1', b, dc[1])
workspace.FeedBlob('S1', S, dc[1])
workspace.RunOperatorOnce(relu_S1)
workspace.RunOperatorOnce(conv_fusion)
S1 = workspace.FetchBlob('S1')

Expand All @@ -189,6 +204,37 @@ def test_convolution_sum_fusion(self, stride, pad, kernel, size,
print(S0.flatten())
print(np.max(np.abs(S1 - S0)))
self.assertTrue(False)

# Auto fusion for Conv + Sum
workspace.ResetWorkspace()
old_net = caffe2_pb2.NetDef()
relu_S0_old = caffe2_pb2.OperatorDef()
relu_S0_old.CopyFrom(relu_S0)
relu_S0_old.device_option.CopyFrom(dc[1])
conv_old = caffe2_pb2.OperatorDef()
conv_old.CopyFrom(conv)
conv_old.device_option.CopyFrom(dc[1])
sum_old = caffe2_pb2.OperatorDef()
sum_old.CopyFrom(sum)
sum_old.device_option.CopyFrom(dc[1])
old_net.op.extend([relu_S0_old, conv_old, sum_old])
workspace.FeedBlob('X0', X, dc[1])
workspace.FeedBlob('w0', w, dc[1])
workspace.FeedBlob('b0', b, dc[1])
workspace.FeedBlob('S0', S, dc[1])
net = core.Net("net")
net.Proto().CopyFrom(old_net)
optimizeForIDEEP(net)
self.assertTrue(len(net.Proto().op) == 2)
self.assertTrue(net.Proto().op[1].type == "ConvFusion")
workspace.RunNetOnce(net.Proto())
S2 = workspace.FetchBlob('S0')
if not np.allclose(S0, S2, atol=0.01, rtol=0.01):
print(S2.flatten())
print(S0.flatten())
print(np.max(np.abs(S2 - S0)))
self.assertTrue(False)

workspace.SwitchWorkspace(old_ws_name)

@given(stride=st.integers(1, 3),
Expand All @@ -204,6 +250,12 @@ def test_convolution_sum_fusion(self, stride, pad, kernel, size,
def test_convolution_sum_relu_fusion(self, stride, pad, kernel, size,
input_channels, output_channels,
batch_size, use_bias, group, gc, dc):
relu_S0 = core.CreateOperator(
"Relu",
["S0"],
["S0"],
device_option=dc[0]
)
conv = core.CreateOperator(
"Conv",
["X0", "w0", "b0"] if use_bias else ["X0", "w0"],
Expand All @@ -226,6 +278,14 @@ def test_convolution_sum_relu_fusion(self, stride, pad, kernel, size,
["S0"],
device_option=dc[0]
)

# Manual fusion for Conv + Sum + ReLU
relu_S1 = core.CreateOperator(
"Relu",
["S1"],
["S1"],
device_option=dc[1]
)
conv_fusion = core.CreateOperator(
"ConvFusion",
["X1", "w1", "b1", "S1"] if use_bias else ["X1", "w1", "S1"],
Expand Down Expand Up @@ -253,6 +313,7 @@ def test_convolution_sum_relu_fusion(self, stride, pad, kernel, size,
Y0 = workspace.FetchBlob('Y0')
S = np.random.rand(*Y0.shape).astype(np.float32) - 0.5
workspace.FeedBlob('S0', S, dc[0])
workspace.RunOperatorOnce(relu_S0)
workspace.RunOperatorOnce(sum)
workspace.RunOperatorOnce(relu)
S0 = workspace.FetchBlob('S0')
Expand All @@ -262,6 +323,7 @@ def test_convolution_sum_relu_fusion(self, stride, pad, kernel, size,
workspace.FeedBlob('w1', w, dc[1])
workspace.FeedBlob('b1', b, dc[1])
workspace.FeedBlob('S1', S, dc[1])
workspace.RunOperatorOnce(relu_S1)
workspace.RunOperatorOnce(conv_fusion)
S1 = workspace.FetchBlob('S1')

Expand All @@ -270,6 +332,40 @@ def test_convolution_sum_relu_fusion(self, stride, pad, kernel, size,
print(S0.flatten())
print(np.max(np.abs(S1 - S0)))
self.assertTrue(False)

# Auto fusion for Conv + Sum + ReLU
workspace.ResetWorkspace()
old_net = caffe2_pb2.NetDef()
relu_S0_old = caffe2_pb2.OperatorDef()
relu_S0_old.CopyFrom(relu_S0)
relu_S0_old.device_option.CopyFrom(dc[1])
conv_old = caffe2_pb2.OperatorDef()
conv_old.CopyFrom(conv)
conv_old.device_option.CopyFrom(dc[1])
sum_old = caffe2_pb2.OperatorDef()
sum_old.CopyFrom(sum)
sum_old.device_option.CopyFrom(dc[1])
relu_old = caffe2_pb2.OperatorDef()
relu_old.CopyFrom(relu)
relu_old.device_option.CopyFrom(dc[1])
old_net.op.extend([relu_S0_old, conv_old, sum_old, relu_old])
workspace.FeedBlob('X0', X, dc[1])
workspace.FeedBlob('w0', w, dc[1])
workspace.FeedBlob('b0', b, dc[1])
workspace.FeedBlob('S0', S, dc[1])
net = core.Net("net")
net.Proto().CopyFrom(old_net)
optimizeForIDEEP(net)
self.assertTrue(len(net.Proto().op) == 2)
self.assertTrue(net.Proto().op[1].type == "ConvFusion")
workspace.RunNetOnce(net.Proto())
S2 = workspace.FetchBlob('S0')
if not np.allclose(S0, S2, atol=0.01, rtol=0.01):
print(S2.flatten())
print(S0.flatten())
print(np.max(np.abs(S2 - S0)))
self.assertTrue(False)

workspace.SwitchWorkspace(old_ws_name)

if __name__ == "__main__":
Expand Down