mirror of
https://github.com/zebrajr/pytorch.git
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f3ac619764
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/10595 Reviewed By: bwasti Differential Revision: D9110099 fbshipit-source-id: e1ed66c7d82b2f9987b7eb9c7f98877a6dbeb902
337 lines
14 KiB
Python
337 lines
14 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from hypothesis import given
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import hypothesis.strategies as st
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import numpy as np
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from caffe2.python.transformations import Transformer
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from caffe2.python import core, workspace, test_util
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transformer = Transformer()
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def str_compare(a, b, encoding="utf8"):
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if isinstance(a, bytes):
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a = a.decode(encoding)
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if isinstance(b, bytes):
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b = b.decode(encoding)
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return a == b
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class TestTransformations(test_util.TestCase):
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def test_transformer_AddNNPACK(self):
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net = core.Net("net")
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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net.Relu(["Y"], ["Y2"])
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transformer.AddNNPACK(net)
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assert str_compare(net.Proto().op[0].engine, "NNPACK")
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def test_transformer_FuseNNPACKConvRelu(self):
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net = core.Net("net")
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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net.Relu(["Y"], ["Y2"])
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transformer.AddNNPACK(net) # get the NNPACK engine
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assert str_compare(net.Proto().op[0].engine, "NNPACK")
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transformer.FuseNNPACKConvRelu(net)
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assert len(net.Proto().op) == 1
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has_activation_arg = False
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for arg in net.Proto().op[0].arg:
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if str_compare(arg.name, "activation"):
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assert str_compare(arg.s, "Relu")
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has_activation_arg = True
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assert has_activation_arg
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def test_noFuseNNPACKConvRelu(self):
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net = core.Net("net")
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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net.Relu(["Y"], ["Y2"])
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net.Relu(["Y"], ["Y3"])
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transformer.AddNNPACK(net) # get the NNPACK engine
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assert str_compare(net.Proto().op[0].engine, "NNPACK")
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transformer.FuseNNPACKConvRelu(net)
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assert len(net.Proto().op) == 3
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has_activation_arg = False
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for arg in net.Proto().op[0].arg:
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if str_compare(arg.name, "activation") and str_compare(arg.s, "Relu"):
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has_activation_arg = True
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assert not has_activation_arg
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def test_transformer_FuseNNPACKConvReluNoInplace(self):
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net = core.Net("net")
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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net.Relu(["Y"], ["X"])
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transformer.AddNNPACK(net) # get the NNPACK engine
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assert str_compare(net.Proto().op[0].engine, "NNPACK")
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transformer.FuseNNPACKConvRelu(net)
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assert len(net.Proto().op) == 1
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has_activation_arg = False
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for arg in net.Proto().op[0].arg:
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if str_compare(arg.name, "activation"):
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assert str_compare(arg.s, "Relu")
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has_activation_arg = True
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assert has_activation_arg
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assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0]
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def test_transformer_FuseNNPACKConvReluInplaceRelu(self):
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net = core.Net("net")
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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net.Relu(["Y"], ["Y"])
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transformer.AddNNPACK(net) # get the NNPACK engine
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assert str_compare(net.Proto().op[0].engine, "NNPACK")
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transformer.FuseNNPACKConvRelu(net)
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assert len(net.Proto().op) == 1
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has_activation_arg = False
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for arg in net.Proto().op[0].arg:
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if str_compare(arg.name, "activation"):
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assert str_compare(arg.s, "Relu")
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has_activation_arg = True
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assert has_activation_arg
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assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0]
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def test_transformer_FuseNNPACKConvReluPingPongNaming(self):
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net = core.Net("net")
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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net.Relu(["Y"], ["X"])
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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transformer.AddNNPACK(net) # get the NNPACK engine
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assert str_compare(net.Proto().op[0].engine, "NNPACK")
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transformer.FuseNNPACKConvRelu(net)
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assert len(net.Proto().op) == 2
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has_activation_arg = False
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for arg in net.Proto().op[0].arg:
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if str_compare(arg.name, "activation"):
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assert str_compare(arg.s, "Relu")
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has_activation_arg = True
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assert has_activation_arg
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assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0]
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assert net.Proto().op[1].output[0] != net.Proto().op[1].input[0]
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def test_transformer_FuseNNPACKConvReluFollowedByMultipleInputOp(self):
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net = core.Net("net")
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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net.Relu(["Y"], ["Y2"])
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net.Conv(["Y2", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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net.Relu(["Y"], ["Y2"])
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transformer.AddNNPACK(net) # get the NNPACK engine
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assert str_compare(net.Proto().op[0].engine, "NNPACK")
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transformer.FuseNNPACKConvRelu(net)
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assert len(net.Proto().op) == 2
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has_activation_arg = False
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for arg in net.Proto().op[0].arg:
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if str_compare(arg.name, "activation"):
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assert str_compare(arg.s, "Relu")
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has_activation_arg = True
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assert has_activation_arg
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assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0]
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assert net.Proto().op[1].output[0] != net.Proto().op[1].input[0]
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def test_transformer_FuseNNPACKConvReluInplaceFollowedByMultipleInputOp(self):
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net = core.Net("net")
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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net.Relu(["Y"], ["Y"])
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net.Conv(["Y", "w", "b"], ["Y2"], stride=1, pad=0, kernel=3, order="NCHW")
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net.Relu(["Y2"], ["Y2"])
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transformer.AddNNPACK(net) # get the NNPACK engine
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assert str_compare(net.Proto().op[0].engine, "NNPACK")
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transformer.FuseNNPACKConvRelu(net)
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assert len(net.Proto().op) == 2
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has_activation_arg = False
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for arg in net.Proto().op[0].arg:
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if str_compare(arg.name, "activation"):
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assert str_compare(arg.s, "Relu")
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has_activation_arg = True
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assert has_activation_arg
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assert net.Proto().op[0].output[0] != net.Proto().op[0].input[0]
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assert net.Proto().op[1].output[0] != net.Proto().op[1].input[0]
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def test_transformer_SinkMaxPool(self):
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net = core.Net("net")
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=3, order="NCHW")
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net.MaxPool(["Y"], ["Y1"], kernel=3)
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net.Relu(["Y1"], ["Y1"])
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transformer.SinkMaxPool(net)
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assert str_compare(net.Proto().op[1].type, "Relu")
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assert str_compare(net.Proto().op[2].type, "MaxPool")
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@given(
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size=st.integers(7, 10),
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input_channels=st.integers(1, 10),
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seed=st.integers(0, 65535),
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order=st.sampled_from(["NCHW", "NHWC"]),
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epsilon=st.floats(min_value=1e-5, max_value=1e-2),
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)
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def test_transformer_FuseConvBN(self, size, input_channels, seed, order, epsilon):
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workspace.ResetWorkspace()
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net = core.Net("net")
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c = input_channels
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h = size
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w = size
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k = 3
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net.Conv(["X", "w", "b"], ["Y"], stride=1, pad=0, kernel=k, order=order)
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net.SpatialBN(
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["Y", "scale", "bias", "mean", "var"],
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["Y2"],
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is_test=True,
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order=order,
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epsilon=epsilon,
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)
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np.random.seed(seed)
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if order == "NCHW":
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workspace.FeedBlob("X", np.random.rand(1, c, h, w).astype(np.float32))
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workspace.FeedBlob("w", np.random.rand(c, c, k, k).astype(np.float32))
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else:
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workspace.FeedBlob("X", np.random.rand(1, h, w, c).astype(np.float32))
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workspace.FeedBlob("w", np.random.rand(c, k, k, c).astype(np.float32))
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workspace.FeedBlob("b", np.random.rand(c).astype(np.float32))
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workspace.FeedBlob("scale", np.random.rand(c).astype(np.float32))
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workspace.FeedBlob("bias", np.random.rand(c).astype(np.float32))
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workspace.FeedBlob("mean", np.random.rand(c).astype(np.float32))
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# This is necessary because 1/sqrt(var) is used and if var is too small
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# we get floating point artifacts that cause test failures
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workspace.FeedBlob("var", np.random.rand(c).astype(np.float32) + 0.5)
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workspace.RunNetOnce(net)
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preTransformOutput = workspace.FetchBlob("Y2").flatten()
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workspace.FeedBlob("Y2", np.zeros((1, 1)))
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transformer.FuseConvBN(net)
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# Ensure fusion
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assert len(net.Proto().op) == 1
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workspace.RunNetOnce(net)
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postTransformOutput = workspace.FetchBlob("Y2").flatten()
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# Check that there is no numerical difference
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assert np.allclose(
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preTransformOutput,
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postTransformOutput,
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rtol=5e-02,
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atol=1e-03
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)
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@given(
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size=st.integers(7, 10),
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input_channels=st.integers(1, 10),
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seed=st.integers(0, 65535),
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order=st.sampled_from(["NCHW", "NHWC"]),
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epsilon=st.floats(min_value=1e-5, max_value=1e-2),
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)
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def test_transformer_FuseConvBNNoConvBias(self, size, input_channels, seed, order, epsilon):
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workspace.ResetWorkspace()
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net = core.Net("net")
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c = input_channels
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h = size
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w = size
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k = 3
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net.Conv(["X", "w"], ["Y"], stride=1, pad=0, kernel=k, order=order)
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net.SpatialBN(
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["Y", "scale", "bias", "mean", "var"],
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["Y2"],
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is_test=True,
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order=order,
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epsilon=epsilon,
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)
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np.random.seed(seed)
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if order == "NCHW":
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workspace.FeedBlob("X", np.random.rand(1, c, h, w).astype(np.float32))
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workspace.FeedBlob("w", np.random.rand(c, c, k, k).astype(np.float32))
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else:
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workspace.FeedBlob("X", np.random.rand(1, h, w, c).astype(np.float32))
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workspace.FeedBlob("w", np.random.rand(c, k, k, c).astype(np.float32))
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workspace.FeedBlob("scale", np.random.rand(c).astype(np.float32))
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workspace.FeedBlob("bias", np.random.rand(c).astype(np.float32))
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workspace.FeedBlob("mean", np.random.rand(c).astype(np.float32))
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# This is necessary because 1/sqrt(var) is used and if var is too small
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# we get floating point artifacts that cause test failures
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workspace.FeedBlob("var", np.random.rand(c).astype(np.float32) + 0.5)
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workspace.RunNetOnce(net)
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preTransformOutput = workspace.FetchBlob("Y2").flatten()
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workspace.FeedBlob("Y2", np.zeros((1, 1)))
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transformer.FuseConvBN(net)
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# Ensure fusion
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assert len(net.Proto().op) == 1
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workspace.RunNetOnce(net)
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postTransformOutput = workspace.FetchBlob("Y2").flatten()
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# Check that there is no numerical difference
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assert np.allclose(
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preTransformOutput,
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postTransformOutput,
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rtol=5e-02,
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atol=1e-03
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)
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@given(
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size=st.integers(7, 10),
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input_channels=st.integers(1, 10),
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seed=st.integers(0, 65535),
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order=st.sampled_from(["NCHW", "NHWC"]),
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epsilon=st.floats(min_value=1e-5, max_value=1e-2),
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)
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def test_transformer_FuseConvBNNoConvBiasDuplicatedName(self, size, input_channels, seed, order, epsilon):
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workspace.ResetWorkspace()
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net = core.Net("net")
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c = input_channels
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h = size
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w = size
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k = 3
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net.Conv(["X", "w"], ["Y"], stride=1, pad=0, kernel=k, order=order)
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net.SpatialBN(
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["Y", "scale", "_bias0", "mean", "var"],
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["Y2"],
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is_test=True,
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order=order,
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epsilon=epsilon,
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)
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np.random.seed(seed)
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if order == "NCHW":
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workspace.FeedBlob("X", np.random.rand(1, c, h, w).astype(np.float32))
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workspace.FeedBlob("w", np.random.rand(c, c, k, k).astype(np.float32))
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else:
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workspace.FeedBlob("X", np.random.rand(1, h, w, c).astype(np.float32))
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workspace.FeedBlob("w", np.random.rand(c, k, k, c).astype(np.float32))
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workspace.FeedBlob("scale", np.random.rand(c).astype(np.float32))
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workspace.FeedBlob("_bias0", np.random.rand(c).astype(np.float32))
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workspace.FeedBlob("mean", np.random.rand(c).astype(np.float32))
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# This is necessary because 1/sqrt(var) is used and if var is too small
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# we get floating point artifacts that cause test failures
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workspace.FeedBlob("var", np.random.rand(c).astype(np.float32) + 0.5)
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workspace.RunNetOnce(net)
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preTransformOutput = workspace.FetchBlob("Y2").flatten()
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workspace.FeedBlob("Y2", np.zeros((1, 1)))
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transformer.FuseConvBN(net)
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# Ensure fusion
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assert len(net.Proto().op) == 1
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workspace.RunNetOnce(net)
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postTransformOutput = workspace.FetchBlob("Y2").flatten()
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print("pre")
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print(preTransformOutput)
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print("after")
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print(postTransformOutput)
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# Check that there is no numerical difference
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assert np.allclose(
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preTransformOutput,
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postTransformOutput,
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rtol=5e-02,
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atol=1e-03
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)
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