mirror of
https://github.com/pytorch/pytorch.git
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a7b82a44c4
Summary: The mkldnn-bridge is upgraded in this PR to support DNNLOWP operators. Meanwhile, APIs have been updated in caffe2 to use latest version. Pull Request resolved: https://github.com/pytorch/pytorch/pull/16308 Differential Revision: D14697018 Pulled By: yinghai fbshipit-source-id: ca952589098accb08295fd5aa92924c61e74d69c
688 lines
25 KiB
Python
688 lines
25 KiB
Python
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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import unittest
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import hypothesis.strategies as st
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from hypothesis import given, settings
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import copy
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import numpy as np
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from caffe2.proto import caffe2_pb2
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from caffe2.python import core, workspace
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from caffe2.python.transformations import optimizeForMKLDNN
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.ideep_test_util as mu
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@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
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class ConvFusionTest(hu.HypothesisTestCase):
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@given(stride=st.integers(1, 3),
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pad=st.integers(0, 3),
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kernel=st.integers(3, 5),
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size=st.integers(8, 20),
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input_channels=st.integers(1, 16),
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output_channels=st.integers(1, 16),
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batch_size=st.integers(1, 3),
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use_bias=st.booleans(),
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group=st.integers(1, 1),
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**mu.gcs)
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def test_convolution_relu_fusion(self, stride, pad, kernel, size,
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input_channels, output_channels,
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batch_size, use_bias, group, gc, dc):
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conv = core.CreateOperator(
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"Conv",
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["X0", "w0", "b0"] if use_bias else ["X0", "w0"],
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["Y0"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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device_option=dc[0]
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)
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relu = core.CreateOperator(
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"Relu",
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["Y0"],
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["Y0"],
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device_option=dc[0]
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)
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# Manual fusion for Conv + ReLU
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conv_fusion = core.CreateOperator(
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"ConvFusion",
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["X1", "w1", "b1"] if use_bias else ["X1", "w1"],
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["Y1"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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fusion_type = 1,
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device_option=dc[1]
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)
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X = np.random.rand(
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batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
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w = np.random.rand(
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output_channels * group, input_channels, kernel, kernel) \
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.astype(np.float32) - 0.5
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b = np.random.rand(output_channels * group).astype(np.float32) - 0.5
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old_ws_name = workspace.CurrentWorkspace()
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workspace.SwitchWorkspace("_device_check_", True)
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workspace.FeedBlob('X0', X, dc[0])
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workspace.FeedBlob('w0', w, dc[0])
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workspace.FeedBlob('b0', b, dc[0])
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workspace.RunOperatorOnce(conv)
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workspace.RunOperatorOnce(relu)
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Y0 = workspace.FetchBlob('Y0')
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workspace.ResetWorkspace()
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workspace.FeedBlob('X1', X, dc[1])
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workspace.FeedBlob('w1', w, dc[1])
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workspace.FeedBlob('b1', b, dc[1])
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workspace.RunOperatorOnce(conv_fusion)
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Y1 = workspace.FetchBlob('Y1')
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if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01):
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print(Y1.flatten())
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print(Y0.flatten())
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print(np.max(np.abs(Y1 - Y0)))
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self.assertTrue(False)
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# Auto fusion for Conv + ReLU
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workspace.ResetWorkspace()
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old_net = caffe2_pb2.NetDef()
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conv_old = caffe2_pb2.OperatorDef()
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conv_old.CopyFrom(conv)
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conv_old.device_option.CopyFrom(dc[1])
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relu_old = caffe2_pb2.OperatorDef()
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relu_old.CopyFrom(relu)
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relu_old.device_option.CopyFrom(dc[1])
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old_net.op.extend([conv_old, relu_old])
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workspace.FeedBlob('X0', X, dc[1])
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workspace.FeedBlob('w0', w, dc[1])
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workspace.FeedBlob('b0', b, dc[1])
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net = core.Net("net")
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net.Proto().CopyFrom(old_net)
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optimizeForMKLDNN(net)
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self.assertTrue(len(net.Proto().op) == 1)
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self.assertTrue(net.Proto().op[0].type == "ConvFusion")
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workspace.RunOperatorOnce(net.Proto().op[0])
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Y2 = workspace.FetchBlob('Y0')
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if not np.allclose(Y0, Y2, atol=0.01, rtol=0.01):
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print(Y2.flatten())
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print(Y0.flatten())
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print(np.max(np.abs(Y2 - Y0)))
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self.assertTrue(False)
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workspace.SwitchWorkspace(old_ws_name)
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@given(stride=st.integers(1, 3),
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pad=st.integers(0, 3),
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kernel=st.integers(3, 5),
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size=st.integers(8, 20),
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input_channels=st.integers(1, 16),
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output_channels=st.integers(1, 16),
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batch_size=st.integers(1, 3),
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use_bias=st.booleans(),
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group=st.integers(1, 1),
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sum_add=st.sampled_from(["Sum", "Add"]),
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**mu.gcs)
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def test_convolution_sum_fusion(self, stride, pad, kernel, size,
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input_channels, output_channels,
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batch_size, use_bias, group, sum_add, gc, dc):
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conv_S0 = core.CreateOperator(
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"Conv",
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["SX0", "Sw0", "Sb0"] if use_bias else ["SX0", "Sw0"],
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["S0"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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device_option=dc[0]
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)
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conv = core.CreateOperator(
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"Conv",
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["X0", "w0", "b0"] if use_bias else ["X0", "w0"],
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["Y0"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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device_option=dc[0]
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)
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sum = core.CreateOperator(
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sum_add,
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["S0", "Y0"],
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["S0"],
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device_option=dc[0]
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)
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# Manual fusion for Conv + Sum
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conv_S1 = core.CreateOperator(
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"Conv",
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["SX1", "Sw1", "Sb1"] if use_bias else ["SX1", "Sw1"],
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["S1"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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device_option=dc[1]
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)
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conv_fusion = core.CreateOperator(
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"ConvFusion",
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["X1", "w1", "b1", "S1"] if use_bias else ["X1", "w1", "S1"],
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["S1"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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fusion_type = 2,
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device_option=dc[1]
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)
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SX = np.random.rand(
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batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
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Sw = np.random.rand(
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output_channels * group, input_channels, kernel, kernel) \
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.astype(np.float32) - 0.5
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Sb = np.random.rand(output_channels * group).astype(np.float32) - 0.5
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X = np.random.rand(
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batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
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w = np.random.rand(
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output_channels * group, input_channels, kernel, kernel) \
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.astype(np.float32) - 0.5
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b = np.random.rand(output_channels * group).astype(np.float32) - 0.5
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old_ws_name = workspace.CurrentWorkspace()
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workspace.SwitchWorkspace("_device_check_", True)
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workspace.FeedBlob('SX0', SX, dc[0])
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workspace.FeedBlob('Sw0', Sw, dc[0])
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workspace.FeedBlob('Sb0', Sb, dc[0])
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workspace.FeedBlob('X0', X, dc[0])
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workspace.FeedBlob('w0', w, dc[0])
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workspace.FeedBlob('b0', b, dc[0])
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workspace.RunOperatorOnce(conv_S0)
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workspace.RunOperatorOnce(conv)
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workspace.RunOperatorOnce(sum)
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S0 = workspace.FetchBlob('S0')
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workspace.ResetWorkspace()
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workspace.FeedBlob('SX1', SX, dc[1])
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workspace.FeedBlob('Sw1', Sw, dc[1])
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workspace.FeedBlob('Sb1', Sb, dc[1])
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workspace.FeedBlob('X1', X, dc[1])
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workspace.FeedBlob('w1', w, dc[1])
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workspace.FeedBlob('b1', b, dc[1])
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workspace.RunOperatorOnce(conv_S1)
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workspace.RunOperatorOnce(conv_fusion)
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S1 = workspace.FetchBlob('S1')
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if not np.allclose(S0, S1, atol=0.01, rtol=0.01):
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print(S1.flatten())
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print(S0.flatten())
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print(np.max(np.abs(S1 - S0)))
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self.assertTrue(False)
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# Auto fusion for Conv + Sum
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workspace.ResetWorkspace()
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old_net = caffe2_pb2.NetDef()
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conv_S0_old = caffe2_pb2.OperatorDef()
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conv_S0_old.CopyFrom(conv_S0)
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conv_S0_old.device_option.CopyFrom(dc[1])
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conv_old = caffe2_pb2.OperatorDef()
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conv_old.CopyFrom(conv)
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conv_old.device_option.CopyFrom(dc[1])
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sum_old = caffe2_pb2.OperatorDef()
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sum_old.CopyFrom(sum)
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sum_old.device_option.CopyFrom(dc[1])
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old_net.op.extend([conv_S0_old, conv_old, sum_old])
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workspace.FeedBlob('SX0', SX, dc[1])
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workspace.FeedBlob('Sw0', Sw, dc[1])
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workspace.FeedBlob('Sb0', Sb, dc[1])
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workspace.FeedBlob('X0', X, dc[1])
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workspace.FeedBlob('w0', w, dc[1])
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workspace.FeedBlob('b0', b, dc[1])
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net = core.Net("net")
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net.Proto().CopyFrom(old_net)
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optimizeForMKLDNN(net)
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self.assertTrue(len(net.Proto().op) == 2)
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self.assertTrue(net.Proto().op[1].type == "ConvFusion")
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workspace.RunNetOnce(net.Proto())
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S2 = workspace.FetchBlob('S0')
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if not np.allclose(S0, S2, atol=0.01, rtol=0.01):
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print(S2.flatten())
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print(S0.flatten())
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print(np.max(np.abs(S2 - S0)))
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self.assertTrue(False)
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workspace.SwitchWorkspace(old_ws_name)
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@given(stride=st.integers(1, 3),
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pad=st.integers(0, 3),
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kernel=st.integers(3, 5),
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size=st.integers(8, 20),
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input_channels=st.integers(1, 16),
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output_channels=st.integers(1, 16),
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batch_size=st.integers(1, 3),
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use_bias=st.booleans(),
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group=st.integers(1, 1),
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sum_add=st.sampled_from(["Sum", "Add"]),
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**mu.gcs)
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def test_convolution_sum_relu_fusion(self, stride, pad, kernel, size,
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input_channels, output_channels,
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batch_size, use_bias, group, sum_add, gc, dc):
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conv_S0 = core.CreateOperator(
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"Conv",
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["SX0", "Sw0", "Sb0"] if use_bias else ["SX0", "Sw0"],
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["S0"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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device_option=dc[0]
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)
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conv = core.CreateOperator(
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"Conv",
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["X0", "w0", "b0"] if use_bias else ["X0", "w0"],
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["Y0"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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device_option=dc[0]
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)
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sum = core.CreateOperator(
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sum_add,
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["S0", "Y0"],
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["S0"],
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device_option=dc[0]
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)
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relu = core.CreateOperator(
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"Relu",
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["S0"],
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["S0"],
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device_option=dc[0]
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)
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# Manual fusion for Conv + Sum + ReLU
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conv_S1 = core.CreateOperator(
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"Conv",
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["SX1", "Sw1", "Sb1"] if use_bias else ["SX1", "Sw1"],
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["S1"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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device_option=dc[1]
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)
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conv_fusion = core.CreateOperator(
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"ConvFusion",
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["X1", "w1", "b1", "S1"] if use_bias else ["X1", "w1", "S1"],
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["S1"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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fusion_type = 3,
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device_option=dc[1]
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)
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SX = np.random.rand(
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batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
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Sw = np.random.rand(
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output_channels * group, input_channels, kernel, kernel) \
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.astype(np.float32) - 0.5
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Sb = np.random.rand(output_channels * group).astype(np.float32) - 0.5
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X = np.random.rand(
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batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
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w = np.random.rand(
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output_channels * group, input_channels, kernel, kernel) \
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.astype(np.float32) - 0.5
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b = np.random.rand(output_channels * group).astype(np.float32) - 0.5
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old_ws_name = workspace.CurrentWorkspace()
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workspace.SwitchWorkspace("_device_check_", True)
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workspace.FeedBlob('SX0', SX, dc[0])
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workspace.FeedBlob('Sw0', Sw, dc[0])
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workspace.FeedBlob('Sb0', Sb, dc[0])
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workspace.FeedBlob('X0', X, dc[0])
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workspace.FeedBlob('w0', w, dc[0])
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workspace.FeedBlob('b0', b, dc[0])
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workspace.RunOperatorOnce(conv_S0)
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workspace.RunOperatorOnce(conv)
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workspace.RunOperatorOnce(sum)
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workspace.RunOperatorOnce(relu)
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S0 = workspace.FetchBlob('S0')
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workspace.ResetWorkspace()
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workspace.FeedBlob('SX1', SX, dc[1])
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workspace.FeedBlob('Sw1', Sw, dc[1])
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workspace.FeedBlob('Sb1', Sb, dc[1])
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workspace.FeedBlob('X1', X, dc[1])
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workspace.FeedBlob('w1', w, dc[1])
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workspace.FeedBlob('b1', b, dc[1])
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workspace.RunOperatorOnce(conv_S1)
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workspace.RunOperatorOnce(conv_fusion)
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S1 = workspace.FetchBlob('S1')
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if not np.allclose(S0, S1, atol=0.01, rtol=0.01):
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print(S1.flatten())
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print(S0.flatten())
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print(np.max(np.abs(S1 - S0)))
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self.assertTrue(False)
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# Auto fusion for Conv + Sum + ReLU
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workspace.ResetWorkspace()
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old_net = caffe2_pb2.NetDef()
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conv_S0_old = caffe2_pb2.OperatorDef()
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conv_S0_old.CopyFrom(conv_S0)
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conv_S0_old.device_option.CopyFrom(dc[1])
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conv_old = caffe2_pb2.OperatorDef()
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conv_old.CopyFrom(conv)
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conv_old.device_option.CopyFrom(dc[1])
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sum_old = caffe2_pb2.OperatorDef()
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sum_old.CopyFrom(sum)
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sum_old.device_option.CopyFrom(dc[1])
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relu_old = caffe2_pb2.OperatorDef()
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relu_old.CopyFrom(relu)
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relu_old.device_option.CopyFrom(dc[1])
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old_net.op.extend([conv_S0_old, conv_old, sum_old, relu_old])
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workspace.FeedBlob('SX0', SX, dc[1])
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workspace.FeedBlob('Sw0', Sw, dc[1])
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workspace.FeedBlob('Sb0', Sb, dc[1])
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workspace.FeedBlob('X0', X, dc[1])
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workspace.FeedBlob('w0', w, dc[1])
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workspace.FeedBlob('b0', b, dc[1])
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net = core.Net("net")
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net.Proto().CopyFrom(old_net)
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optimizeForMKLDNN(net)
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self.assertTrue(len(net.Proto().op) == 2)
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self.assertTrue(net.Proto().op[1].type == "ConvFusion")
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workspace.RunNetOnce(net.Proto())
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S2 = workspace.FetchBlob('S0')
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if not np.allclose(S0, S2, atol=0.01, rtol=0.01):
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print(S2.flatten())
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print(S0.flatten())
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print(np.max(np.abs(S2 - S0)))
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self.assertTrue(False)
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workspace.SwitchWorkspace(old_ws_name)
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@given(stride=st.integers(1, 3),
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pad=st.integers(0, 3),
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kernel=st.integers(3, 5),
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size=st.integers(8, 20),
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input_channels=st.integers(7, 17),
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output_channels=st.integers(5, 15),
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batch_size=st.integers(1, 3),
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use_bias=st.booleans(),
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group=st.integers(2, 5),
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**mu.gcs)
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def test_convolution_grouped_sum_relu_fusion(self, stride, pad, kernel, size,
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input_channels, output_channels,
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batch_size, use_bias, group, gc, dc):
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conv_S0 = core.CreateOperator(
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"Conv",
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["SX0", "Sw0", "Sb0"] if use_bias else ["SX0", "Sw0"],
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["S0"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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device_option=dc[0]
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)
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conv = core.CreateOperator(
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"Conv",
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["X0", "w0", "b0"] if use_bias else ["X0", "w0"],
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["Y0"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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group=group,
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device_option=dc[0]
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)
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|
sum = core.CreateOperator(
|
|
"Sum",
|
|
["S0", "Y0"],
|
|
["S0"],
|
|
device_option=dc[0]
|
|
)
|
|
relu = core.CreateOperator(
|
|
"Relu",
|
|
["S0"],
|
|
["S0"],
|
|
device_option=dc[0]
|
|
)
|
|
|
|
SX = np.random.rand(
|
|
batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
|
|
Sw = np.random.rand(
|
|
output_channels * group, input_channels, kernel, kernel) \
|
|
.astype(np.float32) - 0.5
|
|
Sb = np.random.rand(output_channels * group).astype(np.float32) - 0.5
|
|
X = np.random.rand(
|
|
batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
|
|
w = np.random.rand(
|
|
output_channels * group, input_channels, kernel, kernel) \
|
|
.astype(np.float32) - 0.5
|
|
b = np.random.rand(output_channels * group).astype(np.float32) - 0.5
|
|
|
|
old_ws_name = workspace.CurrentWorkspace()
|
|
workspace.SwitchWorkspace("_device_check_", True)
|
|
workspace.FeedBlob('SX0', SX, dc[0])
|
|
workspace.FeedBlob('Sw0', Sw, dc[0])
|
|
workspace.FeedBlob('Sb0', Sb, dc[0])
|
|
workspace.FeedBlob('X0', X, dc[0])
|
|
workspace.FeedBlob('w0', w, dc[0])
|
|
workspace.FeedBlob('b0', b, dc[0])
|
|
workspace.RunOperatorOnce(conv_S0)
|
|
workspace.RunOperatorOnce(conv)
|
|
workspace.RunOperatorOnce(sum)
|
|
workspace.RunOperatorOnce(relu)
|
|
S0 = workspace.FetchBlob('S0')
|
|
|
|
workspace.ResetWorkspace()
|
|
old_net = caffe2_pb2.NetDef()
|
|
conv_S0_old = caffe2_pb2.OperatorDef()
|
|
conv_S0_old.CopyFrom(conv_S0)
|
|
conv_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([conv_S0_old, conv_old, sum_old, relu_old])
|
|
workspace.FeedBlob('SX0', SX, dc[1])
|
|
workspace.FeedBlob('Sw0', Sw, dc[1])
|
|
workspace.FeedBlob('Sb0', Sb, dc[1])
|
|
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)
|
|
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),
|
|
pad=st.integers(0, 3),
|
|
kernel=st.integers(3, 5),
|
|
size=st.integers(8, 20),
|
|
input_channels=st.integers(1, 16),
|
|
output_channels=st.integers(1, 16),
|
|
batch_size=st.integers(1, 3),
|
|
use_bias=st.booleans(),
|
|
group=st.integers(1, 1),
|
|
inplace=st.sampled_from([True, False]),
|
|
**mu.gcs)
|
|
def test_convolution_bn_folding(
|
|
self, stride, pad, kernel, size, input_channels,
|
|
output_channels, batch_size, use_bias, group,
|
|
inplace, gc, dc):
|
|
conv = core.CreateOperator(
|
|
"Conv",
|
|
["X0", "w0", "b0"] if use_bias else ["X0", "w0"],
|
|
["X1"],
|
|
stride=stride,
|
|
pad=pad,
|
|
kernel=kernel,
|
|
group=group,
|
|
device_option=dc[1]
|
|
)
|
|
bn = core.CreateOperator(
|
|
"SpatialBN",
|
|
["X1", "scale", "bias", "mean", "var"],
|
|
["X1" if inplace else "Y"],
|
|
is_test=True,
|
|
device_option=dc[1]
|
|
)
|
|
|
|
X = np.random.rand(
|
|
batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
|
|
w = np.random.rand(
|
|
output_channels * group, input_channels, kernel, kernel) \
|
|
.astype(np.float32) - 0.5
|
|
b = np.random.rand(output_channels * group).astype(np.float32) - 0.5
|
|
scale = np.random.rand(output_channels).astype(np.float32) + 0.5
|
|
bias = np.random.rand(output_channels).astype(np.float32) - 0.5
|
|
mean = np.random.randn(output_channels).astype(np.float32)
|
|
var = np.absolute(np.random.rand(output_channels).astype(np.float32)) + 0.5
|
|
|
|
old_ws_name = workspace.CurrentWorkspace()
|
|
workspace.SwitchWorkspace("_device_check_", True)
|
|
workspace.FeedBlob('X0', X, dc[1])
|
|
workspace.FeedBlob('w0', w, dc[1])
|
|
workspace.FeedBlob('b0', b, dc[1])
|
|
workspace.FeedBlob('scale', scale, dc[1])
|
|
workspace.FeedBlob('bias', bias, dc[1])
|
|
workspace.FeedBlob('mean', mean, dc[1])
|
|
workspace.FeedBlob('var', var, dc[1])
|
|
workspace.RunOperatorOnce(conv)
|
|
workspace.RunOperatorOnce(bn)
|
|
Y = workspace.FetchBlob('X1' if inplace else "Y")
|
|
|
|
workspace.ResetWorkspace()
|
|
old_net = caffe2_pb2.NetDef()
|
|
conv_old = caffe2_pb2.OperatorDef()
|
|
conv_old.CopyFrom(conv)
|
|
conv_old.device_option.CopyFrom(dc[1])
|
|
bn_old = caffe2_pb2.OperatorDef()
|
|
bn_old.CopyFrom(bn)
|
|
bn_old.device_option.CopyFrom(dc[1])
|
|
old_net.op.extend([conv_old, bn_old])
|
|
workspace.FeedBlob('X0', X, dc[1])
|
|
workspace.FeedBlob('w0', w, dc[1])
|
|
workspace.FeedBlob('b0', b, dc[1])
|
|
workspace.FeedBlob('scale', scale, dc[1])
|
|
workspace.FeedBlob('bias', bias, dc[1])
|
|
workspace.FeedBlob('mean', mean, dc[1])
|
|
workspace.FeedBlob('var', var, dc[1])
|
|
net = core.Net("net")
|
|
net.Proto().CopyFrom(old_net)
|
|
optimizeForMKLDNN(net)
|
|
self.assertTrue(len(net.Proto().op) == 1)
|
|
self.assertTrue(net.Proto().op[0].type == "Conv")
|
|
workspace.RunOperatorOnce(net.Proto().op[0])
|
|
Y1 = workspace.FetchBlob('X1' if inplace else "Y")
|
|
if not np.allclose(Y, Y1, atol=0.01, rtol=0.01):
|
|
print(Y.flatten())
|
|
print(Y1.flatten())
|
|
print(np.max(np.abs(Y - Y1)))
|
|
self.assertTrue(False)
|
|
|
|
workspace.SwitchWorkspace(old_ws_name)
|
|
|
|
@given(stride=st.integers(1, 3),
|
|
pad=st.integers(0, 3),
|
|
kernel=st.integers(3, 5),
|
|
size=st.integers(8, 20),
|
|
input_channels=st.integers(1, 16),
|
|
output_channels=st.integers(1, 16),
|
|
batch_size=st.integers(1, 3),
|
|
use_bias=st.booleans(),
|
|
group=st.integers(1, 1),
|
|
inplace=st.sampled_from([True, False]),
|
|
**mu.gcs)
|
|
def test_convolution_affch_folding(
|
|
self, stride, pad, kernel, size, input_channels,
|
|
output_channels, batch_size, use_bias, group,
|
|
inplace, gc, dc):
|
|
conv = core.CreateOperator(
|
|
"Conv",
|
|
["X0", "w0", "b0"] if use_bias else ["X0", "w0"],
|
|
["X1"],
|
|
stride=stride,
|
|
pad=pad,
|
|
kernel=kernel,
|
|
group=group,
|
|
device_option=dc[1]
|
|
)
|
|
affch = core.CreateOperator(
|
|
"AffineChannel",
|
|
["X1", "scale", "bias"],
|
|
["X1" if inplace else "Y"],
|
|
device_option=dc[1]
|
|
)
|
|
|
|
X = np.random.rand(
|
|
batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
|
|
w = np.random.rand(
|
|
output_channels * group, input_channels, kernel, kernel) \
|
|
.astype(np.float32) - 0.5
|
|
b = np.random.rand(output_channels * group).astype(np.float32) - 0.5
|
|
scale = np.random.rand(output_channels).astype(np.float32) + 0.5
|
|
bias = np.random.rand(output_channels).astype(np.float32) - 0.5
|
|
|
|
old_ws_name = workspace.CurrentWorkspace()
|
|
workspace.SwitchWorkspace("_device_check_", True)
|
|
workspace.FeedBlob('X0', X, dc[1])
|
|
workspace.FeedBlob('w0', w, dc[1])
|
|
workspace.FeedBlob('b0', b, dc[1])
|
|
workspace.FeedBlob('scale', scale, dc[1])
|
|
workspace.FeedBlob('bias', bias, dc[1])
|
|
workspace.RunOperatorOnce(conv)
|
|
workspace.RunOperatorOnce(affch)
|
|
Y = workspace.FetchBlob('X1' if inplace else "Y")
|
|
|
|
workspace.ResetWorkspace()
|
|
old_net = caffe2_pb2.NetDef()
|
|
conv_old = caffe2_pb2.OperatorDef()
|
|
conv_old.CopyFrom(conv)
|
|
conv_old.device_option.CopyFrom(dc[1])
|
|
affch_old = caffe2_pb2.OperatorDef()
|
|
affch_old.CopyFrom(affch)
|
|
affch_old.device_option.CopyFrom(dc[1])
|
|
old_net.op.extend([conv_old, affch_old])
|
|
workspace.FeedBlob('X0', X, dc[1])
|
|
workspace.FeedBlob('w0', w, dc[1])
|
|
workspace.FeedBlob('b0', b, dc[1])
|
|
workspace.FeedBlob('scale', scale, dc[1])
|
|
workspace.FeedBlob('bias', bias, dc[1])
|
|
net = core.Net("net")
|
|
net.Proto().CopyFrom(old_net)
|
|
optimizeForMKLDNN(net)
|
|
self.assertTrue(len(net.Proto().op) == 1)
|
|
self.assertTrue(net.Proto().op[0].type == "Conv")
|
|
workspace.RunOperatorOnce(net.Proto().op[0])
|
|
Y1 = workspace.FetchBlob('X1' if inplace else "Y")
|
|
if not np.allclose(Y, Y1, atol=0.01, rtol=0.01):
|
|
print(Y.flatten())
|
|
print(Y1.flatten())
|
|
print(np.max(np.abs(Y - Y1)))
|
|
self.assertTrue(False)
|
|
|
|
workspace.SwitchWorkspace(old_ws_name)
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|