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Fix MKLDNN sigmoid/softrelu issue #10336

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merged 5 commits into from
Apr 2, 2018
Merged

Fix MKLDNN sigmoid/softrelu issue #10336

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ea0e7b3
Fix MKLDNN sigmoid/softrelu issue
Mar 30, 2018
179963e
Enable Sigmoid and SoftRelu for MKLDNN
Mar 30, 2018
c3f25fa
Add activation kData for backward calculation for MKLDNN
Mar 31, 2018
c85641e
Add tanh support for MKLDNN activation
Apr 2, 2018
1f68df8
Adjust rtol to pass tanh tests for MKLDNN
Apr 2, 2018
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2 changes: 1 addition & 1 deletion src/operator/nn/activation-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -201,7 +201,7 @@ void ActivationGradCompute(const nnvm::NodeAttrs& attrs,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
#if MXNET_USE_CUDNN == 1
#if (MXNET_USE_CUDNN == 1 || MXNET_USE_MKLDNN == 1)
CHECK_EQ(inputs.size(), 3U);
#else
CHECK_EQ(inputs.size(), 2U);
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12 changes: 4 additions & 8 deletions src/operator/nn/activation.cc
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Expand Up @@ -44,7 +44,7 @@ struct ActivationGrad {
const std::vector<nnvm::NodeEntry>& ograds) const {
std::vector<nnvm::NodeEntry> heads(ograds.begin(), ograds.end());
heads.emplace_back(nnvm::NodeEntry{n, activation::kOut, 0});
#if MXNET_USE_CUDNN == 1
#if (MXNET_USE_CUDNN == 1 || MXNET_USE_MKLDNN == 1)
heads.push_back(n->inputs[activation::kData]);
#endif
return MakeGradNode(op_name, n, heads, n->attrs.dict);
Expand Down Expand Up @@ -74,15 +74,11 @@ void ActivationGradComputeExCPU(const nnvm::NodeAttrs& attrs,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
#if MXNET_USE_CUDNN == 1
CHECK_EQ(inputs.size(), 3U);
#else
CHECK_EQ(inputs.size(), 2U);
#endif
const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);
if (SupportMKLDNN(inputs[0])) {
MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs);
MKLDNNActivationBackward(attrs, ctx, inputs[0], inputs[1], req[0],
MKLDNNActivationBackward(attrs, ctx, inputs[0], inputs[2], req[0],
outputs[0]);
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inputs[2] doesn't exist. You need to modify ActivationGrad to pass input data to backward.

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Thanks for the comments, have updated diff to pass input data and updated a few other functions as well.

MKLDNN_OPCHECK_RUN(ActivationGradCompute<cpu>, attrs, ctx, inputs, req, outputs);
return;
Expand Down Expand Up @@ -116,13 +112,13 @@ inline static bool BackwardActStorageType(const nnvm::NodeAttrs& attrs,
DispatchMode* dispatch_mode,
std::vector<int> *in_attrs,
std::vector<int> *out_attrs) {
#if MXNET_USE_CUDNN == 1
#if (MXNET_USE_CUDNN == 1 || MXNET_USE_MKLDNN == 1)
CHECK_EQ(in_attrs->size(), 3U);
#else
CHECK_EQ(in_attrs->size(), 2U);
#endif
CHECK_EQ(out_attrs->size(), 1U);
#if MXNET_USE_CUDNN == 1
#if (MXNET_USE_CUDNN == 1 || MXNET_USE_MKLDNN == 1)
bool ret = ElemwiseStorageType<3, 1, false, false, false>(attrs, dev_mask,
dispatch_mode,
in_attrs, out_attrs);
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9 changes: 3 additions & 6 deletions src/operator/nn/mkldnn/mkldnn_act.cc
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Expand Up @@ -43,13 +43,10 @@ namespace mxnet {
namespace op {

bool SupportMKLDNNAct(const ActivationParam& param) {
// We only enable ReLU for now. It seems other activations have some precision
// problems.
return param.act_type == activation::kReLU;
#if 0
return param.act_type == activation::kReLU
|| param.act_type == activation::kSigmoid
|| param.act_type == activation::kSoftReLU;
#endif
|| param.act_type == activation::kSoftReLU
|| param.act_type == activation::kTanh;
}

static inline mkldnn::algorithm GetMKLDNNActAlgo(const ActivationParam& param) {
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4 changes: 2 additions & 2 deletions tests/python/unittest/test_gluon.py
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Expand Up @@ -717,8 +717,8 @@ def test_lambda():

input_data = mx.nd.random.uniform(shape=(2, 3, 5, 7))
out1, out2, out3 = net1(input_data), net2(input_data), net3(input_data)
assert_almost_equal(out1.asnumpy(), out2.asnumpy())
assert_almost_equal(out1.asnumpy(), out3.asnumpy())
assert_almost_equal(out1.asnumpy(), out2.asnumpy(), rtol=1e-3)
assert_almost_equal(out1.asnumpy(), out3.asnumpy(), rtol=1e-3)
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@zheng-da zheng-da Apr 2, 2018 •
edited
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maybe we should set a smaller tolerance. changing from 1e-5 to 1e-3 seems to be a big jump.

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It would still fail intermittently if setting to 1e-4, by checking another similar function check_consistency(), the threshold is set to 1e-3 for FP32, since for this test_lambda() case the data type is FP32 (mx.nd.random.uniform() default output FP32 type) so I set the rtol to 1e-3 as well.



@with_seed()
Expand Down