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The following commit(s) were added to refs/heads/master by this push:
new 90d9054b7b [submodule] Upgrade oneDNN to v2.6 (#20982)
90d9054b7b is described below
commit 90d9054b7b3f8d38fd693278d440524e5815f59d
Author: bartekkuncer <[email protected]>
AuthorDate: Tue Apr 19 19:23:42 2022 +0200
[submodule] Upgrade oneDNN to v2.6 (#20982)
---
3rdparty/onednn | 2 +-
src/operator/nn/dnnl/dnnl_convolution.cc | 64 ++++++++++++++------------------
tests/cpp/operator/dnnl_test.cc | 2 +-
tools/dependencies/README.md | 2 +-
4 files changed, 31 insertions(+), 39 deletions(-)
diff --git a/3rdparty/onednn b/3rdparty/onednn
index 9a35435c18..52b5f107dd 160000
--- a/3rdparty/onednn
+++ b/3rdparty/onednn
@@ -1 +1 @@
-Subproject commit 9a35435c18722ff17a48fb60bceac42bfdf78754
+Subproject commit 52b5f107dd9cf10910aaa19cb47f3abf9b349815
diff --git a/src/operator/nn/dnnl/dnnl_convolution.cc
b/src/operator/nn/dnnl/dnnl_convolution.cc
index f28f273d3b..60c65ffa47 100644
--- a/src/operator/nn/dnnl/dnnl_convolution.cc
+++ b/src/operator/nn/dnnl/dnnl_convolution.cc
@@ -108,42 +108,34 @@
std::shared_ptr<dnnl::convolution_forward::primitive_desc> GetConvFwdImpl(
int mask = (param.requantize_scales.size() > 1) ? 2 : 0;
attr.set_output_scales(mask, param.requantize_scales);
}
- auto GetConvFwdPd = [¶m, &data, &weights, &output, &attr](
- const dnnl::convolution_forward::desc& desc) {
- auto engine = CpuEngine::Get()->get_engine();
- try {
- // DNNL introduced padded formats since 0.15 which require more memory
- // compared to the actual size of the tensor. Currently, DNNL operators
- // still reuse memory from memory planning, so here we need to select a
- // suboptimal kernel for computation that has the expected memory size
requirements
- auto conv_pd =
- std::make_shared<dnnl::convolution_forward::primitive_desc>(desc,
attr, engine);
- while (
- conv_pd->dst_desc().get_size() != GetArraySize(output) ||
- conv_pd->src_desc().get_size() != GetArraySize(data) ||
- (!param.dnnl_param.quantized &&
- conv_pd->weights_desc().get_size() != GetArraySize(weights)) ||
- // With the upgrade of oneDNN to version 2.4+
- //
tests/python/dnnl/subgraphs/test_conv_subgraph.py::test_pos_conv_add[True-data_shape1]
- // started failing. Switching away from blocking weights in order to
temporarily fix
- // the issue until full fix arrives. Tracking issue:
- // https://github.com/apache/incubator-mxnet/issues/20826.
- (param.dnnl_param.quantized && conv_pd->weights_desc().dims()[1] < 4
&&
- conv_pd->weights_desc().data.padded_dims[1] !=
conv_pd->weights_desc().dims()[1])) {
- // next_impl() will visit desc and engine, please make sure they are
still alive here.
- CHECK(conv_pd->next_impl()) << "No convolution implementation for this
request.";
- }
- return conv_pd;
- } catch (dnnl::error& e) {
- if (e.status == dnnl_unimplemented && param.dnnl_param.quantized) {
- LOG(ERROR) << "AVX512-BW support or Intel(R) MKL dependency is "
- "required for int8 convolution";
- } else {
- LOG(ERROR) << e.message;
- }
- throw;
- }
- };
+ auto GetConvFwdPd =
+ [¶m, &data, &weights, &output, &attr](const
dnnl::convolution_forward::desc& desc) {
+ auto engine = CpuEngine::Get()->get_engine();
+ try {
+ // DNNL introduced padded formats since 0.15 which require more
memory
+ // compared to the actual size of the tensor. Currently, DNNL
operators
+ // still reuse memory from memory planning, so here we need to
select a
+ // suboptimal kernel for computation that has the expected memory
size requirements
+ auto conv_pd =
+
std::make_shared<dnnl::convolution_forward::primitive_desc>(desc, attr, engine);
+ while (conv_pd->dst_desc().get_size() != GetArraySize(output) ||
+ conv_pd->src_desc().get_size() != GetArraySize(data) ||
+ (!param.dnnl_param.quantized &&
+ conv_pd->weights_desc().get_size() !=
GetArraySize(weights))) {
+ // next_impl() will visit desc and engine, please make sure they
are still alive here.
+ CHECK(conv_pd->next_impl()) << "No convolution implementation for
this request.";
+ }
+ return conv_pd;
+ } catch (dnnl::error& e) {
+ if (e.status == dnnl_unimplemented && param.dnnl_param.quantized) {
+ LOG(ERROR) << "AVX512-BW support or Intel(R) MKL dependency is "
+ "required for int8 convolution";
+ } else {
+ LOG(ERROR) << e.message;
+ }
+ throw;
+ }
+ };
if (param.conv_param.dilate.ndim() == 0 && bias_md_ptr == nullptr) {
dnnl::convolution_forward::desc desc(prop,
diff --git a/tests/cpp/operator/dnnl_test.cc b/tests/cpp/operator/dnnl_test.cc
index 2874df1947..a420c2a7b9 100644
--- a/tests/cpp/operator/dnnl_test.cc
+++ b/tests/cpp/operator/dnnl_test.cc
@@ -101,7 +101,7 @@ static void VerifyDefMem(const dnnl::memory& mem) {
TEST(DNNL_UTIL_FUNC, MemFormat) {
// Check whether the number of format is correct.
- CHECK_EQ(dnnl_format_tag_last, 503);
+ CHECK_EQ(dnnl_format_tag_last, 514);
CHECK_EQ(dnnl_nchw, 5);
CHECK_EQ(dnnl_oihw, 5);
}
diff --git a/tools/dependencies/README.md b/tools/dependencies/README.md
index 6d18d1933d..e3e7c85ca6 100644
--- a/tools/dependencies/README.md
+++ b/tools/dependencies/README.md
@@ -57,7 +57,7 @@ The dependencies could be categorized by several groups: BLAS
libraries, CPU-bas
| Dependencies | MXNet Version |
| :------------: |:-------------:|
|OpenBLAS| 0.3.9 |
-|oneDNN| 2.5.2 |
+|oneDNN| 2.6 |
|CUDA| 10.1 |
|cuDNN| 7.5.1 |
|NCCL| 2.4.2 |
