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new b03d717 [BUGFIX] Fix
tests/python/dnnl/subgraphs/test_conv_subgraph.py (#20970)
b03d717 is described below
commit b03d717118621f112f1645da4aa95c6e0704a3f0
Author: bartekkuncer <[email protected]>
AuthorDate: Fri Mar 25 14:40:04 2022 +0100
[BUGFIX] Fix tests/python/dnnl/subgraphs/test_conv_subgraph.py (#20970)
* [BUGFIX] Fix tests/python/dnnl/subgraphs/test_conv_subgraph.py
* Fix sanity
---
src/operator/nn/dnnl/dnnl_convolution.cc | 72 ++++++++++++++++----------------
1 file changed, 36 insertions(+), 36 deletions(-)
diff --git a/src/operator/nn/dnnl/dnnl_convolution.cc
b/src/operator/nn/dnnl/dnnl_convolution.cc
index d38009a..7a21290 100644
--- a/src/operator/nn/dnnl/dnnl_convolution.cc
+++ b/src/operator/nn/dnnl/dnnl_convolution.cc
@@ -108,42 +108,42 @@
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 primitive with weight
dnnl::format_tag
- // ABcd4b16a4b 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] == 16)) {
- // 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)) ||
+ // 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;
+ }
+ };
if (param.conv_param.dilate.ndim() == 0 && bias_md_ptr == nullptr) {
dnnl::convolution_forward::desc desc(prop,
