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Add conv2d_transpose to bf16 list and kernel refator
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@wozna
wozna committed Feb 16, 2021
1 parent 4fa62a2 commit 2a8a832
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Showing 2 changed files with 28 additions and 26 deletions.
6 changes: 3 additions & 3 deletions paddle/fluid/framework/ir/graph_pattern_detector.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -2202,9 +2202,9 @@ PDNode *patterns::Bfloat16Placement::operator()(
const std::unordered_set<std::string> &bfloat16_enabled_op_types) {
std::unordered_set<std::string> supported_op_types =
std::unordered_set<std::string>(
{"concat", "conv2d", "elementwise_add", "elementwise_mul", "fc",
"fusion_gru", "gelu", "layer_norm", "matmul", "pool2d", "reshape2",
"softmax", "sum", "transpose2"});
{"concat", "conv2d", "conv2d_transpose", "elementwise_add",
"elementwise_mul", "fc", "fusion_gru", "gelu", "layer_norm",
"matmul", "pool2d", "reshape2", "softmax", "sum", "transpose2"});
if (!bfloat16_enabled_op_types.empty()) {
supported_op_types = bfloat16_enabled_op_types;
}
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48 changes: 25 additions & 23 deletions paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -125,13 +125,14 @@ class ConvTransposeMKLDNNHandlerT
platform::errors::Unimplemented(
"Now we only support 2d oneDNN convolution transpose op"));

auto input_dims = input->dims();
auto data_dims = framework::slice_ddim(input_dims, 2, input_dims.size());
auto filter_dims = filter->dims();
auto filter_data_dims =
const auto& input_dims = input->dims();
const auto data_dims =
framework::slice_ddim(input_dims, 2, input_dims.size());
const auto& filter_dims = filter->dims();
const auto filter_data_dims =
framework::slice_ddim(filter_dims, 2, filter_dims.size());

auto ksize = framework::vectorize(filter_data_dims);
const auto ksize = framework::vectorize(filter_data_dims);

UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
data_dims, strides, ksize);
Expand All @@ -140,31 +141,30 @@ class ConvTransposeMKLDNNHandlerT
[](int64_t i) { return i - 1; });

const auto src_tz = framework::vectorize(input->dims());

auto weights_tz = GetWeightsTz(filter, groups);

auto dst_tz = framework::vectorize(output->dims());
const auto weights_tz = GetWeightsTz(filter, groups);
const auto dst_tz = framework::vectorize(output->dims());
const auto mkldnn_paddings = platform::ToMkldnnPadding(paddings);

/* create memory descriptor for convolution without specified format
* ('any') which lets a primitive (convolution in this case) choose
* the memory format preferred for best performance
*/
auto chosen_memory_format = MKLDNNMemoryFormat::any;
std::string fuse_activation = ctx.Attr<std::string>("fuse_activation");
float fuse_alpha = ctx.Attr<float>("fuse_alpha");
float fuse_beta = ctx.Attr<float>("fuse_beta");
const auto chosen_memory_format = MKLDNNMemoryFormat::any;
const std::string fuse_activation =
ctx.Attr<std::string>("fuse_activation");
const float fuse_alpha = ctx.Attr<float>("fuse_alpha");
const float fuse_beta = ctx.Attr<float>("fuse_beta");

auto data_type = mkldnn::memory::data_type::f32;
if (ctx.Attr<std::string>("mkldnn_data_type") == "bfloat16" ||
std::is_same<T_out, platform::bfloat16>::value)
data_type = mkldnn::memory::data_type::bf16;

auto src_md =
const auto src_md =
platform::MKLDNNMemDesc(src_tz, data_type, chosen_memory_format);
auto weights_md =
const auto weights_md =
platform::MKLDNNMemDesc(weights_tz, data_type, chosen_memory_format);
auto dst_md = platform::MKLDNNMemDesc(
const auto dst_md = platform::MKLDNNMemDesc(
dst_tz, platform::MKLDNNGetDataType<T_out>(), chosen_memory_format);

const mkldnn::primitive_attr conv_trans_attr =
Expand All @@ -173,7 +173,7 @@ class ConvTransposeMKLDNNHandlerT
: mkldnn::prop_kind::forward_training;
if (bias) {
std::vector<int64_t> bias_tz = framework::vectorize(bias->dims());
auto bias_md =
const auto bias_md =
platform::MKLDNNMemDesc(bias_tz, data_type, MKLDNNMemoryFormat::x);
this->AcquireForwardPrimitiveDescriptor(
conv_trans_attr, fwd_prop_kind,
Expand All @@ -188,8 +188,9 @@ class ConvTransposeMKLDNNHandlerT
}
}

mkldnn::primitive_attr CreatePostOps(std::string fuse_activation,
float fuse_alpha, float fuse_beta) {
mkldnn::primitive_attr CreatePostOps(const std::string& fuse_activation,
const float& fuse_alpha,
const float& fuse_beta) {
mkldnn::primitive_attr conv_attr;
mkldnn::post_ops post_operations;

Expand Down Expand Up @@ -237,7 +238,7 @@ class ConvTransposeMKLDNNHandlerT
}

std::shared_ptr<mkldnn::memory> AcquireWeightsMemoryWithReorder(
const framework::Tensor* filter, const int groups, const bool is_test) {
const framework::Tensor* filter, const int& groups, const bool& is_test) {
// This is workaround to make execution faster, delete
// if statement after including md inside Tensor
auto weights_mem_p = this->AcquireMemory("@weights_mem_p_target");
Expand Down Expand Up @@ -282,7 +283,7 @@ class ConvTransposeMKLDNNHandlerT
}

std::shared_ptr<mkldnn::memory> AcquireBiasMemoryWithReorder(
const framework::Tensor* bias, const bool is_test) {
const framework::Tensor* bias, const bool& is_test) {
auto bias_mem_p = this->AcquireMemory("@bias_mem_p_target");
if (is_test && bias_mem_p) {
return bias_mem_p;
Expand All @@ -305,8 +306,9 @@ class ConvTransposeMKLDNNOpKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
platform::errors::PreconditionNotMet(
"Operator DNNL ConvTranspose must use CPUPlace"));
bool is_bfloat16 = ctx.Attr<std::string>("mkldnn_data_type") == "bfloat16";
bool force_fp32_output = ctx.Attr<bool>("force_fp32_output");
const bool is_bfloat16 =
ctx.Attr<std::string>("mkldnn_data_type") == "bfloat16";
const bool force_fp32_output = ctx.Attr<bool>("force_fp32_output");
if (is_bfloat16) {
if (force_fp32_output)
Execute<float>(ctx);
Expand Down

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✅Congratulation! Your pull request passed all required CI. You could ask reviewer(s) to approve and merge. 🎉

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