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Implemented LRU based cache clearing
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@jczaja
jczaja committed Oct 11, 2021
1 parent 57e8cbe commit 006f33f
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Showing 6 changed files with 136 additions and 146 deletions.
49 changes: 25 additions & 24 deletions paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -78,7 +78,8 @@ class ConvMKLDNNHandlerT
mkldnn::convolution_backward_weights>(
dev_ctx, mkldnn_engine, cpu_place,
platform::CreateKey(dev_ctx, framework::vectorize(input->dims()),
unique_name)) {
unique_name)),
is_test_(ctx.Attr<bool>("is_test")) {
if (!this->isCached()) {
PADDLE_ENFORCE_EQ(
input->layout(), framework::DataLayout::kMKLDNN,
Expand Down Expand Up @@ -159,7 +160,6 @@ class ConvMKLDNNHandlerT
framework::slice_ddim(filter_dims, 2, filter_dims.size());

const auto ksize = framework::vectorize(filter_data_dims);
const bool is_test = ctx.Attr<bool>("is_test");

auto strides_temp = ctx.Attr<std::vector<int>>("strides");
std::vector<int64_t> strides(begin(strides_temp), end(strides_temp));
Expand Down Expand Up @@ -214,9 +214,8 @@ class ConvMKLDNNHandlerT

const auto dst_md = platform::MKLDNNMemDesc(
dst_tz, platform::MKLDNNGetDataType<T_out>(), chosen_memory_format);
const auto fwd_prop_kind = is_test ? mkldnn::prop_kind::forward_inference
: mkldnn::prop_kind::forward_training;

const auto fwd_prop_kind = is_test_ ? mkldnn::prop_kind::forward_inference
: mkldnn::prop_kind::forward_training;
float sum_scale = 1.0f;
std::vector<float> output_shift_scale;
if (platform::is_int8<T>())
Expand Down Expand Up @@ -261,7 +260,8 @@ class ConvMKLDNNHandlerT
mkldnn::convolution_backward_weights>(
dev_ctx, dev_ctx.GetEngine(), cpu_place,
platform::CreateKey(dev_ctx, framework::vectorize(in->dims()),
unique_name)) {
unique_name)),
is_test_(false) {
if (!this->isBwdCached()) {
PADDLE_ENFORCE_EQ(
in->layout(), framework::DataLayout::kMKLDNN,
Expand Down Expand Up @@ -291,7 +291,7 @@ class ConvMKLDNNHandlerT
"Wrong format set for output_grad tensor"));

PADDLE_ENFORCE_EQ(
ctx.Attr<bool>("is_test"), false,
is_test_, false,
platform::errors::InvalidArgument(
"is_test attribute should be set to False in training phase."));

Expand Down Expand Up @@ -557,26 +557,26 @@ class ConvMKLDNNHandlerT
framework::vectorize(in_mem->dims()),
platform::MKLDNNGetDataType<T>(), in_mem->format());
return this->AcquireMemoryWithReorder(
user_mem_md, mem_md, platform::to_void_cast<T>(in_mem_data), key_mem);
user_mem_md, mem_md, platform::to_void_cast<T>(in_mem_data), key_mem,
is_test_);
} else {
const std::string target_key_suffix{key_mem_target};
const auto target_mem_p = this->AcquireMemory(target_key_suffix);
user_mem_p->set_data_handle(platform::to_void_cast<T>(in_mem_data));
if (user_mem_p != target_mem_p) {
this->AcquireReorder(user_mem_p, target_mem_p, key_mem);
this->AcquireReorder(user_mem_p, target_mem_p);
}
return target_mem_p;
}
}

std::shared_ptr<mkldnn::memory> AcquireWeightsMemoryWithReorder(
const framework::Tensor* filter, const int groups, const bool is_conv3d,
const bool is_test, const std::vector<float>& scale_data = {1.0f},
int mask = 0) {
const std::vector<float>& scale_data = {1.0f}, int mask = 0) {
// 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");
if (is_test && weights_mem_p) {
if (is_test_ && weights_mem_p) {
return weights_mem_p;
} else {
const K* filter_data = filter->data<K>();
Expand All @@ -589,16 +589,16 @@ class ConvMKLDNNHandlerT

return this->AcquireMemoryWithReorder(
user_src_md, this->fwd_pd_->weights_desc(),
platform::to_void_cast<K>(filter_data), "@weights_mem_p", is_test, {},
scale_data, mask);
platform::to_void_cast<K>(filter_data), "@weights_mem_p", is_test_,
{}, scale_data, mask);
}
}

std::shared_ptr<mkldnn::memory> AcquireBiasMemoryWithReorder(
const framework::Tensor* bias, const bool is_test,
const framework::Tensor* bias,
const std::vector<float>& scale_data = {1.0f}, int mask = 0) {
auto bias_mem_p = this->AcquireMemory("@bias_mem_p_target");
if (is_test && bias_mem_p) {
if (is_test_ && bias_mem_p) {
return bias_mem_p;
} else {
const K* bias_data = bias->data<K>();
Expand All @@ -608,7 +608,7 @@ class ConvMKLDNNHandlerT

return this->AcquireMemoryWithReorder(
user_bias_md, this->fwd_pd_->bias_desc(),
platform::to_void_cast<K>(bias_data), "@bias_mem_p", is_test, {},
platform::to_void_cast<K>(bias_data), "@bias_mem_p", is_test_, {},
scale_data, mask);
}
}
Expand Down Expand Up @@ -641,7 +641,7 @@ class ConvMKLDNNHandlerT
platform::GetMKLDNNFormat(this->fwd_pd_->dst_desc())) {
auto residual_memory_p = this->AcquireResidualMemory(residual_param);
dst_memory_p = this->template AcquireDstMemory<T_out>(output);
this->AcquireReorder(residual_memory_p, dst_memory_p, "@residual_dst");
this->AcquireReorder(residual_memory_p, dst_memory_p);
} else {
// Changing ShareDataWith to TensorCopy results in performance drop
// on ResNet architectures
Expand All @@ -651,6 +651,9 @@ class ConvMKLDNNHandlerT
}
return dst_memory_p;
}

private:
const bool is_test_;
};

} // anonymous namespace
Expand Down Expand Up @@ -695,7 +698,6 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();

const bool is_test = ctx.Attr<bool>("is_test");
const bool is_conv3d = ctx.Attr<std::vector<int>>("strides").size() == 3U;
const bool fuse_residual_conn = ctx.Attr<bool>("fuse_residual_connection");

Expand All @@ -712,7 +714,7 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
auto src_memory_p = handler.AcquireSrcMemoryWithReorder(input);

auto weights_memory_p = handler.AcquireWeightsMemoryWithReorder(
filter, ctx.Attr<int>("groups"), is_conv3d, is_test);
filter, ctx.Attr<int>("groups"), is_conv3d);

std::shared_ptr<dnnl::memory> dst_memory_p;
if (fuse_residual_conn) {
Expand All @@ -731,7 +733,7 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
{MKLDNN_ARG_DST, *dst_memory_p}};

if (bias) {
auto bias_memory_p = handler.AcquireBiasMemoryWithReorder(bias, is_test);
auto bias_memory_p = handler.AcquireBiasMemoryWithReorder(bias);
args.insert({MKLDNN_ARG_BIAS, *bias_memory_p});
}

Expand Down Expand Up @@ -783,11 +785,10 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
ctx.Attr<std::vector<float>>("Scale_weights");
const bool is_multi_channel = scale_weights_data.size() > 1;
const int& groups = ctx.Attr<int>("groups");
const bool& is_test = ctx.Attr<bool>("is_test");
int mask_reorder =
is_multi_channel ? ((groups != 1) ? (1 << 1) + (1 << 0) : 1 << 0) : 0;
auto weights_memory_p = handler.AcquireWeightsMemoryWithReorder(
filter, groups, false, is_test, scale_weights_data, mask_reorder);
filter, groups, false, scale_weights_data, mask_reorder);

std::shared_ptr<dnnl::memory> dst_memory_p;
if (fuse_residual_conn) {
Expand Down Expand Up @@ -822,7 +823,7 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
handler.get_int8_bias_scales(ctx);

auto bias_memory_p = handler.AcquireBiasMemoryWithReorder(
bias, is_test, scale_bias_data, mask_reorder);
bias, scale_bias_data, mask_reorder);
args.insert({MKLDNN_ARG_BIAS, *bias_memory_p});
}

Expand Down
33 changes: 17 additions & 16 deletions paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -51,10 +51,10 @@ class ConvTransposeMKLDNNHandlerT
: platform::MKLDNNHandlerT<T, mkldnn::deconvolution_forward>(
dev_ctx, mkldnn_engine, cpu_place,
platform::CreateKey(dev_ctx, framework::vectorize(input->dims()),
unique_name)) {
unique_name)),
is_test_(ctx.Attr<bool>("is_test")) {
if (!this->isCached()) {
const bool is_test = ctx.Attr<bool>("is_test");
PADDLE_ENFORCE_EQ(is_test, true,
PADDLE_ENFORCE_EQ(is_test_, true,
platform::errors::InvalidArgument(
"ConvTransposeMKLDNN works only for inference. "
"The attribute \'is_test\' value should be set to "
Expand Down Expand Up @@ -169,8 +169,8 @@ class ConvTransposeMKLDNNHandlerT

const mkldnn::primitive_attr conv_trans_attr =
CreatePostOps(fuse_activation, fuse_alpha, fuse_beta);
auto fwd_prop_kind = is_test ? mkldnn::prop_kind::forward_inference
: mkldnn::prop_kind::forward_training;
auto fwd_prop_kind = is_test_ ? mkldnn::prop_kind::forward_inference
: mkldnn::prop_kind::forward_training;
if (bias) {
std::vector<int64_t> bias_tz = framework::vectorize(bias->dims());
const auto bias_md =
Expand Down Expand Up @@ -231,18 +231,18 @@ class ConvTransposeMKLDNNHandlerT
const auto target_src_mem_p = this->AcquireMemory(target_key_suffix);
user_src_mem_p->set_data_handle(platform::to_void_cast<T>(input_data));
if (user_src_mem_p != target_src_mem_p) {
this->AcquireReorder(user_src_mem_p, target_src_mem_p, "@src_mem_p");
this->AcquireReorder(user_src_mem_p, target_src_mem_p);
}
return target_src_mem_p;
}
}

std::shared_ptr<mkldnn::memory> AcquireWeightsMemoryWithReorder(
const framework::Tensor* filter, const int& groups, const bool& is_test) {
const framework::Tensor* filter, const int& groups) {
// 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");
if (is_test && weights_mem_p) {
if (is_test_ && weights_mem_p) {
return weights_mem_p;
} else {
const K* filter_data = filter->data<K>();
Expand Down Expand Up @@ -277,15 +277,15 @@ class ConvTransposeMKLDNNHandlerT

return this->template AcquireMemoryWithReorder<K>(
user_src_md, this->fwd_pd_->weights_desc(),
platform::to_void_cast<K>(filter_data), "@weights_mem_p", is_test,
platform::to_void_cast<K>(filter_data), "@weights_mem_p", is_test_,
iohw2oihw_reorder);
}
}

std::shared_ptr<mkldnn::memory> AcquireBiasMemoryWithReorder(
const framework::Tensor* bias, const bool& is_test) {
const framework::Tensor* bias) {
auto bias_mem_p = this->AcquireMemory("@bias_mem_p_target");
if (is_test && bias_mem_p) {
if (is_test_ && bias_mem_p) {
return bias_mem_p;
} else {
const K* bias_data = bias->data<K>();
Expand All @@ -294,9 +294,12 @@ class ConvTransposeMKLDNNHandlerT
MKLDNNMemoryFormat::x);
return this->AcquireMemoryWithReorder(
user_bias_md, this->fwd_pd_->bias_desc(),
platform::to_void_cast<K>(bias_data), "@bias_mem_p", is_test);
platform::to_void_cast<K>(bias_data), "@bias_mem_p", is_test_);
}
}

private:
const bool is_test_;
};

template <typename T, typename K>
Expand Down Expand Up @@ -325,8 +328,6 @@ class ConvTransposeMKLDNNOpKernel : public framework::OpKernel<T> {
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();

const bool is_test = ctx.Attr<bool>("is_test");

const auto* input = ctx.Input<Tensor>("Input");
const auto* filter = ctx.Input<Tensor>("Filter");
const auto* bias =
Expand All @@ -340,7 +341,7 @@ class ConvTransposeMKLDNNOpKernel : public framework::OpKernel<T> {
output, unique_name);
auto src_memory_p = handler.AcquireSrcMemoryWithReorder(input);
auto weights_memory_p = handler.AcquireWeightsMemoryWithReorder(
filter, ctx.Attr<int>("groups"), is_test);
filter, ctx.Attr<int>("groups"));

std::shared_ptr<dnnl::memory> dst_memory_p =
handler.template AcquireDstMemory<T_out>(output);
Expand All @@ -352,7 +353,7 @@ class ConvTransposeMKLDNNOpKernel : public framework::OpKernel<T> {
{MKLDNN_ARG_DST, *dst_memory_p}};

if (bias) {
auto bias_memory_p = handler.AcquireBiasMemoryWithReorder(bias, is_test);
auto bias_memory_p = handler.AcquireBiasMemoryWithReorder(bias);
args.insert({MKLDNN_ARG_BIAS, *bias_memory_p});
}
auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
Expand Down
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