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提交 98270c18 编写于 作者: J Jacek Czaja
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- modified UT

上级 2b24a801
隐藏空白更改
内联 并排
Showing with 220 addition and 241 deletion
paddle/fluid/operators/elementwise/mkldnn/elementwise_mkldnn_op.h
浏览文件 @ 98270c18
......@@ -47,7 +47,9 @@ class EltwiseMKLDNNKernel : public framework::OpKernel<T> {
float scale_o = ctx.Attr<float>("Scale_out");
int axis = ctx.Attr<int>("axis");
platform::BinaryMKLDNNHandler<T> handler( BINARY_OP, axis, mkldnn_engine, ctx.GetPlace(), x, y, z, scale_x, scale_y, scale_o);
platform::BinaryMKLDNNHandler<T> handler(BINARY_OP, axis, mkldnn_engine,
ctx.GetPlace(), x, y, z, scale_x,
scale_y, scale_o);
const auto src_x_memory = handler.AcquireSrcMemory(x);
const auto src_y_memory = handler.AcquireSecondSrcMemory(y);
......
paddle/fluid/operators/elementwise/mkldnn/elementwise_mul_mkldnn_op.cc
浏览文件 @ 98270c18
......@@ -48,8 +48,8 @@ class EltwiseMulMKLDNNGradKernel : public ElemwiseGradKernel<T> {
if (dx) {
// dx = dout*y
platform::BinaryMKLDNNHandler<T> handler(
dnnl::algorithm::binary_mul, axis, mkldnn_engine,
ctx.GetPlace(), dout, y, dx, 1.0f, 1.0f, 1.0f);
dnnl::algorithm::binary_mul, axis, mkldnn_engine, ctx.GetPlace(),
dout, y, dx, 1.0f, 1.0f, 1.0f);
const auto src_dout_memory = handler.AcquireSrcMemory(dout);
const auto src_y_memory = handler.AcquireSecondSrcMemory(y);
......@@ -74,8 +74,8 @@ class EltwiseMulMKLDNNGradKernel : public ElemwiseGradKernel<T> {
// Handler is having nullptr passed instead of output tensor as
// we want Dst buffer to be allocated by oneDNN not to use Tensor
platform::BinaryMKLDNNHandler<T> handler(
dnnl::algorithm::binary_mul, axis, mkldnn_engine,
ctx.GetPlace(), dout, x, nullptr, 1.0f, 1.0f, 1.0f);
dnnl::algorithm::binary_mul, axis, mkldnn_engine, ctx.GetPlace(),
dout, x, nullptr, 1.0f, 1.0f, 1.0f);
const auto src_dout_memory = handler.AcquireSrcMemory(dout);
const auto src_x_memory = handler.AcquireSecondSrcMemory(x);
......
paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc
浏览文件 @ 98270c18
......@@ -79,14 +79,15 @@ void eltwise_forward(const framework::ExecutionContext &ctx,
paddle::platform::errors::PreconditionNotMet(
"Operator DNNL eletwise_forward must use CPUPlace"));
auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
const auto &mkldnn_engine = dev_ctx.GetEngine();
const auto *x = ctx.Input<Tensor>("X");
auto *y = ctx.Output<Tensor>("Out");
bool is_inplaced = x->IsSharedBufferWith(*y);
platform::ActivationMKLDNNHandler<T> handler(algorithm, ctx, mkldnn_engine, ctx.GetPlace(), x);
platform::ActivationMKLDNNHandler<T> handler(algorithm, ctx, mkldnn_engine,
ctx.GetPlace(), x);
auto src_memory_p = handler.AcquireSrcMemory(x);
auto dst_memory_p = is_inplaced ? src_memory_p : handler.AcquireDstMemory(y);
......@@ -105,14 +106,14 @@ template <typename T>
void eltwise_grad(const framework::ExecutionContext &ctx,
mkldnn::algorithm algorithm) {
auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
const auto &mkldnn_engine = dev_ctx.GetEngine();
const auto *x = ctx.Input<Tensor>("X");
const auto *diff_y = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto *diff_x = ctx.Output<Tensor>(framework::GradVarName("X"));
platform::ActivationMKLDNNHandler<T> handler(
algorithm, ctx, mkldnn_engine, ctx.GetPlace(), x, diff_y);
platform::ActivationMKLDNNHandler<T> handler(algorithm, ctx, mkldnn_engine,
ctx.GetPlace(), x, diff_y);
auto src_memory_p = handler.AcquireBackwardSrcMemory(x);
auto diff_dst_memory_p = handler.AcquireDiffDstMemory(diff_y);
......
paddle/fluid/operators/mkldnn/scale_mkldnn_op.cc
浏览文件 @ 98270c18
......@@ -37,10 +37,12 @@ class ScaleMKLDNNKernel : public framework::OpKernel<T> {
bool is_inplaced = x->IsSharedBufferWith(*out);
platform::ActivationMKLDNNHandler<T> handler(
mkldnn::algorithm::eltwise_linear, ctx, mkldnn_engine, ctx.GetPlace(), x);
mkldnn::algorithm::eltwise_linear, ctx, mkldnn_engine, ctx.GetPlace(),
x);
auto src_memory_p = handler.AcquireSrcMemory(x);
auto dst_memory_p = is_inplaced ? src_memory_p : handler.AcquireDstMemory(out);
auto dst_memory_p =
is_inplaced ? src_memory_p : handler.AcquireDstMemory(out);
auto activation_p = handler.AcquireForwardPrimitive();
auto& astream = paddle::platform::MKLDNNDeviceContext::tls().get_stream();
......
paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc
浏览文件 @ 98270c18
......@@ -33,12 +33,13 @@ using platform::to_void_cast;
template <typename T>
class SoftmaxMKLDNNHandler
: public platform::MKLDNNHandlerNoCachingT<T, mkldnn::softmax_forward,
mkldnn::softmax_backward> {
mkldnn::softmax_backward> {
public:
SoftmaxMKLDNNHandler(const mkldnn::engine mkldnn_engine,
platform::Place cpu_place, const Tensor* input,
Tensor* output, const int axis)
: platform::MKLDNNHandlerNoCachingT<T, mkldnn::softmax_forward, mkldnn::softmax_backward>(
: platform::MKLDNNHandlerNoCachingT<T, mkldnn::softmax_forward,
mkldnn::softmax_backward>(
mkldnn_engine, cpu_place) {
PADDLE_ENFORCE_EQ(
input->dims(), output->dims(),
......@@ -49,7 +50,8 @@ class SoftmaxMKLDNNHandler
auto md = memory::desc(softmax_tz, platform::MKLDNNGetDataType<T>(),
input->format());
this->AcquireForwardPrimitiveDescriptor(prop_kind::forward_scoring, md, axis);
this->AcquireForwardPrimitiveDescriptor(prop_kind::forward_scoring, md,
axis);
}
SoftmaxMKLDNNHandler(const framework::ExecutionContext& ctx,
......@@ -58,25 +60,26 @@ class SoftmaxMKLDNNHandler
const Tensor* out_grad, Tensor* in_x_grad,
const std::string& unique_name)
: platform::MKLDNNHandlerNoCachingT<T, mkldnn::softmax_forward,
mkldnn::softmax_backward>(mkldnn_engine, cpu_place) {
PADDLE_ENFORCE_EQ(
out_grad->dims(), in_x_grad->dims(),
platform::errors::InvalidArgument("The shape of softmax_grad's input "
"and output must be identical."));
auto dims = out_grad->dims(); // input and output share the same shape
const int axis = CanonicalAxis(ctx.Attr<int>("axis"), dims.size());
auto softmax_tz = framework::vectorize<int64_t>(dims);
auto data_softmax_md = MKLDNNMemDesc(
softmax_tz, platform::MKLDNNGetDataType<T>(), out->format());
auto diff_softmax_md = MKLDNNMemDesc(
softmax_tz, platform::MKLDNNGetDataType<T>(), out_grad->format());
this->AcquireForwardPrimitiveDescriptor(prop_kind::forward_scoring,
data_softmax_md, axis);
this->AcquireBackwardPrimitiveDescriptor(diff_softmax_md, data_softmax_md,
axis);
mkldnn::softmax_backward>(
mkldnn_engine, cpu_place) {
PADDLE_ENFORCE_EQ(
out_grad->dims(), in_x_grad->dims(),
platform::errors::InvalidArgument("The shape of softmax_grad's input "
"and output must be identical."));
auto dims = out_grad->dims(); // input and output share the same shape
const int axis = CanonicalAxis(ctx.Attr<int>("axis"), dims.size());
auto softmax_tz = framework::vectorize<int64_t>(dims);
auto data_softmax_md = MKLDNNMemDesc(
softmax_tz, platform::MKLDNNGetDataType<T>(), out->format());
auto diff_softmax_md = MKLDNNMemDesc(
softmax_tz, platform::MKLDNNGetDataType<T>(), out_grad->format());
this->AcquireForwardPrimitiveDescriptor(prop_kind::forward_scoring,
data_softmax_md, axis);
this->AcquireBackwardPrimitiveDescriptor(diff_softmax_md, data_softmax_md,
axis);
}
};
......@@ -93,7 +96,8 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
const int axis = CanonicalAxis(ctx.Attr<int>("axis"), input->dims().size());
SoftmaxMKLDNNHandler<T> handler(mkldnn_engine, ctx.GetPlace(), input, output, axis);
SoftmaxMKLDNNHandler<T> handler(mkldnn_engine, ctx.GetPlace(), input,
output, axis);
auto softmax_src_memory_p = handler.AcquireSrcMemory(input);
// For Inplace src and and dst are the same memory object
......
paddle/fluid/operators/mkldnn/test_mkldnn_caching.cc
浏览文件 @ 98270c18
......@@ -70,11 +70,16 @@ void RunOperator(const platform::Place &place, const std::string &op_type,
std::map<const std::string, int> num_inputs = {{"softmax", 1},
{"relu", 1},
{"conv2d", 2},
{"elementwise_add", 2},
{"elementwise_mul", 2}};
std::string first_input = inplace == true ? output_name : "x";
std::string first_input_var_name = (op_type == "conv2d") ? "Input" : "X";
std::string second_input_var_name = (op_type == "conv2d") ? "Filter" : "Y";
std::string output_var_name = (op_type == "conv2d") ? "Output" : "Out";
std::vector<InputVars> input_names = {
{first_input, scope.Var(first_input)->GetMutable<framework::LoDTensor>()},
{"x1", num_inputs[op_type] > 1
......@@ -113,68 +118,37 @@ void RunOperator(const platform::Place &place, const std::string &op_type,
auto &pool = platform::DeviceContextPool::Instance();
auto op = num_inputs[op_type] > 1
? framework::OpRegistry::CreateOp(
op_type, {{"X", {first_input}}, {"Y", {"x1"}}},
{{"Out", {output_name}}}, {{"use_mkldnn", {true}}})
: framework::OpRegistry::CreateOp(
op_type, {{"X", {first_input}}}, {{"Out", {output_name}}},
{{"use_mkldnn", {true}}});
auto op =
num_inputs[op_type] > 1
? framework::OpRegistry::CreateOp(
op_type, {{first_input_var_name, {first_input}},
{second_input_var_name, {"x1"}}},
{{output_var_name, {output_name}}}, {{"use_mkldnn", {true}}})
: framework::OpRegistry::CreateOp(
op_type, {{first_input_var_name, {first_input}}},
{{output_var_name, {output_name}}}, {{"use_mkldnn", {true}}});
op->Run(scope, place);
pool.Get(place)->Wait();
}
TEST(test_softmax_reuse_cache, cpu_place) {
framework::DDim dims({32, 64});
framework::DDim dims({1, 16, 32, 64});
platform::CPUPlace p;
CacheTester ct;
RunOperator<float>(p, "softmax", dims, "softmax_out");
RunOperator<float>(p, "softmax", dims, "softmax_out");
RunOperator<float>(p, "conv2d", dims, "conv_out");
RunOperator<float>(p, "conv2d", dims, "conv_out");
PADDLE_ENFORCE_EQ(ct.Analyze(4), true,
platform::errors::InvalidArgument(
"Wrong number of cached oneDNN objects"));
}
TEST(test_softmax_noreuse_cache, cpu_place) {
framework::DDim dims({32, 64});
platform::CPUPlace p;
CacheTester ct;
RunOperator<float>(p, "softmax", dims, "softmax_out");
RunOperator<float>(p, "softmax", dims, "softmax_out2");
PADDLE_ENFORCE_EQ(ct.Analyze(8), true,
platform::errors::InvalidArgument(
"Wrong number of cached oneDNN objects"));
}
TEST(test_softmax_inplace_cache, cpu_place) {
framework::DDim dims({32, 64});
platform::CPUPlace p;
CacheTester ct;
RunOperator<float>(p, "softmax", dims, "softmax_out");
RunOperator<float>(p, "softmax", dims, "softmax_out", true);
PADDLE_ENFORCE_EQ(ct.Analyze(7), true,
platform::errors::InvalidArgument(
"Wrong number of cached oneDNN objects"));
}
TEST(test_relu_inplace_cache, cpu_place) {
framework::DDim dims({32, 64});
platform::CPUPlace p;
CacheTester ct;
RunOperator<float>(p, "relu", dims, "relu_out");
RunOperator<float>(p, "relu", dims, "relu_out", true);
PADDLE_ENFORCE_EQ(ct.Analyze(7), true,
platform::errors::InvalidArgument(
"Wrong number of cached oneDNN objects"));
}
TEST(test_elementwise_add_reuse_cache, cpu_place) {
framework::DDim dims({32, 64});
framework::DDim dims({1, 16, 32, 64});
platform::CPUPlace p;
CacheTester ct;
RunOperator<float>(p, "elementwise_add", dims, "elementwise_add_out");
RunOperator<float>(p, "relu", dims, "elementwise_add_out", true);
RunOperator<float>(p, "conv2d", dims, "conv_out");
RunOperator<float>(p, "conv2d", dims, "conv_out2");
PADDLE_ENFORCE_EQ(ct.Analyze(8), true,
platform::errors::InvalidArgument(
"Wrong number of cached oneDNN objects"));
......
paddle/fluid/platform/mkldnn_reuse.h
浏览文件 @ 98270c18
......@@ -34,40 +34,36 @@ using framework::Tensor;
using user_function = std::function<std::shared_ptr<float>(const float*)>;
using memory = mkldnn::memory;
template <typename T, typename TForward,
typename TBackward = mkldnn_dummy_primitive,
typename TBackward_params = mkldnn_dummy_primitive>
class MKLDNNHandlerNoCachingT {
public:
MKLDNNHandlerNoCachingT(mkldnn::engine engine, platform::Place cpu_place)
: engine_(engine),
place_(cpu_place),
fwd_pd_(nullptr),
bwd_pd_(nullptr) {
: engine_(engine), place_(cpu_place), fwd_pd_(nullptr), bwd_pd_(nullptr) {
platform::MKLDNNDeviceContext::tls().log_lib_version();
}
std::shared_ptr<TForward> AcquireForwardPrimitive() {
return std::make_shared<TForward>(*fwd_pd_);
return std::make_shared<TForward>(*fwd_pd_);
}
std::shared_ptr<TBackward> AcquireBackwardPrimitive() {
return std::make_shared<TBackward>(*bwd_pd_);
return std::make_shared<TBackward>(*bwd_pd_);
}
std::shared_ptr<TBackward_params> AcquireBackwardWeightsPrimitive() {
PADDLE_ENFORCE_NOT_NULL(bwd_w_pd_, platform::errors::Unavailable(
"Error: BWD_PD should be set when "
"getting BWD prim ."));
return std::make_shared<TBackward_params>(*bwd_w_pd_);
PADDLE_ENFORCE_NOT_NULL(bwd_w_pd_, platform::errors::Unavailable(
"Error: BWD_PD should be set when "
"getting BWD prim ."));
return std::make_shared<TBackward_params>(*bwd_w_pd_);
}
std::shared_ptr<mkldnn::memory> AcquireSrcMemory(
const framework::Tensor* input) {
const T* input_data = input->data<T>();
return this->AcquireMemoryFromPrimitive(
fwd_pd_->src_desc(), to_void_cast<T>(input_data));
return this->AcquireMemoryFromPrimitive(fwd_pd_->src_desc(),
to_void_cast<T>(input_data));
}
template <typename T_out = T>
......@@ -93,8 +89,8 @@ class MKLDNNHandlerNoCachingT {
std::shared_ptr<mkldnn::memory> AcquireDiffDstMemory(
const framework::Tensor* diffdst) {
const T* ptr = diffdst->data<T>();
return this->AcquireMemoryFromPrimitive(
bwd_pd_->diff_dst_desc(), to_void_cast<T>(ptr));
return this->AcquireMemoryFromPrimitive(bwd_pd_->diff_dst_desc(),
to_void_cast<T>(ptr));
}
std::shared_ptr<mkldnn::memory> AcquireDiffSrcMemory(
......@@ -113,7 +109,8 @@ class MKLDNNHandlerNoCachingT {
"Error: BWD_W_PD should be set when getting BWD grad of weights."));
T* ptr = diff_weights->mutable_data<T>(
place_, bwd_w_pd_->diff_weights_desc().get_size());
return this->AcquireMemoryFromPrimitive(bwd_w_pd_->diff_weights_desc(), ptr);
return this->AcquireMemoryFromPrimitive(bwd_w_pd_->diff_weights_desc(),
ptr);
}
// Buffer is allocated by oneDNN to store computation results
......@@ -126,14 +123,13 @@ class MKLDNNHandlerNoCachingT {
}
protected:
// If your primitive descriptor requires attributes, pass them as a
// first argument and paramters to descriptor constructor in the following
// arguments. Otherwise, all arguments will be forwarded to descriptor
// constructor, including the first one.
template <typename Arg, typename... Args>
void AcquireForwardPrimitiveDescriptor(Arg&& first_arg, Args&&... args) {
CreateForwardPrimitiveDescriptor(first_arg, std::forward<Args>(args)...);
CreateForwardPrimitiveDescriptor(first_arg, std::forward<Args>(args)...);
}
// Using sfinae to specialise variadic function. Workaround for not having
......@@ -161,9 +157,9 @@ class MKLDNNHandlerNoCachingT {
void AcquireBackwardPrimitiveDescriptor(Args&&... args) {
// fwd_pd_ is set during grad by calling
// AcquireForwardPrimitiveDescriptor
PADDLE_ENFORCE_NOT_NULL(
fwd_pd_,
platform::errors::Unavailable("Get MKLDNN Forward primitive %s failed."));
PADDLE_ENFORCE_NOT_NULL(fwd_pd_,
platform::errors::Unavailable(
"Get MKLDNN Forward primitive %s failed."));
auto bwd_desc = typename TBackward::desc(std::forward<Args>(args)...);
bwd_pd_ = std::make_shared<typename TBackward::primitive_desc>(
bwd_desc, engine_, *fwd_pd_);
......@@ -173,29 +169,29 @@ class MKLDNNHandlerNoCachingT {
void AcquireBackwardWeightsPrimitiveDescriptor(Args&&... args) {
// fwd_pd_ is set during grad by calling
// AcquireForwardPrimitiveDescriptor
PADDLE_ENFORCE_NOT_NULL(
fwd_pd_,
platform::errors::Unavailable("Get MKLDNN Forward primitive %s failed."));
auto bwd_desc =
typename TBackward_params::desc(std::forward<Args>(args)...);
bwd_w_pd_ = std::make_shared<typename TBackward_params::primitive_desc>(
bwd_desc, engine_, *fwd_pd_);
PADDLE_ENFORCE_NOT_NULL(fwd_pd_,
platform::errors::Unavailable(
"Get MKLDNN Forward primitive %s failed."));
auto bwd_desc =
typename TBackward_params::desc(std::forward<Args>(args)...);
bwd_w_pd_ = std::make_shared<typename TBackward_params::primitive_desc>(
bwd_desc, engine_, *fwd_pd_);
}
std::shared_ptr<mkldnn::memory> AcquireMemoryFromPrimitive(
mkldnn::memory::desc md, void* ptr) {
return std::make_shared<mkldnn::memory>(md, engine_, ptr);
return std::make_shared<mkldnn::memory>(md, engine_, ptr);
}
std::shared_ptr<mkldnn::memory> AcquireMemoryFromPrimitive(
mkldnn::memory::desc md) {
return std::make_shared<mkldnn::memory>(md, engine_);
return std::make_shared<mkldnn::memory>(md, engine_);
}
void AcquireReorder(const std::shared_ptr<mkldnn::memory>& user_memory_p,
const std::shared_ptr<mkldnn::memory>& target_memory_p) {
auto reorder_p =
std::make_shared<mkldnn::reorder>(*user_memory_p, *target_memory_p);
auto reorder_p =
std::make_shared<mkldnn::reorder>(*user_memory_p, *target_memory_p);
auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
......@@ -212,33 +208,30 @@ class MKLDNNHandlerNoCachingT {
const mkldnn::memory::desc& target_md, void* ptr,
const std::string& suffix, bool is_persistent = false,
std::function<std::shared_ptr<F>(const F*)> custom_reorder_func = {}) {
std::shared_ptr<mkldnn::memory> target_memory_p;
if (custom_reorder_func) {
auto reordered_data =
custom_reorder_func(reinterpret_cast<const F*>(ptr));
ptr = reinterpret_cast<void*>(reordered_data.get());
}
auto user_memory_p = std::make_shared<dnnl::memory>(user_md, engine_, ptr);
if (user_md != target_md) {
target_memory_p = std::make_shared<mkldnn::memory>(target_md, engine_);
auto reorder_p =
std::make_shared<dnnl::reorder>(*user_memory_p, *target_memory_p);
std::shared_ptr<mkldnn::memory> target_memory_p;
if (custom_reorder_func) {
auto reordered_data =
custom_reorder_func(reinterpret_cast<const F*>(ptr));
ptr = reinterpret_cast<void*>(reordered_data.get());
}
auto user_memory_p =
std::make_shared<dnnl::memory>(user_md, engine_, ptr);
if (user_md != target_md) {
target_memory_p = std::make_shared<mkldnn::memory>(target_md, engine_);
auto reorder_p =
std::make_shared<dnnl::reorder>(*user_memory_p, *target_memory_p);
auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
platform::RecordEvent record_reorder("int_reorder",
platform::EventRole::kUniqueOp);
reorder_p->execute(astream, {{MKLDNN_ARG_FROM, *user_memory_p},
{MKLDNN_ARG_TO, *target_memory_p}});
astream.wait();
} else {
target_memory_p = user_memory_p;
}
auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
platform::RecordEvent record_reorder("int_reorder",
platform::EventRole::kUniqueOp);
reorder_p->execute(astream, {{MKLDNN_ARG_FROM, *user_memory_p},
{MKLDNN_ARG_TO, *target_memory_p}});
astream.wait();
} else {
target_memory_p = user_memory_p;
}
return target_memory_p;
}
mkldnn::engine engine_;
platform::Place place_;
std::shared_ptr<typename TForward::primitive_desc> fwd_pd_;
......@@ -801,63 +794,64 @@ class MKLDNNHandler {
};
template <typename T>
class BinaryMKLDNNHandler : public platform::MKLDNNHandlerNoCachingT<T, dnnl::binary> {
class BinaryMKLDNNHandler
: public platform::MKLDNNHandlerNoCachingT<T, dnnl::binary> {
public:
BinaryMKLDNNHandler(const dnnl::algorithm algo, const int axis,
const mkldnn::engine engine, platform::Place cpu_place,
const Tensor* x, const Tensor* y, Tensor* z,
float scale_x, float scale_y, float scale_z)
: platform::MKLDNNHandlerNoCachingT<T, dnnl::binary>(engine, cpu_place) {
PADDLE_ENFORCE_EQ(
x->layout(), DataLayout::kMKLDNN,
platform::errors::InvalidArgument("Wrong layout set for X tensor."));
PADDLE_ENFORCE_NE(
x->format(), MKLDNNMemoryFormat::undef,
platform::errors::InvalidArgument("Wrong format set for X tensor."));
PADDLE_ENFORCE_EQ(
y->layout(), DataLayout::kMKLDNN,
platform::errors::InvalidArgument("Wrong layout set for Y tensor."));
PADDLE_ENFORCE_NE(
y->format(), MKLDNNMemoryFormat::undef,
platform::errors::InvalidArgument("Wrong format set for Y tensor."));
const auto src_x_tz = framework::vectorize(x->dims());
const auto src_y_tz = framework::vectorize(y->dims());
// if output tensor(z) is nullptr then we are computing into oneDNN
// managed buffer
auto rankdiff = x->dims().size() - y->dims().size();
const auto dst_tz = (z == nullptr) ? (rankdiff > 0 ? src_x_tz : src_y_tz)
: framework::vectorize(z->dims());
auto src0_md = dnnl::memory::desc(
src_x_tz, platform::MKLDNNGetDataType<T>(), x->format());
auto src1_md = dnnl::memory::desc(
src_y_tz, platform::MKLDNNGetDataType<T>(), y->format());
if (rankdiff > 0) { // Second input is of smaller rank than first
std::vector<int64_t> dims1_ex(rankdiff, 1);
dims1_ex.insert(next(dims1_ex.begin(), (axis == -1 ? rankdiff : axis)),
src_y_tz.begin(), src_y_tz.end());
src1_md = src1_md.reshape(dims1_ex);
} else if (rankdiff < 0) { // First input is of smaller than second
std::vector<int64_t> dims0_ex(-rankdiff, 1);
dims0_ex.insert(next(dims0_ex.begin(), (axis == -1 ? -rankdiff : axis)),
src_x_tz.begin(), src_x_tz.end());
src0_md = src0_md.reshape(dims0_ex);
}
const auto dst_md = memory::desc(dst_tz, platform::MKLDNNGetDataType<T>(),
MKLDNNMemoryFormat::any);
PADDLE_ENFORCE_EQ(
x->layout(), DataLayout::kMKLDNN,
platform::errors::InvalidArgument("Wrong layout set for X tensor."));
PADDLE_ENFORCE_NE(
x->format(), MKLDNNMemoryFormat::undef,
platform::errors::InvalidArgument("Wrong format set for X tensor."));
PADDLE_ENFORCE_EQ(
y->layout(), DataLayout::kMKLDNN,
platform::errors::InvalidArgument("Wrong layout set for Y tensor."));
PADDLE_ENFORCE_NE(
y->format(), MKLDNNMemoryFormat::undef,
platform::errors::InvalidArgument("Wrong format set for Y tensor."));
const auto src_x_tz = framework::vectorize(x->dims());
const auto src_y_tz = framework::vectorize(y->dims());
// if output tensor(z) is nullptr then we are computing into oneDNN
// managed buffer
auto rankdiff = x->dims().size() - y->dims().size();
const auto dst_tz = (z == nullptr) ? (rankdiff > 0 ? src_x_tz : src_y_tz)
: framework::vectorize(z->dims());
auto src0_md = dnnl::memory::desc(
src_x_tz, platform::MKLDNNGetDataType<T>(), x->format());
auto src1_md = dnnl::memory::desc(
src_y_tz, platform::MKLDNNGetDataType<T>(), y->format());
if (rankdiff > 0) { // Second input is of smaller rank than first
std::vector<int64_t> dims1_ex(rankdiff, 1);
dims1_ex.insert(next(dims1_ex.begin(), (axis == -1 ? rankdiff : axis)),
src_y_tz.begin(), src_y_tz.end());
src1_md = src1_md.reshape(dims1_ex);
} else if (rankdiff < 0) { // First input is of smaller than second
std::vector<int64_t> dims0_ex(-rankdiff, 1);
dims0_ex.insert(next(dims0_ex.begin(), (axis == -1 ? -rankdiff : axis)),
src_x_tz.begin(), src_x_tz.end());
src0_md = src0_md.reshape(dims0_ex);
}
const auto dst_md = memory::desc(dst_tz, platform::MKLDNNGetDataType<T>(),
MKLDNNMemoryFormat::any);
auto attributes = CreateAttributes(algo, scale_x, scale_y, scale_z);
this->AcquireForwardPrimitiveDescriptor(attributes, algo, src0_md,
src1_md, dst_md);
auto attributes = CreateAttributes(algo, scale_x, scale_y, scale_z);
this->AcquireForwardPrimitiveDescriptor(attributes, algo, src0_md, src1_md,
dst_md);
}
std::shared_ptr<mkldnn::memory> AcquireSecondSrcMemory(
const framework::Tensor* input) {
const T* input_data = input->data<T>();
return this->AcquireMemoryFromPrimitive(
this->fwd_pd_->src1_desc(), to_void_cast<T>(input_data));
return this->AcquireMemoryFromPrimitive(this->fwd_pd_->src1_desc(),
to_void_cast<T>(input_data));
}
private:
......@@ -981,51 +975,51 @@ class ReductionMKLDNNHandler
template <typename T>
class ActivationMKLDNNHandler
: public MKLDNNHandlerNoCachingT<T, mkldnn::eltwise_forward,
mkldnn::eltwise_backward> {
mkldnn::eltwise_backward> {
public:
ActivationMKLDNNHandler(mkldnn::algorithm algorithm,
const framework::ExecutionContext& ctx,
const mkldnn::engine engine, Place cpu_place,
const framework::Tensor* in_x)
: platform::MKLDNNHandlerNoCachingT<T, mkldnn::eltwise_forward,
mkldnn::eltwise_backward>(engine, cpu_place) {
float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 0;
float beta = ctx.HasAttr("beta") ? ctx.Attr<float>("beta") : 0;
// eltwise_linear means we are in scale op
if (algorithm == mkldnn::algorithm::eltwise_linear) {
bool bias_after_scale = ctx.Attr<bool>("bias_after_scale");
auto* scale_tensor = ctx.Input<Tensor>("ScaleTensor");
alpha = (scale_tensor == nullptr) ? ctx.Attr<float>("scale")
: (float)*(scale_tensor->data<T>());
beta = ctx.Attr<float>("bias");
// if bias_after_scale == true
// out = scale*X + bias
// else
// out = scale*(X + bias) = scale*X + scale*bias
if (!bias_after_scale) beta *= alpha;
} else {
// paddle uses beta but mkldnn uses alpha for swish
if (algorithm == mkldnn::algorithm::eltwise_swish) {
std::swap(alpha, beta);
} else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
alpha = ctx.Attr<float>("threshold");
}
mkldnn::eltwise_backward>(engine,
cpu_place) {
float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 0;
float beta = ctx.HasAttr("beta") ? ctx.Attr<float>("beta") : 0;
// eltwise_linear means we are in scale op
if (algorithm == mkldnn::algorithm::eltwise_linear) {
bool bias_after_scale = ctx.Attr<bool>("bias_after_scale");
auto* scale_tensor = ctx.Input<Tensor>("ScaleTensor");
alpha = (scale_tensor == nullptr) ? ctx.Attr<float>("scale")
: (float)*(scale_tensor->data<T>());
beta = ctx.Attr<float>("bias");
// if bias_after_scale == true
// out = scale*X + bias
// else
// out = scale*(X + bias) = scale*X + scale*bias
if (!bias_after_scale) beta *= alpha;
} else {
// paddle uses beta but mkldnn uses alpha for swish
if (algorithm == mkldnn::algorithm::eltwise_swish) {
std::swap(alpha, beta);
} else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
alpha = ctx.Attr<float>("threshold");
}
}
PADDLE_ENFORCE(in_x->dims().size() >= 1 || in_x->dims().size() <= 6,
platform::errors::Unimplemented(
"Input dimension size can be 1, 2, 3, 4, "
"5, or 6, but now the dimension size is",
in_x->dims().size()));
PADDLE_ENFORCE(in_x->dims().size() >= 1 || in_x->dims().size() <= 6,
platform::errors::Unimplemented(
"Input dimension size can be 1, 2, 3, 4, "
"5, or 6, but now the dimension size is",
in_x->dims().size()));
auto src_tz = framework::vectorize<int64_t>(in_x->dims());
auto src_fmt =
src_tz.size() == 2 ? MKLDNNMemoryFormat::nc : in_x->format();
auto md = mkldnn::memory::desc(src_tz, platform::MKLDNNGetDataType<T>(),
src_fmt);
auto src_tz = framework::vectorize<int64_t>(in_x->dims());
auto src_fmt = src_tz.size() == 2 ? MKLDNNMemoryFormat::nc : in_x->format();
auto md =
mkldnn::memory::desc(src_tz, platform::MKLDNNGetDataType<T>(), src_fmt);
this->AcquireForwardPrimitiveDescriptor(
mkldnn::prop_kind::forward_training, algorithm, md, alpha, beta);
this->AcquireForwardPrimitiveDescriptor(mkldnn::prop_kind::forward_training,
algorithm, md, alpha, beta);
}
ActivationMKLDNNHandler(mkldnn::algorithm algorithm,
......@@ -1033,40 +1027,42 @@ class ActivationMKLDNNHandler
const mkldnn::engine engine, Place cpu_place,
const framework::Tensor* in_x, const Tensor* out_grad)
: platform::MKLDNNHandlerNoCachingT<T, mkldnn::eltwise_forward,
mkldnn::eltwise_backward>(engine, cpu_place) {
float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 0;
float beta = ctx.HasAttr("beta") ? ctx.Attr<float>("beta") : 0;
// paddle uses beta but mkldnn uses alpha for swish
if (algorithm == mkldnn::algorithm::eltwise_swish) {
std::swap(alpha, beta);
} else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
alpha = ctx.Attr<float>("threshold");
}
mkldnn::eltwise_backward>(engine,
cpu_place) {
float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 0;
float beta = ctx.HasAttr("beta") ? ctx.Attr<float>("beta") : 0;
// paddle uses beta but mkldnn uses alpha for swish
if (algorithm == mkldnn::algorithm::eltwise_swish) {
std::swap(alpha, beta);
} else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
alpha = ctx.Attr<float>("threshold");
}
auto diff_dst_tz = framework::vectorize<int64_t>(out_grad->dims());
auto diff_dst_tz = framework::vectorize<int64_t>(out_grad->dims());
auto src_fmt =
diff_dst_tz.size() == 2 ? MKLDNNMemoryFormat::nc : in_x->format();
auto diff_fmt =
diff_dst_tz.size() == 2 ? MKLDNNMemoryFormat::nc : out_grad->format();
auto src_fmt =
diff_dst_tz.size() == 2 ? MKLDNNMemoryFormat::nc : in_x->format();
auto diff_fmt =
diff_dst_tz.size() == 2 ? MKLDNNMemoryFormat::nc : out_grad->format();
auto dims = framework::vectorize(in_x->dims());
auto diff_dst_md = platform::MKLDNNMemDesc(
dims, platform::MKLDNNGetDataType<T>(), diff_fmt);
auto src_md = platform::MKLDNNMemDesc(
dims, platform::MKLDNNGetDataType<T>(), src_fmt);
auto dims = framework::vectorize(in_x->dims());
auto diff_dst_md = platform::MKLDNNMemDesc(
dims, platform::MKLDNNGetDataType<T>(), diff_fmt);
auto src_md = platform::MKLDNNMemDesc(
dims, platform::MKLDNNGetDataType<T>(), src_fmt);
this->AcquireForwardPrimitiveDescriptor(
mkldnn::prop_kind::forward_training, algorithm, src_md, alpha, beta);
this->AcquireBackwardPrimitiveDescriptor(algorithm, diff_dst_md, src_md,
alpha, beta);
this->AcquireForwardPrimitiveDescriptor(mkldnn::prop_kind::forward_training,
algorithm, src_md, alpha, beta);
this->AcquireBackwardPrimitiveDescriptor(algorithm, diff_dst_md, src_md,
alpha, beta);
}
std::shared_ptr<mkldnn::memory> AcquireBackwardSrcMemory(
const framework::Tensor* input) {
const T* input_data = input->data<T>();
return this->AcquireMemoryFromPrimitive(this->bwd_pd_->src_desc(), to_void_cast<T>(input_data));
return this->AcquireMemoryFromPrimitive(this->bwd_pd_->src_desc(),
to_void_cast<T>(input_data));
}
};
......
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