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未验证 提交 dc62a227 编写于 作者: C Chen Weihang 提交者： GitHub
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Revert "[oneDNN] Fix to issue #34554 (#34623)" (#34838) 

This reverts commit 0a5c99e8.
上级 dffb0b22
展开全部 隐藏空白更改
内联 并排
Showing with 279 addition and 436 deletion
paddle/fluid/operators/elementwise/mkldnn/elementwise_mkldnn_op.h
浏览文件 @ dc62a227
......@@ -47,24 +47,13 @@ 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, dev_ctx, mkldnn_engine, ctx.GetPlace(), x, y, z,
scale_x, scale_y, scale_o, ctx.OutputName("Out"));
const auto src_x_memory = handler.AcquireSrcMemory(x);
const auto src_y_memory = handler.AcquireSecondSrcMemory(y);
// (jczaja) For Inplace src and dst should be the same memory object.
// So x should share buffer with z. But UT mechanics is testing inplace
// execution for this op not checking that x can be bradcasted to match in
// shape y tensor.
// This is wrong as when x is to be broadcasted then z(out) will match the
// shape of y which is bigger than x. Hence if x is smaller in shape than z
// and they share a buffer (of
// shape x) then this buffer is not big enough to hold result of elementwise
// operation.
auto dst_memory = (x->numel() == z->numel() && x->IsSharedBufferWith(*z))
? src_x_memory
: handler.AcquireDstMemory(z);
const auto dst_memory = handler.AcquireDstMemory(z);
const auto binary_prim = handler.AcquireForwardPrimitive();
......
paddle/fluid/operators/elementwise/mkldnn/elementwise_mul_mkldnn_op.cc
浏览文件 @ dc62a227
......@@ -48,8 +48,9 @@ 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, dev_ctx, mkldnn_engine,
ctx.GetPlace(), dout, y, dx, 1.0f, 1.0f, 1.0f,
ctx.InputName(framework::GradVarName("Out")));
const auto src_dout_memory = handler.AcquireSrcMemory(dout);
const auto src_y_memory = handler.AcquireSecondSrcMemory(y);
......@@ -74,8 +75,9 @@ 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, dev_ctx, mkldnn_engine,
ctx.GetPlace(), dout, x, nullptr, 1.0f, 1.0f, 1.0f,
ctx.InputName(framework::GradVarName("Out")));
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 @ dc62a227
......@@ -79,15 +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 *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, dev_ctx,
ctx.GetPlace(), x,
ctx.InputName("X"), is_inplaced);
auto src_memory_p = handler.AcquireSrcMemory(x);
auto dst_memory_p = is_inplaced ? src_memory_p : handler.AcquireDstMemory(y);
......@@ -106,14 +106,13 @@ 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 *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, dev_ctx, ctx.GetPlace(), x, diff_y, ctx.InputName("X"));
auto src_memory_p = handler.AcquireBackwardSrcMemory(x);
auto diff_dst_memory_p = handler.AcquireDiffDstMemory(diff_y);
......
paddle/fluid/operators/mkldnn/caching_tests.cmake
浏览文件 @ dc62a227
cc_test(test_mkldnn_caching SRCS mkldnn/test_mkldnn_caching.cc DEPS op_registry elementwise_mul_op elementwise_add_op activation_op softmax_op conv_op im2col vol2col softmax scope device_context enforce)
cc_test(test_mkldnn_caching SRCS mkldnn/test_mkldnn_caching.cc DEPS op_registry elementwise_mul_op elementwise_add_op activation_op softmax_op softmax scope device_context enforce)
paddle/fluid/operators/mkldnn/scale_mkldnn_op.cc
浏览文件 @ dc62a227
......@@ -29,7 +29,6 @@ class ScaleMKLDNNKernel : public framework::OpKernel<T> {
void RunKernel(const framework::ExecutionContext& ctx) const {
const auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
auto* x = ctx.Input<Tensor>("X");
auto* out = ctx.Output<Tensor>("Out");
......@@ -37,12 +36,11 @@ 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, dev_ctx, ctx.GetPlace(), x,
ctx.InputName("X"), is_inplaced);
auto src_memory_p = handler.AcquireSrcMemory(x);
auto dst_memory_p =
is_inplaced ? src_memory_p : handler.AcquireDstMemory(out);
auto dst_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 @ dc62a227
......@@ -32,56 +32,69 @@ using platform::to_void_cast;
template <typename T>
class SoftmaxMKLDNNHandler
: public platform::MKLDNNHandlerNoCachingT<T, mkldnn::softmax_forward,
mkldnn::softmax_backward> {
: public platform::MKLDNNHandlerT<T, mkldnn::softmax_forward,
mkldnn::softmax_backward> {
public:
SoftmaxMKLDNNHandler(const mkldnn::engine mkldnn_engine,
SoftmaxMKLDNNHandler(const MKLDNNDeviceContext& dev_ctx,
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>(
mkldnn_engine, cpu_place) {
PADDLE_ENFORCE_EQ(
input->dims(), output->dims(),
platform::errors::InvalidArgument(
"The shape of input and output tensor must be identical."));
auto softmax_tz = framework::vectorize(input->dims());
auto md = memory::desc(softmax_tz, platform::MKLDNNGetDataType<T>(),
input->format());
this->AcquireForwardPrimitiveDescriptor(prop_kind::forward_scoring, md,
axis);
Tensor* output, const int axis,
const std::string uniq_name, bool is_inplaced)
: platform::MKLDNNHandlerT<T, mkldnn::softmax_forward,
mkldnn::softmax_backward>(
dev_ctx, mkldnn_engine, cpu_place,
// Softmax may be inplace then uniq_name is no longer unique
is_inplaced ? platform::CreateKey(
dev_ctx, framework::vectorize(input->dims()),
axis, uniq_name)
: platform::CreateKey(
dev_ctx, framework::vectorize(input->dims()),
uniq_name)) {
if (!this->isCached()) {
PADDLE_ENFORCE_EQ(
input->dims(), output->dims(),
platform::errors::InvalidArgument(
"The shape of input and output tensor must be identical."));
auto softmax_tz = framework::vectorize(input->dims());
auto md = memory::desc(softmax_tz, platform::MKLDNNGetDataType<T>(),
input->format());
this->AcquireForwardPrimitiveDescriptor(prop_kind::forward_scoring, md,
axis);
}
}
SoftmaxMKLDNNHandler(const framework::ExecutionContext& ctx,
const mkldnn::engine mkldnn_engine,
const MKLDNNDeviceContext& dev_ctx,
platform::Place cpu_place, const Tensor* out,
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, but shapes differ, "
"out_grad: %s in_grad: %s",
out_grad->dims(), in_x_grad->dims()));
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);
: platform::MKLDNNHandlerT<T, mkldnn::softmax_forward,
mkldnn::softmax_backward>(
dev_ctx, dev_ctx.GetEngine(), cpu_place,
platform::CreateKey(dev_ctx, framework::vectorize(out->dims()),
unique_name)) {
if (!this->isBwdCached()) {
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);
}
}
};
......@@ -98,8 +111,9 @@ 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(dev_ctx, mkldnn_engine, ctx.GetPlace(),
input, output, axis, ctx.OutputName("Out"),
is_inplaced);
auto softmax_src_memory_p = handler.AcquireSrcMemory(input);
// For Inplace src and and dst are the same memory object
......@@ -135,12 +149,11 @@ class SoftmaxMKLDNNGradKernel : public paddle::framework::OpKernel<T> {
paddle::platform::errors::PreconditionNotMet(
"Operator DNNL SoftmaxGrad must use CPUPlace"));
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
const Tensor* output = ctx.Input<Tensor>("Out");
auto* out_grad = ctx.template Input<Tensor>(framework::GradVarName("Out"));
auto* in_x_grad = ctx.template Output<Tensor>(framework::GradVarName("X"));
SoftmaxMKLDNNHandler<T> handler(ctx, mkldnn_engine, ctx.GetPlace(), output,
SoftmaxMKLDNNHandler<T> handler(ctx, dev_ctx, ctx.GetPlace(), output,
out_grad, in_x_grad, ctx.InputName("Out"));
auto dst_memory_p = handler.AcquireDstMemory(output);
......
paddle/fluid/operators/mkldnn/test_mkldnn_caching.cc
浏览文件 @ dc62a227
......@@ -33,8 +33,6 @@ USE_OP(relu);
USE_OP_DEVICE_KERNEL(relu, MKLDNN);
USE_OP(softmax);
USE_OP_DEVICE_KERNEL(softmax, MKLDNN);
USE_OP(conv2d);
USE_OP_DEVICE_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN, FP32);
namespace paddle {
namespace operators {
......@@ -66,19 +64,16 @@ class CacheTester {
template <typename T>
void RunOperator(const platform::Place &place, const std::string &op_type,
const framework::DDim &dims, const std::string &first_input) {
const framework::DDim &dims, const std::string &output_name,
bool inplace = false) {
framework::Scope scope;
std::map<const std::string, int> num_inputs = {{"softmax", 1},
{"relu", 1},
{"conv2d", 2},
{"elementwise_add", 2},
{"elementwise_mul", 2}};
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::string output_name = "output";
std::string first_input = inplace == true ? output_name : "x";
std::vector<InputVars> input_names = {
{first_input, scope.Var(first_input)->GetMutable<framework::LoDTensor>()},
......@@ -118,40 +113,71 @@ 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, {{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}}});
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}}});
op->Run(scope, place);
pool.Get(place)->Wait();
}
TEST(test_conv2d_reuse_cache, cpu_place) {
framework::DDim dims({1, 16, 32, 64});
TEST(test_softmax_reuse_cache, cpu_place) {
framework::DDim dims({32, 64});
platform::CPUPlace p;
CacheTester ct;
RunOperator<float>(p, "conv2d", dims, "input_signal");
RunOperator<float>(p, "conv2d", dims, "input_signal");
PADDLE_ENFORCE_EQ(ct.Analyze(9), true,
RunOperator<float>(p, "softmax", dims, "softmax_out");
RunOperator<float>(p, "softmax", dims, "softmax_out");
PADDLE_ENFORCE_EQ(ct.Analyze(4), true,
platform::errors::InvalidArgument(
"Invalid number of cached oneDNN objects"));
"Wrong number of cached oneDNN objects"));
}
TEST(test_conv2d_noreuse_cache, cpu_place) {
framework::DDim dims({1, 16, 32, 64});
TEST(test_softmax_noreuse_cache, cpu_place) {
framework::DDim dims({32, 64});
platform::CPUPlace p;
CacheTester ct;
RunOperator<float>(p, "conv2d", dims, "input_signal");
RunOperator<float>(p, "conv2d", dims, "input_signal2");
PADDLE_ENFORCE_EQ(ct.Analyze(18), true,
RunOperator<float>(p, "softmax", dims, "softmax_out");
RunOperator<float>(p, "softmax", dims, "softmax_out2");
PADDLE_ENFORCE_EQ(ct.Analyze(8), true,
platform::errors::InvalidArgument(
"Invalid number of cached oneDNN objects"));
"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});
platform::CPUPlace p;
CacheTester ct;
RunOperator<float>(p, "elementwise_add", dims, "elementwise_add_out");
RunOperator<float>(p, "relu", dims, "elementwise_add_out", true);
PADDLE_ENFORCE_EQ(ct.Analyze(8), true,
platform::errors::InvalidArgument(
"Wrong number of cached oneDNN objects"));
}
} // namespace operators
......
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