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未验证 提交 e8edbb09 编写于 作者: H Hulek 提交者： GitHub
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Migrate mul_mkldnn_op to phi matmul_kernel (#48299) 

* Migrate mul_mkldnn_op to matmul_kernel

* Review fixes - changed mutable_data, changed ctx to dev_ctx, fixed namespaces

* switched some funcs to phi

* Deleted not needed phi:: and changed place checking according to standards
上级 2af82190
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paddle/fluid/operators/mkldnn/mul_mkldnn_op.cc 已删除 100644 → 0
浏览文件 @ 2af82190
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <string>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/mkldnn_reuse.h"
namespace phi {
class DenseTensor;
} // namespace phi
namespace paddle {
namespace operators {
using framework::DDim;
using framework::ExecutionContext;
using phi::OneDNNContext;
using platform::MatMulV2MKLDNNHandler;
using dnnl::inner_product_forward;
using dnnl::memory;
using dnnl::prop_kind;
using dnnl::stream;
template <typename XT, typename YT, typename OT>
class MulPrimitiveFactory {
public:
explicit MulPrimitiveFactory(const dnnl::engine &engine) : engine_(engine) {}
inner_product_forward CreateMulPrimitive(const Tensor *x_input,
const Tensor *y_input,
Tensor *output,
const ExecutionContext &ctx) {
/* check data format and reorder if need */
int x_num_col_dims = ctx.Attr<int>("x_num_col_dims");
int y_num_col_dims = ctx.Attr<int>("y_num_col_dims");
// TODO(intel-minghui) : Remove the restriction that only supports Input(Y)
// as weights
PADDLE_ENFORCE_EQ(
(std::is_same<YT, float>::value),
true,
platform::errors::InvalidArgument(
"Input(Y) must be fp32 data type since only fp32 data type is "
"supported in the current design of MKLDNN INT8."));
auto x_matrix = UpdateDataFormat<XT>(x_input, x_num_col_dims, ctx);
auto y_matrix = UpdateDataFormat<YT>(y_input, y_num_col_dims, ctx);
auto output_dim = output->dims();
if (output_dim.size() != 2) {
output->Resize({x_matrix.dims()[0], y_matrix.dims()[1]});
}
if (mul_) {
UpdateDataPointers(ctx, output, &x_matrix);
Execute();
return *(mul_);
}
auto src_desc = CreateMemDescriptor<XT>(&x_matrix, OneDNNMemoryFormat::nc);
x_input_ = CreateMemory<XT>(src_desc, &x_matrix);
if (is_int8_) {
const auto trans_y = TransposeInputY(&y_matrix);
auto scale_y = ctx.Attr<std::vector<float>>("scale_y");
y_input_ = QuantInputY(trans_y, scale_y);
} else {
y_input_ = TransposeInputY(&y_matrix);
}
auto dst_desc = CreateMemDescriptor<OT>(output, OneDNNMemoryFormat::any);
mul_ = CreateMulPrimitive(*x_input_, *y_input_, dst_desc, output, ctx);
Execute();
return *(mul_);
}
private:
memory ReorderWithScale(const memory::desc &src_desc,
const memory::desc &dst_desc,
void *src_data,
const std::vector<float> &scale) {
auto mask = scale.size() > 1 ? 1 : 0;
dnnl::primitive_attr attr;
attr.set_output_scales(mask, scale);
auto src_mem = memory(src_desc, engine_, src_data);
auto dst_mem = memory(dst_desc, engine_);
auto reorder_pd = dnnl::reorder::primitive_desc(src_mem, dst_mem, attr);
auto reorder = dnnl::reorder(reorder_pd);
auto &astream = OneDNNContext::tls().get_stream();
{
platform::RecordEvent record_reorder(
"int_reorder",
platform::TracerEventType::UserDefined,
2,
platform::EventRole::kUniqueOp);
reorder.execute(astream, src_mem, dst_mem);
astream.wait();
}
return dst_mem;
}
memory QuantInputY(memory input_y, const std::vector<float> &scale_y) {
const auto &dims = input_y.get_desc().data.dims;
auto ndims = input_y.get_desc().data.ndims;
auto y_dims = std::vector<int64_t>(dims, dims + ndims);
auto user_y_desc = CreateMemDescriptor<YT>(y_dims, OneDNNMemoryFormat::oi);
auto y_desc = CreateMemDescriptor<int8_t>(y_dims, OneDNNMemoryFormat::oi);
return ReorderWithScale(
user_y_desc, y_desc, input_y.get_data_handle(), scale_y);
}
dnnl::primitive_attr CreateMulAttr(const ExecutionContext &ctx,
bool force_fp32_output) {
dnnl::primitive_attr mul_attr;
auto scale_y_data = ctx.Attr<std::vector<float>>("scale_y");
auto scale_x_data = ctx.Attr<float>("scale_x");
auto scale_out_data =
force_fp32_output ? 1.0f : ctx.Attr<float>("scale_out");
bool is_multi_channel = scale_y_data.size() > 1;
int count = is_multi_channel ? scale_y_data.size() : 1;
std::vector<float> output_shift_scale(count);
for (int i = 0; i < count; i++) {
if (scale_y_data[i] == 0.0)
output_shift_scale[i] = scale_out_data;
else
output_shift_scale[i] =
scale_out_data / (scale_x_data * scale_y_data[i]);
}
int mul_mask = is_multi_channel ? 1 : 0;
mul_attr.set_output_scales(mul_mask, output_shift_scale);
return mul_attr;
}
inner_product_forward CreateMulPrimitive(const memory &x_memory,
const memory &y_memory,
const memory::desc &dst_desc,
Tensor *output,
const ExecutionContext &ctx) {
const auto x_desc = x_memory.get_desc();
const auto y_desc = y_memory.get_desc();
inner_product_forward::primitive_desc mul_prim_desc;
const auto &mul_desc = inner_product_forward::desc(
prop_kind::forward, x_desc, y_desc, dst_desc);
if (is_int8_) {
bool force_fp32_output = ctx.Attr<bool>("force_fp32_output");
auto mul_attr = CreateMulAttr(ctx, force_fp32_output);
mul_prim_desc =
inner_product_forward::primitive_desc(mul_desc, mul_attr, engine_);
} else {
mul_prim_desc = inner_product_forward::primitive_desc(mul_desc, engine_);
}
output_ = CreateDstMemory(mul_prim_desc, ctx, output);
return inner_product_forward(mul_prim_desc);
}
void Execute() {
auto &astream = OneDNNContext::tls().get_stream();
(*mul_).execute(astream,
{{DNNL_ARG_SRC, *x_input_},
{DNNL_ARG_WEIGHTS, *y_input_},
{DNNL_ARG_DST, *output_}});
astream.wait();
}
template <typename T>
Tensor UpdateDataFormat(const Tensor *data,
int num_col_dims,
const ExecutionContext &ctx) {
Tensor x_tmp;
Tensor data_matrix;
// This code is enforcing plain (non-blocked) memory arrangement
// in order to flatten (reduce dimensionality) of Tensor later
auto src_mdesc = data->mem_desc();
auto dst_mdesc =
data->dims().size() >= 4
? (data->dims().size() == 5
? CreateMemDescriptor<T>(data, OneDNNMemoryFormat::ncdhw)
: CreateMemDescriptor<T>(data, OneDNNMemoryFormat::nchw))
: src_mdesc;
if (src_mdesc != dst_mdesc) {
x_tmp.mutable_data<T>(ctx.GetPlace(), data->memory_size());
Reorder(src_mdesc,
dst_mdesc,
phi::funcs::to_void_cast<T>(data->data<T>()),
phi::funcs::to_void_cast<T>(x_tmp.data<T>()));
x_tmp.Resize(data->dims());
x_tmp.set_mem_desc(dst_mdesc);
data_matrix = framework::ReshapeToMatrix(x_tmp, num_col_dims);
} else {
data_matrix = framework::ReshapeToMatrix(*data, num_col_dims);
}
return data_matrix;
}
void UpdateDataPointers(const ExecutionContext &ctx,
Tensor *out,
const Tensor *in) {
x_input_->set_data_handle(phi::funcs::to_void_cast<XT>(in->data<XT>()));
output_->set_data_handle(out->mutable_data<OT>(ctx.GetPlace()));
out->set_mem_desc(output_->get_desc());
}
template <typename T>
memory::desc CreateMemDescriptor(
const Tensor *tensor,
OneDNNMemoryFormat format,
memory::data_type type = phi::funcs::OneDNNGetDataType<T>()) {
auto dims = phi::vectorize<int64_t>(tensor->dims());
return phi::funcs::OneDNNMemDesc(dims, type, format);
}
template <typename T>
memory::desc CreateMemDescriptor(
const std::vector<int64_t> &dims,
OneDNNMemoryFormat format,
memory::data_type type = phi::funcs::OneDNNGetDataType<T>()) {
return phi::funcs::OneDNNMemDesc(dims, type, format);
}
template <typename T>
memory CreateMemory(const memory::desc &desc, const Tensor *tensor) {
return memory(
desc, engine_, phi::funcs::to_void_cast<T>(tensor->data<T>()));
}
memory CreateDstMemory(
const inner_product_forward::primitive_desc &mul_prim_desc,
const ExecutionContext &ctx,
Tensor *output) {
auto dst_desc = mul_prim_desc.dst_desc();
auto buffer_size = dst_desc.get_size();
OT *output_data = output->mutable_data<OT>(ctx.GetPlace(), buffer_size);
output->set_mem_desc(dst_desc);
return memory(dst_desc, engine_, phi::funcs::to_void_cast<OT>(output_data));
}
memory Reorder(const memory::desc &src_desc,
const memory::desc &dst_desc,
void *src_data,
void *dst_data = NULL) {
auto src_mem = memory(src_desc, engine_, src_data);
auto dst_mem = dst_data ? memory(dst_desc, engine_, dst_data)
: memory(dst_desc, engine_);
auto reorder = dnnl::reorder(src_mem, dst_mem);
auto &astream = OneDNNContext::tls().get_stream();
{
platform::RecordEvent record_reorder(
"int_reorder",
platform::TracerEventType::UserDefined,
2,
platform::EventRole::kUniqueOp);
reorder.execute(astream, src_mem, dst_mem);
astream.wait();
}
return dst_mem;
}
memory TransposeInputY(const Tensor *input_y) {
auto dims = phi::vectorize<int64_t>(input_y->dims());
std::swap(dims[0], dims[1]); // Correct output dimensions
auto src_desc = CreateMemDescriptor<YT>(dims, OneDNNMemoryFormat::io);
auto dst_desc = CreateMemDescriptor<YT>(dims, OneDNNMemoryFormat::oi);
return Reorder(
src_desc, dst_desc, phi::funcs::to_void_cast<YT>(input_y->data<YT>()));
}
const dnnl::engine &engine_;
paddle::optional<memory> x_input_;
paddle::optional<memory> y_input_;
paddle::optional<memory> output_;
paddle::optional<inner_product_forward> mul_;
static constexpr bool is_int8_ =
std::is_same<XT, int8_t>::value || std::is_same<XT, uint8_t>::value;
};
/* OT: output data type */
template <typename XT, typename YT, typename OT>
std::shared_ptr<MulPrimitiveFactory<XT, YT, OT>> GetPrimitiveFactory(
const OneDNNContext &dev_ctx,
const ExecutionContext &ctx,
const Tensor *input_x,
const Tensor *input_y,
const dnnl::engine &mkldnn_engine) {
std::string key =
phi::funcs::CreateKey(dev_ctx,
framework::TransToProtoVarType(input_x->dtype()),
phi::vectorize(input_x->dims()),
framework::TransToProtoVarType(input_y->dtype()),
phi::vectorize(input_y->dims()),
ctx.OutputName("Out"));
key = phi::funcs::ExtendKeyWithThreadInfoIfNeeded(dev_ctx, key);
auto prim_creator = std::static_pointer_cast<MulPrimitiveFactory<XT, YT, OT>>(
dev_ctx.GetBlob(key));
if (prim_creator == nullptr) {
prim_creator =
std::make_shared<MulPrimitiveFactory<XT, YT, OT>>(mkldnn_engine);
dev_ctx.SetBlob(key, prim_creator);
}
return prim_creator;
}
/* XT: input x data type, YT: input y data type */
template <typename XT, typename YT>
inner_product_forward GetMulPrimitive(const OneDNNContext &dev_ctx,
const ExecutionContext &ctx,
const Tensor *input_x,
const Tensor *input_y,
Tensor *output,
const dnnl::engine &mkldnn_engine) {
constexpr bool is_int8 =
std::is_same<XT, int8_t>::value || std::is_same<XT, uint8_t>::value;
bool force_fp32_output = ctx.Attr<bool>("force_fp32_output");
if (is_int8 && !force_fp32_output) {
return GetPrimitiveFactory<XT, YT, int8_t>(
dev_ctx, ctx, input_x, input_y, mkldnn_engine)
->CreateMulPrimitive(input_x, input_y, output, ctx);
} else {
return GetPrimitiveFactory<XT, YT, float>(
dev_ctx, ctx, input_x, input_y, mkldnn_engine)
->CreateMulPrimitive(input_x, input_y, output, ctx);
}
}
/* XT: input x data type */
template <typename XT>
class MulMKLDNNINT8Kernel : public framework::OpKernel<XT> {
public:
void Compute(const ExecutionContext &ctx) const override {
PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()),
true,
paddle::platform::errors::PreconditionNotMet(
"Operator DNNL Mul must use CPUPlace"));
OneDNNContext::tls().log_lib_version();
auto &dev_ctx = ctx.template device_context<OneDNNContext>();
auto &mkldnn_engine = dev_ctx.GetEngine();
const Tensor *x = ctx.Input<phi::DenseTensor>("X");
const Tensor *y = ctx.Input<phi::DenseTensor>("Y");
Tensor *out = ctx.Output<phi::DenseTensor>("Out");
auto out_dims = out->dims();
auto mul =
GetMulPrimitive<XT, float>(dev_ctx, ctx, x, y, out, mkldnn_engine);
if (out_dims.size() != 2) {
out->Resize(out_dims);
}
auto in_md = dnnl::memory::desc(*dnnl_primitive_desc_query_md(
mul.get_primitive_desc(), dnnl_query_dst_md, 0));
out->set_mem_desc(in_md.reshape(phi::vectorize<int64_t>(out->dims())));
}
};
template <typename XT>
class MulMKLDNNKernel : public framework::OpKernel<XT> {
public:
void Compute(const ExecutionContext &ctx) const override { RunKernel(ctx); }
protected:
void ExecuteMatMul(const ExecutionContext &ctx,
const OneDNNContext &dev_ctx,
const dnnl::engine &onednn_engine,
const platform::Place &cpu_place,
const Tensor *x,
const std::vector<int64_t> &x_dims,
bool trans_x,
const Tensor *y,
const std::vector<int64_t> &y_dims,
bool trans_y,
Tensor *out) const {
static const std::vector<int64_t> vec_placeholder;
MatMulV2MKLDNNHandler<XT, XT, XT> handler(ctx,
onednn_engine,
ctx.GetPlace(),
x_dims,
trans_x,
y_dims,
trans_y,
false,
vec_placeholder,
vec_placeholder);
const auto src_memory_p = handler.AcquireSrcMemory(x);
const auto weights_memory_p = handler.AcquireWeightsMemory(y);
const auto dst_memory_p = handler.AcquireDstMemory(out);
auto matmul_p = handler.AcquireForwardPrimitive();
std::unordered_map<int, dnnl::memory> matmul_args = {
{DNNL_ARG_SRC, *src_memory_p},
{DNNL_ARG_WEIGHTS, *weights_memory_p},
{DNNL_ARG_DST, *dst_memory_p}};
auto &astream = OneDNNContext::tls().get_stream();
matmul_p->execute(astream, matmul_args);
astream.wait();
// This kernel is flattening dims so then we need to unflattened version
// that should be set in out reshape require plain layout, but
// MatmulV2MKLDNNHanlder enforces one so it should work
out->set_mem_desc(
dst_memory_p->get_desc().reshape(phi::vectorize<int64_t>(out->dims())));
}
private:
void RunKernel(const ExecutionContext &ctx) const {
const auto &dev_ctx = ctx.template device_context<OneDNNContext>();
const auto &onednn_engine = dev_ctx.GetEngine();
const auto *x = ctx.Input<phi::DenseTensor>("X");
const auto *y = ctx.Input<phi::DenseTensor>("Y");
auto *out = ctx.Output<phi::DenseTensor>("Out");
int x_num_col_dims = ctx.Attr<int>("x_num_col_dims");
int y_num_col_dims = ctx.Attr<int>("y_num_col_dims");
const Tensor x_matrix = x->dims().size() > 2
? framework::ReshapeToMatrix(*x, x_num_col_dims)
: *x;
const Tensor y_matrix = y->dims().size() > 2
? framework::ReshapeToMatrix(*y, y_num_col_dims)
: *y;
// adding mb dim because MatMulV2 handler needs it
std::vector<int64_t> y_dims(3, 1);
std::vector<int64_t> x_dims(3, 1);
y_dims[1] = y_matrix.dims()[0];
y_dims[2] = y_matrix.dims()[1];
x_dims[1] = x_matrix.dims()[0];
x_dims[2] = x_matrix.dims()[1];
ExecuteMatMul(ctx,
dev_ctx,
onednn_engine,
ctx.GetPlace(),
&x_matrix,
x_dims,
false,
&y_matrix,
y_dims,
false,
out);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(mul,
MKLDNN,
::paddle::platform::CPUPlace,
ops::MulMKLDNNINT8Kernel<uint8_t>,
ops::MulMKLDNNINT8Kernel<int8_t>,
ops::MulMKLDNNKernel<paddle::platform::bfloat16>,
ops::MulMKLDNNKernel<float>);
paddle/phi/kernels/onednn/matmul_kernel.cc
浏览文件 @ e8edbb09
...@@ -12,11 +12,20 @@ ...@@ -12,11 +12,20 @@
// See the License for the specific language governing permissions and // See the License for the specific language governing permissions and
// limitations under the License. // limitations under the License.
#include <string>
#include "paddle/phi/kernels/matmul_kernel.h" #include "paddle/phi/kernels/matmul_kernel.h"
#include "paddle/phi/backends/onednn/onednn_reuse.h" #include "paddle/phi/backends/onednn/onednn_reuse.h"
#include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/core/kernel_registry.h"
using dnnl::engine;
using dnnl::inner_product_forward;
using dnnl::memory;
using dnnl::prop_kind;
using dnnl::stream;
using paddle::framework::ReshapeToMatrix;
namespace phi { namespace phi {
DDim GetDimsForInput(const OneDNNContext &dev_ctx, DDim GetDimsForInput(const OneDNNContext &dev_ctx,
...@@ -152,6 +161,418 @@ void MatmulKernel(const Context &dev_ctx, ...@@ -152,6 +161,418 @@ void MatmulKernel(const Context &dev_ctx,
} }
} }
template <typename XT, typename YT, typename OT>
class MulPrimitiveFactory {
public:
explicit MulPrimitiveFactory(const engine &engine) : engine_(engine) {}
inner_product_forward CreateMulPrimitive(const DenseTensor *x_input,
const DenseTensor *y_input,
DenseTensor *output,
int x_num_col_dims,
int y_num_col_dims,
const OneDNNContext &dev_ctx) {
// TODO(intel-minghui) : Remove the restriction that only supports Input(Y)
// as weights
PADDLE_ENFORCE_EQ(
(std::is_same<YT, float>::value),
true,
errors::InvalidArgument(
"Input(Y) must be fp32 data type since only fp32 data type is "
"supported in the current design of OneDNN INT8."));
/* check data format and reorder if need */
auto x_matrix = UpdateDataFormat<XT>(x_input, x_num_col_dims, dev_ctx);
auto y_matrix = UpdateDataFormat<YT>(y_input, y_num_col_dims, dev_ctx);
auto output_dim = output->dims();
if (output_dim.size() != 2) {
output->Resize({x_matrix.dims()[0], y_matrix.dims()[1]});
}
if (mul_) {
UpdateDataPointers(dev_ctx, output, &x_matrix);
Execute();
return *(mul_);
}
auto src_desc =
CreateMemDescriptor<XT>(&x_matrix, funcs::OneDNNMemoryFormat::nc);
x_input_ = CreateMemory<XT>(src_desc, &x_matrix);
if (is_int8_) {
const auto trans_y = TransposeInputY(&y_matrix);
auto scale_y = dev_ctx.HasDnnAttr("scale_y")
? PADDLE_GET_CONST(std::vector<float>,
dev_ctx.GetDnnAttr("scale_y"))
: std::vector<float>();
y_input_ = QuantInputY(trans_y, scale_y);
} else {
y_input_ = TransposeInputY(&y_matrix);
}
auto dst_desc =
CreateMemDescriptor<OT>(output, funcs::OneDNNMemoryFormat::any);
mul_ = CreateMulPrimitive(*x_input_, *y_input_, dst_desc, output, dev_ctx);
Execute();
return *(mul_);
}
private:
memory ReorderWithScale(const memory::desc &src_desc,
const memory::desc &dst_desc,
void *src_data,
const std::vector<float> &scale) {
auto mask = scale.size() > 1 ? 1 : 0;
dnnl::primitive_attr attr;
attr.set_output_scales(mask, scale);
auto src_mem = memory(src_desc, engine_, src_data);
auto dst_mem = memory(dst_desc, engine_);
auto reorder_pd = dnnl::reorder::primitive_desc(src_mem, dst_mem, attr);
auto reorder = dnnl::reorder(reorder_pd);
auto &astream = OneDNNContext::tls().get_stream();
{
paddle::platform::RecordEvent record_reorder(
"int_reorder",
paddle::platform::TracerEventType::UserDefined,
2,
paddle::platform::EventRole::kUniqueOp);
reorder.execute(astream, src_mem, dst_mem);
astream.wait();
}
return dst_mem;
}
memory QuantInputY(memory input_y, const std::vector<float> &scale_y) {
const auto &dims = input_y.get_desc().data.dims;
auto ndims = input_y.get_desc().data.ndims;
auto y_dims = std::vector<int64_t>(dims, dims + ndims);
auto user_y_desc =
CreateMemDescriptor<YT>(y_dims, funcs::OneDNNMemoryFormat::oi);
auto y_desc =
CreateMemDescriptor<int8_t>(y_dims, funcs::OneDNNMemoryFormat::oi);
return ReorderWithScale(
user_y_desc, y_desc, input_y.get_data_handle(), scale_y);
}
dnnl::primitive_attr CreateMulAttr(const OneDNNContext &dev_ctx,
bool force_fp32_output) {
dnnl::primitive_attr mul_attr;
auto scale_y_data = dev_ctx.HasDnnAttr("scale_y")
? PADDLE_GET_CONST(std::vector<float>,
dev_ctx.GetDnnAttr("scale_y"))
: std::vector<float>{1.0};
auto scale_x_data =
dev_ctx.HasDnnAttr("scale_x")
? PADDLE_GET_CONST(float, dev_ctx.GetDnnAttr("scale_x"))
: 1.0f;
auto scale_out =
dev_ctx.HasDnnAttr("scale_out")
? PADDLE_GET_CONST(float, dev_ctx.GetDnnAttr("scale_out"))
: 1.0f;
auto scale_out_data = force_fp32_output ? 1.0f : scale_out;
bool is_multi_channel = scale_y_data.size() > 1;
int count = is_multi_channel ? scale_y_data.size() : 1;
std::vector<float> output_shift_scale(count);
for (int i = 0; i < count; i++) {
if (scale_y_data[i] == 0.0)
output_shift_scale[i] = scale_out_data;
else
output_shift_scale[i] =
scale_out_data / (scale_x_data * scale_y_data[i]);
}
int mul_mask = is_multi_channel ? 1 : 0;
mul_attr.set_output_scales(mul_mask, output_shift_scale);
return mul_attr;
}
inner_product_forward CreateMulPrimitive(const memory &x_memory,
const memory &y_memory,
const memory::desc &dst_desc,
DenseTensor *output,
const OneDNNContext &dev_ctx) {
const auto x_desc = x_memory.get_desc();
const auto y_desc = y_memory.get_desc();
inner_product_forward::primitive_desc mul_prim_desc;
const auto &mul_desc = inner_product_forward::desc(
prop_kind::forward, x_desc, y_desc, dst_desc);
if (is_int8_) {
bool force_fp32_output =
dev_ctx.HasDnnAttr("force_fp32_output")
? PADDLE_GET_CONST(bool, dev_ctx.GetDnnAttr("force_fp32_output"))
: false;
auto mul_attr = CreateMulAttr(dev_ctx, force_fp32_output);
mul_prim_desc =
inner_product_forward::primitive_desc(mul_desc, mul_attr, engine_);
} else {
mul_prim_desc = inner_product_forward::primitive_desc(mul_desc, engine_);
}
output_ = CreateDstMemory(mul_prim_desc, dev_ctx, output);
return inner_product_forward(mul_prim_desc);
}
void Execute() {
auto &astream = OneDNNContext::tls().get_stream();
(*mul_).execute(astream,
{{DNNL_ARG_SRC, *x_input_},
{DNNL_ARG_WEIGHTS, *y_input_},
{DNNL_ARG_DST, *output_}});
astream.wait();
}
template <typename T>
DenseTensor UpdateDataFormat(const DenseTensor *data,
int num_col_dims,
const OneDNNContext &dev_ctx) {
DenseTensor x_tmp;
DenseTensor data_matrix;
// This code is enforcing plain (non-blocked) memory arrangement
// in order to flatten (reduce dimensionality) of DenseTensor later
auto src_mdesc = data->mem_desc();
auto dst_mdesc = data->dims().size() >= 4
? (data->dims().size() == 5
? CreateMemDescriptor<T>(
data, funcs::OneDNNMemoryFormat::ncdhw)
: CreateMemDescriptor<T>(
data, funcs::OneDNNMemoryFormat::nchw))
: src_mdesc;
if (src_mdesc != dst_mdesc) {
dev_ctx.template Alloc<T>(&x_tmp, data->memory_size());
Reorder(src_mdesc,
dst_mdesc,
funcs::to_void_cast<T>(data->data<T>()),
funcs::to_void_cast<T>(x_tmp.data<T>()));
x_tmp.Resize(data->dims());
x_tmp.set_mem_desc(dst_mdesc);
data_matrix = ReshapeToMatrix(x_tmp, num_col_dims);
} else {
data_matrix = ReshapeToMatrix(*data, num_col_dims);
}
return data_matrix;
}
void UpdateDataPointers(const OneDNNContext &dev_ctx,
DenseTensor *out,
const DenseTensor *in) {
x_input_->set_data_handle(funcs::to_void_cast<XT>(in->data<XT>()));
output_->set_data_handle(dev_ctx.template Alloc<OT>(out));
out->set_mem_desc(output_->get_desc());
}
template <typename T>
memory::desc CreateMemDescriptor(
const DenseTensor *tensor,
funcs::OneDNNMemoryFormat format,
memory::data_type type = funcs::OneDNNGetDataType<T>()) {
auto dims = vectorize<int64_t>(tensor->dims());
return funcs::OneDNNMemDesc(dims, type, format);
}
template <typename T>
memory::desc CreateMemDescriptor(
const std::vector<int64_t> &dims,
funcs::OneDNNMemoryFormat format,
memory::data_type type = funcs::OneDNNGetDataType<T>()) {
return funcs::OneDNNMemDesc(dims, type, format);
}
template <typename T>
memory CreateMemory(const memory::desc &desc, const DenseTensor *tensor) {
return memory(desc, engine_, funcs::to_void_cast<T>(tensor->data<T>()));
}
memory CreateDstMemory(
const inner_product_forward::primitive_desc &mul_prim_desc,
const OneDNNContext &dev_ctx,
DenseTensor *output) {
auto dst_desc = mul_prim_desc.dst_desc();
auto buffer_size = dst_desc.get_size();
OT *output_data = dev_ctx.template Alloc<OT>(output, buffer_size);
output->set_mem_desc(dst_desc);
return memory(dst_desc, engine_, funcs::to_void_cast<OT>(output_data));
}
memory Reorder(const memory::desc &src_desc,
const memory::desc &dst_desc,
void *src_data,
void *dst_data = NULL) {
auto src_mem = memory(src_desc, engine_, src_data);
auto dst_mem = dst_data ? memory(dst_desc, engine_, dst_data)
: memory(dst_desc, engine_);
auto reorder = dnnl::reorder(src_mem, dst_mem);
auto &astream = OneDNNContext::tls().get_stream();
{
paddle::platform::RecordEvent record_reorder(
"int_reorder",
paddle::platform::TracerEventType::UserDefined,
2,
paddle::platform::EventRole::kUniqueOp);
reorder.execute(astream, src_mem, dst_mem);
astream.wait();
}
return dst_mem;
}
memory TransposeInputY(const DenseTensor *input_y) {
auto dims = vectorize<int64_t>(input_y->dims());
std::swap(dims[0], dims[1]); // Correct output dimensions
auto src_desc =
CreateMemDescriptor<YT>(dims, funcs::OneDNNMemoryFormat::io);
auto dst_desc =
CreateMemDescriptor<YT>(dims, funcs::OneDNNMemoryFormat::oi);
return Reorder(
src_desc, dst_desc, funcs::to_void_cast<YT>(input_y->data<YT>()));
}
const engine &engine_;
paddle::optional<memory> x_input_;
paddle::optional<memory> y_input_;
paddle::optional<memory> output_;
paddle::optional<inner_product_forward> mul_;
static constexpr bool is_int8_ = funcs::is_int8<XT>();
};
/* OT: output data type */
template <typename XT, typename YT, typename OT>
std::shared_ptr<MulPrimitiveFactory<XT, YT, OT>> GetPrimitiveFactory(
const OneDNNContext &dev_ctx,
const DenseTensor *input_x,
const DenseTensor *input_y,
const engine &onednn_engine) {
std::string key = funcs::CreateKey(dev_ctx,
TransToProtoVarType(input_x->dtype()),
vectorize(input_x->dims()),
TransToProtoVarType(input_y->dtype()),
vectorize(input_y->dims()),
dev_ctx.GetOutputsName("Out")[0]);
key = funcs::ExtendKeyWithThreadInfoIfNeeded(dev_ctx, key);
auto prim_creator = std::static_pointer_cast<MulPrimitiveFactory<XT, YT, OT>>(
dev_ctx.GetBlob(key));
if (prim_creator == nullptr) {
prim_creator =
std::make_shared<MulPrimitiveFactory<XT, YT, OT>>(onednn_engine);
dev_ctx.SetBlob(key, prim_creator);
}
return prim_creator;
}
/* XT: input x data type, YT: input y data type */
template <typename XT, typename YT>
inner_product_forward GetMulPrimitive(const OneDNNContext &dev_ctx,
const DenseTensor *input_x,
const DenseTensor *input_y,
DenseTensor *output,
int x_num_col_dims,
int y_num_col_dims,
const engine &onednn_engine) {
constexpr bool is_int8 = funcs::is_int8<XT>();
bool force_fp32_output =
dev_ctx.HasDnnAttr("force_fp32_output")
? PADDLE_GET_CONST(bool, dev_ctx.GetDnnAttr("force_fp32_output"))
: false;
if (is_int8 && !force_fp32_output) {
return GetPrimitiveFactory<XT, YT, int8_t>(
dev_ctx, input_x, input_y, onednn_engine)
->CreateMulPrimitive(
input_x, input_y, output, x_num_col_dims, y_num_col_dims, dev_ctx);
} else {
return GetPrimitiveFactory<XT, YT, float>(
dev_ctx, input_x, input_y, onednn_engine)
->CreateMulPrimitive(
input_x, input_y, output, x_num_col_dims, y_num_col_dims, dev_ctx);
}
}
/* XT: input x data type */
template <typename XT, typename Context>
void MatmulWithFlattenKernelINT8(const Context &dev_ctx,
const DenseTensor &x,
const DenseTensor &y,
int x_num_col_dims,
int y_num_col_dims,
DenseTensor *out) {
PADDLE_ENFORCE_EQ(dev_ctx.GetPlace().GetType() == AllocationType::CPU,
true,
errors::PreconditionNotMet(
"oneDNN MatmulWithFlatten kernel must use CPUPlace"));
OneDNNContext::tls().log_lib_version();
auto &onednn_engine = dev_ctx.GetEngine();
auto out_dims = out->dims();
auto mul = GetMulPrimitive<XT, float>(
dev_ctx, &x, &y, out, x_num_col_dims, y_num_col_dims, onednn_engine);
if (out_dims.size() != 2) {
out->Resize(out_dims);
}
auto in_md = memory::desc(*dnnl_primitive_desc_query_md(
mul.get_primitive_desc(), dnnl_query_dst_md, 0));
out->set_mem_desc(in_md.reshape(vectorize<int64_t>(out->dims())));
}
template <typename T, typename Context>
void MatmulWithFlattenKernel(const Context &dev_ctx,
const DenseTensor &x,
const DenseTensor &y,
int x_num_col_dims,
int y_num_col_dims,
DenseTensor *out) {
constexpr bool is_int8 = funcs::is_int8<T>();
if (is_int8) {
MatmulWithFlattenKernelINT8<T, Context>(
dev_ctx, x, y, x_num_col_dims, y_num_col_dims, out);
return;
}
const DenseTensor x_matrix =
x.dims().size() > 2 ? ReshapeToMatrix(x, x_num_col_dims) : x;
const DenseTensor y_matrix =
y.dims().size() > 2 ? ReshapeToMatrix(y, y_num_col_dims) : y;
// adding mb dim because MatMulV2 handler needs it
std::vector<int64_t> x_dims(3, 1);
std::vector<int64_t> y_dims(3, 1);
x_dims[1] = x_matrix.dims()[0];
x_dims[2] = x_matrix.dims()[1];
y_dims[1] = y_matrix.dims()[0];
y_dims[2] = y_matrix.dims()[1];
funcs::ExecuteMul<T>(
dev_ctx, x_matrix, y_matrix, x_dims, y_dims, false, false, out);
}
} // namespace phi } // namespace phi
PD_REGISTER_KERNEL(matmul, PD_REGISTER_KERNEL(matmul,
...@@ -162,3 +583,12 @@ PD_REGISTER_KERNEL(matmul, ...@@ -162,3 +583,12 @@ PD_REGISTER_KERNEL(matmul,
phi::dtype::bfloat16, phi::dtype::bfloat16,
int8_t, int8_t,
uint8_t) {} uint8_t) {}
PD_REGISTER_KERNEL(matmul_with_flatten,
OneDNN,
ONEDNN,
phi::MatmulWithFlattenKernel,
float,
phi::dtype::bfloat16,
uint8_t,
int8_t) {}
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