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未验证 提交 6bd2762c 编写于 作者: J Jiabin Yang 提交者： GitHub
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[PHI] Support bmm and bmm_grad in xpu (#45887) 

* support bmm and bmm_grad in xpu

* add error removal

* test=kunlun

* refactor code for better structure

* test=kunlun

* add fp16 kernel for bmm

* test=kunlun
上级 4fac4e77
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Showing with 252 addition and 227 deletion
paddle/fluid/eager/utils.h
浏览文件 @ 6bd2762c
......@@ -223,7 +223,7 @@ class EagerUtils {
const std::vector<paddle::experimental::Tensor*>& out_var,
std::vector<paddle::experimental::Tensor>* result);
// end Intermidate needed
// end Intermidate needed.
static void CheckAndRetainGrad(const paddle::experimental::Tensor& tensor);
static void CheckAndRetainGrad(
......
paddle/fluid/operators/bmm_op_xpu.cc 已删除 100644 → 0
浏览文件 @ 4fac4e77
// Copyright (c) 2020 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.
#ifdef PADDLE_WITH_XPU
#include <string>
#include <vector>
#include "paddle/fluid/operators/matmul_v2_op.h"
#include "paddle/fluid/operators/xpu_api_wrapper.h"
#include "paddle/fluid/platform/device/device_wrapper.h"
namespace paddle {
namespace operators {
template <typename T, typename FCT>
static void MatMulXPUFunction(const Tensor* x,
const Tensor* y,
Tensor* out,
bool trans_x,
bool trans_y,
const paddle::framework::ExecutionContext& ctx) {
using XPUType = typename XPUTypeTrait<T>::Type;
const auto& x_dims = x->dims();
const auto& y_dims = y->dims();
auto& dev_ctx =
ctx.template device_context<paddle::platform::XPUDeviceContext>();
auto mat_dim_a = phi::funcs::CreateMatrixDescriptor(
RowMatrixFromVector(x_dims), 0, trans_x);
auto mat_dim_b = phi::funcs::CreateMatrixDescriptor(
ColumnMatrixFromVector(y_dims), 0, trans_y);
T* data_c = out->data<T>();
int m = mat_dim_a.height_;
int n = mat_dim_b.width_;
int k = mat_dim_a.width_;
int batch_size = mat_dim_a.batch_size_;
// batch matmul
int r = xpu::fc_batched<XPUType, XPUType, XPUType, FCT>(
dev_ctx.x_context(), // Context* ctx,
batch_size, // int batch_size,
mat_dim_a.trans_, // bool x_trans,
mat_dim_b.trans_, // bool w_trans,
m, // int m,
n, // int n,
k, // int k,
1.0, // float alpha,
reinterpret_cast<const XPUType*>(x->data<T>()), // const TX* x,
mat_dim_a.stride_, // int stride_a,
reinterpret_cast<const XPUType*>(y->data<T>()), // const TW* w,
mat_dim_b.stride_, // int stride_b,
0.0, // float beta,
reinterpret_cast<XPUType*>(data_c), // TY* y,
m * n, // int stride_c,
nullptr, // const float* x_maxptr,
nullptr); // const float* w_maxptr
PADDLE_ENFORCE_XDNN_SUCCESS(r, "fc_batched");
}
template <typename T>
class BmmXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Input<Tensor>("Y");
auto* out = ctx.Output<Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
if (x->numel() == 0 || y->numel() == 0) {
return;
}
bool trans_x = false;
bool trans_y = false;
auto x_dims = x->dims();
auto y_dims = y->dims();
PADDLE_ENFORCE_EQ(x_dims.size(),
3,
platform::errors::InvalidArgument(
"Input(X) of BmmOp must be 3-dimensional in BmmOp, "
"but received X's shape: [%s].",
x_dims));
PADDLE_ENFORCE_EQ(y_dims.size(),
3,
platform::errors::InvalidArgument(
"Input(Y) of BmmOp must be 3-dimensional in BmmOp, "
"but received Y's shape: [%s].",
y_dims));
PADDLE_ENFORCE_EQ(
x_dims[0],
y_dims[0],
platform::errors::InvalidArgument(
"Input(X) and Input(Y) must have the same batch size in BmmOp, "
"but received X's batch size: [%s],"
"Y's batch size [%s]",
x_dims[0],
y_dims[0]));
PADDLE_ENFORCE_EQ(
x_dims[2],
y_dims[1],
platform::errors::InvalidArgument(
"Input(X)'s width must be equal with Input(Y)'s height in BmmOp,"
"but receive X's width: [%s],"
"Y's height: [%s].",
x_dims[2],
y_dims[1]));
if (std::is_same<paddle::platform::float16, T>::value) {
MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, ctx);
} else {
if (std::getenv("XPU_PADDLE_FC_INT32") != nullptr) {
MatMulXPUFunction<T, int32_t>(x, y, out, trans_x, trans_y, ctx);
} else if (std::getenv("XPU_PADDLE_FC_LOCAL_INT16") != nullptr) {
MatMulXPUFunction<T, float>(x, y, out, trans_x, trans_y, ctx);
} else {
MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, ctx);
}
}
}
};
template <typename T>
class BmmXPUGradKernel : public framework::OpKernel<T> {
public:
void MatMul(const framework::ExecutionContext& ctx,
const framework::Tensor& a,
bool trans_a,
const framework::Tensor& b,
bool trans_b,
framework::Tensor* out) const {
out->mutable_data<T>(ctx.GetPlace());
if (std::is_same<paddle::platform::float16, T>::value) {
MatMulXPUFunction<T, int16_t>(&a, &b, out, trans_a, trans_b, ctx);
} else {
if (std::getenv("XPU_PADDLE_FC_INT32") != nullptr) {
MatMulXPUFunction<T, int32_t>(&a, &b, out, trans_a, trans_b, ctx);
} else if (std::getenv("XPU_PADDLE_FC_LOCAL_INT16") != nullptr) {
MatMulXPUFunction<T, float>(&a, &b, out, trans_a, trans_b, ctx);
} else {
MatMulXPUFunction<T, int16_t>(&a, &b, out, trans_a, trans_b, ctx);
}
}
}
void CalcInputGrad(const framework::ExecutionContext& context,
const framework::Tensor& a,
bool trans_a,
const framework::Tensor& b,
bool trans_b,
framework::Tensor* out) const {
if (out == nullptr) return;
MatMul(context, a, trans_a, b, trans_b, out);
}
void Compute(const framework::ExecutionContext& context) const override {
auto x = *context.Input<framework::Tensor>("X");
auto y = *context.Input<framework::Tensor>("Y");
auto dout =
*context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* dx = context.Output<framework::Tensor>(framework::GradVarName("X"));
auto* dy = context.Output<framework::Tensor>(framework::GradVarName("Y"));
ReshapeXYOutIntoMatrixSequence(&x, &y, &dout, false, false);
framework::DDim dx_dims;
if (dx) {
dx_dims = dx->dims();
if (dx_dims != x.dims()) {
dx->Resize(x.dims());
}
}
framework::DDim dy_dims;
if (dy) {
dy_dims = dy->dims();
if (dy_dims != y.dims()) {
dy->Resize(y.dims());
}
}
CalcInputGrad(context, dout, false, y, true, dx);
CalcInputGrad(context, x, true, dout, false, dy);
// CalcInputGrad(context, dout, false, false, y, true, false, dx);
// CalcInputGrad(context, x, true, true, dout, false, true, dy);
if (dx) {
if (dx_dims != x.dims()) {
dx->Resize(dx_dims);
}
}
if (dy) {
if (dy_dims != y.dims()) {
dy->Resize(dy_dims);
}
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_XPU_KERNEL(bmm,
ops::BmmXPUKernel<float>,
ops::BmmXPUKernel<plat::float16>);
REGISTER_OP_XPU_KERNEL(bmm_grad,
ops::BmmXPUGradKernel<float>,
ops::BmmXPUGradKernel<plat::float16>);
#endif
paddle/phi/kernels/xpu/bmm_grad_kernel.cc 0 → 100644
浏览文件 @ 6bd2762c
// Copyright (c) 2022 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 "paddle/phi/kernels/bmm_grad_kernel.h"
#include "paddle/phi/kernels/xpu/bmm_xpu_utils.h"
namespace phi {
template <typename T, typename Context>
void MatMul(const Context& dev_ctx,
const DenseTensor& a,
bool trans_a,
const DenseTensor& b,
bool trans_b,
DenseTensor* out) {
dev_ctx.template Alloc<T>(out);
xpu::Context* xpu_ctx = dev_ctx.x_context();
if (std::is_same<paddle::platform::float16, T>::value) {
MatMulXPUFunction<T, int16_t>(a, b, out, trans_a, trans_b, xpu_ctx);
} else {
if (std::getenv("XPU_PADDLE_FC_INT32") != nullptr) {
MatMulXPUFunction<T, int32_t>(a, b, out, trans_a, trans_b, xpu_ctx);
} else if (std::getenv("XPU_PADDLE_FC_LOCAL_INT16") != nullptr) {
MatMulXPUFunction<T, float>(a, b, out, trans_a, trans_b, xpu_ctx);
} else {
MatMulXPUFunction<T, int16_t>(a, b, out, trans_a, trans_b, xpu_ctx);
}
}
}
template <typename T, typename Context>
void CalcInputGrad(const Context& dev_ctx,
const DenseTensor& a,
bool trans_a,
const DenseTensor& b,
bool trans_b,
DenseTensor* out) {
if (out == nullptr) return;
MatMul<T, Context>(dev_ctx, a, trans_a, b, trans_b, out);
}
template <typename T, typename Context>
void BmmGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
const DenseTensor& out_grad,
DenseTensor* x_grad,
DenseTensor* y_grad) {
DenseTensor x_help = x;
DenseTensor y_help = y;
DenseTensor out_grad_help = out_grad;
ReshapeXYOutIntoMatrixSequence(
&x_help, &y_help, &out_grad_help, false, false);
phi::DDim dx_dims;
if (x_grad) {
dx_dims = x_grad->dims();
if (dx_dims != x_help.dims()) {
x_grad->Resize(x_help.dims());
}
}
phi::DDim dy_dims;
if (y_grad) {
dy_dims = y_grad->dims();
if (dy_dims != y_help.dims()) {
y_grad->Resize(y_help.dims());
}
}
CalcInputGrad<T, Context>(
dev_ctx, out_grad_help, false, y_help, true, x_grad);
CalcInputGrad<T, Context>(
dev_ctx, x_help, true, out_grad_help, false, y_grad);
if (x_grad) {
if (dx_dims != x_help.dims()) {
x_grad->Resize(dx_dims);
}
}
if (y_grad) {
if (dy_dims != y_help.dims()) {
y_grad->Resize(dy_dims);
}
}
}
} // namespace phi
PD_REGISTER_KERNEL(bmm_grad,
XPU,
ALL_LAYOUT,
phi::BmmGradKernel,
float,
paddle::platform::float16) {}
paddle/phi/kernels/xpu/bmm_kernel.cc 0 → 100644
浏览文件 @ 6bd2762c
// Copyright (c) 2022 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 "paddle/phi/kernels/bmm_kernel.h"
#include "paddle/phi/kernels/xpu/bmm_xpu_utils.h"
namespace phi {
template <typename T, typename Context>
void BmmKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out) {
dev_ctx.template Alloc<T>(out);
if (x.numel() == 0 || y.numel() == 0) {
return;
}
bool trans_x = false;
bool trans_y = false;
auto x_dims = x.dims();
auto y_dims = y.dims();
PADDLE_ENFORCE_EQ(x_dims.size(),
3,
phi::errors::InvalidArgument(
"Input(X) of BmmOp must be 3-dimensional in BmmOp, "
"but received X's shape: [%s]",
x_dims));
PADDLE_ENFORCE_EQ(y_dims.size(),
3,
phi::errors::InvalidArgument(
"Input(Y) of BmmOp must be 3-dimensional in BmmOp, "
"but received Y's shape: [%s].",
y_dims));
PADDLE_ENFORCE_EQ(
x_dims[0],
y_dims[0],
phi::errors::InvalidArgument(
"Input(X) and Input(Y) must have the same batch size in BmmOp, "
"but received X's batch size: [%s],"
"Y's batch size [%s]",
x_dims[0],
y_dims[0]));
PADDLE_ENFORCE_EQ(
x_dims[2],
y_dims[1],
phi::errors::InvalidArgument(
"Input(X)'s width must be equal with Input(Y)'s height in BmmOp,"
"but receive X's width: [%s],"
"Y's height: [%s].",
x_dims[2],
y_dims[1]));
xpu::Context* xpu_ctx = dev_ctx.x_context();
if (std::is_same<paddle::platform::float16, T>::value) {
MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, xpu_ctx);
} else {
if (std::getenv("XPU_PADDLE_FC_INT32") != nullptr) {
MatMulXPUFunction<T, int32_t>(x, y, out, trans_x, trans_y, xpu_ctx);
} else if (std::getenv("XPU_PADDLE_FC_LOCAL_INT16") != nullptr) {
MatMulXPUFunction<T, float>(x, y, out, trans_x, trans_y, xpu_ctx);
} else {
MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, xpu_ctx);
}
}
}
} // namespace phi
PD_REGISTER_KERNEL(
bmm, XPU, ALL_LAYOUT, phi::BmmKernel, float, paddle::platform::float16) {}
paddle/phi/kernels/xpu/bmm_xpu_utils.h 0 → 100644
浏览文件 @ 6bd2762c
// Copyright (c) 2022 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.
#pragma once
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/impl/matmul_grad_kernel_impl.h"
#include "paddle/phi/kernels/xpu/xpu_api_wrapper.h"
namespace phi {
template <typename T, typename FCT>
static void MatMulXPUFunction(const DenseTensor& x,
const DenseTensor& y,
DenseTensor* out,
bool trans_x,
bool trans_y,
xpu::Context* xpu_ctx) {
using XPUType = typename XPUTypeTrait<T>::Type;
const auto& x_dims = x.dims();
const auto& y_dims = y.dims();
auto mat_dim_a = phi::funcs::CreateMatrixDescriptor(
RowMatrixFromVector(x_dims), 0, trans_x);
auto mat_dim_b = phi::funcs::CreateMatrixDescriptor(
ColumnMatrixFromVector(y_dims), 0, trans_y);
T* data_c = out->data<T>();
int m = mat_dim_a.height_;
int n = mat_dim_b.width_;
int k = mat_dim_a.width_;
int batch_size = mat_dim_a.batch_size_;
// batch matmul
int r = xpu::fc_batched<XPUType, XPUType, XPUType, FCT>(
xpu_ctx, // Context* ctx,
batch_size, // int batch_size,
mat_dim_a.trans_, // bool x_trans,
mat_dim_b.trans_, // bool w_trans,
m, // int m,
n, // int n,
k, // int k,
1.0, // float alpha,
reinterpret_cast<const XPUType*>(x.data<T>()), // const TX* x,
mat_dim_a.stride_, // int stride_a,
reinterpret_cast<const XPUType*>(y.data<T>()), // const TW* w,
mat_dim_b.stride_, // int stride_b,
0.0, // float beta,
reinterpret_cast<XPUType*>(data_c), // TY* y,
m * n, // int stride_c,
nullptr, // const float* x_maxptr,
nullptr); // const float* w_maxptr
PADDLE_ENFORCE_XDNN_SUCCESS(r, "fc_batched");
}
} // namespace phi
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