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Paddle/paddle/fluid/operators/matmul_v2_op_xpu.cc

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// 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 "paddle/fluid/operators/matmul_v2_op.h"
#include <string>
#include <vector>
namespace paddle {
namespace operators {
template <typename T>
void MatMulXPUFunction(const Tensor* X, const Tensor* Y,
const std::vector<std::int64_t>& x_dims,
const std::vector<std::int64_t>& y_dims, Tensor* Out,
bool trans_x, bool trans_y,
const paddle::framework::ExecutionContext& ctx) {
const int x_ndim = x_dims.size();
const int y_ndim = y_dims.size();
auto& dev_ctx =
ctx.template device_context<paddle::platform::XPUDeviceContext>();
// currently only support x_ndim == y_dim and non-broadcast case
PADDLE_ENFORCE_EQ(x_ndim, y_ndim, platform::errors::InvalidArgument(
"Shape mistake in matmul_v2_op"));
for (int i = 0; i < x_ndim - 2; i++) {
PADDLE_ENFORCE_EQ(
x_dims.data()[i], y_dims.data()[i],
platform::errors::InvalidArgument("Shape mistake in matmul_v2_op"));
}
int ret = 0;
if (x_ndim == 1 && y_ndim == 1) {
PADDLE_ENFORCE_EQ(X->numel(), Y->numel(),
platform::errors::InvalidArgument(
"X's numbers is not equal to Y's numbers,"
"when X/Y's dims =1"));
VLOG(3) << "MatMul's case 1";
Out->Resize({1});
Out->mutable_data<T>(ctx.GetPlace());
ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, false, 1, 1,
X->numel(), 1.0f, X->data<T>(),
Y->data<T>(), 0.0f, Out->data<T>());
PADDLE_ENFORCE_EQ(
ret, XPU_SUCCESS,
platform::errors::External(
"XPU API return wrong value[%d] in matmul_v2, please check whether "
"Baidu Kunlun Card is properly installed.",
ret));
return;
}
if (x_ndim == 1) {
const int N = X->numel();
if (trans_y) {
PADDLE_ENFORCE_EQ(
y_dims[y_ndim - 1], N,
platform::errors::InvalidArgument("Input(Y) has error dim."));
} else {
PADDLE_ENFORCE_EQ(
y_dims[y_ndim - 2], N,
platform::errors::InvalidArgument("Input(Y) has error dim."));
}
std::vector<std::int64_t> out_dims(y_ndim - 1);
if (trans_y) {
std::copy_n(y_dims.cbegin(), y_ndim - 1, out_dims.begin());
} else {
std::copy_n(y_dims.cbegin(), y_ndim - 2, out_dims.begin());
out_dims.back() = y_dims.back();
}
Out->Resize(framework::make_ddim(out_dims));
Out->mutable_data<T>(ctx.GetPlace());
if (trans_y) {
const int M = Y->numel() / N;
VLOG(3) << "MatMul's case 2";
ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, true, 1, M, N,
1.0f, X->data<T>(), Y->data<T>(), 0.0f,
Out->data<T>());
PADDLE_ENFORCE_EQ(
ret, XPU_SUCCESS,
platform::errors::External("XPU API return wrong value[%d] in "
"matmul_v2, please check whether "
"Baidu Kunlun Card is properly installed.",
ret));
} else {
const int M = y_dims[y_ndim - 1];
const int batch_size = Y->numel() / (M * N);
for (int i = 0; i < batch_size; i++) {
ret = baidu::xpu::api::fc_int16(
dev_ctx.x_context(), false, false, 1, M, N, 1.0f, X->data<T>(),
Y->data<T>() + i * M * N, 0.0f, Out->data<T>() + i * M);
PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
platform::errors::External(
"XPU API return wrong value[%d] in matmul_v2, "
"please check whether "
"Baidu Kunlun Card is properly installed.",
ret));
}
}
return;
}
if (y_ndim == 1) {
const int N = Y->numel();
if (trans_x) {
PADDLE_ENFORCE_EQ(
x_dims[x_ndim - 2], N,
platform::errors::InvalidArgument("Input(X) has error dim."));
} else {
PADDLE_ENFORCE_EQ(
x_dims[x_ndim - 1], N,
platform::errors::InvalidArgument("Input(X) has error dim."));
}
std::vector<std::int64_t> out_dims(x_ndim - 1);
if (trans_x) {
std::copy_n(x_dims.cbegin(), x_ndim - 2, out_dims.begin());
out_dims.back() = x_dims.back();
} else {
std::copy_n(x_dims.cbegin(), x_ndim - 1, out_dims.begin());
}
Out->Resize(framework::make_ddim(out_dims));
Out->mutable_data<T>(ctx.GetPlace());
if (trans_x) {
const int M = x_dims[x_ndim - 1];
const int batch_size = X->numel() / (M * N);
for (int i = 0; i < batch_size; i++) {
ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), true, false, M, 1,
N, 1.0f, X->data<T>() + i * M * N,
Y->data<T>(), 0.0f,
Out->data<T>() + i * M);
PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
platform::errors::External(
"XPU API return wrong value[%d] in matmul_v2, "
"please check whether "
"Baidu Kunlun Card is properly installed.",
ret));
}
} else {
const int M = X->numel() / N;
VLOG(3) << "MatMul's case 7";
ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, false, M, 1,
N, 1.0f, X->data<T>(), Y->data<T>(), 0.0f,
Out->data<T>());
PADDLE_ENFORCE_EQ(
ret, XPU_SUCCESS,
platform::errors::External("XPU API return wrong value[%d] in "
"matmul_v2, please check whether "
"Baidu Kunlun Card is properly installed.",
ret));
}
return;
}
const int M = trans_x ? x_dims[x_ndim - 1] : x_dims[x_ndim - 2];
const int K = trans_x ? x_dims[x_ndim - 2] : x_dims[x_ndim - 1];
if (trans_y) {
PADDLE_ENFORCE_EQ(y_dims[y_ndim - 1], K, platform::errors::InvalidArgument(
"Input(X) has error dim."));
} else {
PADDLE_ENFORCE_EQ(y_dims[y_ndim - 2], K, platform::errors::InvalidArgument(
"Input(X) has error dim."));
}
const int N = trans_y ? y_dims[y_ndim - 2] : y_dims[y_ndim - 1];
const int ndim = (std::max)(x_ndim, y_ndim);
std::vector<std::int64_t> out_broadcast_dims(ndim);
int batch_size = 1;
for (int i = 0; i < ndim - 2; i++) {
PADDLE_ENFORCE_EQ(
x_dims.data()[i], y_dims.data()[i],
platform::errors::InvalidArgument("Shape mistake in matmul_v2_op"));
out_broadcast_dims[i] = x_dims.data()[i];
batch_size *= x_dims.data()[i];
}
out_broadcast_dims[ndim - 2] = M;
out_broadcast_dims[ndim - 1] = N;
Out->Resize(framework::make_ddim(out_broadcast_dims));
Out->mutable_data<T>(ctx.GetPlace());
ret = baidu::xpu::api::batched_gemm_int16(
dev_ctx.x_context(), trans_x, trans_y, batch_size, M, N, K, 1.0f,
X->data<T>(), Y->data<T>(), Out->data<T>(), nullptr, nullptr);
PADDLE_ENFORCE_EQ(
ret, XPU_SUCCESS,
platform::errors::External(
"XPU API return wrong value[%d] in matmul_v2, please check whether "
"Baidu Kunlun Card is properly installed.",
ret));
}
template <typename T>
class MatMulV2XPUKernel : 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");
bool trans_x = ctx.Attr<bool>("trans_x");
bool trans_y = ctx.Attr<bool>("trans_y");
MatMulXPUFunction<T>(X, Y, vectorize(X->dims()), vectorize(Y->dims()), Out,
trans_x, trans_y, ctx);
}
};
template <typename T>
class MatMulV2XPUGradKernel : public framework::OpKernel<T> {
public:
void MatMul(const framework::ExecutionContext& context,
const framework::Tensor& a, bool trans_a,
const framework::Tensor& b, bool trans_b,
framework::Tensor* out) const {
out->mutable_data<T>(context.GetPlace());
MatMulXPUFunction<T>(&a, &b, vectorize(a.dims()), vectorize(b.dims()), out,
trans_a, trans_b, context);
}
void CalcInputGrad(const framework::ExecutionContext& context,
const framework::Tensor& a, bool trans_a,
bool is_fold_init_dims_a, const framework::Tensor& b,
bool trans_b, bool is_fold_init_dims_b,
framework::Tensor* out) const {
if (out == nullptr) return;
bool need_combine = (a.dims().size() == 3 || b.dims().size() == 3) &&
out->dims().size() == 2;
if (!need_combine) {
MatMul(context, a, trans_a, b, trans_b, out);
} else {
// currently not support this case
}
}
void Compute(const framework::ExecutionContext& ctx) const override {
bool transpose_x = ctx.Attr<bool>("trans_x");
bool transpose_y = ctx.Attr<bool>("trans_y");
auto x = *ctx.Input<framework::Tensor>("X");
auto y = *ctx.Input<framework::Tensor>("Y");
auto dout = *ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
// get dims
std::vector<std::int64_t> x_dims = vectorize(x.dims());
std::vector<std::int64_t> y_dims = vectorize(y.dims());
std::vector<std::int64_t> dout_dims = vectorize(dout.dims());
int x_ndim = x_dims.size();
int y_ndim = y_dims.size();
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
auto& dev_ctx =
ctx.template device_context<paddle::platform::XPUDeviceContext>();
// Case1 : x's or y's dim = 1
int ret = 0;
if (x_ndim == 1 && y_ndim == 1) {
if (dx) {
dx->mutable_data<T>(ctx.GetPlace());
ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, false,
dx->numel(), 1, 1, 1.0f, y.data<T>(),
dout.data<T>(), 0.0f, dx->data<T>());
PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
platform::errors::External(
"XPU API return wrong value[%d] in "
"matmul_v2_grad, please check whether "
"Baidu Kunlun Card is properly installed.",
ret));
}
if (dy) {
dy->mutable_data<T>(ctx.GetPlace());
ret = baidu::xpu::api::fc_int16(dev_ctx.x_context(), false, false,
dy->numel(), 1, 1, 1.0f, x.data<T>(),
dout.data<T>(), 0.0f, dy->data<T>());
PADDLE_ENFORCE_EQ(ret, XPU_SUCCESS,
platform::errors::External(
"XPU API return wrong value[%d] in "
"matmul_v2_grad, please check whether "
"Baidu Kunlun Card is properly installed.",
ret));
}
return;
}
bool is_broadcast = true;
if (x_ndim <= 2 || y_ndim <= 2) {
is_broadcast = false;
} else if (x_ndim != y_ndim) {
is_broadcast = true;
} else {
is_broadcast = !std::equal(x_dims.cbegin(), x_dims.cbegin() + x_ndim - 2,
y_dims.cbegin());
}
// currently only support non-broadcast case
PADDLE_ENFORCE_EQ(
is_broadcast, false,
platform::errors::InvalidArgument("Shape mistake in matmul_v2_op"));
// Case2: no broadcast or no batch size, it aims to speed and it is same as
// matmul in old version.
if (!is_broadcast) {
ReshapeXYOutIntoMatrixSequence(&x, &y, &dout, transpose_x, transpose_y);
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());
}
}
if (transpose_x && transpose_y) {
CalcInputGrad(ctx, y, true, true, dout, true, false, dx);
CalcInputGrad(ctx, dout, true, true, x, true, false, dy);
} else if (transpose_x) {
CalcInputGrad(ctx, y, false, false, dout, true, false, dx);
CalcInputGrad(ctx, x, false, false, dout, false, true, dy);
} else if (transpose_y) {
CalcInputGrad(ctx, dout, false, false, y, false, true, dx);
CalcInputGrad(ctx, dout, true, true, x, false, true, dy);
} else {
CalcInputGrad(ctx, dout, false, false, y, true, false, dx);
CalcInputGrad(ctx, 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;
REGISTER_OP_XPU_KERNEL(matmul_v2, ops::MatMulV2XPUKernel<float>);
REGISTER_OP_XPU_KERNEL(matmul_v2_grad, ops::MatMulV2XPUGradKernel<float>);
#endif
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