Skip to content

P
Paddle

Crayon鑫 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
未验证 提交 ea50282b 编写于 作者: E enzodechine 提交者： GitHub
浏览文件
操作

move one_hot_v2, p_norm, p_norm_grad to phi (#45625) 

* move one_hot_v2, p_norm, p_norm_grad to phi

* move one_hot_v2, p_norm, p_norm_grad to phi

test=kunlun

* remove unused header file

* remove unused header file

test=kunlun
上级 087711c6
隐藏空白更改
内联 并排
Showing with 436 addition and 432 deletion
paddle/fluid/operators/one_hot_v2_op_xpu.cc 已删除 100644 → 0
浏览文件 @ 087711c6
// Copyright (c) 2018 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/one_hot_op.h"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;
template <typename DeviceContext, typename T>
class OneHotV2XPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
int depth = context.Attr<int>("depth");
if (context.HasInput("depth_tensor")) {
auto* depth_tensor = context.Input<Tensor>("depth_tensor");
auto* depth_data = depth_tensor->data<int32_t>();
if (platform::is_xpu_place(depth_tensor->place())) {
xpu_memcpy(static_cast<void*>(&depth),
static_cast<const void*>(depth_data),
sizeof(int32_t),
XPU_DEVICE_TO_HOST);
} else {
depth = depth_data[0];
}
auto out_dims = out->dims();
out_dims[out_dims.size() - 1] = depth;
out->Resize(out_dims);
}
auto& dev_ctx = context.template device_context<DeviceContext>();
int len = in->numel();
int ret = xpu::one_hot<T>(dev_ctx.x_context(),
in->data<T>(),
out->mutable_data<float>(context.GetPlace()),
len,
depth,
1.0,
0.0);
PADDLE_ENFORCE_EQ(ret,
XPU_SUCCESS,
platform::errors::External(
"XPU one_hot kernel return wrong value[%d %s]",
ret,
XPUAPIErrorMsg[ret]));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(
one_hot_v2,
ops::OneHotV2XPUKernel<paddle::platform::XPUDeviceContext, int>,
ops::OneHotV2XPUKernel<paddle::platform::XPUDeviceContext, int64_t>);
#endif
paddle/fluid/operators/p_norm_op_xpu.cc 已删除 100644 → 0
浏览文件 @ 087711c6
/* 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/reduce_ops/reduce_op_xpu.h"
#include "paddle/fluid/platform/device/device_wrapper.h"
#include "paddle/fluid/platform/device/xpu/xpu_header.h"
namespace paddle {
namespace operators {
inline void GetDims(
const phi::DDim& dim, int axis, int* m, int* t, int* n, bool asvector) {
*m = 1;
*n = 1;
*t = dim[axis];
if (asvector) {
*t = product(dim);
} else {
for (int i = 0; i < axis; ++i) {
(*m) *= dim[i];
}
for (int i = axis + 1; i < dim.size(); ++i) {
(*n) *= dim[i];
}
}
}
using Tensor = framework::Tensor;
template <typename DeviceContext, typename T>
class P_NormXPUKernel : public framework::OpKernel<T> {
using XPUType = typename XPUTypeTrait<T>::Type;
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto* out = ctx.Output<framework::Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
float porder = ctx.Attr<float>("porder");
int axis = ctx.Attr<int>("axis");
bool asvector = ctx.Attr<bool>("asvector");
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto xdim = in->dims();
if (axis < 0) axis = xdim.size() + axis;
std::vector<int> r_dim;
std::vector<int> x_dim;
std::vector<int> y_dim;
int m = 1;
int n = 1;
int t = 1;
GetDims(xdim, axis, &m, &t, &n, asvector);
x_dim.push_back(m);
x_dim.push_back(t);
x_dim.push_back(n);
r_dim.push_back(1);
y_dim.push_back(m);
y_dim.push_back(n);
int r = 0;
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
XPUType* tmp_x = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(tmp_x);
r = xpu::abs(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(in->data<T>()),
tmp_x,
m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "abs");
if (porder == INFINITY) {
r = xpu::reduce_max(dev_ctx.x_context(),
tmp_x,
reinterpret_cast<XPUType*>(out->data<T>()),
x_dim,
r_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "reduce_max");
} else if (porder == -INFINITY) {
r = xpu::reduce_min(dev_ctx.x_context(),
tmp_x,
reinterpret_cast<XPUType*>(out->data<T>()),
x_dim,
r_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "reduce_min");
} else if (porder == 0) {
XPUType* zeros = RAII_GUARD.alloc_l3_or_gm<XPUType>(1);
PADDLE_ENFORCE_XDNN_NOT_NULL(zeros);
r = xpu::constant(dev_ctx.x_context(), zeros, 1, 0.0f);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
std::vector<int> zeros_dim(1, 1);
bool* tmp2_x = RAII_GUARD.alloc_l3_or_gm<bool>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(tmp2_x);
r = xpu::broadcast_not_equal(
dev_ctx.x_context(), tmp_x, zeros, tmp2_x, x_dim, zeros_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_not_equal");
XPUType* x_mid = tmp_x;
r = xpu::cast<bool, XPUType>(
dev_ctx.x_context(), tmp2_x, x_mid, m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
r = xpu::reduce_sum(dev_ctx.x_context(),
x_mid,
reinterpret_cast<XPUType*>(out->data<T>()),
x_dim,
r_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "reduce_sum");
} else {
Tensor porder_tensor;
framework::DDim pdim = phi::make_ddim({1});
porder_tensor.mutable_data<float>(pdim, in->place());
r = xpu::constant(
dev_ctx.x_context(), porder_tensor.data<float>(), 1, porder);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
std::vector<int> p_dim(1, 1);
XPUType* tmp2_x = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(tmp2_x);
r = xpu::broadcast_pow(
dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(tmp_x),
reinterpret_cast<const XPUType*>(porder_tensor.data<float>()),
tmp2_x,
x_dim,
p_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_pow");
XPUType* tmp_y = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(tmp_y);
r = xpu::reduce_sum(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(tmp2_x),
tmp_y,
x_dim,
r_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "reduce_sum");
r = xpu::constant(
dev_ctx.x_context(), porder_tensor.data<float>(), 1, 1.0f / porder);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
r = xpu::broadcast_pow(
dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(tmp_y),
reinterpret_cast<const XPUType*>(porder_tensor.data<float>()),
reinterpret_cast<XPUType*>(out->data<T>()),
y_dim,
p_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_pow");
dev_ctx.Wait();
}
}
};
template <typename DeviceContext, typename T>
class P_NormGradXPUKernel : public framework::OpKernel<T> {
using XPUType = typename XPUTypeTrait<T>::Type;
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Input<Tensor>("Out");
auto* dy = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
dx->mutable_data<T>(ctx.GetPlace());
auto xdim = x->dims();
float porder = ctx.Attr<float>("porder");
bool asvector = ctx.Attr<bool>("asvector");
int axis = ctx.Attr<int>("axis");
axis = axis < 0 ? xdim.size() + axis : axis;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
int m, t, n;
GetDims(xdim, axis, &m, &t, &n, asvector);
std::vector<int> r_dim;
std::vector<int> x_dim;
std::vector<int> y_dim;
x_dim.push_back(m);
x_dim.push_back(t);
x_dim.push_back(n);
y_dim.push_back(m);
y_dim.push_back(1);
y_dim.push_back(n);
int r = 0;
if (porder == 0) {
r = xpu::constant(dev_ctx.x_context(),
reinterpret_cast<XPUType*>(dx->data<T>()),
m * t * n,
static_cast<T>(0));
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
} else if (porder == INFINITY || porder == -INFINITY) {
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
XPUType* x_abs = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(x_abs);
r = xpu::abs(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x->data<T>()),
x_abs,
m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "abs");
bool* dx_t = RAII_GUARD.alloc_l3_or_gm<bool>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(dx_t);
XPUType* dx_mid = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(dx_mid);
r = xpu::broadcast_equal<XPUType>(
dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x_abs),
reinterpret_cast<const XPUType*>(y->data<T>()),
dx_t,
x_dim,
y_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_equal");
r = xpu::cast<bool, XPUType>(
dev_ctx.x_context(), dx_t, dx_mid, m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
XPUType* x_sign = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(x_sign);
r = xpu::sign(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x->data<T>()),
x_sign,
m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "sign");
XPUType* dx_pre_dy = x_abs;
r = xpu::mul(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(dx_mid),
reinterpret_cast<const XPUType*>(x_sign),
dx_pre_dy,
m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "mul");
r = xpu::broadcast_mul(dev_ctx.x_context(),
dx_pre_dy,
reinterpret_cast<const XPUType*>(dy->data<T>()),
reinterpret_cast<XPUType*>(dx->data<T>()),
x_dim,
y_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_mul");
} else {
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
XPUType* x_abs = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(x_abs);
r = xpu::abs(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x->data<T>()),
x_abs,
m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "abs");
Tensor porder_tensor;
framework::DDim pdim = phi::make_ddim({1});
porder_tensor.mutable_data<float>(pdim, x->place());
r = xpu::constant(
dev_ctx.x_context(), porder_tensor.data<float>(), 1, porder - 1.0f);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
std::vector<int> p_dim(1, 1);
XPUType* x_pow = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(x_pow);
r = xpu::broadcast_pow(
dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x_abs),
reinterpret_cast<const XPUType*>(porder_tensor.data<float>()),
x_pow,
x_dim,
p_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_pow");
XPUType* y_pow = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(y_pow);
r = xpu::broadcast_pow(
dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(y->data<T>()),
reinterpret_cast<const XPUType*>(porder_tensor.data<float>()),
y_pow,
y_dim,
p_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_pow");
dev_ctx.Wait();
XPUType* dx_t = x_abs;
r = xpu::broadcast_div(
dev_ctx.x_context(), x_pow, y_pow, dx_t, x_dim, y_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_div");
XPUType* x_sign = x_pow;
r = xpu::sign(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x->data<T>()),
x_sign,
m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "sign");
XPUType* dx_mid = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(dx_mid);
r = xpu::broadcast_mul(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x_sign),
reinterpret_cast<const XPUType*>(dy->data<T>()),
dx_mid,
x_dim,
y_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_mul");
r = xpu::broadcast_mul(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(dx_t),
reinterpret_cast<const XPUType*>(dx_mid),
reinterpret_cast<XPUType*>(dx->data<T>()),
x_dim,
x_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_mul");
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(
p_norm, ops::P_NormXPUKernel<paddle::platform::XPUDeviceContext, float>);
REGISTER_OP_XPU_KERNEL(
p_norm_grad,
ops::P_NormGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
#endif
paddle/phi/kernels/one_hot_kernel.cc
浏览文件 @ ea50282b
......@@ -35,3 +35,7 @@ PD_REGISTER_KERNEL(one_hot, CPU, ALL_LAYOUT, phi::OneHotKernel, int, int64_t) {}
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
PD_REGISTER_KERNEL(one_hot, GPU, ALL_LAYOUT, phi::OneHotKernel, int, int64_t) {}
#endif
#ifdef PADDLE_WITH_XPU
PD_REGISTER_KERNEL(one_hot, XPU, ALL_LAYOUT, phi::OneHotKernel, int, int64_t) {}
#endif
paddle/phi/kernels/xpu/one_hot_kernel.cc 0 → 100644
浏览文件 @ ea50282b
// 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/one_hot_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/backends/xpu/xpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/utils/data_type.h"
namespace phi {
template <typename Context, typename InT>
struct OneHotV2OpFunctor {
const DenseTensor* in_;
DenseTensor* out_;
int depth_;
const Context& ctx_;
OneHotV2OpFunctor(const DenseTensor* in,
DenseTensor* out,
int depth,
const Context& ctx)
: in_(in), out_(out), depth_(depth), ctx_(ctx) {}
template <typename OutT>
void apply() const {
auto* p_in_data = in_->data<InT>();
auto numel = in_->numel();
auto* p_out_data = ctx_.template Alloc<float>(out_);
int r = xpu::one_hot<InT>(
ctx_.x_context(), p_in_data, p_out_data, numel, depth_, 1.0, 0.0);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "one_hot");
}
};
template <typename T, typename Context>
void OneHotRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const Scalar& depth,
DataType dtype,
bool allow_out_of_range,
DenseTensor* out) {
auto depth_v = depth.to<int>();
auto out_dims = out->dims();
if (out_dims[out_dims.size() - 1] == -1) {
out_dims[out_dims.size() - 1] = depth_v;
out->Resize(out_dims);
}
phi::VisitDataType(dtype,
OneHotV2OpFunctor<Context, T>(&x, out, depth_v, dev_ctx));
}
} // namespace phi
PD_REGISTER_KERNEL(
one_hot_raw, XPU, ALL_LAYOUT, phi::OneHotRawKernel, int, int64_t) {}
paddle/phi/kernels/xpu/p_norm_grad_kernel.cc 0 → 100644
浏览文件 @ ea50282b
// 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/p_norm_grad_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
inline void GetDims(
const phi::DDim& dim, int axis, int* m, int* t, int* n, bool asvector) {
*m = 1;
*n = 1;
*t = dim[axis];
if (asvector) {
*t = product(dim);
} else {
for (int i = 0; i < axis; ++i) {
(*m) *= dim[i];
}
for (int i = axis + 1; i < dim.size(); ++i) {
(*n) *= dim[i];
}
}
}
template <typename T, typename Context>
void PNormGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out,
const DenseTensor& out_grad,
float porder,
int axis,
float epsilon,
bool keepdim,
bool asvector,
DenseTensor* x_grad) {
using XPUType = typename XPUTypeTrait<T>::Type;
dev_ctx.template Alloc<T>(x_grad);
auto xdim = x.dims();
axis = axis < 0 ? xdim.size() + axis : axis;
int m, t, n;
GetDims(xdim, axis, &m, &t, &n, asvector);
std::vector<int> r_dim;
std::vector<int> x_dim;
std::vector<int> y_dim;
x_dim.push_back(m);
x_dim.push_back(t);
x_dim.push_back(n);
y_dim.push_back(m);
y_dim.push_back(1);
y_dim.push_back(n);
int r = 0;
if (porder == 0) {
r = xpu::constant(dev_ctx.x_context(),
reinterpret_cast<XPUType*>(x_grad->data<T>()),
m * t * n,
static_cast<T>(0));
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
} else if (porder == INFINITY || porder == -INFINITY) {
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
XPUType* x_abs = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(x_abs);
r = xpu::abs(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x.data<T>()),
x_abs,
m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "abs");
bool* dx_t = RAII_GUARD.alloc_l3_or_gm<bool>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(dx_t);
XPUType* dx_mid = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(dx_mid);
r = xpu::broadcast_equal<XPUType>(
dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x_abs),
reinterpret_cast<const XPUType*>(out.data<T>()),
dx_t,
x_dim,
y_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_equal");
r = xpu::cast<bool, XPUType>(dev_ctx.x_context(), dx_t, dx_mid, m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
XPUType* x_sign = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(x_sign);
r = xpu::sign(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x.data<T>()),
x_sign,
m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "sign");
XPUType* dx_pre_dy = x_abs;
r = xpu::mul(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(dx_mid),
reinterpret_cast<const XPUType*>(x_sign),
dx_pre_dy,
m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "mul");
r = xpu::broadcast_mul(dev_ctx.x_context(),
dx_pre_dy,
reinterpret_cast<const XPUType*>(out_grad.data<T>()),
reinterpret_cast<XPUType*>(x_grad->data<T>()),
x_dim,
y_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_mul");
} else {
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
XPUType* x_abs = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(x_abs);
r = xpu::abs(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x.data<T>()),
x_abs,
m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "abs");
DenseTensor porder_tensor;
phi::DDim pdim = phi::make_ddim({1});
porder_tensor.Resize(pdim);
dev_ctx.template Alloc<float>(&porder_tensor);
r = xpu::constant(
dev_ctx.x_context(), porder_tensor.data<float>(), 1, porder - 1.0f);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
std::vector<int> p_dim(1, 1);
XPUType* x_pow = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(x_pow);
r = xpu::broadcast_pow(
dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x_abs),
reinterpret_cast<const XPUType*>(porder_tensor.data<float>()),
x_pow,
x_dim,
p_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_pow");
XPUType* y_pow = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(y_pow);
r = xpu::broadcast_pow(
dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(out.data<T>()),
reinterpret_cast<const XPUType*>(porder_tensor.data<float>()),
y_pow,
y_dim,
p_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_pow");
dev_ctx.Wait();
XPUType* dx_t = x_abs;
r = xpu::broadcast_div(
dev_ctx.x_context(), x_pow, y_pow, dx_t, x_dim, y_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_div");
XPUType* x_sign = x_pow;
r = xpu::sign(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x.data<T>()),
x_sign,
m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "sign");
XPUType* dx_mid = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(dx_mid);
r = xpu::broadcast_mul(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x_sign),
reinterpret_cast<const XPUType*>(out_grad.data<T>()),
dx_mid,
x_dim,
y_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_mul");
r = xpu::broadcast_mul(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(dx_t),
reinterpret_cast<const XPUType*>(dx_mid),
reinterpret_cast<XPUType*>(x_grad->data<T>()),
x_dim,
x_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_mul");
}
}
} // namespace phi
PD_REGISTER_KERNEL(p_norm_grad, XPU, ALL_LAYOUT, phi::PNormGradKernel, float) {}
paddle/phi/kernels/xpu/p_norm_kernel.cc 0 → 100644
浏览文件 @ ea50282b
// 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/p_norm_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
inline void GetDims(
const phi::DDim& dim, int axis, int* m, int* t, int* n, bool asvector) {
*m = 1;
*n = 1;
*t = dim[axis];
if (asvector) {
*t = product(dim);
} else {
for (int i = 0; i < axis; ++i) {
(*m) *= dim[i];
}
for (int i = axis + 1; i < dim.size(); ++i) {
(*n) *= dim[i];
}
}
}
template <typename T, typename Context>
void PNormKernel(const Context& dev_ctx,
const DenseTensor& x,
float porder,
int axis,
float epsilon,
bool keepdim,
bool asvector,
DenseTensor* out) {
using XPUType = typename XPUTypeTrait<T>::Type;
dev_ctx.template Alloc<T>(out);
auto xdim = x.dims();
if (axis < 0) axis = xdim.size() + axis;
std::vector<int> r_dim;
std::vector<int> x_dim;
std::vector<int> y_dim;
int m = 1;
int n = 1;
int t = 1;
GetDims(xdim, axis, &m, &t, &n, asvector);
x_dim.push_back(m);
x_dim.push_back(t);
x_dim.push_back(n);
r_dim.push_back(1);
y_dim.push_back(m);
y_dim.push_back(n);
int r = 0;
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
XPUType* tmp_x = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(tmp_x);
r = xpu::abs(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x.data<T>()),
tmp_x,
m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "abs");
if (porder == INFINITY) {
r = xpu::reduce_max(dev_ctx.x_context(),
tmp_x,
reinterpret_cast<XPUType*>(out->data<T>()),
x_dim,
r_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "reduce_max");
} else if (porder == -INFINITY) {
r = xpu::reduce_min(dev_ctx.x_context(),
tmp_x,
reinterpret_cast<XPUType*>(out->data<T>()),
x_dim,
r_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "reduce_min");
} else if (porder == 0) {
XPUType* zeros = RAII_GUARD.alloc_l3_or_gm<XPUType>(1);
PADDLE_ENFORCE_XDNN_NOT_NULL(zeros);
r = xpu::constant(dev_ctx.x_context(), zeros, 1, 0.0f);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
std::vector<int> zeros_dim(1, 1);
bool* tmp2_x = RAII_GUARD.alloc_l3_or_gm<bool>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(tmp2_x);
r = xpu::broadcast_not_equal(
dev_ctx.x_context(), tmp_x, zeros, tmp2_x, x_dim, zeros_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_not_equal");
XPUType* x_mid = tmp_x;
r = xpu::cast<bool, T>(dev_ctx.x_context(), tmp2_x, x_mid, m * t * n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
r = xpu::reduce_sum(dev_ctx.x_context(),
x_mid,
reinterpret_cast<XPUType*>(out->data<T>()),
x_dim,
r_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "reduce_sum");
} else {
DenseTensor porder_tensor;
phi::DDim pdim = phi::make_ddim({1});
porder_tensor.Resize(pdim);
dev_ctx.template Alloc<T>(&porder_tensor);
r = xpu::constant(
dev_ctx.x_context(), porder_tensor.data<float>(), 1, porder);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
std::vector<int> p_dim(1, 1);
XPUType* tmp2_x = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * t * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(tmp2_x);
r = xpu::broadcast_pow(
dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(tmp_x),
reinterpret_cast<const XPUType*>(porder_tensor.data<float>()),
reinterpret_cast<XPUType*>(tmp2_x),
x_dim,
p_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_pow");
XPUType* tmp_y = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * n);
PADDLE_ENFORCE_XDNN_NOT_NULL(tmp_y);
r = xpu::reduce_sum(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(tmp2_x),
reinterpret_cast<XPUType*>(tmp_y),
x_dim,
r_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "reduce_sum");
r = xpu::constant(
dev_ctx.x_context(), porder_tensor.data<float>(), 1, 1.0f / porder);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
r = xpu::broadcast_pow(
dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(tmp_y),
reinterpret_cast<const XPUType*>(porder_tensor.data<float>()),
reinterpret_cast<XPUType*>(out->data<T>()),
y_dim,
p_dim);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_pow");
dev_ctx.Wait();
}
}
} // namespace phi
PD_REGISTER_KERNEL(p_norm, XPU, ALL_LAYOUT, phi::PNormKernel, float) {}
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或