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“a50230add6ea483fa6ed7de3f08bcd10b8a75ed3”上不存在“...tests/git@gitcode.net:s920243400/PaddleDetection.git”
提交 4d42f4fa 编写于 作者: P phlrain
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paddle/fluid/operators/lu_op.h
浏览文件 @ 4d42f4fa
......@@ -41,9 +41,12 @@ void SetValueCompute(const framework::ExecutionContext& ctx,
auto dtype = framework::TransToProtoVarType(in->dtype());
auto in_dims = in->dims();
CheckAndUpdateSliceAttrs<int64_t>(in_dims, axes, starts, ends, &steps);
auto slice_dims = GetSliceDims(in_dims, axes, *starts, *ends, &steps);
auto decrease_slice_dims = GetDecreasedDims(slice_dims, decrease_axes);
phi::funcs::CheckAndUpdateSliceAttrs<int64_t>(in_dims, axes, starts, ends,
&steps);
auto slice_dims =
phi::funcs::GetSliceDims(in_dims, axes, *starts, *ends, &steps);
auto decrease_slice_dims =
phi::funcs::GetDecreasedDims(slice_dims, decrease_axes);
auto slice_dims_for_assign = decrease_slice_dims;
if (!none_axes.empty()) {
......@@ -281,10 +284,10 @@ void SliceCompute(const framework::ExecutionContext& ctx,
}
}
CheckAndUpdateSliceAttrs(in_dims, axes, &starts, &ends);
slice_dims =
GetSliceDims<int64_t>(in_dims, axes, starts, ends, nullptr, nullptr);
out_dims = GetDecreasedDims(slice_dims, decrease_axis);
phi::funcs::CheckAndUpdateSliceAttrs(in_dims, axes, &starts, &ends);
slice_dims = phi::funcs::GetSliceDims<int64_t>(in_dims, axes, starts, ends,
nullptr, nullptr);
out_dims = phi::funcs::GetDecreasedDims(slice_dims, decrease_axis);
// 2.2 Get output
auto offsets = Eigen::DSizes<Eigen::DenseIndex, D>();
......
paddle/fluid/operators/set_value_op.h
浏览文件 @ 4d42f4fa
......@@ -25,10 +25,10 @@
#include "paddle/fluid/operators/assign_value_op.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/operators/slice_utils.h"
#include "paddle/fluid/operators/strided_slice_op.h"
#include "paddle/fluid/operators/utils.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/phi/kernels/funcs/slice_utils.h"
namespace paddle {
namespace operators {
......@@ -188,9 +188,11 @@ class SetValueKernel : public framework::OpKernel<T> {
}
auto in_dims = in->dims();
CheckAndUpdateSliceAttrs(in_dims, axes, &starts, &ends, &steps);
auto slice_dims = GetSliceDims(in_dims, axes, starts, ends, &steps);
auto decrease_slice_dims = GetDecreasedDims(slice_dims, decrease_axes);
phi::funcs::CheckAndUpdateSliceAttrs(in_dims, axes, &starts, &ends, &steps);
auto slice_dims =
phi::funcs::GetSliceDims(in_dims, axes, starts, ends, &steps);
auto decrease_slice_dims =
phi::funcs::GetDecreasedDims(slice_dims, decrease_axes);
auto slice_dims_for_assign = decrease_slice_dims;
if (!none_axes.empty()) {
......
paddle/fluid/operators/slice_op.cc
浏览文件 @ 4d42f4fa
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <memory>
#include <string>
#include <vector>
#include "paddle/phi/kernels/funcs/slice_utils.h"
namespace paddle {
namespace operators {
......@@ -101,15 +102,17 @@ class SliceOp : public framework::OperatorWithKernel {
"The size of ends must be equal to the size of axes."));
}
CheckAndUpdateSliceAttrs<int>(in_dims, axes, &starts, &ends, nullptr,
&infer_flags);
phi::funcs::CheckAndUpdateSliceAttrs<int>(in_dims, axes, &starts, &ends,
nullptr, &infer_flags);
auto slice_dims =
GetSliceDims<int>(in_dims, axes, starts, ends, nullptr, &infer_flags);
auto slice_dims = phi::funcs::GetSliceDims<int>(in_dims, axes, starts, ends,
nullptr, &infer_flags);
if (ctx->IsRuntime()) {
out_dims = GetDecreasedDims<int>(slice_dims, decrease_axis, &infer_flags);
out_dims = phi::funcs::GetDecreasedDims<int>(slice_dims, decrease_axis,
&infer_flags);
} else {
out_dims = GetDecreasedDims<int>(slice_dims, decrease_axis, nullptr);
out_dims =
phi::funcs::GetDecreasedDims<int>(slice_dims, decrease_axis, nullptr);
}
ctx->SetOutputDim("Out", out_dims);
......
paddle/fluid/operators/slice_op.h
浏览文件 @ 4d42f4fa
......@@ -18,7 +18,6 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/operators/slice_utils.h"
#include "paddle/fluid/operators/utils.h"
#include "paddle/phi/kernels/funcs/math_function.h"
......
paddle/phi/kernels/cpu/slice_grad_kernel.cc
浏览文件 @ 4d42f4fa
......@@ -29,5 +29,4 @@ PD_REGISTER_KERNEL(slice_grad,
double,
phi::dtype::complex<float>,
phi::dtype::complex<double>,
phi::dtype::bfloat16,
phi::dtype::float16) {}
phi::dtype::bfloat16) {}
paddle/phi/kernels/funcs/slice_utils.h
浏览文件 @ 4d42f4fa
......@@ -19,6 +19,8 @@ limitations under the License. */
namespace phi {
namespace funcs {
template <typename T = int64_t>
inline void CheckAndUpdateSliceAttrs(const DDim in_dims,
const std::vector<T>& axes,
......@@ -161,4 +163,5 @@ inline DDim GetDecreasedDims(const DDim slice_dims,
return decreased_dims;
}
} // namespace funcs
} // namespace phi
paddle/phi/kernels/gpu/slice_grad_kernel.cu 已删除 100644 → 0
浏览文件 @ 263b4773
// 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/impl/slice_grad_kernel_impl.h"
#include "paddle/phi/kernels/slice_grad_kernel.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
PD_REGISTER_KERNEL(slice_grad,
GPU,
ALL_LAYOUT,
phi::SliceGradRawKernel,
bool,
int,
int64_t,
float,
double,
phi::dtype::complex<float>,
phi::dtype::complex<double>,
phi::dtype::bfloat16,
phi::dtype::float16) {}
paddle/phi/kernels/gpu/slice_kernel.cu.cc
浏览文件 @ 4d42f4fa
......@@ -29,4 +29,5 @@ PD_REGISTER_KERNEL(slice,
double,
phi::dtype::complex<float>,
phi::dtype::complex<double>,
phi::dtype::bfloat16) {}
phi::dtype::bfloat16,
phi::dtype::float16) {}
paddle/phi/kernels/impl/slice_grad_kernel_impl.h
浏览文件 @ 4d42f4fa
......@@ -66,136 +66,143 @@ void EigenPaddingCompute(
// if dimension less than 3, cannot reduce dimension
LaunchEigenPadding<T, Context, D>(
context, d_input, in_dims, d_out, out_dims, paddings);
} else { // else we can reduce dimension
// count not-zero padding number, and record the dimension
int need_pad_num = 0, pad_dim = -1;
for (size_t i = 0; i < D; i++) {
if (paddings[i].first != 0 || paddings[i].second != 0) {
need_pad_num++;
pad_dim = i;
}
}
// } else { // else we can reduce dimension
// // count not-zero padding number, and record the dimension
// int need_pad_num = 0, pad_dim = -1;
// for (size_t i = 0; i < D; i++) {
// if (paddings[i].first != 0 || paddings[i].second != 0) {
// need_pad_num++;
// pad_dim = i;
// }
// }
// if (need_pad_num == 1) {
// // only need padding one dimension, we can reduce dimension.
// // only the padding dimension is available for us.
// // How to reduce dimension(5 to 3 for example):
// // before(D=5):
// // in_dims: [x1, x2, x3, x4, x5]
// // padding.first: [0, 0, a, 0, 0]
// // padding.second: [0, 0, b, 0, 0]
// // | |
// // V V
// // after(D=3):
// // reshaped_in_dims: [x1*x2, x3, x4*x5]
// // reshaped_padding.first: [0, a, 0]
// // reshaped_padding.second: [0, b, 0]
if (need_pad_num == 1) {
// only need padding one dimension, we can reduce dimension.
// only the padding dimension is available for us.
// How to reduce dimension(5 to 3 for example):
// before(D=5):
// in_dims: [x1, x2, x3, x4, x5]
// padding.first: [0, 0, a, 0, 0]
// padding.second: [0, 0, b, 0, 0]
// | |
// V V
// after(D=3):
// reshaped_in_dims: [x1*x2, x3, x4*x5]
// reshaped_padding.first: [0, a, 0]
// reshaped_padding.second: [0, b, 0]
// if (pad_dim == D - 1) {
// // only last dimension need padding,
// // reshape the dimension of tensor in 2: [preceding, padding]
// std::vector<int64_t> in_tore_shape(2, 1), out_tore_shape(2, 1);
// Eigen::array<std::pair<int64_t, int64_t>, 2> reshaped_padding;
if (pad_dim == D - 1) {
// only last dimension need padding,
// reshape the dimension of tensor in 2: [preceding, padding]
std::vector<int64_t> in_tore_shape(2, 1), out_tore_shape(2, 1);
Eigen::array<std::pair<int64_t, int64_t>, 2> reshaped_padding;
// // first dimension is the accumulate of preceding dimension
// for (int i = 0; i < pad_dim; i++) {
// in_tore_shape[0] *= in_dims[i];
// out_tore_shape[0] *= out_dims[i];
// }
// // second dimension is the padding dimension
// in_tore_shape[1] = in_dims[pad_dim];
// out_tore_shape[1] = out_dims[pad_dim];
// first dimension is the accumulate of preceding dimension
for (int i = 0; i < pad_dim; i++) {
in_tore_shape[0] *= in_dims[i];
out_tore_shape[0] *= out_dims[i];
}
// second dimension is the padding dimension
in_tore_shape[1] = in_dims[pad_dim];
out_tore_shape[1] = out_dims[pad_dim];
// // convert array from std::vector to DDim
// DDim reshaped_in_dims = make_ddim(in_tore_shape);
// DDim reshaped_out_dims = make_ddim(out_tore_shape);
// convert array from std::vector to DDim
DDim reshaped_in_dims = make_ddim(in_tore_shape);
DDim reshaped_out_dims = make_ddim(out_tore_shape);
// // after reshape: the first dimension do not need padding,
// // set padding[0] zero
// reshaped_padding[0].first = reshaped_padding[0].second = 0;
// // the second dimension is the previous padding dimension
// reshaped_padding[1].first = paddings[pad_dim].first;
// reshaped_padding[1].second = paddings[pad_dim].second;
// after reshape: the first dimension do not need padding,
// set padding[0] zero
reshaped_padding[0].first = reshaped_padding[0].second = 0;
// the second dimension is the previous padding dimension
reshaped_padding[1].first = paddings[pad_dim].first;
reshaped_padding[1].second = paddings[pad_dim].second;
// LaunchEigenPadding<T, Context, D>(context, d_input, reshaped_in_dims,
// d_out,
// reshaped_out_dims, reshaped_padding);
// } else if (pad_dim == 0) {
// // only first dimension need padding,
// // reshape the dimension of tensor in 2: [padding, succeeding]
// // similar to (D - 1)
// std::vector<int64_t> in_tore_shape(2, 1), out_tore_shape(2, 1);
// Eigen::array<std::pair<int64_t, int64_t>, 2> reshaped_padding;
LaunchEigenPadding<T, Context>(context,
d_input,
reshaped_in_dims,
d_out,
reshaped_out_dims,
reshaped_padding);
} else if (pad_dim == 0) {
// only first dimension need padding,
// reshape the dimension of tensor in 2: [padding, succeeding]
// similar to (D - 1)
std::vector<int64_t> in_tore_shape(2, 1), out_tore_shape(2, 1);
Eigen::array<std::pair<int64_t, int64_t>, 2> reshaped_padding;
// // first dimension is the padding dimension
// in_tore_shape[0] = in_dims[pad_dim];
// out_tore_shape[0] = out_dims[pad_dim];
// // sencond dimension is the accumulate of succeeding dimension
// for (size_t i = pad_dim + 1; i < D; i++) {
// in_tore_shape[1] *= in_dims[i];
// out_tore_shape[1] *= out_dims[i];
// }
// first dimension is the padding dimension
in_tore_shape[0] = in_dims[pad_dim];
out_tore_shape[0] = out_dims[pad_dim];
// sencond dimension is the accumulate of succeeding dimension
for (size_t i = pad_dim + 1; i < D; i++) {
in_tore_shape[1] *= in_dims[i];
out_tore_shape[1] *= out_dims[i];
}
// // convert array from std::vector to DDim
// DDim reshaped_in_dims = make_ddim(in_tore_shape);
// DDim reshaped_out_dims = make_ddim(out_tore_shape);
// convert array from std::vector to DDim
DDim reshaped_in_dims = make_ddim(in_tore_shape);
DDim reshaped_out_dims = make_ddim(out_tore_shape);
// // after reshape:
// // the first dimension is the previous padding dimension
// reshaped_padding[0].first = paddings[pad_dim].first;
// reshaped_padding[0].second = paddings[pad_dim].second;
// // the second dimension do not need padding, set padding[1] zero
// reshaped_padding[1].first = reshaped_padding[1].second = 0;
// after reshape:
// the first dimension is the previous padding dimension
reshaped_padding[0].first = paddings[pad_dim].first;
reshaped_padding[0].second = paddings[pad_dim].second;
// the second dimension do not need padding, set padding[1] zero
reshaped_padding[1].first = reshaped_padding[1].second = 0;
// LaunchEigenPadding<T, Context, D>(context, d_input, reshaped_in_dims,
// d_out,
// reshaped_out_dims, reshaped_padding);
// } else {
// // other dimension need padding
// // reshape the dimension of tensor in 3:
// // [preceding, padding, succeeding]
// std::vector<int64_t> in_tore_shape(3, 1), out_tore_shape(3, 1);
// Eigen::array<std::pair<int64_t, int64_t>, 3> reshaped_padding;
LaunchEigenPadding<T, Context>(context,
d_input,
reshaped_in_dims,
d_out,
reshaped_out_dims,
reshaped_padding);
} else {
// other dimension need padding
// reshape the dimension of tensor in 3:
// [preceding, padding, succeeding]
std::vector<int64_t> in_tore_shape(3, 1), out_tore_shape(3, 1);
Eigen::array<std::pair<int64_t, int64_t>, 3> reshaped_padding;
// // first dimension is the accumulate of preceding dimension
// for (int i = 0; i < pad_dim; i++) {
// in_tore_shape[0] *= in_dims[i];
// out_tore_shape[0] *= out_dims[i];
// }
// // second dimension is the padding dimension
// in_tore_shape[1] = in_dims[pad_dim];
// out_tore_shape[1] = out_dims[pad_dim];
// // third dimension is the accumulate of succeeding dimension
// for (size_t i = pad_dim + 1; i < D; i++) {
// in_tore_shape[2] *= in_dims[i];
// out_tore_shape[2] *= out_dims[i];
// }
// first dimension is the accumulate of preceding dimension
for (int i = 0; i < pad_dim; i++) {
in_tore_shape[0] *= in_dims[i];
out_tore_shape[0] *= out_dims[i];
}
// second dimension is the padding dimension
in_tore_shape[1] = in_dims[pad_dim];
out_tore_shape[1] = out_dims[pad_dim];
// third dimension is the accumulate of succeeding dimension
for (size_t i = pad_dim + 1; i < D; i++) {
in_tore_shape[2] *= in_dims[i];
out_tore_shape[2] *= out_dims[i];
}
// // convert array from std::vector to DDim
// DDim reshaped_in_dims = make_ddim(in_tore_shape);
// DDim reshaped_out_dims = make_ddim(out_tore_shape);
// convert array from std::vector to DDim
DDim reshaped_in_dims = make_ddim(in_tore_shape);
DDim reshaped_out_dims = make_ddim(out_tore_shape);
// // after reshape:
// // the first dimension do not need padding, set padding[0] zero
// reshaped_padding[0].first = reshaped_padding[2].second = 0;
// // the second dimension is the previous padding dimension
// reshaped_padding[1].first = paddings[pad_dim].first;
// reshaped_padding[1].second = paddings[pad_dim].second;
// // the third dimension do not need padding, set padding[2] zero
// reshaped_padding[2].first = reshaped_padding[2].second = 0;
// after reshape:
// the first dimension do not need padding, set padding[0] zero
reshaped_padding[0].first = reshaped_padding[2].second = 0;
// the second dimension is the previous padding dimension
reshaped_padding[1].first = paddings[pad_dim].first;
reshaped_padding[1].second = paddings[pad_dim].second;
// the third dimension do not need padding, set padding[2] zero
reshaped_padding[2].first = reshaped_padding[2].second = 0;
// LaunchEigenPadding<T, Context, D>(context, d_input, reshaped_in_dims,
// d_out,
// reshaped_out_dims, reshaped_padding);
// }
// } else {
// // need padding at many dimension, cannot reduce dimension
// LaunchEigenPadding<T, Context, D>(context, d_input, in_dims, d_out,
// out_dims,
// paddings);
// }
// }
LaunchEigenPadding<T, Context>(context,
d_input,
reshaped_in_dims,
d_out,
reshaped_out_dims,
reshaped_padding);
}
} else {
// need padding at many dimension, cannot reduce dimension
LaunchEigenPadding<T, Context>(
context, d_input, in_dims, d_out, out_dims, paddings);
}
}
}
template <typename T, typename Context, size_t D>
......
paddle/phi/kernels/impl/slice_kernel_impl.h
浏览文件 @ 4d42f4fa
......@@ -60,10 +60,10 @@ void SliceCompute(const Context& ctx,
}
}
CheckAndUpdateSliceAttrs<int64_t>(in_dims, axes, &starts, &ends);
slice_dims =
GetSliceDims<int64_t>(in_dims, axes, starts, ends, nullptr, nullptr);
out_dims = GetDecreasedDims<int64_t>(slice_dims, decrease_axis);
funcs::CheckAndUpdateSliceAttrs<int64_t>(in_dims, axes, &starts, &ends);
slice_dims = funcs::GetSliceDims<int64_t>(
in_dims, axes, starts, ends, nullptr, nullptr);
out_dims = funcs::GetDecreasedDims<int64_t>(slice_dims, decrease_axis);
// 2.2 Get output
auto offsets = Eigen::DSizes<Eigen::DenseIndex, D>();
......
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