m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

add xpu slice op (#27349)

Browse Source 
* add xpu slice op
test=xpu

* add slice xpu op
test=xpu

* code style
test=kunlun

* style
test=kunlun

* format
test=kunlun
my_2.0rc
Thunderbrook 4 years ago committed by GitHub
parent 8d2cb14f98
commit 04be37c57f
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 374 additions and 0 deletions
Show Stats Download Patch File Download Diff File

203
paddle/fluid/operators/slice_xpu_op.cc
Unescape View File

@ -0,0 +1,203 @@
/* 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 <algorithm>
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/operators/slice_op.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename DeviceContext, typename T>
class SliceXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto in = ctx.Input<framework::Tensor>("Input");
auto out = ctx.Output<framework::Tensor>("Out");
auto axes = ctx.Attr<std::vector<int>>("axes");
auto starts = ctx.Attr<std::vector<int>>("starts");
auto ends = ctx.Attr<std::vector<int>>("ends");
auto in_dims = in->dims();
// prepare starts, ends on XPU
int dim_value = 0, start = 0, end = 0;
// If a negative value is passed for any of the start or end indices,
// it represents number of elements before the end of that dimension.
// If the value passed to start or end is larger than the n
// (the number of elements in this dimension), it represents n.
for (size_t i = 0; i < axes.size(); ++i) {
dim_value = in_dims[axes[i]];
start = starts[i];
end = ends[i];
start = start < 0 ? (start + dim_value) : start;
end = end < 0 ? (end + dim_value) : end;
start = std::max(start, 0);
end = std::max(end, 0);
end = std::min(end, dim_value);
PADDLE_ENFORCE_GT(end, start, platform::errors::InvalidArgument(
"end should greater than start"));
starts[i] = start;
ends[i] = end;
}
size_t shape_size = in_dims.size();
// the slice XPU kernel require that the length of `start`, `end` must be
// equal
// to the dims size of input tensor, therefore, if shape_size > axes.size(),
// the `starts_extension` and `ends_extension` is necessary.
std::vector<int> starts_extension(shape_size, 0);
std::vector<int> ends_extension(shape_size, 0);
if (shape_size > axes.size()) {
for (size_t i = 0; i < shape_size; ++i) {
ends_extension[i] = in_dims[i];
}
for (size_t i = 0; i < axes.size(); ++i) {
starts_extension[axes[i]] = starts[i];
ends_extension[axes[i]] = ends[i];
}
} else {
starts_extension = std::move(starts);
ends_extension = std::move(ends);
}
// prepare shape on XPU
std::vector<int> shape(shape_size, 0);
for (size_t i = 0; i < shape_size; ++i) {
shape[i] = in_dims[i];
}
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto* in_data = in->data<T>();
auto* out_data = out->mutable_data<T>(ctx.GetPlace());
int r = xpu::slice_forward(dev_ctx.x_context(), shape.data(),
starts_extension.data(), ends_extension.data(),
shape_size, in_data, out_data);
PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
platform::errors::External("XPU slice kernel error!"));
}
};
template <typename DeviceContext, typename T>
class SliceGradXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* d_in = ctx.Output<framework::Tensor>(framework::GradVarName("Input"));
d_in->mutable_data<T>(ctx.GetPlace());
auto in_dims = d_in->dims();
auto axes = ctx.Attr<std::vector<int>>("axes");
auto starts = ctx.Attr<std::vector<int>>("starts");
auto ends = ctx.Attr<std::vector<int>>("ends");
// prepare starts, ends on XPU
int dim_value = 0, start = 0, end = 0;
// If a negative value is passed for any of the start or end indices,
// it represents number of elements before the end of that dimension.
// If the value passed to start or end is larger than the n
// (the number of elements in this dimension), it represents n.
for (size_t i = 0; i < axes.size(); ++i) {
dim_value = in_dims[axes[i]];
start = starts[i];
end = ends[i];
start = start < 0 ? (start + dim_value) : start;
end = end < 0 ? (end + dim_value) : end;
start = std::max(start, 0);
end = std::max(end, 0);
end = std::min(end, dim_value);
PADDLE_ENFORCE_GT(end, start, platform::errors::InvalidArgument(
"end should greater than start"));
starts[i] = start;
ends[i] = end;
}
size_t shape_size = in_dims.size();
// the slice XPU kernel require that the length of `start`, `end` must be
// equal
// to the dims size of input tensor, therefore, if shape_size > axes.size(),
// the `starts_extension` and `ends_extension` is necessary.
std::vector<int> starts_extension(shape_size, 0);
std::vector<int> ends_extension(shape_size, 0);
if (shape_size > axes.size()) {
for (size_t i = 0; i < shape_size; ++i) {
ends_extension[i] = in_dims[i];
}
for (size_t i = 0; i < axes.size(); ++i) {
starts_extension[axes[i]] = starts[i];
ends_extension[axes[i]] = ends[i];
}
}
int* starts_device = nullptr;
int* ends_device = nullptr;
int* starts_host =
shape_size > axes.size() ? starts_extension.data() : starts.data();
int* ends_host =
shape_size > axes.size() ? ends_extension.data() : ends.data();
PADDLE_ENFORCE_EQ(
xpu_malloc((void**)(&starts_device), shape_size * sizeof(int)),
XPU_SUCCESS, platform::errors::External("XPU has no enough memory"));
PADDLE_ENFORCE_EQ(
xpu_malloc((void**)(&ends_device), shape_size * sizeof(int)),
XPU_SUCCESS, platform::errors::External("XPU has no enough memory"));
memory::Copy(BOOST_GET_CONST(platform::XPUPlace, ctx.GetPlace()),
starts_device, platform::CPUPlace(), starts_host,
shape_size * sizeof(int));
memory::Copy(BOOST_GET_CONST(platform::XPUPlace, ctx.GetPlace()),
ends_device, platform::CPUPlace(), ends_host,
shape_size * sizeof(int));
// prepare shape on XPU
std::vector<int> shape(shape_size, 0);
for (size_t i = 0; i < shape_size; ++i) {
shape[i] = in_dims[i];
}
int* shape_device = nullptr;
PADDLE_ENFORCE_EQ(
xpu_malloc((void**)(&shape_device), shape_size * sizeof(int)),
XPU_SUCCESS, platform::errors::External("XPU has no enough memory"));
memory::Copy(BOOST_GET_CONST(platform::XPUPlace, ctx.GetPlace()),
shape_device, platform::CPUPlace(), shape.data(),
shape_size * sizeof(int));
auto& dev_ctx = ctx.template device_context<DeviceContext>();
int r =
xpu::slice_backward(dev_ctx.x_context(), shape_device, starts_device,
ends_device, shape_size, d_out->data<T>(),
d_in->data<T>(), d_in->numel(), d_out->numel());
PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
platform::errors::External("xpu slice kernel error"));
dev_ctx.Wait();
// free device data
xpu_free(shape_device);
xpu_free(starts_device);
xpu_free(ends_device);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(
slice, ops::SliceXPUKernel<paddle::platform::XPUDeviceContext, float>);
REGISTER_OP_XPU_KERNEL(
slice_grad,
ops::SliceGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
#endif

171
python/paddle/fluid/tests/unittests/xpu/test_slice_op_xpu.py
Unescape View File

@ -0,0 +1,171 @@
# 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.
from __future__ import print_function
import unittest
import numpy as np
import sys
sys.path.append("..")
import paddle
import paddle.fluid.core as core
from op_test import OpTest
import paddle.fluid as fluid
import paddle.fluid.layers as layers
# Situation 1: starts(list, no tensor), ends(list, no tensor)
# 1.1 without attr(decrease)
class TestSliceOp(OpTest):
def setUp(self):
self.op_type = "slice"
self.config()
self.inputs = {'Input': self.input}
self.outputs = {'Out': self.out}
self.attrs = {
'axes': self.axes,
'starts': self.starts,
'ends': self.ends,
'infer_flags': self.infer_flags,
"use_xpu": True
}
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [1, 0, 2]
self.ends = [3, 3, 4]
self.axes = [0, 1, 2]
self.infer_flags = [1, 1, 1]
self.out = self.input[1:3, 0:3, 2:4, :]
def test_check_output(self):
place = paddle.XPUPlace(0)
self.check_output_with_place(place)
def test_check_grad_normal(self):
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['Input'], 'Out')
class TestCase1(TestSliceOp):
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [-3, 0, 2]
self.ends = [3, 100, -1]
self.axes = [0, 1, 2]
self.infer_flags = [1, 1, 1]
self.out = self.input[-3:3, 0:100, 2:-1, :]
class TestCase2(TestSliceOp):
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [-3, 0, 2]
self.ends = [3, 100, -1]
self.axes = [0, 1, 3]
self.infer_flags = [1, 1, 1]
self.out = self.input[-3:3, 0:100, :, 2:-1]
# 1.2 with attr(decrease)
class TestSliceOp_decs_dim(OpTest):
def setUp(self):
self.op_type = "slice"
self.config()
self.inputs = {'Input': self.input}
self.outputs = {'Out': self.out}
self.attrs = {
'axes': self.axes,
'starts': self.starts,
'ends': self.ends,
'infer_flags': self.infer_flags,
'decrease_axis': self.decrease_axis,
"use_xpu": True
}
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [1, 0, 2]
self.ends = [2, 3, 4]
self.axes = [0, 1, 2]
self.decrease_axis = [0]
self.infer_flags = [1, 1, 1]
self.out = self.input[1, 0:3, 2:4, :]
def test_check_output(self):
place = paddle.XPUPlace(0)
self.check_output_with_place(place)
def test_check_grad_normal(self):
place = paddle.XPUPlace(0)
self.check_grad_with_place(place, ['Input'], 'Out')
class TestSliceOp_decs_dim_2(TestSliceOp_decs_dim):
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [1, 0, 2]
self.ends = [2, 1, 4]
self.axes = [0, 1, 2]
self.decrease_axis = [0, 1]
self.infer_flags = [1, 1, 1]
self.out = self.input[1, 0, 2:4, :]
class TestSliceOp_decs_dim_3(TestSliceOp_decs_dim):
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [-1, 0, 2]
self.ends = [1000000, 1, 4]
self.axes = [0, 1, 2]
self.decrease_axis = [0, 1]
self.infer_flags = [1, 1, 1]
self.out = self.input[-1, 0, 2:4, :]
class TestSliceOp_decs_dim_4(TestSliceOp_decs_dim):
def config(self):
self.input = np.random.random([3, 4, 5, 7]).astype("float64")
self.starts = [0, 1, 2, 3]
self.ends = [1, 2, 3, 4]
self.axes = [0, 1, 2, 3]
self.decrease_axis = [0, 1, 2, 3]
self.infer_flags = [1, 1, 1]
self.out = self.input[0, 1, 2, 3:4]
class TestSliceOp_decs_dim_5(TestSliceOp_decs_dim):
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [-1]
self.ends = [1000000]
self.axes = [3]
self.decrease_axis = [3]
self.infer_flags = [1, 1, 1]
self.out = self.input[:, :, :, -1]
class TestSliceOp_decs_dim_6(TestSliceOp_decs_dim):
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [0, 1, 2, 3]
self.ends = [1, 2, 3, 4]
self.axes = [0, 1, 2, 3]
self.decrease_axis = [0, 1, 2, 3]
self.infer_flags = [1, 1, 1]
self.out = self.input[0, 1, 2, 3:4]
if __name__ == '__main__':
unittest.main()
Write Preview
Loading…
Cancel
Save