提交 98725bc8 编写于 作者: M mindspore-ci-bot 提交者: Gitee

!5790 [MS][GPU][CUDA] Dedicated new user facing Pad API kernel

Merge pull request !5790 from danishnxt/GPU_three
......@@ -18,6 +18,7 @@
#include <stdint.h>
#include "backend/kernel_compiler/gpu/cuda_impl/pad_impl.cuh"
// For internal OP use, not user facing
template <typename T>
__global__ void Pad(const size_t size, const T* input, const int num, const int channels, const int old_height,
const int old_width, const int padded_height, const int padded_width, const int pad_top,
......@@ -37,6 +38,7 @@ __global__ void Pad(const size_t size, const T* input, const int num, const int
return;
}
// For internal OP use, not user facing
template <typename T>
__global__ void PadNHWC(const size_t size, const T* input, const int num, const int old_height, const int old_width,
const int channels, const int padded_height, const int padded_width, const int pad_top,
......@@ -57,6 +59,37 @@ __global__ void PadNHWC(const size_t size, const T* input, const int num, const
return;
}
// Used by user facing 'Pad' API
template <typename T>
__global__ void PadGeneral(const size_t size, const T *input, const int num, const int channels_orig,
const int pad_channel_before, const int pad_channel_after, const int old_height,
const int old_width, const int padded_height, const int padded_width, const int pad_top,
const int pad_left, float pad_value, T *output) {
T pad_value_template = static_cast<T>(pad_value);
for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
int block_num = (pos / padded_width) / padded_height; // total blocks = (batch * channels)
const int padded_w = pos % padded_width; // x coordinate refered to by cur 'pos'
const int padded_h = (pos / padded_width) % padded_height; // y coordinate refered to by cur 'pos'
int channels_new = channels_orig + pad_channel_after + pad_channel_before; // new number of channels from padding
int channel_num = block_num % channels_new; // current channel
int batch_item = block_num / channels_new; // current item in batch
int equiv_block_num = 0; // init variable to select equivalent block to copy data from from input
if (padded_h - pad_top < 0 || padded_w - pad_left < 0 || padded_h - pad_top >= old_height ||
padded_w - pad_left >= old_width || channel_num <= pad_channel_before - 1 ||
channel_num > channels_orig + pad_channel_before - 1) {
output[pos] = pad_value_template;
} else {
// on a block/x,y positon that isn't padding, copy data from the correct block/x,y pos the input
// calculate from number of blocks of padding (due to channel padding) inserted prior
equiv_block_num = block_num - (batch_item * (pad_channel_before + pad_channel_after)) - pad_channel_before;
output[pos] = input[(equiv_block_num * old_height + padded_h - pad_top) * old_width + padded_w - pad_left];
}
}
return;
}
template <typename T>
__global__ void PadGradNHWC(const size_t size, const T* dy, const int num, const int old_height, const int old_width,
const int channels, const int padded_height, const int padded_width, const int pad_top,
......@@ -102,6 +135,17 @@ void CalPadNHWC(const size_t size, const T* input, const int num, const int old_
return;
}
template <typename T>
void CalPadGeneral(const size_t size, const T *input, const int num, const int channels_orig,
const int pad_channel_before, const int pad_channel_after, const int old_height, const int old_width,
const int padded_height, const int padded_width, const int pad_top, const int pad_left,
float pad_value, T *output, cudaStream_t cuda_stream) {
PadGeneral<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, num, channels_orig, pad_channel_before,
pad_channel_after, old_height, old_width, padded_height,
padded_width, pad_top, pad_left, pad_value, output);
return;
}
template <typename T>
void CalPadGradNHWC(const size_t size, const T* dy, const int num, const int old_height, const int old_width,
const int channels, const int padded_height, const int padded_width, const int pad_top,
......@@ -152,3 +196,13 @@ template void CalPadGradNHWC<half>(const size_t size, const half* dy, const int
const int old_width, const int channels, const int padded_height,
const int padded_width, const int pad_top, const int pad_left, half* dx,
cudaStream_t cuda_stream);
template void CalPadGeneral<float>(const size_t size, const float *input, const int num, const int channels_orig,
const int pad_channel_before, const int pad_channel_after, const int old_height,
const int old_width, const int padded_height, const int padded_width,
const int pad_top, const int pad_left, float pad_value, float *output,
cudaStream_t cuda_stream);
template void CalPadGeneral<half>(const size_t size, const half *input, const int num, const int channels_orig,
const int pad_channel_before, const int pad_channel_after, const int old_height,
const int old_width, const int padded_height, const int padded_width,
const int pad_top, const int pad_left, float pad_value, half *output,
cudaStream_t cuda_stream);
......@@ -31,9 +31,13 @@ template <typename T>
void CalPadNHWC(const size_t size, const T* input, const int num, const int old_height, const int old_width,
const int channels, const int padded_height, const int padded_width, const int pad_top, const int pad_left,
float pad_value, T* output, cudaStream_t cuda_stream);
template <typename T>
void CalPadGradNHWC(const size_t size, const T* input, const int num, const int old_height, const int old_width,
const int channels, const int padded_height, const int padded_width, const int pad_top,
const int pad_left, T* output, cudaStream_t cuda_stream);
template <typename T>
void CalPadGeneral(const size_t size, const T *input, const int num, const int channels_orig,
const int pad_channel_before, const int pad_channel_after, const int old_height, const int old_width,
const int padded_height, const int padded_width, const int pad_top, const int pad_left,
float pad_value, T *output, cudaStream_t cuda_stream);
#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_PADIMPL_H_
......@@ -42,9 +42,12 @@ class PadGpuFwdKernel : public GpuKernel {
size_t size = output_size_ / sizeof(T);
int pad_left = paddings[3][0];
int pad_top = paddings[2][0];
int pad_channel_before = paddings[1][0];
int pad_channel_after = paddings[1][1];
T pad_value = 0.0;
CalPad(size, input, input_shape_[0], input_shape_[1], input_shape_[2], input_shape_[3], output_shape_[2],
output_shape_[3], pad_top, pad_left, pad_value, output, reinterpret_cast<cudaStream_t>(stream_ptr));
CalPadGeneral(size, input, input_shape_[0], input_shape_[1], pad_channel_before, pad_channel_after, input_shape_[2],
input_shape_[3], output_shape_[2], output_shape_[3], pad_top, pad_left, pad_value, output,
reinterpret_cast<cudaStream_t>(stream_ptr));
return true;
}
......
......@@ -470,6 +470,8 @@ class Pad(Cell):
for item in paddings:
if len(item) != 2:
raise ValueError('The shape of paddings must be (n, 2).')
if len(paddings) > 4:
raise ValueError('Only padding up to 4 dims is supported')
if mode == "CONSTANT":
self.pad = P.Pad(self.paddings)
else:
......
# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
import pytest
import numpy as np
import mindspore
import mindspore.nn as nn
import mindspore.context as context
from mindspore import Tensor
from mindspore.ops.composite import GradOperation
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pad_basic():
# confirm array is being padded with 0's
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
test_arr = np.array([[1, 2], [3, 4]]).astype(np.float32)
test_arr_expected = np.array(
[[0, 0, 0, 0], [0, 1, 2, 0], [0, 3, 4, 0], [0, 0, 0, 0]]).astype(np.float32)
x_test = Tensor(test_arr, dtype=mindspore.float32)
pad_op = nn.Pad(mode='CONSTANT', paddings=((1, 1), (1, 1)))
y_test = pad_op(x_test).asnumpy()
np.testing.assert_array_equal(y_test, test_arr_expected)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pad_row():
# Confirm correct row padding
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
test_arr_1 = np.random.rand(40, 40).astype(np.float32)
test_paddings_1 = ((2, 3), (0, 0))
test_arr_2 = np.random.randn(3, 10, 30, 30).astype(np.float32)
test_paddings_2 = ((0, 0), (0, 0), (3, 0), (0, 0))
pad_op_row_1 = nn.Pad(mode='CONSTANT', paddings=test_paddings_1)
pad_op_row_2 = nn.Pad(mode='CONSTANT', paddings=test_paddings_2)
x_test_1 = Tensor(np.array(test_arr_1), dtype=mindspore.float32)
x_test_2 = Tensor(np.array(test_arr_2), dtype=mindspore.float32)
y_test_1 = pad_op_row_1(x_test_1).asnumpy()
y_test_2 = pad_op_row_2(x_test_2).asnumpy()
# check size
assert y_test_1.shape == (45, 40)
assert y_test_2.shape == (3, 10, 33, 30)
# check values - select correct sections
np.testing.assert_equal(y_test_1[2:-3, :], test_arr_1)
np.testing.assert_equal(y_test_2[:, :, 3:, :], test_arr_2)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pad_column():
# Confirm correct column padding
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
test_arr_1 = np.random.randn(40, 40).astype(np.float32)
test_paddings_1 = ((0, 0), (3, 3))
test_arr_2 = np.random.randn(3, 10, 30, 30).astype(np.float32)
test_paddings_2 = ((0, 0), (0, 0), (0, 0), (6, 1))
pad_op_col_1 = nn.Pad(mode='CONSTANT', paddings=test_paddings_1)
pad_op_col_2 = nn.Pad(mode='CONSTANT', paddings=test_paddings_2)
x_test_1 = Tensor(np.array(test_arr_1), dtype=mindspore.float32)
x_test_2 = Tensor(np.array(test_arr_2), dtype=mindspore.float32)
y_test_1 = pad_op_col_1(x_test_1).asnumpy()
y_test_2 = pad_op_col_2(x_test_2).asnumpy()
# check size
assert y_test_1.shape == (40, 46)
assert y_test_2.shape == (3, 10, 30, 37)
# check values - select correct sections - should match
np.testing.assert_equal(y_test_1[:, 3:-3], test_arr_1)
np.testing.assert_equal(y_test_2[:, :, :, 6:-1], test_arr_2)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pad_3d_pad():
# Confirm correct 3d padding - row, column, channel
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
test_arr = np.random.randn(5, 3, 30, 30).astype(np.float32)
test_paddings = ((0, 0), (2, 1), (0, 1), (0, 2)) # padding 3 dims now
pad_op_3d = nn.Pad(mode='CONSTANT', paddings=test_paddings)
x_test = Tensor(np.array(test_arr), dtype=mindspore.float32)
y_test = pad_op_3d(x_test).asnumpy()
assert y_test.shape == (5, 6, 31, 32)
np.testing.assert_equal(test_arr, y_test[:, 2:-1, :-1, :-2])
# For testing backprop
class Grad(nn.Cell):
def __init__(self, network):
super(Grad, self).__init__()
self.grad = GradOperation(get_all=True, sens_param=True)
self.network = network
def construct(self, input_, output_grad):
return self.grad(self.network)(input_, output_grad)
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.pad = nn.Pad(mode="CONSTANT", paddings=(
(0, 0), (4, 3), (1, 1), (0, 2)))
def construct(self, x):
return self.pad(x)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pad_3d_backprop():
# Confirm correct 3d padding backprop
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
test_arr = np.random.randn(5, 3, 30, 30).astype(np.float32)
x_test = Tensor(test_arr, dtype=mindspore.float32)
padded_shape = (5, 10, 32, 32)
dy = np.random.randn(*padded_shape).astype(np.float32)
expected_dx = dy[:, 4:-3, 1:-1, :-2]
net = Grad(Net())
dx = net(x_test, Tensor(dy))
dx = dx[0].asnumpy()
np.testing.assert_array_equal(dx, expected_dx)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pad_error_cases():
# Test against common errorneous inputs to catch correctly
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
# TEST 1 - Neg padding values
test_op = nn.Pad(paddings=((0, 0), (-1, -1)), mode="CONSTANT")
test_arr = np.random.randn(3, 3)
test_arr_ms = Tensor(test_arr, dtype=mindspore.float32)
with pytest.raises(ValueError):
test_op(test_arr_ms)
# TEST 2 - Mismatched input size and paddings - 1D tensor
test_op = nn.Pad(paddings=((0, 0), (1, 0)), mode="CONSTANT")
test_arr = np.random.randn(3) # 1D Tensor
test_arr_ms = Tensor(test_arr, dtype=mindspore.float32)
with pytest.raises(ValueError):
test_op(test_arr_ms)
# TEST 3 - Mismatched input size and paddings - 2D tensor, 3D padding
test_op = nn.Pad(paddings=((0, 0), (1, 0)), mode="CONSTANT") # 2D Padding
test_arr = np.random.randn(1, 3, 3) # 3D Tensor
test_arr_ms = Tensor(test_arr, dtype=mindspore.float32)
with pytest.raises(ValueError):
test_op(test_arr_ms)
# TEST 4 - 1D Paddings should not work
with pytest.raises(TypeError):
test_op = nn.Pad(paddings=((0, 2)), mode="CONSTANT")
# TEST 5 - Padding beyond 4d - (added check in nn file in PR)
with pytest.raises(ValueError):
_ = nn.Pad(paddings=((0, 0), (0, 0,), (0, 0), (0, 0),
(1, 0)), mode="CONSTANT") # 2D Padding
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