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!13140 Operator[StridedSlice] not support bool tensor in cpu env. 

From: @wangyanling10
Reviewed-by: 
Signed-off-by:
This commit is contained in:
mindspore-ci-bot 2021-03-15 19:10:43 +08:00 committed by Gitee
parent e3056e77d1 42aa96a1a2
commit 18580ac506
6 changed files with 158 additions and 32 deletions
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44
mindspore/ccsrc/backend/kernel_compiler/cpu/slice_cpu_kernel.cc
View File

@ -23,6 +23,7 @@ constexpr int MAX_DIMS = 8;
void SliceCPUKernel::InitKernel(const CNodePtr &kernel_node) {
CheckParam(kernel_node);
input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0);
std::vector<int64_t> begin_me = AnfAlgo::GetNodeAttr<std::vector<int64_t>>(kernel_node, BEGIN);
(void)std::transform(begin_me.begin(), begin_me.end(), std::back_inserter(begin_),
[](const int64_t &value) { return static_cast<int>(value); });
@ -94,8 +95,25 @@ void SliceCPUKernel::ExpandAllMemberDims() {
bool SliceCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> & /*workspace*/,
const std::vector<kernel::AddressPtr> &outputs) {
auto input_addr = reinterpret_cast<float *>(inputs[0]->addr);
auto output_addr = reinterpret_cast<float *>(outputs[0]->addr);
bool ret{true};
if (dtype_ == kNumberTypeInt32) {
ret = LaunchKernel<int>(inputs, outputs);
} else if (dtype_ == kNumberTypeFloat32) {
ret = LaunchKernel<float>(inputs, outputs);
} else if (dtype_ == kNumberTypeBool) {
ret = LaunchKernel<bool>(inputs, outputs);
} else {
MS_LOG(ERROR) << "Slice op only support input_x int32 and float32";
return false;
}
return ret;
}
template <typename T>
bool SliceCPUKernel::LaunchKernel(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> &outputs) {
T *input_addr = reinterpret_cast<T *>(inputs[0]->addr);
T *output_addr = reinterpret_cast<T *>(outputs[0]->addr);
bool can_copy_memory[3] = {CanCopyMemoryOnAxis(0), CanCopyMemoryOnAxis(1), CanCopyMemoryOnAxis(2)};
int signstride[4] = {SignOfStride(0), SignOfStride(1), SignOfStride(2), SignOfStride(3)};
size_t in_start_offset[3] = {begin_[0] * input_element_num_[0], begin_[1] * input_element_num_[1],
@ -108,7 +126,7 @@ bool SliceCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
for (int i = begin_[0]; signstride[0] * i < signstride[0] * end_[0];
i += strides_[0], in_n_offset += in_step_size[0], out_n_offset += output_element_num_[0]) {
if (can_copy_memory[0]) {
CopyDataToOutput(inputs, in_n_offset, outputs, out_n_offset, input_element_num_[0], 0);
CopyDataToOutput<T>(inputs, in_n_offset, outputs, out_n_offset, input_element_num_[0], 0);
continue;
}
auto in_c_offset = in_start_offset[1];
@ -116,8 +134,8 @@ bool SliceCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
for (int j = begin_[1]; signstride[1] * j < signstride[1] * end_[1];
j += strides_[1], in_c_offset += in_step_size[1], out_c_offset += output_element_num_[1]) {
if (can_copy_memory[1]) {
CopyDataToOutput(inputs, in_n_offset + in_c_offset, outputs, out_n_offset + out_c_offset, input_element_num_[1],
1);
CopyDataToOutput<T>(inputs, in_n_offset + in_c_offset, outputs, out_n_offset + out_c_offset,
input_element_num_[1], 1);
continue;
}
auto in_h_offset = in_start_offset[2];
@ -125,7 +143,7 @@ bool SliceCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
for (int k = begin_[2]; signstride[2] * k < signstride[2] * end_[2];
k += strides_[2], in_h_offset += in_step_size[2], out_h_offset += output_element_num_[2]) {
if (can_copy_memory[2]) {
CopyDataToOutput(inputs, in_n_offset + in_c_offset + in_h_offset, outputs,
CopyDataToOutput<T>(inputs, in_n_offset + in_c_offset + in_h_offset, outputs,
out_n_offset + out_c_offset + out_h_offset, input_element_num_[2], 2);
continue;
}
@ -154,23 +172,25 @@ int SliceCPUKernel::SignOfStride(size_t axis) const {
}
return -1;
}
template <typename T>
void SliceCPUKernel::CopyDataToOutput(const std::vector<kernel::AddressPtr> &inputs, size_t in_offset,
const std::vector<kernel::AddressPtr> &outputs, size_t out_offset,
size_t copy_num, int id) const {
auto input_addr = reinterpret_cast<float *>(inputs[0]->addr);
T *input_addr = reinterpret_cast<T *>(inputs[0]->addr);
auto in_buff_size = inputs[0]->size;
auto output_addr = reinterpret_cast<float *>(outputs[0]->addr);
T *output_addr = reinterpret_cast<T *>(outputs[0]->addr);
auto out_buff_size = outputs[0]->size;
if ((in_offset + copy_num) * sizeof(float) > in_buff_size) {
if ((in_offset + copy_num) * sizeof(T) > in_buff_size) {
MS_LOG(EXCEPTION) << "input memory out of bounds.";
}
if ((out_offset + copy_num) * sizeof(float) > out_buff_size) {
if ((out_offset + copy_num) * sizeof(T) > out_buff_size) {
MS_LOG(EXCEPTION) << id << " output memory out of bounds.";
}
auto ret = memcpy_s(output_addr + out_offset, out_buff_size - out_offset * sizeof(float), input_addr + in_offset,
copy_num * sizeof(float));
auto ret = memcpy_s(output_addr + out_offset, out_buff_size - out_offset * sizeof(T), input_addr + in_offset,
copy_num * sizeof(T));
if (ret != EOK) {
MS_LOG(EXCEPTION) << "memcpy failed. ret:" << ret;
}

12
mindspore/ccsrc/backend/kernel_compiler/cpu/slice_cpu_kernel.h
View File

@ -33,12 +33,15 @@ class SliceCPUKernel : public CPUKernel {
const std::vector<AddressPtr> &outputs) override;
private:
void ExpandAllMemberDims();
bool CanCopyMemoryOnAxis(size_t dim) const;
int SignOfStride(size_t axis) const;
template <typename T>
bool LaunchKernel(const std::vector<kernel::AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &outputs);
template <typename T>
void CopyDataToOutput(const std::vector<kernel::AddressPtr> &inputs, size_t in_offset,
const std::vector<kernel::AddressPtr> &outputs, size_t out_offset, size_t copy_num,
int id) const;
void ExpandAllMemberDims();
bool CanCopyMemoryOnAxis(size_t dim) const;
int SignOfStride(size_t axis) const;
void CheckParam(const CNodePtr &kernel_node) const;
void TransArg();
void ClipBegin();
@ -49,6 +52,7 @@ class SliceCPUKernel : public CPUKernel {
std::vector<size_t> input_element_num_;
std::vector<size_t> output_shape_;
std::vector<size_t> output_element_num_;
TypeId dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL(Slice, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
@ -58,6 +62,8 @@ MS_REG_CPU_KERNEL(StridedSlice, KernelAttr().AddInputAttr(kNumberTypeFloat32).Ad
SliceCPUKernel);
MS_REG_CPU_KERNEL(StridedSlice, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
SliceCPUKernel);
MS_REG_CPU_KERNEL(StridedSlice, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
SliceCPUKernel);
} // namespace kernel
} // namespace mindspore

43
mindspore/ccsrc/backend/kernel_compiler/cpu/slice_grad_cpu_kernel.cc
View File

@ -23,6 +23,7 @@ namespace kernel {
void SliceGradCPUKernel::InitKernel(const CNodePtr &kernel_node) {
CheckParam(kernel_node);
output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0);
dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0);
std::vector<int64_t> begin_me = AnfAlgo::GetNodeAttr<std::vector<int64_t>>(kernel_node, BEGIN);
(void)std::transform(begin_me.begin(), begin_me.end(), std::back_inserter(begin_),
[](const int64_t &value) { return static_cast<int>(value); });
@ -78,8 +79,25 @@ void SliceGradCPUKernel::ExpandAllMemberDims() {
bool SliceGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> & /*workspace*/,
const std::vector<kernel::AddressPtr> &outputs) {
auto input_addr = reinterpret_cast<float *>(inputs[0]->addr);
auto output_addr = reinterpret_cast<float *>(outputs[0]->addr);
bool ret{true};
if (dtype_ == kNumberTypeInt32) {
ret = LaunchKernel<int>(inputs, outputs);
} else if (dtype_ == kNumberTypeFloat32) {
ret = LaunchKernel<float>(inputs, outputs);
} else if (dtype_ == kNumberTypeBool) {
ret = LaunchKernel<bool>(inputs, outputs);
} else {
MS_LOG(ERROR) << "Slice op only support input_x int32 and float32";
return false;
}
return ret;
}
template <typename T>
bool SliceGradCPUKernel::LaunchKernel(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> &outputs) {
T *input_addr = reinterpret_cast<T *>(inputs[0]->addr);
T *output_addr = reinterpret_cast<T *>(outputs[0]->addr);
auto ret = memset_s(output_addr, outputs[0]->size, 0, outputs[0]->size);
if (ret != EOK) {
@ -97,7 +115,7 @@ bool SliceGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
for (int i = begin_[0]; stride_signs[0] * i < stride_signs[0] * end_[0];
i += strides_[0], in_n_offset += input_element_num_[0], out_n_offset += out_step_size[0]) {
if (can_copy_memory[0]) {
CopyDataToOutput(inputs, in_n_offset, outputs, out_n_offset, input_element_num_[0], 0);
CopyDataToOutput<T>(inputs, in_n_offset, outputs, out_n_offset, input_element_num_[0], 0);
continue;
}
auto in_c_offset = 0;
@ -105,8 +123,8 @@ bool SliceGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
for (int j = begin_[1]; stride_signs[1] * j < stride_signs[1] * end_[1];
j += strides_[1], in_c_offset += input_element_num_[1], out_c_offset += out_step_size[1]) {
if (can_copy_memory[1]) {
CopyDataToOutput(inputs, in_n_offset + in_c_offset, outputs, out_n_offset + out_c_offset, input_element_num_[1],
1);
CopyDataToOutput<T>(inputs, in_n_offset + in_c_offset, outputs, out_n_offset + out_c_offset,
input_element_num_[1], 1);
continue;
}
auto in_h_offset = 0;
@ -114,7 +132,7 @@ bool SliceGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
for (int k = begin_[2]; stride_signs[2] * k < stride_signs[2] * end_[2];
k += strides_[2], in_h_offset += input_element_num_[2], out_h_offset += out_step_size[2]) {
if (can_copy_memory[2]) {
CopyDataToOutput(inputs, in_n_offset + in_c_offset + in_h_offset, outputs,
CopyDataToOutput<T>(inputs, in_n_offset + in_c_offset + in_h_offset, outputs,
out_n_offset + out_c_offset + out_h_offset, input_element_num_[2], 2);
continue;
}
@ -143,23 +161,24 @@ int SliceGradCPUKernel::SignOfStride(size_t axis) const {
return -1;
}
template <typename T>
void SliceGradCPUKernel::CopyDataToOutput(const std::vector<kernel::AddressPtr> &inputs, size_t in_offset,
const std::vector<kernel::AddressPtr> &outputs, size_t out_offset,
size_t copy_num, int id) const {
auto input_addr = reinterpret_cast<float *>(inputs[0]->addr);
T *input_addr = reinterpret_cast<T *>(inputs[0]->addr);
auto in_buff_size = inputs[0]->size;
auto output_addr = reinterpret_cast<float *>(outputs[0]->addr);
T *output_addr = reinterpret_cast<T *>(outputs[0]->addr);
auto out_buff_size = outputs[0]->size;
if ((in_offset + copy_num) * sizeof(float) > in_buff_size) {
if ((in_offset + copy_num) * sizeof(T) > in_buff_size) {
MS_LOG(EXCEPTION) << id << "input memory out of bounds.";
}
if ((out_offset + copy_num) * sizeof(float) > out_buff_size) {
if ((out_offset + copy_num) * sizeof(T) > out_buff_size) {
MS_LOG(EXCEPTION) << id << "output memory out of bounds.";
}
auto ret = memcpy_s(output_addr + out_offset, out_buff_size - out_offset * sizeof(float), input_addr + in_offset,
copy_num * sizeof(float));
auto ret = memcpy_s(output_addr + out_offset, out_buff_size - out_offset * sizeof(T), input_addr + in_offset,
copy_num * sizeof(T));
if (ret != EOK) {
MS_LOG(EXCEPTION) << "memcpy failed. ret:" << ret;
}

15
mindspore/ccsrc/backend/kernel_compiler/cpu/slice_grad_cpu_kernel.h
View File

@ -33,12 +33,16 @@ class SliceGradCPUKernel : public CPUKernel {
const std::vector<AddressPtr> &outputs) override;
private:
void ExpandAllMemberDims();
bool CanCopyMemoryOnAxis(size_t dim) const;
int SignOfStride(size_t axis) const;
template <typename T>
bool LaunchKernel(const std::vector<kernel::AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &outputs);
template <typename T>
void CopyDataToOutput(const std::vector<kernel::AddressPtr> &inputs, size_t in_offset,
const std::vector<kernel::AddressPtr> &outputs, size_t out_offset, size_t copy_num,
int id) const;
void ExpandAllMemberDims();
bool CanCopyMemoryOnAxis(size_t dim) const;
int SignOfStride(size_t axis) const;
void CheckParam(const CNodePtr &kernel_node) const;
void FormatArgs(bool stride);
std::vector<int> begin_;
@ -49,6 +53,7 @@ class SliceGradCPUKernel : public CPUKernel {
std::vector<size_t> input_element_num_;
std::vector<size_t> output_shape_;
std::vector<size_t> output_element_num_;
TypeId dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL(
@ -57,6 +62,10 @@ MS_REG_CPU_KERNEL(
SliceGradCPUKernel);
MS_REG_CPU_KERNEL(StridedSliceGrad, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
SliceGradCPUKernel);
MS_REG_CPU_KERNEL(StridedSliceGrad, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
SliceGradCPUKernel);
MS_REG_CPU_KERNEL(StridedSliceGrad, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
SliceGradCPUKernel);
} // namespace kernel
} // namespace mindspore

46
tests/st/ops/cpu/test_slice_grad_op.py
View File

@ -22,6 +22,7 @@ from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.common.api import ms_function
from mindspore.ops.operations import _grad_ops as G
from mindspore.ops import operations as P
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
@ -77,7 +78,52 @@ def test_slice_grad2():
[[0., 0.], [8., 9.], [10., 11.]]]
assert (output.asnumpy() == expect).all()
class StridedSliceGrad(nn.Cell):
def __init__(self, x, begin, end, stride):
super(StridedSliceGrad, self).__init__()
self.shape_op = P.Shape()
self.shapex = self.shape_op(x)
self.begin = begin
self.end = end
self.stride = stride
self.stride_slice = G.StridedSliceGrad()
def construct(self, dy):
return self.stride_slice(dy, self.shapex, self.begin, self.end, self.stride)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_strided_slice_grad_bool_type():
x = Tensor([[[False, False, True], [False, True, False]], [[False, True, False], [True, False, False]],
[[False, True, True], [True, False, True]]], mstype.bool_)
dy = Tensor([False, True, False], mstype.bool_)
begin = (1, 0, 0)
end = (2, 1, 3)
stride = (1, 1, 1)
slice_op = StridedSliceGrad(x, begin, end, stride)
output = slice_op(dy)
expected_output = np.array([[[False, False, False], [False, False, False]],
[[False, True, False], [False, False, False]],
[[False, False, False], [False, False, False]]])
assert (output.asnumpy() == expected_output).all()
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_strided_slice_grad_float32_type():
x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]], mstype.float32)
dy = Tensor([3, 3, 3], mstype.float32)
begin = (1, 0, 0)
end = (2, 1, 3)
stride = (1, 1, 1)
slice_op = StridedSliceGrad(x, begin, end, stride)
output = slice_op(dy)
expected_output = np.array([[[0, 0, 0], [0, 0, 0]], [[3, 3, 3], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]])
assert (output.asnumpy() == expected_output).all()
if __name__ == '__main__':
test_slice_grad()
test_slice_grad2()
test_strided_slice_grad_bool_type()
test_strided_slice_grad_float32_type()

26
tests/st/ops/cpu/test_slice_op.py
View File

@ -160,6 +160,31 @@ def test_slice6():
assert (output[2].asnumpy() == inputx[-10:10:1, :, -10:10:1]).all()
class StridedSlice(nn.Cell):
def __init__(self, begin, end, stride):
super(StridedSlice, self).__init__()
self.begin = begin
self.end = end
self.stride = stride
self.stride_slice = P.StridedSlice()
def construct(self, x):
return self.stride_slice(x, self.begin, self.end, self.stride)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_strided_slice_bool_type():
input_x = Tensor([[[False, False, True], [False, True, False]], [[False, True, False], [True, False, False]],
[[False, True, True], [True, False, True]]], mstype.bool_)
begin = (1, 0, 0)
end = (2, 1, 3)
stride = (1, 1, 1)
slice_op = StridedSlice(begin, end, stride)
output = slice_op(input_x)
expected_output = np.array([False, True, False])
assert (output.asnumpy() == expected_output).all()
if __name__ == '__main__':
test_slice()
test_slice2()
@ -167,3 +192,4 @@ if __name__ == '__main__':
test_slice4()
test_slice5()
test_slice6()
test_strided_slice_bool_type()