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add roll and roll_grad kernels and strided_slice and strided_slice_gr…
…ad kernels, test=kunlun (#47368) * add stat tool * add roll and roll_grad kernels and strided_slice and strided_slice_grad kernels, test=kunlun
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,73 @@ | ||
| // 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. | ||
|
|
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| #include "paddle/phi/kernels/roll_grad_kernel.h" | ||
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| #include "paddle/phi/backends/xpu/enforce_xpu.h" | ||
| #include "paddle/phi/core/kernel_registry.h" | ||
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| namespace phi { | ||
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| template <typename T, typename Context> | ||
| void RollGradKernel(const Context& dev_ctx, | ||
| const DenseTensor& x, | ||
| const DenseTensor& out_grad, | ||
| const IntArray& shifts, | ||
| const std::vector<int64_t>& axis, | ||
| DenseTensor* x_grad) { | ||
| using XPUType = typename XPUTypeTrait<T>::Type; | ||
| auto shifts_data = shifts.GetData(); | ||
| dev_ctx.template Alloc<T>(x_grad); | ||
| DDim input_dim = x.dims(); | ||
| std::vector<int> xshape; | ||
| size_t nums = shifts_data.size(); | ||
| for (int i = 0; i < input_dim.size(); ++i) { | ||
| xshape.emplace_back(input_dim[i]); | ||
| } | ||
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| auto dims = axis; | ||
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| // axis = none, reshape to 1-D tensor | ||
| if (dims.size() == 0) { | ||
| dims.push_back(0l); | ||
| input_dim = phi::Dim<1>(x.numel()); | ||
| } | ||
| std::vector<int> shifts_in; | ||
| std::vector<int> axis_in; | ||
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| for (size_t i = 0; i < nums; ++i) { | ||
| int a = dims[i]; | ||
| if (a < 0) { | ||
| a += (input_dim.size()); | ||
| } | ||
| axis_in.emplace_back(a); | ||
| int sh = (0 - shifts_data[i]) % input_dim[a]; | ||
| if (sh < 0) { | ||
| sh += input_dim[a]; | ||
| } | ||
| shifts_in.emplace_back(sh); | ||
| } | ||
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| int r = xpu::roll(dev_ctx.x_context(), | ||
| reinterpret_cast<const XPUType*>(out_grad.data<T>()), | ||
| reinterpret_cast<XPUType*>(x_grad->data<T>()), | ||
| xshape, | ||
| shifts_in, | ||
| axis_in); | ||
| PADDLE_ENFORCE_XDNN_SUCCESS(r, "roll"); | ||
| } | ||
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| } // namespace phi | ||
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| PD_REGISTER_KERNEL(roll_grad, XPU, ALL_LAYOUT, phi::RollGradKernel, float) {} |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,70 @@ | ||
| // 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. | ||
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| #include "paddle/phi/kernels/roll_kernel.h" | ||
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| #include "paddle/phi/backends/xpu/enforce_xpu.h" | ||
| #include "paddle/phi/core/kernel_registry.h" | ||
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| namespace phi { | ||
| template <typename T, typename Context> | ||
| void RollKernel(const Context& dev_ctx, | ||
| const DenseTensor& x, | ||
| const IntArray& shifts, | ||
| const std::vector<int64_t>& axis, | ||
| DenseTensor* out) { | ||
| using XPUType = typename XPUTypeTrait<T>::Type; | ||
| auto shifts_data = shifts.GetData(); | ||
| dev_ctx.template Alloc<T>(out); | ||
| DDim input_dim = x.dims(); | ||
| std::vector<int> xshape; | ||
| size_t nums = shifts_data.size(); | ||
| for (int i = 0; i < input_dim.size(); ++i) { | ||
| xshape.emplace_back(input_dim[i]); | ||
| } | ||
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| auto dims = axis; | ||
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| // axis = none, reshape to 1-D tensor | ||
| if (dims.size() == 0) { | ||
| dims.push_back(0l); | ||
| input_dim = phi::Dim<1>(x.numel()); | ||
| } | ||
| std::vector<int> shifts_in; | ||
| std::vector<int> axis_in; | ||
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| for (size_t i = 0; i < nums; ++i) { | ||
| int a = dims[i]; | ||
| if (a < 0) { | ||
| a += (input_dim.size()); | ||
| } | ||
| axis_in.emplace_back(a); | ||
| int sh = shifts_data[i] % input_dim[a]; | ||
| if (sh < 0) { | ||
| sh += input_dim[a]; | ||
| } | ||
| shifts_in.emplace_back(sh); | ||
| } | ||
| int r = xpu::roll(dev_ctx.x_context(), | ||
| reinterpret_cast<const XPUType*>(x.data<T>()), | ||
| reinterpret_cast<XPUType*>(out->data<T>()), | ||
| xshape, | ||
| shifts_in, | ||
| axis_in); | ||
| PADDLE_ENFORCE_XDNN_SUCCESS(r, "roll"); | ||
| } | ||
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| } // namespace phi | ||
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| PD_REGISTER_KERNEL(roll, XPU, ALL_LAYOUT, phi::RollKernel, float) {} |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,100 @@ | ||
| // 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. | ||
|
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| #include "paddle/phi/kernels/strided_slice_grad_kernel.h" | ||
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| #include "paddle/phi/backends/xpu/enforce_xpu.h" | ||
| #include "paddle/phi/core/kernel_registry.h" | ||
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| namespace phi { | ||
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| template <typename T, typename Context> | ||
| void StridedSliceRawGradKernel(const Context& dev_ctx, | ||
| const DenseTensor& x, | ||
| const DenseTensor& out_grad, | ||
| const std::vector<int>& axes, | ||
| const IntArray& starts, | ||
| const IntArray& ends, | ||
| const IntArray& strides, | ||
| const std::vector<int>& infer_flags, | ||
| const std::vector<int>& decrease_axis, | ||
| DenseTensor* x_grad) { | ||
| using XPUType = typename XPUTypeTrait<T>::Type; | ||
| DDim in_dims = x.dims(); | ||
| dev_ctx.template Alloc<T>(x_grad); | ||
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| auto starts_ = starts.GetData(); | ||
| auto ends_ = ends.GetData(); | ||
| auto strides_ = strides.GetData(); | ||
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| std::vector<int> xshape; | ||
| std::vector<int> starts_in(in_dims.size(), 0); | ||
| std::vector<int> ends_in; | ||
| std::vector<int> strides_in(in_dims.size(), 1); | ||
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| for (int i = 0; i < in_dims.size(); ++i) { | ||
| xshape.emplace_back(in_dims[i]); | ||
| ends_in.emplace_back(in_dims[i]); | ||
| } | ||
| int num = axes.size(); | ||
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| for (int i = 0; i < num; ++i) { | ||
| PADDLE_ENFORCE_EQ( | ||
| strides_[i] > 0, | ||
| true, | ||
| errors::InvalidArgument("Currently, XPU strided slice kernel does not", | ||
| "support reverse strided slice")); | ||
| int cur_axe = axes[i]; | ||
| int st = starts_[i]; | ||
| if (st > xshape[cur_axe]) { | ||
| st = xshape[cur_axe]; | ||
| } | ||
| if (st < 0) { | ||
| st += xshape[cur_axe]; | ||
| } | ||
| starts_in[cur_axe] = st; | ||
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| int end = ends_[i]; | ||
| if (end > xshape[cur_axe]) { | ||
| end = xshape[cur_axe]; | ||
| } | ||
| if (end < 0) { | ||
| end += xshape[cur_axe]; | ||
| } | ||
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| ends_in[cur_axe] = end; | ||
| strides_in[cur_axe] = strides_[i]; | ||
| } | ||
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| int r = xpu::strided_slice_grad( | ||
| dev_ctx.x_context(), | ||
| reinterpret_cast<const XPUType*>(out_grad.data<T>()), | ||
| reinterpret_cast<XPUType*>(x_grad->data<T>()), | ||
| xshape, | ||
| starts_in, | ||
| ends_in, | ||
| strides_in); | ||
| PADDLE_ENFORCE_XDNN_SUCCESS(r, "strided_slice_grad"); | ||
| } | ||
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| } // namespace phi | ||
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| PD_REGISTER_KERNEL(strided_slice_raw_grad, | ||
| XPU, | ||
| ALL_LAYOUT, | ||
| phi::StridedSliceRawGradKernel, | ||
| int, | ||
| int16_t, | ||
| float, | ||
| phi::dtype::float16) {} |
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