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[XPU]. Add meshgrid op on xpu and bindings of floordiv/min/mod.
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,120 @@ | ||
| // 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 "lite/kernels/xpu/meshgrid_compute.h" | ||
| #include <vector> | ||
| #include "lite/backends/xpu/math.h" | ||
| #include "lite/backends/xpu/xpu_header_sitter.h" | ||
| #include "lite/core/op_registry.h" | ||
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| namespace paddle { | ||
| namespace lite { | ||
| namespace kernels { | ||
| namespace xpu { | ||
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| template <typename T, PrecisionType PType> | ||
| void MeshgridCompute<T, PType>::Run() { | ||
| auto& param = this->template Param<operators::MeshgridParam>(); | ||
| auto& ctx = this->ctx_->template As<XPUContext>(); | ||
| std::vector<lite::Tensor*>& ins = param.X; | ||
| std::vector<lite::Tensor*>& outs = param.Out; | ||
| int64_t size = ins.size(); | ||
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| std::vector<const T*> x_list; | ||
| std::vector<std::vector<int>> x_shape_list; | ||
| for (int i = 0; i < size; ++i) { | ||
| std::vector<int> x_shape(1); | ||
| switch (ins[i]->dims().size()) { | ||
| case 0: | ||
| x_shape[0] = 1; | ||
| break; | ||
| case 1: | ||
| x_shape[0] = ins[i]->dims()[0]; | ||
| break; | ||
| default: | ||
| LOG(FATAL) << "Meshgrid Op expected scalar or 1D tensor in the input " | ||
| "tensor list"; | ||
| break; | ||
| } | ||
| x_shape_list.push_back(x_shape); | ||
| x_list.push_back(reinterpret_cast<const T*>(ins[i]->template data<T>())); | ||
| } | ||
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| std::vector<T*> out_ptrs; | ||
| for (auto out : outs) { | ||
| out_ptrs.push_back(out->template mutable_data<T>(TARGET(kXPU))); | ||
| } | ||
|
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| int r = | ||
| xdnn::meshgrid<T>(ctx.GetRawContext(), x_list, out_ptrs, x_shape_list); | ||
|
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| CHECK_EQ(r, 0); | ||
| } | ||
|
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| } // namespace xpu | ||
| } // namespace kernels | ||
| } // namespace lite | ||
| } // namespace paddle | ||
|
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| using meshgridFP32 = | ||
| paddle::lite::kernels::xpu::MeshgridCompute<float, PRECISION(kFloat)>; | ||
| REGISTER_LITE_KERNEL(meshgrid, kXPU, kFloat, kAny, meshgridFP32, float32) | ||
| .BindInput("X", | ||
| {LiteType::GetTensorTy(TARGET(kXPU), | ||
| PRECISION(kFloat), | ||
| DATALAYOUT(kAny))}) | ||
| .BindOutput("Out", | ||
| {LiteType::GetTensorTy(TARGET(kXPU), | ||
| PRECISION(kFloat), | ||
| DATALAYOUT(kAny))}) | ||
| .Finalize(); | ||
|
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| using meshgridFP16 = | ||
| paddle::lite::kernels::xpu::MeshgridCompute<float16, PRECISION(kFP16)>; | ||
| REGISTER_LITE_KERNEL(meshgrid, kXPU, kFP16, kAny, meshgridFP16, float16) | ||
| .BindInput("X", | ||
| {LiteType::GetTensorTy(TARGET(kXPU), | ||
| PRECISION(kFP16), | ||
| DATALAYOUT(kAny))}) | ||
| .BindOutput("Out", | ||
| {LiteType::GetTensorTy(TARGET(kXPU), | ||
| PRECISION(kFP16), | ||
| DATALAYOUT(kAny))}) | ||
| .Finalize(); | ||
|
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| using meshgridInt32 = | ||
| paddle::lite::kernels::xpu::MeshgridCompute<int, PRECISION(kFloat)>; | ||
| REGISTER_LITE_KERNEL(meshgrid, kXPU, kFloat, kAny, meshgridInt32, int32) | ||
| .BindInput("X", | ||
| {LiteType::GetTensorTy(TARGET(kXPU), | ||
| PRECISION(kInt32), | ||
| DATALAYOUT(kAny))}) | ||
| .BindOutput("Out", | ||
| {LiteType::GetTensorTy(TARGET(kXPU), | ||
| PRECISION(kInt32), | ||
| DATALAYOUT(kAny))}) | ||
| .Finalize(); | ||
|
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| using meshgridInt64 = | ||
| paddle::lite::kernels::xpu::MeshgridCompute<int64_t, PRECISION(kFloat)>; | ||
| REGISTER_LITE_KERNEL(meshgrid, kXPU, kFloat, kAny, meshgridInt64, int64) | ||
| .BindInput("X", | ||
| {LiteType::GetTensorTy(TARGET(kXPU), | ||
| PRECISION(kInt64), | ||
| DATALAYOUT(kAny))}) | ||
| .BindOutput("Out", | ||
| {LiteType::GetTensorTy(TARGET(kXPU), | ||
| PRECISION(kInt64), | ||
| DATALAYOUT(kAny))}) | ||
| .Finalize(); |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,38 @@ | ||
| // 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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| #pragma once | ||
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| #include "lite/core/kernel.h" | ||
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| namespace paddle { | ||
| namespace lite { | ||
| namespace kernels { | ||
| namespace xpu { | ||
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| template <typename T, PrecisionType PType> | ||
| class MeshgridCompute | ||
| : public KernelLite<TARGET(kXPU), PType, DATALAYOUT(kAny)> { | ||
| public: | ||
| using param_t = operators::MeshgridParam; | ||
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| virtual void Run(); | ||
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| virtual ~MeshgridCompute() = default; | ||
| }; | ||
|
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| } // namespace xpu | ||
| } // namespace kernels | ||
| } // namespace lite | ||
| } // namespace paddle |
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