【API2.0】add masked_select Op for API2.0 (#26374)
wangchaochaohu
5 years ago
committed by
GitHub
parent
c09de13ec4
commit
e167e87974
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/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/masked_select_op.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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class MaskedSelectOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "Input", "MaskedSelect");
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OP_INOUT_CHECK(ctx->HasInput("Mask"), "Input", "Mask", "MaskedSelect");
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OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Out", "MaskedSelect");
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framework::DDim output_dims(ctx->GetInputDim("X"));
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ctx->SetOutputDim("Y", output_dims);
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ctx->ShareLoD("X", /*->*/ "Y");
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
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return framework::OpKernelType(data_type, ctx.device_context());
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}
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};
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class MaskedSelectOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "The input tensor.");
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AddInput("Mask",
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"The mask of Input Tensor to be selected which is a bool Tensor.");
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AddOutput(
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"Y",
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"The returned tensor, the data type "
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"is same as input, will be on the same device with the input Tensor.");
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AddComment(R"DOC(
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Size Operator.
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Return a new 0-D tensor which indexes the indexed tensor according
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the mask which is a tensor withe data type bool.
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)DOC");
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}
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};
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class MaskedSelectOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Input",
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"Input", "MaskedSelect");
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OP_INOUT_CHECK(ctx->HasInput("Mask"), "Input", "Mask", "MaskedSelect");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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ctx->ShareLoD("X", /*-->*/ framework::GradVarName("X"));
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
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ctx, framework::GradVarName("Y")),
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ctx.device_context());
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}
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};
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template <typename T>
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class MaskedSelectGradOpMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> op) const override {
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op->SetType("masked_select_grad");
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op->SetInput("X", this->Input("X"));
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op->SetInput("Mask", this->Input("Mask"));
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op->SetInput(framework::GradVarName("Y"), this->OutputGrad("Y"));
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op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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}
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};
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DECLARE_NO_NEED_BUFFER_VARS_INFERER(MaskedSelectedGradNoNeedBufferVarsInferer,
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"X");
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(masked_select, ops::MaskedSelectOp, ops::MaskedSelectOpMaker,
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ops::MaskedSelectGradOpMaker<paddle::framework::OpDesc>,
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ops::MaskedSelectGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(masked_select_grad, ops::MaskedSelectOpGrad,
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ops::MaskedSelectedGradNoNeedBufferVarsInferer);
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REGISTER_OP_CPU_KERNEL(
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masked_select,
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ops::MaskedSelectKernel<paddle::platform::CPUDeviceContext, float>,
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ops::MaskedSelectKernel<paddle::platform::CPUDeviceContext, double>,
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ops::MaskedSelectKernel<paddle::platform::CPUDeviceContext, int>,
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ops::MaskedSelectKernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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masked_select_grad,
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ops::MaskedSelectGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::MaskedSelectGradKernel<paddle::platform::CPUDeviceContext, double>,
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ops::MaskedSelectGradKernel<paddle::platform::CPUDeviceContext, int>,
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ops::MaskedSelectGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
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@ -0,0 +1,179 @@
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/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <thrust/device_ptr.h>
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#include <thrust/device_vector.h>
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#include <thrust/reverse.h>
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#include <thrust/scan.h>
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#include "paddle/fluid/operators/masked_select_op.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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using DDim = framework::DDim;
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__global__ void SetMaskArray(const bool* mask, int32_t* mask_array, int size) {
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int idx = blockDim.x * blockIdx.x + threadIdx.x;
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for (; idx < size; idx += blockDim.x * gridDim.x) {
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if (mask[idx])
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mask_array[idx] = 1;
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else
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mask_array[idx] = 0;
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}
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}
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template <typename T>
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__global__ void SelectWithPrefixMask(const int32_t* mask_prefix_sum,
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const bool* mask, const T* input, T* out,
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int size) {
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int idx = blockDim.x * blockIdx.x + threadIdx.x;
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for (; idx < size; idx += blockDim.x * gridDim.x) {
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if (mask[idx]) {
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int index = mask_prefix_sum[idx];
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out[index] = input[idx];
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}
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}
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}
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template <typename T>
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__global__ void SelectGradWithPrefixMask(const int32_t* mask_prefix_sum,
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const bool* mask, const T* input,
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T* out, int size) {
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int idx = blockDim.x * blockIdx.x + threadIdx.x;
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for (; idx < size; idx += blockDim.x * gridDim.x) {
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if (mask[idx]) {
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int index = mask_prefix_sum[idx];
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out[idx] = input[index];
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} else {
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out[idx] = 0;
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}
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}
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}
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template <typename DeviceContext, typename T>
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class MaskedSelectCUDAKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto input = ctx.Input<framework::Tensor>("X");
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auto mask = ctx.Input<framework::Tensor>("Mask");
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auto out = ctx.Output<framework::Tensor>("Y");
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auto* mask_data = mask->data<bool>();
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auto input_data = input->data<T>();
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auto mask_size = mask->numel();
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auto input_dim = input->dims();
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auto mask_dim = mask->dims();
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PADDLE_ENFORCE_EQ(
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input_dim, mask_dim,
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platform::errors::InvalidArgument(
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"The dim size of input and mask in OP(masked_selected) "
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"must be equal, but got input dim:(%ld), mask dim: "
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"(%ld). Please check input "
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"value.",
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input_dim, mask_dim));
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thrust::device_ptr<const bool> mask_dev_ptr =
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thrust::device_pointer_cast(mask_data);
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thrust::device_vector<T> mask_vec(mask_dev_ptr, mask_dev_ptr + mask_size);
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auto out_size = thrust::count(mask_vec.begin(), mask_vec.end(), true);
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framework::DDim out_dim{out_size};
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out->Resize(out_dim);
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auto out_data = out->mutable_data<T>(ctx.GetPlace());
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Tensor mask_array;
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Tensor mask_prefix_sum;
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mask_array.Resize(mask_dim);
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mask_prefix_sum.Resize(mask_dim);
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int32_t* mask_array_data = mask_array.mutable_data<int32_t>(ctx.GetPlace());
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int32_t* mask_prefix_sum_data =
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mask_prefix_sum.mutable_data<int32_t>(ctx.GetPlace());
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int threads = 512;
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int grid = (mask_size + threads - 1) / threads;
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auto stream = ctx.cuda_device_context().stream();
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SetMaskArray<<<grid, threads, 0, stream>>>(mask_data, mask_array_data,
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mask_size);
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thrust::device_ptr<int32_t> mask_array_dev_ptr =
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thrust::device_pointer_cast(mask_array_data);
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thrust::device_vector<int32_t> mask_array_vec(
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mask_array_dev_ptr, mask_array_dev_ptr + mask_size);
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thrust::exclusive_scan(thrust::device, mask_array_vec.begin(),
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mask_array_vec.end(), mask_prefix_sum_data);
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SelectWithPrefixMask<T><<<grid, threads, 0, stream>>>(
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mask_prefix_sum_data, mask_data, input_data, out_data, mask_size);
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}
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};
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template <typename DeviceContext, typename T>
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class MaskedSelectGradCUDAKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto input = ctx.Input<framework::Tensor>(framework::GradVarName("Y"));
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auto mask = ctx.Input<framework::Tensor>("Mask");
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auto out = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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auto* mask_data = mask->data<bool>();
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auto* input_data = input->data<T>();
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auto* out_data = out->mutable_data<T>(ctx.GetPlace());
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auto input_size = input->numel();
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auto mask_size = mask->numel();
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auto mask_dim = mask->dims();
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auto out_size = mask_size;
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Tensor mask_array;
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Tensor mask_prefix_sum;
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mask_array.Resize(mask_dim);
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mask_prefix_sum.Resize(mask_dim);
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int32_t* mask_array_data = mask_array.mutable_data<int32_t>(ctx.GetPlace());
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int32_t* mask_prefix_sum_data =
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mask_prefix_sum.mutable_data<int32_t>(ctx.GetPlace());
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int threads = 512;
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int grid = (mask_size + threads - 1) / threads;
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auto stream = ctx.cuda_device_context().stream();
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SetMaskArray<<<grid, threads, 0, stream>>>(mask_data, mask_array_data,
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mask_size);
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thrust::device_ptr<int32_t> mask_array_dev_ptr =
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thrust::device_pointer_cast(mask_array_data);
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thrust::device_vector<int32_t> mask_array_vec(
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mask_array_dev_ptr, mask_array_dev_ptr + mask_size);
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thrust::exclusive_scan(thrust::device, mask_array_vec.begin(),
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mask_array_vec.end(), mask_prefix_sum_data);
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SelectGradWithPrefixMask<T><<<grid, threads, 0, stream>>>(
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mask_prefix_sum_data, mask_data, input_data, out_data, mask_size);
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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masked_select,
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ops::MaskedSelectCUDAKernel<paddle::platform::CUDADeviceContext, float>,
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ops::MaskedSelectCUDAKernel<paddle::platform::CUDADeviceContext, double>,
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ops::MaskedSelectCUDAKernel<paddle::platform::CUDADeviceContext, int>,
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ops::MaskedSelectCUDAKernel<paddle::platform::CUDADeviceContext, int64_t>);
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REGISTER_OP_CUDA_KERNEL(
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masked_select_grad,
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ops::MaskedSelectGradCUDAKernel<paddle::platform::CUDADeviceContext, float>,
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ops::MaskedSelectGradCUDAKernel<paddle::platform::CUDADeviceContext,
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double>,
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ops::MaskedSelectGradCUDAKernel<paddle::platform::CUDADeviceContext, int>,
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ops::MaskedSelectGradCUDAKernel<paddle::platform::CUDADeviceContext,
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int64_t>);
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@ -0,0 +1,94 @@
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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using DDim = framework::DDim;
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template <typename DeviceContext, typename T>
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class MaskedSelectKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto input = context.Input<framework::Tensor>("X");
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auto mask = context.Input<framework::Tensor>("Mask");
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auto out = context.Output<framework::Tensor>("Y");
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auto* mask_data = mask->data<bool>();
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auto input_data = input->data<T>();
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auto mask_size = mask->numel();
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auto input_dim = input->dims();
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auto mask_dim = mask->dims();
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PADDLE_ENFORCE_EQ(
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input_dim, mask_dim,
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platform::errors::InvalidArgument(
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"The dim size of input and mask in OP(masked_selected) "
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"must be equal, but got input dim:(%ld), mask dim: "
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"(%ld). Please check input "
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"value.",
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input_dim, mask_dim));
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int out_size = 0;
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for (int i = 0; i < mask_size; i++) {
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if (mask_data[i]) out_size++;
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}
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|
||||||
|
framework::DDim out_dim{out_size};
|
||||||
|
out->Resize(out_dim);
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|
auto out_data = out->mutable_data<T>(context.GetPlace());
|
||||||
|
|
||||||
|
int index = 0;
|
||||||
|
for (int i = 0; i < mask_size; i++) {
|
||||||
|
if (mask_data[i]) {
|
||||||
|
out_data[index] = input_data[i];
|
||||||
|
index++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
template <typename DeviceContext, typename T>
|
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|
class MaskedSelectGradKernel : public framework::OpKernel<T> {
|
||||||
|
public:
|
||||||
|
void Compute(const framework::ExecutionContext& context) const override {
|
||||||
|
auto out = context.Output<framework::Tensor>(framework::GradVarName("X"));
|
||||||
|
auto mask = context.Input<framework::Tensor>("Mask");
|
||||||
|
auto input = context.Input<framework::Tensor>(framework::GradVarName("Y"));
|
||||||
|
|
||||||
|
auto* mask_data = mask->data<bool>();
|
||||||
|
auto* input_data = input->data<T>();
|
||||||
|
auto* out_data = out->mutable_data<T>(context.GetPlace());
|
||||||
|
int mask_size = mask->numel();
|
||||||
|
|
||||||
|
int index = 0;
|
||||||
|
for (int i = 0; i < mask_size; i++) {
|
||||||
|
if (mask_data[i]) {
|
||||||
|
out_data[i] = input_data[index];
|
||||||
|
index++;
|
||||||
|
} else {
|
||||||
|
out_data[i] = 0;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace operators
|
||||||
|
} // namespace paddle
|
||||||
@ -0,0 +1,124 @@
|
|||||||
|
# Copyright (c) 2020 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
|
||||||
|
from op_test import OpTest
|
||||||
|
import paddle.fluid as fluid
|
||||||
|
import paddle
|
||||||
|
|
||||||
|
|
||||||
|
def np_masked_select(x, mask):
|
||||||
|
result = np.empty(shape=(0), dtype=x.dtype)
|
||||||
|
for ele, ma in zip(np.nditer(x), np.nditer(mask)):
|
||||||
|
if ma:
|
||||||
|
result = np.append(result, ele)
|
||||||
|
return result.flatten()
|
||||||
|
|
||||||
|
|
||||||
|
class TestMaskedSelectOp(OpTest):
|
||||||
|
def setUp(self):
|
||||||
|
self.init()
|
||||||
|
self.op_type = "masked_select"
|
||||||
|
x = np.random.random(self.shape).astype("float64")
|
||||||
|
mask = np.array(np.random.randint(2, size=self.shape, dtype=bool))
|
||||||
|
out = np_masked_select(x, mask)
|
||||||
|
self.inputs = {'X': x, 'Mask': mask}
|
||||||
|
self.outputs = {'Y': out}
|
||||||
|
|
||||||
|
def test_check_output(self):
|
||||||
|
self.check_output()
|
||||||
|
|
||||||
|
def test_check_grad(self):
|
||||||
|
self.check_grad(['X'], 'Y')
|
||||||
|
|
||||||
|
def init(self):
|
||||||
|
self.shape = (50, 3)
|
||||||
|
|
||||||
|
|
||||||
|
class TestMaskedSelectOp1(TestMaskedSelectOp):
|
||||||
|
def init(self):
|
||||||
|
self.shape = (6, 8, 9, 18)
|
||||||
|
|
||||||
|
|
||||||
|
class TestMaskedSelectOp2(TestMaskedSelectOp):
|
||||||
|
def init(self):
|
||||||
|
self.shape = (168, )
|
||||||
|
|
||||||
|
|
||||||
|
class TestMaskedSelectAPI(unittest.TestCase):
|
||||||
|
def test_imperative_mode(self):
|
||||||
|
paddle.disable_static()
|
||||||
|
shape = (88, 6, 8)
|
||||||
|
np_x = np.random.random(shape).astype('float32')
|
||||||
|
np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))
|
||||||
|
x = paddle.to_tensor(np_x)
|
||||||
|
mask = paddle.to_tensor(np_mask)
|
||||||
|
out = paddle.masked_select(x, mask)
|
||||||
|
np_out = np_masked_select(np_x, np_mask)
|
||||||
|
self.assertEqual(np.allclose(out.numpy(), np_out), True)
|
||||||
|
paddle.enable_static()
|
||||||
|
|
||||||
|
def test_static_mode(self):
|
||||||
|
shape = [8, 9, 6]
|
||||||
|
x = paddle.data(shape=shape, dtype='float32', name='x')
|
||||||
|
mask = paddle.data(shape=shape, dtype='bool', name='mask')
|
||||||
|
np_x = np.random.random(shape).astype('float32')
|
||||||
|
np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))
|
||||||
|
|
||||||
|
out = paddle.masked_select(x, mask)
|
||||||
|
np_out = np_masked_select(np_x, np_mask)
|
||||||
|
|
||||||
|
exe = paddle.static.Executor(place=paddle.CPUPlace())
|
||||||
|
|
||||||
|
res = exe.run(paddle.static.default_main_program(),
|
||||||
|
feed={"x": np_x,
|
||||||
|
"mask": np_mask},
|
||||||
|
fetch_list=[out])
|
||||||
|
self.assertEqual(np.allclose(res, np_out), True)
|
||||||
|
|
||||||
|
|
||||||
|
class TestMaskedSelectError(unittest.TestCase):
|
||||||
|
def test_error(self):
|
||||||
|
with paddle.static.program_guard(paddle.static.Program(),
|
||||||
|
paddle.static.Program()):
|
||||||
|
|
||||||
|
shape = [8, 9, 6]
|
||||||
|
x = paddle.data(shape=shape, dtype='float32', name='x')
|
||||||
|
mask = paddle.data(shape=shape, dtype='bool', name='mask')
|
||||||
|
mask_float = paddle.data(
|
||||||
|
shape=shape, dtype='float32', name='mask_float')
|
||||||
|
np_x = np.random.random(shape).astype('float32')
|
||||||
|
np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))
|
||||||
|
|
||||||
|
def test_x_type():
|
||||||
|
paddle.masked_select(np_x, mask)
|
||||||
|
|
||||||
|
self.assertRaises(TypeError, test_x_type)
|
||||||
|
|
||||||
|
def test_mask_type():
|
||||||
|
paddle.masked_select(x, np_mask)
|
||||||
|
|
||||||
|
self.assertRaises(TypeError, test_mask_type)
|
||||||
|
|
||||||
|
def test_mask_dtype():
|
||||||
|
paddle.masked_select(x, mask_float)
|
||||||
|
|
||||||
|
self.assertRaises(TypeError, test_mask_dtype)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
unittest.main()
|
||||||
Loading…
Reference in new issue