supports distributed classification #18690
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| // Copyright (c) 2019 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/fluid/operators/shard_index_op.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| class ShardIndexOp : public framework::OperatorWithKernel { | ||
| public: | ||
| using framework::OperatorWithKernel::OperatorWithKernel; | ||
| void InferShape(framework::InferShapeContext* ctx) const override { | ||
| PADDLE_ENFORCE(ctx->HasInput("X"), | ||
| "Input(X) of ShardIndexOp should not be null."); | ||
| PADDLE_ENFORCE(ctx->HasOutput("Out"), | ||
| "Output(Out) of ShardIndexOp should not be null."); | ||
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| auto x_dims = ctx->GetInputDim("X"); | ||
| PADDLE_ENFORCE_GE(x_dims.size(), 2, | ||
| "Rank of Input(X) should be at least 2."); | ||
| if (ctx->IsRuntime() || x_dims[x_dims.size() - 1] > 0) { | ||
| PADDLE_ENFORCE_GE(x_dims[x_dims.size() - 1], 1U, | ||
| "Last dimension of Input(X) should be 1."); | ||
| } | ||
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| ctx->SetOutputDim("Out", x_dims); | ||
| ctx->ShareLoD("X", /* --> */ "Out"); | ||
| } | ||
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| protected: | ||
| framework::OpKernelType GetExpectedKernelType( | ||
| const framework::ExecutionContext& ctx) const override { | ||
| return framework::OpKernelType(ctx.Input<framework::Tensor>("X")->type(), | ||
| ctx.device_context()); | ||
| } | ||
| }; | ||
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| class ShardIndexOpMaker : public framework::OpProtoAndCheckerMaker { | ||
| public: | ||
| void Make() override { | ||
| AddInput("X", | ||
| "(LoDTensor, LoDTensor<int|int64>) Input variable. Each value " | ||
| "of X is an index."); | ||
| AddOutput( | ||
| "Out", | ||
| "(Tensor, Tensor<int|int64>) Output tensor with same shape as X. " | ||
| "The tensor consists of sharding representations of values in X."); | ||
| AddAttr<int>("index_range", | ||
| "A positive integer to specify the range of the input X."); | ||
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| AddAttr<int>("nshards", | ||
| "A positive integer to specify the number of shards."); | ||
| AddAttr<int>("shard_id", "The current shard id"); | ||
| AddAttr<int>("ignore_value", "An ingeter value out of sharded range") | ||
| .SetDefault(-1); | ||
| AddComment(R"DOC( | ||
| This layer creates the sharded index for input. This layers is used in | ||
| model- and data- parallel mixed training generally, in which the index | ||
| data (usually the label) should be recaculated in each trainer according | ||
| to | ||
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| .. math:: | ||
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| assert index_range % nshards == 0 | ||
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| shard_range = index_range / nshards | ||
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| y = x % shard_range if x / shard_range == shard_id else ignore_value | ||
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| We take the distributed one-hot representation to show what this layer is | ||
| used for. The distributed one-hot representation is seperated into multiple | ||
| shards, and each shard is filling zeros except the one with the index | ||
| inside. In order to create these sharded representation in each trainer, | ||
| the original index should be recalculated (i.e. sharded) before. | ||
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| Examples: | ||
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| X is a Tensor of integer values: | ||
| X.shape = [4, 1] | ||
| X.data = [[1], [6], [12], [19]] | ||
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| suppose index_range = 20 and nshards = 2, then we get shard_range = 10 | ||
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| if shard_id == 0, we get the Out: | ||
| Out.shape = [4, 1] | ||
| Out.data = [[1], [6], [-1], [-1]] | ||
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| if shard_id == 1, we get the Out: | ||
| Out.shape = [4, 1] | ||
| Out.data = [[-1], [-1], [2], [9]] | ||
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| the default `ignore_value` -1 is used in this example. | ||
| )DOC"); | ||
| } | ||
| }; | ||
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| } // namespace operators | ||
| } // namespace paddle | ||
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| namespace ops = paddle::operators; | ||
| REGISTER_OP_WITHOUT_GRADIENT(shard_index, ops::ShardIndexOp, | ||
| ops::ShardIndexOpMaker); | ||
| REGISTER_OP_CPU_KERNEL(shard_index, ops::ShardIndexCPUKernel<int>, | ||
| ops::ShardIndexCPUKernel<int64_t>); |
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| // Copyright (c) 2019 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/fluid/operators/shard_index_op.h" | ||
| #include "paddle/fluid/platform/cuda_primitives.h" | ||
| #include "paddle/fluid/platform/gpu_info.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| using platform::PADDLE_CUDA_NUM_THREADS; | ||
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| template <typename T> | ||
| __global__ void ShardIndexInner(const T* in_data, T* out_data, | ||
| const int64_t numel, const int index_range, | ||
| const int nshards, const int shard_id, | ||
| const int ignore_value) { | ||
| int shard_range = index_range / nshards; | ||
| int idx = blockIdx.x * blockDim.x + threadIdx.x; | ||
| if (idx < numel) { | ||
| assert(in_data[idx] >= 0 && in_data[idx] < index_range); | ||
| if (in_data[idx] / shard_range == shard_id) { | ||
| out_data[idx] = in_data[idx] % shard_range; | ||
| } else { | ||
| out_data[idx] = ignore_value; | ||
| } | ||
| } | ||
| } | ||
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| using LoDTensor = framework::LoDTensor; | ||
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| template <typename T> | ||
| class ShardIndexCUDAKernel : public framework::OpKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& context) const override { | ||
| auto* in = context.Input<LoDTensor>("X"); | ||
| auto* out = context.Output<LoDTensor>("Out"); | ||
| int index_range = context.Attr<int>("index_range"); | ||
| int nshards = context.Attr<int>("nshards"); | ||
| int shard_id = context.Attr<int>("shard_id"); | ||
| int ignore_value = context.Attr<int>("ignore_value"); | ||
| PADDLE_ENFORCE_GT(index_range, 0); | ||
| PADDLE_ENFORCE_GT(nshards, 0); | ||
| PADDLE_ENFORCE(shard_id >= 0 && shard_id < nshards, | ||
| "shard_id(%d) is not in range [0, %d)", shard_id, nshards); | ||
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| out->Resize(in->dims()); | ||
| out->set_lod(in->lod()); | ||
| auto* in_data = in->data<T>(); | ||
| auto* out_data = out->mutable_data<T>(context.GetPlace()); | ||
| int64_t numel = in->numel(); | ||
| auto stream = | ||
| context.template device_context<platform::CUDADeviceContext>().stream(); | ||
| ShardIndexInner<<<(numel + PADDLE_CUDA_NUM_THREADS - 1) / | ||
| PADDLE_CUDA_NUM_THREADS, | ||
| PADDLE_CUDA_NUM_THREADS, 0, stream>>>( | ||
| in_data, out_data, numel, index_range, nshards, shard_id, ignore_value); | ||
| } | ||
| }; | ||
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| } // namespace operators | ||
| } // namespace paddle | ||
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| namespace ops = paddle::operators; | ||
| REGISTER_OP_CUDA_KERNEL(shard_index, ops::ShardIndexCUDAKernel<int>, | ||
| ops::ShardIndexCUDAKernel<int64_t>); |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,58 @@ | ||
| // Copyright (c) 2019 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 | ||
| #include "paddle/fluid/framework/op_registry.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| using LoDTensor = framework::LoDTensor; | ||
| template <typename T> | ||
| class ShardIndexCPUKernel : public framework::OpKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& context) const override { | ||
| auto* in = context.Input<LoDTensor>("X"); | ||
| auto* out = context.Output<LoDTensor>("Out"); | ||
| int index_range = context.Attr<int>("index_range"); | ||
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| int nshards = context.Attr<int>("nshards"); | ||
| int shard_id = context.Attr<int>("shard_id"); | ||
| int ignore_value = context.Attr<int>("ignore_value"); | ||
| PADDLE_ENFORCE_GT(index_range, 0); | ||
| PADDLE_ENFORCE_GT(nshards, 0); | ||
| PADDLE_ENFORCE(shard_id >= 0 && shard_id < nshards, | ||
| "shard_id(%d) is not in range [0, %d)", shard_id, nshards); | ||
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| int shard_range = index_range / nshards; | ||
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| out->Resize(in->dims()); | ||
| out->set_lod(in->lod()); | ||
| auto* in_data = in->data<T>(); | ||
| auto* out_data = out->mutable_data<T>(context.GetPlace()); | ||
| int64_t numel = in->numel(); | ||
| for (int64_t i = 0; i < numel; ++i) { | ||
| PADDLE_ENFORCE(in_data[i] >= 0 && in_data[i] < index_range, | ||
| "Input index(%d) is out of range [0,%d)", in_data[i], | ||
| index_range); | ||
| if (in_data[i] / shard_range == shard_id) { | ||
| out_data[i] = in_data[i] % shard_range; | ||
| } else { | ||
| out_data[i] = ignore_value; | ||
| } | ||
| } | ||
| } | ||
| }; | ||
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| } // namespace operators | ||
| } // namespace paddle | ||
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are you gavin?
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囧，忙晕了
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done