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[cherry-pick]Add sparse attention cherrypick (#36447)
The code of this PR can only support CUDA 11.2. Currently, CI does not have GPU with CUDA 11.2 , and all tests will be skipped automatically.
The new OP is paddle._C_ops.sparse_attention. Regarding the work of the python API, it will be resolved in a follow-up PR.
The code of this PR lacks tests on dynamic graphs and static graphs, and will be added in subsequent PRs.- Loading branch information
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| /* Copyright (c) 2021 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 <string> | ||
| #include <vector> | ||
| #include "paddle/fluid/framework/data_type.h" | ||
| #include "paddle/fluid/framework/op_registry.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| class SparseAttentionOpMaker : public framework::OpProtoAndCheckerMaker { | ||
| public: | ||
| void Make() override { | ||
| AddInput( | ||
| "Q", | ||
| "(Tensor), The input tensor of query in attention, " | ||
| "whose dimension : `[batch_size, num_heads, target_len, head_dim]`."); | ||
| AddInput( | ||
| "K", | ||
| "(Tensor), The input tensor of key in attention, " | ||
| "whose dimension : `[batch_size, num_heads, target_len, head_dim]`."); | ||
| AddInput( | ||
| "V", | ||
| "(Tensor), The input tensor of value in attention, " | ||
| "whose dimension : `[batch_size, num_heads, target_len, head_dim]`."); | ||
| AddInput("Offset", | ||
| "(Tensor, default: Tensor<int32>), The input tensor of offset in " | ||
| "CSR sparse format, " | ||
| "whose dimension : `[batch_size, num_heads, target_len + 1]`."); | ||
| AddInput("Columns", | ||
| "(Tensor, default: Tensor<int32>), The input tensor of columns in " | ||
| "CSR sparse format, " | ||
| "whose dimension : `[batch_size, num_heads, sparse_nnz_num]`."); | ||
| AddOutput( | ||
| "Out", | ||
| "(Tensor), The output tensor of result in attention, " | ||
| "whose dimension : `[batch_size, num_heads, target_len, head_dim]`."); | ||
| AddOutput("SparseDotSdd", | ||
| "(Tensor), The output tensor of result in SparseDotSdd step, " | ||
| "whose dimension : `[batch_size, num_heads, sparse_nnz_dim]`.") | ||
| .AsIntermediate(); | ||
| AddOutput("Softmax", | ||
| "(Tensor), The output tensor of result in Softmax step, " | ||
| "whose dimension : `[batch_size, num_heads, sparse_nnz_dim]`.") | ||
| .AsIntermediate(); | ||
| AddComment(R"DOC( | ||
| Compute the value of the sparse attention module. Its input value includes five tensors. | ||
| Q, K, and V represent query, key, and value in the Attention module, respectively. | ||
| The CSR format is used to represent the sparsity feature in the Attention module. | ||
| The CSR format contains two tensors, offset and columns. | ||
| )DOC"); | ||
| } | ||
| }; | ||
|
|
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| class SparseAttentionOp : public framework::OperatorWithKernel { | ||
| public: | ||
| using framework::OperatorWithKernel::OperatorWithKernel; | ||
| void InferShape(framework::InferShapeContext* ctx) const override { | ||
| OP_INOUT_CHECK(ctx->HasInput("Q"), "Input", "Q", "sparse_attention"); | ||
| OP_INOUT_CHECK(ctx->HasInput("K"), "Input", "K", "sparse_attention"); | ||
| OP_INOUT_CHECK(ctx->HasInput("V"), "Input", "V", "sparse_attention"); | ||
| OP_INOUT_CHECK(ctx->HasInput("Offset"), "Input", "Offset", | ||
| "sparse_attention"); | ||
| OP_INOUT_CHECK(ctx->HasInput("Columns"), "Input", "Columns", | ||
| "sparse_attention"); | ||
| OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "sparse_attention"); | ||
| OP_INOUT_CHECK(ctx->HasOutput("SparseDotSdd"), "Output", "SparseDotSdd", | ||
| "sparse_attention"); | ||
| OP_INOUT_CHECK(ctx->HasOutput("Softmax"), "Output", "Softmax", | ||
| "sparse_attention"); | ||
|
|
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| auto dims_q = ctx->GetInputDim("Q"); | ||
| auto dims_k = ctx->GetInputDim("K"); | ||
| auto dims_v = ctx->GetInputDim("V"); | ||
| auto dims_columns = ctx->GetInputDim("Columns"); | ||
|
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| PADDLE_ENFORCE_EQ(dims_q.size(), static_cast<size_t>(4), | ||
| platform::errors::InvalidArgument( | ||
| "Dimension in query' shapes should be 4.")); | ||
| PADDLE_ENFORCE_EQ(dims_k.size(), static_cast<size_t>(4), | ||
| platform::errors::InvalidArgument( | ||
| "Dimension in key' shapes should be 4.")); | ||
| PADDLE_ENFORCE_EQ(dims_v.size(), static_cast<size_t>(4), | ||
| platform::errors::InvalidArgument( | ||
| "Dimension in value' shapes should be 4.")); | ||
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| auto batch_size = dims_q[0]; | ||
| auto num_heads = dims_q[1]; | ||
| auto M = dims_q[2]; | ||
| auto N = dims_q[3]; | ||
| auto sparse_nnz = dims_columns[2]; | ||
| ctx->SetOutputDim("Out", {batch_size, num_heads, M, N}); | ||
| ctx->SetOutputDim("SparseDotSdd", {batch_size, num_heads, sparse_nnz}); | ||
| ctx->SetOutputDim("Softmax", {batch_size, num_heads, sparse_nnz}); | ||
| ctx->ShareLoD("Q", "Out"); | ||
| } | ||
|
|
||
| protected: | ||
| framework::OpKernelType GetExpectedKernelType( | ||
| const framework::ExecutionContext& ctx) const override { | ||
| auto input_data_type = | ||
| OperatorWithKernel::IndicateOrPromoteVarDataTypes(ctx, "Q", "K"); | ||
| return framework::OpKernelType(input_data_type, ctx.GetPlace()); | ||
| } | ||
| }; | ||
|
|
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| class SparseAttentionOpGrad : public framework::OperatorWithKernel { | ||
| public: | ||
| using framework::OperatorWithKernel::OperatorWithKernel; | ||
|
|
||
| protected: | ||
| void InferShape(framework::InferShapeContext* ctx) const override { | ||
| OP_INOUT_CHECK(ctx->HasInput("Q"), "Input", "Q", "sparse_attention_grad"); | ||
| OP_INOUT_CHECK(ctx->HasInput("K"), "Input", "K", "sparse_attention_grad"); | ||
| OP_INOUT_CHECK(ctx->HasInput("V"), "Input", "V", "sparse_attention_grad"); | ||
| OP_INOUT_CHECK(ctx->HasInput("Offset"), "Input", "Offset", | ||
| "sparse_attention_grad"); | ||
| OP_INOUT_CHECK(ctx->HasInput("Columns"), "Input", "Columns", | ||
| "sparse_attention_grad"); | ||
| OP_INOUT_CHECK(ctx->HasInput("SparseDotSdd"), "Input", "SparseDotSdd", | ||
| "sparse_attention_grad"); | ||
| OP_INOUT_CHECK(ctx->HasInput("Softmax"), "Input", "Softmax", | ||
| "sparse_attention_grad"); | ||
| OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input", | ||
| "Out@GRAD", "sparse_attention_grad"); | ||
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| auto x_grad_name = framework::GradVarName("Q"); | ||
| auto y_grad_name = framework::GradVarName("K"); | ||
| auto z_grad_name = framework::GradVarName("V"); | ||
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| if (ctx->HasOutput(x_grad_name)) { | ||
| ctx->SetOutputDim(x_grad_name, ctx->GetInputDim("Q")); | ||
| } | ||
| if (ctx->HasOutput(y_grad_name)) { | ||
| ctx->SetOutputDim(y_grad_name, ctx->GetInputDim("K")); | ||
| } | ||
| if (ctx->HasOutput(z_grad_name)) { | ||
| ctx->SetOutputDim(z_grad_name, ctx->GetInputDim("V")); | ||
| } | ||
| } | ||
|
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| framework::OpKernelType GetExpectedKernelType( | ||
| const framework::ExecutionContext& ctx) const override { | ||
| return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType( | ||
| ctx, framework::GradVarName("Out")), | ||
| ctx.GetPlace()); | ||
| } | ||
| }; | ||
|
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| template <typename T> | ||
| class SparseAttentionGradOpMaker : public framework::SingleGradOpMaker<T> { | ||
| public: | ||
| using framework::SingleGradOpMaker<T>::SingleGradOpMaker; | ||
|
|
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| protected: | ||
| void Apply(GradOpPtr<T> op) const override { | ||
| op->SetType("sparse_attention_grad"); | ||
| op->SetInput("Q", this->Input("Q")); | ||
| op->SetInput("K", this->Input("K")); | ||
| op->SetInput("V", this->Input("V")); | ||
| op->SetInput("Offset", this->Input("Offset")); | ||
| op->SetInput("Columns", this->Input("Columns")); | ||
| op->SetInput("SparseDotSdd", this->Output("SparseDotSdd")); | ||
| op->SetInput("Softmax", this->Output("Softmax")); | ||
| op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out")); | ||
| op->SetOutput(framework::GradVarName("Q"), this->InputGrad("Q")); | ||
| op->SetOutput(framework::GradVarName("K"), this->InputGrad("K")); | ||
| op->SetOutput(framework::GradVarName("V"), this->InputGrad("V")); | ||
| } | ||
| }; | ||
|
|
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| } // namespace operators | ||
| } // namespace paddle | ||
|
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| namespace ops = paddle::operators; | ||
| REGISTER_OPERATOR(sparse_attention, ops::SparseAttentionOp, | ||
| ops::SparseAttentionOpMaker, | ||
| ops::SparseAttentionGradOpMaker<paddle::framework::OpDesc>, | ||
| ops::SparseAttentionGradOpMaker<paddle::imperative::OpBase>); | ||
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| REGISTER_OPERATOR(sparse_attention_grad, ops::SparseAttentionOpGrad); |
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