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[cherry-pick]add op: fused_feedforward(forward) (#36729)
This is a fusion operator to compute feed forward layer in transformer model architecture.
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zhangkaihuo
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Oct 26, 2021
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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. */ | ||
|
|
||
| #include <algorithm> | ||
| #include <utility> | ||
| #include <vector> | ||
| #include "paddle/fluid/framework/op_registry.h" | ||
| #include "paddle/fluid/framework/op_version_registry.h" | ||
| #include "paddle/fluid/operators/math/blas.h" | ||
| #include "paddle/fluid/operators/matmul_v2_op.h" | ||
|
|
||
| namespace paddle { | ||
| namespace operators { | ||
| using Tensor = framework::Tensor; | ||
|
|
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| class FusedFeedForwardOp : public framework::OperatorWithKernel { | ||
| public: | ||
| using framework::OperatorWithKernel::OperatorWithKernel; | ||
|
|
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| protected: | ||
| void InferShape(framework::InferShapeContext *context) const override { | ||
| OP_INOUT_CHECK(context->HasInput("X"), "Input", "X", "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasInput("Linear1Weight"), "Input", "Linear1Weight", | ||
| "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasInput("Linear2Weight"), "Input", "Linear2Weight", | ||
| "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasOutput("Out"), "Output", "Out", | ||
| "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasOutput("Dropout1Mask"), "Output", "Dropout1Mask", | ||
| "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasOutput("Dropout2Mask"), "Output", "Dropout2Mask", | ||
| "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasOutput("Ln1Mean"), "Output", "Ln1Mean", | ||
| "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasOutput("Ln1Variance"), "Output", "Ln1Variance", | ||
| "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasOutput("Ln2Mean"), "Output", "Ln2Mean", | ||
| "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasOutput("Ln2Variance"), "Output", "Ln2Variance", | ||
| "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasOutput("Linear1Out"), "Output", "Linear1Out", | ||
| "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasOutput("Ln1Out"), "Output", "Ln1Out", | ||
| "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasOutput("Dropout1Out"), "Output", "Dropout1Out", | ||
| "fused_feedforward"); | ||
| OP_INOUT_CHECK(context->HasOutput("Dropout2Out"), "Output", "Dropout2Out", | ||
| "fused_feedforward"); | ||
|
|
||
| auto dim_x = context->GetInputDim("X"); | ||
| auto mat_dim_x = | ||
| math::CreateMatrixDescriptor(RowMatrixFromVector(dim_x), 0, false); | ||
| // verify for the pre layer_norm, the feature size must be larger than 1 | ||
| PADDLE_ENFORCE_GT( | ||
| mat_dim_x.width_, static_cast<size_t>(1), | ||
| platform::errors::InvalidArgument("Product from the X shape[1] to " | ||
| "shape[n-1] must be larger than 1!")); | ||
| auto dim_Linear1Weight = context->GetInputDim("Linear1Weight"); | ||
| auto tmp_dim_x = dim_x; | ||
| tmp_dim_x[dim_x.size() - 1] = | ||
| dim_Linear1Weight[dim_Linear1Weight.size() - 1]; | ||
| context->SetOutputDim("Out", dim_x); | ||
| if (context->Attrs().Get<bool>("dropout1_is_test") == false) { | ||
| context->SetOutputDim("Dropout1Mask", tmp_dim_x); | ||
| } | ||
| context->SetOutputDim("Dropout1Out", tmp_dim_x); | ||
| context->SetOutputDim("Linear1Out", tmp_dim_x); | ||
| context->SetOutputDim("Ln1Out", dim_x); | ||
| context->SetOutputDim("Dropout2Out", dim_x); | ||
|
|
||
| if (context->Attrs().Get<bool>("dropout2_is_test") == false) { | ||
| context->SetOutputDim("Dropout2Mask", dim_x); | ||
| } | ||
| framework::DDim mean_dim = | ||
| framework::make_ddim({mat_dim_x.batch_size_ * mat_dim_x.height_}); | ||
| context->SetOutputDim("Ln1Mean", mean_dim); | ||
| context->SetOutputDim("Ln1Variance", mean_dim); | ||
| context->SetOutputDim("Ln2Mean", mean_dim); | ||
| context->SetOutputDim("Ln2Variance", mean_dim); | ||
| context->ShareLoD("X", "Out"); | ||
| } | ||
|
|
||
| framework::OpKernelType GetExpectedKernelType( | ||
| const framework::ExecutionContext &ctx) const override { | ||
| return framework::OpKernelType( | ||
| OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace()); | ||
| } | ||
| }; | ||
|
|
||
| class FusedFeedForwardOpMaker : public framework::OpProtoAndCheckerMaker { | ||
| public: | ||
| void Make() override { | ||
| AddInput("X", "The input of FusedFeedForward op"); | ||
| AddInput( | ||
| "Dropout1Seed", | ||
| "The seed of first dropout op, it has higher priority than the attr " | ||
| "fix_seed and seed") | ||
| .AsDispensable(); | ||
| AddInput( | ||
| "Dropout2Seed", | ||
| "The seed of second dropout op, it has higher priority than the attr " | ||
| "fix_seed and seed") | ||
| .AsDispensable(); | ||
|
|
||
| AddInput("Linear1Weight", "The linear1 weight of FusedFeedForward op"); | ||
| AddInput("Linear1Bias", "The linear1 bias of FusedFeedForward op") | ||
| .AsDispensable(); | ||
| AddInput("Linear2Weight", "The linear2 weight of FusedFeedForward op"); | ||
| AddInput("Linear2Bias", "The linear2 bias input of FusedFeedForward op") | ||
| .AsDispensable(); | ||
| AddInput("Ln1Scale", "The layer_norm1 scale of FusedFeedForward op") | ||
| .AsDispensable(); | ||
| AddInput("Ln1Bias", "The layer_norm1 bias of FusedFeedForward op") | ||
| .AsDispensable(); | ||
| AddInput("Ln2Scale", "The layer_norm2 scale of FusedFeedForward op") | ||
| .AsDispensable(); | ||
| AddInput("Ln2Bias", "The layer_norm2 bias of FusedFeedForward op") | ||
| .AsDispensable(); | ||
| AddOutput("Out", "The output of FusedFeedForward op"); | ||
| AddOutput("Dropout1Mask", "The mask of dropout1").AsIntermediate(); | ||
| AddOutput("Dropout2Mask", "The mask of dropout2").AsIntermediate(); | ||
| AddOutput("Ln1Mean", "The mean of layer_norm1").AsIntermediate(); | ||
| AddOutput("Ln1Variance", "The variance of layer_norm1").AsIntermediate(); | ||
| AddOutput("Ln2Mean", "The mean of layer_nomr2").AsIntermediate(); | ||
| AddOutput("Ln2Variance", "The variance of layer_norm2").AsIntermediate(); | ||
| AddOutput("Linear1Out", "The output of linear1").AsIntermediate(); | ||
| AddOutput("Ln1Out", "The output of layer_norm1").AsIntermediate(); | ||
| AddOutput("Dropout1Out", "The output of dropout1").AsIntermediate(); | ||
| AddOutput("Dropout2Out", "The output of dropout2").AsIntermediate(); | ||
|
|
||
| AddAttr<bool>("pre_layer_norm", "true is pre layernorm").SetDefault(false); | ||
| AddAttr<float>("ln1_epsilon", "epsilon of pre layer_norm") | ||
| .SetDefault(1e-5f); | ||
| AddAttr<float>("ln2_epsilon", "epsilon of post layer_norm") | ||
| .SetDefault(1e-5f); | ||
| AddAttr<std::string>("act_method", "act_method").SetDefault("gelu"); | ||
| AddAttr<float>("dropout1_rate", "the dropout rate of first dropout") | ||
| .SetDefault(.5f) | ||
| .AddCustomChecker([](const float &drop_p) { | ||
| PADDLE_ENFORCE_EQ( | ||
| drop_p >= 0.0f && drop_p <= 1.0f, true, | ||
| platform::errors::InvalidArgument( | ||
| "'dropout1_rate' must be between 0.0 and 1.0.")); | ||
| }); | ||
| AddAttr<float>("dropout2_rate", "the dropout rate of second dropout") | ||
| .SetDefault(.5f) | ||
| .AddCustomChecker([](const float &drop_p) { | ||
| PADDLE_ENFORCE_EQ( | ||
| drop_p >= 0.0f && drop_p <= 1.0f, true, | ||
| platform::errors::InvalidArgument( | ||
| "'dropout2_rate' must be between 0.0 and 1.0.")); | ||
| }); | ||
| AddAttr<std::string>("dropout1_implementation", | ||
| "the dropout implementation of first dropout") | ||
| .SetDefault("downgrade_in_infer") | ||
| .AddCustomChecker([](const std::string &type) { | ||
| PADDLE_ENFORCE_EQ( | ||
| type == "downgrade_in_infer" || type == "upscale_in_train", true, | ||
| platform::errors::InvalidArgument( | ||
| "dropout1_implementation can only be downgrade_in_infer or " | ||
| "upscale_in_train")); | ||
| }); | ||
| AddAttr<std::string>("dropout2_implementation", | ||
| "the dropout implementation of second dropout") | ||
| .SetDefault("downgrade_in_infer") | ||
| .AddCustomChecker([](const std::string &type) { | ||
| PADDLE_ENFORCE_EQ( | ||
| type == "downgrade_in_infer" || type == "upscale_in_train", true, | ||
| platform::errors::InvalidArgument( | ||
| "dropout2_implementation can only be downgrade_in_infer or " | ||
| "upscale_in_train")); | ||
| }); | ||
| AddAttr<bool>("dropout1_is_test", "the is_test of first dropout") | ||
| .SetDefault(false); | ||
| AddAttr<bool>("dropout2_is_test", "the is_test of second dropout") | ||
| .SetDefault(false); | ||
| AddAttr<bool>("dropout1_fix_seed", "the is_test of first dropout") | ||
| .SetDefault(false); | ||
| AddAttr<bool>("dropout2_fix_seed", "the is_test of second dropout") | ||
| .SetDefault(false); | ||
| AddAttr<int>("dropout1_seed", "Dropout1 random seed.").SetDefault(0); | ||
| AddAttr<int>("dropout2_seed", "Dropout2 random seed.").SetDefault(0); | ||
| AddComment(R"DOC( | ||
| the function of fused_feedforward operator is the same as the following pseudo code: | ||
| residual = src; | ||
| ln1_out = src; | ||
| if(pre_layer_norm){ | ||
| ln1_out = layer_norm(src); | ||
| } | ||
| out = linear(dropout(activation(dropout(linear(ln1_out))))); | ||
| if(!pre_layer_norm) { | ||
| out = layer_norm(out); | ||
| } | ||
| )DOC"); | ||
| } | ||
| }; | ||
|
|
||
| } // namespace operators | ||
| } // namespace paddle | ||
|
|
||
| namespace ops = paddle::operators; | ||
| REGISTER_OPERATOR(fused_feedforward, ops::FusedFeedForwardOp, | ||
| ops::FusedFeedForwardOpMaker); |
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