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Merge pull request #3927 from Xreki/core_add_fc_op
Port fully connected operator
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| /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. | ||
| 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/framework/op_registry.h" | ||
| #include "paddle/operators/net_op.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| class FCOp : public NetOp { | ||
| public: | ||
| FCOp(const std::string &type, const framework::VariableNameMap &inputs, | ||
| const framework::VariableNameMap &outputs, | ||
| const framework::AttributeMap &attrs) | ||
| : NetOp(type, inputs, outputs, attrs) { | ||
| PADDLE_ENFORCE(!Inputs("X").empty(), | ||
| "Inputs(X) of FCOp should not be null."); | ||
| PADDLE_ENFORCE(!Inputs("W").empty(), | ||
| "Inputs(W) of FCOp should not be null."); | ||
| PADDLE_ENFORCE(!Outputs("MulOut").empty(), | ||
| "Outputs(MulOut) of FCOp should not be null."); | ||
| PADDLE_ENFORCE_NE(Output("Out"), framework::kEmptyVarName, | ||
| "Output(Out) of FCOp should not be null."); | ||
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| auto x = Inputs("X"); | ||
| auto w = Inputs("W"); | ||
| auto mul_out = Outputs("MulOut"); | ||
| PADDLE_ENFORCE_EQ( | ||
| x.size(), w.size(), | ||
| "The size of inputs X(%d) should be the same as that of weights W(%d).", | ||
| x.size(), w.size()); | ||
| PADDLE_ENFORCE_EQ(mul_out.size(), x.size(), | ||
| "The size of intermediate mul_out(%d) should be the same " | ||
| "as that of inputs X(%d).", | ||
| mul_out.size(), x.size()); | ||
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| size_t n = x.size(); | ||
| PADDLE_ENFORCE_GE(n, static_cast<size_t>(1), | ||
| "The size of inputs X(%d) should be no less than 1.", n); | ||
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| auto x_num_col_dims = Attr<std::vector<int>>("xNumColDims"); | ||
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| // Set all values or set no values (use the default value) | ||
| if (!x_num_col_dims.empty()) { | ||
| PADDLE_ENFORCE_EQ(x_num_col_dims.size(), n, | ||
| "The size of attribute xNumColDims(%d) should be the " | ||
| "same as that of inputs X(%d).", | ||
| x_num_col_dims.size(), n); | ||
| } else { | ||
| x_num_col_dims.resize(n); | ||
| for (size_t i = 0; i < n; i++) { | ||
| x_num_col_dims[i] = 1; | ||
| } | ||
| } | ||
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| // mul_out[i] = X[i] * W[i] | ||
| for (size_t i = 0; i < n; i++) { | ||
| framework::AttributeMap mul_attr; | ||
| mul_attr["x_num_col_dims"] = static_cast<int>(x_num_col_dims[i]); | ||
| mul_attr["y_num_col_dims"] = static_cast<int>(1); | ||
| AppendOp( | ||
| framework::OpRegistry::CreateOp("mul", {{"X", {x[i]}}, {"Y", {w[i]}}}, | ||
| {{"Out", {mul_out[i]}}}, mul_attr)); | ||
| } | ||
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| // sum_out = X[0] * W[0] + ... + X[n-1] * W[n-1] | ||
| auto sum_out = mul_out[0]; | ||
| if (n > 1) { | ||
| PADDLE_ENFORCE_NE(Output("SumOut"), framework::kEmptyVarName, | ||
| "Output(SumOut) of FCOp should not be null when the " | ||
| "size of Inputs(X) > 1."); | ||
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| sum_out = Output("SumOut"); | ||
| AppendOp(framework::OpRegistry::CreateOp("sum", {{"X", {mul_out}}}, | ||
| {{"Out", {sum_out}}}, {})); | ||
| } else { | ||
| if (Output("SumOut") != framework::kEmptyVarName) { | ||
| this->Rename(Output("SumOut"), framework::kEmptyVarName); | ||
| } | ||
| } | ||
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| // add_out = sum_out + b | ||
| auto b = Input("B"); | ||
| auto add_out = sum_out; | ||
| if (b != framework::kEmptyVarName) { | ||
| PADDLE_ENFORCE_NE( | ||
| Output("AddOut"), framework::kEmptyVarName, | ||
| "Output(AddOut) of FCOp should not be null when Input(B) is set."); | ||
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| add_out = Output("AddOut"); | ||
| AppendOp(framework::OpRegistry::CreateOp( | ||
| "rowwise_add", {{"X", {sum_out}}, {"b", {Input("B")}}}, | ||
| {{"Out", {add_out}}}, {})); | ||
| } else { | ||
| if (Output("AddOut") != framework::kEmptyVarName) { | ||
| this->Rename(Output("AddOut"), framework::kEmptyVarName); | ||
| } | ||
| } | ||
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| auto activation = Attr<std::string>("activation"); | ||
| AppendOp(framework::OpRegistry::CreateOp(activation, {{"X", {add_out}}}, | ||
| {{"Y", {Output("Out")}}}, {})); | ||
| CompleteAddOp(false); | ||
| } | ||
| }; | ||
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| class FCOpMaker : public framework::OpProtoAndCheckerMaker { | ||
| public: | ||
| FCOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) | ||
| : OpProtoAndCheckerMaker(proto, op_checker) { | ||
| AddInput("X", | ||
| "(A vector of Tensors) each input Tensor can be of arbitrary " | ||
| "dimension, and will be reshaped to a 2-D matrix of size " | ||
| "(minibatch, number_of_input_features) according to attribute " | ||
| "xNumColDims.") | ||
| .AsDuplicable(); | ||
| AddInput("W", | ||
| "(A vector of Tensors) the weights of FC operator, a " | ||
| "vector of 2-D matrix of size " | ||
| "(number_of_input_features, number_of_neurons).") | ||
| .AsDuplicable(); | ||
| AddInput("B", | ||
| "(Tensor) the bias of FC operator, a 1-D vector of size " | ||
| "number_of_neurons."); | ||
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| AddOutput("Out", | ||
| "(Tensor) the activated output matrix of FC operator, a 2-D " | ||
| "matrix of size (minibatch, number_of_neurons)."); | ||
| AddOutput("MulOut", | ||
| "(A vector of Tensors) the intermediate outputs of FC operator, " | ||
| "each Tensor saving the product of X_i * W_i.") | ||
| .AsIntermediate() | ||
| .AsDuplicable(); | ||
| AddOutput( | ||
| "SumOut", | ||
| "(Tensor) the intermediate output of FC operator, " | ||
| "saving the sum of the products of X and W, that is sum{X_i * W_i}.") | ||
| .AsIntermediate(); | ||
| AddOutput("AddOut", | ||
| "(Tensor) the non-actived output of FC operator, " | ||
| "saving sum{X_i * W_i} + B.") | ||
| .AsIntermediate(); | ||
| AddAttr<std::string>( | ||
| "activation", | ||
| "(string, default identity) the activation type of FC operator.") | ||
| .SetDefault("identity") | ||
| .InEnum({"identity", "sigmoid", "softmax"}); | ||
| AddAttr<std::vector<int>>( | ||
| "xNumColDims", | ||
| "(std::vector<int>) The inputs Tensors of FC operator can be of " | ||
| "more than 2 dimensions. In that case, each input Tensor `X_i` will be " | ||
| "reshaped to a 2-D matrix. The matrix's first dimension " | ||
| "(the length of column) will be the product of `X_i`'s last " | ||
| "`xNumColDims_i` dimensions, that is " | ||
| "`X_i.dims[0] x ... x X_i.dims[xNumColDims_i - 1]`. " | ||
| "The matrix's second dimension (the length of row) will be the product " | ||
| "of `X_i`'s first `rank - xNumColDims_i` dimensions, that is " | ||
| "`X_i.dims[xNumColDims_i] x ... x X_i.dims[rank - 1]`)") | ||
| .SetDefault(std::vector<int>{}); | ||
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| AddComment(R"DOC( | ||
| Fully Connected Operator, known as Fully Connected Layer or Inner Product Layer | ||
| in Convolutional Neural Networks. Neurons in a fully connected layer have | ||
| full connections to all activations in the previous layer. | ||
| It computes an inner product of a set of | ||
| learned weights with a matrix multiplication followed by a bias offset | ||
| (optionally). | ||
| Equation: | ||
| Out = Act(sum_n{X_i * W_i} + B) | ||
| where X_i is Tensor that will be reshaped to a 2-D matrix of size (M x K), | ||
| usually M is the minibatch size and K is the number of input features. | ||
| W_i is a 2-D matrix of size (K x N), where N means the number of neurons | ||
| in the fully connected layer. B is a 1-D vector of size N. | ||
| Thus, the output Out is a 2-D matrix of size (M x N). | ||
| Activation type can be set to `identity` (default), `sigmoid` or `softmax`. | ||
| )DOC"); | ||
| } | ||
| }; | ||
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| } // namespace operators | ||
| } // namespace paddle | ||
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| namespace ops = paddle::operators; | ||
| REGISTER_OP_WITHOUT_GRADIENT(fc, ops::FCOp, ops::FCOpMaker); |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,62 @@ | ||
| import unittest | ||
| import numpy as np | ||
| from op_test import OpTest | ||
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| class TestFCOp1(OpTest): | ||
| def setUp(self): | ||
| x0 = np.random.random((16, 32)).astype("float32") | ||
| w0 = np.random.random((32, 10)).astype("float32") | ||
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| mul_out0 = np.dot(x0, w0) | ||
| identity_out = mul_out0 | ||
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| self.op_type = "fc" | ||
| self.inputs = {"X": [("X0", x0)], "W": [("W0", w0)]} | ||
| self.outputs = {"MulOut": [("MulOut0", mul_out0)], "Out": identity_out} | ||
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| def test_check_output(self): | ||
| self.check_output() | ||
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| def test_check_grad(self): | ||
| self.check_grad(["X0", "W0"], "Out", max_relative_error=0.01) | ||
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| class TestFCOp2(OpTest): | ||
| def setUp(self): | ||
| x0 = np.random.random((16, 4, 8)).astype("float32") | ||
| x1 = np.random.random((4, 4, 32)).astype("float32") | ||
| w0 = np.random.random((32, 10)).astype("float32") | ||
| w1 = np.random.random((32, 10)).astype("float32") | ||
| b = np.random.random(10).astype("float32") | ||
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| mul_out0 = np.dot(x0.reshape(16, 4 * 8), w0) | ||
| mul_out1 = np.dot(x1.reshape(4 * 4, 32), w1) | ||
| sum_out = mul_out0 + mul_out1 | ||
| add_out = np.add(sum_out, b) | ||
| sigmoid_out = 1 / (1 + np.exp(-add_out)) | ||
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| self.op_type = "fc" | ||
| self.inputs = { | ||
| "X": [("X0", x0), ("X1", x1)], | ||
| "W": [("W0", w0), ("W1", w1)], | ||
| "B": b | ||
| } | ||
| self.attrs = {"xNumColDims": [1, 2], "activation": "sigmoid"} | ||
| self.outputs = { | ||
| "MulOut": [("MulOut0", mul_out0), ("MulOut1", mul_out1)], | ||
| "SumOut": sum_out, | ||
| "AddOut": add_out, | ||
| "Out": sigmoid_out | ||
| } | ||
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| def test_check_output(self): | ||
| self.check_output() | ||
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| def test_check_grad(self): | ||
| self.check_grad( | ||
| ["X0", "X1", "W0", "W1", "B"], "Out", max_relative_error=0.01) | ||
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| if __name__ == '__main__': | ||
| unittest.main() |
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