[OpDevOptimize] Add common infershape functions (#26096)
* add unchaged infershape function * add broadcast infershape function * fix bug * rename infershape functions * add UnaryOpUnchangedInferShapeCheckAxis * add error message * add test for common infer shape functions * dont update existed ops * dont update op_desc.h * add more test * add error check, refine error messagerevert-24895-update_cub
Leo Chen
5 years ago
committed by
GitHub
parent
2d95280e1f
commit
ffe52b4452
@ -0,0 +1,166 @@
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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/common_infer_shape_functions.h"
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#include <algorithm>
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#include <vector>
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// This file almostly contains all the infershape functions that are used in
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// operators.
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namespace paddle {
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namespace operators {
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namespace details {
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inline void GetBroadcastDimsArrays(const framework::DDim &x_dims,
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const framework::DDim &y_dims,
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int *x_dims_array, int *y_dims_array,
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int *out_dims_array, const int max_dim,
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const int axis) {
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PADDLE_ENFORCE_GE(
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axis, 0,
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platform::errors::InvalidArgument(
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"Axis should be great than or equal to 0, but received axis is %d.",
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axis));
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PADDLE_ENFORCE_LT(axis, max_dim,
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platform::errors::InvalidArgument(
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"Axis should be less than %d, but received axis is %d.",
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max_dim, axis));
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if (x_dims.size() > y_dims.size()) {
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std::fill(y_dims_array, y_dims_array + axis, 1);
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if (axis + y_dims.size() < max_dim) {
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std::fill(y_dims_array + axis + y_dims.size(), y_dims_array + max_dim, 1);
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}
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std::copy(x_dims.Get(), x_dims.Get() + x_dims.size(), x_dims_array);
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std::copy(y_dims.Get(), y_dims.Get() + y_dims.size(), y_dims_array + axis);
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} else {
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std::fill(x_dims_array, x_dims_array + axis, 1);
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if (axis + x_dims.size() < max_dim) {
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std::fill(x_dims_array + axis + x_dims.size(), x_dims_array + max_dim, 1);
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}
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std::copy(x_dims.Get(), x_dims.Get() + x_dims.size(), x_dims_array + axis);
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std::copy(y_dims.Get(), y_dims.Get() + y_dims.size(), y_dims_array);
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}
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for (int i = 0; i < max_dim; i++) {
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PADDLE_ENFORCE_EQ(
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x_dims_array[i] == y_dims_array[i] || x_dims_array[i] <= 1 ||
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y_dims_array[i] <= 1,
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true, platform::errors::InvalidArgument(
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"Broadcast dimension mismatch. Operands could "
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"not be broadcast together with the shape of X = [%s] and "
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"the shape of Y = [%s]. Received [%d] in X is not equal to "
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"[%d] in Y at i:%d.",
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x_dims, y_dims, x_dims_array[i], y_dims_array[i], i));
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if ((x_dims_array[i] > 1 || y_dims_array[i] > 1) ||
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(x_dims_array[i] == 1 && y_dims_array[i] == 1)) {
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out_dims_array[i] = std::max(x_dims_array[i], y_dims_array[i]);
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} else {
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out_dims_array[i] = -1;
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}
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}
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}
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} // namespace details
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// shape input(0) -> output(0) without change.
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void UnaryOpUnchangedInferShape(framework::InferShapeContext *ctx) {
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auto x_name = ctx->GetInputNameByIdx(0);
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auto out_name = ctx->GetOutputNameByIdx(0);
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ctx->ShareDim(x_name, /*->*/ out_name);
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ctx->ShareLoD(x_name, /*->*/ out_name);
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}
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// shape input(0) -> output(0) without change, check if axis in range [-Rank(x),
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// Rank(x)-1]
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void UnaryOpUnchangedInferShapeCheckAxis(framework::InferShapeContext *ctx) {
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auto x_name = ctx->GetInputNameByIdx(0);
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auto out_name = ctx->GetOutputNameByIdx(0);
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auto x_dim = ctx->GetInputDim(x_name);
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auto x_rank = x_dim.size();
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auto axis = ctx->Attrs().Get<int>("axis");
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PADDLE_ENFORCE_GE(
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axis, -x_rank,
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platform::errors::InvalidArgument(
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"Attr(axis) value should be in range [-R, R-1], "
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"R is the rank of Input(X). But received axis: %d, R: %d.",
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axis, x_rank));
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PADDLE_ENFORCE_LT(
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axis, x_rank,
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platform::errors::InvalidArgument(
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"Attr(axis) value should be in range [-R, R-1], "
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"R is the rank of Input(X). But received axis: %d, R: %d.",
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axis, x_rank));
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ctx->ShareDim(x_name, /*->*/ out_name);
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ctx->ShareLoD(x_name, /*->*/ out_name);
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}
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// broadcast input(0) and input(1) -> output(0)
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void BinaryOpBroadcastInferShape(framework::InferShapeContext *ctx) {
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auto x_name = ctx->GetInputNameByIdx(0);
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auto y_name = ctx->GetInputNameByIdx(1);
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auto out_name = ctx->GetOutputNameByIdx(0);
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auto x_dims = ctx->GetInputDim(x_name);
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auto y_dims = ctx->GetInputDim(y_name);
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PADDLE_ENFORCE_EQ(
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ctx->GetInputsVarType(y_name).front(),
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framework::proto::VarType::LOD_TENSOR,
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platform::errors::InvalidArgument(
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"The var type of input %s should be LoDTensor, but got %s.",
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ctx->Inputs(y_name).front(), ctx->GetInputsVarType(y_name).front()));
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if (ctx->GetInputsVarType(x_name).front() ==
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framework::proto::VarType::SELECTED_ROWS) {
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PADDLE_ENFORCE_EQ(y_dims.size(), 1u,
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platform::errors::InvalidArgument(
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"For binary broadcastable operator, if X is "
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"Sparse(VarType.SELECTED_ROWS"
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"), Y must be scalar, and the size of Y should be 1. "
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"But reveived the size of Y = %s.",
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y_dims.size()));
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PADDLE_ENFORCE_EQ(
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y_dims[0], 1,
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platform::errors::InvalidArgument(
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"For binary broadcastable operator, if X is "
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"Sparse(VarType.SELECTED_ROWS"
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"), Y must be scalar, the first dimension of Y should be 1. "
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"But reveived the first dimension of Y = %s.",
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y_dims[0]));
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} else if (ctx->GetInputsVarType(x_name).front() !=
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framework::proto::VarType::LOD_TENSOR) {
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PADDLE_THROW(platform::errors::InvalidArgument(
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"For binary broadcastable operator, the var type of input X should "
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"be LOD_TENSOR, but got %s",
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ctx->GetInputsVarType(x_name).front()));
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}
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if (x_dims == y_dims) {
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ctx->ShareDim(x_name, /*->*/ out_name);
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ctx->ShareLoD(x_name, /*->*/ out_name);
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} else {
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int max_dim = std::max(x_dims.size(), y_dims.size());
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int axis = ctx->Attrs().Get<int>("axis");
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axis = (axis == -1 ? std::abs(x_dims.size() - y_dims.size()) : axis);
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std::vector<int> x_dims_array(max_dim);
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std::vector<int> y_dims_array(max_dim);
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std::vector<int> out_dims_array(max_dim);
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details::GetBroadcastDimsArrays(x_dims, y_dims, x_dims_array.data(),
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y_dims_array.data(), out_dims_array.data(),
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max_dim, axis);
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ctx->SetOutputDim(out_name, framework::make_ddim(out_dims_array));
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ctx->ShareLoD(x_name, /*->*/ out_name);
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}
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}
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,35 @@
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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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#pragma once
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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// This file almostly contains all the infershape functions that are used in
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// operators.
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namespace paddle {
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namespace operators {
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// shape input(0) -> output(0) without change.
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void UnaryOpUnchangedInferShape(framework::InferShapeContext* ctx);
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// shape input(0) -> output(0) without change, check if axis in range [-Rank(x),
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// Rank(x)-1]
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void UnaryOpUnchangedInferShapeCheckAxis(framework::InferShapeContext* ctx);
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// broadcast input(0) and input(1) -> output(0)
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void BinaryOpBroadcastInferShape(framework::InferShapeContext* ctx);
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,145 @@
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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 "gtest/gtest.h"
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#include "paddle/fluid/framework/ddim.h"
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/framework/var_type.h"
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#include "paddle/fluid/imperative/infer_shape_context.h"
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#include "paddle/fluid/imperative/layer.h"
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#include "paddle/fluid/operators/common_infer_shape_functions.h"
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USE_OP(relu);
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USE_OP(elementwise_add);
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USE_OP(softmax);
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namespace paddle {
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namespace operators {
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namespace details {
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class DygraphInferShapeTest {
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public:
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void AddInput(const std::string& name, const framework::DDim& dim) {
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std::shared_ptr<imperative::VarBase> vin(
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new imperative::VarBase(false, name));
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vin->MutableVar()->GetMutable<framework::LoDTensor>()->Resize(dim);
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ins_[name] = {vin};
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}
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void AddOutput(const std::string& name, const framework::DDim& expected_dim) {
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std::shared_ptr<imperative::VarBase> vout(
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new imperative::VarBase(false, name));
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vout->MutableVar()
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->GetMutable<framework::LoDTensor>(); // InitializeVariable
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outs_[name] = {vout};
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expected_dims_[name] = expected_dim;
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}
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void AddAttrs(const framework::AttributeMap& attrs) { attrs_ = attrs; }
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void SetOpType(const std::string& op_type) { op_type_ = op_type; }
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void Run(std::function<void(framework::InferShapeContext* ctx)> infer_shape) {
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imperative::DygraphInferShapeContext<imperative::VarBase> ctx(
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&ins_, &outs_, &attrs_, op_type_);
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infer_shape(&ctx);
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for (const auto& pair : expected_dims_) {
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auto out = outs_[pair.first][0];
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ASSERT_EQ(pair.second,
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out->MutableVar()->GetMutable<framework::LoDTensor>()->dims());
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}
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}
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private:
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imperative::NameVarBaseMap ins_;
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imperative::NameVarBaseMap outs_;
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framework::AttributeMap attrs_;
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std::string op_type_;
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std::map<std::string, framework::DDim> expected_dims_;
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};
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} // namespace details
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TEST(test_UnaryOpUnchangedInferShape, test_shape) {
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details::DygraphInferShapeTest test;
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test.AddInput("X", {2, 10});
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test.AddOutput("Out", {2, 10});
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test.SetOpType("relu");
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test.Run(UnaryOpUnchangedInferShape);
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}
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TEST(test_BinaryOpBroadcastInferShape, test_same_shape) {
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details::DygraphInferShapeTest test;
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test.AddInput("X", {2, 3, 4, 5});
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test.AddInput("Y", {2, 3, 4, 5});
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test.AddOutput("Out", {2, 3, 4, 5});
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test.SetOpType("elementwise_add");
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test.Run(BinaryOpBroadcastInferShape);
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}
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TEST(test_BinaryOpBroadcastInferShape, test_broadcast1) {
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details::DygraphInferShapeTest test;
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test.AddInput("X", {2, 3, 4, 5});
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test.AddInput("Y", {4, 5});
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test.AddOutput("Out", {2, 3, 4, 5});
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test.AddAttrs({
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{"axis", -1},
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});
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test.SetOpType("elementwise_add");
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test.Run(BinaryOpBroadcastInferShape);
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}
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TEST(test_BinaryOpBroadcastInferShape, test_broadcast2) {
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details::DygraphInferShapeTest test;
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test.AddInput("X", {2, 10, 5, 1});
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test.AddInput("Y", {10, 1, 1});
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test.AddOutput("Out", {2, 10, 5, 1});
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test.AddAttrs({
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{"axis", -1},
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});
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test.SetOpType("elementwise_add");
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test.Run(BinaryOpBroadcastInferShape);
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}
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TEST(test_BinaryOpBroadcastInferShape, test_broadcast3) {
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details::DygraphInferShapeTest test;
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test.AddInput("X", {10, 1, 1});
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test.AddInput("Y", {2, 10, 5, 5});
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test.AddOutput("Out", {2, 10, 5, 5});
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test.AddAttrs({
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{"axis", -1},
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});
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test.SetOpType("elementwise_add");
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test.Run(BinaryOpBroadcastInferShape);
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}
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TEST(test_UnaryOpUnchangedInferShapeCheckAxis, test_shape) {
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details::DygraphInferShapeTest test;
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test.AddInput("X", {2, 10});
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test.AddOutput("Out", {2, 10});
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test.AddAttrs({
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{"axis", -1},
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});
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test.SetOpType("softmax");
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test.Run(UnaryOpUnchangedInferShapeCheckAxis);
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}
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TEST(test_UnaryOpUnchangedInferShapeCheckAxis, test_axis_exception) {
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details::DygraphInferShapeTest test;
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test.AddInput("X", {2, 10});
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test.AddOutput("Out", {2, 10});
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test.AddAttrs({
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{"axis", 2},
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});
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test.SetOpType("softmax");
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ASSERT_ANY_THROW(test.Run(UnaryOpUnchangedInferShapeCheckAxis));
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}
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} // namespace operators
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} // namespace paddle
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