wehub-resource-sync
85 lines
3.2 KiB
C++
85 lines
3.2 KiB
C++
/* Copyright 2017 The TensorFlow 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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==============================================================================*/
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#include <string>
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#include "absl/status/status.h"
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#include "absl/strings/str_cat.h"
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#include "tensorflow/compiler/tf2xla/mlir_xla_op_kernel.h"
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#include "tensorflow/compiler/tf2xla/xla_helpers.h"
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#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
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#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
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#include "xla/hlo/builder/xla_builder.h"
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#include "tensorflow/core/framework/tensor.h"
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#include "tensorflow/core/framework/tensor_shape.h"
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#include "tensorflow/core/util/tensor_format.h"
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namespace tensorflow {
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namespace {
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class BiasOp : public XlaOpKernel {
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public:
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explicit BiasOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
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std::string data_format;
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if (ctx->GetAttr("data_format", &data_format).ok()) {
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OP_REQUIRES(ctx, FormatFromString(data_format, &data_format_),
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absl::InvalidArgumentError("Invalid data format"));
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} else {
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data_format_ = FORMAT_NHWC;
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}
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}
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void Compile(XlaOpKernelContext* ctx) override {
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const TensorShape input_shape = ctx->InputShape(0);
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const TensorShape bias_shape = ctx->InputShape(1);
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OP_REQUIRES(
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ctx, TensorShapeUtils::IsMatrixOrHigher(input_shape),
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absl::InvalidArgumentError(absl::StrCat(
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"Input tensor must be at least 2D: ", input_shape.DebugString())));
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OP_REQUIRES(ctx, TensorShapeUtils::IsVector(bias_shape),
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absl::InvalidArgumentError(absl::StrCat(
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"Biases must be 1D: ", bias_shape.DebugString())));
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// feature_dim is the channel (C) dimension of the data.
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int feature_dim = (data_format_ == FORMAT_NHWC)
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? input_shape.dims() - 1
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: /*data_format == FORMAT_NCHW*/ 1;
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OP_REQUIRES(ctx, feature_dim >= 0,
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absl::InvalidArgumentError(
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"Input tensor does not have enough dimensions "
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"to contain the feature dimension"));
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OP_REQUIRES(
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ctx, bias_shape.dim_size(0) == input_shape.dim_size(feature_dim),
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absl::InvalidArgumentError(absl::StrCat(
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"Must provide as many biases as the last dimension "
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"of the input tensor: ",
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bias_shape.DebugString(), " vs. ", input_shape.DebugString())));
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xla::XlaOp result = xla::Add(ctx->Input(0), ctx->Input(1), {feature_dim});
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ctx->SetOutput(0, result);
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}
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private:
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TensorFormat data_format_;
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};
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REGISTER_XLA_OP(Name("BiasAdd"), BiasOp);
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REGISTER_XLA_OP(Name("BiasAddV1"), BiasOp);
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REGISTER_XLA_OP(Name("BiasAddGrad"), MlirXlaOpKernel);
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} // namespace
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} // namespace tensorflow
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