wehub-resource-sync
94 lines
3.4 KiB
C++
94 lines
3.4 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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// Native XLA implementations of indexing ops.
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#include "tensorflow/compiler/tf2xla/kernels/index_ops.h"
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#include <cstdint>
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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/type_util.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/lib/arithmetic.h"
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#include "xla/hlo/builder/xla_builder.h"
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#include "xla/xla_data.pb.h"
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#include "tensorflow/core/framework/op_kernel.h"
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#include "tensorflow/core/framework/op_requires.h"
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#include "tensorflow/core/framework/tensor_shape.h"
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#include "tensorflow/core/framework/types.pb.h"
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#include "tensorflow/core/platform/errors.h"
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namespace tensorflow {
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XlaArgMinMaxOp::XlaArgMinMaxOp(OpKernelConstruction* ctx, bool is_min)
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: XlaOpKernel(ctx), is_min_(is_min) {}
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void XlaArgMinMaxOp::Compile(XlaOpKernelContext* ctx) {
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const TensorShape input_shape = ctx->InputShape(0);
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const TensorShape dimension_shape = ctx->InputShape(1);
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OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(dimension_shape),
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absl::InvalidArgumentError(absl::StrCat(
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"dim must be a scalar, but received tensor of shape: ",
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dimension_shape.DebugString())));
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int64_t dim;
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OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar(1, &dim));
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const int input_dims = input_shape.dims();
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const int axis = dim < 0 ? dim + input_dims : dim;
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OP_REQUIRES(ctx, axis >= 0 && axis < input_dims,
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absl::InvalidArgumentError(
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absl::StrCat("Expected dimension in the range [", -input_dims,
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", ", input_dims, "), but got ", dim)));
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const int64_t axis_size = input_shape.dim_size(axis);
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OP_REQUIRES(ctx, axis_size > 0,
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absl::InvalidArgumentError(
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absl::StrCat("Reduction axis ", dim, " is empty in shape ",
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input_shape.DebugString())));
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DataType index_type = output_type(0);
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xla::PrimitiveType index_xla_type;
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OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(index_type, &index_xla_type));
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xla::XlaOp input = ctx->Input(0);
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xla::XlaOp output =
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xla::ArgMinMax(input, index_xla_type, axis, /*is_min=*/is_min_);
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ctx->SetOutput(0, output);
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}
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XlaArgMaxOp::XlaArgMaxOp(OpKernelConstruction* ctx)
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: XlaArgMinMaxOp(ctx, /*is_min=*/false) {}
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REGISTER_XLA_OP(Name("ArgMax").CompileTimeConstantInput("dimension"),
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XlaArgMaxOp);
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namespace {
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class XlaArgMinOp : public XlaArgMinMaxOp {
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public:
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explicit XlaArgMinOp(OpKernelConstruction* ctx);
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};
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XlaArgMinOp::XlaArgMinOp(OpKernelConstruction* ctx)
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: XlaArgMinMaxOp(ctx, /*is_min=*/true) {}
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REGISTER_XLA_OP(Name("ArgMin").CompileTimeConstantInput("dimension"),
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XlaArgMinOp);
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} // namespace
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} // namespace tensorflow
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