102 lines
4.1 KiB
C++
102 lines
4.1 KiB
C++
/* Copyright 2018 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 "absl/algorithm/container.h"
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#include "tensorflow/compiler/tf2xla/shape_util.h"
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#include "tensorflow/compiler/tf2xla/xla_compiler.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 "tensorflow/compiler/xla/client/xla_builder.h"
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#include "tensorflow/compiler/xla/client/xla_computation.h"
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#include "tensorflow/core/framework/function.h"
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#include "tensorflow/core/framework/op_kernel.h"
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namespace tensorflow {
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namespace {
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class XlaReduceOp : public XlaOpKernel {
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public:
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explicit XlaReduceOp(OpKernelConstruction* context) : XlaOpKernel(context) {
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OP_REQUIRES_OK(context, context->GetAttr("reducer", &reducer_));
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OP_REQUIRES_OK(context, context->GetAttr("dimensions_to_reduce",
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&dimensions_to_reduce_));
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std::set<int64> dims_set(dimensions_to_reduce_.begin(),
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dimensions_to_reduce_.end());
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OP_REQUIRES(
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context, dims_set.size() == dimensions_to_reduce_.size(),
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errors::InvalidArgument("Duplicate dimension in dimensions_to_reduce "
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"argument to XlaReduce"));
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}
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void Compile(XlaOpKernelContext* context) override {
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const TensorShape input_shape = context->InputShape("input");
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const TensorShape init_value_shape = context->InputShape("init_value");
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const DataType dtype = context->input_type(0);
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const int rank = input_shape.dims();
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OP_REQUIRES(context, TensorShapeUtils::IsScalar(init_value_shape),
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errors::InvalidArgument("init_value must be a scalar"));
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auto dim_in_range = [rank](int64 dim) { return dim >= 0 && dim < rank; };
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OP_REQUIRES(context,
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rank >= dimensions_to_reduce_.size() &&
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absl::c_all_of(dimensions_to_reduce_, dim_in_range),
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errors::InvalidArgument(
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"Invalid dimensions_to_reduce argument to XlaReduce"));
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// Build the reducer function.
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XlaCompiler::Argument reducer_arg;
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reducer_arg.kind = XlaCompiler::Argument::kParameter;
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reducer_arg.type = dtype;
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reducer_arg.shape = TensorShape();
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XlaCompiler::CompileOptions compile_options;
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compile_options.use_tuple_arg = false;
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compile_options.always_return_tuple = false;
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compile_options.is_entry_computation = false;
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XlaCompiler::CompilationResult reducer;
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OP_REQUIRES_OK(context, context->compiler()->CompileFunction(
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compile_options, *reducer_,
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{reducer_arg, reducer_arg}, &reducer));
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xla::Shape scalar_shape;
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OP_REQUIRES_OK(context,
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TensorShapeToXLAShape(dtype, TensorShape(), &scalar_shape));
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OP_REQUIRES(
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context,
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xla::ShapeUtil::Compatible(reducer.xla_output_shape, scalar_shape),
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errors::InvalidArgument(
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"Invalid output shape of XlaReduce reducer. Expected ",
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xla::ShapeUtil::HumanString(scalar_shape), " got ",
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xla::ShapeUtil::HumanString(reducer.xla_output_shape)));
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xla::XlaOp output =
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xla::Reduce(context->Input("input"), context->Input("init_value"),
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*reducer.computation, dimensions_to_reduce_);
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context->SetOutput(0, output);
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}
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private:
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const NameAttrList* reducer_;
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std::vector<int64> dimensions_to_reduce_;
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TF_DISALLOW_COPY_AND_ASSIGN(XlaReduceOp);
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};
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REGISTER_XLA_OP(Name("XlaReduce"), XlaReduceOp);
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} // namespace
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} // namespace tensorflow
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