wehub-resource-sync
202 lines
8.0 KiB
C++
202 lines
8.0 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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// XLA-specific sequence and range Ops.
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#include <cstdint>
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#include <type_traits>
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#include "absl/status/statusor.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/constants.h"
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#include "xla/hlo/builder/value_inference.h"
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#include "xla/hlo/builder/xla_builder.h"
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#include "xla/literal.h"
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#include "xla/primitive_util.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.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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#include "tensorflow/core/platform/types.h"
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namespace tensorflow {
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namespace {
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// The type-specific part of the implementation of Range.
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template <typename T>
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absl::StatusOr<xla::XlaOp> CreateRangeTensor(
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const xla::LiteralSlice& start_literal,
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const xla::LiteralSlice& limit_literal,
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const xla::LiteralSlice& delta_literal, xla::XlaBuilder* builder) {
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T start = start_literal.Get<T>({});
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T limit = limit_literal.Get<T>({});
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T delta = delta_literal.Get<T>({});
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if (delta == 0) {
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return errors::InvalidArgument("Requires delta != 0: ", delta);
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}
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if (delta > 0) {
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if (start > limit) {
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return errors::InvalidArgument(
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"Requires start <= limit when delta > 0: ", start, "/", limit);
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}
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} else {
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if (start < limit) {
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return errors::InvalidArgument(
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"Requires start >= limit when delta < 0: ", start, "/", limit);
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}
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}
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int64_t size =
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(std::is_integral<T>::value
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? static_cast<T>(
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limit == start
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? 0
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: (std::abs(limit - start) - 1) / std::abs(delta) + 1)
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: std::ceil(std::abs((limit - start) / delta)));
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return xla::ConstantR0(builder, start) +
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xla::ConstantR0(builder, delta) *
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xla::Iota(builder, xla::primitive_util::NativeToPrimitiveType<T>(),
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size);
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}
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class RangeOp : public XlaOpKernel {
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public:
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explicit RangeOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
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void Compile(XlaOpKernelContext* ctx) override {
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const TensorShape start_in_shape = ctx->InputShape(0);
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const TensorShape limit_in_shape = ctx->InputShape(1);
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const TensorShape delta_in_shape = ctx->InputShape(2);
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OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(start_in_shape),
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errors::InvalidArgument("start must be a scalar, not shape ",
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start_in_shape.DebugString()));
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OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(limit_in_shape),
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errors::InvalidArgument("limit must be a scalar, not shape ",
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limit_in_shape.DebugString()));
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OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(delta_in_shape),
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errors::InvalidArgument("delta must be a scalar, not shape ",
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delta_in_shape.DebugString()));
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xla::Literal start, limit, delta;
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OP_REQUIRES_OK(ctx, ctx->ConstantInput(
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0, &start, xla::ValueInferenceMode::kLowerBound));
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OP_REQUIRES_OK(ctx, ctx->ConstantInput(
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1, &limit, xla::ValueInferenceMode::kUpperBound));
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OP_REQUIRES_OK(ctx, ctx->ConstantInput(2, &delta));
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DataType type = input_type(0);
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absl::StatusOr<xla::XlaOp> output;
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switch (type) {
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case DT_INT32:
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output =
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CreateRangeTensor<int32_t>(start, limit, delta, ctx->builder());
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break;
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case DT_INT64:
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output =
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CreateRangeTensor<int64_t>(start, limit, delta, ctx->builder());
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break;
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case DT_FLOAT:
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output = CreateRangeTensor<float>(start, limit, delta, ctx->builder());
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break;
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case DT_DOUBLE:
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output = CreateRangeTensor<double>(start, limit, delta, ctx->builder());
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break;
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default:
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output = errors::InvalidArgument("Invalid type for Range ",
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DataTypeString(type));
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}
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OP_REQUIRES_OK(ctx, output.status());
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bool start_is_dynamic = false;
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OP_REQUIRES_OK(ctx,
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ctx->ResolveInputDynamismIntoPred(0, &start_is_dynamic));
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bool limit_is_dynamic = false;
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OP_REQUIRES_OK(ctx,
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ctx->ResolveInputDynamismIntoPred(1, &limit_is_dynamic));
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if (start_is_dynamic || limit_is_dynamic) {
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xla::XlaOp delta = ctx->Input(2);
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xla::XlaOp limit = ctx->Input(1);
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xla::XlaOp start = ctx->Input(0);
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if (type == DT_INT32 || type == DT_INT64) {
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auto dynamic_size = (xla::Abs(limit - start) + xla::Abs(delta) -
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xla::One(ctx->builder(), ctx->input_xla_type(0))) /
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xla::Abs(delta);
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dynamic_size = xla::ConvertElementType(dynamic_size, xla::S32);
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output = xla::SetDimensionSize(output.value(), dynamic_size, 0);
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} else {
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auto dynamic_size = (xla::Ceil(xla::Abs((limit - start) / delta)));
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dynamic_size = xla::ConvertElementType(dynamic_size, xla::S32);
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output = xla::SetDimensionSize(output.value(), dynamic_size, 0);
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}
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}
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ctx->SetOutput(0, output.value());
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}
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};
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REGISTER_XLA_OP(Name("Range")
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.CompileTimeConstantInput("start")
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.CompileTimeConstantInput("limit")
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.CompileTimeConstantInput("delta"),
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RangeOp);
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class LinSpaceOp : public XlaOpKernel {
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public:
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explicit LinSpaceOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
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void Compile(XlaOpKernelContext* ctx) override {
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const TensorShape start_in_shape = ctx->InputShape("start");
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const TensorShape stop_in_shape = ctx->InputShape("stop");
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const TensorShape num_in_shape = ctx->InputShape("num");
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OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(start_in_shape),
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errors::InvalidArgument("start must be a scalar, not shape ",
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start_in_shape.DebugString()));
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OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(stop_in_shape),
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errors::InvalidArgument("stop must be a scalar, not shape ",
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stop_in_shape.DebugString()));
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OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(num_in_shape),
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errors::InvalidArgument("num must be a scalar, not shape ",
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num_in_shape.DebugString()));
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int64_t num;
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OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar("num", &num));
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OP_REQUIRES(ctx, num > 0,
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errors::InvalidArgument("Requires num > 0: ", num));
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xla::XlaOp start = ctx->Input("start");
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xla::XlaOp stop = ctx->Input("stop");
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xla::XlaOp iota = xla::Iota(ctx->builder(), ctx->output_xla_type(0), num);
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xla::XlaOp step =
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(stop - start) / xla::ScalarLike(start, (num > 1 ? num - 1 : num));
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xla::XlaOp result = iota * step + start;
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if (num > 1) {
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// According to linspace spec, start has to be the first element and end
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// has to be last element.
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xla::XlaOp mask = xla::Iota(ctx->builder(), xla::S64, num);
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xla::XlaOp eq = xla::Eq(mask, xla::ScalarLike(mask, num - 1));
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result = xla::Select(eq, stop, result);
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}
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ctx->SetOutput(0, result);
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}
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};
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REGISTER_XLA_OP(Name("LinSpace").CompileTimeConstantInput("num"), LinSpaceOp);
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} // namespace
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} // namespace tensorflow
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