11caab3c13
StringPiece is an alias for absl::string_view, InlinedVector is aliased to absl::InlinedVector. StrCat is compatible, so swapping it out is safe. PiperOrigin-RevId: 211691840
247 lines
8.4 KiB
C++
247 lines
8.4 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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// XLA-specific Shape Ops.
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#include "tensorflow/compiler/tf2xla/kernels/shape_util.h"
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#include "tensorflow/compiler/tf2xla/type_util.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 "tensorflow/compiler/xla/client/xla_builder.h"
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#include "tensorflow/core/framework/kernel_def_builder.h"
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#include "tensorflow/core/kernels/bounds_check.h"
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namespace tensorflow {
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namespace {
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class ShapeOp : public XlaOpKernel {
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public:
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explicit ShapeOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
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OP_REQUIRES_OK(ctx, ctx->GetAttr("out_type", &out_dtype_));
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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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Tensor shape_constant(out_dtype_, TensorShape({input_shape.dims()}));
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OP_REQUIRES_OK(ctx, TensorShapeToConstant(input_shape, &shape_constant));
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ctx->SetConstantOutput(0, shape_constant);
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}
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private:
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DataType out_dtype_;
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};
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REGISTER_XLA_OP(Name("Shape").CompilationOnly(), ShapeOp);
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class ShapeNOp : public XlaOpKernel {
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public:
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explicit ShapeNOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
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OP_REQUIRES_OK(ctx, ctx->GetAttr("out_type", &out_dtype_));
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}
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void Compile(XlaOpKernelContext* ctx) override {
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for (int i = 0; i < ctx->num_inputs(); ++i) {
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const TensorShape input_shape = ctx->InputShape(i);
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Tensor shape_constant(out_dtype_, TensorShape({input_shape.dims()}));
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OP_REQUIRES_OK(ctx, TensorShapeToConstant(input_shape, &shape_constant));
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ctx->SetConstantOutput(i, shape_constant);
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}
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}
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bool IsExpensive() override { return false; }
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private:
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DataType out_dtype_;
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};
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REGISTER_XLA_OP(Name("ShapeN").CompilationOnly(), ShapeNOp);
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class RankOp : public XlaOpKernel {
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public:
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explicit RankOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
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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 int rank = input_shape.dims();
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Tensor rank_constant(DT_INT32, TensorShape({}));
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rank_constant.scalar<int32>()() = rank;
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ctx->SetConstantOutput(0, rank_constant);
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}
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};
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REGISTER_XLA_OP(Name("Rank").CompilationOnly(), RankOp);
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class SizeOp : public XlaOpKernel {
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public:
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explicit SizeOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
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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 int64 size = input_shape.num_elements();
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OP_REQUIRES(ctx, FastBoundsCheck(size, std::numeric_limits<int32>::max()),
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errors::InvalidArgument("Size does not work for tensors > "
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"int32 max."));
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Tensor size_constant(DT_INT32, TensorShape({}));
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size_constant.scalar<int32>()() = static_cast<int32>(size);
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ctx->SetConstantOutput(0, size_constant);
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}
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};
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REGISTER_XLA_OP(Name("Size").CompilationOnly(), SizeOp);
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class ExpandDimsOp : public XlaOpKernel {
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public:
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explicit ExpandDimsOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
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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 dim_shape = ctx->InputShape(1);
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// TODO(phawkins): the standard implementation of ExpandDimsOp seems to
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// accept legacy scalars, even when they should be forbidden by the graphdef
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// version.
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OP_REQUIRES(ctx, dim_shape.num_elements() == 1,
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errors::InvalidArgument(absl::StrCat(
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"dim input to ExpandDims must be a scalar; got ",
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dim_shape.DebugString())));
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xla::Literal literal;
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OP_REQUIRES_OK(ctx, ctx->ConstantInputReshaped(1, {1}, &literal));
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int dim = literal.data<int32>()[0];
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OP_REQUIRES(ctx,
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(dim >= -1 - input_shape.dims() && dim <= input_shape.dims()),
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errors::InvalidArgument("Tried to expand dim index ", dim,
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" for tensor with ", input_shape.dims(),
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" dimensions."));
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auto existing_dims = input_shape.dim_sizes();
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// Safe - # elements in tensor dims bounded.
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const int existing_dims_size = static_cast<int>(existing_dims.size());
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std::vector<int64> new_shape(existing_dims_size);
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for (size_t i = 0; i < new_shape.size(); ++i) {
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new_shape[i] = existing_dims[i];
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}
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// We emulate numpy's interpretation of the dim axis when
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// -input.dims() >= dim <= input.dims().
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if (dim < 0) {
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dim += existing_dims.size() + 1;
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}
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// Clamp to the end if needed.
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dim = std::min<int32>(dim, existing_dims_size);
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new_shape.emplace(new_shape.begin() + dim, 1);
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ctx->SetOutput(0, xla::Reshape(ctx->Input(0), new_shape));
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}
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};
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REGISTER_XLA_OP(Name("ExpandDims").CompileTimeConstInput("dim"), ExpandDimsOp);
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class SqueezeOp : public XlaOpKernel {
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public:
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explicit SqueezeOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
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std::vector<int32> squeeze_dims;
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OP_REQUIRES_OK(ctx, ctx->GetAttr("squeeze_dims", &squeeze_dims));
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squeeze_dims_.insert(squeeze_dims.begin(), squeeze_dims.end());
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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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auto existing_dims = input_shape.dim_sizes();
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int existing_dims_size = input_shape.dims();
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std::vector<int64> new_shape;
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std::unordered_set<int32> wrapped_squeeze_dims;
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wrapped_squeeze_dims.reserve(squeeze_dims_.size());
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// Validate squeeze dims against the input.
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for (int32 dim : squeeze_dims_) {
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OP_REQUIRES(ctx, (dim >= -input_shape.dims() && dim < input_shape.dims()),
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errors::InvalidArgument("Tried to squeeze dim index ", dim,
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" for tensor with ",
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input_shape.dims(), " dimensions."));
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// If dim is < 0, we wrap around (-1 means the last element).
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if (dim < 0) {
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dim = existing_dims_size + dim;
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}
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wrapped_squeeze_dims.insert(dim);
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}
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for (int i = 0; i < existing_dims_size; ++i) {
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auto existing_dim = existing_dims[i];
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// If squeeze_set is non-empty, only squeeze those dimensions.
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if (!wrapped_squeeze_dims.empty()) {
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if (wrapped_squeeze_dims.count(i) > 0) {
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OP_REQUIRES(ctx, existing_dim == 1,
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errors::InvalidArgument(
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"Tried to explicitly squeeze dimension ", i,
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" but dimension was not 1: ", existing_dim));
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} else {
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// This dimension is not being squeezed.
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new_shape.push_back(existing_dim);
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}
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} else {
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// Copy over all non-1-length dimensions.
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if (existing_dim != 1) {
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new_shape.push_back(existing_dim);
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}
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}
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}
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ctx->SetOutput(0, xla::Reshape(ctx->Input(0), new_shape));
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}
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private:
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std::unordered_set<int32> squeeze_dims_;
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};
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REGISTER_XLA_OP(Name("Squeeze"), SqueezeOp);
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class ZerosLikeOp : public XlaOpKernel {
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public:
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explicit ZerosLikeOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
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void Compile(XlaOpKernelContext* ctx) override {
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const TensorShape input_shape = ctx->InputShape(0);
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auto zero = XlaHelpers::Zero(ctx->builder(), input_type(0));
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ctx->SetOutput(0, xla::Broadcast(zero, input_shape.dim_sizes()));
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}
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};
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REGISTER_XLA_OP(Name("ZerosLike"), ZerosLikeOp);
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class OnesLikeOp : public XlaOpKernel {
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public:
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explicit OnesLikeOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
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void Compile(XlaOpKernelContext* ctx) override {
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const TensorShape input_shape = ctx->InputShape(0);
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auto one = XlaHelpers::One(ctx->builder(), input_type(0));
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ctx->SetOutput(0, xla::Broadcast(one, input_shape.dim_sizes()));
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}
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};
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REGISTER_XLA_OP(Name("OnesLike"), OnesLikeOp);
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} // namespace
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} // namespace tensorflow
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