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STT-tensorflow/tensorflow/compiler/tf2xla/kernels/binary_ops.cc
Srinivas Vasudevan b946521465 Add XLA registration for Polygamma, to be used for Digamma gradients. 
PiperOrigin-RevId: 332557647
Change-Id: I97fba661240412a49716544c5cf106a700ab89a4
2020-09-18 17:40:20 -07:00

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/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// Native XLA implementations of simple binary Ops
#include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h"
#include "tensorflow/compiler/tf2xla/lib/broadcast.h"
#include "tensorflow/compiler/tf2xla/shape_util.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
#include "tensorflow/compiler/xla/client/client_library.h"
#include "tensorflow/compiler/xla/client/lib/constants.h"
#include "tensorflow/compiler/xla/client/lib/math.h"
#include "tensorflow/compiler/xla/client/xla_builder.h"
#include "tensorflow/compiler/xla/primitive_util.h"
#include "tensorflow/compiler/xla/xla_data.pb.h"
#include "tensorflow/core/framework/kernel_def_builder.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/types.h"
namespace tensorflow {
namespace {
// A subclass of a XlaBinaryOp must build the computation that
// describes the (tensor,tensor)->tensor function to apply to each element of
// the input.
#define XLA_MAKE_BINARY(NAME, HLO) \
class NAME##Op : public XlaBinaryOp { \
public: \
explicit NAME##Op(OpKernelConstruction* ctx) : XlaBinaryOp(ctx) {} \
xla::XlaOp Computation( \
XlaOpKernelContext* ctx, const xla::XlaOp& lhs, \
const absl::Span<const int64>& lhs_shape, const xla::XlaOp& rhs, \
const absl::Span<const int64>& rhs_shape, \
const BCast& broadcast_helper, \
const std::vector<int64>& extend_dimensions) override { \
xla::XlaBuilder* b = ctx->builder(); \
(void)b; \
(void)lhs_shape; \
(void)rhs_shape; \
(void)extend_dimensions; \
return HLO; \
} \
}; \
REGISTER_XLA_OP(Name(#NAME), NAME##Op)
XLA_MAKE_BINARY(Add, xla::Add(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(AddV2, xla::Add(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(Sub, xla::Sub(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(Mul, xla::Mul(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(Div, xla::Div(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(Atan2, xla::Atan2(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(Complex, xla::Complex(lhs, rhs, extend_dimensions));
// Implementation of DivNoNan. Pseudo-code:
// if (y == 0) {
// return 0
// } else {
// return x / y;
// }
static xla::XlaOp DivNoNanImpl(xla::XlaBuilder* b, DataType dtype, xla::XlaOp x,
xla::XlaOp y, const BCast& broadcast_helper) {
std::tie(x, y) = XlaBinaryOp::Broadcast(x, y, broadcast_helper);
auto zero = XlaHelpers::Zero(b, dtype);
auto y_equals_0 = xla::Eq(y, zero);
auto zeros = xla::ZerosLike(x);
auto result = xla::Select(y_equals_0, zeros, xla::Div(x, y));
return result;
}
XLA_MAKE_BINARY(DivNoNan,
DivNoNanImpl(b, input_type(0), lhs, rhs, broadcast_helper));
// Implementation of MulNoNan. Pseudo-code:
// if (y == 0) {
// return 0
// } else {
// return x * y;
// }
static xla::XlaOp MulNoNanImpl(xla::XlaBuilder* b, DataType dtype, xla::XlaOp x,
xla::XlaOp y, const BCast& broadcast_helper) {
std::tie(x, y) = XlaBinaryOp::Broadcast(x, y, broadcast_helper);
auto zero = XlaHelpers::Zero(b, dtype);
auto y_equals_0 = xla::Eq(y, zero);
auto zeros = xla::ZerosLike(x);
auto result = xla::Select(y_equals_0, zeros, xla::Mul(x, y));
return result;
}
XLA_MAKE_BINARY(MulNoNan,
MulNoNanImpl(b, input_type(0), lhs, rhs, broadcast_helper));
// Implementation of FloorDiv.
//
// For floating-point values, simply returns floor(x / y). For integers, does:
//
// if ((x < 0) != (y < 0)) {
// T abs_x = std::abs(x);
// T abs_y = std::abs(y);
// return -(abs_x + abs_y - 1) / abs_y;
// } else {
// return x / y;
// }
static xla::XlaOp FloorDivImpl(xla::XlaBuilder* b, DataType dtype, xla::XlaOp x,
xla::XlaOp y, const BCast& broadcast_helper) {
std::tie(x, y) = XlaBinaryOp::Broadcast(x, y, broadcast_helper);
if (DataTypeIsFloating(dtype)) {
if (dtype == DataType::DT_BFLOAT16) {
// The result of a BF16 division may produce the Ceil of what was
// computed by F32 division, so avoid end user confusion by doing the
// intermediate divide in F32.
return xla::ConvertElementType(
xla::Floor(xla::Div(xla::ConvertElementType(x, xla::F32),
xla::ConvertElementType(y, xla::F32))),
xla::BF16);
} else {
return xla::Floor(xla::Div(x, y));
}
}
if (DataTypeIsUnsigned(dtype)) {
return xla::Div(x, y);
}
auto zero = XlaHelpers::Zero(b, dtype);
auto one = XlaHelpers::One(b, dtype);
auto different_sign = xla::Ne(xla::Lt(x, zero), xla::Lt(y, zero));
auto abs_x = xla::Abs(x);
auto abs_y = xla::Abs(y);
auto t = xla::Neg(xla::Sub(xla::Add(abs_x, abs_y), one));
return xla::Select(different_sign, xla::Div(t, abs_y), xla::Div(x, y));
}
XLA_MAKE_BINARY(FloorDiv,
FloorDivImpl(b, input_type(0), lhs, rhs, broadcast_helper));
xla::XlaOp XlogyImpl(xla::XlaOp x, xla::XlaOp y,
const BCast& broadcast_helper) {
std::tie(x, y) = XlaBinaryOp::Broadcast(x, y, broadcast_helper);
auto zero = xla::ZerosLike(x);
auto is_zero = xla::Eq(x, zero);
return xla::Select(is_zero, zero, xla::Mul(x, xla::Log(y)));
}
XLA_MAKE_BINARY(Xlogy, XlogyImpl(lhs, rhs, broadcast_helper));
xla::XlaOp Xlog1pyImpl(xla::XlaOp x, xla::XlaOp y,
const BCast& broadcast_helper) {
std::tie(x, y) = XlaBinaryOp::Broadcast(x, y, broadcast_helper);
auto non_zero = xla::Mul(x, xla::Log1p(y));
auto zero = xla::ZerosLike(non_zero);
auto x_is_zero = xla::Eq(x, zero);
return xla::Select(x_is_zero, zero, non_zero);
}
XLA_MAKE_BINARY(Xlog1py, Xlog1pyImpl(lhs, rhs, broadcast_helper));
xla::XlaOp XdivyImpl(xla::XlaOp x, xla::XlaOp y,
const BCast& broadcast_helper) {
std::tie(x, y) = XlaBinaryOp::Broadcast(x, y, broadcast_helper);
auto zero = xla::ZerosLike(x);
auto is_zero = xla::Eq(x, zero);
return xla::Select(is_zero, zero, xla::Div(x, y));
}
XLA_MAKE_BINARY(Xdivy, XdivyImpl(lhs, rhs, broadcast_helper));
// Implementation of FloorMod. Pseudo-code:
// T trunc_mod = std::fmod(x, y);
// return trunc_mod != 0 && (y < 0 != trunc_mod < 0) ? trunc_mod + y
// : trunc_mod;
static xla::XlaOp FloorModImpl(xla::XlaBuilder* b, DataType dtype, xla::XlaOp x,
xla::XlaOp y, const BCast& broadcast_helper) {
std::tie(x, y) = XlaBinaryOp::Broadcast(x, y, broadcast_helper);
auto zero = XlaHelpers::Zero(b, dtype);
auto trunc_mod = xla::Rem(x, y);
auto trunc_mod_not_zero = xla::Ne(trunc_mod, zero);
auto do_plus = xla::And(xla::Ne(xla::Lt(trunc_mod, zero), xla::Lt(y, zero)),
trunc_mod_not_zero);
return xla::Select(do_plus, xla::Add(trunc_mod, y), trunc_mod);
}
XLA_MAKE_BINARY(FloorMod,
FloorModImpl(b, input_type(0), lhs, rhs, broadcast_helper));
XLA_MAKE_BINARY(BitwiseAnd, xla::And(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(BitwiseOr, xla::Or(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(BitwiseXor, xla::Xor(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(LeftShift, xla::ShiftLeft(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(RightShift,
(DataTypeIsUnsigned(ctx->input_type(0))
? xla::ShiftRightLogical(lhs, rhs, extend_dimensions)
: xla::ShiftRightArithmetic(lhs, rhs, extend_dimensions)));
XLA_MAKE_BINARY(LogicalAnd, xla::And(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(LogicalOr, xla::Or(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(Mod, xla::Rem(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(Maximum, xla::Max(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(Minimum, xla::Min(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(RealDiv, xla::Div(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(ReciprocalGrad, xla::Neg(xla::Mul(rhs, xla::Mul(lhs, lhs))));
XLA_MAKE_BINARY(
RsqrtGrad,
xla::Mul((lhs * lhs) * lhs,
xla::Div(rhs, XlaHelpers::IntegerLiteral(b, input_type(0), -2)),
extend_dimensions));
XLA_MAKE_BINARY(
SqrtGrad,
xla::Div(xla::Mul(rhs, XlaHelpers::FloatLiteral(b, input_type(0), 0.5)),
lhs, extend_dimensions));
XLA_MAKE_BINARY(TruncateDiv, xla::Div(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(TruncateMod, xla::Rem(lhs, rhs, extend_dimensions));
// Comparison ops
XLA_MAKE_BINARY(Equal, xla::Eq(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(NotEqual, xla::Ne(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(Greater, xla::Gt(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(GreaterEqual, xla::Ge(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(Less, xla::Lt(lhs, rhs, extend_dimensions));
XLA_MAKE_BINARY(LessEqual, xla::Le(lhs, rhs, extend_dimensions));
// Non-linear ops
XLA_MAKE_BINARY(SigmoidGrad,
xla::Mul(xla::Mul(rhs, lhs),
xla::Sub(XlaHelpers::One(b, input_type(0)), lhs)));
XLA_MAKE_BINARY(SoftplusGrad, xla::Mul(lhs, xla::Logistic(rhs)));
// softsigngrad(gradients, features) = gradients / (1 + abs(features)) ** 2
XLA_MAKE_BINARY(SoftsignGrad,
xla::Div(lhs,
xla::Square(xla::Add(XlaHelpers::One(b, input_type(0)),
xla::Abs(rhs)))));
XLA_MAKE_BINARY(TanhGrad,
xla::Mul(rhs, xla::Sub(XlaHelpers::One(b, input_type(0)),
xla::Mul(lhs, lhs))));
XLA_MAKE_BINARY(Pow, xla::Pow(lhs, rhs, extend_dimensions));
xla::XlaOp SquaredDifferenceImpl(DataType dtype, xla::XlaOp x, xla::XlaOp y,
const std::vector<int64>& extend_dimensions) {
auto difference = xla::Sub(x, y, extend_dimensions);
if (DataTypeIsComplex(dtype)) {
return xla::Conj(difference) * difference;
} else {
return xla::Square(difference);
}
}
XLA_MAKE_BINARY(SquaredDifference,
SquaredDifferenceImpl(input_type(0), lhs, rhs,
extend_dimensions));
xla::XlaOp IgammaImpl(xla::XlaOp x, xla::XlaOp y,
const BCast& broadcast_helper) {
std::tie(x, y) = XlaBinaryOp::Broadcast(x, y, broadcast_helper);
return xla::Igamma(x, y);
}
XLA_MAKE_BINARY(Igamma, IgammaImpl(lhs, rhs, broadcast_helper));
xla::XlaOp IgammaGradAImpl(xla::XlaOp x, xla::XlaOp y,
const BCast& broadcast_helper) {
std::tie(x, y) = XlaBinaryOp::Broadcast(x, y, broadcast_helper);
return xla::IgammaGradA(x, y);
}
XLA_MAKE_BINARY(IgammaGradA, IgammaGradAImpl(lhs, rhs, broadcast_helper));
xla::XlaOp RandomGammaGradImpl(xla::XlaOp x, xla::XlaOp y,
const BCast& broadcast_helper) {
std::tie(x, y) = XlaBinaryOp::Broadcast(x, y, broadcast_helper);
return xla::RandomGammaGrad(x, y);
}
XLA_MAKE_BINARY(RandomGammaGrad,
RandomGammaGradImpl(lhs, rhs, broadcast_helper));
xla::XlaOp IgammacImpl(xla::XlaOp x, xla::XlaOp y,
const BCast& broadcast_helper) {
std::tie(x, y) = XlaBinaryOp::Broadcast(x, y, broadcast_helper);
return xla::Igammac(x, y);
}
XLA_MAKE_BINARY(Igammac, IgammacImpl(lhs, rhs, broadcast_helper));
xla::XlaOp PolygammaImpl(xla::XlaOp n, xla::XlaOp x,
const BCast& broadcast_helper) {
std::tie(n, x) = XlaBinaryOp::Broadcast(n, x, broadcast_helper);
return xla::Polygamma(n, x);
}
XLA_MAKE_BINARY(Polygamma, PolygammaImpl(lhs, rhs, broadcast_helper));
xla::XlaOp ZetaImpl(xla::XlaOp x, xla::XlaOp q, const BCast& broadcast_helper) {
std::tie(x, q) = XlaBinaryOp::Broadcast(x, q, broadcast_helper);
return xla::Zeta(x, q);
}
XLA_MAKE_BINARY(Zeta, ZetaImpl(lhs, rhs, broadcast_helper));
#undef XLA_MAKE_BINARY
class ApproximateEqualOp : public XlaOpKernel {
public:
explicit ApproximateEqualOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
OP_REQUIRES_OK(ctx, ctx->GetAttr("tolerance", &tolerance_));
}
// Computes the max of the scalar input x and 0.
void Compile(XlaOpKernelContext* ctx) override {
xla::XlaBuilder* b = ctx->builder();
auto abs = xla::Abs(xla::Sub(ctx->Input(0), ctx->Input(1)));
auto abs_shape = b->GetShape(abs);
OP_REQUIRES_OK(ctx, abs_shape.status());
auto abs_type = abs_shape.ValueOrDie().element_type();
auto result =
xla::Lt(abs, xla::ConvertElementType(
xla::ConstantR0<float>(b, tolerance_), abs_type));
ctx->SetOutput(0, result);
}
private:
float tolerance_;
};
REGISTER_XLA_OP(Name("ApproximateEqual"), ApproximateEqualOp);
} // namespace
} // namespace tensorflow
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