499 lines
22 KiB
C++
499 lines
22 KiB
C++
/* Copyright 2020 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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// Enable the use of M_* math constants.
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// NOTE: this must be first in the file to ensure that if cmath is transitively
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// included by any other header it has the define set on first processing.
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// https://docs.microsoft.com/en-us/cpp/c-runtime-library/math-constants
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#define _USE_MATH_DEFINES
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#include <cmath>
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#include <numeric>
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#include <vector>
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#include "mlir-hlo/Dialect/mhlo/IR/chlo_ops.h"
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#include "mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
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#include "mlir-hlo/Dialect/mhlo/transforms/map_chlo_to_hlo_op.h"
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#include "mlir-hlo/Dialect/mhlo/transforms/rewriters.h"
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#include "mlir-hlo/utils/broadcast_utils.h"
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#include "mlir/Dialect/SCF/SCF.h"
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#include "mlir/Dialect/Shape/IR/Shape.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/IR/Attributes.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/MLIRContext.h"
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#include "mlir/IR/OperationSupport.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/Transforms/DialectConversion.h"
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namespace mlir {
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namespace chlo {
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namespace {
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struct ConvertConstantLikeOp : public OpConversionPattern<ConstantLikeOp> {
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using OpConversionPattern<ConstantLikeOp>::OpConversionPattern;
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LogicalResult matchAndRewrite(
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ConstantLikeOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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auto result_ty = op.getType().cast<ShapedType>();
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// Unranked uses are not supported. Consider `transform-unranked-hlo`.
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if (!result_ty.hasRank()) return failure();
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// Lower to MHLO constant if statically shaped.
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if (result_ty.hasStaticShape()) {
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rewriter.replaceOpWithNewOp<mhlo::ConstOp>(
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op, DenseElementsAttr::get(result_ty, op.value()));
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return success();
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}
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// Lower to broadcasted constant.
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ConstantLikeOp::Adaptor transformed(operands);
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auto loc = op.getLoc();
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Type extent_tensor_type = shape::getExtentTensorType(op.getContext());
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Value constant = rewriter.create<mhlo::ConstOp>(loc, op.value());
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Value uncasted_shape = rewriter.create<shape::ShapeOfOp>(
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loc, extent_tensor_type, transformed.operand());
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Type shape_ty =
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RankedTensorType::get({result_ty.getRank()}, rewriter.getIndexType());
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Value shape =
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rewriter.create<tensor::CastOp>(loc, shape_ty, uncasted_shape);
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rewriter.replaceOpWithNewOp<mhlo::DynamicBroadcastInDimOp>(
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op, result_ty, constant, shape, rewriter.getI64TensorAttr({}));
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return success();
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}
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};
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template <typename FTy>
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Value MaterializePolynomialApproximation(ConversionPatternRewriter &rewriter,
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Location loc, Value x,
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const std::vector<FTy> &coefficients) {
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Value poly = chlo::getConstantLike(rewriter, loc, 0.0, x);
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for (FTy c : coefficients) {
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poly = rewriter.create<mhlo::MulOp>(loc, x.getType(), poly, x);
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poly = rewriter.create<mhlo::AddOp>(
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loc, x.getType(), poly, chlo::getConstantLike(rewriter, loc, c, x));
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}
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return poly;
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}
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// Precondition is |x| >= 1. Use erf approximation, otherwise.
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//
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// We rely on multiple polynomial approximations for x >= 1. We pass |x| as an
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// argument and derive the final approximation for all |x| >= 1.
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// This implementation is based on Cephes.
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Value MaterializeErfcApproximationF64ForMagnituteGEOne(
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ConversionPatternRewriter &rewriter, Location loc, Value x) {
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assert(x.getType().cast<ShapedType>().getElementType().isF64() &&
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"expect f64 element type");
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const double kMaxlog = 7.09782712893383996843E2;
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const std::vector<double> kErfcPCoefficients{
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2.46196981473530512524E-10, 5.64189564831068821977E-1,
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7.46321056442269912687E0, 4.86371970985681366614E1,
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1.96520832956077098242E2, 5.26445194995477358631E2,
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9.34528527171957607540E2, 1.02755188689515710272E3,
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5.57535335369399327526E2};
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const std::vector<double> kErfcQCoefficients{
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1.00000000000000000000E0, 1.32281951154744992508E1,
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8.67072140885989742329E1, 3.54937778887819891062E2,
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9.75708501743205489753E2, 1.82390916687909736289E3,
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2.24633760818710981792E3, 1.65666309194161350182E3,
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5.57535340817727675546E2};
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const std::vector<double> kErfcRCoefficients{
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5.64189583547755073984E-1, 1.27536670759978104416E0,
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5.01905042251180477414E0, 6.16021097993053585195E0,
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7.40974269950448939160E0, 2.97886665372100240670E0};
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const std::vector<double> kErfcSCoefficients{
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1.00000000000000000000E0, 2.26052863220117276590E0,
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9.39603524938001434673E0, 1.20489539808096656605E1,
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1.70814450747565897222E1, 9.60896809063285878198E0,
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3.36907645100081516050E0};
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// Let z = -x^2.
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Value x_sq = rewriter.create<mhlo::MulOp>(loc, x, x);
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Value z = rewriter.create<mhlo::NegOp>(loc, x_sq);
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// Materialize polynomial approximation for x in [1, 8) as
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// erfc(x) = exp(z) P(|x|) / Q(|x|).
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Value exp_z = rewriter.create<mhlo::ExpOp>(loc, z);
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Value abs_x = rewriter.create<mhlo::AbsOp>(loc, x);
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Value poly_p = MaterializePolynomialApproximation(rewriter, loc, abs_x,
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kErfcPCoefficients);
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Value exp_z_mul_poly_p = rewriter.create<mhlo::MulOp>(loc, exp_z, poly_p);
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Value poly_q = MaterializePolynomialApproximation(rewriter, loc, abs_x,
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kErfcQCoefficients);
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Value erfc_approx_1_8 =
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rewriter.create<mhlo::DivOp>(loc, exp_z_mul_poly_p, poly_q);
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// Materialize polynomial approximation for x in >= 8 as
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// erfc(x) exp(z) R(|x|) / S(|x|).
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Value poly_r = MaterializePolynomialApproximation(rewriter, loc, abs_x,
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kErfcRCoefficients);
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Value exp_z_mul_poly_r = rewriter.create<mhlo::MulOp>(loc, exp_z, poly_r);
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Value poly_s = MaterializePolynomialApproximation(rewriter, loc, abs_x,
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kErfcSCoefficients);
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Value erfc_approx_8_inf =
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rewriter.create<mhlo::DivOp>(loc, exp_z_mul_poly_r, poly_s);
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// Combine polynomial approximations for x >= 1.
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const StringAttr kLT = rewriter.getStringAttr(
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mhlo::stringifyComparisonDirection(mhlo::ComparisonDirection::LT));
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Value eight = chlo::getConstantLike(rewriter, loc, 8.0, x);
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Value abs_x_lt_8 = rewriter.create<mhlo::CompareOp>(loc, abs_x, eight, kLT);
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Value erfc_approx = rewriter.create<mhlo::SelectOp>(
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loc, abs_x_lt_8, erfc_approx_1_8, erfc_approx_8_inf);
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// Clamp to prevent overflow and materialize approximation for large x as
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// erfc(x) = 0.
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Value z_lt_neg_maxlog = rewriter.create<mhlo::CompareOp>(
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loc, z, chlo::getConstantLike(rewriter, loc, -kMaxlog, x), kLT);
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Value zero = chlo::getConstantLike(rewriter, loc, 0.0, x);
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Value erfc_approx_clamped =
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rewriter.create<mhlo::SelectOp>(loc, z_lt_neg_maxlog, zero, erfc_approx);
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// Derive approximation for x <= -1 as
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// erfc(x) = 2 - erfc(-x).
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// Reuse previously materialized approximations all of which take |x| as their
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// argument.
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Value x_lt_zero = rewriter.create<mhlo::CompareOp>(loc, x, zero, kLT);
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Value two = chlo::getConstantLike(rewriter, loc, 2.0, x);
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Value two_sub_erfc_approx_clamped =
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rewriter.create<mhlo::SubOp>(loc, two, erfc_approx_clamped);
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return rewriter.create<mhlo::SelectOp>(
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loc, x_lt_zero, two_sub_erfc_approx_clamped, erfc_approx_clamped);
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}
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// Precondition is |x| <= 1. Use erfc approximation, otherwise.
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// This implementation is based on Cephes.
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Value MaterializeErfApproximationF64ForMagnituteLEOne(
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ConversionPatternRewriter &rewriter, Location loc, Value x) {
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assert(x.getType().cast<ShapedType>().getElementType().isF64() &&
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"expect f64 element type");
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const std::vector<double> kErfTCoefficients{
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9.60497373987051638749E0, 9.00260197203842689217E1,
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2.23200534594684319226E3, 7.00332514112805075473E3,
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5.55923013010394962768E4};
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const std::vector<double> kErfUCoefficients{
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1.00000000000000000000E0, 3.35617141647503099647E1,
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5.21357949780152679795E2, 4.59432382970980127987E3,
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2.26290000613890934246E4, 4.92673942608635921086E4};
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// Materialize polynomial approximation for |x| <= 1 as
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// erf(x) = x T(x^2) / U(x^2).
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Value x_sq = rewriter.create<mhlo::MulOp>(loc, x, x);
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Value poly_t = MaterializePolynomialApproximation(rewriter, loc, x_sq,
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kErfTCoefficients);
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Value x_mul_poly_t = rewriter.create<mhlo::MulOp>(loc, x, poly_t);
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Value poly_u = MaterializePolynomialApproximation(rewriter, loc, x_sq,
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kErfUCoefficients);
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return rewriter.create<mhlo::DivOp>(loc, x_mul_poly_t, poly_u);
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}
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// This implementation is based on Cephes.
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Value MaterializeErfApproximationF64(ConversionPatternRewriter &rewriter,
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Location loc, Value x) {
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assert(x.getType().cast<ShapedType>().getElementType().isF64() &&
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"expect f64 element type");
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// Rely on erf approximation for |x| < 1
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// erf(x) = erf_approx(x)
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Value erf_approx =
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MaterializeErfApproximationF64ForMagnituteLEOne(rewriter, loc, x);
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// Rely on erfc approximation for |x| >= 1 and materialize erf as
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// erf(x) = 1 - erfc_approx(x)
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Value one = chlo::getConstantLike(rewriter, loc, 1.0, x);
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Value erfc_approx =
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MaterializeErfcApproximationF64ForMagnituteGEOne(rewriter, loc, x);
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Value erfc_based_approx = rewriter.create<mhlo::SubOp>(loc, one, erfc_approx);
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// Materialize approximation selection based on argument.
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Value abs_x = rewriter.create<mhlo::AbsOp>(loc, x);
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const StringAttr kLT = rewriter.getStringAttr(
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mhlo::stringifyComparisonDirection(mhlo::ComparisonDirection::LT));
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Value abs_x_lt_one = rewriter.create<mhlo::CompareOp>(loc, abs_x, one, kLT);
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return rewriter.create<mhlo::SelectOp>(loc, abs_x_lt_one, erf_approx,
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erfc_based_approx);
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}
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Value MaterializeErfcApproximationF64(ConversionPatternRewriter &rewriter,
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Location loc, Value x) {
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assert(x.getType().cast<ShapedType>().getElementType().isF64() &&
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"expect f64 element type");
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// Rely on erfc approximation for |x| >= 1
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// erfc(x) = erfc_approx(x)
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Value erfc_approx =
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MaterializeErfcApproximationF64ForMagnituteGEOne(rewriter, loc, x);
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// Rely on erf approximation for |x| < 1 and materialize erfc as
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// erfc(x) = 1 - erf_approx(x)
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Value one = chlo::getConstantLike(rewriter, loc, 1.0, x);
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Value erf_approx =
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MaterializeErfApproximationF64ForMagnituteLEOne(rewriter, loc, x);
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Value erf_based_approx = rewriter.create<mhlo::SubOp>(loc, one, erf_approx);
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// Materialize approximation selection based on argument.
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Value abs_x = rewriter.create<mhlo::AbsOp>(loc, x);
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const StringAttr kLT = rewriter.getStringAttr(
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mhlo::stringifyComparisonDirection(mhlo::ComparisonDirection::LT));
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Value abs_x_lt_one = rewriter.create<mhlo::CompareOp>(loc, abs_x, one, kLT);
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return rewriter.create<mhlo::SelectOp>(loc, abs_x_lt_one, erf_based_approx,
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erfc_approx);
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}
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// This is the same approximation as used in Eigen.
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Value MaterializeErfApproximationF32(ConversionPatternRewriter &rewriter,
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Location loc, Value operand) {
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assert(operand.getType().cast<ShapedType>().getElementType().isF32() &&
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"expect f32 element type");
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const std::vector<float> kAlpha{
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-2.72614225801306e-10f, 2.77068142495902e-08f, -2.10102402082508e-06f,
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-5.69250639462346e-05f, -7.34990630326855e-04f, -2.95459980854025e-03f,
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-1.60960333262415e-02f,
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};
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const std::vector<float> kBeta{
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-1.45660718464996e-05f, -2.13374055278905e-04f, -1.68282697438203e-03f,
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-7.37332916720468e-03f, -1.42647390514189e-02f,
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};
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// Clamp argument between -4 and 4.
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Value lb = chlo::getConstantLike(rewriter, loc, -4.0, operand);
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Value ub = chlo::getConstantLike(rewriter, loc, 4.0, operand);
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Value x =
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rewriter.create<mhlo::ClampOp>(loc, operand.getType(), lb, operand, ub);
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Value x_sq = rewriter.create<mhlo::MulOp>(loc, x, x);
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// Materialize polynomial approximation for x in [-4, 4] as
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// erf(x) = x * Alpha(x^2) / Beta(x^2).
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Value alpha_poly =
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MaterializePolynomialApproximation(rewriter, loc, x_sq, kAlpha);
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Value beta_poly =
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MaterializePolynomialApproximation(rewriter, loc, x_sq, kBeta);
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Value x_mul_alpha_poly = rewriter.create<mhlo::MulOp>(loc, x, alpha_poly);
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return rewriter.create<mhlo::DivOp>(loc, x_mul_alpha_poly, beta_poly);
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}
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struct ConvertErfOp : public OpConversionPattern<ErfOp> {
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using OpConversionPattern<ErfOp>::OpConversionPattern;
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LogicalResult matchAndRewrite(
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ErfOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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Location loc = op.getLoc();
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ErfOp::Adaptor transformed(operands);
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Value x = transformed.operand();
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Type ty = x.getType().cast<ShapedType>().getElementType();
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// For now, we support only f64, f32, and f16.
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if (!ty.isF64() && !ty.isF32() && !ty.isF16()) return failure();
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if (ty.isF64()) {
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rewriter.replaceOp(op, MaterializeErfApproximationF64(rewriter, loc, x));
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return success();
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}
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// Cast argument to f32 tensor if needed.
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assert((ty.isF16() || ty.isF32()) && "expect f16 or f32 at this point");
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if (ty.isF16()) {
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x = rewriter.create<mhlo::ConvertOp>(loc, x, rewriter.getF32Type());
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}
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Value result = MaterializeErfApproximationF32(rewriter, loc, x);
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// Cast back if needed.
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if (ty.isF16()) {
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result = rewriter.create<mhlo::ConvertOp>(loc, result, ty);
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}
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rewriter.replaceOp(op, result);
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return success();
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}
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};
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struct ConvertErfcOp : public OpConversionPattern<ErfcOp> {
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using OpConversionPattern<ErfcOp>::OpConversionPattern;
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LogicalResult matchAndRewrite(
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ErfcOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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Location loc = op.getLoc();
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ErfcOp::Adaptor transformed(operands);
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Value x = transformed.operand();
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Type ty = x.getType().cast<ShapedType>().getElementType();
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// For now, we support only f64.
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if (!ty.isF64()) return failure();
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rewriter.replaceOp(op, MaterializeErfcApproximationF64(rewriter, loc, x));
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return success();
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}
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};
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// Converts binary ops that statically are determined to not broadcast directly
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// to the corresponding mhlo non-broadcasting op.
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template <typename ChloOpTy, typename HloOpTy, typename Adaptor>
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struct ConvertTrivialNonBroadcastBinaryOp
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: public OpConversionPattern<ChloOpTy> {
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using OpConversionPattern<ChloOpTy>::OpConversionPattern;
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LogicalResult matchAndRewrite(
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ChloOpTy op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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// Only rewrite for statically determinable non-broadcasting cases.
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typename ChloOpTy::Adaptor transformed(operands);
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auto lhs_type =
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transformed.lhs().getType().template dyn_cast<RankedTensorType>();
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auto rhs_type =
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transformed.rhs().getType().template dyn_cast<RankedTensorType>();
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if (!lhs_type || !rhs_type) return failure();
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// Requires rank broadcast.
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if (lhs_type.getRank() != rhs_type.getRank()) return failure();
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// Any dynamic dimension may require broadcasting and requires more
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// analysis.
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if (!lhs_type.hasStaticShape() || !rhs_type.hasStaticShape())
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return failure();
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for (auto extents : llvm::zip(lhs_type.getShape(), rhs_type.getShape())) {
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auto lhs_extent = std::get<0>(extents);
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auto rhs_extent = std::get<1>(extents);
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if (lhs_extent != rhs_extent) {
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return failure();
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}
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}
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rewriter.replaceOp(
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op, {Adaptor::CreateOp(op, op.getResult().getType(), operands[0],
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operands[1], rewriter)});
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return success();
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}
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};
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// Converts a binary op with ranked broadcasting operands to explicitly
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// broadcast and invoke the corresponding mhlo non-broadcasting op.
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// Note that dynamic broadcasting supported by this pattern is only valid for
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// "numpy" broadcasting semantics as defined here:
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// https://docs.scipy.org/doc/numpy/reference/ufuncs.html
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// Specifically, this includes the following cases:
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// - Same rank broadcast (operands have the same static rank).
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// - Different-rank broadcast, either without a broadcast_dims attribte or
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// with the broadcast_dims attribute set to map to a prefix padding.
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// - Legal combinations of degenerate (1-dim) implicit broadcasting.
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// The restriction on broadcast_dims derives from the definition of the
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// `shape.broadcast` op, which only supports prefix-padding.
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template <typename ChloOpTy, typename HloOpTy, typename Adaptor>
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struct ConvertRankedDynamicBroadcastBinaryOp
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: public OpConversionPattern<ChloOpTy> {
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using OpConversionPattern<ChloOpTy>::OpConversionPattern;
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LogicalResult matchAndRewrite(
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ChloOpTy op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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|
// Only support ranked operands.
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|
typename ChloOpTy::Adaptor transformed(operands);
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Value lhs = transformed.lhs();
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Value rhs = transformed.rhs();
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auto lhs_type = lhs.getType().dyn_cast<RankedTensorType>();
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auto rhs_type = rhs.getType().dyn_cast<RankedTensorType>();
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|
auto result_type =
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op.getResult().getType().template dyn_cast<RankedTensorType>();
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if (!lhs_type || !rhs_type || !result_type) return failure();
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|
|
|
// Check for "numpy"-style rank broadcast.
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|
auto broadcast_dimensions = op.broadcast_dimensions();
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|
if (broadcast_dimensions &&
|
|
!hlo::IsLegalNumpyRankedBroadcast(lhs, rhs, *broadcast_dimensions)) {
|
|
// Note: It is unclear whether the general specification of explicit
|
|
// broadcast_dimensions on binary ops is a feature we want to carry
|
|
// forward. While it can technically be implemented for ranked-dynamic,
|
|
// it is incompatible with unranked inputs. If this warning is emitted
|
|
// in real programs, it is an indication that the feature should be
|
|
// implemented versus just falling back on the more standard definition
|
|
// of numpy-like prefix-padding.
|
|
op.emitWarning() << "unsupported non prefix-padded dynamic rank "
|
|
<< "broadcast_dimensions = " << *broadcast_dimensions;
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|
return failure();
|
|
}
|
|
|
|
// Compute result shape.
|
|
auto loc = op.getLoc();
|
|
|
|
// Insert a constraint on the shapes being broadcastable and insert all
|
|
// future code into an assuming block reliant on the constraint.
|
|
Value lhs_shape = rewriter.create<shape::ShapeOfOp>(loc, lhs);
|
|
Value rhs_shape = rewriter.create<shape::ShapeOfOp>(loc, rhs);
|
|
auto broadcastable_cstr =
|
|
rewriter.create<shape::CstrBroadcastableOp>(loc, lhs_shape, rhs_shape);
|
|
auto assuming_op = rewriter.create<shape::AssumingOp>(
|
|
loc, ArrayRef<Type>{result_type}, broadcastable_cstr.result());
|
|
|
|
OpBuilder::InsertionGuard guard(rewriter);
|
|
rewriter.createBlock(&assuming_op.doRegion());
|
|
|
|
int64_t result_rank = std::max(lhs_type.getRank(), rhs_type.getRank());
|
|
Value result_extents =
|
|
hlo::ComputeBinaryElementwiseBroadcastingResultExtents(
|
|
loc, lhs, rhs, rewriter, /*unsafe_as_extent_tensor=*/true);
|
|
|
|
// Note that we unconditionally emit DynamicBroadcastInDim ops and let
|
|
// downstream canonicalizations fold them away if possible. This is
|
|
// because, in the dynamic case, there are many corner cases regarding
|
|
// when it is safe to omit, and some of them require analysis to prove
|
|
// properly.
|
|
auto lhs_broadcast_dimensions = llvm::to_vector<4>(
|
|
llvm::seq<int64_t>(result_rank - lhs_type.getRank(), result_rank));
|
|
Value broadcasted_lhs = rewriter.create<mhlo::DynamicBroadcastInDimOp>(
|
|
loc,
|
|
RankedTensorType::get(result_type.getShape(),
|
|
lhs_type.getElementType()),
|
|
lhs, result_extents,
|
|
rewriter.getI64TensorAttr(lhs_broadcast_dimensions));
|
|
auto rhs_broadcast_dimensions = llvm::to_vector<4>(
|
|
llvm::seq<int64_t>(result_rank - rhs_type.getRank(), result_rank));
|
|
Value broadcasted_rhs = rewriter.create<mhlo::DynamicBroadcastInDimOp>(
|
|
loc,
|
|
RankedTensorType::get(result_type.getShape(),
|
|
rhs_type.getElementType()),
|
|
rhs, result_extents,
|
|
rewriter.getI64TensorAttr(rhs_broadcast_dimensions));
|
|
|
|
// And generate the final non-broadcasted binary op.
|
|
Value final_result = Adaptor::CreateOp(op, result_type, broadcasted_lhs,
|
|
broadcasted_rhs, rewriter);
|
|
rewriter.create<shape::AssumingYieldOp>(loc, final_result);
|
|
rewriter.replaceOp(op, {assuming_op.getResult(0)});
|
|
return success();
|
|
}
|
|
};
|
|
|
|
#include "generated_chlo_legalize_to_hlo.inc"
|
|
} // namespace
|
|
|
|
void PopulateLegalizeChloToHloPatterns(MLIRContext *context,
|
|
OwningRewritePatternList *patterns) {
|
|
populateWithGenerated(context, *patterns);
|
|
|
|
// Instantiate conversion templates for conforming binary elementwise ops
|
|
// that do not have different dtypes between operands and results and do
|
|
// not have special attributes that need to be preserved.
|
|
PopulateForBroadcastingBinaryOp<ConvertTrivialNonBroadcastBinaryOp>(
|
|
context, patterns, 10);
|
|
PopulateForBroadcastingBinaryOp<ConvertRankedDynamicBroadcastBinaryOp>(
|
|
context, patterns, 5);
|
|
|
|
// Other patterns.
|
|
patterns->insert<ConvertConstantLikeOp, ConvertErfOp, ConvertErfcOp>(context);
|
|
}
|
|
|
|
} // namespace chlo
|
|
} // namespace mlir
|