263 lines
11 KiB
C++
263 lines
11 KiB
C++
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//===- ArithToAMDGPU.cpp - Arith to AMDGPU dialect conversion ---------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Conversion/ArithToAMDGPU/ArithToAMDGPU.h"
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#include "mlir/Dialect/AMDGPU/IR/AMDGPUDialect.h"
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/Arith/Utils/Utils.h"
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#include "mlir/Dialect/Vector/IR/VectorOps.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/IR/TypeUtilities.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Support/LogicalResult.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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namespace mlir {
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#define GEN_PASS_DEF_ARITHTOAMDGPUCONVERSIONPASS
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#include "mlir/Conversion/Passes.h.inc"
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} // namespace mlir
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using namespace mlir;
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namespace {
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struct ArithToAMDGPUConversionPass final
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: impl::ArithToAMDGPUConversionPassBase<ArithToAMDGPUConversionPass> {
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using impl::ArithToAMDGPUConversionPassBase<
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ArithToAMDGPUConversionPass>::ArithToAMDGPUConversionPassBase;
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void runOnOperation() override;
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};
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struct ExtFOnFloat8RewritePattern final : OpRewritePattern<arith::ExtFOp> {
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using OpRewritePattern::OpRewritePattern;
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LogicalResult match(arith::ExtFOp op) const override;
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void rewrite(arith::ExtFOp op, PatternRewriter &rewriter) const override;
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};
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struct TruncFToFloat8RewritePattern final : OpRewritePattern<arith::TruncFOp> {
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bool saturateFP8 = false;
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TruncFToFloat8RewritePattern(MLIRContext *ctx, bool saturateFP8)
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: OpRewritePattern::OpRewritePattern(ctx), saturateFP8(saturateFP8) {}
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LogicalResult match(arith::TruncFOp op) const override;
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void rewrite(arith::TruncFOp op, PatternRewriter &rewriter) const override;
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};
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} // end namespace
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static Value castF32To(Type elementType, Value f32, Location loc,
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PatternRewriter &rewriter) {
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if (elementType.isF32())
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return f32;
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if (elementType.getIntOrFloatBitWidth() < 32)
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return rewriter.create<arith::TruncFOp>(loc, elementType, f32);
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if (elementType.getIntOrFloatBitWidth() > 32)
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return rewriter.create<arith::ExtFOp>(loc, elementType, f32);
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llvm_unreachable("The only 32-bit float type is f32");
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}
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LogicalResult ExtFOnFloat8RewritePattern::match(arith::ExtFOp op) const {
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Type inType = op.getIn().getType();
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if (auto inVecType = inType.dyn_cast<VectorType>()) {
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if (inVecType.isScalable())
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return failure();
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if (inVecType.getShape().size() > 1)
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// Multi-dimensional vectors are currently unsupported.
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return failure();
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inType = inVecType.getElementType();
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}
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return success(inType.isFloat8E5M2FNUZ() || inType.isFloat8E4M3FNUZ());
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}
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void ExtFOnFloat8RewritePattern::rewrite(arith::ExtFOp op,
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PatternRewriter &rewriter) const {
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Location loc = op.getLoc();
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Value in = op.getIn();
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Type outElemType = getElementTypeOrSelf(op.getOut().getType());
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if (!in.getType().isa<VectorType>()) {
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Value asFloat = rewriter.create<amdgpu::ExtPackedFp8Op>(
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loc, rewriter.getF32Type(), in, 0);
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Value result = castF32To(outElemType, asFloat, loc, rewriter);
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return rewriter.replaceOp(op, result);
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}
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VectorType inType = in.getType().cast<VectorType>();
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int64_t numElements = inType.getNumElements();
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Value zero = rewriter.createOrFold<arith::ConstantOp>(
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loc, outElemType, rewriter.getFloatAttr(outElemType, 0.0));
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Value result =
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rewriter.createOrFold<vector::SplatOp>(loc, op.getOut().getType(), zero);
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if (inType.getShape().empty()) {
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Value scalarIn =
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rewriter.create<vector::ExtractOp>(loc, in, ArrayRef<int64_t>{});
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// Recurse to send the 0-D vector case to the 1-D vector case
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Value scalarExt =
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rewriter.create<arith::ExtFOp>(loc, outElemType, scalarIn);
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result = rewriter.create<vector::InsertOp>(loc, scalarExt, zero,
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ArrayRef<int64_t>{});
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return rewriter.replaceOp(op, result);
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}
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for (int64_t i = 0; i < numElements; i += 4) {
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int64_t elemsThisOp = std::min(numElements, i + 4) - i;
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Value inSlice = rewriter.create<vector::ExtractStridedSliceOp>(
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loc, in, i, elemsThisOp, 1);
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for (int64_t j = 0; j < elemsThisOp; ++j) {
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Value asFloat = rewriter.create<amdgpu::ExtPackedFp8Op>(
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loc, rewriter.getF32Type(), inSlice, j);
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Value asType = castF32To(outElemType, asFloat, loc, rewriter);
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result = rewriter.create<vector::InsertOp>(loc, asType, result, i + j);
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}
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}
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rewriter.replaceOp(op, result);
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}
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static Value castToF32(Value value, Location loc, PatternRewriter &rewriter) {
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Type type = value.getType();
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if (type.isF32())
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return value;
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if (type.getIntOrFloatBitWidth() < 32)
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return rewriter.create<arith::ExtFOp>(loc, rewriter.getF32Type(), value);
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if (type.getIntOrFloatBitWidth() > 32)
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return rewriter.create<arith::TruncFOp>(loc, rewriter.getF32Type(), value);
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llvm_unreachable("The only 32-bit float type is f32");
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}
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// If `in` is a finite value, clamp it between the maximum and minimum values
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// of `outElemType` so that subsequent conversion instructions don't
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// overflow those out-of-range values to NaN. These semantics are commonly
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// used in machine-learning contexts where failure to clamp would lead to
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// excessive NaN production.
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static Value clampInput(PatternRewriter &rewriter, Location loc,
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Type outElemType, Value source) {
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Type sourceType = source.getType();
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const llvm::fltSemantics &sourceSem =
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cast<FloatType>(getElementTypeOrSelf(sourceType)).getFloatSemantics();
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const llvm::fltSemantics &targetSem =
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cast<FloatType>(outElemType).getFloatSemantics();
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APFloat min = APFloat::getLargest(targetSem, /*Negative=*/true);
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APFloat max = APFloat::getLargest(targetSem, /*Negative=*/false);
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bool ignoredLosesInfo = false;
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// We can ignore conversion failures here because this conversion promotes
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// from a smaller type to a larger one - ex. there can be no loss of precision
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// when casting fp8 to f16.
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(void)min.convert(sourceSem, APFloat::rmNearestTiesToEven, &ignoredLosesInfo);
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(void)max.convert(sourceSem, APFloat::rmNearestTiesToEven, &ignoredLosesInfo);
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Value minCst = createScalarOrSplatConstant(rewriter, loc, sourceType, min);
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Value maxCst = createScalarOrSplatConstant(rewriter, loc, sourceType, max);
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Value inf = createScalarOrSplatConstant(
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rewriter, loc, sourceType,
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APFloat::getInf(sourceSem, /*Negative=*/false));
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Value negInf = createScalarOrSplatConstant(
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rewriter, loc, sourceType, APFloat::getInf(sourceSem, /*Negative=*/true));
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Value isInf = rewriter.createOrFold<arith::CmpFOp>(
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loc, arith::CmpFPredicate::OEQ, source, inf);
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Value isNegInf = rewriter.createOrFold<arith::CmpFOp>(
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loc, arith::CmpFPredicate::OEQ, source, negInf);
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Value isNan = rewriter.createOrFold<arith::CmpFOp>(
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loc, arith::CmpFPredicate::UNO, source, source);
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Value isNonFinite = rewriter.create<arith::OrIOp>(
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loc, rewriter.create<arith::OrIOp>(loc, isInf, isNegInf), isNan);
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Value clampedBelow = rewriter.create<arith::MaximumFOp>(loc, source, minCst);
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Value clamped = rewriter.create<arith::MinimumFOp>(loc, clampedBelow, maxCst);
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Value res =
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rewriter.create<arith::SelectOp>(loc, isNonFinite, source, clamped);
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return res;
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}
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LogicalResult TruncFToFloat8RewritePattern::match(arith::TruncFOp op) const {
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Type outType = op.getOut().getType();
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if (auto outVecType = outType.dyn_cast<VectorType>()) {
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if (outVecType.isScalable())
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return failure();
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if (outVecType.getShape().size() > 1)
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// Multi-dimensional vectors are currently unsupported.
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return failure();
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outType = outVecType.getElementType();
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}
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auto inType = dyn_cast<FloatType>(getElementTypeOrSelf(op.getIn().getType()));
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if (inType && inType.getWidth() <= 8 && saturateFP8)
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// Conversion between 8-bit floats is not supported with truncation enabled.
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return failure();
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return success(outType.isFloat8E5M2FNUZ() || outType.isFloat8E4M3FNUZ());
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}
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void TruncFToFloat8RewritePattern::rewrite(arith::TruncFOp op,
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PatternRewriter &rewriter) const {
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Location loc = op.getLoc();
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Value in = op.getIn();
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Type outElemType = getElementTypeOrSelf(op.getOut().getType());
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if (saturateFP8)
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in = clampInput(rewriter, loc, outElemType, in);
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VectorType truncResType = VectorType::get(4, outElemType);
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if (!in.getType().isa<VectorType>()) {
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Value asFloat = castToF32(in, loc, rewriter);
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Value asF8s = rewriter.create<amdgpu::PackedTrunc2xFp8Op>(
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loc, truncResType, asFloat, /*sourceB=*/nullptr, 0,
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/*existing=*/nullptr);
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Value result = rewriter.create<vector::ExtractOp>(loc, asF8s, 0);
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return rewriter.replaceOp(op, result);
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}
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VectorType outType = op.getOut().getType().cast<VectorType>();
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int64_t numElements = outType.getNumElements();
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Value zero = rewriter.createOrFold<arith::ConstantOp>(
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loc, outElemType, rewriter.getFloatAttr(outElemType, 0.0));
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Value result = rewriter.createOrFold<vector::SplatOp>(loc, outType, zero);
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if (outType.getShape().empty()) {
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Value scalarIn =
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rewriter.create<vector::ExtractOp>(loc, in, ArrayRef<int64_t>{});
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// Recurse to send the 0-D vector case to the 1-D vector case
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Value scalarTrunc =
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rewriter.create<arith::TruncFOp>(loc, outElemType, scalarIn);
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result = rewriter.create<vector::InsertOp>(loc, scalarTrunc, zero,
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ArrayRef<int64_t>{});
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return rewriter.replaceOp(op, result);
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}
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for (int64_t i = 0; i < numElements; i += 4) {
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int64_t elemsThisOp = std::min(numElements, i + 4) - i;
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Value thisResult = nullptr;
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for (int64_t j = 0; j < elemsThisOp; j += 2) {
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Value elemA = rewriter.create<vector::ExtractOp>(loc, in, i + j);
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Value asFloatA = castToF32(elemA, loc, rewriter);
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Value asFloatB = nullptr;
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if (j + 1 < elemsThisOp) {
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Value elemB = rewriter.create<vector::ExtractOp>(loc, in, i + j + 1);
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asFloatB = castToF32(elemB, loc, rewriter);
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}
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thisResult = rewriter.create<amdgpu::PackedTrunc2xFp8Op>(
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loc, truncResType, asFloatA, asFloatB, j / 2, thisResult);
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}
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if (elemsThisOp < 4)
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thisResult = rewriter.create<vector::ExtractStridedSliceOp>(
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loc, thisResult, 0, elemsThisOp, 1);
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result = rewriter.create<vector::InsertStridedSliceOp>(loc, thisResult,
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result, i, 1);
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}
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rewriter.replaceOp(op, result);
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}
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void mlir::arith::populateArithToAMDGPUConversionPatterns(
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RewritePatternSet &patterns, bool saturateFP8TruncF) {
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patterns.add<ExtFOnFloat8RewritePattern>(patterns.getContext());
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patterns.add<TruncFToFloat8RewritePattern>(patterns.getContext(),
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saturateFP8TruncF);
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}
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void ArithToAMDGPUConversionPass::runOnOperation() {
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Operation *op = getOperation();
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RewritePatternSet patterns(op->getContext());
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arith::populateArithToAMDGPUConversionPatterns(patterns, saturateFP8Truncf);
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if (failed(applyPatternsAndFoldGreedily(op, std::move(patterns))))
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return signalPassFailure();
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}
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