mirror of https://github.com/llvm/torch-mlir
239 lines
8.8 KiB
C++
239 lines
8.8 KiB
C++
//===----------------------------------------------------------------------===//
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//
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// This file is licensed 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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// Also available under a BSD-style license. See LICENSE.
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//
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//===----------------------------------------------------------------------===//
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#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
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#include "mlir/IR/BuiltinDialect.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
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using namespace mlir;
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using namespace mlir::torch;
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using namespace mlir::torch::Torch;
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int64_t Torch::toPositiveDim(int64_t dim, int64_t inputRank) {
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return dim >= 0 ? dim : dim + inputRank;
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}
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bool Torch::isValidDim(int64_t dim, int64_t inputRank) {
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return dim >= 0 && dim < inputRank;
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}
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llvm::Optional<int64_t>
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Torch::matchLegalConstantIndexIntoListOfSize(Value v, int64_t length) {
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int64_t dim;
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if (!matchPattern(v, m_TorchConstantInt(&dim)))
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return std::nullopt;
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dim = toPositiveDim(dim, length);
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if (!isValidDim(dim, length))
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return std::nullopt;
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return dim;
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}
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bool Torch::getListConstructElements(Value v, SmallVectorImpl<Value> &elems) {
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auto listConstruct = v.getDefiningOp<PrimListConstructOp>();
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if (!listConstruct)
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return false;
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elems = llvm::to_vector<4>(listConstruct.getElements());
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return true;
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}
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torch_upstream::ScalarType Torch::getScalarTypeForType(Type type) {
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if (type.isa<Float32Type>())
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return torch_upstream::ScalarType::Float;
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if (type.isa<Float64Type>())
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return torch_upstream::ScalarType::Double;
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if (type.isSignedInteger(64))
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return torch_upstream::ScalarType::Long;
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if (type.isSignedInteger(32))
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return torch_upstream::ScalarType::Int;
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if (type.isSignlessInteger(1))
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return torch_upstream::ScalarType::Bool;
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if (type.isBF16())
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return torch_upstream::ScalarType::BFloat16;
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if (type.isF16())
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return torch_upstream::ScalarType::Half;
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if (type.isUnsignedInteger(8))
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return torch_upstream::ScalarType::Byte;
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if (type.isSignedInteger(8))
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return torch_upstream::ScalarType::Char;
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llvm::report_fatal_error("unhandled type for getScalarTypeForType");
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}
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Type Torch::getTypeForTorchType(
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MLIRContext *context, Type type,
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mlir::IntegerType::SignednessSemantics signedness) {
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if (type.isa<Torch::IntType>())
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return IntegerType::get(context, 64, signedness);
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if (type.isa<Torch::FloatType>())
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return Float64Type::get(context);
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llvm::report_fatal_error("unhandled type for getTypeForTorchType");
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}
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Type Torch::getTypeForScalarType(
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MLIRContext *context, torch_upstream::ScalarType dtypeInt,
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mlir::IntegerType::SignednessSemantics signedness) {
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switch (dtypeInt) {
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case torch_upstream::ScalarType::Float:
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return Float32Type::get(context);
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case torch_upstream::ScalarType::Double:
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return Float64Type::get(context);
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case torch_upstream::ScalarType::Long:
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return IntegerType::get(context, 64, signedness);
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case torch_upstream::ScalarType::Int:
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return IntegerType::get(context, 32, signedness);
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case torch_upstream::ScalarType::Bool:
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return IntegerType::get(context, 1);
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case torch_upstream::ScalarType::BFloat16:
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return mlir::FloatType::getBF16(context);
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case torch_upstream::ScalarType::Half:
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return mlir::FloatType::getF16(context);
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case torch_upstream::ScalarType::Byte:
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case torch_upstream::ScalarType::Char:
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return mlir::IntegerType::get(context, 8, signedness);
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default:
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return Type();
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}
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}
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FailureOr<Type>
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Torch::getTorchTypeForScalarType(MLIRContext *context,
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torch_upstream::ScalarType dtypeInt) {
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switch (dtypeInt) {
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case torch_upstream::ScalarType::Double:
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return Torch::FloatType::get(context);
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case torch_upstream::ScalarType::Long:
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return Torch::IntType::get(context);
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default:
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return failure();
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}
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}
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Type Torch::getDefaultDtypeForTorchScalar(Type type) {
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MLIRContext *context = type.getContext();
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if (type.isa<Torch::FloatType>()) {
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// For now, use float32 which is the initial default dtype returned by
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// `torch.get_default_dtype`.
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return Float32Type::get(context);
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}
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if (type.isa<Torch::IntType>())
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return IntegerType::get(context, 64, IntegerType::Signed);
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if (type.isa<Torch::BoolType>())
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return IntegerType::get(context, 1);
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llvm_unreachable(
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"getDefaultDtypeForTorchScalar called on an unsupported type");
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}
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Type Torch::getBuiltInTypeForTorchScalar(Type type) {
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MLIRContext *context = type.getContext();
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if (type.isa<Torch::FloatType>())
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return Float64Type::get(context);
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if (type.isa<Torch::IntType>())
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return IntegerType::get(context, 64, IntegerType::Signed);
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if (type.isa<Torch::BoolType>())
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return IntegerType::get(context, 1);
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llvm_unreachable(
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"getBuiltInTypeForTorchScalar called on an unsupported type");
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}
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Value Torch::getDtypeIntValueForType(PatternRewriter &rewriter, Location loc,
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Type dtype) {
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int intType = (int)getScalarTypeForType(dtype);
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return rewriter.create<ConstantIntOp>(loc,
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rewriter.getI64IntegerAttr(intType));
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}
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// Helper to convert a tensor to a specific scalar type.
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Value Torch::convertTensorToDtype(PatternRewriter &rewriter, Location loc,
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Value input, Type dtype) {
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BaseTensorType origType = input.getType().cast<BaseTensorType>();
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Type newType = origType.getWithSizesAndDtype(origType.getSizes(), dtype);
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// `convertIntVal` contains the corresponding integer for the dtype which is
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// used by the aten.to.dtype op.
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Value convertIntVal = getDtypeIntValueForType(rewriter, loc, dtype);
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Value falseVal = rewriter.create<ConstantBoolOp>(loc, false);
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Value noneVal = rewriter.create<ConstantNoneOp>(loc);
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Value converted = rewriter.create<AtenToDtypeOp>(
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loc, newType, input, convertIntVal, falseVal, falseVal, noneVal);
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return converted;
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}
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bool Torch::isBuiltInType(Type type) {
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return isa<BuiltinDialect>(type.getDialect());
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}
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Optional<unsigned> Torch::getTensorRank(Value tensor) {
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BaseTensorType tensorType = tensor.getType().cast<BaseTensorType>();
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if (!tensorType.hasSizes())
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return std::nullopt;
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return tensorType.getSizes().size();
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}
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bool Torch::isViewLikeOp(Operation *op) {
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// AtenContiguousOp might return a view, so this is conservatively
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// correct. We could potentially be more precise and identify the cases
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// that it does not return a view and treat those as having value
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// semantics.
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return isa<AtenBroadcastToOp, AtenContiguousOp, AtenDetachOp, AtenExpandAsOp,
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AtenExpandOp, AtenFlattenUsingIntsOp, AtenPermuteOp, AtenReshapeOp,
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Aten_ReshapeAliasOp, AtenSelectIntOp, AtenSliceTensorOp,
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AtenSqueezeDimOp, AtenSqueezeOp, AtenTOp, AtenToDtypeOp,
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AtenTransposeIntOp, AtenUnsqueezeOp, AtenViewOp,
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TensorStaticInfoCastOp, AtenToDtypeLayoutOp, AtenNumpyTOp,
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AtenNarrowOp, AtenToDeviceOp>(op);
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}
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Value Torch::getConstantWithGivenDtypeAndValue(PatternRewriter &rewriter,
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Location loc, float value,
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Type dtype) {
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// Creating constants satisfying backend contract.
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if (dtype.isInteger(64) || dtype.isInteger(32) || dtype.isInteger(8) ||
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dtype.isInteger(1))
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return rewriter.create<ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr((int64_t)value));
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if (dtype.isF64() || dtype.isF32() || dtype.isF16() || dtype.isBF16())
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return rewriter.create<ConstantFloatOp>(loc,
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rewriter.getF64FloatAttr(value));
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llvm::report_fatal_error(
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"unhandled type for getConstantWithGivenDtypeAndValue");
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}
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// Return the number of elements of a tensor if the shape is static; otherwise,
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// return -1.
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int64_t Torch::getNumberOfElements(RankedTensorType inputType) {
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if (!inputType.hasStaticShape())
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return -1;
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SmallVector<int64_t> inputShape =
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makeShapeTorchCompatible(inputType.getShape());
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int64_t numel = 1;
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for (int64_t i = 0; i < inputType.getRank(); i++)
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numel *= inputShape[i];
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return numel;
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}
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SmallVector<int64_t> Torch::makeShapeLLVMCompatible(ArrayRef<int64_t> shape) {
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SmallVector<int64_t> updatedShape(shape);
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int64_t kDynamic = ShapedType::kDynamic;
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for (unsigned i = 0; i < shape.size(); i++) {
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assert(shape[i] >= 0 || shape[i] == kUnknownSize);
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if (shape[i] == kUnknownSize)
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updatedShape[i] = kDynamic;
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}
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return updatedShape;
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}
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SmallVector<int64_t> Torch::makeShapeTorchCompatible(ArrayRef<int64_t> shape) {
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SmallVector<int64_t> updatedShape(shape);
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int64_t kDynamic = ShapedType::kDynamic;
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for (unsigned i = 0; i < shape.size(); i++) {
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assert(shape[i] >= 0 || shape[i] == kDynamic);
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if (shape[i] == kDynamic)
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updatedShape[i] = kUnknownSize;
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}
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return updatedShape;
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}
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