mirror of https://github.com/llvm/torch-mlir
Clang format refresh (#2812)
After noticing a number of commits with unrelated formatting changes, I think something was changed with clang-format at one point and we're seeing a number of unrelated changes. Doing a refresh can help avoid this. The changes made here came from ``` find lib -iname *.h -o -iname *.cpp | xargs clang-format -i --style=llvm find include -iname *.h -o -iname *.cpp | xargs clang-format -i --style=llvm find projects -iname *.h -o -iname *.cpp | xargs clang-format -i --style=llvm ```pull/2823/head
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commit
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@ -10,9 +10,9 @@
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#ifndef TORCH_MLIR_DIALECTS_DIALECT_TMTENSOR_IR_TMTENSORINTERFACES_H_
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#define TORCH_MLIR_DIALECTS_DIALECT_TMTENSOR_IR_TMTENSORINTERFACES_H_
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#include "mlir/IR/IRMapping.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/IRMapping.h"
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#include "mlir/IR/OpDefinition.h"
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#include "mlir/Support/LLVM.h"
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@ -97,7 +97,8 @@ struct OpBinder {
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return success();
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}
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ParseResult tensorResultTypeAtIndex(Torch::ValueTensorType &typeIdx, int64_t idx) {
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ParseResult tensorResultTypeAtIndex(Torch::ValueTensorType &typeIdx,
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int64_t idx) {
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if (idx >= op->getNumResults())
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return failure();
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auto t = toValidTensorType(op->getResult(idx).getType());
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@ -37,8 +37,8 @@ TosaOpT createBinaryOpAndCast(PatternRewriter &rewriter, Operation *op,
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return CreateOpAndInfer<TosaOpT>(rewriter, op->getLoc(), outType, lhs, rhs);
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}
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// This specialization is for Div op. Unlike other binary ops, it doesn't support
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// floating type.
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// This specialization is for Div op. Unlike other binary ops, it doesn't
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// support floating type.
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template <>
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tosa::DivOp createBinaryOpAndCast<DivOp>(PatternRewriter &rewriter,
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Operation *op, TensorType outType,
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@ -53,9 +53,8 @@ std::optional<Value> convertTorchIndexToTfIndices(PatternRewriter &rewriter,
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// Lowers torch.aten.Gather operators to a sequence of TOSA ops.
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// Revised from
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// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/tosa/transforms/legalize_common.cc
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std::optional<Value> convertGatherNdOp(PatternRewriter &rewriter,
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Operation *op, Type out_type,
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Value params_value,
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std::optional<Value> convertGatherNdOp(PatternRewriter &rewriter, Operation *op,
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Type out_type, Value params_value,
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Value indices_value);
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std::optional<Value> convertScatterNdOp(PatternRewriter &rewriter,
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@ -63,7 +62,6 @@ std::optional<Value> convertScatterNdOp(PatternRewriter &rewriter,
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Value paramsValue, Value indicesValue,
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Value fillValues);
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// Lowers ReduceAll to a sequence of TOSA ops.
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std::optional<Value>
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convertReduceAllOp(PatternRewriter &rewriter, Operation *op,
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@ -36,8 +36,7 @@ class HasValueSemantics
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// This is a weaker form of HasValueSemantics, since that trait also requires no
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// aliasing. That is, HasValueSemantics implies this trait.
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template <typename ConcreteType>
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class ReadOnly
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: public ::mlir::OpTrait::TraitBase<ConcreteType, ReadOnly> {};
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class ReadOnly : public ::mlir::OpTrait::TraitBase<ConcreteType, ReadOnly> {};
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// If a Torch op has this trait, it means that the op is a "trailing underscore"
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// op variant that performs an in-place operation on its first argument. These
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@ -62,7 +61,8 @@ class AllowsTypeRefinement
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// by the IValue importer.
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template <typename ConcreteType>
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class AllowedInModuleInitializer
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: public ::mlir::OpTrait::TraitBase<ConcreteType, AllowedInModuleInitializer> {};
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: public ::mlir::OpTrait::TraitBase<ConcreteType,
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AllowedInModuleInitializer> {};
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} // namespace OpTrait
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} // namespace Torch
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@ -61,7 +61,8 @@ struct TorchLoweringPipelineOptions
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Option<std::string> extraLibrary{
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*this, "extra-library",
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llvm::cl::desc("Filename of MLIR module for splicing into the abstract interpretation library.")};
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llvm::cl::desc("Filename of MLIR module for splicing into the abstract "
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"interpretation library.")};
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};
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/// Creates a pipeline that lowers the object graph IR that is produced by
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@ -125,8 +126,7 @@ createSimplifyDtypeCalculationsPass();
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std::unique_ptr<OperationPass<func::FuncOp>>
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createDropAbstractInterpCalculationsPass();
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std::unique_ptr<OperationPass<ModuleOp>>
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createEraseModuleInitializerPass();
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std::unique_ptr<OperationPass<ModuleOp>> createEraseModuleInitializerPass();
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std::unique_ptr<OperationPass<ModuleOp>>
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createLowerToBackendContractPass(int maxIterations, bool decompose,
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@ -140,12 +140,7 @@ enum Reduction { None, Mean, Sum, END };
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// Source:
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// https://github.com/pytorch/pytorch/blob/master/c10/core/MemoryFormat.h
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//===----------------------------------------------------------------------===//
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enum MemoryFormat {
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Contiguous,
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Preserve,
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ChannelsLast,
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ChannelsLast3d
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};
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enum MemoryFormat { Contiguous, Preserve, ChannelsLast, ChannelsLast3d };
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//===----------------------------------------------------------------------===//
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// Possible values for `layout` argument in PyTorch ops that support it.
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@ -121,8 +121,7 @@ LogicalResult checkDefaultStrideHelper(Operation *op, PatternRewriter &rewriter,
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// Helper to create a tensor filled with the given scalar. Scalar would be
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// converted the to the element type of the given tensor type.
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Value createInitTensor(PatternRewriter &rewriter, Location loc,
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BaseTensorType resultType, Value scalar,
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Value sizeList);
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BaseTensorType resultType, Value scalar, Value sizeList);
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// Helper to create a rank 0 tensor filled with the given `scalar`. `scalar`
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// would be converted to the element type of the given `inputType`.
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@ -9,7 +9,8 @@
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#include "torch-mlir-c/Dialects.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
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#include "mlir/CAPI/Registration.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
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MLIR_DEFINE_CAPI_DIALECT_REGISTRATION(Torch, torch, mlir::torch::Torch::TorchDialect)
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MLIR_DEFINE_CAPI_DIALECT_REGISTRATION(Torch, torch,
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mlir::torch::Torch::TorchDialect)
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@ -30,6 +30,4 @@ namespace {
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#include "torch-mlir/Conversion/Passes.h.inc"
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} // end namespace
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void mlir::torch::registerConversionPasses() {
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::registerPasses();
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}
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void mlir::torch::registerConversionPasses() { ::registerPasses(); }
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@ -82,7 +82,8 @@ public:
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// temp = multiplier * currentSeed + incrementStep
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Value mul = rewriter.create<arith::MulIOp>(loc, currentSeed, multiplier);
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Value seed = rewriter.create<arith::AddIOp>(loc, mul, incrementStep);
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globalVar = rewriter.create<tensor::InsertOp>(loc, seed, globalVar, ValueRange());
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globalVar =
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rewriter.create<tensor::InsertOp>(loc, seed, globalVar, ValueRange());
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rewriter.create<ml_program::GlobalStoreOp>(
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loc, SymbolRefAttr::get(op->getContext(), getSeedGobalVarName()),
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globalVar);
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@ -29,7 +29,8 @@ using namespace mlir::torch::onnx_c;
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// thing here, so we simplify.
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void mlir::torch::onnx_c::populateDefaultDomainGtoP(
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OnnxCustomOpConversionPattern &patterns) {
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patterns.onOp("HardSigmoid", 6,
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patterns.onOp(
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"HardSigmoid", 6,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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Value tensorOperand;
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binder.tensorResultType(resultType))
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return failure();
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// HardSigmoid computes the following expression: max(0, min(1, alpha * x + beta))
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// HardSigmoid computes the following expression:
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// max(0, min(1, alpha * x + beta))
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Value constAlpha = rewriter.create<Torch::ConstantFloatOp>(
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binder.getLoc(), rewriter.getType<Torch::FloatType>(),
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rewriter.getF64FloatAttr(alpha));
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// Expression: alpha * x + beta
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Value alpha_x_plus_beta = rewriter.create<Torch::AtenAddScalarOp>(
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binder.getLoc(), resultType, tensorOperand, constBeta, /*alpha=*/constAlpha);
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binder.getLoc(), resultType, tensorOperand, constBeta,
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/*alpha=*/constAlpha);
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// Expression: min(1, alpha * x + beta)
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Value constantOne = rewriter.create<Torch::ConstantIntOp>(
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binder.op, resultType, lhs, rhs);
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return success();
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});
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patterns.onOp("Max", 1,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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patterns.onOp(
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"Max", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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llvm::SmallVector<Value> operands;
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if (binder.tensorOperandsList(operands) ||
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binder.tensorResultType(resultType) ||
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operands.size() == 0) {
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binder.tensorResultType(resultType) || operands.size() == 0) {
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return failure();
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}
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Value result = operands[0];
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@ -349,13 +351,12 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
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rewriter.replaceOp(binder.op, result.getDefiningOp());
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return success();
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});
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patterns.onOp("Min", 1,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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patterns.onOp(
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"Min", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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llvm::SmallVector<Value> operands;
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if (binder.tensorOperandsList(operands) ||
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binder.tensorResultType(resultType) ||
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operands.size() == 0) {
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binder.tensorResultType(resultType) || operands.size() == 0) {
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return failure();
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}
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Value result = operands[0];
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result = rewriter.create<Torch::AtenMinimumOp>(
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binder.getLoc(), resultType, result, operands[i]);
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}
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rewriter.replaceOp(
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binder.op, result.getDefiningOp());
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rewriter.replaceOp(binder.op, result.getDefiningOp());
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return success();
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});
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patterns.onOp("Neg", 1,
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binder.getLoc(),
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Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
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cstStrides);
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Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
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Value cstFalse =
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rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
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Value cstCeilMode = cstFalse;
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Value cstCountIncludePad = cstFalse;
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Value cstNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
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@ -42,7 +42,8 @@ Value getItemOp(OpBinder binder, ConversionPatternRewriter &rewriter,
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void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
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OnnxCustomOpConversionPattern &patterns) {
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patterns.onOp("QuantizeLinear", 1,
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patterns.onOp(
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"QuantizeLinear", 1,
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[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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llvm::SmallVector<Value> operands;
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auto scaleTy = scale.getType().dyn_cast<Torch::ValueTensorType>();
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if (!scaleTy || !scaleTy.hasSizes())
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return rewriter.notifyMatchFailure(binder.op,
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"requires known rank");
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return rewriter.notifyMatchFailure(binder.op, "requires known rank");
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if (!resultType.hasDtype())
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return rewriter.notifyMatchFailure(
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binder.op, "requires known result dtype");
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return rewriter.notifyMatchFailure(binder.op,
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"requires known result dtype");
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if (scaleTy.getSizes().size() == 0) {
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Type qTy = resultType.getDtype();
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} else if (qTy.isSignedInteger(32)) {
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qTy = rewriter.getType<Torch::QInt32Type>();
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} else {
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return rewriter.notifyMatchFailure(binder.op, "unsupported result dtype");
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return rewriter.notifyMatchFailure(binder.op,
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"unsupported result dtype");
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}
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auto qTensorTy = rewriter.getType<Torch::ValueTensorType>(resultType.getOptionalSizes(), qTy);
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auto qTensorTy = rewriter.getType<Torch::ValueTensorType>(
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resultType.getOptionalSizes(), qTy);
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auto torchqTy = Torch::getScalarTypeForType(qTy);
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Value tyConst = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), static_cast<int64_t>(torchqTy)));
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rewriter.getIntegerAttr(rewriter.getIntegerType(64),
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static_cast<int64_t>(torchqTy)));
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scale = rewriter.create<Torch::AtenItemOp>(binder.getLoc(), rewriter.getType<Torch::FloatType>(), scale);
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zeropoint = rewriter.create<Torch::AtenItemOp>(binder.getLoc(), rewriter.getType<Torch::IntType>(), zeropoint);
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scale = rewriter.create<Torch::AtenItemOp>(
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binder.getLoc(), rewriter.getType<Torch::FloatType>(), scale);
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zeropoint = rewriter.create<Torch::AtenItemOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(), zeropoint);
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auto quantize = rewriter.create<Torch::AtenQuantizePerTensorOp>(binder.getLoc(), qTensorTy, operand, scale, zeropoint, tyConst);
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rewriter.replaceOpWithNewOp<Torch::AtenIntReprOp>(binder.op, resultType, quantize);
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auto quantize = rewriter.create<Torch::AtenQuantizePerTensorOp>(
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binder.getLoc(), qTensorTy, operand, scale, zeropoint, tyConst);
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rewriter.replaceOpWithNewOp<Torch::AtenIntReprOp>(
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binder.op, resultType, quantize);
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return success();
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}
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LogicalResult
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matchAndRewrite(AtenDimOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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auto rank = rewriter.create<tensor::RankOp>(op->getLoc(), adaptor.getSelf());
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auto rank =
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rewriter.create<tensor::RankOp>(op->getLoc(), adaptor.getSelf());
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rewriter.replaceOpWithNewOp<arith::IndexCastOp>(
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op, getTypeConverter()->convertType(op.getType()), rank);
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return success();
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@ -74,7 +75,8 @@ public:
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matchAndRewrite(AtenOp op,
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typename OpConversionPattern<AtenOp>::OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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rewriter.template replaceOpWithNewOp<BinOp>(op, adaptor.getA(), adaptor.getB());
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rewriter.template replaceOpWithNewOp<BinOp>(op, adaptor.getA(),
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adaptor.getB());
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return success();
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}
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};
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@ -112,10 +114,10 @@ public:
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typename OpConversionPattern<AtenDivIntOp>::OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Location loc = op.getLoc();
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Value a =
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convertScalarToDtype(rewriter, loc, adaptor.getA(), rewriter.getF64Type());
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Value b =
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convertScalarToDtype(rewriter, loc, adaptor.getB(), rewriter.getF64Type());
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Value a = convertScalarToDtype(rewriter, loc, adaptor.getA(),
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rewriter.getF64Type());
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Value b = convertScalarToDtype(rewriter, loc, adaptor.getB(),
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rewriter.getF64Type());
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rewriter.replaceOpWithNewOp<arith::DivFOp>(op, a, b);
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return success();
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}
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@ -178,13 +180,14 @@ public:
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auto shapedType =
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RankedTensorType::get(type.getShape(), builtinTensorElemTy);
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auto rawData = elements.getRawData();
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DenseElementsAttr newAttr = DenseElementsAttr::getFromRawBuffer(
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shapedType, rawData);
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DenseElementsAttr newAttr =
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DenseElementsAttr::getFromRawBuffer(shapedType, rawData);
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rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, newAttr);
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return success();
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}
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}
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if (auto elements = op.getValueAttr().dyn_cast<DenseResourceElementsAttr>()) {
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if (auto elements =
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op.getValueAttr().dyn_cast<DenseResourceElementsAttr>()) {
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if (auto type = elements.getType().dyn_cast<RankedTensorType>()) {
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if (auto intType = type.getElementType().dyn_cast<IntegerType>()) {
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Type builtinTensorElemTy =
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@ -360,7 +363,8 @@ public:
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// -----------------------------------------------------------------------------
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namespace {
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class ConvertTorchToArith : public ConvertTorchToArithBase<ConvertTorchToArith> {
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class ConvertTorchToArith
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: public ConvertTorchToArithBase<ConvertTorchToArith> {
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public:
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry.insert<func::FuncDialect>();
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@ -110,22 +110,32 @@ LogicalResult prepareArgumentsForSlicingOp(OpTy op, OpAdaptor adaptor,
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// Example:
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// input = tensor([[[0., 1., 2., 3.],
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// [4., 5., 6., 7.]]])
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// torch.ops.aten.reflection_pad1d(input, (3,1)) ; padding_left = 3, padding_right = 1
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// tensor([[[3., 2., 1., 0., 1., 2., 3., 2.],
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// torch.ops.aten.reflection_pad1d(input, (3,1));
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// padding_left = 3,
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// padding_right = 1
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// output = tensor([[[3., 2., 1., 0., 1., 2., 3., 2.],
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// [7., 6., 5., 4., 5., 6., 7., 6.]]])
|
||||
// Checks: 1) Each of padding_left and padding_right must be non-negative less than size of last dimension
|
||||
// Implementation: a) Construct a result tensor of shape of input tensor except for the last dimension.
|
||||
// The last dimension of the result tensor should be last dimension of input tensor +
|
||||
// left padding size + right padding size. INitialize result tensor to all zeros
|
||||
// b) Setup affine map to take slice from input tensor of size left padding starting from
|
||||
// second column onwards as first column is reflection boundary
|
||||
// Checks: 1) Each of padding_left and padding_right must be non-negative and
|
||||
// less than the size of the last dimension.
|
||||
// Implementation: a) Construct a result tensor of
|
||||
// shape of input tensor except for the last dimension.
|
||||
// The last dimension of the result tensor should be last
|
||||
// dimension of input tensor + left padding size + right
|
||||
// padding size. Initialize result tensor to all zeros
|
||||
// b) Setup affine map to take slice from input tensor of size
|
||||
// left padding starting from
|
||||
// second column onwards as first column is reflection
|
||||
// boundary
|
||||
// c) Reflect the affine map to have resultant slice reflected
|
||||
// d) Take the slice and write from begining in result tensor
|
||||
// e) write the original tensor next into result tensor
|
||||
// f) Setup affine map to take slice from input tensor of right padding size ending
|
||||
// at second last column as last column is reflection boundary for right padding
|
||||
// f) Setup affine map to take slice from input tensor of right
|
||||
// padding size ending
|
||||
// at second last column as last column is reflection
|
||||
// boundary for right padding
|
||||
// g) Reflect the affine map to have resultant slice reflected
|
||||
// h) Take the slice and write from left padding size + orignal tensor last dim size
|
||||
// h) Take the slice and write from left padding size + orignal
|
||||
// tensor last dim size
|
||||
// into result tensor
|
||||
// Uses the ideas/code used for AtenReflectionPad2dOp
|
||||
namespace {
|
||||
|
@ -165,43 +175,56 @@ public:
|
|||
Value zero = getConstant(rewriter, loc, 0, indexType);
|
||||
Value one = getConstant(rewriter, loc, 1, indexType);
|
||||
auto inputType = llvm::cast<RankedTensorType>(input.getType());
|
||||
auto outputType = llvm::cast<RankedTensorType>(getTypeConverter()->convertType(op->getResult(0).getType()));
|
||||
auto outputType = llvm::cast<RankedTensorType>(
|
||||
getTypeConverter()->convertType(op->getResult(0).getType()));
|
||||
unsigned numDims = inputType.getRank();
|
||||
assert(numDims >= 2 && "Not enough input dimensions");
|
||||
int64_t lastDim = numDims - 1;
|
||||
SmallVector<Value> inputShape = getTensorSizes(rewriter, loc, input);
|
||||
Value lastDimSize = inputShape[lastDim]; // input [1,2,4], then lastDim = 2, inputShape[2] will give 4
|
||||
Value lastDimSize = inputShape[lastDim]; // input [1,2,4], then lastDim = 2,
|
||||
// inputShape[2] will give 4
|
||||
|
||||
Value tileWidth[3], extractOffset[3], insertOffset[3];
|
||||
|
||||
tileWidth[PAD_LEFT] = getConstant(rewriter, loc, padInts[PAD_LEFT], indexType);
|
||||
tileWidth[PAD_RIGHT] = getConstant(rewriter, loc, padInts[PAD_RIGHT], indexType);
|
||||
tileWidth[PAD_LEFT] =
|
||||
getConstant(rewriter, loc, padInts[PAD_LEFT], indexType);
|
||||
tileWidth[PAD_RIGHT] =
|
||||
getConstant(rewriter, loc, padInts[PAD_RIGHT], indexType);
|
||||
tileWidth[PAD_CENTER] = lastDimSize;
|
||||
|
||||
extractOffset[PAD_LEFT] = one;
|
||||
// for (1,2,4) input, padding (3,1) lastDimSize=4, 4 - 1 - 1 = 2 [3,5, 6,7], so start offset to 6, which is right
|
||||
// lasDimSize - (tileWidth[PAD_RIGHT] + one)
|
||||
extractOffset[PAD_RIGHT] = createISub(lastDimSize, createIAdd(tileWidth[PAD_RIGHT], one));
|
||||
// The offset for the right hand padding "bar" is:
|
||||
// [right] lastDimSize - (tileWidth[PAD_RIGHT] + one)
|
||||
extractOffset[PAD_RIGHT] =
|
||||
createISub(lastDimSize, createIAdd(tileWidth[PAD_RIGHT], one));
|
||||
extractOffset[PAD_CENTER] = zero;
|
||||
|
||||
insertOffset[PAD_LEFT] = zero;
|
||||
insertOffset[PAD_RIGHT] = createIAdd(lastDimSize, tileWidth[PAD_LEFT]);
|
||||
insertOffset[PAD_CENTER] = tileWidth[PAD_LEFT];
|
||||
|
||||
|
||||
SmallVector<Value> resultShape{inputShape};
|
||||
// Result's last dimension will have shape lastDimSize + left padding size + right padding size
|
||||
resultShape[lastDim] = createIAdd(resultShape[lastDim], createIAdd(tileWidth[PAD_LEFT], tileWidth[PAD_RIGHT]));
|
||||
Value resultTensor = createZeroInitTensor(rewriter, loc, resultShape, inputType.getElementType());
|
||||
// Result's last dimension will have size:
|
||||
// lastDimSize + left padding size + right padding size
|
||||
resultShape[lastDim] =
|
||||
createIAdd(resultShape[lastDim],
|
||||
createIAdd(tileWidth[PAD_LEFT], tileWidth[PAD_RIGHT]));
|
||||
Value resultTensor = createZeroInitTensor(rewriter, loc, resultShape,
|
||||
inputType.getElementType());
|
||||
|
||||
// Helper to reflect/reverse the i-th dimension of an affine map without symbols. This only works if applied on a tensor
|
||||
// for which the corresponding dimension has a statically known size
|
||||
auto reflectDim = [](AffineMap map, unsigned numDims, int64_t i, int64_t size) {
|
||||
// Helper to reflect/reverse the i-th dimension of an affine map without
|
||||
// symbols. This only works if applied on a tensor for which the
|
||||
// corresponding dimension has a statically known size
|
||||
auto reflectDim = [](AffineMap map, unsigned numDims, int64_t i,
|
||||
int64_t size) {
|
||||
AffineExpr d = map.getResult(i);
|
||||
return map.replace(d, size - d - 1, numDims, 0); // left reflect for (3,1) on input shape (1,2,4). size = 3, lastDim=2, numDims=3
|
||||
return map.replace(d, size - d - 1, numDims,
|
||||
0); // left reflect for (3,1) on input shape (1,2,4).
|
||||
// size = 3, lastDim=2, numDims=3
|
||||
};
|
||||
|
||||
SmallVector<utils::IteratorType> iteratorTypes{numDims, utils::IteratorType::parallel};
|
||||
SmallVector<utils::IteratorType> iteratorTypes{
|
||||
numDims, utils::IteratorType::parallel};
|
||||
auto idMap = AffineMap::getMultiDimIdentityMap(numDims, context);
|
||||
SmallVector<Value> allOneStrides(numDims, one);
|
||||
|
||||
|
@ -214,22 +237,26 @@ public:
|
|||
Value tile = rewriter.create<tensor::ExtractSliceOp>(
|
||||
loc, input, extractOffsets, extractShape, allOneStrides);
|
||||
|
||||
|
||||
auto inputMap = AffineMap::getMultiDimIdentityMap(numDims, context);
|
||||
// Setup the affine map function to resverse the tile along the horizontal for left and right slices
|
||||
// Setup the affine map function to resverse the tile along the horizontal
|
||||
// for left and right slices
|
||||
if (padPosition < PAD_CENTER) {
|
||||
inputMap = reflectDim(inputMap, numDims, lastDim, padInts[padPosition]);
|
||||
// Take reflected slice as per inputMap
|
||||
tile = rewriter.create<linalg::GenericOp>(loc, llvm::cast<RankedTensorType>(tile.getType()), tile,
|
||||
tile = rewriter
|
||||
.create<linalg::GenericOp>(
|
||||
loc, llvm::cast<RankedTensorType>(tile.getType()), tile,
|
||||
tile, ArrayRef({inputMap, idMap}), iteratorTypes,
|
||||
[](OpBuilder &b, Location nestedLoc, ValueRange args) {
|
||||
b.create<linalg::YieldOp>(nestedLoc, args[0]);
|
||||
}).getResult(0);
|
||||
})
|
||||
.getResult(0);
|
||||
}
|
||||
// Insert the tile in the resultTensor
|
||||
SmallVector<Value> insertOffsets(numDims, zero);
|
||||
insertOffsets[lastDim] = insertOffset[padPosition];
|
||||
resultTensor = rewriter.create<tensor::InsertSliceOp>(loc, tile, resultTensor, insertOffsets, extractShape, allOneStrides);
|
||||
resultTensor = rewriter.create<tensor::InsertSliceOp>(
|
||||
loc, tile, resultTensor, insertOffsets, extractShape, allOneStrides);
|
||||
};
|
||||
|
||||
if (padInts[PAD_LEFT] > 0)
|
||||
|
@ -242,7 +269,7 @@ public:
|
|||
return success();
|
||||
}
|
||||
};
|
||||
}
|
||||
} // namespace
|
||||
|
||||
namespace {
|
||||
|
||||
|
|
|
@ -79,7 +79,8 @@ public:
|
|||
int64_t dim;
|
||||
if (!matchPattern(dimValue, m_TorchConstantInt(&dim)))
|
||||
return op.emitError("unimplemented: dim is not constant");
|
||||
int64_t inputRank = adaptor.getSelf().getType().cast<RankedTensorType>().getRank();
|
||||
int64_t inputRank =
|
||||
adaptor.getSelf().getType().cast<RankedTensorType>().getRank();
|
||||
dim = toPositiveDim(dim, inputRank);
|
||||
if (!isValidDim(dim, inputRank))
|
||||
return rewriter.notifyMatchFailure(op, "dim is statically invalid");
|
||||
|
@ -248,9 +249,9 @@ public:
|
|||
}
|
||||
|
||||
if (modeInt != torch_upstream::EmbeddingBagMode::MODE_SUM) {
|
||||
return rewriter.notifyMatchFailure(
|
||||
op,
|
||||
"Unimplemented: Mean and Max mode are not supported yet for EmbeddingBag.");
|
||||
return rewriter.notifyMatchFailure(op,
|
||||
"Unimplemented: Mean and Max mode are "
|
||||
"not supported yet for EmbeddingBag.");
|
||||
}
|
||||
|
||||
bool isSparse;
|
||||
|
@ -351,10 +352,10 @@ public:
|
|||
|
||||
Value indexI = b.create<linalg::IndexOp>(loc, /*value=*/0);
|
||||
Value indexIToInt = castIndexToInt64(b, loc, indexI);
|
||||
Value one = getConstant(
|
||||
b, loc, 1,
|
||||
mlir::IntegerType::get(getContext(), 64,
|
||||
IntegerType::Signless));
|
||||
Value one =
|
||||
getConstant(b, loc, 1,
|
||||
mlir::IntegerType::get(
|
||||
getContext(), 64, IntegerType::Signless));
|
||||
Value offsetIndexPlusOneInt =
|
||||
b.create<arith::AddIOp>(loc, indexIToInt, one);
|
||||
|
||||
|
@ -393,14 +394,13 @@ public:
|
|||
castIntToIndex(b, loc, indexInIndices));
|
||||
indexIntoWeight.push_back(
|
||||
b.create<linalg::IndexOp>(loc, /*value=*/2));
|
||||
Value weightElem = b.create<tensor::ExtractOp>(
|
||||
loc, weight, indexIntoWeight);
|
||||
Value weightElem =
|
||||
b.create<tensor::ExtractOp>(loc, weight, indexIntoWeight);
|
||||
|
||||
Value addResult = b.create<arith::AddFOp>(loc, weightElem,
|
||||
initTensorElem);
|
||||
Value select =
|
||||
b.create<arith::SelectOp>(loc, indicesIndexWithinBounds,
|
||||
addResult, initTensorElem);
|
||||
Value addResult =
|
||||
b.create<arith::AddFOp>(loc, weightElem, initTensorElem);
|
||||
Value select = b.create<arith::SelectOp>(
|
||||
loc, indicesIndexWithinBounds, addResult, initTensorElem);
|
||||
b.create<linalg::YieldOp>(loc, select);
|
||||
})
|
||||
.getResult(0);
|
||||
|
@ -552,7 +552,8 @@ static Value makeIndexValuePositive(OpBuilder &b, Location loc, Value index,
|
|||
// e.g. x: [2, 3]
|
||||
// x[[4], [6, 1]] -> x[6, 4]
|
||||
namespace {
|
||||
class ConvertAtenIndexTensorHackedTwinOp : public OpConversionPattern<AtenIndexTensorHackedTwinOp> {
|
||||
class ConvertAtenIndexTensorHackedTwinOp
|
||||
: public OpConversionPattern<AtenIndexTensorHackedTwinOp> {
|
||||
public:
|
||||
using OpConversionPattern::OpConversionPattern;
|
||||
LogicalResult
|
||||
|
|
|
@ -165,7 +165,8 @@ public:
|
|||
Location loc = op->getLoc();
|
||||
MLIRContext *context = op.getContext();
|
||||
Value self = adaptor.getSelf();
|
||||
auto selfRank = adaptor.getSelf().getType().cast<RankedTensorType>().getRank();
|
||||
auto selfRank =
|
||||
adaptor.getSelf().getType().cast<RankedTensorType>().getRank();
|
||||
Type elementType =
|
||||
adaptor.getSelf().getType().cast<RankedTensorType>().getElementType();
|
||||
Value c1 =
|
||||
|
@ -535,7 +536,8 @@ public:
|
|||
RankedTensorType lhsType = lhs.getType().cast<RankedTensorType>();
|
||||
RankedTensorType rhsType = rhs.getType().cast<RankedTensorType>();
|
||||
Type newResultType = getTypeConverter()->convertType(op.getType());
|
||||
Type resultElementType = newResultType.cast<RankedTensorType>().getElementType();
|
||||
Type resultElementType =
|
||||
newResultType.cast<RankedTensorType>().getElementType();
|
||||
Type lhsElementType = lhsType.cast<RankedTensorType>().getElementType();
|
||||
Type rhsElementType = rhsType.cast<RankedTensorType>().getElementType();
|
||||
|
||||
|
@ -547,13 +549,15 @@ public:
|
|||
// Convert the inputs element type equivalent to the result' element type.
|
||||
if (lhsElementType != rhsElementType) {
|
||||
if (lhsElementType != resultElementType) {
|
||||
// True if the lhs element type is not equal to the result' element type.
|
||||
lhs = torch_to_linalg::convertTensorToElementType(
|
||||
rewriter, loc, lhs, resultElementType);
|
||||
// True if the lhs element type is not equal to the result' element
|
||||
// type.
|
||||
lhs = torch_to_linalg::convertTensorToElementType(rewriter, loc, lhs,
|
||||
resultElementType);
|
||||
} else {
|
||||
// True if the rhs element type is not equal to the result' element type.
|
||||
rhs = torch_to_linalg::convertTensorToElementType(
|
||||
rewriter, loc, rhs, resultElementType);
|
||||
// True if the rhs element type is not equal to the result' element
|
||||
// type.
|
||||
rhs = torch_to_linalg::convertTensorToElementType(rewriter, loc, rhs,
|
||||
resultElementType);
|
||||
}
|
||||
}
|
||||
|
||||
|
@ -571,7 +575,8 @@ public:
|
|||
checkDimEqualHelper(rewriter, loc, lhsDim2, rhsDim1);
|
||||
|
||||
Value initTensor0 = createZeroInitTensor(
|
||||
rewriter, loc, ValueRange{lhsDim0, lhsDim1, rhsDim2}, resultElementType);
|
||||
rewriter, loc, ValueRange{lhsDim0, lhsDim1, rhsDim2},
|
||||
resultElementType);
|
||||
|
||||
Value bmm =
|
||||
rewriter
|
||||
|
@ -634,7 +639,8 @@ public:
|
|||
return rewriter.notifyMatchFailure(op,
|
||||
"only support constant int strides");
|
||||
SmallVector<int64_t> dilationInts;
|
||||
if (!matchPattern(op.getDilation(), m_TorchListOfConstantInts(dilationInts)))
|
||||
if (!matchPattern(op.getDilation(),
|
||||
m_TorchListOfConstantInts(dilationInts)))
|
||||
return rewriter.notifyMatchFailure(op,
|
||||
"only support constant int dilations");
|
||||
|
||||
|
@ -838,8 +844,10 @@ public:
|
|||
|
||||
Value conv;
|
||||
// the code so far is able to respect all numSpacialDims
|
||||
// the code below this point is numSpacialDims specific and groupSize specific
|
||||
// TODO: factor out the above code into a helper function, and then separate convolution into:
|
||||
// the code below this point is numSpacialDims specific and groupSize
|
||||
// specific
|
||||
// TODO: factor out the above code into a helper function, and then separate
|
||||
// convolution into:
|
||||
// - grouped 1d-3d
|
||||
// - ungrouped 1d-3d
|
||||
if (groupSize == 1) {
|
||||
|
@ -854,19 +862,19 @@ public:
|
|||
.getResult(0);
|
||||
break;
|
||||
case 2:
|
||||
conv =
|
||||
rewriter
|
||||
conv = rewriter
|
||||
.create<linalg::Conv2DNchwFchwOp>(
|
||||
loc, outputTensor.getType(), ValueRange{paddedInput, weight},
|
||||
outputTensor, stridesAttr, dilationAttr)
|
||||
loc, outputTensor.getType(),
|
||||
ValueRange{paddedInput, weight}, outputTensor,
|
||||
stridesAttr, dilationAttr)
|
||||
.getResult(0);
|
||||
break;
|
||||
case 3:
|
||||
conv =
|
||||
rewriter
|
||||
conv = rewriter
|
||||
.create<linalg::Conv3DNcdhwFcdhwOp>(
|
||||
loc, outputTensor.getType(), ValueRange{paddedInput, weight},
|
||||
outputTensor, stridesAttr, dilationAttr)
|
||||
loc, outputTensor.getType(),
|
||||
ValueRange{paddedInput, weight}, outputTensor,
|
||||
stridesAttr, dilationAttr)
|
||||
.getResult(0);
|
||||
break;
|
||||
default:
|
||||
|
|
|
@ -194,7 +194,6 @@ public:
|
|||
};
|
||||
} // namespace
|
||||
|
||||
|
||||
void mlir::torch::torch_to_linalg::populateRandomPatternsAndLegality(
|
||||
TypeConverter &typeConverter, RewritePatternSet &patterns,
|
||||
ConversionTarget &target) {
|
||||
|
|
|
@ -144,8 +144,7 @@ public:
|
|||
}
|
||||
|
||||
Value filledTensorVal =
|
||||
rewriter.create<linalg::FillOp>(loc, fillValue, initTensorVal)
|
||||
.result();
|
||||
rewriter.create<linalg::FillOp>(loc, fillValue, initTensorVal).result();
|
||||
|
||||
// Create the affine expressions that will be used to
|
||||
// iterate over the input and output tensors.
|
||||
|
@ -220,8 +219,8 @@ public:
|
|||
});
|
||||
|
||||
// This cast is required to fix the shape in the case of keepDim=True
|
||||
Value valuesCast = rewriter.create<tensor::CastOp>(
|
||||
loc, valResultType, linalgOp.getResult(0));
|
||||
Value valuesCast = rewriter.create<tensor::CastOp>(loc, valResultType,
|
||||
linalgOp.getResult(0));
|
||||
Value idxCast = rewriter.create<tensor::CastOp>(loc, idxResultType,
|
||||
linalgOp.getResult(1));
|
||||
rewriter.replaceOp(op, {valuesCast, idxCast});
|
||||
|
@ -345,7 +344,8 @@ static Value createLinalgPayloadForReduceOp(OpBuilder &b, Location loc,
|
|||
Value self = convertScalarToDtype(b, loc, elem, resultElementType);
|
||||
auto abs = b.create<math::AbsFOp>(loc, self);
|
||||
AtenLinalgVectorNormOp::Adaptor adaptor(operands);
|
||||
Value ord = convertScalarToDtype(b, loc, adaptor.getOrd(), resultElementType);
|
||||
Value ord =
|
||||
convertScalarToDtype(b, loc, adaptor.getOrd(), resultElementType);
|
||||
auto pow = b.create<math::PowFOp>(loc, abs, ord);
|
||||
return b.create<arith::AddFOp>(loc, pow, result);
|
||||
} else if (isa<AtenFrobeniusNormDimOp>(op)) {
|
||||
|
@ -427,8 +427,8 @@ private:
|
|||
opInfo.tensorOperand = operands[0];
|
||||
auto inputType = opInfo.tensorOperand.getType().cast<RankedTensorType>();
|
||||
|
||||
// `AtenSumOp`, `AtenMaxOp`, and `AtenMinOp` each reduce along all the dimensions of the
|
||||
// input tensor.
|
||||
// `AtenSumOp`, `AtenMaxOp`, and `AtenMinOp` each reduce along all the
|
||||
// dimensions of the input tensor.
|
||||
for (int64_t i = 0; i < inputType.getRank(); i++)
|
||||
opInfo.dimSet.insert(i);
|
||||
|
||||
|
|
|
@ -120,13 +120,18 @@ namespace {
|
|||
Value vDimSize = inputShape[vDim];
|
||||
|
||||
enum tileHLoc { LEFT = 0, HCENTER = 1, RIGHT = 2 };
|
||||
enum tileVLoc { TOP = 0, VCENTER = 2, BOTTOM = 1, };
|
||||
enum tileVLoc {
|
||||
TOP = 0,
|
||||
VCENTER = 2,
|
||||
BOTTOM = 1,
|
||||
};
|
||||
// vTile denotes the vertical size of the tile
|
||||
// hTile denotes the horizontal size of the tile
|
||||
// The padding results are composed of following tiles:
|
||||
// vTile[TOP]hTile[LEFT], vTile[TOP]hTile[HCENTER], vTile[TOP]hTile[RIGHT]
|
||||
// vTile[VCENTER]hTile[LEFT], vTile[VCENTER]hTile[HCENTER], vTile[VCENTER]hTile[RIGHT]
|
||||
// vTile[BOTTOM]hTile[LEFT], vTile[BOTTOM]hTile[HCENTER], vTile[BOTTOM]hTile[RIGHT]
|
||||
// vTile[VCENTER]hTile[LEFT], vTile[VCENTER]hTile[HCENTER],
|
||||
// vTile[VCENTER]hTile[RIGHT] vTile[BOTTOM]hTile[LEFT],
|
||||
// vTile[BOTTOM]hTile[HCENTER], vTile[BOTTOM]hTile[RIGHT]
|
||||
// vTile[VCENTER]hTile[HCENTER] is the original input tensor
|
||||
Type indexType = rewriter.getIndexType();
|
||||
Value vTile[3];
|
||||
|
@ -215,16 +220,19 @@ namespace {
|
|||
SmallVector<int64_t> lowPadding(4, 0);
|
||||
SmallVector<int64_t> highPadding(4, 0);
|
||||
lowPadding[2] = padInts[2];
|
||||
vLeftSlice = torch_to_linalg::getPaddedTensor(op, rewriter, vLeftSlice, lowPadding, highPadding, topLeftValue);
|
||||
vLeftSlice = torch_to_linalg::getPaddedTensor(
|
||||
op, rewriter, vLeftSlice, lowPadding, highPadding, topLeftValue);
|
||||
}
|
||||
if (hasBottomPadding) {
|
||||
Value bottomLeftValue = rewriter.create<tensor::ExtractOp> (loc, input, ValueRange{zero, zero, vDimSizeMinusOne, zero});
|
||||
Value bottomLeftValue = rewriter.create<tensor::ExtractOp>(
|
||||
loc, input, ValueRange{zero, zero, vDimSizeMinusOne, zero});
|
||||
|
||||
// pad vLeftSlice at the bottom
|
||||
SmallVector<int64_t> lowPadding(4, 0);
|
||||
SmallVector<int64_t> highPadding(4, 0);
|
||||
highPadding[2] = padInts[3];
|
||||
vLeftSlice = torch_to_linalg::getPaddedTensor(op, rewriter, vLeftSlice, lowPadding, highPadding, bottomLeftValue);
|
||||
vLeftSlice = torch_to_linalg::getPaddedTensor(
|
||||
op, rewriter, vLeftSlice, lowPadding, highPadding, bottomLeftValue);
|
||||
}
|
||||
for (auto i = 0; i < padInts[0]; ++i) {
|
||||
tensorsLeft.push_back(vLeftSlice);
|
||||
|
@ -256,27 +264,34 @@ namespace {
|
|||
loc, input, extractOffsetsRight, extractShapeLR, allOneStrides);
|
||||
Value vRightSlice = vCenterRightSlice;
|
||||
if (hasTopPadding) {
|
||||
Value topRightValue = rewriter.create<tensor::ExtractOp> (loc, input, ValueRange{zero, zero, zero, hDimSizeMinusOne});
|
||||
Value topRightValue = rewriter.create<tensor::ExtractOp>(
|
||||
loc, input, ValueRange{zero, zero, zero, hDimSizeMinusOne});
|
||||
|
||||
// pad vCenterRightSlice on the top
|
||||
SmallVector<int64_t> lowPadding(4, 0);
|
||||
SmallVector<int64_t> highPadding(4, 0);
|
||||
lowPadding[2] = padInts[2];
|
||||
vRightSlice = torch_to_linalg::getPaddedTensor(op, rewriter, vRightSlice, lowPadding, highPadding, topRightValue);
|
||||
vRightSlice = torch_to_linalg::getPaddedTensor(
|
||||
op, rewriter, vRightSlice, lowPadding, highPadding, topRightValue);
|
||||
}
|
||||
if (hasBottomPadding) {
|
||||
Value bottomRightValue = rewriter.create<tensor::ExtractOp> (loc, input, ValueRange{zero, zero, vDimSizeMinusOne, hDimSizeMinusOne});
|
||||
Value bottomRightValue = rewriter.create<tensor::ExtractOp>(
|
||||
loc, input,
|
||||
ValueRange{zero, zero, vDimSizeMinusOne, hDimSizeMinusOne});
|
||||
|
||||
// Pad vCenterRightSlice or vRightTopPaddedSlice at the bottom.
|
||||
SmallVector<int64_t> lowPadding(4, 0);
|
||||
SmallVector<int64_t> highPadding(4, 0);
|
||||
highPadding[2] = padInts[3];
|
||||
vRightSlice = torch_to_linalg::getPaddedTensor(op, rewriter, vRightSlice, lowPadding, highPadding, bottomRightValue);
|
||||
vRightSlice = torch_to_linalg::getPaddedTensor(
|
||||
op, rewriter, vRightSlice, lowPadding, highPadding,
|
||||
bottomRightValue);
|
||||
}
|
||||
for (auto i = 0; i < padInts[1]; ++i) {
|
||||
tensorsRight.push_back(vRightSlice);
|
||||
}
|
||||
Value rightPadTile = rewriter.create<tensor::ConcatOp>(loc, 3, tensorsRight);
|
||||
Value rightPadTile =
|
||||
rewriter.create<tensor::ConcatOp>(loc, 3, tensorsRight);
|
||||
tensorsRes.push_back(rightPadTile);
|
||||
}
|
||||
Value resTensor = rewriter.create<tensor::ConcatOp>(loc, 3, tensorsRes);
|
||||
|
@ -285,7 +300,7 @@ namespace {
|
|||
return success();
|
||||
}
|
||||
};
|
||||
}
|
||||
} // namespace
|
||||
|
||||
namespace {
|
||||
// Converts constant tensor allocation like ops.
|
||||
|
@ -348,8 +363,8 @@ public:
|
|||
// Create an uninitialized tensor of `resultSize` shape and fill it with
|
||||
// value `fillVal`.
|
||||
Value constVal = getConstant(rewriter, loc, fillVal, resultElementType);
|
||||
Value outputTensor =
|
||||
createInitTensor(rewriter, loc, resultSizeIndex, resultElementType, constVal);
|
||||
Value outputTensor = createInitTensor(rewriter, loc, resultSizeIndex,
|
||||
resultElementType, constVal);
|
||||
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, outputTensor);
|
||||
return success();
|
||||
}
|
||||
|
@ -384,7 +399,8 @@ public:
|
|||
// Only `none`, `contiguous` and `preserve` memory_format is supported.
|
||||
if (!op.getMemoryFormat().getType().isa<Torch::NoneType>()) {
|
||||
int64_t memoryFormat;
|
||||
if (!matchPattern(op.getMemoryFormat(), m_TorchConstantInt(&memoryFormat)))
|
||||
if (!matchPattern(op.getMemoryFormat(),
|
||||
m_TorchConstantInt(&memoryFormat)))
|
||||
return rewriter.notifyMatchFailure(
|
||||
op, "unimplemented: the memory format should be specified in "
|
||||
"an integer constant");
|
||||
|
@ -495,7 +511,8 @@ public:
|
|||
typeConverter->convertType(op->getResult(0).getType())
|
||||
.cast<RankedTensorType>();
|
||||
Type dtype = resultType.getElementType();
|
||||
Value start = convertScalarToDtype(rewriter, loc, adaptor.getStart(), dtype);
|
||||
Value start =
|
||||
convertScalarToDtype(rewriter, loc, adaptor.getStart(), dtype);
|
||||
Value end = convertScalarToDtype(rewriter, loc, adaptor.getEnd(), dtype);
|
||||
Value step = convertScalarToDtype(rewriter, loc, adaptor.getStep(), dtype);
|
||||
|
||||
|
|
|
@ -429,7 +429,8 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
|
|||
if (isa<AtenIsinfOp>(op)) {
|
||||
Value abs = b.create<math::AbsFOp>(loc, payloadArgs[0]);
|
||||
Value infinity = b.create<arith::ConstantOp>(
|
||||
loc, b.getFloatAttr(abs.getType(), std::numeric_limits<double>::infinity()));
|
||||
loc,
|
||||
b.getFloatAttr(abs.getType(), std::numeric_limits<double>::infinity()));
|
||||
return createEqual(b, loc, abs.getType(), abs, infinity);
|
||||
}
|
||||
if (isa<AtenSigmoidOp>(op)) {
|
||||
|
|
|
@ -7,13 +7,13 @@
|
|||
//
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
#include "mlir/Dialect/Tensor/Utils/Utils.h"
|
||||
#include "../PassDetail.h"
|
||||
#include "PopulatePatterns.h"
|
||||
#include "mlir/Dialect/Arith/IR/Arith.h"
|
||||
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
|
||||
#include "mlir/Dialect/Linalg/IR/Linalg.h"
|
||||
#include "mlir/Dialect/Tensor/IR/Tensor.h"
|
||||
#include "mlir/Dialect/Tensor/Utils/Utils.h"
|
||||
#include "mlir/IR/Matchers.h"
|
||||
#include "torch-mlir/Conversion/TorchToLinalg/Utils.h"
|
||||
#include "torch-mlir/Conversion/Utils/Utils.h"
|
||||
|
|
|
@ -923,8 +923,7 @@ LogicalResult ConvertAtenOp<AtenScalarImplicitOp>::matchAndRewrite(
|
|||
op.getA().getType().template cast<BaseTensorType>().getDtype();
|
||||
Type resultType =
|
||||
this->getTypeConverter()->convertType(op->getResult(0).getType());
|
||||
auto result =
|
||||
rewriter.create<tensor::ExtractOp>(loc, adaptor.getA());
|
||||
auto result = rewriter.create<tensor::ExtractOp>(loc, adaptor.getA());
|
||||
|
||||
rewriter.replaceOp(
|
||||
op, convertScalarToDtype(rewriter, loc, result, resultType, inputDtype));
|
||||
|
@ -1797,8 +1796,7 @@ void mlir::torch::torch_to_stablehlo::populateBasicOpPatternsAndLegality(
|
|||
|
||||
#define INSERT_TENSOR_TO_SCALAR_PATTERN(AtenOp) \
|
||||
target.addIllegalOp<AtenOp>(); \
|
||||
patterns.add<ConvertAtenTensorToScalarLikeOp<AtenOp>>(typeConverter, \
|
||||
context)
|
||||
patterns.add<ConvertAtenTensorToScalarLikeOp<AtenOp>>(typeConverter, context)
|
||||
|
||||
INSERT_TENSOR_TO_SCALAR_PATTERN(AtenIntTensorOp);
|
||||
INSERT_TENSOR_TO_SCALAR_PATTERN(AtenFloatTensorOp);
|
||||
|
|
|
@ -35,7 +35,8 @@ static Value createInitialValueForAtenPoolingOp(Operation *op, Type elementTy,
|
|||
PatternRewriter &rewriter) {
|
||||
auto constType = RankedTensorType::get({}, elementTy);
|
||||
// Avg pooling
|
||||
if (isa<AtenAvgPool1dOp, AtenAdaptiveAvgPool2dOp, AtenAvgPool2dOp, AtenCumsumOp>(op)) {
|
||||
if (isa<AtenAvgPool1dOp, AtenAdaptiveAvgPool2dOp, AtenAvgPool2dOp,
|
||||
AtenCumsumOp>(op)) {
|
||||
if (elementTy.isa<mlir::FloatType>()) {
|
||||
auto constAttr = DenseElementsAttr::get(
|
||||
constType, {APFloat::getZero(
|
||||
|
@ -373,7 +374,6 @@ LogicalResult ConvertAtenOp<AtenMaxPool2dWithIndicesOp>::matchAndRewrite(
|
|||
return success();
|
||||
}
|
||||
|
||||
|
||||
namespace {
|
||||
template <typename AtenOpT, int Dim>
|
||||
class ConvertAtenAvgPoolOp : public ConvertAtenOp<AtenOpT> {
|
||||
|
@ -392,7 +392,6 @@ public:
|
|||
.template cast<RankedTensorType>();
|
||||
auto outShape = outTy.getShape();
|
||||
|
||||
|
||||
if (inputRank <= Dim) {
|
||||
return op.emitError(
|
||||
"avg_pooling1d/2d only supports inputs with rank higher than 1/2");
|
||||
|
@ -407,15 +406,16 @@ public:
|
|||
op, "non-const int kernel size unsupported!");
|
||||
}
|
||||
if (!(matchPattern(op.getStride(), m_TorchListOfConstantInts(stride)))) {
|
||||
return rewriter.notifyMatchFailure(op, "non-const int stride unsupported!");
|
||||
return rewriter.notifyMatchFailure(op,
|
||||
"non-const int stride unsupported!");
|
||||
}
|
||||
if (!(matchPattern(op.getPadding(), m_TorchListOfConstantInts(padding)))) {
|
||||
return rewriter.notifyMatchFailure(op,
|
||||
"non-const int padding unsupported!");
|
||||
}
|
||||
if (!(matchPattern(op.getCeilMode(), m_TorchConstantBool(&ceilMode)))) {
|
||||
return rewriter.notifyMatchFailure(op,
|
||||
"non-const bool ceil_mode unsupported!");
|
||||
return rewriter.notifyMatchFailure(
|
||||
op, "non-const bool ceil_mode unsupported!");
|
||||
}
|
||||
if (!(matchPattern(op.getCountIncludePad(),
|
||||
m_TorchConstantBool(&countIncludePad)))) {
|
||||
|
@ -450,10 +450,12 @@ public:
|
|||
stablehloPadding[stablehloPadding.size() - 1] = padding[1];
|
||||
}
|
||||
|
||||
Value initVal = createInitialValueForAtenPoolingOp(op, inputElemTy, rewriter);
|
||||
Value initVal =
|
||||
createInitialValueForAtenPoolingOp(op, inputElemTy, rewriter);
|
||||
|
||||
DenseIntElementsAttr windowDimensions = DenseIntElementsAttr::get(
|
||||
RankedTensorType::get({static_cast<int64_t>(stablehloKernelSize.size())},
|
||||
RankedTensorType::get(
|
||||
{static_cast<int64_t>(stablehloKernelSize.size())},
|
||||
rewriter.getI64Type()),
|
||||
stablehloKernelSize);
|
||||
DenseIntElementsAttr windowStrides = DenseIntElementsAttr::get(
|
||||
|
@ -518,8 +520,8 @@ public:
|
|||
windowSizeConst =
|
||||
hlo::promoteType(rewriter, op.getLoc(), windowSizeConst, outTy);
|
||||
const auto &options = ConvertAtenOp<AtenOpT>::getOptions();
|
||||
auto inputShapeVec =
|
||||
*hlo::getDimSizesOfTensor(rewriter, op, input, options.dimSizeIndexBits);
|
||||
auto inputShapeVec = *hlo::getDimSizesOfTensor(rewriter, op, input,
|
||||
options.dimSizeIndexBits);
|
||||
auto inputShapeTensor = rewriter.create<mlir::tensor::FromElementsOp>(
|
||||
op->getLoc(), inputShapeVec);
|
||||
|
||||
|
@ -555,12 +557,9 @@ public:
|
|||
rewriter.replaceOpWithNewOp<stablehlo::DivOp>(
|
||||
op, outTy, reduceWindowSum.getResult(0), reduceWindowSize.getResult(0));
|
||||
return success();
|
||||
|
||||
}
|
||||
|
||||
};
|
||||
}
|
||||
|
||||
} // namespace
|
||||
|
||||
// AtenCumsumOp
|
||||
template <>
|
||||
|
@ -662,8 +661,8 @@ void mlir::torch::torch_to_stablehlo::populatePoolingOpPatternsAndLegality(
|
|||
patterns.add<ConvertAtenOp<AtenCumsumOp>>(typeConverter, context, options);
|
||||
#define INSERT_ATEN_AVGPOOL_PATTERN(AtenOp, Dim) \
|
||||
target.addIllegalOp<AtenOp>(); \
|
||||
patterns.add<ConvertAtenAvgPoolOp<AtenOp, Dim>>( \
|
||||
typeConverter, context, options)
|
||||
patterns.add<ConvertAtenAvgPoolOp<AtenOp, Dim>>(typeConverter, context, \
|
||||
options)
|
||||
INSERT_ATEN_AVGPOOL_PATTERN(AtenAvgPool1dOp, 1);
|
||||
INSERT_ATEN_AVGPOOL_PATTERN(AtenAvgPool2dOp, 2);
|
||||
#undef INSERT_ATEN_AVGPOOL_PATTERN
|
||||
|
|
|
@ -16,13 +16,13 @@
|
|||
#include "mlir/Dialect/Tensor/IR/Tensor.h"
|
||||
#include "stablehlo/dialect/ChloOps.h"
|
||||
#include "stablehlo/dialect/StablehloOps.h"
|
||||
#include "torch-mlir/Conversion/TorchToStablehlo/StablehloLegalizeUtils.h"
|
||||
#include "torch-mlir/Conversion/Utils/Utils.h"
|
||||
#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
|
||||
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
|
||||
#include "torch-mlir/Dialect/Torch/Utils/TorchUpstream.h"
|
||||
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
|
||||
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
|
||||
#include "torch-mlir/Conversion/TorchToStablehlo/StablehloLegalizeUtils.h"
|
||||
|
||||
using namespace mlir;
|
||||
using namespace mlir::torch;
|
||||
|
|
|
@ -15,6 +15,7 @@
|
|||
#include "mlir/Dialect/Arith/IR/Arith.h"
|
||||
#include "mlir/Dialect/Tensor/IR/Tensor.h"
|
||||
#include "stablehlo/dialect/StablehloOps.h"
|
||||
#include "torch-mlir/Conversion/TorchToStablehlo/StablehloLegalizeUtils.h"
|
||||
#include "torch-mlir/Conversion/Utils/Utils.h"
|
||||
#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
|
||||
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
|
||||
|
@ -22,7 +23,6 @@
|
|||
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
|
||||
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionDialect.h"
|
||||
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
|
||||
#include "torch-mlir/Conversion/TorchToStablehlo/StablehloLegalizeUtils.h"
|
||||
#include <numeric>
|
||||
|
||||
using namespace mlir;
|
||||
|
@ -403,7 +403,8 @@ LogicalResult ConvertAtenOp<AtenUnsqueezeOp>::matchAndRewrite(
|
|||
int64_t dim;
|
||||
if (!matchPattern(op.getDim(), m_TorchConstantInt(&dim)))
|
||||
return op->emitError("dim must be a Scalar constant");
|
||||
int64_t inputRank = adaptor.getSelf().getType().cast<RankedTensorType>().getRank();
|
||||
int64_t inputRank =
|
||||
adaptor.getSelf().getType().cast<RankedTensorType>().getRank();
|
||||
dim = toPositiveDim(dim, inputRank + 1);
|
||||
if (!isValidDim(dim, inputRank + 1))
|
||||
return rewriter.notifyMatchFailure(op, "dim is statically invalid");
|
||||
|
|
|
@ -210,9 +210,9 @@ std::optional<Value> convertTorchIndexToTfIndices(PatternRewriter &rewriter,
|
|||
// Lowers Gather operators to a sequence of TOSA ops.
|
||||
// taken from
|
||||
// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/tosa/transforms/legalize_common.cc
|
||||
std::optional<Value> convertGatherNdOp(PatternRewriter &rewriter,
|
||||
Operation *op, Type outType,
|
||||
Value paramsValue, Value indicesValue) {
|
||||
std::optional<Value> convertGatherNdOp(PatternRewriter &rewriter, Operation *op,
|
||||
Type outType, Value paramsValue,
|
||||
Value indicesValue) {
|
||||
auto resultType = outType.dyn_cast<ShapedType>();
|
||||
auto paramsType = paramsValue.getType().dyn_cast<RankedTensorType>();
|
||||
auto indicesType = indicesValue.getType().dyn_cast<RankedTensorType>();
|
||||
|
@ -683,7 +683,6 @@ std::optional<Value> convertScatterNdOp(PatternRewriter &rewriter,
|
|||
.getResult();
|
||||
}
|
||||
|
||||
|
||||
// Common function for lowering reduce operations to TOSA ops.
|
||||
template <typename T>
|
||||
std::optional<Value> convertReduceOpCommon(
|
||||
|
@ -721,9 +720,8 @@ std::optional<Value> convertReduceOpCommon(
|
|||
auto axis_attr = rewriter.getI32IntegerAttr(axis_val);
|
||||
|
||||
shape_vec[axis_val] = 1;
|
||||
RankedTensorType reduce_type = RankedTensorType::get(
|
||||
shape_vec,
|
||||
reduce_element_type);
|
||||
RankedTensorType reduce_type =
|
||||
RankedTensorType::get(shape_vec, reduce_element_type);
|
||||
|
||||
auto reduce_op = CreateOpAndInfer<T>(rewriter, op->getLoc(), reduce_type,
|
||||
val, axis_attr);
|
||||
|
|
|
@ -176,7 +176,8 @@ std::optional<Value> getZerosLikeTensor(PatternRewriter &rewriter,
|
|||
// Default template creates a constant tensor in T.
|
||||
template <typename T>
|
||||
std::optional<Value> getConstTensor(PatternRewriter &rewriter, Operation *op,
|
||||
ArrayRef<T> vec, ArrayRef<int64_t> shape, std::optional<Type> dtype) {
|
||||
ArrayRef<T> vec, ArrayRef<int64_t> shape,
|
||||
std::optional<Type> dtype) {
|
||||
uint64_t num_total_elements = 1;
|
||||
for (int64_t a : shape) {
|
||||
num_total_elements *= a;
|
||||
|
@ -209,7 +210,8 @@ std::optional<Value> getConstTensor(PatternRewriter &rewriter, Operation *op,
|
|||
template <>
|
||||
std::optional<Value> getConstTensor<APInt>(PatternRewriter &rewriter,
|
||||
Operation *op, ArrayRef<APInt> vec,
|
||||
ArrayRef<int64_t> shape, std::optional<Type> dtype) {
|
||||
ArrayRef<int64_t> shape,
|
||||
std::optional<Type> dtype) {
|
||||
uint64_t num_total_elements = 1;
|
||||
for (int64_t a : shape) {
|
||||
num_total_elements *= a;
|
||||
|
@ -238,7 +240,8 @@ std::optional<Value> getConstTensor<APInt>(PatternRewriter &rewriter,
|
|||
template <>
|
||||
std::optional<Value> getConstTensor<float>(PatternRewriter &rewriter,
|
||||
Operation *op, ArrayRef<float> vec,
|
||||
ArrayRef<int64_t> shape, std::optional<Type> dtype) {
|
||||
ArrayRef<int64_t> shape,
|
||||
std::optional<Type> dtype) {
|
||||
uint64_t num_total_elements = 1;
|
||||
for (int64_t a : shape) {
|
||||
num_total_elements *= a;
|
||||
|
@ -347,23 +350,17 @@ Value promoteType(PatternRewriter &rewriter, Value input, TensorType outType) {
|
|||
}
|
||||
|
||||
// Template instantiation
|
||||
template std::optional<Value> getConstTensor<bool>(PatternRewriter &,
|
||||
Operation *,
|
||||
ArrayRef<bool> vec,
|
||||
ArrayRef<int64_t> shape,
|
||||
std::optional<Type> dtype);
|
||||
template std::optional<Value>
|
||||
getConstTensor<bool>(PatternRewriter &, Operation *, ArrayRef<bool> vec,
|
||||
ArrayRef<int64_t> shape, std::optional<Type> dtype);
|
||||
|
||||
template std::optional<Value> getConstTensor<int32_t>(PatternRewriter &,
|
||||
Operation *,
|
||||
ArrayRef<int32_t> vec,
|
||||
ArrayRef<int64_t> shape,
|
||||
std::optional<Type> dtype);
|
||||
template std::optional<Value>
|
||||
getConstTensor<int32_t>(PatternRewriter &, Operation *, ArrayRef<int32_t> vec,
|
||||
ArrayRef<int64_t> shape, std::optional<Type> dtype);
|
||||
|
||||
template std::optional<Value> getConstTensor<int64_t>(PatternRewriter &,
|
||||
Operation *,
|
||||
ArrayRef<int64_t> vec,
|
||||
ArrayRef<int64_t> shape,
|
||||
std::optional<Type> dtype);
|
||||
template std::optional<Value>
|
||||
getConstTensor<int64_t>(PatternRewriter &, Operation *, ArrayRef<int64_t> vec,
|
||||
ArrayRef<int64_t> shape, std::optional<Type> dtype);
|
||||
|
||||
LogicalResult getAvgPool2dAccType(PatternRewriter &rewriter, Value input,
|
||||
TypeAttr &accType) {
|
||||
|
|
|
@ -87,7 +87,8 @@ static TMTensorOp createTMTensorOpOnBuffers(ConversionPatternRewriter &rewriter,
|
|||
ValueRange outputs) {
|
||||
SmallVector<Value, 8> newOperands = inputs;
|
||||
newOperands.append(outputs.begin(), outputs.end());
|
||||
return cast<TMTensorOp>(tmtensorOp.clone(rewriter, tmtensorOp->getLoc(), {}, newOperands));
|
||||
return cast<TMTensorOp>(
|
||||
tmtensorOp.clone(rewriter, tmtensorOp->getLoc(), {}, newOperands));
|
||||
}
|
||||
|
||||
/// Generic conversion pattern that matches any TMTensorOp. This avoids template
|
||||
|
|
|
@ -203,8 +203,8 @@ static Value getScalarFloatValue(Value input, Location loc,
|
|||
//===----------------------------------------------------------------------===//
|
||||
|
||||
LogicalResult MethodOp::verifySymbolUses(SymbolTableCollection &symbolTable) {
|
||||
auto func =
|
||||
symbolTable.lookupNearestSymbolFrom<func::FuncOp>(*this, getFunctionAttr());
|
||||
auto func = symbolTable.lookupNearestSymbolFrom<func::FuncOp>(
|
||||
*this, getFunctionAttr());
|
||||
if (!func)
|
||||
return emitError() << "'@" << getFunction()
|
||||
<< "' does not reference a valid function";
|
||||
|
@ -453,11 +453,13 @@ void PrimIfOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
|
|||
// If the condition is constant, delete the dead branch and inline the live
|
||||
// branch.
|
||||
patterns.add(+[](PrimIfOp op, PatternRewriter &rewriter) {
|
||||
auto constantBool = op.getCondition().getDefiningOp<Torch::ConstantBoolOp>();
|
||||
auto constantBool =
|
||||
op.getCondition().getDefiningOp<Torch::ConstantBoolOp>();
|
||||
if (!constantBool)
|
||||
return rewriter.notifyMatchFailure(op, "non-constant condition");
|
||||
replaceOpWithRegion(
|
||||
rewriter, op, constantBool.getValue() ? op.getThenRegion() : op.getElseRegion());
|
||||
replaceOpWithRegion(rewriter, op,
|
||||
constantBool.getValue() ? op.getThenRegion()
|
||||
: op.getElseRegion());
|
||||
return success();
|
||||
});
|
||||
// If the thenRegion and elseRegion yield the same Value's, then use those
|
||||
|
@ -515,14 +517,16 @@ void PrimIfOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
|
|||
continue;
|
||||
newResultTypes.push_back(op->getResult(i).getType());
|
||||
}
|
||||
auto newIf =
|
||||
rewriter.create<PrimIfOp>(op->getLoc(), newResultTypes, op.getCondition());
|
||||
auto newIf = rewriter.create<PrimIfOp>(op->getLoc(), newResultTypes,
|
||||
op.getCondition());
|
||||
rewriter.inlineRegionBefore(op.getThenRegion(), newIf.getThenRegion(),
|
||||
newIf.getThenRegion().end());
|
||||
rewriter.inlineRegionBefore(op.getElseRegion(), newIf.getElseRegion(),
|
||||
newIf.getElseRegion().end());
|
||||
newIf.getThenRegion().front().getTerminator()->eraseOperands(resultsToErase);
|
||||
newIf.getElseRegion().front().getTerminator()->eraseOperands(resultsToErase);
|
||||
newIf.getThenRegion().front().getTerminator()->eraseOperands(
|
||||
resultsToErase);
|
||||
newIf.getElseRegion().front().getTerminator()->eraseOperands(
|
||||
resultsToErase);
|
||||
SmallVector<Value> replacementValues;
|
||||
for (int i = 0, e = op->getNumResults(), nextNewValue = 0; i < e; ++i) {
|
||||
if (resultsToErase[i])
|
||||
|
@ -900,8 +904,8 @@ void AtenToOtherOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
|
|||
auto getRhsDtype = rewriter.create<PrimDtypeOp>(op.getLoc(), rhs);
|
||||
rewriter.replaceOpWithNewOp<AtenToDeviceOp>(
|
||||
op, op.getType(), lhs, getRhsDevice.getResult(),
|
||||
getRhsDtype.getResult(), op.getNonBlocking(),
|
||||
op.getCopy(), op.getMemoryFormat());
|
||||
getRhsDtype.getResult(), op.getNonBlocking(), op.getCopy(),
|
||||
op.getMemoryFormat());
|
||||
return success();
|
||||
});
|
||||
}
|
||||
|
@ -2045,7 +2049,8 @@ void Aten__Getitem__TOp::getCanonicalizationPatterns(
|
|||
// compiler treat the size as having value semantics?
|
||||
// There's a small number of such ops, and they are marked as `inplace_view`
|
||||
// in PyTorch's `native_functions.yaml` file.
|
||||
rewriter.replaceOpWithNewOp<AtenSizeIntOp>(op, sizeOp.getSelf(), op.getIdx());
|
||||
rewriter.replaceOpWithNewOp<AtenSizeIntOp>(op, sizeOp.getSelf(),
|
||||
op.getIdx());
|
||||
return success();
|
||||
});
|
||||
}
|
||||
|
@ -2073,11 +2078,13 @@ OpFoldResult AtenIsFloatingPointOp::fold(FoldAdaptor adaptor) {
|
|||
void AtenAddTOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
|
||||
MLIRContext *context) {
|
||||
patterns.add(+[](AtenAddTOp op, PatternRewriter &rewriter) {
|
||||
auto lhsListConstruct = op.getA().getDefiningOp<Torch::PrimListConstructOp>();
|
||||
auto lhsListConstruct =
|
||||
op.getA().getDefiningOp<Torch::PrimListConstructOp>();
|
||||
if (!lhsListConstruct || isListPotentiallyMutated(lhsListConstruct))
|
||||
return failure();
|
||||
|
||||
auto rhsListConstruct = op.getB().getDefiningOp<Torch::PrimListConstructOp>();
|
||||
auto rhsListConstruct =
|
||||
op.getB().getDefiningOp<Torch::PrimListConstructOp>();
|
||||
if (!rhsListConstruct || isListPotentiallyMutated(rhsListConstruct))
|
||||
return failure();
|
||||
|
||||
|
@ -2195,7 +2202,8 @@ LogicalResult PrimTupleConstructOp::verify() {
|
|||
void PrimTupleIndexOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
|
||||
MLIRContext *context) {
|
||||
patterns.add(+[](PrimTupleIndexOp op, PatternRewriter &rewriter) {
|
||||
auto tupleConstruct = op.getTup().getDefiningOp<Torch::PrimTupleConstructOp>();
|
||||
auto tupleConstruct =
|
||||
op.getTup().getDefiningOp<Torch::PrimTupleConstructOp>();
|
||||
if (!tupleConstruct)
|
||||
return failure();
|
||||
|
||||
|
@ -2245,7 +2253,8 @@ void PrimUninitializedOp::getCanonicalizationPatterns(
|
|||
void PrimTupleUnpackOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
|
||||
MLIRContext *context) {
|
||||
patterns.add(+[](PrimTupleUnpackOp op, PatternRewriter &rewriter) {
|
||||
auto tupleConstruct = op.getTup().getDefiningOp<Torch::PrimTupleConstructOp>();
|
||||
auto tupleConstruct =
|
||||
op.getTup().getDefiningOp<Torch::PrimTupleConstructOp>();
|
||||
if (!tupleConstruct)
|
||||
return failure();
|
||||
|
||||
|
@ -2400,9 +2409,7 @@ atenBinaryFloatOperatorFoldHelper(ArrayRef<Attribute> operands,
|
|||
// AtenAliasOp
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
OpFoldResult AtenAliasOp::fold(FoldAdaptor adaptor) {
|
||||
return getOperand();
|
||||
}
|
||||
OpFoldResult AtenAliasOp::fold(FoldAdaptor adaptor) { return getOperand(); }
|
||||
|
||||
//===----------------------------------------------------------------------===//
|
||||
// AtenFloordivIntOp
|
||||
|
@ -2484,10 +2491,8 @@ OpFoldResult AtenSliceTensorOp::fold(FoldAdaptor adaptor) {
|
|||
int64_t start, end, step;
|
||||
if (matchPattern(getStart(), m_TorchConstantInt(&start)) &&
|
||||
matchPattern(getEnd(), m_TorchConstantInt(&end)) &&
|
||||
matchPattern(getStep(), m_TorchConstantInt(&step))
|
||||
&& step == 1
|
||||
&& start == 0
|
||||
&& end == std::numeric_limits<int64_t>::max())
|
||||
matchPattern(getStep(), m_TorchConstantInt(&step)) && step == 1 &&
|
||||
start == 0 && end == std::numeric_limits<int64_t>::max())
|
||||
return getOperand(0);
|
||||
|
||||
auto inType = getOperand(0).getType().dyn_cast<BaseTensorType>();
|
||||
|
@ -2955,7 +2960,6 @@ LogicalResult AtenPermuteOp::verify() {
|
|||
<< " elements, the output has rank " << outRank << '.';
|
||||
}
|
||||
|
||||
|
||||
// Initialization of the reverse permutation. -1 denotes an unknown
|
||||
// permutation index.
|
||||
SmallVector<int64_t> reversePermutation(outRank, -1);
|
||||
|
|
|
@ -556,9 +556,9 @@ Type Torch::meetTensorTypes(BaseTensorType lhs, BaseTensorType rhs) {
|
|||
|
||||
// TODO: These are not DRY in that the two type predicates AnyTorchDictKeyType
|
||||
// and AnyTorchType generate the exact same code (in TorchOps.cpp.inc).
|
||||
// Unfortunately the generated implementations aren't visible/exposed ("static" linkage)
|
||||
// and the predicates themselves can't be added/used in the specification of the parameters
|
||||
// of the Torch_DictType.
|
||||
// Unfortunately the generated implementations aren't visible/exposed ("static"
|
||||
// linkage) and the predicates themselves can't be added/used in the
|
||||
// specification of the parameters of the Torch_DictType.
|
||||
static bool isAnyTorchDictKeyType(Type type) {
|
||||
return type.isa<Torch::AnyType>() || type.isa<Torch::IntType>() ||
|
||||
type.isa<Torch::BoolType>() || type.isa<Torch::FloatType>() ||
|
||||
|
|
|
@ -457,7 +457,6 @@ static LogicalResult performMatmul(PatternRewriter &rewriter, Location loc,
|
|||
return success();
|
||||
}
|
||||
|
||||
|
||||
static Value performLastReduceAndPermute(PatternRewriter &rewriter,
|
||||
Location loc, Type outType,
|
||||
Value input,
|
||||
|
@ -1269,7 +1268,8 @@ public:
|
|||
};
|
||||
} // namespace
|
||||
|
||||
// Decompose `AtenArgMaxOp` into `AtenMaxDimOp` as well as `AtenArgMinOp` into `AtenMinDimOp`
|
||||
// Decompose `AtenArgMaxOp` into `AtenMaxDimOp` as well as `AtenArgMinOp` into
|
||||
// `AtenMinDimOp`
|
||||
namespace {
|
||||
template <typename OpTy, typename DecompOpTy>
|
||||
class DecomposeAtenArgMinMaxOp : public OpRewritePattern<OpTy> {
|
||||
|
@ -1300,9 +1300,9 @@ public:
|
|||
.cast<BaseTensorType>();
|
||||
|
||||
// If the dim type is `NoneType` i.e. reduce along all the dimensions.
|
||||
// `AtenMaxDimOp` and `AtenMinDimOp` do not support dim as `NoneType` so first the input
|
||||
// tensor is flattened to 1d tensor and then the reduction happens on the
|
||||
// 0th dimension.
|
||||
// `AtenMaxDimOp` and `AtenMinDimOp` do not support dim as `NoneType` so
|
||||
// first the input tensor is flattened to 1d tensor and then the reduction
|
||||
// happens on the 0th dimension.
|
||||
if (dim.getType().isa<Torch::NoneType>()) {
|
||||
BaseTensorType flattenType =
|
||||
inputType
|
||||
|
@ -1318,8 +1318,8 @@ public:
|
|||
|
||||
Value resultArg =
|
||||
rewriter
|
||||
.create<DecompOpTy>(loc, valueTensorType, indicesTensorType,
|
||||
input, dim, keepDim)
|
||||
.create<DecompOpTy>(loc, valueTensorType, indicesTensorType, input,
|
||||
dim, keepDim)
|
||||
.getIndices();
|
||||
|
||||
rewriter.replaceOp(op, resultArg);
|
||||
|
@ -1961,8 +1961,10 @@ public:
|
|||
double alpha = 1.6732632423543772848170429916717;
|
||||
|
||||
// Create constants for λ and α
|
||||
Value scaleVal = rewriter.create<Torch::ConstantFloatOp>(loc, rewriter.getF64FloatAttr(scale));
|
||||
Value alphaVal = rewriter.create<Torch::ConstantFloatOp>(loc, rewriter.getF64FloatAttr(alpha));
|
||||
Value scaleVal = rewriter.create<Torch::ConstantFloatOp>(
|
||||
loc, rewriter.getF64FloatAttr(scale));
|
||||
Value alphaVal = rewriter.create<Torch::ConstantFloatOp>(
|
||||
loc, rewriter.getF64FloatAttr(alpha));
|
||||
|
||||
// Create zero tensor for comparison
|
||||
Value constantZero =
|
||||
|
@ -1972,17 +1974,21 @@ public:
|
|||
// Calculate positive and negative parts
|
||||
Value constantOne =
|
||||
rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1.0));
|
||||
Value positiveOutput = rewriter.create<AtenMaximumOp>(loc, resType, zeroTensor, input);
|
||||
Value positiveOutput =
|
||||
rewriter.create<AtenMaximumOp>(loc, resType, zeroTensor, input);
|
||||
Value minZeroX =
|
||||
rewriter.create<AtenMinimumOp>(loc, resType, zeroTensor, input);
|
||||
Value expInput = rewriter.create<AtenExpOp>(loc, resType, minZeroX);
|
||||
Value expInputMinusOne = rewriter.create<AtenSubScalarOp>(loc, resType, expInput, constantOne, constantOne);
|
||||
Value negativeOutput = rewriter.create<AtenMulScalarOp>(loc, resType, expInputMinusOne, alphaVal);
|
||||
Value expInputMinusOne = rewriter.create<AtenSubScalarOp>(
|
||||
loc, resType, expInput, constantOne, constantOne);
|
||||
Value negativeOutput = rewriter.create<AtenMulScalarOp>(
|
||||
loc, resType, expInputMinusOne, alphaVal);
|
||||
|
||||
// Multiply the result by λ
|
||||
Value seluOutput = rewriter.create<AtenAddTensorOp>(
|
||||
loc, resType, positiveOutput, negativeOutput, constantOne);
|
||||
seluOutput = rewriter.create<AtenMulScalarOp>(loc, resType, seluOutput, scaleVal);
|
||||
seluOutput =
|
||||
rewriter.create<AtenMulScalarOp>(loc, resType, seluOutput, scaleVal);
|
||||
|
||||
// Replace the original operation
|
||||
rewriter.replaceOp(op, seluOutput);
|
||||
|
@ -2594,7 +2600,8 @@ namespace {
|
|||
|
||||
static LogicalResult createTorchTransposeOpForConvTbc(PatternRewriter &rewriter,
|
||||
Location loc, Value input,
|
||||
int64_t dimA, int64_t dimB,
|
||||
int64_t dimA,
|
||||
int64_t dimB,
|
||||
Value &transposed) {
|
||||
Type transposedType;
|
||||
if (failed(getTransposedType(input.getType().cast<Torch::BaseTensorType>(),
|
||||
|
@ -2615,17 +2622,18 @@ namespace {
|
|||
LogicalResult matchAndRewrite(AtenConvTbcOp op,
|
||||
PatternRewriter &rewriter) const override {
|
||||
Value emptyList = rewriter.create<PrimListConstructOp>(
|
||||
op.getLoc(),
|
||||
Torch::ListType::get(Torch::IntType::get(op.getContext())),
|
||||
op.getLoc(), Torch::ListType::get(Torch::IntType::get(op.getContext())),
|
||||
SmallVector<Value>());
|
||||
Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(op.getLoc(), false);
|
||||
Value oneList = rewriter.create<PrimListConstructOp>(
|
||||
op.getLoc(), Torch::ListType::get(Torch::IntType::get(op.getContext())),
|
||||
SmallVector<Value>{rewriter.create<Torch::ConstantIntOp>(op.getLoc(), rewriter.getI64IntegerAttr(1))});
|
||||
SmallVector<Value>{rewriter.create<Torch::ConstantIntOp>(
|
||||
op.getLoc(), rewriter.getI64IntegerAttr(1))});
|
||||
Value padding = rewriter.create<PrimListConstructOp>(
|
||||
op.getLoc(), Torch::ListType::get(Torch::IntType::get(op.getContext())),
|
||||
SmallVector<Value>{op.getPad()});
|
||||
Value groups = rewriter.create<Torch::ConstantIntOp>(op.getLoc(), rewriter.getI64IntegerAttr(1));
|
||||
Value groups = rewriter.create<Torch::ConstantIntOp>(
|
||||
op.getLoc(), rewriter.getI64IntegerAttr(1));
|
||||
|
||||
// convtbc has WNC layout for input and output
|
||||
// and WCF layout for weight
|
||||
|
@ -2634,37 +2642,45 @@ namespace {
|
|||
Value selfWnc = op.getSelf();
|
||||
Value selfNwc;
|
||||
Value selfNcw;
|
||||
if(failed(createTorchTransposeOpForConvTbc(rewriter, op.getLoc(), selfWnc, 0, 1, selfNwc)))
|
||||
return rewriter.notifyMatchFailure(op, "failed to transpose input to Nwc");
|
||||
if(failed(createTorchTransposeOpForConvTbc(rewriter, op.getLoc(), selfNwc, 1, 2, selfNcw)))
|
||||
return rewriter.notifyMatchFailure(op, "failed to transpose input to Ncw");
|
||||
if (failed(createTorchTransposeOpForConvTbc(rewriter, op.getLoc(), selfWnc,
|
||||
0, 1, selfNwc)))
|
||||
return rewriter.notifyMatchFailure(op,
|
||||
"failed to transpose input to Nwc");
|
||||
if (failed(createTorchTransposeOpForConvTbc(rewriter, op.getLoc(), selfNwc,
|
||||
1, 2, selfNcw)))
|
||||
return rewriter.notifyMatchFailure(op,
|
||||
"failed to transpose input to Ncw");
|
||||
|
||||
Value weightWcf = op.getWeight();
|
||||
Value weightFcw;
|
||||
if(failed(createTorchTransposeOpForConvTbc(rewriter, op.getLoc(), weightWcf, 0, 2, weightFcw)))
|
||||
return rewriter.notifyMatchFailure(op, "failed to transpose weight to Fcw");
|
||||
|
||||
if (failed(createTorchTransposeOpForConvTbc(rewriter, op.getLoc(),
|
||||
weightWcf, 0, 2, weightFcw)))
|
||||
return rewriter.notifyMatchFailure(op,
|
||||
"failed to transpose weight to Fcw");
|
||||
|
||||
Value outputNcw = rewriter.create<AtenConvolutionOp>(
|
||||
op.getLoc(), op->getResultTypes(), selfNcw, weightFcw, op.getBias(), /*stride*/oneList,
|
||||
op.getLoc(), op->getResultTypes(), selfNcw, weightFcw, op.getBias(),
|
||||
/*stride*/ oneList,
|
||||
/*padding*/ padding, /*dilation*/ oneList,
|
||||
/*transpose*/ cstFalse, /*output_padding*/ emptyList,
|
||||
groups);
|
||||
/*transpose*/ cstFalse, /*output_padding*/ emptyList, groups);
|
||||
|
||||
// convert output from Ncw to Wnc
|
||||
Value outputNwc;
|
||||
Value outputWnc;
|
||||
if(failed(createTorchTransposeOpForConvTbc(rewriter, op.getLoc(), outputNcw, 1, 2, outputNwc)))
|
||||
return rewriter.notifyMatchFailure(op, "failed to transpose output to Nwc");
|
||||
if(failed(createTorchTransposeOpForConvTbc(rewriter, op.getLoc(), outputNwc, 0, 1, outputWnc)))
|
||||
return rewriter.notifyMatchFailure(op, "failed to transpose output to Wnc");
|
||||
if (failed(createTorchTransposeOpForConvTbc(rewriter, op.getLoc(),
|
||||
outputNcw, 1, 2, outputNwc)))
|
||||
return rewriter.notifyMatchFailure(op,
|
||||
"failed to transpose output to Nwc");
|
||||
if (failed(createTorchTransposeOpForConvTbc(rewriter, op.getLoc(),
|
||||
outputNwc, 0, 1, outputWnc)))
|
||||
return rewriter.notifyMatchFailure(op,
|
||||
"failed to transpose output to Wnc");
|
||||
rewriter.replaceOp(op, outputWnc);
|
||||
|
||||
return success();
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
} // namespace
|
||||
|
||||
// Decompose aten.conv1d to aten.convolution
|
||||
namespace {
|
||||
|
@ -3815,8 +3831,8 @@ public:
|
|||
/*device=*/none, /*pin_memory=*/none, /*memory_format=*/none);
|
||||
Value stdRandN =
|
||||
rewriter.create<AtenMulScalarOp>(loc, resultType, randN, std);
|
||||
rewriter.replaceOpWithNewOp<AtenAddScalarOp>(op, resultType, stdRandN,
|
||||
mean, /*alpha=*/one);
|
||||
rewriter.replaceOpWithNewOp<AtenAddScalarOp>(op, resultType, stdRandN, mean,
|
||||
/*alpha=*/one);
|
||||
return success();
|
||||
}
|
||||
};
|
||||
|
@ -6654,8 +6670,10 @@ public:
|
|||
addPatternIfTargetOpIsIllegal<DecomposeAtenConvTranspose2dOp>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenArangeOp>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenArangeStartOp>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenArgMinMaxOp<AtenArgmaxOp, AtenMaxDimOp>>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenArgMinMaxOp<AtenArgminOp, AtenMinDimOp>>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<
|
||||
DecomposeAtenArgMinMaxOp<AtenArgmaxOp, AtenMaxDimOp>>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<
|
||||
DecomposeAtenArgMinMaxOp<AtenArgminOp, AtenMinDimOp>>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenSquareOp>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenVarOp>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenStdOp>(patterns);
|
||||
|
@ -6768,8 +6786,6 @@ public:
|
|||
addPatternIfTargetOpIsIllegal<DecomposeAtenConv2dOp>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenConv3dOp>(patterns);
|
||||
|
||||
|
||||
|
||||
GreedyRewriteConfig config;
|
||||
config.useTopDownTraversal = true;
|
||||
config.maxIterations = GreedyRewriteConfig::kNoLimit;
|
||||
|
|
|
@ -170,8 +170,8 @@ private:
|
|||
auto attr = std::get<1>(t);
|
||||
nameStack.push_back(attr.getName().str());
|
||||
if (attr.getType().isa<NnModuleType>()) {
|
||||
if (failed(
|
||||
recursivelyTraverse(slot.getValue().getDefiningOp<NnModuleOp>())))
|
||||
if (failed(recursivelyTraverse(
|
||||
slot.getValue().getDefiningOp<NnModuleOp>())))
|
||||
return failure();
|
||||
} else if (usedSlots.find(slot) != usedSlots.end()) {
|
||||
// Only create the GlobalSlotOp if the slot is used at all.
|
||||
|
@ -190,8 +190,8 @@ private:
|
|||
}
|
||||
for (auto method : classType.getOps<MethodOp>()) {
|
||||
nameStack.push_back(method.getName().str());
|
||||
funcLinkageInfo[{nnModule,
|
||||
symbolTable.lookup<func::FuncOp>(method.getFunction())}] =
|
||||
funcLinkageInfo[{
|
||||
nnModule, symbolTable.lookup<func::FuncOp>(method.getFunction())}] =
|
||||
LinkageInfo{llvm::join(nameStack, "."), method.getIsPrivate()};
|
||||
nameStack.pop_back();
|
||||
}
|
||||
|
@ -501,21 +501,24 @@ static LogicalResult rewriteMonomorphizedFuncClone(
|
|||
|
||||
SmallVector<Operation *> toErase;
|
||||
auto handlePrimSetAttr = [&](PrimSetAttrOp op) {
|
||||
auto instance = mapping.lookup(op.getReceiver()).getDefiningOp<NnModuleOp>();
|
||||
auto instance =
|
||||
mapping.lookup(op.getReceiver()).getDefiningOp<NnModuleOp>();
|
||||
SlotOp affectedSlot;
|
||||
for (auto slot : instance.getOps<SlotOp>()) {
|
||||
if (slot.getName() == op.getName())
|
||||
affectedSlot = slot;
|
||||
}
|
||||
OpBuilder(op).create<GlobalSlotSetOp>(
|
||||
op.getLoc(), objectGraphInfo.getGlobalSlotFor(affectedSlot).getSymName(),
|
||||
op.getLoc(),
|
||||
objectGraphInfo.getGlobalSlotFor(affectedSlot).getSymName(),
|
||||
op.getValue());
|
||||
toErase.push_back(op);
|
||||
return WalkResult::advance();
|
||||
};
|
||||
auto handlePrimGetAttr = [&](PrimGetAttrOp op) {
|
||||
if (!op.getType().isa<NnModuleType>()) {
|
||||
auto instance = mapping.lookup(op.getReceiver()).getDefiningOp<NnModuleOp>();
|
||||
auto instance =
|
||||
mapping.lookup(op.getReceiver()).getDefiningOp<NnModuleOp>();
|
||||
SlotOp affectedSlot;
|
||||
for (auto slot : instance.getOps<SlotOp>()) {
|
||||
if (slot.getName() == op.getName())
|
||||
|
|
|
@ -163,7 +163,8 @@ LogicalResult InlineGlobalSlotsAnalysis::initialize(Operation *top) {
|
|||
}
|
||||
if (auto globalSlotSet = dyn_cast<Torch::GlobalSlotSetOp>(op)) {
|
||||
auto *state = getOrCreate<InlineGlobalSlotsAnalysisState>(
|
||||
getProgramPoint<FlatSymbolRefProgramPoint>(globalSlotSet.getSlotAttr()));
|
||||
getProgramPoint<FlatSymbolRefProgramPoint>(
|
||||
globalSlotSet.getSlotAttr()));
|
||||
propagateIfChanged(state, state->setSafe(false));
|
||||
}
|
||||
// Save the InitializeGlobalSlotsOp for later referencee
|
||||
|
@ -211,8 +212,8 @@ LogicalResult InlineGlobalSlotsAnalysis::visit(ProgramPoint point) {
|
|||
auto it =
|
||||
llvm::find(initializeGlobalSlotsOp.getSlotSymNames(),
|
||||
static_cast<Attribute>(flatSymbolRefPoint->getValue()));
|
||||
Value value = initializeGlobalSlotsOp->getOperand(
|
||||
std::distance(initializeGlobalSlotsOp.getSlotSymNames().begin(), it));
|
||||
Value value = initializeGlobalSlotsOp->getOperand(std::distance(
|
||||
initializeGlobalSlotsOp.getSlotSymNames().begin(), it));
|
||||
auto *flatSymbolRefState =
|
||||
getOrCreateFor<InlineGlobalSlotsAnalysisState>(value,
|
||||
flatSymbolRefPoint);
|
||||
|
@ -331,7 +332,8 @@ class InlineGlobalSlotsPass
|
|||
|
||||
DenseSet</*FlatSymbolRefAttr*/ Attribute> safeToInline;
|
||||
for (int i = 0, e = initialize->getNumOperands(); i != e; i++) {
|
||||
auto slotSymName = initialize.getSlotSymNames()[i].cast<FlatSymbolRefAttr>();
|
||||
auto slotSymName =
|
||||
initialize.getSlotSymNames()[i].cast<FlatSymbolRefAttr>();
|
||||
Value operand = initialize.getOperand(i);
|
||||
auto symbolRefPoint = solver.getProgramPoint<FlatSymbolRefProgramPoint>(
|
||||
initialize.getSlotSymNames()[i].cast<FlatSymbolRefAttr>());
|
||||
|
@ -405,7 +407,8 @@ class InlineGlobalSlotsPass
|
|||
SmallVector<Attribute> newSlotSymNames;
|
||||
SmallVector<Value> newInitialValues;
|
||||
for (int i = 0, e = initialize.getNumOperands(); i != e; i++) {
|
||||
auto slotSymName = initialize.getSlotSymNames()[i].cast<FlatSymbolRefAttr>();
|
||||
auto slotSymName =
|
||||
initialize.getSlotSymNames()[i].cast<FlatSymbolRefAttr>();
|
||||
if (!safeToInline.count(slotSymName)) {
|
||||
newSlotSymNames.push_back(slotSymName);
|
||||
newInitialValues.push_back(initialize.getOperand(i));
|
||||
|
|
|
@ -202,15 +202,16 @@ static bool satisfiesBackendContract(ModuleOp module,
|
|||
// Check for unimplemented operators first to give more direct diagnostics.
|
||||
walkResult0 = module.walk([&](Torch::OperatorOp op) {
|
||||
if (llvm::all_of(op.getResults(), [&op](auto res) {
|
||||
return succeeded(
|
||||
checkType(op.getOperation(), res.getType(), /*actuallyEmitDiagnostics=*/false));
|
||||
return succeeded(checkType(op.getOperation(), res.getType(),
|
||||
/*actuallyEmitDiagnostics=*/false));
|
||||
})) {
|
||||
return WalkResult::advance();
|
||||
}
|
||||
|
||||
if (actuallyEmitDiagnostics) {
|
||||
op->emitError("unsupported by backend contract: Unimplemented operator '"
|
||||
+ op.getName() + "'");
|
||||
op->emitError(
|
||||
"unsupported by backend contract: Unimplemented operator '" +
|
||||
op.getName() + "'");
|
||||
}
|
||||
return WalkResult::interrupt();
|
||||
});
|
||||
|
@ -309,12 +310,14 @@ public:
|
|||
<< " iterations of the simplification pipeline\n";
|
||||
});
|
||||
}
|
||||
|
||||
private:
|
||||
llvm::StringSet<> backendLegalOpsSet;
|
||||
};
|
||||
|
||||
class VerifyBackendContractNoDecompositionsPass
|
||||
: public VerifyBackendContractNoDecompositionsBase<VerifyBackendContractNoDecompositionsPass> {
|
||||
: public VerifyBackendContractNoDecompositionsBase<
|
||||
VerifyBackendContractNoDecompositionsPass> {
|
||||
public:
|
||||
VerifyBackendContractNoDecompositionsPass() = default;
|
||||
|
||||
|
|
|
@ -158,9 +158,11 @@ void Torch::importLibraryFunctions(ModuleOp module, ModuleOp library,
|
|||
}
|
||||
}
|
||||
|
||||
FailureOr<Value> Torch::adjustFunctionArg(
|
||||
OpBuilder &b, Location loc, Value operand, Type desiredType,
|
||||
function_ref<Value(OpBuilder &, Location, Value, Type)> baseTransformation) {
|
||||
FailureOr<Value>
|
||||
Torch::adjustFunctionArg(OpBuilder &b, Location loc, Value operand,
|
||||
Type desiredType,
|
||||
function_ref<Value(OpBuilder &, Location, Value, Type)>
|
||||
baseTransformation) {
|
||||
operand = baseTransformation(b, loc, operand, desiredType);
|
||||
|
||||
// No need for adjustment if they already match.
|
||||
|
|
|
@ -90,7 +90,8 @@ public:
|
|||
PatternRewriter &rewriter) const override {
|
||||
SmallVector<std::optional<int64_t>> ranks;
|
||||
SmallVector<int64_t> dtypes;
|
||||
if (!matchPattern(op.getRanks(), m_TorchListOfOptionalConstantInts(ranks))) {
|
||||
if (!matchPattern(op.getRanks(),
|
||||
m_TorchListOfOptionalConstantInts(ranks))) {
|
||||
return rewriter.notifyMatchFailure(
|
||||
op, "Expected `ranks` to be a list of optional constant ints");
|
||||
}
|
||||
|
|
|
@ -54,13 +54,13 @@ void TorchConversionDialect::initialize() {
|
|||
addInterfaces<TorchConversionInlinerInterface>();
|
||||
}
|
||||
|
||||
|
||||
//===----------------------------------------------------------------------===//
|
||||
// Constant materializer.
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
Operation *TorchConversionDialect::materializeConstant(OpBuilder &builder,
|
||||
Attribute value, Type type,
|
||||
Attribute value,
|
||||
Type type,
|
||||
Location loc) {
|
||||
if (auto integerType = type.dyn_cast<Torch::IntType>())
|
||||
return builder.create<Torch::ConstantIntOp>(loc, value.cast<IntegerAttr>());
|
||||
|
|
|
@ -7,8 +7,8 @@
|
|||
//
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
|
||||
#include "torch-mlir/Dialect/TorchConversion/Transforms/BackendTypeConversion.h"
|
||||
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
|
||||
|
||||
using namespace mlir;
|
||||
using namespace mlir::torch;
|
||||
|
@ -57,8 +57,8 @@ static void setupTorchBoolToI1Conversion(ConversionTarget &target,
|
|||
typeConverter.addConversion([](Torch::BoolType type) -> std::optional<Type> {
|
||||
return IntegerType::get(type.getContext(), 1);
|
||||
});
|
||||
typeConverter.addTargetMaterialization([](OpBuilder &builder,
|
||||
IntegerType type, ValueRange inputs,
|
||||
typeConverter.addTargetMaterialization(
|
||||
[](OpBuilder &builder, IntegerType type, ValueRange inputs,
|
||||
Location loc) -> std::optional<Value> {
|
||||
// Other builtin integer types could be handled by other materializers.
|
||||
if (!(type.getWidth() == 1 && type.isSignless()))
|
||||
|
@ -83,8 +83,8 @@ static void setupTorchIntToI64Conversion(ConversionTarget &target,
|
|||
typeConverter.addConversion([](Torch::IntType type) -> std::optional<Type> {
|
||||
return IntegerType::get(type.getContext(), 64);
|
||||
});
|
||||
typeConverter.addTargetMaterialization([](OpBuilder &builder,
|
||||
IntegerType type, ValueRange inputs,
|
||||
typeConverter.addTargetMaterialization(
|
||||
[](OpBuilder &builder, IntegerType type, ValueRange inputs,
|
||||
Location loc) -> std::optional<Value> {
|
||||
// Other builtin integer types could be handled by other materializers.
|
||||
if (!(type.getWidth() == 64 && type.isSignless()))
|
||||
|
@ -112,8 +112,8 @@ static void setupTorchFloatToF64Conversion(ConversionTarget &target,
|
|||
typeConverter.addConversion([](Torch::FloatType type) -> std::optional<Type> {
|
||||
return Float64Type::get(type.getContext());
|
||||
});
|
||||
typeConverter.addTargetMaterialization([](OpBuilder &builder,
|
||||
Float64Type type, ValueRange inputs,
|
||||
typeConverter.addTargetMaterialization(
|
||||
[](OpBuilder &builder, Float64Type type, ValueRange inputs,
|
||||
Location loc) -> std::optional<Value> {
|
||||
assert(inputs.size() == 1);
|
||||
assert(inputs[0].getType().isa<Torch::FloatType>());
|
||||
|
@ -133,11 +133,12 @@ static void setupTorchGeneratorToI64Conversion(ConversionTarget &target,
|
|||
TypeConverter &typeConverter) {
|
||||
target.addLegalOp<TorchConversion::GeneratorToI64Op,
|
||||
TorchConversion::I64ToGeneratorOp>();
|
||||
typeConverter.addConversion([](Torch::GeneratorType type) -> std::optional<Type> {
|
||||
typeConverter.addConversion(
|
||||
[](Torch::GeneratorType type) -> std::optional<Type> {
|
||||
return IntegerType::get(type.getContext(), 64);
|
||||
});
|
||||
typeConverter.addTargetMaterialization([](OpBuilder &builder,
|
||||
IntegerType type, ValueRange inputs,
|
||||
typeConverter.addTargetMaterialization(
|
||||
[](OpBuilder &builder, IntegerType type, ValueRange inputs,
|
||||
Location loc) -> std::optional<Value> {
|
||||
// Other builtin integer types could be handled by other materializers.
|
||||
if (!(type.getWidth() == 64 && type.isSignless()))
|
||||
|
|
|
@ -18,8 +18,8 @@
|
|||
#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
|
||||
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
|
||||
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
|
||||
#include "torch-mlir/Dialect/TorchConversion/Transforms/Passes.h"
|
||||
#include "torch-mlir/Dialect/TorchConversion/Transforms/BackendTypeConversion.h"
|
||||
#include "torch-mlir/Dialect/TorchConversion/Transforms/Passes.h"
|
||||
|
||||
using namespace mlir;
|
||||
using namespace mlir::torch;
|
||||
|
@ -65,7 +65,8 @@ public:
|
|||
|
||||
auto getConstantIntegerFromDefiningOp = [](Value operand,
|
||||
int &extractedInt) {
|
||||
auto castOp = dyn_cast<mlir::UnrealizedConversionCastOp>(operand.getDefiningOp());
|
||||
auto castOp =
|
||||
dyn_cast<mlir::UnrealizedConversionCastOp>(operand.getDefiningOp());
|
||||
if (!castOp) {
|
||||
return failure();
|
||||
}
|
||||
|
@ -83,7 +84,8 @@ public:
|
|||
return failure();
|
||||
}
|
||||
int unpackedBitWidth;
|
||||
if (failed(getConstantIntegerFromDefiningOp(unpackedTypeWidth, unpackedBitWidth))) {
|
||||
if (failed(getConstantIntegerFromDefiningOp(unpackedTypeWidth,
|
||||
unpackedBitWidth))) {
|
||||
return failure();
|
||||
}
|
||||
if (unpackedBitWidth !=
|
||||
|
@ -103,32 +105,35 @@ public:
|
|||
// expand lhs
|
||||
std::vector<int64_t> lhsExpandedShape = {lhsShape[0], lhsShape[1],
|
||||
lhsReductDimSize / gs, gs};
|
||||
RankedTensorType lhsExpandedType = RankedTensorType::get(lhsExpandedShape, elementType);
|
||||
RankedTensorType lhsExpandedType =
|
||||
RankedTensorType::get(lhsExpandedShape, elementType);
|
||||
SmallVector<ReassociationIndices, 4> lhsReassociation = {{0}, {1}, {2, 3}};
|
||||
Value lhsExpanded = rewriter.create<tensor::ExpandShapeOp>(
|
||||
loc, lhsExpandedType, lhs, lhsReassociation);
|
||||
|
||||
// expand rhs
|
||||
std::vector<int64_t> rhsExpandedShape = {rhsShape[0], rhsReductDimSize/gs, gs};
|
||||
RankedTensorType rhsExpandedType = RankedTensorType::get(rhsExpandedShape, rhsElementType);
|
||||
std::vector<int64_t> rhsExpandedShape = {rhsShape[0], rhsReductDimSize / gs,
|
||||
gs};
|
||||
RankedTensorType rhsExpandedType =
|
||||
RankedTensorType::get(rhsExpandedShape, rhsElementType);
|
||||
SmallVector<ReassociationIndices, 4> rhsReassociation = {{0}, {1, 2}};
|
||||
Value rhsExpanded = rewriter.create<tensor::ExpandShapeOp>(
|
||||
loc, rhsExpandedType, rhsQuant, rhsReassociation);
|
||||
Value cst0 = rewriter.create<arith::ConstantOp>(
|
||||
loc, FloatAttr::get(elementType, 0.0));
|
||||
|
||||
Value emptyDequant = rewriter.create<tensor::EmptyOp>(
|
||||
loc, rhsExpandedShape, elementType);
|
||||
Value emptyDequant =
|
||||
rewriter.create<tensor::EmptyOp>(loc, rhsExpandedShape, elementType);
|
||||
SmallVector<Value> dynDims;
|
||||
for (int i = 0; i < lhsType.getRank(); i++) {
|
||||
if (lhsType.isDynamicDim(i)) {
|
||||
dynDims.push_back(rewriter.create<tensor::DimOp>(loc, lhs, i));
|
||||
}
|
||||
}
|
||||
Value empty = rewriter.create<tensor::EmptyOp>(
|
||||
loc, resultShape, elementType, dynDims);
|
||||
Value output = rewriter.create<linalg::FillOp>(
|
||||
loc, cst0, empty).getResult(0);
|
||||
Value empty = rewriter.create<tensor::EmptyOp>(loc, resultShape,
|
||||
elementType, dynDims);
|
||||
Value output =
|
||||
rewriter.create<linalg::FillOp>(loc, cst0, empty).getResult(0);
|
||||
|
||||
AffineExpr d0, d1, d2, d3, d4;
|
||||
bindDims(getContext(), d0, d1, d2, d3, d4);
|
||||
|
@ -141,12 +146,12 @@ public:
|
|||
SmallVector<AffineMap, 4> dqIndexingMaps = {map, map1, map1, map};
|
||||
SmallVector<AffineMap, 4> matIndexingMaps = {map2, map3, map4};
|
||||
|
||||
SmallVector<utils::IteratorType> dequantIteratorTypes(3, utils::IteratorType::parallel);
|
||||
SmallVector<utils::IteratorType> dequantIteratorTypes(
|
||||
3, utils::IteratorType::parallel);
|
||||
SmallVector<utils::IteratorType> matmulIteratorTypes = {
|
||||
utils::IteratorType::parallel, utils::IteratorType::parallel,
|
||||
utils::IteratorType::parallel, utils::IteratorType::reduction,
|
||||
utils::IteratorType::reduction
|
||||
};
|
||||
utils::IteratorType::reduction};
|
||||
|
||||
Value rhsDequant =
|
||||
rewriter
|
||||
|
@ -157,9 +162,12 @@ public:
|
|||
/*iteratorTypes=*/dequantIteratorTypes,
|
||||
[&](OpBuilder &b, Location loc, ValueRange args) {
|
||||
Value w = args[0], scale = args[1], zeroPoint = args[2];
|
||||
Value extw = b.create<arith::ExtUIOp>(loc, rewriter.getI32Type(), w);
|
||||
Value fp_extw = b.create<arith::UIToFPOp>(loc, rewriter.getF16Type(), extw);
|
||||
Value shifted = b.create<arith::SubFOp>(loc, fp_extw, zeroPoint);
|
||||
Value extw =
|
||||
b.create<arith::ExtUIOp>(loc, rewriter.getI32Type(), w);
|
||||
Value fp_extw = b.create<arith::UIToFPOp>(
|
||||
loc, rewriter.getF16Type(), extw);
|
||||
Value shifted =
|
||||
b.create<arith::SubFOp>(loc, fp_extw, zeroPoint);
|
||||
Value dqw = b.create<arith::MulFOp>(loc, shifted, scale);
|
||||
b.create<linalg::YieldOp>(loc, dqw);
|
||||
})
|
||||
|
@ -168,8 +176,8 @@ public:
|
|||
Value matmulDequant =
|
||||
rewriter
|
||||
.create<linalg::GenericOp>(
|
||||
loc, output.getType(),
|
||||
ValueRange{lhsExpanded, rhsDequant}, output,
|
||||
loc, output.getType(), ValueRange{lhsExpanded, rhsDequant},
|
||||
output,
|
||||
/*indexingMaps=*/matIndexingMaps,
|
||||
/*iteratorTypes=*/matmulIteratorTypes,
|
||||
[&](OpBuilder &b, Location loc, ValueRange args) {
|
||||
|
@ -188,7 +196,8 @@ public:
|
|||
|
||||
namespace {
|
||||
class ConvertCustomQuantOpPass
|
||||
: public TorchConversion::ConvertCustomQuantOpBase<ConvertCustomQuantOpPass> {
|
||||
: public TorchConversion::ConvertCustomQuantOpBase<
|
||||
ConvertCustomQuantOpPass> {
|
||||
void getDependentDialects(DialectRegistry ®istry) const override {
|
||||
registry.insert<arith::ArithDialect>();
|
||||
registry.insert<func::FuncDialect>();
|
||||
|
@ -213,8 +222,8 @@ class ConvertCustomQuantOpPass
|
|||
target.addIllegalOp<OperatorOp>();
|
||||
patterns.add<ConvertCustomQuantizedMatmulOp>(typeConverter, context);
|
||||
|
||||
if (failed(
|
||||
applyPartialConversion(getOperation(), target, std::move(patterns))))
|
||||
if (failed(applyPartialConversion(getOperation(), target,
|
||||
std::move(patterns))))
|
||||
signalPassFailure();
|
||||
}
|
||||
};
|
||||
|
|
|
@ -33,7 +33,6 @@ using namespace mlir::torch;
|
|||
using namespace mlir::torch::TorchConversion;
|
||||
using namespace TMTensor;
|
||||
|
||||
|
||||
namespace {
|
||||
class VerifyLinalgOnTensorsBackendContractPass
|
||||
: public VerifyLinalgOnTensorsBackendContractBase<
|
||||
|
@ -96,7 +95,8 @@ class VerifyLinalgOnTensorsBackendContractPass
|
|||
// We avoid `module.emitError()` so that mlir-print-op-on-diagnostics
|
||||
// doesn't unnecessarily spew out the entire module.
|
||||
emitError(module.getLoc())
|
||||
<< "Module does not conform to the linalg-on-tensors backend contract. "
|
||||
<< "Module does not conform to the linalg-on-tensors backend "
|
||||
"contract. "
|
||||
"See dialect conversion legality information above.";
|
||||
return signalPassFailure();
|
||||
}
|
||||
|
|
|
@ -45,7 +45,8 @@ class VerifyStablehloBackendContractPass
|
|||
ConversionTarget target(*context);
|
||||
|
||||
// Structural operations.
|
||||
target.addDynamicallyLegalOp<ModuleOp, func::FuncOp, func::ReturnOp>(opHasLegalTypes);
|
||||
target.addDynamicallyLegalOp<ModuleOp, func::FuncOp, func::ReturnOp>(
|
||||
opHasLegalTypes);
|
||||
// Shape operations.
|
||||
target.addDynamicallyLegalOp<shape::ShapeOfOp>(opHasLegalTypes);
|
||||
|
||||
|
|
|
@ -35,14 +35,14 @@ TorchMlirBackendData::TorchMlirBackendData(
|
|||
: BackendData(device, shape), info_(info) {
|
||||
PRINT_FUNCTION();
|
||||
}
|
||||
TorchMlirBackendData::TorchMlirBackendData(
|
||||
const at::Scalar& scalar, BackendDevice device)
|
||||
TorchMlirBackendData::TorchMlirBackendData(const at::Scalar &scalar,
|
||||
BackendDevice device)
|
||||
: BackendData(device, Shape(scalar.type(), {})),
|
||||
info_(std::make_shared<TorchMlirBackendData::Info>(scalar)) {
|
||||
PRINT_FUNCTION();
|
||||
}
|
||||
TorchMlirBackendData::TorchMlirBackendData(
|
||||
const at::Tensor& tensor, BackendDevice device, Shape shape)
|
||||
TorchMlirBackendData::TorchMlirBackendData(const at::Tensor &tensor,
|
||||
BackendDevice device, Shape shape)
|
||||
: BackendData(device, shape),
|
||||
info_(std::make_shared<TorchMlirBackendData::Info>(tensor)) {
|
||||
PRINT_FUNCTION();
|
||||
|
@ -55,8 +55,7 @@ BackendData::Handle TorchMlirBackendData::GetHandle() {
|
|||
void TorchMlirBackendData::Assign(const BackendData &data) {
|
||||
const TorchMlirBackendData *torch_mlir_data =
|
||||
dynamic_cast<const TorchMlirBackendData *>(&data);
|
||||
TORCH_CHECK(
|
||||
torch_mlir_data,
|
||||
TORCH_CHECK(torch_mlir_data,
|
||||
"Invalid Backend Data Pointer. Expected TorchMlirBackendData.");
|
||||
|
||||
info_ = torch_mlir_data->info_;
|
||||
|
@ -99,8 +98,9 @@ BackendDataPtr TorchMlirBackendImpl::MakeComputationDataFromScalar(
|
|||
return std::make_shared<TorchMlirBackendData>(scalar, device);
|
||||
}
|
||||
|
||||
BackendDataPtr TorchMlirBackendImpl::CreateDataPlaceholder(
|
||||
const BackendDevice& device, const Shape& shape) const {
|
||||
BackendDataPtr
|
||||
TorchMlirBackendImpl::CreateDataPlaceholder(const BackendDevice &device,
|
||||
const Shape &shape) const {
|
||||
PRINT_FUNCTION();
|
||||
return std::make_shared<TorchMlirBackendData>(device, shape);
|
||||
}
|
||||
|
@ -122,8 +122,7 @@ at::Tensor TorchMlirBackendImpl::MakeTensorFromComputationData(
|
|||
|
||||
TorchMlirBackendData *torch_mlir_data =
|
||||
dynamic_cast<TorchMlirBackendData *>(data.get());
|
||||
TORCH_CHECK(
|
||||
torch_mlir_data,
|
||||
TORCH_CHECK(torch_mlir_data,
|
||||
"Invalid Backend Data Pointer. Expected TorchMlirBackendData.");
|
||||
|
||||
TorchMlirBackendData::Info *info =
|
||||
|
@ -141,7 +140,8 @@ at::Tensor TorchMlirBackendImpl::MakeTensorFromComputationData(
|
|||
|
||||
std::unique_ptr<LoweringContext> TorchMlirBackendImpl::CreateLoweringContext(
|
||||
const std::string &name, BackendDevice device,
|
||||
c10::ArrayRef<const Node*> post_order, Util::EmissionMap emit_status) const {
|
||||
c10::ArrayRef<const Node *> post_order,
|
||||
Util::EmissionMap emit_status) const {
|
||||
PRINT_FUNCTION();
|
||||
return std::make_unique<TorchMlirLoweringContext>(
|
||||
name, std::forward<BackendDevice>(device),
|
||||
|
@ -149,8 +149,9 @@ std::unique_ptr<LoweringContext> TorchMlirBackendImpl::CreateLoweringContext(
|
|||
std::forward<Util::EmissionMap>(emit_status));
|
||||
}
|
||||
|
||||
std::unique_ptr<LoweringContext> TorchMlirBackendImpl::CreateLoweringContext(
|
||||
const std::string& name, BackendDevice device) const {
|
||||
std::unique_ptr<LoweringContext>
|
||||
TorchMlirBackendImpl::CreateLoweringContext(const std::string &name,
|
||||
BackendDevice device) const {
|
||||
PRINT_FUNCTION();
|
||||
return std::make_unique<TorchMlirLoweringContext>(
|
||||
name, std::forward<BackendDevice>(device));
|
||||
|
@ -175,8 +176,7 @@ at::DeviceType TorchMlirBackendImpl::EagerFallbackDeviceType() const {
|
|||
// Query all available backend devices
|
||||
std::vector<BackendDevice> TorchMlirBackendImpl::GetBackendDevices() const {
|
||||
PRINT_FUNCTION();
|
||||
return {
|
||||
GetBackendDevice(c10::Device(c10::kLazy, 0)),
|
||||
return {GetBackendDevice(c10::Device(c10::kLazy, 0)),
|
||||
GetBackendDevice(c10::Device(c10::kCPU, 0))};
|
||||
}
|
||||
|
||||
|
|
|
@ -50,10 +50,11 @@ public:
|
|||
};
|
||||
|
||||
TorchMlirBackendData(BackendDevice device, Shape shape);
|
||||
TorchMlirBackendData(BackendDevice device, Shape shape, std::shared_ptr<BackendData::Info> info);
|
||||
TorchMlirBackendData(BackendDevice device, Shape shape,
|
||||
std::shared_ptr<BackendData::Info> info);
|
||||
TorchMlirBackendData(const at::Scalar &scalar, BackendDevice device);
|
||||
TorchMlirBackendData(
|
||||
const at::Tensor& tensor, BackendDevice device, Shape shape);
|
||||
TorchMlirBackendData(const at::Tensor &tensor, BackendDevice device,
|
||||
Shape shape);
|
||||
|
||||
virtual BackendData::Handle GetHandle() override;
|
||||
|
||||
|
@ -91,19 +92,22 @@ public:
|
|||
* Data Transfer
|
||||
* */
|
||||
|
||||
virtual BackendDataPtr MakeComputationDataFromTensor(
|
||||
const at::Tensor& tensor, const Shape& shape,
|
||||
virtual BackendDataPtr
|
||||
MakeComputationDataFromTensor(const at::Tensor &tensor, const Shape &shape,
|
||||
const BackendDevice &device) const override;
|
||||
|
||||
virtual BackendDataPtr MakeComputationDataFromScalar(
|
||||
const at::Scalar& scalar, const BackendDevice& device) const override;
|
||||
virtual BackendDataPtr
|
||||
MakeComputationDataFromScalar(const at::Scalar &scalar,
|
||||
const BackendDevice &device) const override;
|
||||
|
||||
virtual BackendDataPtr CreateDataPlaceholder(
|
||||
const BackendDevice& device, const Shape& shape) const override;
|
||||
virtual BackendDataPtr
|
||||
CreateDataPlaceholder(const BackendDevice &device,
|
||||
const Shape &shape) const override;
|
||||
|
||||
// Gets backend data if the node is a device data node. Otherwise returns
|
||||
// nullptr.
|
||||
virtual BackendDataPtr GetComputationDataFromNode(const Node*) const override;
|
||||
virtual BackendDataPtr
|
||||
GetComputationDataFromNode(const Node *) const override;
|
||||
|
||||
virtual at::Tensor MakeTensorFromComputationData(
|
||||
const BackendDataPtr data,
|
||||
|
@ -113,13 +117,14 @@ public:
|
|||
* Lowering, Compilation, Execution
|
||||
* */
|
||||
|
||||
virtual std::unique_ptr<LoweringContext> CreateLoweringContext(
|
||||
const std::string& name, BackendDevice device,
|
||||
virtual std::unique_ptr<LoweringContext>
|
||||
CreateLoweringContext(const std::string &name, BackendDevice device,
|
||||
c10::ArrayRef<const Node *> post_order,
|
||||
Util::EmissionMap emit_status) const override;
|
||||
|
||||
virtual std::unique_ptr<LoweringContext> CreateLoweringContext(
|
||||
const std::string& name, BackendDevice device) const override;
|
||||
virtual std::unique_ptr<LoweringContext>
|
||||
CreateLoweringContext(const std::string &name,
|
||||
BackendDevice device) const override;
|
||||
|
||||
// TODO(whc) need to keep this?
|
||||
// virtual std::vector<std::string> GetCompilationDevices(
|
||||
|
|
|
@ -16,15 +16,13 @@ namespace torch {
|
|||
namespace lazy {
|
||||
|
||||
DimensionNode::DimensionNode(OpKind op, OpList operands, hash_t hash_seed)
|
||||
: TorchMlirNode(
|
||||
op, operands, /*num_outputs=*/1,
|
||||
: TorchMlirNode(op, operands, /*num_outputs=*/1,
|
||||
/* hash_seed */ HashCombine(op.hash(), hash_seed)) {}
|
||||
|
||||
std::string DimensionNode::ToString() const { return "DimensionNode"; }
|
||||
|
||||
SizeNode::SizeNode(Value input, size_t dim)
|
||||
: DimensionNode(
|
||||
OpKind{c10::Symbol::fromQualString("aten::size")}, {input},
|
||||
: DimensionNode(OpKind{c10::Symbol::fromQualString("aten::size")}, {input},
|
||||
MHash(dim)),
|
||||
dim_(dim){};
|
||||
|
||||
|
@ -40,7 +38,8 @@ SizeAdd::SizeAdd(Value a, Value b)
|
|||
: DimensionNode(OpKind{c10::Symbol::fromQualString("aten::add")}, {a, b}){};
|
||||
|
||||
int64_t SizeAdd::getStaticValue() const {
|
||||
return dynamic_cast<const DimensionNode*>(operand(0).node)->getStaticValue() +
|
||||
return dynamic_cast<const DimensionNode *>(operand(0).node)
|
||||
->getStaticValue() +
|
||||
dynamic_cast<const DimensionNode *>(operand(1).node)->getStaticValue();
|
||||
}
|
||||
|
||||
|
@ -50,7 +49,8 @@ SizeMul::SizeMul(Value a, Value b)
|
|||
: DimensionNode(OpKind{c10::Symbol::fromQualString("aten::mul")}, {a, b}){};
|
||||
|
||||
int64_t SizeMul::getStaticValue() const {
|
||||
return dynamic_cast<const DimensionNode*>(operand(0).node)->getStaticValue() *
|
||||
return dynamic_cast<const DimensionNode *>(operand(0).node)
|
||||
->getStaticValue() *
|
||||
dynamic_cast<const DimensionNode *>(operand(1).node)->getStaticValue();
|
||||
}
|
||||
|
||||
|
@ -64,7 +64,8 @@ int64_t SizeDiv::getStaticValue() const {
|
|||
dynamic_cast<const DimensionNode *>(operand(1).node)->getStaticValue() !=
|
||||
0,
|
||||
"Can't divide a dimension by zero");
|
||||
return dynamic_cast<const DimensionNode*>(operand(0).node)->getStaticValue() /
|
||||
return dynamic_cast<const DimensionNode *>(operand(0).node)
|
||||
->getStaticValue() /
|
||||
dynamic_cast<const DimensionNode *>(operand(1).node)->getStaticValue();
|
||||
}
|
||||
|
||||
|
|
|
@ -12,14 +12,14 @@
|
|||
|
||||
#include <iostream>
|
||||
|
||||
#include <torch/csrc/jit/api/compilation_unit.h>
|
||||
#include <torch/csrc/jit/passes/refine_tuple_types.h>
|
||||
#include <torch/csrc/lazy/core/lazy_graph_executor.h>
|
||||
#include <torch/csrc/lazy/core/config.h>
|
||||
#include "torch-mlir-c/Registration.h"
|
||||
#include "torch-mlir-c/Transforms.h"
|
||||
#include "mlir-c/IR.h"
|
||||
#include "mlir-c/Pass.h"
|
||||
#include "torch-mlir-c/Registration.h"
|
||||
#include "torch-mlir-c/Transforms.h"
|
||||
#include <torch/csrc/jit/api/compilation_unit.h>
|
||||
#include <torch/csrc/jit/passes/refine_tuple_types.h>
|
||||
#include <torch/csrc/lazy/core/config.h>
|
||||
#include <torch/csrc/lazy/core/lazy_graph_executor.h>
|
||||
|
||||
#include "backend_impl.h"
|
||||
#include "jit_ir_importer/function_importer.h"
|
||||
|
@ -38,8 +38,8 @@ namespace lazy {
|
|||
// TorchMlir Lowering Context
|
||||
///////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
TorchMlirLoweringContext::TorchMlirLoweringContext(
|
||||
const std::string& name, BackendDevice device)
|
||||
TorchMlirLoweringContext::TorchMlirLoweringContext(const std::string &name,
|
||||
BackendDevice device)
|
||||
: LoweringContext(name, std::forward<BackendDevice>(device)),
|
||||
graph_(std::make_shared<torch::jit::Graph>()),
|
||||
function_(
|
||||
|
@ -50,7 +50,8 @@ TorchMlirLoweringContext::TorchMlirLoweringContext(
|
|||
|
||||
TorchMlirLoweringContext::TorchMlirLoweringContext(
|
||||
const std::string &name, BackendDevice device,
|
||||
c10::ArrayRef<const torch::lazy::Node*> post_order, Util::EmissionMap emit_status)
|
||||
c10::ArrayRef<const torch::lazy::Node *> post_order,
|
||||
Util::EmissionMap emit_status)
|
||||
: LoweringContext(
|
||||
name, std::forward<BackendDevice>(device),
|
||||
std::forward<c10::ArrayRef<const torch::lazy::Node *>>(post_order),
|
||||
|
@ -90,9 +91,9 @@ void TorchMlirLoweringContext::SetUpAlias(
|
|||
|
||||
bool TorchMlirLoweringContext::CheckResultShape(
|
||||
const BackendDataPtr ¶meter_data, size_t result_idx) {
|
||||
TORCH_CHECK(
|
||||
result_idx < root_tuple_.size(), "Tried getting result shape at index ",
|
||||
result_idx, " which is out of bounds!");
|
||||
TORCH_CHECK(result_idx < root_tuple_.size(),
|
||||
"Tried getting result shape at index ", result_idx,
|
||||
" which is out of bounds!");
|
||||
|
||||
torch::jit::Value *output = root_tuple_[result_idx];
|
||||
|
||||
|
@ -120,9 +121,10 @@ size_t TorchMlirLoweringContext::AddResult(const Output& output) {
|
|||
// Associates the given output with the input parameter of the given index and
|
||||
// shape. Only used for the operator-by-operator execution, mostly for
|
||||
// debugging purposes.
|
||||
void TorchMlirLoweringContext::AddParameter(
|
||||
const torch::lazy::Output& output, size_t index,
|
||||
const torch::lazy::Shape& shape, const std::string& name) {
|
||||
void TorchMlirLoweringContext::AddParameter(const torch::lazy::Output &output,
|
||||
size_t index,
|
||||
const torch::lazy::Shape &shape,
|
||||
const std::string &name) {
|
||||
UNIMPLEMENTED_FUNCTION_ERROR();
|
||||
}
|
||||
|
||||
|
@ -152,7 +154,6 @@ ComputationPtr TorchMlirLoweringContext::Build() {
|
|||
/*getArgAttribute=*/[](int) -> MlirAttribute { return {nullptr}; },
|
||||
/*importOptions=*/{/*assumeTensorsHaveValueSemantics=*/true});
|
||||
|
||||
|
||||
// Convert MlirOperation to MlirModule.
|
||||
MlirLocation loc = mlirLocationUnknownGet(mlir_context_);
|
||||
MlirModule module_op = mlirModuleCreateEmpty(loc);
|
||||
|
@ -162,14 +163,10 @@ ComputationPtr TorchMlirLoweringContext::Build() {
|
|||
// Apply passes to verify generated MLIR.
|
||||
auto pass_manager = mlirPassManagerCreate(mlir_context_);
|
||||
mlirPassManagerAddOwnedPass(
|
||||
pass_manager,
|
||||
mlirCreateVerifyBackendContractNoDecompositions()
|
||||
);
|
||||
pass_manager, mlirCreateVerifyBackendContractNoDecompositions());
|
||||
|
||||
MlirLogicalResult result = mlirPassManagerRunOnOp(
|
||||
pass_manager,
|
||||
mlirModuleGetOperation(module_op)
|
||||
);
|
||||
MlirLogicalResult result =
|
||||
mlirPassManagerRunOnOp(pass_manager, mlirModuleGetOperation(module_op));
|
||||
|
||||
if (mlirLogicalResultIsFailure(result)) {
|
||||
throw std::runtime_error("MLIR verification has failed.");
|
||||
|
@ -178,9 +175,11 @@ ComputationPtr TorchMlirLoweringContext::Build() {
|
|||
return CreateComputation(module_op);
|
||||
}
|
||||
|
||||
ComputationPtr TorchMlirLoweringContext::CreateComputation(MlirModule module_op) {
|
||||
return std::make_shared<TorchMlirComputation>(
|
||||
module_op, mlir_context_, graph_, parameter_names_, input_output_aliases_);
|
||||
ComputationPtr
|
||||
TorchMlirLoweringContext::CreateComputation(MlirModule module_op) {
|
||||
return std::make_shared<TorchMlirComputation>(module_op, mlir_context_,
|
||||
graph_, parameter_names_,
|
||||
input_output_aliases_);
|
||||
}
|
||||
|
||||
torch::jit::Value *TorchMlirLoweringContext::GetOutputOp(const Output &output) {
|
||||
|
@ -195,15 +194,14 @@ torch::jit::Value* TorchMlirLoweringContext::GetOutputOp(const Output& output) {
|
|||
// At this point the output better be present, otherwise there is an issue
|
||||
// with the lowering code.
|
||||
it = emitted_outputs_.find(output);
|
||||
TORCH_CHECK(
|
||||
it != emitted_outputs_.end(),
|
||||
TORCH_CHECK(it != emitted_outputs_.end(),
|
||||
"No MLIR operation emitted for output: ", output.ToString());
|
||||
}
|
||||
return it->second;
|
||||
}
|
||||
|
||||
void TorchMlirLoweringContext::AssignOutputOp(
|
||||
const Output& output, torch::jit::Value* op) {
|
||||
void TorchMlirLoweringContext::AssignOutputOp(const Output &output,
|
||||
torch::jit::Value *op) {
|
||||
PRINT_FUNCTION();
|
||||
|
||||
auto torch_mlir_node =
|
||||
|
@ -234,17 +232,13 @@ void TorchMlirLoweringContext::AssignOutputOp(
|
|||
});
|
||||
|
||||
if (!source_files.empty()) {
|
||||
op->node()->ss_(
|
||||
c10::Symbol::attr("source_files"), source_files);
|
||||
op->node()->ss_(
|
||||
c10::Symbol::attr("functions"), functions);
|
||||
op->node()->is_(
|
||||
c10::Symbol::attr("line_numbers"), line_numbers);
|
||||
op->node()->ss_(c10::Symbol::attr("source_files"), source_files);
|
||||
op->node()->ss_(c10::Symbol::attr("functions"), functions);
|
||||
op->node()->is_(c10::Symbol::attr("line_numbers"), line_numbers);
|
||||
}
|
||||
}
|
||||
auto scope = ::c10::Symbol::scope(metadata.scope);
|
||||
op->node()->setScope(
|
||||
c10::make_intrusive<torch::jit::Scope>()->push(scope));
|
||||
op->node()->setScope(c10::make_intrusive<torch::jit::Scope>()->push(scope));
|
||||
|
||||
emitted_outputs_[output] = std::move(op);
|
||||
}
|
||||
|
@ -266,7 +260,8 @@ torch::jit::Value* TorchMlirLoweringContext::GetParameter(BackendDataPtr data) {
|
|||
torch::jit::Value *param =
|
||||
graph_->addInput(c10::str("p", parameters_.size()));
|
||||
|
||||
auto* info = dynamic_cast<TorchMlirBackendData::Info*>(mlir_data->mlir_info());
|
||||
auto *info =
|
||||
dynamic_cast<TorchMlirBackendData::Info *>(mlir_data->mlir_info());
|
||||
TORCH_CHECK(info, "Expected TorchMlirBackendData::Info");
|
||||
if (info->scalar.has_value()) {
|
||||
auto &scalar = info->scalar.value();
|
||||
|
@ -275,8 +270,8 @@ torch::jit::Value* TorchMlirLoweringContext::GetParameter(BackendDataPtr data) {
|
|||
} else if (scalar.isIntegral(true)) {
|
||||
param->setType(c10::IntType::get());
|
||||
} else {
|
||||
TORCH_CHECK(
|
||||
false, "Unhandled scalar type: ", c10::toString(scalar.type()));
|
||||
TORCH_CHECK(false,
|
||||
"Unhandled scalar type: ", c10::toString(scalar.type()));
|
||||
}
|
||||
} else {
|
||||
// Save parameter shape information.
|
||||
|
@ -313,8 +308,8 @@ size_t TorchMlirLoweringContext::AddResult(torch::jit::Value* op) {
|
|||
|
||||
// Sync vector of c10::Argument with type specified from parallel list of
|
||||
// jit::Value. There must be a 1:1 map between elements of args and values.
|
||||
std::vector<c10::Argument> sync_argument_types(
|
||||
const std::vector<c10::Argument>& args,
|
||||
std::vector<c10::Argument>
|
||||
sync_argument_types(const std::vector<c10::Argument> &args,
|
||||
c10::ArrayRef<torch::jit::Value *> values) {
|
||||
TORCH_CHECK(
|
||||
args.size() == values.size(),
|
||||
|
@ -377,9 +372,7 @@ TorchMlirComputation::TorchMlirComputation(
|
|||
}
|
||||
}
|
||||
|
||||
int TorchMlirComputation::parameters_size() const {
|
||||
return num_parameters_;
|
||||
}
|
||||
int TorchMlirComputation::parameters_size() const { return num_parameters_; }
|
||||
|
||||
const std::vector<torch::lazy::Shape> &
|
||||
TorchMlirComputation::parameter_shapes() const {
|
||||
|
@ -392,7 +385,8 @@ const std::vector<std::string>& TorchMlirComputation::parameter_names() const {
|
|||
return parameter_names_;
|
||||
}
|
||||
|
||||
const std::unordered_map<int, std::string>& TorchMlirComputation::parameters_map() const {
|
||||
const std::unordered_map<int, std::string> &
|
||||
TorchMlirComputation::parameters_map() const {
|
||||
return parameters_map_;
|
||||
}
|
||||
|
||||
|
@ -411,13 +405,9 @@ MlirOperation TorchMlirComputation::func_op() const {
|
|||
return mlirBlockGetFirstOperation(block);
|
||||
}
|
||||
|
||||
MlirModule TorchMlirComputation::module_op() const {
|
||||
return module_op_;
|
||||
}
|
||||
MlirModule TorchMlirComputation::module_op() const { return module_op_; }
|
||||
|
||||
MlirContext TorchMlirComputation::mlir_context() const {
|
||||
return mlir_context_;
|
||||
}
|
||||
MlirContext TorchMlirComputation::mlir_context() const { return mlir_context_; }
|
||||
|
||||
const std::string TorchMlirComputation::debug_string() const {
|
||||
std::stringstream ss;
|
||||
|
@ -462,7 +452,8 @@ const std::string TorchMlirComputation::to_string() const {
|
|||
// Setup flags for MLIR serialization.
|
||||
MlirOpPrintingFlags flags = mlirOpPrintingFlagsCreate();
|
||||
mlirOpPrintingFlagsEnableDebugInfo(flags, FLAGS_torch_lazy_ir_debug, false);
|
||||
mlirOperationPrintWithFlags(mlirModuleGetOperation(module_op_), flags, print_callback, &ss);
|
||||
mlirOperationPrintWithFlags(mlirModuleGetOperation(module_op_), flags,
|
||||
print_callback, &ss);
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
|
|
|
@ -39,24 +39,23 @@ public:
|
|||
};
|
||||
using InputOutputAliases = std::vector<InputOutputAlias>;
|
||||
|
||||
TorchMlirLoweringContext(
|
||||
const std::string& name, torch::lazy::BackendDevice device);
|
||||
TorchMlirLoweringContext(
|
||||
const std::string& name, torch::lazy::BackendDevice device,
|
||||
TorchMlirLoweringContext(const std::string &name,
|
||||
torch::lazy::BackendDevice device);
|
||||
TorchMlirLoweringContext(const std::string &name,
|
||||
torch::lazy::BackendDevice device,
|
||||
c10::ArrayRef<const torch::lazy::Node *> post_order,
|
||||
torch::lazy::Util::EmissionMap emit_status);
|
||||
|
||||
void Lower(const Node *node);
|
||||
|
||||
// Adds a new input/output alias.
|
||||
void SetUpAlias(
|
||||
const std::vector<int64_t>& output_index, int64_t param_number,
|
||||
const std::vector<int64_t>& param_index,
|
||||
void SetUpAlias(const std::vector<int64_t> &output_index,
|
||||
int64_t param_number, const std::vector<int64_t> ¶m_index,
|
||||
bool must_alias = false) override;
|
||||
|
||||
// Check if parameter shape matches result at index.
|
||||
bool CheckResultShape(
|
||||
const BackendDataPtr& parameter_data, size_t result_idx) override;
|
||||
bool CheckResultShape(const BackendDataPtr ¶meter_data,
|
||||
size_t result_idx) override;
|
||||
|
||||
// Adds the given output as a component of the result tuple and returns its
|
||||
// assigned position within the tuple.
|
||||
|
@ -65,9 +64,9 @@ public:
|
|||
// Associates the given output with the input parameter of the given index and
|
||||
// shape. Only used for the operator-by-operator execution, mostly for
|
||||
// debugging purposes.
|
||||
void AddParameter(
|
||||
const torch::lazy::Output& output, size_t index,
|
||||
const torch::lazy::Shape& shape, const std::string& name) override;
|
||||
void AddParameter(const torch::lazy::Output &output, size_t index,
|
||||
const torch::lazy::Shape &shape,
|
||||
const std::string &name) override;
|
||||
|
||||
// Build the computation capturing all the operations created with the
|
||||
// embedded builder (returned by the builder() API).
|
||||
|
@ -122,8 +121,7 @@ public:
|
|||
using InputOutputAliases = TorchMlirLoweringContext::InputOutputAliases;
|
||||
using InputOutputAlias = TorchMlirLoweringContext::InputOutputAlias;
|
||||
|
||||
TorchMlirComputation(
|
||||
MlirModule module_op, MlirContext mlir_context,
|
||||
TorchMlirComputation(MlirModule module_op, MlirContext mlir_context,
|
||||
const std::shared_ptr<torch::jit::Graph> &graph,
|
||||
std::unordered_map<int, std::string> parameters_map,
|
||||
InputOutputAliases input_output_aliases);
|
||||
|
|
|
@ -10,8 +10,8 @@
|
|||
// https://github.com/pytorch/pytorch/blob/master/torch/csrc/lazy/ts_backend/ts_native_functions.cpp
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
#include <ATen/CompositeExplicitAutogradNonFunctionalFunctions.h>
|
||||
#include <ATen/CompositeExplicitAutogradFunctions.h>
|
||||
#include <ATen/CompositeExplicitAutogradNonFunctionalFunctions.h>
|
||||
#include <ATen/FunctionalTensorWrapper.h>
|
||||
#include <ATen/InferSize.h>
|
||||
#include <ATen/MetaFunctions.h>
|
||||
|
@ -33,11 +33,11 @@
|
|||
#include "generated/LazyIr.h"
|
||||
#include "generated/LazyNativeFunctions.h"
|
||||
#include "generated/shape_inference.h"
|
||||
#include "ops/to_copy.h"
|
||||
#include "ops/unbind_int.h"
|
||||
#include "ops/split.h"
|
||||
#include "ops/index.h"
|
||||
#include "ops/ivalue.h"
|
||||
#include "ops/split.h"
|
||||
#include "ops/to_copy.h"
|
||||
#include "ops/unbind_int.h"
|
||||
#include "utils/exception.h"
|
||||
#include "utils/sys_utils.h"
|
||||
|
||||
|
@ -76,7 +76,8 @@ std::vector<at::Tensor> to_meta(at::ITensorListRef t_list) {
|
|||
return outs;
|
||||
}
|
||||
|
||||
c10::List<c10::optional<at::Tensor>> to_meta(const c10::List<c10::optional<at::Tensor>>& t_list) {
|
||||
c10::List<c10::optional<at::Tensor>>
|
||||
to_meta(const c10::List<c10::optional<at::Tensor>> &t_list) {
|
||||
c10::List<c10::optional<at::Tensor>> outs;
|
||||
outs.reserve(t_list.size());
|
||||
for (const auto &tensor : t_list) {
|
||||
|
@ -91,8 +92,8 @@ namespace lazy {
|
|||
|
||||
namespace {
|
||||
|
||||
at::Tensor CreateLtcTensor(
|
||||
const at::Tensor& tensor,
|
||||
at::Tensor
|
||||
CreateLtcTensor(const at::Tensor &tensor,
|
||||
const c10::optional<torch::lazy::BackendDevice> &device) {
|
||||
if (tensor.defined() && device) {
|
||||
return torch::lazy::CreateAtenFromLtcTensor(
|
||||
|
@ -112,13 +113,12 @@ GetLtcDevice(const c10::optional<c10::Device>& device) {
|
|||
return torch::lazy::atenDeviceToBackendDevice(*device);
|
||||
}
|
||||
|
||||
torch::lazy::Value MaybeExpand(
|
||||
const torch::lazy::Value& input, const torch::lazy::Shape& target_shape) {
|
||||
torch::lazy::Value MaybeExpand(const torch::lazy::Value &input,
|
||||
const torch::lazy::Shape &target_shape) {
|
||||
if (input.shape().sizes() == target_shape.sizes()) {
|
||||
return input;
|
||||
}
|
||||
return torch::lazy::MakeExpand(
|
||||
input, target_shape.sizes().vec(),
|
||||
return torch::lazy::MakeExpand(input, target_shape.sizes().vec(),
|
||||
/*is_scalar_expand=*/false);
|
||||
}
|
||||
|
||||
|
@ -128,8 +128,8 @@ void copy_(torch::lazy::LazyTensorPtr& input, torch::lazy::LazyTensorPtr& src) {
|
|||
if (input->dtype() == src->dtype()) {
|
||||
copy_value = src->GetIrValue();
|
||||
} else {
|
||||
copy_value = torch::lazy::MakeCast(
|
||||
src->GetIrValue(), input->dtype(), src->dtype());
|
||||
copy_value = torch::lazy::MakeCast(src->GetIrValue(), input->dtype(),
|
||||
src->dtype());
|
||||
}
|
||||
input->SetIrValue(MaybeExpand(copy_value, input->shape()));
|
||||
} else {
|
||||
|
@ -146,15 +146,17 @@ void copy_(torch::lazy::LazyTensorPtr& input, torch::lazy::LazyTensorPtr& src) {
|
|||
|
||||
// clone is special in LT because we make it a no-op.
|
||||
// This should be safe to do, because every operator in the LT is functional.
|
||||
at::Tensor LazyNativeFunctions::clone(
|
||||
const at::Tensor& self, c10::optional<at::MemoryFormat> memory_format) {
|
||||
at::Tensor
|
||||
LazyNativeFunctions::clone(const at::Tensor &self,
|
||||
c10::optional<at::MemoryFormat> memory_format) {
|
||||
auto self_lt = torch::lazy::TryGetLtcTensor(self);
|
||||
return torch::lazy::CreateAtenFromLtcTensor(
|
||||
self_lt->Create(self_lt->GetIrValue(), self_lt->GetDevice()));
|
||||
}
|
||||
|
||||
at::Tensor LazyNativeFunctions::_copy_from(
|
||||
const at::Tensor& self, const at::Tensor& dst, bool non_blocking) {
|
||||
at::Tensor LazyNativeFunctions::_copy_from(const at::Tensor &self,
|
||||
const at::Tensor &dst,
|
||||
bool non_blocking) {
|
||||
TORCH_LAZY_FN_COUNTER("lazy::");
|
||||
auto dst_tensor = torch::lazy::TryGetLtcTensor(dst);
|
||||
auto self_tensor = torch::lazy::TryGetLtcTensor(self);
|
||||
|
@ -207,8 +209,8 @@ at::Tensor LazyNativeFunctions::_copy_from(
|
|||
return dst;
|
||||
}
|
||||
|
||||
at::Tensor LazyNativeFunctions::_copy_from_and_resize(
|
||||
const at::Tensor& self, const at::Tensor& dst) {
|
||||
at::Tensor LazyNativeFunctions::_copy_from_and_resize(const at::Tensor &self,
|
||||
const at::Tensor &dst) {
|
||||
TORCH_LAZY_FN_COUNTER("lazy::");
|
||||
auto dst_tensor = torch::lazy::TryGetLtcTensor(dst);
|
||||
auto self_tensor = torch::lazy::TryGetLtcTensor(self);
|
||||
|
@ -239,8 +241,9 @@ at::Tensor LazyNativeFunctions::_to_copy(
|
|||
PRINT_FUNCTION();
|
||||
auto options = self.options();
|
||||
if (dtype) {
|
||||
// I put each of these setters in a conditional instead of doing `self.options().dtype(dtype).layout(layout)...
|
||||
// because calling .dtype(nullopt) on an options() that already has dtype appears to wipe it
|
||||
// I put each of these setters in a conditional instead of doing
|
||||
// `self.options().dtype(dtype).layout(layout)... because calling
|
||||
// .dtype(nullopt) on an options() that already has dtype appears to wipe it
|
||||
options = options.dtype(dtype);
|
||||
}
|
||||
if (layout) {
|
||||
|
@ -261,8 +264,9 @@ at::Tensor LazyNativeFunctions::_to_copy(
|
|||
if (!lazy_self && device && device->type() == c10::kLazy) {
|
||||
// Case 1: eager->lazy (we create a new lazy tensor)
|
||||
// See Note [Lazy Tensor Functionalization]
|
||||
// Invariant: if the functionalization key is in the exclude set, then we're expected
|
||||
// to return an ordinary tensor, which will be "lifted" into a functional wrapper later.
|
||||
// Invariant: if the functionalization key is in the exclude set, then we're
|
||||
// expected to return an ordinary tensor, which will be "lifted" into a
|
||||
// functional wrapper later.
|
||||
bool functionalize_output =
|
||||
!c10::impl::tls_local_dispatch_key_set().excluded_.has(
|
||||
c10::DispatchKey::Functionalize);
|
||||
|
@ -270,7 +274,8 @@ at::Tensor LazyNativeFunctions::_to_copy(
|
|||
self, options, *device, /*non_blocking=*/non_blocking,
|
||||
/*functionalize_output=*/functionalize_output);
|
||||
} else if (device && device->type() != c10::kLazy) {
|
||||
// Case 2: lazy->eager (forces a graph break since we are materializing a tensor)
|
||||
// Case 2: lazy->eager (forces a graph break since we are materializing a
|
||||
// tensor)
|
||||
|
||||
TORCH_INTERNAL_ASSERT(lazy_self);
|
||||
auto eager_tensor = lazy_self->ToTensor(/*detached=*/true);
|
||||
|
@ -278,22 +283,24 @@ at::Tensor LazyNativeFunctions::_to_copy(
|
|||
auto moved_eager_tensor =
|
||||
eager_tensor.to(options, /*non_blocking=*/non_blocking, /*copy=*/true);
|
||||
return moved_eager_tensor;
|
||||
} else if (
|
||||
device && device->type() == c10::kLazy && device->has_index() &&
|
||||
} else if (device && device->type() == c10::kLazy && device->has_index() &&
|
||||
device->index() != self.device().index()) {
|
||||
// Case 3: lazy:0 -> lazy:1
|
||||
|
||||
// TODO(whc) what do we actually want to do here?
|
||||
// option 1: materialize, move eager tensor, create new lazy tensor
|
||||
// - this should be our default, as it is what would happen before we implemented _to_copy
|
||||
// - this should be our default, as it is what would happen before we
|
||||
// implemented _to_copy
|
||||
// - actually combines case 1 + case 2
|
||||
// option 2: support multiple devices inside one lazy/TS executor (case 4)
|
||||
// - but: we may have other assumptions that there is just one device per executor? so don't take this lightly
|
||||
// - but: we may have other assumptions that there is just one device
|
||||
// per executor? so don't take this lightly
|
||||
|
||||
TORCH_INTERNAL_ASSERT(lazy_self);
|
||||
auto eager_tensor = lazy_self->ToTensor(/*detached=*/true);
|
||||
// we move the eager tensor to the 'eager' equivalent of our lazy device
|
||||
// e.g. if our device is lazy:1, the backend maps that to cuda:1, which is what we use
|
||||
// e.g. if our device is lazy:1, the backend maps that to cuda:1, which is
|
||||
// what we use
|
||||
auto eager_device = c10::Device(
|
||||
torch::lazy::getBackend()->EagerFallbackDeviceType(), device->index());
|
||||
options = options.device(eager_device);
|
||||
|
@ -305,12 +312,14 @@ at::Tensor LazyNativeFunctions::_to_copy(
|
|||
return torch::lazy::CreateAtenFromLtcTensor(lazy_self);
|
||||
|
||||
} else {
|
||||
// Case 4: lazy->lazy (special case: keep the _to_copy INSIDE the lazy graph)
|
||||
// Case 4: lazy->lazy (special case: keep the _to_copy INSIDE the lazy
|
||||
// graph)
|
||||
|
||||
// Note: captured _to_copy will be executed with real eager tensors, not lazy tensors.
|
||||
// We DO NOT want to burn 'lazy:0' as the device into this captured IR, or we will try to
|
||||
// convert an eager tensor back to a lazy one inside the torchscript executor
|
||||
// lazy:0 -> lazy:1 is handled in case3, so we can safely drop the device argument
|
||||
// Note: captured _to_copy will be executed with real eager tensors, not
|
||||
// lazy tensors. We DO NOT want to burn 'lazy:0' as the device into this
|
||||
// captured IR, or we will try to convert an eager tensor back to a lazy one
|
||||
// inside the torchscript executor lazy:0 -> lazy:1 is handled in case3, so
|
||||
// we can safely drop the device argument
|
||||
device = c10::nullopt;
|
||||
|
||||
auto shapes = torch::lazy::compute_shape__to_copy(
|
||||
|
@ -327,10 +336,11 @@ at::Tensor LazyNativeFunctions::_to_copy(
|
|||
}
|
||||
};
|
||||
|
||||
at::Tensor LazyNativeFunctions::_unsafe_view(
|
||||
const at::Tensor& self, at::IntArrayRef size) {
|
||||
at::Tensor LazyNativeFunctions::_unsafe_view(const at::Tensor &self,
|
||||
at::IntArrayRef size) {
|
||||
TORCH_LAZY_FN_COUNTER("lazy::");
|
||||
return LazyNativeFunctions::view_copy_symint(self, c10::fromIntArrayRefSlow(size));
|
||||
return LazyNativeFunctions::view_copy_symint(self,
|
||||
c10::fromIntArrayRefSlow(size));
|
||||
}
|
||||
|
||||
at::Tensor LazyNativeFunctions::t(const at::Tensor &self) {
|
||||
|
@ -338,156 +348,180 @@ at::Tensor LazyNativeFunctions::t(const at::Tensor& self) {
|
|||
return at::functionalization::functionalize_aten_op<ATEN_OP(t)>::call(self);
|
||||
}
|
||||
|
||||
std::vector<at::Tensor> LazyNativeFunctions::unbind_copy(const at::Tensor & self, int64_t dim) {
|
||||
std::vector<at::Tensor> LazyNativeFunctions::unbind_copy(const at::Tensor &self,
|
||||
int64_t dim) {
|
||||
TORCH_LAZY_FN_COUNTER("lazy::");
|
||||
auto common_device = torch::lazy::GetBackendDevice(self);
|
||||
TORCH_INTERNAL_ASSERT(common_device);
|
||||
|
||||
LazyTensorPtr lazy_self = torch::lazy::GetLtcTensorOrCreateForWrappedNumber(self, *common_device);
|
||||
torch::lazy::NodePtr node = torch::lazy::ReuseNode<UnbindCopyInt>(lazy_self->GetIrValue(), dim);
|
||||
LazyTensorPtr lazy_self =
|
||||
torch::lazy::GetLtcTensorOrCreateForWrappedNumber(self, *common_device);
|
||||
torch::lazy::NodePtr node =
|
||||
torch::lazy::ReuseNode<UnbindCopyInt>(lazy_self->GetIrValue(), dim);
|
||||
if (!node) {
|
||||
auto self_meta = to_meta(self);
|
||||
auto out_meta = at::compositeexplicitautogradnonfunctional::unbind_copy(self_meta, dim);
|
||||
auto out_meta =
|
||||
at::compositeexplicitautogradnonfunctional::unbind_copy(self_meta, dim);
|
||||
|
||||
std::vector<torch::lazy::Shape> shapes;
|
||||
for (const auto &shape : out_meta) {
|
||||
shapes.push_back(
|
||||
torch::lazy::Shape(shape.scalar_type(), shape.sizes().vec())
|
||||
);
|
||||
torch::lazy::Shape(shape.scalar_type(), shape.sizes().vec()));
|
||||
}
|
||||
|
||||
if (torch::lazy::symbolicShapeEnabled()) {
|
||||
std::vector<torch::jit::IValue> inputs = {self, dim};
|
||||
const char* schema_str = "aten::unbind_copy.int(Tensor self, int dim=0) -> Tensor[]";
|
||||
const char *schema_str =
|
||||
"aten::unbind_copy.int(Tensor self, int dim=0) -> Tensor[]";
|
||||
applySymbolicShapesOnLT(schema_str, inputs, shapes);
|
||||
}
|
||||
|
||||
node = torch::lazy::MakeNode<UnbindCopyInt>(lazy_self->GetIrValue(), dim, std::move(shapes));
|
||||
node = torch::lazy::MakeNode<UnbindCopyInt>(lazy_self->GetIrValue(), dim,
|
||||
std::move(shapes));
|
||||
CacheNode(node);
|
||||
}
|
||||
|
||||
std::vector<at::Tensor> result;
|
||||
for (size_t i = 0; i < node->num_outputs(); ++i) {
|
||||
result.push_back(
|
||||
torch::lazy::CreateAtenFromLtcTensor(
|
||||
torch::lazy::LazyTensor::Create(torch::lazy::Value(node, i), *common_device)
|
||||
)
|
||||
);
|
||||
torch::lazy::CreateAtenFromLtcTensor(torch::lazy::LazyTensor::Create(
|
||||
torch::lazy::Value(node, i), *common_device)));
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
std::vector<at::Tensor> LazyNativeFunctions::split_with_sizes_copy_symint(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) {
|
||||
std::vector<at::Tensor> LazyNativeFunctions::split_with_sizes_copy_symint(
|
||||
const at::Tensor &self, c10::SymIntArrayRef split_sizes, int64_t dim) {
|
||||
TORCH_LAZY_FN_COUNTER("lazy::");
|
||||
auto common_device = torch::lazy::GetBackendDevice(self);
|
||||
TORCH_INTERNAL_ASSERT(common_device);
|
||||
|
||||
LazyTensorPtr lazy_self = torch::lazy::GetLtcTensorOrCreateForWrappedNumber(self, *common_device);
|
||||
torch::lazy::NodePtr node = torch::lazy::ReuseNode<SplitWithSizesCopy>(lazy_self->GetIrValue(), GetSymIntArrayRefValue(split_sizes), dim);
|
||||
LazyTensorPtr lazy_self =
|
||||
torch::lazy::GetLtcTensorOrCreateForWrappedNumber(self, *common_device);
|
||||
torch::lazy::NodePtr node = torch::lazy::ReuseNode<SplitWithSizesCopy>(
|
||||
lazy_self->GetIrValue(), GetSymIntArrayRefValue(split_sizes), dim);
|
||||
if (!node) {
|
||||
auto self_meta = to_meta(self);
|
||||
auto out_meta = at::compositeexplicitautogradnonfunctional::split_with_sizes_copy_symint(self_meta, split_sizes, dim);
|
||||
auto out_meta = at::compositeexplicitautogradnonfunctional::
|
||||
split_with_sizes_copy_symint(self_meta, split_sizes, dim);
|
||||
|
||||
std::vector<torch::lazy::Shape> shapes;
|
||||
for (const auto &shape : out_meta) {
|
||||
shapes.push_back(
|
||||
torch::lazy::Shape(shape.scalar_type(), shape.sizes().vec())
|
||||
);
|
||||
torch::lazy::Shape(shape.scalar_type(), shape.sizes().vec()));
|
||||
}
|
||||
|
||||
if (torch::lazy::symbolicShapeEnabled()) {
|
||||
std::vector<torch::jit::IValue> inputs = {self, split_sizes, dim};
|
||||
const char* schema_str = "aten::split_with_sizes_copy(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[]";
|
||||
const char *schema_str = "aten::split_with_sizes_copy(Tensor self, "
|
||||
"SymInt[] split_sizes, int dim=0) -> Tensor[]";
|
||||
applySymbolicShapesOnLT(schema_str, inputs, shapes);
|
||||
}
|
||||
|
||||
node = torch::lazy::MakeNode<SplitWithSizesCopy>(lazy_self->GetIrValue(), GetSymIntArrayRefValue(split_sizes), dim, std::move(shapes));
|
||||
node = torch::lazy::MakeNode<SplitWithSizesCopy>(
|
||||
lazy_self->GetIrValue(), GetSymIntArrayRefValue(split_sizes), dim,
|
||||
std::move(shapes));
|
||||
CacheNode(node);
|
||||
}
|
||||
|
||||
std::vector<at::Tensor> result;
|
||||
for (size_t i = 0; i < node->num_outputs(); ++i) {
|
||||
result.push_back(
|
||||
torch::lazy::CreateAtenFromLtcTensor(
|
||||
torch::lazy::LazyTensor::Create(torch::lazy::Value(node, i), *common_device)
|
||||
)
|
||||
);
|
||||
torch::lazy::CreateAtenFromLtcTensor(torch::lazy::LazyTensor::Create(
|
||||
torch::lazy::Value(node, i), *common_device)));
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
std::vector<at::Tensor> LazyNativeFunctions::split_copy_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim) {
|
||||
std::vector<at::Tensor>
|
||||
LazyNativeFunctions::split_copy_symint(const at::Tensor &self,
|
||||
c10::SymInt split_size, int64_t dim) {
|
||||
TORCH_LAZY_FN_COUNTER("lazy::");
|
||||
auto common_device = torch::lazy::GetBackendDevice(self);
|
||||
TORCH_INTERNAL_ASSERT(common_device);
|
||||
LazyTensorPtr lazy_self = torch::lazy::GetLtcTensorOrCreateForWrappedNumber(self, *common_device);
|
||||
torch::lazy::NodePtr node = torch::lazy::ReuseNode<SplitCopyTensor>(lazy_self->GetIrValue(), GetSymIntValue(split_size), dim);
|
||||
LazyTensorPtr lazy_self =
|
||||
torch::lazy::GetLtcTensorOrCreateForWrappedNumber(self, *common_device);
|
||||
torch::lazy::NodePtr node = torch::lazy::ReuseNode<SplitCopyTensor>(
|
||||
lazy_self->GetIrValue(), GetSymIntValue(split_size), dim);
|
||||
if (!node) {
|
||||
auto self_meta = to_meta(self);
|
||||
auto out_meta = at::compositeexplicitautogradnonfunctional::split_copy_symint(self_meta, split_size, dim);
|
||||
auto out_meta =
|
||||
at::compositeexplicitautogradnonfunctional::split_copy_symint(
|
||||
self_meta, split_size, dim);
|
||||
|
||||
std::vector<torch::lazy::Shape> shapes;
|
||||
for (const auto &shape : out_meta) {
|
||||
shapes.push_back(
|
||||
torch::lazy::Shape(shape.scalar_type(), shape.sizes().vec())
|
||||
);
|
||||
torch::lazy::Shape(shape.scalar_type(), shape.sizes().vec()));
|
||||
}
|
||||
const size_t num_outputs = shapes.size();
|
||||
|
||||
if (torch::lazy::symbolicShapeEnabled()) {
|
||||
std::vector<torch::jit::IValue> inputs = {self, split_size, dim};
|
||||
const char* schema_str = "aten::split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]";
|
||||
const char *schema_str = "aten::split_copy.Tensor(Tensor self, SymInt "
|
||||
"split_size, int dim=0) -> Tensor[]";
|
||||
applySymbolicShapesOnLT(schema_str, inputs, shapes);
|
||||
}
|
||||
|
||||
node = torch::lazy::MakeNode<SplitCopyTensor>(lazy_self->GetIrValue(), GetSymIntValue(split_size), dim, std::move(shapes), num_outputs);
|
||||
node = torch::lazy::MakeNode<SplitCopyTensor>(
|
||||
lazy_self->GetIrValue(), GetSymIntValue(split_size), dim,
|
||||
std::move(shapes), num_outputs);
|
||||
CacheNode(node);
|
||||
}
|
||||
|
||||
std::vector<at::Tensor> result;
|
||||
for (size_t i = 0; i < node->num_outputs(); ++i) {
|
||||
result.push_back(
|
||||
torch::lazy::CreateAtenFromLtcTensor(
|
||||
torch::lazy::LazyTensor::Create(torch::lazy::Value(node, i), *common_device)
|
||||
)
|
||||
);
|
||||
torch::lazy::CreateAtenFromLtcTensor(torch::lazy::LazyTensor::Create(
|
||||
torch::lazy::Value(node, i), *common_device)));
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
at::Tensor LazyNativeFunctions::index(const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices) {
|
||||
at::Tensor LazyNativeFunctions::index(
|
||||
const at::Tensor &self,
|
||||
const c10::List<c10::optional<at::Tensor>> &indices) {
|
||||
TORCH_LAZY_FN_COUNTER("lazy::");
|
||||
auto common_device = torch::lazy::GetBackendDevice(self);
|
||||
TORCH_INTERNAL_ASSERT(common_device);
|
||||
LazyTensorPtr lazy_self = torch::lazy::GetLtcTensorOrCreateForWrappedNumber(self, *common_device);
|
||||
LazyTensorPtr lazy_self =
|
||||
torch::lazy::GetLtcTensorOrCreateForWrappedNumber(self, *common_device);
|
||||
|
||||
std::vector<torch::lazy::Value> values;
|
||||
for (const auto &it : indices) {
|
||||
c10::optional<at::Tensor> tensor = it;
|
||||
LazyTensorPtr lazy_tensor = torch::lazy::TryGetLtcTensor(tensor.value_or(at::Tensor()));
|
||||
values.push_back(lazy_tensor ? lazy_tensor->GetIrValue() : torch::lazy::Value(MakeNode<IValueConstant>(c10::IValue()), 0));
|
||||
LazyTensorPtr lazy_tensor =
|
||||
torch::lazy::TryGetLtcTensor(tensor.value_or(at::Tensor()));
|
||||
values.push_back(
|
||||
lazy_tensor
|
||||
? lazy_tensor->GetIrValue()
|
||||
: torch::lazy::Value(MakeNode<IValueConstant>(c10::IValue()), 0));
|
||||
}
|
||||
|
||||
auto list = MakeNode<TorchMlirOptionalTensorList>(values);
|
||||
|
||||
torch::lazy::NodePtr node = torch::lazy::ReuseNode<IndexTensor>(lazy_self->GetIrValue(), list);
|
||||
torch::lazy::NodePtr node =
|
||||
torch::lazy::ReuseNode<IndexTensor>(lazy_self->GetIrValue(), list);
|
||||
|
||||
if (!node) {
|
||||
auto self_meta = to_meta(self);
|
||||
auto indices_meta = to_meta(indices);
|
||||
auto out_meta = at::meta::index(self_meta, indices_meta);
|
||||
|
||||
std::vector<torch::lazy::Shape> shapes{torch::lazy::Shape(out_meta.scalar_type(), out_meta.sizes().vec())};
|
||||
std::vector<torch::lazy::Shape> shapes{
|
||||
torch::lazy::Shape(out_meta.scalar_type(), out_meta.sizes().vec())};
|
||||
TORCH_INTERNAL_ASSERT(shapes.size() == 1);
|
||||
if (torch::lazy::symbolicShapeEnabled()) {
|
||||
std::vector<torch::jit::IValue> inputs = {self, indices};
|
||||
const char* schema_str = "aten::index.Tensor(Tensor self, Tensor?[] indices) -> Tensor";
|
||||
const char *schema_str =
|
||||
"aten::index.Tensor(Tensor self, Tensor?[] indices) -> Tensor";
|
||||
applySymbolicShapesOnLT(schema_str, inputs, shapes);
|
||||
}
|
||||
|
||||
node = torch::lazy::MakeNode<IndexTensor>(lazy_self->GetIrValue(), list, std::move(shapes));
|
||||
node = torch::lazy::MakeNode<IndexTensor>(lazy_self->GetIrValue(), list,
|
||||
std::move(shapes));
|
||||
CacheNode(node);
|
||||
}
|
||||
|
||||
|
@ -497,40 +531,56 @@ at::Tensor LazyNativeFunctions::index(const at::Tensor & self, const c10::List<c
|
|||
return result;
|
||||
}
|
||||
|
||||
at::Tensor LazyNativeFunctions::index_put(const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate) {
|
||||
at::Tensor LazyNativeFunctions::index_put(
|
||||
const at::Tensor &self, const c10::List<c10::optional<at::Tensor>> &indices,
|
||||
const at::Tensor &values, bool accumulate) {
|
||||
TORCH_LAZY_FN_COUNTER("lazy::");
|
||||
auto common_device = torch::lazy::GetBackendDevice(self);
|
||||
TORCH_INTERNAL_ASSERT(common_device);
|
||||
LazyTensorPtr lazy_self = torch::lazy::GetLtcTensorOrCreateForWrappedNumber(self, *common_device);
|
||||
LazyTensorPtr lazy_valeus = torch::lazy::GetLtcTensorOrCreateForWrappedNumber(values, *common_device);
|
||||
LazyTensorPtr lazy_self =
|
||||
torch::lazy::GetLtcTensorOrCreateForWrappedNumber(self, *common_device);
|
||||
LazyTensorPtr lazy_valeus =
|
||||
torch::lazy::GetLtcTensorOrCreateForWrappedNumber(values, *common_device);
|
||||
|
||||
std::vector<torch::lazy::Value> indices_vector;
|
||||
for (const auto &it : indices) {
|
||||
c10::optional<at::Tensor> tensor = it;
|
||||
LazyTensorPtr lazy_tensor = torch::lazy::TryGetLtcTensor(tensor.value_or(at::Tensor()));
|
||||
indices_vector.push_back(lazy_tensor ? lazy_tensor->GetIrValue() : torch::lazy::Value(MakeNode<IValueConstant>(c10::IValue()), 0));
|
||||
LazyTensorPtr lazy_tensor =
|
||||
torch::lazy::TryGetLtcTensor(tensor.value_or(at::Tensor()));
|
||||
indices_vector.push_back(
|
||||
lazy_tensor
|
||||
? lazy_tensor->GetIrValue()
|
||||
: torch::lazy::Value(MakeNode<IValueConstant>(c10::IValue()), 0));
|
||||
}
|
||||
|
||||
auto indices_list = MakeNode<TorchMlirOptionalTensorList>(indices_vector);
|
||||
|
||||
torch::lazy::NodePtr node = torch::lazy::ReuseNode<IndexPut>(lazy_self->GetIrValue(), indices_list, lazy_valeus->GetIrValue(), accumulate);
|
||||
torch::lazy::NodePtr node =
|
||||
torch::lazy::ReuseNode<IndexPut>(lazy_self->GetIrValue(), indices_list,
|
||||
lazy_valeus->GetIrValue(), accumulate);
|
||||
|
||||
if (!node) {
|
||||
auto self_meta = to_meta(self);
|
||||
auto indices_meta = to_meta(indices);
|
||||
auto values_meta = to_meta(values);
|
||||
|
||||
auto out_meta = at::compositeexplicitautograd::index_put(self_meta, indices_meta, values_meta, accumulate);
|
||||
auto out_meta = at::compositeexplicitautograd::index_put(
|
||||
self_meta, indices_meta, values_meta, accumulate);
|
||||
|
||||
std::vector<torch::lazy::Shape> shapes{torch::lazy::Shape(out_meta.scalar_type(), out_meta.sizes().vec())};
|
||||
std::vector<torch::lazy::Shape> shapes{
|
||||
torch::lazy::Shape(out_meta.scalar_type(), out_meta.sizes().vec())};
|
||||
TORCH_INTERNAL_ASSERT(shapes.size() == 1);
|
||||
if (torch::lazy::symbolicShapeEnabled()) {
|
||||
std::vector<torch::jit::IValue> inputs = {self, indices, values};
|
||||
const char* schema_str = "aten::index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor";
|
||||
const char *schema_str =
|
||||
"aten::index_put(Tensor self, Tensor?[] indices, Tensor values, bool "
|
||||
"accumulate=False) -> Tensor";
|
||||
applySymbolicShapesOnLT(schema_str, inputs, shapes);
|
||||
}
|
||||
|
||||
node = torch::lazy::MakeNode<IndexPut>(lazy_self->GetIrValue(), indices_list, lazy_valeus->GetIrValue(), accumulate, std::move(shapes));
|
||||
node = torch::lazy::MakeNode<IndexPut>(
|
||||
lazy_self->GetIrValue(), indices_list, lazy_valeus->GetIrValue(),
|
||||
accumulate, std::move(shapes));
|
||||
CacheNode(node);
|
||||
}
|
||||
|
||||
|
@ -542,7 +592,8 @@ at::Tensor LazyNativeFunctions::index_put(const at::Tensor & self, const c10::Li
|
|||
|
||||
// This is needed by the torch.tensor constructor.
|
||||
// LazyTensor always opts into functionalization.
|
||||
// "lifting" a tensor for functionalization means wrapping it in a FunctionalTensorWrapper object.
|
||||
// "lifting" a tensor for functionalization means wrapping it in a
|
||||
// FunctionalTensorWrapper object.
|
||||
at::Tensor LazyNativeFunctions::lift(const at::Tensor &tensor) {
|
||||
TORCH_INTERNAL_ASSERT(
|
||||
!at::functionalization::impl::isFunctionalTensor(tensor));
|
||||
|
@ -555,29 +606,27 @@ at::Tensor LazyNativeFunctions::lift_fresh(const at::Tensor& tensor) {
|
|||
return at::functionalization::impl::to_functional_tensor(tensor);
|
||||
}
|
||||
|
||||
// All of the below ops correspond to CompositeExplicitAutograd kernels from core
|
||||
// that call into view operators internally.
|
||||
// These are all composite ops that LTC can technically re-use / get for free,
|
||||
// but we need to "functionalize" them to remove the view ops before we can use them.
|
||||
// All of the below ops correspond to CompositeExplicitAutograd kernels from
|
||||
// core that call into view operators internally. These are all composite ops
|
||||
// that LTC can technically re-use / get for free, but we need to
|
||||
// "functionalize" them to remove the view ops before we can use them.
|
||||
at::Tensor LazyNativeFunctions::block_diag(at::TensorList tensors) {
|
||||
return at::functionalization::functionalize_aten_op<ATEN_OP(
|
||||
block_diag)>::call(tensors);
|
||||
}
|
||||
at::Tensor LazyNativeFunctions::new_empty_strided_symint(
|
||||
const at::Tensor& self,
|
||||
c10::SymIntArrayRef size,
|
||||
c10::SymIntArrayRef stride,
|
||||
c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout,
|
||||
c10::optional<at::Device> device,
|
||||
const at::Tensor &self, c10::SymIntArrayRef size,
|
||||
c10::SymIntArrayRef stride, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout, c10::optional<at::Device> device,
|
||||
c10::optional<bool> pin_memory) {
|
||||
if (!device || device->type() == c10::DeviceType::Lazy) {
|
||||
return at::functionalization::functionalize_aten_op_symint<
|
||||
ATEN_OP(new_empty_strided)>::call(self, size, stride, dtype, layout,
|
||||
device, pin_memory);
|
||||
return at::functionalization::functionalize_aten_op_symint<ATEN_OP(
|
||||
new_empty_strided)>::call(self, size, stride, dtype, layout, device,
|
||||
pin_memory);
|
||||
}
|
||||
// For cases when device != lazy, for example: lazy_tensor.new_empty_strided(..., "cpu")
|
||||
// we need to avoid explicit functionalization. To do that we create regular cpu tensors.
|
||||
// For cases when device != lazy, for example:
|
||||
// lazy_tensor.new_empty_strided(..., "cpu") we need to avoid explicit
|
||||
// functionalization. To do that we create regular cpu tensors.
|
||||
at::Tensor t = at::empty_symint(
|
||||
size, (dtype ? dtype : c10::optional<at::ScalarType>(self.scalar_type())),
|
||||
(layout ? layout : c10::optional<at::Layout>(self.layout())), device,
|
||||
|
@ -585,43 +634,39 @@ at::Tensor LazyNativeFunctions::new_empty_strided_symint(
|
|||
return t.as_strided_symint(size, stride, /*storage_offset=*/0);
|
||||
}
|
||||
|
||||
at::Tensor LazyNativeFunctions::narrow_copy_symint(
|
||||
const at::Tensor& self,
|
||||
at::Tensor LazyNativeFunctions::narrow_copy_symint(const at::Tensor &self,
|
||||
int64_t dim,
|
||||
c10::SymInt start,
|
||||
c10::SymInt length) {
|
||||
return at::functionalization::functionalize_aten_op_symint<ATEN_OP(
|
||||
narrow_copy)>::call(self, dim, start, length);
|
||||
}
|
||||
at::Tensor LazyNativeFunctions::pixel_shuffle(
|
||||
const at::Tensor& self, int64_t upscale_factor) {
|
||||
at::Tensor LazyNativeFunctions::pixel_shuffle(const at::Tensor &self,
|
||||
int64_t upscale_factor) {
|
||||
return at::functionalization::functionalize_aten_op<ATEN_OP(
|
||||
pixel_shuffle)>::call(self, upscale_factor);
|
||||
}
|
||||
at::Tensor LazyNativeFunctions::pixel_unshuffle(
|
||||
const at::Tensor& self, int64_t downscale_factor) {
|
||||
at::Tensor LazyNativeFunctions::pixel_unshuffle(const at::Tensor &self,
|
||||
int64_t downscale_factor) {
|
||||
return at::functionalization::functionalize_aten_op<ATEN_OP(
|
||||
pixel_unshuffle)>::call(self, downscale_factor);
|
||||
}
|
||||
at::Tensor LazyNativeFunctions::select_backward(
|
||||
const at::Tensor& grad_output, at::IntArrayRef input_sizes, int64_t dim,
|
||||
int64_t index) {
|
||||
at::Tensor LazyNativeFunctions::select_backward(const at::Tensor &grad_output,
|
||||
at::IntArrayRef input_sizes,
|
||||
int64_t dim, int64_t index) {
|
||||
return at::functionalization::functionalize_aten_op<ATEN_OP(
|
||||
select_backward)>::call(grad_output, input_sizes, dim, index);
|
||||
}
|
||||
at::Tensor LazyNativeFunctions::slice_backward_symint(
|
||||
const at::Tensor& grad_output,
|
||||
at::SymIntArrayRef input_sizes,
|
||||
int64_t dim,
|
||||
c10::SymInt start,
|
||||
c10::SymInt end,
|
||||
c10::SymInt step) {
|
||||
const at::Tensor &grad_output, at::SymIntArrayRef input_sizes, int64_t dim,
|
||||
c10::SymInt start, c10::SymInt end, c10::SymInt step) {
|
||||
return at::functionalization::functionalize_aten_op_symint<ATEN_OP(
|
||||
slice_backward)>::call(grad_output, input_sizes, dim, start, end, step);
|
||||
}
|
||||
at::Tensor LazyNativeFunctions::diagonal_backward(
|
||||
const at::Tensor& grad_output, at::IntArrayRef input_sizes, int64_t offset,
|
||||
int64_t dim1, int64_t dim2) {
|
||||
at::Tensor LazyNativeFunctions::diagonal_backward(const at::Tensor &grad_output,
|
||||
at::IntArrayRef input_sizes,
|
||||
int64_t offset, int64_t dim1,
|
||||
int64_t dim2) {
|
||||
return at::functionalization::functionalize_aten_op<ATEN_OP(
|
||||
diagonal_backward)>::call(grad_output, input_sizes, offset, dim1, dim2);
|
||||
}
|
||||
|
@ -629,8 +674,9 @@ at::Tensor LazyNativeFunctions::_trilinear(
|
|||
const at::Tensor &i1, const at::Tensor &i2, const at::Tensor &i3,
|
||||
at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3,
|
||||
at::IntArrayRef sumdim, int64_t unroll_dim) {
|
||||
return at::functionalization::functionalize_aten_op<ATEN_OP(_trilinear)>::
|
||||
call(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim);
|
||||
return at::functionalization::functionalize_aten_op<ATEN_OP(
|
||||
_trilinear)>::call(i1, i2, i3, expand1, expand2, expand3, sumdim,
|
||||
unroll_dim);
|
||||
}
|
||||
at::Tensor LazyNativeFunctions::linalg_pinv(
|
||||
const at::Tensor &self, const c10::optional<at::Tensor> &atol,
|
||||
|
@ -640,10 +686,11 @@ at::Tensor LazyNativeFunctions::linalg_pinv(
|
|||
}
|
||||
|
||||
// functionalize_aten_op can't handle out= ops directly.
|
||||
// Instead, we can call the composite kernel from core, and copy and mutations back to the inputs.
|
||||
at::Tensor& LazyNativeFunctions::logsumexp_out(
|
||||
const at::Tensor& self, at::IntArrayRef dim, bool keepdim,
|
||||
at::Tensor& out) {
|
||||
// Instead, we can call the composite kernel from core, and copy and mutations
|
||||
// back to the inputs.
|
||||
at::Tensor &LazyNativeFunctions::logsumexp_out(const at::Tensor &self,
|
||||
at::IntArrayRef dim,
|
||||
bool keepdim, at::Tensor &out) {
|
||||
auto self_wrapped = at::functionalization::impl::to_functional_tensor(self);
|
||||
auto out_wrapped = at::functionalization::impl::to_functional_tensor(out);
|
||||
// directly call the composite kernel from core.
|
||||
|
|
|
@ -18,8 +18,7 @@ namespace lazy {
|
|||
|
||||
namespace {
|
||||
|
||||
hash_t OperandHashes(
|
||||
const OpList& operands, const c10::ArrayRef<Shape>& shapes,
|
||||
hash_t OperandHashes(const OpList &operands, const c10::ArrayRef<Shape> &shapes,
|
||||
const hash_t &seed, bool bakeInSizes) {
|
||||
hash_t hash = seed;
|
||||
for (auto &operand : operands) {
|
||||
|
@ -38,21 +37,20 @@ hash_t OperandHashes(
|
|||
|
||||
} // namespace
|
||||
|
||||
|
||||
// Adds a static hook that is run after every single TorchMlirNode is initialized
|
||||
// Adds a static hook that is run after every single TorchMlirNode is
|
||||
// initialized
|
||||
static std::vector<std::function<void(TorchMlirNode *)>> constructor_hooks;
|
||||
void TorchMlirNode::addConstructorHook(std::function<void(TorchMlirNode *)> f) {
|
||||
constructor_hooks.emplace_back(f);
|
||||
}
|
||||
|
||||
TorchMlirNode::TorchMlirNode(
|
||||
OpKind op, OpList operands, std::vector<Shape>&& shapes, size_t num_outputs,
|
||||
TorchMlirNode::TorchMlirNode(OpKind op, OpList operands,
|
||||
std::vector<Shape> &&shapes, size_t num_outputs,
|
||||
hash_t hash_seed)
|
||||
: Node(op, operands, std::move(shapes), num_outputs) {
|
||||
hash_seed = HashCombine(op.hash(), hash_seed);
|
||||
shape_hash_ = OperandHashes(operands, this->shapes(), hash_seed, true);
|
||||
dag_hash_ =
|
||||
(enableDynamicShape()
|
||||
dag_hash_ = (enableDynamicShape()
|
||||
? OperandHashes(operands, this->shapes(), hash_seed, false)
|
||||
: shape_hash_);
|
||||
|
||||
|
@ -61,28 +59,27 @@ TorchMlirNode::TorchMlirNode(
|
|||
}
|
||||
}
|
||||
|
||||
TorchMlirNode::TorchMlirNode(
|
||||
OpKind op, OpList operands, const std::function<Shape()>& shape_fn,
|
||||
TorchMlirNode::TorchMlirNode(OpKind op, OpList operands,
|
||||
const std::function<Shape()> &shape_fn,
|
||||
size_t num_outputs, hash_t hash_seed)
|
||||
: TorchMlirNode(
|
||||
op, operands, std::vector<Shape>{}, num_outputs, hash_seed) {
|
||||
: TorchMlirNode(op, operands, std::vector<Shape>{}, num_outputs,
|
||||
hash_seed) {
|
||||
addComputedShape(shape_fn);
|
||||
}
|
||||
|
||||
TorchMlirNode::TorchMlirNode(
|
||||
OpKind op, OpList operands, size_t num_outputs, hash_t hash_seed)
|
||||
: TorchMlirNode(
|
||||
op, operands, std::vector<Shape>{}, num_outputs, hash_seed) {}
|
||||
TorchMlirNode::TorchMlirNode(OpKind op, OpList operands, size_t num_outputs,
|
||||
hash_t hash_seed)
|
||||
: TorchMlirNode(op, operands, std::vector<Shape>{}, num_outputs,
|
||||
hash_seed) {}
|
||||
|
||||
TorchMlirNode::TorchMlirNode(
|
||||
OpKind op, Shape shape, size_t num_outputs, hash_t hash_seed)
|
||||
TorchMlirNode::TorchMlirNode(OpKind op, Shape shape, size_t num_outputs,
|
||||
hash_t hash_seed)
|
||||
: TorchMlirNode(op, {}, {std::move(shape)}, num_outputs, hash_seed) {}
|
||||
|
||||
hash_t TorchMlirNode::hash() const { return dag_hash_; }
|
||||
|
||||
hash_t TorchMlirNode::shapeHash() const { return shape_hash_; }
|
||||
|
||||
|
||||
TorchMlirNode *TorchMlirNode::mlir_node(int index) const {
|
||||
return dynamic_cast<TorchMlirNode *>(operands_.at(index).get());
|
||||
}
|
||||
|
@ -107,8 +104,9 @@ TorchMlirTensorList::TorchMlirTensorList(OpList values)
|
|||
/*num_outputs=*/1,
|
||||
/*hash_seed=*/kHashSeed) {}
|
||||
|
||||
torch::lazy::TorchMlirOpVector TorchMlirTensorList::Lower(
|
||||
TorchMlirFunction function, TorchMlirLoweringContext* loctx) const {
|
||||
torch::lazy::TorchMlirOpVector
|
||||
TorchMlirTensorList::Lower(TorchMlirFunction function,
|
||||
TorchMlirLoweringContext *loctx) const {
|
||||
std::vector<torch::jit::Value *> tensor_list;
|
||||
CHECK(!operands().empty());
|
||||
for (const torch::lazy::Output &operand : operands()) {
|
||||
|
@ -140,16 +138,17 @@ TorchMlirOptionalTensorList::TorchMlirOptionalTensorList(OpList values)
|
|||
/*num_outputs=*/1,
|
||||
/*hash_seed=*/kHashSeed) {}
|
||||
|
||||
torch::lazy::TorchMlirOpVector TorchMlirOptionalTensorList::Lower(
|
||||
TorchMlirFunction function, TorchMlirLoweringContext* loctx) const {
|
||||
torch::lazy::TorchMlirOpVector
|
||||
TorchMlirOptionalTensorList::Lower(TorchMlirFunction function,
|
||||
TorchMlirLoweringContext *loctx) const {
|
||||
std::vector<torch::jit::Value *> tensor_list;
|
||||
CHECK(!operands().empty());
|
||||
for (const torch::lazy::Output &operand : operands()) {
|
||||
tensor_list.emplace_back(loctx->GetOutputOp(operand));
|
||||
}
|
||||
auto graph = function->graph();
|
||||
auto listnode =
|
||||
graph->insertNode(graph->createList(c10::OptionalType::create(c10::TensorType::get()), tensor_list));
|
||||
auto listnode = graph->insertNode(graph->createList(
|
||||
c10::OptionalType::create(c10::TensorType::get()), tensor_list));
|
||||
return {listnode->output()};
|
||||
}
|
||||
|
||||
|
|
|
@ -27,22 +27,21 @@ namespace lazy {
|
|||
|
||||
class TORCH_API TorchMlirNode : public torch::lazy::Node {
|
||||
public:
|
||||
TorchMlirNode(
|
||||
OpKind op, OpList operands, std::vector<Shape>&& shapes,
|
||||
TorchMlirNode(OpKind op, OpList operands, std::vector<Shape> &&shapes,
|
||||
size_t num_outputs, hash_t hash_seed = kHashSeed);
|
||||
|
||||
TorchMlirNode(
|
||||
OpKind op, OpList operands, const std::function<Shape()>& shape_fn,
|
||||
size_t num_outputs, hash_t hash_seed = kHashSeed);
|
||||
|
||||
TorchMlirNode(
|
||||
OpKind op, OpList operands, size_t num_outputs,
|
||||
TorchMlirNode(OpKind op, OpList operands,
|
||||
const std::function<Shape()> &shape_fn, size_t num_outputs,
|
||||
hash_t hash_seed = kHashSeed);
|
||||
|
||||
TorchMlirNode(
|
||||
OpKind op, Shape shape, size_t num_outputs, hash_t hash_seed = kHashSeed);
|
||||
TorchMlirNode(OpKind op, OpList operands, size_t num_outputs,
|
||||
hash_t hash_seed = kHashSeed);
|
||||
|
||||
// Adds a static hook that is run after every single TorchMlirNode is constructed
|
||||
TorchMlirNode(OpKind op, Shape shape, size_t num_outputs,
|
||||
hash_t hash_seed = kHashSeed);
|
||||
|
||||
// Adds a static hook that is run after every single TorchMlirNode is
|
||||
// constructed
|
||||
static void addConstructorHook(std::function<void(TorchMlirNode *)>);
|
||||
|
||||
~TorchMlirNode() override = default;
|
||||
|
@ -53,8 +52,8 @@ public:
|
|||
|
||||
TorchMlirNode *mlir_node(int index) const;
|
||||
|
||||
virtual TorchMlirOpVector
|
||||
Lower(TorchMlirFunction function, TorchMlirLoweringContext* loctx) const;
|
||||
virtual TorchMlirOpVector Lower(TorchMlirFunction function,
|
||||
TorchMlirLoweringContext *loctx) const;
|
||||
|
||||
private:
|
||||
// The hash of the dag WITH size info. Used for shape caching
|
||||
|
@ -86,21 +85,22 @@ struct TORCH_API TorchMlirTensorList : public TorchMlirNode {
|
|||
TorchMlirTensorList() = delete;
|
||||
TorchMlirTensorList(OpList values);
|
||||
|
||||
torch::lazy::TorchMlirOpVector Lower(
|
||||
TorchMlirFunction function,
|
||||
torch::lazy::TorchMlirOpVector
|
||||
Lower(TorchMlirFunction function,
|
||||
TorchMlirLoweringContext *loctx) const override;
|
||||
};
|
||||
|
||||
// TorchMlirOptionalTensorList is similar to TorchMlirTensorList but it can also represent
|
||||
// optional tensors, so the output type for this op is !torch.list<optional<vtensor>>.
|
||||
// TorchMlirOptionalTensorList is similar to TorchMlirTensorList but it can also
|
||||
// represent optional tensors, so the output type for this op is
|
||||
// !torch.list<optional<vtensor>>.
|
||||
struct TORCH_API TorchMlirOptionalTensorList : public TorchMlirNode {
|
||||
static OpKind ClassOpKind();
|
||||
|
||||
TorchMlirOptionalTensorList() = delete;
|
||||
TorchMlirOptionalTensorList(OpList values);
|
||||
|
||||
torch::lazy::TorchMlirOpVector Lower(
|
||||
TorchMlirFunction function,
|
||||
torch::lazy::TorchMlirOpVector
|
||||
Lower(TorchMlirFunction function,
|
||||
TorchMlirLoweringContext *loctx) const override;
|
||||
};
|
||||
|
||||
|
|
|
@ -31,8 +31,8 @@
|
|||
namespace torch {
|
||||
namespace lazy {
|
||||
|
||||
TorchMlirOpVector LowerTorchMlirBuiltin(
|
||||
TorchMlirFunction function, c10::Symbol sym,
|
||||
TorchMlirOpVector
|
||||
LowerTorchMlirBuiltin(TorchMlirFunction function, c10::Symbol sym,
|
||||
const std::vector<c10::TypePtr> tensor_types,
|
||||
const std::vector<torch::jit::NamedValue> &arguments,
|
||||
const std::vector<torch::jit::NamedValue> &kwarguments) {
|
||||
|
@ -43,9 +43,11 @@ TorchMlirOpVector LowerTorchMlirBuiltin(
|
|||
for (auto arg : arguments) {
|
||||
torch::jit::Value *value = arg.value(dummy_graph);
|
||||
if (value->type()->kind() == c10::TypeKind::ListType) {
|
||||
auto list_element_type = value->type()->cast<c10::ListType>()->getElementType();
|
||||
auto list_element_type =
|
||||
value->type()->cast<c10::ListType>()->getElementType();
|
||||
if (list_element_type->cast<c10::OptionalType>()) {
|
||||
value->setType(c10::ListType::create(c10::OptionalType::create(c10::TensorType::get())));
|
||||
value->setType(c10::ListType::create(
|
||||
c10::OptionalType::create(c10::TensorType::get())));
|
||||
} else {
|
||||
value->setType(c10::ListType::create(c10::TensorType::get()));
|
||||
}
|
||||
|
@ -61,16 +63,18 @@ TorchMlirOpVector LowerTorchMlirBuiltin(
|
|||
|
||||
TorchMlirOpVector results;
|
||||
if (sv->getValue()->type()->kind() == c10::TypeKind::ListType) {
|
||||
// Unpack dynamic multi-output operations like aten::split with Tensor[] output type.
|
||||
// This is required to have consistent input types for multi-output node consumers.
|
||||
torch::jit::Node * node = function->graph()->createListUnpack(sv->getValue(), tensor_types.size());
|
||||
// Unpack dynamic multi-output operations like aten::split with Tensor[]
|
||||
// output type. This is required to have consistent input types for
|
||||
// multi-output node consumers.
|
||||
torch::jit::Node *node = function->graph()->createListUnpack(
|
||||
sv->getValue(), tensor_types.size());
|
||||
function->graph()->insertNode(node);
|
||||
for (const auto &output : node->outputs()) {
|
||||
results.push_back(output);
|
||||
}
|
||||
} else if (sv->getValue()->type()->kind() == c10::TypeKind::TupleType) {
|
||||
// Op returns multiple values and the number of outputs is static and defined
|
||||
// by the operation schema.
|
||||
// Op returns multiple values and the number of outputs is static and
|
||||
// defined by the operation schema.
|
||||
const auto tuple_call_result = sv->asTuple({}, *function);
|
||||
for (const auto &tuple_component : tuple_call_result) {
|
||||
auto tuple_component_sv =
|
||||
|
@ -97,16 +101,15 @@ TorchMlirOpVector LowerTorchMlirBuiltin(
|
|||
}
|
||||
|
||||
// Ensure that we use up all the known tensor type information available.
|
||||
TORCH_CHECK(
|
||||
tensor_type_idx == tensor_types.size(), tensor_type_idx,
|
||||
" known types were injected into jit::Value, but ", tensor_types.size(),
|
||||
" were provided from lazy::Node!");
|
||||
TORCH_CHECK(tensor_type_idx == tensor_types.size(), tensor_type_idx,
|
||||
" known types were injected into jit::Value, but ",
|
||||
tensor_types.size(), " were provided from lazy::Node!");
|
||||
|
||||
return results;
|
||||
}
|
||||
|
||||
TorchMlirOpVector LowerTorchMlirBuiltin(
|
||||
TorchMlirFunction function, c10::Symbol sym,
|
||||
TorchMlirOpVector
|
||||
LowerTorchMlirBuiltin(TorchMlirFunction function, c10::Symbol sym,
|
||||
const c10::ArrayRef<Shape> result_shapes,
|
||||
const std::vector<torch::jit::NamedValue> &arguments,
|
||||
const std::vector<torch::jit::NamedValue> &kwarguments) {
|
||||
|
@ -122,27 +125,27 @@ TorchMlirOpVector LowerTorchMlirBuiltin(
|
|||
/*requires_grad=*/c10::nullopt));
|
||||
}
|
||||
|
||||
return LowerTorchMlirBuiltin(
|
||||
function, sym, tensor_types, arguments, kwarguments);
|
||||
return LowerTorchMlirBuiltin(function, sym, tensor_types, arguments,
|
||||
kwarguments);
|
||||
}
|
||||
|
||||
TorchMlirOpVector LowerBuiltin(
|
||||
const torch::lazy::Node* node, TorchMlirFunction function,
|
||||
TorchMlirOpVector
|
||||
LowerBuiltin(const torch::lazy::Node *node, TorchMlirFunction function,
|
||||
const std::vector<torch::jit::NamedValue> &arguments,
|
||||
const std::vector<torch::jit::NamedValue> &kwarguments = {}) {
|
||||
return LowerTorchMlirBuiltin(
|
||||
function, node->op().op, node->shapes(), arguments, kwarguments);
|
||||
return LowerTorchMlirBuiltin(function, node->op().op, node->shapes(),
|
||||
arguments, kwarguments);
|
||||
}
|
||||
TorchMlirOpVector LowerBuiltin(
|
||||
c10::Symbol sym, const c10::ArrayRef<Shape> result_shapes,
|
||||
TorchMlirOpVector
|
||||
LowerBuiltin(c10::Symbol sym, const c10::ArrayRef<Shape> result_shapes,
|
||||
TorchMlirFunction function,
|
||||
const std::vector<torch::jit::NamedValue> &arguments,
|
||||
const std::vector<torch::jit::NamedValue> &kwarguments = {}) {
|
||||
return LowerTorchMlirBuiltin(
|
||||
function, sym, result_shapes, arguments, kwarguments);
|
||||
return LowerTorchMlirBuiltin(function, sym, result_shapes, arguments,
|
||||
kwarguments);
|
||||
}
|
||||
TorchMlirOpVector LowerBuiltin(
|
||||
c10::Symbol sym, const std::vector<c10::TypePtr> types,
|
||||
TorchMlirOpVector
|
||||
LowerBuiltin(c10::Symbol sym, const std::vector<c10::TypePtr> types,
|
||||
TorchMlirFunction function,
|
||||
const std::vector<torch::jit::NamedValue> &arguments,
|
||||
const std::vector<torch::jit::NamedValue> &kwarguments = {}) {
|
||||
|
@ -181,14 +184,14 @@ std::vector<torch::lazy::Shape> compute_shape_copy(c10::TypePtr value_type) {
|
|||
TORCH_CHECK(maybe_dims.has_value(), "Cannot copy unranked tensor!");
|
||||
|
||||
auto scalar_type = tensor_type.scalarType();
|
||||
TORCH_CHECK(
|
||||
scalar_type.has_value(), "Unable to copy due to lack of scalar type!");
|
||||
TORCH_CHECK(scalar_type.has_value(),
|
||||
"Unable to copy due to lack of scalar type!");
|
||||
return {Shape(scalar_type.value(), maybe_dims.value())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_slice(
|
||||
c10::TypePtr value_type, int64_t dim, int64_t start, int64_t end,
|
||||
int64_t step) {
|
||||
std::vector<torch::lazy::Shape> compute_shape_slice(c10::TypePtr value_type,
|
||||
int64_t dim, int64_t start,
|
||||
int64_t end, int64_t step) {
|
||||
c10::TensorType &tensor_type = cast_tensor_type(value_type);
|
||||
|
||||
auto maybe_dims = get_tensor_type_shape(tensor_type);
|
||||
|
@ -217,13 +220,13 @@ std::vector<torch::lazy::Shape> compute_shape_slice(
|
|||
}
|
||||
|
||||
auto scalar_type = tensor_type.scalarType();
|
||||
TORCH_CHECK(
|
||||
scalar_type.has_value(), "Unable to slice due to lack of scalar type!");
|
||||
TORCH_CHECK(scalar_type.has_value(),
|
||||
"Unable to slice due to lack of scalar type!");
|
||||
return {Shape(scalar_type.value(), dims)};
|
||||
}
|
||||
|
||||
torch::jit::Value*
|
||||
GenerateClone(torch::jit::Value* val, TorchMlirFunction function) {
|
||||
torch::jit::Value *GenerateClone(torch::jit::Value *val,
|
||||
TorchMlirFunction function) {
|
||||
std::vector<torch::jit::NamedValue> clone_arguments;
|
||||
clone_arguments.emplace_back(val);
|
||||
|
||||
|
@ -234,20 +237,19 @@ GenerateClone(torch::jit::Value* val, TorchMlirFunction function) {
|
|||
return cloned.front();
|
||||
}
|
||||
|
||||
void GenerateCopy(
|
||||
torch::jit::Value* destination, torch::jit::Value* source,
|
||||
void GenerateCopy(torch::jit::Value *destination, torch::jit::Value *source,
|
||||
TorchMlirFunction function) {
|
||||
std::vector<torch::jit::NamedValue> arguments;
|
||||
arguments.emplace_back(destination);
|
||||
arguments.emplace_back(source);
|
||||
LowerBuiltin(
|
||||
at::aten::copy_, c10::ArrayRef<Shape>(compute_shape_copy(source->type())),
|
||||
LowerBuiltin(at::aten::copy_,
|
||||
c10::ArrayRef<Shape>(compute_shape_copy(source->type())),
|
||||
function, arguments);
|
||||
}
|
||||
|
||||
torch::jit::Value* GenerateSlice(
|
||||
torch::jit::Value* base, int64_t dim, int64_t start, int64_t end,
|
||||
int64_t step, TorchMlirFunction function) {
|
||||
torch::jit::Value *GenerateSlice(torch::jit::Value *base, int64_t dim,
|
||||
int64_t start, int64_t end, int64_t step,
|
||||
TorchMlirFunction function) {
|
||||
std::vector<torch::jit::NamedValue> arguments;
|
||||
arguments.emplace_back(base);
|
||||
arguments.emplace_back(dim);
|
||||
|
@ -255,10 +257,10 @@ torch::jit::Value* GenerateSlice(
|
|||
arguments.emplace_back(end);
|
||||
arguments.emplace_back(step);
|
||||
|
||||
TorchMlirOpVector selected = LowerBuiltin(
|
||||
at::aten::slice,
|
||||
c10::ArrayRef<Shape>(
|
||||
compute_shape_slice(base->type(), dim, start, end, step)),
|
||||
TorchMlirOpVector selected =
|
||||
LowerBuiltin(at::aten::slice,
|
||||
c10::ArrayRef<Shape>(compute_shape_slice(base->type(), dim,
|
||||
start, end, step)),
|
||||
function, arguments);
|
||||
TORCH_CHECK_EQ(selected.size(), 1);
|
||||
return selected.front();
|
||||
|
@ -267,8 +269,8 @@ torch::jit::Value* GenerateSlice(
|
|||
// Node Lowerings
|
||||
|
||||
// Default Node Lowering
|
||||
TorchMlirOpVector TorchMlirNode::Lower(
|
||||
TorchMlirFunction function, TorchMlirLoweringContext* loctx) const {
|
||||
TorchMlirOpVector TorchMlirNode::Lower(TorchMlirFunction function,
|
||||
TorchMlirLoweringContext *loctx) const {
|
||||
std::vector<torch::jit::NamedValue> arguments;
|
||||
for (const torch::lazy::Output &output : operands()) {
|
||||
arguments.emplace_back(loctx->GetOutputOp(output));
|
||||
|
@ -280,16 +282,16 @@ TorchMlirOpVector TorchMlirNode::Lower(
|
|||
|
||||
// Non-native nodes
|
||||
|
||||
TorchMlirOpVector
|
||||
Cast::Lower(TorchMlirFunction function, TorchMlirLoweringContext* loctx) const {
|
||||
TorchMlirOpVector Cast::Lower(TorchMlirFunction function,
|
||||
TorchMlirLoweringContext *loctx) const {
|
||||
std::vector<torch::jit::NamedValue> arguments;
|
||||
arguments.emplace_back(loctx->GetOutputOp(operand(0)));
|
||||
arguments.emplace_back(dtype);
|
||||
return LowerBuiltin(at::aten::to, shapes(), function, arguments);
|
||||
}
|
||||
|
||||
TorchMlirOpVector DeviceData::Lower(
|
||||
TorchMlirFunction function, TorchMlirLoweringContext* loctx) const {
|
||||
TorchMlirOpVector DeviceData::Lower(TorchMlirFunction function,
|
||||
TorchMlirLoweringContext *loctx) const {
|
||||
auto infoptr = data_->info();
|
||||
auto deviceDataInfoPtr =
|
||||
(torch::lazy::LazyGraphExecutor::DeviceDataInfo *)infoptr;
|
||||
|
@ -300,8 +302,8 @@ TorchMlirOpVector DeviceData::Lower(
|
|||
return {loctx->GetParameter(data_)};
|
||||
}
|
||||
|
||||
TorchMlirOpVector Scalar::Lower(
|
||||
TorchMlirFunction function, TorchMlirLoweringContext* loctx) const {
|
||||
TorchMlirOpVector Scalar::Lower(TorchMlirFunction function,
|
||||
TorchMlirLoweringContext *loctx) const {
|
||||
auto options =
|
||||
at::TensorOptions()
|
||||
.device(torch::lazy::getBackend()->EagerFallbackDeviceType())
|
||||
|
@ -309,8 +311,8 @@ TorchMlirOpVector Scalar::Lower(
|
|||
return {loctx->graph()->insertConstant(at::scalar_tensor(value, options))};
|
||||
}
|
||||
|
||||
TorchMlirOpVector Expand::Lower(
|
||||
TorchMlirFunction function, TorchMlirLoweringContext* loctx) const {
|
||||
TorchMlirOpVector Expand::Lower(TorchMlirFunction function,
|
||||
TorchMlirLoweringContext *loctx) const {
|
||||
std::vector<torch::jit::NamedValue> arguments;
|
||||
arguments.emplace_back(loctx->GetOutputOp(operand(0)));
|
||||
arguments.emplace_back(size);
|
||||
|
|
|
@ -2,16 +2,14 @@
|
|||
|
||||
#include <torch/csrc/lazy/core/ir_builder.h>
|
||||
|
||||
#include "device_data.h"
|
||||
#include "../backend_impl.h"
|
||||
#include "device_data.h"
|
||||
|
||||
namespace torch {
|
||||
namespace lazy {
|
||||
|
||||
DeviceData::DeviceData(std::shared_ptr<BackendData> data)
|
||||
: TorchMlirNode(
|
||||
ClassOpKind(),
|
||||
data->shape(),
|
||||
: TorchMlirNode(ClassOpKind(), data->shape(),
|
||||
/*num_outputs=*/1,
|
||||
/*hash_seed=*/static_cast<uint32_t>(101)),
|
||||
data_(std::move(data)) {
|
||||
|
@ -21,9 +19,11 @@ DeviceData::DeviceData(std::shared_ptr<BackendData> data)
|
|||
void DeviceData::propagate_name() {
|
||||
if (data_ && name_ != "") {
|
||||
// Add device data name to backend data
|
||||
TorchMlirBackendData* mlir_data = dynamic_cast<TorchMlirBackendData*>(data_.get());
|
||||
TorchMlirBackendData *mlir_data =
|
||||
dynamic_cast<TorchMlirBackendData *>(data_.get());
|
||||
TORCH_CHECK(mlir_data);
|
||||
auto* info = dynamic_cast<TorchMlirBackendData::Info*>(mlir_data->mlir_info());
|
||||
auto *info =
|
||||
dynamic_cast<TorchMlirBackendData::Info *>(mlir_data->mlir_info());
|
||||
TORCH_CHECK(info);
|
||||
info->name = name_;
|
||||
}
|
||||
|
|
|
@ -6,15 +6,12 @@
|
|||
#include <torch/csrc/lazy/backend/backend_data.h>
|
||||
#include <torch/csrc/lazy/core/internal_ops/ltc_ops.h>
|
||||
|
||||
|
||||
namespace torch {
|
||||
namespace lazy {
|
||||
|
||||
class TORCH_API DeviceData : public TorchMlirNode {
|
||||
public:
|
||||
static OpKind ClassOpKind() {
|
||||
return ltc_device_data;
|
||||
}
|
||||
static OpKind ClassOpKind() { return ltc_device_data; }
|
||||
|
||||
explicit DeviceData(std::shared_ptr<BackendData> data);
|
||||
|
||||
|
@ -31,7 +28,8 @@ class TORCH_API DeviceData : public TorchMlirNode {
|
|||
|
||||
void SetData(std::shared_ptr<BackendData> data);
|
||||
|
||||
TorchMlirOpVector Lower(TorchMlirFunction function, TorchMlirLoweringContext* loctx) const override;
|
||||
TorchMlirOpVector Lower(TorchMlirFunction function,
|
||||
TorchMlirLoweringContext *loctx) const override;
|
||||
|
||||
static const DeviceData *Cast(const Node *node);
|
||||
|
||||
|
|
|
@ -15,11 +15,7 @@
|
|||
namespace torch {
|
||||
namespace lazy {
|
||||
|
||||
Generic::Generic(
|
||||
OpKind op,
|
||||
OpList operands,
|
||||
Shape shape,
|
||||
size_t num_outputs,
|
||||
Generic::Generic(OpKind op, OpList operands, Shape shape, size_t num_outputs,
|
||||
hash_t hash_seed)
|
||||
: TorchMlirNode(op, operands, {std::move(shape)}, num_outputs, hash_seed),
|
||||
hash_seed_(hash_seed) {}
|
||||
|
|
|
@ -24,11 +24,7 @@ namespace lazy {
|
|||
// Doing the former would limit IR introspection.
|
||||
class TORCH_API Generic : public TorchMlirNode {
|
||||
public:
|
||||
Generic(
|
||||
OpKind op,
|
||||
OpList operands,
|
||||
Shape shape,
|
||||
size_t num_outputs = 1,
|
||||
Generic(OpKind op, OpList operands, Shape shape, size_t num_outputs = 1,
|
||||
hash_t hash_seed = static_cast<uint32_t>(0x5a2d296e9));
|
||||
|
||||
private:
|
||||
|
|
|
@ -17,25 +17,28 @@
|
|||
namespace torch {
|
||||
namespace lazy {
|
||||
|
||||
|
||||
// This IR was copied from code-generated output, but the entire _to_copy operator
|
||||
// cannot be trivially code genereated since it is only desirable to capture IR for
|
||||
// certain permutaions of _to_copy (e.g. dtype), and for the others it is difficult to even invoke
|
||||
// the aten/eager fallback necessitating directly implementing the right to(device) behavior
|
||||
// This IR was copied from code-generated output, but the entire _to_copy
|
||||
// operator cannot be trivially code genereated since it is only desirable to
|
||||
// capture IR for certain permutaions of _to_copy (e.g. dtype), and for the
|
||||
// others it is difficult to even invoke the aten/eager fallback necessitating
|
||||
// directly implementing the right to(device) behavior
|
||||
class ToCopy : public torch::lazy::TorchMlirNode {
|
||||
public:
|
||||
ToCopy(const torch::lazy::Value& self, const c10::optional<at::ScalarType>& dtype, const c10::optional<at::Layout>& layout, const c10::optional<at::Device>& device, const c10::optional<bool>& pin_memory, const bool& non_blocking, const c10::optional<at::MemoryFormat>& memory_format, std::vector<torch::lazy::Shape>&& shapes)
|
||||
: torch::lazy::TorchMlirNode(torch::lazy::OpKind(at::aten::_to_copy),
|
||||
{self}, std::move(shapes),
|
||||
ToCopy(const torch::lazy::Value &self,
|
||||
const c10::optional<at::ScalarType> &dtype,
|
||||
const c10::optional<at::Layout> &layout,
|
||||
const c10::optional<at::Device> &device,
|
||||
const c10::optional<bool> &pin_memory, const bool &non_blocking,
|
||||
const c10::optional<at::MemoryFormat> &memory_format,
|
||||
std::vector<torch::lazy::Shape> &&shapes)
|
||||
: torch::lazy::TorchMlirNode(
|
||||
torch::lazy::OpKind(at::aten::_to_copy), {self}, std::move(shapes),
|
||||
/* num_outputs */ 1,
|
||||
torch::lazy::MHash(dtype, layout, device, pin_memory, non_blocking, memory_format)),
|
||||
torch::lazy::MHash(dtype, layout, device, pin_memory, non_blocking,
|
||||
memory_format)),
|
||||
|
||||
dtype(dtype),
|
||||
layout(layout),
|
||||
device(device),
|
||||
pin_memory(pin_memory),
|
||||
non_blocking(non_blocking),
|
||||
memory_format(memory_format) {}
|
||||
dtype(dtype), layout(layout), device(device), pin_memory(pin_memory),
|
||||
non_blocking(non_blocking), memory_format(memory_format) {}
|
||||
|
||||
std::string ToString() const override {
|
||||
std::stringstream ss;
|
||||
|
@ -69,7 +72,8 @@ class ToCopy : public torch::lazy::TorchMlirNode {
|
|||
return ss.str();
|
||||
}
|
||||
|
||||
torch::lazy::TorchMlirOpVector Lower(TorchMlirFunction function,
|
||||
torch::lazy::TorchMlirOpVector
|
||||
Lower(TorchMlirFunction function,
|
||||
torch::lazy::TorchMlirLoweringContext *loctx) const override {
|
||||
std::vector<torch::jit::NamedValue> arguments;
|
||||
std::vector<torch::jit::NamedValue> kwarguments;
|
||||
|
@ -83,11 +87,12 @@ class ToCopy : public torch::lazy::TorchMlirNode {
|
|||
kwarguments.emplace_back("pin_memory", pin_memory);
|
||||
kwarguments.emplace_back("non_blocking", non_blocking);
|
||||
kwarguments.emplace_back("memory_format", memory_format);
|
||||
torch::lazy::TorchMlirOpVector _to_copy_out = torch::lazy::LowerTorchMlirBuiltin(function, op().op, shapes(), arguments, kwarguments);
|
||||
torch::lazy::TorchMlirOpVector _to_copy_out =
|
||||
torch::lazy::LowerTorchMlirBuiltin(function, op().op, shapes(),
|
||||
arguments, kwarguments);
|
||||
TORCH_CHECK_EQ(_to_copy_out.size(), 1);
|
||||
|
||||
return _to_copy_out;
|
||||
|
||||
}
|
||||
|
||||
c10::optional<at::ScalarType> dtype;
|
||||
|
|
|
@ -27,7 +27,6 @@ std::vector<torch::lazy::Shape> compute_shape_add(const at::Tensor& self,
|
|||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_sub(const at::Tensor &self,
|
||||
const at::Scalar &other,
|
||||
const at::Scalar &alpha) {
|
||||
|
@ -96,9 +95,8 @@ std::vector<torch::lazy::Shape> compute_shape_quantize_per_channel(
|
|||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_max_pool3d_with_indices(
|
||||
const at::Tensor& self, at::IntArrayRef kernel_size,
|
||||
at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation,
|
||||
bool ceil_mode) {
|
||||
const at::Tensor &self, at::IntArrayRef kernel_size, at::IntArrayRef stride,
|
||||
at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) {
|
||||
auto in_sizes = self.sizes().vec();
|
||||
std::vector<int64_t> dhw(3, 0);
|
||||
std::vector<int64_t> paddings = padding.vec();
|
||||
|
@ -107,17 +105,18 @@ std::vector<torch::lazy::Shape> compute_shape_max_pool3d_with_indices(
|
|||
std::vector<int64_t> strides = stride.vec();
|
||||
TORCH_CHECK(in_sizes.size() == 5, "max_pool3d requires 5D inputs, but got ",
|
||||
in_sizes);
|
||||
TORCH_CHECK(kernel_size.size() == 3 &&
|
||||
stride.size() == 3 &&
|
||||
padding.size() == 3 &&
|
||||
dilation.size() == 3, "max_pool3d requires 3D operands, but got ",
|
||||
kernel_size, stride, padding, dilation);
|
||||
TORCH_CHECK(kernel_size.size() == 3 && stride.size() == 3 &&
|
||||
padding.size() == 3 && dilation.size() == 3,
|
||||
"max_pool3d requires 3D operands, but got ", kernel_size, stride,
|
||||
padding, dilation);
|
||||
int64_t batch = in_sizes[0];
|
||||
int64_t channel = in_sizes[1]; // NCDHW
|
||||
// https://pytorch.org/docs/stable/generated/torch.nn.MaxPool3d.html
|
||||
for (auto i = 0UL; i < 3; ++i) {
|
||||
double out_size = (in_sizes[2+i] + 2 * paddings[i] - dilations[i] *
|
||||
(ksizes[i] - 1) - 1) / (double)strides[i] + 1;
|
||||
double out_size = (in_sizes[2 + i] + 2 * paddings[i] -
|
||||
dilations[i] * (ksizes[i] - 1) - 1) /
|
||||
(double)strides[i] +
|
||||
1;
|
||||
if (ceil_mode)
|
||||
dhw[i] = (int64_t)std::ceil(out_size);
|
||||
else
|
||||
|
@ -136,8 +135,9 @@ std::vector<torch::lazy::Shape> compute_shape_max_pool3d_with_indices_backward(
|
|||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_mse_loss_backward(
|
||||
const at::Tensor& grad_output, const at::Tensor& self,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_mse_loss_backward(const at::Tensor &grad_output,
|
||||
const at::Tensor &self,
|
||||
const at::Tensor &target, int64_t reduction) {
|
||||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
@ -147,21 +147,22 @@ std::vector<torch::lazy::Shape> compute_shape_mul(const at::Tensor& self,
|
|||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_var(
|
||||
const at::Tensor& self, at::OptionalIntArrayRef dim,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_var(const at::Tensor &self, at::OptionalIntArrayRef dim,
|
||||
const c10::optional<at::Scalar> &correction, bool keepdim) {
|
||||
// Result of variance is scalar tensor.
|
||||
return {Shape(self.scalar_type(), {})};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_nan_to_num(
|
||||
const at::Tensor & self, c10::optional<double> nan,
|
||||
c10::optional<double> posinf, c10::optional<double> neginf) {
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_nan_to_num(const at::Tensor &self, c10::optional<double> nan,
|
||||
c10::optional<double> posinf,
|
||||
c10::optional<double> neginf) {
|
||||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_hardtanh(
|
||||
const at::Tensor& self, const at::Scalar& min_val,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_hardtanh(const at::Tensor &self, const at::Scalar &min_val,
|
||||
const at::Scalar &max_val) {
|
||||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
@ -201,9 +202,9 @@ std::vector<torch::lazy::Shape> compute_shape_where(const at::Tensor& condition,
|
|||
return {Shape(out_meta.scalar_type(), out_meta.sizes().vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_bucketize(
|
||||
const at::Tensor& self, const at::Tensor& boundaries, bool out_int32,
|
||||
bool right) {
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_bucketize(const at::Tensor &self, const at::Tensor &boundaries,
|
||||
bool out_int32, bool right) {
|
||||
auto dtype = out_int32 ? at::kInt : at::kLong;
|
||||
return {Shape(dtype, self.sizes().vec())};
|
||||
}
|
||||
|
@ -214,8 +215,8 @@ std::vector<torch::lazy::Shape> compute_shape_copy(const at::Tensor& self,
|
|||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_floor_divide(
|
||||
const at::Tensor& self, const at::Tensor& other) {
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_floor_divide(const at::Tensor &self, const at::Tensor &other) {
|
||||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
||||
|
@ -244,9 +245,10 @@ std::vector<torch::lazy::Shape> compute_shape_native_group_norm(
|
|||
return shapes;
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_im2col(
|
||||
const at::Tensor& self, at::IntArrayRef kernel_size,
|
||||
at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) {
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_im2col(const at::Tensor &self, at::IntArrayRef kernel_size,
|
||||
at::IntArrayRef dilation, at::IntArrayRef padding,
|
||||
at::IntArrayRef stride) {
|
||||
|
||||
auto self_meta = at::native::empty_strided_meta_symint(
|
||||
self.sym_sizes(), self.sym_strides(),
|
||||
|
@ -280,8 +282,8 @@ std::vector<torch::lazy::Shape> compute_shape_native_group_norm_backward(
|
|||
|
||||
return shapes;
|
||||
}
|
||||
std::vector<torch::lazy::Shape> compute_shape_remainder(
|
||||
const at::Tensor& self, const at::Scalar& other) {
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_remainder(const at::Tensor &self, const at::Scalar &other) {
|
||||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
||||
|
@ -313,20 +315,21 @@ compute_shape_reflection_pad2d(const at::Tensor &self,
|
|||
return {Shape(self.scalar_type(), out_sizes)};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_uniform(
|
||||
const at::Tensor& self, double from, double to,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_uniform(const at::Tensor &self, double from, double to,
|
||||
c10::optional<at::Generator> generator) {
|
||||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_normal_functional(
|
||||
const at::Tensor& self, double mean, double std,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_normal_functional(const at::Tensor &self, double mean, double std,
|
||||
c10::optional<at::Generator> generator) {
|
||||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_multinomial(
|
||||
const at::Tensor& self, int64_t num_samples, bool replacement,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_multinomial(const at::Tensor &self, int64_t num_samples,
|
||||
bool replacement,
|
||||
c10::optional<at::Generator> generator) {
|
||||
// Input tensor can be either 1D or 2D. The last dim of output
|
||||
// should be 'num_samples'. So the output shape can be either
|
||||
|
@ -337,27 +340,30 @@ std::vector<torch::lazy::Shape> compute_shape_multinomial(
|
|||
return {Shape(at::kLong, ishape)};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_eye(
|
||||
int64_t n, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout, c10::optional<at::Device> device,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_eye(int64_t n, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout,
|
||||
c10::optional<at::Device> device,
|
||||
c10::optional<bool> pin_memory) {
|
||||
auto out_meta =
|
||||
at::eye(n, dtype, layout, c10::Device(c10::kMeta), pin_memory);
|
||||
return {Shape(out_meta.scalar_type(), out_meta.sizes().vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_eye(
|
||||
int64_t n, int64_t m, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout, c10::optional<at::Device> device,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_eye(int64_t n, int64_t m, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout,
|
||||
c10::optional<at::Device> device,
|
||||
c10::optional<bool> pin_memory) {
|
||||
auto out_meta =
|
||||
at::eye(n, m, dtype, layout, c10::Device(c10::kMeta), pin_memory);
|
||||
return {Shape(out_meta.scalar_type(), out_meta.sizes().vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_arange(
|
||||
const at::Scalar& end, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout, c10::optional<at::Device> device,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_arange(const at::Scalar &end, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout,
|
||||
c10::optional<at::Device> device,
|
||||
c10::optional<bool> pin_memory) {
|
||||
auto out_meta =
|
||||
at::arange(end, dtype, layout, c10::Device(c10::kMeta), pin_memory);
|
||||
|
@ -390,25 +396,28 @@ std::vector<torch::lazy::Shape> compute_shape_full(
|
|||
Shape(dtype.value_or(at::get_default_dtype_as_scalartype()), size.vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_ones(
|
||||
at::IntArrayRef size, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout, c10::optional<at::Device> device,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_ones(at::IntArrayRef size, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout,
|
||||
c10::optional<at::Device> device,
|
||||
c10::optional<bool> pin_memory) {
|
||||
return {
|
||||
Shape(dtype.value_or(at::get_default_dtype_as_scalartype()), size.vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_zeros(
|
||||
at::IntArrayRef size, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout, c10::optional<at::Device> device,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_zeros(at::IntArrayRef size, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout,
|
||||
c10::optional<at::Device> device,
|
||||
c10::optional<bool> pin_memory) {
|
||||
return {
|
||||
Shape(dtype.value_or(at::get_default_dtype_as_scalartype()), size.vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_empty(
|
||||
at::IntArrayRef size, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout, c10::optional<at::Device> device,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_empty(at::IntArrayRef size, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout,
|
||||
c10::optional<at::Device> device,
|
||||
c10::optional<bool> pin_memory,
|
||||
c10::optional<at::MemoryFormat> memory_format) {
|
||||
return {
|
||||
|
@ -433,9 +442,10 @@ std::vector<torch::lazy::Shape> compute_shape_fill(const at::Tensor& self,
|
|||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_randn(
|
||||
at::IntArrayRef size, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout, c10::optional<at::Device> device,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_randn(at::IntArrayRef size, c10::optional<at::ScalarType> dtype,
|
||||
c10::optional<at::Layout> layout,
|
||||
c10::optional<at::Device> device,
|
||||
c10::optional<bool> pin_memory) {
|
||||
return {
|
||||
Shape(dtype.value_or(at::get_default_dtype_as_scalartype()), size.vec())};
|
||||
|
@ -457,14 +467,14 @@ std::vector<torch::lazy::Shape> compute_shape_randint(
|
|||
Shape(dtype.value_or(at::get_default_dtype_as_scalartype()), size.vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_resize(
|
||||
const at::Tensor & self, at::IntArrayRef size,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_resize(const at::Tensor &self, at::IntArrayRef size,
|
||||
c10::optional<at::MemoryFormat> memory_format) {
|
||||
return {Shape(self.scalar_type(), size.vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_bernoulli(
|
||||
const at::Tensor& self, const at::Tensor &p,
|
||||
std::vector<torch::lazy::Shape>
|
||||
compute_shape_bernoulli(const at::Tensor &self, const at::Tensor &p,
|
||||
c10::optional<at::Generator> generator) {
|
||||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
@ -476,17 +486,20 @@ std::vector<torch::lazy::Shape> compute_shape_scalar_tensor(
|
|||
return {Shape(dtype.value_or(s.type()), c10::ArrayRef<int64_t>{})};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_roll(
|
||||
const at::Tensor& self, at::IntArrayRef shifts, at::IntArrayRef dims) {
|
||||
std::vector<torch::lazy::Shape> compute_shape_roll(const at::Tensor &self,
|
||||
at::IntArrayRef shifts,
|
||||
at::IntArrayRef dims) {
|
||||
return {Shape(self.scalar_type(), self.sizes().vec())};
|
||||
}
|
||||
|
||||
std::vector<torch::lazy::Shape> compute_shape_linspace(const at::Scalar & start, const at::Scalar & end, int64_t steps, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
|
||||
auto out_meta =
|
||||
at::linspace(start, end, steps, dtype, layout, c10::Device(c10::kMeta), pin_memory);
|
||||
std::vector<torch::lazy::Shape> compute_shape_linspace(
|
||||
const at::Scalar &start, const at::Scalar &end, int64_t steps,
|
||||
c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout,
|
||||
c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
|
||||
auto out_meta = at::linspace(start, end, steps, dtype, layout,
|
||||
c10::Device(c10::kMeta), pin_memory);
|
||||
return {Shape(out_meta.scalar_type(), out_meta.sizes().vec())};
|
||||
}
|
||||
|
||||
|
||||
} // namespace lazy
|
||||
} // namespace torch
|
||||
|
|
|
@ -14,8 +14,8 @@
|
|||
namespace torch {
|
||||
namespace lazy {
|
||||
|
||||
at::Tensor CreateFunctionalizedAtenFromLtcTensor(
|
||||
const LazyTensorPtr& ltc_tensor) {
|
||||
at::Tensor
|
||||
CreateFunctionalizedAtenFromLtcTensor(const LazyTensorPtr <c_tensor) {
|
||||
at::Tensor tensor = CreateAtenFromLtcTensor(ltc_tensor);
|
||||
if (!c10::impl::tls_is_dispatch_key_excluded(
|
||||
c10::DispatchKey::Functionalize) &&
|
||||
|
|
|
@ -18,7 +18,8 @@ namespace lazy {
|
|||
// should have explicit tensor functinoalization. Otherwise we can get
|
||||
// unfanctionalized primitives or in the worst case if we apply inplace
|
||||
// operations to unfunctionalized tensor it won't be captured in LTC graph.
|
||||
TORCH_API at::Tensor CreateFunctionalizedAtenFromLtcTensor(const LazyTensorPtr& ltc_tensor);
|
||||
TORCH_API at::Tensor
|
||||
CreateFunctionalizedAtenFromLtcTensor(const LazyTensorPtr <c_tensor);
|
||||
|
||||
} // namespace lazy
|
||||
} // namespace torch
|
||||
|
|
|
@ -21,8 +21,8 @@
|
|||
}
|
||||
|
||||
#define UNIMPLEMENTED_FUNCTION_ERROR() \
|
||||
UNIMPLEMENTED_ERROR( \
|
||||
"\n\t" << __FILE__ << ":" << __LINE__ << " " << __PRETTY_FUNCTION__)
|
||||
UNIMPLEMENTED_ERROR("\n\t" << __FILE__ << ":" << __LINE__ << " " \
|
||||
<< __PRETTY_FUNCTION__)
|
||||
|
||||
#define UNSUPPORTED_ERROR(msg) \
|
||||
{ \
|
||||
|
|
|
@ -27,12 +27,10 @@ void ConvertScalarImplicit(std::shared_ptr<Graph>& graph) {
|
|||
node_type = c10::aten::FloatImplicit;
|
||||
output_type = FloatType::get();
|
||||
} else {
|
||||
throw std::runtime_error(
|
||||
"Expected isIntegralType or isFloatingType");
|
||||
throw std::runtime_error("Expected isIntegralType or isFloatingType");
|
||||
}
|
||||
|
||||
Value * output = graph
|
||||
->create(node_type, {input})
|
||||
Value *output = graph->create(node_type, {input})
|
||||
->insertBefore(node)
|
||||
->output()
|
||||
->setType(output_type);
|
||||
|
|
|
@ -1,15 +1,17 @@
|
|||
#pragma once
|
||||
|
||||
#include <string>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
|
||||
template <typename T>
|
||||
std::ostream& string_join(std::ostream& out, const std::vector<T>& v, const std::string& delimiter) {
|
||||
std::ostream &string_join(std::ostream &out, const std::vector<T> &v,
|
||||
const std::string &delimiter) {
|
||||
size_t i = 0;
|
||||
for (const T &e : v) {
|
||||
if ((i++) > 0) { out << delimiter; }
|
||||
if ((i++) > 0) {
|
||||
out << delimiter;
|
||||
}
|
||||
out << e;
|
||||
}
|
||||
return out;
|
||||
|
@ -22,10 +24,8 @@ std::string string_join(const std::vector<T>& v, const std::string& delimiter) {
|
|||
return joined.str();
|
||||
}
|
||||
|
||||
inline std::vector<std::string> string_split(
|
||||
const std::string& str,
|
||||
const std::string& sep
|
||||
) {
|
||||
inline std::vector<std::string> string_split(const std::string &str,
|
||||
const std::string &sep) {
|
||||
std::vector<std::string> tokens;
|
||||
std::size_t pos1 = str.find_first_not_of(sep);
|
||||
while (pos1 != std::string::npos) {
|
||||
|
|
|
@ -14,7 +14,8 @@ static T GetEnv(const std::string& name, const T& default_value = T(0)) {
|
|||
return T(std::atoi(env));
|
||||
}
|
||||
|
||||
static std::string GetEnvString(const std::string& name, const std::string& default_value) {
|
||||
static std::string GetEnvString(const std::string &name,
|
||||
const std::string &default_value) {
|
||||
const char *env = std::getenv(name.c_str());
|
||||
if (!env) {
|
||||
return default_value;
|
||||
|
|
|
@ -3,7 +3,6 @@
|
|||
#include "../generated/LazyIr.h"
|
||||
#include "../mlir_node.h"
|
||||
|
||||
|
||||
namespace torch {
|
||||
namespace lazy {
|
||||
|
||||
|
@ -15,9 +14,12 @@ bool is_detach_copy(const torch::lazy::Value& value) {
|
|||
}
|
||||
|
||||
torch::lazy::Node *extract_non_detach_copy_node(torch::lazy::Node *node) {
|
||||
if (!node) { return nullptr; }
|
||||
if (!node) {
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
torch::lazy::TorchMlirNode* mlir_node = dynamic_cast<torch::lazy::TorchMlirNode*>(node);
|
||||
torch::lazy::TorchMlirNode *mlir_node =
|
||||
dynamic_cast<torch::lazy::TorchMlirNode *>(node);
|
||||
while (mlir_node && is_detach_copy(mlir_node)) {
|
||||
mlir_node = mlir_node->mlir_node(0);
|
||||
}
|
||||
|
@ -27,10 +29,14 @@ torch::lazy::Node* extract_non_detach_copy_node(torch::lazy::Node* node) {
|
|||
return mlir_node;
|
||||
}
|
||||
|
||||
const torch::lazy::Node* extract_non_detach_copy_node(const torch::lazy::Node* node) {
|
||||
if (!node) { return nullptr; }
|
||||
const torch::lazy::Node *
|
||||
extract_non_detach_copy_node(const torch::lazy::Node *node) {
|
||||
if (!node) {
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
const torch::lazy::TorchMlirNode* mlir_node = dynamic_cast<const torch::lazy::TorchMlirNode*>(node);
|
||||
const torch::lazy::TorchMlirNode *mlir_node =
|
||||
dynamic_cast<const torch::lazy::TorchMlirNode *>(node);
|
||||
while (mlir_node && is_detach_copy(mlir_node)) {
|
||||
mlir_node = mlir_node->mlir_node(0);
|
||||
}
|
||||
|
@ -40,7 +46,6 @@ const torch::lazy::Node* extract_non_detach_copy_node(const torch::lazy::Node* n
|
|||
return mlir_node;
|
||||
}
|
||||
|
||||
|
||||
torch::lazy::DeviceData *device_data_cast(torch::lazy::Node *node) {
|
||||
if (!node) {
|
||||
return nullptr;
|
||||
|
@ -68,14 +73,15 @@ torch::lazy::DeviceData* device_data_cast(const torch::lazy::Value& value) {
|
|||
return device_data_cast(value.node.get());
|
||||
}
|
||||
|
||||
torch::lazy::DeviceData* device_data_cast(
|
||||
const at::Tensor& tensor, c10::optional<torch::lazy::BackendDevice> device
|
||||
) {
|
||||
torch::lazy::DeviceData *
|
||||
device_data_cast(const at::Tensor &tensor,
|
||||
c10::optional<torch::lazy::BackendDevice> device) {
|
||||
if (!device) {
|
||||
device = torch::lazy::GetBackendDevice(tensor);
|
||||
}
|
||||
TORCH_CHECK(device);
|
||||
torch::lazy::LazyTensorPtr lazy_tensor = torch::lazy::GetLtcTensorOrCreateForWrappedNumber(tensor, *device);
|
||||
torch::lazy::LazyTensorPtr lazy_tensor =
|
||||
torch::lazy::GetLtcTensorOrCreateForWrappedNumber(tensor, *device);
|
||||
if (lazy_tensor) {
|
||||
return device_data_cast(lazy_tensor->GetIrValue());
|
||||
}
|
||||
|
|
|
@ -12,14 +12,17 @@ TORCH_API bool is_detach_copy(const torch::lazy::Node*);
|
|||
TORCH_API bool is_detach_copy(const torch::lazy::Value &);
|
||||
|
||||
TORCH_API torch::lazy::Node *extract_non_detach_copy_node(torch::lazy::Node *);
|
||||
TORCH_API const torch::lazy::Node* extract_non_detach_copy_node(const torch::lazy::Node*);
|
||||
TORCH_API const torch::lazy::Node *
|
||||
extract_non_detach_copy_node(const torch::lazy::Node *);
|
||||
|
||||
TORCH_API torch::lazy::DeviceData *device_data_cast(torch::lazy::Node *);
|
||||
TORCH_API const torch::lazy::DeviceData* device_data_cast(const torch::lazy::Node*);
|
||||
TORCH_API torch::lazy::DeviceData* device_data_cast(const torch::lazy::Value& value);
|
||||
TORCH_API const torch::lazy::DeviceData *
|
||||
device_data_cast(const torch::lazy::Node *);
|
||||
TORCH_API torch::lazy::DeviceData *
|
||||
device_data_cast(const torch::lazy::Value &value);
|
||||
TORCH_API torch::lazy::DeviceData *device_data_cast(
|
||||
const at::Tensor& tensor, c10::optional<torch::lazy::BackendDevice> device = c10::nullopt
|
||||
);
|
||||
const at::Tensor &tensor,
|
||||
c10::optional<torch::lazy::BackendDevice> device = c10::nullopt);
|
||||
|
||||
} // namespace lazy
|
||||
} // namespace torch
|
||||
|
|
|
@ -73,10 +73,8 @@ public:
|
|||
// Vendor backend specific lowering can be exec here before returning.
|
||||
for (const auto& instance : instances) {
|
||||
TORCH_CHECK(
|
||||
instance->in_mark_step,
|
||||
"Compile outside of mark step:\n",
|
||||
GetComputationBackendText(instance)
|
||||
);
|
||||
instance->in_mark_step, "Compile outside of mark step:\n",
|
||||
GetComputationBackendText(instance));
|
||||
// Store computation instance for external access after compilation.
|
||||
GetLatestComputation() = instance;
|
||||
}
|
||||
|
@ -114,12 +112,13 @@ public:
|
|||
// Convert any lazy devices to cpu devices to ensure
|
||||
// that the values are actually computed
|
||||
if (node->outputs().size() == 1 &&
|
||||
node->output()->type()->kind() ==
|
||||
c10::TypeKind::DeviceObjType) {
|
||||
node->output()->type()->kind() == c10::TypeKind::DeviceObjType) {
|
||||
auto value_sym = torch::jit::Symbol::attr("value");
|
||||
TORCH_CHECK(node->hasAttribute(value_sym),
|
||||
TORCH_CHECK(
|
||||
node->hasAttribute(value_sym),
|
||||
"Expected node to have 'value' attribute.");
|
||||
TORCH_CHECK(node->kindOf(value_sym) == torch::jit::AttributeKind::s,
|
||||
TORCH_CHECK(
|
||||
node->kindOf(value_sym) == torch::jit::AttributeKind::s,
|
||||
"Expected 'value' attribute to be a string.");
|
||||
if (beginswith(node->s(value_sym), "lazy")) {
|
||||
node->s_(value_sym, "cpu");
|
||||
|
@ -132,7 +131,8 @@ public:
|
|||
for (const auto& argument : arguments) {
|
||||
const auto mlir_data =
|
||||
std::static_pointer_cast<TorchMlirBackendData>(argument);
|
||||
auto* info = dynamic_cast<TorchMlirBackendData::Info*>(mlir_data->mlir_info());
|
||||
auto* info =
|
||||
dynamic_cast<TorchMlirBackendData::Info*>(mlir_data->mlir_info());
|
||||
TORCH_CHECK(info);
|
||||
if (info->scalar.has_value()) {
|
||||
stack.emplace_back(info->scalar.value());
|
||||
|
|
|
@ -8,8 +8,8 @@
|
|||
//===----------------------------------------------------------------------===//
|
||||
|
||||
#include "torch/csrc/jit/python/pybind.h"
|
||||
#include "torch/csrc/lazy/core/config.h"
|
||||
#include "torch/csrc/lazy/backend/backend_interface.h"
|
||||
#include "torch/csrc/lazy/core/config.h"
|
||||
|
||||
#include <base_lazy_backend/mlir_lowering_context.h>
|
||||
#include <base_lazy_backend/utils/string_utils.h>
|
||||
|
@ -82,9 +82,11 @@ PYBIND11_MODULE(_REFERENCE_LAZY_BACKEND, m) {
|
|||
torch::lazy::GetLatestComputation().get());
|
||||
return py::cast(computation);
|
||||
});
|
||||
m.def("set_parameter_name",
|
||||
m.def(
|
||||
"set_parameter_name",
|
||||
[](const at::Tensor& tensor, const std::string& name) -> bool {
|
||||
torch::lazy::DeviceData* ir_node = torch::lazy::device_data_cast(tensor);
|
||||
torch::lazy::DeviceData* ir_node =
|
||||
torch::lazy::device_data_cast(tensor);
|
||||
if (ir_node) {
|
||||
ir_node->SetName(name);
|
||||
return true;
|
||||
|
|
Loading…
Reference in New Issue