mirror of https://github.com/llvm/torch-mlir
Implement lowering of torch.aten.hstack (#3563)
parent
04740824ae
commit
1c4b9d6a0e
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@ -14121,6 +14121,29 @@ def Torch_AtenStackOp : Torch_Op<"aten.stack", [
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}];
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}
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def Torch_AtenHstackOp : Torch_Op<"aten.hstack", [
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AllowsTypeRefinement,
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HasValueSemantics,
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ReadOnly
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]> {
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let summary = "Generated op for `aten::hstack : (Tensor[]) -> (Tensor)`";
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let arguments = (ins
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AnyTorchListOfTensorType:$tensors
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);
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let results = (outs
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AnyTorchOptionalTensorType:$result
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);
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let hasCustomAssemblyFormat = 1;
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let extraClassDefinition = [{
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ParseResult AtenHstackOp::parse(OpAsmParser &parser, OperationState &result) {
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return parseDefaultTorchOp(parser, result, 1, 1);
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}
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void AtenHstackOp::print(OpAsmPrinter &printer) {
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printDefaultTorchOp(printer, *this, 1, 1);
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}
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}];
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}
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def Torch_AtenAppendTOp : Torch_Op<"aten.append.t", [
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AllowsTypeRefinement
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]> {
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@ -10639,6 +10639,31 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
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" %0 = call @__torch__.torch.jit._shape_functions.stack(%arg0, %arg1) : (!torch.list<list<int>>, !torch.int) -> !torch.list<int>\n"
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" return %0 : !torch.list<int>\n"
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" }\n"
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" func.func @\"__torch_mlir_shape_fn.aten.hstack\"(%arg0: !torch.list<list<int>>) -> !torch.list<int> {\n"
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" %true = torch.constant.bool true\n"
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" %int0 = torch.constant.int 0\n"
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" %int1 = torch.constant.int 1\n"
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" %0 = torch.prim.ListConstruct : () -> !torch.list<list<int>>\n"
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" %1 = torch.aten.len.t %arg0 : !torch.list<list<int>> -> !torch.int\n"
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" torch.prim.Loop %1, %true, init() {\n"
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" ^bb0(%arg1: !torch.int):\n"
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" %6 = torch.aten.__getitem__.t %arg0, %arg1 : !torch.list<list<int>>, !torch.int -> !torch.list<int>\n"
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" %7 = func.call @\"__torch_mlir_shape_fn.aten.atleast_1d\"(%6) : (!torch.list<int>) -> !torch.list<int>\n"
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" %8 = torch.aten.append.t %0, %7 : !torch.list<list<int>>, !torch.list<int> -> !torch.list<list<int>>\n"
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" torch.prim.Loop.condition %true, iter()\n"
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" } : (!torch.int, !torch.bool) -> ()\n"
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" %2 = torch.aten.__getitem__.t %0, %int0 : !torch.list<list<int>>, !torch.int -> !torch.list<int>\n"
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" %3 = torch.aten.len.t %2 : !torch.list<int> -> !torch.int\n"
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" %4 = torch.aten.eq.int %3, %int1 : !torch.int, !torch.int -> !torch.bool\n"
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" %5 = torch.prim.If %4 -> (!torch.list<int>) {\n"
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" %6 = func.call @__torch__.torch.jit._shape_functions.cat(%0, %int0) : (!torch.list<list<int>>, !torch.int) -> !torch.list<int>\n"
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" torch.prim.If.yield %6 : !torch.list<int>\n"
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" } else {\n"
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" %6 = func.call @__torch__.torch.jit._shape_functions.cat(%0, %int1) : (!torch.list<list<int>>, !torch.int) -> !torch.list<int>\n"
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" torch.prim.If.yield %6 : !torch.list<int>\n"
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" }\n"
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" return %5 : !torch.list<int>\n"
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" }\n"
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" func.func @\"__torch_mlir_shape_fn.aten.fft_fft\"(%arg0: !torch.list<int>, %arg1: !torch.optional<int>, %arg2: !torch.int, %arg3: !torch.optional<str>) -> !torch.list<int> {\n"
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" return %arg0 : !torch.list<int>\n"
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" }\n"
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@ -15185,6 +15210,33 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
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" %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" return %0#1 : !torch.int\n"
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" }\n"
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" func.func @\"__torch_mlir_dtype_fn.aten.hstack\"(%arg0: !torch.list<tuple<int, int>>) -> !torch.int {\n"
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" %true = torch.constant.bool true\n"
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" %none = torch.constant.none\n"
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" %str = torch.constant.str \"AssertionError: \"\n"
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" %int0 = torch.constant.int 0\n"
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" %0 = torch.prim.ListConstruct : () -> !torch.list<optional<int>>\n"
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" %1 = torch.prim.ListConstruct : () -> !torch.list<int>\n"
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" %2 = torch.aten.len.t %arg0 : !torch.list<tuple<int, int>> -> !torch.int\n"
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" %3 = torch.aten.ne.int %2, %int0 : !torch.int, !torch.int -> !torch.bool\n"
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" torch.prim.If %3 -> () {\n"
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" torch.prim.If.yield\n"
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" } else {\n"
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" torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
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" torch.prim.If.yield\n"
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" }\n"
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" %4 = torch.aten.len.t %arg0 : !torch.list<tuple<int, int>> -> !torch.int\n"
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" torch.prim.Loop %4, %true, init() {\n"
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" ^bb0(%arg1: !torch.int):\n"
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" %6 = torch.aten.__getitem__.t %arg0, %arg1 : !torch.list<tuple<int, int>>, !torch.int -> !torch.tuple<int, int>\n"
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" %7:2 = torch.prim.TupleUnpack %6 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %8 = torch.aten.append.t %0, %7#0 : !torch.list<optional<int>>, !torch.int -> !torch.list<optional<int>>\n"
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" %9 = torch.aten.append.t %1, %7#1 : !torch.list<int>, !torch.int -> !torch.list<int>\n"
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" torch.prim.Loop.condition %true, iter()\n"
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" } : (!torch.int, !torch.bool) -> ()\n"
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" %5 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%0, %1) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
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" return %5 : !torch.int\n"
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" }\n"
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" func.func @\"__torch_mlir_dtype_fn.aten.einsum\"(%arg0: !torch.str, %arg1: !torch.list<tuple<int, int>>, %arg2: !torch.optional<list<int>>) -> !torch.int {\n"
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" %true = torch.constant.bool true\n"
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" %none = torch.constant.none\n"
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@ -3813,6 +3813,58 @@ public:
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};
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} // namespace
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// Decompose `aten.hstack` into `aten.at_least1d` and `aten.cat`.
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// https://github.com/pytorch/pytorch/blob/207564bab1c4fe42750931765734ee604032fb69/torch/_refs/__init__.py#L3908
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namespace {
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class DecomposeAtenHstackOp : public OpRewritePattern<AtenHstackOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(AtenHstackOp op,
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PatternRewriter &rewriter) const override {
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Location loc = op.getLoc();
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// Get SmallVector<Value> from Value.
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SmallVector<Value> tensors;
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if (!getListConstructElements(op.getTensors(), tensors))
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return rewriter.notifyMatchFailure(
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op, "unimplemented: the tensor list is not from list construct");
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// Execute AtenAtleast1dOp on every tensor inside tensors.
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SmallVector<Value> atleast1dTensors;
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for (auto tensor : tensors) {
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std::optional<unsigned> tensorRank = getTensorRank(tensor);
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// Check if the tensor is already of rank >= 1.
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if (*tensorRank < 1) {
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auto atleast1dTensor =
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rewriter.create<AtenAtleast1dOp>(loc, tensor.getType(), tensor);
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atleast1dTensors.push_back(atleast1dTensor);
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} else {
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atleast1dTensors.push_back(tensor);
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}
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}
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// Make Value list from atleast1dTensors variable.
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auto elemType = cast<BaseTensorType>(atleast1dTensors[0].getType())
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.getWithSizesAndDtype(std::nullopt, nullptr);
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Value atleast1dTensorList = rewriter.create<PrimListConstructOp>(
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loc, Torch::ListType::get(elemType), atleast1dTensors);
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// Replace hstack with cat operator.
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if (getTensorRank(atleast1dTensors[0]) == 1)
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rewriter.replaceOpWithNewOp<AtenCatOp>(
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op, op.getType(), atleast1dTensorList,
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rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(0)));
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else
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rewriter.replaceOpWithNewOp<AtenCatOp>(
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op, op.getType(), atleast1dTensorList,
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rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1)));
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return success();
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}
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};
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} // namespace
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// Decompose aten.roll into aten.slice and aten.cat ops.
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// https://pytorch.org/docs/stable/generated/torch.roll.html
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namespace {
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@ -9567,6 +9619,7 @@ public:
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addPatternIfTargetOpIsIllegal<
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DecomposeConstantTensorAllocLikeOp<AtenZerosLikeOp, 0>>(patterns);
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addPatternIfTargetOpIsIllegal<DecomposeAtenStackOp>(patterns);
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addPatternIfTargetOpIsIllegal<DecomposeAtenHstackOp>(patterns);
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addPatternIfTargetOpIsIllegal<DecomposeAtenRollOp>(patterns);
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addPatternIfTargetOpIsIllegal<DecomposeAtenRepeatOp>(patterns);
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addPatternIfTargetOpIsIllegal<DecomposeAtenRepeatInterleaveSelfIntOp>(
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@ -380,6 +380,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
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target.addIllegalOp<AtenOnesLikeOp>();
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target.addIllegalOp<AtenZerosLikeOp>();
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target.addIllegalOp<AtenStackOp>();
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target.addIllegalOp<AtenHstackOp>();
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target.addIllegalOp<AtenRollOp>();
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target.addIllegalOp<AtenRepeatOp>();
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target.addIllegalOp<AtenRepeatInterleaveSelfIntOp>();
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@ -1213,6 +1213,10 @@ STABLEHLO_PASS_SET = {
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"GridSamplerBasic4_basic",
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"GtFloatIntModule_basic",
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"GtIntModule_basic",
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"HstackBasicComplexModule_basic",
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"HstackBasicFloatModule_basic",
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"HstackBasicIntFloatModule_basic",
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"HstackBasicIntModule_basic",
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"IndexTensorMultiIndexStaticModule_basic",
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"IndexTensorStaticModule_basic",
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"IntFloatModule_basic",
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@ -2215,6 +2219,11 @@ MAKE_FX_TOSA_PASS_SET = (
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# failed to legalize operation 'torch.aten.rrelu_with_noise'
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"ElementwiseRreluEvalModule_basic",
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"ElementwiseRreluEvalStaticModule_basic",
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# incompatible return type failure for tosa.concat.
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"HstackBasicComplexModule_basic",
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"HstackBasicFloatModule_basic",
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"HstackBasicIntFloatModule_basic",
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"HstackBasicIntModule_basic",
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# Shape Related failures
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"PrimListUnpackNumMismatchModule_basic",
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"ReshapeExpandModule_basic",
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@ -2623,6 +2632,10 @@ ONNX_XFAIL_SET = {
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"GtFloatIntModule_basic",
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"GtIntModule_basic",
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"HardtanhBackward_basic",
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"HstackBasicComplexModule_basic",
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"HstackBasicFloatModule_basic",
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"HstackBasicIntFloatModule_basic",
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"HstackBasicIntModule_basic",
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"IndexPutImpl1DFloatAccumulateModule_basic",
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"IndexPutImpl1DFloatNonAccumulateModule_basic",
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"IndexPutImpl1DIntAccumulateModule_basic",
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@ -2159,6 +2159,19 @@ def aten〇atleast_2d〡shape(self: List[int]) -> List[int]:
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def aten〇stack〡shape(tensors: List[List[int]], dim: int = 0) -> List[int]:
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return upstream_shape_functions.stack(tensors, dim)
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@check_shape_function([
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Invocation([LongTensorOfShape(2, 4, 3), LongTensorOfShape(2, 5, 3)]), # Basic case.
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])
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def aten〇hstack〡shape(tensors: List[List[int]]) -> List[int]:
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tensors_atleast1d = [aten〇atleast_1d〡shape(tensor) for tensor in tensors]
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if len(tensors_atleast1d[0]) == 1:
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return upstream_shape_functions.cat(tensors_atleast1d, dim=0)
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return upstream_shape_functions.cat(tensors_atleast1d, dim=1)
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def aten〇fft_fft〡shape(self: List[int], n: Optional[int] = None, dim: int = -1, norm: Optional[str] = None) -> List[int]:
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return self
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@ -5325,6 +5338,23 @@ def aten〇atleast_2d〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
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self_rank, self_dtype = self_rank_dtype
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return self_dtype
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@check_dtype_function(
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[Invocation([NonZeroDTensorWithDtype(torch.bool), NonZeroDTensorWithDtype(torch.int32), NonZeroDTensorWithDtype(torch.int64)]),
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Invocation([NonZeroDTensorWithDtype(torch.float32), NonZeroDTensorWithDtype(torch.int32)]),
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Invocation([NonZeroDTensorWithDtype(torch.float16), NonZeroDTensorWithDtype(torch.float64)]),
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Invocation([NonZeroDTensorWithDtype(torch.float32), NonZeroDTensorWithDtype(torch.int32),
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NonZeroDTensorWithDtype(torch.complex64)])])
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def aten〇hstack〡dtype(tensors_rank_dtype: List[Tuple[int, int]]) -> int:
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ranks: List[Optional[int]] = []
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dtypes: List[int] = []
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assert len(tensors_rank_dtype) != 0
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for tensor_rank_dtype in tensors_rank_dtype:
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tensor_rank, tensor_dtype = tensor_rank_dtype
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ranks.append(tensor_rank)
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dtypes.append(tensor_dtype)
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return promote_dtypes(ranks, dtypes)
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@check_dtype_function(
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[Invocation("i,j->ij", [TensorOfShape(1, dtype=torch.float32),
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TensorOfShape(1, dtype=torch.int32)]),])
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@ -1015,6 +1015,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
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has_folder=True,
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)
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emit("aten::stack : (Tensor[], int) -> (Tensor)")
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emit("aten::hstack : (Tensor[]) -> (Tensor)")
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emit("aten::append.t : (t[], t) -> (t[])")
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emit("aten::add.t : (t[], t[]) -> (t[])", has_canonicalizer=True)
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emit("aten::eq.int_list : (int[], int[]) -> (bool)", has_folder=True)
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@ -1308,6 +1308,107 @@ def TensorsStackPromoteDTypeModule_basic(module, tu: TestUtils):
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# ==============================================================================
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class HstackBasicIntModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args(
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[
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None,
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([2, 3, 4], torch.bool, True),
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([2, 3, 4], torch.int32, True),
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([2, 3, 4], torch.int64, True),
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]
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)
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def forward(self, x, y, z):
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return torch.ops.aten.hstack([x, y, z])
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@register_test_case(module_factory=lambda: HstackBasicIntModule())
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def HstackBasicIntModule_basic(module, tu: TestUtils):
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module.forward(
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tu.randint(2, 3, 4, low=0, high=2).bool(),
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tu.randint(2, 3, 4, low=0, high=100).int(),
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tu.randint(2, 3, 4, low=0, high=100).long(),
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)
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class HstackBasicFloatModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args(
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[
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None,
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([2, 6, 4], torch.int32, True),
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([2, 3, 4], torch.float64, True),
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]
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)
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def forward(self, x, y):
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return torch.ops.aten.hstack([x, y])
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@register_test_case(module_factory=lambda: HstackBasicFloatModule())
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def HstackBasicFloatModule_basic(module, tu: TestUtils):
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module.forward(
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tu.rand(2, 6, 4).int(),
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tu.rand(2, 3, 4).double(),
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)
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class HstackBasicIntFloatModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args(
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[
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None,
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([-1, -1, -1, -1], torch.int32, True),
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([-1, -1, -1, -1], torch.float32, True),
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]
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)
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def forward(self, x, y):
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return torch.ops.aten.hstack([x, y])
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@register_test_case(module_factory=lambda: HstackBasicIntFloatModule())
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def HstackBasicIntFloatModule_basic(module, tu: TestUtils):
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module.forward(
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tu.randint(4, 6, 4, 2, low=1, high=50).int(),
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tu.rand(4, 3, 4, 2),
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)
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class HstackBasicComplexModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args(
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[
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None,
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([-1, -1, -1, -1], torch.complex64, True),
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([-1, -1, -1, -1], torch.complex128, True),
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]
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)
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def forward(self, x, y):
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return torch.ops.aten.hstack([x, y])
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@register_test_case(module_factory=lambda: HstackBasicComplexModule())
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def HstackBasicComplexModule_basic(module, tu: TestUtils):
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module.forward(
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tu.rand(4, 6, 4, 2).type(torch.complex64),
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tu.rand(4, 3, 4, 2).type(torch.complex128),
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)
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# ==============================================================================
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class GatherModule(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
|
Loading…
Reference in New Issue