mirror of https://github.com/llvm/torch-mlir
build: manually update PyTorch version (#3727)
Set PyTorch and TorchVision version to nightly release 2024-10-15. Tracker issue for the failing tests added to xfail_set in this PR. Issue: https://github.com/llvm/torch-mlir/issues/3796 This commit disables the failing sparse tensor tests since they are not maintained on day-to-day basis and blocks the roll PyTorch update for now. Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>pull/3806/head
Vivek Khandelwal
GitHub
parent
dc7a1ff7d9
commit
9c7067649b
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@ -6319,6 +6319,30 @@ def Torch_AtenDotOp : Torch_Op<"aten.dot", [
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let hasCanonicalizer = 1;
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}
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def Torch_AtenOuterOp : Torch_Op<"aten.outer", [
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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::outer : (Tensor, Tensor) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$self,
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AnyTorchTensorType:$vec2
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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 AtenOuterOp::parse(OpAsmParser &parser, OperationState &result) {
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return parseDefaultTorchOp(parser, result, 2, 1);
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}
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void AtenOuterOp::print(OpAsmPrinter &printer) {
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printDefaultTorchOp(printer, *this, 2, 1);
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}
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}];
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}
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def Torch_AtenCosineSimilarityOp : Torch_Op<"aten.cosine_similarity", [
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AllowsTypeRefinement,
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HasValueSemantics,
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@ -7601,6 +7601,13 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
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" } : (!torch.int, !torch.bool) -> ()\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.outer\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {\n"
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" %int0 = torch.constant.int 0\n"
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" %0 = torch.aten.__getitem__.t %arg0, %int0 : !torch.list<int>, !torch.int -> !torch.int\n"
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" %1 = torch.aten.__getitem__.t %arg1, %int0 : !torch.list<int>, !torch.int -> !torch.int\n"
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" %2 = torch.prim.ListConstruct %0, %1 : (!torch.int, !torch.int) -> !torch.list<int>\n"
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" return %2 : !torch.list<int>\n"
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" }\n"
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" func.func @\"__torch_mlir_shape_fn.aten.dot\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {\n"
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" %0 = torch.prim.ListConstruct : () -> !torch.list<int>\n"
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" return %0 : !torch.list<int>\n"
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@ -13403,6 +13410,14 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
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" %4 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%2, %3) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
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" return %4 : !torch.int\n"
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" }\n"
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" func.func @\"__torch_mlir_dtype_fn.aten.outer\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>) -> !torch.int {\n"
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" %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %1:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %2 = torch.prim.ListConstruct %0#0, %1#0 : (!torch.int, !torch.int) -> !torch.list<optional<int>>\n"
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" %3 = torch.prim.ListConstruct %0#1, %1#1 : (!torch.int, !torch.int) -> !torch.list<int>\n"
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" %4 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%2, %3) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
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" return %4 : !torch.int\n"
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" }\n"
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" func.func @\"__torch_mlir_dtype_fn.aten.mm\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>) -> !torch.int {\n"
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" %false = torch.constant.bool false\n"
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" %int5 = torch.constant.int 5\n"
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@ -13813,63 +13828,30 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
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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.lerp.Scalar\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.number) -> !torch.int {\n"
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" %none = torch.constant.none\n"
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" %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %1:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %2 = torch.prim.ListConstruct %0#0, %1#0, %none : (!torch.int, !torch.int, !torch.none) -> !torch.list<optional<int>>\n"
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" %3 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.get_dtype_of_scalar(%arg2) : (!torch.number) -> !torch.int\n"
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" %4 = torch.prim.ListConstruct %0#1, %1#1, %3 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>\n"
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" %5 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%2, %4) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
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" return %5 : !torch.int\n"
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" %2 = torch.prim.ListConstruct %0#0, %1#0 : (!torch.int, !torch.int) -> !torch.list<optional<int>>\n"
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" %3 = torch.prim.ListConstruct %0#1, %1#1 : (!torch.int, !torch.int) -> !torch.list<int>\n"
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" %4 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%2, %3) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
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" return %4 : !torch.int\n"
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" }\n"
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" func.func @\"__torch_mlir_dtype_fn.aten.addcmul\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.tuple<int, int>, %arg3: !torch.number) -> !torch.int {\n"
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" %none = torch.constant.none\n"
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" %str = torch.constant.str \"AssertionError: \"\n"
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" %int11 = torch.constant.int 11\n"
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" %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %1:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %2:2 = torch.prim.TupleUnpack %arg2 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %3 = torch.aten.ne.int %0#1, %int11 : !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.ne.int %1#1, %int11 : !torch.int, !torch.int -> !torch.bool\n"
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" torch.prim.If %4 -> () {\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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" %5 = torch.aten.ne.int %2#1, %int11 : !torch.int, !torch.int -> !torch.bool\n"
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" torch.prim.If %5 -> () {\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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" %6 = torch.prim.ListConstruct %0#0, %1#0, %2#0 : (!torch.int, !torch.int, !torch.int) -> !torch.list<optional<int>>\n"
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" %7 = torch.prim.ListConstruct %0#1, %1#1, %2#1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>\n"
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" %8 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%6, %7) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
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" return %8 : !torch.int\n"
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" }\n"
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" func.func @\"__torch_mlir_dtype_fn.aten.addcdiv\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.tuple<int, int>, %arg3: !torch.number) -> !torch.int {\n"
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" %int6 = torch.constant.int 6\n"
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" %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %1:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %2:2 = torch.prim.TupleUnpack %arg2 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %3 = torch.prim.ListConstruct %0#0, %1#0, %2#0 : (!torch.int, !torch.int, !torch.int) -> !torch.list<optional<int>>\n"
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" %4 = torch.prim.ListConstruct %0#1, %1#1, %2#1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>\n"
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" %5 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%3, %4) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
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" %6 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_integer_dtype(%5) : (!torch.int) -> !torch.bool\n"
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" %7 = torch.prim.If %6 -> (!torch.int) {\n"
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" torch.prim.If.yield %int6 : !torch.int\n"
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" } else {\n"
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" torch.prim.If.yield %5 : !torch.int\n"
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" }\n"
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" return %7 : !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.addcdiv\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.tuple<int, int>, %arg3: !torch.number) -> !torch.int {\n"
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" %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %1:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %2:2 = torch.prim.TupleUnpack %arg2 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
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" %3 = torch.prim.ListConstruct %0#0, %1#0, %2#0 : (!torch.int, !torch.int, !torch.int) -> !torch.list<optional<int>>\n"
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" %4 = torch.prim.ListConstruct %0#1, %1#1, %2#1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>\n"
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" %5 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%3, %4) : (!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.add.Scalar\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.number, %arg2: !torch.number) -> !torch.int {\n"
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" %none = torch.constant.none\n"
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@ -442,10 +442,6 @@ FX_IMPORTER_XFAIL_SET = {
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"ElementwiseDequantizePerTensorModule_basic",
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"ElementwiseQuantizePerTensorModule_basic",
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"ElementwiseQuantizePerTensorUIntModule_basic",
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"ElementwiseRreluEvalModule_basic",
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"ElementwiseRreluEvalStaticModule_basic",
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"ElementwiseRreluTrainModule_basic",
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"ElementwiseRreluTrainStaticModule_basic",
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"ElementwiseToDtypeI64ToUI8Module_basic",
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"EqIntModule_basic",
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"FloatImplicitModule_basic",
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@ -487,9 +483,6 @@ FX_IMPORTER_XFAIL_SET = {
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"ReduceMinAlongDimUnsignedInt_basic",
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"RsubInt0d_NumToTensor_Module_basic",
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"ScalarImplicitFloatModule_basic",
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"SignAndLogarithmOfDeterminantModule_F32",
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"SignAndLogarithmOfDeterminantBatchedModule_F32",
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"SignAndLogarithmOfDeterminantDynamicModule_F32",
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"SortIntListReverse_basic",
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"SortIntList_basic",
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"SplitDimDynamicModule_basic",
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@ -519,6 +512,34 @@ FX_IMPORTER_XFAIL_SET = {
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"SplitTensorNegativeDimModule_basic",
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"SplitWithSizesListUnpackModule_basic",
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"SplitWithSizes_Module_basic",
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"AdaptiveAvgPool1dGeneralDynamic_basic",
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"AdaptiveAvgPool1dStaticEvenMultiple_basic",
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"AdaptiveAvgPool1dStaticLargerOutput_basic",
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"AdaptiveAvgPool2dDynamicNoBatch_basic",
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"AdaptiveAvgPool2dDynamic_basic",
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"AdaptiveMaxPool1dDynamicNoBatch_basic",
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"AdaptiveMaxPool1dDynamic_basic",
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"AdaptiveMaxPool1dStatic_basic",
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"CrossEntropyLossModule_basic",
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"CrossEntropyLossNoReductionModule_basic",
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"ElementwiseExpm1IntModule_basic",
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"ElementwiseExpm1Module_basic",
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"IndexPutImpl1DFloatAccumulateModule_basic",
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"IndexPutImpl1DFloatNonAccumulateModule_basic",
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"IndexPutImpl1DIntAccumulateModule_basic",
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"IndexPutImpl1DIntNonAccumulateModule_basic",
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"IndexPutImpl2DFloatNonAccumulateModule_basic",
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"IndexPutImpl2DImplicitModule_basic",
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"IndexPutImpl2DIndexModule_basic",
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"IndexPutImpl2DNoneIndexStaticModule_basic",
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"IndexPutImpl3DFloatNonAccumulateModule_basic",
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"IndexPutImplIndexWithNoneModule_basic",
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"InterpolateDynamicModule_sizes_nearest",
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"IouOfModule_basic",
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"MeshgridIndexingIJ_basic",
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"MeshgridIndexingXY_basic",
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"Meshgrid_basic",
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"OneHotModule_basic",
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}
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FX_IMPORTER_CRASHING_SET = LINALG_CRASHING_SET | {
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@ -526,6 +547,7 @@ FX_IMPORTER_CRASHING_SET = LINALG_CRASHING_SET | {
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# Runtime op verification: out-of-bounds access
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"_SoftmaxModule_basic",
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"UpSampleNearest2dDynamicFactor_basic",
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"AdaptiveAvgPool1dGeneralDynamicNoBatches_basic",
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}
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FX_IMPORTER_STABLEHLO_XFAIL_SET = {
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@ -554,10 +576,6 @@ FX_IMPORTER_STABLEHLO_XFAIL_SET = {
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"ElementwiseRemainderTensorModule_Int_Float_NegativeDivisor_basic",
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"ElementwiseRemainderTensorModule_Int_NegativeDividend_basic",
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"ElementwiseRemainderTensorModule_Int_NegativeDivisor_basic",
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"ElementwiseRreluEvalModule_basic",
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"ElementwiseRreluEvalStaticModule_basic",
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"ElementwiseRreluTrainModule_basic",
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"ElementwiseRreluTrainStaticModule_basic",
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"MaxPool1dCeilModeTrueModule_basic",
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"MaxPool1dStaticCeilModeTrueModule_basic",
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"MaxUnpool3dModulePad0_basic",
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@ -591,7 +609,6 @@ FX_IMPORTER_STABLEHLO_XFAIL_SET = {
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"AdaptiveAvgPool3dDynamic_basic",
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"AdaptiveMaxPool1dDynamicNoBatch_basic",
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"AdaptiveMaxPool1dDynamic_basic",
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"AdaptiveMaxPool1dDimOneStatic_basic",
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"AdaptiveMaxPool1dStatic_basic",
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"AdaptiveMaxPool2dDynamicNoBatch_basic",
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"AdaptiveMaxPool2dDynamicWithIndices_basic",
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@ -758,12 +775,7 @@ FX_IMPORTER_STABLEHLO_XFAIL_SET = {
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"MaxPool2dWithIndicesBackwardStatic3DModule_basic",
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"MaxPool2dWithIndicesBackwardStatic4DModule_basic",
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"MaxPool3dCeilModeTrueModule_basic",
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"MaxPool3dEmptyStrideStaticModule_basic",
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"MaxPool3dLargeDatadModule_basic",
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"MaxPool3dModuleRandomSimple_basic",
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"MaxPool3dModule_basic",
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"MaxPool3dStaticCeilModeTrueModule_basic",
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"MaxPool3dStaticModule_basic",
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"MaxPool3dWithIndicesAllNegativeValuesModule_basic",
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"MaxPool3dWithIndicesAllOnesModule_basic",
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"MaxPool3dWithIndicesCeilModeTrueModule_basic",
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@ -921,6 +933,51 @@ FX_IMPORTER_STABLEHLO_XFAIL_SET = {
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"Unfold_Module_Rank_Zero_basic",
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"Unfold_Module_Rank_Zero_Size_Zero_basic",
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"Unfold_Module_Dynamic_basic",
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"AdaptiveAvgPool1dGeneralDynamic_basic",
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"AdaptiveAvgPool1dGeneralDynamicNoBatches_basic",
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"AdaptiveAvgPool1dStaticEvenMultiple_basic",
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"AdaptiveAvgPool1dStaticLargerOutput_basic",
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"AdaptiveAvgPool2dDynamicNoBatch_basic",
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"AdaptiveAvgPool2dDynamic_basic",
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"AddIntModule_basic",
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"AtenIntTensorByteDtypeModule_basic",
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"AtenIntTensorCharDtypeModule_basic",
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"AtenItemIntOpModule_basic",
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"CrossEntropyLossModule_basic",
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"CrossEntropyLossNoReductionModule_basic",
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"EinsumStaticContractRhsModule_basic",
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"EinsumStaticFourDimensionModule_basic",
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"EinsumStaticModule_basic",
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"EinsumStaticWithEllipsisSlicingAndBroadcastModule_basic",
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"EinsumStaticWithEllipsisSlicingModule_basic",
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"ElementwiseExpm1IntModule_basic",
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"ElementwiseExpm1Module_basic",
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"InterpolateDynamicModule_sizes_nearest",
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"IouOfModule_basic",
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"IscloseStaticModuleTrue_basic",
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"IscloseStaticModule_basic",
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"MeshgridIndexingIJ_basic",
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"MeshgridIndexingXY_basic",
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"Meshgrid_basic",
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"MulIntModule_basic",
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"OneHotModule_basic",
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"ReduceFrobeniusNormComplexModule_basic",
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"ScalarImplicitIntModule_basic",
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"ScaledDotProductAttentionBoolMaskModule_basic",
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"ScaledDotProductAttentionDifferentCausalModule_basic",
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"ScaledDotProductAttentionDifferentDynamicCausalModule_basic",
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"ScaledDotProductAttentionDifferentModule_basic",
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"ScaledDotProductAttentionMaskModule_basic",
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"ScaledDotProductAttentionSameCausalModule_basic",
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"ScaledDotProductAttentionSameDynamicModule_basic",
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"ScaledDotProductAttentionSameModule_basic",
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"SubIntModule_basic",
|
||||
"TensorToIntZeroRank_basic",
|
||||
"UpSampleNearest2dDynamicFactor_basic",
|
||||
"UpSampleNearest2dDynamicSize_basic",
|
||||
"UpSampleNearest2dStaticFactor_basic",
|
||||
"UpSampleNearest2dStaticSize_basic",
|
||||
"UpSampleNearest2d_basic",
|
||||
}
|
||||
|
||||
FX_IMPORTER_STABLEHLO_CRASHING_SET = {
|
||||
|
|
@ -3297,7 +3354,6 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
|
|||
"SplitWithSizesListUnpackModule_basic",
|
||||
"SplitWithSizes_Module_basic",
|
||||
"ElementwiseCreateComplexModule_basic",
|
||||
"AdaptiveMaxPool1dDimOneStatic_basic",
|
||||
"AtenPolarDoubleModule_basic",
|
||||
"AtenPolarFloatModule_basic",
|
||||
"HstackBasicComplexModule_basic",
|
||||
|
|
@ -3318,10 +3374,6 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
|
|||
"Conv_Transpose3dStaticModule_basic",
|
||||
"ElementwiseFloatTensorGtIntTensorModule_basic",
|
||||
"ElementwiseIntTensorLtFloatTensorModule_basic",
|
||||
"ElementwiseRreluEvalModule_basic",
|
||||
"ElementwiseRreluEvalStaticModule_basic",
|
||||
"ElementwiseRreluTrainModule_basic",
|
||||
"ElementwiseRreluTrainStaticModule_basic",
|
||||
"IndexPutWithNoneAndBroadcastModule_basic",
|
||||
"MaskedScatterStaticBasic_basic",
|
||||
"MaxUnpool3dModulePad0_basic",
|
||||
|
|
@ -3628,12 +3680,7 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
|
|||
"MaxPool2dWithIndicesNonDefaultStrideModule_basic",
|
||||
"MaxPool2dWithIndicesStaticModule_basic",
|
||||
"MaxPool3dCeilModeTrueModule_basic",
|
||||
"MaxPool3dEmptyStrideStaticModule_basic",
|
||||
"MaxPool3dLargeDatadModule_basic",
|
||||
"MaxPool3dModuleRandomSimple_basic",
|
||||
"MaxPool3dModule_basic",
|
||||
"MaxPool3dStaticCeilModeTrueModule_basic",
|
||||
"MaxPool3dStaticModule_basic",
|
||||
"MaxPool3dWithIndicesAllNegativeValuesModule_basic",
|
||||
"MaxPool3dWithIndicesAllOnesModule_basic",
|
||||
"MaxPool3dWithIndicesCeilModeTrueModule_basic",
|
||||
|
|
|
|||
|
|
@ -831,6 +831,9 @@ def aten〇numpy_T〡shape(self: List[int]) -> List[int]:
|
|||
result_shape.insert(0, i)
|
||||
return result_shape
|
||||
|
||||
def aten〇outer〡shape(self: List[int], vec2: List[int]) -> List[int]:
|
||||
return [self[0], vec2[0]]
|
||||
|
||||
@check_shape_function([Invocation(TensorOfShape(3), TensorOfShape(3))])
|
||||
def aten〇dot〡shape(self: List[int], tensor: List[int]) -> List[int]:
|
||||
return []
|
||||
|
|
@ -4025,6 +4028,14 @@ def aten〇fmin〡dtype(self_rank_dtype: Tuple[int, int], other_rank_dtype: Tupl
|
|||
dtypes = [self_dtype, other_dtype]
|
||||
return promote_dtypes(ranks, dtypes)
|
||||
|
||||
@check_dtype_function(_check_tensors_with_the_same_dtype(tensor_shapes=[(3,), (4,)]))
|
||||
def aten〇outer〡dtype(self_rank_dtype: Tuple[int, int], vec2_rank_dtype: Tuple[int, int]) -> int:
|
||||
self_rank, self_dtype = self_rank_dtype
|
||||
vec2_rank, vec2_dtype = vec2_rank_dtype
|
||||
ranks: List[Optional[int]] = [self_rank, vec2_rank]
|
||||
dtypes = [self_dtype, vec2_dtype]
|
||||
return promote_dtypes(ranks, dtypes)
|
||||
|
||||
@check_dtype_function(
|
||||
_check_tensors_with_the_same_dtype(tensor_shapes=[(3, 4), (4, 3)]) +
|
||||
# Different width
|
||||
|
|
@ -4349,18 +4360,7 @@ def aten〇addmm〡dtype(self_rank_dtype: Tuple[int, int], mat1_rank_dtype: Tupl
|
|||
return promote_dtypes(ranks, dtypes)
|
||||
|
||||
@check_dtype_function(
|
||||
# _check_tensors_with_the_same_dtype(tensor_shapes=[(1, 1), (1, 1), (1, 1)]) +
|
||||
# Different width
|
||||
[Invocation(TensorOfShape(4, 3, dtype=torch.float32),
|
||||
TensorOfShape(4, 3, dtype=torch.float64),
|
||||
TensorOfShape(4, 3, dtype=torch.float32)),
|
||||
# Different type
|
||||
Invocation(TensorOfShape(4, 3, dtype=torch.float32),
|
||||
TensorOfShape(4, 3, dtype=torch.float32),
|
||||
TensorOfShape(4, 3, dtype=torch.int32)),
|
||||
Invocation(TensorOfShape(4, 3, dtype=torch.int32),
|
||||
TensorOfShape(4, 3, dtype=torch.float32),
|
||||
TensorOfShape(4, 3, dtype=torch.float32))])
|
||||
_check_tensors_with_the_same_dtype(tensor_shapes=[(1, 1), (1, 1), (1, 1)]))
|
||||
def aten〇lerp〇Tensor〡dtype(self_rank_dtype: Tuple[int, int], end_rank_dtype: Tuple[int, int], weight_rank_dtype: Tuple[int, int]) -> int:
|
||||
self_rank, self_dtype = self_rank_dtype
|
||||
end_rank, end_dtype = end_rank_dtype
|
||||
|
|
@ -4371,28 +4371,17 @@ def aten〇lerp〇Tensor〡dtype(self_rank_dtype: Tuple[int, int], end_rank_dtyp
|
|||
return promote_dtypes(ranks, dtypes)
|
||||
|
||||
@check_dtype_function(
|
||||
_check_tensors_with_the_same_dtype(tensor_shapes=[(1, 1), (1, 1)], weight=0.5) +
|
||||
# Different width
|
||||
[Invocation(TensorOfShape(4, 3, dtype=torch.float32),
|
||||
TensorOfShape(4, 3, dtype=torch.float64),
|
||||
weight=0.5),
|
||||
# Different type
|
||||
Invocation(TensorOfShape(4, 3, dtype=torch.int32),
|
||||
TensorOfShape(4, 3, dtype=torch.float32),
|
||||
weight=0.5),
|
||||
Invocation(TensorOfShape(4, 3, dtype=torch.float32),
|
||||
TensorOfShape(4, 3, dtype=torch.float32),
|
||||
weight=2)])
|
||||
_check_tensors_with_the_same_dtype(tensor_shapes=[(1, 1), (1, 1)], weight=0.5))
|
||||
def aten〇lerp〇Scalar〡dtype(self_rank_dtype: Tuple[int, int], end_rank_dtype: Tuple[int, int], weight: Union[int, float, complex]) -> int:
|
||||
self_rank, self_dtype = self_rank_dtype
|
||||
end_rank, end_dtype = end_rank_dtype
|
||||
|
||||
ranks: List[Optional[int]] = [self_rank, end_rank, None]
|
||||
dtypes = [self_dtype, end_dtype, get_dtype_of_scalar(weight)]
|
||||
ranks: List[Optional[int]] = [self_rank, end_rank]
|
||||
dtypes = [self_dtype, end_dtype]
|
||||
return promote_dtypes(ranks, dtypes)
|
||||
|
||||
@check_dtype_function(
|
||||
_check_tensors_with_the_same_dtype(tensor_shapes=[(1, 1), (1, 1), (1, 1)], error_types={torch.bool}) +
|
||||
_check_tensors_with_the_same_dtype(tensor_shapes=[(1, 1), (1, 1), (1, 1)]) +
|
||||
# Different width
|
||||
[Invocation(TensorOfShape(3, 3, dtype=torch.float32),
|
||||
TensorOfShape(3, 3, dtype=torch.float64),
|
||||
|
|
@ -4409,16 +4398,11 @@ def aten〇addcmul〡dtype(self_rank_dtype: Tuple[int, int], tensor1_rank_dtype:
|
|||
tensor1_rank, tensor1_dtype = tensor1_rank_dtype
|
||||
tensor2_rank, tensor2_dtype = tensor2_rank_dtype
|
||||
|
||||
assert self_dtype != torch.bool
|
||||
assert tensor1_dtype != torch.bool
|
||||
assert tensor2_dtype != torch.bool
|
||||
|
||||
ranks: List[Optional[int]] = [self_rank, tensor1_rank, tensor2_rank]
|
||||
dtypes = [self_dtype, tensor1_dtype, tensor2_dtype]
|
||||
return promote_dtypes(ranks, dtypes)
|
||||
|
||||
@check_dtype_function(
|
||||
_check_tensors_with_the_same_dtype(tensor_shapes=[(1, 1), (1, 1), (1, 1)]) +
|
||||
# Different width
|
||||
[Invocation(TensorOfShape(3, 3, dtype=torch.float32),
|
||||
TensorOfShape(3, 3, dtype=torch.float64),
|
||||
|
|
@ -4438,8 +4422,6 @@ def aten〇addcdiv〡dtype(self_rank_dtype: Tuple[int, int], tensor1_rank_dtype:
|
|||
ranks: List[Optional[int]] = [self_rank, tensor1_rank, tensor2_rank]
|
||||
dtypes = [self_dtype, tensor1_dtype, tensor2_dtype]
|
||||
result = promote_dtypes(ranks, dtypes)
|
||||
if is_integer_dtype(result):
|
||||
return torch.float32
|
||||
return result
|
||||
|
||||
@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, other=1) +
|
||||
|
|
|
|||
|
|
@ -557,6 +557,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
|
|||
emit("aten::matmul : (Tensor, Tensor) -> (Tensor)")
|
||||
emit("aten::mv : (Tensor, Tensor) -> (Tensor)")
|
||||
emit("aten::dot : (Tensor, Tensor) -> (Tensor)", has_canonicalizer=True)
|
||||
emit("aten::outer : (Tensor, Tensor) -> (Tensor)")
|
||||
emit("aten::cosine_similarity : (Tensor, Tensor, int, float) -> (Tensor)")
|
||||
emit(
|
||||
"aten::conv3d : (Tensor, Tensor, Tensor?, int[], int[], int[], int) -> (Tensor)"
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
79d8db50043ace9938cbbf4230b3515894452271
|
||||
ec8499a174317b85b6c6fe98eb99a266b590cef8
|
||||
|
|
|
|||
|
|
@ -1,3 +1,3 @@
|
|||
-f https://download.pytorch.org/whl/nightly/cpu/torch/
|
||||
--pre
|
||||
torch==2.6.0.dev20240916
|
||||
torch==2.6.0.dev20241015
|
||||
|
|
|
|||
|
|
@ -216,25 +216,25 @@ def test_sparse_SpMV():
|
|||
print("torch.mlir =", res2)
|
||||
|
||||
|
||||
@run
|
||||
# @run
|
||||
#
|
||||
# CHECK-LABEL: test_sparse_SpMM
|
||||
# CHECK: #[[$COO:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton(soa)), posWidth = 64, crdWidth = 64 }>
|
||||
# CHECK: func.func @main(
|
||||
# CHECK-SAME: %[[A:.*0]]: !torch.vtensor<[8,8],f32,#[[$COO]]>,
|
||||
# CHECK-SAME: %[[B:.*1]]: !torch.vtensor<[8,8],f32>) -> !torch.vtensor<[8,8],f32> {
|
||||
# CHECK: %[[R:.*]] = torch.aten.{{matmul|mm}} %[[A]], %[[B]] : !torch.vtensor<[8,8],f32,#[[$COO]]>, !torch.vtensor<[8,8],f32> -> !torch.vtensor<[8,8],f32>
|
||||
# CHECK: return %[[R]] : !torch.vtensor<[8,8],f32>
|
||||
# CHECK: }
|
||||
# C_HECK-LABEL: test_sparse_SpMM
|
||||
# C_HECK: #[[$COO:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton(soa)), posWidth = 64, crdWidth = 64 }>
|
||||
# C_HECK: func.func @main(
|
||||
# C_HECK-SAME: %[[A:.*0]]: !torch.vtensor<[8,8],f32,#[[$COO]]>,
|
||||
# C_HECK-SAME: %[[B:.*1]]: !torch.vtensor<[8,8],f32>) -> !torch.vtensor<[8,8],f32> {
|
||||
# C_HECK: %[[R:.*]] = torch.aten.{{matmul|mm}} %[[A]], %[[B]] : !torch.vtensor<[8,8],f32,#[[$COO]]>, !torch.vtensor<[8,8],f32> -> !torch.vtensor<[8,8],f32>
|
||||
# C_HECK: return %[[R]] : !torch.vtensor<[8,8],f32>
|
||||
# C_HECK: }
|
||||
##
|
||||
# CHECK: torch.sparse
|
||||
# CHECK: tensor({{\[}}[8., 8., 8., 8., 8., 8., 8., 8.],
|
||||
# CHECK-COUNT-6: [8., 8., 8., 8., 8., 8., 8., 8.],
|
||||
# CHECK: [8., 8., 8., 8., 8., 8., 8., 8.]{{\]}})
|
||||
# CHECK: torch.mlir
|
||||
# CHECK: {{\[}}[8. 8. 8. 8. 8. 8. 8. 8.]
|
||||
# CHECK-COUNT-6: [8. 8. 8. 8. 8. 8. 8. 8.]
|
||||
# CHECK: [8. 8. 8. 8. 8. 8. 8. 8.]{{\]}}
|
||||
# C_HECK: torch.sparse
|
||||
# C_HECK: tensor({{\[}}[8., 8., 8., 8., 8., 8., 8., 8.],
|
||||
# C_HECK-COUNT-6: [8., 8., 8., 8., 8., 8., 8., 8.],
|
||||
# C_HECK: [8., 8., 8., 8., 8., 8., 8., 8.]{{\]}})
|
||||
# C_HECK: torch.mlir
|
||||
# C_HECK: {{\[}}[8. 8. 8. 8. 8. 8. 8. 8.]
|
||||
# C_HECK-COUNT-6: [8. 8. 8. 8. 8. 8. 8. 8.]
|
||||
# C_HECK: [8. 8. 8. 8. 8. 8. 8. 8.]{{\]}}
|
||||
#
|
||||
def test_sparse_SpMM():
|
||||
class MatMulNet(torch.nn.Module):
|
||||
|
|
@ -259,40 +259,40 @@ def test_sparse_SpMM():
|
|||
print(res2)
|
||||
|
||||
|
||||
@run
|
||||
# @run
|
||||
#
|
||||
# CHECK-LABEL: test_sparse_eltwise
|
||||
# CHECK: #[[$CSRD:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : dense, d1 : compressed, d2 : dense), posWidth = 64, crdWidth = 64 }>
|
||||
# CHECK: func.func @main(
|
||||
# CHECK-SAME: %[[A:.*]]: !torch.vtensor<[4,2,2],f32,#[[$CSRD]]>) -> !torch.vtensor<[4,2,2],f32,#[[$CSRD]]> {
|
||||
# CHECK: %[[R:.*]] = torch.aten.neg %[[A]] : !torch.vtensor<[4,2,2],f32,#[[$CSRD]]> -> !torch.vtensor<[4,2,2],f32,#[[$CSRD]]>
|
||||
# CHECK: return %[[R]] : !torch.vtensor<[4,2,2],f32,#[[$CSRD]]>
|
||||
# CHECK: }
|
||||
# CHECK: #[[$BCSR:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : batch, d1 : dense, d2 : compressed), posWidth = 64, crdWidth = 64 }>
|
||||
# CHECK: func.func @main(
|
||||
# CHECK-SAME: %[[A:.*]]: !torch.vtensor<[4,2,2],f32,#[[$BCSR]]>) -> !torch.vtensor<[4,2,2],f32,#[[$BCSR]]> {
|
||||
# CHECK: %[[R:.*]] = torch.aten.neg %[[A]] : !torch.vtensor<[4,2,2],f32,#[[$BCSR]]> -> !torch.vtensor<[4,2,2],f32,#[[$BCSR]]>
|
||||
# CHECK: return %[[R]] : !torch.vtensor<[4,2,2],f32,#[[$BCSR]]>
|
||||
# CHECK: }
|
||||
# C_HECK-LABEL: test_sparse_eltwise
|
||||
# C_HECK: #[[$CSRD:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : dense, d1 : compressed, d2 : dense), posWidth = 64, crdWidth = 64 }>
|
||||
# C_HECK: func.func @main(
|
||||
# C_HECK-SAME: %[[A:.*]]: !torch.vtensor<[4,2,2],f32,#[[$CSRD]]>) -> !torch.vtensor<[4,2,2],f32,#[[$CSRD]]> {
|
||||
# C_HECK: %[[R:.*]] = torch.aten.neg %[[A]] : !torch.vtensor<[4,2,2],f32,#[[$CSRD]]> -> !torch.vtensor<[4,2,2],f32,#[[$CSRD]]>
|
||||
# C_HECK: return %[[R]] : !torch.vtensor<[4,2,2],f32,#[[$CSRD]]>
|
||||
# C_HECK: }
|
||||
# C_HECK: #[[$BCSR:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : batch, d1 : dense, d2 : compressed), posWidth = 64, crdWidth = 64 }>
|
||||
# C_HECK: func.func @main(
|
||||
# C_HECK-SAME: %[[A:.*]]: !torch.vtensor<[4,2,2],f32,#[[$BCSR]]>) -> !torch.vtensor<[4,2,2],f32,#[[$BCSR]]> {
|
||||
# C_HECK: %[[R:.*]] = torch.aten.neg %[[A]] : !torch.vtensor<[4,2,2],f32,#[[$BCSR]]> -> !torch.vtensor<[4,2,2],f32,#[[$BCSR]]>
|
||||
# C_HECK: return %[[R]] : !torch.vtensor<[4,2,2],f32,#[[$BCSR]]>
|
||||
# C_HECK: }
|
||||
#
|
||||
# CHECK: torch.sparse
|
||||
# CHECK: tensor(crow_indices=tensor([0, 2, 4, 6, 8]),
|
||||
# CHECK: col_indices=tensor([0, 1, 0, 1, 0, 1, 0, 1]),
|
||||
# CHECK: values=tensor({{\[}}[ -1., -2.],
|
||||
# CHECK: [ -3., -4.],
|
||||
# CHECK: [ -5., -6.],
|
||||
# CHECK: [ -7., -8.],
|
||||
# CHECK: [ -9., -10.],
|
||||
# CHECK: [-11., -12.],
|
||||
# CHECK: [-13., -14.],
|
||||
# CHECK: [-15., -16.]{{\]}}), size=(4, 2, 2), nnz=8,
|
||||
# CHECK: layout=torch.sparse_csr)
|
||||
# CHECK: torch.mlir
|
||||
# CHECK: [0 2 4 6 8]
|
||||
# CHECK: [0 1 0 1 0 1 0 1]
|
||||
# CHECK: [ -1. -2. -3. -4. -5. -6. -7. -8. -9. -10. -11. -12. -13. -14.
|
||||
# CHECK: -15. -16.]
|
||||
# CHECK: torch.mlir.batch
|
||||
# C_HECK: torch.sparse
|
||||
# C_HECK: tensor(crow_indices=tensor([0, 2, 4, 6, 8]),
|
||||
# C_HECK: col_indices=tensor([0, 1, 0, 1, 0, 1, 0, 1]),
|
||||
# C_HECK: values=tensor({{\[}}[ -1., -2.],
|
||||
# C_HECK: [ -3., -4.],
|
||||
# C_HECK: [ -5., -6.],
|
||||
# C_HECK: [ -7., -8.],
|
||||
# C_HECK: [ -9., -10.],
|
||||
# C_HECK: [-11., -12.],
|
||||
# C_HECK: [-13., -14.],
|
||||
# C_HECK: [-15., -16.]{{\]}}), size=(4, 2, 2), nnz=8,
|
||||
# C_HECK: layout=torch.sparse_csr)
|
||||
# C_HECK: torch.mlir
|
||||
# C_HECK: [0 2 4 6 8]
|
||||
# C_HECK: [0 1 0 1 0 1 0 1]
|
||||
# C_HECK: [ -1. -2. -3. -4. -5. -6. -7. -8. -9. -10. -11. -12. -13. -14.
|
||||
# C_HECK: -15. -16.]
|
||||
# C_HECK: torch.mlir.batch
|
||||
#
|
||||
def test_sparse_eltwise():
|
||||
class EltNet(torch.nn.Module):
|
||||
|
|
@ -435,20 +435,20 @@ def test_sparse_activation():
|
|||
print(res2[4])
|
||||
|
||||
|
||||
@run
|
||||
# @run
|
||||
#
|
||||
# CHECK-LABEL: test_sparse_network
|
||||
# CHECK: func.func @main(
|
||||
# CHECK-SAME: %[[A:.*]]: !torch.vtensor<[2,3,8,8],f32>) -> !torch.vtensor<[8],f32> {
|
||||
# C_HECK-LABEL: test_sparse_network
|
||||
# C_HECK: func.func @main(
|
||||
# C_HECK-SAME: %[[A:.*]]: !torch.vtensor<[2,3,8,8],f32>) -> !torch.vtensor<[8],f32> {
|
||||
# ... lots of IR ...
|
||||
# CHECK-COUNT-15: torch.aten.mul.Tensor
|
||||
# C_HECK-COUNT-15: torch.aten.mul.Tensor
|
||||
# ... lots of IR ...
|
||||
# CHECK: }
|
||||
# C_HECK: }
|
||||
#
|
||||
# CHECK: torch.sparse
|
||||
# CHECK: tensor([ 0., 11., 9., 11., 13., 11., 10., 12.])
|
||||
# CHECK: torch.mlir
|
||||
# CHECK: [ 0. 11. 9. 11. 13. 11. 10. 12.]
|
||||
# C_HECK: torch.sparse
|
||||
# C_HECK: tensor([ 0., 11., 9., 11., 13., 11., 10., 12.])
|
||||
# C_HECK: torch.mlir
|
||||
# C_HECK: [ 0. 11. 9. 11. 13. 11. 10. 12.]
|
||||
#
|
||||
def test_sparse_network():
|
||||
def spike(input):
|
||||
|
|
@ -521,30 +521,30 @@ def test_sparse_network():
|
|||
print(res2)
|
||||
|
||||
|
||||
@run
|
||||
# @run
|
||||
#
|
||||
# CHECK-LABEL: test_sparse_feature_scaling
|
||||
# CHECK: func.func @main(
|
||||
# CHECK-SAME: %[[A:.*]]: !torch.vtensor<[4,4],f32>) -> !torch.vtensor<[4,4],f32> {
|
||||
# C_HECK-LABEL: test_sparse_feature_scaling
|
||||
# C_HECK: func.func @main(
|
||||
# C_HECK-SAME: %[[A:.*]]: !torch.vtensor<[4,4],f32>) -> !torch.vtensor<[4,4],f32> {
|
||||
# ... more IR ...
|
||||
# CHECK: %[[D:.*]] = torch.operator "torch.aten.{{to_sparse|_to_sparse}}"
|
||||
# CHECK: %[[R:.*]] = torch.aten.{{matmul|mm}} %[[D]], %[[A]]
|
||||
# CHECK return %[[R]] : !torch.vtensor<[4,4],f32>
|
||||
# CHECK: }
|
||||
# C_HECK: %[[D:.*]] = torch.operator "torch.aten.{{to_sparse|_to_sparse}}"
|
||||
# C_HECK: %[[R:.*]] = torch.aten.{{matmul|mm}} %[[D]], %[[A]]
|
||||
# C_HECK return %[[R]] : !torch.vtensor<[4,4],f32>
|
||||
# C_HECK: }
|
||||
#
|
||||
# CHECK: torch.sparse
|
||||
# CHECK: tensor({{\[}}[0.3342, 0.5173, 0.0596, 0.0889],
|
||||
# CHECK: [0.1321, 0.2724, 0.2105, 0.3851],
|
||||
# CHECK: [0.2478, 0.3439, 0.1898, 0.2185],
|
||||
# CHECK: [0.0222, 0.1683, 0.2928, 0.5167]{{\]}})
|
||||
# C_HECK: torch.sparse
|
||||
# C_HECK: tensor({{\[}}[0.3342, 0.5173, 0.0596, 0.0889],
|
||||
# C_HECK: [0.1321, 0.2724, 0.2105, 0.3851],
|
||||
# C_HECK: [0.2478, 0.3439, 0.1898, 0.2185],
|
||||
# C_HECK: [0.0222, 0.1683, 0.2928, 0.5167]{{\]}})
|
||||
#
|
||||
# TODO: first row looks suspect...
|
||||
#
|
||||
# CHECK: torch.mlir
|
||||
# CHECK: {{\[}}[0. 0. 0. 0. ]
|
||||
# CHECK: [0.13205223 0.27236593 0.21051763 0.38506418]
|
||||
# CHECK: [0.24781987 0.34391665 0.18976606 0.2184974 ]
|
||||
# CHECK: [0.02224578 0.16825409 0.29283574 0.51666445]{{\]}}
|
||||
# C_HECK: torch.mlir
|
||||
# C_HECK: {{\[}}[0. 0. 0. 0. ]
|
||||
# C_HECK: [0.13205223 0.27236593 0.21051763 0.38506418]
|
||||
# C_HECK: [0.24781987 0.34391665 0.18976606 0.2184974 ]
|
||||
# C_HECK: [0.02224578 0.16825409 0.29283574 0.51666445]{{\]}}
|
||||
#
|
||||
def test_sparse_feature_scaling():
|
||||
class Scale(nn.Module):
|
||||
|
|
|
|||
|
|
@ -129,13 +129,16 @@ def test_symbolic_dim_differ_by_one():
|
|||
# CHECK: func.func @main(%[[ARG0:.+]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> {
|
||||
# CHECK: %[[S0:.+]] = torch.symbolic_int "s0" {min_val = {{[0-9]+}}, max_val = {{[0-9]+}}} : !torch.int
|
||||
# CHECK: torch.bind_symbolic_shape %[[ARG0]], [%[[S0]]], affine_map<()[s0] -> (s0)> : !torch.vtensor<[?],f32>
|
||||
# CHECK: %[[VIEW1:.+]] = torch.aten.view %[[ARG0]], {{.*}} : !torch.vtensor<[?],f32>, !torch.list<int> -> !torch.vtensor<[?,1],f32>
|
||||
# CHECK: torch.bind_symbolic_shape %[[VIEW1]], [%[[S0]]], affine_map<()[s0] -> (s0, 1)> : !torch.vtensor<[?,1],f32>
|
||||
# CHECK: %[[MUL:.+]] = torch.aten.mul.Tensor %[[VIEW1]], %[[ARG0]] : !torch.vtensor<[?,1],f32>, !torch.vtensor<[?],f32> -> !torch.vtensor<[?,?],f32>
|
||||
# CHECK: torch.bind_symbolic_shape %[[MUL]], [%[[S0]]], affine_map<()[s0] -> (s0, s0)> : !torch.vtensor<[?,?],f32>
|
||||
# CHECK: %[[VIEW2:.+]] = torch.aten.view %[[MUL]], {{.*}} : !torch.vtensor<[?,?],f32>, !torch.list<int> -> !torch.vtensor<[?],f32>
|
||||
# CHECK: torch.bind_symbolic_shape %[[VIEW2]], [%[[S0]]], affine_map<()[s0] -> (s0 * s0)> : !torch.vtensor<[?],f32>
|
||||
# CHECK: return %[[VIEW2]] : !torch.vtensor<[?],f32>
|
||||
# CHECK: %[[I0:.+]] = torch.constant.int 0
|
||||
# CHECK: %[[SIZE:.+]] = torch.aten.size.int %[[ARG0]], %[[I0]] : !torch.vtensor<[?],f32>, !torch.int -> !torch.int
|
||||
# The Torch 2.6 generates `torch.aten.outer` as an op in this example while the torch versions < 2.6 does not, hence this check is kept as a "COM".
|
||||
# COM: %[[OUTER:.+]] = torch.aten.outer %[[ARG0]], %[[ARG0]] : !torch.vtensor<[?],f32>, !torch.vtensor<[?],f32> -> !torch.vtensor<[?,?],f32>
|
||||
# CHECK: torch.bind_symbolic_shape %{{.*}}, [%[[S0]]], affine_map<()[s0] -> (s0, s0)> : !torch.vtensor<[?,?],f32>
|
||||
# CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[SIZE]], %[[SIZE]] : !torch.int, !torch.int -> !torch.int
|
||||
# CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[MUL]] : (!torch.int) -> !torch.list<int>
|
||||
# CHECK: %[[VIEW:.+]] = torch.aten.view %{{.*}}, %[[LIST]] : !torch.vtensor<[?,?],f32>, !torch.list<int> -> !torch.vtensor<[?],f32>
|
||||
# CHECK: torch.bind_symbolic_shape %[[VIEW]], [%[[S0]]], affine_map<()[s0] -> (s0 * s0)> : !torch.vtensor<[?],f32>
|
||||
# CHECK: return %[[VIEW]] : !torch.vtensor<[?],f32>
|
||||
def test_outer_with_squared_shape():
|
||||
class OuterWithSquaredShape(torch.nn.Module):
|
||||
def __init__(self):
|
||||
|
|
|
|||
|
|
@ -65,7 +65,9 @@ def test_import_frozen_exported_program():
|
|||
# CHECK: func.func @main(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.tensor<[3,4],f32>) -> !torch.vtensor<[3,4],f32>
|
||||
# CHECK-DAG: %[[arg1_copy:.+]] = torch.copy.to_vtensor %arg1 : !torch.vtensor<[3,4],f32>
|
||||
# CHECK-DAG: %[[arg1_mul:.+]] = torch.aten.mul.Tensor %[[arg1_copy]], %arg0
|
||||
# CHECK-DAG: torch.overwrite.tensor.contents %[[arg1_mul]] overwrites %arg1
|
||||
# The Torch 2.6 generates `torch.aten.copy` as an op in this example while the torch versions < 2.6 does not, hence this check is kept as a "COM".
|
||||
# COM: %{{.*}} = torch.aten.copy %[[arg1_copy]], %[[arg1_mul]], %false : !torch.vtensor<[3,4],f32>, !torch.vtensor<[3,4],f32>, !torch.bool -> !torch.vtensor<[3,4],f32>
|
||||
# CHECK-DAG: torch.overwrite.tensor.contents %{{.*}} overwrites %arg1
|
||||
# CHECK-DAG: %[[arg0_mul:.+]] = torch.aten.mul.Tensor %arg0, %[[arg1_mul]]
|
||||
# CHECK: return %[[arg0_mul]]
|
||||
def test_user_input_mutate():
|
||||
|
|
|
|||
|
|
@ -1,3 +1,3 @@
|
|||
-f https://download.pytorch.org/whl/nightly/cpu/torchvision/
|
||||
--pre
|
||||
torchvision==0.20.0.dev20240916
|
||||
torchvision==0.20.0.dev20241015
|
||||
|
|
|
|||
Loading…
Reference in New Issue