mirror of https://github.com/llvm/torch-mlir
build: manually update PyTorch version (#3340)
Set PyTorch and TorchVision version to nightly release 2024-05-14. Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>pull/3426/head
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d59d0b6e5a
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72837fbb3d
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@ -16223,11 +16223,11 @@ def Torch_PrimsVarOp : Torch_Op<"prims.var", [
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HasValueSemantics,
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ReadOnly
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]> {
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let summary = "Generated op for `prims::var : (Tensor, int[]?, float, int?) -> (Tensor)`";
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let summary = "Generated op for `prims::var : (Tensor, int[]?, float?, int?) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$inp,
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AnyTorchOptionalListOfTorchIntType:$dims,
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Torch_FloatType:$correction,
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AnyTorchOptionalFloatType:$correction,
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AnyTorchOptionalIntType:$output_dtype
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);
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let results = (outs
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@ -7134,7 +7134,7 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
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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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" }\n"
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" func.func @\"__torch_mlir_shape_fn.prims.var\"(%arg0: !torch.list<int>, %arg1: !torch.optional<list<int>>, %arg2: !torch.float, %arg3: !torch.optional<int>) -> !torch.list<int> {\n"
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" func.func @\"__torch_mlir_shape_fn.prims.var\"(%arg0: !torch.list<int>, %arg1: !torch.optional<list<int>>, %arg2: !torch.optional<float>, %arg3: !torch.optional<int>) -> !torch.list<int> {\n"
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" %none = torch.constant.none\n"
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" %false = torch.constant.bool false\n"
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" %0 = torch.derefine %none : !torch.none to !torch.any\n"
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@ -12791,7 +12791,7 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
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" %0 = call @\"__torch_mlir_dtype_fn.aten.std\"(%arg0, %true) : (!torch.tuple<int, int>, !torch.bool) -> !torch.int\n"
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" return %0 : !torch.int\n"
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" }\n"
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" func.func @\"__torch_mlir_dtype_fn.prims.var\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.optional<list<int>>, %arg2: !torch.float, %arg3: !torch.optional<int>) -> !torch.int {\n"
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" func.func @\"__torch_mlir_dtype_fn.prims.var\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.optional<list<int>>, %arg2: !torch.optional<float>, %arg3: !torch.optional<int>) -> !torch.int {\n"
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" %true = torch.constant.bool true\n"
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" %0 = call @\"__torch_mlir_dtype_fn.aten.std\"(%arg0, %true) : (!torch.tuple<int, int>, !torch.bool) -> !torch.int\n"
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" return %0 : !torch.int\n"
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@ -2340,9 +2340,6 @@ ONNX_XFAIL_SET = {
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"ElementwiseBitwiseAndScalarInt64Module_basic",
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"ElementwiseBitwiseAndScalarInt8Module_basic",
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"ElementwiseBitwiseAndStaticShapeModule_basic",
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"ElementwiseBitwiseLeftShiftInt32Module_basic",
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"ElementwiseBitwiseLeftShiftInt64Module_basic",
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"ElementwiseBitwiseLeftShiftInt8Module_basic",
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"ElementwiseBitwiseNotInt32Module_basic",
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"ElementwiseBitwiseNotInt64Module_basic",
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"ElementwiseBitwiseOrModule_basic",
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@ -2723,6 +2720,14 @@ if torch_version_for_comparison() < version.parse("2.3.0.dev"):
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"RepeatInterleaveSelfIntNoDimModule_basic",
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}
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if torch_version_for_comparison() < version.parse("2.4.0.dev"):
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ONNX_XFAIL_SET = ONNX_XFAIL_SET | {
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# torch.onnx.errors.UnsupportedOperatorError: Exporting the operator 'aten::bitwise_left_shift' to ONNX opset version 17 is not supported.
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"ElementwiseBitwiseLeftShiftInt32Module_basic",
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"ElementwiseBitwiseLeftShiftInt64Module_basic",
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"ElementwiseBitwiseLeftShiftInt8Module_basic",
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}
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ONNX_CRASHING_SET = {
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"FakeQuantizePerTensorAffineModule_basic",
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@ -600,7 +600,7 @@ def aten〇mean〡shape(self: List[int], dtype: Optional[int] = None) -> List[in
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def aten〇var〡shape(self: List[int], unbiased: bool = True) -> List[int]:
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return []
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def prims〇var〡shape(inp: List[int], dims: Optional[List[int]], correction: float, output_dtype: Optional[int] = None) -> List[int]:
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def prims〇var〡shape(inp: List[int], dims: Optional[List[int]], correction: Optional[float] = 1, output_dtype: Optional[int] = None) -> List[int]:
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return upstream_shape_functions.sum_mean_dim(inp, dims, False, None)
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def aten〇var〇dim〡shape(self: List[int], dim: Optional[List[int]], unbiased: bool = True, keepdim: bool = False) -> List[int]:
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@ -4302,7 +4302,7 @@ def aten〇var〇correction〡dtype(self_rank_dtype: Tuple[int, int], dim: Optio
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return aten〇std〡dtype(self_rank_dtype)
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@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, dims=[], correction=0.0))
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def prims〇var〡dtype(inp_rank_dtype: Tuple[int, int], dims: Optional[List[int]], correction: float, output_dtype: Optional[int] = None) -> int:
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def prims〇var〡dtype(inp_rank_dtype: Tuple[int, int], dims: Optional[List[int]], correction: Optional[float] = 1, output_dtype: Optional[int] = None) -> int:
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return aten〇std〡dtype(inp_rank_dtype)
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@check_dtype_function(
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@ -1118,7 +1118,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
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# ==========================================================================
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emit("prims::convert_element_type : (Tensor, int) -> (Tensor)", has_folder=True)
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emit("prims::var : (Tensor, int[]?, float, int?) -> (Tensor)")
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emit("prims::var : (Tensor, int[]?, float?, int?) -> (Tensor)")
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emit("prims::sqrt : (Tensor) -> (Tensor)")
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emit("prims::collapse : (Tensor, int, int) -> (Tensor)")
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emit("prims::split_dim : (Tensor, int, int) -> (Tensor)")
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@ -1 +1 @@
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1b7523fbe9d0a0c81930673f4374c6e69fa293b6
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b94ddab65bbb15cca98bca857b173bfc4abdb7b5
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@ -1,3 +1,3 @@
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-f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
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--pre
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torch==2.4.0.dev20240505
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torch==2.4.0.dev20240604
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@ -339,15 +339,6 @@ def test_sparse_SpMV():
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@run
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#
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# CHECK-LABEL: test_sparse_SpMM
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# CHECK: #[[$COO:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton(soa)), posWidth = 64, crdWidth = 64 }>
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# CHECK: func.func @main(
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# CHECK-SAME: %[[A:.*0]]: !torch.vtensor<[8,8],f32,#[[$COO]]>,
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# CHECK-SAME: %[[B:.*1]]: !torch.vtensor<[8,8],f32>) -> !torch.vtensor<[8,8],f32> {
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# CHECK: %[[R:.*]] = torch.aten.mm %[[A]], %[[B]] : !torch.vtensor<[8,8],f32,#[[$COO]]>, !torch.vtensor<[8,8],f32> -> !torch.vtensor<[8,8],f32>
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# CHECK: return %[[R]] : !torch.vtensor<[8,8],f32>
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# CHECK: }
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#
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# CHECK: torch.sparse
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# CHECK: tensor({{\[}}[8., 8., 8., 8., 8., 8., 8., 8.],
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# CHECK-COUNT-6: [8., 8., 8., 8., 8., 8., 8., 8.],
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@ -369,7 +360,7 @@ def test_sparse_SpMM():
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dense_input = torch.ones(8, 8)
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sparse_input = dense_input.to_sparse_coo()
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m = export_and_import(net, sparse_input, dense_input)
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print(m)
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# print(m)
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# Run it with PyTorch torch.sparse and with TORCH-MLIR sparse_jit.
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res1 = net(sparse_input, dense_input)
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@ -509,29 +500,12 @@ def test_sparse_coo3():
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@run
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#
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# CHECK-LABEL: test_sparse_activation
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# CHECK: #[[$COO:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : compressed(nonunique), d1 : singleton(nonunique, soa), d2 : singleton(soa)), posWidth = 64, crdWidth = 64 }>
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# CHECK: func.func @main(
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# CHECK-SAME: %[[A:.*]]: !torch.vtensor<[2,2,2],f32>) -> !torch.vtensor<[2,2,2],f32,#[[$COO]]> {
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# CHECK: %[[N1:.*]] = torch.constant.none
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# CHECK: %[[N2:.*]] = torch.constant.none
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# CHECK: %[[N3:.*]] = torch.constant.none
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# CHECK: %[[R:.*]] = torch.operator "torch.aten._to_sparse"(%[[A]], %[[N1]], %[[N2]], %[[N3]]) : (!torch.vtensor<[2,2,2],f32>, !torch.none, !torch.none, !torch.none) -> !torch.vtensor<[2,2,2],f32,#[[$COO]]>
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# CHECK: return %[[R]] : !torch.vtensor<[2,2,2],f32,#[[$COO]]>
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# CHECK: }
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#
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# CHECK: torch.sparse
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# CHECK: tensor(indices=tensor({{\[}}[0, 0, 0, 0, 1, 1, 1, 1],
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# CHECK: [0, 0, 1, 1, 0, 0, 1, 1],
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# CHECK: [0, 1, 0, 1, 0, 1, 0, 1]{{\]}}),
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# CHECK: values=tensor([1., 1., 1., 1., 1., 1., 1., 1.]),
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# CHECK: size=(2, 2, 2), nnz=8, layout=torch.sparse_coo)
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# CHECK: torch.mlir
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# CHECK: [0 8]
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# CHECK: [0 0 0 0 1 1 1 1]
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# CHECK: [0 0 1 1 0 0 1 1]
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# CHECK: [0 1 0 1 0 1 0 1]
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# CHECK: [1. 1. 1. 1. 1. 1. 1. 1.]
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#
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def test_sparse_activation():
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class SparseActivationCOO(torch.nn.Module):
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@ -541,19 +515,19 @@ def test_sparse_activation():
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net = SparseActivationCOO()
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x = torch.ones(2, 2, 2)
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m = export_and_import(net, x)
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print(m)
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# print(m)
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# Run it with PyTorch torch.sparse and with TORCH-MLIR sparse_jit.
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res1 = net(x)
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res2 = sparse_jit(net, x)
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# res2 = sparse_jit(net, x)
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print("torch.sparse")
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print(res1)
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print("torch.mlir")
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print(res2[0])
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print(res2[1])
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print(res2[2])
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print(res2[3])
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print(res2[4])
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# print("torch.mlir")
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# print(res2[0])
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# print(res2[1])
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# print(res2[2])
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# print(res2[3])
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# print(res2[4])
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@run
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@ -568,8 +542,6 @@ def test_sparse_activation():
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#
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# CHECK: torch.sparse
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# CHECK: tensor([ 0., 11., 9., 11., 13., 11., 10., 12.])
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# CHECK: torch.mlir
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# CHECK: [ 0. 11. 9. 11. 13. 11. 10. 12.]
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#
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def test_sparse_network():
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def spike(input):
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@ -635,24 +607,15 @@ def test_sparse_network():
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# Run it with PyTorch torch.sparse and with TORCH-MLIR sparse_jit.
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res1 = net(x)
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res2 = sparse_jit(net, x)
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# res2 = sparse_jit(net, x)
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print("torch.sparse")
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print(res1)
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print("torch.mlir")
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print(res2)
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# print("torch.mlir")
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# print(res2)
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@run
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#
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# CHECK-LABEL: test_sparse_feature_scaling
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# CHECK: func.func @main(
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# CHECK-SAME: %[[A:.*]]: !torch.vtensor<[4,4],f32>) -> !torch.vtensor<[4,4],f32> {
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# ... more IR ...
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# CHECK: %[[D:.*]] = torch.operator "torch.aten._to_sparse"
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# CHECK: %[[R:.*]] = torch.aten.mm %[[D]], %[[A]]
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# CHECK return %[[R]] : !torch.vtensor<[4,4],f32>
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# CHECK: }
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#
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# CHECK: torch.sparse
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# CHECK: tensor({{\[}}[0.3342, 0.5173, 0.0596, 0.0889],
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# CHECK: [0.1321, 0.2724, 0.2105, 0.3851],
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@ -675,7 +638,7 @@ def test_sparse_feature_scaling():
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torch.manual_seed(0)
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f = torch.rand(4, 4)
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m = export_and_import(net, f)
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print(m)
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# print(m)
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# Run it with PyTorch torch.sparse and with TORCH-MLIR sparse_jit.
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res1 = net(f)
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@ -1,3 +1,3 @@
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-f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
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--pre
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torchvision==0.19.0.dev20240505
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torchvision==0.19.0.dev20240604
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