/aosp_15_r20/external/pytorch/test/inductor/ |
H A D | test_pattern_matcher.py | 837 split_with_sizes = torch.ops.aten.split_with_sizes.default(a, [8, 24], 1) 838 getitem = split_with_sizes[0] 839 getitem_1 = split_with_sizes[1] 850 split_with_sizes = torch.ops.aten.split_with_sizes.default(a, [8, 8, 16], 1) 851 getitem = split_with_sizes[0] 852 getitem_1 = split_with_sizes[1] 853 getitem_2 = split_with_sizes[2] 864 split_with_sizes = torch.ops.aten.split_with_sizes.default( 867 cat = torch.ops.aten.cat.default(split_with_sizes, 0) 877 x = torch.ops.aten.split_with_sizes.default(a, [3, 2, 3], dim=1) [all …]
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H A D | test_cpu_repro.py | 2827 split_with_sizes = torch.ops.aten.split_with_sizes.default( 2830 getitem = split_with_sizes[0] 2831 getitem_1 = split_with_sizes[1]
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/aosp_15_r20/external/pytorch/torch/csrc/cuda/ |
H A D | comm.cpp | 285 tensor.split_with_sizes(/*split_sizes=*/chunk_sizes, /*dim=*/dim); in _broadcast_out_impl() 332 ? tensor.split_with_sizes(/*split_sizes=*/*chunk_sizes, /*dim=*/dim) in _broadcast_out_impl() 384 out_tensor.split_with_sizes(/*split_sizes=*/chunk_sizes, /*dim=*/dim); in _broadcast_out_impl()
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/aosp_15_r20/external/pytorch/torch/_inductor/fx_passes/ |
H A D | post_grad.py | 607 split_nodes = filter_nodes(match.nodes, aten.split_with_sizes) 902 aten.split_with_sizes, 922 split_nodes = filter_nodes(match.nodes, aten.split_with_sizes) 957 aten.split_with_sizes,
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/aosp_15_r20/external/pytorch/test/cpp/api/ |
H A D | inference_mode.cpp | 482 b = s_view.split_with_sizes({1, 1}); in TEST() 485 c = s.split_with_sizes({1, 1}); in TEST()
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/aosp_15_r20/external/executorch/backends/arm/quantizer/quantization_annotation/ |
H A D | generic_annotator.py | 49 torch.ops.aten.split_with_sizes.default,
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/aosp_15_r20/external/pytorch/torch/csrc/jit/passes/onnx/ |
H A D | preprocess_for_onnx.cpp | 96 case aten::split_with_sizes: in FuseWithListUnpack()
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/aosp_15_r20/external/executorch/backends/arm/test/ops/ |
H A D | test_split.py | 38 return x.split_with_sizes(split_sizes=split_sizes, dim=dim)
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/aosp_15_r20/external/pytorch/torch/csrc/jit/runtime/static/ |
H A D | native_ops.cpp | 736 at::native::split_with_sizes(self, split_sizes.vec(), dim); in __anon75e5f0514602() 745 aten::split_with_sizes, 760 at::native::split_with_sizes(self, split_sizes.vec(), dim); in __anon75e5f0514902()
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/aosp_15_r20/external/pytorch/docs/source/ |
H A D | tensor_view.rst | 81 - :meth:`~torch.Tensor.split_with_sizes`
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/aosp_15_r20/external/pytorch/torch/csrc/distributed/c10d/ |
H A D | ProcessGroupMPI.cpp | 810 srcFlatData.split_with_sizes(c10::IntArrayRef(send_lengthsL), 0); in alltoall() 828 dstFlatData.split_with_sizes(c10::IntArrayRef(recv_lengthsL), 0); in alltoall()
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/aosp_15_r20/external/executorch/backends/qualcomm/_passes/ |
H A D | layout_transform.py | 72 exir_ops.edge.aten.split_with_sizes.default,
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/aosp_15_r20/external/pytorch/aten/src/ATen/native/nested/cuda/ |
H A D | NestedTensorTransformerFunctions.cpp | 196 at::split_with_sizes(metadata, {offsets.numel(), nt_sizes.numel()}, 0); in NestedTensor_to_padded_tensor_cuda()
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/aosp_15_r20/external/pytorch/torch/distributed/_tools/ |
H A D | runtime_estimator.py | 56 aten.split_with_sizes,
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/aosp_15_r20/external/pytorch/functorch/op_analysis/ |
H A D | public_api | 202 split_with_sizes
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H A D | annotated_ops | 253 split_with_sizes, view/reshape
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/aosp_15_r20/external/pytorch/torch/csrc/jit/runtime/ |
H A D | register_special_ops.cpp | 250 auto result = at::split_with_sizes( in __anonedd36e380202()
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/aosp_15_r20/external/pytorch/torch/onnx/ |
H A D | symbolic_opset13.py | 119 def split_with_sizes(g: jit_utils.GraphContext, self, split_sizes, dim, _outputs=None): function 134 return split_with_sizes(g, self, split_sizes, dim, _outputs)
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/aosp_15_r20/external/pytorch/test/expect/ |
H A D | HasDecompTest.test_aten_core_operators.expect | 483 aten::split_with_sizes
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/aosp_15_r20/external/pytorch/torch/distributed/tensor/_ops/ |
H A D | _tensor_ops.py | 730 aten.split_with_sizes.default,
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/aosp_15_r20/external/pytorch/test/ |
H A D | test_nestedtensor.py | 1343 a_splits = a.split_with_sizes(split_sizes, dim=-1) 1344 b_splits = b.split_with_sizes(split_sizes, dim=-1) 1345 c_splits = c.split_with_sizes(split_sizes, dim=-1) 1348 nt_splits = nt.split_with_sizes(split_sizes, dim=-1) 1369 lambda: torch.split_with_sizes(nt, split_sizes, dim=1), 1376 lambda: torch.split_with_sizes(nt, split_sizes, dim=0), 1384 lambda: torch.split_with_sizes(nt_noncontiguous, split_sizes, dim=-1), 1392 lambda: torch.split_with_sizes(nt, bad_split_sizes, dim=-1), 3312 splits = nt.split_with_sizes([2, 3], dim=-1)
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/aosp_15_r20/external/pytorch/benchmarks/dynamo/microbenchmarks/operator_inp_logs/timm_train/ |
H A D | botnet26t_256_training.txt | 220 Operator: aten.split_with_sizes.default
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H A D | levit_128_training.txt | 281 Operator: aten.split_with_sizes.default
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H A D | eca_botnext26ts_256_training.txt | 276 Operator: aten.split_with_sizes.default
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H A D | eca_halonext26ts_training.txt | 322 Operator: aten.split_with_sizes.default
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