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/aosp_15_r20/external/pytorch/test/inductor/
H A Dtest_pattern_matcher.py837 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 …]
/aosp_15_r20/external/pytorch/test/jit/
H A Dtest_autodiff_subgraph_slicing.py462 # to merge both split_with_sizes in relu in one graph
476 # Case 2: aliasing between relu and split_with_sizes
478 # to merge both split_with_sizes in relu in one graph
479 # i.e. relu and split_with_sizes should be in different
486 %2 : Tensor[] = aten::split_with_sizes(%b, %0, %1)
495 ).check("Tensor = aten::relu").check_not("aten::split_with_sizes").run(
500 # Both `split_with_sizes` should be unfused
507 %2 : Tensor[] = aten::split_with_sizes(%b, %s1, %1)
508 %3 : Tensor[] = aten::split_with_sizes(%b, %s2, %1)
517 ).check("Tensor = aten::relu").check_not("aten::split_with_sizes").run(
/aosp_15_r20/external/pytorch/aten/src/ATen/native/nested/
H A DNestedTensorUtils.cpp119 TORCH_CHECK_INDEX(false, "split_with_sizes() cannot be applied to a 0-dim tensor."); in split_with_sizes_nested()
123 … "split_with_sizes for nested tensors is currently only supported for the last dimension."); in split_with_sizes_nested()
126 "split_with_sizes expects number of splits to be greater than 0, got: ", num_splits); in split_with_sizes_nested()
127 TORCH_CHECK(self.is_contiguous(), "split_with_sizes expects `self` to be contiguous."); in split_with_sizes_nested()
137 "split_with_sizes expects split_sizes to sum exactly to ", self_size, in split_with_sizes_nested()
/aosp_15_r20/external/pytorch/torch/csrc/jit/passes/onnx/
H A Dpreprocess_for_onnx.cpp41 // split_with_sizes(Tensor self, int[] split_sizes, int dim=0) -> Tensor[]
46 // %8 : Tensor[] = aten::split_with_sizes(%input, %13, %7)
59 // aten::split_with_sizes(%input, %13, %7, %8) return (%14, %15, %16)
96 case aten::split_with_sizes: in FuseWithListUnpack()
/aosp_15_r20/external/executorch/backends/qualcomm/builders/
H A Dop_split_with_sizes.py20 target = ["aten.split_with_sizes.default"]
43 # split_with_sizes will return a tuple since it has multiple outputs
/aosp_15_r20/external/pytorch/torch/_inductor/fx_passes/
H A Dpost_grad.py607 split_nodes = filter_nodes(match.nodes, aten.split_with_sizes)
625 # For example, if the split_node like split_with_sizes(input, [2, 2, 3], 1),
902 aten.split_with_sizes,
922 split_nodes = filter_nodes(match.nodes, aten.split_with_sizes)
957 aten.split_with_sizes,
/aosp_15_r20/external/pytorch/torch/csrc/cuda/
H A Dcomm.cpp285 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()
/aosp_15_r20/external/pytorch/test/
H A Dtest_functionalization.py858 # test: view ops that return multiple tensors (split_with_sizes)
860 y1, y2 = x.split_with_sizes([2, 2])
908 split_with_sizes = torch.ops.aten.split_with_sizes.default(arg0_1, [2, 2])
909 getitem = split_with_sizes[0]
910 getitem_1 = split_with_sizes[1]; split_with_sizes = getitem_1 = None
913 split_with_sizes_1 = torch.ops.aten.split_with_sizes.default(arg0_1, [2, 2])
918 split_with_sizes_2 = torch.ops.aten.split_with_sizes.default(slice_scatter, [2, 2])
H A Dtest_proxy_tensor.py1572 …split_with_sizes = torch.ops.aten.split_with_sizes.default(values_1, [_local_scalar_dense, _local_…
1573 getitem = split_with_sizes[0]
1574 getitem_1 = split_with_sizes[1]
1575 getitem_2 = split_with_sizes[2]; split_with_sizes = None
H A Dtest_nestedtensor.py1343 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)
1368 … "split_with_sizes for nested tensors is currently only supported for the last dimension.",
1369 lambda: torch.split_with_sizes(nt, split_sizes, dim=1),
1375 … "split_with_sizes for nested tensors is currently only supported for the last dimension.",
1376 lambda: torch.split_with_sizes(nt, split_sizes, dim=0),
1383 "split_with_sizes expects `self` to be contiguous.",
1384 lambda: torch.split_with_sizes(nt_noncontiguous, split_sizes, dim=-1),
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/aosp_15_r20/external/pytorch/torch/csrc/jit/runtime/static/
H A Dnative_ops.cpp736 at::native::split_with_sizes(self, split_sizes.vec(), dim); in __anon75e5f0514602()
745 aten::split_with_sizes,
749 … "aten::split_with_sizes(Tensor(a -> *) self, int[] split_sizes, int dim=0) -> Tensor(a)[]")) && in __anon75e5f0514902()
751 … "aten::split_with_sizes(Tensor(a -> *) self, int[] split_sizes, int dim=0) -> (Tensor[])"))) { in __anon75e5f0514902()
760 at::native::split_with_sizes(self, split_sizes.vec(), dim); in __anon75e5f0514902()
/aosp_15_r20/external/pytorch/torch/csrc/jit/runtime/
H A Dregister_special_ops.cpp248 RECORD_FUNCTION("split_with_sizes", last(stack, 3)); in __anonedd36e380202()
250 auto result = at::split_with_sizes( in __anonedd36e380202()
H A Dsymbolic_script.cpp609 def split_with_sizes(self,
617 return torch.split_with_sizes(self, split_sizes, dim), backward
644 grad_tensors = torch.split_with_sizes(grad_output, split_sizes, dim)
/aosp_15_r20/external/pytorch/torch/onnx/
H A Dsymbolic_opset13.py118 @_onnx_symbolic("aten::split_with_sizes")
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)
H A Dsymbolic_opset11.py78 "split_with_sizes",
636 @_onnx_symbolic("aten::split_with_sizes")
638 def split_with_sizes(g: jit_utils.GraphContext, self, split_sizes, dim, _outputs=None): function
/aosp_15_r20/external/pytorch/test/cpp/api/
H A Dinference_mode.cpp482 b = s_view.split_with_sizes({1, 1}); in TEST()
485 c = s.split_with_sizes({1, 1}); in TEST()
/aosp_15_r20/external/executorch/backends/arm/quantizer/quantization_annotation/
H A Dgeneric_annotator.py49 torch.ops.aten.split_with_sizes.default,
/aosp_15_r20/external/pytorch/aten/src/ATen/native/
H A DTensorShape.cpp164 #include <ATen/ops/split_with_sizes.h>
946 // We need to call split_with_sizes in the case where split_size and dimension size are 0, because in chunk()
948 // 0-sized chunks adding up to 0). So, call split_with_sizes with the correct number of chunks, in chunk()
2621 std::vector<Tensor> split_with_sizes(const Tensor& self, IntArrayRef split_sizes, int64_t dim) { in split_with_sizes() function
2632 "split_with_sizes expects split_sizes have only non-negative ", in split_with_sizes()
2638 "split_with_sizes expects split_sizes to sum exactly to ", dim_size, in split_with_sizes()
2644 auto result = at::native::split_with_sizes(self, split_sizes, dim); in unsafe_split_with_sizes()
4059 auto tmp = self.split_with_sizes(split_sizes, dim); in split_with_sizes_copy_out()
H A DMathBitFallThroughLists.h37 m.impl("split_with_sizes", torch::CppFunction::makeFallthrough()); \
/aosp_15_r20/external/pytorch/aten/src/ATen/native/cuda/
H A DTensorShape.cu728 "split_with_sizes expects split_sizes have only non-negative ", in split_with_sizes_copy_out_cuda()
735 "split_with_sizes expects split_sizes to sum exactly to ", in split_with_sizes_copy_out_cuda()
/aosp_15_r20/external/pytorch/functorch/op_analysis/
H A Dpublic_api202 split_with_sizes
/aosp_15_r20/external/executorch/backends/arm/test/ops/
H A Dtest_split.py38 return x.split_with_sizes(split_sizes=split_sizes, dim=dim)
/aosp_15_r20/external/pytorch/docs/source/
H A Dtensor_view.rst81 - :meth:`~torch.Tensor.split_with_sizes`
H A Dconf.py1354 "split_with_sizes",
1401 "split_with_sizes",
1690 "split_with_sizes",
/aosp_15_r20/external/pytorch/torch/csrc/distributed/c10d/
H A DProcessGroupMPI.cpp810 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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