| /aosp_15_r20/external/pytorch/test/ |
| H A D | test_sparse.py | 121 " Please use torch.sparse_coo_tensor((0,), dtype=)"
122 x_ref = torch.sparse_coo_tensor((0,), dtype=torch.float64)
128 " Please use torch.sparse_coo_tensor(x._indices(), x._values(), x.shape)"
131 y = torch.sparse_coo_tensor(x._indices(), x._values(), x.shape)
137 " Please use torch.sparse_coo_tensor(indices, values, dtype=, device=)"
138 … x_ref = torch.sparse_coo_tensor([[0, 0, 1, 1], [0, 1, 0, 1]], [1, 2, 3, 4], dtype=torch.float64)
145 " Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=)"
146 …x_ref = torch.sparse_coo_tensor([[0, 0, 1, 1], [0, 1, 0, 1]], [1, 2, 3, 4], (2, 3), dtype=torch.fl…
153 " Please use torch.sparse_coo_tensor(shape, dtype=, device=)"
154 x_ref = torch.sparse_coo_tensor((2, 3), dtype=torch.float64)
[all …]
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| H A D | test_numba_integration.py | 72 sparse_t = torch.sparse_coo_tensor(indices_t, cput)
79 sparse_cuda_t = torch.sparse_coo_tensor(indices_t, cput).cuda()
183 sparset = torch.sparse_coo_tensor(cput[None, :], cput)
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| H A D | test_testing.py | 1026 actual = torch.sparse_coo_tensor(indices, values, size=(2, 2)).coalesce()
1038 actual = torch.sparse_coo_tensor(indices, values, size=(2, 2))
1059 actual = torch.sparse_coo_tensor(actual_indices, actual_values, size=(2, 2))
1066 expected = torch.sparse_coo_tensor(expected_indices, expected_values, size=(2, 2))
1078 actual = torch.sparse_coo_tensor(actual_indices, actual_values, size=(2, 2))
1085 expected = torch.sparse_coo_tensor(expected_indices, expected_values, size=(2, 2))
1097 actual = torch.sparse_coo_tensor(actual_indices, actual_values, size=(2, 2))
1104 expected = torch.sparse_coo_tensor(expected_indices, expected_values, size=(2, 2))
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| H A D | test_masked.py | 403 expected_sparse = torch.sparse_coo_tensor(
407 outmask = torch.sparse_coo_tensor(sparse.indices(),
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| /aosp_15_r20/external/pytorch/test/typing/pass/ |
| H A D | creation_ops.py | 23 # torch.sparse_coo_tensor
26 torch.sparse_coo_tensor(i, v, [2, 4])
27 torch.sparse_coo_tensor(i, v)
28 torch.sparse_coo_tensor(
31 torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1])
32 torch.sparse_coo_tensor(torch.empty([1, 0]), torch.empty([0, 2]), [1, 2])
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| /aosp_15_r20/external/pytorch/test/typing/reveal/ |
| H A D | tensor_constructors.py | 25 # torch.sparse_coo_tensor
28 reveal_type(torch.sparse_coo_tensor(i, v, [2, 4])) # E: {Tensor}
29 reveal_type(torch.sparse_coo_tensor(i, v)) # E: {Tensor}
31 torch.sparse_coo_tensor(
35 reveal_type(torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1])) # E: {Tensor}
37 torch.sparse_coo_tensor(torch.empty([1, 0]), torch.empty([0, 2]), [1, 2])
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| /aosp_15_r20/external/pytorch/torch/csrc/utils/ |
| H A D | tensor_new.cpp | 314 // vs sparse_coo_tensor). in internal_new_from_data()
623 " Please use torch.sparse_coo_tensor((0,), dtype=)."); in legacy_sparse_tensor_generic_ctor_new()
632 " Please use torch.sparse_coo_tensor(x._indices(), x._values(), x.shape)."); in legacy_sparse_tensor_generic_ctor_new()
641 " Please use torch.sparse_coo_tensor(indices, values, dtype=, device=)."); in legacy_sparse_tensor_generic_ctor_new()
648 return at::sparse_coo_tensor(r.tensor(0), r.tensor(1)); in legacy_sparse_tensor_generic_ctor_new()
653 " Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=)."); in legacy_sparse_tensor_generic_ctor_new()
660 return at::sparse_coo_tensor(r.tensor(0), r.tensor(1), r.intlist(2)); in legacy_sparse_tensor_generic_ctor_new()
671 … "torch.sparse.SparseTensor(sequence) only accepts sizes. Please use torch.sparse_coo_tensor() " in legacy_sparse_tensor_generic_ctor_new()
675 "SparseTensor.new(sequence) only accepts sizes. Please use torch.sparse_coo_tensor() " in legacy_sparse_tensor_generic_ctor_new()
682 " Please use torch.sparse_coo_tensor(shape, dtype=, device=)."); in legacy_sparse_tensor_generic_ctor_new()
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| /aosp_15_r20/external/pytorch/benchmarks/distributed/rpc/parameter_server/ |
| H A D | utils.py | 13 sparse_tensor (torch.Tensor): sparse_coo_tensor represented as a list
21 A helper function creates a sparse_coo_tensor from indices, values, and size.
23 sparse_rpc_format (list): sparse_coo_tensor represented as a list
25 return torch.sparse_coo_tensor(
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| /aosp_15_r20/external/pytorch/docs/source/ |
| H A D | sparse.rst | 356 :func:`torch.sparse_coo_tensor`.
366 >>> s = torch.sparse_coo_tensor(i, v, (2, 3))
382 >>> s = torch.sparse_coo_tensor(list(zip(*i)), v, (2, 3))
384 >>> s = torch.sparse_coo_tensor(torch.tensor(i).t(), v, (2, 3))
385 >>> torch.sparse_coo_tensor(i.t(), v, torch.Size([2,3])).to_dense()
392 >>> torch.sparse_coo_tensor(size=(2, 3))
431 >>> s = torch.sparse_coo_tensor(i, v, (2, 3, 2))
489 >>> s=torch.sparse_coo_tensor(i, v, (3,))
522 >>> a = torch.sparse_coo_tensor([[1, 1]], [5, 6], (2,))
523 >>> b = torch.sparse_coo_tensor([[0, 0]], [7, 8], (2,))
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| H A D | jit_unsupported.rst | 75 * :func:`torch.sparse_coo_tensor`
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| /aosp_15_r20/external/pytorch/aten/src/ATen/native/sparse/ |
| H A D | SparseTensor.cpp | 34 #include <ATen/ops/sparse_coo_tensor.h>
58 #include <ATen/ops/sparse_coo_tensor.h>
262 Tensor sparse_coo_tensor(IntArrayRef size, in sparse_coo_tensor() function
288 Tensor sparse_coo_tensor(const Tensor& indices, const Tensor& values_, in sparse_coo_tensor() function
453 Tensor sparse_coo_tensor(const Tensor& indices, const Tensor& values, IntArrayRef size, in sparse_coo_tensor() function
489 // NOTE: _sparse_coo_tensor_unsafe() differs from sparse_coo_tensor()
810 auto mask_template = at::sparse_coo_tensor( in sparse_mask()
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| H A D | SparseFactories.cpp | 13 #include <ATen/ops/sparse_coo_tensor.h>
83 auto result_coo = at::sparse_coo_tensor(indices, values, shape); in spdiags()
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| /aosp_15_r20/external/pytorch/torch/csrc/autograd/ |
| H A D | python_torch_functions_manual.cpp | 224 …"sparse_coo_tensor(PyObject* indices, PyObject* values, *, ScalarType dtype=None, Device? device=N… in THPVariable_sparse_coo_tensor()
225 …"sparse_coo_tensor(PyObject* indices, PyObject* values, IntArrayRef size, *, ScalarType dtype=None… in THPVariable_sparse_coo_tensor()
226 …"sparse_coo_tensor(IntArrayRef size, *, ScalarType dtype=None, Device? device=None, bool requires_… in THPVariable_sparse_coo_tensor()
235 jit::tracer::warn("torch.sparse_coo_tensor", jit::tracer::WARN_CONSTRUCTOR); in THPVariable_sparse_coo_tensor()
389 {"sparse_coo_tensor",
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| /aosp_15_r20/external/pytorch/test/distributed/fsdp/ |
| H A D | test_fsdp_sharded_grad_scaler.py | 105 s = torch.sparse_coo_tensor(
120 s1.grad = torch.sparse_coo_tensor(
131 s1 = torch.sparse_coo_tensor(
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| /aosp_15_r20/external/pytorch/benchmarks/sparse/ |
| H A D | utils.py | 48 return torch.sparse_coo_tensor(indices, values, size=shape)
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| /aosp_15_r20/external/pytorch/aten/src/ATen/native/mkl/ |
| H A D | SparseBlasImpl.cpp | 24 #include <ATen/ops/sparse_coo_tensor.h>
640 …const auto t_expanded_coo = at::sparse_coo_tensor(expanded_indices, expanded_values, t_coo.sizes()… in triangular_solve_out_sparse_csr()
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| /aosp_15_r20/external/pytorch/torch/sparse/ |
| H A D | __init__.py | 231 >>> S = torch.sparse_coo_tensor(I, V, size)
651 a = torch.sparse_coo_tensor(
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| /aosp_15_r20/external/pytorch/torch/utils/ |
| H A D | _device.py | 42 torch.sparse_coo_tensor,
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| /aosp_15_r20/external/pytorch/torch/masked/maskedtensor/ |
| H A D | unary.py | 132 result_data = torch.sparse_coo_tensor(i, v, size=s)
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| H A D | binary.py | 124 result_data = torch.sparse_coo_tensor(i, v, size)
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| /aosp_15_r20/external/pytorch/torch/utils/benchmark/examples/sparse/ |
| H A D | compare.py | 73 return torch.sparse_coo_tensor(indices, values, size, dtype=dtype, device=device)
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| /aosp_15_r20/external/pytorch/torch/utils/benchmark/utils/ |
| H A D | sparse_fuzzer.py | 88 x = torch.sparse_coo_tensor(i, v, torch.Size(size))
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| /aosp_15_r20/external/pytorch/functorch/op_analysis/ |
| H A D | public_api | 579 sparse_coo_tensor
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| /aosp_15_r20/external/pytorch/torch/csrc/api/src/optim/ |
| H A D | adagrad.cpp | 108 return torch::sparse_coo_tensor( in step()
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| /aosp_15_r20/external/pytorch/test/expect/ |
| H A D | HasDecompTest.test_has_decomposition.expect | 1181 aten::sparse_coo_tensor.size
1182 aten::sparse_coo_tensor.size_out
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