Searched full:randn_like (Results 1 – 25 of 95) sorted by relevance
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| /external/pytorch/test/functorch/ |
| D | test_ops.py | 227 return tree_map(torch.randn_like, output)
270 tree_map(lambda x: torch.randn_like(x, requires_grad=True), result)
292 tangents = _as_tuple(tree_map(lambda x: torch.randn_like(x), primals))
315 tangents = _as_tuple(tree_map(lambda x: torch.randn_like(x), primals))
323 tangents = _as_tuple(tree_map(lambda x: torch.randn_like(x), primals))
693 orig_tangents = tree_map(lambda x: torch.randn_like(x), primals)
808 cotangents = tree_map(lambda x: torch.randn_like(x), result)
903 cotangents = tree_map(lambda x: torch.randn_like(x), result)
1088 cotangents = tree_map(lambda x: torch.randn_like(x), result)
1726 cotangents = tree_map(lambda x: torch.randn_like(x), result)
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| D | discover_coverage.py | 597 "torch.randn_like", # randomness
680 "randn_like", # randomness
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| /external/pytorch/aten/src/ATen/native/vulkan/ops/ |
| D | Random.cpp | 123 static Tensor randn_like( in randn_like() function
139 m.impl(TORCH_SELECTIVE_NAME("aten::randn_like"), TORCH_FN(randn_like)); in TORCH_LIBRARY_IMPL()
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| /external/pytorch/test/typing/reveal/ |
| D | tensor_sampling.py | 51 # randn_like
53 reveal_type(torch.randn_like(c)) # E: {Tensor}
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| /external/pytorch/torch/distributed/_shard/sharded_tensor/_ops/ |
| D | init.py | 121 torch.randn_like: sharded_tensor.randn,
151 register_tensor_creation_op(torch.randn_like)
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| /external/pytorch/test/optim/ |
| D | test_swa_utils.py | 97 p.detach().add_(torch.randn_like(p))
146 p.detach().add_(torch.randn_like(p))
201 p.detach().add_(torch.randn_like(p))
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| /external/pytorch/test/mobile/model_test/ |
| D | sampling_ops.py | 25 torch.randn_like(a),
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| /external/pytorch/torch/testing/_internal/opinfo/definitions/ |
| D | nested.py | 161 njt2 = torch.randn_like(njt1)
248 njt, kwargs={"mask": (torch.randn_like(njt, requires_grad=False) < 0.0)}
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| /external/pytorch/test/nn/ |
| D | test_embedding.py | 802 torch.randn_like(idx1, device=device, dtype=w_dtype),
884 per_sample_weights = torch.randn_like(input, dtype=torch.double, device=device)
1012 per_sample_weights = torch.randn_like(
1028 grad = torch.randn_like(expected)
1076 per_sample_weights = torch.randn_like(
1092 grad = torch.randn_like(expected).to(dtype=dtypes[2], device=device)
1152 per_sample_weights = torch.randn_like(
1177 grad = torch.randn_like(expected)
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| /external/pytorch/aten/src/ATen/functorch/ |
| D | BatchRulesFactory.cpp | 230 VMAP_SUPPORT(randn_like, BASIC_UNARY_BATCH_RULE(ATEN_FN(randn_like))); in TORCH_LIBRARY_IMPL()
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| D | BatchRulesRandomness.cpp | 477 …m.impl("randn_like", tensor_like_random_batch_rule<decltype(&ATEN_FN(randn_like)), &ATEN_FN(randn_… in TORCH_LIBRARY_IMPL()
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| /external/pytorch/docs/source/ |
| D | torch.rst | 38 :func:`torch.randn_like`
210 randn_like
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| D | jit_unsupported.rst | 63 * :func:`torch.randn_like`
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| /external/pytorch/benchmarks/transformer/ |
| D | sdpa.py | 111 dOut = torch.randn_like(out_torch)
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| D | score_mod.py | 206 dOut = torch.randn_like(out_eager)
219 dOut = torch.randn_like(out_compile)
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| /external/pytorch/torch/utils/benchmark/utils/ |
| D | sparse_fuzzer.py | 85 v = torch.cat([v, torch.randn_like(v)], 0)
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| /external/pytorch/functorch/op_analysis/ |
| D | public_api | 389 randn_like
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| D | annotated_ops | 216 randn_like, factory
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| /external/pytorch/test/ |
| D | test_bundled_inputs.py | 376 output[k] = torch.randn_like(v)
391 output.append(torch.randn_like(v))
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| /external/libopus/dnn/torch/osce/models/ |
| D | shape_up_48.py | 160 noise = self.noise_amplitude * torch.randn_like(y32)
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| /external/pytorch/benchmarks/fastrnns/ |
| D | factory.py | 50 grad_output = torch.randn_like(output)
239 grad = torch.randn_like(padded)
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| /external/executorch/docs/source/ |
| D | extension-tensor.md | 396 | `at::randn_like(tensor)` | `randn_like(tensor)` |
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| /external/pytorch/torch/testing/_internal/ |
| D | composite_compliance.py | 512 return torch.randn_like(t)
514 return [torch.randn_like(e) if e.requires_grad else None for e in t]
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| /external/pytorch/aten/src/ATen/ |
| D | VmapModeRegistrations.cpp | 75 …m.impl("randn_like", unsupportedRandomOp<const Tensor&, TENSOROPTIONS, std::optional<MemoryFormat>… in TORCH_LIBRARY_IMPL()
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| /external/pytorch/torch/_inductor/fx_passes/ |
| D | pad_mm.py | 437 return torch.randn_like(t)
495 input_pad = torch.randn_like(input)
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