| /external/pytorch/test/inductor/ |
| D | test_b2b_gemm.py | 21 def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
25 def f_32(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
55 def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
59 def f_32(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
81 def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
84 def f_32(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
106 def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
109 def f_32(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
130 def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
151 def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
[all …]
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| /external/pytorch/torch/_inductor/ |
| D | decomposition.py | 126 def assert_async_msg_decomp(tensor: torch.Tensor, msg: str) -> None:
132 def functional_assert_async_msg_decomp(tensor: torch.Tensor, msg: str) -> None:
149 x: torch.Tensor,
191 grad_output: torch.Tensor,
192 input: torch.Tensor,
193 weight: torch.Tensor,
223 def round_dec(x: torch.Tensor, decimals: int = 0) -> torch.Tensor:
231 self: torch.Tensor,
232 batch2: torch.Tensor,
254 self: torch.Tensor,
[all …]
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| /external/executorch/backends/cadence/aot/ |
| D | ops_registrations.py | 264 input: torch.Tensor,
276 input: torch.Tensor,
288 src: torch.Tensor,
289 weight: torch.Tensor,
290 bias: torch.Tensor,
292 weight_zero_point: torch.Tensor,
293 out_multiplier: torch.Tensor,
294 out_shift: torch.Tensor,
296 offset: Optional[torch.Tensor],
310 src: torch.Tensor,
[all …]
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| /external/pytorch/torch/distributed/_symmetric_memory/ |
| D | __init__.py | 137 shard: torch.Tensor,
138 shard_consumer: Callable[[torch.Tensor, int], None],
139 ag_out: torch.Tensor,
195 chunk_producer: Callable[[int, torch.Tensor], None],
196 output: torch.Tensor,
289 A_shard: torch.Tensor,
290 Bs: List[torch.Tensor],
319 def unflatten(t: torch.Tensor) -> torch.Tensor:
333 def shard_consumer(shard: torch.Tensor, rank: int) -> None:
348 A_shard: torch.Tensor,
[all …]
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| /external/pytorch/torch/ao/quantization/fx/ |
| D | _decomposed.py | 52 input: torch.Tensor,
89 input: torch.Tensor,
114 input: torch.Tensor,
115 scale: torch.Tensor,
116 zero_point: torch.Tensor,
139 input: torch.Tensor,
140 scale: torch.Tensor,
141 zero_point: torch.Tensor,
171 input: torch.Tensor,
172 scale: torch.Tensor,
[all …]
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| /external/pytorch/torch/_decomp/ |
| D | decompositions_for_jvp.py | 102 def trace(self: Tensor) -> Tensor:
107 def log_sigmoid_forward(self: Tensor) -> Tuple[Tensor, Tensor]:
118 input: Tensor, rstd: Tensor, inner_dim_indices: List[int], keepdim: bool
133 grad_out: Tensor,
134 input: Tensor,
136 mean: Tensor,
137 rstd: Tensor,
138 weight: Optional[Tensor],
139 bias: Optional[Tensor],
217 grad_out: Tensor,
[all …]
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| D | decompositions.py | 106 def _unsqueeze_to_dim(x: Tensor, dim: int) -> Tensor:
115 def tanh_backward(out_grad: Tensor, y: Tensor):
122 def sigmoid_backward(out_grad: Tensor, y: Tensor):
129 def softplus_backward(out_grad: Tensor, x: Tensor, beta: float, threshold: float):
138 grad_output: Tensor,
143 self_or_result: Tensor,
168 def fill_tensor(self, value: Tensor):
179 def hardsigmoid(self: Tensor) -> Tensor:
186 def hardsigmoid_backward(grad_output: Tensor, self: Tensor):
197 grad_output: Tensor, self: Tensor, min_val: float, max_val: float
[all …]
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| /external/executorch/examples/qualcomm/oss_scripts/llama2/model/ |
| D | static_llama.py | 19 def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
34 x: torch.Tensor, freqs_cos: torch.Tensor, freqs_sin: torch.Tensor
102 hidden_states: torch.Tensor,
103 freqs_cos: torch.Tensor,
104 freqs_sin: torch.Tensor,
105 atten_mask: torch.Tensor,
106 k_caches: List[torch.Tensor],
107 v_caches: List[torch.Tensor],
138 hidden_states: torch.Tensor,
139 freqs_cos: torch.Tensor,
[all …]
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| /external/pytorch/benchmarks/fastrnns/ |
| D | cells.py | 29 input: Tensor,
30 hidden: Tuple[Tensor, Tensor], argument
31 w_ih: Tensor,
32 w_hh: Tensor,
33 b_ih: Tensor,
34 b_hh: Tensor,
53 input: Tensor,
54 hx: Tensor,
55 cx: Tensor,
56 w_ih: Tensor,
[all …]
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| /external/pytorch/torch/nn/ |
| D | functional.py | 437 input: Tensor,
442 _random_samples: Optional[Tensor] = None,
514 input: Tensor,
519 _random_samples: Optional[Tensor] = None,
549 input: Tensor,
554 _random_samples: Optional[Tensor] = None,
630 input: Tensor,
635 _random_samples: Optional[Tensor] = None,
665 input: Tensor,
718 input: Tensor,
[all …]
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| /external/pytorch/torch/optim/ |
| D | adam.py | 36 lr: Union[float, Tensor] = 1e-3, argument
321 params: List[Tensor],
322 grads: List[Tensor],
323 exp_avgs: List[Tensor],
324 exp_avg_sqs: List[Tensor],
325 max_exp_avg_sqs: List[Tensor],
326 state_steps: List[Tensor],
327 grad_scale: Optional[Tensor],
328 found_inf: Optional[Tensor],
440 params: List[Tensor],
[all …]
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| D | adamw.py | 36 lr: Union[float, Tensor] = 1e-3, argument
318 params: List[Tensor],
319 grads: List[Tensor],
320 exp_avgs: List[Tensor],
321 exp_avg_sqs: List[Tensor],
322 max_exp_avg_sqs: List[Tensor],
323 state_steps: List[Tensor],
324 grad_scale: Optional[Tensor],
325 found_inf: Optional[Tensor],
330 lr: Union[Tensor, float],
[all …]
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| D | _adafactor.py | 25 lr: Union[float, Tensor] = 1e-2, argument
327 params: List[Tensor],
328 grads: List[Tensor],
333 row_vars: List[Optional[Tensor]],
334 col_vars: List[Optional[Tensor]],
335 variances: List[Optional[Tensor]],
336 state_steps: List[Tensor],
337 grad_scale: Optional[Tensor],
338 found_inf: Optional[Tensor],
341 lr: Union[Tensor, float],
[all …]
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| D | adadelta.py | 31 lr: Union[float, Tensor] = 1.0, argument
86 params_with_grad: List[Tensor],
87 grads: List[Tensor],
88 square_avgs: List[Tensor],
89 acc_deltas: List[Tensor],
90 state_steps: List[Tensor],
246 params: List[Tensor],
247 grads: List[Tensor],
248 square_avgs: List[Tensor],
249 acc_deltas: List[Tensor],
[all …]
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| /external/pytorch/torch/distributed/tensor/experimental/ |
| D | _attention.py | 69 def _maybe_wait(tensor: torch.Tensor) -> torch.Tensor:
89 def _merge_one(self, block_out: torch.Tensor, block_lse: torch.Tensor) -> None:
103 def step(self, out: torch.Tensor, lse: torch.Tensor) -> None:
122 query: torch.Tensor,
123 key: torch.Tensor,
124 value: torch.Tensor,
148 query: torch.Tensor,
149 key: torch.Tensor,
150 value: torch.Tensor,
151 attn_bias: Optional[torch.Tensor] = None,
[all …]
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| /external/pytorch/torch/ao/quantization/experimental/ |
| D | quantizer.py | 23 alpha: torch.Tensor,
24 gamma: torch.Tensor,
25 quantization_levels: torch.Tensor,
26 level_indices: torch.Tensor,
42 def quantize(self, tensor2quantize: Tensor):
97 def quant_dequant(self, tensor2quantize: Tensor) -> Tensor:
121 tensor2quantize: Tensor,
122 alpha: Tensor,
123 gamma: Tensor,
124 quantization_levels: Tensor,
[all …]
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| /external/pytorch/torch/masked/ |
| D | _ops.py | 400 def _reduction_identity(op_name: str, input: Tensor, *args):
485 def _sparse_coo_flatten_indices(indices: Tensor, shape: tuple):
494 def _any(input: Tensor, dim: tuple, keepdim: bool):
503 def _sparse_coo_where(mask: Tensor, input: Tensor, fill_value: Tensor) -> Tensor:
617 mask_input: Tensor,
737 mask_input: Tensor,
821 def _sparse_csr_where(mask: Tensor, input: Tensor, fill_value: Tensor) -> Tensor:
829 def _where(mask: Tensor, input: Tensor, fill_value: Tensor) -> Tensor:
865 def _input_mask(input: Union[Tensor, MaskedTensor], *args, **kwargs) -> Tensor:
950 def _output_mask(op, input: Tensor, *args, **kwargs) -> Tensor:
[all …]
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| /external/pytorch/torch/_higher_order_ops/ |
| D | flex_attention.py | 49 def _permute_strides(out: torch.Tensor, query_strides: Tuple[int, ...]) -> torch.Tensor:
94 query: torch.Tensor,
95 key: torch.Tensor,
96 value: torch.Tensor,
131 query: torch.Tensor,
132 key: torch.Tensor,
133 value: torch.Tensor,
134 out: torch.Tensor,
135 logsumexp: torch.Tensor,
136 grad_out: torch.Tensor,
[all …]
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| /external/pytorch/torch/nn/modules/ |
| D | transformer.py | 47 def _get_seq_len(src: Tensor, batch_first: bool) -> Optional[int]:
107 activation: Union[str, Callable[[Tensor], Tensor]] = F.relu, argument
174 src: Tensor,
175 tgt: Tensor,
176 src_mask: Optional[Tensor] = None,
177 tgt_mask: Optional[Tensor] = None,
178 memory_mask: Optional[Tensor] = None,
179 src_key_padding_mask: Optional[Tensor] = None,
180 tgt_key_padding_mask: Optional[Tensor] = None,
181 memory_key_padding_mask: Optional[Tensor] = None,
[all …]
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| /external/pytorch/torch/ao/nn/quantized/modules/ |
| D | functional_modules.py | 51 def add(self, x: Tensor, y: Tensor) -> Tensor:
58 def add_scalar(self, x: Tensor, y: float) -> Tensor:
66 def mul(self, x: Tensor, y: Tensor) -> Tensor:
73 def mul_scalar(self, x: Tensor, y: float) -> Tensor:
81 def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
88 def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
96 def matmul(self, x: Tensor, y: Tensor) -> Tensor:
123 def add(self, x: Tensor, y: Tensor) -> Tensor:
129 def add_scalar(self, x: Tensor, y: float) -> Tensor:
135 def mul(self, x: Tensor, y: Tensor) -> Tensor:
[all …]
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| /external/executorch/examples/models/llama/source_transformation/ |
| D | sdpa.py | 42 input_pos: torch.Tensor,
43 q: torch.Tensor,
44 k: torch.Tensor,
45 v: torch.Tensor,
125 input_pos: torch.Tensor,
126 q: torch.Tensor,
127 k: torch.Tensor,
128 v: torch.Tensor,
151 def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
183 input_pos: torch.Tensor,
[all …]
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| /external/executorch/backends/arm/test/ops/ |
| D | test_mul.py | 61 input_: torch.Tensor,
62 other_: torch.Tensor,
67 self, module: torch.nn.Module, test_data: tuple[torch.Tensor, torch.Tensor]
88 self, module: torch.nn.Module, test_data: tuple[torch.Tensor, torch.Tensor]
113 test_data: tuple[torch.Tensor, torch.Tensor],
135 input_: torch.Tensor,
136 other_: torch.Tensor,
145 input_: torch.Tensor,
146 other_: torch.Tensor,
156 input_: torch.Tensor,
[all …]
|
| /external/pytorch/test/jit/ |
| D | test_await.py | 34 def fn(x: Tensor):
49 def fn(x: Tensor):
65 def __init__(self, a: Tensor, b: Tensor):
72 def fn(x: Tensor):
89 def __init__(self, a: Tensor, b: Tensor):
101 def fn(x: Tensor):
120 def __init__(self, a: Tensor, b: Tensor):
131 def fn(x: Tensor):
149 def __init__(self, a: Tensor, b: Tensor):
163 def fn(x: Tensor):
[all …]
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| /external/pytorch/torch/ao/quantization/pt2e/ |
| D | qat_utils.py | 88 x: torch.Tensor,
89 conv_weight: torch.Tensor,
90 conv_bias: torch.Tensor,
91 bn_weight: torch.Tensor,
92 bn_bias: torch.Tensor,
93 bn_running_mean: torch.Tensor,
94 bn_running_var: torch.Tensor,
108 x: torch.Tensor,
109 conv_weight: torch.Tensor,
110 conv_bias: torch.Tensor,
[all …]
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| /external/pytorch/torch/_inductor/fx_passes/ |
| D | pad_mm.py | 63 def get_alignment_size(x: Tensor) -> int:
76 def check_device(a: Tensor, b: Tensor) -> bool:
80 def check_dtype(a: Tensor, b: Tensor) -> bool:
85 mat1: Tensor, mat2: Tensor, input: Optional[Tensor] = None
89 def valid_shape_and_stride(t: Optional[Tensor]) -> bool:
131 def pad_dim(x: Tensor, padded_length: int, dim: int) -> Tensor:
139 input: Tensor, mat1: Tensor, mat2: Tensor, beta: float, alpha: float
152 input: Optional[Tensor],
153 mat1: Tensor,
154 mat2: Tensor,
[all …]
|