def check_training_status(self): """Checks if loss is admissible. If not, training is stopped after max_nans consecutive inadmissible loss loss corresponds to the training loss of the model. `max_nans` is the maximum number of consecutive NaNs after which a ValueError will be """ loss_...
Consistency check (5 runs): Run 1: N: 100, Result: nan N: 10, Result: inf N: 100, Result: nan Run 2: N: 100, Result: nan N: 10, Result: inf N: 100, Result: nan Run 3: N: 100, Result: nan N: 10, Result: inf ...
torch.is_anomaly_check_nan_enabled, torch.is_grad_enabled, torch.merge_type_from_type_comment, torch.parse_ir, torch.parse_schema, torch.parse_type_comment, torch.set_anomaly_enabled, torch.set_flush_denormal, torch.set_num_interop_threads, torch.set_num_threads, torch.wait, torch.as_tens...
isnan(probs).any()): break if just_prob: return probs #with torch.no_grad(): # add eps for unexpected torch error # probs = nn.functional.softmax(new_logits, dim=1) # selected_index = torch.multinomial(probs + eps, 2, False) with torch.no_grad(): # add eps for unexpected...
Checkout this example: import torch # Trace-based only class LoopModel(torch.nn.Module): def forward(self, x, y): for i in range(y): x = x + i return x model = LoopModel() dummy_input = torch.ones(2, 3, dtype=torch.long) loop_count = torch.tensor(5, dtype=torch.long) ...
_amp_foreach_non_finite_check_and_unscale_ _amp_update_scale _assert _autograd_functions _baddbmm_mkl_ _batch_norm_impl_index _bmm _cast_Byte _cast_Char _cast_Double _cast_Float _cast_Half _cast_Int _cast_Long _cast_Short _cat _choose_qparams_per_tensor _classes _compute_linear_...
开发者ID:pytorch,项目名称:ignite,代码行数:20,代码来源:terminate_on_nan.py 示例15: _check_gradients ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importisfinite[as 别名]def_check_gradients(harn):""" Checks that the the accumulated gradients are all finite. ...
# 需要导入模块: import torch [as 别名]# 或者: from torch importnumel[as 别名]defloss(self, proposal_classes: Tensor,gt_proposal_classes: Tensor, batch_size,batch_indices)-> Tuple[Tensor, Tensor]:# assert np.any(np.isnan(np.array(proposal_classes)))==False# assert np.any(np.isnan(np...
def npu_check_overflow(grad): if is_support_inf_nan(): if isinstance(grad, float): cpu_sum = grad elif isinstance(grad, torch.Tensor): cpu_sum = float(grad.float().sum()) else: raise RuntimeError("Unsupported type." + pta_error(ErrCode.TYPE)) if cpu_sum == float('...
"init_dump", "set_dump", "finalize_dump", "get_soc_version", "is_support_inf_nan", "is_bf16_supported", "get_npu_overflow_flag", "npu_check_overflow", "clear_npu_overflow_flag", "current_blas_handle"] def synchronize(device=None): ...