torch_dtype=torch.bfloat16).to(device)optimizer=torch.optim.Adam(model.parameters(),lr=5e-5)input_ids=tokenizer.encode(input,return_tensors="pt").to(device)output=model(input_ids,labels=input_ids)output.loss.backward
🐛 Describe the bug import torch.nn as nn import torch as th If using CPU as the device, the following codes run perfectly rnn = nn.LSTM(10, 20, 2).to(device="cpu", dtype=th.bfloat16) input = th.randn(5, 3, 10).to(device="cpu", dtype=th.b...
bfloat16]: hidden_states = hidden_states.to(self.weight.dtype) return self.weight * hidden_states class RotaryEmbedding(torch.nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.inv_freq = 1.0 / (base ** ...
def float_conversion(val): if val is None: return val val_typecheck = val if isinstance(val_typecheck, (torch.nn.parameter.Parameter, torch.autograd.Variable)): val_typecheck = val.data if val_typecheck.dtype in [torch.float16, torch.bfloat16]: ...
(self.lm._init_weights)self.cross_entropy = nn.CrossEntropyLoss()self.model_args = model_argsdef gradient_checkpointing_enable(self, **kwargs):self.lm.gradient_checkpointing_enable(**kwargs)def forward(self,encoder_input_ids, encoder_...
from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.float16, device_map="auto") return model 函数详细说明 函数名:load_model 函数传入参数: model_path:平台上传模型的路径; kwargs:其他参数,目前未使用。当未来功能升级时,做向前兼容使用。 函数输出结果: model:模型实例 样例: llama...
False, 'torchscript': False, 'torch_dtype': None, 'use_bfloat16': False, 'tf_legacy_loss': False, 'pruned_heads': {}, 'tie_word_embeddings': False, 'is_encoder_decoder': False, 'is_decoder': False, 'cross_attention_hidden_size': None, 'add_cross_attention': False, 'tie_encod...
模型具体实现:mindformers/models/glm3 glm3 ├── __init__.py └── glm3_tokenizer.py # tokenizer glm3的模型结构和config同glm2 模型配置:configs/glm3 glm3 ├── export_glm3_6b.yaml# 导出mindir配置├── run_glm3_6b_finetune_2k_910b.yaml# Atlas 800T A2最佳性能...
import torch as t from torch.autograd import Function from model.utils.roi_cupy import kernel_backward, kernel_forward Stream = namedtuple('Stream', ['ptr']) @cupy.util.memoize(for_each_device=True) def load_kernel(kernel_name, code, **kwargs): ...
在YOLOv6-N/S/M上实验了Focal Los、Polyloss、QFL和VFL。如表8所示,与Focal Loss相比,VFL对YOLOv6-N/S/M分别带来0.2%/0.3%/0.1%的AP改善。所以,选择VFL作为分类损失函数 Focal Loss修改了传统的交叉熵损失,以解决正负样本或难易样本之间的类不平衡问题。为了解决训练和推理之间质量估计和分类的不一致使用,Qu...