With safetensors.torch.load_file since you're memory mapping, I am guessing it's issuing read calls for each individual tensors (I have no clue why Windows might do that instead of just reading the entire file in memory). It could be either the Windows implementation of torch.Storage or...
import safetensors; safetensors.torchseems to fail because torch is not a member ofsafetensors(which is true), whilefrom safetensors.torch import load_fileworks because it imports correctly from the module. I don't remember specifically why Python does this, but seems by design:https://sta...
pip uninstall torch torchvision 然后，安装用cuda编译的torch，如：
pip uninstall torch torchvision 然后，安装用cuda编译的torch，如：
r}") print("-" * 80) #TODO: update it to your chosen epoch llm = LLM( model=trained_model_path, load_format="safetensors", kv_cache_dtype="auto", ) sampling_params = SamplingParams(max_tokens=16, temperature=0.5) conversation = [ {"role": "system", "content": "You are a ...
在原脚本基础上,增加from_diffusers和to_diffusers两个参数传入:from_diffusers激活时可读取pytorch diffusers模型,to_diffusers激活时可保存pytorch safetensors模型 暂时需要等待修复才能解决的问题 需要等待PaddleNLP各类Tokenizer对huggingface hub subfolder的读取问题修复才能解决从huggingface下载模型错误的问题 具体参考:http...
--image_size 模型训练时使用的图像尺寸。...--to_safetensors 是否将管道存储为safetensors格式。 --dump_path 输出模型的路径。 --device 要使用的设备(例如cpu,cuda:0,cuda:1等)。...--dump_path 输出模型的路径。 --lora_prefix_unet safetensors中UNet权重的前缀。
Dynamo在trace的时候,会把所有的Tensor包在FakeTensor里,在避免进行真正的运算的同时,也能知道每个op的结果是什么shape什么dtype。通过这些信息,Dynamo理论上也能知道operator.xxx对应的是哪一个aten op,哪一个overload,但是Dynamo似乎并没有记录下来,而是把这个工作交给了Inductor。
param_group (dict) – Specifies what Tensors should be optimized along with group specific optimization options. 参数组(字典格式)-指明优化参数和特定的优化选项; load_state_dict(state_dict)加载状态词典,加载优化器的状态数据; Loads the optimizer state. ...
# create output tensors outputs = [None] * len(self.output_names) for i, output_name in enumerate(self.output_names): idx = self.engine.get_binding_index(output_name) dtype = torch_dtype_from_trt(self.engine.get_binding_dtype(idx)) ...