大模型Expected all tensors to be on the same device

Question

大模型小bai，四张卡推理，加载模型时

```python
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=load_type, device_map="auto").half().cuda()

指定了device_map="auto"，模型被划分到四块卡上，看着好像没有问题

```bash
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.23.06              Driver Version: 545.23.06    CUDA Version: 12.3     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA A800 80GB PCIe          Off | 00000000:4F:00.0 Off |                    0 |
| N/A   38C    P0              66W / 300W |  65255MiB / 81920MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   1  NVIDIA A800 80GB PCIe          Off | 00000000:50:00.0 Off |                    0 |
| N/A   39C    P0              66W / 300W |  17787MiB / 81920MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   2  NVIDIA A800 80GB PCIe          Off | 00000000:53:00.0 Off |                    0 |
| N/A   40C    P0              68W / 300W |  17787MiB / 81920MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   3  NVIDIA A800 80GB PCIe          Off | 00000000:57:00.0 Off |                    0 |
| N/A   39C    P0              69W / 300W |  14287MiB / 81920MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+

但是当调用推理后，报错：

ERROR:    Exception in ASGI application
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/uvicorn/protocols/http/h11_impl.py", line 429, in run_asgi
    result = await app(  # type: ignore[func-returns-value]
  File "/usr/local/lib/python3.8/site-packages/uvicorn/middleware/proxy_headers.py", line 78, in __call__
    return await self.app(scope, receive, send)
  File "/usr/local/lib/python3.8/site-packages/fastapi/applications.py", line 276, in __call__
    await super().__call__(scope, receive, send)
  File "/usr/local/lib/python3.8/site-packages/starlette/applications.py", line 122, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/usr/local/lib/python3.8/site-packages/starlette/middleware/errors.py", line 184, in __call__
    raise exc
  File "/usr/local/lib/python3.8/site-packages/starlette/middleware/errors.py", line 162, in __call__
    await self.app(scope, receive, _send)
  File "/usr/local/lib/python3.8/site-packages/starlette/middleware/cors.py", line 84, in __call__
    await self.app(scope, receive, send)
  File "/usr/local/lib/python3.8/site-packages/starlette/middleware/exceptions.py", line 79, in __call__
    raise exc
  File "/usr/local/lib/python3.8/site-packages/starlette/middleware/exceptions.py", line 68, in __call__
    await self.app(scope, receive, sender)
  File "/usr/local/lib/python3.8/site-packages/fastapi/middleware/asyncexitstack.py", line 21, in __call__
    raise e
  File "/usr/local/lib/python3.8/site-packages/fastapi/middleware/asyncexitstack.py", line 18, in __call__
    await self.app(scope, receive, send)
  File "/usr/local/lib/python3.8/site-packages/starlette/routing.py", line 718, in __call__
    await route.handle(scope, receive, send)
  File "/usr/local/lib/python3.8/site-packages/starlette/routing.py", line 276, in handle
    await self.app(scope, receive, send)
  File "/usr/local/lib/python3.8/site-packages/starlette/routing.py", line 66, in app
    response = await func(request)
  File "/usr/local/lib/python3.8/site-packages/fastapi/routing.py", line 237, in app
    raw_response = await run_endpoint_function(
  File "/usr/local/lib/python3.8/site-packages/fastapi/routing.py", line 163, in run_endpoint_function
    return await dependant.call(**values)
  File "openai_api_codellama-34b-2.py", line 194, in create_chat_completion
    generation_output = model.generate(
  File "/usr/local/lib/python3.8/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/transformers-4.33.0-py3.8.egg/transformers/generation/utils.py", line 1681, in generate
    return self.beam_search(
  File "/usr/local/lib/python3.8/site-packages/transformers-4.33.0-py3.8.egg/transformers/generation/utils.py", line 3020, in beam_search
    outputs = self(
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/accelerate/hooks.py", line 165, in new_forward
    output = old_forward(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/transformers-4.33.0-py3.8.egg/transformers/models/llama/modeling_llama.py", line 820, in forward
    outputs = self.model(
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/transformers-4.33.0-py3.8.egg/transformers/models/llama/modeling_llama.py", line 708, in forward
    layer_outputs = decoder_layer(
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/accelerate/hooks.py", line 165, in new_forward
    output = old_forward(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/transformers-4.33.0-py3.8.egg/transformers/models/llama/modeling_llama.py", line 421, in forward
    hidden_states = self.input_layernorm(hidden_states)
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/accelerate/hooks.py", line 165, in new_forward
    output = old_forward(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/transformers-4.33.0-py3.8.egg/transformers/models/llama/modeling_llama.py", line 89, in forward
    return self.weight * hidden_states.to(input_dtype)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cuda:1!

我明白这个报错是说模型和数据不在同一张卡上，但是device_map=auto本来是指自动切分模型(?),从显存占用量来看每块卡的使用率也是不同的，所以我不明白 本来就是要将模型分散到不同卡上，可是现在看起来不合并又不行？请问这要怎么改呢？
以及，torchrun的方式是不是就是把模型复制到四张卡上一模一样，然后就不会报这个错？

Answer 1

【以下回答由 GPT 生成】

问题：能否添加更多有关您代码的信息，特别是在处理模型加载方面的问题时，例如您想讨论的错误消息，以便更好地理解上下文。此外，您是否尝试过在加载模型时手动指定GPU设备的参数？

回答：非常抱歉，您提供的问题比较模糊，没有明确的代码和错误消息。为了更好地理解上下文，请提供相关代码和错误消息，以便我可以帮助您解决问题。

关于手动指定GPU设备的参数，在许多深度学习框架中，例如TensorFlow和PyTorch，我们可以通过指定设备的索引或名称来将模型加载到特定的GPU上。下面是使用PyTorch加载模型并指定GPU设备的示例代码：

import torch

# 检查是否有可用的GPU设备
if torch.cuda.is_available():
    device = torch.device("cuda")  # 使用默认的GPU设备
    # 或者可以指定特定的GPU设备
    # device = torch.device("cuda:0")  # 使用索引为0的GPU设备
    # device = torch.device("cuda:1")  # 使用索引为1的GPU设备

    # 加载模型到指定的GPU设备
    model = Model().to(device)

    # 进行训练或推理等操作
    output = model(input_tensor.to(device))
else:
    device = torch.device("cpu")  # 如果没有可用的GPU设备，则使用CPU设备

    model = Model().to(device)

    output = model(input_tensor)

请提供更多详细信息，以便我能够更好地帮助您解决问题。

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