Fix minicpm device map (modelscope#978)

docs/source/Multi-Modal/minicpm-v-2.5最佳实践.md

@@ -10,7 +10,9 @@
 
 ## 环境准备
 ```shell
-pip install 'ms-swift[llm]' -U
+git clone https://github.com/modelscope/swift.git
+cd swift
+pip install -e '.[llm]'
 ```
 模型链接:
 - minicpm-v-v2_5-chat: [https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5/summary](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5/summary)

docs/source/Multi-Modal/minicpm-v-2最佳实践.md

@@ -10,7 +10,9 @@
 
 ## 环境准备
 ```shell
-pip install 'ms-swift[llm]' -U
+git clone https://github.com/modelscope/swift.git
+cd swift
+pip install -e '.[llm]'
 ```
 
 ## 推理

swift/llm/utils/model.py

@@ -3870,6 +3870,52 @@ def get_model_tokenizer_yi_vl(model_dir: str,
     return model, tokenizer
 
 
+def _patch_minicpm_v_device_map(model) -> None:
+    if not hasattr(model, 'hf_device_map') or len(model.hf_device_map.values()) == 1:
+        return
+    if hasattr(model.llm, '__old_forward'):
+        # avoid double patching
+        return
+    device = list(model.hf_device_map.values())[0]
+    if hasattr(model, 'get_vision_embedding'):  # minicpm-v-v2-chat
+        _old_get_vision_embedding = model.get_vision_embedding
+
+        def _get_vision_embedding(pixel_values):
+            if len(pixel_values) == 0:
+                return _old_get_vision_embedding(pixel_values)
+            output = _old_get_vision_embedding(pixel_values)
+            return output.to(device=device)
+
+        model._old_get_vision_embedding = _old_get_vision_embedding
+        model.get_vision_embedding = _get_vision_embedding
+
+    if hasattr(model, 'resampler'):  # minicpm-v-v2_5-chat
+        __old_resampler_forward = model.resampler.forward
+
+        def _new_resampler_forward(*args, **kwargs) -> Tensor:
+            output = __old_resampler_forward(*args, **kwargs)
+            return output.to(device=device)
+
+        model.resampler.forward = _new_resampler_forward
+
+    __old_forward = model.llm.forward
+
+    def _new_forward(*args, **kwargs) -> Tensor:
+        inputs = kwargs.get('inputs_embeds')
+        if inputs is None:
+            inputs = kwargs.get('input_ids')
+        device = inputs.device
+        output = __old_forward(*args, **kwargs)
+        if output.logits is not None:
+            output.logits = output.logits.to(device)
+        if output.loss is not None:
+            output.loss = output.loss.to(device)
+        return output
+
+    model.llm.forward = _new_forward
+    model.llm.__old_forward = __old_forward
+
+
 @register_model(
     ModelType.minicpm_v_3b_chat,
     'OpenBMB/MiniCPM-V',
@@ -3904,6 +3950,7 @@ def get_model_tokenizer_minicpm_v(model_dir: str,
     model, tokenizer = get_model_tokenizer_with_flash_attn(model_dir, torch_dtype, model_kwargs, load_model, **kwargs)
     if load_model:
         model.resampler.to(torch_dtype)  # fix float32
+        _patch_minicpm_v_device_map(model)
         func_list = ['generate', 'get_input_embeddings', 'forward']
         _use_submodel_func(model, 'llm', func_list)
         if patching_embedding: