[Model] Support DeepSeek-V4

vllm_mlu/model_executor/models/utils.py

@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-MLU project
+
+import json
+import os
+
+import torch
+
+import vllm.envs as envs
+from vllm.config import ModelConfig
+from vllm.forward_context import get_forward_context
+
+def set_attn_compute_dtype_v1(attn_metadata, dtype: torch.dtype):
+    '''
+    set attn compute_dtype for v1
+    '''
+    if isinstance(attn_metadata, dict):
+        for _, metadata in attn_metadata.items():
+            metadata.compute_dtype = dtype
+    else:
+        metadata.compute_dtype = dtype
+
+def set_attn_compute_dtype(dtype: torch.dtype):
+    '''
+    set attn compute_dtype.
+
+    TODO: FA may standardize on half precision computation in the future
+          set_attn_compute_dtype might be deprecated and removed
+    '''
+    forward_context = get_forward_context()
+    attn_metadata = forward_context.attn_metadata
+    if attn_metadata is None:
+        return
+    set_attn_compute_dtype_v1(attn_metadata, dtype)
+
+def is_tie_word_embeddings(
+    model_config: ModelConfig,
+    org_tie_word_embeddings: bool
+) -> bool:
+    '''
+    Vllm language model config for multimodal model may have wrong tie_word_embeddings,
+    for example, InternVL3.5-38B, InternVL3.5-30B-A3B, etc.
+
+    This function is a WorkAround.
+    '''
+    from vllm.lora.utils import get_adapter_absolute_path
+
+    if not model_config.is_multimodal_model:
+        return org_tie_word_embeddings
+
+    model_path = get_adapter_absolute_path(model_config.model)
+    config_path = os.path.join(model_path, "config.json")
+    if not os.path.exists(config_path):
+        return org_tie_word_embeddings
+
+    tie_word_embeddings = org_tie_word_embeddings
+    with open(config_path) as f:
+        config = json.load(f)
+    # first, we find if tie_word_embeddings config is in overall config
+    if config.get("tie_word_embeddings") is not None:
+        tie_word_embeddings = config["tie_word_embeddings"]
+    # then, we find if tie_word_embeddings config is in language model config
+    if (config.get("llm_config") is not None
+        and config["llm_config"].get("tie_word_embeddings") is not None):
+        tie_word_embeddings = config["llm_config"]["tie_word_embeddings"]
+
+    return tie_word_embeddings