fix phi3-small (#1148)

swift/llm/utils/model.py

@@ -451,6 +451,7 @@ class LoRATM(NamedTuple):
     glm4v = ['self_attention.query_key_value']
     phi = ['Wqkv']
     phi3 = ['qkv_proj']
+    phi3_small = ['query_key_value']  # what the hell???
     internlm2 = ['wqkv']
     mamba = ['in_proj', 'x_proj', 'embeddings', 'out_proj']
     telechat = ['key_value', 'query']
@@ -1605,16 +1606,6 @@ def _output_device_map_hook(module, input, output):
     support_vllm=True,
     tags=['general'],
     hf_model_id='microsoft/Phi-3-mini-128k-instruct')
-@register_model(
-    ModelType.phi3_small_128k_instruct,
-    'LLM-Research/Phi-3-small-128k-instruct',
-    LoRATM.phi3,
-    TemplateType.phi3,
-    requires=['transformers>=4.36'],
-    support_flash_attn=True,
-    support_vllm=True,
-    tags=['general'],
-    hf_model_id='microsoft/Phi-3-small-128k-instruct')
 @register_model(
     ModelType.phi3_medium_128k_instruct,
     'LLM-Research/Phi-3-medium-128k-instruct',
@@ -2361,6 +2352,49 @@ def get_model_tokenizer_with_flash_attn(model_dir: str,
         model_dir, torch_dtype, model_kwargs, load_model, model_config=model_config, **kwargs)
 
 
+@register_model(
+    ModelType.phi3_small_128k_instruct,
+    'LLM-Research/Phi-3-small-128k-instruct',
+    LoRATM.phi3_small,
+    TemplateType.phi3,
+    requires=['transformers>=4.36'],
+    support_flash_attn=True,
+    support_gradient_checkpointing=False,
+    support_vllm=True,
+    tags=['general'],
+    hf_model_id='microsoft/Phi-3-small-128k-instruct')
+def get_model_tokenizer_phi3_small(model_dir: str,
+                                   torch_dtype: Dtype,
+                                   model_kwargs: Dict[str, Any],
+                                   load_model: bool = True,
+                                   model_config=None,
+                                   **kwargs):
+    if model_config is None:
+        model_config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True)
+    use_flash_attn = kwargs.pop('use_flash_attn', False)
+    if version.parse(transformers.__version__) >= version.parse('4.36'):
+        if use_flash_attn:
+            model_config._attn_implementation = 'flash_attention_2'
+    else:
+        model_config._flash_attn_2_enabled = use_flash_attn
+    model, tokenizer = get_model_tokenizer_from_repo(
+        model_dir, torch_dtype, model_kwargs, load_model, model_config=model_config, **kwargs)
+
+    def rotary_emb(self, query_states, key_states, **kwargs):
+        q_type = query_states.dtype
+        k_type = key_states.dtype
+        query_states, key_states = self.rotory_emb_origin(query_states, key_states, **kwargs)
+        query_states = query_states.to(q_type)
+        key_states = key_states.to(k_type)
+        return query_states, key_states
+
+    for i in range(32):
+        re = model.model.layers[i].self_attn.rotary_emb
+        re.rotory_emb_origin = re.forward
+        re.forward = MethodType(rotary_emb, re)
+    return model, tokenizer
+
+
 @register_model(
     ModelType.qwen2_57b_a14b_instruct_int4,
     'qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4',