[BugFix] Fix GGUF tp>1 when vocab_size is not divisible by 64 (vllm-project#12230)

Signed-off-by: NickLucche <nlucches@redhat.com>
Signed-off-by: Isotr0py <2037008807@qq.com>

Author: NickLucche

tests/models/decoder_only/language/test_gguf.py

@@ -66,12 +66,20 @@ def gguf_model(self):
     gguf_filename="starcoder2-3b.Q6_K.gguf",
 )
 
+DOLPHIN_CONFIG = GGUFTestConfig(
+    # Test VocabParallelEmbedding sharding issue.
+    original_model="cognitivecomputations/TinyDolphin-2.8-1.1b",
+    gguf_repo="tsunemoto/TinyDolphin-2.8-1.1b-GGUF",
+    gguf_filename="tinydolphin-2.8-1.1b.Q6_K.gguf",
+)
+
 MODELS = [
     LLAMA_CONFIG,
     QWEN2_CONFIG,
     PHI3_CONFIG,
     GPT2_CONFIG,
     STABLELM_CONFIG,
+    DOLPHIN_CONFIG
     # STARCODER_CONFIG, # broken
 ]
 
@@ -107,6 +115,7 @@ def test_models(
 
     # Run unquantized model.
     with vllm_runner(model_name=model.original_model,
+                     enforce_eager=True, # faster tests
                      dtype=dtype,
                      max_model_len=MAX_MODEL_LEN,
                      tensor_parallel_size=tp_size) as original_model:
@@ -115,6 +124,7 @@ def test_models(
 
     # Run gguf model.
     with vllm_runner(model_name=model.gguf_model,
+                     enforce_eager=True,
                      tokenizer_name=model.original_model,
                      dtype=dtype,
                      max_model_len=MAX_MODEL_LEN,

vllm/model_executor/layers/vocab_parallel_embedding.py

@@ -355,7 +355,7 @@ def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
         elif isinstance(param, UninitializedParameter):
             shape = list(loaded_weight.shape)
             if output_dim is not None:
-                shape[output_dim] = shape[output_dim] // self.tp_size
+                shape[output_dim] = self.num_embeddings_per_partition
             param.materialize(tuple(shape), dtype=loaded_weight.dtype)
 
         # If parameter does not have output dim, then it should
@@ -381,7 +381,7 @@ def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
         else:
             assert loaded_weight.shape[output_dim] == self.org_vocab_size
 
-        # Copy the data.
+        # Copy the data. Select chunk corresponding to current shard.
         loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
 
         if current_platform.is_hpu():