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[BugFix] Fix GGUF tp>1 models when vocab_size is not divisible by 64 #12230

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merged 2 commits into from
Jan 21, 2025
Merged

[BugFix] Fix GGUF tp>1 models when vocab_size is not divisible by 64 #12230

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1191485
fix vocab_parallel_embedding sharding
NickLucche Jan 20, 2025
5389ab6
add tests
NickLucche Jan 20, 2025
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10 changes: 10 additions & 0 deletions tests/models/decoder_only/language/test_gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
]

Expand Down Expand Up @@ -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:
Expand All @@ -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,
Expand Down
4 changes: 2 additions & 2 deletions vllm/model_executor/layers/vocab_parallel_embedding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -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():
Expand Down
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