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Fix initializing GGUF weights for ColumnParallelLinear when using tensor parallel > 1 #13023

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Merged
merged 2 commits into from
Feb 11, 2025
+12 −7
Merged

Fix initializing GGUF weights for ColumnParallelLinear when using tensor parallel > 1 #13023

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215d598
Fix initializing GGUF weights when using tensor parallel
SzymonOzog Feb 10, 2025
a61e3ae
remove bnb specific param
SzymonOzog Feb 10, 2025
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19 changes: 12 additions & 7 deletions vllm/model_executor/layers/linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -335,6 +335,12 @@ def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
tp_rank = get_tensor_model_parallel_rank()
output_dim = getattr(param, "output_dim", None)

is_sharded_weight = getattr(param, "is_sharded_weight", False)
use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
# bitsandbytes loads the weights of the specific portion
# no need to narrow
is_sharded_weight = is_sharded_weight or use_bitsandbytes_4bit

# Special case for GGUF
is_gguf_weight = getattr(param, "is_gguf_weight", False)
is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False)
Expand All @@ -343,13 +349,12 @@ def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):

# Materialize GGUF UninitializedParameter
if is_gguf_weight and isinstance(param, UninitializedParameter):
param.materialize(loaded_weight.shape, dtype=loaded_weight.dtype)

use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
is_sharded_weight = getattr(param, "is_sharded_weight", False)
# bitsandbytes loads the weights of the specific portion
# no need to narrow
is_sharded_weight = is_sharded_weight or use_bitsandbytes_4bit
final_shape = list(loaded_weight.shape)
if output_dim is not None:
tp_size = get_tensor_model_parallel_world_size()
assert final_shape[output_dim] % tp_size == 0
final_shape[output_dim] = final_shape[output_dim] // tp_size
param.materialize(final_shape, dtype=loaded_weight.dtype)

param_data = param.data
if output_dim is not None and not is_sharded_weight:
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