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Add INT4 compressed-tensors + LoRA support (including MoE) #28791

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8ef757a
feat: Add INT4 compressed-tensors + LoRA support
sheikheddy Nov 15, 2025
2a0f94e
fix: Add LoRA compatibility to compressed-tensors MoE methods
sheikheddy Nov 15, 2025
8fd7c16
fix: Correct embedding dimension logic in LoRA dummy creation
sheikheddy Nov 16, 2025
038244d
fix: Add LoRA compatibility to W4A4 and W4A8 MoE methods
sheikheddy Nov 16, 2025
22bf730
Update test_quant_model.py to fix ruff check
sheikheddy Nov 17, 2025
57faaea
Merge branch 'main' into feat/int4-compressed-tensors-lora-support
sheikheddy Nov 17, 2025
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6 changes: 6 additions & 0 deletions examples/offline_inference/lora_with_quantization_inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,6 +114,12 @@ def main():
"quantization": "gptq",
"lora_repo": "jashing/tinyllama-colorist-lora",
},
{
"name": "compressed_tensors_inference_with_lora_example",
"model": "neuralmagic/TinyLlama-1.1B-Chat-v1.0-INT4",
"quantization": "compressed-tensors",
"lora_repo": "jashing/tinyllama-colorist-lora",
},
]

for test_config in test_configs:
Expand Down
20 changes: 17 additions & 3 deletions tests/lora/test_quant_model.py
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Expand Up @@ -35,6 +35,10 @@ class ModelWithQuantization:
ModelWithQuantization(
model_path="TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ", quantization="gptq"
),
ModelWithQuantization(
model_path="neuralmagic/TinyLlama-1.1B-Chat-v1.0-INT4",
quantization="compressed-tensors",
),
]


Expand Down Expand Up @@ -99,11 +103,21 @@ def test_quant_model_lora(tinyllama_lora_files, model):
"#f08800: This is",
"#f07788 \n#",
]
elif model.quantization == "compressed-tensors":
# Compressed-tensors output (INT4 quantization)
# Similar to other quantized models, outputs may vary slightly
expected_lora_output = [
"#", # Placeholder, will check prefix only
"#", # Placeholder, will check prefix only
]

def expect_match(output, expected_output):
# HACK: GPTQ lora outputs are just incredibly unstable.
# Assert that the outputs changed.
if model.quantization == "gptq" and expected_output is expected_lora_output:
if (
model.quantization in ("gptq", "compressed-tensors")
and expected_output is expected_lora_output
):
for i, o in enumerate(output):
assert o.startswith("#"), (
f"Expected example {i} to start with # but got {o}"
Expand Down Expand Up @@ -132,8 +146,8 @@ def expect_match(output, expected_output):
def test_quant_model_tp_equality(tinyllama_lora_files, num_gpus_available, model):
if num_gpus_available < 2:
pytest.skip(f"Not enough GPUs for tensor parallelism {2}")
if model.quantization == "gptq":
pytest.skip("GPTQ lora outputs are just incredibly unstable")
if model.quantization in ("gptq", "compressed-tensors"):
pytest.skip(f"{model.quantization} lora outputs are just incredibly unstable")
llm_tp1 = vllm.LLM(
model=model.model_path,
enable_lora=True,
Expand Down
55 changes: 39 additions & 16 deletions vllm/lora/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -614,22 +614,45 @@ def create_dummy_lora(
if module_name not in self.packed_modules:
assert embedding_modules is not None
if parts[-1] in embedding_modules:
input_dim = (
module.base_layer.org_vocab_size
+ self.lora_config.lora_extra_vocab_size
if hasattr(module.base_layer, "org_vocab_size")
else module.base_layer.weight.shape[1]
)
output_dim = (
module.base_layer.embedding_dim
if hasattr(module.base_layer, "embedding_dim")
else module.base_layer.weight.shape[0]
)
embeddings_tensor_dim = (
module.base_layer.embedding_dim
if hasattr(module.base_layer, "embedding_dim")
else module.base_layer.weight.shape[1]
)
# Try to get dimensions from layer attributes first
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@HDCharles HDCharles Nov 19, 2025 •
edited
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better to detect if we're doing Lora and write that in one if branch and normal logic in the other.

easier to read

if A
   input_dim = X1
   output_dim = Y1
   embedding_dim = Z1
elif B
   input_dim = X2
   output_dim = Y2
   embedding_dim = Z2
else C
   input_dim = X3
   output_dim = Y3
   embedding_dim = Z3

than

if A
   input_dim = X1
elif B
   input_dim = X2
else C
   input_dim = X3

if A
   output_dim = Y1
elif B
   output_dim = Y2
else C
   output_dim = Y3

...etc

if hasattr(module.base_layer, "org_vocab_size"):
input_dim = (
module.base_layer.org_vocab_size
+ self.lora_config.lora_extra_vocab_size
)
elif hasattr(module.base_layer, "input_size"):
input_dim = module.base_layer.input_size
elif hasattr(module.base_layer, "weight_shape"):
# Compressed tensors: weight_shape stores [output, input]
# For embeddings: [vocab_size, embedding_dim]
input_dim = module.base_layer.weight_shape[0].item()
else:
# For embeddings: weight.shape = [vocab_size, embedding_dim]
input_dim = module.weight.shape[0]
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@HDCharles HDCharles Nov 19, 2025

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shouldn't it be
module.base_layer.weight.shape[1]
?

worrying that tests passed with an issue like this

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@sheikheddy sheikheddy Nov 19, 2025

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Good catch. Not sure the tests cover this branch.


if hasattr(module.base_layer, "embedding_dim"):
output_dim = module.base_layer.embedding_dim
elif hasattr(module.base_layer, "output_size"):
output_dim = module.base_layer.output_size
elif hasattr(module.base_layer, "weight_shape"):
# Compressed tensors: weight_shape stores [output, input]
# For embeddings: [vocab_size, embedding_dim]
output_dim = module.base_layer.weight_shape[1].item()
else:
# For embeddings: weight.shape = [vocab_size, embedding_dim]
output_dim = module.weight.shape[1]
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@HDCharles HDCharles Nov 19, 2025

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also backward, should be shape[0]


if hasattr(module.base_layer, "embedding_dim"):
embeddings_tensor_dim = module.base_layer.embedding_dim
elif hasattr(module.base_layer, "output_size"):
embeddings_tensor_dim = module.base_layer.output_size
elif hasattr(module.base_layer, "weight_shape"):
# Compressed tensors: weight_shape stores [output, input]
# For embeddings: [vocab_size, embedding_dim]
embeddings_tensor_dim = module.base_layer.weight_shape[1].item()
else:
# For embeddings: weight.shape = [vocab_size, embedding_dim]
embeddings_tensor_dim = module.weight.shape[1]
lora = LoRALayerWeights.create_dummy_lora_weights(
module_name,
input_dim,
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19 changes: 19 additions & 0 deletions vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py
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Expand Up @@ -203,6 +203,10 @@ def create_weights(
params_dtype: torch.dtype,
**extra_weight_attrs,
):
# Set layer attributes needed for LoRA compatibility
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@HDCharles HDCharles Nov 19, 2025

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isn't this only needed for W4A16?

layer.hidden_size = hidden_size
layer.intermediate_size_per_partition = intermediate_size_per_partition
layer.local_num_experts = num_experts
layer.num_experts = num_experts
layer.params_dtype = params_dtype

Expand Down Expand Up @@ -1367,6 +1371,11 @@ def create_weights(
params_dtype: torch.dtype,
**extra_weight_attrs,
):
# Set layer attributes needed for LoRA compatibility
layer.hidden_size = hidden_size
layer.intermediate_size_per_partition = intermediate_size_per_partition
layer.local_num_experts = num_experts

intermediate_size_full = extra_weight_attrs.pop("intermediate_size_full")

# Will transpose the loaded weight along the
Expand Down Expand Up @@ -1738,6 +1747,11 @@ def create_weights(
params_dtype: torch.dtype,
**extra_weight_attrs,
):
# Set layer attributes needed for LoRA compatibility
layer.hidden_size = hidden_size
layer.intermediate_size_per_partition = intermediate_size_per_partition
layer.local_num_experts = num_experts

# Will transpose the loaded weight along the
# intermediate and hidden dim sizes. Will
# shard for TP along the transposed dims
Expand Down Expand Up @@ -2013,6 +2027,11 @@ def create_weights(
**extra_weight_attrs,
):
# Shapes per local rank (TP/EP):
# Set layer attributes needed for LoRA compatibility
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@HDCharles HDCharles Nov 19, 2025

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isn't this only needed for W4A16 as of now?

layer.hidden_size = hidden_size
layer.intermediate_size_per_partition = intermediate_size_per_partition
layer.local_num_experts = num_experts

# w13: [E, 2*I_local, H] int8 (int4 values in [-8,7])
# w2 : [E, H, I_local] int8
# Scales:
Expand Down