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[https://nvbugs/5441729][test] Fix test_modeling_llama_min_latency.py failures #7478

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Merged
nvpohanh merged 1 commit into from
Oct 13, 2025
Merged

[https://nvbugs/5441729][test] Fix test_modeling_llama_min_latency.py failures #7478

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22 changes: 17 additions & 5 deletions tensorrt_llm/_torch/models/modeling_llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1002,16 +1002,28 @@ def __init__(self, model_config: ModelConfig[Llama4Config], *args,

self.dtype = self.pretrained_config.text_config.torch_dtype

def load_weights(self):
def load_weights(self, weights: Dict):
module_dict = nn.ModuleDict({
"vision_model":
Llama4VisionModel(self.pretrained_config.vision_config),
"multi_modal_projector":
Llama4MultiModalProjector(self.pretrained_config),
})
load_sharded_checkpoint(module_dict,
self.pretrained_config._name_or_path,
strict=False)

# If the named params are present in the weights, load them directly.
param_names = [name for name, _ in module_dict.named_parameters()]
if all(name in weights for name in param_names):
vision_encoder_weights = {
name: weights[name]
for name in param_names
}
module_dict.load_state_dict(vision_encoder_weights)

# Otherwise, load the weights from the checkpoint.
else:
load_sharded_checkpoint(module_dict,
self.pretrained_config._name_or_path,
strict=False)

self.vision_model = module_dict["vision_model"].to(self.device)
self.mm_projector = module_dict["multi_modal_projector"].to(self.device)
Expand Down Expand Up @@ -1294,7 +1306,7 @@ def infer_max_seq_len(self):

def load_weights(self, weights: Dict, weight_mapper: BaseWeightMapper):
if not DISAGG:
self.mm_encoder.load_weights()
self.mm_encoder.load_weights(weights)
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nvpohanh marked this conversation as resolved.

# Temporarily detach mm_encoder so the TRT-LLM loader doesn't try to load it
had_mm_encoder = hasattr(self, "mm_encoder")
Expand Down
8 changes: 5 additions & 3 deletions tests/unittest/_torch/modeling/test_modeling_llama_min_latency.py
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Expand Up @@ -266,10 +266,12 @@ def test_llama_allclose_to_hf(self, scenario: AllCloseScenario) -> None:
attention_backend = "TRTLLM"
metadata_cls = get_attention_backend(attention_backend).Metadata

if transformers.__version__ >= "4.55.0":
if transformers.__version__ >= "4.55.0" \
and transformers.__version__ < "4.56.1":
self.skipTest(
"The transformers 4.55.0 has accuracy issues while 4.33.1 works fine. "
"https://nvbugspro.nvidia.com/bug/5441729")
"The transformers between 4.55.0 and 4.56.1 have accuracy "
"issues for Llama4. See: "
"https://github.com/huggingface/transformers/pull/40609")

torch.random.manual_seed(0)
config_dict = deepcopy(LLAMA_4_MAVERICK_TWO_LAYER_CONFIG)
Expand Down