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[main][Bugfix] Fixed an problem related to embeddings sharing #5967

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Merged
yiz-liu merged 1 commit into from
Jan 20, 2026
Merged

[main][Bugfix] Fixed an problem related to embeddings sharing #5967

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9 changes: 7 additions & 2 deletions tests/ut/spec_decode/test_eagle_proposer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -156,14 +156,19 @@ def test_load_model_pp1(self, mock_pp_group, mock_get_model,
"layer3": mock_draft_layer3
}]

weight = torch.zeros(0)

mock_model = MagicMock()
mock_model.supports_multimodal = False
mock_model.model.embed_tokens = MagicMock()
mock_model.lm_head = MagicMock()
mock_model.multimodal_cpu_fields = None
mock_model.merge_by_field_config = None
mock_get_model.return_value = MagicMock()
mock_model.model.embed_tokens = MagicMock()
mock_model.model.embed_tokens.weight = weight

self.proposer.name = SpecDcodeType.EAGLE
mock_get_model.return_value = MagicMock()
mock_get_model.return_value.model.embed_tokens.weight = weight

self.proposer.load_model(mock_model)
mock_get_model.assert_called_once()
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23 changes: 8 additions & 15 deletions vllm_ascend/spec_decode/eagle_proposer.py
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Expand Up @@ -177,28 +177,21 @@ def load_model(self, model: nn.Module) -> None:
raise AttributeError(
"Target model does not have 'embed_tokens' or 'embedding' attribute"
)
if self.method == "mtp":
if self.vllm_config.model_config.is_deepseek_mla and \
# If pp>1, the weights of mtp and the main model's embedding are not on the same device.
# check if mtp model use main model's embedding and LMhead
if hasattr(model, "model") and hasattr(model.model, "embed_tokens") and \
torch.equal(self.model.model.embed_tokens.weight,
model.model.embed_tokens.weight):
# If pp>1, the weights of mtp and the main model's embedding are not on the same device.
# check if mtp model use main model's embedding and LMhead
logger.info(
"The MTP head shares the same vocab embedding" \
" with the target model."
)
self.model.model.embed_tokens = target_embed_tokens
else:
logger.info(
" The MTP head loaded its own vocab embedding" \
" weights instead of sharing them with the target model."
)
else:
logger.info(
"The EAGLE head shares the same vocab embedding" \
" with the target model."
)
self.model.model.embed_tokens = target_embed_tokens
else:
logger.info(
" The EAGLE head loaded its own vocab embedding" \
" weights instead of sharing them with the target model."
)
else:
logger.info(
"Since PP > 1 or other reasons the model head loaded its own vocab embedding" \
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