biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Core] [Bugfix] Add Input Embeddings (#15428)

Browse Source 
Signed-off-by: Andrew Sansom <andrew@protopia.ai>
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: 临景 <linjing.yx@alibaba-inc.com>
Co-authored-by: Bryce1010 <bryceyx@gmail.com>
Co-authored-by: Nan2018 <nan@protopia.ai>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Andrew Sansom
2025-05-02 03:06:39 -05:00
committed by GitHub
parent 9e2de9b9e9
commit cc2a77d7f1
22 changed files with 691 additions and 113 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
vllm/engine/llm_engine.py
View File

@@ -753,6 +753,12 @@ class LLMEngine:
if arrival_time is None:
arrival_time = time.time()
if (isinstance(prompt, dict)
and prompt.get("prompt_embeds", None) is not None
and not prompt.get("prompt_token_ids", None)):
seq_len = prompt["prompt_embeds"].shape[0]
prompt["prompt_token_ids"] = [0] * seq_len
if self.tokenizer is not None:
self._validate_token_prompt(
prompt,
@@ -1267,11 +1273,13 @@ class LLMEngine:
if self.scheduler_config.is_multi_step:
is_prefill_append = seq.data.get_num_uncomputed_tokens(
) == 0
seq.append_token_id(sample.output_token, sample.logprobs)
seq.append_token_id(sample.output_token, sample.logprobs,
sample.output_embed)
if not is_prefill_append:
seq_group.update_num_computed_tokens(1)
else:
seq.append_token_id(sample.output_token, sample.logprobs)
seq.append_token_id(sample.output_token, sample.logprobs,
sample.output_embed)
def step(self) -> List[Union[RequestOutput, PoolingRequestOutput]]:
"""Performs one decoding iteration and returns newly generated results.
@@ -2032,10 +2040,12 @@ class LLMEngine:
tokenizer = (None if self.tokenizer is None else
self.tokenizer.get_lora_tokenizer(lora_request))
prompt_ids = prompt_inputs["prompt_token_ids"]
prompt_ids = prompt_inputs.get("prompt_token_ids", [])
if not prompt_ids:
if prompt_type == "encoder" and model_config.is_multimodal_model:
pass # Mllama may have empty encoder inputs for text-only data
if prompt_inputs["type"] == "embeds":
pass
else:
raise ValueError(f"The {prompt_type} prompt cannot be empty")