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Open AI API hidden states (#6716)

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This commit is contained in:
kyle-pena-kuzco
2025-06-10 17:37:29 -04:00
committed by GitHub
parent ce5ee3bdf0
commit b56de8f943
17 changed files with 606 additions and 44 deletions
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8
python/sglang/srt/managers/io_struct.py
View File

@@ -99,7 +99,7 @@ class GenerateReqInput:
custom_logit_processor: Optional[Union[List[Optional[str]], str]] = None
# Whether to return hidden states
return_hidden_states: bool = False
return_hidden_states: Union[List[bool], bool] = False
# For disaggregated inference
bootstrap_host: Optional[Union[List[str], str]] = None
@@ -409,7 +409,11 @@ class GenerateReqInput:
if self.custom_logit_processor is not None
else None
),
return_hidden_states=self.return_hidden_states,
return_hidden_states=(
self.return_hidden_states[i]
if isinstance(self.return_hidden_states, list)
else self.return_hidden_states
),
# if `__getitem__` is called, the bootstrap_host, bootstrap_port, bootstrap_room must be a list
bootstrap_host=(
self.bootstrap_host[i] if self.bootstrap_host is not None else None

14
python/sglang/srt/managers/tokenizer_manager.py
View File

@@ -418,6 +418,20 @@ class TokenizerManager:
obj.normalize_batch_and_arguments()
if isinstance(obj, GenerateReqInput):
return_hidden_states = obj.return_hidden_states
has_return_hidden_states = return_hidden_states == True or (
isinstance(return_hidden_states, list) and any(return_hidden_states)
)
if (
not self.server_args.enable_return_hidden_states
and has_return_hidden_states
):
raise ValueError(
"return_hidden_states=True requires the server to be started "
"with --enable-return-hidden-states (ServerArgs.enable_return_hidden_states)."
)
if self.log_requests:
max_length, skip_names, _ = self.log_request_metadata
logger.info(

63
python/sglang/srt/model_executor/cuda_graph_runner.py
View File

@@ -235,6 +235,10 @@ class CudaGraphRunner:
self.model_runner.server_args.speculative_num_draft_tokens
)
# If returning hidden states is enabled, set initial capture hidden mode to full to avoid double-capture on startup
if model_runner.server_args.enable_return_hidden_states:
self.capture_hidden_mode = CaptureHiddenMode.FULL
# Attention backend
self.max_bs = max(self.capture_bs)
self.max_num_token = self.max_bs * self.num_tokens_per_bs
@@ -342,11 +346,29 @@ class CudaGraphRunner:
else True
)
requested_capture_hidden_mode = max(
forward_batch.capture_hidden_mode,
(
forward_batch.spec_info.capture_hidden_mode
if getattr(forward_batch.spec_info, "capture_hidden_mode", None)
is not None
else CaptureHiddenMode.NULL
),
)
capture_hidden_mode_matches = (
requested_capture_hidden_mode == CaptureHiddenMode.NULL
or requested_capture_hidden_mode == self.capture_hidden_mode
)
is_tbo_supported = (
forward_batch.can_run_tbo if self.enable_two_batch_overlap else True
)
return is_bs_supported and is_encoder_lens_supported and is_tbo_supported
return (
is_bs_supported
and is_encoder_lens_supported
and is_tbo_supported
and capture_hidden_mode_matches
)
def capture(self) -> None:
profile_context = empty_context()
@@ -541,21 +563,34 @@ class CudaGraphRunner:
return graph, out
def recapture_if_needed(self, forward_batch: ForwardBatch):
# If the capture_hidden_mode changes, we need to recapture the graph
hidden_mode_from_spec_info = getattr(
# If the required capture_hidden_mode changes, we need to recapture the graph
# These are the different factors that can influence the capture_hidden_mode
capture_hidden_mode_required_by_forward_batch = (
forward_batch.capture_hidden_mode
)
capture_hidden_mode_required_by_spec_info = getattr(
forward_batch.spec_info, "capture_hidden_mode", CaptureHiddenMode.NULL
)
if (
forward_batch.capture_hidden_mode == CaptureHiddenMode.FULL
and self.capture_hidden_mode != CaptureHiddenMode.FULL
):
self.capture_hidden_mode = CaptureHiddenMode.FULL
self.capture()
elif (
forward_batch.capture_hidden_mode != CaptureHiddenMode.FULL
and self.capture_hidden_mode != hidden_mode_from_spec_info
):
self.capture_hidden_mode = hidden_mode_from_spec_info
capture_hidden_mode_required_for_returning_hidden_states = (
CaptureHiddenMode.FULL
if self.model_runner.server_args.enable_return_hidden_states
else CaptureHiddenMode.NULL
)
# Determine the highest capture_hidden_mode required
# (If we have FULL, we can emulate LAST or NULL)
# (If we have LAST, we can emulate NULL)
required_capture_hidden_mode = max(
capture_hidden_mode_required_by_forward_batch,
capture_hidden_mode_required_by_spec_info,
capture_hidden_mode_required_for_returning_hidden_states,
)
# If the current hidden mode is no longer aligned with the required hidden mode, we need to set it to what is required and re-capture
if self.capture_hidden_mode != required_capture_hidden_mode:
self.capture_hidden_mode = required_capture_hidden_mode
self.capture()
def replay_prepare(

13
python/sglang/srt/model_executor/forward_batch_info.py
View File

@@ -31,6 +31,7 @@ from __future__ import annotations
from dataclasses import dataclass
from enum import IntEnum, auto
from functools import total_ordering
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
import torch
@@ -117,13 +118,14 @@ class ForwardMode(IntEnum):
return self == ForwardMode.DECODE or self == ForwardMode.IDLE
@total_ordering
class CaptureHiddenMode(IntEnum):
# Do not capture anything.
NULL = auto()
# Capture hidden states of all tokens.
FULL = auto()
NULL = 0
# Capture a hidden state of the last token.
LAST = auto()
LAST = 1
# Capture hidden states of all tokens.
FULL = 2
def need_capture(self):
return self != CaptureHiddenMode.NULL
@@ -134,6 +136,9 @@ class CaptureHiddenMode(IntEnum):
def is_last(self):
return self == CaptureHiddenMode.LAST
def __lt__(self, other):
return self.value < other.value
@dataclass
class ForwardBatch:

92
python/sglang/srt/openai_api/adapter.py
View File

@@ -542,6 +542,7 @@ def v1_generate_request(
logprob_start_lens = []
top_logprobs_nums = []
lora_paths = []
return_hidden_states = []
for request in all_requests:
# NOTE: with openai API, the prompt's logprobs are always not computed
@@ -588,6 +589,7 @@ def v1_generate_request(
top_logprobs_nums.append(
request.logprobs if request.logprobs is not None else 0
)
return_hidden_states.append(request.return_hidden_states)
if len(all_requests) == 1:
if isinstance(prompts[0], str) or isinstance(prompts[0][0], str):
@@ -599,6 +601,7 @@ def v1_generate_request(
logprob_start_lens = logprob_start_lens[0]
top_logprobs_nums = top_logprobs_nums[0]
lora_paths = lora_paths[0]
return_hidden_states = return_hidden_states[0]
else:
if isinstance(prompts[0], str) or isinstance(prompts[0][0], str):
prompt_kwargs = {"text": prompts}
@@ -615,6 +618,7 @@ def v1_generate_request(
stream=all_requests[0].stream,
rid=request_ids,
lora_path=lora_paths,
return_hidden_states=return_hidden_states,
bootstrap_host=all_requests[0].bootstrap_host,
bootstrap_port=all_requests[0].bootstrap_port,
bootstrap_room=all_requests[0].bootstrap_room,
@@ -683,6 +687,16 @@ def v1_generate_response(
else:
logprobs = None
hidden_states = None
if isinstance(request, list) and request[idx].return_hidden_states:
hidden_states = ret_item["meta_info"].get("hidden_states", None)
elif (not isinstance(request, list)) and request.return_hidden_states:
hidden_states = ret_item["meta_info"].get("hidden_states", None)
if hidden_states is not None:
hidden_states = (
hidden_states[-1] if hidden_states and len(hidden_states) > 1 else []
)
finish_reason = ret_item["meta_info"]["finish_reason"]
if to_file:
@@ -698,6 +712,8 @@ def v1_generate_response(
else None
),
}
if hidden_states is not None:
choice_data["hidden_states"] = hidden_states
else:
choice_data = CompletionResponseChoice(
index=idx,
@@ -709,6 +725,7 @@ def v1_generate_response(
if finish_reason and "matched" in finish_reason
else None
),
hidden_states=hidden_states,
)
choices.append(choice_data)
@@ -777,6 +794,7 @@ async def v1_completions(tokenizer_manager, raw_request: Request):
prompt_tokens = {}
completion_tokens = {}
cached_tokens = {}
hidden_states = {}
try:
async for content in tokenizer_manager.generate_request(
@@ -791,6 +809,9 @@ async def v1_completions(tokenizer_manager, raw_request: Request):
prompt_tokens[index] = content["meta_info"]["prompt_tokens"]
completion_tokens[index] = content["meta_info"]["completion_tokens"]
cached_tokens[index] = content["meta_info"].get("cached_tokens", 0)
hidden_states[index] = content["meta_info"].get(
"hidden_states", None
) or hidden_states.get(index)
if not stream_buffer: # The first chunk
if request.echo:
@@ -873,6 +894,27 @@ async def v1_completions(tokenizer_manager, raw_request: Request):
n_prev_tokens[index] = n_prev_token
yield f"data: {chunk.model_dump_json()}\n\n"
if request.return_hidden_states and hidden_states:
for index, choice_hidden_states in hidden_states.items():
last_token_hidden_states = (
choice_hidden_states[-1]
if choice_hidden_states and len(choice_hidden_states) > 1
else []
)
hidden_states_chunk = CompletionStreamResponse(
id=content["meta_info"]["id"],
created=created,
choices=[
CompletionResponseStreamChoice(
text="",
index=index,
hidden_states=last_token_hidden_states,
finish_reason=None,
)
],
model=request.model,
)
yield f"data: {hidden_states_chunk.model_dump_json()}\n\n"
if request.stream_options and request.stream_options.include_usage:
total_prompt_tokens = sum(
tokens
@@ -973,6 +1015,7 @@ def v1_chat_generate_request(
top_logprobs_nums = []
modalities_list = []
lora_paths = []
return_hidden_states = []
# NOTE: with openai API, the prompt's logprobs are always not computed
@@ -1215,6 +1258,7 @@ def v1_chat_generate_request(
image_data_list.append(image_data)
audio_data_list.append(audio_data)
modalities_list.append(modalities)
return_hidden_states.append(request.return_hidden_states)
if len(all_requests) == 1:
if is_multimodal:
# processor will need text input
@@ -1233,6 +1277,7 @@ def v1_chat_generate_request(
modalities_list = modalities_list[0]
lora_paths = lora_paths[0]
request_ids = request_ids[0]
return_hidden_states = return_hidden_states[0]
else:
if tokenizer_manager.model_config.is_multimodal:
# processor will need text input
@@ -1259,6 +1304,7 @@ def v1_chat_generate_request(
bootstrap_host=all_requests[0].bootstrap_host,
bootstrap_port=all_requests[0].bootstrap_port,
bootstrap_room=all_requests[0].bootstrap_room,
return_hidden_states=return_hidden_states,
)
return adapted_request, all_requests if len(all_requests) > 1 else all_requests[0]
@@ -1319,6 +1365,20 @@ def v1_chat_generate_response(
else:
choice_logprobs = None
if isinstance(request, list) and request[idx].return_hidden_states:
include_hidden_states = True
elif not isinstance(request, list) and request.return_hidden_states:
include_hidden_states = True
else:
include_hidden_states = False
if include_hidden_states and ret_item["meta_info"].get("hidden_states", None):
hidden_states = ret_item["meta_info"]["hidden_states"]
hidden_states = (
hidden_states[-1] if hidden_states and len(hidden_states) > 1 else []
)
else:
hidden_states = None
finish_reason = ret_item["meta_info"]["finish_reason"]
tool_calls = None
@@ -1391,6 +1451,8 @@ def v1_chat_generate_response(
else None
),
}
if hidden_states is not None:
choice_data["hidden_states"] = hidden_states
else:
choice_data = ChatCompletionResponseChoice(
index=idx,
@@ -1407,6 +1469,7 @@ def v1_chat_generate_response(
if finish_reason and "matched" in finish_reason
else None
),
hidden_states=hidden_states,
)
choices.append(choice_data)
@@ -1486,12 +1549,16 @@ async def v1_chat_completions(
prompt_tokens = {}
completion_tokens = {}
cached_tokens = {}
hidden_states = {}
try:
async for content in tokenizer_manager.generate_request(
adapted_request, raw_request
):
index = content.get("index", 0)
text = content["text"]
hidden_states[index] = content["meta_info"].get(
"hidden_states", None
) or hidden_states.get(index)
is_first = is_firsts.get(index, True)
stream_buffer = stream_buffers.get(index, "")
@@ -1613,6 +1680,7 @@ async def v1_chat_completions(
if (delta and len(delta) == 0) or not delta:
stream_buffers[index] = new_stream_buffer
is_firsts[index] = is_first
n_prev_tokens[index] = n_prev_token
continue
if request.tool_choice != "none" and request.tools:
@@ -1702,6 +1770,7 @@ async def v1_chat_completions(
stream_buffers[index] = new_stream_buffer
is_firsts[index] = is_first
n_prev_tokens[index] = n_prev_token
else:
# No tool calls => just treat this as normal text
@@ -1734,6 +1803,7 @@ async def v1_chat_completions(
yield f"data: {chunk.model_dump_json()}\n\n"
stream_buffers[index] = new_stream_buffer
is_firsts[index] = is_first
n_prev_tokens[index] = n_prev_token
if finish_reason_type == "stop" and request.tool_choice != "none":
parser = FunctionCallParser(
tools=request.tools,
@@ -1769,6 +1839,28 @@ async def v1_chat_completions(
else:
usage = None
if request.return_hidden_states and hidden_states:
for index, choice_hidden_states in hidden_states.items():
last_token_hidden_states = (
choice_hidden_states[-1]
if choice_hidden_states and len(choice_hidden_states) > 1
else []
)
hidden_states_chunk = ChatCompletionStreamResponse(
id=content["meta_info"]["id"],
created=created,
choices=[
ChatCompletionResponseStreamChoice(
index=index,
delta=DeltaMessage(
hidden_states=last_token_hidden_states
),
finish_reason=finish_reason_type,
)
],
model=request.model,
)
yield f"data: {hidden_states_chunk.model_dump_json()}\n\n"
final_usage_chunk = ChatCompletionStreamResponse(
id=content["meta_info"]["id"],
created=created,

31
python/sglang/srt/openai_api/protocol.py
View File

@@ -16,7 +16,7 @@
import time
from typing import Dict, List, Optional, Union
from pydantic import BaseModel, Field, root_validator
from pydantic import BaseModel, Field, model_serializer, root_validator
from typing_extensions import Literal
@@ -182,6 +182,7 @@ class CompletionRequest(BaseModel):
skip_special_tokens: bool = True
lora_path: Optional[Union[List[Optional[str]], Optional[str]]] = None
session_params: Optional[Dict] = None
return_hidden_states: Optional[bool] = False
# For PD disaggregation
bootstrap_host: Optional[str] = None
@@ -195,6 +196,11 @@ class CompletionResponseChoice(BaseModel):
logprobs: Optional[LogProbs] = None
finish_reason: Literal["stop", "length", "content_filter", "abort"]
matched_stop: Union[None, int, str] = None
hidden_states: Optional[object] = None
@model_serializer
def _serialize(self):
return exclude_if_none(self, ["hidden_states"])
class CompletionResponse(BaseModel):
@@ -212,6 +218,11 @@ class CompletionResponseStreamChoice(BaseModel):
logprobs: Optional[LogProbs] = None
finish_reason: Optional[Literal["stop", "length", "content_filter"]] = None
matched_stop: Union[None, int, str] = None
hidden_states: Optional[object] = None
@model_serializer
def _serialize(self):
return exclude_if_none(self, ["hidden_states"])
class CompletionStreamResponse(BaseModel):
@@ -405,6 +416,9 @@ class ChatCompletionRequest(BaseModel):
bootstrap_port: Optional[int] = None
bootstrap_room: Optional[int] = None
# Hidden States
return_hidden_states: Optional[bool] = False
class ChatMessage(BaseModel):
role: Optional[str] = None
@@ -421,6 +435,11 @@ class ChatCompletionResponseChoice(BaseModel):
"stop", "length", "tool_calls", "content_filter", "function_call", "abort"
]
matched_stop: Union[None, int, str] = None
hidden_states: Optional[object] = None
@model_serializer
def _serialize(self):
return exclude_if_none(self, ["hidden_states"])
class ChatCompletionResponse(BaseModel):
@@ -437,6 +456,11 @@ class DeltaMessage(BaseModel):
content: Optional[str] = None
reasoning_content: Optional[str] = None
tool_calls: Optional[List[ToolCall]] = Field(default=None, examples=[None])
hidden_states: Optional[object] = None
@model_serializer
def _serialize(self):
return exclude_if_none(self, ["hidden_states"])
class ChatCompletionResponseStreamChoice(BaseModel):
@@ -513,3 +537,8 @@ class ScoringResponse(BaseModel):
model: str
usage: Optional[UsageInfo] = None
object: str = "scoring"
def exclude_if_none(obj, field_names: List[str]):
omit_if_none_fields = {k for k, v in obj.model_fields.items() if k in field_names}
return {k: v for k, v in obj if k not in omit_if_none_fields or v is not None}

7
python/sglang/srt/server_args.py
View File

@@ -215,6 +215,7 @@ class ServerArgs:
disable_chunked_prefix_cache: bool = False
disable_fast_image_processor: bool = False
warmups: Optional[str] = None
enable_return_hidden_states: bool = False
# Debug tensor dumps
debug_tensor_dump_output_folder: Optional[str] = None
@@ -1456,6 +1457,12 @@ class ServerArgs:
default=ServerArgs.debug_tensor_dump_inject,
help="Inject the outputs from jax as the input of every layer.",
)
parser.add_argument(
"--enable-return-hidden-states",
action="store_true",
help="Enable returning hidden states with responses.",
)
parser.add_argument(
"--debug-tensor-dump-prefill-only",
action="store_true",

4
python/sglang/srt/speculative/eagle_draft_cuda_graph_runner.py
View File

@@ -117,9 +117,7 @@ class EAGLEDraftCudaGraphRunner:
hidden_states = self.hidden_states[:num_seqs]
spec_info = EagleDraftInput(
topk_p=topk_p,
topk_index=topk_index,
hidden_states=hidden_states,
topk_p=topk_p, topk_index=topk_index, hidden_states=hidden_states
)
# Forward batch

12
python/sglang/srt/speculative/eagle_worker.py
View File

@@ -290,6 +290,7 @@ class EAGLEWorker(TpModelWorker):
A tuple of the final logit output of the target model, next tokens accepted,
the batch id (used for overlap schedule), and number of accepted tokens.
"""
if batch.forward_mode.is_decode():
with self.draft_tp_context(self.draft_model_runner.tp_group):
spec_info = self.draft(batch)
@@ -431,10 +432,10 @@ class EAGLEWorker(TpModelWorker):
batch.out_cache_loc = out_cache_loc
batch.seq_lens_sum = torch.sum(batch.seq_lens).item()
spec_info.positions = batch.seq_lens.repeat_interleave(self.topk, dim=0)
# Get forward batch
spec_info.capture_hidden_mode = CaptureHiddenMode.LAST
batch.return_hidden_states = False
model_worker_batch = batch.get_model_worker_batch()
assert model_worker_batch.capture_hidden_mode == CaptureHiddenMode.LAST
forward_batch = ForwardBatch.init_new(
model_worker_batch, self.draft_model_runner
)
@@ -547,11 +548,13 @@ class EAGLEWorker(TpModelWorker):
def verify(self, batch: ScheduleBatch, spec_info: EagleVerifyInput):
spec_info.prepare_for_verify(batch, self.page_size)
batch.return_hidden_states = False
batch.forward_mode = ForwardMode.TARGET_VERIFY
batch.spec_info = spec_info
model_worker_batch = batch.get_model_worker_batch(
seq_lens_cpu_cache=spec_info.seq_lens_cpu
)
assert model_worker_batch.capture_hidden_mode == spec_info.capture_hidden_mode
if batch.has_grammar:
retrieve_next_token_cpu = spec_info.retrive_next_token.cpu()
@@ -687,15 +690,18 @@ class EAGLEWorker(TpModelWorker):
hidden_states: Hidden states from the target model forward
next_token_ids: Next token ids generated from the target forward.
"""
# Sometimes we get hidden states produced by CaptureHiddenMode.FULL, so we have to select just the last
batch.spec_info = EagleDraftInput(
hidden_states=hidden_states,
verified_id=next_token_ids,
)
batch.return_hidden_states = False
batch.spec_info.prepare_for_extend(batch)
batch.spec_info.capture_hidden_mode = CaptureHiddenMode.LAST
model_worker_batch = batch.get_model_worker_batch(
seq_lens_cpu_cache=seq_lens_cpu
)
assert model_worker_batch.capture_hidden_mode == CaptureHiddenMode.LAST
forward_batch = ForwardBatch.init_new(
model_worker_batch, self.draft_model_runner
)
@@ -718,7 +724,9 @@ class EAGLEWorker(TpModelWorker):
batch,
self.speculative_num_steps,
)
batch.return_hidden_states = False
model_worker_batch = batch.get_model_worker_batch()
assert model_worker_batch.capture_hidden_mode == CaptureHiddenMode.LAST
forward_batch = ForwardBatch.init_new(
model_worker_batch, self.draft_model_runner
)