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[Auto Sync] Update io_struct.py (20250909) (#10236)

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Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: cctry <shiyang@x.ai>
This commit is contained in:
Lianmin Zheng
2025-09-09 14:15:21 -07:00
committed by GitHub
parent 8cbe1538ef
commit bf72b80122
4 changed files with 147 additions and 12 deletions
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4
python/sglang/srt/managers/detokenizer_manager.py
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@@ -246,6 +246,8 @@ class DetokenizerManager(MultiHttpWorkerDetokenizerMixin):
output_token_ids_logprobs_val=recv_obj.output_token_ids_logprobs_val,
output_token_ids_logprobs_idx=recv_obj.output_token_ids_logprobs_idx,
output_hidden_states=recv_obj.output_hidden_states,
placeholder_tokens_idx=None,
placeholder_tokens_val=None,
)
def handle_multimodal_decode_req(self, recv_obj: BatchMultimodalDecodeReq):
@@ -257,6 +259,8 @@ class DetokenizerManager(MultiHttpWorkerDetokenizerMixin):
prompt_tokens=recv_obj.prompt_tokens,
completion_tokens=recv_obj.completion_tokens,
cached_tokens=recv_obj.cached_tokens,
placeholder_tokens_idx=None,
placeholder_tokens_val=None,
)
def handle_freeze_gc_req(self, recv_req: FreezeGCReq):

137
python/sglang/srt/managers/io_struct.py
View File

@@ -121,6 +121,7 @@ class GenerateReqInput:
bootstrap_host: Optional[Union[List[str], str]] = None
bootstrap_port: Optional[Union[List[Optional[int]], int]] = None
bootstrap_room: Optional[Union[List[int], int]] = None
bootstrap_pair_key: Optional[Union[List[str], str]] = None
# For data parallel rank routing
data_parallel_rank: Optional[int] = None
@@ -128,6 +129,15 @@ class GenerateReqInput:
# For background responses (OpenAI responses API)
background: bool = False
# Conversation id used for tracking requests
conversation_id: Optional[str] = None
# Label for the request
label: Optional[str] = None
# Image gen grpc migration
return_bytes: bool = False
def contains_mm_input(self) -> bool:
return (
has_valid_data(self.image_data)
@@ -258,6 +268,7 @@ class GenerateReqInput:
self._normalize_sampling_params(num)
self._normalize_logprob_params(num)
self._normalize_custom_logit_processor(num)
self._normalize_bootstrap_params(num)
def _expand_inputs(self, num):
"""Expand the main inputs (text, input_ids, input_embeds) for parallel sampling."""
@@ -297,6 +308,11 @@ class GenerateReqInput:
self.image_data = [[self.image_data]] * num
self.modalities = ["image"] * num
elif isinstance(self.image_data, list):
# Handle empty list case - treat as no images
if len(self.image_data) == 0:
self.image_data = [None] * num
return
if len(self.image_data) != self.batch_size:
raise ValueError(
"The length of image_data should be equal to the batch size."
@@ -421,6 +437,40 @@ class GenerateReqInput:
"Cannot use list custom_logit_processor with parallel_sample_num > 1"
)
def _normalize_bootstrap_params(self, num):
"""Normalize bootstrap parameters for batch processing."""
# Normalize bootstrap_host
if self.bootstrap_host is None:
self.bootstrap_host = [None] * num
elif not isinstance(self.bootstrap_host, list):
self.bootstrap_host = [self.bootstrap_host] * num
elif isinstance(self.bootstrap_host, list):
self.bootstrap_host = self.bootstrap_host * self.parallel_sample_num
# Normalize bootstrap_port
if self.bootstrap_port is None:
self.bootstrap_port = [None] * num
elif not isinstance(self.bootstrap_port, list):
self.bootstrap_port = [self.bootstrap_port] * num
elif isinstance(self.bootstrap_port, list):
self.bootstrap_port = self.bootstrap_port * self.parallel_sample_num
# Normalize bootstrap_room
if self.bootstrap_room is None:
self.bootstrap_room = [None] * num
elif not isinstance(self.bootstrap_room, list):
self.bootstrap_room = [self.bootstrap_room + i for i in range(num)]
elif isinstance(self.bootstrap_room, list):
self.bootstrap_room = self.bootstrap_room * self.parallel_sample_num
# Normalize bootstrap_pair_key
if self.bootstrap_pair_key is None:
self.bootstrap_pair_key = [None] * num
elif not isinstance(self.bootstrap_pair_key, list):
self.bootstrap_pair_key = [self.bootstrap_pair_key] * num
elif isinstance(self.bootstrap_pair_key, list):
self.bootstrap_pair_key = self.bootstrap_pair_key * self.parallel_sample_num
def _validate_session_params(self):
"""Validate that session parameters are properly formatted."""
if self.session_params is not None:
@@ -453,7 +503,13 @@ class GenerateReqInput:
return_text_in_logprobs=self.return_text_in_logprobs,
stream=self.stream,
log_metrics=self.log_metrics,
return_hidden_states=(
self.return_hidden_states[i]
if isinstance(self.return_hidden_states, list)
else self.return_hidden_states
),
modalities=self.modalities[i] if self.modalities else None,
session_params=self.session_params,
lora_path=self.lora_path[i] if self.lora_path is not None else None,
lora_id=self.lora_id[i] if self.lora_id is not None else None,
custom_logit_processor=(
@@ -461,11 +517,6 @@ class GenerateReqInput:
if self.custom_logit_processor is not None
else None
),
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
@@ -476,9 +527,17 @@ class GenerateReqInput:
bootstrap_room=(
self.bootstrap_room[i] if self.bootstrap_room is not None else None
),
bootstrap_pair_key=(
self.bootstrap_pair_key[i]
if self.bootstrap_pair_key is not None
else None
),
data_parallel_rank=(
self.data_parallel_rank if self.data_parallel_rank is not None else None
),
conversation_id=self.conversation_id,
label=self.label,
return_bytes=self.return_bytes,
)
@@ -504,27 +563,28 @@ class TokenizedGenerateReqInput:
token_ids_logprob: List[int]
# Whether to stream output
stream: bool
# Whether to return hidden states
return_hidden_states: bool = False
# LoRA related
lora_id: Optional[str] = None # None means just use the base model
# The input embeds
input_embeds: Optional[Union[List[List[List[float]]], List[List[float]]]] = None
# Session info for continual prompting
session_params: Optional[SessionParams] = None
# LoRA related
lora_id: Optional[str] = None # None means just use the base model
# Custom logit processor for advanced sampling control. Must be a serialized instance
# of `CustomLogitProcessor` in python/sglang/srt/sampling/custom_logit_processor.py
# Use the processor's `to_str()` method to generate the serialized string.
custom_logit_processor: Optional[str] = None
# Whether to return hidden states
return_hidden_states: bool = False
# For disaggregated inference
bootstrap_host: Optional[str] = None
bootstrap_port: Optional[int] = None
bootstrap_room: Optional[int] = None
bootstrap_pair_key: Optional[str] = None
# For data parallel rank routing
data_parallel_rank: Optional[int] = None
@@ -532,6 +592,12 @@ class TokenizedGenerateReqInput:
# For dp balance
dp_balance_id: int = -1
# Label for the request
label: Optional[str] = None
# Image gen grpc migration
return_bytes: bool = False
@dataclass
class BatchTokenizedGenerateReqInput:
@@ -738,9 +804,26 @@ class BatchTokenIDOut:
# Hidden states
output_hidden_states: List[List[float]]
# The information of placeholder tokens (e.g., image token)
# idx is the index of the token in the prompt after expansion.
# val is the length of padded tokens after expansion.
placeholder_tokens_idx: List[Optional[List[int]]]
placeholder_tokens_val: List[Optional[List[int]]]
@dataclass
class BatchMultimodalDecodeReq:
decoded_ids: List[int]
input_token_logprobs_val: List[float]
input_token_logprobs_idx: List[int]
output_token_logprobs_val: List[float]
output_token_logprobs_idx: List[int]
read_offsets: List[int]
skip_special_tokens: List[bool]
spaces_between_special_tokens: List[bool]
image_resolutions: List[List[int]]
resize_image_resolutions: List[List[int]]
# The request id
rids: List[str]
finished_reasons: List[BaseFinishReason]
@@ -750,6 +833,12 @@ class BatchMultimodalDecodeReq:
completion_tokens: List[int]
cached_tokens: List[int]
# Placeholder token info
placeholder_tokens_idx: List[Optional[List[int]]]
placeholder_tokens_val: List[Optional[List[int]]]
return_bytes: bool = False
@dataclass
class BatchStrOut:
@@ -785,6 +874,9 @@ class BatchStrOut:
# Hidden states
output_hidden_states: List[List[float]]
placeholder_tokens_idx: List[Optional[List[int]]]
placeholder_tokens_val: List[Optional[List[int]]]
@dataclass
class BatchMultimodalOut:
@@ -792,14 +884,26 @@ class BatchMultimodalOut:
rids: List[str]
# The finish reason
finished_reasons: List[dict]
decoded_ids: List[List[int]]
# The outputs
outputs: List[List[Dict]]
outputs: Union[List[str | bytes], List[List[Dict]]]
# probability values for input tokens and output tokens
input_token_logprobs_val: List[List[float]]
input_token_logprobs_idx: List[List[int]]
output_token_logprobs_val: List[List[float]]
output_token_logprobs_idx: List[List[int]]
# Token counts
prompt_tokens: List[int]
completion_tokens: List[int]
cached_tokens: List[int]
placeholder_tokens_idx: List[Optional[List[int]]]
placeholder_tokens_val: List[Optional[List[int]]]
return_bytes: List[bool]
@dataclass
class BatchEmbeddingOut:
@@ -812,6 +916,9 @@ class BatchEmbeddingOut:
# Token counts
prompt_tokens: List[int]
cached_tokens: List[int]
# Placeholder token info
placeholder_tokens_idx: List[Optional[List[int]]]
placeholder_tokens_val: List[Optional[List[int]]]
@dataclass
@@ -844,6 +951,12 @@ class UpdateWeightFromDiskReqInput:
abort_all_requests: bool = False
# Optional: Update weight version along with weights
weight_version: Optional[str] = None
# Whether to update weights asynchronously
is_async: bool = False
# Whether to empty torch cache
torch_empty_cache: bool = False
# Whether to keep the scheduler paused after weight update
keep_pause: bool = False
@dataclass
@@ -983,6 +1096,7 @@ class AbortReq:
abort_all: bool = False
# The finished reason data
finished_reason: Optional[Dict[str, Any]] = None
abort_reason: Optional[str] = None
# used in MultiTokenzierManager mode
rids: Optional[Union[List[str], str]] = None
@@ -1061,6 +1175,7 @@ class ConfigureLoggingReq:
log_requests_level: Optional[int] = None
dump_requests_folder: Optional[str] = None
dump_requests_threshold: Optional[int] = None
crash_dump_folder: Optional[str] = None
@dataclass

8
python/sglang/srt/managers/multi_tokenizer_mixin.py
View File

@@ -195,6 +195,8 @@ def _handle_output_by_index(output, i):
if output.output_hidden_states
else None
),
placeholder_tokens_idx=None,
placeholder_tokens_val=None,
)
elif isinstance(output, BatchEmbeddingOut):
new_output = BatchEmbeddingOut(
@@ -211,6 +213,8 @@ def _handle_output_by_index(output, i):
cached_tokens=(
[output.cached_tokens[i]] if len(output.cached_tokens) > i else None
),
placeholder_tokens_idx=None,
placeholder_tokens_val=None,
)
elif isinstance(output, BatchStrOut):
new_output = BatchStrOut(
@@ -307,6 +311,8 @@ def _handle_output_by_index(output, i):
if output.output_hidden_states
else None
),
placeholder_tokens_idx=None,
placeholder_tokens_val=None,
)
elif isinstance(output, BatchMultimodalOut):
new_output = BatchMultimodalOut(
@@ -328,6 +334,8 @@ def _handle_output_by_index(output, i):
cached_tokens=(
[output.cached_tokens[i]] if len(output.cached_tokens) > i else None
),
placeholder_tokens_idx=None,
placeholder_tokens_val=None,
)
else:
new_output = output

10
python/sglang/srt/managers/scheduler_output_processor_mixin.py
View File

@@ -700,6 +700,8 @@ class SchedulerOutputProcessorMixin:
output_token_ids_logprobs_val,
output_token_ids_logprobs_idx,
output_hidden_states,
placeholder_tokens_idx=None,
placeholder_tokens_val=None,
)
)
@@ -719,6 +721,12 @@ class SchedulerOutputProcessorMixin:
cached_tokens.append(req.cached_tokens)
self.send_to_detokenizer.send_pyobj(
BatchEmbeddingOut(
rids, finished_reasons, embeddings, prompt_tokens, cached_tokens
rids,
finished_reasons,
embeddings,
prompt_tokens,
cached_tokens,
placeholder_tokens_idx=None,
placeholder_tokens_val=None,
)
)