814 lines
30 KiB
Python
814 lines
30 KiB
Python
"""
|
||
Copyright 2023-2024 SGLang Team
|
||
Licensed under the Apache License, Version 2.0 (the "License");
|
||
you may not use this file except in compliance with the License.
|
||
You may obtain a copy of the License at
|
||
|
||
http://www.apache.org/licenses/LICENSE-2.0
|
||
|
||
Unless required by applicable law or agreed to in writing, software
|
||
distributed under the License is distributed on an "AS IS" BASIS,
|
||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
See the License for the specific language governing permissions and
|
||
limitations under the License.
|
||
"""
|
||
|
||
"""Meta data for requests and batches"""
|
||
|
||
import logging
|
||
import warnings
|
||
from dataclasses import dataclass
|
||
from typing import List, Optional, Union
|
||
|
||
import torch
|
||
from flashinfer.sampling import top_k_top_p_sampling_from_probs
|
||
|
||
import sglang.srt.sampling.penaltylib as penaltylib
|
||
from sglang.global_config import global_config
|
||
from sglang.srt.constrained import RegexGuide
|
||
from sglang.srt.constrained.jump_forward import JumpForwardMap
|
||
from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
|
||
from sglang.srt.mem_cache.chunk_cache import ChunkCache
|
||
from sglang.srt.mem_cache.memory_pool import BaseTokenToKVPool, ReqToTokenPool
|
||
|
||
INIT_INCREMENTAL_DETOKENIZATION_OFFSET = 5
|
||
|
||
# Put some global args for easy access
|
||
global_server_args_dict = {
|
||
"disable_flashinfer": False,
|
||
"disable_flashinfer_sampling": False,
|
||
"attention_reduce_in_fp32": False,
|
||
"enable_mla": False,
|
||
}
|
||
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
|
||
class BaseFinishReason:
|
||
def __init__(self, is_error: bool = False):
|
||
self.is_error = is_error
|
||
|
||
def __str__(self):
|
||
raise NotImplementedError("Subclasses must implement this method")
|
||
|
||
|
||
class FINISH_MATCHED_TOKEN(BaseFinishReason):
|
||
def __init__(self, matched: Union[int, List[int]]):
|
||
super().__init__()
|
||
self.matched = matched
|
||
|
||
def __str__(self) -> str:
|
||
return f"FINISH_MATCHED_TOKEN: {self.matched}"
|
||
|
||
|
||
class FINISH_LENGTH(BaseFinishReason):
|
||
def __init__(self, length: int):
|
||
super().__init__()
|
||
self.length = length
|
||
|
||
def __str__(self) -> str:
|
||
return f"FINISH_LENGTH: {self.length}"
|
||
|
||
|
||
class FINISH_MATCHED_STR(BaseFinishReason):
|
||
def __init__(self, matched: str):
|
||
super().__init__()
|
||
self.matched = matched
|
||
|
||
def __str__(self) -> str:
|
||
return f"FINISH_MATCHED_STR: {self.matched}"
|
||
|
||
|
||
class FINISH_ABORT(BaseFinishReason):
|
||
def __init__(self):
|
||
super().__init__(is_error=True)
|
||
|
||
def __str__(self) -> str:
|
||
return "FINISH_ABORT"
|
||
|
||
|
||
class Req:
|
||
"""Store all inforamtion of a request."""
|
||
|
||
def __init__(self, rid, origin_input_text, origin_input_ids):
|
||
# Input and output info
|
||
self.rid = rid
|
||
self.origin_input_text = origin_input_text
|
||
self.origin_input_ids_unpadded = origin_input_ids # Before image padding
|
||
self.origin_input_ids = origin_input_ids
|
||
self.output_ids = [] # Each decode stage's output ids
|
||
self.fill_ids = None # fill_ids = origin_input_ids + output_ids
|
||
|
||
# Memory info
|
||
self.req_pool_idx = None
|
||
|
||
# For incremental decoding
|
||
# ----- | --------- read_ids -------|
|
||
# ----- | surr_ids |
|
||
# xxxxx | xxxxxxxxxxx | xxxxxxxxxxx |
|
||
# ----- ^ ----------- ^ ----------- ^
|
||
# ----- 1 ----------- 2 ----------- 3
|
||
# 1: surr_offset
|
||
# 2: read_offset
|
||
# 3: last token
|
||
self.vid = 0 # version id to sync decode status with in detokenizer_manager
|
||
self.decoded_text = ""
|
||
self.surr_offset = None # Surrounding offset to defeat the cleanup algorithm
|
||
self.read_offset = None
|
||
|
||
# The number of decoded tokens for token usage report. Note that
|
||
# this does not include the jump forward tokens.
|
||
self.completion_tokens_wo_jump_forward = 0
|
||
|
||
# For vision input
|
||
self.pixel_values = None
|
||
self.image_size = None
|
||
self.image_offset = None
|
||
self.pad_value = None
|
||
|
||
# Prefix info
|
||
self.extend_input_len = 0
|
||
self.prefix_indices = []
|
||
self.last_node = None
|
||
|
||
# Sampling parameters
|
||
self.sampling_params = None
|
||
self.stream = False
|
||
|
||
# Check finish
|
||
self.tokenizer = None
|
||
self.finished_reason = None
|
||
|
||
# Logprobs
|
||
self.return_logprob = False
|
||
self.embedding = None
|
||
self.logprob_start_len = 0
|
||
self.top_logprobs_num = 0
|
||
self.normalized_prompt_logprob = None
|
||
self.input_token_logprobs = None
|
||
self.input_top_logprobs = None
|
||
self.output_token_logprobs = []
|
||
self.output_top_logprobs = []
|
||
# The tokens is prefilled but need to be considered as decode tokens
|
||
# and should be updated for the decode logprobs
|
||
self.last_update_decode_tokens = 0
|
||
|
||
# Constrained decoding
|
||
self.regex_fsm: RegexGuide = None
|
||
self.regex_fsm_state: int = 0
|
||
self.jump_forward_map: JumpForwardMap = None
|
||
|
||
# whether request reached finished condition
|
||
def finished(self) -> bool:
|
||
return self.finished_reason is not None
|
||
|
||
def init_next_round_input(self, tree_cache: Optional[BasePrefixCache] = None):
|
||
self.fill_ids = self.origin_input_ids + self.output_ids
|
||
if tree_cache is not None:
|
||
self.prefix_indices, self.last_node = tree_cache.match_prefix(
|
||
rid=self.rid, key=self.adjust_max_prefix_ids()
|
||
)
|
||
self.extend_input_len = len(self.fill_ids) - len(self.prefix_indices)
|
||
|
||
def adjust_max_prefix_ids(self):
|
||
self.fill_ids = self.origin_input_ids + self.output_ids
|
||
input_len = len(self.fill_ids)
|
||
max_prefix_len = input_len
|
||
|
||
if self.sampling_params.max_new_tokens > 0:
|
||
# Need at least one token to compute logits
|
||
max_prefix_len = min(max_prefix_len, input_len - 1)
|
||
|
||
if self.return_logprob:
|
||
max_prefix_len = min(max_prefix_len, self.logprob_start_len)
|
||
|
||
if self.normalized_prompt_logprob is None:
|
||
# Need at least two tokens to compute normalized logprob
|
||
max_prefix_len = min(max_prefix_len, input_len - 2)
|
||
|
||
return self.fill_ids[:max_prefix_len]
|
||
|
||
# Based on https://github.com/vllm-project/vllm/blob/7a64d24aad69e4d2548aa0bf528d9fe63428ab01/vllm/transformers_utils/detokenizer.py#L194-L313
|
||
def init_incremental_detokenize(self):
|
||
first_iter = self.surr_offset is None or self.read_offset is None
|
||
|
||
if first_iter:
|
||
self.read_offset = len(self.origin_input_ids_unpadded)
|
||
self.surr_offset = max(
|
||
self.read_offset - INIT_INCREMENTAL_DETOKENIZATION_OFFSET, 0
|
||
)
|
||
|
||
all_ids = self.origin_input_ids_unpadded + self.output_ids
|
||
return all_ids[self.surr_offset :], self.read_offset - self.surr_offset
|
||
|
||
def get_next_inc_detokenization(self):
|
||
if self.tokenizer is None:
|
||
return False, ""
|
||
read_ids, read_offset = self.init_incremental_detokenize()
|
||
surr_ids = read_ids[:read_offset]
|
||
|
||
surr_text = self.tokenizer.decode(
|
||
surr_ids,
|
||
skip_special_tokens=self.sampling_params.skip_special_tokens,
|
||
spaces_between_special_tokens=self.sampling_params.spaces_between_special_tokens,
|
||
)
|
||
new_text = self.tokenizer.decode(
|
||
read_ids,
|
||
skip_special_tokens=self.sampling_params.skip_special_tokens,
|
||
spaces_between_special_tokens=self.sampling_params.spaces_between_special_tokens,
|
||
)
|
||
|
||
if len(new_text) > len(surr_text) and not new_text.endswith("<EFBFBD>"):
|
||
return True, new_text[len(surr_text) :]
|
||
|
||
return False, ""
|
||
|
||
def check_finished(self):
|
||
if self.finished():
|
||
return
|
||
|
||
if len(self.output_ids) >= self.sampling_params.max_new_tokens:
|
||
self.finished_reason = FINISH_LENGTH(
|
||
length=self.sampling_params.max_new_tokens
|
||
)
|
||
return
|
||
|
||
last_token_id = self.output_ids[-1]
|
||
if self.tokenizer is None:
|
||
matched_eos = last_token_id in self.sampling_params.stop_token_ids
|
||
else:
|
||
matched_eos = last_token_id == self.tokenizer.eos_token_id
|
||
if matched_eos and not self.sampling_params.ignore_eos:
|
||
self.finished_reason = FINISH_MATCHED_TOKEN(matched=last_token_id)
|
||
return
|
||
|
||
if len(self.sampling_params.stop_strs) > 0:
|
||
tail_str = self.tokenizer.decode(
|
||
self.output_ids[-(self.sampling_params.stop_str_max_len + 1) :]
|
||
)
|
||
|
||
for stop_str in self.sampling_params.stop_strs:
|
||
if stop_str in tail_str or stop_str in self.decoded_text:
|
||
self.finished_reason = FINISH_MATCHED_STR(matched=stop_str)
|
||
return
|
||
|
||
def jump_forward_and_retokenize(self, jump_forward_str, next_state):
|
||
if self.origin_input_text is None:
|
||
# Recovering text can only use unpadded ids
|
||
self.origin_input_text = self.tokenizer.decode(
|
||
self.origin_input_ids_unpadded
|
||
)
|
||
|
||
all_text = self.origin_input_text + self.decoded_text + jump_forward_str
|
||
all_ids = self.tokenizer.encode(all_text)
|
||
prompt_tokens = len(self.origin_input_ids_unpadded)
|
||
|
||
if all_ids[prompt_tokens - 1] != self.origin_input_ids_unpadded[-1]:
|
||
# TODO(lsyin): fix token fusion
|
||
warnings.warn(
|
||
"Token fusion between input and output, try to avoid this by removing the space at the end of the input."
|
||
)
|
||
return False
|
||
|
||
old_output_ids = self.output_ids
|
||
self.output_ids = all_ids[prompt_tokens:]
|
||
self.decoded_text = self.decoded_text + jump_forward_str
|
||
self.surr_offset = prompt_tokens
|
||
self.read_offset = len(all_ids)
|
||
|
||
# NOTE: A trick to reduce the surrouding tokens decoding overhead
|
||
for i in range(0, INIT_INCREMENTAL_DETOKENIZATION_OFFSET):
|
||
surr_text_ = self.tokenizer.decode(
|
||
all_ids[self.read_offset - i : self.read_offset]
|
||
)
|
||
if not surr_text_.endswith("<EFBFBD>"):
|
||
self.surr_offset = self.read_offset - i
|
||
break
|
||
|
||
self.regex_fsm_state = next_state
|
||
|
||
if self.return_logprob:
|
||
# For fast-forward part's logprobs
|
||
k = 0
|
||
for i, old_id in enumerate(old_output_ids):
|
||
if old_id == self.output_ids[i]:
|
||
k = k + 1
|
||
else:
|
||
break
|
||
self.output_token_logprobs = self.output_token_logprobs[:k]
|
||
self.output_top_logprobs = self.output_top_logprobs[:k]
|
||
self.logprob_start_len = prompt_tokens + k
|
||
self.last_update_decode_tokens = len(self.output_ids) - k
|
||
|
||
return True
|
||
|
||
def __repr__(self):
|
||
return f"rid(n={self.rid}, " f"input_ids={self.origin_input_ids}, "
|
||
|
||
|
||
@dataclass
|
||
class ScheduleBatch:
|
||
"""Store all inforamtion of a batch."""
|
||
|
||
# Request, memory pool, and cache
|
||
reqs: List[Req]
|
||
req_to_token_pool: ReqToTokenPool
|
||
token_to_kv_pool: BaseTokenToKVPool
|
||
tree_cache: BasePrefixCache
|
||
|
||
# Batched arguments to model runner
|
||
input_ids: torch.Tensor = None
|
||
req_pool_indices: torch.Tensor = None
|
||
seq_lens: torch.Tensor = None
|
||
position_ids_offsets: torch.Tensor = None
|
||
out_cache_loc: torch.Tensor = None
|
||
extend_num_tokens: int = None
|
||
|
||
# For processing logprobs
|
||
return_logprob: bool = False
|
||
top_logprobs_nums: List[int] = None
|
||
|
||
# Batched sampling params
|
||
temperatures: torch.Tensor = None
|
||
top_ps: torch.Tensor = None
|
||
top_ks: torch.Tensor = None
|
||
penalizer_orchestrator: penaltylib.BatchedPenalizerOrchestrator = None
|
||
logit_bias: torch.Tensor = None
|
||
|
||
@classmethod
|
||
def init_new(cls, reqs, req_to_token_pool, token_to_kv_pool, tree_cache):
|
||
return_logprob = any(req.return_logprob for req in reqs)
|
||
|
||
return cls(
|
||
reqs=reqs,
|
||
req_to_token_pool=req_to_token_pool,
|
||
token_to_kv_pool=token_to_kv_pool,
|
||
tree_cache=tree_cache,
|
||
return_logprob=return_logprob,
|
||
)
|
||
|
||
def batch_size(self):
|
||
return len(self.reqs) if self.reqs is not None else 0
|
||
|
||
def is_empty(self):
|
||
return len(self.reqs) == 0
|
||
|
||
def has_stream(self) -> bool:
|
||
# Return whether batch has at least 1 streaming request
|
||
return any(r.stream for r in self.reqs)
|
||
|
||
def alloc_req_slots(self, num_reqs):
|
||
req_pool_indices = self.req_to_token_pool.alloc(num_reqs)
|
||
if req_pool_indices is None:
|
||
raise RuntimeError(
|
||
"Out of memory. "
|
||
"Please set a smaller number for `--max-running-requests`."
|
||
)
|
||
return req_pool_indices
|
||
|
||
def alloc_token_slots(self, num_tokens: int):
|
||
out_cache_loc = self.token_to_kv_pool.alloc(num_tokens)
|
||
|
||
if out_cache_loc is None:
|
||
if self.tree_cache is not None:
|
||
self.tree_cache.evict(num_tokens, self.token_to_kv_pool.free)
|
||
out_cache_loc = self.token_to_kv_pool.alloc(num_tokens)
|
||
|
||
if out_cache_loc is None:
|
||
logger.error("Prefill out of memory. Try to lower your batch size.")
|
||
if self.tree_cache is not None:
|
||
self.tree_cache.pretty_print()
|
||
exit(1)
|
||
|
||
return out_cache_loc
|
||
|
||
def batch_sampling_params(self, vocab_size, int_token_logit_bias):
|
||
device = "cuda"
|
||
bs, reqs = self.batch_size(), self.reqs
|
||
self.temperatures = torch.tensor(
|
||
[r.sampling_params.temperature for r in reqs],
|
||
dtype=torch.float,
|
||
device=device,
|
||
).view(-1, 1)
|
||
self.top_ps = torch.tensor(
|
||
[r.sampling_params.top_p for r in reqs], dtype=torch.float, device=device
|
||
)
|
||
self.top_ks = torch.tensor(
|
||
[r.sampling_params.top_k for r in reqs], dtype=torch.int, device=device
|
||
)
|
||
|
||
# Each penalizers will do nothing if they evaluate themselves as not required by looking at
|
||
# the sampling_params of the requests (See {_is_required()} of each penalizers). So this
|
||
# should not add hefty computation overhead other than simple checks.
|
||
#
|
||
# While we choose not to even create the class instances if they are not required, this
|
||
# could add additional complexity to the {ScheduleBatch} class, especially we need to
|
||
# handle {filter_batch()} and {merge()} cases as well.
|
||
self.penalizer_orchestrator = penaltylib.BatchedPenalizerOrchestrator(
|
||
vocab_size=vocab_size,
|
||
batch=self,
|
||
device=device,
|
||
Penalizers={
|
||
penaltylib.BatchedFrequencyPenalizer,
|
||
penaltylib.BatchedMinNewTokensPenalizer,
|
||
penaltylib.BatchedPresencePenalizer,
|
||
penaltylib.BatchedRepetitionPenalizer,
|
||
},
|
||
)
|
||
|
||
# Handle logit bias but only allocate when needed
|
||
self.logit_bias = None
|
||
for i in range(bs):
|
||
if reqs[i].sampling_params.dtype == "int":
|
||
if self.logit_bias is None:
|
||
self.logit_bias = torch.zeros(
|
||
(bs, vocab_size), dtype=torch.float32, device=device
|
||
)
|
||
self.logit_bias[i][: len(int_token_logit_bias)] = int_token_logit_bias
|
||
|
||
def prepare_for_extend(self, vocab_size: int, int_token_logit_bias: torch.Tensor):
|
||
bs = self.batch_size()
|
||
reqs = self.reqs
|
||
input_ids = [r.fill_ids[len(r.prefix_indices) :] for r in reqs]
|
||
extend_num_tokens = sum(len(ids) for ids in input_ids)
|
||
seq_lens = []
|
||
|
||
# Allocate memory
|
||
req_pool_indices_cpu = self.alloc_req_slots(bs)
|
||
out_cache_loc = self.alloc_token_slots(extend_num_tokens)
|
||
|
||
pt = 0
|
||
for i, req in enumerate(reqs):
|
||
req.req_pool_idx = req_pool_indices_cpu[i]
|
||
pre_len, seq_len = len(req.prefix_indices), len(req.fill_ids)
|
||
ext_len = seq_len - pre_len
|
||
seq_lens.append(seq_len)
|
||
|
||
if pre_len > 0:
|
||
self.req_to_token_pool.req_to_token[req.req_pool_idx][
|
||
:pre_len
|
||
] = req.prefix_indices
|
||
|
||
self.req_to_token_pool.req_to_token[req.req_pool_idx][pre_len:seq_len] = (
|
||
out_cache_loc[pt : pt + ext_len]
|
||
)
|
||
pt += ext_len
|
||
|
||
# Set fields
|
||
with torch.device("cuda"):
|
||
self.input_ids = torch.tensor(sum(input_ids, []), dtype=torch.int32)
|
||
self.req_pool_indices = torch.tensor(req_pool_indices_cpu)
|
||
self.seq_lens = torch.tensor(seq_lens, dtype=torch.int32)
|
||
self.position_ids_offsets = torch.zeros((bs,), dtype=torch.int64)
|
||
|
||
self.extend_num_tokens = extend_num_tokens
|
||
self.out_cache_loc = out_cache_loc
|
||
self.top_logprobs_nums = [r.top_logprobs_num for r in reqs]
|
||
|
||
self.batch_sampling_params(vocab_size, int_token_logit_bias)
|
||
|
||
def check_decode_mem(self):
|
||
bs = self.batch_size()
|
||
if self.token_to_kv_pool.available_size() >= bs:
|
||
return True
|
||
|
||
self.tree_cache.evict(bs, self.token_to_kv_pool.free)
|
||
|
||
if self.token_to_kv_pool.available_size() >= bs:
|
||
return True
|
||
|
||
return False
|
||
|
||
def retract_decode(self):
|
||
sorted_indices = [i for i in range(len(self.reqs))]
|
||
|
||
# TODO(lsyin): improve retraction policy for radix cache
|
||
sorted_indices.sort(
|
||
key=lambda i: (
|
||
len(self.reqs[i].output_ids),
|
||
-len(self.reqs[i].origin_input_ids),
|
||
),
|
||
reverse=True,
|
||
)
|
||
|
||
retracted_reqs = []
|
||
seq_lens_cpu = self.seq_lens.cpu().numpy()
|
||
while (
|
||
self.token_to_kv_pool.available_size()
|
||
< len(sorted_indices) * global_config.retract_decode_steps
|
||
):
|
||
if len(sorted_indices) == 1:
|
||
# Corner case: only one request left
|
||
assert (
|
||
self.token_to_kv_pool.available_size() > 0
|
||
), "No space left for only one request"
|
||
break
|
||
|
||
idx = sorted_indices.pop()
|
||
req = self.reqs[idx]
|
||
retracted_reqs.append(req)
|
||
|
||
if isinstance(self.tree_cache, ChunkCache):
|
||
# ChunkCache does not have eviction
|
||
token_indices = self.req_to_token_pool.req_to_token[req.req_pool_idx][
|
||
: seq_lens_cpu[idx]
|
||
]
|
||
self.token_to_kv_pool.free(token_indices)
|
||
self.req_to_token_pool.free(req.req_pool_idx)
|
||
del self.tree_cache.entries[req.rid]
|
||
else:
|
||
# TODO: apply more fine-grained retraction
|
||
last_uncached_pos = len(req.prefix_indices)
|
||
token_indices = self.req_to_token_pool.req_to_token[req.req_pool_idx][
|
||
last_uncached_pos : seq_lens_cpu[idx]
|
||
]
|
||
self.token_to_kv_pool.free(token_indices)
|
||
self.req_to_token_pool.free(req.req_pool_idx)
|
||
|
||
# release the last node
|
||
self.tree_cache.dec_lock_ref(req.last_node)
|
||
|
||
# NOTE(lsyin): we should use the newly evictable memory instantly.
|
||
residual_size = (
|
||
len(sorted_indices) * global_config.retract_decode_steps
|
||
- self.token_to_kv_pool.available_size()
|
||
)
|
||
residual_size = max(0, residual_size)
|
||
self.tree_cache.evict(residual_size, self.token_to_kv_pool.free)
|
||
|
||
req.prefix_indices = []
|
||
req.last_node = None
|
||
req.extend_input_len = 0
|
||
|
||
# For incremental logprobs
|
||
req.last_update_decode_tokens = 0
|
||
req.logprob_start_len = 10**9
|
||
|
||
self.filter_batch(sorted_indices)
|
||
|
||
# Reqs in batch are filtered
|
||
total_decoded_tokens = sum(len(r.output_ids) for r in self.reqs)
|
||
total_max_new_tokens = sum(r.sampling_params.max_new_tokens for r in self.reqs)
|
||
|
||
new_estimate_ratio = (
|
||
total_decoded_tokens + global_config.retract_decode_steps * len(self.reqs)
|
||
) / total_max_new_tokens
|
||
new_estimate_ratio = min(1.0, new_estimate_ratio)
|
||
|
||
return retracted_reqs, new_estimate_ratio
|
||
|
||
def check_for_jump_forward(self, model_runner):
|
||
jump_forward_reqs = []
|
||
filter_indices = [i for i in range(len(self.reqs))]
|
||
|
||
for i, req in enumerate(self.reqs):
|
||
if req.jump_forward_map is not None:
|
||
jump_forward_bytes = req.jump_forward_map.jump_forward_byte(
|
||
req.regex_fsm_state
|
||
)
|
||
if jump_forward_bytes is not None and len(jump_forward_bytes) > 1:
|
||
suffix_bytes = []
|
||
continuation_range = range(0x80, 0xC0)
|
||
cur_state = req.regex_fsm_state
|
||
while (
|
||
len(jump_forward_bytes)
|
||
and jump_forward_bytes[0][0] in continuation_range
|
||
):
|
||
# continuation bytes
|
||
byte_edge = jump_forward_bytes.pop(0)
|
||
suffix_bytes.append(byte_edge[0])
|
||
cur_state = byte_edge[1]
|
||
|
||
suffix_tokens = [f"<0x{hex(b)[2:].upper()}>" for b in suffix_bytes]
|
||
suffix_ids = req.tokenizer.convert_tokens_to_ids(suffix_tokens)
|
||
|
||
# Current ids, for cache and revert
|
||
cur_all_ids = tuple(req.origin_input_ids + req.output_ids)[:-1]
|
||
cur_output_ids = req.output_ids
|
||
|
||
req.output_ids.extend(suffix_ids)
|
||
decode_res, new_text = req.get_next_inc_detokenization()
|
||
if not decode_res:
|
||
req.output_ids = cur_output_ids
|
||
continue
|
||
|
||
(
|
||
jump_forward_str,
|
||
next_state,
|
||
) = req.jump_forward_map.jump_forward_symbol(cur_state)
|
||
|
||
# Make the incrementally decoded text part of jump_forward_str
|
||
# so that the UTF-8 will not corrupt
|
||
jump_forward_str = new_text + jump_forward_str
|
||
if not req.jump_forward_and_retokenize(
|
||
jump_forward_str, next_state
|
||
):
|
||
req.output_ids = cur_output_ids
|
||
continue
|
||
|
||
# The decode status has diverged from detokenizer_manager
|
||
req.vid += 1
|
||
|
||
# insert the old request into tree_cache
|
||
self.tree_cache.cache_finished_req(req, cur_all_ids)
|
||
|
||
# re-applying image padding
|
||
if req.pixel_values is not None:
|
||
(
|
||
req.origin_input_ids,
|
||
req.image_offset,
|
||
) = model_runner.model.pad_input_ids(
|
||
req.origin_input_ids_unpadded,
|
||
req.pad_value,
|
||
req.pixel_values.shape,
|
||
req.image_size,
|
||
)
|
||
|
||
jump_forward_reqs.append(req)
|
||
filter_indices.remove(i)
|
||
|
||
self.filter_batch(filter_indices)
|
||
|
||
return jump_forward_reqs
|
||
|
||
def prepare_for_decode(self, input_ids=None):
|
||
if input_ids is None:
|
||
input_ids = [
|
||
r.output_ids[-1] if r.output_ids else r.origin_input_ids[-1]
|
||
for r in self.reqs
|
||
]
|
||
else:
|
||
self.penalizer_orchestrator.cumulate_input_tokens(input_ids)
|
||
|
||
self.input_ids = torch.tensor(input_ids, dtype=torch.int32, device="cuda")
|
||
self.seq_lens.add_(1)
|
||
|
||
# Alloc mem
|
||
bs = self.batch_size()
|
||
self.out_cache_loc = self.alloc_token_slots(bs)
|
||
|
||
self.req_to_token_pool.req_to_token[
|
||
self.req_pool_indices, self.seq_lens - 1
|
||
] = self.out_cache_loc
|
||
|
||
def filter_batch(self, unfinished_indices: List[int]):
|
||
if unfinished_indices is None or len(unfinished_indices) == 0:
|
||
# Filter out all requests
|
||
self.reqs = []
|
||
return
|
||
|
||
if len(unfinished_indices) == len(self.reqs):
|
||
# No need to filter
|
||
return
|
||
|
||
self.reqs = [self.reqs[i] for i in unfinished_indices]
|
||
new_indices = torch.tensor(unfinished_indices, dtype=torch.int32, device="cuda")
|
||
self.seq_lens = self.seq_lens[new_indices]
|
||
self.input_ids = None
|
||
self.req_pool_indices = self.req_pool_indices[new_indices]
|
||
self.position_ids_offsets = self.position_ids_offsets[new_indices]
|
||
self.out_cache_loc = None
|
||
self.top_logprobs_nums = [self.top_logprobs_nums[i] for i in unfinished_indices]
|
||
self.return_logprob = any(req.return_logprob for req in self.reqs)
|
||
|
||
self.penalizer_orchestrator.filter(unfinished_indices, new_indices)
|
||
|
||
for item in [
|
||
"temperatures",
|
||
"top_ps",
|
||
"top_ks",
|
||
"logit_bias",
|
||
]:
|
||
self_val = getattr(self, item, None)
|
||
if self_val is not None: # logit_bias can be None
|
||
setattr(self, item, self_val[new_indices])
|
||
|
||
def merge(self, other: "ScheduleBatch"):
|
||
# Penalizer orchestrator must be merged before Batch.reqs is merged. This is because
|
||
# orchestrator.merge() depends on Batch.reqs during preparation of each penalizers, so it
|
||
# needs to be called with pre-merged Batch.reqs.
|
||
self.penalizer_orchestrator.merge(other.penalizer_orchestrator)
|
||
|
||
self.reqs.extend(other.reqs)
|
||
|
||
self.req_pool_indices = torch.concat(
|
||
[self.req_pool_indices, other.req_pool_indices]
|
||
)
|
||
self.seq_lens = torch.concat([self.seq_lens, other.seq_lens])
|
||
self.position_ids_offsets = torch.concat(
|
||
[self.position_ids_offsets, other.position_ids_offsets]
|
||
)
|
||
self.out_cache_loc = None
|
||
self.top_logprobs_nums.extend(other.top_logprobs_nums)
|
||
self.return_logprob = any(req.return_logprob for req in self.reqs)
|
||
|
||
for item in [
|
||
"temperatures",
|
||
"top_ps",
|
||
"top_ks",
|
||
]:
|
||
self_val = getattr(self, item, None)
|
||
other_val = getattr(other, item, None)
|
||
setattr(self, item, torch.concat([self_val, other_val]))
|
||
|
||
# logit_bias can be None
|
||
if self.logit_bias is not None or other.logit_bias is not None:
|
||
vocab_size = (
|
||
self.logit_bias.shape[1]
|
||
if self.logit_bias is not None
|
||
else other.logit_bias.shape[1]
|
||
)
|
||
if self.logit_bias is None:
|
||
self.logit_bias = torch.zeros(
|
||
(len(self.reqs), vocab_size), dtype=torch.float32, device="cuda"
|
||
)
|
||
if other.logit_bias is None:
|
||
other.logit_bias = torch.zeros(
|
||
(len(other.reqs), vocab_size), dtype=torch.float32, device="cuda"
|
||
)
|
||
self.logit_bias = torch.concat([self.logit_bias, other.logit_bias])
|
||
|
||
def sample(self, logits: torch.Tensor):
|
||
# TODO(lsyin): move this into a part of layer and run with CUDA Graph
|
||
# Post process logits
|
||
logits = logits.contiguous()
|
||
logits.div_(self.temperatures)
|
||
if self.logit_bias is not None:
|
||
logits.add_(self.logit_bias)
|
||
|
||
has_regex = any(req.regex_fsm is not None for req in self.reqs)
|
||
if has_regex:
|
||
allowed_mask = torch.empty_like(logits[0], dtype=torch.bool)
|
||
for i, req in enumerate(self.reqs):
|
||
if req.regex_fsm is not None:
|
||
allowed_mask.zero_()
|
||
allowed_mask[
|
||
req.regex_fsm.get_next_instruction(req.regex_fsm_state).tokens
|
||
] = 1
|
||
logits[i].masked_fill_(~allowed_mask, float("-inf"))
|
||
|
||
logits = self.penalizer_orchestrator.apply(logits)
|
||
|
||
probs = torch.softmax(logits, dim=-1)
|
||
|
||
if not global_server_args_dict["disable_flashinfer_sampling"]:
|
||
max_top_k_round, batch_size = 32, probs.shape[0]
|
||
uniform_samples = torch.rand(
|
||
(max_top_k_round, batch_size), device=probs.device
|
||
)
|
||
batch_next_token_ids, success = top_k_top_p_sampling_from_probs(
|
||
probs, uniform_samples, self.top_ks, self.top_ps
|
||
)
|
||
else:
|
||
# Here we provide a slower fallback implementation.
|
||
batch_next_token_ids, success = top_k_top_p_sampling_from_probs_torch(
|
||
probs, self.top_ks, self.top_ps
|
||
)
|
||
|
||
if not torch.all(success):
|
||
warnings.warn("Sampling failed, fallback to top_k=1 strategy")
|
||
probs = probs.masked_fill(torch.isnan(probs), 0.0)
|
||
argmax_ids = torch.argmax(probs, dim=-1)
|
||
batch_next_token_ids = torch.where(
|
||
success, batch_next_token_ids, argmax_ids
|
||
)
|
||
|
||
if has_regex:
|
||
batch_next_token_ids_cpu = batch_next_token_ids.cpu().numpy()
|
||
for i, req in enumerate(self.reqs):
|
||
if req.regex_fsm is not None:
|
||
req.regex_fsm_state = req.regex_fsm.get_next_state(
|
||
req.regex_fsm_state, batch_next_token_ids_cpu[i]
|
||
)
|
||
|
||
self.penalizer_orchestrator.cumulate_output_tokens(batch_next_token_ids)
|
||
|
||
return batch_next_token_ids
|
||
|
||
|
||
def top_k_top_p_sampling_from_probs_torch(
|
||
probs: torch.Tensor, top_ks: torch.Tensor, top_ps: torch.Tensor
|
||
):
|
||
"""A top-k and top-k sampling implementation with native pytorch operations."""
|
||
probs_sort, probs_idx = probs.sort(dim=-1, descending=True)
|
||
probs_sum = torch.cumsum(probs_sort, dim=-1)
|
||
probs_sort[(probs_sum - probs_sort) > top_ps.view(-1, 1)] = 0.0
|
||
probs_sort[
|
||
torch.arange(0, probs.shape[-1], device=probs.device).view(1, -1)
|
||
>= top_ks.view(-1, 1)
|
||
] = 0.0
|
||
probs_sort.div_(probs_sort.max(dim=-1, keepdim=True)[0])
|
||
try:
|
||
sampled_index = torch.multinomial(probs_sort, num_samples=1)
|
||
except RuntimeError:
|
||
batch_next_token_ids = torch.zeros(
|
||
(probs_sort.shape[0],), dtype=torch.int32, device=probs.device
|
||
)
|
||
success = torch.zeros(probs.shape[0], dtype=torch.bool, device=probs.device)
|
||
return batch_next_token_ids, success
|
||
|
||
batch_next_token_ids = torch.gather(probs_idx, dim=1, index=sampled_index).view(-1)
|
||
success = torch.ones(probs.shape[0], dtype=torch.bool, device=probs.device)
|
||
return batch_next_token_ids, success
|