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from enum import Enum, auto
from itertools import count
from jetengine_ext.sampling_params import SamplingParams
class SequenceStatus(Enum):
WAITING = auto() # Has a prompt part to prefill
PREFILLING = auto() # Is currently in a prefill model run
DENOISING = auto() # Is ready for or in a denoise model run
SAVING = auto() # Is ready for or in a save model run
FINISHED = auto()
class RunType(Enum):
PREFILL = auto()
DENOISE = auto()
class Sequence:
block_size = 256
counter = count()
def __init__(self, prompt_token_ids: list[int], mask_token_id: int, sampling_params=SamplingParams()):
self.seq_id = next(Sequence.counter)
self.block_length = sampling_params.block_length
self.prompt_token_ids = prompt_token_ids
prompt_len = len(self.prompt_token_ids)
self.num_prefill_tokens = (prompt_len // self.block_length) * self.block_length
prefill_part = self.prompt_token_ids[:self.num_prefill_tokens]
first_denoise_part = self.prompt_token_ids[self.num_prefill_tokens:]
self.token_ids = prefill_part
self.num_tokens = len(self.token_ids)
self.num_prompt_tokens = prompt_len # Keep track of the original full prompt length
self.intermediate_block_tokens = first_denoise_part + [mask_token_id] * (self.block_length - len(first_denoise_part))
self.num_to_transfer = 0
self.current_denoising_step = 0
self.first_unmask_steps: list[int] = []
self.block_first_unmask_steps: list[int] | None = [0] * len(self.intermediate_block_tokens)
self.global_denoising_step = 0
# initial status based on whether prefill is needed.
if self.num_prefill_tokens > 0:
self.status = SequenceStatus.WAITING
else:
self.status = SequenceStatus.DENOISING
# Block Diffusion parameters
self.temperature = sampling_params.temperature
self.stop_words = sampling_params.stop_words if sampling_params.stop_words is not None else []
self.top_k = sampling_params.topk
self.top_p = sampling_params.topp
self.max_tokens = sampling_params.max_tokens
self.ignore_eos = sampling_params.ignore_eos
self.denoising_steps = sampling_params.denoising_steps
self.remasking_strategy = sampling_params.remasking_strategy
self.dynamic_threshold = sampling_params.dynamic_threshold
self.eb_threshold = sampling_params.eb_threshold
self.mask_token_id = mask_token_id
self.num_transfer_tokens_per_step = self._get_num_transfer_tokens()
# State for KV Caching
self.num_cached_tokens = 0
self.block_table = []
def __len__(self):
return self.num_tokens
def __getitem__(self, key):
return self.token_ids[key]
def _get_num_transfer_tokens(self):
base = self.block_length // self.denoising_steps
remainder = self.block_length % self.denoising_steps
num_tokens = [base] * self.denoising_steps
for i in range(remainder):
num_tokens[i] += 1
return num_tokens
def start_new_block(self):
self.current_denoising_step = 0
self.intermediate_block_tokens = [self.mask_token_id] * self.block_length
self.status = SequenceStatus.DENOISING
'''
def commit_block(self, block_tokens: list[int]):
# Trim block if it exceeds max_tokens or contains EOS
final_block = []
for token_id in block_tokens:
if not self.ignore_eos and (token_id == self.eos_token_id or token_id in self.stop_words):
final_block.append(token_id)
self.status = SequenceStatus.FINISHED
break
if self.num_completion_tokens + len(final_block) >= self.max_tokens:
self.status = SequenceStatus.FINISHED
break
final_block.append(token_id)
self.token_ids.extend(final_block)
self.num_tokens = len(self.token_ids)
self.intermediate_block_tokens = []
if self.num_tokens >= self.num_prompt_tokens + self.max_tokens:
self.status = SequenceStatus.FINISHED'''
def commit_block(self, block_tokens: list[int]):
# 1) take token one by one, stop when EOS / reach max_tokens
final_block = []
k = 0
for token_id in block_tokens:
if not self.ignore_eos and (token_id == self.eos_token_id or token_id in self.stop_words):
final_block.append(token_id)
k += 1
self.status = SequenceStatus.FINISHED
break
if self.num_completion_tokens + k >= self.max_tokens:
self.status = SequenceStatus.FINISHED
break
final_block.append(token_id)
k += 1
# 2) self.token_ids
before_ntok = self.num_tokens # pre length
self.token_ids.extend(final_block)
self.num_tokens = len(self.token_ids)
self.intermediate_block_tokens = []
# 3) merge first unmask step into list
# only completion
if self.block_first_unmask_steps is not None:
prompt_gap = max(0, self.num_prompt_tokens - before_ntok)
# completion start index
start = min(prompt_gap, k)
if start < k:
self.first_unmask_steps.extend(self.block_first_unmask_steps[start:k])
self.block_first_unmask_steps = None
if self.num_tokens >= self.num_prompt_tokens + self.max_tokens:
self.status = SequenceStatus.FINISHED
def get_len_for_next_step(self):
return self.num_tokens + self.block_length
def num_new_blocks_needed(self, block_size: int) -> int:
if not self.block_table:
return (self.num_tokens + self.block_length + block_size - 1) // block_size
last_block_capacity = block_size - (self.num_tokens % block_size)
if last_block_capacity == block_size: # Current tokens perfectly fill blocks
last_block_capacity = 0
remaining_tokens_to_add = self.block_length - last_block_capacity
if remaining_tokens_to_add <= 0:
return 0
return (remaining_tokens_to_add + block_size - 1) // block_size
@property
def is_finished(self):
return self.status == SequenceStatus.FINISHED
@property
def num_completion_tokens(self):
return self.num_tokens - self.num_prompt_tokens
@property
def completion_token_ids(self):
return self.token_ids[self.num_prompt_tokens:]
@property
def num_cached_blocks(self):
return self.num_cached_tokens // self.block_size
@property
def num_blocks(self):
return (self.num_tokens + self.block_size - 1) // self.block_size
@property
def last_block_num_tokens(self):
return self.num_tokens - (self.num_blocks - 1) * self.block_size
def block(self, i):
assert 0 <= i < self.num_blocks
return self.token_ids[i*self.block_size: (i+1)*self.block_size]
def append_token(self, token_id: int):
self.token_ids.append(token_id)
self.last_token = token_id
self.num_tokens += 1
'''
def __getstate__(self):
# Simplified for multiprocessing; customize as needed
return (self.seq_id, self.status, self.token_ids, self.num_tokens, self.num_prompt_tokens,
self.num_cached_tokens, self.block_table, self.intermediate_block_tokens, self.current_denoising_step)
def __setstate__(self, state):
(self.seq_id, self.status, self.token_ids, self.num_tokens, self.num_prompt_tokens,
self.num_cached_tokens, self.block_table, self.intermediate_block_tokens, self.current_denoising_step) = state'''
def __getstate__(self):
# Simplified for multiprocessing; customize as needed
return (self.seq_id, self.status, self.token_ids, self.num_tokens, self.num_prompt_tokens,
self.num_cached_tokens, self.block_table, self.intermediate_block_tokens, self.current_denoising_step,
self.first_unmask_steps, self.block_first_unmask_steps, self.global_denoising_step)
def __setstate__(self, state):
(self.seq_id, self.status, self.token_ids, self.num_tokens, self.num_prompt_tokens,
self.num_cached_tokens, self.block_table, self.intermediate_block_tokens, self.current_denoising_step,
self.first_unmask_steps, self.block_first_unmask_steps, self.global_denoising_step) = state
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