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from enum import Enum, auto
from itertools import count
import random
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
# Initialize first block with optional random tokens (will be applied after vocab_size is set)
mask_fill_length = self.block_length - len(first_denoise_part)
self.intermediate_block_tokens = first_denoise_part + [mask_token_id] * mask_fill_length
self._first_block_base_tokens = first_denoise_part # Store for later random initialization
self._first_block_mask_length = mask_fill_length
self.random_init_positions = set() # Track positions that were randomly initialized (to treat as "mask-like")
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.random_init_ratio = sampling_params.random_init_ratio
self.mask_token_id = mask_token_id
self.num_transfer_tokens_per_step = self._get_num_transfer_tokens()
self.vocab_size = None # Will be set by LLMEngine
self.eos_token_id = None # Will be set by LLMEngine
# State for KV Caching
self.num_cached_tokens = 0
self.block_table = []
def _apply_random_init_to_first_block(self):
"""Apply random initialization to the first block if vocab_size is set."""
if hasattr(self, '_first_block_base_tokens') and self.random_init_ratio > 0.0:
if self.vocab_size is not None and self.vocab_size > 0:
self.intermediate_block_tokens, random_positions = self._init_block_with_random(
self._first_block_base_tokens,
self._first_block_mask_length,
self.mask_token_id
)
# Store random positions relative to block start
self.random_init_positions = random_positions
# Clear the stored values after use
delattr(self, '_first_block_base_tokens')
delattr(self, '_first_block_mask_length')
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 _init_block_with_random(self, base_tokens: list[int], mask_fill_length: int, mask_token_id: int) -> tuple[list[int], set[int]]:
"""
Initialize a block with base_tokens + mask tokens, optionally replacing some masks with random tokens.
Args:
base_tokens: Initial tokens (e.g., from prompt)
mask_fill_length: Number of mask tokens to add
mask_token_id: The mask token ID
Returns:
Tuple of (block tokens, set of random initialized positions relative to block start)
"""
block = base_tokens + [mask_token_id] * mask_fill_length
random_positions_set = set()
# Apply random initialization if enabled
if hasattr(self, 'random_init_ratio') and self.random_init_ratio > 0.0:
vocab_size = getattr(self, 'vocab_size', None)
if vocab_size is not None and vocab_size > 0:
# Only randomize mask positions
mask_positions = [i for i in range(len(base_tokens), len(block)) if block[i] == mask_token_id]
num_random = int(len(mask_positions) * self.random_init_ratio)
if num_random > 0 and mask_positions:
random_positions = random.sample(mask_positions, min(num_random, len(mask_positions)))
# Avoid special tokens (mask_token_id, eos_token_id, pad_token_id)
special_tokens = {mask_token_id}
if hasattr(self, 'eos_token_id') and self.eos_token_id is not None:
special_tokens.add(self.eos_token_id)
for pos in random_positions:
# Sample a random token, retry if it's a special token
max_retries = 10
for _ in range(max_retries):
random_token = random.randint(0, vocab_size - 1)
if random_token not in special_tokens:
block[pos] = random_token
random_positions_set.add(pos)
break
return block, random_positions_set
def start_new_block(self):
self.current_denoising_step = 0
# Use random initialization if enabled
self.intermediate_block_tokens, random_positions = self._init_block_with_random(
[], self.block_length, self.mask_token_id
)
# Store random positions relative to current block (always starts at 0 for new blocks)
self.random_init_positions = random_positions
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], early_termination_threshold: float = 0.95):
# 1) take token one by one, stop when EOS / reach max_tokens
# early_termination_threshold: finish early if completion >= threshold * max_tokens (to free resources)
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
# Early termination: if we're close to max_tokens and resources are tight, finish early
# This helps free up KV cache when resources are constrained
if self.num_completion_tokens + k >= int(self.max_tokens * early_termination_threshold):
# Only early terminate if we're in the last block (to avoid cutting off mid-thought)
# Check if we're past the minimum reasonable length
if self.num_completion_tokens + k >= max(32, int(self.max_tokens * 0.8)):
final_block.append(token_id)
k += 1
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,
self.random_init_positions)
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,
self.random_init_positions) = state
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