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# coding=utf-8
# Copyright 2025 The HuggingFace Inc. 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.
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Optional
import torch
from ...utils.logging import logging
from ...utils.metrics import traced
# We centralize the logger here to coordinate between logging and progress bar
logger = logging.getLogger("ContinuousBatchingLogger")
# logger.setLevel(logging.INFO)
def get_device_and_memory_breakdown() -> tuple[torch.device, int, int, int]:
if torch.cuda.is_available():
device = torch.device("cuda")
torch.cuda.empty_cache()
torch.cuda.synchronize()
total_memory = torch.cuda.get_device_properties(device).total_memory
reserved_memory = torch.cuda.memory_reserved(device)
allocated_memory = torch.cuda.memory_allocated(device)
elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
device = torch.device("mps")
# MPS memory reporting (PyTorch 2.0+)
total_memory = torch.mps.driver_allocated_memory()
allocated_memory = total_memory - torch.mps.recommended_max_memory()
reserved_memory = 0 # MPS does not track reserved separately
else:
device = torch.device("cpu")
total_memory = None
reserved_memory = 0
allocated_memory = 0
return device, total_memory, reserved_memory, allocated_memory
class RequestStatus(Enum):
"""Status of a generation request through its lifecycle."""
PENDING = "pending"
PREFILLING = "prefilling"
PREFILLING_SPLIT = "prefilling_split"
SPLIT_PENDING_REMAINDER = "split_pending_remainder"
DECODING = "decoding"
FINISHED = "finished"
FAILED = "failed"
@dataclass
class GenerationOutput:
"""Tracks the output of a generation request.
Attributes:
request_id (str): The ID of the generation request.
prompt_ids (list[int]): The IDs of the prompt tokens.
generated_tokens (list[int]): The generated tokens.
logprobs (list[float]): The log probabilities of the generated tokens.
error (Optional[str]): Any error message associated with the request. When None, the request was successful.
status (RequestStatus): The status of the request.
created_time (float): The time the request was created.
"""
request_id: str
prompt_ids: list[int] = field(default_factory=list)
generated_tokens: list[int] = field(default_factory=list)
logprobs: list[float] = field(default_factory=list)
error: Optional[str] = None
status: RequestStatus = RequestStatus.PENDING
created_time: float = field(default_factory=time.time)
@dataclass
class RequestState:
"""Tracks the state of a generation request through its lifecycle.
Attributes:
request_id (str): The ID of the generation request.
full_prompt_ids (list[int] | None): The tokens IDs of the full prompt.
prompt_ids (list[int] | None): The tokens IDs currently being processed.
remaining_prompt_ids (list[int]): The tokens IDs remaining to be processed (for split requests).
static_outputs (list[int]): The generated tokens.
allocated_blocks (int): The number of blocks allocated to the request.
position_offset (int): The current position in the sequence for position_ids.
status (RequestStatus): The status of the request: can be one of PENDING, PREFILLING, PREFILLING_SPLIT,
SPLIT_PENDING_REMAINDER, DECODING, FINISHED, FAILED
max_new_tokens (int): The maximum number of new tokens to generate.
eos_token_id (int): The ID of the end-of-sequence token.
created_time (float): The time the request was created.
error (Optional[str]): Any error message associated with the request. When None, has had no error yet.
"""
# Required fields
request_id: str
full_prompt_ids: Optional[list[int]] = None # Full initial prompt
prompt_ids: Optional[list[int]] = None # Tokens IDs currently being processed (initial + generated)
remaining_prompt_ids: list[int] = field(default_factory=list) # For split requests, prefill left to process
static_outputs: list[int] = field(default_factory=list) # Generated tokens
allocated_blocks: int = 0 # Number of blocks allocated to the request
position_offset: int = 0 # Current position in the sequence for position_ids
_status: RequestStatus = RequestStatus.PENDING # Status of the request, hidden behind a property
max_new_tokens: int = 20 # Maximum number of new tokens to generate
eos_token_id: int = -1 # ID of the end-of-sequence token
created_time: float = field(default_factory=time.time) # Time the request was created
error: Optional[str] = None # Error message if the request failed
lifespan: tuple[float, float] = (-1, -1) # (time request was no longer pending, time request finished)
@property
def status(self) -> RequestStatus:
return self._status
@status.setter
def status(self, value: RequestStatus):
if self._status == RequestStatus.PENDING:
self.lifespan = (time.time(), -1)
elif value == RequestStatus.FINISHED:
self.lifespan = (self.lifespan[0], time.time())
self.log_end_of_request()
self._status = value
def log_end_of_request(self):
prefill_len = len(self.full_prompt_ids)
decode_len = self.generated_len()
start_time = self.lifespan[0] - self.created_time
end_time = self.lifespan[1] - self.created_time
logger.info(
f"Request {self.request_id} finished: {prefill_len = } {decode_len = } {start_time = } {end_time = }"
)
def current_len(self) -> int:
"""Get the current length of the sequence (prompt + generated tokens)."""
return self.position_offset
def generated_len(self) -> int:
"""Get the number of tokens generated so far."""
return len(self.static_outputs)
# TODO: this logic seems one token off, check it out
@traced
def update_with_token(self, token_id: int) -> bool:
"""Update the request with a newly generated token and check for completion.
Args:
token_id: The token ID to add to the output sequence
Returns:
bool: True if the request is now complete, False otherwise
"""
# Only update if we're in decoding state
if self.status != RequestStatus.DECODING:
return False
is_eos = token_id == self.eos_token_id and self.eos_token_id != -1
is_max_len = self.generated_len() >= self.max_new_tokens
# Only add the token if we're not finishing due to max length
# (EOS tokens should still be added to the output)
if not (is_max_len and not is_eos):
self.static_outputs.extend([token_id])
if is_eos or is_max_len:
self.status = RequestStatus.FINISHED
return True
return False
def __repr__(self):
msg = [
f"request_id={self.request_id}",
f"status={self._status}",
f"out_tokens={self.generated_len()}",
f"query_length={len(self.prompt_ids)}",
f"remaining_tokens={len(self.remaining_prompt_ids)}",
f"kv_length={self.position_offset}",
f"full_prompt_length={len(self.full_prompt_ids)}",
f"allocated_blocks={self.allocated_blocks}",
f"generated_tokens={self.static_outputs}",
]
return "RequestState(\n\t" + ",\n\t".join(msg) + "\n)"
def to_generation_output(self):
"""Convert the request state to a GenerationOutput object."""
return GenerationOutput(
request_id=self.request_id,
prompt_ids=self.full_prompt_ids,
status=self.status,
generated_tokens=self.static_outputs,
logprobs=[],
error=self.error,
)
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