mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2025-09-06 04:30:03 +00:00
roll back ktransformers backend, add max_tokens, max_completion_tokens param
This commit is contained in:
qiyuxinlin
parent
a1162eea01
commit
03a65d6bea
10 changed files with 144 additions and 161 deletions
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@ -207,7 +207,7 @@ async def chat_completion(request: Request, create: ChatCompletionCreate):
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"<tools▁end>":"<｜tool▁calls▁end｜>"
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}
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# Use check_client_connected for early stopping
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async for res in interface.inference(input_message, id, create.temperature, create.top_p):
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async for res in interface.inference(input_message, id, create.temperature, create.top_p, create.max_tokens, create.max_completion_tokens):
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if isinstance(res, RawUsage):
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# Final return on utilization
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raw_usage = res
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@ -371,7 +371,7 @@ async def chat_completion(request: Request, create: ChatCompletionCreate):
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"<tool▁end>":"<｜tool▁call▁end｜>",
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"<tools▁end>":"<｜tool▁calls▁end｜>"
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}
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async for res in interface.inference(input_message, id, create.temperature, create.top_p):
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async for res in interface.inference(input_message, id, create.temperature, create.top_p, create.max_tokens, create.max_completion_tokens):
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if isinstance(res, RawUsage):
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raw_usage = res
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usage = CompletionUsage(
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@ -11,7 +11,7 @@ from ktransformers.server.schemas.endpoints.chat import RawUsage
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router = APIRouter()
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@router.post("/completions",tags=['openai'])
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async def create_completion(request:Request,create:CompletionCreate):
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async def create_completion(request:Request, create:CompletionCreate):
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id = str(uuid4())
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interface = get_interface()
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@ -20,7 +20,7 @@ async def create_completion(request:Request,create:CompletionCreate):
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if create.stream:
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async def inner():
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async for res in interface.inference(create.prompt,id,create.temperature,create.top_p):
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async for res in interface.inference(create.prompt, id, create.temperature, create.top_p, create.max_tokens, create.max_completion_tokens):
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if isinstance(res, RawUsage):
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raw_usage = res
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else:
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@ -32,7 +32,7 @@ async def create_completion(request:Request,create:CompletionCreate):
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return stream_response(request,inner())
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else:
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comp = CompletionObject(id=id,object='text_completion',created=int(time()))
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async for res in interface.inference(create.prompt,id,create.temperature,create.top_p):
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async for res in interface.inference(create.prompt,id,create.temperature,create.top_p, create.max_tokens, create.max_completion_tokens):
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if isinstance(res, RawUsage):
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raw_usage = res
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else:
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@ -80,6 +80,7 @@ def fill_generated_tokens(query_updates: list[sched_ext.QueryUpdate], generated_
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query_updates[i].generated_token = generated_tokens[i].item()
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if not query_manager.query_map[query_updates[i].id].is_prefill:
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pos = query_updates[i].active_position
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if pos < query_manager.query_map[query_updates[i].id].max_length:
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query_manager.query_map[query_updates[i].id].query_tokens[pos] = generated_tokens[i]
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def report_last_time_performance(profiler: Profiler):
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@ -314,19 +315,26 @@ class BalanceServeInterface(BackendInterfaceBase):
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start_event.wait()
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def get_sampling_params(self, temperature: Optional[float] = None, top_p: Optional[float] = None) -> tuple[float, float]:
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def get_params(self, temperature: Optional[float] = None, top_p: Optional[float] = None,
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max_tokens: Optional[float] = None, max_completion_tokens: Optional[float] = None) -> tuple[float, float]:
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"""Get sampling parameters and handle default values and edge cases"""
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if max_tokens is not None:
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max_completion_tokens = max_tokens
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if max_completion_tokens is None:
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max_completion_tokens = self.args.max_new_tokens
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else:
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max_completion_tokens = min(self.args.max_new_tokens, max_completion_tokens)
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if temperature is None:
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temperature = Config().temperature
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temperature = self.args.temperature
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if top_p is None:
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top_p = Config().top_p
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top_p = self.args.top_p
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if temperature == 0:
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temperature = 0.0001
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if top_p == 0:
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top_p = 0.0001
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return temperature, top_p
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return temperature, top_p, max_completion_tokens
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def run_queue_proxy(self):
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loop = asyncio.new_event_loop()
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@ -380,7 +388,8 @@ class BalanceServeInterface(BackendInterfaceBase):
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logger.debug(f"get input ids of shape {input_ids.shape}")
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return input_ids
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async def inference(self, local_messages, thread_id: str, temperature: Optional[float] = None, top_p: Optional[float] = None):
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async def inference(self, local_messages, thread_id: str, temperature: Optional[float] = None, top_p: Optional[float] = None,
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max_tokens: Optional[float] = None, max_completion_tokens: Optional[float] = None):
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profiler = Profiler()
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profiler.create_and_start_timer("tokenize")
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@ -409,17 +418,17 @@ class BalanceServeInterface(BackendInterfaceBase):
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stop_criteria = [self.tokenizer.encode(self.tokenizer.eos_token, add_special_tokens=False),self.tokenizer.encode("<|im_end|>")]
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query_add.stop_criteria = stop_criteria
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temperature, top_p = self.get_sampling_params(temperature, top_p)
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temperature, top_p, max_new_tokens = self.get_params(temperature, top_p, max_tokens, max_completion_tokens)
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query_add.sample_options.temperature = temperature
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query_add.sample_options.top_p = top_p
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query_add.estimated_length = min(self.args.cache_lens, query_length+self.args.max_new_tokens)
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query_add.estimated_length = min(self.args.cache_lens, query_length+max_new_tokens)
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if query_add.estimated_length < query_add.query_length:
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raise Exception(f'query too long: estimated_length={query_add.estimated_length} < query_length={query_add.query_length}')
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query_id = self.sched_client.add_query(query_add)
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queue = asyncio.Queue(maxsize=self.args.max_new_tokens)
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queue = asyncio.Queue(maxsize=max_new_tokens)
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self.queue_map[query_id] = queue
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self.thread_map[thread_id] = query_id
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is_first_token = True
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@ -439,7 +448,7 @@ class BalanceServeInterface(BackendInterfaceBase):
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profiler.pause_timer("decode")
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report_last_time_performance(profiler)
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yield self.streamer.end(), None
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if profiler.get_counter('decode') >= self.args.max_new_tokens - 1:
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if profiler.get_counter('decode') >= max_new_tokens - 1:
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yield "", "length"
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else:
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yield "", "stop"
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@ -129,8 +129,14 @@ class KTransformersInterface(TransformersInterface):
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@torch.no_grad
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def prefill(self, input_ids: torch.Tensor, is_new: bool, temperature: Optional[float], top_p: Optional[float]):
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def prefill(self, input_ids: torch.Tensor, is_new: bool, temperature: Optional[float] = None, top_p: Optional[float] = None, max_tokens: Optional[float] = None, max_completion_tokens: Optional[float] = None):
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input_ids_length = input_ids.shape[-1]
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if max_tokens is not None:
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max_completion_tokens = max_tokens
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if max_completion_tokens is None:
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max_new_tokens = self.args.max_new_tokens
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else:
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max_new_tokens = min(self.args.max_new_tokens, max_completion_tokens)
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if(input_ids_length >= self.args.cache_lens):
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logger.warning(f"input_ids_length {input_ids_length} > cache_lens {self.args.cache_lens}")
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self.seq_length = input_ids_length
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@ -147,7 +153,7 @@ class KTransformersInterface(TransformersInterface):
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if getattr(self, 'generated_ids', None) is None:
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self.generated_ids = torch.zeros(
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self.args.batch_size,
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input_ids.shape[-1] + self.args.max_new_tokens + 1,
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input_ids.shape[-1] + max_new_tokens + 1,
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dtype=torch.int,
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device=self.args.device,
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)
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@ -174,7 +180,7 @@ class KTransformersInterface(TransformersInterface):
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former_seq_length = self.seq_length
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self.seq_length += input_ids_length
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expected_length = min(self.seq_length + self.args.max_new_tokens + 1, self.args.cache_lens)
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expected_length = min(self.seq_length + max_new_tokens + 1, self.args.cache_lens)
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delta_length = expected_length - self.generated_ids.shape[-1]
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if delta_length > 0:
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new_generate_ids = torch.zeros(
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@ -222,6 +228,7 @@ class KTransformersInterface(TransformersInterface):
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MLAWrapperSingleton.reset_buffer()
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self.prepare_logits_wrapper(input_ids, device, temperature, top_p)
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next_token = self.logits_to_token(logits[0, -1, :])
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self.max_new_tokens = min(max_new_tokens, self.args.cache_lens - self.seq_length) - 1
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yield self.append_new_tokens(next_token)
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@property
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@ -229,9 +236,9 @@ class KTransformersInterface(TransformersInterface):
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device = self.device_map.get("blk.0.self_attn", {}).get("generate_device", "cuda:0")
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return torch.tensor([self.seq_length - 1], device=device)
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async def inference(self, local_messages, thread_id: str, temperature: Optional[float] = None, top_p: Optional[float] = None, tools: Optional[List] = None):
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async def inference(self, local_messages, thread_id: str, temperature: Optional[float] = None, top_p: Optional[float] = None, max_tokens: Optional[float] = None, max_completion_tokens: Optional[float] = None):
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async with self._infer_lock:
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async for v in super().inference(local_messages, thread_id, temperature, top_p, tools):
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async for v in super().inference(local_messages, thread_id, temperature, top_p, max_tokens, max_completion_tokens):
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yield v
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# return this inference raw usage
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@ -262,10 +262,15 @@ class TransformersInterface(BackendInterfaceBase):
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return self.logits_to_token(logits)
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@torch.no_grad
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def prefill(self, input_ids: torch.Tensor, is_new: bool, temperature: Optional[float] = None, top_p: Optional[float] = None):
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def prefill(self, input_ids: torch.Tensor, is_new: bool, temperature: Optional[float] = None, top_p: Optional[float] = None, max_tokens: Optional[float] = None, max_completion_tokens: Optional[float] = None):
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input_ids_length = input_ids.shape[-1]
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logger.debug(f"input_ids: {input_ids.shape}")
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if max_tokens is not None:
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max_completion_tokens = max_tokens
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if max_completion_tokens is None:
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max_new_tokens = self.args.max_new_tokens
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else:
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max_new_tokens = min(self.args.max_new_tokens, max_completion_tokens)
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if is_new:
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self.ever_generated_ids.clear()
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same_prefix = 0
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@ -274,7 +279,7 @@ class TransformersInterface(BackendInterfaceBase):
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if getattr(self, 'generated_ids', None) is None:
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self.generated_ids = torch.zeros(
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self.args.batch_size,
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input_ids.shape[-1] + self.args.max_new_tokens + 1,
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input_ids.shape[-1] + max_new_tokens + 1,
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dtype=torch.int,
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device=self.args.device,
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)
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@ -301,7 +306,7 @@ class TransformersInterface(BackendInterfaceBase):
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logger.debug(f"generate_ids: {self.generated_ids.shape}")
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former_seq_length = self.seq_length
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self.seq_length += input_ids_length
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expected_length = self.seq_length + self.args.max_new_tokens + 1
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expected_length = self.seq_length + max_new_tokens + 1
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delta_length = expected_length - self.generated_ids.shape[-1]
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if delta_length > 0:
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new_generate_ids = torch.zeros(
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@ -330,17 +335,16 @@ class TransformersInterface(BackendInterfaceBase):
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self.prepare_logits_wrapper(input_ids, device, temperature, top_p)
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next_token = self.logits_to_token(logits[0, -1, :])
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self.max_new_tokens = min(max_new_tokens, self.args.cache_lens - self.seq_length) - 1
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yield self.append_new_tokens(next_token)
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@torch.no_grad
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def generate(self):
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self.max_new_tokens = min(self.args.max_new_tokens, self.args.cache_lens - self.seq_length) - 1
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logger.info(f"args.max_new_tokens: {self.args.max_new_tokens}, cache_lens: {self.args.cache_lens}, seq_length: {self.seq_length}")
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if(self.max_new_tokens <= 0):
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logger.warning("max_new_tokens is less than 0")
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yield self.streamer.end(), "length"
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return
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logger.info(f"max_new_tokens: {self.max_new_tokens}")
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self.profiler.set_counter("decode", 0)
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for i in range(1, self.max_new_tokens):
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@ -378,17 +382,15 @@ class TransformersInterface(BackendInterfaceBase):
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self.last_request_id = thread_id
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return True
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async def inference(self, local_messages, thread_id: str, temperature: Optional[float] = None, top_p: Optional[float] = None, tools: Optional[List] = None):
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async def inference(self, local_messages, thread_id: str, temperature: Optional[float] = None, top_p: Optional[float] = None, max_tokens: Optional[float] = None, max_completion_tokens: Optional[float] = None):
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self.streamer.reset()
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self.profiler.create_and_start_timer("tokenize")
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# Check if tools are present
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has_tools = tools is not None and len(tools) > 0
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if isinstance(local_messages, List):
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input_ids = self.format_and_tokenize_input_ids(thread_id, local_messages)
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elif isinstance(local_messages, str):
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#local_messages = local_messages[0]['content']
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input_ids = self.tokenize_prompt(local_messages)
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#input_ids = torch.tensor([[6366]], device=input_ids.device)
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else:
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raise ValueError("local_messages should be List or str")
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@ -399,6 +401,7 @@ class TransformersInterface(BackendInterfaceBase):
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)
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self.profiler.pause_timer("tokenize")
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self.profiler.create_and_start_timer("prefill")
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if Config().user_force_think:
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@ -406,119 +409,18 @@ class TransformersInterface(BackendInterfaceBase):
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print(think, end="",flush=True)
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yield think, None
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for t in self.prefill(input_ids, self.check_is_new(thread_id), temperature, top_p):
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for t in self.prefill(input_ids, self.check_is_new(thread_id), temperature, top_p, max_tokens, max_completion_tokens):
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# output think token after prefill done
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if t is not None:
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print(t, end="",flush=True)
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yield t, None
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self.profiler.pause_timer("prefill")
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self.profiler.create_and_start_timer("decode")
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# Handle tool calling
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if has_tools:
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# Start collecting tokens until we detect a tool call
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collected_tokens = ""
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is_collecting_tool_call = False
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is_function_name_collected = False
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function_name = ""
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collected_arguments = ""
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brackets_count = 0
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for t, finish_reason in self.generate():
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if t is not None:
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print(t, end="", flush=True)
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collected_tokens += t
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# Check if we're starting a tool call
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if not is_collecting_tool_call and any(keyword in collected_tokens.lower() for keyword in ['"function"', 'function', 'tool_call', 'tool call']):
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is_collecting_tool_call = True
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# Generate a unique tool call ID
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tool_call_id = f"call_{uuid.uuid4().hex.replace('-', '')}"
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# Send first tool call info
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if len(tools) > 0 and hasattr(tools[0], 'function') and hasattr(tools[0].function, 'name'):
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# If tools are provided, use the first one's name
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recommended_function = tools[0].function.name
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else:
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# Otherwise try to extract from context
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function_match = re.search(r'"name":\s*"([^"]+)"', collected_tokens)
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recommended_function = function_match.group(1) if function_match else ""
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yield {
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'tool_call': {
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'id': tool_call_id,
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'type': 'function',
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'index': 0,
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'function': {
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'name': recommended_function,
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'arguments': ""
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}
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},
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'first_chunk': True
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}
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# Extract function name if we're collecting tool call
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if is_collecting_tool_call and not is_function_name_collected:
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name_match = re.search(r'"name":\s*"([^"]+)"', collected_tokens)
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if name_match:
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function_name = name_match.group(1)
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is_function_name_collected = True
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# Track argument collection
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if is_collecting_tool_call and is_function_name_collected:
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args_position = collected_tokens.find('"arguments"')
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if args_position > -1:
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# Find the start of the JSON object after "arguments":
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json_start = collected_tokens.find('{', args_position)
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if json_start > -1:
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for i in range(json_start, len(collected_tokens)):
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char = collected_tokens[i]
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collected_arguments += char
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if char == '{':
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brackets_count += 1
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elif char == '}':
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brackets_count -= 1
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# Check if we've completed the arguments JSON
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if brackets_count == 0:
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# Send argument chunk
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yield {
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'tool_call': {
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'id': tool_call_id,
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'type': 'function',
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'function': {
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'name': function_name,
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'arguments': collected_arguments
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}
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},
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'argument_chunk': collected_arguments,
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'last_chunk': True,
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'prompt_tokens': 176,
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'completion_tokens': 20
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}
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# Reset for next potential tool call
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collected_tokens = ""
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is_collecting_tool_call = False
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is_function_name_collected = False
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function_name = ""
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collected_arguments = ""
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brackets_count = 0
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break
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# Handle finish reason
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||||
if finish_reason is not None:
|
||||
yield "", finish_reason
|
||||
|
||||
print("")
|
||||
else:
|
||||
# Regular text generation (no tools)
|
||||
for t, finish_reason in self.generate():
|
||||
if t is not None:
|
||||
print(t, end="",flush=True)
|
||||
yield t, finish_reason
|
||||
print("")
|
||||
|
||||
self.profiler.pause_timer("decode")
|
||||
self.report_last_time_performance()
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
from typing import List, Optional, Union, Dict, Any
|
||||
from typing_extensions import Literal
|
||||
from enum import Enum
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from ktransformers.server.schemas.base import Object
|
||||
|
|
@ -11,7 +10,6 @@ from openai.types.chat.chat_completion_chunk import Choice
|
|||
|
||||
from uuid import uuid4
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
class Role(Enum):
|
||||
system = 'system'
|
||||
|
|
@ -67,6 +65,8 @@ class ChatCompletionCreate(BaseModel):
|
|||
stream_options: Optional[Dict[str, Any]] = None
|
||||
frequency_penalty: float = 0
|
||||
presence_penalty: float = 0
|
||||
max_tokens: Optional[int] = Field(default=50)
|
||||
max_completion_tokens: Optional[int] = Field(default=50)
|
||||
|
||||
def get_tokenizer_messages(self):
|
||||
return [m.to_tokenizer_message() for m in self.messages]
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
from typing import List, Optional
|
||||
from enum import Enum
|
||||
|
||||
from pydantic import BaseModel
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from ..base import Object
|
||||
|
||||
|
|
@ -9,8 +8,10 @@ class CompletionCreate(BaseModel):
|
|||
model: str
|
||||
prompt: str | List[str]
|
||||
stream: bool = False
|
||||
temperature: Optional[float] = None
|
||||
top_p: Optional[float] = None
|
||||
temperature: Optional[float] = Field(default=0.6)
|
||||
top_p: Optional[float] = Field(default=1)
|
||||
max_tokens: Optional[int] = Field(default=50)
|
||||
max_completion_tokens: Optional[int] = Field(default=50)
|
||||
|
||||
def get_tokenizer_messages(self):
|
||||
if isinstance(self.prompt,List):
|
||||
|
|
|
|||
45
ktransformers/tests/function_call_test.py
Normal file
45
ktransformers/tests/function_call_test.py
Normal file
|
|
@ -0,0 +1,45 @@
|
|||
from openai import OpenAI
|
||||
|
||||
def send_messages(messages):
|
||||
response = client.chat.completions.create(
|
||||
model="deepseek-chat",
|
||||
messages=messages,
|
||||
tools=tools
|
||||
)
|
||||
return response.choices[0].message
|
||||
|
||||
client = OpenAI(
|
||||
api_key="placeholder",
|
||||
base_url="http://0.0.0.0:10002/v1",
|
||||
)
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get weather of an location, the user shoud supply a location first",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
}
|
||||
},
|
||||
"required": ["location"]
|
||||
},
|
||||
}
|
||||
},
|
||||
]
|
||||
|
||||
messages = [{"role": "user", "content": "How's the weather in Hangzhou?"}]
|
||||
message = send_messages(messages)
|
||||
print(f"User>\t {messages[0]['content']}")
|
||||
print(message)
|
||||
tool = message.tool_calls[0]
|
||||
messages.append(message)
|
||||
|
||||
messages.append({"role": "tool", "tool_call_id": tool.id, "content": "24℃"})
|
||||
message = send_messages(messages)
|
||||
print(f"Model>\t {message.content}")
|
||||
|
|
@ -15,18 +15,9 @@ SERVER_URL = "http://localhost:10002/v1/chat/completions"
|
|||
bf_list = [1]
|
||||
decodesz_list = [128]
|
||||
prompt_list = ['Please elaborate on modern world history.', 'Please introduce Harry Potter.', 'I want to learn Python. Please give me some advice.', 'Please tell me a joke ']
|
||||
async def fetch_event_stream(session, request_id):
|
||||
async def fetch_event_stream(session, payload, request_id):
|
||||
try:
|
||||
payload = {
|
||||
"messages": [
|
||||
{"role": "system", "content": ""},
|
||||
{"role": "user", "content": prompt_list[request_id]}
|
||||
],
|
||||
"model": "DeepSeek-V3",
|
||||
"temperature": 0.3,
|
||||
"top_p": 1.0,
|
||||
"stream": True # 开启流式输出
|
||||
}
|
||||
|
||||
|
||||
headers = {
|
||||
'accept': 'application/json',
|
||||
|
|
@ -103,7 +94,35 @@ async def fetch_event_stream(session, request_id):
|
|||
|
||||
async def main(prompt_id):
|
||||
async with aiohttp.ClientSession() as session:
|
||||
tasks = [fetch_event_stream(session, prompt_id)]
|
||||
payload = {
|
||||
"messages": [
|
||||
{"role": "system", "content": ""},
|
||||
{"role": "user", "content": prompt_list[prompt_id]}
|
||||
],
|
||||
"model": "DeepSeek-V3",
|
||||
"stream": True,
|
||||
"max_completion_tokens": 2,
|
||||
# "temperature": 0.3,
|
||||
# "top_p": 1.0,
|
||||
# "max_tokens" : 20,
|
||||
}
|
||||
tasks = [fetch_event_stream(session, payload, prompt_id)]
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
payload["temperature"] = 0.3
|
||||
tasks = [fetch_event_stream(session, payload, prompt_id)]
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
payload["top_p"] = 1
|
||||
tasks = [fetch_event_stream(session, payload, prompt_id)]
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
payload["max_tokens"] = 200
|
||||
tasks = [fetch_event_stream(session, payload, prompt_id)]
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
payload["stream"] = False
|
||||
tasks = [fetch_event_stream(session, payload, prompt_id)]
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
2
third_party/llamafile/iqk_mul_mat.inc
vendored
2
third_party/llamafile/iqk_mul_mat.inc
vendored
|
|
@ -3326,7 +3326,7 @@ bool MulMat::set_mul_mat(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
|
|||
|
||||
default:
|
||||
{
|
||||
printf("case:%d",typeA);
|
||||
// printf("case:%d",typeA);
|
||||
return false;
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue