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#
# 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 asyncio
import os
import platform
from collections.abc import AsyncGenerator
from threading import Thread
from typing import TYPE_CHECKING, Any, Optional
import torch
from typing_extensions import override
from ..data import get_template_and_fix_tokenizer
from ..extras import logging
from ..extras.constants import EngineName
from ..model import load_model, load_tokenizer
from .base_engine import BaseEngine, Response
if TYPE_CHECKING:
from transformers import PreTrainedTokenizer
from trl import PreTrainedModelWrapper
from ..data.mm_plugin import AudioInput, ImageInput, VideoInput
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
from ktransformers.operators.flashinfer_wrapper import flashinfer_enabled
from ktransformers.server.config.config import Config
from ktransformers.util.utils import (
get_compute_capability,
prefill_and_generate_capture,
)
from ktransformers.util.vendors import GPUVendor, device_manager
logger = logging.get_logger(__name__)
class KTransformersEngine(BaseEngine):
def __init__(
self,
model_args: "ModelArguments",
data_args: "DataArguments",
finetuning_args: "FinetuningArguments",
generating_args: "GeneratingArguments",
) -> None:
self.name = EngineName.KT
self.can_generate = finetuning_args.stage == "sft"
tok_mod = load_tokenizer(model_args)
self.tokenizer = tok_mod["tokenizer"]
self.tokenizer.padding_side = "left" if self.can_generate else "right"
self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args)
self.model = load_model(
self.tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=(not self.can_generate)
)
self.generating_args = generating_args.to_dict()
self.max_new_tokens = model_args.kt_maxlen
self.use_cuda_graph = model_args.kt_use_cuda_graph
self.mode = model_args.kt_mode
self.force_think = model_args.kt_force_think
self.chunk_size = model_args.chunk_size
try:
asyncio.get_event_loop()
except RuntimeError:
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
self.semaphore = asyncio.Semaphore(int(os.getenv("MAX_CONCURRENT", "1")))
@staticmethod
@torch.inference_mode()
def _get_scores(
model: "PreTrainedModelWrapper",
tokenizer: "PreTrainedTokenizer",
batch_input: list[str],
input_kwargs: Optional[dict[str, Any]] = {},
) -> list[float]:
max_length: Optional[int] = input_kwargs.pop("max_length", None)
device = getattr(model.pretrained_model, "device", "cuda")
inputs = tokenizer(
batch_input,
padding=True,
truncation=True,
max_length=max_length or getattr(model.config, "max_position_embeddings", 1024),
return_tensors="pt",
add_special_tokens=False,
).to(device)
values: torch.Tensor = model(**inputs, return_dict=True, use_cache=False)[-1]
scores = values.gather(dim=-1, index=(inputs["attention_mask"].sum(dim=-1, keepdim=True) - 1))
return scores
async def _generate(
self,
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
paired = messages + [{"role": "assistant", "content": ""}]
prompt_ids, _ = self.template.encode_oneturn(self.tokenizer, paired, system, tools)
prompt_len = len(prompt_ids)
max_length: Optional[int] = input_kwargs.pop("max_length", None)
max_new_tokens: Optional[int] = input_kwargs.pop("max_new_tokens", None)
if "max_new_tokens" in self.generating_args:
max_tokens = int(self.generating_args["max_new_tokens"])
elif "max_length" in self.generating_args:
gl = int(self.generating_args["max_length"])
max_tokens = gl - prompt_len if gl > prompt_len else 1
else:
max_tokens = self.max_new_tokens or 256
if max_length is not None:
max_tokens = max(max_length - prompt_len, 1)
if max_new_tokens is not None:
max_tokens = int(max_new_tokens)
max_tokens = max(1, int(max_tokens))
if self.mode == "long_context":
max_len_cfg = Config().long_context_config["max_seq_len"]
need = prompt_len + max_tokens
assert max_len_cfg > need, f"please set max_seq_len > {need} in ~/.ktransformers/config.yaml"
device = next(self.model.parameters()).device
input_tensor = torch.tensor([prompt_ids], dtype=torch.long, device=device)
if self.force_think:
think = torch.tensor(
[self.tokenizer.encode("<think>\n", add_special_tokens=False)], dtype=torch.long, device=device
)
input_tensor = torch.cat([input_tensor, think], dim=1)
use_flashinfer = (
platform.system() != "Windows"
and getattr(self.model.config, "architectures", [""])[0]
in {"DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"}
and flashinfer_enabled
and get_compute_capability() >= 8
and device_manager.gpu_vendor == GPUVendor.NVIDIA
)
def make_gen():
if use_flashinfer:
return prefill_and_generate_capture(
self.model,
self.tokenizer,
input_tensor,
max_tokens,
self.use_cuda_graph,
mode=self.mode,
force_think=self.force_think,
chunk_size=self.chunk_size,
use_flashinfer_mla=True,
num_heads=self.model.config.num_attention_heads,
head_dim_ckv=getattr(self.model.config, "kv_lora_rank", 0),
head_dim_kpe=getattr(self.model.config, "qk_rope_head_dim", 0),
q_head_dim=getattr(self.model.config, "qk_rope_head_dim", 0)
+ getattr(self.model.config, "qk_nope_head_dim", 0),
echo_stream=False,
)
else:
return prefill_and_generate_capture(
self.model,
self.tokenizer,
input_tensor,
max_tokens,
self.use_cuda_graph,
mode=self.mode,
force_think=self.force_think,
chunk_size=self.chunk_size,
echo_stream=False,
)
loop = asyncio.get_running_loop()
q: asyncio.Queue[Optional[str]] = asyncio.Queue()
def producer():
try:
gen = make_gen()
if hasattr(gen, "__aiter__"):
async def drain_async():
async for t in gen:
loop.call_soon_threadsafe(q.put_nowait, t if isinstance(t, str) else str(t))
asyncio.run(drain_async())
elif hasattr(gen, "__iter__"):
for t in gen:
loop.call_soon_threadsafe(q.put_nowait, t if isinstance(t, str) else str(t))
else:
loop.call_soon_threadsafe(q.put_nowait, gen if isinstance(gen, str) else str(gen))
finally:
loop.call_soon_threadsafe(q.put_nowait, None)
Thread(target=producer, daemon=True).start()
while True:
item = await q.get()
if item is None:
break
yield item
@override
async def chat(
self,
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> list["Response"]:
if not self.can_generate:
raise ValueError("The current model does not support `chat`.")
async with self.semaphore:
produced = ""
final_text = ""
async for t in self._generate(messages, system, tools, **input_kwargs):
delta = t
produced = produced + delta
if delta:
final_text += delta
prompt_ids, _ = self.template.encode_oneturn(
self.tokenizer, messages + [{"role": "assistant", "content": ""}], system, tools
)
return [
Response(
response_text=final_text,
response_length=len(self.tokenizer.encode(final_text, add_special_tokens=False)),
prompt_length=len(prompt_ids),
finish_reason="stop",
)
]
@override
async def stream_chat(
self,
messages: list[dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
images: Optional[list["ImageInput"]] = None,
videos: Optional[list["VideoInput"]] = None,
audios: Optional[list["AudioInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
if not self.can_generate:
raise ValueError("The current model does not support `stream_chat`.")
async with self.semaphore:
produced = ""
async for t in self._generate(messages, system, tools, **input_kwargs):
delta = t[len(produced) :] if t.startswith(produced) else t
produced = t
if delta:
yield delta
@override
async def get_scores(
self,
batch_input: list[str],
**input_kwargs,
) -> list[float]:
if self.can_generate:
raise ValueError("Cannot get scores using an auto-regressive model.")
args = (self.model, self.tokenizer, batch_input, input_kwargs)
async with self.semaphore:
return await asyncio.to_thread(self._get_scores, *args)
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