364 lines
10 KiB
Python
364 lines
10 KiB
Python
import gc
|
|
import json
|
|
import random
|
|
from pathlib import Path
|
|
from time import time
|
|
|
|
from exllamav2 import (
|
|
ExLlamaV2,
|
|
ExLlamaV2Cache,
|
|
ExLlamaV2Cache_Q4,
|
|
ExLlamaV2Cache_Q6,
|
|
ExLlamaV2Cache_Q8,
|
|
ExLlamaV2Config,
|
|
ExLlamaV2Tokenizer,
|
|
)
|
|
from exllamav2.generator import (
|
|
ExLlamaV2DynamicGenerator,
|
|
ExLlamaV2DynamicJob,
|
|
ExLlamaV2Sampler,
|
|
)
|
|
from jinja2 import Template
|
|
|
|
from comfy.model_management import soft_empty_cache, unload_all_models
|
|
from comfy.utils import ProgressBar
|
|
from folder_paths import add_model_folder_path, get_folder_paths, models_dir
|
|
|
|
_CATEGORY = "Zuellni/ExLlama"
|
|
_MAPPING = "ZuellniExLlama"
|
|
|
|
|
|
class Loader:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
add_model_folder_path("llm", str(Path(models_dir) / "llm"))
|
|
|
|
for folder in get_folder_paths("llm"):
|
|
for path in Path(folder).rglob("*/"):
|
|
if (path / "config.json").is_file():
|
|
parent = path.relative_to(folder).parent
|
|
cls._MODELS[str(parent / path.name)] = path
|
|
|
|
models = list(cls._MODELS.keys())
|
|
caches = list(cls._CACHES.keys())
|
|
default = models[0] if models else None
|
|
|
|
return {
|
|
"required": {
|
|
"model": (models, {"default": default}),
|
|
"cache_bits": (caches, {"default": 4}),
|
|
"fast_tensors": ("BOOLEAN", {"default": True}),
|
|
"flash_attention": ("BOOLEAN", {"default": True}),
|
|
"max_seq_len": ("INT", {"default": 2048, "max": 2**20, "step": 256}),
|
|
},
|
|
}
|
|
|
|
_CACHES = {
|
|
4: lambda m: ExLlamaV2Cache_Q4(m, lazy=True),
|
|
6: lambda m: ExLlamaV2Cache_Q6(m, lazy=True),
|
|
8: lambda m: ExLlamaV2Cache_Q8(m, lazy=True),
|
|
16: lambda m: ExLlamaV2Cache(m, lazy=True),
|
|
}
|
|
_MODELS = {}
|
|
CATEGORY = _CATEGORY
|
|
FUNCTION = "setup"
|
|
RETURN_NAMES = ("MODEL",)
|
|
RETURN_TYPES = ("EXL_MODEL",)
|
|
|
|
def setup(self, model, cache_bits, fast_tensors, flash_attention, max_seq_len):
|
|
self.unload()
|
|
self.cache_bits = cache_bits
|
|
|
|
self.config = ExLlamaV2Config(__class__._MODELS[model])
|
|
self.config.fasttensors = fast_tensors
|
|
self.config.no_flash_attn = not flash_attention
|
|
|
|
if max_seq_len:
|
|
self.config.max_seq_len = max_seq_len
|
|
|
|
if self.config.max_input_len > max_seq_len:
|
|
self.config.max_input_len = max_seq_len
|
|
self.config.max_attention_len = max_seq_len**2
|
|
|
|
self.tokenizer = ExLlamaV2Tokenizer(self.config)
|
|
return (self,)
|
|
|
|
def load(self):
|
|
if (
|
|
hasattr(self, "model")
|
|
and hasattr(self, "cache")
|
|
and hasattr(self, "generator")
|
|
and self.model
|
|
and self.cache
|
|
and self.generator
|
|
):
|
|
return
|
|
|
|
self.model = ExLlamaV2(self.config)
|
|
self.cache = __class__._CACHES[self.cache_bits](self.model)
|
|
|
|
progress = ProgressBar(len(self.model.modules))
|
|
self.model.load_autosplit(self.cache, callback=lambda _, __: progress.update(1))
|
|
|
|
self.generator = ExLlamaV2DynamicGenerator(
|
|
model=self.model,
|
|
cache=self.cache,
|
|
tokenizer=self.tokenizer,
|
|
paged=not self.config.no_flash_attn,
|
|
)
|
|
|
|
def unload(self):
|
|
if hasattr(self, "model") and self.model:
|
|
self.model.unload()
|
|
|
|
self.model = None
|
|
self.cache = None
|
|
self.generator = None
|
|
|
|
gc.collect()
|
|
soft_empty_cache()
|
|
|
|
|
|
class Formatter:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"model": ("EXL_MODEL",),
|
|
"messages": ("EXL_MESSAGES",),
|
|
"add_assistant_role": ("BOOLEAN", {"default": True}),
|
|
},
|
|
}
|
|
|
|
CATEGORY = _CATEGORY
|
|
FUNCTION = "format"
|
|
RETURN_NAMES = ("TEXT",)
|
|
RETURN_TYPES = ("STRING",)
|
|
|
|
def raise_exception(self, message):
|
|
raise Exception(message)
|
|
|
|
def render(self, template, messages, add_assistant_role):
|
|
return (
|
|
template.render(
|
|
add_generation_prompt=add_assistant_role,
|
|
raise_exception=self.raise_exception,
|
|
messages=messages,
|
|
bos_token="",
|
|
),
|
|
)
|
|
|
|
def format(self, model, messages, add_assistant_role):
|
|
template = model.tokenizer.tokenizer_config_dict["chat_template"]
|
|
template = Template(template)
|
|
|
|
try:
|
|
return self.render(template, messages, add_assistant_role)
|
|
except:
|
|
system = None
|
|
merged = []
|
|
|
|
for message in messages:
|
|
if message["role"] == "system":
|
|
system = {"role": "user", "content": message["content"]}
|
|
merged.append(system)
|
|
elif system and message["role"] == "user":
|
|
index = merged.index(system)
|
|
merged[index]["content"] += "\n" + message["content"]
|
|
system = None
|
|
else:
|
|
merged.append(message)
|
|
system = None
|
|
|
|
return self.render(template, merged, add_assistant_role)
|
|
|
|
|
|
class Tokenizer:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"model": ("EXL_MODEL",),
|
|
"text": ("STRING", {"forceInput": True}),
|
|
"add_bos_token": ("BOOLEAN", {"default": True}),
|
|
"encode_special_tokens": ("BOOLEAN", {"default": True}),
|
|
},
|
|
}
|
|
|
|
CATEGORY = _CATEGORY
|
|
FUNCTION = "tokenize"
|
|
RETURN_NAMES = ("TOKENS",)
|
|
RETURN_TYPES = ("EXL_TOKENS",)
|
|
|
|
def tokenize(self, model, text, add_bos_token, encode_special_tokens):
|
|
return (
|
|
model.tokenizer.encode(
|
|
text=text,
|
|
add_bos=add_bos_token,
|
|
encode_special_tokens=encode_special_tokens,
|
|
),
|
|
)
|
|
|
|
|
|
class Settings:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"temperature": ("FLOAT", {"default": 1, "max": 10, "step": 0.01}),
|
|
"penalty": ("FLOAT", {"default": 1, "min": 1, "max": 10, "step": 0.01}),
|
|
"top_k": ("INT", {"default": 1, "max": 1000}),
|
|
"top_p": ("FLOAT", {"max": 1, "step": 0.01}),
|
|
"top_a": ("FLOAT", {"max": 1, "step": 0.01}),
|
|
"min_p": ("FLOAT", {"max": 1, "step": 0.01}),
|
|
"tfs": ("FLOAT", {"max": 1, "step": 0.01}),
|
|
"typical": ("FLOAT", {"max": 1, "step": 0.01}),
|
|
"temperature_last": ("BOOLEAN", {"default": True}),
|
|
},
|
|
}
|
|
|
|
CATEGORY = _CATEGORY
|
|
FUNCTION = "set"
|
|
RETURN_NAMES = ("SETTINGS",)
|
|
RETURN_TYPES = ("EXL_SETTINGS",)
|
|
|
|
def set(
|
|
self,
|
|
temperature,
|
|
penalty,
|
|
top_k,
|
|
top_p,
|
|
top_a,
|
|
min_p,
|
|
tfs,
|
|
typical,
|
|
temperature_last,
|
|
):
|
|
settings = ExLlamaV2Sampler.Settings()
|
|
settings.temperature = temperature
|
|
settings.token_repetition_penalty = penalty
|
|
settings.top_k = top_k
|
|
settings.top_p = top_p
|
|
settings.top_a = top_a
|
|
settings.min_p = min_p
|
|
settings.tfs = tfs
|
|
settings.typical = typical
|
|
settings.temperature_last = temperature_last
|
|
return (settings,)
|
|
|
|
|
|
class Generator:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"model": ("EXL_MODEL",),
|
|
"tokens": ("EXL_TOKENS",),
|
|
"unload": ("BOOLEAN", {"default": False}),
|
|
"stop_conditions": ("STRING", {"default": r'"\n"'}),
|
|
"max_tokens": ("INT", {"default": 128, "max": 2**20}),
|
|
"seed": ("INT", {"max": 2**64 - 1}),
|
|
},
|
|
"optional": {"settings": ("EXL_SETTINGS",)},
|
|
"hidden": {"info": "EXTRA_PNGINFO", "id": "UNIQUE_ID"},
|
|
}
|
|
|
|
CATEGORY = _CATEGORY
|
|
FUNCTION = "generate"
|
|
RETURN_NAMES = ("TEXT",)
|
|
RETURN_TYPES = ("STRING",)
|
|
|
|
def generate(
|
|
self,
|
|
model,
|
|
tokens,
|
|
unload,
|
|
stop_conditions,
|
|
max_tokens,
|
|
seed,
|
|
settings=None,
|
|
info=None,
|
|
id=None,
|
|
):
|
|
if unload:
|
|
unload_all_models()
|
|
model.unload()
|
|
|
|
model.load()
|
|
random.seed(seed)
|
|
tokens_len = tokens.shape[-1]
|
|
max_len = model.config.max_seq_len - tokens_len
|
|
stop = [model.tokenizer.eos_token_id]
|
|
|
|
if not max_tokens or max_tokens > max_len:
|
|
max_tokens = max_len
|
|
|
|
if stop_conditions.strip():
|
|
stop_conditions = json.loads(f"[{stop_conditions}]")
|
|
stop.extend(stop_conditions)
|
|
|
|
if not settings:
|
|
settings = ExLlamaV2Sampler.Settings()
|
|
settings.greedy()
|
|
|
|
job = ExLlamaV2DynamicJob(
|
|
input_ids=tokens,
|
|
max_new_tokens=max_tokens,
|
|
stop_conditions=stop,
|
|
gen_settings=settings,
|
|
)
|
|
|
|
progress = ProgressBar(max_tokens)
|
|
model.generator.enqueue(job)
|
|
start = time()
|
|
eos = False
|
|
chunks = []
|
|
count = 0
|
|
|
|
while not eos:
|
|
for response in model.generator.iterate():
|
|
if response["stage"] == "streaming":
|
|
chunk = response.get("text", "")
|
|
eos = response["eos"]
|
|
chunks.append(chunk)
|
|
progress.update(1)
|
|
count += 1
|
|
|
|
output = "".join(chunks).strip()
|
|
total = round(time() - start, 2)
|
|
speed = round(count / total, 2)
|
|
|
|
print(
|
|
f"Output generated in {total} seconds",
|
|
f"({tokens_len} context, {count} tokens, {speed}t/s)",
|
|
)
|
|
|
|
if unload:
|
|
model.unload()
|
|
|
|
if id and info and "workflow" in info:
|
|
nodes = info["workflow"]["nodes"]
|
|
node = next((n for n in nodes if str(n["id"]) == id), None)
|
|
|
|
if node:
|
|
node["widgets_values"] = [output]
|
|
|
|
return (output,)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
f"{_MAPPING}Loader": Loader,
|
|
f"{_MAPPING}Formatter": Formatter,
|
|
f"{_MAPPING}Tokenizer": Tokenizer,
|
|
f"{_MAPPING}Settings": Settings,
|
|
f"{_MAPPING}Generator": Generator,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
f"{_MAPPING}Loader": "Loader",
|
|
f"{_MAPPING}Formatter": "Formatter",
|
|
f"{_MAPPING}Tokenizer": "Tokenizer",
|
|
f"{_MAPPING}Settings": "Settings",
|
|
f"{_MAPPING}Generator": "Generator",
|
|
}
|