123 lines
3.5 KiB
Python
123 lines
3.5 KiB
Python
from time import time
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import torch
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from comfy.utils import ProgressBar
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from exllamav2 import ExLlamaV2, ExLlamaV2Cache, ExLlamaV2Config, ExLlamaV2Tokenizer
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from exllamav2.generator import ExLlamaV2Sampler, ExLlamaV2StreamingGenerator
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class Loader:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model_dir": ("STRING", {"default": ""}),
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"max_seq_len": ("INT", {"default": 2048, "min": 1, "max": 8192}),
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},
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}
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CATEGORY = "Zuellni/ExLlama"
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FUNCTION = "load"
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RETURN_NAMES = ("MODEL",)
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RETURN_TYPES = ("EL_MODEL",)
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def load(self, model_dir, max_seq_len):
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config = ExLlamaV2Config()
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config.model_dir = model_dir
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config.prepare()
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config.max_seq_len = max_seq_len
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model = ExLlamaV2(config)
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model.load()
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cache = ExLlamaV2Cache(model)
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tokenizer = ExLlamaV2Tokenizer(config)
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generator = ExLlamaV2StreamingGenerator(model, cache, tokenizer)
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settings = ExLlamaV2Sampler.Settings()
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return ((tokenizer, generator, settings),)
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class Generator:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("EL_MODEL",),
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"stop_on_newline": ((False, True),),
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"max_tokens": ("INT", {"default": 128, "min": 1, "max": 8192}),
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"temperature": ("FLOAT", {"default": 0.7, "max": 2, "step": 0.01}),
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"top_k": ("INT", {"default": 20, "max": 200}),
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"top_p": ("FLOAT", {"default": 0.9, "max": 1, "step": 0.01}),
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"typical": ("FLOAT", {"default": 1, "max": 1, "step": 0.01}),
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"penalty": ("FLOAT", {"default": 1.15, "min": 1, "max": 2, "step": 0.01}),
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"seed": ("INT", {"max": 2**64 - 1}),
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"text": ("STRING", {"multiline": True}),
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},
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}
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CATEGORY = "Zuellni/ExLlama"
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FUNCTION = "generate"
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RETURN_NAMES = ("TEXT",)
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RETURN_TYPES = ("STRING",)
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def generate(
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self,
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model,
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stop_on_newline,
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max_tokens,
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temperature,
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top_k,
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top_p,
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typical,
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penalty,
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seed,
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text,
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):
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if not text:
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return ("",)
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tokenizer, generator, settings = model
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progress = ProgressBar(max_tokens)
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prompt = tokenizer.encode(text)
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stop_conditions = [tokenizer.eos_token_id]
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stop_on_newline and stop_conditions.append(tokenizer.newline_token_id)
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generator.set_stop_conditions(stop_conditions)
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settings.temperature = temperature
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settings.top_k = top_k
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settings.top_p = top_p
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settings.typical = typical
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settings.token_repetition_penalty = penalty
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torch.manual_seed(seed)
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generator.begin_stream(prompt, settings)
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start = time()
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eos = False
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output = ""
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tokens = 0
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while not eos and tokens < max_tokens:
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chunk, eos, _ = generator.stream()
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progress.update(1)
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output += chunk
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tokens += 1
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total = round(time() - start, 2)
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speed = round(tokens / total, 2)
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print(f"Output generated in {total} seconds ({tokens} tokens, {speed} tokens/s)")
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return (output.strip(),)
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NODE_CLASS_MAPPINGS = {
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"ZuellniExLlamaLoader": Loader,
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"ZuellniExLlamaGenerator": Generator,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ZuellniExLlamaLoader": "Loader",
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"ZuellniExLlamaGenerator": "Generator",
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}
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