87 lines
3.2 KiB
Python
87 lines
3.2 KiB
Python
import modal
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from pathlib import PurePosixPath
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# Setup - define our infrastructure with code!
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app = modal.App("pricer-service")
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secrets = [modal.Secret.from_name("huggingface-secret")]
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image = modal.Image.debian_slim().pip_install(
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"huggingface", "torch", "transformers", "bitsandbytes",
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"accelerate", "peft", "huggingface_hub[hf_transfer]"
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).env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})
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# This is where we cache model files to avoid redownloading each time a container is started
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hf_cache_vol = modal.Volume.from_name("hf-cache", create_if_missing=True)
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GPU = "T4"
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# Keep N containers active to avoid cold starts
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MIN_CONTAINERS = 0
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BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B"
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PROJECT_NAME = "pricer"
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HF_USER = "ed-donner" # your HF name here! Or use mine if you just want to reproduce my results.
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RUN_NAME = "2024-09-13_13.04.39"
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PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}"
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REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36"
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FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}"
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# Mount for cache location
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MODEL_DIR = PurePosixPath("/models")
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BASE_DIR = MODEL_DIR / BASE_MODEL
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FINETUNED_DIR = MODEL_DIR / FINETUNED_MODEL
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QUESTION = "How much does this cost to the nearest dollar?"
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PREFIX = "Price is $"
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@app.cls(image=image, secrets=secrets, gpu=GPU, timeout=1800, min_containers=MIN_CONTAINERS, volumes={MODEL_DIR: hf_cache_vol})
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class Pricer:
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@modal.enter()
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def setup(self):
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import torch
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from huggingface_hub import snapshot_download
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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from peft import PeftModel
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# Download and cache model files to the volume
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snapshot_download(BASE_MODEL, local_dir=BASE_DIR)
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snapshot_download(FINETUNED_MODEL, revision=REVISION, local_dir=FINETUNED_DIR)
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# Quant Config
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quant_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_quant_type="nf4"
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)
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# Load model and tokenizer
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self.tokenizer = AutoTokenizer.from_pretrained(BASE_DIR)
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self.tokenizer.pad_token = self.tokenizer.eos_token
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self.tokenizer.padding_side = "right"
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self.base_model = AutoModelForCausalLM.from_pretrained(
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BASE_DIR,
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quantization_config=quant_config,
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device_map="auto"
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)
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self.fine_tuned_model = PeftModel.from_pretrained(self.base_model, FINETUNED_DIR, revision=REVISION)
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@modal.method()
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def price(self, description: str) -> float:
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import re, torch
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from transformers import set_seed
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set_seed(42)
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prompt = f"{QUESTION}\n\n{description}\n\n{PREFIX}"
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inputs = self.tokenizer.encode(prompt, return_tensors="pt").to("cuda")
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attention_mask = torch.ones(inputs.shape, device="cuda")
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outputs = self.fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1)
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result = self.tokenizer.decode(outputs[0])
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contents = result.split("Price is $")[1]
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contents = contents.replace(',','')
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match = re.search(r"[-+]?\d*\.\d+|\d+", contents)
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return float(match.group()) if match else 0
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