Unit test harness started

week8/community_contributions/pricer_test/pricer/ci.py

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+from pathlib import Path
+
+import modal
+
+ROOT_PATH = Path(__file__).parent.parent
+
+image = (
+    modal.Image.debian_slim()
+    .pip_install("pytest")
+    .pip_install_from_requirements(ROOT_PATH / "requirements.txt")
+)
+
+app = modal.App("pricer-ci-testing", image=image)
+
+# mount: add local files to the remote container
+tests = modal.Mount.from_local_dir(ROOT_PATH / "tests", remote_path="/root/tests")
+
+@app.function(gpu="any", mounts=[tests])
+def pytest():
+    import subprocess
+    subprocess.run(["pytest", "-vs"], check=True, cwd="/root")
+
+secrets = [modal.Secret.from_name("huggingface-secret")]
+
+# Constants
+
+GPU = "T4"
+BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B"
+PROJECT_NAME = "pricer"
+HF_USER = "ed-donner" # your HF name here! Or use mine if you just want to reproduce my results.
+RUN_NAME = "2024-09-13_13.04.39"
+PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}"
+REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36"
+FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}"
+MODEL_DIR = "hf-cache/"
+BASE_DIR = MODEL_DIR + BASE_MODEL
+FINETUNED_DIR = MODEL_DIR + FINETUNED_MODEL
+
+QUESTION = "How much does this cost to the nearest dollar?"
+PREFIX = "Price is $"
+
+@app.cls(image=image, secrets=secrets, gpu=GPU, timeout=1800)
+class Pricer:
+    @modal.build()
+    def download_model_to_folder(self):
+        from huggingface_hub import snapshot_download
+        import os
+        os.makedirs(MODEL_DIR, exist_ok=True)
+        snapshot_download(BASE_MODEL, local_dir=BASE_DIR)
+        snapshot_download(FINETUNED_MODEL, revision=REVISION, local_dir=FINETUNED_DIR)
+
+    @modal.enter()
+    def setup(self):
+        import os
+        import torch
+        from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
+        from peft import PeftModel
+        
+        # Quant Config
+        quant_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_compute_dtype=torch.bfloat16,
+            bnb_4bit_quant_type="nf4"
+        )
+    
+        # Load model and tokenizer
+        
+        self.tokenizer = AutoTokenizer.from_pretrained(BASE_DIR)
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+        self.tokenizer.padding_side = "right"
+        
+        self.base_model = AutoModelForCausalLM.from_pretrained(
+            BASE_DIR, 
+            quantization_config=quant_config,
+            device_map="auto"
+        )
+    
+        self.fine_tuned_model = PeftModel.from_pretrained(self.base_model, FINETUNED_DIR, revision=REVISION)
+
+    @modal.method()
+    def price(self, description: str) -> float:
+        import os
+        import re
+        import torch
+        from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
+        from peft import PeftModel
+    
+        set_seed(42)
+        prompt = f"{QUESTION}\n\n{description}\n\n{PREFIX}"
+        inputs = self.tokenizer.encode(prompt, return_tensors="pt").to("cuda")
+        attention_mask = torch.ones(inputs.shape, device="cuda")
+        outputs = self.fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1)
+        result = self.tokenizer.decode(outputs[0])
+    
+        contents = result.split("Price is $")[1]
+        contents = contents.replace(',','')
+        match = re.search(r"[-+]?\d*\.\d+|\d+", contents)
+        return float(match.group()) if match else 0
+
+    @modal.method()
+    def wake_up(self) -> str:
+        return "ok"