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Browse files- app.py +51 -0
- requirements.txt +5 -0
app.py
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import gradio as gr
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import numpy as np
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import onnxruntime as ort
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from huggingface_hub import hf_hub_download
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import torch
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HF_MODEL_ID = "mistralai/Mistral-Nemo-Instruct-2407"
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HF_ONNX_REPO = "techAInewb/mistral-nemo-2407-fp32"
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ONNX_MODEL_FILE = "model.onnx"
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(HF_MODEL_ID)
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# Load PyTorch model
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pt_model = AutoModelForCausalLM.from_pretrained(HF_MODEL_ID, torch_dtype=torch.float32)
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pt_model.eval()
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# Load ONNX model
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onnx_path = hf_hub_download(repo_id=HF_ONNX_REPO, filename=ONNX_MODEL_FILE)
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onnx_session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
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def compare_outputs(prompt):
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inputs = tokenizer(prompt, return_tensors="np", padding=False)
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torch_inputs = tokenizer(prompt, return_tensors="pt")
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# Run PyTorch
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with torch.no_grad():
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pt_outputs = pt_model(**torch_inputs).logits
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pt_top = torch.topk(pt_outputs[0, -1], 5).indices.tolist()
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# Run ONNX
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ort_outputs = onnx_session.run(None, {
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"input_ids": inputs["input_ids"],
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"attention_mask": inputs["attention_mask"]
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})
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ort_logits = ort_outputs[0]
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ort_top = np.argsort(ort_logits[0, -1])[::-1][:5].tolist()
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pt_tokens = tokenizer.convert_ids_to_tokens(pt_top)
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ort_tokens = tokenizer.convert_ids_to_tokens(ort_top)
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return f"PyTorch Top Tokens: {pt_tokens}", f"ONNX Top Tokens: {ort_tokens}"
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iface = gr.Interface(fn=compare_outputs,
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inputs=gr.Textbox(lines=2, placeholder="Enter a prompt..."),
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outputs=["text", "text"],
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title="ONNX vs PyTorch Model Comparison",
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description="Run both PyTorch and ONNX inference on a prompt and compare top predicted tokens.")
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iface.launch()
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requirements.txt
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gradio
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transformers
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torch
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onnxruntime
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huggingface_hub
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