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# app.py
"""
FastAPI app to inspect Hugging Face transformer model "sizing":
- total # parameters
- trainable # parameters
- approximate memory footprint in bytes (and human-readable)
- saved disk size (by saving model files temporarily)
- model config summary (hidden layers, hidden_size if available)
Usage:
pip install fastapi "uvicorn[standard]" transformers torch
uvicorn app:app --reload
Endpoints:
GET / -> simple HTML UI (submit model id, e.g. "bert-base-uncased")
GET /inspect?model=... -> JSON with sizing info
"""
import os
import shutil
import tempfile
import math
from typing import Optional
from fastapi import FastAPI, Query, HTTPException
from fastapi.responses import HTMLResponse, JSONResponse
from pydantic import BaseModel
from transformers import AutoModel, AutoConfig, AutoTokenizer, logging as hf_logging
import torch
# reduce transformers logging noise
hf_logging.set_verbosity_error()
app = FastAPI(title="HuggingFace Transformer Sizing API")
def humanize_bytes(n: int) -> str:
"""Return human-readable size string (e.g. '1.2 GB')."""
if n < 1024:
return f"{n} B"
units = ["B", "KB", "MB", "GB", "TB", "PB"]
idx = int(math.floor(math.log(n, 1024)))
val = n / (1024 ** idx)
return f"{val:.2f} {units[idx]}"
def model_parameter_counts(model: torch.nn.Module):
"""Return total and trainable parameter counts and memory bytes (approx)"""
total = 0
trainable = 0
bytes_total = 0
bytes_trainable = 0
for p in model.parameters():
n_elem = p.numel()
elem_size = p.element_size() # bytes per element (e.g., 4 for float32)
total += n_elem
bytes_total += n_elem * elem_size
if p.requires_grad:
trainable += n_elem
bytes_trainable += n_elem * elem_size
return {
"total_params": total,
"trainable_params": trainable,
"approx_bytes": bytes_total,
"trainable_bytes": bytes_trainable,
"approx_bytes_human": humanize_bytes(bytes_total),
"trainable_bytes_human": humanize_bytes(bytes_trainable),
}
def folder_size_bytes(path: str) -> int:
"""Return total size in bytes of files under `path`."""
total = 0
for root, _, files in os.walk(path):
for f in files:
try:
total += os.path.getsize(os.path.join(root, f))
except OSError:
pass
return total
class InspectResult(BaseModel):
model_id: str
backbone_class: str
config: dict
sizing: dict
saved_size_bytes: Optional[int]
saved_size_human: Optional[str]
notes: Optional[str]
@app.get("/", response_class=HTMLResponse)
def index():
html = """
<html>
<head>
<title>Transformer Sizing Inspector</title>
<style>
body { font-family: Arial, sans-serif; max-width: 800px; margin: 40px auto; }
input[type=text] { width: 70%; padding: 8px; }
button { padding: 8px 12px; }
pre { background: #f7f7f7; padding: 12px; border-radius: 6px; }
</style>
</head>
<body>
<h2>Hugging Face Transformer Sizing</h2>
<form action="/inspect" method="get">
<label>Model ID (e.g. <code>bert-base-uncased</code>):</label><br/>
<input type="text" name="model" value="bert-base-uncased" />
<button type="submit">Inspect</button>
</form>
<p>Example models: <code>bert-base-uncased</code>, <code>roberta-base</code>, <code>google/bert_uncased_L-2_H-128_A-2</code>, <code>distilbert-base-uncased</code></p>
<hr/>
<p>Result will be shown in JSON. If the model is large it may take time to download.</p>
</body>
</html>
"""
return HTMLResponse(content=html)
@app.get("/inspect", response_model=InspectResult)
def inspect(model: str = Query(..., description="Hugging Face model identifier or local path (e.g. 'bert-base-uncased')"),
use_auth_token: Optional[str] = Query(None, description="Optional HF token if you need private model access"),
save_to_disk: bool = Query(True, description="If true, save model to temp dir to calculate saved disk size (default: true)")):
"""
Inspect a Hugging Face model's size and config.
Example:
GET /inspect?model=bert-base-uncased
"""
# Basic validation
if not model:
raise HTTPException(status_code=400, detail="model query parameter is required")
# Attempt to load config first (fast) to get basic info and avoid unnecessary download of large weights
try:
config = AutoConfig.from_pretrained(model, use_auth_token=use_auth_token)
except Exception as e:
raise HTTPException(status_code=400, detail=f"Failed to load model config for '{model}': {e}")
# Now load model weights into CPU (to inspect parameters). We'll use low_cpu_mem_usage if available.
# Note: large models may still consume a lot of RAM.
model_obj = None
notes = []
try:
# prefer CPU to avoid accidental GPU usage
model_obj = AutoModel.from_pretrained(model, config=config, torch_dtype=torch.float32, low_cpu_mem_usage=True, use_auth_token=use_auth_token).to("cpu")
except TypeError:
# older transformers may not support low_cpu_mem_usage param
model_obj = AutoModel.from_pretrained(model, config=config, use_auth_token=use_auth_token).to("cpu")
except Exception as e:
raise HTTPException(status_code=500, detail=f"Failed to load model weights for '{model}': {e}")
sizing = model_parameter_counts(model_obj)
# compute saved disk size by using model.save_pretrained to a temp dir
saved_size_bytes = None
saved_size_human = None
temp_dir = None
if save_to_disk:
try:
temp_dir = tempfile.mkdtemp(prefix="hf_model_")
# save model + config + tokenizer if available
model_obj.save_pretrained(temp_dir)
try:
tok = AutoTokenizer.from_pretrained(model, use_auth_token=use_auth_token)
tok.save_pretrained(temp_dir)
except Exception:
# tokenizer may not be available / may fail; that's ok
notes.append("tokenizer save failed or not available")
saved_size_bytes = folder_size_bytes(temp_dir)
saved_size_human = humanize_bytes(saved_size_bytes)
except Exception as e:
notes.append(f"Failed to save model to temp dir: {e}")
finally:
# clean up the temp dir (we measured size already)
if temp_dir and os.path.exists(temp_dir):
try:
shutil.rmtree(temp_dir)
except Exception:
pass
# attempt to surface useful common config items (hidden_size, num_hidden_layers, vocab_size)
config_summary = {}
for k in ("hidden_size", "d_model", "n_embd", "num_hidden_layers", "num_attention_heads", "vocab_size", "intermediate_size"):
if hasattr(config, k):
config_summary[k] = getattr(config, k)
result = {
"model_id": model,
"backbone_class": model_obj.__class__.__name__,
"config": config_summary,
"sizing": {
"total_params": sizing["total_params"],
"trainable_params": sizing["trainable_params"],
"approx_bytes": sizing["approx_bytes"],
"approx_bytes_human": sizing["approx_bytes_human"],
"trainable_bytes": sizing["trainable_bytes"],
"trainable_bytes_human": sizing["trainable_bytes_human"],
},
"saved_size_bytes": saved_size_bytes,
"saved_size_human": saved_size_human,
"notes": "; ".join(notes) if notes else None
}
# free model (optional)
try:
del model_obj
torch.cuda.empty_cache()
except Exception:
pass
return JSONResponse(content=result)
# If you prefer to run 'python app.py' directly for dev, include a simple runner.
if __name__ == "__main__":
import uvicorn
uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)
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