Create memory-requirements-quantized-vs-dequantized.py

memory-requirements-quantized-vs-dequantized.py

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+# Measure and compare VRAM with and without MXFP4 dequantize
+import gc
+import torch
+from transformers import AutoModelForCausalLM, Mxfp4Config
+
+MODEL_ID = "openai/gpt-oss-20b"
+DEVICE = "cuda:0"
+
+def get_used_gb():
+    free, total = torch.cuda.mem_get_info()
+    return (total - free) / (1024**3), total / (1024**3)
+
+def clear_memory():
+    del_vars = [k for k in list(globals().keys()) if k.startswith("_tmp_")]
+    for k in del_vars:
+        globals().pop(k, None)
+    gc.collect()
+    torch.cuda.empty_cache()
+    torch.cuda.synchronize()
+
+assert torch.cuda.is_available(), "CUDA is not available."
+
+# --- Dequantized (heavier) ---
+clear_memory()
+before_deq_used, total_gb = get_used_gb()
+qconf = Mxfp4Config(dequantize=True)
+model_deq = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype="auto",
+    device_map=DEVICE,
+    quantization_config=qconf,
+).eval()
+after_deq_used, _ = get_used_gb()
+
+# --- Quantized (lighter) ---
+del model_deq
+clear_memory()
+before_q_used, _ = get_used_gb()
+model_q = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype="auto",
+    device_map=DEVICE,
+).eval()
+after_q_used, _ = get_used_gb()
+
+print(f"[dequantized] used before: {before_deq_used:.2f} GB, after: {after_deq_used:.2f} GB / total {total_gb:.2f} GB")
+print(f"[quantized  ] used before: {before_q_used:.2f} GB, after: {after_q_used:.2f} GB / total {total_gb:.2f} GB")
+
+# Make these available for plotting
+mx_results = {
+    "total_gb": total_gb,
+    "after_dequantized_gb": after_deq_used,
+    "after_quantized_gb": after_q_used,
+}
+
+# Outputs:
+# [dequantized] used before: 0.41 GB, after: 43.18 GB / total 79.25 GB
+# [quantized  ] used before: 0.49 GB, after: 13.37 GB / total 79.25 GB