biondizzle
b5d569218c
- model_opt_nvfp4_full.py: Full NVFP4 quantization (not experts-only) Uses --gpu_max_mem_percentage 0.9 instead of --use_seq_device_map - dequant_fp8_to_bf16.py: Now handles INT4-packed experts + FP8 shared experts + FP8 attention. Complete dequant to pure BF16.
68 lines
2.8 KiB
Python
68 lines
2.8 KiB
Python
#!/usr/bin/env python3
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"""
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ModelOpt NVFP4 quantization — full model.
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Quantizes ALL weights (attention + experts + shared MLP) to NVFP4.
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Requires a pure BF16 source model (from scripts/dequant_fp8_to_bf16.py)
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to avoid FP8/INT4 kernel issues on Blackwell GPUs.
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Available NVFP4 quantization strategies (from modelopt huggingface_example.sh):
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- nvfp4 : Full model NVFP4 quantization (this script)
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- nvfp4_experts_only : Only MoE expert weights
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- nvfp4_mlp_only : Only MLP layers (experts + shared MLP)
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- nvfp4_omlp_only : Only output + MLP layers
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- nvfp4_awq : NVFP4 with AWQ calibration
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- nvfp4_mse : NVFP4 with MSE calibration
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- w4a8_nvfp4_fp8 : W4A8 NVFP4 weights + FP8 activations
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- w4a8_mxfp4_fp8 : W4A8 MXFP4 weights + FP8 activations
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- nvfp4_svdquant : NVFP4 with SVDQuant
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- nvfp4_local_hessian : NVFP4 with local Hessian calibration
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Strategy: Copy this file to model_opt_nvfp4_<strategy>.py and tweak as needed.
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By the end, we'll have working quantized weights for each successful strategy.
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Output dir naming: DeepSeek-V4-Pro_NVFP4-<strategy>_kv_fp8_cast
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"""
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import subprocess
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import sys
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import os
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# ── Config ──────────────────────────────────────────────────────────────────
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MODEL = "/root/nvidia-meeting/DeepSeek-V4-Pro-BF16" # Dequantized BF16 (from scripts/dequant_fp8_to_bf16.py)
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QUANT = "nvfp4"
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TP = 8
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CALIB = 256
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KV_CACHE_QUANT = "fp8_cast"
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# No --use_seq_device_map (causes OOM on 2.8TB RAM with 782GB+ model)
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# Use gpu_max_mem_percentage to keep model on GPU, reduce CPU RAM pressure
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EXTRA_FLAGS = "--trust_remote_code --gpu_max_mem_percentage 0.9"
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# Output dir follows modelopt convention: <model>_<quant>_kv_<kv_quant>
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# We override the model name to make the strategy clear
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OUTPUT_NAME = f"DeepSeek-V4-Pro_NVFP4-{QUANT}_kv_{KV_CACHE_QUANT}"
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SCRIPT_DIR = "/root/nvidia-meeting/modelopt-repo/examples/llm_ptq"
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LOG_FILE = f"/root/nvidia-meeting/modelopt_{QUANT}.log"
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# ── Run ─────────────────────────────────────────────────────────────────────
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cmd = f"""cd {SCRIPT_DIR} && \\
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source /root/nvidia-meeting/venv/bin/activate && \\
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PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \\
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bash scripts/huggingface_example.sh \\
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--model {MODEL} \\
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--quant {QUANT} \\
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--tp {TP} \\
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--calib {CALIB} \\
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--kv_cache_quant {KV_CACHE_QUANT} \\
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{EXTRA_FLAGS} 2>&1 | tee {LOG_FILE}"""
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print(f"Running: {QUANT} quantization on {MODEL}")
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print(f"Output: {OUTPUT_NAME}")
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print(f"Log: {LOG_FILE}")
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print(f"Command:\n{cmd}\n")
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ret = subprocess.call(cmd, shell=True)
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sys.exit(ret)
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