| import os, time, torch, json |
| os.environ.setdefault("HF_HOME","/workspace/.hf_home") |
| from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
| from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training |
| M="/workspace/models/gemma4-qat-unquantized" |
| torch.cuda.init(); _=torch.zeros(1,device="cuda:0") |
| bnb=BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_quant_type="nf4", |
| bnb_4bit_compute_dtype=torch.bfloat16,bnb_4bit_use_double_quant=True) |
| tok=AutoTokenizer.from_pretrained(M) |
| m=AutoModelForCausalLM.from_pretrained(M,quantization_config=bnb,dtype=torch.bfloat16, |
| device_map={"":0},attn_implementation="sdpa") |
| base=torch.cuda.memory_allocated(0)/2**30 |
| |
| enc=tok.apply_chat_template([{"role":"user","content":"Reply with ONLY this JSON: {\"answer\":\"A\"}"}], |
| add_generation_prompt=True, return_dict=True, return_tensors="pt").to(0) |
| with torch.no_grad(): o=m.generate(**enc,max_new_tokens=24,do_sample=False) |
| print("GEN:",repr(tok.decode(o[0][enc['input_ids'].shape[-1]:],skip_special_tokens=True))[:160]) |
| print(f"BASE_WEIGHTS {base:.2f} GiB") |
|
|
| m.config.use_cache=False |
| m=prepare_model_for_kbit_training(m, use_gradient_checkpointing=False) |
| TM=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"] |
| res={} |
| for rank in (128, 256): |
| mm=get_peft_model(m, LoraConfig(r=rank,lora_alpha=rank*2,lora_dropout=0.05,bias="none", |
| task_type="CAUSAL_LM",target_modules=TM)) |
| tr=sum(p.numel() for p in mm.parameters() if p.requires_grad) |
| for seq,mb in ((2048,4),(2048,8),(4096,4),(8192,2)): |
| try: |
| torch.cuda.empty_cache(); torch.cuda.reset_peak_memory_stats(0) |
| ids=torch.randint(0,250000,(mb,seq),device=0) |
| opt=torch.optim.AdamW([p for p in mm.parameters() if p.requires_grad],lr=1e-5) |
| for i in range(3): |
| if i==1: torch.cuda.synchronize(); t=time.time() |
| out=mm(input_ids=ids,labels=ids); out.loss.backward(); opt.step(); opt.zero_grad(set_to_none=True) |
| torch.cuda.synchronize(); dt=(time.time()-t)/2 |
| pk=torch.cuda.max_memory_allocated(0)/2**30 |
| tps=mb*seq/dt |
| res[f"r{rank}_s{seq}_mb{mb}"]={"tok_s":round(tps),"peak_gib":round(pk,1),"s_step":round(dt,2)} |
| print(f"r={rank} seq={seq} mb={mb}: {tps:8.0f} tok/s peak {pk:5.1f} GiB {dt:.2f}s/step trainable {tr/1e6:.0f}M") |
| del opt,ids,out |
| except torch.cuda.OutOfMemoryError: |
| print(f"r={rank} seq={seq} mb={mb}: OOM"); torch.cuda.empty_cache() |
| mm=mm.unload() if hasattr(mm,'unload') else mm |
| m=getattr(mm,'base_model',m) |
| json.dump(res,open("/workspace/aditi/logs/bench_train.json","w"),indent=2) |
|
|
