fix: use DeepSpeed ZeRO-3 directly (skip CPU loading)
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@@ -59,36 +59,8 @@ def train(config_path):
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except Exception as e:
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print(f"✗ Failed: {e}")
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# Strategy 2: Load bf16 to CPU with low_cpu_mem_usage
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print("\n[2/4] Trying: bf16 model to CPU...")
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try:
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model = AutoModelForCausalLM.from_pretrained(
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config["base_model"],
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torch_dtype=torch.bfloat16,
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device_map="cpu",
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low_cpu_mem_usage=True,
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trust_remote_code=True,
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)
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print("✓ Success: bf16 model loaded to CPU")
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except Exception as e:
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print(f"✗ Failed: {e}")
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# Strategy 3: Load fp16 with auto placement
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print("\n[3/4] Trying: fp16 model with auto placement...")
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try:
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model = AutoModelForCausalLM.from_pretrained(
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config["base_model"],
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torch_dtype=torch.float16,
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device_map="auto",
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low_cpu_mem_usage=True,
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trust_remote_code=True,
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)
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print("✓ Success: fp16 model loaded")
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except Exception as e:
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print(f"✗ Failed: {e}")
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# Strategy 4: DeepSpeed ZeRO-3 with CPU offload
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print("\n[4/4] Trying: DeepSpeed ZeRO-3 CPU offload...")
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# Strategy 2: DeepSpeed ZeRO-3 (distribute across GPUs)
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print("\n[2/4] Trying: DeepSpeed ZeRO-3...")
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try:
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import deepspeed
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model = AutoModelForCausalLM.from_pretrained(
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@@ -112,7 +84,7 @@ def train(config_path):
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"bf16": {"enabled": True},
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}
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optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)
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optimizer = torch.optim.AdamW(model.parameters(), lr=float(config["train_params"]["learning_rate"]))
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model, optimizer, _, _ = deepspeed.initialize(
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model=model,
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model_parameters=model.parameters(),
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