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fix: use DeepSpeed ZeRO-3 directly (skip CPU loading)

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Christian Medina
2026-07-01 14:41:48 -04:00
parent 3760aaf316
commit 3fa2cfe4cc
1 changed files with 27 additions and 55 deletions
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82
training/scripts/train.py
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@@ -59,70 +59,42 @@ def train(config_path):
except Exception as e: except Exception as e:
print(f"✗ Failed: {e}") print(f"✗ Failed: {e}")
# Strategy 2: Load bf16 to CPU with low_cpu_mem_usage # Strategy 2: DeepSpeed ZeRO-3 (distribute across GPUs)
print("\n[2/4] Trying: bf16 model to CPU...") print("\n[2/4] Trying: DeepSpeed ZeRO-3...")
try: try:
import deepspeed
model = AutoModelForCausalLM.from_pretrained( model = AutoModelForCausalLM.from_pretrained(
config["base_model"], config["base_model"],
torch_dtype=torch.bfloat16, torch_dtype=torch.bfloat16,
device_map="cpu", device_map=None,
low_cpu_mem_usage=True, low_cpu_mem_usage=True,
trust_remote_code=True, trust_remote_code=True,
) )
print("✓ Success: bf16 model loaded to CPU")
ds_config = {
"train_micro_batch_size_per_gpu": 1,
"gradient_accumulation_steps": 1,
"zero_optimization": {
"stage": 3,
"contiguous_gradients": True,
"overlap_comm": True,
"offload_optimizer": {"device": "cpu", "pin_memory": True},
"offload_param": {"device": "cpu", "pin_memory": True},
},
"bf16": {"enabled": True},
}
optimizer = torch.optim.AdamW(model.parameters(), lr=float(config["train_params"]["learning_rate"]))
model, optimizer, _, _ = deepspeed.initialize(
model=model,
model_parameters=model.parameters(),
optimizer=optimizer,
config=ds_config,
)
print("✓ Success: DeepSpeed ZeRO-3 loaded")
except Exception as e: except Exception as e:
print(f"✗ Failed: {e}") print(f"✗ Failed: {e}")
raise RuntimeError("All loading strategies failed!")
# Strategy 3: Load fp16 with auto placement
print("\n[3/4] Trying: fp16 model with auto placement...")
try:
model = AutoModelForCausalLM.from_pretrained(
config["base_model"],
torch_dtype=torch.float16,
device_map="auto",
low_cpu_mem_usage=True,
trust_remote_code=True,
)
print("✓ Success: fp16 model loaded")
except Exception as e:
print(f"✗ Failed: {e}")
# Strategy 4: DeepSpeed ZeRO-3 with CPU offload
print("\n[4/4] Trying: DeepSpeed ZeRO-3 CPU offload...")
try:
import deepspeed
model = AutoModelForCausalLM.from_pretrained(
config["base_model"],
torch_dtype=torch.bfloat16,
device_map=None,
low_cpu_mem_usage=True,
trust_remote_code=True,
)
ds_config = {
"train_micro_batch_size_per_gpu": 1,
"gradient_accumulation_steps": 1,
"zero_optimization": {
"stage": 3,
"contiguous_gradients": True,
"overlap_comm": True,
"offload_optimizer": {"device": "cpu", "pin_memory": True},
"offload_param": {"device": "cpu", "pin_memory": True},
},
"bf16": {"enabled": True},
}
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)
model, optimizer, _, _ = deepspeed.initialize(
model=model,
model_parameters=model.parameters(),
optimizer=optimizer,
config=ds_config,
)
print("✓ Success: DeepSpeed ZeRO-3 loaded")
except Exception as e:
print(f"✗ Failed: {e}")
raise RuntimeError("All loading strategies failed!")
# Prepare model for k-bit training # Prepare model for k-bit training
from peft import prepare_model_for_kbit_training from peft import prepare_model_for_kbit_training