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from typing import Set |
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import torch |
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from deepspeed.accelerator import get_accelerator |
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from deepspeed.runtime.zero.offload_config import OffloadStateTypeEnum |
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def _make_offload_state_key(key): |
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return f"{key}_offload_buffer" |
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def offload_adam_states(optimizer, device, pin_memory: bool = False, non_blocking: bool = False): |
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"""Move optimizer states to device. Note that this assumes the state structure of DeepSpeed Adam.""" |
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def move_key(state, key): |
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offload_buf_key = _make_offload_state_key(key) |
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if offload_buf_key not in state: |
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state[offload_buf_key] = torch.empty_like(state[key], device=device) |
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if pin_memory: |
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state[offload_buf_key] = get_accelerator().pin_memory(state[offload_buf_key]) |
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state[offload_buf_key].copy_(state[key], non_blocking=non_blocking) |
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state[key].data = state[offload_buf_key] |
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for _, state in optimizer.state.items(): |
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if "exp_avg" in state: |
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move_key(state, "exp_avg") |
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if "exp_avg_sq" in state: |
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move_key(state, "exp_avg_sq") |
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def reload_adam_states(optimizer, device, non_blocking: bool = False): |
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"""Move optimizer states to device. Note that this assumes the state structure of DeepSpeed Adam.""" |
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def move_back_key(state, key): |
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state[key].data = state[_make_offload_state_key(key)].to(device, non_blocking=non_blocking) |
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for _, state in optimizer.state.items(): |
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if "exp_avg" in state: |
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move_back_key(state, "exp_avg") |
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if "exp_avg_sq" in state: |
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move_back_key(state, "exp_avg_sq") |
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def get_state_devices(model, state: OffloadStateTypeEnum) -> Set[torch.device]: |
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"""Retrieve the devices of the specified state of the model. |
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Args: |
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model (DeepSpeedEngine): The model whose device allocations are to be checked. |
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state (OffloadStateTypeEnum): The specific state for which the devices should be retrieved. |
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Returns: |
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Set[torch.device]: A set of devices of the specified state. |
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""" |
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if state == OffloadStateTypeEnum.hp_params: |
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return set(model.optimizer.get_hp_param_device(p) for p in model.parameters()) |
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elif state == OffloadStateTypeEnum.lp_params: |
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return set(p.ds_tensor.device for p in model.parameters()) |
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elif state == OffloadStateTypeEnum.lp_grads: |
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return {model.optimizer.grad_partitions_flat_buffer.device} |
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elif state == OffloadStateTypeEnum.optim_states: |
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return set(model.optimizer.get_hp_param_device(p, "exp_avg") for p in model.parameters()) | \ |
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set(model.optimizer.get_hp_param_device(p, "exp_avg_sq") for p in model.parameters()) |
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elif state == OffloadStateTypeEnum.contiguous_grad_buffer: |
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if model.optimizer._DeepSpeedZeroOptimizer_Stage3__ipg_bucket_flat_buffer == None: |
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return {} |
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return {model.optimizer._DeepSpeedZeroOptimizer_Stage3__ipg_bucket_flat_buffer.device} |
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