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118 lines
4.3 KiB
118 lines
4.3 KiB
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import torch
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from torch.utils.checkpoint import check_backward_validity, detach_variable
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from colossalai.context.random import get_states, get_current_mode, set_seed_states, set_mode, sync_states
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class CheckpointFunction(torch.autograd.Function):
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@staticmethod
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def forward(ctx, run_function, *args):
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check_backward_validity(args)
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ctx.run_function = run_function
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# preserve rng states
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ctx.fwd_cpu_rng_state = torch.get_rng_state()
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sync_states()
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ctx.fwd_seed_states = get_states(copy=True)
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ctx.fwd_current_mode = get_current_mode()
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if hasattr(torch, 'is_autocast_enabled'):
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ctx.had_autocast_in_fwd = torch.is_autocast_enabled()
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else:
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ctx.had_autocast_in_fwd = False
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# Save non-tensor inputs in ctx, keep a placeholder None for tensors
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# to be filled out during the backward.
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ctx.inputs = []
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ctx.tensor_indices = []
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tensor_inputs = []
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for i, arg in enumerate(args):
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if torch.is_tensor(arg):
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tensor_inputs.append(arg)
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ctx.tensor_indices.append(i)
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ctx.inputs.append(None)
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else:
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ctx.inputs.append(arg)
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ctx.save_for_backward(*tensor_inputs)
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with torch.no_grad():
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outputs = run_function(*args)
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return outputs
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@staticmethod
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def backward(ctx, *args):
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if not torch.autograd._is_checkpoint_valid():
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raise RuntimeError(
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"Checkpointing is not compatible with .grad() or when an `inputs` parameter"
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" is passed to .backward(). Please use .backward() and do not pass its `inputs`"
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" argument.")
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# Copy the list to avoid modifying original list.
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inputs = list(ctx.inputs)
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tensor_indices = ctx.tensor_indices
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tensors = ctx.saved_tensors
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# store the current states
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bwd_cpu_rng_state = torch.get_rng_state()
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sync_states()
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bwd_seed_states = get_states(copy=True)
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bwd_current_mode = get_current_mode()
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# set the states to what it used to be
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torch.set_rng_state(ctx.fwd_cpu_rng_state)
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for parallel_mode, state in ctx.fwd_seed_states.items():
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set_seed_states(parallel_mode, state)
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set_mode(ctx.fwd_current_mode)
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# Fill in inputs with appropriate saved tensors.
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for i, idx in enumerate(tensor_indices):
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inputs[idx] = tensors[i]
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detached_inputs = detach_variable(tuple(inputs))
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if ctx.had_autocast_in_fwd:
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with torch.enable_grad(), torch.cuda.amp.autocast():
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outputs = ctx.run_function(*detached_inputs)
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else:
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with torch.enable_grad():
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outputs = ctx.run_function(*detached_inputs)
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if isinstance(outputs, torch.Tensor):
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outputs = (outputs,)
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# recover the rng states
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torch.set_rng_state(bwd_cpu_rng_state)
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for parallel_mode, state in bwd_seed_states.items():
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set_seed_states(parallel_mode, state)
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set_mode(bwd_current_mode)
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# run backward() with only tensor that requires grad
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outputs_with_grad = []
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args_with_grad = []
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for i in range(len(outputs)):
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if torch.is_tensor(outputs[i]) and outputs[i].requires_grad:
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outputs_with_grad.append(outputs[i])
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args_with_grad.append(args[i])
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if len(outputs_with_grad) == 0:
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raise RuntimeError(
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"none of output has requires_grad=True,"
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" this checkpoint() is not necessary")
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torch.autograd.backward(outputs_with_grad, args_with_grad)
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grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else None
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for inp in detached_inputs)
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return (None,) + grads
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def checkpoint(function, *args):
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"""Checkpoint the computation while preserve the rng states, modified from Pytorch torch.utils.checkpoint
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:param function: Describe the forward pass function. It should know how to handle the input tuples.
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:param args: Tuple containing the parameters of the function
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:return: Output of running function with provided args
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"""
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return CheckpointFunction.apply(function, *args)
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