mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
118 lines
4.3 KiB
118 lines
4.3 KiB
#!/usr/bin/env python
|
|
# -*- encoding: utf-8 -*-
|
|
|
|
import torch
|
|
from torch.utils.checkpoint import check_backward_validity, detach_variable
|
|
|
|
from colossalai.context.random import get_states, get_current_mode, set_seed_states, set_mode, sync_states
|
|
|
|
|
|
class CheckpointFunction(torch.autograd.Function):
|
|
|
|
@staticmethod
|
|
def forward(ctx, run_function, *args):
|
|
check_backward_validity(args)
|
|
ctx.run_function = run_function
|
|
|
|
# preserve rng states
|
|
ctx.fwd_cpu_rng_state = torch.get_rng_state()
|
|
sync_states()
|
|
ctx.fwd_seed_states = get_states(copy=True)
|
|
ctx.fwd_current_mode = get_current_mode()
|
|
|
|
if hasattr(torch, 'is_autocast_enabled'):
|
|
ctx.had_autocast_in_fwd = torch.is_autocast_enabled()
|
|
else:
|
|
ctx.had_autocast_in_fwd = False
|
|
|
|
# Save non-tensor inputs in ctx, keep a placeholder None for tensors
|
|
# to be filled out during the backward.
|
|
ctx.inputs = []
|
|
ctx.tensor_indices = []
|
|
tensor_inputs = []
|
|
for i, arg in enumerate(args):
|
|
if torch.is_tensor(arg):
|
|
tensor_inputs.append(arg)
|
|
ctx.tensor_indices.append(i)
|
|
ctx.inputs.append(None)
|
|
else:
|
|
ctx.inputs.append(arg)
|
|
|
|
ctx.save_for_backward(*tensor_inputs)
|
|
|
|
with torch.no_grad():
|
|
outputs = run_function(*args)
|
|
return outputs
|
|
|
|
@staticmethod
|
|
def backward(ctx, *args):
|
|
if not torch.autograd._is_checkpoint_valid():
|
|
raise RuntimeError(
|
|
"Checkpointing is not compatible with .grad() or when an `inputs` parameter"
|
|
" is passed to .backward(). Please use .backward() and do not pass its `inputs`"
|
|
" argument.")
|
|
# Copy the list to avoid modifying original list.
|
|
inputs = list(ctx.inputs)
|
|
tensor_indices = ctx.tensor_indices
|
|
tensors = ctx.saved_tensors
|
|
|
|
# store the current states
|
|
bwd_cpu_rng_state = torch.get_rng_state()
|
|
sync_states()
|
|
bwd_seed_states = get_states(copy=True)
|
|
bwd_current_mode = get_current_mode()
|
|
|
|
# set the states to what it used to be
|
|
torch.set_rng_state(ctx.fwd_cpu_rng_state)
|
|
for parallel_mode, state in ctx.fwd_seed_states.items():
|
|
set_seed_states(parallel_mode, state)
|
|
set_mode(ctx.fwd_current_mode)
|
|
|
|
# Fill in inputs with appropriate saved tensors.
|
|
for i, idx in enumerate(tensor_indices):
|
|
inputs[idx] = tensors[i]
|
|
|
|
detached_inputs = detach_variable(tuple(inputs))
|
|
if ctx.had_autocast_in_fwd:
|
|
with torch.enable_grad(), torch.cuda.amp.autocast():
|
|
outputs = ctx.run_function(*detached_inputs)
|
|
else:
|
|
with torch.enable_grad():
|
|
outputs = ctx.run_function(*detached_inputs)
|
|
|
|
if isinstance(outputs, torch.Tensor):
|
|
outputs = (outputs,)
|
|
|
|
# recover the rng states
|
|
torch.set_rng_state(bwd_cpu_rng_state)
|
|
for parallel_mode, state in bwd_seed_states.items():
|
|
set_seed_states(parallel_mode, state)
|
|
set_mode(bwd_current_mode)
|
|
|
|
# run backward() with only tensor that requires grad
|
|
outputs_with_grad = []
|
|
args_with_grad = []
|
|
for i in range(len(outputs)):
|
|
if torch.is_tensor(outputs[i]) and outputs[i].requires_grad:
|
|
outputs_with_grad.append(outputs[i])
|
|
args_with_grad.append(args[i])
|
|
if len(outputs_with_grad) == 0:
|
|
raise RuntimeError(
|
|
"none of output has requires_grad=True,"
|
|
" this checkpoint() is not necessary")
|
|
torch.autograd.backward(outputs_with_grad, args_with_grad)
|
|
grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else None
|
|
for inp in detached_inputs)
|
|
|
|
return (None,) + grads
|
|
|
|
|
|
def checkpoint(function, *args):
|
|
'''Checkpoint the computation while preserve the rng states, modified from Pytorch torch.utils.checkpoint
|
|
|
|
:param function: describe the forward pass function. It should know how to handle the input tuples.
|
|
:param args: tuple containing inputs to the function
|
|
:return: Output of running function on \*args
|
|
'''
|
|
return CheckpointFunction.apply(function, *args)
|