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111 lines
3.0 KiB
111 lines
3.0 KiB
from collections import OrderedDict
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from functools import partial
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import torch
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from torch import Tensor
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from colossalai.legacy.constants import (
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INPUT_GROUP_3D,
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INPUT_X_WEIGHT_3D,
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OUTPUT_GROUP_3D,
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OUTPUT_X_WEIGHT_3D,
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WEIGHT_GROUP_3D,
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)
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from colossalai.legacy.core import global_context as gpc
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from colossalai.legacy.global_variables import tensor_parallel_env as env
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def get_depth_from_env() -> int:
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try:
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depth = env.depth_3d
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assert depth > 0, "DEPTH must be greater than zero"
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return depth
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except KeyError:
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raise EnvironmentError(
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"DEPTH is not found in the current environment, "
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"please make sure that you have used the correct process group initializer"
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)
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def get_parallel_mode_from_env(group):
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assert group in [
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INPUT_GROUP_3D,
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WEIGHT_GROUP_3D,
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OUTPUT_GROUP_3D,
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INPUT_X_WEIGHT_3D,
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OUTPUT_X_WEIGHT_3D,
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], f"{group} is not valid for 3D tensor parallelism."
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return getattr(env, group)
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def swap_in_out_group():
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env.input_group_3d, env.output_group_3d = env.output_group_3d, env.input_group_3d
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env.input_x_weight_group_3d, env.output_x_weight_group_3d = (
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env.output_x_weight_group_3d,
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env.input_x_weight_group_3d,
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)
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def dbg_check_shape(tensor: Tensor, shape: tuple):
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rank = gpc.get_global_rank()
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if rank == 0:
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print(tensor.shape)
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assert tensor.shape == shape, "{} does not match {}".format(tensor.shape, shape)
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class AsyncGradientBucket(object):
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def __init__(self):
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self.bucket = OrderedDict()
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def __len__(self):
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return len(self.bucket)
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def push(self, async_op, grad_tensor, param_id):
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self.bucket[param_id] = tuple((async_op, grad_tensor))
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return torch.zeros_like(grad_tensor, dtype=grad_tensor.dtype, device=grad_tensor.device)
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def pop(self, param_id):
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grad = None
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if param_id in self.bucket:
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op, grad = self.bucket.pop(param_id)
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if op is not None:
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op.wait()
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return grad
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def synchronize(self, params):
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for p in params:
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i = id(p)
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if i in self.bucket:
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op, grad = self.bucket.pop(i)
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if op is not None:
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op.wait()
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p.grad.add_(grad)
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_async_grad_bucket = AsyncGradientBucket()
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def push_async_grad(op, grad, param_id):
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return _async_grad_bucket.push(op, grad, param_id)
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def pop_async_grad(param_id):
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return _async_grad_bucket.pop(param_id)
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def _async_grad_hook(grad, param_id):
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grad.add_(pop_async_grad(param_id))
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return grad
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def register_async_grad_hook(param):
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param.register_hook(partial(_async_grad_hook, param_id=id(param)))
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def synchronize(params=list()):
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_async_grad_bucket.synchronize(params)
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torch.cuda.default_stream().synchronize()
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if len(_async_grad_bucket) > 0:
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raise RuntimeError(f"{len(_async_grad_bucket)} asynchronous gradient(s) not collected.")
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