#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import os
import random
import socket
from pathlib import Path
from typing import Callable, List, Union, Dict, Optional
import functools
import torch
from torch._six import inf
from torch.nn.parameter import Parameter
try:
import colossal_C
except:
pass
from contextlib import contextmanager
import torch.distributed as dist
from colossalai.constants import (IS_TENSOR_PARALLEL, NUM_PARTITIONS, TENSOR_PARALLEL_ATTRIBUTES)
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.global_variables import tensor_parallel_env as env
from .multi_tensor_apply import multi_tensor_applier
from colossalai.tensor import ColoParameter, ProcessGroup
from collections import defaultdict
def print_rank_0(msg: str, logger=None):
"""Print messages and save logs(optional). This is executed only if you are the rank-0 gpu.
Args:
msg (str): A string message to output.
logger (:class:`colossalai.logging.DistributedLogger`, optional):
The logger to record the message, defaults to None.
"""
if gpc.get_global_rank() == 0:
if logger is None:
print(msg, flush=True)
else:
logger.info(msg)
def ensure_path_exists(filename: str):
# ensure the path exists
dirpath = os.path.dirname(filename)
if not os.path.exists(dirpath):
Path(dirpath).mkdir(parents=True, exist_ok=True)
def free_port():
while True:
try:
sock = socket.socket()
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
port = random.randint(20000, 65000)
sock.bind(('localhost', port))
sock.close()
return port
except Exception:
continue
def sync_model_param(model, parallel_mode):
r"""Make sure data parameters are consistent during Data Parallel Mode.
Args:
model (:class:`torch.nn.Module`): A pyTorch model on whose parameters you check the consistency.
parallel_mode (:class:`colossalai.context.ParallelMode`): Parallel mode to be checked.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
if gpc.is_initialized(parallel_mode) and gpc.get_world_size(parallel_mode) > 1:
for param in model.parameters():
ranks = gpc.get_ranks_in_group(parallel_mode)
dist.broadcast(param, src=ranks[0], group=gpc.get_group(parallel_mode))
def is_dp_rank_0():
return not gpc.is_initialized(ParallelMode.DATA) or gpc.is_first_rank(ParallelMode.DATA)
def is_tp_rank_0():
return not gpc.is_initialized(ParallelMode.TENSOR) or gpc.is_first_rank(ParallelMode.TENSOR)
def is_no_pp_or_last_stage():
return not gpc.is_initialized(ParallelMode.PIPELINE) or gpc.is_last_rank(ParallelMode.PIPELINE)
def is_using_ddp():
return gpc.is_initialized(ParallelMode.DATA) and gpc.get_world_size(ParallelMode.DATA) > 1
def is_using_pp():
return gpc.is_initialized(ParallelMode.PIPELINE) and gpc.get_world_size(ParallelMode.PIPELINE) > 1
def is_using_sequence():
return gpc.is_initialized(ParallelMode.SEQUENCE) and gpc.get_world_size(ParallelMode.SEQUENCE) > 1
@contextmanager
def conditional_context(context_manager, enable=True):
if enable:
with context_manager:
yield
else:
yield
class model_branch_context(object):
def __enter__(self):
self.env_status = env.save()
def __exit__(self, *exc_info):
env.load(**self.env_status)
def is_model_parallel_parameter(p):
return hasattr(p, IS_TENSOR_PARALLEL) and getattr(p, IS_TENSOR_PARALLEL)
def _calc_l2_norm(grads):
norm = 0.0
if len(grads) > 0:
dummy_overflow_buf = torch.cuda.IntTensor([0])
norm, _ = multi_tensor_applier(
colossal_C.multi_tensor_l2norm,
dummy_overflow_buf,
[grads],
False # no per-parameter norm
)
return norm
def _calc_lp(grads, norm_type):
norm = 0.0
for grad in grads:
grad_norm = torch.norm(grad, norm_type)
norm += grad_norm**norm_type
return norm
def _move_norm_to_cuda(norm: Union[float, torch.Tensor]) -> Union[float, torch.Tensor]:
if torch.is_tensor(norm) and norm.device.type != 'cuda':
norm = norm.to(torch.cuda.current_device())
return norm
def _get_tensor_norm(norm: Union[float, torch.Tensor], move_to_cuda) -> torch.Tensor:
if isinstance(norm, float):
norm = torch.Tensor([norm])
if move_to_cuda:
norm = norm.to(torch.cuda.current_device())
return norm
# ======== Gradient Clipping =========
def _compute_local_lp(params: List[ColoParameter], norm_type: float) -> float:
if len(params) == 0:
return 0.0
grads = [p.grad for p in params]
use_cuda_kernel = grads[0].device.type == 'cuda'
if norm_type == inf:
local_lp = max([g.abs().max() for g in grads])
elif norm_type == 2.0 and use_cuda_kernel:
local_lp = _calc_l2_norm(grads)**norm_type
else:
local_lp = _calc_lp(grads, norm_type)
if isinstance(local_lp, torch.Tensor):
return local_lp.item()
return local_lp
def _compute_buckets_lp(params: List[ColoParameter], norm_type: float) -> float:
if len(params) == 0:
return 0.0
buckets: Dict[Optional[ProcessGroup], List[ColoParameter]] = defaultdict(list)
for p in params:
if p.is_replicate():
buckets[None].append(p)
else:
buckets[p.get_process_group().tp_process_group()].append(p)
total_lp = 0.0
for group, bucket in buckets.items():
local_lp = _compute_local_lp(bucket, norm_type)
if group is not None:
local_lp_tensor = torch.tensor([local_lp], device=torch.cuda.current_device())
if norm_type == inf:
dist.all_reduce(local_lp_tensor, op=dist.ReduceOp.MAX, group=group)
else:
dist.all_reduce(local_lp_tensor, group=group)
local_lp = local_lp_tensor.item()
if norm_type == inf:
total_lp = max(total_lp, local_lp)
else:
total_lp += local_lp
return total_lp
def _compute_pp_grad_lp(total_lp: float, norm_type: float) -> float:
if gpc.is_initialized(ParallelMode.PIPELINE) and gpc.get_world_size(ParallelMode.PIPELINE) > 1:
total_lp_tensor = torch.tensor([total_lp], device=torch.cuda.current_device())
if norm_type == inf:
dist.all_reduce(total_lp_tensor, op=dist.ReduceOp.MAX, group=gpc.get_group(ParallelMode.PIPELINE))
else:
dist.all_reduce(total_lp_tensor, group=gpc.get_group(ParallelMode.PIPELINE))
total_lp = total_lp_tensor.item()
return total_lp
def _compute_grad_lp(parameters, norm_type: float = 2.0) -> float:
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
grad_dtype = None
cpu_grad_params: List[ColoParameter] = []
cuda_grad_params: List[ColoParameter] = []
for p in parameters:
if p.grad is None:
continue
assert isinstance(p, ColoParameter)
if grad_dtype is None:
grad_dtype = p.grad.dtype
assert p.grad.dtype == grad_dtype, f'Expected all grads are {grad_dtype}, got {p.grad.dtype}'
if p.grad.device.type == 'cuda':
cuda_grad_params.append(p)
else:
cpu_grad_params.append(p)
norm_type = float(norm_type)
cpu_lp = _compute_buckets_lp(cpu_grad_params, norm_type)
cuda_lp = _compute_buckets_lp(cuda_grad_params, norm_type)
if norm_type == inf:
total_lp = max(cpu_lp, cuda_lp)
else:
total_lp = cpu_lp + cuda_lp
return _compute_pp_grad_lp(total_lp, norm_type)
def compute_grad_norm(parameters, norm_type: float = 2.0) -> float:
norm_type = float(norm_type)
total_norm = _compute_grad_lp(parameters, norm_type)
if norm_type != inf:
total_norm = total_norm**(1 / norm_type)
return total_norm
def _clip_grad_norm(parameters, max_norm: float, total_norm: float) -> None:
clip_coef = max_norm / (total_norm + 1e-6)
if clip_coef < 1.0:
cuda_grads: List[torch.Tensor] = []
cpu_grads: List[torch.Tensor] = []
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
for p in parameters:
if p.grad is None:
continue
if p.grad.device.type == 'cuda':
cuda_grads.append(p.grad.detach())
else:
cpu_grads.append(p.grad.detach())
if len(cuda_grads) > 0:
dummy_overflow_buf = torch.cuda.IntTensor([0])
multi_tensor_applier(colossal_C.multi_tensor_scale, dummy_overflow_buf, [cuda_grads, cuda_grads], clip_coef)
for g in cpu_grads:
g.mul_(clip_coef)
def clip_grad_norm(parameters, max_norm: float, norm_type: float = 2.0) -> float:
total_norm = compute_grad_norm(parameters, norm_type)
_clip_grad_norm(parameters, max_norm, total_norm)
return total_norm
def clip_grad_norm_fp32(parameters, max_norm, norm_type=2):
"""Clips gradient norm of an iterable of parameters whose gradients are in fp32.
This is adapted from :func:`torch.nn.utils.clip_grad.clip_grad_norm_` and
added functionality to handle model parallel parameters.
Note:
the gradients are modified in place.
Args:
parameters (Iterable[:class:`torch.tensor`] or :class:`torch.tensor`):
An iterable of Tensors or a single Tensor that will have gradients normalized.
max_norm (Union[float, int]): Max norm of the gradients.
norm_type (Union[float, int, 'inf']): Type of the used p-norm. Can be ``'inf'`` for infinity norm.
Returns:
float: Total norm of the parameters.
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
# Filter parameters based on:
# - grad should not be none
# - parameter should not be shared
# - should not be a replica due to tensor model parallelism
params: List[Parameter] = []
has_zero_shared_param: bool = False
for param in parameters:
if param.grad is not None:
# Make sure the grads are in fp32
assert param.grad.dtype == torch.float, \
f'expected gradient to be dtype torch.float, but got {param.grad.type()}'
if hasattr(param, 'colo_attr') and param.colo_attr.sharded_data_tensor.is_sharded:
has_zero_shared_param = True
params.append(param)
if len(params) == 0:
enable_cuda_kernels = False
else:
enable_cuda_kernels = params[0].grad.device.type == 'cuda'
# Norm parameters.
max_norm = float(max_norm)
norm_type = float(norm_type)
# Parameters can be on CPU or CUDA
# If parameters are on CPU, disable CUDA kernerls
# Calculate norm.
if norm_type == inf:
total_norm = max(p.grad.data.abs().max() for p in params)
total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
# Take max across all model-parallel GPUs.
if gpc.is_initialized(ParallelMode.MODEL) and gpc.get_world_size(ParallelMode.MODEL) > 1:
dist.all_reduce(total_norm_cuda,
op=dist.ReduceOp.MAX,
group=gpc.get_group(ParallelMode.MODEL),
async_op=False)
if has_zero_shared_param:
dist.all_reduce(total_norm_cuda,
op=dist.ReduceOp.MAX,
group=gpc.get_group(ParallelMode.DATA),
async_op=False)
total_norm = total_norm_cuda[0].item()
else:
tensor_parallel_grads = []
no_tensor_parallel_grads = []
zero_sharded_grads = []
for p in params:
if is_model_parallel_parameter(p):
reductor = (gpc.get_world_size(ParallelMode.TENSOR) / getattr(p, NUM_PARTITIONS))**(1 / norm_type)
tensor_parallel_grads.append(p.grad.data / reductor)
elif hasattr(p, 'colo_attr') and p.colo_attr.sharded_data_tensor.is_sharded:
zero_sharded_grads.append(p.grad.data)
else:
no_tensor_parallel_grads.append(p.grad.data)
if norm_type == 2.0 and enable_cuda_kernels:
tensor_parallel_norm = _calc_l2_norm(tensor_parallel_grads)**norm_type
no_tensor_parallel_norm = _calc_l2_norm(no_tensor_parallel_grads)**norm_type
zero_sharded_norm = _calc_l2_norm(zero_sharded_grads)**norm_type
else:
tensor_parallel_norm = _calc_lp(tensor_parallel_grads, norm_type)
no_tensor_parallel_norm = _calc_lp(no_tensor_parallel_grads, norm_type)
zero_sharded_norm = _calc_lp(zero_sharded_grads, norm_type)
# If norm is type of float, then we convert them into torch.Tensor.
tensor_parallel_norm = _get_tensor_norm(tensor_parallel_norm, enable_cuda_kernels)
no_tensor_parallel_norm = _get_tensor_norm(no_tensor_parallel_norm, enable_cuda_kernels)
zero_sharded_norm = _get_tensor_norm(zero_sharded_norm, enable_cuda_kernels)
# If grads are on CPU, the norms is also on CPU. Cast them to CUDA tensors
if not enable_cuda_kernels:
tensor_parallel_norm = _move_norm_to_cuda(tensor_parallel_norm)
no_tensor_parallel_norm = _move_norm_to_cuda(no_tensor_parallel_norm)
zero_sharded_norm = _move_norm_to_cuda(zero_sharded_norm)
# Sum across all model-parallel GPUs.
if gpc.is_initialized(ParallelMode.TENSOR) and len(tensor_parallel_grads) > 0:
dist.all_reduce(tensor_parallel_norm, op=dist.ReduceOp.SUM, group=gpc.get_group(ParallelMode.TENSOR))
# Sum across all zero sharded GPUs
if len(zero_sharded_grads) > 0:
dist.all_reduce(zero_sharded_norm, group=gpc.get_group(ParallelMode.DATA))
no_tensor_parallel_norm += zero_sharded_norm
total_norm = tensor_parallel_norm + no_tensor_parallel_norm
if gpc.is_initialized(ParallelMode.PIPELINE) and gpc.get_world_size(ParallelMode.PIPELINE) > 1:
dist.all_reduce(total_norm, op=dist.ReduceOp.SUM, group=gpc.get_group(ParallelMode.PIPELINE))
total_norm = total_norm**(1.0 / norm_type)
if torch.is_tensor(total_norm):
total_norm = total_norm.item()
# Scale.
clip_coeff = max_norm / (total_norm + 1.0e-6)
if clip_coeff < 1.0:
if enable_cuda_kernels:
grads = [p.grad.detach() for p in params]
dummy_overflow_buf = torch.cuda.IntTensor([0])
multi_tensor_applier(colossal_C.multi_tensor_scale, dummy_overflow_buf, [grads, grads], clip_coeff)
else:
for p in params:
p.grad.detach().mul_(clip_coeff)
return total_norm
def count_zeros_fp32(parameters):
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
# Filter parameters based on:
# - grad should not be none
# - parameter should not be shared
# - should not be a replica due to tensor model parallelism
total_num_zeros = 0.0
for param in parameters:
grad_not_none = param.grad is not None
is_not_tp_duplicate = param_is_not_tensor_parallel_duplicate(param)
if grad_not_none and is_not_tp_duplicate:
grad = param.grad.detach()
num_zeros = grad.numel() - torch.count_nonzero(grad)
total_num_zeros = num_zeros + total_num_zeros
total_num_zeros = torch.IntTensor([int(total_num_zeros)]).cuda()
# Sum across all model-parallel GPUs.
ops = []
ops.append(
dist.all_reduce(total_num_zeros, op=dist.ReduceOp.SUM, group=gpc.get_group(ParallelMode.TENSOR), async_op=True))
if gpc.is_initialized(ParallelMode.PIPELINE):
ops.append(
dist.all_reduce(total_num_zeros,
op=dist.ReduceOp.SUM,
group=gpc.get_group(ParallelMode.PIPELINE),
async_op=True))
for req in ops:
req.wait()
total_num_zeros = total_num_zeros.item()
return total_num_zeros
def copy_tensor_parallel_attributes(src_tensor, dst_tensor):
for attr in TENSOR_PARALLEL_ATTRIBUTES:
if hasattr(src_tensor, attr):
val = getattr(src_tensor, attr)
setattr(dst_tensor, attr, val)
def param_is_not_tensor_parallel_duplicate(param):
return (hasattr(param, IS_TENSOR_PARALLEL) and getattr(param, IS_TENSOR_PARALLEL)) or (gpc.get_local_rank(
ParallelMode.TENSOR) == 0)
@contextmanager
def switch_virtual_pipeline_parallel_rank(rank):
prev_rank = gpc.virtual_pipeline_parallel_rank
try:
gpc.set_virtual_pipeline_parallel_rank(rank)
yield
finally:
gpc.set_virtual_pipeline_parallel_rank(prev_rank)
def disposable(func: Callable) -> Callable:
executed = False
@functools.wraps(func)
def wrapper(*args, **kwargs):
nonlocal executed
if not executed:
executed = True
return func(*args, **kwargs)
return wrapper