540 lines
15 KiB
Python
Executable File
540 lines
15 KiB
Python
Executable File
"""
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# https://github.com/enhuiz/pytorch-training-utilities
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"""
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from .distributed import global_rank, local_rank, global_leader_only
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import gc
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import logging
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import pandas as pd
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import numpy as np
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import re
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import torch
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import random
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import time
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import psutil
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import math
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import logging
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_logger = logging.getLogger(__name__)
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from coloredlogs import ColoredFormatter
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from logging import StreamHandler
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from pathlib import Path
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from torch import Tensor, nn
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from tqdm.auto import tqdm
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from typing import Callable, TypeVar, overload
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from contextlib import contextmanager
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T = TypeVar("T")
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def truncate_json( str ):
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def fun( match ):
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return "{:.4f}".format(float(match.group()))
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return re.sub(r"\d+\.\d{8,}", fun, str)
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def do_gc():
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gc.collect()
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torch.cuda.empty_cache()
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def flatten_dict(d):
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records = pd.json_normalize(d).to_dict(orient="records")
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return records[0] if records else {}
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def set_seed(seed=None):
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if not seed:
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seed = int(time.time())
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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def _get_named_modules(module, attrname):
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for name, module in module.named_modules():
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if hasattr(module, attrname):
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yield name, module
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def gather_attribute(module, attrname, delete=True, prefix=True):
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ret = {}
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for name, module in _get_named_modules(module, attrname):
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ret[name] = getattr(module, attrname)
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if delete:
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try:
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delattr(module, attrname)
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except Exception as e:
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raise RuntimeError(f"{name} {module} {attrname}") from e
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if prefix:
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ret = {attrname: ret}
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ret = flatten_dict(ret)
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# remove consecutive dots
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ret = {re.sub(r"\.+", ".", k): v for k, v in ret.items()}
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return ret
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def dispatch_attribute(
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module,
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attrname,
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value,
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filter_fn: Callable[[nn.Module], bool] | None = None,
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):
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for _, module in _get_named_modules(module, attrname):
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if filter_fn is None or filter_fn(module):
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setattr(module, attrname, value)
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def load_state_dict_non_strict(model, state_dict, logger=None):
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model_state_dict = model.state_dict()
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provided = set(state_dict)
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required = set(model_state_dict)
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agreed = provided & required
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for k in list(agreed):
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if model_state_dict[k].shape != state_dict[k].shape:
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agreed.remove(k)
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provided.remove(k)
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state_dict = {k: state_dict[k] for k in agreed}
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if logger is not None and (diff := provided - required):
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logger.warning(
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f"Extra parameters are found. "
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f"Provided but not required parameters: \n{diff}."
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)
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if logger is not None and (diff := required - provided):
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logger.warning(
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f"Some parameters are missing. "
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f"Required but not provided parameters: \n{diff}."
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)
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model.load_state_dict(state_dict, strict=False)
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class TqdmLoggingHandler(logging.Handler):
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def __init__(self, level=logging.INFO):
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super().__init__(level)
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def emit(self, record):
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try:
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msg = self.format(record)
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tqdm.write(msg)
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self.flush()
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except Exception as e:
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self.handleError(record)
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@global_leader_only
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def setup_logging(log_dir: str | Path | None = None, log_level="info"):
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handlers = []
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#stdout_handler = StreamHandler()
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stdout_handler = TqdmLoggingHandler()
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stdout_handler.setLevel(logging.INFO)
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formatter = ColoredFormatter(
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f"%(asctime)s - %(name)s - %(levelname)s - GR={global_rank()};LR={local_rank()} - \n%(message)s"
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)
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stdout_handler.setFormatter(formatter)
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handlers.append(stdout_handler)
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if log_dir is not None:
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filename = Path(log_dir) / f"log.txt"
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filename.parent.mkdir(parents=True, exist_ok=True)
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file_handler = logging.FileHandler(filename, mode="a")
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file_handler.setLevel(logging.DEBUG)
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handlers.append(file_handler)
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logging.basicConfig(
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level=logging.getLevelName(log_level.upper()),
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format="%(asctime)s - %(name)s - %(levelname)s - \n%(message)s",
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handlers=handlers,
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)
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@overload
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def tree_map(fn: Callable, x: list[T]) -> list[T]:
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...
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@overload
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def tree_map(fn: Callable, x: tuple[T]) -> tuple[T]:
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...
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@overload
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def tree_map(fn: Callable, x: dict[str, T]) -> dict[str, T]:
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...
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@overload
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def tree_map(fn: Callable, x: T) -> T:
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...
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def tree_map(fn: Callable, x):
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if isinstance(x, list):
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x = [tree_map(fn, xi) for xi in x]
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elif isinstance(x, tuple):
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x = (tree_map(fn, xi) for xi in x)
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elif isinstance(x, dict):
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x = {k: tree_map(fn, v) for k, v in x.items()}
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elif isinstance(x, Tensor):
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x = fn(x)
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return x
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def to_device(x: T | None, *args, **kwargs) -> T:
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if x is None:
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return
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return tree_map(lambda t: t.to(*args, **kwargs), x)
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def coalese( *arg, return_last=True ):
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return [ x for x in arg if x is not None ][-1 if return_last else 0]
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# checks if a module name is within a given whitelist/blacklist policy dict
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def passes_policy( policy, name ):
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if policy is None:
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return True
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if "exclude" in policy:
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for term in policy["exclude"]:
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if term in name:
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return False
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if "include" in policy:
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for term in policy["include"]:
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if term in name:
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return True
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return False
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# handles generically converting to a specific tensor type and converting back (implemented solely for bfloat16)
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@contextmanager
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def autocast(input, from_dtype, to_dtype):
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if input.dtype == from_dtype:
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input = input.to(to_dtype)
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yield input
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input = input.to(from_dtype)
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else:
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yield input
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@contextmanager
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def autocasts(input, from_dtype, to_dtype):
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if input.dtype in from_dtype:
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from_dtype = input.dtype
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input = input.to(to_dtype)
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yield input
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input = input.to(from_dtype)
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else:
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yield input
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# handles temporarily upcasting 'index tensors' so torch will stop bitching
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def autocast_forward( func ):
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def wrapper( self, input, *args, **kwargs ):
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with autocasts( input, [torch.int16, torch.int8, torch.uint8, torch.float16, torch.bfloat16], torch.int32 ) as k:
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return func( self, k, *args, **kwargs )
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return wrapper
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# handles migrating an input tensor to a given devicve
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def auto_align_inputs_forward( module, device=None, name = None ):
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func = module.forward
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if device is None:
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if hasattr( module, 'device' ):
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device = module.device
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else:
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try:
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device = next(module.parameters() if [*module.parameters()] else module.buffers()).device
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except Exception as e:
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return func
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def wrapper( *args, **kwargs ):
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args = [*args]
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# search through args and kwargs for any Tensor arguments
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for i, arg in enumerate(args):
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if not isinstance( arg, torch.Tensor ):
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continue
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args[i] = arg.to( device=device )
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for k, v in kwargs.items():
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if not isinstance( v, torch.Tensor ):
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continue
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kwargs[k] = v.to( device=device )
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# disgusting patch
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if "position_embeddings" in kwargs:
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kwargs["position_embeddings"] = tuple([ t.to(device=device) for t in kwargs["position_embeddings"] ])
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return func( *args, **kwargs )
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return wrapper
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# disgusting kludge, but it works (just realized BitNet has its own replacement routine)
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# generalizing this would be super sugoi but the there's no catch all for arguments
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def replace_linear( model, klass, target=torch.nn.Linear, verbose=False ):
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bnb = cfg.optimizations.bitsandbytes and cfg.optimizations.linear and not cfg.optimizations.bitnet
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device = next(model.parameters()).device
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dtype = next(model.parameters()).dtype
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modules = [k.split('.') for k, m in model.named_modules() if isinstance(m, target)]
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for *parent, k in modules:
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name = '.'.join(parent)
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m = getattr( model.get_submodule(name), k )
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if isinstance(m, klass):
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continue
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kwargs = dict(
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in_features = m.in_features,
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out_features = m.out_features,
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bias = m.bias is not None,
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) if not bnb else dict(
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input_features=m.in_features,
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output_features=m.out_features,
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bias=m.bias is not None,
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)
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# overwrite
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setattr(
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model.get_submodule(name), k,
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klass( **kwargs ).to(device=device, dtype=dtype)
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)
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if verbose:
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_logger.info(f"Replacing {name}.{k} to: {klass}")
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return model
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def replace_embedding( model, klass, target=torch.nn.Embedding, verbose=False ):
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device = next(model.parameters()).device
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dtype = next(model.parameters()).dtype
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modules = [k.split('.') for k, m in model.named_modules() if isinstance(m, target)]
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for *parent, k in modules:
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name = '.'.join(parent)
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m = getattr( model.get_submodule(name), k )
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if isinstance(m, klass):
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continue
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kwargs = dict(
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num_embeddings=m.num_embeddings,
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embedding_dim=m.embedding_dim,
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padding_idx=m.padding_idx,
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max_norm=m.max_norm,
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norm_type=m.norm_type,
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scale_grad_by_freq=m.scale_grad_by_freq,
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sparse=m.sparse,
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)
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# overwrite
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setattr(
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model.get_submodule(name), k,
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klass( **kwargs ).to(device=device, dtype=dtype)
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)
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if verbose:
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_logger.info(f"Replacing {name}.{k} to: {klass}")
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return model
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# cannot feasibly do default arguments here sad
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def replace_attention( model, klass, target, mode="math", verbose=False ):
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device = next(model.parameters()).device
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dtype = next(model.parameters()).dtype
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modules = [k.split('.') for k, m in model.named_modules() if isinstance(m, target)]
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for *parent, k in modules:
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name = '.'.join(parent)
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m = getattr( model.get_submodule(name), k )
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if isinstance(m, klass):
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continue
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kwargs = dict(
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config = m.config,
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layer_idx = m.layer_idx,
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mode = mode,
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)
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# overwrite
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setattr(
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model.get_submodule(name), k,
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klass( **kwargs ).to(device=device, dtype=dtype)
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)
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if verbose:
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_logger.info(f"Replacing {name}.{k} to: {klass}")
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return model
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# trim/expand a tensor (for example, in a state dict)
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def resize_weight( weight, target, dim=0, random=True ):
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# trim
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if target < weight.shape[dim]:
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return weight[:target]
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# expand
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if target > weight.shape[dim]:
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fn = torch.rand if random else torch.zeros
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return torch.stack(
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[ x for x in weight ] +
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[ fn( weight[0].shape ).to(device=weight[0].device, dtype=weight[0].dtype) for _ in range( target - weight.shape[dim] ) ]
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)
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return weight
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def get_devices():
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return [f'{"cuda"}:{i}' for i in range(torch.cuda.device_count())] + ['cpu']
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# grabs the memory properties of a given device
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def get_device_properties( device ):
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if 'cuda' in device:
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props = torch.cuda.get_device_properties(device)
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free, total = torch.cuda.mem_get_info(device)
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else:
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props = psutil.virtual_memory()
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free, total = props.available, props.total
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return {"name": device, "total": total, "free": free, "props": props}
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# gets the rough size for a given module's parameters
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def get_module_size( module ):
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param_size = sum([p.nelement() * p.element_size() for p in module.parameters()])
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buffer_size = sum([b.nelement() * b.element_size() for b in module.buffers()])
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return param_size + buffer_size
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# to-do: rewrite all this shit, I don't know what I was thinking when implementing it this way
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# it'd be better to just attach to layers itself rather than every single module
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# assigns modules to requested devices for a given policy
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def get_model_offload_policy(module, policy=None):
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# handle any other weird values this is set to
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if not isinstance(policy, dict):
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policy = {}
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# default to only include the core model, and not the other modules (embeddings) in the splitting policy
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if "include" not in policy:
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policy["include"] = ["model"]
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if "limits" not in policy:
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policy["limits"] = []
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if "assign" not in policy:
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policy["assign"] = []
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if "devices" not in policy:
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policy["devices"] = get_devices() # + cpu to spill the remainder on CPU if overbudget
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# create initial device info
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devices = [ get_device_properties(device) | {"modules": []} for device in policy["devices"] ]
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modules = [ (name, get_module_size(module)) for name, module in module.named_modules() if not [*module.named_children()] and passes_policy( policy, name ) ]
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# filter
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modules = [ (name, size) for name, size in modules if name and size ]
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total_size = sum([size for name, size in modules])
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# set caps if requested in the policy
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for i, cap in enumerate(policy["limits"]):
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# no limit, skip
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if cap <= 0:
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continue
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# is fractional, scale to total size
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if cap < 1:
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cap = math.floor(total_size * cap)
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# available space is below cap, don't set
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if devices[i]["free"] < cap:
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continue
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# cap to requested size
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devices[i]["free"] = cap
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# assign if specific parts of the model are requested for assignment
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if policy["assign"]:
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discarded = []
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# yuck, there has to be a better way
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for device_index, includes in enumerate( policy["assign"] ):
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device = devices[device_index]
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buffered_modules = []
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buffered_size = device["free"]
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# iterate through list of modules to compare against includes
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for name, size in modules:
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# doesn't pass policy
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if not passes_policy( {"include": includes}, name ):
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continue
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# check if within budget
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if buffered_size - size >= 0:
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# add to buffer
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buffered_modules.append( (name, size) )
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buffered_size -= size
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# budget exceeded, flush buffer
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else:
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discarded += buffered_modules
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buffered_modules = []
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buffered_size = 0
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break
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if buffered_modules and buffered_size:
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device["modules"] += [ name for name, size in buffered_modules ]
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device["free"] = buffered_size
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modules = discarded
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device_index = 0
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module_index = 0
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# assign modules to each device
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while module_index < len(modules) and device_index < len(devices):
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device = devices[device_index]
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name, size = modules[module_index]
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# fits within budget
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if device["free"] - size >= 0:
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device["modules"].append( name )
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device["free"] -= size
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module_index += 1
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# does not fit in budget, increase device index
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else:
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device_index += 1
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_logger.info(f"Over budget for device: {device['name']}, shifting to next device: {name}, {size / (1024 ** 2)}MiB")
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# to-do: check that all modules are exhausted
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assert module_index >= len(modules)
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# only return devices with modules assigned
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return [ device for device in devices if device["modules"] ]
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# handles naively splitting a model's layers across multiple devices
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# this apparently works for training too? the test trainer seemed fine with it split between GPU and CPU
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def offload_model( model, policy=None ):
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policy = get_model_offload_policy(model, policy=policy)
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# move modules to respective devices
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for i, device in enumerate( policy ):
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# nothing assigned, skip
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if not device["modules"]:
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continue
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for name in device["modules"]:
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module = model.get_submodule(name)
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module = module.to( device["name"] )
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module.device = device['name']
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# wrap modules with forward to ensure all inputs are matched to its device
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for name, module in model.named_modules():
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if not hasattr( module, 'forward' ):
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continue
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module.forward = auto_align_inputs_forward(module)
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"""
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# Validate that the layers are all in the right spot
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for name, module in model.named_modules():
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if not not [*module.named_children()]:
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continue
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try:
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_logger.info( name, next(module.parameters()).device )
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except Exception as e:
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_logger.info( name, "?" )
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pass
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"""
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return model |