"""Utility functions for training and inference.""" import math import pickle import sys from contextlib import nullcontext from io import BytesIO from pathlib import Path from typing import ContextManager, Dict, List, Mapping, Optional, TypeVar, Union import lightning as L import torch import torch.nn as nn import torch.utils._device from lightning.fabric.strategies import FSDPStrategy from lightning.fabric.utilities.load import _lazy_load as lazy_load from torch.serialization import normalize_storage_type def find_multiple(n: int, k: int) -> int: assert k > 0 if n % k == 0: return n return n + k - (n % k) def num_parameters(module: nn.Module, requires_grad: Optional[bool] = None) -> int: total = 0 for p in module.parameters(): if requires_grad is None or p.requires_grad == requires_grad: if hasattr(p, "quant_state"): # bitsandbytes 4bit layer support total += math.prod(p.quant_state[1]) else: total += p.numel() return total def gptq_quantization(enabled: bool = False) -> ContextManager: if not enabled: return nullcontext() from lightning.fabric.plugins.precision.utils import _ClassReplacementContextManager from quantize.gptq import ColBlockQuantizedLinear class QuantizedLinear(ColBlockQuantizedLinear): def __init__(self, *args, **kwargs): super().__init__(*args, bits=4, tile_cols=-1, **kwargs) return _ClassReplacementContextManager({"torch.nn.Linear": QuantizedLinear}) def check_valid_checkpoint_dir(checkpoint_dir: Path) -> None: files = { "lit_model.pth": (checkpoint_dir / "lit_model.pth").is_file(), "lit_config.json": (checkpoint_dir / "lit_config.json").is_file(), "tokenizer.json OR tokenizer.model": (checkpoint_dir / "tokenizer.json").is_file() or ( checkpoint_dir / "tokenizer.model" ).is_file(), "tokenizer_config.json": (checkpoint_dir / "tokenizer_config.json").is_file(), } if checkpoint_dir.is_dir(): if all(files.values()): # we're good return problem = f" is missing the files: {[f for f, exists in files.items() if not exists]!r}" else: problem = " is not a checkpoint directory" # list locally available checkpoints available = list(Path("checkpoints").glob("*/*")) if available: options = "\n --checkpoint_dir ".join([""] + [repr(str(p.resolve())) for p in available]) extra = f"\nYou have downloaded locally:{options}\n" else: extra = "" error_message = ( f"--checkpoint_dir {str(checkpoint_dir.absolute())!r}{problem}." "\nFind download instructions at https://github.com/Lightning-AI/lit-gpt/blob/main/tutorials\n" f"{extra}\nSee all download options by running:\n python scripts/download.py" ) print(error_message, file=sys.stderr) raise SystemExit(1) class SavingProxyForStorage: def __init__(self, obj, saver, protocol_version=5): self.protocol_version = protocol_version self.saver = saver if not (isinstance(obj, torch.storage.TypedStorage) or torch.is_storage(obj)): raise TypeError(f"expected storage, not {type(obj)}") # this logic is taken from PyTorch 2.0+ torch/serialization.py if isinstance(obj, torch.storage.TypedStorage): # PT upstream wants to deprecate this eventually... storage = obj._untyped_storage storage_type_str = obj._pickle_storage_type() storage_type = getattr(torch, storage_type_str) storage_numel = obj._size() else: storage = obj storage_type = normalize_storage_type(type(obj)) storage_numel = storage.nbytes() storage_key = saver._write_storage_and_return_key(storage) location = torch.serialization.location_tag(storage) self.storage_info = ("storage", storage_type, storage_key, location, storage_numel) def __reduce_ex__(self, protocol_version): assert False, "this should be handled with out of band" class SavingProxyForTensor: def __init__(self, tensor, saver, protocol_version=5): self.protocol_version = protocol_version self.reduce_ret_fn, reduce_args = tensor.__reduce_ex__(protocol_version) if reduce_args[0] == torch._utils._rebuild_tensor_v2: # for Tensors with Python attributes (a0, a1, (storage, *a2_other), *other_reduce_args) = reduce_args assert isinstance(storage, torch.storage.TypedStorage), "Please check for updates" storage_proxy = SavingProxyForStorage(storage, saver, protocol_version=protocol_version) self.reduce_args = (a0, a1, (storage_proxy, *a2_other), *other_reduce_args) else: (storage, *other_reduce_args) = reduce_args assert isinstance(storage, torch.storage.TypedStorage), "Please check for updates" storage_proxy = SavingProxyForStorage(storage, saver, protocol_version=protocol_version) self.reduce_args = (storage_proxy, *other_reduce_args) def __reduce_ex__(self, protocol_version): if protocol_version != self.protocol_version: raise RuntimeError(f"Unexpected protocol version: expected {self.protocol_version}, got {protocol_version}") return self.reduce_ret_fn, self.reduce_args class IncrementalPyTorchPickler(pickle.Pickler): def __init__(self, saver, *args, **kwargs): super().__init__(*args, **kwargs) self.storage_dtypes = {} self.saver = saver self.id_map = {} # this logic is taken from PyTorch 2.0+ torch/serialization.py def persistent_id(self, obj): # FIXME: the docs say that persistent_id should only return a string # but torch store returns tuples. This works only in the binary protocol # see # https://docs.python.org/2/library/pickle.html#pickling-and-unpickling-external-objects # https://github.com/python/cpython/blob/master/Lib/pickle.py#L527-L537 if isinstance(obj, SavingProxyForStorage): return obj.storage_info if isinstance(obj, torch.storage.TypedStorage) or torch.is_storage(obj): if isinstance(obj, torch.storage.TypedStorage): # TODO: Once we decide to break serialization FC, this case # can be deleted storage = obj._untyped_storage storage_dtype = obj.dtype storage_type_str = obj._pickle_storage_type() storage_type = getattr(torch, storage_type_str) storage_numel = obj._size() else: storage = obj storage_dtype = torch.uint8 storage_type = normalize_storage_type(type(obj)) storage_numel = storage.nbytes() # If storage is allocated, ensure that any other saved storages # pointing to the same data all have the same dtype. If storage is # not allocated, don't perform this check if storage.data_ptr() != 0: if storage.data_ptr() in self.storage_dtypes: if storage_dtype != self.storage_dtypes[storage.data_ptr()]: raise RuntimeError( "Cannot save multiple tensors or storages that view the same data as different types" ) else: self.storage_dtypes[storage.data_ptr()] = storage_dtype storage_key = self.id_map.get(storage._cdata) if storage_key is None: storage_key = self.saver._write_storage_and_return_key(storage) self.id_map[storage._cdata] = storage_key location = torch.serialization.location_tag(storage) return ("storage", storage_type, storage_key, location, storage_numel) return None class incremental_save: def __init__(self, name): self.name = name self.zipfile = torch._C.PyTorchFileWriter(str(name)) self.has_saved = False self.next_key = 0 def __enter__(self): return self def store_early(self, tensor): if isinstance(tensor, torch.Tensor): return SavingProxyForTensor(tensor, self) raise TypeError(f"can only store tensors early, not {type(tensor)}") def save(self, obj): if self.has_saved: raise RuntimeError("have already saved") # Write the pickle data for `obj` data_buf = BytesIO() pickler = IncrementalPyTorchPickler(self, data_buf, protocol=5) pickler.dump(obj) data_value = data_buf.getvalue() self.zipfile.write_record("data.pkl", data_value, len(data_value)) self.has_saved = True def _write_storage_and_return_key(self, storage): if self.has_saved: raise RuntimeError("have already saved") key = self.next_key self.next_key += 1 name = f"data/{key}" if storage.device.type != "cpu": storage = storage.cpu() num_bytes = storage.nbytes() self.zipfile.write_record(name, storage.data_ptr(), num_bytes) return key def __exit__(self, type, value, traceback): self.zipfile.write_end_of_file() T = TypeVar("T") def chunked_cross_entropy( logits: Union[torch.Tensor, List[torch.Tensor]], targets: torch.Tensor, chunk_size: int = 128 ) -> torch.Tensor: # with large max_sequence_lengths, the beginning of `backward` allocates a large memory chunk which can dominate # the memory usage in fine-tuning settings with low number of parameters. # as a workaround hack, the cross entropy computation is chunked to force it to deallocate on the go, reducing # the memory spike's magnitude # lm_head was chunked (we are fine-tuning) if isinstance(logits, list): # don't want to chunk cross entropy if chunk_size == 0: logits = torch.cat(logits, dim=1) logits = logits.reshape(-1, logits.size(-1)) targets = targets.reshape(-1) return torch.nn.functional.cross_entropy(logits, targets, ignore_index=-1) # chunk cross entropy logit_chunks = [logit_chunk.reshape(-1, logit_chunk.size(-1)) for logit_chunk in logits] target_chunks = [target_chunk.reshape(-1) for target_chunk in targets.split(logits[0].size(1), dim=1)] loss_chunks = [ torch.nn.functional.cross_entropy(logit_chunk, target_chunk, ignore_index=-1, reduction="none") for logit_chunk, target_chunk in zip(logit_chunks, target_chunks) ] return torch.cat(loss_chunks).mean() # no chunking at all logits = logits.reshape(-1, logits.size(-1)) targets = targets.reshape(-1) if chunk_size == 0: return torch.nn.functional.cross_entropy(logits, targets, ignore_index=-1) # lm_head wasn't chunked, chunk cross entropy logit_chunks = logits.split(chunk_size) target_chunks = targets.split(chunk_size) loss_chunks = [ torch.nn.functional.cross_entropy(logit_chunk, target_chunk, ignore_index=-1, reduction="none") for logit_chunk, target_chunk in zip(logit_chunks, target_chunks) ] return torch.cat(loss_chunks).mean() def map_old_state_dict_weights(state_dict: Dict, mapping: Mapping, prefix: str) -> Dict: for checkpoint_name, attribute_name in mapping.items(): full_checkpoint_name = prefix + checkpoint_name if full_checkpoint_name in state_dict: full_attribute_name = prefix + attribute_name state_dict[full_attribute_name] = state_dict.pop(full_checkpoint_name) return state_dict def get_default_supported_precision(training: bool) -> str: """Return default precision that is supported by the hardware: either `bf16` or `16`. Args: training: `-mixed` or `-true` version of the precision to use Returns: default precision that is suitable for the task and is supported by the hardware """ from lightning.fabric.accelerators import MPSAccelerator if MPSAccelerator.is_available() or (torch.cuda.is_available() and not torch.cuda.is_bf16_supported()): return "16-mixed" if training else "16-true" return "bf16-mixed" if training else "bf16-true" def load_checkpoint(fabric: L.Fabric, model: nn.Module, checkpoint_path: Path, strict: bool = True) -> None: if isinstance(fabric.strategy, FSDPStrategy): fabric.load_raw(checkpoint_path, model, strict=strict) else: state_dict = lazy_load(checkpoint_path) state_dict = state_dict.get("model", state_dict) model.load_state_dict(state_dict, strict=strict)