TSAI_S22 / tsai_gpt /utils.py
ToletiSri's picture
Upload 26 files
dca102c
"""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 TYPE_CHECKING, 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
if TYPE_CHECKING:
from model import GPT
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)
def flops_per_param(max_seq_length: int, n_layer: int, n_embd: int, n_params: int) -> int:
flops_per_token = 2 * n_params # each parameter is used for a MAC (2 FLOPS) per network operation
# this assumes that all samples have a fixed length equal to the block size
# which is most likely false during finetuning
flops_per_seq = flops_per_token * max_seq_length
attn_flops_per_seq = n_layer * 2 * 2 * (n_embd * (max_seq_length**2))
return flops_per_seq + attn_flops_per_seq
def estimate_flops(model: "GPT", training: bool) -> int:
"""Measures estimated FLOPs for MFU.
Refs:
* https://ar5iv.labs.arxiv.org/html/2205.05198#A1
* https://ar5iv.labs.arxiv.org/html/2204.02311#A2
"""
# using all parameters for this is a naive over estimation because not all model parameters actually contribute to
# this FLOP computation (e.g. embedding, norm). For this reason, the result will be higher by a fixed percentage
# (~10%) compared to the measured FLOPs, making those lower but more realistic.
# For a proper estimate, this needs a more fine-grained calculation as in Appendix A of the paper.
n_trainable_params = num_parameters(model, requires_grad=True)
trainable_flops = flops_per_param(
model.max_seq_length, model.config.n_layer, model.config.n_embd, n_trainable_params
)
# forward + backward + gradients (assumes no gradient accumulation)
ops_per_step = 3 if training else 1
n_frozen_params = num_parameters(model, requires_grad=False)
frozen_flops = flops_per_param(model.max_seq_length, model.config.n_layer, model.config.n_embd, n_frozen_params)
# forward + backward
frozen_ops_per_step = 2 if training else 1
return ops_per_step * trainable_flops + frozen_ops_per_step * frozen_flops