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# This file may have been modified by Flash-VStream Authors (Flash-VStream Modifications”). All Flash-VStream Modifications are Copyright 2024 Flash-VStream Authors.
# ------------------------------------------------------------------------
# Based on https://github.com/haotian-liu/LLaVA. Below is the original copyright:
# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
import torch.nn as nn
from torch.utils.data import Sampler
from transformers import Trainer
from transformers.trainer import (
is_sagemaker_mp_enabled,
get_parameter_names,
has_length,
ALL_LAYERNORM_LAYERS,
ShardedDDPOption,
logger,
)
from typing import List, Optional
def maybe_zero_3(param, ignore_status=False, name=None):
from deepspeed import zero
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
if hasattr(param, "ds_id"):
if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
if not ignore_status:
print(name, 'no ignore status')
with zero.GatheredParameters([param]):
param = param.data.detach().cpu().clone()
else:
param = param.detach().cpu().clone()
return param
def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
to_return = {k: maybe_zero_3(v, ignore_status=True, name=k).cpu() for k, v in to_return.items()}
return to_return
def split_to_even_chunks(indices, lengths, num_chunks):
"""
Split a list of indices into `chunks` chunks of roughly equal lengths.
"""
if len(indices) % num_chunks != 0:
return [indices[i::num_chunks] for i in range(num_chunks)]
num_indices_per_chunk = len(indices) // num_chunks
chunks = [[] for _ in range(num_chunks)]
chunks_lengths = [0 for _ in range(num_chunks)]
for index in indices:
shortest_chunk = chunks_lengths.index(min(chunks_lengths))
chunks[shortest_chunk].append(index)
chunks_lengths[shortest_chunk] += lengths[index]
if len(chunks[shortest_chunk]) == num_indices_per_chunk:
chunks_lengths[shortest_chunk] = float("inf")
return chunks
def get_modality_length_grouped_indices(lengths, batch_size, world_size, generator=None):
# We need to use torch for the random part as a distributed sampler will set the random seed for torch.
assert all(l != 0 for l in lengths), "Should not have zero length."
if all(l > 0 for l in lengths) or all(l < 0 for l in lengths):
# all samples are in the same modality
return get_length_grouped_indices(lengths, batch_size, world_size, generator=generator)
mm_indices, mm_lengths = zip(*[(i, l) for i, l in enumerate(lengths) if l > 0])
lang_indices, lang_lengths = zip(*[(i, -l) for i, l in enumerate(lengths) if l < 0])
mm_shuffle = [mm_indices[i] for i in get_length_grouped_indices(mm_lengths, batch_size, world_size, generator=None)]
lang_shuffle = [lang_indices[i] for i in get_length_grouped_indices(lang_lengths, batch_size, world_size, generator=None)]
megabatch_size = world_size * batch_size
mm_megabatches = [mm_shuffle[i : i + megabatch_size] for i in range(0, len(mm_shuffle), megabatch_size)]
lang_megabatches = [lang_shuffle[i : i + megabatch_size] for i in range(0, len(lang_shuffle), megabatch_size)]
last_mm = mm_megabatches[-1]
last_lang = lang_megabatches[-1]
additional_batch = last_mm + last_lang
megabatches = mm_megabatches[:-1] + lang_megabatches[:-1]
megabatch_indices = torch.randperm(len(megabatches), generator=generator)
megabatches = [megabatches[i] for i in megabatch_indices]
if len(additional_batch) > 0:
megabatches.append(sorted(additional_batch))
return [i for megabatch in megabatches for i in megabatch]
def get_length_grouped_indices(lengths, batch_size, world_size, generator=None, merge=True):
# We need to use torch for the random part as a distributed sampler will set the random seed for torch.
indices = torch.randperm(len(lengths), generator=generator)
megabatch_size = world_size * batch_size
megabatches = [indices[i : i + megabatch_size].tolist() for i in range(0, len(lengths), megabatch_size)]
megabatches = [sorted(megabatch, key=lambda i: lengths[i], reverse=True) for megabatch in megabatches]
megabatches = [split_to_even_chunks(megabatch, lengths, world_size) for megabatch in megabatches]
return [i for megabatch in megabatches for batch in megabatch for i in batch]
class LengthGroupedSampler(Sampler):
r"""
Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while
keeping a bit of randomness.
"""
def __init__(
self,
batch_size: int,
world_size: int,
lengths: Optional[List[int]] = None,
generator=None,
group_by_modality: bool = False,
):
if lengths is None:
raise ValueError("Lengths must be provided.")
self.batch_size = batch_size
self.world_size = world_size
self.lengths = lengths
self.generator = generator
self.group_by_modality = group_by_modality
def __len__(self):
return len(self.lengths)
def __iter__(self):
if self.group_by_modality:
indices = get_modality_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator)
else:
indices = get_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator)
return iter(indices)
class VStreamTrainer(Trainer):
def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
if self.train_dataset is None or not has_length(self.train_dataset):
return None
if self.args.group_by_modality_length:
lengths = self.train_dataset.modality_lengths
return LengthGroupedSampler(
self.args.train_batch_size,
world_size=self.args.world_size * self.args.gradient_accumulation_steps,
lengths=lengths,
group_by_modality=True,
)
else:
return super()._get_train_sampler()
def create_optimizer(self):
"""
Setup the optimizer.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass and override this method in a subclass.
"""
if is_sagemaker_mp_enabled():
return super().create_optimizer()
if self.sharded_ddp == ShardedDDPOption.SIMPLE:
return super().create_optimizer()
opt_model = self.model
if self.optimizer is None:
decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
decay_parameters = [name for name in decay_parameters if "bias" not in name]
if self.args.mm_projector_lr is not None:
projector_parameters = [name for name, _ in opt_model.named_parameters() if "mm_projector" in name]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in opt_model.named_parameters() if (n in decay_parameters and n not in projector_parameters and p.requires_grad)
],
"weight_decay": self.args.weight_decay,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n not in projector_parameters and p.requires_grad)
],
"weight_decay": 0.0,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n in decay_parameters and n in projector_parameters and p.requires_grad)
],
"weight_decay": self.args.weight_decay,
"lr": self.args.mm_projector_lr,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n in projector_parameters and p.requires_grad)
],
"weight_decay": 0.0,
"lr": self.args.mm_projector_lr,
},
]
else:
optimizer_grouped_parameters = [
{
"params": [
p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad)
],
"weight_decay": self.args.weight_decay,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)
],
"weight_decay": 0.0,
},
]
optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(self.args)
self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
if optimizer_cls.__name__ == "Adam8bit":
import bitsandbytes
manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
skipped = 0
for module in opt_model.modules():
if isinstance(module, nn.Embedding):
skipped += sum({p.data_ptr(): p.numel() for p in module.parameters()}.values())
logger.info(f"skipped {module}: {skipped/2**20}M params")
manager.register_module_override(module, "weight", {"optim_bits": 32})
logger.debug(f"bitsandbytes: will optimize {module} in fp32")
logger.info(f"skipped: {skipped/2**20}M params")
return self.optimizer
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