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1636 lines (1481 loc) · 59.9 KB
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#!/usr/bin/env python
# coding=utf-8
# Copyright 2021-2022 The HuggingFace & DALL·E Mini team. All rights reserved.
#
# 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.
"""
Training DALL·E Mini.
Script adapted from run_summarization_flax.py
"""
import io
import logging
import os
import sys
import tempfile
import time
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Any, Callable, NamedTuple, Optional
import datasets
import flax
import jax
import jax.numpy as jnp
import jaxlib
import numpy as np
import optax
import transformers
import wandb
from datasets import Dataset
from flax import core, struct, traverse_util
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.serialization import from_bytes, to_bytes
from flax.training.common_utils import onehot
from jax.experimental import PartitionSpec, maps
from jax.experimental.compilation_cache import compilation_cache as cc
from jax.experimental.pjit import pjit, with_sharding_constraint
from scalable_shampoo.distributed_shampoo import GraftingType, distributed_shampoo
from tqdm import tqdm
from transformers import HfArgumentParser
import dalle_mini
from dalle_mini.data import Dataset
from dalle_mini.model import (
DalleBart,
DalleBartConfig,
DalleBartTokenizer,
set_partitions,
)
try:
from google.cloud import storage
except:
storage = None
cc.initialize_cache("./jax_cache", max_cache_size_bytes=10 * 2**30)
logger = logging.getLogger(__name__)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": "The model checkpoint for weights initialization. "
"Don't set if you want to train a model from scratch. "
"W&B artifact references are supported in addition to the sources supported by `PreTrainedModel`."
},
)
config_name: Optional[str] = field(
default=None,
metadata={
"help": "Pretrained config name or path if not the same as model_name_or_path"
},
)
tokenizer_name: Optional[str] = field(
default=None,
metadata={
"help": "Pretrained tokenizer name or path if not the same as model_name_or_path"
},
)
dtype: Optional[str] = field(
default="float32",
metadata={
"help": "Floating-point format in which the computations will be performed (not the model weights). Choose one of `[float32, float16, bfloat16]`."
},
)
restore_state: Optional[bool] = field(
default=False,
metadata={
"help": "Restore optimizer and training state. Can be True (will retrieve associated wandb artifact), a local directory or a Google bucket path."
},
)
dropout: Optional[float] = field(
default=None,
metadata={"help": "Dropout rate. Overwrites config."},
)
activation_dropout: Optional[float] = field(
default=None,
metadata={"help": "Activation dropout rate. Overwrites config."},
)
attention_dropout: Optional[float] = field(
default=None,
metadata={"help": "Attention dropout rate. Overwrites config."},
)
def __post_init__(self):
if self.tokenizer_name is None:
self.tokenizer_name = self.model_name_or_path
assert (
self.tokenizer_name is not None
), "Tokenizer name or model name/path needs to be specified"
if self.restore_state:
assert self.model_name_or_path is not None and (
"/model-" in self.model_name_or_path
), "Restoring state only available with W&B artifact reference"
def get_metadata(self):
if self.model_name_or_path is not None and ":" in self.model_name_or_path:
if jax.process_index() == 0:
artifact = wandb.run.use_artifact(self.model_name_or_path)
else:
artifact = wandb.Api().artifact(self.model_name_or_path)
return artifact.metadata
else:
return dict()
def get_opt_state(self):
with tempfile.TemporaryDirectory() as tmp_dir: # avoid multiple artifact copies
if self.restore_state is True:
# wandb artifact
state_artifact = self.model_name_or_path.replace(
"/model-", "/state-", 1
)
if jax.process_index() == 0:
artifact = wandb.run.use_artifact(state_artifact)
else:
artifact = wandb.Api().artifact(state_artifact)
if artifact.metadata.get("bucket_path"):
# we will read directly file contents
self.restore_state = artifact.metadata["bucket_path"]
else:
artifact_dir = artifact.download(tmp_dir)
self.restore_state = str(Path(artifact_dir) / "opt_state.msgpack")
if self.restore_state.startswith("gs://"):
bucket_path = Path(self.restore_state[5:]) / "opt_state.msgpack"
bucket, blob_name = str(bucket_path).split("/", 1)
assert (
storage is not None
), 'Could not find google.storage. Install with "pip install google-cloud-storage"'
client = storage.Client()
bucket = client.bucket(bucket)
blob = bucket.blob(blob_name)
return blob.download_as_bytes()
with Path(self.restore_state).open("rb") as f:
return f.read()
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
text_column: Optional[str] = field(
default="caption",
metadata={
"help": "The name of the column in the datasets containing the full texts (for summarization)."
},
)
encoding_column: Optional[str] = field(
default="encoding",
metadata={
"help": "The name of the column in the datasets containing the image encodings."
},
)
dataset_repo_or_path: str = field(
default=None,
metadata={"help": "The dataset repository containing encoded files."},
)
train_file: Optional[str] = field(
default=None,
metadata={
"help": "The input training data file (glob & braceexpand acceptable)."
},
)
validation_file: Optional[str] = field(
default=None,
metadata={
"help": "An optional input evaluation data file (glob & braceexpand acceptable)."
},
)
# data loading should not be a bottleneck so we use "streaming" mode by default
streaming: Optional[bool] = field(
default=True,
metadata={"help": "Whether to stream the dataset."},
)
use_auth_token: Optional[bool] = field(
default=False,
metadata={
"help": "Whether to use the authentication token for private datasets."
},
)
shard_by_host: Optional[bool] = field(
default=False,
metadata={
"help": "Whether to shard data files by host in multi-host environments."
},
)
blank_caption_prob: Optional[float] = field(
default=0.0,
metadata={
"help": "Probability of removing some captions for classifier-free guidance."
},
)
clip_score_column: Optional[str] = field(
default="clip_score",
metadata={"help": "Column that containts clip score for filtering."},
)
min_clip_score: Optional[float] = field(
default=None,
metadata={"help": "Minimum clip score required."},
)
max_clip_score: Optional[float] = field(
default=None,
metadata={"help": "Maximum clip score required."},
)
filter_column: Optional[str] = field(
default=None,
metadata={"help": "Column that containts classes to be filtered."},
)
filter_value: Optional[str] = field(
default=None,
metadata={"help": "Class value to be kept during filtering."},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples."
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={
"help": "The number of processes to use for the preprocessing. Not used in streaming mode."
},
)
overwrite_cache: bool = field(
default=False,
metadata={
"help": "Overwrite the cached training and evaluation sets. Not used in streaming mode."
},
)
# default seed of None ensures we don't repeat the same items if script was interrupted during an epoch
seed_dataset: int = field(
default=None,
metadata={
"help": "Random seed for the dataset that will be set at the beginning of training."
},
)
def __post_init__(self):
if self.dataset_repo_or_path is None:
raise ValueError("Need a dataset repository or path.")
@dataclass
class TrainingArguments:
"""
Arguments pertaining to training parameters.
"""
output_dir: str = field(
metadata={
"help": "The output directory where the model predictions and checkpoints will be written."
},
)
overwrite_output_dir: bool = field(
default=False,
metadata={
"help": (
"Overwrite the content of the output directory. "
"Use this to continue training if output_dir points to a checkpoint directory."
)
},
)
do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
do_eval: bool = field(
default=False, metadata={"help": "Whether to run eval on the validation set."}
)
per_device_train_batch_size: int = field(
default=8,
metadata={"help": "Batch size per data parallel device for training."},
)
per_device_eval_batch_size: Optional[int] = field(
default=None,
metadata={
"help": "Batch size per data parallel device for evaluation. Same as training batch size if not set."
},
)
gradient_accumulation_steps: int = field(
default=1,
metadata={
"help": "Number of updates steps to accumulate before performing an update pass."
},
)
gradient_checkpointing: bool = field(
default=False, metadata={"help": "Use gradient checkpointing."}
)
learning_rate: float = field(
default=5e-5, metadata={"help": "The initial learning rate."}
)
optim: str = field(
default="distributed_shampoo",
metadata={
"help": 'The optimizer to use. Can be "distributed_shampoo" (default), "adam" or "adafactor"'
},
)
weight_decay: float = field(
default=0.0, metadata={"help": "Weight decay applied to parameters."}
)
beta1: float = field(
default=0.9,
metadata={"help": "Beta1 for Adam & Distributed Shampoo."},
)
beta2: float = field(
default=0.999,
metadata={"help": "Beta2 for for Adam & Distributed Shampoo."},
)
adam_epsilon: float = field(
default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}
)
max_grad_norm: float = field(
default=1.0, metadata={"help": "Max gradient norm for Adafactor."}
)
block_size: int = field(
default=1024,
metadata={"help": "Chunked size for large layers with Distributed Shampoo."},
)
preconditioning_compute_steps: int = field(
default=10, metadata={"help": "Number of steps to update preconditioner."}
)
skip_preconditioning_dim_size_gt: int = field(
default=4096,
metadata={"help": "Max size for preconditioning with Distributed Shampoo."},
)
graft_type: str = field(
default="rmsprop_normalized",
metadata={
"help": "The type of grafting to use. Can be 'rmsprop_normalized' (default), 'rmsprop', 'adagrad', 'adagrad_normalized', 'sgd' or 'sqrt_n'"
},
)
nesterov: bool = field(
default=False,
metadata={"help": "Use Nesterov momentum for Distributed Shampoo."},
)
optim_quantized: bool = field(
default=False,
metadata={
"help": "Whether to quantize optimizer (only supported with Distributed Shampoo)."
},
)
shard_shampoo_across: str = field(
default="dp",
metadata={
"help": "Whether to shard the optimizer across data devices (dp), model devices (mp) or both (2d)."
},
)
num_train_epochs: int = field(
default=3, metadata={"help": "Total number of training epochs to perform."}
)
warmup_steps: int = field(
default=0, metadata={"help": "Linear warmup over warmup_steps."}
)
lr_decay: str = field(
default=None,
metadata={
"help": "Decay to be used in the learning rate scheduler. Can be None (default), linear or exponential."
},
)
lr_transition_steps: int = field(
default=None,
metadata={
"help": "Number of transition steps associated with learning rate decay when using exponential decay."
},
)
lr_decay_rate: float = field(
default=None,
metadata={
"help": "Decay rate associated with learning rate when using exponential decay."
},
)
lr_staircase: bool = field(
default=False,
metadata={
"help": "Whether to use staircase or continuous learning rate when using exponential decay."
},
)
lr_offset: int = field(
default=0,
metadata={"help": "Number of steps to offset learning rate and keep it at 0."},
)
logging_steps: int = field(
default=40, metadata={"help": "Log every X updates steps."}
)
eval_steps: int = field(
default=400, metadata={"help": "Run an evaluation every X steps."}
)
save_steps: int = field(
default=4000, metadata={"help": "Save checkpoint every X updates steps."}
)
log_model: bool = field(
default=False,
metadata={"help": "Log model to wandb at `save_steps` frequency."},
)
log_norm_steps: int = field(
default=True,
metadata={"help": "Log parameters and gradients norm at this frequency."},
)
log_histogram_steps: int = field(
default=False,
metadata={
"help": "Log parameters and gradients histograms at this frequency. Slows down training."
},
)
seed_model: int = field(
default=42,
metadata={
"help": "Random seed for the model that will be set at the beginning of training."
},
)
wandb_entity: Optional[str] = field(
default=None,
metadata={"help": "The wandb entity to use (for teams)."},
)
wandb_project: str = field(
default="dalle-mini",
metadata={"help": "The name of the wandb project."},
)
wandb_job_type: str = field(
default="Seq2Seq",
metadata={"help": "The name of the wandb job type."},
)
assert_TPU_available: bool = field(
default=False,
metadata={"help": "Verify that TPU is not in use."},
)
use_vmap_trick: bool = field(
default=True,
metadata={"help": "Verify that TPU is not in use."},
)
mp_devices: Optional[int] = field(
default=1,
metadata={
"help": "Number of devices required for model parallelism. The other dimension of available devices is used for data parallelism."
},
)
dp_devices: int = field(init=False)
def __post_init__(self):
if self.assert_TPU_available:
assert (
jax.local_device_count() == 8
), "TPUs in use, please check running processes"
if self.output_dir.startswith("gs://"):
assert (
storage is not None
), 'Could not find google.storage. Install with "pip install google-cloud-storage"'
assert self.optim in [
"distributed_shampoo",
"adam",
"adafactor",
], f"Selected optimizer not supported: {self.optim}"
if self.optim == "adafactor" and self.weight_decay == 0:
self.weight_decay = None
assert self.graft_type in [
"rmsprop_normalized",
"rmsprop",
"adagrad",
"adagrad_normalized",
"sgd",
"sqrt_n",
], f"Selected graft type not supported: {self.graft_type}"
assert self.lr_decay in [
None,
"linear",
"exponential",
], f"Selected learning rate decay not supported: {self.lr_decay}"
if self.per_device_eval_batch_size is None:
self.per_device_eval_batch_size = self.per_device_train_batch_size
if self.log_norm_steps is True:
self.log_norm_steps = self.logging_steps
if not self.do_train:
self.num_train_epochs = 1
if (
os.path.exists(self.output_dir)
and os.listdir(self.output_dir)
and self.do_train
and not self.overwrite_output_dir
):
raise ValueError(
f"Output directory ({self.output_dir}) already exists and is not empty."
"Use --overwrite_output_dir to overcome."
)
assert self.shard_shampoo_across in [
"dp",
"mp",
"2d",
], f"Shard shampoo across {self.shard_shampoo_across} not supported."
assert (
self.mp_devices > 0
), f"Number of devices for model parallelism must be > 0"
assert (
jax.device_count() % self.mp_devices == 0
), f"Number of available devices ({jax.device_count()} must be divisible by number of devices used for model parallelism ({self.mp_devices})."
self.dp_devices = jax.device_count() // self.mp_devices
def split_params(data):
"""Split params between scanned and non-scanned"""
flat = traverse_util.flatten_dict(unfreeze(data))
split = {"standard": {}, "scanned_encoder": {}, "scanned_decoder": {}}
for k, v in flat.items():
if "FlaxBartEncoderLayers" in k:
split["scanned_encoder"][k] = v
elif "FlaxBartDecoderLayers" in k:
split["scanned_decoder"][k] = v
else:
split["standard"][k] = v
# remove empty keys
split = {k: v for k, v in split.items() if v}
for k, v in split.items():
split[k] = freeze(traverse_util.unflatten_dict(v))
return split
def unsplit_params(data):
flat = {}
for k in ["standard", "scanned_encoder", "scanned_decoder"]:
if k in data:
flat.update(traverse_util.flatten_dict(unfreeze(data[k])))
return freeze(traverse_util.unflatten_dict(flat))
class TrainState(struct.PyTreeNode):
step: int
params: core.FrozenDict[str, Any]
opt_state: optax.OptState
apply_fn: Callable = struct.field(pytree_node=False)
tx: optax.GradientTransformation = struct.field(pytree_node=False)
dropout_rng: jnp.ndarray = None
epoch: int = 0
train_time: float = 0.0 # total time the model trained
train_samples: int = 0 # number of samples seen
def apply_gradients(self, *, grads, **kwargs):
grads = split_params(grads)
params = split_params(self.params)
opt_state = {}
# we loop over keys: "standard", "scanned_encoder", "scanned_decoder"
for k, param in params.items():
update_fn = self.tx[k].update
if "scanned" in k:
update_fn = jax.vmap(update_fn, in_axes=(0, 0, 0), out_axes=(0, 0))
updates, new_opt_state = update_fn(grads[k], self.opt_state[k], param)
params[k] = optax.apply_updates(param, updates)
opt_state[k] = new_opt_state
params = unsplit_params(params)
return self.replace(
step=self.step + 1,
params=params,
opt_state=freeze(opt_state),
**kwargs,
)
@classmethod
def create(cls, *, apply_fn, params, tx, **kwargs):
opt_state = {}
for k, p in split_params(params).items():
init_fn = tx[k].init
if "scanned" in k:
init_fn = jax.vmap(init_fn)
opt_state[k] = init_fn(p)
return cls(
step=0,
apply_fn=apply_fn,
params=params,
tx=tx,
opt_state=freeze(opt_state),
**kwargs,
)
def main():
# See all possible arguments by passing the --help flag to this script.
parser = HfArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments)
)
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(
json_file=os.path.abspath(sys.argv[1])
)
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# check arguments
if training_args.mp_devices > jax.local_device_count():
assert (
data_args.seed_dataset is not None
), "Seed dataset must be provided when model is split over multiple hosts"
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
if jax.process_index() == 0:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# Set the verbosity to info of the Transformers logger (on main process only):
logger.info(f"Training/evaluation parameters {training_args}")
# Load dataset
dataset = Dataset(
**asdict(data_args),
do_train=training_args.do_train,
do_eval=training_args.do_eval,
)
logger.info(f"Local TPUs: {jax.local_device_count()}")
logger.info(f"Global TPUs: {jax.device_count()}")
# Set up wandb run
if jax.process_index() == 0:
wandb.init(
entity=training_args.wandb_entity,
project=training_args.wandb_project,
job_type=training_args.wandb_job_type,
config=parser.parse_args(),
)
# Set up our new model config
config_args = {
k: getattr(model_args, k)
for k in ["dropout", "activation_dropout", "attention_dropout"]
if getattr(model_args, k) is not None
}
if model_args.config_name:
config = DalleBartConfig.from_pretrained(model_args.config_name)
config.gradient_checkpointing = training_args.gradient_checkpointing
for k, v in config_args.items():
setattr(config, k, v)
else:
config = None
# Load or create new model
if model_args.model_name_or_path:
model, params = DalleBart.from_pretrained(
model_args.model_name_or_path,
config=config,
seed=training_args.seed_model,
dtype=getattr(jnp, model_args.dtype),
_do_init=False, # we overwrite them with loaded checkpoint
gradient_checkpointing=training_args.gradient_checkpointing,
**config_args,
)
else:
model = DalleBart(
config,
seed=training_args.seed_model,
dtype=getattr(jnp, model_args.dtype),
_do_init=False,
)
params = None
params_shape = model.params_shape_tree
# get model metadata
model_metadata = model_args.get_metadata()
# get PartitionSpec for model params (required to be a dict)
param_spec = set_partitions(params_shape, model.config.use_scan)
params_shape = freeze(params_shape)
if params is not None:
params = freeze(params)
# Load tokenizer
tokenizer = DalleBartTokenizer.from_pretrained(
model_args.tokenizer_name, use_fast=True
)
# Preprocessing the datasets.
# We need to normalize and tokenize inputs and targets.
dataset.preprocess(tokenizer=tokenizer, config=model.config)
# Initialize our training
dropout_rng = jax.random.PRNGKey(training_args.seed_model)
# Store some constant
num_epochs = training_args.num_train_epochs
# batch size
batch_size_per_node_per_grad_step = (
training_args.per_device_train_batch_size
* jax.local_device_count()
// training_args.mp_devices
)
batch_size_per_node = (
batch_size_per_node_per_grad_step * training_args.gradient_accumulation_steps
)
batch_size_per_step = batch_size_per_node * jax.process_count()
eval_batch_size_per_node = (
training_args.per_device_eval_batch_size
* jax.local_device_count()
// training_args.mp_devices
)
eval_batch_size_per_step = eval_batch_size_per_node * jax.process_count()
len_train_dataset, len_eval_dataset = dataset.length
steps_per_epoch = (
len_train_dataset // batch_size_per_node
if len_train_dataset is not None
else None
)
num_train_steps = (
steps_per_epoch * num_epochs if steps_per_epoch is not None else None
)
num_params = model.num_params(params_shape)
logger.info("***** Running training *****")
logger.info(f" Num examples = {len_train_dataset}")
logger.info(f" Num Epochs = {num_epochs}")
logger.info(
f" Batch size per dp device = {training_args.per_device_train_batch_size}"
)
logger.info(f" Number of devices = {jax.device_count()}")
logger.info(
f" Gradient accumulation steps = {training_args.gradient_accumulation_steps}"
)
logger.info(f" Batch size per update = {batch_size_per_step}")
logger.info(f" Model parameters = {num_params:,}")
# set up wandb run
if jax.process_index() == 0:
# set default x-axis as 'train/step'
wandb.define_metric("*", step_metric="train/step")
# add interesting config parameters
wandb.config.update(
{
"len_train_dataset": len_train_dataset,
"len_eval_dataset": len_eval_dataset,
"batch_size_per_step": batch_size_per_step,
"num_params": num_params,
"model_config": model.config.to_dict(),
"num_devices": jax.device_count(),
"versions": {
"jax": jax.__version__,
"jaxlib": jaxlib.__version__,
"flax": flax.__version__,
"transformers": transformers.__version__,
"datasets": datasets.__version__,
"wandb": wandb.__version__,
"dalle_mini": dalle_mini.__version__,
},
}
)
# Create learning rate schedule
def create_learning_rate_fn() -> Callable[[int], jnp.array]:
"""Create the learning rate function."""
warmup_fn = optax.linear_schedule(
init_value=0.0,
end_value=training_args.learning_rate,
transition_steps=training_args.warmup_steps + 1, # ensure not 0
)
last_boundary = training_args.warmup_steps
# offset step when resuming
if training_args.lr_offset:
warmup_fn = optax.join_schedules(
schedules=[optax.constant_schedule(0.0), warmup_fn],
boundaries=[training_args.lr_offset],
)
last_boundary += training_args.lr_offset
if training_args.lr_decay is None:
return warmup_fn
elif training_args.lr_decay == "linear":
assert (
num_train_steps is not None
), "linear decay requires knowing the dataset length"
decay_fn = optax.linear_schedule(
init_value=training_args.learning_rate,
end_value=0,
transition_steps=num_train_steps - training_args.warmup_steps,
)
elif training_args.lr_decay == "exponential":
decay_fn = optax.exponential_decay(
init_value=training_args.learning_rate,
transition_steps=training_args.lr_transition_steps,
decay_rate=training_args.lr_decay_rate,
staircase=training_args.lr_staircase,
)
schedule_fn = optax.join_schedules(
schedules=[warmup_fn, decay_fn],
boundaries=[last_boundary],
)
return schedule_fn
learning_rate_fn = create_learning_rate_fn()
# create adam optimizer
if training_args.optim == "distributed_shampoo":
# parameters from https://github.com/tensorflow/lingvo/blob/03ee9d7cd50764b0424c7c863733c91fc0b053ec/lingvo/jax/optimizers.py#L729
graft_type = {
"sgd": GraftingType.SGD,
"adagrad": GraftingType.ADAGRAD,
"rmsprop": GraftingType.RMSPROP,
"rmsprop_normalized": GraftingType.RMSPROP_NORMALIZED,
"sqrt_n": GraftingType.SQRT_N,
"adagrad_normalized": GraftingType.ADAGRAD_NORMALIZED,
}[training_args.graft_type]
statistics_partition_spec = (
PartitionSpec(None, training_args.shard_shampoo_across, None)
if training_args.shard_shampoo_across != "2d"
else PartitionSpec(None, "dp", "mp")
)
opt = distributed_shampoo(
learning_rate_fn,
block_size=training_args.block_size,
beta1=training_args.beta1,
beta2=training_args.beta2,
diagonal_epsilon=1e-10,
matrix_epsilon=1e-6,
weight_decay=training_args.weight_decay,
start_preconditioning_step=max(
training_args.preconditioning_compute_steps + 1, 101
),
preconditioning_compute_steps=training_args.preconditioning_compute_steps,
statistics_compute_steps=1,
best_effort_shape_interpretation=True,
graft_type=graft_type,
nesterov=training_args.nesterov,
exponent_override=0,
statistics_partition_spec=statistics_partition_spec,
preconditioner_partition_spec=PartitionSpec(
training_args.shard_shampoo_across, None, None
)
if training_args.shard_shampoo_across != "2d"
else PartitionSpec(
"mp" if training_args.mp_devices > training_args.dp_devices else "dp",
None,
None,
),
num_devices_for_pjit=training_args.dp_devices,
shard_optimizer_states=True,
inverse_failure_threshold=0.1,
moving_average_for_momentum=True,
skip_preconditioning_dim_size_gt=training_args.skip_preconditioning_dim_size_gt,
clip_by_scaled_gradient_norm=None,
precision=jax.lax.Precision.HIGHEST,
best_effort_memory_usage_reduction=training_args.optim_quantized,
)
# get the real optimizer and helper functions
update_fn = opt.update
optimizer = {}
opt_fn = {}
for k, p in split_params(params_shape).items():
if "scanned" in k:
p = jax.eval_shape(lambda x: jax.tree_map(lambda y: y[0], x), p)
optimizer[k] = opt.init(p)
opt_fn[k] = NamedTuple("opt_fn", pspec_fn=Any, shape_and_dtype_fn=Any)(
optimizer[k].pspec_fn, optimizer[k].shape_and_dtype_fn
)
optimizer[k] = optax.GradientTransformation(optimizer[k].init_fn, update_fn)
elif training_args.optim == "adam":
optimizer = optax.adamw(
learning_rate=learning_rate_fn,
b1=training_args.beta1,
b2=training_args.beta2,
eps=training_args.adam_epsilon,
weight_decay=training_args.weight_decay,
)
optimizer = {k: optimizer for k in split_params(params_shape)}
elif training_args.optim == "adafactor":
# We use the default parameters here to initialize adafactor,
# For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
optimizer = optax.adafactor(
learning_rate=learning_rate_fn,
clipping_threshold=training_args.max_grad_norm,
weight_decay_rate=training_args.weight_decay,
)
optimizer = {k: optimizer for k in split_params(params_shape)}
# get PartitionSpec for optimizer state
def get_opt_state_spec_and_shape():
# get opt_state shape without actual init
opt_state_shape = {}
for k, p in split_params(params_shape).items():
if "scanned" not in k:
opt_state_shape[k] = jax.eval_shape(optimizer[k].init, p)
else:
opt_state_shape[k] = jax.eval_shape(jax.vmap(optimizer[k].init), p)
if training_args.optim == "adafactor":
# factorized state must be replicated (rank different than params)
opt_state_spec = {k: None for k in split_params(params_shape)}
elif training_args.optim in ["adam", "distributed_shampoo"]:
def _opt_state_spec_per_leaf(x, spec):
if isinstance(x, FrozenDict):
# variables with same structure as params
return spec
else:
# other variables such as count
return None
split_spec = split_params(set_partitions(params_shape, False))
opt_state_spec = {}
for k, p in split_params(params_shape).items():
if "scanned" in k:
p = jax.eval_shape(lambda x: jax.tree_map(lambda y: y[0], x), p)
if training_args.optim == "adam":
opt_state_spec[k] = jax.tree_map(
_opt_state_spec_per_leaf,
opt_state_shape[k],
split_spec[k],
# return None spec for empty elements
is_leaf=lambda x: isinstance(x, (FrozenDict, optax.EmptyState)),
)
elif training_args.optim == "distributed_shampoo":
opt_state_spec[k] = opt_fn[k].pspec_fn(
p,
split_spec[k],
statistics_partition_spec,
)
# add dimension for scanned params
if "scanned" in k:
opt_state_spec[k] = jax.tree_map(
lambda x: PartitionSpec(*(None,) + x)
if x is not None
else None,
opt_state_spec[k],
is_leaf=lambda x: isinstance(x, PartitionSpec),
)
else:
raise NotImplementedError
return freeze(opt_state_spec), freeze(opt_state_shape)
opt_state_spec, opt_state_shape = get_opt_state_spec_and_shape()
# create a mesh
mesh_shape = (training_args.dp_devices, training_args.mp_devices)
devices = np.asarray(jax.devices()).reshape(*mesh_shape)