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a20151e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | import argparse
import os
import jax
import flax
import copy
import json
import optax
import torch
import wandb
import numpy as np
from tqdm import tqdm
import jax.numpy as jnp
from flax import linen as nn
from typing import Any, Dict, List
from flax.jax_utils import replicate, unreplicate
from flax.training import checkpoints, train_state
from flax.core.frozen_dict import freeze, unfreeze
from flax.traverse_util import flatten_dict, unflatten_dict
from flax.training.common_utils import get_metrics, onehot, shard
from transformers.models.vit.modeling_flax_vit import ViTConfig, FlaxViTForImageClassification
from model import OldLMCFlaxViTForImageClassification
from lmc_model import LMCFlaxViTForImageClassification, print_model, print_model_with_prefix
from datasets import build_dataset
import multiprocessing as mp
from pprint import pprint
def imagenet_data_loader(args):
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
dataset_val, _ = build_dataset(is_train=False, args=args)
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
data_loader_train = torch.utils.data.DataLoader(
dataset_train, sampler=sampler_train, batch_size=args.batch_size,
num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=True
)
data_loader_val = torch.utils.data.DataLoader(
dataset_val, sampler=sampler_val, batch_size=args.batch_size,
num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=False
)
return data_loader_train, data_loader_val
def prepare_image_batch(images:torch.Tensor,labels:torch.Tensor) -> Dict[str, Any]:
images, labels = jnp.array(images),jnp.array(labels)
return {'images': shard(images),'labels': shard(labels)}
def accuracy(logits, labels, topk=(1,)):
maxk = max(topk)
batch_size = labels.shape[0]
topk_preds = jnp.argsort(logits, axis=-1)[:, -maxk:][:, ::-1] # Top-k predictions
res = []
for k in topk:
correct = (topk_preds[:, :k] == labels[:, None])
correct = jnp.any(correct, axis=1)
correct = jnp.sum(correct)
res.append(100.0 * correct / batch_size)
return res # list of [acc@1, acc@5]
def pretrained_new2old(new_params,old_params,config):
new_params = unfreeze(new_params)
old_params = unfreeze(old_params)
print_model(new_params)
print_model(old_params)
# 1. Copy top-level params (embeddings, layernorm, classifier)
new_params["vit"]["embeddings"] = copy.deepcopy(old_params["vit"]["embeddings"])
new_params["vit"]["layernorm"] = copy.deepcopy(old_params["vit"]["layernorm"])
new_params["classifier"] = copy.deepcopy(old_params["classifier"])
# 2. Copy encoder layers
for i in range(config.num_hidden_layers):
str_i = str(i)
ref_layer = old_params["vit"]["encoder"]["layer"][str_i] # use layer 0 from pretrained
target_layer = new_params["vit"]["encoder"]["layer"][str_i]
# Copy shared parts
target_layer["layernorm_before"] = copy.deepcopy(ref_layer["layernorm_before"])
target_layer["layernorm_after"] = copy.deepcopy(ref_layer["layernorm_after"])
target_layer["moe"]['shared_experts']['intermediate'] = copy.deepcopy(ref_layer["mlp"]['intermediate']['dense'])
target_layer["moe"]['shared_experts']['output'] = copy.deepcopy(ref_layer["mlp"]['output']['dense'])
#Copy Attention weights
target_layer["attention"] = copy.deepcopy(ref_layer["attention"])
return freeze(new_params)
def main(args: argparse.Namespace):
train_loader, val_loader = imagenet_data_loader(args)
save_path = "/mnt/data/vinhbk/weights/imagenet/lr0.0005-rope-epochs300-batch256-shared1-routed0-topk0/"
old_config = ViTConfig.from_pretrained('/mnt/data/vinhbk/weights/imagenet/lr0.0005-rope-epochs300-batch256/config.json')
old_model = OldLMCFlaxViTForImageClassification(old_config,dtype=jnp.bfloat16)
new_config = copy.deepcopy(old_config)
new_config.position_embeddings = old_config.position_embeddings
new_config.rotary_value = old_config.rotary_value
new_config.q_lora_rank = 8
new_config.qk_rope_head_dim = 64
new_config.kv_lora_rank = 8
new_config.v_head_dim = 64
new_config.qk_nope_head_dim = 64
new_config.attention_bias = True
new_config.routed_scaling_factor = 1.0
new_config.lmc_layer_indices = []
new_config.num_shared_experts = 1
new_config.num_routed_experts = 0
new_config.topk = 0
# 1. Recreate LR schedule
lr_schedule = optax.warmup_cosine_decay_schedule(
init_value=1e-6,
peak_value=5e-4,
warmup_steps=5*5004,
decay_steps=300*5004,
end_value=1e-5,
)
# 2. Recreate optimizer tx
tx = optax.adamw(
learning_rate=lr_schedule,
b1=0.9,
b2=0.999,
eps=1e-8,
weight_decay=0.01,
)
old_state = train_state.TrainState.create(apply_fn=old_model.__call__, params=old_model.params, tx=tx)
old_params = old_state.params
new_model = LMCFlaxViTForImageClassification(new_config,input_shape=(1,new_config.image_size, new_config.image_size, new_config.num_channels),seed=args.seed,dtype=jnp.bfloat16)
new_model.params = pretrained_new2old(new_params=copy.deepcopy(new_model.params),old_params=copy.deepcopy(old_state.params),config=new_model.config)
# print(new_model.params['classifier']['bias'])
# 3. Create new TrainState with new_model
new_state = train_state.TrainState.create(apply_fn=new_model.__call__,params=new_model.params,tx=tx,)
print(old_state.params['classifier']['bias'])
print(new_state.params['classifier']['bias'])
# 4. Load old values
os.makedirs(save_path,exist_ok=True)
new_model.config.save_pretrained(save_path)
# checkpoints.save_checkpoint(ckpt_dir=save_path,target=new_state,step=old_state.step,prefix="best_",keep=1,overwrite=True)
def train_step(state, batch, rng):
dropout_rng, new_dropout_rng = jax.random.split(rng)
def loss_fn(params):
outputs = state.apply_fn(params=params,pixel_values=batch["images"],train=True,dropout_rng=dropout_rng,)
logits = outputs.logits if hasattr(outputs, "logits") else outputs[0]
loss = optax.softmax_cross_entropy_with_integer_labels(logits, batch["labels"]).mean()
return loss, logits
(loss, logits), grads = jax.value_and_grad(loss_fn, has_aux=True)(state.params)
grads = jax.lax.pmean(grads, axis_name="batch")
state = state.apply_gradients(grads=grads)
acc1, acc5 = accuracy(logits, batch["labels"], topk=(1, 5))
metrics = {"loss": loss,"acc1": acc1,"acc5": acc5,"learning_rate": lr_schedule(state.step),}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return state, metrics, new_dropout_rng
def eval_step(state, batch):
outputs = state.apply_fn(params=state.params,pixel_values=batch["images"],train=False,)
logits = outputs.logits if hasattr(outputs, "logits") else outputs[0]
loss = optax.softmax_cross_entropy_with_integer_labels(logits, batch["labels"]).mean()
acc1, acc5 = accuracy(logits, batch["labels"], topk=(1, 5))
metrics = {"loss": loss,"acc1": acc1,"acc5": acc5,}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return metrics
parallel_train_step = jax.pmap(train_step, "batch")
parallel_eval_step = jax.pmap(eval_step, "batch")
state = replicate(new_state)
rng = jax.random.PRNGKey(0)
train_metrics_stack = []
train_loss = 0.0
best_val_acc1 = 0.0
###JUST FOR TESTING####
eval_results = []
pbar = tqdm(enumerate(val_loader), desc="Evaluating", leave=False)
for batch_idx, (images, labels) in pbar:
batch = prepare_image_batch(images, labels)
# Run eval step
eval_metric = parallel_eval_step(state, batch)
eval_results.append(eval_metric) # Run train step
# Compute mean metrics across all eval batches
eval_metrics = get_metrics(eval_results)
eval_metrics = unreplicate(eval_metrics)
eval_metrics = jax.tree_util.tree_map(lambda x: x.mean(), eval_metrics)
val_loss, val_acc1, val_acc5 = float(eval_metrics["loss"]), float(eval_metrics["acc1"]), float(eval_metrics["acc5"])
print("-" * 100)
print(f"valid loss {val_loss:5.4f} | valid acc@1 {val_acc1:6.2f}% | valid acc@5 {val_acc5:6.2f}%")
print("-" * 100)
if __name__ =="__main__":
parser = argparse.ArgumentParser(description="Fine-tune ViT with MoE on Imagenet")
parser.add_argument("--batch-size", type=int, default=256)
parser.add_argument("--data-path", type=str, required=True)
parser.add_argument('--data-set', default='IMNET', choices=['CIFAR', 'IMNET', 'INAT', 'INAT19'])
parser.add_argument("--input-size", type=int, default=224)
parser.add_argument('--num_workers', type=int, default=8)
parser.add_argument('--pin-mem', action='store_true')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--color-jitter', type=float, default=0.4)
parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1')
parser.add_argument('--train-interpolation', type=str, default='bicubic')
parser.add_argument('--reprob', type=float, default=0.25)
parser.add_argument('--remode', type=str, default='pixel')
parser.add_argument('--recount', type=int, default=1)
parser.add_argument("--dtype", choices=["float32", "float16", "bfloat16"], default="bfloat16", help="model datatype")
args = parser.parse_args()
main(args) |