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eval.py
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eval.py
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# Written by Yukang Chen
# Some code based on https://github.com/epfml/landmark-attention
#
# 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 math
import torch
import argparse
import random
import numpy as np
from tqdm import tqdm
import transformers
from peft import PeftModel
from llama_attn_replace import replace_llama_attn
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--batch_size', type=int, default=32, help='batch size during inference')
parser.add_argument('--base_model', type=str, default="/data1/pretrained-models/llama-7b-hf")
parser.add_argument('--cache_dir', type=str, default="./cache")
parser.add_argument('--seq_len', type=int, default=2048, help='context length during evaluation')
parser.add_argument('--context_size', type=int, default=-1, help='context size during fine-tuning')
parser.add_argument('--peft_model', type=str, default=None, help='')
parser.add_argument('--flash_attn', type=bool, default=True, help='')
parser.add_argument('--data_path', type=str, default="./test.bin", help='')
args = parser.parse_args()
return args
def get_as_batch(data, seq_length, batch_size, device='cpu', sliding_window=256):
all_ix = list(range(0, len(data) - seq_length, sliding_window))
all_ix.pop()
for idx in range(0, len(all_ix), batch_size):
ix = all_ix[idx:idx+batch_size]
assert all([idx + seq_length + 1 <= len(data) for idx in ix])
x = torch.stack([torch.from_numpy((data[i:i+seq_length]).astype(np.int64)) for i in ix])
y = torch.stack([torch.from_numpy((data[i+1:i+1+seq_length]).astype(np.int64)) for i in ix])
if device != 'cpu':
x, y = x.pin_memory().to(device, non_blocking=True), y.pin_memory().to(device, non_blocking=True)
yield x, y
def iceildiv(x, y):
return (x + y - 1) // y
def evaluate(model, data, batch_size, device, seq_length, sliding_window=256, use_cache=False):
stats = {}
model.eval()
loss_list_val, acc_list = [], []
loss_step_list_val = []
with torch.no_grad():
print(f"Using seq length {seq_length}")
torch.set_printoptions(sci_mode=False)
for idx, (x, y) in tqdm(
enumerate(
get_as_batch(
data['val'],
seq_length,
batch_size,
device=device,
sliding_window=sliding_window
)
),
total=iceildiv(
iceildiv(len(data['val']), sliding_window),
batch_size
)
):
val_loss = 0.
acc = 0.
cnt = 0
for part_idx, i in enumerate(range(0, x.shape[1], seq_length)):
part_len = x[:, i:i + seq_length].shape[1]
outputs = model(
input_ids=x[:, i:i + seq_length],
labels=x[:, i:i+seq_length].contiguous(),
use_cache=use_cache)
val_loss = outputs.loss * part_len + val_loss
acc = ((outputs.logits.argmax(-1) == y[:, i:i+seq_length]).float().sum()) + acc
cnt += part_len
while len(loss_step_list_val) <= part_idx:
loss_step_list_val.append([])
loss_step_list_val[part_idx].append(outputs.loss.item())
val_loss /= cnt
acc /= cnt
loss_list_val.append(val_loss.item())
acc_list.append(acc.item())
stats['val_acc'] = torch.as_tensor(acc_list).mean().item()
stats['val_loss'] = torch.as_tensor(loss_list_val).mean().item()
stats['val_perplexity'] = 2.71828 ** stats['val_loss']
stats['val_perplexity_per_chunk'] = torch.exp(torch.as_tensor(loss_step_list_val).mean(dim=1))
return stats
def main(args):
device = "cuda:0"
seed = 2
torch.cuda.set_device(device)
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
data = {'val': np.memmap(args.data_path, dtype=np.uint16, mode='r')}
print(f"Num validation tokens: {len(data['val'])}")
print("data path", args.data_path)
print("base model", args.base_model)
print("peft model", args.peft_model)
if args.flash_attn:
replace_llama_attn(use_flash_attn=True, use_full=True)
# Set RoPE scaling factor
config = transformers.AutoConfig.from_pretrained(
args.base_model,
cache_dir=args.cache_dir,
)
context_size = args.context_size if args.context_size > 0 else args.seq_len
orig_ctx_len = getattr(config, "max_position_embeddings", None) # this value should be 4096 for LLaMA2 models
if orig_ctx_len and context_size > orig_ctx_len:
scaling_factor = float(math.ceil(context_size / orig_ctx_len))
config.rope_scaling = {"type": "linear", "factor": scaling_factor}
# Load model and tokenizer
model = transformers.AutoModelForCausalLM.from_pretrained(
args.base_model,
config=config,
cache_dir=args.cache_dir,
torch_dtype=torch.float16,
device_map="auto",
)
model.resize_token_embeddings(32001)
if args.peft_model:
trainable_params = os.path.join(args.peft_model, "trainable_params.bin")
if os.path.isfile(trainable_params):
model.load_state_dict(torch.load(trainable_params, map_location=model.device), strict=False)
else:
raise ValueError("Trainable input embedding and normalization are required.")
model = PeftModel.from_pretrained(
model,
args.peft_model,
device_map="auto",
torch_dtype=torch.float16,
)
stats = evaluate(model, data, args.batch_size, device, args.seq_len, sliding_window=256)
print(stats)
if __name__ == "__main__":
args = parse_config()
main(args)