File size: 6,055 Bytes
55a580f |
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 |
#
# Copyright © 2023 Advanced Micro Devices, Inc. All rights reserved.
#
import torch
import logging
import time
import random
import numpy as np
prompts = [ "What is the meaning of life?",
"Tell me something you don't know.",
"What does Xilinx do?",
"What is the mass of earth?",
"What is a poem?",
"What is recursion?",
"Tell me a one line joke.",
"Who is Gilgamesh?",
"Tell me something about cryptocurrency.",
"How did it all begin?"
]
def warmup(model, tokenizer, max_new_tokens=30):
print(f"Warming up ... ")
for prompt in prompts[0:1]:
inputs = tokenizer(prompt, return_tensors="pt")
generate_ids = model.generate(inputs.input_ids, attention_mask=inputs.attention_mask, max_new_tokens=max_new_tokens)
_ = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(f"Warm up DONE!! ")
def decode_prompt(model, tokenizer, prompt, input_ids=None, max_new_tokens=30):
if input_ids is None:
print(f"prompt: {prompt}")
start = time.time()
inputs = tokenizer(prompt, return_tensors="pt")
end = time.time()
logging.critical(f"[PROFILE][WARMUP] tokenizer: {end-start}")
else:
logging.critical(f"[PROFILE][WARMUP] tokenizer: na") # for logging consistency
start, end = 0, 0
prompt_tokens = 0
input_ids_ = input_ids if prompt is None else inputs.input_ids
# attention_mask = torch.ones((1, input_ids.numel())) if prompt is None else inputs.attention_mask
start = time.time()
generate_ids = model.generate(input_ids_, max_new_tokens=max_new_tokens,eos_token_id=None)
# generate_ids = model.generate(input_ids_, attention_mask=attention_mask, max_new_tokens=max_new_tokens)
end = time.time()
prompt_tokens = input_ids_.shape[1]
num_tokens_out = generate_ids.shape[1]
new_tokens_generated = num_tokens_out - prompt_tokens
generate_time = (end - start)
# print(generate_time)
time_per_token = (generate_time/new_tokens_generated)*1e3
# print(time_per_token)
logging.critical(f"[PROFILE][AIE] generate: {generate_time} for {num_tokens_out} tokens; prompt-tokens: {prompt_tokens}; time per generated token: {time_per_token}")
start = time.time()
response = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
end = time.time()
logging.critical(f"[PROFILE][WARMUP] tokenizer decode: {end-start}")
print(f"response: {response}")
logging.critical(f"response: {response}")
def decode_prompts(model, tokenizer, max_new_tokens=30):
for prompt in prompts:
logging.critical("*"*40)
print("*"*40)
decode_prompt(model, tokenizer, prompt, max_new_tokens=max_new_tokens)
def get_wikitext2(tokenizer, dataset="non-raw", nsamples=128, seqlen=2048):
""" gptq """
from datasets import load_dataset
if dataset == "non-raw":
traindata = load_dataset('wikitext', 'wikitext-2-v1', split='train')
testdata = load_dataset('wikitext', 'wikitext-2-v1', split='test')
elif dataset == "raw":
traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')
testdata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
else:
raise ValueError(
"You are using an unsupported dataset, only support wikitext2-raw-v1 and wikitext2-v1."
"Using wikitext2-raw-v1 with --dataset=raw and wikitext2-v1 with --dataset=non-raw."
)
trainenc = tokenizer("\n\n".join(traindata['text']), return_tensors='pt')
testenc = tokenizer("\n\n".join(testdata['text']), return_tensors='pt')
dataloader = []
for _ in range(nsamples):
i = random.randint(0, testenc.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
inp = testenc.input_ids[:, i:j]
tar = inp.clone()
tar[:, :-1] = -100
dataloader.append((inp, tar))
return dataloader, testenc
def perplexity(model, tokenizer, dataset, framework="pytorch"):
random.seed(0)
np.random.seed(0)
torch.random.manual_seed(0)
print(f"Calculating Perplexity on wikitext2 test set ...")
model = model#.cuda()
dataloader, testenc = get_wikitext2(tokenizer, dataset=dataset)
model.seqlen = 2048
test_enc = testenc.input_ids
nsamples = 2 #test_enc.numel() // model.seqlen
if framework == "pytorch":
dtype = next(iter(model.parameters())).dtype
loss = torch.nn.CrossEntropyLoss()
nlls = []
with torch.no_grad():
attention_mask = torch.ones((1, test_enc.numel()))#.cuda()
for i in range(nsamples):
batch = test_enc[:, (i * model.seqlen):((i + 1) * model.seqlen)]#.cuda()
if framework == "pytorch":
out = model(
batch,
attention_mask=attention_mask[:, (i * model.seqlen):((i + 1) * model.seqlen)].reshape((1, -1))
)
else :
out = model(
batch,
attention_mask=batch.new_ones(batch.shape)
)
shift_labels = test_enc[
:, (i * model.seqlen):((i + 1) * model.seqlen)
][:, 1:]#.cuda()
loss_fct = torch.nn.CrossEntropyLoss()
loss = loss_fct(out.logits[0][:-1, :], shift_labels.view(-1))
neg_log_likelihood = loss.float() * model.seqlen
nlls.append(neg_log_likelihood)
ppl = torch.exp(torch.stack(nlls).sum() / (nsamples * model.seqlen))
print('Perplexity:', ppl.item())
|