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54976e1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | import torch
import torch.nn as nn
import json
import tiktoken
from datasets import load_dataset
from model import GPTModel
from safetensors.torch import load_file
import tqdm
def load_custom_model():
with open("config.json") as f:
cfg = json.load(f)
model = GPTModel(cfg)
model.load_state_dict(load_file("model.safetensors"), strict=False)
model.cuda()
model.eval()
return model
def evaluate_wikitext_perplexity(model, tokenizer):
max_length = model.pos_emb.weight.shape[0]
print(f"Loading WikiText-2 test set (Context Window: {max_length})...")
dataset = load_dataset("Salesforce/wikitext", "wikitext-2-raw-v1", split="test")
encodings = tokenizer.encode("\n\n".join(dataset["text"]))
seq_len = len(encodings)
nlls = []
print(f"Evaluating Perplexity on {seq_len} tokens...")
loss_fn = nn.CrossEntropyLoss()
# Process in chunks of max_length
with torch.no_grad():
with torch.amp.autocast("cuda"):
for begin_loc in tqdm.tqdm(range(0, seq_len, max_length)):
end_loc = min(begin_loc + max_length, seq_len)
# We need at least 2 tokens to compute loss
if end_loc - begin_loc < 2:
break
input_ids = torch.tensor(encodings[begin_loc:end_loc]).unsqueeze(0).cuda()
target_ids = input_ids.clone()
logits = model(input_ids)
# Shift logits and labels for next token prediction
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = target_ids[..., 1:].contiguous()
# Compute loss
loss = loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
# Multiply by the number of tokens in this batch to get sum of losses
nlls.append(loss * (end_loc - begin_loc - 1))
# Calculate overall perplexity
total_tokens = sum([(min(b + max_length, seq_len) - b - 1) for b in range(0, seq_len, max_length) if min(b + max_length, seq_len) - b >= 2])
ppl = torch.exp(torch.stack(nlls).sum() / total_tokens)
return ppl.item()
def evaluate_sciq_accuracy(model, tokenizer):
print("Loading SciQ test set for Multiple Choice Accuracy...")
dataset = load_dataset("sciq", split="test")
correct_count = 0
total = len(dataset)
loss_fn = nn.CrossEntropyLoss(reduction='none')
print(f"Evaluating Zero-Shot Accuracy on {total} questions...")
with torch.no_grad():
with torch.amp.autocast("cuda"):
for item in tqdm.tqdm(dataset):
q = item['question']
choices = [item['correct_answer'], item['distractor1'], item['distractor2'], item['distractor3']]
best_loss = float('inf')
best_idx = -1
for i, choice in enumerate(choices):
prompt_text = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
f"### Instruction:\nAnswer the following question:\n{q}\n\n### Response:\n"
)
full_text = f"{prompt_text}{choice}"
prompt_tokens = tokenizer.encode(prompt_text)
full_tokens = tokenizer.encode(full_text)
# The tokens we actually want to measure the loss on (the answer)
answer_len = len(full_tokens) - len(prompt_tokens)
if answer_len == 0:
continue
input_ids = torch.tensor(full_tokens).unsqueeze(0).cuda()
logits = model(input_ids)
# Shift for next token prediction
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = input_ids[..., 1:].contiguous()
# Compute token-wise loss
token_losses = loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
# We only care about the loss of the ANSWER tokens!
# The answer tokens are at the very end of the sequence.
answer_losses = token_losses[-answer_len:]
avg_answer_loss = answer_losses.mean().item()
if avg_answer_loss < best_loss:
best_loss = avg_answer_loss
best_idx = i
if best_idx == 0: # 0 is the correct_answer
correct_count += 1
accuracy = (correct_count / total) * 100
return accuracy
def evaluate_mmlu_accuracy(model, tokenizer):
print("Loading MMLU test set...")
# Load all subjects, but sample 1000 questions to keep benchmark time reasonable
dataset = load_dataset("cais/mmlu", "all", split="test")
dataset = dataset.shuffle(seed=42).select(range(1000))
correct_count = 0
total = len(dataset)
loss_fn = nn.CrossEntropyLoss(reduction='none')
print(f"Evaluating Zero-Shot Accuracy on {total} MMLU questions...")
with torch.no_grad():
with torch.amp.autocast("cuda"):
for item in tqdm.tqdm(dataset):
q = item['question']
choices = item['choices']
correct_idx = item['answer']
best_loss = float('inf')
best_idx = -1
# Check if this question will exceed our context window before evaluating
skip_question = False
for i, choice in enumerate(choices):
prompt_text = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
f"### Instruction:\nAnswer the following multiple choice question:\n{q}\n\n### Response:\n"
)
full_text = f"{prompt_text}{choice}"
prompt_tokens = tokenizer.encode(prompt_text)
full_tokens = tokenizer.encode(full_text)
# Prevent CUDA crash if the question is longer than the model's brain (512 tokens)
if len(full_tokens) > model.pos_emb.weight.shape[0]:
skip_question = True
break
answer_len = len(full_tokens) - len(prompt_tokens)
if answer_len == 0:
continue
input_ids = torch.tensor(full_tokens).unsqueeze(0).cuda()
logits = model(input_ids)
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = input_ids[..., 1:].contiguous()
token_losses = loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
answer_losses = token_losses[-answer_len:]
avg_answer_loss = answer_losses.mean().item()
if avg_answer_loss < best_loss:
best_loss = avg_answer_loss
best_idx = i
if skip_question:
# Subtract from total since we couldn't evaluate it
total -= 1
continue
if best_idx == correct_idx:
correct_count += 1
accuracy = (correct_count / total) * 100 if total > 0 else 0
return accuracy
if __name__ == "__main__":
print("Loading 124M Custom Model...")
model = load_custom_model()
tokenizer = tiktoken.get_encoding("gpt2")
ppl = evaluate_wikitext_perplexity(model, tokenizer)
sciq_acc = evaluate_sciq_accuracy(model, tokenizer)
mmlu_acc = evaluate_mmlu_accuracy(model, tokenizer)
print(f"\n" + "="*50)
print(f"WikiText-2 Perplexity: {ppl:.2f}")
print(f"SciQ (Zero-Shot) Accuracy: {sciq_acc:.2f}%")
print(f"MMLU (Zero-Shot) Accuracy: {mmlu_acc:.2f}%")
print("="*50 + "\n")
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