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import os
current_directory = os.path.dirname(os.path.abspath(__file__))
os.chdir(current_directory)
import torch
import torch.nn as nn
from torch.nn import functional as F
device = 'cuda' if torch.cuda.is_available() else 'cpu'
from tokenizer import Tokenizer
tokenizer = Tokenizer()
vocab_size = tokenizer.get_vocab()
from model import Transformer
model = Transformer(vocab_size)
checkpoint_path = '/content/drive/MyDrive/base-500m.pth'
checkpoint = torch.load(checkpoint_path)
model.load_state_dict(checkpoint)
m = model.to(device)
class Generate:
def __init__(self):
self.vocab_size = vocab_size
self.block_size = m.block_size
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=0):
"""
generate new tokens using the trained model
Args:
- idx (Tensor): input tensor representing initial token indices
- max_new_tokens (int): max no of new tokens to generate
- temperature (float): softmax temperature for sampling
- top_k (int): no of top tokens to consider in sampling
Returns:
- generated_tokens (list): list of generated token indices
"""
generated_tokens = []
for _ in range(max_new_tokens):
idx_cond = idx[:, -m.block_size:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :]
scaled_logits = logits / temperature
if top_k > 0:
scaled_logits = self._top_k_filtering(scaled_logits, top_k)
probs = F.softmax(scaled_logits, dim=-1)
sampled_idx = torch.multinomial(probs, num_samples=1)
generated_tokens.append(sampled_idx.item())
idx = torch.cat((idx, sampled_idx), dim=1)
return generated_tokens
def generate_masked_tokens(self, idx, masked_indices, temperature=1.0, top_k=0):
"""
Generate predictions for masked tokens using the trained model.
Args:
- idx (Tensor): input tensor representing token indices
- masked_indices (Tensor): tensor of indices indicating masked positions
- temperature (float): softmax temperature for sampling
- top_k (int): no of top tokens to consider in sampling
Returns:
- predicted_tokens (Tensor): tensor of predicted token indices
"""
B, T = idx.shape
toked_model = m.toked_model(idx)
pos_encod = m.pos_encod(torch.arange(T, device=device))
x = toked_model + pos_encod
for layer in m.enc_layer:
x_out = layer(x)
for layer in m.dec_layer:
x_final = layer(x, x_out)
x_masked = x_final.clone()
x_masked[masked_indices] = m.toked_model(torch.tensor([6], device=device))
x_masked = m.norm_final(x_masked)
logits = m.linear_final(x_masked)
masked_logits = logits[masked_indices].view(-1, logits.size(-1))
scaled_logits = masked_logits / temperature
if top_k > 0:
scaled_logits = self._top_k_filtering(scaled_logits, top_k)
probs = F.softmax(scaled_logits, dim=-1)
predicted_indices = torch.argmax(probs, dim=-1)
return predicted_indices
def _top_k_filtering(self, logits, top_k):
"""
filter logits to keep only the top-k tokens
Args:
- logits (Tensor): input tensor representing unscaled logits
- top_k (int): no of top tokens to keep
Returns:
- filtered_logits (Tensor): filtered logits with only top-k tokens remaining
"""
values, indices = torch.topk(logits, top_k, dim=-1)
min_value = values[:, -1].unsqueeze(-1).expand_as(logits)
filtered_logits = torch.where(logits < min_value, torch.ones_like(logits) * -float('inf'), logits)
return filtered_logits
generator = Generate()
target_text = "I was in the market when"
context = torch.tensor([tokenizer.encode(target_text)], dtype=torch.long, device=device)
generated_output = tokenizer.decode(generator.generate(context, max_new_tokens=50))
print(target_text, generated_output) |