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added new generator file + removed generate function
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import os | ||
current_directory = os.path.dirname(os.path.abspath(__file__)) | ||
os.chdir(current_directory) | ||
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import torch | ||
import torch.nn as nn | ||
from torch.nn import functional as F | ||
device = 'cuda' if torch.cuda.is_available() else 'cpu' | ||
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from tokenizer import Tokenizer | ||
tokenizer = Tokenizer() | ||
vocab_size = tokenizer.get_vocab() | ||
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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) | ||
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class Generate: | ||
def __init__(self): | ||
self.vocab_size = vocab_size | ||
self.block_size = m.block_size | ||
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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 = [] | ||
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for _ in range(max_new_tokens): | ||
idx_cond = idx[:, -m.block_size:] | ||
logits, _ = self(idx_cond) | ||
logits = logits[:, -1, :] | ||
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scaled_logits = logits / temperature | ||
if top_k > 0: | ||
scaled_logits = self._top_k_filtering(scaled_logits, top_k) | ||
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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) | ||
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return generated_tokens | ||
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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 | ||
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toked_model = m.toked_model(idx) | ||
pos_encod = m.pos_encod(torch.arange(T, device=device)) | ||
x = toked_model + pos_encod | ||
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for layer in m.enc_layer: | ||
x_out = layer(x) | ||
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for layer in m.dec_layer: | ||
x_final = layer(x, x_out) | ||
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x_masked = x_final.clone() | ||
x_masked[masked_indices] = m.toked_model(torch.tensor([6], device=device)) | ||
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x_masked = m.norm_final(x_masked) | ||
logits = m.linear_final(x_masked) | ||
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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) | ||
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probs = F.softmax(scaled_logits, dim=-1) | ||
predicted_indices = torch.argmax(probs, dim=-1) | ||
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return predicted_indices | ||
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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) | ||
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return filtered_logits | ||
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generator = Generate() | ||
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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) |
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