Update generate.py

inference/generate.py

@@ -11,21 +11,33 @@ from safetensors.torch import load_model
 from model import Transformer, ModelArgs
 
 
-def sample(logits, temperature: float = 1.0):
+def sample(logits, temperature: float = 1.0, top_k: int = 40, p: float = 0.9):
     """
-    Samples a token from the logits using temperature scaling.
+    Samples a token from the logits using temperature scaling, top-k, or nucleus sampling.
     
     Args:
         logits (torch.Tensor): The logits tensor for token predictions.
         temperature (float, optional): Temperature for scaling logits. Defaults to 1.0.
+        top_k (int, optional): Top-k for sampling. Defaults to 40.
+        p (float, optional): Nucleus sampling threshold. Defaults to 0.9.
 
     Returns:
         torch.Tensor: The sampled token.
     """
     logits = logits / max(temperature, 1e-5)
+    if top_k > 0:
+        top_k_values, top_k_indices = torch.topk(logits, top_k)
+        top_k_probs = torch.softmax(top_k_values, dim=-1)
+        next_token = top_k_indices[torch.multinomial(top_k_probs, 1)].squeeze()
+    elif p < 1.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
+        sorted_indices_to_keep = sorted_indices[cumulative_probs <= p]
+        next_token = sorted_indices_to_keep[torch.multinomial(torch.softmax(sorted_logits, dim=-1), 1)].squeeze()
+    else:
         probs = torch.softmax(logits, dim=-1)
-    return probs.div_(torch.empty_like(probs).exponential_(1)).argmax(dim=-1)
-
+        next_token = torch.multinomial(probs, 1).squeeze()
+    return next_token
 
 @torch.inference_mode()
 def generate(