Add greedy sampler

cacheflow/models/sample.py

@@ -0,0 +1,45 @@
+from typing import Dict, List, Tuple
+
+import torch
+import torch.nn as nn
+
+from cacheflow.models import InputMetadata
+
+
+class Sampler(nn.Module):
+
+    def __init__(
+        self,
+        embedding: torch.Tensor,
+    ) -> None:
+        super().__init__()
+        self.embedding = embedding.t()  # [hidden_size, vocab_size]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        input_metadata: InputMetadata,
+    ) -> Dict[int, Tuple[int, int]]:
+        # Get the hidden states of the last tokens.
+        start_idx = 0
+        last_token_indicies: List[int] = []
+        for prompt_len in input_metadata.prompt_lens:
+            last_token_indicies.append(start_idx + prompt_len - 1)
+            start_idx += prompt_len
+        last_token_indicies.extend(
+            range(start_idx, start_idx + input_metadata.num_generation_tokens))
+        hidden_states = hidden_states[last_token_indicies]
+
+        # Get the logits for the next tokens.
+        logits = torch.matmul(hidden_states, self.embedding)
+
+        # Sample the next tokens.
+        # TODO(woosuk): Implement other sampling methods.
+        next_token_ids = torch.argmax(logits, dim=-1)
+        next_token_ids = next_token_ids.tolist()
+
+        # Return the next tokens.
+        next_tokens: Dict[int, Tuple[int, int]] = {}
+        for seq_id, token_id in zip(input_metadata.seq_ids, next_token_ids):
+            next_tokens[seq_id] = (seq_id, token_id)
+        return next_tokens