Create generation.py

llama/generation.py

@@ -0,0 +1,77 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the GNU General Public License version 3.
+
+from typing import List
+
+import torch
+
+from llama.tokenizer import Tokenizer
+from llama.model import Transformer
+
+
+class LLaMA:
+    def __init__(self, model: Transformer, tokenizer: Tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    def generate(
+        self,
+        prompts: List[str],
+        max_gen_len: int,
+        temperature: float = 0.8,
+        top_p: float = 0.95,
+    ) -> List[str]:
+        bsz = len(prompts)
+        params = self.model.params
+        assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
+
+        prompt_tokens = [self.tokenizer.encode(x, bos=True, eos=False) for x in prompts]
+
+        min_prompt_size = min([len(t) for t in prompt_tokens])
+        max_prompt_size = max([len(t) for t in prompt_tokens])
+
+        total_len = min(params.max_seq_len, max_gen_len + max_prompt_size)
+
+        tokens = torch.full((bsz, total_len), self.tokenizer.pad_id).cuda().long()
+        for k, t in enumerate(prompt_tokens):
+            tokens[k, : len(t)] = torch.tensor(t).long()
+        input_text_mask = tokens != self.tokenizer.pad_id
+        start_pos = min_prompt_size
+        prev_pos = 0
+        for cur_pos in range(start_pos, total_len):
+            logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
+            if temperature > 0:
+                probs = torch.softmax(logits / temperature, dim=-1)
+                next_token = sample_top_p(probs, top_p)
+            else:
+                next_token = torch.argmax(logits, dim=-1)
+            next_token = next_token.reshape(-1)
+            # only replace token if prompt has already been generated
+            next_token = torch.where(
+                input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
+            )
+            tokens[:, cur_pos] = next_token
+            prev_pos = cur_pos
+
+        decoded = []
+        for i, t in enumerate(tokens.tolist()):
+            # cut to max gen len
+            t = t[: len(prompt_tokens[i]) + max_gen_len]
+            # cut to eos tok if any
+            try:
+                t = t[: t.index(self.tokenizer.eos_id)]
+            except ValueError:
+                pass
+            decoded.append(self.tokenizer.decode(t))
+        return decoded
+
+
+def sample_top_p(probs, p):
+    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
+    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    mask = probs_sum - probs_sort > p
+    probs_sort[mask] = 0.0
+    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
+    next_token = torch.multinomial(probs_sort, num_samples=1)
+    next_token = torch.gather(probs_idx, -1, next_token)
+    return next_token