[Inference] add logit processor and request handler (#5166)

* add logit processor and request handler

* add

* add

* add

* fix

* add search tokens and update func

* finish request handler

* add running list test

* fix test

* fix some bug

* add

* add

* fix bugs

* fix some bugs

* fix bug

* fix

* fix

* add copy fun

* del useless attn

* fix request status

---------

Co-authored-by: CjhHa1 <cjh18671720497outlook.com>

colossalai/inference/sampler.py

@@ -0,0 +1,62 @@
+from typing import List, Tuple
+
+import torch
+
+
+def greedy_sample(
+    generation_config,
+    logprobs: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Sample tokens greedyly.
+    """
+    results = torch.argmax(logprobs, dim=-1).cpu()
+    return results
+
+
+def multinomial_sample(
+    generation_config,
+    probs: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Sample tokens in a random phase.
+    """
+    max_best_of = generation_config.best_of
+    random_results = torch.multinomial(probs, num_samples=max_best_of, replacement=True).cpu()
+    return random_results
+
+
+def beam_search_sample(
+    generation_config,
+    logprobs: torch.Tensor,
+    is_prompt: bool = False,
+) -> List[Tuple[List[int], List[int]]]:
+    """
+    Sample tokens with beam search.
+    We sample 2 * beam_width candidates to make sure that with high probability we can get `beam_width` candidates in addition to
+    the finished sequences for the next iteration.
+
+    ref:
+        https://github.com/tensorflow/tensor2tensor/blob/bafdc1b67730430d38d6ab802cbd51f9d053ba2e/tensor2tensor/utils/beam_search.py#L557-L563
+    for details. See also HF reference:
+        https://github.com/huggingface/transformers/blob/a4dd53d88e4852f023332d284ff07a01afcd5681/src/transformers/generation/utils.py#L3063-L3065
+
+    # NOTE: this beam search sample function is wrong now.
+    """
+
+    beam_width = generation_config.best_of
+    results = []
+    if is_prompt:
+        # Prompt phase.
+        parent_ids = [0] * (2 * beam_width)
+        _, next_token_ids = torch.topk(logprobs[0], 2 * beam_width)
+        next_token_ids = next_token_ids.tolist()
+    else:
+        # Generation phase.
+        # cumulative_logprobs = [seq_data[seq_id].cumulative_logprob for seq_id in seq_ids]
+        cumulative_logprobs = torch.tensor(logprobs, dtype=torch.float, device=seq_group_logprobs.device)
+        seq_group_logprobs = seq_group_logprobs + cumulative_logprobs.unsqueeze(dim=1)
+        _, topk_ids = torch.topk(logprobs.flatten(), 2 * beam_width)
+
+    results.append((next_token_ids, parent_ids))
+    return results