Port of vllm-project#6532 - remove unnecessary sampling

vllm/model_executor/layers/sampler.py

@@ -724,7 +724,11 @@ def _get_logprobs(
     next_token_ids: List[int] = []
     # The largest requested number of logprobs. We find logprobs as many as the
     # largest num logprobs in this API.
-    largest_num_logprobs = 1
+    # largest num logprobs in this API. If every logprobs is None, it will be
+    # set to -1.
+    largest_num_logprobs = -1
+    # If beam search is enabled.
+    use_beam_search = False
 
     # Select indices to compute logprob from, ranks of token ids, and the top
     # k token ids from logprobs.
@@ -756,6 +760,7 @@ def _get_logprobs(
             if sampling_params.logprobs is not None:
                 largest_num_logprobs = max(largest_num_logprobs,
                                            sampling_params.logprobs)
+            use_beam_search = use_beam_search or sampling_params.use_beam_search
 
         assert len(next_token_ids) == len(query_indices)
 
@@ -764,35 +769,40 @@ def _get_logprobs(
         empty_prompt_logprob: Optional[PromptLogprobs] = None
         return [empty_prompt_logprob], [empty_sampled_logprob]
 
-    query_indices_gpu = torch.tensor(query_indices, device=logprobs.device)
-    next_token_ids_gpu = torch.tensor(next_token_ids, device=logprobs.device)
-
-    # (num_selected_query_tokens, num_logprobs). Note that query_indices can
-    # contain duplicates if beam search is enabled.
-    selected_logprobs = logprobs[[
-        query_indices_gpu,
-        next_token_ids_gpu,
-    ]]
-    ranks = _get_ranks(
-        logprobs[query_indices_gpu],
-        next_token_ids_gpu,
-    )
-    assert selected_logprobs.shape[0] == ranks.shape[0]
-
-    # Logprobs of topk tokens for a batch of sequence groups.
-    # (num_query_tokens_across_batch).
-    if largest_num_logprobs > 0:
-        top_logprobs, top_token_ids = torch.topk(logprobs,
-                                                 largest_num_logprobs,
-                                                 dim=-1)
-    else:
-        top_logprobs, top_token_ids = None, None
+    selected_logprobs, ranks = None, None
+    top_logprobs, top_token_ids = None, None
+
+    # If largest_num_logprobs == -1, i.e. no logprobs are requested, we can
+    # skip the whole logprob calculation.
+    if largest_num_logprobs >= 0 or use_beam_search:
+        query_indices_gpu = torch.tensor(query_indices, device=logprobs.device)
+        next_token_ids_gpu = torch.tensor(next_token_ids,
+                                          device=logprobs.device)
+
+        # (num_selected_query_tokens, num_logprobs). Note that query_indices can
+        # contain duplicates if beam search is enabled.
+        selected_logprobs = logprobs[[
+            query_indices_gpu,
+            next_token_ids_gpu,
+        ]]
+        ranks = _get_ranks(
+            logprobs[query_indices_gpu],
+            next_token_ids_gpu,
+        )
+        assert selected_logprobs.shape[0] == ranks.shape[0]
 
-    selected_logprobs = selected_logprobs.to('cpu')
-    ranks = ranks.to('cpu')
-    if top_logprobs is not None and top_token_ids is not None:
-        top_logprobs = top_logprobs.to('cpu')
-        top_token_ids = top_token_ids.to('cpu')
+        # We need to compute top k only if there exists logprobs > 0.
+        if largest_num_logprobs > 0:
+            # Logprobs of topk tokens for a batch of sequence groups.
+            # (num_query_tokens_across_batch).
+            top_logprobs, top_token_ids = torch.topk(logprobs,
+                                                     largest_num_logprobs,
+                                                     dim=-1)
+            top_logprobs = top_logprobs.to('cpu')
+            top_token_ids = top_token_ids.to('cpu')
+
+        selected_logprobs = selected_logprobs.to('cpu')
+        ranks = ranks.to('cpu')
 
     # Find prompt/sample logprobs.
     prompt_logprobs_per_seq_group: List[Optional[PromptLogprobs]] = []
@@ -889,46 +899,53 @@ def _get_sampled_logprob_if_needed(
 ):
     """Compute the sample logprob if needed."""
     seq_ids = seq_group.seq_ids
-    num_logprobs = seq_group.sampling_params.logprobs or 0
+    num_logprobs = seq_group.sampling_params.logprobs
+    use_beam_search = seq_group.sampling_params.use_beam_search
     sampled_logprobs: SampleLogprobs = []
     next_token_ids, parent_seq_ids = sample_result
 
     if seq_group.do_sample:
         assert len(next_token_ids) > 0
-        # Pre-select items from tensor. tolist() is faster than repetitive
-        # `.item()` calls.
-        selected_logprob_items = selected_logprobs[
-            selected_logprobs_idx:selected_logprobs_idx +
-            len(next_token_ids)].tolist()
-        rank_items = ranks[selected_logprobs_idx:selected_logprobs_idx +
-                           len(next_token_ids)].tolist()
-        for idx, (next_token_id,
-                  parent_id) in enumerate(zip(next_token_ids, parent_seq_ids)):
-            # Get the logprob of a sampled token.
-            sampled_logprobs_dict = {
-                next_token_id: (selected_logprob_items[idx], rank_items[idx])
-            }
-            # Get top K logprobs.
-            if num_logprobs > 0:
-                top_ids = top_token_ids[top_logprob_idx +
-                                        parent_id, :num_logprobs].tolist()
-                top_probs = top_logprobs[top_logprob_idx +
-                                         parent_id, :num_logprobs].tolist()
-                # Top K is already sorted by rank, so we can use 1 ~
-                # num_logprobs + 1 for rank.
-                top_ranks = range(1, num_logprobs + 1)
-                sampled_logprobs_dict.update({
-                    top_id: (top_prob, rank)
-                    for top_id, top_prob, rank in zip(top_ids, top_probs,
-                                                      top_ranks)
+        if num_logprobs is None and not use_beam_search:
+            for next_token_id in next_token_ids:
+                # Use a dummy logprob
+                sampled_logprobs.append({next_token_id: Logprob(0.0)})
+        else:
+            # Pre-select items from tensor. tolist() is faster than repetitive
+            # `.item()` calls.
+            selected_logprob_items = selected_logprobs[
+                selected_logprobs_idx:selected_logprobs_idx +
+                len(next_token_ids)].tolist()
+            rank_items = ranks[selected_logprobs_idx:selected_logprobs_idx +
+                               len(next_token_ids)].tolist()
+            for idx, (next_token_id, parent_id) in enumerate(
+                    zip(next_token_ids, parent_seq_ids)):
+                # Get the logprob of a sampled token.
+                sampled_logprobs_dict = {
+                    next_token_id:
+                    (selected_logprob_items[idx], rank_items[idx])
+                }
+                if num_logprobs is not None and num_logprobs > 0:
+                    # Get top K logprobs.
+                    top_ids = top_token_ids[top_logprob_idx +
+                                            parent_id, :num_logprobs].tolist()
+                    top_probs = top_logprobs[
+                        top_logprob_idx + parent_id, :num_logprobs].tolist()
+                    # Top K is already sorted by rank, so we can use 1 ~
+                    # num_logprobs + 1 for rank.
+                    top_ranks = range(1, num_logprobs + 1)
+                    sampled_logprobs_dict.update({
+                        top_id: (top_prob, rank)
+                        for top_id, top_prob, rank in zip(
+                            top_ids, top_probs, top_ranks)
+                    })
+
+                sampled_logprobs.append({
+                    token_id: Logprob(*logprob_and_rank)
+                    for token_id, logprob_and_rank in
+                    sampled_logprobs_dict.items()
                 })
 
-            sampled_logprobs.append({
-                token_id: Logprob(*logprob_and_rank)
-                for token_id, logprob_and_rank in
-                sampled_logprobs_dict.items()
-            })
-
         # NOTE: This part of code is not intuitive. `selected_logprobs` include
         # logprobs for the current step, which has len(next_token_ids) tokens
         # per sequence group. `logprobs` includes logprobs from the previous
@@ -1070,4 +1087,4 @@ def _get_next_prompt_tokens(seq_group: SequenceGroupToSample) -> List[int]:
                                len(prompt_tokens))
     next_prompt_tokens = prompt_tokens[
         next_token_index_start:next_token_index_end]
-    return next_prompt_tokens
+    return next_prompt_tokens