[Core] Reduce unnecessary compute when logprobs=None

tests/samplers/test_logprobs.py

@@ -14,7 +14,7 @@
 @pytest.mark.parametrize("dtype",
                          ["float"])  # needed for comparing logprobs with HF
 @pytest.mark.parametrize("chunked_prefill_token_size", [1, 4, 16, -1])
-@pytest.mark.parametrize("num_top_logprobs", [6])  # 32000 == vocab_size
+@pytest.mark.parametrize("num_top_logprobs", [0, 6])  # 32000 == vocab_size
 @pytest.mark.parametrize("detokenize", [True, False])
 def test_get_prompt_logprobs(
     hf_runner,
@@ -63,7 +63,10 @@ def test_get_prompt_logprobs(
         assert result.outputs[0].logprobs is not None
         assert len(result.outputs[0].logprobs) == max_tokens
         for logprobs in result.outputs[0].logprobs:
-            assert len(logprobs) == num_top_logprobs
+            # If the output token is not included in the top X
+            # logprob, it can return 1 more data
+            assert (len(logprobs) == num_top_logprobs
+                    or len(logprobs) == num_top_logprobs + 1)
         output_text = result.outputs[0].text
         output_string_from_most_likely_tokens_lst: List[str] = []
         for top_logprobs in result.outputs[0].logprobs:
@@ -135,3 +138,35 @@ def test_max_logprobs():
     bad_sampling_params = SamplingParams(logprobs=2)
     with pytest.raises(ValueError):
         runner.generate(["Hello world"], sampling_params=bad_sampling_params)
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("chunked_prefill_token_size", [1, 4, 16, -1])
+@pytest.mark.parametrize("detokenize", [True, False])
+def test_none_logprobs(vllm_runner, model, chunked_prefill_token_size: int,
+                       detokenize: bool, example_prompts):
+    max_num_seqs = 256
+    enable_chunked_prefill = False
+    max_num_batched_tokens = None
+    if chunked_prefill_token_size != -1:
+        enable_chunked_prefill = True
+        max_num_seqs = min(chunked_prefill_token_size, max_num_seqs)
+        max_num_batched_tokens = chunked_prefill_token_size
+    max_tokens = 5
+
+    with vllm_runner(
+            model,
+            enable_chunked_prefill=enable_chunked_prefill,
+            max_num_batched_tokens=max_num_batched_tokens,
+            max_num_seqs=max_num_seqs,
+    ) as vllm_model:
+        sampling_params_logprobs_none = SamplingParams(max_tokens=max_tokens,
+                                                       logprobs=None,
+                                                       temperature=0.0,
+                                                       detokenize=detokenize)
+        results_logprobs_none = vllm_model.model.generate(
+            example_prompts, sampling_params=sampling_params_logprobs_none)
+
+    for i in range(len(results_logprobs_none)):
+        assert results_logprobs_none[i].outputs[0].logprobs is None
+        assert results_logprobs_none[i].outputs[0].cumulative_logprob is None

vllm/model_executor/layers/sampler.py

@@ -1,5 +1,6 @@
 """A layer that samples the next tokens from the model's outputs."""
 import itertools
+from math import inf
 from typing import Dict, List, Optional, Tuple
 
 import torch
@@ -774,8 +775,11 @@ def _get_logprobs(
     # The next token ids to get the logprob value from.
     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.
@@ -808,42 +812,49 @@ def _get_logprobs(
                 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)
 
     if len(query_indices) == 0:
         empty_sampled_logprob: SampleLogprobs = []
         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]
+
+        # 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')
-    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')
+        selected_logprobs = selected_logprobs.to('cpu')
+        ranks = ranks.to('cpu')
 
     # Find prompt/sample logprobs.
     prompt_logprobs_per_seq_group: List[Optional[PromptLogprobs]] = []
@@ -940,46 +951,53 @@ 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(inf)})
+        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

vllm/outputs.py

@@ -29,7 +29,7 @@ class CompletionOutput:
     index: int
     text: str
     token_ids: Tuple[int, ...]
-    cumulative_logprob: float
+    cumulative_logprob: Optional[float]
     logprobs: Optional[SampleLogprobs]
     finish_reason: Optional[str] = None
     stop_reason: Union[int, str, None] = None
@@ -124,13 +124,14 @@ def from_seq_group(cls, seq_group: SequenceGroup) -> "RequestOutput":
         include_logprobs = seq_group.sampling_params.logprobs is not None
         text_buffer_length = seq_group.sampling_params.output_text_buffer_length
         outputs = [
-            CompletionOutput(seqs.index(seq),
-                             seq.get_output_text_to_return(text_buffer_length),
-                             seq.get_output_token_ids(),
-                             seq.get_cumulative_logprob(),
-                             seq.output_logprobs if include_logprobs else None,
-                             SequenceStatus.get_finished_reason(seq.status),
-                             seq.stop_reason) for seq in top_n_seqs
+            CompletionOutput(
+                seqs.index(seq),
+                seq.get_output_text_to_return(text_buffer_length),
+                seq.get_output_token_ids(),
+                seq.get_cumulative_logprob() if include_logprobs else None,
+                seq.output_logprobs if include_logprobs else None,
+                SequenceStatus.get_finished_reason(seq.status),
+                seq.stop_reason) for seq in top_n_seqs
         ]
 
         # Every sequence in the sequence group should have the same prompt.

vllm/sampling_params.py

@@ -92,11 +92,12 @@ class SamplingParams:
         min_tokens: Minimum number of tokens to generate per output sequence
             before EOS or stop_token_ids can be generated
         logprobs: Number of log probabilities to return per output token.
-            Note that the implementation follows the OpenAI API: The return
-            result includes the log probabilities on the `logprobs` most likely
-            tokens, as well the chosen tokens. The API will always return the
-            log probability of the sampled token, so there  may be up to
-            `logprobs+1` elements in the response.
+            When set to None, no probability is returned. If set to a non-None
+            value, the result includes the log probabilities of the specified
+            number of most likely tokens, as well as the chosen tokens.
+            Note that the implementation follows the OpenAI API: The API will
+            always return the log probability of the sampled token, so there
+            may be up to `logprobs+1` elements in the response.
         prompt_logprobs: Number of log probabilities to return per prompt token.
         detokenize: Whether to detokenize the output. Defaults to True.
         skip_special_tokens: Whether to skip special tokens in the output.
@@ -168,8 +169,8 @@ def __init__(
         self.ignore_eos = ignore_eos
         self.max_tokens = max_tokens
         self.min_tokens = min_tokens
-        self.logprobs = logprobs
-        self.prompt_logprobs = prompt_logprobs
+        self.logprobs = 1 if logprobs is True else logprobs
+        self.prompt_logprobs = 1 if prompt_logprobs is True else prompt_logprobs
         # NOTE: This parameter is only exposed at the engine level for now.
         # It is not exposed in the OpenAI API server, as the OpenAI API does
         # not support returning only a list of token IDs.