Added logits processor API to sampling params (#1469)

tests/samplers/test_sampler.py

@@ -183,3 +183,37 @@ def test_sampler_mixed(seed: int):
             continue
         for nth_output in sequence_output.samples:
             assert nth_output.output_token in expected_tokens
+
+
+@pytest.mark.parametrize("seed", RANDOM_SEEDS)
+def test_sampler_logits_processors(seed: int):
+    set_random_seed(seed)
+    batch_size = random.randint(1, 256)
+    input_tensor, _, sampler, worker = _prepare_test(batch_size)
+
+    # This sample logits processor gives infinite score to the i-th token,
+    # where i is the length of the input sequence.
+    # We therefore expect the output token sequence to be [0, 1, 2, ...]
+    def pick_ith(token_ids, logits):
+        logits[len(token_ids)] = float("inf")
+        return logits
+
+    seq_group_metadata_list = []
+    for i in range(batch_size):
+        seq_group_metadata_list.append(
+            SequenceGroupMetadata(
+                request_id=f"test_{i}",
+                is_prompt=True,
+                seq_data={0: SequenceData([1, 2, 3])},
+                sampling_params=SamplingParams(temperature=0,
+                                               logits_processors=[pick_ith]),
+                block_tables={0: [1]},
+            ))
+
+    _, _, input_metadata = worker._prepare_inputs(seq_group_metadata_list)
+    sampler_output = sampler(embedding=None,
+                             hidden_states=input_tensor,
+                             input_metadata=input_metadata)
+    for i, sequence_output in enumerate(sampler_output):
+        for idx, nth_output in enumerate(sequence_output.samples):
+            assert nth_output.output_token == idx

vllm/model_executor/layers/sampler.py

@@ -47,6 +47,8 @@ class Sampler(nn.Module):
         logits = _get_logits(hidden_states, embedding, embedding_bias,
                              self.vocab_size)
 
+        # Apply logits processors (if any).
+        logits = _apply_logits_processors(logits, input_metadata)
         # Apply presence and frequency penalties.
         output_tokens = _get_output_tokens(input_metadata)
         assert len(output_tokens) == logits.shape[0]
@@ -155,6 +157,28 @@ def _get_output_tokens(input_metadata: InputMetadata) -> List[List[int]]:
     return output_tokens
 
 
+def _apply_logits_processors(logits: torch.Tensor,
+                             input_metadata: InputMetadata) -> torch.Tensor:
+    logits_row_idx = 0
+    found_logits_processors = False
+    for seq_ids, sampling_params in input_metadata.seq_groups:
+        logits_processors = sampling_params.logits_processors
+        if logits_processors:
+            found_logits_processors = True
+            for seq_id in seq_ids:
+                logits_row = logits[logits_row_idx]
+                token_ids = input_metadata.seq_data[seq_id].output_token_ids
+                for logits_processor in logits_processors:
+                    logits_row = logits_processor(token_ids, logits_row)
+                logits[logits_row_idx] = logits_row
+                logits_row_idx += 1
+        else:
+            logits_row_idx += len(seq_ids)
+    if found_logits_processors:
+        assert logits_row_idx == logits.shape[0]
+    return logits
+
+
 def _apply_penalties(
     logits: torch.Tensor,
     output_tokens: List[List[int]],

vllm/sampling_params.py

 """Sampling parameters for text generation."""
 from enum import IntEnum
 from functools import cached_property
-from typing import List, Optional, Union
+from typing import Callable, List, Optional, Union
+import torch
 
 _SAMPLING_EPS = 1e-5
 
@@ -12,6 +13,12 @@ class SamplingType(IntEnum):
     BEAM = 2
 
 
+LogitsProcessor = Callable[[List[int], torch.Tensor], torch.Tensor]
+"""LogitsProcessor is a function that takes a list of previously generated
+tokens and a tensor of the logits for the next token, and returns a modified
+tensor of logits to sample from."""
+
+
 class SamplingParams:
     """Sampling parameters for text generation.
 
@@ -73,6 +80,8 @@ class SamplingParams:
         skip_special_tokens: Whether to skip special tokens in the output.
         spaces_between_special_tokens: Whether to add spaces between special
             tokens in the output.  Defaults to True.
+        logits_processors: List of functions that modify logits based on
+            previously generated tokens.
     """
 
     def __init__(
@@ -96,6 +105,7 @@ class SamplingParams:
         prompt_logprobs: Optional[int] = None,
         skip_special_tokens: bool = True,
         spaces_between_special_tokens: bool = True,
+        logits_processors: Optional[List[LogitsProcessor]] = None,
     ) -> None:
         self.n = n
         self.best_of = best_of if best_of is not None else n
@@ -124,7 +134,7 @@ class SamplingParams:
         self.prompt_logprobs = prompt_logprobs
         self.skip_special_tokens = skip_special_tokens
         self.spaces_between_special_tokens = spaces_between_special_tokens
-
+        self.logits_processors = logits_processors
         self._verify_args()
         if self.use_beam_search:
             self._verify_beam_search()