[BugFix] Fix use of per-request seed with pipeline parallel

tests/samplers/test_rejection_sampler.py

@@ -150,10 +150,9 @@ def test_no_crash_with_varying_dims(k: int, vocab_size: int, batch_size: int,
                                     high=vocab_size,
                                     size=(batch_size, k),
                                     dtype=torch.int64)
-    generators = [None] * batch_size
 
     rejection_sampler(target_probs, bonus_token_ids, draft_probs,
-                      draft_token_ids, generators)
+                      draft_token_ids)
 
 
 @pytest.mark.parametrize("frac_seeded", [0.0, 0.25, 0.5, 1.0])
@@ -185,14 +184,13 @@ def test_deterministic_when_seeded(k: int, vocab_size: int, batch_size: int,
 
     results = []
     for _ in range(n_rep):
-        generators = [
-            torch.Generator(
-                device=device).manual_seed(i) if seeded_mask[i] else None
-            for i in range(batch_size)
-        ]
+        seeded_seqs = {
+            i: torch.Generator(device=device).manual_seed(i)
+            for i in range(batch_size) if seeded_mask[i]
+        }
         results.append(
             rejection_sampler(target_probs, bonus_token_ids, draft_probs,
-                              draft_token_ids, generators))
+                              draft_token_ids, seeded_seqs))
 
     for i in range(batch_size):
         if seeded_mask[i]:
@@ -242,11 +240,10 @@ def test_raises_when_vocab_oob(above_or_below_vocab_range: str,
         raise AssertionError()
 
     oob_token_ids[0][0] = rogue_token_id
-    generators = [None] * batch_size
 
     with pytest.raises(AssertionError):
         rejection_sampler(target_probs, bonus_token_ids, draft_probs,
-                          draft_token_ids, generators)
+                          draft_token_ids)
 
 
 @pytest.mark.parametrize("draft_and_target_probs_equal", [True, False])
@@ -417,15 +414,11 @@ def _estimate_rejection_sampling_pdf(
                                       dtype=torch.int64,
                                       device="cuda").repeat(num_samples, 1)
 
-        # unseeded
-        generators = [None]
-
         # Get output tokens via rejection sampling.
         output_token_ids = self.rejection_sampler(target_probs.to("cuda"),
                                                   bonus_token_ids.to("cuda"),
                                                   draft_probs.to("cuda"),
-                                                  draft_token_ids.to("cuda"),
-                                                  generators)
+                                                  draft_token_ids.to("cuda"))
 
         # Remove bonus tokens
         output_token_ids = output_token_ids[:, :-1].flatten()

tests/samplers/test_sampler.py

@@ -510,13 +510,16 @@ def test_sampler_mixed(seed: int, device: str):
             ))
         seq_lens.append(seq_group_metadata_list[-1].seq_data[0].get_len())
 
+    generators: Dict[str, torch.Generator] = {}
+
     def test_sampling():
         sampling_metadata = SamplingMetadata.prepare(
             seq_group_metadata_list,
             seq_lens,
             query_lens=seq_lens,
             device=device,
-            pin_memory=is_pin_memory_available())
+            pin_memory=is_pin_memory_available(),
+            generators=generators)
         sampler_output = sampler(logits=fake_logits,
                                  sampling_metadata=sampling_metadata)
 

tests/spec_decode/e2e/test_mlp_correctness.py

@@ -21,7 +21,8 @@
 
 import pytest
 
-from .conftest import run_greedy_equality_correctness_test
+from .conftest import (run_equality_correctness_test,
+                       run_greedy_equality_correctness_test)
 
 # main model
 MAIN_MODEL = "JackFram/llama-160m"
@@ -77,6 +78,57 @@ def test_mlp_e2e_greedy_correctness(baseline_llm_generator, test_llm_generator,
                                          force_output_len=True)
 
 
+@pytest.mark.parametrize(
+    "common_llm_kwargs",
+    [{
+        # Skip cuda graph recording for fast test.
+        "enforce_eager": True,
+
+        # Required for spec decode.
+        "use_v2_block_manager": True,
+
+        # Print spec metrics.
+        "disable_log_stats": False,
+
+        # Precision
+        "dtype": PRECISION,
+
+        # Main model
+        "model": MAIN_MODEL,
+
+        # Speculative model
+        "speculative_model": SPEC_MODEL,
+    }])
+@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
+@pytest.mark.parametrize("baseline_llm_kwargs", [{"seed": 1}])
+@pytest.mark.parametrize("test_llm_kwargs", [{"seed": 5}])
+@pytest.mark.parametrize("output_len", [64])
+@pytest.mark.parametrize("batch_size", [1, 32])
+@pytest.mark.parametrize("temperature", [0.1, 1.0])
+@pytest.mark.parametrize("seed", [None])
+def test_mlp_e2e_seeded_correctness(baseline_llm_generator, test_llm_generator,
+                                    batch_size: int, output_len: int,
+                                    temperature: float):
+    """Verify seeded runs produce the same output."""
+    run_equality_correctness_test(baseline_llm_generator,
+                                  test_llm_generator,
+                                  batch_size,
+                                  max_output_len=output_len,
+                                  temperature=temperature,
+                                  seeded=True,
+                                  force_output_len=True)
+
+    # Ensure this same test does fail if we _don't_ include per-request seeds
+    with pytest.raises(AssertionError):
+        run_equality_correctness_test(baseline_llm_generator,
+                                      test_llm_generator,
+                                      batch_size,
+                                      max_output_len=output_len,
+                                      temperature=temperature,
+                                      seeded=False,
+                                      force_output_len=True)
+
+
 @pytest.mark.parametrize(
     "common_llm_kwargs",
     [{

tests/spec_decode/e2e/test_seed.py

@@ -29,7 +29,7 @@
     "output_len",
     [
         # Use smaller output len for fast test.
-        10,
+        20,
     ])
 @pytest.mark.parametrize("seed", [None])
 def test_seeded_consistency(baseline_llm_generator, test_llm_generator,

tests/spec_decode/test_batch_expansion.py

@@ -86,6 +86,7 @@ def test_create_single_target_seq_group_metadata(k: int):
         input_seq_id,
         target_seq_id,
         token_ids,
+        input_seq_group_metadata.sampling_params,
     )
 
     assert output.request_id == input_seq_group_metadata.request_id

tests/utils.py

@@ -178,6 +178,37 @@ def compare_two_settings(model: str, arg1: List[str], arg2: List[str]):
                 "usage": completion.usage,
             })
 
+            # test seeded random sampling
+            completion = client.completions.create(model=model,
+                                                   prompt=prompt,
+                                                   max_tokens=5,
+                                                   seed=33,
+                                                   temperature=1.0)
+
+            results.append({
+                "test": "seeded_sampling",
+                "text": completion.choices[0].text,
+                "finish_reason": completion.choices[0].finish_reason,
+                "usage": completion.usage,
+            })
+
+            # test seeded random sampling with multiple prompts
+            completion = client.completions.create(model=model,
+                                                   prompt=[prompt, prompt],
+                                                   max_tokens=5,
+                                                   seed=33,
+                                                   temperature=1.0)
+
+            results.append({
+                "test":
+                "seeded_sampling",
+                "text": [choice.text for choice in completion.choices],
+                "finish_reason":
+                [choice.finish_reason for choice in completion.choices],
+                "usage":
+                completion.usage,
+            })
+
             # test simple list
             batch = client.completions.create(
                 model=model,

vllm/core/scheduler.py

@@ -1029,7 +1029,6 @@ def schedule(self) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs]:
                 token_chunk_size=token_chunk_size,
                 lora_request=seq_group.lora_request,
                 computed_block_nums=common_computed_block_nums,
-                state=seq_group.state,
                 # `multi_modal_data` will only be present for the 1st comm
                 # between engine and worker.
                 # the subsequent comms can still use delta, but

vllm/model_executor/layers/rejection_sampler.py

@@ -1,5 +1,5 @@
 from functools import cached_property
-from typing import List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import torch
 import torch.jit
@@ -36,7 +36,7 @@ def forward(
         bonus_token_ids: torch.Tensor,
         draft_probs: torch.Tensor,
         draft_token_ids: torch.Tensor,
-        generators: List[Optional[torch.Generator]],
+        seeded_seqs: Optional[Dict[int, torch.Generator]] = None,
     ) -> torch.Tensor:
         """Sample token ids using rejection sampling. This accepts or rejects
         tokens proposed by the draft model using the probability of each token
@@ -66,6 +66,9 @@ def forward(
                 probabilities.
             shape = [batch_size, num_speculative_tokens]
 
+            seeded_seqs: Dict of batch row index to torch generator, for
+                sequences using seeded generation.
+
         Returns:
             output_token_ids: The token ids sampled via rejection sampling,
                 or -1 if unable to sample a token because the previous token
@@ -83,7 +86,7 @@ def forward(
                 target_probs,
                 draft_probs,
                 draft_token_ids,
-                generators,
+                seeded_seqs,
             ))
 
         output_token_ids = self._create_output(
@@ -100,7 +103,7 @@ def _batch_modified_rejection_sampling(
         target_probs: torch.Tensor,  # [batch_size, k, vocab_size]
         draft_probs: torch.Tensor,  # [batch_size, k, vocab_size]
         draft_token_ids: torch.Tensor,  # [batch_size, k]
-        generators: List[Optional[torch.Generator]],
+        seeded_seqs: Optional[Dict[int, torch.Generator]],
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         """Perform modified rejection sampling on each sequence.
 
@@ -117,23 +120,17 @@ def _batch_modified_rejection_sampling(
 
         # shape [batch_size, k]
         accepted = self._get_accepted(target_probs, draft_probs,
-                                      draft_token_ids, generators)
+                                      draft_token_ids, seeded_seqs)
 
         recovered_probs = self._get_recovered_probs(
             target_probs, draft_probs).reshape(batch_size * k, vocab_size)
 
-        seed_indices, non_seed_indices = self._split_batch_by_seeded(
-            generators, k=k)
-
         # NOTE: the recovered_probs are overwritten by this method.
         recovered_token_ids = _multinomial(
             recovered_probs,
             num_samples=1,
             k=k,
-            generators=generators,
-            seed_indices=seed_indices,
-            # this arg is unused when None but torch.jit requires a list
-            non_seed_indices=non_seed_indices or [],
+            seeded_seqs=seeded_seqs or {},
         ).reshape(batch_size, k)
 
         return accepted, recovered_token_ids
@@ -143,7 +140,7 @@ def _get_accepted(
         target_probs: torch.Tensor,  # [batch_size, k, vocab_size]
         draft_probs: torch.Tensor,  # [batch_size, k, vocab_size]
         draft_token_ids: torch.Tensor,  # [batch_size, k]
-        generators: List[Optional[torch.Generator]],
+        seeded_seqs: Optional[Dict[int, torch.Generator]],
     ) -> torch.Tensor:
         r"""Create bool matrix over the proposed draft tokens. If
         True, then a token can be accepted, else it should be
@@ -178,24 +175,26 @@ def _get_accepted(
         selected_target_probs = target_probs[batch_indices, probs_indicies,
                                              draft_token_ids]
 
-        seed_indices, non_seed_indices = self._split_batch_by_seeded(
-            generators)
-
-        if len(seed_indices) == 0:
+        if not seeded_seqs:
             uniform_rand = torch.rand_like(selected_target_probs)
         else:
             uniform_rand = torch.empty_like(selected_target_probs)
 
-            for idx in seed_indices:
-                uniform_rand[idx, :] = torch.rand(1,
-                                                  k,
-                                                  dtype=self.probs_dtype,
-                                                  device=target_probs.device,
-                                                  generator=generators[idx])
-
-            if non_seed_indices:
-                uniform_rand[non_seed_indices, :] = torch.rand(
-                    len(non_seed_indices),
+            non_seeded_indices = []
+            for idx in range(batch_size):
+                generator = seeded_seqs.get(idx)
+                if generator is None:
+                    non_seeded_indices.append(idx)
+                else:
+                    uniform_rand[idx, :] = torch.rand(
+                        1,
+                        k,
+                        dtype=self.probs_dtype,
+                        device=target_probs.device,
+                        generator=generator)
+            if non_seeded_indices:
+                uniform_rand[non_seeded_indices, :] = torch.rand(
+                    len(non_seeded_indices),
                     k,
                     dtype=self.probs_dtype,
                     device=target_probs.device)
@@ -272,27 +271,6 @@ def _smallest_positive_value(self) -> float:
         """
         return torch.finfo(self.probs_dtype).tiny
 
-    # partition batch into indices for which a generator is provided
-    # and indicies for which no generator is provided
-    @staticmethod
-    def _split_batch_by_seeded(
-        generators: List[Optional[torch.Generator]],
-        k: int = 1,
-    ) -> Tuple[List[int], Optional[List[int]]]:
-
-        if all(generator is None for generator in generators):
-            seed_indices: List[int] = []
-            non_seed_indices: Optional[List[int]] = None
-        else:
-            seed_indices, non_seed_indices = [], []
-            for i, generator in enumerate(generators):
-                if generator is None:
-                    non_seed_indices.extend(range(k * i, k * (i + 1)))
-                else:
-                    seed_indices.extend(range(k * i, k * (i + 1)))
-
-        return seed_indices, non_seed_indices
-
 
 # torch.multinomial forces a GPU<->CPU sync.
 # Therefore, we use an optimized implementation instead that skips the sync.
@@ -304,9 +282,7 @@ def _multinomial(
     probs: torch.Tensor,
     num_samples: int,
     k: int,
-    generators: List[Optional[torch.Generator]],
-    seed_indices: List[int],
-    non_seed_indices: List[int],
+    seeded_seqs: Dict[int, torch.Generator],
 ) -> torch.Tensor:
 
     if num_samples > 1:
@@ -315,13 +291,20 @@ def _multinomial(
         probs = probs[:, None, :].expand(probs.shape[0], num_samples,
                                          probs.shape[1]).contiguous().view(
                                              -1, probs.shape[1])
-
     q = torch.empty_like(probs)
-    if len(seed_indices) == 0:
+    if not seeded_seqs:
         q.exponential_(1.0)
     else:
-        q[non_seed_indices].exponential_(1.0)
-        for idx in seed_indices:
-            q[idx].exponential_(1.0, generator=generators[idx // k])
+        non_seeded_indices: List[int] = []
+        start = 0
+        for idx in range(len(q) // k):
+            end = start + k
+            generator = seeded_seqs.get(idx)
+            if generator is None:
+                non_seeded_indices.extend(list(range(start, end)))
+            else:
+                q[start:end].exponential_(1.0, generator=generator)
+            start = end
+        q[non_seeded_indices].exponential_(1.0)
 
     return probs.div_(q).argmax(dim=1).view(-1, num_samples)