[Bugfix] Make spec. decode respect per-request seed. (vllm-project#6034)

Signed-off-by: Thomas Parnell <[email protected]>
Co-authored-by: Nick Hill <[email protected]>

Author: 2 people

tests/samplers/test_rejection_sampler.py

@@ -150,9 +150,54 @@ 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)
+                      draft_token_ids, generators)
+
+
+@pytest.mark.parametrize("frac_seeded", [0.0, 0.25, 0.5, 1.0])
+@pytest.mark.parametrize("k", [1, 3, 6])
+@pytest.mark.parametrize("vocab_size", [30_000, 50_000])
+@pytest.mark.parametrize("batch_size", [1, 8, 32, 128])
+@pytest.mark.parametrize("n_rep", [100])
+@pytest.mark.parametrize("device", CUDA_DEVICES)
+@torch.inference_mode()
+def test_deterministic_when_seeded(k: int, vocab_size: int, batch_size: int,
+                                   frac_seeded: float, n_rep: int,
+                                   device: str):
+    torch.set_default_device(device)
+    rejection_sampler = RejectionSampler()
+    rejection_sampler.init_gpu_tensors(rank=0)
+
+    draft_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
+    target_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
+    bonus_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, 1),
+                                    dtype=torch.int64)
+    draft_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, k),
+                                    dtype=torch.int64)
+
+    seeded_mask = torch.rand(batch_size, dtype=torch.float32) <= frac_seeded
+
+    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)
+        ]
+        results.append(
+            rejection_sampler(target_probs, bonus_token_ids, draft_probs,
+                              draft_token_ids, generators))
+
+    for i in range(batch_size):
+        if seeded_mask[i]:
+            for j in range(1, n_rep):
+                assert torch.equal(results[j][i], results[0][i])
 
 
 @pytest.mark.parametrize("above_or_below_vocab_range", ["above", "below"])
@@ -197,10 +242,11 @@ 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)
+                          draft_token_ids, generators)
 
 
 @pytest.mark.parametrize("draft_and_target_probs_equal", [True, False])
@@ -371,11 +417,15 @@ 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"))
+                                                  draft_token_ids.to("cuda"),
+                                                  generators)
 
         # Remove bonus tokens
         output_token_ids = output_token_ids[:, :-1].flatten()

tests/spec_decode/e2e/conftest.py

@@ -1,6 +1,6 @@
 import asyncio
 from itertools import cycle
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Dict, List, Optional, Sequence, Tuple, Union
 
 import pytest
 import ray
@@ -128,7 +128,9 @@ async def get_output(prompt, sampling_param) -> RequestOutput:
         try:
             for i in range(num_requests):
                 prompt = prompts[i] if prompts is not None else None
-                res = asyncio.run(get_output(prompt, sampling_params))
+                params = sampling_params[i] if isinstance(
+                    sampling_params, Sequence) else sampling_params
+                res = asyncio.run(get_output(prompt, params))
                 outputs.append(res)
         finally:
             ray.shutdown()
@@ -267,7 +269,31 @@ def run_greedy_equality_correctness_test(baseline_llm_generator,
     the test LLM. It asserts greedy equality, e.g. that the outputs are exactly
     the same when temperature is zero.
     """
-    temperature = 0.0
+
+    run_equality_correctness_test(baseline_llm_generator,
+                                  test_llm_generator,
+                                  batch_size,
+                                  max_output_len,
+                                  force_output_len,
+                                  temperature=0.0,
+                                  seeded=False,
+                                  print_tokens=print_tokens,
+                                  ensure_all_accepted=ensure_all_accepted)
+
+
+def run_equality_correctness_test(baseline_llm_generator,
+                                  test_llm_generator,
+                                  batch_size,
+                                  max_output_len,
+                                  force_output_len: bool,
+                                  temperature: float,
+                                  seeded: bool,
+                                  print_tokens: bool = False,
+                                  ensure_all_accepted: bool = False):
+    """Helper method that compares the outputs of both the baseline LLM and
+    the test LLM. It asserts greedy equality, e.g. that the outputs are exactly
+    the same when temperature is zero (or when temperature is > 0 and seeded).
+    """
 
     prompts = [
         "Hello, my name is",
@@ -286,11 +312,21 @@ def run_greedy_equality_correctness_test(baseline_llm_generator,
     # sampling params to ignore eos token.
     ignore_eos = force_output_len
 
-    sampling_params = SamplingParams(
-        max_tokens=max_output_len,
-        ignore_eos=ignore_eos,
-        temperature=temperature,
-    )
+    if seeded:
+        sampling_params = [
+            SamplingParams(
+                max_tokens=max_output_len,
+                ignore_eos=ignore_eos,
+                temperature=temperature,
+                seed=i,
+            ) for i in range(len(prompts))
+        ]
+    else:
+        sampling_params = SamplingParams(
+            max_tokens=max_output_len,
+            ignore_eos=ignore_eos,
+            temperature=temperature,
+        )
 
     (spec_batch_tokens, spec_batch_token_ids,
      acceptance_rate) = get_output_from_llm_generator(test_llm_generator,

tests/spec_decode/e2e/test_seed.py

@@ -0,0 +1,44 @@
+import pytest
+
+from .conftest import run_equality_correctness_test
+
+
+@pytest.mark.parametrize(
+    "common_llm_kwargs",
+    [{
+        "model": "JackFram/llama-68m",
+
+        # Skip cuda graph recording for fast test.
+        "enforce_eager": True,
+
+        # Required for spec decode.
+        "use_v2_block_manager": True,
+
+        # speculative model
+        "speculative_model": "JackFram/llama-160m",
+
+        # num speculative tokens
+        "num_speculative_tokens": 3,
+    }])
+@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
+@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
+@pytest.mark.parametrize("batch_size", [1, 8, 32])
+@pytest.mark.parametrize("temperature", [0.1, 1.0])
+@pytest.mark.parametrize(
+    "output_len",
+    [
+        # Use smaller output len for fast test.
+        10,
+    ])
+@pytest.mark.parametrize("seed", [1])
+def test_seeded_consistency(baseline_llm_generator, batch_size: int,
+                            temperature: float, output_len: int):
+    """Verify outputs are consistent across multiple runs with same seed
+    """
+    run_equality_correctness_test(baseline_llm_generator,
+                                  baseline_llm_generator,
+                                  batch_size,
+                                  max_output_len=output_len,
+                                  temperature=temperature,
+                                  seeded=True,
+                                  force_output_len=True)

vllm/model_executor/layers/rejection_sampler.py

@@ -1,14 +1,14 @@
 from functools import cached_property
-from typing import Tuple
+from typing import List, Optional, Tuple
 
 import torch
 import torch.jit
 
 from vllm.model_executor.layers.spec_decode_base_sampler import (
-    SpecDecodeBaseSampler)
+    SpecDecodeStochasticBaseSampler)
 
 
-class RejectionSampler(SpecDecodeBaseSampler):
+class RejectionSampler(SpecDecodeStochasticBaseSampler):
     """Apply modified rejection sampling as described in "Accelerating Large
         Language Model Decoding with Speculative Sampling"
         https://arxiv.org/pdf/2302.01318.pdf.
@@ -36,6 +36,7 @@ def forward(
         bonus_token_ids: torch.Tensor,
         draft_probs: torch.Tensor,
         draft_token_ids: torch.Tensor,
+        generators: List[Optional[torch.Generator]],
     ) -> torch.Tensor:
         """Sample token ids using rejection sampling. This accepts or rejects
         tokens proposed by the draft model using the probability of each token
@@ -82,6 +83,7 @@ def forward(
                 target_probs,
                 draft_probs,
                 draft_token_ids,
+                generators,
             ))
 
         output_token_ids = self._create_output(
@@ -94,10 +96,11 @@ def forward(
         return output_token_ids
 
     def _batch_modified_rejection_sampling(
-            self,
-            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]
+        self,
+        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]],
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         """Perform modified rejection sampling on each sequence.
 
@@ -114,22 +117,33 @@ def _batch_modified_rejection_sampling(
 
         # shape [batch_size, k]
         accepted = self._get_accepted(target_probs, draft_probs,
-                                      draft_token_ids)
+                                      draft_token_ids, generators)
 
         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).reshape(
-                                               batch_size, k)
+        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 [],
+        ).reshape(batch_size, k)
+
         return accepted, recovered_token_ids
 
     def _get_accepted(
-            self,
-            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]
+        self,
+        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]],
     ) -> torch.Tensor:
         r"""Create bool matrix over the proposed draft tokens. If
         True, then a token can be accepted, else it should be
@@ -164,10 +178,28 @@ def _get_accepted(
         selected_target_probs = target_probs[batch_indices, probs_indicies,
                                              draft_token_ids]
 
-        uniform_rand = torch.rand(batch_size,
-                                  k,
-                                  dtype=self.probs_dtype,
-                                  device=target_probs.device)
+        seed_indices, non_seed_indices = self._split_batch_by_seeded(
+            generators)
+
+        if len(seed_indices) == 0:
+            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),
+                    k,
+                    dtype=self.probs_dtype,
+                    device=target_probs.device)
+
         capped_ratio = torch.minimum(
             selected_target_probs / selected_draft_probs,
             torch.full((1, ), 1, device=target_probs.device))
@@ -240,6 +272,27 @@ 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.
@@ -250,12 +303,25 @@ def _smallest_positive_value(self) -> float:
 def _multinomial(
     probs: torch.Tensor,
     num_samples: int,
+    k: int,
+    generators: List[Optional[torch.Generator]],
+    seed_indices: List[int],
+    non_seed_indices: List[int],
 ) -> torch.Tensor:
+
     if num_samples > 1:
         # This is equivalent to torch.repeat_interleaved (which also
         # forces a GPU<->CPU sync).
         probs = probs[:, None, :].expand(probs.shape[0], num_samples,
                                          probs.shape[1]).contiguous().view(
                                              -1, probs.shape[1])
-    q = torch.empty_like(probs).exponential_(1.0)
+
+    q = torch.empty_like(probs)
+    if len(seed_indices) == 0:
+        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])
+
     return probs.div_(q).argmax(dim=1).view(-1, num_samples)