[Bugfix][SpecDecode] kv corruption with bonus tokens in spec decode (#9730)

Co-authored-by: LiuXiaoxuanPKU <lilyliupku@gmail.com>

tests/spec_decode/test_multi_step_worker.py

@@ -5,6 +5,8 @@ from unittest.mock import MagicMock
 import pytest
 import torch
 
+from vllm.attention.selector import (_Backend,
+                                     global_force_attn_backend_context_manager)
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.model_executor.utils import set_random_seed
 from vllm.sequence import (ExecuteModelRequest, HiddenStates, Logprob,
@@ -303,6 +305,7 @@ def test_multi_step_with_batch_expansion_correct_output():
         seed,
         model_runner_cls=TP1DraftModelRunner,
     )
+    multi_step_worker.set_include_gpu_probs_tensor()
     worker = create_worker(
         Worker,
         model_name,
@@ -397,6 +400,7 @@ def test_multi_step_with_batch_expansion_incorrect_output():
         seed,
         model_runner_cls=TP1DraftModelRunner,
     )
+    multi_step_worker.set_include_gpu_probs_tensor()
     worker = create_worker(
         Worker,
         model_name,
@@ -477,6 +481,109 @@ def test_multi_step_with_batch_expansion_incorrect_output():
     assert (num_mismatch > 0)
 
 
+@torch.inference_mode()
+@pytest.mark.parametrize('num_steps', [1, 2, 3, 4])
+# The choice of backends forces the multi_step_worker to choose between
+# the vanilla model_runner and TP1DraftModelRunner and that we can test
+# both code paths.
+@pytest.mark.parametrize('attn_backend',
+                         [_Backend.XFORMERS, _Backend.FLASH_ATTN])
+def test_multi_step_correct_kvcache(num_steps, attn_backend):
+    """Verify that the KV cache of the draft model 
+    is correctly updated for sequences with bonus token.
+    """
+    seed = 100
+    model_name = "JackFram/llama-68m"
+
+    block_size = 16
+    num_gpu_blocks = 2048 // block_size
+    batch_size = 1
+
+    with global_force_attn_backend_context_manager(attn_backend):
+        dtype = 'float16' if attn_backend == _Backend.FLASH_ATTN else 'float32'
+        multi_step_worker = create_worker(MultiStepWorker,
+                                          model_name,
+                                          block_size,
+                                          num_gpu_blocks,
+                                          seed,
+                                          model_runner_cls=TP1DraftModelRunner,
+                                          dtype=dtype)
+        multi_step_worker.set_include_gpu_probs_tensor()
+        worker = create_worker(Worker,
+                               model_name,
+                               block_size,
+                               num_gpu_blocks,
+                               seed,
+                               dtype=dtype)
+
+        prompts = [[0] for _ in range(batch_size)]
+        # Already generate two tokens for the sequence
+        # so that we can simulate the bonus token case
+        multi_step_continuations = [[
+            random.randint(0, 1000),
+            random.randint(0, 1000)
+        ] for _ in prompts]
+        final_prompt_lens = [len(prompt) + 2 + num_steps for prompt in prompts]
+
+        seq_ids_with_bonus_token_in_last_step = set(range(batch_size))
+        seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+            prompts,
+            num_gpu_blocks,
+            block_size,
+            continuations=multi_step_continuations,
+            final_prompt_lens=final_prompt_lens)
+
+        # Run multi-step.
+        zero_kv_cache(multi_step_worker.cache_engine)
+        multi_step_worker.sampler_output(execute_model_req=ExecuteModelRequest(
+            seq_group_metadata_list=seq_group_metadata_list),
+                                         sample_len=num_steps,
+                                         seq_ids_with_bonus_token_in_last_step=
+                                         seq_ids_with_bonus_token_in_last_step)
+
+        # Run single-step repeatedly.
+        zero_kv_cache(worker.cache_engine)
+        # Generate the kv cache for the bonus token first
+        single_step_continuations = [c[:1] for c in multi_step_continuations]
+        seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+            prompts,
+            num_gpu_blocks,
+            block_size,
+            continuations=single_step_continuations,
+            final_prompt_lens=final_prompt_lens)
+        single_step_output = worker.execute_model(
+            execute_model_req=ExecuteModelRequest(
+                seq_group_metadata_list=seq_group_metadata_list))
+        for _ in range(num_steps):
+            seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+                prompts,
+                num_gpu_blocks,
+                block_size,
+                continuations=multi_step_continuations,
+                final_prompt_lens=final_prompt_lens)
+
+            single_step_output = worker.execute_model(
+                execute_model_req=ExecuteModelRequest(
+                    seq_group_metadata_list=seq_group_metadata_list))
+
+            for i, seq_group_output in enumerate(single_step_output[-1]):
+                multi_step_continuations[i].append(
+                    seq_group_output.samples[0].output_token)
+
+        # Verify that the KV cache of the single-step and
+        # multi-step workers are the same.
+        single_step_gpu_cache = worker.cache_engine[0].gpu_cache
+        multi_step_gpu_cache = multi_step_worker.cache_engine[0].gpu_cache
+        num_layers = len(single_step_gpu_cache)
+        allclose = lambda a, b: torch.allclose(
+            a.cuda(), b.cuda(), rtol=1e-2, atol=1e-2)
+        for i in range(num_layers):
+            assert allclose(single_step_gpu_cache[i][0],
+                            multi_step_gpu_cache[i][0])
+            assert allclose(single_step_gpu_cache[i][1],
+                            multi_step_gpu_cache[i][1])
+
+
 @torch.inference_mode()
 def test_draft_proposals_full_speculation_len():
     """Verify Top1Proposer correctly handles case where all sequences

tests/spec_decode/utils.py

@@ -68,12 +68,14 @@ def create_worker(cls: Callable[..., T],
                   seed: int,
                   is_driver_worker: bool = True,
                   enforce_eager: bool = True,
-                  model_runner_cls: Optional[ModelRunner] = None) -> T:
+                  model_runner_cls: Optional[ModelRunner] = None,
+                  dtype: Optional[str] = "auto") -> T:
     engine_args = EngineArgs(
         model=model_name,
         seed=seed,
         block_size=block_size,
         enforce_eager=enforce_eager,
+        dtype=dtype,
     )
     engine_config = engine_args.create_engine_config()
 

vllm/spec_decode/draft_model_runner.py

@@ -54,6 +54,8 @@ class TP1DraftModelRunner(ModelRunner):
 
         super().__init__(*args, **kwargs)
 
+        self.indices_of_seq_with_bonus_tokens = None
+
     def _update_sampling_metadata(self, sampling_metadata, num_seqs,
                                   num_queries):
 
@@ -159,6 +161,10 @@ class TP1DraftModelRunner(ModelRunner):
         # TODO: Add soft-tuning prompt adapter support
         return not self.prompt_adapter_config
 
+    def set_indices_of_seq_with_bonus_tokens(self,
+                                             indices_of_seq_with_bonus_tokens):
+        self.indices_of_seq_with_bonus_tokens = indices_of_seq_with_bonus_tokens
+
     @torch.inference_mode()
     def execute_model(
         self,
@@ -284,11 +290,30 @@ class TP1DraftModelRunner(ModelRunner):
                                                model_input.sampling_metadata)
 
             # Sample the next token.
-            outputs.append(
-                self.model.sample(
+            output = self.model.sample(
                 logits=logits,
                 sampling_metadata=model_input.sampling_metadata,
-                ))
+            )
+            outputs.append(output)
+
+            if model_input.attn_metadata.num_prefills == 0 \
+                and self.indices_of_seq_with_bonus_tokens is not None:
+                assert output.sampled_token_ids is not None
+                # output.sampled_token_ids should be of shape (num_seqs, 1)
+                nums_seqs, num_tokens_per_seq = output.sampled_token_ids.shape
+                assert num_tokens_per_seq == 1
+                count = 0
+                for i in range(nums_seqs):
+                    bonus_seq_idx = self.indices_of_seq_with_bonus_tokens[
+                        count]
+                    if i != bonus_seq_idx:
+                        # The following might cause a cpu->gpu sync
+                        # However, the performance impact is negligible as we
+                        # benchmarked on H100.
+                        output.sampled_token_ids[
+                            i, :] = model_input.input_tokens[bonus_seq_idx]
+                    else:
+                        count += 1
 
             # Prepare inputs for the next step
             if step != num_steps - 1:

vllm/spec_decode/multi_step_worker.py

@@ -81,6 +81,8 @@ class MultiStepWorker(Worker, ProposerWorkerBase):
             # Here we run the draft_model_runner with multi-step prepare
             # on the GPU directly
             expanded_request.num_steps = sample_len
+            self.model_runner.set_indices_of_seq_with_bonus_tokens(
+                indices_of_seq_with_bonus_tokens)
             model_outputs = self.execute_model(
                 execute_model_req=expanded_request)
         else:
@@ -97,7 +99,8 @@ class MultiStepWorker(Worker, ProposerWorkerBase):
                 model_output = model_output[0]
 
                 self._append_new_tokens(
-                    model_output, expanded_request.seq_group_metadata_list)
+                    model_output, expanded_request.seq_group_metadata_list,
+                    indices_of_seq_with_bonus_tokens)
                 model_outputs.append(model_output)
 
         filtered_model_outputs = self._filter_model_output(
@@ -221,13 +224,15 @@ class MultiStepWorker(Worker, ProposerWorkerBase):
     @staticmethod
     def _append_new_tokens(
             model_output: List[SamplerOutput],
-            seq_group_metadata_list: List[SequenceGroupMetadata]) -> None:
+            seq_group_metadata_list: List[SequenceGroupMetadata],
+            indices_of_seq_with_bonus_tokens: List[int]) -> None:
         """Given model output from a single run, append the tokens to the
         sequences. This is normally done outside of the worker, but it is
         required if the worker is to perform multiple forward passes.
         """
-        for seq_group_metadata, sequence_group_outputs in zip(
-                seq_group_metadata_list, model_output):
+        count = 0
+        for index, (seq_group_metadata, sequence_group_outputs) in enumerate(
+                zip(seq_group_metadata_list, model_output)):
             seq_group_metadata.is_prompt = False
 
             for seq_output in sequence_group_outputs.samples:
@@ -237,6 +242,16 @@ class MultiStepWorker(Worker, ProposerWorkerBase):
 
                 token_id = seq_output.output_token
                 token_logprob = seq_output.logprobs[token_id]
+                # Determine the actual token ID to be generated,
+                # considering bonus tokens
+                if index != indices_of_seq_with_bonus_tokens[count]:
+                    bonus_seq_metadata = seq_group_metadata_list[
+                        indices_of_seq_with_bonus_tokens[count]]
+                    _, bonus_token_seq_data = next(
+                        iter(bonus_seq_metadata.seq_data.items()))
+                    token_id = bonus_token_seq_data.output_token_ids[-1]
+                else:
+                    count += 1
 
                 seq.append_token_id(token_id, token_logprob.logprob)
                 seq.update_num_computed_tokens(1)