[Speculative Decoding] Enabling bonus token in speculative decoding f…

…or KV cache based models (vllm-project#5765)

tests/spec_decode/test_dynamic_spec_decode.py

@@ -70,14 +70,17 @@ def test_disable_spec_tokens(queue_size: int, batch_size: int, k: int,
     if queue_size < disable_by_batch_size:
         # Should raise exception when executing the mocked draft model.
         with pytest.raises(ValueError, match=exception_secret):
-            proposer.get_spec_proposals(execute_model_req=ExecuteModelRequest(
-                seq_group_metadata_list=seq_group_metadata_list,
-                num_lookahead_slots=k), )
+            proposer.get_spec_proposals(
+                execute_model_req=ExecuteModelRequest(
+                    seq_group_metadata_list=seq_group_metadata_list,
+                    num_lookahead_slots=k),
+                seq_ids_with_bonus_token_in_last_step=set())
     else:
         # Should not execute the draft model because spec decode is disabled
         # for all requests. Accordingly, the proposal length should be 0.
         proposals = proposer.get_spec_proposals(
             execute_model_req=ExecuteModelRequest(
                 seq_group_metadata_list=seq_group_metadata_list,
-                num_lookahead_slots=k), )
+                num_lookahead_slots=k),
+            seq_ids_with_bonus_token_in_last_step=set())
         assert proposals.proposal_lens.tolist() == [0] * batch_size

tests/spec_decode/test_multi_step_worker.py

@@ -118,7 +118,8 @@ def test_same_output_for_single_step():
     actual_output, _ = multi_step_worker.sampler_output(
         execute_model_req=ExecuteModelRequest(
             seq_group_metadata_list=multi_step_seq_group),
-        sample_len=num_steps)
+        sample_len=num_steps,
+        seq_ids_with_bonus_token_in_last_step=set())
     assert len(actual_output) == num_steps
     actual_output = actual_output[0]
 
@@ -210,7 +211,8 @@ def test_same_output_for_multi_step():
     multi_step_output, _ = multi_step_worker.sampler_output(
         execute_model_req=ExecuteModelRequest(
             seq_group_metadata_list=seq_group_metadata_list),
-        sample_len=num_steps)
+        sample_len=num_steps,
+        seq_ids_with_bonus_token_in_last_step=set())
 
     # Run single-step repeatedly.
     zero_kv_cache(worker.cache_engine)
@@ -277,6 +279,203 @@ def test_same_output_for_multi_step():
                                       single_step_logprobs)
 
 
+@torch.inference_mode()
+def test_multi_step_with_batch_expansion_correct_output():
+    """
+    In this test we verify that the MultiStepWorker is able to handle bonus
+    tokens correctly. The test verifies that if a sequence has a
+    bonus token then the MultiStepWorker is able to expand the batch by adding
+    new sequences corresponding to the sequences with bonus tokens. The
+    expanded batch is then used for predicting the next tokens.
+    """
+    seed = 100
+    model_name = 'JackFram/llama-68m'
+
+    block_size = 16
+    num_gpu_blocks = 2048 // block_size
+    batch_size = 128
+    multi_step_worker = create_worker(
+        MultiStepWorker,
+        model_name,
+        block_size,
+        num_gpu_blocks,
+        seed,
+        model_runner_cls=TP1DraftModelRunner,
+    )
+    worker = create_worker(
+        Worker,
+        model_name,
+        block_size,
+        num_gpu_blocks,
+        seed,
+    )
+    random.seed(seed)
+    prompts = [[0] for _ in range(batch_size)]
+    num_steps = 2
+    final_prompt_lens = [(num_steps + 1) for prompt in prompts]
+    rand_seeds = list(random.randint(0, 100) for _ in range(num_steps))
+    multi_step_worker.execute_model = patch_execute_model_with_seeds(
+        multi_step_worker, rand_seeds)
+    worker.execute_model = patch_execute_model_with_seeds(worker, rand_seeds)
+    # Create the test continuations
+    continuations = [[random.randint(0, 1000)] for _ in prompts]
+    seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        continuations=continuations,
+        final_prompt_lens=final_prompt_lens)
+
+    # Run single-step twice to generate 2 tokens. This
+    # will simulate the bonus token case with the second token
+    # being the bonus token.
+    zero_kv_cache(worker.cache_engine)
+    single_step_output: List[SamplerOutput] = []
+    set_random_seed(seed)
+    for _ in range(num_steps):
+        seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+            prompts,
+            num_gpu_blocks,
+            block_size,
+            continuations=continuations,
+            final_prompt_lens=final_prompt_lens)
+        single_step_output.extend(
+            worker.execute_model(execute_model_req=ExecuteModelRequest(
+                seq_group_metadata_list=seq_group_metadata_list)))
+        # Append output tokens to new sequence data.
+        for i, seq_group_output in enumerate(single_step_output[-1]):
+            continuations[i].append(seq_group_output.samples[0].output_token)
+
+    # Create continuations for the MultiStepWorker. The continuations have
+    # 2 tokens in order to simulate the bonus token case.
+    multi_step_continuations = []
+    for continuation in continuations:
+        multi_step_continuations.append(continuation[:2])
+    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 and verify that the third token prediction is accurate
+    # for all sequences.
+    zero_kv_cache(multi_step_worker.cache_engine)
+    all_seq_ids = {i for i in range(batch_size)}
+    multi_step_output, _ = multi_step_worker.sampler_output(
+        execute_model_req=ExecuteModelRequest(
+            seq_group_metadata_list=seq_group_metadata_list),
+        sample_len=1,
+        seq_ids_with_bonus_token_in_last_step=all_seq_ids)
+    for index, output in enumerate(multi_step_output[-1].outputs):
+        assert (continuations[index][-1] == output.samples[0].output_token)
+
+
+@torch.inference_mode()
+def test_multi_step_with_batch_expansion_incorrect_output():
+    """
+    Tests the MultiStepWorker's ability to handle batch expansion with bonus
+    tokens in a negative case scenario. This test provides the MultiStepWorker
+    with a batch containing sequences with bonus tokens but specifies the
+    sequence IDs with bonus tokens incorrectly. The test verifies that the
+    MultiStepWorker generates correct tokens for the sequences where the
+    sequence ID is specified correctly and incorrect tokens for those where
+    the sequence ID is specified incorrectly.
+    """
+    seed = 100
+    model_name = 'JackFram/llama-68m'
+
+    block_size = 16
+    num_gpu_blocks = 2048 // block_size
+    batch_size = 128
+    multi_step_worker = create_worker(
+        MultiStepWorker,
+        model_name,
+        block_size,
+        num_gpu_blocks,
+        seed,
+        model_runner_cls=TP1DraftModelRunner,
+    )
+    worker = create_worker(
+        Worker,
+        model_name,
+        block_size,
+        num_gpu_blocks,
+        seed,
+    )
+    random.seed(seed)
+    prompts = [[0] for _ in range(batch_size)]
+    num_steps = 2
+    final_prompt_lens = [(num_steps + 1) for prompt in prompts]
+    rand_seeds = list(random.randint(0, 100) for _ in range(num_steps))
+    multi_step_worker.execute_model = patch_execute_model_with_seeds(
+        multi_step_worker, rand_seeds)
+    worker.execute_model = patch_execute_model_with_seeds(worker, rand_seeds)
+    # Create the test continuations
+    continuations = [[random.randint(0, 1000)] for _ in prompts]
+    seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+        prompts,
+        num_gpu_blocks,
+        block_size,
+        continuations=continuations,
+        final_prompt_lens=final_prompt_lens)
+    # Run single-step twice to generate 2 tokens. This
+    # will simulate the bonus token case with the second token
+    # being the bonus token.
+    zero_kv_cache(worker.cache_engine)
+    single_step_output: List[SamplerOutput] = []
+    set_random_seed(seed)
+    for _ in range(num_steps):
+        seq_group_metadata_list = create_seq_group_metadata_from_prompts(
+            prompts,
+            num_gpu_blocks,
+            block_size,
+            continuations=continuations,
+            final_prompt_lens=final_prompt_lens)
+        single_step_output.extend(
+            worker.execute_model(execute_model_req=ExecuteModelRequest(
+                seq_group_metadata_list=seq_group_metadata_list)))
+        # Append output tokens to new sequence data.
+        for i, seq_group_output in enumerate(single_step_output[-1]):
+            continuations[i].append(seq_group_output.samples[0].output_token)
+
+    # Create continuations for the MultiStepWorker. The continuations have
+    # 2 tokens in order to simulate the bonus token case.
+    multi_step_continuations = []
+    for continuation in continuations:
+        multi_step_continuations.append(continuation[:2])
+    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. In this run INCORRECTLY specify that only the odd number
+    # sequences have bonus tokens. Verify that with this setting the third token
+    # prediction is accurate only for the odd numbered sequences. Also verify
+    # that the prediction might be wrong for some of the even numbered
+    # sequences.
+    zero_kv_cache(multi_step_worker.cache_engine)
+    set_random_seed(seed)
+    odd_seq_ids = {i for i in range(batch_size) if i % 2 != 0}
+    multi_step_output, _ = multi_step_worker.sampler_output(
+        execute_model_req=ExecuteModelRequest(
+            seq_group_metadata_list=seq_group_metadata_list),
+        sample_len=1,
+        seq_ids_with_bonus_token_in_last_step=odd_seq_ids)
+    num_mismatch = 0
+    for index, output in enumerate(multi_step_output[-1].outputs):
+        if (index % 2) != 0:
+            assert (continuations[index][-1] == output.samples[0].output_token)
+        elif (continuations[index][-1] != output.samples[0].output_token):
+            num_mismatch += 1
+    # The prediction is accurate for some of the sequences even without proper
+    # handling of the bonus tokens. Hence verify that the number of sequences
+    # for which there is a mismatch is > 0.
+    assert (num_mismatch > 0)
+
+
 @torch.inference_mode()
 def test_draft_proposals_full_speculation_len():
     """Verify Top1Proposer correctly handles case where all sequences
@@ -318,7 +517,8 @@ def test_draft_proposals_full_speculation_len():
     proposals = proposer.get_spec_proposals(
         execute_model_req=ExecuteModelRequest(
             seq_group_metadata_list=seq_group_metadata_list,
-            num_lookahead_slots=k), )
+            num_lookahead_slots=k),
+        seq_ids_with_bonus_token_in_last_step=set())
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)
@@ -356,7 +556,8 @@ def test_draft_proposals_no_speculations():
     proposals = proposer.get_spec_proposals(
         execute_model_req=ExecuteModelRequest(
             seq_group_metadata_list=seq_group_metadata_list,
-            num_lookahead_slots=k), )
+            num_lookahead_slots=k),
+        seq_ids_with_bonus_token_in_last_step=set())
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)
@@ -428,7 +629,8 @@ def test_draft_proposals_mixed_k():
     proposals = proposer.get_spec_proposals(
         execute_model_req=ExecuteModelRequest(
             seq_group_metadata_list=seq_group_metadata_list,
-            num_lookahead_slots=k), )
+            num_lookahead_slots=k),
+        seq_ids_with_bonus_token_in_last_step=set())
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)

tests/spec_decode/test_ngram_worker.py

@@ -53,7 +53,8 @@ def test_ngram_algo_correctness_for_single_no_match():
     proposals = proposer.get_spec_proposals(
         execute_model_req=ExecuteModelRequest(
             seq_group_metadata_list=seq_group_metadata_list,
-            num_lookahead_slots=proposal_len), )
+            num_lookahead_slots=proposal_len),
+        seq_ids_with_bonus_token_in_last_step=None)
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)
@@ -121,7 +122,8 @@ def test_ngram_algo_correctness_for_batches_not_match_all():
     proposals = proposer.get_spec_proposals(
         execute_model_req=ExecuteModelRequest(
             seq_group_metadata_list=seq_group_metadata_list,
-            num_lookahead_slots=proposal_len), )
+            num_lookahead_slots=proposal_len),
+        seq_ids_with_bonus_token_in_last_step=None)
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)
@@ -193,7 +195,8 @@ def test_ngram_algo_correctness_for_batches_match_all():
     proposals = proposer.get_spec_proposals(
         execute_model_req=ExecuteModelRequest(
             seq_group_metadata_list=seq_group_metadata_list,
-            num_lookahead_slots=proposal_len), )
+            num_lookahead_slots=proposal_len),
+        seq_ids_with_bonus_token_in_last_step=None)
 
     assert torch.is_tensor(proposals.proposal_token_ids)
     assert torch.is_tensor(proposals.proposal_probs)