[Spec Decode] Unified Parallel Drafting (#32887)

Signed-off-by: Benjamin Chislett <bchislett@nvidia.com>

examples/offline_inference/spec_decode.py

@@ -75,6 +75,7 @@ def parse_args():
     parser.add_argument("--gpu-memory-utilization", type=float, default=0.9)
     parser.add_argument("--disable-padded-drafter-batch", action="store_true")
     parser.add_argument("--max-num-seqs", type=int, default=None)
+    parser.add_argument("--parallel-drafting", action="store_true")
     parser.add_argument("--allowed-local-media-path", type=str, default="")
     return parser.parse_args()
 
@@ -121,6 +122,7 @@ def main(args):
             "model": eagle_dir,
             "num_speculative_tokens": args.num_spec_tokens,
             "disable_padded_drafter_batch": args.disable_padded_drafter_batch,
+            "parallel_drafting": args.parallel_drafting,
         }
     elif args.method == "ngram":
         speculative_config = {
@@ -137,6 +139,7 @@ def main(args):
             "num_speculative_tokens": args.num_spec_tokens,
             "enforce_eager": args.enforce_eager,
             "max_model_len": args.max_model_len,
+            "parallel_drafting": args.parallel_drafting,
         }
     elif args.method == "mtp":
         speculative_config = {

tests/v1/e2e/test_spec_decode.py

@@ -13,15 +13,12 @@ from vllm import LLM, SamplingParams
 from vllm.assets.base import VLLM_S3_BUCKET_URL
 from vllm.assets.image import VLM_IMAGES_DIR
 from vllm.benchmarks.datasets import InstructCoderDataset
-from vllm.config.vllm import VllmConfig
+from vllm.config import VllmConfig
 from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.engine.arg_utils import EngineArgs
 from vllm.platforms import current_platform
 from vllm.v1.metrics.reader import Metric
-from vllm.v1.spec_decode.draft_model import (
-    create_vllm_config_for_draft_model,
-    merge_toks_kernel,
-)
+from vllm.v1.spec_decode.utils import create_vllm_config_for_draft_model
 
 MTP_SIMILARITY_RATE = 0.8
 
@@ -625,6 +622,8 @@ class ArgsTest:
     expected_acceptance_rate: float
     expected_acceptance_len: float
     # Defaults
+    enforce_eager: bool = True
+    parallel_drafting: bool = False
     target_tensor_parallel_size: int = 1
     draft_tensor_parallel_size: int = 1
     max_model_len: int = 1024
@@ -658,7 +657,8 @@ cases = [
 @pytest.mark.parametrize("args", cases)
 @pytest.mark.parametrize("enforce_eager", [True, False])
 def test_draft_model_correctness(args: ArgsTest, enforce_eager: bool):
-    assert_draft_model_correctness(args, enforce_eager)
+    args.enforce_eager = enforce_eager
+    assert_draft_model_correctness(args)
 
 
 def test_draft_model_realistic_example():
@@ -668,11 +668,28 @@ def test_draft_model_realistic_example():
         dataset="likaixin/InstructCoder",
         num_speculative_tokens=3,
         sampling_config=greedy_sampling(),
+        enforce_eager=False,
         # values below are not derived, but just prevent a regression
         expected_acceptance_len=2.8,
         expected_acceptance_rate=0.55,
     )
-    assert_draft_model_correctness(args, enforce_eager=False)
+    assert_draft_model_correctness(args)
+
+
+def test_draft_model_parallel_drafting():
+    args = ArgsTest(
+        target_model="Qwen/Qwen3-1.7B",
+        draft_model="amd/PARD-Qwen3-0.6B",
+        dataset="likaixin/InstructCoder",
+        num_speculative_tokens=3,
+        sampling_config=greedy_sampling(),
+        parallel_drafting=True,
+        enforce_eager=False,
+        # values below are collected from a stable run, with ~5% tolerance
+        expected_acceptance_len=2.375,
+        expected_acceptance_rate=0.45,
+    )
+    assert_draft_model_correctness(args)
 
 
 @pytest.mark.parametrize(
@@ -691,8 +708,9 @@ def test_draft_model_quantization(models: tuple[str, str], enforce_eager: bool):
         target_model=tgt_model,
         draft_model=draft_model,
         **some_high_acceptance_metrics(),
+        enforce_eager=enforce_eager,
     )
-    assert_draft_model_correctness(sd_case, enforce_eager)
+    assert_draft_model_correctness(sd_case)
 
 
 def test_draft_model_tensor_parallelism():
@@ -704,8 +722,9 @@ def test_draft_model_tensor_parallelism():
         draft_model="Qwen/Qwen3-0.6B",
         draft_tensor_parallel_size=2,
         **some_high_acceptance_metrics(),
+        enforce_eager=False,
     )
-    assert_draft_model_correctness(sd_case, enforce_eager=False)
+    assert_draft_model_correctness(sd_case)
 
 
 def test_draft_model_engine_args_tensor_parallelism():
@@ -750,7 +769,7 @@ def test_draft_model_engine_args_rejects_invalid_tp_argname():
         engine_args.create_engine_config()
 
 
-def assert_draft_model_correctness(args: ArgsTest, enforce_eager: bool):
+def assert_draft_model_correctness(args: ArgsTest):
     """Compare the outputs using and not using speculative decoding.
     In the greedy decoding case, the outputs must match EXACTLY."""
     test_prompts: list[Messages] = get_messages(
@@ -764,14 +783,15 @@ def assert_draft_model_correctness(args: ArgsTest, enforce_eager: bool):
             "method": "draft_model",
             "num_speculative_tokens": args.num_speculative_tokens,
             "max_model_len": args.max_model_len,
-            "enforce_eager": enforce_eager,
+            "enforce_eager": args.enforce_eager,
             "draft_tensor_parallel_size": args.draft_tensor_parallel_size,
+            "parallel_drafting": args.parallel_drafting,
         },
         max_num_seqs=100,  # limit cudagraph capture runtime
         max_model_len=args.max_model_len,
         gpu_memory_utilization=args.gpu_memory_utilization,
         tensor_parallel_size=args.target_tensor_parallel_size,
-        enforce_eager=enforce_eager,
+        enforce_eager=args.enforce_eager,
         disable_log_stats=False,  # enables get_metrics()
     )
     # we don't check the outputs, only check the metrics
@@ -813,57 +833,6 @@ def some_high_acceptance_metrics() -> dict:
     }
 
 
-def test_merge_toks_kernel():
-    device = "cuda"
-    merged_len = 5 + 2  # len(target_toks) = 5, batch_size = 2
-    merged = torch.full((merged_len,), -100, device=device)  # -100 is arbitrary
-    is_rejected_tok = torch.full((merged_len,), True, device=device)
-    grid = (2,)
-    merge_toks_kernel[grid](
-        target_toks_ptr=torch.tensor([0, 1, 2, 0, 1], device=device),
-        next_toks_ptr=torch.tensor([3, 2], device=device),
-        query_start_locs_ptr=torch.tensor([0, 3], device=device),
-        query_end_locs_ptr=torch.tensor([2, 4], device=device),
-        out_ptr_merged_toks=merged,
-        out_ptr_is_rejected_tok=is_rejected_tok,
-        target_toks_size=5,
-        rejected_tok_fill=-1,
-    )
-    expected_merged = torch.tensor([0, 1, 2, 3, 0, 1, 2], device=device)
-    assert torch.allclose(merged, expected_merged)
-
-    expected_rejected_toks = torch.tensor([False] * merged_len, device=device)
-    assert torch.allclose(is_rejected_tok, expected_rejected_toks)
-
-
-def test_merge_toks_kernel_with_rejected_tokens():
-    device = "cuda"
-    merged_size = 9 + 2  # len(target_toks) = 9, batch_size = 2
-    merged = torch.full((merged_size,), -100, device=device)
-    is_rejected_tok = torch.full((merged_size,), True, device=device)
-    grid = (2,)
-    merge_toks_kernel[grid](
-        #                                       rejected tokens
-        #                                       ↓   ↓   ↓         ↓
-        target_toks_ptr=torch.tensor([0, 1, 2, 13, 14, 15, 0, 1, 22], device=device),
-        next_toks_ptr=torch.tensor([3, 2], device=device),
-        query_start_locs_ptr=torch.tensor([0, 6], device=device),
-        query_end_locs_ptr=torch.tensor([2, 7], device=device),
-        out_ptr_merged_toks=merged,
-        out_ptr_is_rejected_tok=is_rejected_tok,
-        target_toks_size=9,
-        rejected_tok_fill=-1,
-    )
-    expected_merged = torch.tensor([0, 1, 2, 3, -1, -1, -1, 0, 1, 2, -1], device=device)
-    assert torch.allclose(merged, expected_merged)
-
-    expected_rejected_toks = torch.tensor(
-        [False, False, False, False, True, True, True, False, False, False, True],
-        device=device,
-    )
-    assert torch.allclose(is_rejected_tok, expected_rejected_toks)
-
-
 def compute_acceptance_rate(metrics: list[Metric]) -> float:
     name2metric = {metric.name: metric for metric in metrics}
     n_draft_toks = name2metric["vllm:spec_decode_num_draft_tokens"].value  # type: ignore

tests/v1/spec_decode/test_eagle.py

@@ -27,6 +27,7 @@ from vllm.config.load import LoadConfig
 from vllm.model_executor.models.llama import LlamaForCausalLM
 from vllm.platforms import current_platform
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
+from vllm.v1.spec_decode.draft_model import DraftModelProposer
 from vllm.v1.spec_decode.eagle import EagleProposer
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
 from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
@@ -34,6 +35,7 @@ from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
 model_dir = "meta-llama/Llama-3.1-8B-Instruct"
 eagle_dir = "yuhuili/EAGLE-LLaMA3.1-Instruct-8B"
 eagle3_dir = "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
+ar_draft_model_dir = "amd/PARD-Llama-3.2-1B"  # Compatible with parallel and AR drafting
 
 
 def _create_proposer(
@@ -41,11 +43,19 @@ def _create_proposer(
     num_speculative_tokens: int,
     attention_backend: str | None = None,
     speculative_token_tree: list[tuple[int, ...]] | None = None,
+    parallel_drafting: bool = False,
 ) -> EagleProposer:
     model_config = ModelConfig(model=model_dir, runner="generate", max_model_len=100)
 
-    # Choose model directory based on method
-    draft_model_dir = eagle_dir if method == "eagle" else eagle3_dir
+    # Method-dependent setup
+    if method == "eagle":
+        draft_model_dir = eagle_dir
+    elif method == "eagle3":
+        draft_model_dir = eagle3_dir
+    elif method == "draft_model":
+        draft_model_dir = ar_draft_model_dir
+    else:
+        raise ValueError(f"Unknown method: {method}")
 
     spec_token_tree_str = None
     if speculative_token_tree is not None:
@@ -59,13 +69,18 @@ def _create_proposer(
         method=method,
         num_speculative_tokens=num_speculative_tokens,
         speculative_token_tree=spec_token_tree_str,
+        parallel_drafting=parallel_drafting,
     )
+    if parallel_drafting:
+        # Overwrite pard_token to avoid crash during init
+        speculative_config.draft_model_config.hf_config.pard_token = 0
 
+    device = current_platform.device_type
     vllm_config = VllmConfig(
         model_config=model_config,
         cache_config=CacheConfig(),
         speculative_config=speculative_config,
-        device_config=DeviceConfig(device=current_platform.device_type),
+        device_config=DeviceConfig(device=device),
         parallel_config=ParallelConfig(),
         load_config=LoadConfig(),
         scheduler_config=SchedulerConfig(
@@ -75,7 +90,10 @@ def _create_proposer(
         attention_config=AttentionConfig(backend=attention_backend),
     )
 
-    return EagleProposer(vllm_config=vllm_config, device=current_platform.device_type)
+    if "eagle" in method:
+        return EagleProposer(vllm_config=vllm_config, device=device)
+    else:
+        return DraftModelProposer(vllm_config=vllm_config, device=device)
 
 
 def test_prepare_next_token_ids():
@@ -321,6 +339,390 @@ def test_prepare_inputs_padded():
     assert torch.equal(token_indices_to_sample, expected_token_indices_to_sample)
 
 
+def test_set_inputs_first_pass_default_eagle():
+    """
+    Test for set_inputs_first_pass without extra input slots (default EAGLE).
+
+    This tests the path where needs_extra_input_slots=False, which is the
+    default EAGLE pathway. In this case:
+    - Input IDs are rotated (shifted by one)
+    - The next_token_ids are inserted at the last position of each request
+    - Positions are copied as-is
+    - Hidden states are copied as-is
+    - The CommonAttentionMetadata is returned unchanged
+
+    Setup:
+    - 3 requests with query_lens [3, 2, 4]
+    - Tokens: [a1, a2, a3, b1, b2, c1, c2, c3, c4]
+    - After rotation: [a2, a3, -, b2, -, c2, c3, c4, -]
+    - After inserting next_tokens [100, 200, 300]:
+        [a2, a3, 100, b2, 200, c2, c3, c4, 300]
+    """
+    device = torch.device(current_platform.device_type)
+
+    num_speculative_tokens = 3
+    proposer = _create_proposer("eagle", num_speculative_tokens)
+
+    # Setup batch with 3 requests
+    batch_spec = BatchSpec(
+        seq_lens=[10, 8, 12],  # Arbitrary context lengths
+        query_lens=[3, 2, 4],
+    )
+
+    common_attn_metadata = create_common_attn_metadata(
+        batch_spec,
+        block_size=16,
+        device=device,
+    )
+
+    # Input tensors
+    # Request 0: tokens [10, 11, 12] at positions [7, 8, 9]
+    # Request 1: tokens [20, 21] at positions [6, 7]
+    # Request 2: tokens [30, 31, 32, 33] at positions [8, 9, 10, 11]
+    target_token_ids = torch.tensor(
+        [10, 11, 12, 20, 21, 30, 31, 32, 33], dtype=torch.int32, device=device
+    )
+    target_positions = torch.tensor(
+        [7, 8, 9, 6, 7, 8, 9, 10, 11], dtype=torch.int64, device=device
+    )
+    target_hidden_states = torch.randn(
+        9, proposer.hidden_size, dtype=proposer.dtype, device=device
+    )
+    next_token_ids = torch.tensor([100, 200, 300], dtype=torch.int32, device=device)
+
+    num_tokens, token_indices_to_sample, output_cad = proposer.set_inputs_first_pass(
+        target_token_ids=target_token_ids,
+        next_token_ids=next_token_ids,
+        target_positions=target_positions,
+        target_hidden_states=target_hidden_states,
+        token_indices_to_sample=None,
+        cad=common_attn_metadata,
+        num_rejected_tokens_gpu=None,
+    )
+
+    assert num_tokens == 9  # Total tokens unchanged
+
+    expected_token_indices_to_sample = torch.tensor(
+        [2, 4, 8], dtype=torch.int32, device=device
+    )
+    assert torch.equal(token_indices_to_sample, expected_token_indices_to_sample)
+
+    assert output_cad is common_attn_metadata
+
+    # Verify input_ids are rotated and next_tokens inserted
+    # Original: [10, 11, 12, 20, 21, 30, 31, 32, 33]
+    # After shift by 1: [11, 12, 12, 21, 21, 31, 32, 33, 33]
+    # After inserting at last indices [2, 4, 8]: [11, 12, 100, 21, 200, 31, 32, 33, 300]
+    expected_input_ids = torch.tensor(
+        [11, 12, 100, 21, 200, 31, 32, 33, 300], dtype=torch.int32, device=device
+    )
+    assert torch.equal(proposer.input_ids[:num_tokens], expected_input_ids)
+
+    # Verify positions are copied as-is
+    assert torch.equal(proposer.positions[:num_tokens], target_positions)
+
+    # Verify hidden states are copied as-is
+    assert torch.equal(proposer.hidden_states[:num_tokens], target_hidden_states)
+
+
+def test_set_inputs_first_pass_draft_model():
+    """
+    Test for set_inputs_first_pass with a draft model (extra input slots,
+    no shift).
+
+    This tests the path where needs_extra_input_slots=True and
+    shift_input_ids=False (draft model case). In this case:
+    - Input IDs are NOT shifted
+    - Each request gets extra_slots_per_request (1) new slots
+    - The kernel handles copying tokens and inserting bonus/padding tokens
+    - A new CommonAttentionMetadata is returned with updated query_start_loc
+
+    Setup:
+    - 2 requests
+    - Request 0: tokens [10, 11, 12] at positions [0, 1, 2]
+      - Only tokens [10, 11] are "valid" (query_end_loc=1),
+        token 12 is a rejected token from previous speculation
+    - Request 1: tokens [20, 21] at positions [0, 1], both valid.
+      - Note: this is less than num_speculative_tokens (2) to ensure
+        we handle variable lengths correctly.
+    - next_token_ids: [100, 200] (bonus tokens)
+
+    With extra_slots_per_request=1 and shift=False:
+    Expected output layout:
+    Request 0 (indices 0-3):
+      - idx 0: token 10, pos 0
+      - idx 1: token 11, pos 1
+      - idx 2: token 100, pos 2 (bonus token)
+      - idx 3: padding_token_id, is_rejected=True
+    Request 1 (indices 4-6):
+      - idx 4: token 20, pos 0
+      - idx 5: token 21, pos 1
+      - idx 6: token 200, pos 2 (bonus token)
+    """
+    device = torch.device(current_platform.device_type)
+
+    num_speculative_tokens = 2
+    block_size = 16
+
+    # Create a proposer configured as a draft model (pass_hidden_states=False)
+    # We need to mock this since _create_proposer defaults to EAGLE
+    proposer = _create_proposer("draft_model", num_speculative_tokens)
+
+    proposer.parallel_drafting_token_id = 0
+    proposer.is_rejected_token_mask = torch.zeros(
+        proposer.max_num_tokens, dtype=torch.bool, device=device
+    )
+    proposer.is_masked_token_mask = torch.zeros(
+        proposer.max_num_tokens, dtype=torch.bool, device=device
+    )
+
+    # Mock the attn_metadata_builder to avoid needing the full model setup
+    mock_kv_cache_spec = mock.MagicMock()
+    mock_kv_cache_spec.block_size = block_size
+    mock_builder = mock.MagicMock()
+    mock_builder.kv_cache_spec = mock_kv_cache_spec
+    proposer.attn_metadata_builder = mock_builder
+
+    # Request 0: query_len=3 (but 1 rejected), Request 1: query_len=2
+    batch_spec = BatchSpec(
+        seq_lens=[3, 2],
+        query_lens=[3, 2],
+    )
+
+    common_attn_metadata = create_common_attn_metadata(
+        batch_spec,
+        block_size=block_size,
+        device=device,
+        arange_block_indices=True,  # Use predictable block indices
+    )
+
+    # Input tensors
+    target_token_ids = torch.tensor(
+        [10, 11, 12, 20, 21], dtype=torch.int32, device=device
+    )
+    target_positions = torch.tensor([0, 1, 2, 0, 1], dtype=torch.int64, device=device)
+    target_hidden_states = torch.randn(
+        5, proposer.hidden_size, dtype=proposer.dtype, device=device
+    )
+    next_token_ids = torch.tensor([100, 200], dtype=torch.int32, device=device)
+
+    num_rejected_tokens_gpu = torch.tensor([1, 0], dtype=torch.int32, device=device)
+
+    num_tokens, token_indices_to_sample, output_cad = proposer.set_inputs_first_pass(
+        target_token_ids=target_token_ids,
+        next_token_ids=next_token_ids,
+        target_positions=target_positions,
+        target_hidden_states=target_hidden_states,
+        token_indices_to_sample=None,
+        cad=common_attn_metadata,
+        num_rejected_tokens_gpu=num_rejected_tokens_gpu,
+    )
+
+    assert proposer.net_num_new_slots_per_request == 1
+    assert proposer.needs_extra_input_slots
+
+    # total_output_tokens = total_input_tokens + net_num_new_slots * batch_size
+    assert num_tokens == 7
+
+    # Request 0: [10, 11, 100, padding_token (0)]
+    # Request 1: [20, 21, 200]
+    # Combined: [10, 11, 100, 0, 20, 21, 200]
+    expected_input_ids = torch.tensor(
+        [10, 11, 100, 0, 20, 21, 200], dtype=torch.int32, device=device
+    )
+    assert torch.equal(proposer.input_ids[:num_tokens], expected_input_ids)
+
+    # Verify positions
+    # Request 0: [0, 1, 2, 0 (don't care)]
+    # Request 1: [0, 1, 2]
+    # Combined: [0, 1, 2, 0, 0, 1, 2]
+    expected_positions = torch.tensor(
+        [0, 1, 2, 0, 0, 1, 2], dtype=torch.int64, device=device
+    )
+    assert torch.equal(
+        proposer.positions[:num_tokens],
+        expected_positions,
+    )
+
+    # Verify rejection mask
+    expected_is_rejected = torch.zeros(7, dtype=torch.bool, device=device)
+    expected_is_rejected[3] = True  # padding token at index 3
+    assert torch.equal(
+        proposer.is_rejected_token_mask[:num_tokens], expected_is_rejected
+    )
+
+    # Verify masked token mask (should all be False for non-parallel drafting)
+    expected_is_masked = torch.zeros(7, dtype=torch.bool, device=device)
+    assert torch.equal(proposer.is_masked_token_mask[:num_tokens], expected_is_masked)
+
+    # Verify token_indices_to_sample (bonus tokens at indices 2 and 6)
+    expected_token_indices_to_sample = torch.tensor(
+        [2, 6], dtype=torch.int32, device=device
+    )
+    assert torch.equal(token_indices_to_sample, expected_token_indices_to_sample)
+
+    # Verify the new CAD has updated query_start_loc
+    # Original: [0, 3, 5] -> New: [0, 4, 7] (each request gains 1 slot)
+    expected_query_start_loc = torch.tensor([0, 4, 7], dtype=torch.int32, device=device)
+    assert torch.equal(output_cad.query_start_loc, expected_query_start_loc)
+
+
+def test_set_inputs_first_pass_parallel_drafting():
+    """
+    Test for set_inputs_first_pass with parallel drafting (extra input slots,
+    with shift).
+
+    This tests the path where needs_extra_input_slots=True and
+    shift_input_ids=True (parallel drafting case). In this case:
+    - Input IDs ARE shifted (like default EAGLE)
+    - Each request gets extra_slots_per_request (3) new slots
+    - Parallel drafting tokens are inserted and marked as masked
+    - Hidden states are mapped correctly
+
+    Setup:
+    - 2 requests with query_lens [4, 4] (1 bonus + 3 spec tokens each)
+    - Request 0: tokens [10, 11, 12, 13] at positions [5, 6, 7, 8]
+      - Only tokens [10, 11, 12] are "valid", token 13 is rejected
+    - Request 1: tokens [20, 21, 22, 23] at positions [10, 11, 12, 13], all valid.
+    - next_token_ids: [100, 200] (bonus tokens)
+
+    With shift_input_ids=True, extra_slots_per_request=3:
+    Expected output layout:
+    Request 0 (6 output slots = 4 - 1 + 3):
+      - idx 0-2: shifted tokens [11, 12, 100]
+      - idx 3-4: parallel_drafting_tokens, is_masked=True
+      - idx 5: padding_token, is_rejected=True
+    Request 1 (6 output slots = 4 - 1 + 3):
+      - idx 6-8: shifted tokens [21, 22, 23]
+      - idx 9: bonus token 200
+      - idx 10-11: parallel_drafting_tokens, is_masked=True
+    """
+    device = torch.device(current_platform.device_type)
+
+    num_speculative_tokens = 3
+    block_size = 16
+
+    proposer = _create_proposer("eagle", num_speculative_tokens, parallel_drafting=True)
+
+    # Override to simulate parallel drafting behavior
+    proposer.parallel_drafting_token_id = -2
+    proposer.parallel_drafting_hidden_state_tensor = torch.zeros(
+        proposer.hidden_size, dtype=proposer.dtype, device=device
+    )
+    proposer.is_rejected_token_mask = torch.zeros(
+        proposer.max_num_tokens, dtype=torch.bool, device=device
+    )
+    proposer.is_masked_token_mask = torch.zeros(
+        proposer.max_num_tokens, dtype=torch.bool, device=device
+    )
+
+    # Mock the attn_metadata_builder
+    mock_kv_cache_spec = mock.MagicMock()
+    mock_kv_cache_spec.block_size = block_size
+    mock_builder = mock.MagicMock()
+    mock_builder.kv_cache_spec = mock_kv_cache_spec
+    proposer.attn_metadata_builder = mock_builder
+
+    # Request 0: query_len=4 (1 rejected), Request 1: query_len=4 (all valid)
+    batch_spec = BatchSpec(
+        seq_lens=[9, 14],
+        query_lens=[4, 4],
+    )
+
+    common_attn_metadata = create_common_attn_metadata(
+        batch_spec,
+        block_size=block_size,
+        device=device,
+        arange_block_indices=True,
+    )
+
+    # Input tensors
+    target_token_ids = torch.tensor(
+        [10, 11, 12, 13, 20, 21, 22, 23], dtype=torch.int32, device=device
+    )
+    target_positions = torch.tensor(
+        [5, 6, 7, 8, 10, 11, 12, 13], dtype=torch.int64, device=device
+    )
+    target_hidden_states = torch.arange(
+        8 * proposer.hidden_size, dtype=proposer.dtype, device=device
+    ).view(8, proposer.hidden_size)
+    next_token_ids = torch.tensor([100, 200], dtype=torch.int32, device=device)
+
+    num_rejected_tokens_gpu = torch.tensor([1, 0], dtype=torch.int32, device=device)
+
+    num_tokens, token_indices_to_sample, output_cad = proposer.set_inputs_first_pass(
+        target_token_ids=target_token_ids,
+        next_token_ids=next_token_ids,
+        target_positions=target_positions,
+        target_hidden_states=target_hidden_states,
+        token_indices_to_sample=None,
+        cad=common_attn_metadata,
+        num_rejected_tokens_gpu=num_rejected_tokens_gpu,
+    )
+
+    # total_output_tokens = total_input_tokens + net_num_new_slots * batch_size
+    # = 8 + 2 * 2 = 12
+    assert num_tokens == 12
+
+    # Request 0: [11, 12, 100, -2, -2, 0(padding)]
+    # Request 1: [21, 22, 23, 200, -2, -2]
+    expected_input_ids = torch.tensor(
+        [11, 12, 100, -2, -2, 0, 21, 22, 23, 200, -2, -2],
+        dtype=torch.int32,
+        device=device,
+    )
+    assert torch.equal(proposer.input_ids[:num_tokens], expected_input_ids)
+
+    # Verify positions
+    # Request 0: [5, 6, 7, 8, 9, 0 (don't care)]
+    # Request 1: [10, 11, 12, 13, 14, 15]
+    expected_positions = torch.tensor(
+        [5, 6, 7, 8, 9, 0, 10, 11, 12, 13, 14, 15], dtype=torch.int64, device=device
+    )
+    assert torch.equal(
+        proposer.positions[:num_tokens],
+        expected_positions,
+    )
+
+    # Verify rejection mask
+    expected_is_rejected = torch.zeros(12, dtype=torch.bool, device=device)
+    expected_is_rejected[5] = True
+    assert torch.equal(
+        proposer.is_rejected_token_mask[:num_tokens], expected_is_rejected
+    )
+
+    # Verify masked token mask (parallel drafting slots should be masked)
+    expected_is_masked = torch.zeros(12, dtype=torch.bool, device=device)
+    expected_is_masked[3] = True
+    expected_is_masked[4] = True
+    expected_is_masked[10] = True
+    expected_is_masked[11] = True
+    assert torch.equal(proposer.is_masked_token_mask[:num_tokens], expected_is_masked)
+
+    # Verify token_indices_to_sample (bonus + parallel drafting tokens)
+    # Request 0: bonus at 2, parallel at 3, 4
+    # Request 1: bonus at 9, parallel at 10, 11
+    expected_token_indices_to_sample = torch.tensor(
+        [2, 3, 4, 9, 10, 11], dtype=torch.int32, device=device
+    )
+    assert torch.equal(token_indices_to_sample, expected_token_indices_to_sample)
+
+    # Verify the new CAD has updated query_start_loc
+    # Original query_lens: [4, 4] -> Output: [6, 6]
+    expected_query_start_loc = torch.tensor(
+        [0, 6, 12], dtype=torch.int32, device=device
+    )
+    assert torch.equal(output_cad.query_start_loc, expected_query_start_loc)
+
+    # Verify masked positions have the parallel drafting hidden state (zeros)
+    parallel_drafting_hs = proposer.parallel_drafting_hidden_state_tensor
+    for i in range(num_tokens):
+        if expected_is_masked[i]:
+            assert torch.equal(proposer.hidden_states[i], parallel_drafting_hs), (
+                f"Masked position {i} should have parallel drafting hidden state"
+            )
+
+
 @pytest.mark.parametrize("method", ["eagle", "eagle3"])
 @pytest.mark.parametrize("attn_backend", get_attn_backend_list_based_on_platform())
 @pytest.mark.parametrize("pp_size", [1, 2])
@@ -579,7 +981,7 @@ def test_propose(method, attn_backend, num_speculative_tokens, monkeypatch):
         target_positions=target_positions,
         target_hidden_states=target_hidden_states,
         next_token_ids=next_token_ids,
-        last_token_indices=None,
+        token_indices_to_sample=None,
         common_attn_metadata=common_attn_metadata,
         sampling_metadata=sampling_metadata,
     )
@@ -737,7 +1139,7 @@ def test_propose_tree(spec_token_tree):
         target_positions=target_positions,
         target_hidden_states=target_hidden_states,
         next_token_ids=next_token_ids,
-        last_token_indices=None,
+        token_indices_to_sample=None,
         common_attn_metadata=common_attn_metadata,
         sampling_metadata=sampling_metadata,
     )

tests/v1/spec_decode/test_mtp.py

@@ -204,7 +204,7 @@ def test_mtp_propose(num_speculative_tokens, monkeypatch):
         target_positions=target_positions,
         target_hidden_states=target_hidden_states,
         next_token_ids=next_token_ids,
-        last_token_indices=None,
+        token_indices_to_sample=None,
         common_attn_metadata=common_attn_metadata,
         sampling_metadata=sampling_metadata,
     )

vllm/config/speculative.py

@@ -116,9 +116,16 @@ class SpeculativeConfig:
     """Minimum size of ngram token window when using Ngram proposer, if
     provided. Defaults to 1."""
 
+    # Alternative drafting strategies
     speculative_token_tree: str | None = None
     """Specifies the tree structure for speculative token generation.
     """
+    parallel_drafting: bool = False
+    """Enable parallel drafting, where all speculative tokens are generated
+    in parallel rather than sequentially. This can improve performance but
+    requires the speculative model be trained to support parallel drafting.
+    Only compatible with EAGLE and draft model methods."""
+
     # required configuration params passed from engine
     target_model_config: SkipValidation[ModelConfig] = None  # type: ignore
     """The configuration of the target model."""

vllm/config/vllm.py

@@ -604,10 +604,13 @@ class VllmConfig:
             # Currently, async scheduling only support eagle speculative
             # decoding.
             if self.speculative_config is not None:
-                if self.speculative_config.method not in get_args(EagleModelTypes):
+                if (
+                    self.speculative_config.method not in get_args(EagleModelTypes)
+                    and self.speculative_config.method != "draft_model"
+                ):
                     raise ValueError(
                         "Currently, async scheduling is only supported "
-                        "with EAGLE/MTP kind of speculative decoding."
+                        "with EAGLE/MTP/Draft Model kind of speculative decoding."
                     )
                 if self.speculative_config.disable_padded_drafter_batch:
                     raise ValueError(
@@ -1298,16 +1301,21 @@ class VllmConfig:
         computed_compile_ranges_split_points = []
 
         # The upper bound of the compile ranges is the max_num_batched_tokens.
-        # For speculative decoding with draft model, the compile range must be extended
-        # by 1 for each sequence.
+        # For speculative decoding, the compile range must be extended
+        # - Sequential: + 1 * max_num_seqs (one draft token per iteration)
+        # - Parallel draft: + num_speculative_tokens * max_num_seqs
         compile_range_end = self.scheduler_config.max_num_batched_tokens
         if compile_range_end is not None:
-            do_extend: bool = (
-                self.speculative_config is not None
-                and self.speculative_config.uses_draft_model()
-            )
-            if do_extend:
-                compile_range_end += self.scheduler_config.max_num_seqs
+            if self.speculative_config is not None and (
+                self.speculative_config.uses_draft_model()
+                or self.speculative_config.use_eagle()
+            ):
+                multiplier = (
+                    self.speculative_config.num_speculative_tokens
+                    if self.speculative_config.parallel_drafting
+                    else 1
+                )
+                compile_range_end += multiplier * self.scheduler_config.max_num_seqs
 
             computed_compile_ranges_split_points.append(compile_range_end)
 

vllm/model_executor/models/llama_eagle3.py

@@ -52,13 +52,16 @@ class LlamaDecoderLayer(LlamaDecoderLayer):
         # Subsequent layers use hidden_size (only hidden_states, no embeds)
         qkv_input_size = 2 * self.hidden_size if layer_idx == 0 else self.hidden_size
 
-        # override qkv
+        # Parallel drafting checkpoints may have attention bias enabled
+        qkv_bias = getattr(config, "attention_bias", False)
+
+        # Override qkv_proj with correct input size and bias setting
         self.self_attn.qkv_proj = QKVParallelLinear(
             qkv_input_size,
             self.self_attn.head_dim,
             self.self_attn.total_num_heads,
             self.self_attn.total_num_kv_heads,
-            bias=False,
+            bias=qkv_bias,
             quant_config=quant_config,
             prefix=maybe_prefix(prefix, "qkv_proj"),
         )
@@ -293,6 +296,19 @@ class Eagle3LlamaForCausalLM(LlamaForCausalLM):
             requires_grad=False,
         )
 
+        self.use_parallel_drafting = vllm_config.speculative_config.parallel_drafting
+
+        if self.use_parallel_drafting:
+            self.register_buffer(
+                "mask_hidden",
+                torch.zeros(
+                    1,
+                    (3 if self.model.use_aux_hidden_state else 1)
+                    * self.config.hidden_size,
+                ),
+                persistent=False,
+            )
+
     def embed_input_ids(
         self,
         input_ids: torch.Tensor,
@@ -347,12 +363,25 @@ class Eagle3LlamaForCausalLM(LlamaForCausalLM):
         model_weights = {}
         includes_draft_id_mapping = False
         includes_embed_tokens = False
+        includes_mask_hidden = False
         for name, loaded_weight in weights:
             if "t2d" in name:
                 continue
             if "d2t" in name:
                 name = name.replace("d2t", "draft_id_to_target_id")
                 includes_draft_id_mapping = True
+            elif "mask_hidden" in name:
+                # Load mask_hidden directly into buffer
+                if not self.use_parallel_drafting:
+                    logger.warning(
+                        "mask_hidden found in weights but "
+                        "model is not configured for parallel drafting. "
+                        "Skipping loading mask_hidden."
+                    )
+                    continue
+                self.mask_hidden.copy_(loaded_weight.view(1, -1))
+                includes_mask_hidden = True
+                continue
             elif "lm_head" not in name:
                 name = "model." + name
             if "embed_tokens" in name:
@@ -360,7 +389,14 @@ class Eagle3LlamaForCausalLM(LlamaForCausalLM):
             model_weights[name] = loaded_weight
             process_eagle_weight(self, name)
 
-        skip_substrs = []
+        if not includes_mask_hidden and self.use_parallel_drafting:
+            raise ValueError(
+                "mask_hidden not found in weights but "
+                "model is configured for parallel drafting. "
+                "Please provide mask_hidden in the weights."
+            )
+
+        skip_substrs = ["mask_hidden"]
         if not includes_draft_id_mapping:
             skip_substrs.append("draft_id_to_target_id")
         if not includes_embed_tokens:

vllm/v1/attention/backend.py

@@ -480,9 +480,14 @@ class AttentionMetadataBuilder(ABC, Generic[M]):
                 speculative_config is not None
                 and speculative_config.num_speculative_tokens is not None
             ):
+                max_num_queries_for_spec = (
+                    1
+                    + (2 if speculative_config.parallel_drafting else 1)
+                    * speculative_config.num_speculative_tokens
+                )
                 self.reorder_batch_threshold = max(
                     self.reorder_batch_threshold,
-                    1 + speculative_config.num_speculative_tokens,
+                    max_num_queries_for_spec,
                 )
 
         if (

vllm/v1/attention/backends/flashinfer.py

@@ -60,7 +60,7 @@ from vllm.v1.attention.backends.utils import (
 )
 from vllm.v1.attention.ops.common import cp_lse_ag_out_rs
 from vllm.v1.attention.ops.merge_attn_states import merge_attn_states
-from vllm.v1.kv_cache_interface import AttentionSpec
+from vllm.v1.kv_cache_interface import AttentionSpec, UniformTypeKVCacheSpecs
 from vllm.v1.utils import CpuGpuBuffer
 
 FLASHINFER_WORKSPACE_BUFFER_SIZE_BATCH_INVARIANT = 2048 * 1024 * 1024
@@ -658,12 +658,36 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
         vllm_config: VllmConfig,
         kv_cache_spec: AttentionSpec,
     ) -> AttentionCGSupport:
-        has_trtllm_support = can_use_trtllm_attention(
-            num_qo_heads=vllm_config.model_config.get_num_attention_heads(
-                vllm_config.parallel_config
-            ),
-            num_kv_heads=kv_cache_spec.num_kv_heads,
+        """Get the cudagraph support level for FlashInfer attention.
+
+        This depends on whether we can use TRTLLM attention for decodes, since we can
+        only do UNIFORM_SINGLE_TOKEN_DECODE if it is unavailable.
+        To check this, we must call can_use_trtllm_attention with the number of KV
+        heads from the kv_cache_spec. We check all available KV cache specs and
+        only return UNIFORM_BATCH if all of them support TRTLLM attention.
+        """
+        # For UniformTypeKVCacheSpecs, check all contained specs
+        kv_specs = (
+            kv_cache_spec.kv_cache_specs.values()
+            if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs)
+            else [kv_cache_spec]
         )
+        num_qo_heads = vllm_config.model_config.get_num_attention_heads(
+            vllm_config.parallel_config
+        )
+        has_trtllm_support: bool = len(kv_specs) > 0
+        for spec in kv_specs:
+            if not isinstance(spec, AttentionSpec):
+                # FlashInfer only applies to attention, so we don't consider other types
+                # of KV spec (e.g. Mamba) here. This is mostly for type checking.
+                continue
+            if not can_use_trtllm_attention(
+                num_qo_heads=num_qo_heads,
+                num_kv_heads=spec.num_kv_heads,
+            ):
+                has_trtllm_support = False
+                break
+
         if has_trtllm_support:
             return AttentionCGSupport.UNIFORM_BATCH
         else:

vllm/v1/attention/backends/utils.py

@@ -825,38 +825,6 @@ def get_dcp_local_seq_lens(
     return dcp_local_seq_lens.squeeze(1)
 
 
-def extend_all_queries_by_1(
-    common_attn_metadata: CommonAttentionMetadata,
-    arange: torch.Tensor,
-    new_slot_mapping: torch.Tensor,
-) -> CommonAttentionMetadata:
-    """
-    Creates a new CommonAttentionMetadata with all query lengths increased by 1.
-    Also all seq lens are increased by 1.
-    This is useful e.g. in speculative decoding with draft models, where we
-    extend each sequence by 1 token.
-    The slot mapping is computed externally, as it requires more information.
-    """
-    cad = common_attn_metadata
-    # query start loc must be increased by [+0, +1, +2, ..., +batch_size]
-    new_query_start_loc = cad.query_start_loc + arange[: len(cad.query_start_loc)]
-    new_query_start_loc_cpu = cad.query_start_loc_cpu + torch.arange(
-        len(cad.query_start_loc_cpu), dtype=torch.int32
-    )
-    new_cad = cad.replace(
-        query_start_loc=new_query_start_loc,
-        query_start_loc_cpu=new_query_start_loc_cpu,
-        seq_lens=cad.seq_lens + 1,
-        # each request is extended by 1 token -> batch_size tokens are added
-        num_actual_tokens=cad.num_actual_tokens + cad.batch_size(),
-        # All query lens increase by 1, so max query len increases by 1
-        max_query_len=cad.max_query_len + 1,
-        max_seq_len=cad.max_seq_len + 1,
-        slot_mapping=new_slot_mapping,
-    )
-    return new_cad
-
-
 def mamba_get_block_table_tensor(
     block_table: torch.Tensor,
     seq_lens: torch.Tensor,

vllm/v1/spec_decode/draft_model.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Any
 
 import torch
+import torch.nn as nn
+from typing_extensions import override
 
-from vllm.config import VllmConfig, get_layers_from_vllm_config, replace
+from vllm.config import VllmConfig
 from vllm.logger import init_logger
-from vllm.model_executor.layers.attention import Attention
 from vllm.model_executor.model_loader import get_model
-from vllm.triton_utils import tl, triton
-from vllm.v1.attention.backends.utils import (
-    CommonAttentionMetadata,
-    extend_all_queries_by_1,
-)
-from vllm.v1.spec_decode.eagle import PADDING_SLOT_ID, SpecDecodeBaseProposer
+from vllm.v1.spec_decode.eagle import SpecDecodeBaseProposer
+from vllm.v1.spec_decode.utils import create_vllm_config_for_draft_model
 
 logger = init_logger(__name__)
 
@@ -31,37 +27,9 @@ class DraftModelProposer(SpecDecodeBaseProposer):
             pass_hidden_states_to_model=False,
             runner=runner,
         )
-        self._raise_if_multimodal()
-        self._raise_if_mrope()
-        self._raise_if_padded_drafter_batch_disabled()
         self._raise_if_vocab_size_mismatch()
         self._raise_if_draft_tp_mismatch()
 
-    def _block_size(self) -> int:
-        builder = self._get_attention_metadata_builder()
-        return builder.kv_cache_spec.block_size
-
-    def _raise_if_multimodal(self):
-        if self.supports_mm_inputs:
-            raise NotImplementedError(
-                "Speculative Decoding with draft models "
-                "does not support multimodal models yet"
-            )
-
-    def _raise_if_mrope(self):
-        if self.draft_model_config.uses_mrope:
-            raise NotImplementedError(
-                "Speculative Decoding with draft models does not support M-RoPE yet"
-            )
-
-    def _raise_if_padded_drafter_batch_disabled(self):
-        if self.speculative_config.disable_padded_drafter_batch:
-            raise NotImplementedError(
-                "Speculative Decoding with draft models only supports "
-                "padded drafter batch. Please don't pass --disable-padded-drafter-batch"
-                " in the speculative_config."
-            )
-
     def _raise_if_vocab_size_mismatch(self):
         self.speculative_config.verify_equal_vocab_size_if_draft_model()
 
@@ -82,193 +50,26 @@ class DraftModelProposer(SpecDecodeBaseProposer):
                 "Please pass 'draft_tensor_parallel_size' in the speculative_config."
             )
 
-    def set_inputs_first_pass(
-        self,
-        target_token_ids: torch.Tensor,
-        next_token_ids: torch.Tensor,
-        target_positions: torch.Tensor,
-        last_token_indices: torch.Tensor | None,
-        cad: CommonAttentionMetadata,
-        num_rejected_tokens_gpu: torch.Tensor | None,
-    ) -> tuple[int, torch.Tensor, CommonAttentionMetadata]:
-        batch_size = cad.batch_size()
-        grid = (batch_size,)
-        start_locs = cad.query_start_loc[:-1]
-        end_locs = cad.query_start_loc[1:] - 1
-        if num_rejected_tokens_gpu is not None:
-            end_locs -= num_rejected_tokens_gpu
-
-        num_tokens = target_token_ids.shape[0] + batch_size
-        is_rejected_tok = torch.empty(
-            (num_tokens,), device=self.input_ids.device, dtype=torch.bool
-        )
-        merge_toks_kernel[grid](
-            target_toks_ptr=target_token_ids,
-            next_toks_ptr=next_token_ids,
-            query_start_locs_ptr=start_locs,
-            query_end_locs_ptr=end_locs,
-            out_ptr_merged_toks=self.input_ids,
-            out_ptr_is_rejected_tok=is_rejected_tok,
-            target_toks_size=target_token_ids.shape[0],
-            # passing a negative rejected_tok_fill value will raise an error
-            # when the value is used to index into embeddings.
-            # Therefore, we pass a valid integer, e.g. 0.
-            rejected_tok_fill=0,
-        )
-        merge_toks_kernel[grid](
-            target_toks_ptr=target_positions,
-            next_toks_ptr=target_positions[end_locs] + 1,
-            query_start_locs_ptr=start_locs,
-            query_end_locs_ptr=end_locs,
-            out_ptr_merged_toks=self.positions,
-            out_ptr_is_rejected_tok=is_rejected_tok,
-            target_toks_size=target_positions.shape[0],
-            rejected_tok_fill=0,
-        )
-
-        # recompute slot mapping
-        new_slot_mapping = compute_new_slot_mapping(
-            cad=cad,
-            new_positions=self.positions[:num_tokens],
-            is_rejected_token_mask=is_rejected_tok,
-            block_size=self._block_size(),
-            max_model_len=self.max_model_len,
-        )
-        # update common_attn_metadata
-        new_cad: CommonAttentionMetadata = extend_all_queries_by_1(
-            cad,
-            arange=self.arange,
-            new_slot_mapping=new_slot_mapping,
-        )
-
-        new_last_token_indices = new_cad.query_start_loc[1:] - 1
-        if num_rejected_tokens_gpu is not None:
-            new_last_token_indices -= num_rejected_tokens_gpu
-
-        return num_tokens, new_last_token_indices, new_cad
-
-    def load_model(self, target_model: Any) -> None:
-        """Takes target_model to satisfy the type checker."""
-
-        # This must be computed before loading the draft model
-        # because that mutates the forward_context of the vllm_config
-        target_attn_layer_names = set(
-            get_layers_from_vllm_config(self.vllm_config, Attention).keys()
-        )
-
+    @override
+    def _get_model(self) -> nn.Module:
+        # Draft models may be quantized or on different parallelism,
+        # so we load them with a modified vllm config
         from vllm.compilation.backends import set_model_tag
 
-        draft_vllm_config: VllmConfig = create_vllm_config_for_draft_model(
-            target_model_vllm_config=self.vllm_config
-        )
-        logger.info(
-            "Starting to load draft model %s. TP=%d, rank=%d",
-            draft_vllm_config.model_config.model,
-            draft_vllm_config.parallel_config.tensor_parallel_size,
-            draft_vllm_config.parallel_config.rank,
-        )
+        temp_vllm_config = create_vllm_config_for_draft_model(self.vllm_config)
         with set_model_tag("draft_model"):
-            self.model = get_model(vllm_config=draft_vllm_config, prefix="draft_model")
-
-        # This must be computed after loading the draft model
-        # because that mutates the forward_context of the vllm_config
-        draft_attn_layer_names = (
-            get_layers_from_vllm_config(self.vllm_config, Attention).keys()
-            - target_attn_layer_names
-        )
-        self.attn_layer_names = list(draft_attn_layer_names)
-
-
-def create_vllm_config_for_draft_model(
-    target_model_vllm_config: VllmConfig,
-) -> VllmConfig:
-    """The vllm_config is configured for the target model, e.g.
-    its quant_config and parallel_config. But the draft model is potentially
-    quantized differently, and has potentially different tensor_parallel_size.
-    This function creates a new vllm_config configured for the draft model.
-    The vllm_config is useful when loading the draft model with get_model().
-    """
-    old = target_model_vllm_config
-    assert old.speculative_config is not None, "speculative_config is not set"
-    old_spec_config = old.speculative_config
-    new_parallel_config = replace(
-        old_spec_config.draft_parallel_config,
-        rank=old.parallel_config.rank,
-    )
-    new: VllmConfig = replace(
-        old,
-        quant_config=None,  # quant_config is recomputed in __init__()
-        model_config=old_spec_config.draft_model_config,
-        parallel_config=new_parallel_config,
-    )
-    return new
-
-
-def compute_new_slot_mapping(
-    cad: CommonAttentionMetadata,
-    new_positions: torch.Tensor,
-    is_rejected_token_mask: torch.Tensor,
-    block_size: int,
-    max_model_len: int,
-):
-    batch_size, n_blocks_per_req = cad.block_table_tensor.shape
-    req_indices = torch.arange(batch_size, device=cad.query_start_loc.device)
-    req_indices = torch.repeat_interleave(
-        req_indices, cad.naive_query_lens() + 1, output_size=len(new_positions)
-    )
-    # Clamp the positions to prevent an out-of-bounds error when indexing
-    # into block_table_tensor.
-    clamped_positions = torch.clamp(new_positions, max=max_model_len - 1)
-    block_table_indices = (
-        req_indices * n_blocks_per_req + clamped_positions // block_size
-    )
-    block_nums = cad.block_table_tensor.view(-1)[block_table_indices]
-    block_offsets = clamped_positions % block_size
-    new_slot_mapping = block_nums * block_size + block_offsets
-    # Mask out the position ids that exceed the max model length.
-    exceeds_max_model_len = new_positions >= max_model_len
-    new_slot_mapping.masked_fill_(exceeds_max_model_len, PADDING_SLOT_ID)
-    # Mask out rejected tokens to prevent saves to the KV cache.
-    new_slot_mapping.masked_fill_(is_rejected_token_mask, PADDING_SLOT_ID)
-    return new_slot_mapping
-
+            model = get_model(
+                vllm_config=temp_vllm_config,
+                prefix="draft_model",
+            )
+        return model
 
-@triton.jit
-def merge_toks_kernel(
-    target_toks_ptr,
-    next_toks_ptr,
-    query_start_locs_ptr,
-    query_end_locs_ptr,
-    out_ptr_merged_toks,
-    out_ptr_is_rejected_tok,
-    target_toks_size,
-    rejected_tok_fill,
-):
-    """
-    Merges the `target_toks_ptr` and the `next_toks_ptr` into a new tensor
-    called `out_ptr_merged_toks`. Rejected tokens are those after the
-    `query_end_locs_ptr` and before the next `query_start_locs_ptr`. Fills the
-    rejected tokens positions with the value `rejected_tok_fill`. Also fills a mask
-    of the rejected tokens in `out_ptr_is_rejected_tok`.
-    """
-    pid = tl.program_id(0)
-    start_loc = tl.load(query_start_locs_ptr + pid)
-    is_last_program = pid == tl.num_programs(0) - 1
-    if is_last_program:
-        next_start_loc = target_toks_size.to(tl.int32)
-    else:
-        next_start_loc = tl.load(query_start_locs_ptr + pid + 1).to(tl.int32)
+    @override
+    def _maybe_share_embeddings(self, target_language_model: nn.Module) -> None:
+        # Draft models don't share embeddings with the target model
+        pass
 
-    end_loc = tl.load(query_end_locs_ptr + pid)
-    new_val = tl.load(next_toks_ptr + pid)
-    for i in range(start_loc, next_start_loc + 1):
-        if i <= end_loc:  # copy existing tokens
-            old_val = tl.load(target_toks_ptr + i)
-            tl.store(out_ptr_merged_toks + pid + i, old_val)
-            tl.store(out_ptr_is_rejected_tok + pid + i, False)
-        elif i == end_loc + 1:  # copy bonus token
-            tl.store(out_ptr_merged_toks + pid + i, new_val)
-            tl.store(out_ptr_is_rejected_tok + pid + i, False)
-        else:  # fill rejected tokens
-            tl.store(out_ptr_merged_toks + pid + i, rejected_tok_fill)
-            tl.store(out_ptr_is_rejected_tok + pid + i, True)
+    @override
+    def _maybe_share_lm_head(self, target_language_model: nn.Module) -> None:
+        # Draft models don't share lm_head with the target model
+        pass

vllm/v1/spec_decode/utils.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+
+from vllm.config import VllmConfig, replace
 from vllm.triton_utils import tl, triton
+from vllm.v1.attention.backends.utils import (
+    CommonAttentionMetadata,
+)
+
+PADDING_SLOT_ID = -1
 
 
 @triton.jit
@@ -107,3 +115,243 @@ def eagle_prepare_next_token_padded_kernel(
             tl.store(next_token_ids_ptr + req_idx, backup_token)
 
         tl.store(valid_sampled_tokens_count_ptr + req_idx, valid_count)
+
+
+def compute_new_slot_mapping(
+    cad: CommonAttentionMetadata,
+    new_positions: torch.Tensor,
+    is_rejected_token_mask: torch.Tensor,
+    block_size: int,
+    num_new_tokens: int,
+    max_model_len: int,
+):
+    batch_size, n_blocks_per_req = cad.block_table_tensor.shape
+    req_indices = torch.arange(batch_size, device=cad.query_start_loc.device)
+    req_indices = torch.repeat_interleave(
+        req_indices,
+        cad.naive_query_lens() + num_new_tokens,
+        output_size=len(new_positions),
+    )
+    # Clamp the positions to prevent an out-of-bounds error when indexing
+    # into block_table_tensor.
+    clamped_positions = torch.clamp(new_positions, max=max_model_len - 1)
+    block_table_indices = (
+        req_indices * n_blocks_per_req + clamped_positions // block_size
+    )
+    block_nums = cad.block_table_tensor.view(-1)[block_table_indices]
+    block_offsets = clamped_positions % block_size
+    new_slot_mapping = block_nums * block_size + block_offsets
+    # Mask out the position ids that exceed the max model length.
+    exceeds_max_model_len = new_positions >= max_model_len
+    new_slot_mapping.masked_fill_(exceeds_max_model_len, PADDING_SLOT_ID)
+    # Mask out rejected tokens to prevent saves to the KV cache.
+    new_slot_mapping.masked_fill_(is_rejected_token_mask, PADDING_SLOT_ID)
+    return new_slot_mapping
+
+
+def create_vllm_config_for_draft_model(
+    target_model_vllm_config: VllmConfig,
+) -> VllmConfig:
+    """The vllm_config is configured for the target model, e.g.
+    its quant_config and parallel_config. But the draft model is potentially
+    quantized differently, and has potentially different tensor_parallel_size.
+    This function creates a new vllm_config configured for the drafter.
+    The vllm_config is useful when loading the draft model with get_model().
+    """
+    old = target_model_vllm_config
+    assert old.speculative_config is not None, "speculative_config is not set"
+    old_spec_config = old.speculative_config
+    new_parallel_config = replace(
+        old_spec_config.draft_parallel_config, rank=old.parallel_config.rank
+    )
+    new: VllmConfig = replace(
+        old,
+        quant_config=None,
+        parallel_config=new_parallel_config,
+        model_config=old_spec_config.draft_model_config,
+    )
+    return new
+
+
+def extend_all_queries_by_N(
+    common_attn_metadata: CommonAttentionMetadata,
+    N: int,
+    arange: torch.Tensor,
+    new_slot_mapping: torch.Tensor,
+) -> CommonAttentionMetadata:
+    """
+    Creates a new CommonAttentionMetadata with all query lengths increased by N.
+    Also all seq lens are increased by N.
+    This is useful e.g. in speculative decoding with parallel drafting, where we
+    extend each sequence by N tokens and predict all tokens in one pass.
+    The slot mapping is computed externally, as it requires more information.
+    """
+    cad = common_attn_metadata
+    # query start loc must be increased by [+0, +N, +2N, ..., +batch_size * N]
+    new_query_start_loc = cad.query_start_loc + N * arange[: len(cad.query_start_loc)]
+    new_query_start_loc_cpu = cad.query_start_loc_cpu + N * torch.arange(
+        len(cad.query_start_loc_cpu), dtype=torch.int32
+    )
+    new_cad = cad.replace(
+        query_start_loc=new_query_start_loc,
+        query_start_loc_cpu=new_query_start_loc_cpu,
+        seq_lens=cad.seq_lens + N,
+        # each request is extended by N tokens -> batch_size * N tokens are added
+        num_actual_tokens=cad.num_actual_tokens + cad.batch_size() * N,
+        # All query lens increase by N, so max query len increases by N
+        max_query_len=cad.max_query_len + N,
+        max_seq_len=cad.max_seq_len + N,
+        slot_mapping=new_slot_mapping,
+    )
+    return new_cad
+
+
+# Unified copy/expand kernel
+@triton.jit
+def copy_and_expand_eagle_inputs_kernel(
+    # (Padded) Inputs from the target model
+    target_token_ids_ptr,  # [total_tokens_in_batch]
+    target_positions_ptr,  # [total_tokens_in_batch]
+    next_token_ids_ptr,  # [num_reqs]
+    # Outputs to the drafting buffers
+    out_input_ids_ptr,  # [total_draft_tokens_in_batch] (output)
+    out_positions_ptr,  # [total_draft_tokens_in_batch] (output)
+    out_is_rejected_token_mask_ptr,  # [total_draft_tokens_in_batch] (output)
+    out_is_masked_token_mask_ptr,  # [total_draft_tokens_in_batch] (output)
+    out_new_token_indices_ptr,  # [num_padding_slots_per_request * num_reqs] (output)
+    out_hidden_state_mapping_ptr,  # [total_tokens_in_batch]
+    # Input metadata
+    query_start_loc_ptr,  # [num_reqs + 1], last value is the total num input tokens
+    query_end_loc_ptr,  # [num_reqs]
+    padding_token_id,  # tl.int32
+    parallel_drafting_token_id,  # tl.int32
+    # Sizing info
+    total_input_tokens,  # tl.int32
+    num_padding_slots_per_request,  # tl.int32
+    shift_input_ids,  # tl.bool
+    BLOCK_SIZE_TOKENS: tl.constexpr,  # Blocks along token dim to handle prefills
+):
+    """
+    Copy and expand inputs from the target model to the drafting buffers for Eagle
+    speculative decoding. This kernel handles padding slots and parallel drafting
+    tokens, if enabled.
+    """
+    request_idx = tl.program_id(axis=0)
+    token_batch_idx = tl.program_id(axis=1)
+
+    # Load query locations
+    query_start_loc = tl.load(query_start_loc_ptr + request_idx)
+    next_query_start_loc = tl.load(query_start_loc_ptr + request_idx + 1)
+    query_end_loc = tl.load(query_end_loc_ptr + request_idx)
+
+    # Calculate number of valid tokens to copy and input offset
+    # With shift_input_ids=True, we skip the first token
+    # Output layout: each request gets (input_len + num_padding_slots_per_request) slots
+    # But with shift, we lose one token per request
+    if shift_input_ids:
+        num_valid_tokens = query_end_loc - query_start_loc
+        input_offset = 1
+        output_start = query_start_loc + request_idx * (
+            num_padding_slots_per_request - 1
+        )
+    else:
+        num_valid_tokens = query_end_loc - query_start_loc + 1
+        input_offset = 0
+        output_start = query_start_loc + request_idx * num_padding_slots_per_request
+
+    # Number of rejected tokens from previous speculation
+    num_rejected = next_query_start_loc - query_end_loc - 1
+
+    # Total output tokens for this request
+    total_output_tokens = (
+        num_valid_tokens + num_padding_slots_per_request + num_rejected
+    )
+
+    # Process tokens in this block
+    j = token_batch_idx * BLOCK_SIZE_TOKENS + tl.arange(0, BLOCK_SIZE_TOKENS)
+
+    # Compute masks for different output regions:
+    # [0, num_valid_tokens): valid tokens copied from input
+    # [num_valid_tokens]: bonus token from next_token_ids
+    # (num_valid_tokens, num_valid_tokens + num_padding_slots_per_request):
+    #     parallel drafting slots
+    # [num_valid_tokens + num_padding_slots_per_request, total_output_tokens):
+    #     rejected slots
+    in_bounds = j < total_output_tokens
+    is_valid_region = j < num_valid_tokens
+    is_bonus_region = j == num_valid_tokens
+    is_parallel_draft_region = (j > num_valid_tokens) & (
+        j < num_valid_tokens + num_padding_slots_per_request
+    )
+    is_rejected_region = j >= num_valid_tokens + num_padding_slots_per_request
+
+    # Compute output indices
+    out_idx = output_start + j
+
+    # For valid tokens, compute input index
+    in_idx = query_start_loc + input_offset + j
+    # Clamp to avoid out-of-bounds access (masked loads still need valid addresses)
+    in_idx_clamped = tl.minimum(in_idx, total_input_tokens - 1)
+
+    # Load input tokens (masked to valid region)
+    token_ids = tl.load(
+        target_token_ids_ptr + in_idx_clamped, mask=is_valid_region & in_bounds, other=0
+    )
+
+    # Load the starting position for this request (first position in the sequence)
+    start_pos = tl.load(target_positions_ptr + query_start_loc)
+
+    # Load bonus token for this request
+    bonus_token = tl.load(next_token_ids_ptr + request_idx)
+
+    # Build final token_ids based on region
+    token_ids = tl.where(is_bonus_region, bonus_token, token_ids)
+    token_ids = tl.where(
+        is_parallel_draft_region, parallel_drafting_token_id, token_ids
+    )
+    token_ids = tl.where(is_rejected_region, padding_token_id, token_ids)
+
+    # Build final positions:
+    # Positions are NOT shifted - they start from the first input position and increment
+    # Output position j gets start_pos + j
+    # (e.g., input positions [5,6,7] -> output [5,6,7,8,9,...])
+    positions = start_pos + j
+    # Rejected positions are don't-care, set to 0
+    positions = tl.where(is_rejected_region, 0, positions)
+
+    # Compute output masks
+    is_rejected_out = is_rejected_region & in_bounds
+    is_masked_out = is_parallel_draft_region & in_bounds
+
+    # Compute indices of new tokens (bonus + parallel drafting) for sampling
+    # New tokens are at positions
+    #     [num_valid_tokens, num_valid_tokens + num_padding_slots_per_request)
+    is_new_token_region = (j >= num_valid_tokens) & (
+        j < num_valid_tokens + num_padding_slots_per_request
+    )
+    new_token_local_idx = (
+        j - num_valid_tokens
+    )  # 0 for bonus, 1, 2, ... for parallel drafting
+    new_token_out_idx = (
+        request_idx * num_padding_slots_per_request + new_token_local_idx
+    )
+
+    # Compute hidden state mapping (source index -> destination index)
+    # This maps each input position to its corresponding output position
+    # Hidden states don't get shifted, so we map all input tokens (including rejected)
+    if shift_input_ids:
+        num_input_tokens_this_request = next_query_start_loc - query_start_loc
+        is_input_region = j < num_input_tokens_this_request
+        src_idx = query_start_loc + j
+        tl.store(out_hidden_state_mapping_ptr + src_idx, out_idx, mask=is_input_region)
+
+    # Store outputs
+    tl.store(out_input_ids_ptr + out_idx, token_ids, mask=in_bounds)
+    tl.store(out_positions_ptr + out_idx, positions, mask=in_bounds)
+    tl.store(out_is_rejected_token_mask_ptr + out_idx, is_rejected_out, mask=in_bounds)
+    tl.store(out_is_masked_token_mask_ptr + out_idx, is_masked_out, mask=in_bounds)
+    tl.store(
+        out_new_token_indices_ptr + new_token_out_idx,
+        out_idx,
+        mask=is_new_token_region & in_bounds,
+    )

vllm/v1/worker/gpu_model_runner.py

@@ -4090,7 +4090,7 @@ class GPUModelRunner(
                 target_positions=target_positions,
                 target_hidden_states=target_hidden_states,
                 next_token_ids=next_token_ids,
-                last_token_indices=token_indices_to_sample,
+                token_indices_to_sample=token_indices_to_sample,
                 sampling_metadata=sampling_metadata,
                 common_attn_metadata=common_attn_metadata,
                 mm_embed_inputs=mm_embed_inputs,