iHore/vLLM-4x-RTX-3090
8
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
27f50debe0eea590d4aee2b07327873eb392071e
vLLM-4x-RTX-3090/patches/0066-cudagraph-allow-FULL-decode-graphs-for-one-token-pro.patch
T
MOVZX 777e910dd0 Initial commit 
Signed-off-by: MOVZX <movzx@yahoo.com>
2026-10-15 01:01:52 +07:00

455 lines
22 KiB
Diff
Raw Blame History

From f0949d57a607a8882d0cba5a9937fa03bb82eefc Mon Sep 17 00:00:00 2001
From: halt95 <halt95@users.noreply.github.com>
Date: Mon, 28 Sep 2026 12:48:52 -0700
Subject: [PATCH 066/103] cudagraph: allow FULL decode graphs for one-token
prompt tails (port of upstream #58400)
Port of the upstream vLLM change #58400 (Apache-2.0). A prompt tail of one token can run under the FULL decode graph. The
speculative-row rule of v0.30.0 (num_scheduled_tokens == drafts + 1) is kept, because the upstream rule depends
on a later change not in this base.
---
tests/v1/spec_decode/test_dynamic_sd_cug.py | 12 ++--
tests/v1/worker/test_gpu_batch_ordering.py | 69 +++++++++++++++++--
tests/v1/worker/test_gpu_batch_shard.py | 1 +
.../worker/test_mamba_hybrid_model_state.py | 1 +
vllm/v1/worker/gpu/input_batch.py | 8 +++
vllm/v1/worker/gpu/model_runner.py | 49 ++++++++++---
.../worker/gpu/model_states/mamba_hybrid.py | 9 ++-
vllm/v1/worker/gpu/pcp_manager.py | 5 +-
.../spec_decode/autoregressive/speculator.py | 2 +-
.../multi_module_mtp/speculator.py | 2 +-
vllm/v1/worker/gpu/ubatch_utils.py | 5 ++
vllm/v1/worker/utils.py | 6 +-
12 files changed, 140 insertions(+), 29 deletions(-)
diff --git a/tests/v1/spec_decode/test_dynamic_sd_cug.py b/tests/v1/spec_decode/test_dynamic_sd_cug.py
index a5cbd11..1ca8486 100644
--- a/tests/v1/spec_decode/test_dynamic_sd_cug.py
+++ b/tests/v1/spec_decode/test_dynamic_sd_cug.py
@@ -128,7 +128,7 @@ def test_dynamic_sd_full_cudagraph_covers_all_uniform_decode_shapes(monkeypatch)
num_reqs,
num_tokens,
max_query_len,
- has_prefill=False,
+ decode_graph_eligible=True,
)
# The scheduler should mark every one of these shapes as a uniform
@@ -266,14 +266,14 @@ def test_prompt_chunks_shaped_like_spec_decode_miss_the_full_graph(monkeypatch):
# The batch shape is indistinguishable from a full batch of spec decodes.
assert (
get_uniform_decode_token_count(
- num_reqs, num_tokens, decode_query_len, has_prefill=False
+ num_reqs, num_tokens, decode_query_len, decode_graph_eligible=True
)
== decode_query_len
)
# Two of the requests are decode_query_len tokens into a longer prompt.
uniform_tok_count = get_uniform_decode_token_count(
- num_reqs, num_tokens, decode_query_len, has_prefill=True
+ num_reqs, num_tokens, decode_query_len, decode_graph_eligible=False
)
assert uniform_tok_count is None
desc = manager.dispatch(
@@ -287,7 +287,7 @@ def test_prompt_chunks_shaped_like_spec_decode_miss_the_full_graph(monkeypatch):
# The same shape with every request decoding still gets its FULL graph.
uniform_tok_count = get_uniform_decode_token_count(
- num_reqs, num_tokens, decode_query_len, has_prefill=False
+ num_reqs, num_tokens, decode_query_len, decode_graph_eligible=True
)
assert uniform_tok_count == decode_query_len
desc = manager.dispatch(
@@ -341,7 +341,7 @@ def test_basic_sd_does_not_capture_shorter_full_decode_shapes(monkeypatch):
num_reqs,
num_tokens,
max_query_len,
- has_prefill=False,
+ decode_graph_eligible=True,
)
assert uniform_tok_count == max_query_len
@@ -407,7 +407,7 @@ def test_dynamic_sd_only_captures_scheduled_query_lengths(monkeypatch):
num_reqs,
num_tokens,
max_query_len,
- has_prefill=False,
+ decode_graph_eligible=True,
)
assert uniform_tok_count == max_query_len
diff --git a/tests/v1/worker/test_gpu_batch_ordering.py b/tests/v1/worker/test_gpu_batch_ordering.py
index 5037dcc..db9e080 100644
--- a/tests/v1/worker/test_gpu_batch_ordering.py
+++ b/tests/v1/worker/test_gpu_batch_ordering.py
@@ -56,6 +56,7 @@ def _make_runner(
runner: Any = GPUModelRunner.__new__(GPUModelRunner)
runner.decode_query_len = decode_query_len
runner.adaptive_verification = None
+ runner.pcp_manager = None
runner.req_states = SimpleNamespace(
req_id_to_index={req_id: i for i, req_id in enumerate(req_states)},
# The runner keeps this as min(num_computed_tokens, prefill_len).
@@ -128,6 +129,65 @@ def test_prompt_chunk_of_decode_query_len_is_not_uniform_decode():
assert _uniform_token_count({"p0": (0, 8)}, 8) is None
+def test_one_new_prompt_token_over_context_runs_as_decode():
+ # A prompt tail padded with placeholder drafts and a one-token mid-prompt
+ # chunk both compute like decodes. A fresh one-token prompt and a real
+ # multi-token chunk don't.
+ batch = {"d0": (16, 16), "tail": (128, 129), "mid": (64, 129)}
+ batch.update({"fresh": (0, 1), "chunk": (120, 129)})
+ runner = _make_runner(batch, decode_query_len=4)
+ scheduler_output = SimpleNamespace(
+ num_scheduled_tokens={"d0": 4, "tail": 4, "mid": 1, "fresh": 1, "chunk": 4},
+ total_num_scheduled_tokens=14,
+ scheduled_spec_decode_tokens={"d0": [1, 2, 3], "tail": [-1] * 3},
+ )
+ state, _ = runner.gather_batch_req_state(scheduler_output, False)
+ assert state is not None
+ runs_as_decode = dict(zip(state.req_ids, state.prefill_runs_as_decode_np))
+ assert runs_as_decode == {
+ "d0": False,
+ "tail": True,
+ "mid": True,
+ "fresh": False,
+ "chunk": False,
+ }
+
+
+def test_one_token_prompt_tail_with_prior_context_is_uniform_decode():
+ # Replaying the last prompt token over existing context (full prefix-cache
+ # hit or P/D KV import) computes exactly like a decode.
+ batch = {"d0": (16, 16), "d1": (16, 16), "p0": (128, 129)}
+ assert _uniform_token_count(batch, 1) == 1
+
+ # A fresh 1-token prompt alongside it has no prior state.
+ batch["p1"] = (0, 1)
+ assert _uniform_token_count(batch, 1) is None
+
+ # Only one-token tails qualify, not K+1 chunks with prior context.
+ assert _uniform_token_count({"d0": (16, 16), "p0": (120, 129)}, 8) is None
+
+
+def test_padded_prompt_tail_with_prior_context_is_uniform_decode():
+ # The scheduler pads a one-token prompt tail with K placeholder drafts to
+ # keep the K+1 spec-decode shape; it verifies like any other decode.
+ batch = {"d0": (16, 16), "d1": (16, 16), "p0": (128, 129)}
+ runner = _make_runner(batch, decode_query_len=4)
+ scheduler_output = SimpleNamespace(
+ num_scheduled_tokens={req_id: 4 for req_id in batch},
+ total_num_scheduled_tokens=12,
+ scheduled_spec_decode_tokens={req_id: [-1] * 3 for req_id in batch},
+ )
+ state, uniform_tok_count = runner.gather_batch_req_state(scheduler_output, False)
+ assert state.has_prefill and state.decode_graph_eligible
+ assert uniform_tok_count == 4
+
+ # Without the placeholder drafts, it is a real 4-token prompt chunk.
+ del scheduler_output.scheduled_spec_decode_tokens["p0"]
+ state, uniform_tok_count = runner.gather_batch_req_state(scheduler_output, False)
+ assert not state.decode_graph_eligible
+ assert uniform_tok_count is None
+
+
def test_dummy_batches_stay_uniform_decode():
# Dummy batches have no request state to consult and must stay classified
# by shape alone; rejecting them would make capture and replay disagree.
@@ -185,6 +245,7 @@ def test_uniform_decode_uses_state_index_not_batch_position():
runner: Any = GPUModelRunner.__new__(GPUModelRunner)
runner.decode_query_len = 8
runner.adaptive_verification = None
+ runner.pcp_manager = None
# State arrays in state-index order: a prefilling request, then two decodes.
runner.req_states = SimpleNamespace(
req_id_to_index={"prefilling": 0, "decode_a": 1, "decode_b": 2},
@@ -214,11 +275,11 @@ def test_uniform_decode_uses_state_index_not_batch_position():
def test_uniform_decode_predicate():
- # Shape and prefill state must both pass.
- assert get_uniform_decode_token_count(2, 16, 8, False) == 8
- assert get_uniform_decode_token_count(2, 16, 8, True) is None
+ # Shape and decode-graph eligibility must both pass.
+ assert get_uniform_decode_token_count(2, 16, 8, True) == 8
+ assert get_uniform_decode_token_count(2, 16, 8, False) is None
# 12 tokens over 2 requests is no shared query length.
- assert get_uniform_decode_token_count(2, 12, 8, False) is None
+ assert get_uniform_decode_token_count(2, 12, 8, True) is None
def test_no_speculator_dispatches_on_query_length_alone():
diff --git a/tests/v1/worker/test_gpu_batch_shard.py b/tests/v1/worker/test_gpu_batch_shard.py
index 1309686..64c7187 100644
--- a/tests/v1/worker/test_gpu_batch_shard.py
+++ b/tests/v1/worker/test_gpu_batch_shard.py
@@ -96,6 +96,7 @@ def _make_batch(
num_computed_prefill_tokens_np=np.zeros(num_reqs, dtype=np.int32),
is_prefilling_np=np.zeros(num_reqs, dtype=np.bool_),
has_prefill=False,
+ decode_graph_eligible=True,
input_ids=torch.zeros(num_tokens, dtype=torch.int32, device=DEVICE),
positions=torch.arange(num_tokens, dtype=torch.int64, device=DEVICE),
is_padding=torch.zeros(num_tokens, dtype=torch.bool, device=DEVICE),
diff --git a/tests/v1/worker/test_mamba_hybrid_model_state.py b/tests/v1/worker/test_mamba_hybrid_model_state.py
index 0cdd91e..caec567 100644
--- a/tests/v1/worker/test_mamba_hybrid_model_state.py
+++ b/tests/v1/worker/test_mamba_hybrid_model_state.py
@@ -37,6 +37,7 @@ def test_prepare_attn_forwards_positions(monkeypatch: pytest.MonkeyPatch) -> Non
seq_lens_cpu_upper_bound=torch.tensor([1537], dtype=torch.int32),
seq_lens=torch.tensor([1537], dtype=torch.int32),
is_prefilling_np=torch.tensor([False]).numpy(),
+ prefill_runs_as_decode_np=None,
dcp_local_seq_lens=None,
positions=positions,
prompt_lens=torch.tensor([1024], dtype=torch.int32),
diff --git a/vllm/v1/worker/gpu/input_batch.py b/vllm/v1/worker/gpu/input_batch.py
index d3c65bd..cdeb6e1 100644
--- a/vllm/v1/worker/gpu/input_batch.py
+++ b/vllm/v1/worker/gpu/input_batch.py
@@ -86,6 +86,8 @@ class InputBatch:
is_prefilling_np: np.ndarray
# == np.any(is_prefilling_np)
has_prefill: bool
+ # No prefills, or only prefill rows that run as decodes.
+ decode_graph_eligible: bool
# [num_tokens_after_padding]
input_ids: torch.Tensor
@@ -118,6 +120,11 @@ class InputBatch:
# [num_reqs] set only under PCP+DCP (see CommonAttentionMetadata).
dcp_local_seq_lens_cpu_upper_bound: torch.Tensor | None = None
+ # [num_reqs] prefilling rows that schedule one new prompt token (excluding
+ # drafts) over existing context and so compute exactly like decodes.
+ # None if there are no prefills.
+ prefill_runs_as_decode_np: np.ndarray | None = None
+
@classmethod
def make_dummy(
cls,
@@ -200,6 +207,7 @@ class InputBatch:
num_computed_prefill_tokens_np=np.zeros(num_reqs, dtype=np.int32),
is_prefilling_np=np.zeros(num_reqs, dtype=np.bool_),
has_prefill=False,
+ decode_graph_eligible=True,
input_ids=input_ids,
positions=positions,
is_padding=is_padding,
diff --git a/vllm/v1/worker/gpu/model_runner.py b/vllm/v1/worker/gpu/model_runner.py
index 92ae922..246fe4c 100644
--- a/vllm/v1/worker/gpu/model_runner.py
+++ b/vllm/v1/worker/gpu/model_runner.py
@@ -1324,7 +1324,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
if dummy_run:
# Dummy batches are uniform by construction.
return None, get_uniform_decode_token_count(
- num_reqs, num_toks, max_query_len, has_prefill=False
+ num_reqs, num_toks, max_query_len, decode_graph_eligible=True
)
draft_tokens = scheduler_output.scheduled_spec_decode_tokens
@@ -1343,24 +1343,48 @@ class GPUModelRunner(LoRAModelRunnerMixin):
idx_mapping_np
]
is_prefilling_np = num_computed_prefill_tokens_np < prefill_len_np
+ has_prefill = bool(is_prefilling_np.any())
- if self.adaptive_verification is not None and draft_tokens:
- num_toks = self.adaptive_verification.get_num_tokens(
- num_tokens_per_req, draft_tokens
+ num_draft_tokens_np = None
+ if draft_tokens:
+ num_drafts_iter = (len(draft_tokens.get(req_id, ())) for req_id in req_ids)
+ num_draft_tokens_np = np.fromiter(
+ num_drafts_iter, dtype=np.int32, count=num_reqs
+ )
+ if self.adaptive_verification is not None:
+ num_toks = self.adaptive_verification.get_num_tokens(
+ num_tokens_per_req, draft_tokens
+ )
+
+ prefill_runs_as_decode_np = None
+ decode_graph_eligible = not has_prefill
+ if has_prefill and self.pcp_manager is None:
+ # One new prompt token over existing context (possibly padded with
+ # placeholder drafts) runs as a decode. PCP partitions prefill rows.
+ num_new_tokens = num_scheduled_tokens
+ if num_draft_tokens_np is not None:
+ num_new_tokens = num_new_tokens - num_draft_tokens_np
+ one_token_ext = (num_new_tokens == 1) & (num_computed_prefill_tokens_np > 0)
+ prefill_runs_as_decode_np = is_prefilling_np & one_token_ext
+ decode_graph_eligible = bool(
+ (prefill_runs_as_decode_np | ~is_prefilling_np).all()
)
batch_state = BatchReqState(
req_ids=req_ids,
num_scheduled_tokens=num_scheduled_tokens,
num_tokens=num_toks,
+ num_draft_tokens_np=num_draft_tokens_np,
idx_mapping_np=idx_mapping_np,
prefill_len_np=prefill_len_np,
num_computed_prefill_tokens_np=num_computed_prefill_tokens_np,
is_prefilling_np=is_prefilling_np,
- has_prefill=bool(is_prefilling_np.any()),
+ has_prefill=has_prefill,
+ prefill_runs_as_decode_np=prefill_runs_as_decode_np,
+ decode_graph_eligible=decode_graph_eligible,
)
return batch_state, get_uniform_decode_token_count(
- num_reqs, num_toks, max_query_len, batch_state.has_prefill
+ num_reqs, num_toks, max_query_len, decode_graph_eligible
)
def prepare_inputs(
@@ -1401,11 +1425,9 @@ class GPUModelRunner(LoRAModelRunnerMixin):
num_reqs, dtype=torch.int32, device=self.device
)
else:
- num_draft_tokens_per_req = np.fromiter(
- (len(draft_tokens.get(req_id, ())) for req_id in req_ids),
- dtype=np.int32,
- count=num_reqs,
- )
+ num_draft_tokens_per_req = batch_req_state.num_draft_tokens_np
+ if num_draft_tokens_per_req is None:
+ num_draft_tokens_per_req = np.zeros(num_reqs, dtype=np.int32)
num_bonus_tokens = self.model_state.num_new_sampled_tokens_per_step
total_num_draft_tokens = int(num_draft_tokens_per_req.sum())
total_num_logits = num_reqs * num_bonus_tokens + total_num_draft_tokens
@@ -1543,6 +1565,8 @@ class GPUModelRunner(LoRAModelRunnerMixin):
num_computed_prefill_tokens_np=batch_req_state.num_computed_prefill_tokens_np,
is_prefilling_np=batch_req_state.is_prefilling_np,
has_prefill=batch_req_state.has_prefill,
+ prefill_runs_as_decode_np=batch_req_state.prefill_runs_as_decode_np,
+ decode_graph_eligible=batch_req_state.decode_graph_eligible,
input_ids=self.input_buffers.input_ids[:num_tokens_after_padding],
positions=self.input_buffers.positions[:num_tokens_after_padding],
is_padding=self.input_buffers.is_padding[:num_tokens_after_padding],
@@ -2597,11 +2621,14 @@ class BatchReqState(NamedTuple):
# May be less than scheduler_output.total_num_scheduled_tokens:
# adaptive verification trims the draft budget before running.
num_tokens: int
+ num_draft_tokens_np: np.ndarray | None # [num_reqs], None if no drafts
idx_mapping_np: np.ndarray # [num_reqs]
prefill_len_np: np.ndarray # [num_reqs]
num_computed_prefill_tokens_np: np.ndarray # [num_reqs]
is_prefilling_np: np.ndarray # [num_reqs]
has_prefill: bool
+ prefill_runs_as_decode_np: np.ndarray | None # [num_reqs]
+ decode_graph_eligible: bool
def sort_batch_req_ids(
diff --git a/vllm/v1/worker/gpu/model_states/mamba_hybrid.py b/vllm/v1/worker/gpu/model_states/mamba_hybrid.py
index 0a8b5d7..6b6e624 100644
--- a/vllm/v1/worker/gpu/model_states/mamba_hybrid.py
+++ b/vllm/v1/worker/gpu/model_states/mamba_hybrid.py
@@ -275,10 +275,13 @@ class MambaHybridModelState(DefaultModelState):
else:
max_seq_len = seq_lens_cpu_upper_bound[:num_reqs].max().item()
+ is_prefilling_np = input_batch.is_prefilling_np
+ if input_batch.prefill_runs_as_decode_np is not None:
+ # A prompt tail the scheduler padded with placeholder drafts must run
+ # as a spec-decode row: the prefill kernels can't roll them back.
+ is_prefilling_np = is_prefilling_np & ~input_batch.prefill_runs_as_decode_np
is_prefilling = torch.zeros(num_reqs, dtype=torch.bool, device="cpu")
- is_prefilling[: input_batch.num_reqs] = torch.from_numpy(
- input_batch.is_prefilling_np
- )
+ is_prefilling[: input_batch.num_reqs] = torch.from_numpy(is_prefilling_np)
# During CUDAGraph capture, num_decode_draft_tokens_cpu and num_accepted_tokens
# are created by attn_metadata_builder.build_for_cudagraph_capture, so we only
# compute them during actual (non-capture) forward execution.
diff --git a/vllm/v1/worker/gpu/pcp_manager.py b/vllm/v1/worker/gpu/pcp_manager.py
index 376e0f2..ea9648d 100644
--- a/vllm/v1/worker/gpu/pcp_manager.py
+++ b/vllm/v1/worker/gpu/pcp_manager.py
@@ -593,6 +593,7 @@ class PCPManager:
)
local_is_prefilling_np = np.zeros(num_reqs_after_padding, dtype=np.bool_)
local_is_prefilling_np[:num_local_reqs] = real_local_is_prefilling_np
+ local_has_prefill = bool(local_is_prefilling_np.any())
seq_lens_cpu_upper_bound_np = np.zeros(num_reqs_after_padding, dtype=np.int32)
seq_lens_cpu_upper_bound_np[:num_local_reqs] = (
local_start_pos_np + local_num_scheduled_tokens
@@ -643,7 +644,9 @@ class PCPManager:
prefill_len_np=local_prefill_len_np,
num_computed_prefill_tokens_np=local_num_computed_prefill_tokens_np,
is_prefilling_np=local_is_prefilling_np,
- has_prefill=bool(local_is_prefilling_np.any()),
+ has_prefill=local_has_prefill,
+ decode_graph_eligible=not local_has_prefill,
+ prefill_runs_as_decode_np=None,
input_ids=input_buffers.input_ids[:num_local_tokens_padded],
positions=input_buffers.positions[:num_local_tokens_padded],
is_padding=is_padding,
diff --git a/vllm/v1/worker/gpu/spec_decode/autoregressive/speculator.py b/vllm/v1/worker/gpu/spec_decode/autoregressive/speculator.py
index 3d243b6..52d389b 100644
--- a/vllm/v1/worker/gpu/spec_decode/autoregressive/speculator.py
+++ b/vllm/v1/worker/gpu/spec_decode/autoregressive/speculator.py
@@ -289,7 +289,7 @@ class AutoRegressiveSpeculator(DraftModelSpeculator):
# the target model during FULL cudagraph.
num_tokens,
max_query_len,
- input_batch.has_prefill,
+ input_batch.decode_graph_eligible,
)
prefill_batch_desc, prefill_batch_sync = dispatch_cg_and_sync_dp(
self.prefill_cudagraph_manager,
diff --git a/vllm/v1/worker/gpu/spec_decode/multi_module_mtp/speculator.py b/vllm/v1/worker/gpu/spec_decode/multi_module_mtp/speculator.py
index 1c6ae31..e755a65 100644
--- a/vllm/v1/worker/gpu/spec_decode/multi_module_mtp/speculator.py
+++ b/vllm/v1/worker/gpu/spec_decode/multi_module_mtp/speculator.py
@@ -189,7 +189,7 @@ class MultiModuleMTPSpeculator(DraftModelSpeculator):
# When all requests are decoding (no true prefills), each has
# num_speculative_steps + 1 tokens, enabling FULL graph replay.
uniform_token_count = get_uniform_decode_token_count(
- num_reqs, num_tokens, max_query_len, input_batch.has_prefill
+ num_reqs, num_tokens, max_query_len, input_batch.decode_graph_eligible
)
batch_desc, batch_sync = dispatch_cg_and_sync_dp(
self.cudagraph_manager,
diff --git a/vllm/v1/worker/gpu/ubatch_utils.py b/vllm/v1/worker/gpu/ubatch_utils.py
index e3cade2..a28d1c2 100644
--- a/vllm/v1/worker/gpu/ubatch_utils.py
+++ b/vllm/v1/worker/gpu/ubatch_utils.py
@@ -181,6 +181,11 @@ def _slice_input_batch(
req_start:req_stop
],
is_prefilling_np=input_batch.is_prefilling_np[req_start:req_stop],
+ prefill_runs_as_decode_np=(
+ None
+ if input_batch.prefill_runs_as_decode_np is None
+ else input_batch.prefill_runs_as_decode_np[req_start:req_stop]
+ ),
input_ids=input_batch.input_ids[tok_start:tok_stop],
positions=input_batch.positions[tok_start:tok_stop],
is_padding=input_batch.is_padding[tok_start:tok_stop],
diff --git a/vllm/v1/worker/utils.py b/vllm/v1/worker/utils.py
index 71c1a2d..61698bf 100644
--- a/vllm/v1/worker/utils.py
+++ b/vllm/v1/worker/utils.py
@@ -764,10 +764,12 @@ def is_uniform_query_len(num_reqs: int, num_tokens: int, max_query_len: int) ->
def get_uniform_decode_token_count(
- num_reqs: int, num_tokens: int, max_query_len: int, has_prefill: bool
+ num_reqs: int, num_tokens: int, max_query_len: int, decode_graph_eligible: bool
) -> int | None:
"""Per-request token count of a uniform decode batch, or None."""
- if not has_prefill and is_uniform_query_len(num_reqs, num_tokens, max_query_len):
+ if decode_graph_eligible and is_uniform_query_len(
+ num_reqs, num_tokens, max_query_len
+ ):
return max_query_len
return None
--
2.43.0
Reference in New Issue View Git Blame Copy Permalink