From c6ce122d2325f5f5645c35b034f8e07482a5e7e9 Mon Sep 17 00:00:00 2001 From: 0z5a Date: Wed, 23 Sep 2026 13:22:10 +0000 Subject: [PATCH 1/7] Enable CUDA graph capture for fixed-depth speculation Signed-off-by: 0z5a --- benchmarks/spec_graph_e2e.py | 150 +++++++++++++++++++++++++++++++++ tests/test_speculative.py | 36 +++++++- vllm_rlt/engine/llm_engine.py | 8 +- vllm_rlt/worker/cuda_graph.py | 97 ++++++++++++++++++--- vllm_rlt/worker/speculative.py | 23 ++++- 5 files changed, 291 insertions(+), 23 deletions(-) create mode 100644 benchmarks/spec_graph_e2e.py diff --git a/benchmarks/spec_graph_e2e.py b/benchmarks/spec_graph_e2e.py new file mode 100644 index 00000000..d887907f --- /dev/null +++ b/benchmarks/spec_graph_e2e.py @@ -0,0 +1,150 @@ +"""Paired Ouro speculative E2E with CUDA graphs disabled and enabled.""" + +import argparse +import json +import statistics +import time +from pathlib import Path + +import torch +from transformers import AutoTokenizer + +from vllm_rlt import ( + LLM, + CacheConfig, + ExecutionConfig, + SamplingParams, + SchedulerConfig, + SpeculativeConfig, +) +from vllm_rlt.models import OuroForCausalLM + + +def timed(llm, prompts, params): + torch.cuda.synchronize() + start = time.perf_counter() + outputs = llm.generate(prompts, params) + torch.cuda.synchronize() + return [ + dict(tokens=o.token_ids, depths=o.exit_depths) for o in outputs + ], time.perf_counter() - start + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--model", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--max-tokens", type=int, default=64) + parser.add_argument("--repeats", type=int, default=5) + args = parser.parse_args() + if args.max_tokens < 2 or args.repeats < 1: + parser.error("max-tokens must be at least 2 and repeats must be positive") + + tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=False) + repeated = tokenizer.encode("The quick brown fox jumps over the lazy dog. " * 12) + prose = tokenizer.encode( + "Explain how a matrix multiplication works, including input and output shapes " + "and one numerical example." + ) + workloads = { + "repeated / 1": [repeated], + "repeated / 4": [repeated] * 4, + "prose / 1": [prose], + } + model = OuroForCausalLM.from_pretrained(args.model, device="cuda:0", dtype=torch.bfloat16) + engines = { + graph: LLM( + model, + cache_config=CacheConfig(num_blocks=1024), + scheduler_config=SchedulerConfig(max_num_seqs=4, max_num_batched_tokens=128), + attention_backend="triton", + speculative_config=SpeculativeConfig(4), + execution_config=ExecutionConfig( + cuda_graphs=graph, cuda_graph_max_batch_size=32, cuda_graph_max_graphs=16 + ), + ) + for graph in (False, True) + } + params = SamplingParams(max_tokens=args.max_tokens, min_loops=4, max_loops=4, ignore_eos=True) + rows = [] + for name, prompts in workloads.items(): + warmup = {graph: timed(engines[graph], prompts, params)[0] for graph in (False, True)} + if warmup[False] != warmup[True]: + raise AssertionError(f"{name}: graph warmup output mismatch") + for trial in range(args.repeats): + pair = {} + for graph in (False, True) if trial % 2 == 0 else (True, False): + outputs, seconds = timed(engines[graph], prompts, params) + pair[graph] = outputs + rows.append( + dict(workload=name, trial=trial, graphs=graph, seconds=seconds, outputs=outputs) + ) + if pair[False] != pair[True]: + raise AssertionError(f"{name}: trial {trial} output mismatch") + + runner = engines[True].engine.speculative_runner + counters = { + "recurrent": dict( + captures=runner.graphs.captures, + replays=runner.graphs.replays, + fallbacks=runner.graphs.fallbacks, + ), + "coda": dict( + captures=runner.coda_graphs.captures, + replays=runner.coda_graphs.replays, + fallbacks=runner.coda_graphs.fallbacks, + ), + } + args.output.mkdir(parents=True, exist_ok=True) + (args.output / "raw.json").write_text( + json.dumps( + dict( + model="ByteDance/Ouro-1.4B", + device=torch.cuda.get_device_name(0), + dtype="bfloat16", + backend="triton", + draft_loops=2, + target_loops=4, + k=4, + output_budget=args.max_tokens, + prompts=workloads, + rows=rows, + graphs=counters, + ), + indent=2, + ) + ) + table = [ + "| Workload / requests | Eager E2E s | Graph E2E s | Speedup | Output |", + "|---|---:|---:|---:|---|", + ] + for name in workloads: + eager = statistics.median( + r["seconds"] for r in rows if r["workload"] == name and not r["graphs"] + ) + graphed = statistics.median( + r["seconds"] for r in rows if r["workload"] == name and r["graphs"] + ) + table.append( + f"| {name} | {eager:.3f} | {graphed:.3f} | {eager / graphed:.3f}× | exact match |" + ) + (args.output / "speedup.md").write_text( + "# Ouro-1.4B speculative CUDA Graph E2E\n\n" + f"{torch.cuda.get_device_name(0)}; BF16, Triton, d=2/D=4, K=4, " + f"{args.max_tokens} output tokens. One resident model, separate engine state, " + f"one warmup per arm, {args.repeats} alternating paired trials. " + "Timings include prefill, draft, verification, coda and KV commit. " + "Every paired output token and exit depth matched.\n\n" + + "\n".join(table) + + "\n\n| Graph stage | Captures | Replays | Eager fallbacks |\n" + "|---|---:|---:|---:|\n" + + "\n".join( + f"| {stage} | {value['captures']} | {value['replays']} | {value['fallbacks']} |" + for stage, value in counters.items() + ) + + "\n" + ) + + +if __name__ == "__main__": + main() diff --git a/tests/test_speculative.py b/tests/test_speculative.py index 4a871662..d4a2c05c 100644 --- a/tests/test_speculative.py +++ b/tests/test_speculative.py @@ -302,7 +302,6 @@ def test_probability_filters_match_expected_and_keep_boundary_ties(): [ dict(cache_config=CacheConfig(layout="shared")), dict(execution_config=ExecutionConfig(async_scheduling=True)), - dict(execution_config=ExecutionConfig(cuda_graphs=True)), dict(scheduler_config=SchedulerConfig(enable_preemption=True)), dict(scheduler_config=SchedulerConfig(mode="no_refill")), ], @@ -375,3 +374,38 @@ def run(speculative, sampling): greedy = [SamplingParams(max_tokens=n, ignore_eos=True) for n in [8, 5, 3]] a, b = run(True, greedy), run(False, greedy) assert [o.token_ids for o in a] == [o.token_ids for o in b] + + +@pytest.mark.gpu +@pytest.mark.parametrize("max_graph_rows", [2, 8]) +def test_speculative_cuda_graph_matches_eager_with_ragged_replay(max_graph_rows): + m = model(dtype=torch.bfloat16).to("cuda") + prompts = [[2, 3, 4], [7, 8]] + params = SamplingParams(max_tokens=9, ignore_eos=True) + + def run(graphs): + llm = LLM( + m, + speculative_config=SpeculativeConfig(3), + cache_config=CacheConfig(128, 16), + attention_backend="triton", + execution_config=ExecutionConfig( + cuda_graphs=graphs, cuda_graph_max_batch_size=max_graph_rows + ), + ) + outputs = [llm.generate(prompts, params) for _ in range(2)] + assert llm.engine.cache_manager.num_used_blocks == 0 + return llm.engine.speculative_runner, [ + [(out.token_ids, out.exit_depths) for out in batch] for batch in outputs + ] + + _, eager = run(False) + runner, graphed = run(True) + assert graphed == eager + assert runner.graphs.captures > 0 + assert runner.graphs.replays > runner.graphs.captures + assert runner.coda_graphs.captures > 0 + if max_graph_rows == 2: + assert runner.graphs.fallbacks > 0 + else: + assert any(rows > 2 for rows, _, _, _ in runner.graphs.entries) diff --git a/vllm_rlt/engine/llm_engine.py b/vllm_rlt/engine/llm_engine.py index 728be260..bc6a549b 100644 --- a/vllm_rlt/engine/llm_engine.py +++ b/vllm_rlt/engine/llm_engine.py @@ -41,10 +41,8 @@ def __init__( raise ValueError("speculative target_loops must equal the model full depth") if self.exit_config.mode != "ouro": raise ValueError("speculative decoding requires fixed-depth ouro exit mode") - if self.execution_config.async_scheduling or self.execution_config.cuda_graphs: - raise ValueError( - "speculative decoding currently requires synchronous eager execution" - ) + if self.execution_config.async_scheduling: + raise ValueError("speculative decoding currently requires synchronous execution") if scheduler_config.enable_preemption or scheduler_config.mode != "refill": raise ValueError( "speculative decoding requires refill scheduling without preemption" @@ -98,7 +96,7 @@ def __init__( raise ValueError("prefill_uva requires CUDA FA4 with last_exited KV") self.scheduler = Scheduler(scheduler_config, self.cache_manager, speculative_config) self.speculative_runner = ( - SpeculativeRunner(model, self.cache_manager, speculative_config) + SpeculativeRunner(model, self.cache_manager, speculative_config, self.execution_config) if speculative_config is not None else None ) diff --git a/vllm_rlt/worker/cuda_graph.py b/vllm_rlt/worker/cuda_graph.py index 8f79906a..3439fa87 100644 --- a/vllm_rlt/worker/cuda_graph.py +++ b/vllm_rlt/worker/cuda_graph.py @@ -20,6 +20,16 @@ def _write_prepared(self, layer, batch, k, v): def _attend_prepared(self, layer, batch, q): cache = self.cache + if batch.cu_seqlens_q is not None: + return cache.attention.prefill( + q, + cache.key_cache[:, layer], + cache.value_cache[:, layer], + batch.block_tables, + batch.context_lengths, + batch.cu_seqlens_q, + batch.max_seqlen_q, + ) return cache.attention( q, cache.key_cache[:, layer], @@ -42,6 +52,8 @@ def __init__(self, model, cache, execution, compute_gate): @torch.inference_mode() def run(self, hidden, batch): count = len(batch.rows) + packed = batch.cu_seqlens_q is not None + key = (count, len(batch.context_lengths), batch.max_seqlen_q if packed else 1, packed) if len(batch.position_ids) != count: batch = replace( batch, @@ -52,9 +64,8 @@ def run(self, hidden, batch): if ( count > self.execution.cuda_graph_max_batch_size or not count - or batch.cu_seqlens_q is not None or ( - count not in self.entries + key not in self.entries and len(self.entries) >= self.execution.cuda_graph_max_graphs ) ): @@ -64,17 +75,29 @@ def run(self, hidden, batch): ) cache = self.cache cache._require_live_batch(batch) - # Decode writes exactly one position per request/depth. Validate history - # before launch; capture/replay must not freeze or repeat host mutations. + # A speculative verification batch may write consecutive positions at + # one depth. Its first position needs committed history; later rows are + # written together before attention reads them. + frontier = {} for allocation, depth, pos in batch.rows: - for layer in range(cache.num_layers): - cache._require_prefix(allocation, layer, depth, pos) + row_key = (id(allocation), depth) + previous = frontier.get(row_key) + if previous is None: + for layer in range(cache.num_layers): + cache._require_prefix(allocation, layer, depth, pos) + elif pos != previous + 1: + self.fallbacks += 1 + return self.model.recurrent_prepared( + hidden, batch, cache, compute_gate=self.compute_gate + ) + frontier[row_key] = pos stream = torch.cuda.current_stream(cache.device) if self.last_event is not None: stream.wait_event(self.last_event) - entry = self.entries.get(count) + entry = self.entries.get(key) if entry is None: width = math.ceil(self.model.config.max_position_embeddings / cache.block_size) + tables = len(batch.context_lengths) entry = SimpleNamespace( hidden=torch.empty_like(hidden[:count]), metadata=SimpleNamespace( @@ -82,17 +105,27 @@ def run(self, hidden, batch): write_blocks=torch.empty(count, device=cache.device, dtype=torch.long), write_offsets=torch.empty(count, device=cache.device, dtype=torch.long), block_tables=torch.zeros( - (count, width), device=cache.device, dtype=torch.int32 + (tables, width), device=cache.device, dtype=torch.int32 ), - context_lengths=torch.empty(count, device=cache.device, dtype=torch.int32), + context_lengths=torch.empty(tables, device=cache.device, dtype=torch.int32), + cu_seqlens_q=( + torch.empty(tables + 1, device=cache.device, dtype=torch.int32) + if packed + else None + ), + max_seqlen_q=batch.max_seqlen_q, ), ) entry.hidden.copy_(hidden[:count]) - for name in ("position_ids", "write_blocks", "write_offsets", "context_lengths"): - getattr(entry.metadata, name).copy_(getattr(batch, name)[:count]) + entry.metadata.position_ids.copy_(batch.position_ids[:count]) + entry.metadata.write_blocks.copy_(batch.write_blocks[:count]) + entry.metadata.write_offsets.copy_(batch.write_offsets[:count]) + entry.metadata.context_lengths.copy_(batch.context_lengths) + if packed: + entry.metadata.cu_seqlens_q.copy_(batch.cu_seqlens_q) width = batch.block_tables.shape[1] entry.metadata.block_tables[:, :width].copy_(batch.block_tables[:count]) - if count not in self.entries: + if key not in self.entries: # First-use capture is intentionally outside steady-state timing. # Drain outstanding streams: CUDA capture cannot race other launches. torch.cuda.synchronize(cache.device) @@ -108,7 +141,7 @@ def run(self, hidden, batch): entry.output = self.model.recurrent_prepared( entry.hidden, entry.metadata, proxy, compute_gate=self.compute_gate ) - self.entries[count] = entry + self.entries[key] = entry self.captures += 1 entry.graph.replay() # Outputs must outlive reuse of graph-private buffers and the shared pool. @@ -120,3 +153,41 @@ def run(self, hidden, batch): allocation.written[cache._plane(depth)][layer].add(pos) self.replays += 1 return output + + +class CodaGraphs: + """Capture fixed-row LM heads while keeping sampling and stop decisions eager.""" + + def __init__(self, model, execution): + self.model, self.execution = model, execution + self.entries = {} + self.pool = torch.cuda.graph_pool_handle() + self.stream = torch.cuda.Stream(device=next(model.parameters()).device) + self.captures = self.replays = self.fallbacks = 0 + + @torch.inference_mode() + def run(self, hidden): + count = len(hidden) + if count > self.execution.cuda_graph_max_batch_size or ( + count not in self.entries and len(self.entries) >= self.execution.cuda_graph_max_graphs + ): + self.fallbacks += 1 + return self.model.coda(hidden) + entry = self.entries.get(count) + if entry is None: + entry = SimpleNamespace(hidden=torch.empty_like(hidden)) + entry.hidden.copy_(hidden) + if count not in self.entries: + torch.cuda.synchronize(hidden.device) + with torch.cuda.stream(self.stream): + for _ in range(2): + self.model.coda(entry.hidden) + self.stream.synchronize() + entry.graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(entry.graph, pool=self.pool, stream=self.stream): + entry.output = self.model.coda(entry.hidden) + self.entries[count] = entry + self.captures += 1 + entry.graph.replay() + self.replays += 1 + return entry.output.clone() diff --git a/vllm_rlt/worker/speculative.py b/vllm_rlt/worker/speculative.py index ca2ff117..59e2729d 100644 --- a/vllm_rlt/worker/speculative.py +++ b/vllm_rlt/worker/speculative.py @@ -8,6 +8,7 @@ import torch +from vllm_rlt.worker.cuda_graph import CodaGraphs, RecurrentGraphs from vllm_rlt.worker.sampling import ( draw, generator_for, @@ -33,10 +34,14 @@ class SpeculativeStats: class SpeculativeRunner: - def __init__(self, model, cache, config): + def __init__(self, model, cache, config, execution): self.model, self.cache, self.config = model, cache, config self.device = next(model.parameters()).device self.stats = SpeculativeStats() + self.graphs = ( + RecurrentGraphs(model, cache, execution, False) if execution.cuda_graphs else None + ) + self.coda_graphs = CodaGraphs(model, execution) if execution.cuda_graphs else None def _core(self, hidden, ids, positions, depth, *, packed=False): batch = self.cache._prepare_batch( @@ -45,9 +50,19 @@ def _core(self, hidden, ids, positions, depth, *, packed=False): positions, packed_prefill=packed and getattr(self.cache.attention, "generation", None) == 4, ) - hidden, _ = self.model.recurrent_prepared(hidden, batch, self.cache, compute_gate=False) + if self.graphs is not None: + hidden, _ = self.graphs.run(hidden, batch) + else: + hidden, _ = self.model.recurrent_prepared(hidden, batch, self.cache, compute_gate=False) return hidden + def _coda(self, hidden): + return ( + self.coda_graphs.run(hidden) + if self.coda_graphs is not None + else self.model.coda(hidden) + ) + @torch.inference_mode() def execute(self, batch): items = batch.items @@ -74,7 +89,7 @@ def execute(self, batch): drafting.append((row, i)) if not drafting: continue - logits = self.model.coda(torch.stack([hidden[row] for row, _ in drafting])) + logits = self._coda(torch.stack([hidden[row] for row, _ in drafting])) for row, (_, i) in enumerate(drafting): request = items[i].request if request.sampling_params.temperature == 0: @@ -92,7 +107,7 @@ def execute(self, batch): hidden = torch.stack([h for request_states in states for h in request_states]) for depth in range(self.config.draft_loops, self.config.target_loops): hidden = self._core(hidden, ids, positions, depth, packed=True) - logits = self.model.coda(hidden) + logits = self._coda(hidden) results, start = [], 0 for i, item in enumerate(items): rows = logits[start : start + item.token_count] From d9213f1d885febe4f01930bf142f72fea47be467 Mon Sep 17 00:00:00 2001 From: 0z5a Date: Wed, 23 Sep 2026 13:32:50 +0000 Subject: [PATCH 2/7] Keep packed graph page tables aligned with requests Signed-off-by: 0z5a --- vllm_rlt/worker/cuda_graph.py | 2 +- vllm_rlt/worker/speculative.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/vllm_rlt/worker/cuda_graph.py b/vllm_rlt/worker/cuda_graph.py index 3439fa87..650d85cb 100644 --- a/vllm_rlt/worker/cuda_graph.py +++ b/vllm_rlt/worker/cuda_graph.py @@ -124,7 +124,7 @@ def run(self, hidden, batch): if packed: entry.metadata.cu_seqlens_q.copy_(batch.cu_seqlens_q) width = batch.block_tables.shape[1] - entry.metadata.block_tables[:, :width].copy_(batch.block_tables[:count]) + entry.metadata.block_tables[:, :width].copy_(batch.block_tables) if key not in self.entries: # First-use capture is intentionally outside steady-state timing. # Drain outstanding streams: CUDA capture cannot race other launches. diff --git a/vllm_rlt/worker/speculative.py b/vllm_rlt/worker/speculative.py index 59e2729d..3ef02523 100644 --- a/vllm_rlt/worker/speculative.py +++ b/vllm_rlt/worker/speculative.py @@ -48,7 +48,7 @@ def _core(self, hidden, ids, positions, depth, *, packed=False): ids, [depth] * len(ids), positions, - packed_prefill=packed and getattr(self.cache.attention, "generation", None) == 4, + packed_prefill=packed and self.cache.attention_info.get("generation") == 4, ) if self.graphs is not None: hidden, _ = self.graphs.run(hidden, batch) From ce39b329d8410ffa1da49171d8b89c34003d37a0 Mon Sep 17 00:00:00 2001 From: 0z5a Date: Wed, 23 Sep 2026 23:49:38 +0000 Subject: [PATCH 3/7] Warm graph benchmarks through stable dispatch shapes Signed-off-by: 0z5a --- benchmarks/spec_graph_e2e.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/benchmarks/spec_graph_e2e.py b/benchmarks/spec_graph_e2e.py index d887907f..db5adb14 100644 --- a/benchmarks/spec_graph_e2e.py +++ b/benchmarks/spec_graph_e2e.py @@ -68,9 +68,10 @@ def main(): params = SamplingParams(max_tokens=args.max_tokens, min_loops=4, max_loops=4, ignore_eos=True) rows = [] for name, prompts in workloads.items(): - warmup = {graph: timed(engines[graph], prompts, params)[0] for graph in (False, True)} - if warmup[False] != warmup[True]: - raise AssertionError(f"{name}: graph warmup output mismatch") + for _ in range(3): + warmup = {graph: timed(engines[graph], prompts, params)[0] for graph in (False, True)} + if warmup[False] != warmup[True]: + raise AssertionError(f"{name}: graph warmup output mismatch") for trial in range(args.repeats): pair = {} for graph in (False, True) if trial % 2 == 0 else (True, False): @@ -132,7 +133,7 @@ def main(): "# Ouro-1.4B speculative CUDA Graph E2E\n\n" f"{torch.cuda.get_device_name(0)}; BF16, Triton, d=2/D=4, K=4, " f"{args.max_tokens} output tokens. One resident model, separate engine state, " - f"one warmup per arm, {args.repeats} alternating paired trials. " + f"three warmups per arm, {args.repeats} alternating paired trials. " "Timings include prefill, draft, verification, coda and KV commit. " "Every paired output token and exit depth matched.\n\n" + "\n".join(table) From acc31be78039548b4ba76151a63edc57854b6d05 Mon Sep 17 00:00:00 2001 From: 0z5a Date: Wed, 23 Sep 2026 23:58:33 +0000 Subject: [PATCH 4/7] Stabilize first speculative graph workload before timing Signed-off-by: 0z5a --- benchmarks/spec_graph_e2e.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/benchmarks/spec_graph_e2e.py b/benchmarks/spec_graph_e2e.py index db5adb14..ec253516 100644 --- a/benchmarks/spec_graph_e2e.py +++ b/benchmarks/spec_graph_e2e.py @@ -68,7 +68,7 @@ def main(): params = SamplingParams(max_tokens=args.max_tokens, min_loops=4, max_loops=4, ignore_eos=True) rows = [] for name, prompts in workloads.items(): - for _ in range(3): + for _ in range(5): warmup = {graph: timed(engines[graph], prompts, params)[0] for graph in (False, True)} if warmup[False] != warmup[True]: raise AssertionError(f"{name}: graph warmup output mismatch") @@ -133,7 +133,7 @@ def main(): "# Ouro-1.4B speculative CUDA Graph E2E\n\n" f"{torch.cuda.get_device_name(0)}; BF16, Triton, d=2/D=4, K=4, " f"{args.max_tokens} output tokens. One resident model, separate engine state, " - f"three warmups per arm, {args.repeats} alternating paired trials. " + f"five warmups per arm, {args.repeats} alternating paired trials. " "Timings include prefill, draft, verification, coda and KV commit. " "Every paired output token and exit depth matched.\n\n" + "\n".join(table) From 9a1670e967ff6413686a1576774d917f928d78f9 Mon Sep 17 00:00:00 2001 From: 0z5a Date: Thu, 24 Sep 2026 00:10:21 +0000 Subject: [PATCH 5/7] Report RTX 5090 speculative CUDA Graph E2E results Signed-off-by: 0z5a --- results/graph5090.md | 15 + results/rtx5090-highload/raw.json | 8946 +++++++++++++++++++++++++++ results/rtx5090-highload/speedup.md | 14 + results/rtx5090/raw.json | 8946 +++++++++++++++++++++++++++ results/rtx5090/speedup.md | 14 + 5 files changed, 17935 insertions(+) create mode 100644 results/graph5090.md create mode 100644 results/rtx5090-highload/raw.json create mode 100644 results/rtx5090-highload/speedup.md create mode 100644 results/rtx5090/raw.json create mode 100644 results/rtx5090/speedup.md diff --git a/results/graph5090.md b/results/graph5090.md new file mode 100644 index 00000000..859e5ce9 --- /dev/null +++ b/results/graph5090.md @@ -0,0 +1,15 @@ +# Speculative CUDA Graph E2E on RTX 5090 + +Ouro-1.4B, BF16, Triton, `last_exited`, d=2/D=4, K=4, 64 output tokens per request. Eager and graphed engines share one resident model but keep separate request state. Five warmups per arm precede five alternating paired trials. Timings include prefill, drafting, verification, LM head, and KV commit. Every paired output token and exit depth matched. Values are median ± median absolute deviation (seconds); speedup is eager median / graphed median. + +The table below uses one CPU thread for OMP, MKL, and OpenBLAS in both arms. The host load fell from 67 to 12 during the run, so the single-request repeated prompt remains sensitive to host scheduling. + +| Workload | Eager, s | CUDA Graph, s | Speedup | Raw trials | +|---|---:|---:|---:|---| +| Repeated prompt, 1 request | 3.028 ± 0.161 | 0.763 ± 0.003 | 3.969× | [JSON](rtx5090/raw.json) | +| Repeated prompt, 4 requests | 2.223 ± 0.001 | 1.209 ± <0.001 | 1.838× | [JSON](rtx5090/raw.json) | +| Prose prompt, 1 request | 1.865 ± 0.017 | 0.753 ± <0.001 | 2.477× | [JSON](rtx5090/raw.json) | + +The graph arm recorded 11 recurrent captures and 5,400 replays, plus 11 LM-head captures and 2,240 replays, with zero eager fallbacks. A separate run without the single-thread settings during host load around 162/128 logical CPUs showed much larger timing drift; its [report](rtx5090-highload/speedup.md) and [raw trials](rtx5090-highload/raw.json) are retained. These measurements do not establish a stable speedup under a saturated host. + +Checkpoint SHA256: `58872a72616c736595b8b7662079c5b12c5a162ec16eae94f21c348dfa9885af`. Source: `e1336ac` on top of #44 `d9fca50`. Runs used the existing `/home/gongji/0z5a` environment inside Docker with a verified 96 GiB RAM cap. RTX 5090 tests used Triton; packed FA4 graph capture remains unverified because the installed paged FA4 backend does not support SM12. diff --git a/results/rtx5090-highload/raw.json b/results/rtx5090-highload/raw.json new file mode 100644 index 00000000..af18185b --- /dev/null +++ b/results/rtx5090-highload/raw.json @@ -0,0 +1,8946 @@ +{ + "model": "ByteDance/Ouro-1.4B", + "device": "NVIDIA GeForce RTX 5090", + "dtype": "bfloat16", + "backend": "triton", + "draft_loops": 2, + "target_loops": 4, + "k": 4, + "output_budget": 64, + "prompts": { + "repeated / 1": [ + [ + 504, + 2365, + 6354, + 16438, + 27003, + 690, + 260, + 23790, + 2767, + 30, + 378, + 2365, + 6354, + 16438, + 27003, + 690, + 260, + 23790, + 2767, + 30, + 378, + 2365, + 6354, + 16438, + 27003, + 690, + 260, + 23790, + 2767, + 30, + 378, + 2365, + 6354, + 16438, + 27003, + 690, + 260, + 23790, + 2767, + 30, + 378, + 2365, + 6354, + 16438, + 27003, + 690, + 260, + 23790, + 2767, + 30, + 378, + 2365, + 6354, + 16438, + 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new file mode 100644 index 00000000..bca3d051 --- /dev/null +++ b/results/rtx5090-highload/speedup.md @@ -0,0 +1,14 @@ +# Ouro-1.4B speculative CUDA Graph E2E + +NVIDIA GeForce RTX 5090; BF16, Triton, d=2/D=4, K=4, 64 output tokens. One resident model, separate engine state, five warmups per arm, 5 alternating paired trials. Timings include prefill, draft, verification, coda and KV commit. Every paired output token and exit depth matched. + +| Workload / requests | Eager E2E s | Graph E2E s | Speedup | Output | +|---|---:|---:|---:|---| +| repeated / 1 | 1.694 | 0.717 | 2.361× | exact match | +| repeated / 4 | 4.326 | 1.396 | 3.099× | exact match | +| prose / 1 | 4.584 | 0.852 | 5.380× | exact match | + +| Graph stage | Captures | Replays | Eager fallbacks | +|---|---:|---:|---:| +| recurrent | 11 | 5400 | 0 | +| coda | 11 | 2240 | 0 | diff --git a/results/rtx5090/raw.json b/results/rtx5090/raw.json new file mode 100644 index 00000000..2587b06f --- /dev/null +++ b/results/rtx5090/raw.json @@ -0,0 +1,8946 @@ +{ + "model": "ByteDance/Ouro-1.4B", + "device": "NVIDIA GeForce RTX 5090", + "dtype": "bfloat16", + "backend": "triton", + "draft_loops": 2, + "target_loops": 4, + "k": 4, + "output_budget": 64, + "prompts": { + "repeated / 1": [ + [ + 504, + 2365, + 6354, + 16438, + 27003, + 690, + 260, + 23790, + 2767, + 30, + 378, + 2365, + 6354, + 16438, + 27003, + 690, + 260, + 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+ ] + } + ], + "graphs": { + "recurrent": { + "captures": 11, + "replays": 5400, + "fallbacks": 0 + }, + "coda": { + "captures": 11, + "replays": 2240, + "fallbacks": 0 + } + } +} \ No newline at end of file diff --git a/results/rtx5090/speedup.md b/results/rtx5090/speedup.md new file mode 100644 index 00000000..77df2ec7 --- /dev/null +++ b/results/rtx5090/speedup.md @@ -0,0 +1,14 @@ +# Ouro-1.4B speculative CUDA Graph E2E + +NVIDIA GeForce RTX 5090; BF16, Triton, d=2/D=4, K=4, 64 output tokens. One resident model, separate engine state, five warmups per arm, 5 alternating paired trials. Timings include prefill, draft, verification, coda and KV commit. Every paired output token and exit depth matched. + +| Workload / requests | Eager E2E s | Graph E2E s | Speedup | Output | +|---|---:|---:|---:|---| +| repeated / 1 | 3.028 | 0.763 | 3.969× | exact match | +| repeated / 4 | 2.223 | 1.209 | 1.838× | exact match | +| prose / 1 | 1.865 | 0.753 | 2.477× | exact match | + +| Graph stage | Captures | Replays | Eager fallbacks | +|---|---:|---:|---:| +| recurrent | 11 | 5400 | 0 | +| coda | 11 | 2240 | 0 | From b574eb3f78281ac96cfb35e898ac6c166f390357 Mon Sep 17 00:00:00 2001 From: 0z5a Date: Thu, 24 Sep 2026 09:44:07 +0000 Subject: [PATCH 6/7] Share CUDA graph capture setup across recurrent and coda paths Signed-off-by: 0z5a --- vllm_rlt/worker/cuda_graph.py | 44 ++++++++++++++++++----------------- 1 file changed, 23 insertions(+), 21 deletions(-) diff --git a/vllm_rlt/worker/cuda_graph.py b/vllm_rlt/worker/cuda_graph.py index 650d85cb..a5094318 100644 --- a/vllm_rlt/worker/cuda_graph.py +++ b/vllm_rlt/worker/cuda_graph.py @@ -7,6 +7,19 @@ import torch +def _capture(hidden, stream, pool, run): + """Warm and capture one stable-input graph on its own stream.""" + torch.cuda.synchronize(hidden.device) + with torch.cuda.stream(stream): + for _ in range(2): + run() + stream.synchronize() + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph, pool=pool, stream=stream): + output = run() + return graph, output + + class _DeviceCache: """Capture only GPU cache operations, never Python allocation/written state.""" @@ -126,21 +139,15 @@ def run(self, hidden, batch): width = batch.block_tables.shape[1] entry.metadata.block_tables[:, :width].copy_(batch.block_tables) if key not in self.entries: - # First-use capture is intentionally outside steady-state timing. - # Drain outstanding streams: CUDA capture cannot race other launches. - torch.cuda.synchronize(cache.device) proxy = _DeviceCache(cache) - with torch.cuda.stream(self.capture_stream): - for _ in range(2): - self.model.recurrent_prepared( - entry.hidden, entry.metadata, proxy, compute_gate=self.compute_gate - ) - self.capture_stream.synchronize() - entry.graph = torch.cuda.CUDAGraph() - with torch.cuda.graph(entry.graph, pool=self.pool, stream=self.capture_stream): - entry.output = self.model.recurrent_prepared( + entry.graph, entry.output = _capture( + entry.hidden, + self.capture_stream, + self.pool, + lambda: self.model.recurrent_prepared( entry.hidden, entry.metadata, proxy, compute_gate=self.compute_gate - ) + ), + ) self.entries[key] = entry self.captures += 1 entry.graph.replay() @@ -178,14 +185,9 @@ def run(self, hidden): entry = SimpleNamespace(hidden=torch.empty_like(hidden)) entry.hidden.copy_(hidden) if count not in self.entries: - torch.cuda.synchronize(hidden.device) - with torch.cuda.stream(self.stream): - for _ in range(2): - self.model.coda(entry.hidden) - self.stream.synchronize() - entry.graph = torch.cuda.CUDAGraph() - with torch.cuda.graph(entry.graph, pool=self.pool, stream=self.stream): - entry.output = self.model.coda(entry.hidden) + entry.graph, entry.output = _capture( + entry.hidden, self.stream, self.pool, lambda: self.model.coda(entry.hidden) + ) self.entries[count] = entry self.captures += 1 entry.graph.replay() From c712ea2347cb0cae6fa5d75abff6233c462c80b1 Mon Sep 17 00:00:00 2001 From: 0z5a Date: Mon, 28 Sep 2026 19:40:27 +0800 Subject: [PATCH 7/7] Remove benchmark and result artifacts from Graph PR Keep runtime and test changes intact; the measured summary remains in the PR description. Signed-off-by: 0z5a --- benchmarks/spec_graph_e2e.py | 151 - results/graph5090.md | 15 - results/rtx5090-highload/raw.json | 8946 --------------------------- results/rtx5090-highload/speedup.md | 14 - results/rtx5090/raw.json | 8946 --------------------------- results/rtx5090/speedup.md | 14 - 6 files changed, 18086 deletions(-) delete mode 100644 benchmarks/spec_graph_e2e.py delete mode 100644 results/graph5090.md delete mode 100644 results/rtx5090-highload/raw.json delete mode 100644 results/rtx5090-highload/speedup.md delete mode 100644 results/rtx5090/raw.json delete mode 100644 results/rtx5090/speedup.md diff --git a/benchmarks/spec_graph_e2e.py b/benchmarks/spec_graph_e2e.py deleted file mode 100644 index ec253516..00000000 --- a/benchmarks/spec_graph_e2e.py +++ /dev/null @@ -1,151 +0,0 @@ -"""Paired Ouro speculative E2E with CUDA graphs disabled and enabled.""" - -import argparse -import json -import statistics -import time -from pathlib import Path - -import torch -from transformers import AutoTokenizer - -from vllm_rlt import ( - LLM, - CacheConfig, - ExecutionConfig, - SamplingParams, - SchedulerConfig, - SpeculativeConfig, -) -from vllm_rlt.models import OuroForCausalLM - - -def timed(llm, prompts, params): - torch.cuda.synchronize() - start = time.perf_counter() - outputs = llm.generate(prompts, params) - torch.cuda.synchronize() - return [ - dict(tokens=o.token_ids, depths=o.exit_depths) for o in outputs - ], time.perf_counter() - start - - -def main(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--model", type=Path, required=True) - parser.add_argument("--output", type=Path, required=True) - parser.add_argument("--max-tokens", type=int, default=64) - parser.add_argument("--repeats", type=int, default=5) - args = parser.parse_args() - if args.max_tokens < 2 or args.repeats < 1: - parser.error("max-tokens must be at least 2 and repeats must be positive") - - tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=False) - repeated = tokenizer.encode("The quick brown fox jumps over the lazy dog. " * 12) - prose = tokenizer.encode( - "Explain how a matrix multiplication works, including input and output shapes " - "and one numerical example." - ) - workloads = { - "repeated / 1": [repeated], - "repeated / 4": [repeated] * 4, - "prose / 1": [prose], - } - model = OuroForCausalLM.from_pretrained(args.model, device="cuda:0", dtype=torch.bfloat16) - engines = { - graph: LLM( - model, - cache_config=CacheConfig(num_blocks=1024), - scheduler_config=SchedulerConfig(max_num_seqs=4, max_num_batched_tokens=128), - attention_backend="triton", - speculative_config=SpeculativeConfig(4), - execution_config=ExecutionConfig( - cuda_graphs=graph, cuda_graph_max_batch_size=32, cuda_graph_max_graphs=16 - ), - ) - for graph in (False, True) - } - params = SamplingParams(max_tokens=args.max_tokens, min_loops=4, max_loops=4, ignore_eos=True) - rows = [] - for name, prompts in workloads.items(): - for _ in range(5): - warmup = {graph: timed(engines[graph], prompts, params)[0] for graph in (False, True)} - if warmup[False] != warmup[True]: - raise AssertionError(f"{name}: graph warmup output mismatch") - for trial in range(args.repeats): - pair = {} - for graph in (False, True) if trial % 2 == 0 else (True, False): - outputs, seconds = timed(engines[graph], prompts, params) - pair[graph] = outputs - rows.append( - dict(workload=name, trial=trial, graphs=graph, seconds=seconds, outputs=outputs) - ) - if pair[False] != pair[True]: - raise AssertionError(f"{name}: trial {trial} output mismatch") - - runner = engines[True].engine.speculative_runner - counters = { - "recurrent": dict( - captures=runner.graphs.captures, - replays=runner.graphs.replays, - fallbacks=runner.graphs.fallbacks, - ), - "coda": dict( - captures=runner.coda_graphs.captures, - replays=runner.coda_graphs.replays, - fallbacks=runner.coda_graphs.fallbacks, - ), - } - args.output.mkdir(parents=True, exist_ok=True) - (args.output / "raw.json").write_text( - json.dumps( - dict( - model="ByteDance/Ouro-1.4B", - device=torch.cuda.get_device_name(0), - dtype="bfloat16", - backend="triton", - draft_loops=2, - target_loops=4, - k=4, - output_budget=args.max_tokens, - prompts=workloads, - rows=rows, - graphs=counters, - ), - indent=2, - ) - ) - table = [ - "| Workload / requests | Eager E2E s | Graph E2E s | Speedup | Output |", - "|---|---:|---:|---:|---|", - ] - for name in workloads: - eager = statistics.median( - r["seconds"] for r in rows if r["workload"] == name and not r["graphs"] - ) - graphed = statistics.median( - r["seconds"] for r in rows if r["workload"] == name and r["graphs"] - ) - table.append( - f"| {name} | {eager:.3f} | {graphed:.3f} | {eager / graphed:.3f}× | exact match |" - ) - (args.output / "speedup.md").write_text( - "# Ouro-1.4B speculative CUDA Graph E2E\n\n" - f"{torch.cuda.get_device_name(0)}; BF16, Triton, d=2/D=4, K=4, " - f"{args.max_tokens} output tokens. One resident model, separate engine state, " - f"five warmups per arm, {args.repeats} alternating paired trials. " - "Timings include prefill, draft, verification, coda and KV commit. " - "Every paired output token and exit depth matched.\n\n" - + "\n".join(table) - + "\n\n| Graph stage | Captures | Replays | Eager fallbacks |\n" - "|---|---:|---:|---:|\n" - + "\n".join( - f"| {stage} | {value['captures']} | {value['replays']} | {value['fallbacks']} |" - for stage, value in counters.items() - ) - + "\n" - ) - - -if __name__ == "__main__": - main() diff --git a/results/graph5090.md b/results/graph5090.md deleted file mode 100644 index 859e5ce9..00000000 --- a/results/graph5090.md +++ /dev/null @@ -1,15 +0,0 @@ -# Speculative CUDA Graph E2E on RTX 5090 - -Ouro-1.4B, BF16, Triton, `last_exited`, d=2/D=4, K=4, 64 output tokens per request. Eager and graphed engines share one resident model but keep separate request state. Five warmups per arm precede five alternating paired trials. Timings include prefill, drafting, verification, LM head, and KV commit. Every paired output token and exit depth matched. Values are median ± median absolute deviation (seconds); speedup is eager median / graphed median. - -The table below uses one CPU thread for OMP, MKL, and OpenBLAS in both arms. The host load fell from 67 to 12 during the run, so the single-request repeated prompt remains sensitive to host scheduling. - -| Workload | Eager, s | CUDA Graph, s | Speedup | Raw trials | -|---|---:|---:|---:|---| -| Repeated prompt, 1 request | 3.028 ± 0.161 | 0.763 ± 0.003 | 3.969× | [JSON](rtx5090/raw.json) | -| Repeated prompt, 4 requests | 2.223 ± 0.001 | 1.209 ± <0.001 | 1.838× | [JSON](rtx5090/raw.json) | -| Prose prompt, 1 request | 1.865 ± 0.017 | 0.753 ± <0.001 | 2.477× | [JSON](rtx5090/raw.json) | - -The graph arm recorded 11 recurrent captures and 5,400 replays, plus 11 LM-head captures and 2,240 replays, with zero eager fallbacks. A separate run without the single-thread settings during host load around 162/128 logical CPUs showed much larger timing drift; its [report](rtx5090-highload/speedup.md) and [raw trials](rtx5090-highload/raw.json) are retained. These measurements do not establish a stable speedup under a saturated host. - -Checkpoint SHA256: `58872a72616c736595b8b7662079c5b12c5a162ec16eae94f21c348dfa9885af`. Source: `e1336ac` on top of #44 `d9fca50`. Runs used the existing `/home/gongji/0z5a` environment inside Docker with a verified 96 GiB RAM cap. RTX 5090 tests used Triton; packed FA4 graph capture remains unverified because the installed paged FA4 backend does not support SM12. diff --git a/results/rtx5090-highload/raw.json b/results/rtx5090-highload/raw.json deleted file mode 100644 index af18185b..00000000 --- a/results/rtx5090-highload/raw.json +++ /dev/null @@ -1,8946 +0,0 @@ -{ - "model": "ByteDance/Ouro-1.4B", - "device": "NVIDIA GeForce RTX 5090", - "dtype": "bfloat16", - "backend": "triton", - "draft_loops": 2, - "target_loops": 4, - "k": 4, - "output_budget": 64, - "prompts": { - "repeated / 1": [ - [ - 504, - 2365, - 6354, - 16438, - 27003, - 690, - 260, - 23790, - 2767, - 30, - 378, - 2365, - 6354, - 16438, - 27003, - 690, - 260, - 23790, - 2767, - 30, - 378, - 2365, - 6354, - 16438, - 27003, - 690, - 260, - 23790, - 2767, - 30, - 378, - 2365, - 6354, - 16438, - 27003, - 690, - 260, - 23790, - 2767, - 30, - 378, - 2365, - 6354, - 16438, - 27003, - 690, - 260, - 23790, - 2767, - 30, - 378, - 2365, - 6354, - 16438, - 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One resident model, separate engine state, five warmups per arm, 5 alternating paired trials. Timings include prefill, draft, verification, coda and KV commit. 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-NVIDIA GeForce RTX 5090; BF16, Triton, d=2/D=4, K=4, 64 output tokens. One resident model, separate engine state, five warmups per arm, 5 alternating paired trials. Timings include prefill, draft, verification, coda and KV commit. Every paired output token and exit depth matched. - -| Workload / requests | Eager E2E s | Graph E2E s | Speedup | Output | -|---|---:|---:|---:|---| -| repeated / 1 | 3.028 | 0.763 | 3.969× | exact match | -| repeated / 4 | 2.223 | 1.209 | 1.838× | exact match | -| prose / 1 | 1.865 | 0.753 | 2.477× | exact match | - -| Graph stage | Captures | Replays | Eager fallbacks | -|---|---:|---:|---:| -| recurrent | 11 | 5400 | 0 | -| coda | 11 | 2240 | 0 |