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perf: batch decode stream callbacks into one dispatch per step

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- add BatchedStreamCallback sink type: TaskManager resolves a decode step's (task_id, token) events under one lock and delivers each sink a single list instead of one call per token
- keep the plain Callable[[str]] callback contract: per-token callbacks still receive one call per event, and invoke_callback/cancel_task wrap single events for batched sinks
- collect aborted, text, and finish STOP events in the scheduler decode loop and dispatch once per step instead of once per token
- register one _ResultSink per generate call (replacing per-task closures) so GenerateResult takes its lock and wakes waiters once per step, with late-bind replay for tasks that start decoding before add_task returns their id
- apply GenerateResult batches under a single condition hold via append_batch; append delegates to it
- update engine test fakes to the batched contract and add coverage for event grouping, single-event dispatch, cancel STOP, and late-bind replay

Benchmark: NVIDIA L20 (idle), CUDA 12.8, torch 2.11.0+cu128, 1.2B bf16 checkpoint, prompt 512, 256 greedy tokens, CUDA graph on, serving-level decode, 3 trials
- batch 32: 7.808 -> 7.506 ms/token (4098 -> 4263 batch tok/s, +4.0%)
- batch 1/8: unchanged within noise (3.768 -> 3.797 / 4.699 -> 4.607 ms/token)
- full suite: 896 passed
This commit is contained in:
ViperEkura
2026-09-05 00:03:36 +08:00
parent 1798474316
commit 074642b6d2
6 changed files with 265 additions and 46 deletions
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tests/inference/test_engine.py
+64 -21
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@@ -1,6 +1,7 @@
"""Unit tests for GenerateResult accumulator and InferenceEngine.generate()."""
import asyncio
import itertools
import threading
from unittest.mock import MagicMock, patch
@@ -8,7 +9,12 @@ import pytest
from astrai.extension import TorchNativeBackend, attn_backend
from astrai.inference import STOP
from astrai.inference.engine import GenerateResult, InferenceEngine, build_engine
from astrai.inference.engine import (
GenerateResult,
InferenceEngine,
_ResultSink,
build_engine,
)
from tests.helpers import FakeTokenizer, make_model
@@ -50,6 +56,33 @@ def test_result_stop_does_not_double_count():
assert r._completed == 1
def test_result_append_batch_updates_state_in_one_commit():
r = GenerateResult(count=2)
r.append_batch([(0, "he"), (1, "wo"), (0, "llo"), (1, "rld")])
r.append_batch([(0, STOP), (1, STOP)])
assert r.results == ["hello", "world"]
assert r._completed == 2
assert r.pop_all() == [
(0, "he"),
(1, "wo"),
(0, "llo"),
(1, "rld"),
(0, STOP),
(1, STOP),
]
def test_result_sink_replays_events_arriving_before_bind():
r = GenerateResult(count=1)
sink = _ResultSink(r)
sink([("t0", "he")]) # task id not bound yet: buffered, not applied
assert r.results == [""]
sink.bind("t0", 0)
sink([("t0", "llo"), ("t0", STOP)])
assert r.results == ["hello"]
assert r._completed == 1
def test_result_pop_all_returns_and_clears():
r = GenerateResult(count=2)
r.append("a", 0)
@@ -118,8 +151,8 @@ def test_engine_generate_non_streaming_single():
def fake_add(prompt, **kw):
cb = kw["stream_callback"]
cb("response")
cb(STOP)
cb([("task-1", "response"), ("task-1", STOP)])
return "task-1"
instance.add_task.side_effect = fake_add
instance.remove_task.return_value = []
@@ -136,6 +169,7 @@ def test_engine_generate_streaming_yields_tokens():
def capture_cb(prompt, **kw):
callbacks_saved.append(kw.get("stream_callback"))
return "task-0"
with patch("astrai.inference.engine.InferenceScheduler") as MockSched:
instance = MockSched.return_value
@@ -146,9 +180,9 @@ def test_engine_generate_streaming_yields_tokens():
gen = eng.generate("hello", stream=True)
cb = callbacks_saved[0]
cb("t1")
cb("t2")
cb(STOP)
cb([("task-0", "t1")])
cb([("task-0", "t2")])
cb([("task-0", STOP)])
tokens = list(gen)
assert tokens == ["t1", "t2"]
@@ -169,7 +203,7 @@ def test_engine_stream_close_cancels_unfinished_task():
engine = InferenceEngine(mock_model, mock_tokenizer, max_batch_size=1)
stream = engine.generate("hello", stream=True)
callbacks_saved[0]("t1")
callbacks_saved[0]([("task-1", "t1")])
assert next(stream) == "t1"
stream.close()
@@ -182,6 +216,7 @@ def test_engine_generate_async_yields_tokens_until_stop():
def capture_cb(prompt, **kw):
callbacks_saved.append(kw.get("stream_callback"))
return "task-0"
with patch("astrai.inference.engine.InferenceScheduler") as MockSched:
instance = MockSched.return_value
@@ -198,9 +233,9 @@ def test_engine_generate_async_yields_tokens_until_stop():
return out
cb = callbacks_saved[0]
cb("t1")
cb("t2")
cb(STOP)
cb([("task-0", "t1")])
cb([("task-0", "t2")])
cb([("task-0", STOP)])
assert asyncio.run(collect()) == ["t1", "t2"]
@@ -219,7 +254,7 @@ def test_engine_async_close_cancels_unfinished_task():
engine = InferenceEngine(mock_model, mock_tokenizer, max_batch_size=1)
stream = engine.generate_async("hello")
callbacks_saved[0]("t1")
callbacks_saved[0]([("task-1", "t1")])
async def consume_then_close():
assert await anext(stream) == "t1"
@@ -233,20 +268,25 @@ def test_engine_async_close_cancels_unfinished_task():
def test_engine_generate_non_streaming_batch():
mock_model, mock_tokenizer = _make_engine_mocks(decode="r")
counter = itertools.count()
task_ids = []
def fake_add(prompt, **kw):
cb = kw["stream_callback"]
tid = f"task-{next(counter)}"
task_ids.append(tid)
cb([(tid, "r"), (tid, STOP)])
return tid
with patch("astrai.inference.engine.InferenceScheduler") as MockSched:
instance = MockSched.return_value
def fake_add(prompt, **kw):
cb = kw["stream_callback"]
cb("r")
cb(STOP)
instance.add_task.side_effect = fake_add
instance.remove_task.return_value = []
eng = InferenceEngine(mock_model, mock_tokenizer, max_batch_size=2)
results = eng.generate(["hello", "world"])
assert results == ["r", "r"]
assert task_ids == ["task-0", "task-1"]
def test_engine_generate_zero_max_tokens_returns_empty():
@@ -294,7 +334,7 @@ def test_generate_captures_calling_backend_context():
def fake_add(prompt, **kwargs):
captured.append(kwargs["backend"])
kwargs["stream_callback"](STOP)
kwargs["stream_callback"]([("task", STOP)])
return "task"
instance.add_task.side_effect = fake_add
@@ -328,9 +368,12 @@ def test_build_engine_passes_engine_kwargs_through():
model, _ = make_model("cpu", max_position_embeddings=64)
backend = TorchNativeBackend()
with patch("astrai.inference.engine.InferenceScheduler") as MockSched:
MockSched.return_value.add_task.side_effect = lambda *args, **k: (
k["stream_callback"](STOP) or "task"
)
def fake_add(*args, **k):
k["stream_callback"]([("task", STOP)])
return "task"
MockSched.return_value.add_task.side_effect = fake_add
engine = build_engine(
model=model,
tokenizer=FakeTokenizer(),