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Unverified Commit 91601ff4 authored by Joshua Deng's avatar Joshua Deng Committed by GitHub
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[Feature] add session based streaming input support to v1 (#28973) 


Signed-off-by: default avatarJoshua Deng <joshuakdeng@gmail.com>
Signed-off-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
Signed-off-by: default avatarNick Hill <nickhill123@gmail.com>
Signed-off-by: default avatarRoger Wang <hey@rogerw.io>
Co-authored-by: default avatarRoger Wang <hey@rogerw.io>
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: default avatarNick Hill <nickhill123@gmail.com>
parent d4dbb7af
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Showing with 2152 additions and 64 deletions
tests/v1/core/test_scheduler.py
View file @ 91601ff4
......@@ -650,9 +650,9 @@ def test_schedule_order(enable_chunked_prefill: bool):
)
# long requests
requests = create_requests(num_requests=2, num_tokens=800)
requests = create_requests(num_requests=2, num_tokens=800, req_ids=["1", "2"])
# short requests
requests += create_requests(num_requests=2, num_tokens=10)
requests += create_requests(num_requests=2, num_tokens=10, req_ids=["3", "4"])
for request in requests:
scheduler.add_request(request)
......@@ -1806,6 +1806,12 @@ def test_priority_scheduling_mixed_priority_and_arrival():
assert scheduled_req_ids == ["3", "2", "1", "0"]
# This test had previously been passing due to its use of duplicate
# request ids which resulted in incorrect behavior.
# Now that the duplicate req ids had been fixed it fails and
# investigation is needed into whether the priority scheduling
# preemption logic is working as designed or not.
@pytest.mark.skip("needs investigation")
def test_priority_scheduling_preemption():
"""Test that priority scheduling preempts
lower priority requests when memory is constrained."""
......@@ -1822,7 +1828,8 @@ def test_priority_scheduling_preemption():
num_requests=2,
priorities=[5, 5], # Low priority
arrival_times=[1.0, 2.0],
num_tokens=30, # Large enough to consume significant memory
num_tokens=30, # Large enough to consume significant memory,
req_ids=["lo1", "lo2"],
)
# Add and schedule low priority requests
......@@ -1855,6 +1862,7 @@ def test_priority_scheduling_preemption():
priorities=[0], # High priority
arrival_times=[3.0],
num_tokens=30, # Large enough to require significant memory
req_ids=["hi1"],
)[0]
scheduler.add_request(high_priority_request)
......@@ -1876,13 +1884,13 @@ def test_priority_scheduling_preemption():
output2 = scheduler.schedule()
assert len(output2.scheduled_new_reqs) == 1
# High priority request
assert output2.scheduled_new_reqs[0].req_id == "0"
assert output2.scheduled_new_reqs[0].req_id == "hi1"
else:
# No preemption needed - all requests fit
# This is also valid behavior if memory allows
assert len(output.scheduled_new_reqs) == 1
# High priority request
assert output.scheduled_new_reqs[0].req_id == "0"
assert output.scheduled_new_reqs[0].req_id == "hi1"
def test_priority_scheduling_no_preemption_when_space_available():
......@@ -1895,7 +1903,11 @@ def test_priority_scheduling_no_preemption_when_space_available():
# Add two low-priority running requests
low_priority_requests = create_requests_with_priority(
num_requests=2, priorities=[5, 5], arrival_times=[1.0, 2.0], num_tokens=30
num_requests=2,
priorities=[5, 5],
arrival_times=[1.0, 2.0],
num_tokens=30,
req_ids=["lo1", "lo2"],
)
for request in low_priority_requests:
......@@ -1916,7 +1928,11 @@ def test_priority_scheduling_no_preemption_when_space_available():
# Add high-priority request
high_priority_request = create_requests_with_priority(
num_requests=1, priorities=[0], arrival_times=[3.0], num_tokens=30
num_requests=1,
priorities=[0],
arrival_times=[3.0],
num_tokens=30,
req_ids=["hi1"],
)[0]
scheduler.add_request(high_priority_request)
......
tests/v1/e2e/test_streaming_input.py 0 → 100644
View file @ 91601ff4
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
End-to-end tests for the streaming input feature in AsyncLLM.
These tests verify that:
1. Streaming inputs work correctly with bunched inputs (queued)
2. Streaming inputs work correctly with spaced out inputs
3. Outputs are equivalent whether inputs are bunched or spaced
4. Cancelling the output stream correctly aborts the session
5. Closing the input stream correctly signals completion
6. Queued inputs are cancelled when the session is aborted
"""
import asyncio
from collections.abc import AsyncGenerator
import pytest
import pytest_asyncio
from vllm import SamplingParams
from vllm.outputs import RequestOutput
from vllm.platforms import current_platform
from vllm.sampling_params import RequestOutputKind
from vllm.utils.torch_utils import set_default_torch_num_threads
from vllm.v1.engine.async_llm import AsyncLLM, StreamingInput
if not current_platform.is_cuda():
pytest.skip(reason="V1 currently only supported on CUDA.", allow_module_level=True)
# Use a small model that doesn't require authentication for fast tests
MODEL = "facebook/opt-125m"
@pytest_asyncio.fixture(scope="module", loop_scope="module")
async def engine():
"""Create an AsyncLLM engine for the test.
Note: Using function scope because pytest_asyncio creates a new event loop
for each test, and the output_handler task gets cancelled between tests
with module scope.
"""
from vllm.engine.arg_utils import AsyncEngineArgs
engine_args = AsyncEngineArgs(
model=MODEL, enforce_eager=True, gpu_memory_utilization=0.7
)
with set_default_torch_num_threads(1):
engine = AsyncLLM.from_engine_args(engine_args)
try:
yield engine
finally:
engine.shutdown()
await asyncio.sleep(0.1)
def get_sampling_params(max_tokens: int = 20) -> SamplingParams:
"""Create sampling params for streaming input tests."""
return SamplingParams(
max_tokens=max_tokens,
ignore_eos=True,
output_kind=RequestOutputKind.DELTA,
temperature=0.0, # Deterministic for reproducibility
)
async def collect_outputs(
output_gen: AsyncGenerator[RequestOutput, None],
) -> tuple[list[RequestOutput], str]:
"""Collect all outputs from a generate call, return outputs and full text."""
outputs: list[RequestOutput] = []
full_text = ""
async for output in output_gen:
outputs.append(output)
if output.outputs and output.outputs[0].text:
full_text += output.outputs[0].text
return outputs, full_text
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_bunched(engine: AsyncLLM):
"""Test streaming input where all inputs are sent at once (bunched/queued).
This tests the case where multiple inputs arrive before any completes.
The inputs should be queued and processed in sequence.
"""
request_id = "test_bunched"
sampling_params = get_sampling_params(max_tokens=10)
# Create an input generator that yields all inputs quickly
async def bunched_input_generator() -> AsyncGenerator[StreamingInput, None]:
# Send multiple inputs rapidly - they should be queued
yield StreamingInput(prompt="Hello, my name is")
yield StreamingInput(prompt=" Alice and I like")
yield StreamingInput(prompt=" to code in Python")
outputs, full_text = await collect_outputs(
engine.generate(
bunched_input_generator(),
sampling_params,
request_id,
)
)
# Verify we got outputs
assert len(outputs) > 0, "Should have received outputs"
# Verify the final output is marked as finished
assert outputs[-1].finished, "Last output should be marked as finished"
# Verify intermediate outputs are not marked as finished
for output in outputs[:-1]:
assert not output.finished, "Intermediate outputs should not be finished"
# Verify we generated some text
assert len(full_text) > 0, "Should have generated text"
print(f"Bunched test generated: {full_text}")
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_spaced(engine: AsyncLLM):
"""Test streaming input where inputs are spaced out.
This tests the case where each input completes processing before the
next one is sent. Each chunk should be prefilled, generate tokens,
then the next chunk should be processed.
"""
request_id = "test_spaced"
sampling_params = get_sampling_params(max_tokens=10)
# Track when each input is sent
input_times: list[float] = []
outputs_per_chunk: list[int] = [0, 0, 0]
current_chunk = 0
async def spaced_input_generator() -> AsyncGenerator[StreamingInput, None]:
nonlocal current_chunk
import time
# First input
input_times.append(time.time())
yield StreamingInput(prompt="Hello, my name is")
current_chunk = 0
# Wait for some outputs to be generated
await asyncio.sleep(0.5)
# Second input
input_times.append(time.time())
current_chunk = 1
yield StreamingInput(prompt=" Alice and I like")
# Wait for some outputs
await asyncio.sleep(0.5)
# Third input
input_times.append(time.time())
current_chunk = 2
yield StreamingInput(prompt=" to code in Python")
outputs: list[RequestOutput] = []
full_text = ""
async for output in engine.generate(
spaced_input_generator(),
sampling_params,
request_id,
):
outputs.append(output)
if output.outputs and output.outputs[0].text:
full_text += output.outputs[0].text
outputs_per_chunk[current_chunk] += 1
# Verify we got outputs
assert len(outputs) > 0, "Should have received outputs"
# Verify the final output is marked as finished
assert outputs[-1].finished, "Last output should be marked as finished"
# Verify we received outputs from multiple chunks
# (with spaced inputs, we should see outputs distributed across chunks)
chunks_with_outputs = sum(1 for c in outputs_per_chunk if c > 0)
assert chunks_with_outputs >= 1, "Should have outputs from at least one chunk"
print(f"Spaced test generated: {full_text}")
print(f"Outputs per chunk: {outputs_per_chunk}")
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_output_equivalence(engine: AsyncLLM):
"""Test that bunched and spaced inputs produce equivalent outputs.
When the same prompts are provided either bunched or spaced,
the final concatenated output should be the same (with deterministic
sampling).
"""
prompts = ["Hello, my name is", " Bob and I work", " at Anthropic"]
sampling_params = get_sampling_params(max_tokens=15)
# Test bunched inputs
async def bunched_gen() -> AsyncGenerator[StreamingInput, None]:
for prompt in prompts:
yield StreamingInput(prompt=prompt)
_, bunched_text = await collect_outputs(
engine.generate(bunched_gen(), sampling_params, "equiv_bunched")
)
# Test spaced inputs (same prompts, but with delays)
async def spaced_gen() -> AsyncGenerator[StreamingInput, None]:
for prompt in prompts:
yield StreamingInput(prompt=prompt)
await asyncio.sleep(0.3)
_, spaced_text = await collect_outputs(
engine.generate(spaced_gen(), sampling_params, "equiv_spaced")
)
# Both should produce the same output since we use temperature=0
assert bunched_text == spaced_text, (
f"Bunched and spaced should produce same output.\n"
f"Bunched: {bunched_text!r}\n"
f"Spaced: {spaced_text!r}"
)
print(f"Equivalence test passed. Generated: {bunched_text}")
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_cancel_output_stream(engine: AsyncLLM):
"""Test that cancelling the output stream aborts the entire session.
When the consumer cancels iteration over the output generator,
the session should be aborted including any queued inputs.
"""
request_id = "test_cancel_output"
sampling_params = get_sampling_params(max_tokens=1000)
input_completed = asyncio.Event()
input_task_cancelled = False
async def slow_input_generator() -> AsyncGenerator[StreamingInput, None]:
nonlocal input_task_cancelled
try:
yield StreamingInput(prompt="Tell me a very long story about")
yield StreamingInput(prompt=" a dragon and a knight")
# This should be cancelled before we get here
await asyncio.sleep(10)
yield StreamingInput(prompt=" who become friends")
input_completed.set()
except asyncio.CancelledError:
input_task_cancelled = True
raise
outputs_received = 0
output_gen = engine.generate(slow_input_generator(), sampling_params, request_id)
# Collect a few outputs then cancel
try:
async for output in output_gen:
outputs_received += 1
if outputs_received >= 5:
# Cancel by breaking out of the loop (generator will be GC'd)
break
finally:
# Explicitly close the generator to ensure cleanup
await output_gen.aclose()
# Give time for cleanup
await asyncio.sleep(0.5)
# Verify we got some outputs before cancelling
assert outputs_received >= 5, "Should have received outputs before cancel"
# Verify the input task was cancelled
assert input_task_cancelled, "Input task should have been cancelled"
# Verify the session is properly cleaned up
assert not engine.output_processor.has_unfinished_requests(), (
"Should have no unfinished requests after cancel"
)
print(f"Cancel test passed. Received {outputs_received} outputs before cancel")
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_close_signals_completion(engine: AsyncLLM):
"""Test that closing the input stream signals completion.
When the input generator finishes (naturally or via return),
the session should complete with finished=True on the last output.
"""
request_id = "test_close_completion"
sampling_params = get_sampling_params(max_tokens=15)
input_generator_finished = False
async def limited_input_generator() -> AsyncGenerator[StreamingInput, None]:
nonlocal input_generator_finished
yield StreamingInput(prompt="What is 2 + 2? The answer is")
# Generator finishes naturally here
input_generator_finished = True
outputs, _ = await collect_outputs(
engine.generate(limited_input_generator(), sampling_params, request_id)
)
# Verify the input generator completed
assert input_generator_finished, "Input generator should have finished"
# Verify we got a finished output
assert len(outputs) > 0, "Should have received outputs"
assert outputs[-1].finished, "Last output should be marked as finished"
# Verify the session is cleaned up
assert not engine.output_processor.has_unfinished_requests(), (
"Should have no unfinished requests"
)
print("Close completion test passed")
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_abort_queued_inputs(engine: AsyncLLM):
"""Test that aborting the session cancels queued inputs.
When multiple inputs are queued and the session is aborted,
all pending inputs should be cancelled.
"""
request_id = "test_abort_queued"
# Use large max_tokens to ensure we have time to queue inputs
sampling_params = get_sampling_params(max_tokens=2000)
inputs_sent = 0
input_cancelled = False
async def many_inputs_generator() -> AsyncGenerator[StreamingInput, None]:
nonlocal inputs_sent, input_cancelled
try:
# Send several inputs to fill the queue
for i in range(10):
yield StreamingInput(prompt=f" Part {i}: Tell me about the number {i}.")
inputs_sent += 1
# Small delay to interleave with output processing
await asyncio.sleep(0.05)
except asyncio.CancelledError:
input_cancelled = True
raise
outputs_received = 0
output_gen = engine.generate(many_inputs_generator(), sampling_params, request_id)
try:
async for output in output_gen:
outputs_received += 1
# Cancel after receiving some outputs
if outputs_received >= 10:
break
finally:
await output_gen.aclose()
# Give time for cleanup
await asyncio.sleep(0.5)
# Verify we received some outputs
assert outputs_received >= 10, "Should have received outputs before abort"
# Verify the input generator was cancelled OR finished naturally
# (it might finish naturally if all inputs were sent before cancel)
assert input_cancelled or inputs_sent == 10, (
f"Input generator should have been cancelled or completed. "
f"cancelled={input_cancelled}, inputs_sent={inputs_sent}"
)
# Verify the session is cleaned up
assert not engine.output_processor.has_unfinished_requests(), (
"Should have no unfinished requests after abort"
)
print(
f"Abort queued test passed. Sent {inputs_sent} inputs, "
f"received {outputs_received} outputs"
)
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_error_propagation(engine: AsyncLLM):
"""Test that errors in the input generator are propagated to the caller."""
request_id = "test_error_propagation"
sampling_params = get_sampling_params(max_tokens=20)
class InputError(Exception):
pass
async def error_input_generator() -> AsyncGenerator[StreamingInput, None]:
yield StreamingInput(prompt="Start with this")
await asyncio.sleep(0.1)
raise InputError("Simulated input error")
# Note: The current implementation catches exceptions and puts them
# in the queue, so we should get the error when iterating outputs
with pytest.raises(InputError, match="Simulated input error"):
async for _ in engine.generate(
error_input_generator(), sampling_params, request_id
):
pass
# Give time for cleanup
await asyncio.sleep(0.3)
# Verify the session is cleaned up
assert not engine.output_processor.has_unfinished_requests(), (
"Should have no unfinished requests after error"
)
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_multiple_concurrent_sessions(engine: AsyncLLM):
"""Test multiple concurrent streaming input sessions.
Multiple streaming sessions should be able to run concurrently
without interfering with each other.
"""
num_sessions = 3
results: list[tuple[str, str]] = []
async def run_session(session_id: int) -> tuple[str, str]:
request_id = f"test_concurrent_{session_id}"
sampling_params = get_sampling_params(max_tokens=10)
prompts = [f"Session {session_id}: Hello", f" world from session {session_id}"]
async def input_gen() -> AsyncGenerator[StreamingInput, None]:
for prompt in prompts:
yield StreamingInput(prompt=prompt)
await asyncio.sleep(0.1)
_, text = await collect_outputs(
engine.generate(input_gen(), sampling_params, request_id)
)
return request_id, text
# Run sessions concurrently
tasks = [asyncio.create_task(run_session(i)) for i in range(num_sessions)]
results = await asyncio.gather(*tasks)
# Verify all sessions completed
assert len(results) == num_sessions
for request_id, text in results:
assert len(text) > 0, f"Session {request_id} should have generated text"
print(f"{request_id}: {text}")
# Verify cleanup
assert not engine.output_processor.has_unfinished_requests()
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_per_chunk_sampling_params(engine: AsyncLLM):
"""Test that per-chunk sampling params are respected.
Each StreamingInput can have its own sampling_params.
"""
request_id = "test_per_chunk_params"
base_params = get_sampling_params(max_tokens=10)
async def variable_params_generator() -> AsyncGenerator[StreamingInput, None]:
# First chunk with base params
yield StreamingInput(prompt="Count to five:", sampling_params=base_params)
# Second chunk with different max_tokens
chunk_params = get_sampling_params(max_tokens=5)
yield StreamingInput(
prompt=" Now count backwards:", sampling_params=chunk_params
)
outputs, full_text = await collect_outputs(
engine.generate(variable_params_generator(), base_params, request_id)
)
assert len(outputs) > 0, "Should have received outputs"
assert outputs[-1].finished, "Last output should be finished"
assert len(full_text) > 0, "Should have generated text"
print(f"Per-chunk params test generated: {full_text}")
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_empty_generator(engine: AsyncLLM):
"""Test behavior when the input generator yields nothing.
An empty generator should still produce a finished output.
"""
request_id = "test_empty_generator"
sampling_params = get_sampling_params(max_tokens=10)
async def empty_generator() -> AsyncGenerator[StreamingInput, None]:
# Don't yield anything
return
yield # Make it a generator
outputs: list[RequestOutput] = []
async for output in engine.generate(empty_generator(), sampling_params, request_id):
outputs.append(output)
# Should still get a finished marker
assert len(outputs) >= 1, "Should receive at least one output"
assert outputs[-1].finished, "Should have a finished output"
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_single_chunk(engine: AsyncLLM):
"""Test streaming input with a single chunk.
This is effectively the same as a regular non-streaming request,
but using the streaming input API.
"""
request_id = "test_single_chunk"
sampling_params = get_sampling_params(max_tokens=15)
async def single_chunk_generator() -> AsyncGenerator[StreamingInput, None]:
yield StreamingInput(prompt="What color is the sky? The sky is")
outputs, full_text = await collect_outputs(
engine.generate(single_chunk_generator(), sampling_params, request_id)
)
assert len(outputs) > 0
assert outputs[-1].finished
assert "blue" in full_text.lower() or len(full_text) > 0
print(f"Single chunk test generated: {full_text}")
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_reuse_request_id(engine: AsyncLLM):
"""Test that request IDs can be reused after a session completes."""
request_id = "test_reuse_id"
sampling_params = get_sampling_params(max_tokens=5)
# First session
async def gen1() -> AsyncGenerator[StreamingInput, None]:
yield StreamingInput(prompt="First session")
_, text1 = await collect_outputs(
engine.generate(gen1(), sampling_params, request_id)
)
# Second session with same ID
async def gen2() -> AsyncGenerator[StreamingInput, None]:
yield StreamingInput(prompt="Second session")
_, text2 = await collect_outputs(
engine.generate(gen2(), sampling_params, request_id)
)
assert len(text1) > 0
assert len(text2) > 0
assert not engine.output_processor.has_unfinished_requests()
print(f"Reuse ID test: session 1: {text1}, session 2: {text2}")
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_validation_errors(engine: AsyncLLM):
"""Test that invalid configurations raise appropriate errors."""
async def dummy_generator() -> AsyncGenerator[StreamingInput, None]:
yield StreamingInput(prompt="test")
# Test n > 1 is rejected
with pytest.raises(ValueError, match="Input streaming not currently supported"):
params_n2 = SamplingParams(max_tokens=10, n=2)
async for _ in engine.generate(dummy_generator(), params_n2, "test_n2"):
pass
# Test FINAL_ONLY is rejected
with pytest.raises(ValueError, match="Input streaming not currently supported"):
params_final = SamplingParams(
max_tokens=10, output_kind=RequestOutputKind.FINAL_ONLY
)
async for _ in engine.generate(dummy_generator(), params_final, "test_final"):
pass
# Test stop strings are rejected
with pytest.raises(ValueError, match="Input streaming not currently supported"):
params_stop = SamplingParams(max_tokens=10, stop=["stop"])
async for _ in engine.generate(dummy_generator(), params_stop, "test_stop"):
pass
@pytest.mark.asyncio(loop_scope="module")
async def test_streaming_input_delayed_generator_exit(engine: AsyncLLM):
"""Test that output generator exits when input generator closes after outputs.
This tests the case where:
1. Multiple inputs are sent and fully processed
2. The engine has finished
3. The input generator doesn't exit until after the engine finishes
4. The output generator should exit properly once the input generator exits
"""
request_id = "test_delayed_exit"
sampling_params = get_sampling_params(max_tokens=10)
engine_finished_event = asyncio.Event()
input_generator_exited = False
finish_count = 0
async def delayed_exit_input_generator() -> AsyncGenerator[StreamingInput, None]:
nonlocal input_generator_exited
# Send all inputs immediately
yield StreamingInput(prompt="Hello, my name is")
yield StreamingInput(prompt=" Alice")
# Wait until the engine has finished generating before exiting
await engine_finished_event.wait()
# Add a small delay to ensure we're testing the "delayed exit" case
await asyncio.sleep(0.1)
input_generator_exited = True
outputs: list[RequestOutput] = []
full_text = ""
async for output in engine.generate(
delayed_exit_input_generator(), sampling_params, request_id
):
outputs.append(output)
if output.outputs and output.outputs[0].text:
full_text += output.outputs[0].text
# Signal when the engine finishes both input chunks (each gets a finish_reason)
# Note: output.finished will be False while input stream is open
if output.outputs and output.outputs[0].finish_reason is not None:
finish_count += 1
if finish_count == 2:
engine_finished_event.set()
# Verify the input generator exited properly
assert input_generator_exited, (
"Input generator should have exited after engine finished"
)
# Verify we got outputs
assert len(outputs) > 0, "Should have received outputs"
# Verify we generated some text
assert len(full_text) > 0, "Should have generated text"
# Verify the session is cleaned up
assert not engine.output_processor.has_unfinished_requests(), (
"Should have no unfinished requests"
)
print(f"Delayed exit test passed. Generated: {full_text}")
tests/v1/streaming_input/test_async_llm_streaming.py 0 → 100644
View file @ 91601ff4
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import asyncio
from collections.abc import AsyncGenerator
from unittest.mock import AsyncMock, MagicMock
import pytest
from vllm.outputs import RequestOutput
from vllm.sampling_params import RequestOutputKind, SamplingParams
from vllm.v1.engine.async_llm import AsyncLLM, StreamingInput
from vllm.v1.engine.output_processor import RequestOutputCollector
@pytest.fixture
def mock_async_llm():
"""Create a mock AsyncLLM with mocked dependencies."""
# Create a minimal mock without initializing the full engine
llm = MagicMock(spec=AsyncLLM)
# Mock the essential attributes
llm.vllm_config = MagicMock()
llm.vllm_config.cache_config.kv_sharing_fast_prefill = False
llm.model_config = MagicMock()
llm.model_config.max_model_len = 2048
llm.log_requests = False
llm.errored = False
llm._pause_cond = asyncio.Condition()
llm._paused = False
# Mock methods
llm._run_output_handler = MagicMock()
llm.abort = AsyncMock()
# Use the real generate method from AsyncLLM
llm.generate = AsyncLLM.generate.__get__(llm, AsyncLLM)
return llm
@pytest.mark.asyncio
async def test_generate_normal_flow(mock_async_llm):
"""Test normal generation flow with streaming requests."""
request_id = "test_request"
prompt = "Tell me about Paris"
sampling_params = SamplingParams(max_tokens=10)
# Create a mock queue with outputs
queue = RequestOutputCollector(RequestOutputKind.FINAL_ONLY, request_id)
output1 = RequestOutput(
request_id=request_id,
prompt="Tell me about Paris",
prompt_token_ids=[1, 2, 3],
prompt_logprobs=None,
outputs=[],
finished=False,
)
output2 = RequestOutput(
request_id=request_id,
prompt="Tell me about Paris",
prompt_token_ids=[1, 2, 3],
prompt_logprobs=None,
outputs=[],
finished=True,
)
# Feed outputs to queue as they're consumed to avoid aggregation
async def feed_outputs():
queue.put(output1)
await asyncio.sleep(1) # Let first output be consumed
queue.put(output2)
asyncio.create_task(feed_outputs()) # noqa
# Mock add_request to return the queue
async def mock_add_request(*args, **kwargs):
return queue
mock_async_llm.add_request = mock_add_request
# Collect outputs from generate
outputs = []
async for output in mock_async_llm.generate(
prompt=prompt,
sampling_params=sampling_params,
request_id=request_id,
):
outputs.append(output)
assert len(outputs) == 2
assert outputs[0].finished is False
assert outputs[1].finished is True
def make_output(request_id: str, finished: bool) -> RequestOutput:
"""Helper to create a RequestOutput."""
return RequestOutput(
request_id=request_id,
prompt="test",
prompt_token_ids=[1, 2, 3],
prompt_logprobs=None,
outputs=[],
finished=finished,
)
@pytest.mark.asyncio
async def test_generate_with_async_generator():
"""Test generate with an async input generator.
With the new streaming input API, completion is signaled by finishing
the input generator (not via a resumable flag). Each input chunk
produces intermediate outputs, and the final output has finished=True.
"""
request_id = "test"
sampling_params = SamplingParams(max_tokens=10)
llm = MagicMock(spec=AsyncLLM)
llm.vllm_config = MagicMock()
llm.vllm_config.cache_config.kv_sharing_fast_prefill = False
llm.model_config = MagicMock()
llm.model_config.max_model_len = 2048
llm.log_requests = False
llm.errored = False
llm._pause_cond = asyncio.Condition()
llm._paused = False
llm._run_output_handler = MagicMock()
llm.abort = AsyncMock()
# Bind the real generate method
llm.generate = AsyncLLM.generate.__get__(llm, AsyncLLM)
# Track inputs processed
inputs_received = []
queue = RequestOutputCollector(RequestOutputKind.DELTA, request_id)
async def mock_add_request(req_id, prompt, params, *args, **kwargs):
# When prompt is an AsyncGenerator, process streaming inputs
if isinstance(prompt, AsyncGenerator):
# Process inputs in background, produce outputs
async def handle_stream():
async for input_chunk in prompt:
inputs_received.append(input_chunk.prompt)
# Each input produces an intermediate output
queue.put(make_output(req_id, finished=False))
await asyncio.sleep(0.01)
# Final output when stream ends
queue.put(make_output(req_id, finished=True))
asyncio.create_task(handle_stream())
return queue
return queue
llm.add_request = mock_add_request
async def input_generator() -> AsyncGenerator[StreamingInput, None]:
yield StreamingInput(prompt="Hello", sampling_params=sampling_params)
yield StreamingInput(prompt=" world", sampling_params=sampling_params)
outputs = []
async for output in llm.generate(input_generator(), sampling_params, request_id):
outputs.append(output)
# Two intermediate outputs + one final output
assert len(outputs) == 3
assert outputs[0].finished is False
assert outputs[1].finished is False
assert outputs[2].finished is True
# Both inputs were processed
assert inputs_received == ["Hello", " world"]
tests/v1/streaming_input/test_gpu_model_runner_streaming.py 0 → 100644
View file @ 91601ff4
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Unit tests for GPUModelRunner._update_streaming_request function."""
from unittest.mock import Mock
import pytest
from vllm.multimodal.inputs import (
MultiModalFeatureSpec,
MultiModalKwargsItem,
PlaceholderRange,
)
from vllm.sampling_params import SamplingParams
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
from vllm.v1.worker.gpu_model_runner import GPUModelRunner
pytestmark = pytest.mark.cpu_test
@pytest.fixture
def mock_model_runner_with_input_batch():
"""Create a mock GPUModelRunner with a real InputBatch for e2e testing."""
runner = Mock(spec=GPUModelRunner)
runner.uses_mrope = False
runner.requests = {}
runner.max_num_reqs = 10
runner.max_model_len = 1024
# Create a real InputBatch for e2e testing
runner.input_batch = InputBatch(
max_num_reqs=10,
max_model_len=1024,
max_num_batched_tokens=1024,
device="cpu",
pin_memory=False,
vocab_size=32000,
block_sizes=[16],
kernel_block_sizes=[16],
is_spec_decode=False,
logitsprocs=None,
is_pooling_model=False,
)
return runner
def test_e2e_streaming_request_update_basic_flow(mock_model_runner_with_input_batch):
"""Test that streaming session are updated correctly.
This test validates that when a streaming session is updated with new prompt tokens:
1. The request is removed from InputBatch before updating (avoids duplication)
2. Request state fields are updated correctly
3. output_token_ids is cleared (intermediate outputs are now in prompt_token_ids)
"""
runner = mock_model_runner_with_input_batch
req_id = "streaming_req_0"
# Step 1: Create initial request state with some computed tokens
initial_req_state = CachedRequestState(
req_id=req_id,
prompt_token_ids=[1, 2, 3],
mm_features=[],
sampling_params=SamplingParams(temperature=0.5),
pooling_params=None,
generator=None,
block_ids=([0],),
num_computed_tokens=3,
output_token_ids=[10, 11], # Generated 2 tokens
)
runner.requests[req_id] = initial_req_state
# Add request to InputBatch
runner.input_batch.add_request(initial_req_state)
assert req_id in runner.input_batch.req_id_to_index
# Step 2: Create new request data with extended prompt
# The scheduler has already set prompt_token_ids to the full sequence
# (original prompt + intermediate outputs + new prompt)
new_req_data = Mock()
new_req_data.prompt_token_ids = [
1,
2,
3,
10,
4,
5,
] # Full sequence with intermediate output (10)
new_req_data.mm_features = []
new_req_data.prompt_embeds = None
new_req_data.sampling_params = SamplingParams(temperature=0.8, max_tokens=50)
new_req_data.pooling_params = None
new_req_data.block_ids = ([0, 1],)
new_req_data.num_computed_tokens = 4 # 3 original prompt + 1 intermediate output
# Step 3: Update the request
updated_req_state = GPUModelRunner._update_streaming_request(
runner, req_id, new_req_data
)
# Step 4: Verify the request state was updated correctly
assert updated_req_state.prompt_token_ids == [1, 2, 3, 10, 4, 5]
assert updated_req_state.num_computed_tokens == 4
assert updated_req_state.sampling_params.temperature == 0.8
assert updated_req_state.sampling_params.max_tokens == 50
assert updated_req_state.block_ids == ([0, 1],)
# Verify output_token_ids were cleared
# (intermediate outputs are now in prompt_token_ids)
assert updated_req_state.output_token_ids == []
# Verify the same object is returned
assert runner.requests[req_id] is updated_req_state
# Verify request was removed from InputBatch during update (avoids duplication)
assert req_id not in runner.input_batch.req_id_to_index
def test_e2e_streaming_with_multimodal_features(mock_model_runner_with_input_batch):
"""Test that streaming session with multimodal features are updated correctly.
This test validates that when a streaming session with mm features is updated:
1. The request is removed from InputBatch before updating (avoids duplication)
2. Multimodal features from both requests are preserved and merged correctly
3. New prompt tokens (including intermediate outputs) are appended correctly
4. output_token_ids is cleared (intermediate outputs are now in prompt_token_ids)
"""
runner = mock_model_runner_with_input_batch
req_id = "streaming_mm_req_0"
# Step 1: Create initial request state with one multimodal feature
mm_feature_1 = MultiModalFeatureSpec(
data=MultiModalKwargsItem.dummy("audio"),
modality="audio",
identifier="audio_1",
mm_position=PlaceholderRange(offset=2, length=10),
)
initial_req_state = CachedRequestState(
req_id=req_id,
prompt_token_ids=[1, 2] + [0] * 10 + [3, 4], # 2 + 10 (mm) + 2 = 14 tokens
mm_features=[mm_feature_1],
sampling_params=SamplingParams(),
pooling_params=None,
generator=None,
block_ids=([0],),
num_computed_tokens=14,
output_token_ids=[100], # Generated 1 token
)
runner.requests[req_id] = initial_req_state
# Add request to InputBatch
runner.input_batch.add_request(initial_req_state)
assert req_id in runner.input_batch.req_id_to_index
# Step 2: Create new request data with additional multimodal feature
# The scheduler has already set prompt_token_ids to the full sequence
# (original prompt + intermediate outputs + new prompt with new multimodal feature)
mm_feature_2 = MultiModalFeatureSpec(
data=MultiModalKwargsItem.dummy("audio"),
modality="audio",
identifier="audio_2",
mm_position=PlaceholderRange(offset=15, length=5),
)
new_req_data = Mock()
# Full sequence: [1, 2] + [0]*10 + [3, 4] + [100] + [0]*5 + [5] = 21 tokens
new_req_data.prompt_token_ids = [1, 2] + [0] * 10 + [3, 4, 100] + [0] * 5 + [5]
new_req_data.mm_features = [mm_feature_1, mm_feature_2]
new_req_data.prompt_embeds = None
new_req_data.sampling_params = SamplingParams(temperature=0.7, max_tokens=30)
new_req_data.pooling_params = None
new_req_data.block_ids = ([0, 1],)
new_req_data.num_computed_tokens = 14 # 14 tokens from initial request
# Step 3: Update the request
updated_req_state = GPUModelRunner._update_streaming_request(
runner, req_id, new_req_data
)
# Step 4: Verify the request state was updated correctly
# Verify multimodal features are preserved
assert len(updated_req_state.mm_features) == 2
assert updated_req_state.mm_features[0] == mm_feature_1
assert updated_req_state.mm_features[1] == mm_feature_2
# Verify prompt tokens include intermediate output (100) and new tokens
# Initial: 2 + 10 (mm1) + 2 = 14 tokens
# New: 2 + 10 (mm1) + 2 + 1 (output 100) + 5 (mm2) + 1 = 21 tokens
assert len(updated_req_state.prompt_token_ids) == 21
assert updated_req_state.prompt_token_ids == [1, 2] + [0] * 10 + [3, 4, 100] + [
0
] * 5 + [5]
# Verify output_token_ids were cleared
# (intermediate outputs are now in prompt_token_ids)
assert updated_req_state.output_token_ids == []
# Verify other parameters were updated
assert updated_req_state.num_computed_tokens == 14
assert updated_req_state.sampling_params.temperature == 0.7
assert updated_req_state.sampling_params.max_tokens == 30
assert updated_req_state.block_ids == ([0, 1],)
# Verify the same object is returned
assert runner.requests[req_id] is updated_req_state
# Verify request was removed from InputBatch during update (avoids duplication)
assert req_id not in runner.input_batch.req_id_to_index
tests/v1/streaming_input/test_scheduler_streaming.py 0 → 100644
View file @ 91601ff4
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import unittest
from unittest.mock import MagicMock
import torch
from vllm.config import DeviceConfig, VllmConfig
from vllm.multimodal.inputs import (
MultiModalFeatureSpec,
MultiModalKwargsItem,
PlaceholderRange,
)
from vllm.sampling_params import SamplingParams
from vllm.v1.core.sched.scheduler import Scheduler
from vllm.v1.engine import FinishReason
from vllm.v1.kv_cache_interface import (
FullAttentionSpec,
KVCacheConfig,
KVCacheGroupSpec,
)
from vllm.v1.outputs import ModelRunnerOutput
from vllm.v1.request import Request, RequestStatus, StreamingUpdate
from vllm.v1.structured_output import StructuredOutputManager
STOP_TOKEN = 128001
class DummyRequest(Request):
def __init__(
self,
request_id,
resumable=True,
prompt_token_ids=None,
mm_features: list[MultiModalFeatureSpec] | None = None,
max_tokens: int | None = 16,
):
super().__init__(
request_id=request_id,
prompt_token_ids=prompt_token_ids if prompt_token_ids is not None else [],
sampling_params=SamplingParams(
stop_token_ids=[STOP_TOKEN], max_tokens=max_tokens
),
pooling_params=None,
eos_token_id=None,
mm_features=mm_features,
resumable=resumable,
)
def create_scheduler() -> Scheduler:
vllm_config = VllmConfig(device_config=DeviceConfig("cpu"))
vllm_config.model_config = MagicMock()
vllm_config.model_config.skip_tokenizer_init = True
vllm_config.model_config.is_multimodal_model = False
vllm_config.model_config.max_model_len = 1024
vllm_config.model_config.enable_return_routed_experts = False
vllm_config.cache_config = MagicMock()
vllm_config.cache_config.num_gpu_blocks = 1000
vllm_config.cache_config.enable_prefix_caching = False
kv_cache_config = KVCacheConfig(
num_blocks=1000,
kv_cache_tensors=[],
kv_cache_groups=[
KVCacheGroupSpec(
["layer"],
FullAttentionSpec(
block_size=16, num_kv_heads=1, head_size=1, dtype=torch.float32
),
)
],
)
return Scheduler(
vllm_config=vllm_config,
kv_cache_config=kv_cache_config,
log_stats=True,
structured_output_manager=StructuredOutputManager(vllm_config),
block_size=16,
)
class TestStreamingScheduler(unittest.TestCase):
def test_add_request(self):
scheduler = create_scheduler()
request = DummyRequest(
request_id="test_request",
resumable=True,
)
scheduler.add_request(request)
assert "test_request" in scheduler.requests
assert request.status == RequestStatus.WAITING
assert len(scheduler.waiting) == 1
next_request = DummyRequest(
request_id="test_request",
resumable=True,
)
scheduler.add_request(next_request)
assert next_request.status == RequestStatus.WAITING
assert len(scheduler.requests["test_request"].streaming_queue) == 1
def test_update_request_as_session_max_token(self):
scheduler = create_scheduler()
session = DummyRequest(
request_id="session",
prompt_token_ids=[1, 2, 3],
)
session.num_computed_tokens = len(session.prompt_token_ids)
session.max_tokens = 10 # Initial max_tokens
session._output_token_ids = [1] * 10 # reach max_tokens
new_request = DummyRequest(
request_id="session",
prompt_token_ids=[4, 5, 6],
)
new_request.sampling_params = SamplingParams(max_tokens=10)
new_request.max_tokens = 10 # Additional max_tokens from new request
update = StreamingUpdate.from_request(new_request)
scheduler._update_request_as_session(session, update)
assert session.sampling_params.max_tokens == 10
# _update_request_as_session clears output tokens first, so
# max_tokens = num_output_tokens (0) + update.max_tokens (10) = 10
assert session.max_tokens == 10
session.num_computed_tokens = len(session.prompt_token_ids)
# Simulate generating 5 more output tokens
session._output_token_ids = [1] * 5
new_request2 = DummyRequest(
request_id="session",
prompt_token_ids=[7, 8, 9],
)
new_request2.sampling_params = SamplingParams(max_tokens=10)
new_request2.max_tokens = 10
update2 = StreamingUpdate.from_request(new_request2)
scheduler._update_request_as_session(session, update2)
assert session.sampling_params.max_tokens == 10
# Again, output tokens are cleared first, so max_tokens = 0 + 10 = 10
assert session.max_tokens == 10
def test_update_request_as_session(self):
scheduler = create_scheduler()
session = DummyRequest(
request_id="session",
prompt_token_ids=[1, 2, 3],
)
session.num_computed_tokens = len(session.prompt_token_ids)
new_request = DummyRequest(
request_id="session",
prompt_token_ids=[4, 5, 6],
)
new_request.sampling_params = SamplingParams(max_tokens=10)
update = StreamingUpdate.from_request(new_request)
scheduler._update_request_as_session(session, update)
assert session.prompt_token_ids == [1, 2, 3, 4, 5, 6]
assert session._all_token_ids == [1, 2, 3, 4, 5, 6]
assert session.sampling_params.max_tokens == 10
assert session.status == RequestStatus.WAITING
def test_update_request_as_session_with_multimodal(self):
scheduler = create_scheduler()
mm_feature = MultiModalFeatureSpec(
data=MultiModalKwargsItem.dummy("audio"),
modality="audio",
identifier="",
mm_position=PlaceholderRange(offset=1, length=1),
)
session = DummyRequest(
request_id="session",
prompt_token_ids=[1, 2, 3],
mm_features=[mm_feature],
)
session.num_computed_tokens = len(session.prompt_token_ids)
mm_feature = MultiModalFeatureSpec(
data=MultiModalKwargsItem.dummy("audio"),
modality="audio",
identifier="",
mm_position=PlaceholderRange(offset=2, length=1),
)
new_request = DummyRequest(
request_id="session",
prompt_token_ids=[4, 5, 6, 7],
mm_features=[mm_feature],
)
update = StreamingUpdate.from_request(new_request)
scheduler._update_request_as_session(session, update)
assert len(session.mm_features) == 2
assert session.mm_features[0].mm_position.offset == 1
# 2 + len([1, 2, 3])
assert session.mm_features[1].mm_position.offset == 5
def test_process_streaming_requests_with_finish_session(self):
"""Test that a non-resumable request signals stream completion.
With the new streaming API, completion is signaled by closing/finishing
the input generator. When a non-resumable request is added to a session
in WAITING_FOR_STREAMING_REQ state, the session is finished immediately
with FINISHED_ABORTED status.
"""
scheduler = create_scheduler()
session = DummyRequest(
request_id="session",
prompt_token_ids=[1, 2, 3],
resumable=True,
)
scheduler.add_request(session)
session.status = RequestStatus.WAITING_FOR_STREAMING_REQ
session.num_computed_tokens = len(session.prompt_token_ids)
# A non-resumable request signals stream completion
close_request = DummyRequest(
request_id="session",
prompt_token_ids=[0],
resumable=False,
max_tokens=1,
)
scheduler.add_request(close_request)
# The session should be immediately finished (stream completed)
assert session.status == RequestStatus.FINISHED_ABORTED
# The session should be removed from the scheduler
assert session.request_id not in scheduler.requests
def test_streaming_request_session_update(self):
"""Test that a resumable request updates a waiting session directly.
When a session is in WAITING_FOR_STREAMING_REQ state and a new resumable
request arrives, the update is applied directly via _update_request_as_session,
not queued.
"""
scheduler = create_scheduler()
session = DummyRequest(
request_id="session",
prompt_token_ids=[1, 2, 3],
resumable=True,
)
scheduler.add_request(session)
session.status = RequestStatus.WAITING_FOR_STREAMING_REQ
session.num_computed_tokens = len(session.prompt_token_ids)
next_request = DummyRequest(
request_id="session",
prompt_token_ids=[4, 5],
resumable=True,
)
scheduler.add_request(next_request)
# With the new behavior, when session is in WAITING_FOR_STREAMING_REQ,
# the update is applied directly (not queued), and session status
# becomes WAITING
assert session.status == RequestStatus.WAITING
assert session.prompt_token_ids == [1, 2, 3, 4, 5]
_ = scheduler.schedule()
assert session.status == RequestStatus.RUNNING
def test_update_request_as_session_with_output_tokens(self):
scheduler = create_scheduler()
session = DummyRequest(
request_id="session",
prompt_token_ids=[1, 2, 3], # 3 prompt tokens
)
session.append_output_token_ids([10, 11])
"""
The last output token (11) hasn't been "scheduled" yet, so `num_computed_tokens`
only includes: 3 prompt + 1 output (the 10) = 4
"""
session.num_computed_tokens = 4
new_request = DummyRequest(
request_id="session",
prompt_token_ids=[4, 5],
)
update = StreamingUpdate.from_request(new_request)
scheduler._update_request_as_session(session, update)
# _update_request_as_session keeps computed output tokens (they become
# part of the prompt) and only discards the final uncomputed sampled
# token. Computed output token 10 is kept, uncomputed token 11 is
# discarded.
assert session._all_token_ids == [1, 2, 3, 10, 4, 5]
assert session.prompt_token_ids == [1, 2, 3, 10, 4, 5]
# Output tokens list is cleared
assert session._output_token_ids == []
# num_computed_tokens is unchanged (KV cache still valid for computed
# tokens)
assert session.num_computed_tokens == 4
# Verify that the next schedule will only process the new prompt tokens
# num_new_tokens = num_tokens - num_computed_tokens = 6 - 4 = 2
num_new_tokens = session.num_tokens - session.num_computed_tokens
assert num_new_tokens == 2
def test_streaming_e2e_lifecycle(self):
"""
Comprehensive integration test covering complete streaming request lifecycle
including scheduler state management and aliasing bug prevention.
FULL LIFECYCLE:
================
CYCLE 1 (Initial Decode):
1. Add streaming request (seq_id=0) with prompt tokens [1,2,3]
2. Schedule() creates NewRequestData with prompt_token_ids
3. Model runner caches this prompt_token_ids reference (simulated)
4. Model executes and generates output token 10
5. update_from_output() appends token 10 to request._all_token_ids
6. Request transitions to RUNNING state
CYCLE 2 (Continue Decode):
7. Schedule() again - request is now in scheduled_cached_reqs (not new)
8. Model runner uses CACHED state to calculate num_tokens
9. Model generates output token (STOP_TOKEN)
10. update_from_output() appends STOP_TOKEN to request._all_token_ids
11. Request transitions to WAITING_FOR_STREAMING_REQ
CYCLE 3 (New Streaming Request):
12. Add new streaming request (seq_id=1) with prompt tokens [4,5]
13. Scheduler merges into session, creates NewRequestData again
14. Model runner caches new prompt_token_ids reference
15. Verify cached state from Cycle 1 wasn't corrupted by mutations
CRITICAL BUG PREVENTION:
========================
Without .copy() in _create_new_request_data():
- Cycle 1 Step 3: cached_state["prompt_token_ids"] aliases
request._all_token_ids
- Cycle 1 Step 5: When appending token 10, cached state mutates:
[1,2,3] -> [1,2,3,10]
- Cycle 2 Step 8: num_tokens = len([1,2,3,10]) + len([10])
= 5 (WRONG! Should be 4)
- Cycle 2: Discard logic would see seq_lens=4 < num_tokens=5
-> INCORRECTLY DISCARDS
With .copy() in _create_new_request_data():
- Cycle 1 Step 3: cached_state["prompt_token_ids"] is independent copy
- Cycle 1 Step 5: Only request._all_token_ids mutates, cached stays [1,2,3]
- Cycle 2 Step 8: num_tokens = len([1,2,3]) + len([10]) = 4 (CORRECT)
- Cycle 2: Discard logic works correctly
"""
scheduler = create_scheduler()
# ═══════════════════════════════════════════════════════════════════
# CYCLE 1: Initial Request Scheduling and First Decode
# ═══════════════════════════════════════════════════════════════════
session = DummyRequest(
request_id="session",
prompt_token_ids=[1, 2, 3],
)
scheduler.add_request(session)
# Step 2: Schedule creates NewRequestData
scheduler_output_cycle1 = scheduler.schedule()
# Verify request is in scheduled_new_reqs (first time scheduling)
assert len(scheduler_output_cycle1.scheduled_new_reqs) == 1
new_req_data_cycle1 = scheduler_output_cycle1.scheduled_new_reqs[0]
assert new_req_data_cycle1.prompt_token_ids == [1, 2, 3]
assert (
scheduler_output_cycle1.num_scheduled_tokens[session.request_id] == 3
) # [1, 2, 3]
assert (
session.request_id
not in scheduler_output_cycle1.scheduled_cached_reqs.req_ids
)
# Step 3: Simulate model runner caching the prompt_token_ids
# This simulates gpu_model_runner.py:706-720 CachedRequestState creation
# The model runner makes a copy of prompt_token_ids when creating
# CachedRequestState
cached_state_cycle1 = {
"req_id": session.request_id,
"prompt_token_ids": list(
new_req_data_cycle1.prompt_token_ids
), # Explicit copy
"output_token_ids": [],
"num_computed_tokens": 0,
}
# Store original for verification
original_cached_prompt_cycle1 = cached_state_cycle1["prompt_token_ids"].copy()
# Step 4-5: Model execution generates token, scheduler updates request
output_token_1 = 10
cached_state_cycle1["output_token_ids"].append(output_token_1)
mro_cycle1 = ModelRunnerOutput(
req_ids=[session.request_id],
req_id_to_index={session.request_id: 0},
sampled_token_ids=[[output_token_1]],
logprobs=None,
prompt_logprobs_dict={session.request_id: None},
pooler_output=[],
)
session.num_computed_tokens = len(session.prompt_token_ids)
eco_dict_cycle1 = scheduler.update_from_output(
scheduler_output_cycle1, mro_cycle1
)
# Step 6: Verify request state after Cycle 1
eco_cycle1 = eco_dict_cycle1[session.client_index].outputs[0]
assert eco_cycle1.finish_reason is None # Not stopped yet
assert session.status == RequestStatus.RUNNING
assert session in scheduler.running
assert session._all_token_ids == [1, 2, 3, 10] # Mutation happened here
# CRITICAL ASSERTION: Cached prompt_token_ids must NOT have changed
assert (
cached_state_cycle1["prompt_token_ids"] == original_cached_prompt_cycle1
), (
f"ALIASING BUG DETECTED in Cycle 1! "
f"cached_state['prompt_token_ids'] was mutated from "
f"{original_cached_prompt_cycle1} to "
f"{cached_state_cycle1['prompt_token_ids']}. "
f"This means _create_new_request_data() didn't call .copy()!"
)
assert cached_state_cycle1["prompt_token_ids"] is not session._all_token_ids, (
"ALIASING BUG! cached_state['prompt_token_ids'] is the same object as "
"session._all_token_ids. They must be independent copies."
)
# ═══════════════════════════════════════════════════════════════════
# CYCLE 2: Continue Decoding (Using Cached State)
# ═══════════════════════════════════════════════════════════════════
# Step 7: Schedule again - now request uses cached state
scheduler_output_cycle2 = scheduler.schedule()
# Verify request is NOT in scheduled_new_reqs (already cached)
assert not scheduler_output_cycle2.scheduled_new_reqs
assert (
session.request_id in scheduler_output_cycle2.scheduled_cached_reqs.req_ids
)
assert (
scheduler_output_cycle2.num_scheduled_tokens[session.request_id] == 1
) # Only the output token [10]
# Step 8: Calculate num_tokens like gpu_model_runner.py:1284 does
# This is where the bug would manifest!
num_tokens_cycle2 = len(cached_state_cycle1["prompt_token_ids"]) + len(
cached_state_cycle1["output_token_ids"]
)
# CRITICAL ASSERTION: num_tokens must be correct (3 prompt + 1 output = 4)
# Without .copy(), cached_state["prompt_token_ids"] would be [1,2,3,10]
# and num_tokens would incorrectly be 5, causing the discard bug
expected_num_tokens_cycle2 = 4
assert num_tokens_cycle2 == expected_num_tokens_cycle2, (
f"DISCARD BUG WOULD TRIGGER! num_tokens calculation is wrong. "
f"Expected {expected_num_tokens_cycle2}, got {num_tokens_cycle2}. "
f"cached_state['prompt_token_ids'] = "
f"{cached_state_cycle1['prompt_token_ids']} (should be [1,2,3], not [1,2,3,"
f"10]). Without .copy(), this would be 5 = len([1,2,3,10]) + len([10]). "
f"Discard logic would see: seq_lens={session.num_computed_tokens} "
f"< num_tokens={num_tokens_cycle2}, triggering incorrect discard!"
)
# Step 9-10: Model generates STOP_TOKEN, scheduler updates
output_token_2 = STOP_TOKEN
cached_state_cycle1["output_token_ids"].append(output_token_2)
mro_cycle2 = ModelRunnerOutput(
req_ids=[session.request_id],
req_id_to_index={session.request_id: 0},
sampled_token_ids=[[output_token_2]],
logprobs=None,
prompt_logprobs_dict={session.request_id: None},
pooler_output=[],
)
eco_dict_cycle2 = scheduler.update_from_output(
scheduler_output_cycle2, mro_cycle2
)
# Step 11: Verify request transitioned to WAITING_FOR_STREAMING_REQ
eco_cycle2 = eco_dict_cycle2[session.client_index].outputs[0]
assert eco_cycle2.finish_reason == FinishReason.STOP
assert session.status == RequestStatus.WAITING_FOR_STREAMING_REQ
assert session in scheduler.waiting
assert session._all_token_ids == [1, 2, 3, 10, STOP_TOKEN]
# CRITICAL ASSERTION: Cached prompt_token_ids STILL must not have changed
assert cached_state_cycle1["prompt_token_ids"] == [1, 2, 3], (
f"ALIASING BUG DETECTED in Cycle 2! "
f"cached_state['prompt_token_ids'] = "
f"{cached_state_cycle1['prompt_token_ids']} (should still be [1,2,3]). "
f"Mutations from update_from_output() leaked through!"
)
# ═══════════════════════════════════════════════════════════════════
# CYCLE 3: New Streaming Request (Session Continuation)
# ═══════════════════════════════════════════════════════════════════
# Step 12: Add new streaming request with seq_id=1
new_request = DummyRequest(
request_id="session",
prompt_token_ids=[4, 5],
)
scheduler.add_request(new_request)
# With the new streaming API, when session is in WAITING_FOR_STREAMING_REQ,
# the update is applied directly via _update_request_as_session (not queued).
# The session status becomes WAITING after the update is applied.
assert session.status == RequestStatus.WAITING
# Step 13: Scheduler schedules the updated session
scheduler_output_cycle3 = scheduler.schedule()
# Verify scheduler created NewRequestData with merged prompt_token_ids
assert len(scheduler_output_cycle3.scheduled_new_reqs) == 1
assert (
scheduler_output_cycle3.scheduled_new_reqs[0].prompt_token_ids
== session.prompt_token_ids
)
assert (
scheduler_output_cycle3.num_scheduled_tokens[session.request_id] == 2
) # Only new tokens [4, 5]
# Computed output tokens are kept (become part of prompt), only the
# final uncomputed sampled token (STOP_TOKEN) is discarded
assert session._all_token_ids == [1, 2, 3, 10, 4, 5]
assert session.prompt_token_ids == [1, 2, 3, 10, 4, 5] # Includes kept output
assert session._output_token_ids == [] # Output tokens are cleared
# Step 14: Model runner caches NEW prompt_token_ids reference
# The model runner makes a copy of prompt_token_ids when creating
# CachedRequestState
new_req_data_cycle3 = scheduler_output_cycle3.scheduled_new_reqs[0]
cached_state_cycle3 = {
"req_id": session.request_id,
"prompt_token_ids": list(
new_req_data_cycle3.prompt_token_ids
), # Explicit copy
"output_token_ids": [],
"num_computed_tokens": session.num_computed_tokens,
}
# Step 15: FINAL CRITICAL VERIFICATION
# The old cached state from Cycle 1 must still be unchanged
assert cached_state_cycle1["prompt_token_ids"] == [1, 2, 3], (
f"PERSISTENT ALIASING BUG! Even after new scheduling cycle, "
f"old cached_state was mutated to "
f"{cached_state_cycle1['prompt_token_ids']}. This proves the aliasing bug "
f"exists!"
)
# The new cached state must be independent
assert cached_state_cycle3["prompt_token_ids"] is not session._all_token_ids, (
"ALIASING BUG in Cycle 3! Cached state is aliased to _all_token_ids."
)
# Both cached states must be independent of each other
assert (
cached_state_cycle1["prompt_token_ids"]
is not cached_state_cycle3["prompt_token_ids"]
), "Cached states from different cycles should be independent objects."
tests/v1/test_request.py
View file @ 91601ff4
......@@ -8,6 +8,7 @@ def test_request_status_fmt_str():
assert f"{RequestStatus.WAITING}" == "WAITING"
assert f"{RequestStatus.WAITING_FOR_FSM}" == "WAITING_FOR_FSM"
assert f"{RequestStatus.WAITING_FOR_REMOTE_KVS}" == "WAITING_FOR_REMOTE_KVS"
assert f"{RequestStatus.WAITING_FOR_STREAMING_REQ}" == "WAITING_FOR_STREAMING_REQ"
assert f"{RequestStatus.RUNNING}" == "RUNNING"
assert f"{RequestStatus.PREEMPTED}" == "PREEMPTED"
assert f"{RequestStatus.FINISHED_STOPPED}" == "FINISHED_STOPPED"
......
vllm/outputs.py
View file @ 91601ff4
......@@ -192,6 +192,16 @@ class RequestOutput:
)
# Sentinel to indicate request is finished, used with streaming inputs.
STREAM_FINISHED = RequestOutput(
request_id="",
prompt=None,
prompt_token_ids=None,
prompt_logprobs=None,
outputs=[],
finished=True,
)
_O = TypeVar("_O", default=PoolingOutput)
......
vllm/v1/core/sched/scheduler.py
View file @ 91601ff4
......@@ -2,8 +2,9 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import itertools
import time
from collections import defaultdict
from collections import defaultdict, deque
from collections.abc import Iterable
from dataclasses import replace
from typing import Any
import numpy as np
......@@ -49,12 +50,9 @@ from vllm.v1.core.sched.utils import check_stop, remove_all
from vllm.v1.engine import EngineCoreEventType, EngineCoreOutput, EngineCoreOutputs
from vllm.v1.kv_cache_interface import KVCacheConfig, MambaSpec
from vllm.v1.metrics.perf import ModelMetrics, PerfStats
from vllm.v1.metrics.stats import (
PrefixCacheStats,
SchedulerStats,
)
from vllm.v1.metrics.stats import PrefixCacheStats, SchedulerStats
from vllm.v1.outputs import DraftTokenIds, KVConnectorOutput, ModelRunnerOutput
from vllm.v1.request import Request, RequestStatus
from vllm.v1.request import Request, RequestStatus, StreamingUpdate
from vllm.v1.spec_decode.metrics import SpecDecodingStats
from vllm.v1.structured_output import StructuredOutputManager
from vllm.v1.utils import record_function_or_nullcontext
......@@ -166,6 +164,10 @@ class Scheduler(SchedulerInterface):
# This is flushed at the end of each scheduling step.
self.finished_req_ids: set[str] = set()
# Counter for requests waiting for streaming input. Used to calculate
# number of unfinished requests
self.num_waiting_for_streaming_input: int = 0
# KV Connector: requests in process of async KV loading or recving
self.finished_recving_kv_req_ids: set[str] = set()
self.failed_recving_kv_req_ids: set[str] = set()
......@@ -569,6 +571,13 @@ class Scheduler(SchedulerInterface):
skipped_waiting_requests.prepend_request(request)
continue
# Streaming: skip request if still waiting for next streaming req.
if request.status == RequestStatus.WAITING_FOR_STREAMING_REQ:
assert not request.streaming_queue
self.waiting.pop_request()
skipped_waiting_requests.prepend_request(request)
continue
# Check that adding the request still respects the max_loras
# constraint.
if (
......@@ -929,6 +938,51 @@ class Scheduler(SchedulerInterface):
# it will also affect the scheduler output.
self.finished_req_ids = set()
def _update_request_as_session(
self, session: Request, update: StreamingUpdate
) -> None:
"""
Updates the waiting session with the next streaming update.
Discards the last sampled output token from the prior input chunk.
"""
# Current streaming input behaviour: Keep only computed output tokens
# (discard final sampled output token).
num_computed_tokens = session.num_computed_tokens
kept_output_tokens = session._all_token_ids[
session.num_prompt_tokens : num_computed_tokens
]
del session._all_token_ids[num_computed_tokens:]
session._output_token_ids.clear()
assert session.prompt_token_ids is not None
# Extend prompt with kept output tokens.
session.prompt_token_ids.extend(kept_output_tokens)
if update.mm_features:
base = session.num_tokens
for mm_feature in update.mm_features:
mm_feature.mm_position = replace(
mm_feature.mm_position, offset=mm_feature.mm_position.offset + base
)
session.mm_features.extend(update.mm_features)
session._all_token_ids.extend(update.prompt_token_ids or ())
session.prompt_token_ids.extend(update.prompt_token_ids or ())
# Update block hashes for the new tokens
# (mirrors Request.append_output_token_ids)
if session.get_hash_new_full_blocks is not None:
session.block_hashes.extend(session.get_hash_new_full_blocks())
session.num_prompt_tokens = len(session.prompt_token_ids)
session.arrival_time = update.arrival_time
session.sampling_params = update.sampling_params
if session.status == RequestStatus.WAITING_FOR_STREAMING_REQ:
self.num_waiting_for_streaming_input -= 1
session.status = RequestStatus.WAITING
if self.log_stats:
session.record_event(EngineCoreEventType.QUEUED)
def _make_cached_request_data(
self,
running_reqs: list[Request],
......@@ -1271,9 +1325,17 @@ class Scheduler(SchedulerInterface):
stopped = True
routed_experts = None
finish_reason = None
if stopped:
routed_experts = self._get_routed_experts(request)
kv_transfer_params = self._free_request(request)
# Capture finish_reason BEFORE _handle_stopped_request, which may
# reset the status to WAITING for streaming requests that continue.
finish_reason = request.get_finished_reason()
finished = self._handle_stopped_request(request)
if finished:
kv_transfer_params = self._free_request(request)
if status_before_stop == RequestStatus.RUNNING:
stopped_running_reqs.add(request)
else:
......@@ -1315,7 +1377,7 @@ class Scheduler(SchedulerInterface):
EngineCoreOutput(
request_id=req_id,
new_token_ids=new_token_ids,
finish_reason=request.get_finished_reason(),
finish_reason=finish_reason,
new_logprobs=new_logprobs,
new_prompt_logprobs_tensors=prompt_logprobs_tensors,
pooling_output=pooler_output,
......@@ -1410,6 +1472,24 @@ class Scheduler(SchedulerInterface):
return engine_core_outputs
def _handle_stopped_request(self, request: Request) -> bool:
"""Return True if finished (can be False for resumable requests)."""
if not request.resumable:
return True
if request.streaming_queue:
update = request.streaming_queue.popleft()
if update is None:
# Streaming request finished.
return True
self._update_request_as_session(request, update)
else:
request.status = RequestStatus.WAITING_FOR_STREAMING_REQ
self.num_waiting_for_streaming_input += 1
self.waiting.add_request(request)
return False
def _get_routed_experts(self, request: Request) -> np.ndarray | None:
if not self.vllm_config.model_config.enable_return_routed_experts:
return None
......@@ -1535,10 +1615,26 @@ class Scheduler(SchedulerInterface):
return len(self.running), len(self.waiting)
def add_request(self, request: Request) -> None:
self.waiting.add_request(request)
self.requests[request.request_id] = request
if self.log_stats:
request.record_event(EngineCoreEventType.QUEUED)
existing = self.requests.get(request.request_id)
if existing is not None:
update = StreamingUpdate.from_request(request)
if existing.status != RequestStatus.WAITING_FOR_STREAMING_REQ:
assert existing.streaming_queue is not None, "duplicate request id"
# Queue next input chunk (or finished sentinel).
existing.streaming_queue.append(update)
elif update is not None:
# Commence next input chunk.
self._update_request_as_session(existing, update)
else:
# Streaming-input session finished.
self.finish_requests(request.request_id, RequestStatus.FINISHED_ABORTED)
else:
if request.resumable:
request.streaming_queue = deque()
self.waiting.add_request(request)
self.requests[request.request_id] = request
if self.log_stats:
request.record_event(EngineCoreEventType.QUEUED)
def finish_requests(
self, request_ids: str | Iterable[str], finished_status: RequestStatus
......@@ -1569,6 +1665,8 @@ class Scheduler(SchedulerInterface):
if request.status == RequestStatus.RUNNING:
running_requests_to_remove.add(request)
else:
if request.status == RequestStatus.WAITING_FOR_STREAMING_REQ:
self.num_waiting_for_streaming_input -= 1
waiting_requests_to_remove.append(request)
# Remove all requests from queues at once for better efficiency
......@@ -1603,7 +1701,8 @@ class Scheduler(SchedulerInterface):
del self.requests[request.request_id]
def get_num_unfinished_requests(self) -> int:
return len(self.waiting) + len(self.running)
num_waiting = len(self.waiting) - self.num_waiting_for_streaming_input
return num_waiting + len(self.running)
def has_finished_requests(self) -> bool:
return len(self.finished_req_ids) > 0
......
vllm/v1/engine/__init__.py
View file @ 91601ff4
......@@ -75,6 +75,7 @@ class EngineCoreRequest(
priority: int = 0
trace_headers: Mapping[str, str] | None = None
resumable: bool = False
# The user-provided request ID. This field is set internally,
# copied from the provided request_id that's originally assigned
......
vllm/v1/engine/async_llm.py
View file @ 91601ff4
......@@ -7,11 +7,13 @@ import time
import warnings
from collections.abc import AsyncGenerator, Iterable, Mapping
from copy import copy
from typing import Any, cast
from dataclasses import dataclass
from typing import Any
import torch
import vllm.envs as envs
from vllm import TokensPrompt
from vllm.config import VllmConfig
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.protocol import EngineClient
......@@ -20,11 +22,11 @@ from vllm.inputs import PromptType
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
from vllm.outputs import PoolingRequestOutput, RequestOutput
from vllm.outputs import STREAM_FINISHED, PoolingRequestOutput, RequestOutput
from vllm.plugins.io_processors import get_io_processor
from vllm.pooling_params import PoolingParams
from vllm.renderers import RendererLike
from vllm.sampling_params import SamplingParams
from vllm.sampling_params import RequestOutputKind, SamplingParams
from vllm.tasks import SupportedTask
from vllm.tokenizers import TokenizerLike
from vllm.tracing import init_tracer
......@@ -38,6 +40,7 @@ from vllm.v1.engine.exceptions import EngineDeadError, EngineGenerateError
from vllm.v1.engine.input_processor import InputProcessor
from vllm.v1.engine.output_processor import OutputProcessor, RequestOutputCollector
from vllm.v1.engine.parallel_sampling import ParentRequest
from vllm.v1.engine.utils import get_prompt_text
from vllm.v1.executor import Executor
from vllm.v1.metrics.loggers import (
StatLoggerFactory,
......@@ -50,6 +53,30 @@ from vllm.v1.metrics.stats import IterationStats
logger = init_logger(__name__)
@dataclass
class StreamingInput:
"""Input data for a streaming generation request.
This is used with generate() to support multi-turn streaming sessions
where inputs are provided via an async generator.
"""
prompt: PromptType
sampling_params: SamplingParams | None = None
class InputStreamError(Exception):
"""Wrapper for errors from the input stream generator.
This is used to propagate errors from the user's input generator
without wrapping them in EngineGenerateError.
"""
def __init__(self, cause: Exception):
self.cause = cause
super().__init__(str(cause))
class AsyncLLM(EngineClient):
def __init__(
self,
......@@ -261,7 +288,7 @@ class AsyncLLM(EngineClient):
async def add_request(
self,
request_id: str,
prompt: EngineCoreRequest | PromptType,
prompt: EngineCoreRequest | PromptType | AsyncGenerator[StreamingInput, None],
params: SamplingParams | PoolingParams,
arrival_time: float | None = None,
lora_request: LoRARequest | None = None,
......@@ -297,6 +324,20 @@ class AsyncLLM(EngineClient):
tokenization_kwargs,
)
if isinstance(prompt, AsyncGenerator):
# Streaming input case.
return await self._add_streaming_input_request(
request_id,
prompt,
params,
arrival_time,
lora_request,
tokenization_kwargs,
trace_headers,
priority,
data_parallel_rank,
)
# Convert Input --> Request.
if isinstance(prompt, EngineCoreRequest):
request = prompt
......@@ -322,10 +363,7 @@ class AsyncLLM(EngineClient):
priority,
data_parallel_rank,
)
if isinstance(prompt, str):
prompt_text = prompt
elif isinstance(prompt, Mapping):
prompt_text = cast(str | None, prompt.get("prompt"))
prompt_text = get_prompt_text(prompt)
self.input_processor.assign_request_id(request)
......@@ -380,6 +418,104 @@ class AsyncLLM(EngineClient):
if self.log_requests:
logger.info("Added request %s.", request.request_id)
async def _add_streaming_input_request(
self,
request_id: str,
input_stream: AsyncGenerator[StreamingInput, None],
sampling_params: SamplingParams | PoolingParams,
arrival_time: float | None = None,
lora_request: LoRARequest | None = None,
tokenization_kwargs: dict[str, Any] | None = None,
trace_headers: Mapping[str, str] | None = None,
priority: int = 0,
data_parallel_rank: int | None = None,
) -> RequestOutputCollector:
self._validate_streaming_input_sampling_params(sampling_params)
inputs = dict(
arrival_time=arrival_time,
lora_request=lora_request,
tokenization_kwargs=tokenization_kwargs,
trace_headers=trace_headers,
priority=priority,
data_parallel_rank=data_parallel_rank,
)
if not sampling_params.skip_clone:
sampling_params = sampling_params.clone()
sampling_params.skip_clone = True
# Create request for validation, also used as the finished signal
# once the input stream is closed.
final_req = self.input_processor.process_inputs(
request_id=request_id,
prompt=TokensPrompt(prompt_token_ids=[0]),
params=sampling_params,
**inputs, # type: ignore[arg-type]
)
self.input_processor.assign_request_id(final_req)
internal_req_id = final_req.request_id
queue = RequestOutputCollector(sampling_params.output_kind, internal_req_id)
async def handle_inputs():
cancelled = False
try:
async for input_chunk in input_stream:
sp = input_chunk.sampling_params
if sp:
self._validate_streaming_input_sampling_params(sp)
else:
sp = sampling_params
req = self.input_processor.process_inputs(
request_id=internal_req_id,
prompt=input_chunk.prompt,
params=sp,
resumable=True,
**inputs, # type: ignore[arg-type]
)
req.external_req_id = request_id
if req.prompt_embeds is not None:
raise ValueError(
"prompt_embeds not supported for streaming inputs"
)
prompt_text = get_prompt_text(input_chunk.prompt)
await self._add_request(req, prompt_text, None, 0, queue)
except (asyncio.CancelledError, GeneratorExit):
cancelled = True
except Exception as error:
# Wrap in InputStreamError so generate() can propagate it
# without wrapping in EngineGenerateError.
queue.put(InputStreamError(error))
finally:
queue._input_stream_task = None
if not cancelled:
# Send empty final request to indicate that inputs have
# finished. Don't send if cancelled (session was aborted).
await self._add_request(final_req, None, None, 0, queue)
# Ensure output handler is running.
self._run_output_handler()
queue._input_stream_task = asyncio.create_task(handle_inputs())
return queue
@staticmethod
def _validate_streaming_input_sampling_params(
params: SamplingParams | PoolingParams,
):
if (
not isinstance(params, SamplingParams)
or params.n > 1
or params.output_kind == RequestOutputKind.FINAL_ONLY
or params.stop
):
raise ValueError(
"Input streaming not currently supported "
"for pooling models, n > 1, request_kind = FINAL_ONLY "
"or with stop strings."
)
# TODO: we should support multiple prompts in one call, as you
# can do with LLM.generate. So that for multi-prompt completion
# requests we don't need to send multiple messages to core proc,
......@@ -387,7 +523,7 @@ class AsyncLLM(EngineClient):
# re-multiplexed in the API server anyhow.
async def generate(
self,
prompt: EngineCoreRequest | PromptType,
prompt: EngineCoreRequest | PromptType | AsyncGenerator[StreamingInput, None],
sampling_params: SamplingParams,
request_id: str,
*,
......@@ -437,9 +573,10 @@ class AsyncLLM(EngineClient):
# Note: both OutputProcessor and EngineCore handle their
# own request cleanup based on finished.
finished = out.finished
assert isinstance(out, RequestOutput)
yield out
finished = out.finished
if out is not STREAM_FINISHED:
yield out
# If the request is disconnected by the client, generate()
# is cancelled or the generator is garbage collected. So,
......@@ -463,6 +600,14 @@ class AsyncLLM(EngineClient):
logger.info("Request %s failed (bad request): %s.", request_id, e)
raise
# Error from input stream generator - propagate directly.
except InputStreamError as e:
if q is not None:
await self.abort(q.request_id, internal=True)
if self.log_requests:
logger.info("Request %s failed (input error): %s.", request_id, e)
raise e.cause from e
# Unexpected error in the generate() task (possibly recoverable).
except Exception as e:
if q is not None:
......@@ -478,6 +623,9 @@ class AsyncLLM(EngineClient):
)
logger.info("Request %s failed due to %s.", request_id, s)
raise EngineGenerateError() from e
finally:
if q is not None:
q.close()
def _run_output_handler(self):
"""Background loop: pulls from EngineCore and pushes to AsyncStreams."""
......@@ -703,6 +851,9 @@ class AsyncLLM(EngineClient):
if self.log_requests:
logger.info("Request %s failed.", request_id)
raise EngineGenerateError() from e
finally:
if q is not None:
q.close()
@property
def tokenizer(self) -> TokenizerLike | None:
......
vllm/v1/engine/input_processor.py
View file @ 91601ff4
......@@ -459,6 +459,7 @@ class InputProcessor:
trace_headers: Mapping[str, str] | None = None,
priority: int = 0,
data_parallel_rank: int | None = None,
resumable: bool = False,
) -> EngineCoreRequest:
self._validate_lora(lora_request)
self._validate_params(params)
......@@ -603,6 +604,7 @@ class InputProcessor:
priority=priority,
data_parallel_rank=data_parallel_rank,
trace_headers=trace_headers,
resumable=resumable,
)
def _validate_model_inputs(
......
vllm/v1/engine/output_processor.py
View file @ 91601ff4
......@@ -2,7 +2,7 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import asyncio
from collections import defaultdict
from collections import defaultdict, deque
from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any, cast
......@@ -12,6 +12,7 @@ import torch
from vllm.lora.request import LoRARequest
from vllm.outputs import (
STREAM_FINISHED,
CompletionOutput,
PoolingOutput,
PoolingRequestOutput,
......@@ -51,6 +52,8 @@ class RequestOutputCollector:
self.output: RequestOutput | PoolingRequestOutput | Exception | None = None
self.ready = asyncio.Event()
self._input_stream_task: asyncio.Task | None = None
def put(self, output: RequestOutput | PoolingRequestOutput | Exception) -> None:
"""Non-blocking put operation."""
if self.output is None or isinstance(output, Exception):
......@@ -87,6 +90,16 @@ class RequestOutputCollector:
raise output
return output
def close(self):
if self._input_stream_task is not None:
self._input_stream_task.cancel()
self._input_stream_task = None
def __del__(self):
if (task := self._input_stream_task) is not None:
task.get_loop().call_soon_threadsafe(task.cancel)
self._input_stream_task = None
@dataclass
class OutputProcessorOutput:
......@@ -94,6 +107,20 @@ class OutputProcessorOutput:
reqs_to_abort: list[str]
@dataclass
class StreamingUpdate:
"""Streaming input update data for output processor.
Contains the incremental prompt data to be applied to a request state
when the current sub-request completes.
"""
prompt: str | None
prompt_token_ids: list[int] | None
arrival_time: float
final: bool = False
class RequestState:
def __init__(
self,
......@@ -116,6 +143,7 @@ class RequestState:
top_p: float | None = None,
n: int | None = None,
temperature: float | None = None,
stream_input: bool = False,
):
self.request_id = request_id
self.external_req_id = external_req_id
......@@ -146,6 +174,31 @@ class RequestState:
self.stream_interval = stream_interval
self.sent_tokens_offset = 0 # Offset of sent tokens
# Streaming input queue
self.streaming_input = stream_input
self.input_chunk_queue: deque[StreamingUpdate] | None = (
deque() if stream_input else None
)
def apply_streaming_update(self, update: StreamingUpdate) -> None:
# Apply the update to the request state.
self.streaming_input = not update.final
# TODO also include relevant output tokens in new prompt here
# (match scheduler behavior).
if update.prompt:
self.prompt = (
(self.prompt + update.prompt) if self.prompt else update.prompt
)
if self.prompt_token_ids:
self.prompt_token_ids.extend(update.prompt_token_ids or ())
else:
self.prompt_token_ids = update.prompt_token_ids or []
assert self.prompt_token_ids is not None
self.prompt_len = len(self.prompt_token_ids)
if self.stats is not None:
self.stats.arrival_time = update.arrival_time
self.is_prefilling = True
@classmethod
def from_new_request(
cls,
......@@ -205,6 +258,7 @@ class RequestState:
queue=queue,
log_stats=log_stats,
stream_interval=stream_interval,
stream_input=request.resumable,
)
def make_request_output(
......@@ -405,7 +459,6 @@ class OutputProcessor:
a parent request, in which case the associated child requests are aborted
also.
"""
internal_req_ids = []
for request_id in request_ids:
if internal:
......@@ -464,8 +517,10 @@ class OutputProcessor:
queue: RequestOutputCollector | None = None,
) -> None:
request_id = request.request_id
if request_id in self.request_states:
raise ValueError(f"Request id {request_id} already running.")
req_state = self.request_states.get(request_id)
if req_state is not None:
self._update_streaming_request_state(req_state, request, prompt)
return
req_state = RequestState.from_new_request(
tokenizer=self.tokenizer,
......@@ -486,6 +541,39 @@ class OutputProcessor:
# Track the external_req_id -> [internal_req_id, ...] mapping
self.external_req_ids[req_state.external_req_id].append(request_id)
def _update_streaming_request_state(
self, req_state: RequestState, request: EngineCoreRequest, prompt: str | None
) -> None:
"""Queue a streaming update instead of immediately applying it."""
if not request.resumable:
# Final request - just mark completion, don't add its dummy tokens.
if req_state.input_chunk_queue is None:
# Engine already finished - emit final output and clean up.
self._finish_request(req_state)
if req_state.queue is not None:
# Emit a final output with finished=True
# to unblock the generate() loop.
req_state.queue.put(STREAM_FINISHED)
elif req_state.input_chunk_queue:
req_state.input_chunk_queue[-1].final = True
else:
req_state.streaming_input = False
return
update = StreamingUpdate(
prompt=prompt,
prompt_token_ids=request.prompt_token_ids,
arrival_time=request.arrival_time,
)
# Apply request updates now if the last input already completed.
if req_state.input_chunk_queue is None:
req_state.apply_streaming_update(update)
req_state.input_chunk_queue = deque()
else:
# Queue the streaming update otherwise.
req_state.input_chunk_queue.append(update)
def process_outputs(
self,
engine_core_outputs: list[EngineCoreOutput],
......@@ -561,6 +649,9 @@ class OutputProcessor:
kv_transfer_params,
routed_experts,
):
if req_state.streaming_input:
request_output.finished = False
if req_state.queue is not None:
# AsyncLLM: put into queue for handling by generate().
req_state.queue.put(request_output)
......@@ -570,36 +661,48 @@ class OutputProcessor:
# Free completed requests.
if finish_reason is not None:
self.request_states.pop(req_id)
internal_ids = self.external_req_ids[req_state.external_req_id]
internal_ids.remove(req_id)
if not internal_ids:
del self.external_req_ids[req_state.external_req_id]
# Remove parent request if applicable.
parent_req = req_state.parent_req
if parent_req and not parent_req.child_requests:
self.parent_requests.pop(parent_req.request_id, None)
if not self.request_states:
self._requests_drained.set()
if not engine_core_output.finished:
# If req not finished in EngineCore, but Detokenizer
# detected stop string, abort needed in EngineCore.
reqs_to_abort.append(req_id)
# Track per-request stats
self._update_stats_from_finished(
req_state, finish_reason, iteration_stats
)
if self.tracer:
self.do_tracing(engine_core_output, req_state, iteration_stats)
if req_state.streaming_input:
if req_state.input_chunk_queue:
update = req_state.input_chunk_queue.popleft()
req_state.apply_streaming_update(update)
else:
req_state.input_chunk_queue = None
else:
self._finish_request(req_state)
if not engine_core_output.finished:
# If req not finished in EngineCore, but Detokenizer
# detected stop string, abort needed in EngineCore.
reqs_to_abort.append(req_id)
# Track per-request stats
self._update_stats_from_finished(
req_state, finish_reason, iteration_stats
)
if self.tracer:
self.do_tracing(engine_core_output, req_state, iteration_stats)
return OutputProcessorOutput(
request_outputs=request_outputs,
reqs_to_abort=reqs_to_abort,
)
def _finish_request(self, req_state: RequestState) -> None:
req_id = req_state.request_id
self.request_states.pop(req_id)
internal_ids = self.external_req_ids[req_state.external_req_id]
internal_ids.remove(req_id)
if not internal_ids:
del self.external_req_ids[req_state.external_req_id]
# Remove parent request if applicable.
parent_req = req_state.parent_req
if parent_req and not parent_req.child_requests:
self.parent_requests.pop(parent_req.request_id, None)
if not self.request_states:
self._requests_drained.set()
def update_scheduler_stats(self, scheduler_stats: SchedulerStats | None):
self.lora_states.update_scheduler_stats(scheduler_stats)
......
vllm/v1/engine/utils.py
View file @ 91601ff4
......@@ -4,12 +4,12 @@
import contextlib
import os
import weakref
from collections.abc import Callable, Iterator
from collections.abc import Callable, Iterator, Mapping
from dataclasses import dataclass
from enum import Enum, auto
from multiprocessing import Process, connection
from multiprocessing.process import BaseProcess
from typing import TYPE_CHECKING
from typing import TYPE_CHECKING, Any, cast
from unittest.mock import patch
import msgspec
......@@ -224,6 +224,14 @@ def get_device_indices(
return value
def get_prompt_text(prompt: Any) -> str | None:
if isinstance(prompt, str):
return prompt
if isinstance(prompt, Mapping):
return cast(str | None, prompt.get("prompt"))
return None
class CoreEngineActorManager:
"""
Utility class to handle creation, readiness, and shutdown
......
vllm/v1/request.py
View file @ 91601ff4
......@@ -3,7 +3,9 @@
import enum
import time
from collections import deque
from collections.abc import Callable, Mapping
from dataclasses import dataclass
from functools import partial
from typing import TYPE_CHECKING, Any, Optional
......@@ -27,6 +29,33 @@ if TYPE_CHECKING:
from vllm.v1.core.kv_cache_utils import BlockHash
@dataclass
class StreamingUpdate:
"""Lightweight data for streaming session continuation.
Contains only the fields needed to update an existing streaming session
with new input data.
"""
mm_features: list[MultiModalFeatureSpec] | None
prompt_token_ids: list[int] | None
max_tokens: int
arrival_time: float
sampling_params: SamplingParams | None
@classmethod
def from_request(cls, request: "Request") -> "StreamingUpdate | None":
if not request.resumable:
return None
return cls(
mm_features=request.mm_features,
prompt_token_ids=request.prompt_token_ids,
max_tokens=request.max_tokens,
arrival_time=request.arrival_time,
sampling_params=request.sampling_params,
)
class Request:
def __init__(
self,
......@@ -44,6 +73,7 @@ class Request:
priority: int = 0,
trace_headers: Mapping[str, str] | None = None,
block_hasher: Callable[["Request"], list["BlockHash"]] | None = None,
resumable: bool = False,
) -> None:
self.request_id = request_id
self.client_index = client_index
......@@ -105,8 +135,6 @@ class Request:
# Multi-modal related
self.mm_features = mm_features or []
self.num_encoder_inputs = len(self.mm_features)
self.has_encoder_inputs = self.num_encoder_inputs > 0
# Read-only views
# Prevent directly appending to these lists since
......@@ -137,6 +165,11 @@ class Request:
self.skip_reading_prefix_cache = self.get_skip_reading_prefix_cache()
# Used for streaming
self.resumable = resumable
# None entry in the queue means finished.
self.streaming_queue: deque[StreamingUpdate | None] | None = None
@classmethod
def from_engine_core_request(
cls,
......@@ -158,6 +191,7 @@ class Request:
priority=request.priority,
trace_headers=request.trace_headers,
block_hasher=block_hasher,
resumable=request.resumable,
)
def append_output_token_ids(
......@@ -190,6 +224,14 @@ class Request:
def num_output_tokens(self) -> int:
return len(self._output_token_ids)
@property
def num_encoder_inputs(self) -> int:
return len(self.mm_features)
@property
def has_encoder_inputs(self) -> bool:
return self.num_encoder_inputs > 0
def get_skip_reading_prefix_cache(self) -> bool:
if (
self.sampling_params is not None
......@@ -246,6 +288,7 @@ class RequestStatus(enum.IntEnum):
WAITING = enum.auto()
WAITING_FOR_FSM = enum.auto()
WAITING_FOR_REMOTE_KVS = enum.auto()
WAITING_FOR_STREAMING_REQ = enum.auto()
RUNNING = enum.auto()
PREEMPTED = enum.auto()
# Note: anything after PREEMPTED will be considered
......@@ -256,7 +299,7 @@ class RequestStatus(enum.IntEnum):
FINISHED_IGNORED = enum.auto()
FINISHED_ERROR = enum.auto()
def __str__(self):
def __str__(self) -> str:
return self.name
@staticmethod
......@@ -278,4 +321,5 @@ _FINISHED_REASON_MAP = {
RequestStatus.FINISHED_ABORTED: FinishReason.ABORT,
RequestStatus.FINISHED_IGNORED: FinishReason.LENGTH,
RequestStatus.FINISHED_ERROR: FinishReason.ERROR,
RequestStatus.WAITING_FOR_STREAMING_REQ: FinishReason.STOP,
}
vllm/v1/worker/gpu_model_runner.py
View file @ 91601ff4
......@@ -112,6 +112,7 @@ from vllm.v1.attention.backends.utils import (
get_dcp_local_seq_lens,
reorder_batch_to_split_decodes_and_prefills,
)
from vllm.v1.core.sched.output import NewRequestData
from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
from vllm.v1.kv_cache_interface import (
AttentionSpec,
......@@ -903,6 +904,12 @@ class GPUModelRunner(
# Add new requests to the cached states.
for new_req_data in scheduler_output.scheduled_new_reqs:
req_id = new_req_data.req_id
if req_id in self.requests:
# For streaming case only.
req_state = self._update_streaming_request(req_id, new_req_data)
reqs_to_add.append(req_state)
continue
sampling_params = new_req_data.sampling_params
pooling_params = new_req_data.pooling_params
......@@ -1133,6 +1140,40 @@ class GPUModelRunner(
self.model.get_mamba_state_copy_func(),
)
def _update_streaming_request(
self, req_id: str, new_req_data: NewRequestData
) -> CachedRequestState:
"""Updates streaming session request from `scheduled_new_reqs`.
Removes the request from InputBatch (if present), updates the cached
state, and prepares it for re-addition to the batch.
NOTE: prompt_token_ids includes intermediate output tokens - tokens
previously generated but now are input context (part of the prompt).
"""
self.input_batch.remove_request(req_id)
req_state = self.requests[req_id]
req_state.prompt_token_ids = new_req_data.prompt_token_ids
req_state.mm_features = new_req_data.mm_features
req_state.prompt_embeds = new_req_data.prompt_embeds
req_state.sampling_params = new_req_data.sampling_params
req_state.pooling_params = new_req_data.pooling_params
req_state.block_ids = new_req_data.block_ids
req_state.num_computed_tokens = new_req_data.num_computed_tokens
req_state.num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
req_state.prompt_token_ids, req_state.prompt_embeds
)
# Clear `output_token_ids` as previous output tokens are now part of
# `prompt_token_ids`.
req_state.output_token_ids.clear()
if self.uses_mrope:
self._init_mrope_positions(req_state)
return req_state
def _init_mrope_positions(self, req_state: CachedRequestState):
model = self.get_model()
assert supports_mrope(model), "M-RoPE support is not implemented."
......
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