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ComfyUI/tests/execution/test_async_nodes.py

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Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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import pytest
import time
import torch
import urllib.error
import numpy as np
import subprocess
from pytest import fixture
from comfy_execution.graph_utils import GraphBuilder
from tests.execution.test_execution import ComfyClient, run_warmup
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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@pytest.mark.execution
class TestAsyncNodes:
@fixture(scope="class", autouse=True, params=[
(False, 0),
(True, 100),
])
def _server(self, args_pytest, request):
pargs = [
'python','main.py',
'--output-directory', args_pytest["output_dir"],
'--listen', args_pytest["listen"],
'--port', str(args_pytest["port"]),
'--extra-model-paths-config', 'tests/execution/extra_model_paths.yaml',
'--cpu',
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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]
use_lru, lru_size = request.param
if use_lru:
pargs += ['--cache-lru', str(lru_size)]
Multi-threaded load of models from disk (big load time speedups & Offload to disk) (CORE-43,CORE-152,CORE-164,CORE-165,CORE-117) (#13802) * model_management: disable non-dynamic smart memory Disable smart memory outright for non dynamic models. This is a minor step towards deprecation of --disable-dynamic-vram and the legacy ModelPatcher. This is needed for estimate-free model development, where new models can opt-out of supplying a memory estimate and not have to worry about hard VRAM allocations due to legacy non-dynamic model patchers This is also a general stability increase for a lot of stray use cases where estimates may still be off and going forward we are not going to accurately maintain such estimates. * pinned_memory: implement with aimdo growable buffer Use a single growable buffer so we can do threaded pre-warming on pinned memory. * mm: use aimdo to do transfer from disk to pin Aimdo implements a faster threaded loader. * Add stream host pin buffer for AIMDO casts Introduce per-offload-stream HostBuffer reuse for pinned staging, include it in cast buffer reset synchronization. Defer actual casts that go via this pin path to a separate pass such that the buffer can be allocated monolithically (to avoid cudaHostRegister thrash). * remove old pin path * Implement JIT pinned memory pressure Replace the predictive pin pressure mechanism with JIT PIN memory pressure. * LowVRAMPatch: change to two-phase visit * lora: re-implement as inplace swiss-army-knife operation * prepare for multiple pin sets * implement pinned loras * requirements: comfy-aimdo 0.4.0 * ops: remove unused arg This was defeatured in aimdo iteration * ops: sync the CPU with only the offload stream activity This was syncing with the offload stream which itself is synced with the compute stream, so this was syncing CPU with compute transitively. Define the event to sync it more gently. * pins: implement freeing intermediate for pinned memory Pinning is more important than inactive intermediates and the stream pin buffer is more important than even active intermediates. * execution: implement pin eviction on RAM presure Add back proper pin freeing on RAM pressure * implement pin registration swaps Uncap the windows pins from 50% by extending the pool and have a pressure mechanism to move the pin reservations om demand. This unfortunately implies a GPU sync to do the freeing so significant hysterisis needs to be added to consolidate these pressure events. * cli_args/execution: Implement lower background cache-ram threshold Limit the amount of RAM background intermediates can use, so that switching workflows doesn't degrade performance too much. * make default * bump aimdo * model-patcher: force-cast tiny weights Flux 2 gets crazy stalls due to a mix of tiny and giant weights creating lopsided steam buffer rotations which creates stalls. * ops: refactor in prep for chunking * mm: delegate pin-on-the-way to aimdo Aimdo is able to chunk and slice this on the way for better CPU->GPU overlap. The main advantage is the ability to shorten the bus contention window between previous weight transfer and the next weights vbar fault. * bump aimdo * pinning updates * specify hostbuf max allocation size There a signs of virtual memory exhaustion on some linux systems when throwing 128GB for every little piece. Pass the actual to save aimdo from over-estimates * tests: update execution tests for caching The default caching changed to ram-cache so update these tests accordingly. Remove the LRU 0 test as this also falls through to RAM cache.
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else:
pargs += ['--cache-classic']
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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# Running server with args: pargs
p = subprocess.Popen(pargs)
yield
p.kill()
torch.cuda.empty_cache()
@fixture(scope="class", autouse=True)
def shared_client(self, args_pytest, _server):
client = ComfyClient()
n_tries = 5
for i in range(n_tries):
time.sleep(4)
try:
client.connect(listen=args_pytest["listen"], port=args_pytest["port"])
except ConnectionRefusedError:
# Retrying...
pass
else:
break
yield client
del client
torch.cuda.empty_cache()
@fixture
def client(self, shared_client, request):
shared_client.set_test_name(f"async_nodes[{request.node.name}]")
yield shared_client
@fixture
def builder(self, request):
yield GraphBuilder(prefix=request.node.name)
# Happy Path Tests
def test_basic_async_execution(self, client: ComfyClient, builder: GraphBuilder):
"""Test that a basic async node executes correctly."""
g = builder
image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
sleep_node = g.node("TestSleep", value=image.out(0), seconds=0.1)
output = g.node("SaveImage", images=sleep_node.out(0))
result = client.run(g)
# Verify execution completed
assert result.did_run(sleep_node), "Async sleep node should have executed"
assert result.did_run(output), "Output node should have executed"
# Verify the image passed through correctly
result_images = result.get_images(output)
assert len(result_images) == 1, "Should have 1 image"
assert np.array(result_images[0]).min() == 0 and np.array(result_images[0]).max() == 0, "Image should be black"
def test_multiple_async_parallel_execution(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks):
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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"""Test that multiple async nodes execute in parallel."""
# Warmup execution to ensure server is fully initialized
run_warmup(client)
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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g = builder
image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
# Create multiple async sleep nodes with different durations
sleep1 = g.node("TestSleep", value=image.out(0), seconds=0.3)
sleep2 = g.node("TestSleep", value=image.out(0), seconds=0.4)
sleep3 = g.node("TestSleep", value=image.out(0), seconds=0.5)
# Add outputs for each
_output1 = g.node("PreviewImage", images=sleep1.out(0))
_output2 = g.node("PreviewImage", images=sleep2.out(0))
_output3 = g.node("PreviewImage", images=sleep3.out(0))
start_time = time.time()
result = client.run(g)
elapsed_time = time.time() - start_time
# Should take ~0.5s (max duration) not 1.2s (sum of durations)
if not skip_timing_checks:
assert elapsed_time < 0.8, f"Parallel execution took {elapsed_time}s, expected < 0.8s"
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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# Verify all nodes executed
assert result.did_run(sleep1) and result.did_run(sleep2) and result.did_run(sleep3)
def test_async_with_dependencies(self, client: ComfyClient, builder: GraphBuilder):
"""Test async nodes with proper dependency handling."""
g = builder
image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
# Chain of async operations
sleep1 = g.node("TestSleep", value=image1.out(0), seconds=0.2)
sleep2 = g.node("TestSleep", value=image2.out(0), seconds=0.2)
# Average depends on both async results
average = g.node("TestVariadicAverage", input1=sleep1.out(0), input2=sleep2.out(0))
output = g.node("SaveImage", images=average.out(0))
result = client.run(g)
# Verify execution order
assert result.did_run(sleep1) and result.did_run(sleep2)
assert result.did_run(average) and result.did_run(output)
# Verify averaged result
result_images = result.get_images(output)
avg_value = np.array(result_images[0]).mean()
assert abs(avg_value - 127.5) < 1, f"Average value {avg_value} should be ~127.5"
def test_async_validate_inputs(self, client: ComfyClient, builder: GraphBuilder):
"""Test async VALIDATE_INPUTS function."""
g = builder
# Create a test node with async validation
validation_node = g.node("TestAsyncValidation", value=5.0, threshold=10.0)
g.node("SaveImage", images=validation_node.out(0))
# Should pass validation
result = client.run(g)
assert result.did_run(validation_node)
# Test validation failure
validation_node.inputs['threshold'] = 3.0 # Will fail since value > threshold
with pytest.raises(urllib.error.HTTPError):
client.run(g)
def test_async_lazy_evaluation(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks):
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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"""Test async nodes with lazy evaluation."""
# Warmup execution to ensure server is fully initialized
run_warmup(client, prefix="warmup_lazy")
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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g = builder
input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
mask = g.node("StubMask", value=0.0, height=512, width=512, batch_size=1)
# Create async nodes that will be evaluated lazily
sleep1 = g.node("TestSleep", value=input1.out(0), seconds=0.3)
sleep2 = g.node("TestSleep", value=input2.out(0), seconds=0.3)
# Use lazy mix that only needs sleep1 (mask=0.0)
lazy_mix = g.node("TestLazyMixImages", image1=sleep1.out(0), image2=sleep2.out(0), mask=mask.out(0))
g.node("SaveImage", images=lazy_mix.out(0))
start_time = time.time()
result = client.run(g)
elapsed_time = time.time() - start_time
# Should only execute sleep1, not sleep2
if not skip_timing_checks:
assert elapsed_time < 0.5, f"Should skip sleep2, took {elapsed_time}s"
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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assert result.did_run(sleep1), "Sleep1 should have executed"
assert not result.did_run(sleep2), "Sleep2 should have been skipped"
def test_async_check_lazy_status(self, client: ComfyClient, builder: GraphBuilder):
"""Test async check_lazy_status function."""
g = builder
# Create a node with async check_lazy_status
lazy_node = g.node("TestAsyncLazyCheck",
input1="value1",
input2="value2",
condition=True)
g.node("SaveImage", images=lazy_node.out(0))
result = client.run(g)
assert result.did_run(lazy_node)
# Error Handling Tests
def test_async_execution_error(self, client: ComfyClient, builder: GraphBuilder):
"""Test that async execution errors are properly handled."""
g = builder
image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
# Create an async node that will error
error_node = g.node("TestAsyncError", value=image.out(0), error_after=0.1)
g.node("SaveImage", images=error_node.out(0))
try:
client.run(g)
assert False, "Should have raised an error"
except Exception as e:
assert 'prompt_id' in e.args[0], f"Did not get proper error message: {e}"
assert e.args[0]['node_id'] == error_node.id, "Error should be from async error node"
def test_async_validation_error(self, client: ComfyClient, builder: GraphBuilder):
"""Test async validation error handling."""
g = builder
# Node with async validation that will fail
validation_node = g.node("TestAsyncValidationError", value=15.0, max_value=10.0)
g.node("SaveImage", images=validation_node.out(0))
with pytest.raises(urllib.error.HTTPError) as exc_info:
client.run(g)
# Verify it's a validation error
assert exc_info.value.code == 400
def test_async_timeout_handling(self, client: ComfyClient, builder: GraphBuilder):
"""Test handling of async operations that timeout."""
g = builder
image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
# Very long sleep that would timeout
timeout_node = g.node("TestAsyncTimeout", value=image.out(0), timeout=0.5, operation_time=2.0)
g.node("SaveImage", images=timeout_node.out(0))
try:
client.run(g)
assert False, "Should have raised a timeout error"
except Exception as e:
assert 'timeout' in str(e).lower(), f"Expected timeout error, got: {e}"
def test_concurrent_async_error_recovery(self, client: ComfyClient, builder: GraphBuilder):
"""Test that workflow can recover after async errors."""
g = builder
image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
# First run with error
error_node = g.node("TestAsyncError", value=image.out(0), error_after=0.1)
g.node("SaveImage", images=error_node.out(0))
try:
client.run(g)
except Exception:
pass # Expected
# Second run should succeed
g2 = GraphBuilder(prefix="recovery_test")
image2 = g2.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
sleep_node = g2.node("TestSleep", value=image2.out(0), seconds=0.1)
g2.node("SaveImage", images=sleep_node.out(0))
result = client.run(g2)
assert result.did_run(sleep_node), "Should be able to run after error"
def test_sync_error_during_async_execution(self, client: ComfyClient, builder: GraphBuilder):
"""Test handling when sync node errors while async node is executing."""
g = builder
image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
# Async node that takes time
sleep_node = g.node("TestSleep", value=image.out(0), seconds=0.5)
# Sync node that will error immediately
error_node = g.node("TestSyncError", value=image.out(0))
# Both feed into output
g.node("PreviewImage", images=sleep_node.out(0))
g.node("PreviewImage", images=error_node.out(0))
try:
client.run(g)
assert False, "Should have raised an error"
except Exception as e:
# Verify the sync error was caught even though async was running
assert 'prompt_id' in e.args[0]
# Edge Cases
def test_async_with_execution_blocker(self, client: ComfyClient, builder: GraphBuilder):
"""Test async nodes with execution blockers."""
g = builder
image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
# Async sleep nodes
sleep1 = g.node("TestSleep", value=image1.out(0), seconds=0.2)
sleep2 = g.node("TestSleep", value=image2.out(0), seconds=0.2)
# Create list of images
image_list = g.node("TestMakeListNode", value1=sleep1.out(0), value2=sleep2.out(0))
# Create list of blocking conditions - [False, True] to block only the second item
int1 = g.node("StubInt", value=1)
int2 = g.node("StubInt", value=2)
block_list = g.node("TestMakeListNode", value1=int1.out(0), value2=int2.out(0))
# Compare each value against 2, so first is False (1 != 2) and second is True (2 == 2)
compare = g.node("TestIntConditions", a=block_list.out(0), b=2, operation="==")
# Block based on the comparison results
blocker = g.node("TestExecutionBlocker", input=image_list.out(0), block=compare.out(0), verbose=False)
output = g.node("PreviewImage", images=blocker.out(0))
result = client.run(g)
images = result.get_images(output)
assert len(images) == 1, "Should have blocked second image"
def test_async_caching_behavior(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks):
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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"""Test that async nodes are properly cached."""
# Warmup execution to ensure server is fully initialized
run_warmup(client, prefix="warmup_cache")
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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g = builder
image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
sleep_node = g.node("TestSleep", value=image.out(0), seconds=0.2)
g.node("SaveImage", images=sleep_node.out(0))
# First run
result1 = client.run(g)
assert result1.did_run(sleep_node), "Should run first time"
# Second run - should be cached
start_time = time.time()
result2 = client.run(g)
elapsed_time = time.time() - start_time
assert not result2.did_run(sleep_node), "Should be cached"
if not skip_timing_checks:
assert elapsed_time < 0.1, f"Cached run took {elapsed_time}s, should be instant"
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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def test_async_with_dynamic_prompts(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks):
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
2025-07-10 11:46:19 -07:00
"""Test async nodes within dynamically generated prompts."""
# Warmup execution to ensure server is fully initialized
run_warmup(client, prefix="warmup_dynamic")
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
2025-07-10 11:46:19 -07:00
g = builder
image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
# Node that generates async nodes dynamically
dynamic_async = g.node("TestDynamicAsyncGeneration",
image1=image1.out(0),
image2=image2.out(0),
num_async_nodes=5,
sleep_duration=0.4)
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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g.node("SaveImage", images=dynamic_async.out(0))
start_time = time.time()
result = client.run(g)
elapsed_time = time.time() - start_time
# Should execute async nodes in parallel within dynamic prompt
if not skip_timing_checks:
assert elapsed_time < 1.0, f"Dynamic async execution took {elapsed_time}s"
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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assert result.did_run(dynamic_async)
def test_async_resource_cleanup(self, client: ComfyClient, builder: GraphBuilder):
"""Test that async resources are properly cleaned up."""
g = builder
image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
# Create multiple async nodes that use resources
resource_nodes = []
for i in range(5):
node = g.node("TestAsyncResourceUser",
value=image.out(0),
resource_id=f"resource_{i}",
duration=0.1)
resource_nodes.append(node)
g.node("PreviewImage", images=node.out(0))
result = client.run(g)
# Verify all nodes executed
for node in resource_nodes:
assert result.did_run(node)
# Run again to ensure resources were cleaned up
result2 = client.run(g)
# Should be cached but not error due to resource conflicts
for node in resource_nodes:
assert not result2.did_run(node), "Should be cached"
def test_async_cancellation(self, client: ComfyClient, builder: GraphBuilder):
"""Test cancellation of async operations."""
# This would require implementing cancellation in the client
# For now, we'll test that long-running async operations can be interrupted
pass # TODO: Implement when cancellation API is available
def test_mixed_sync_async_execution(self, client: ComfyClient, builder: GraphBuilder):
"""Test workflows with both sync and async nodes."""
g = builder
image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
# Mix of sync and async operations
# Sync: lazy mix images
sync_op1 = g.node("TestLazyMixImages", image1=image1.out(0), image2=image2.out(0), mask=mask.out(0))
# Async: sleep
async_op1 = g.node("TestSleep", value=sync_op1.out(0), seconds=0.2)
# Sync: custom validation
sync_op2 = g.node("TestCustomValidation1", input1=async_op1.out(0), input2=0.5)
# Async: sleep again
async_op2 = g.node("TestSleep", value=sync_op2.out(0), seconds=0.2)
output = g.node("SaveImage", images=async_op2.out(0))
result = client.run(g)
# Verify all nodes executed in correct order
assert result.did_run(sync_op1)
assert result.did_run(async_op1)
assert result.did_run(sync_op2)
assert result.did_run(async_op2)
# Image should be a mix of black and white (gray)
result_images = result.get_images(output)
avg_value = np.array(result_images[0]).mean()
assert abs(avg_value - 63.75) < 5, f"Average value {avg_value} should be ~63.75"