fix(multimodal): stop the media loaders from importing torch

[ayaangazali]

Summary

Fixes #11172.

from dynamo.common.multimodal import ImageLoader pulls in torch and the rest of the ML stack. image_loader.py itself depends only on asyncio, PIL and the local http helpers, so the cost comes entirely from the package __init__: it eagerly re-exports AsyncEncoderCache and the embedding-transfer members, and those reach torch through dynamo.common.memory.multimodal_embedding_cache_manager. Those imports run before the loader re-export, so importing the lightweight name pays for the heavy one.

The practical effect is the one the issue reports. The URL-loading and request-deduplication logic cannot be imported or unit-tested in a CPU-only environment without installing torch or stubbing it into sys.modules first, which is brittle scaffolding for code that has no ML dependency.

Reproduced on current main (d023c6af5) before changing anything:

>>> from dynamo.common.multimodal import ImageLoader
>>> "torch" in sys.modules
True

What changed

I measured which submodules are actually heavy rather than deferring the whole package. Importing each one in a fresh interpreter with the package __init__ emptied:

submodule	imports torch
async_encoder_cache	yes
embedding_transfer	yes
audio_loader	no
image_loader	no
video_loader	no

So only those two move behind a module-level __getattr__ (PEP 562). The three loaders stay eager, because they cost nothing and keeping them eager means the common import path does no extra work. Each resolved attribute is cached into the module globals, so repeat access skips __getattr__ entirely.

EMBEDDING_SENDER_FACTORIES and EMBEDDING_RECEIVER_FACTORIES had to become lazy as well. They are built from the embedding-transfer classes, so evaluating them at import time would have kept the eager import regardless of what the re-exports did. That is the one part of the change that goes beyond the issue's option 1, and it is why option 1 alone would not have worked. Their contents are unchanged, including the NIXL_READ max_items=0 lambda and its existing FIXME.

This is option 1 from the issue rather than 2 or 3: it keeps the public surface identical, so no caller has to change. __all__ is the same, __dir__ reports it, and the names are re-declared under TYPE_CHECKING so type checkers and IDEs still resolve them.

Validation

Run on a CPU-only macOS box with torch installed, so the import assertions are meaningful (the package resolves torch if anything asks for it).

• ImageLoader, AudioLoader and VideoLoader each import with "torch" not in sys.modules afterwards. Before this change all three pulled it in.
• The heavy names still work and still resolve torch on demand: AsyncEncoderCache, TransferRequest, and both factory dicts return 3 entries each with the same keys and the same class objects as before.
• Unknown attributes still raise AttributeError, not ImportError.
• pytest components/src/dynamo/common/tests/multimodal/ is 175 passed, 3 skipped, 7 failed. The 7 failures are all in test_embedding_transfer.py and are identical on clean main with this change stashed; they need NIXL and a GPU, neither of which exists here.
• Added test_lazy_imports.py, which shells out to a fresh interpreter per case because once any earlier test has imported torch an eager import here would go unnoticed. Confirmed red before the fix: the three loader cases fail with importing <name> pulled in torch. The fourth case asserts AsyncEncoderCache still does import torch, so the test cannot be satisfied by simply deleting functionality.
• mypy clean on the changed file. pre-commit run --files on both files passes with zero failing hooks.

Not verified here: no GPU and no NIXL, so I did not exercise the NIXL sender/receiver paths at runtime. The factory dicts were checked for identical keys and identical class objects rather than by constructing NIXL receivers, since NixlReadEmbeddingReceiver(max_items=0) needs a real NIXL agent.

---

Summary by CodeRabbit

• Performance

  • Improved multimodal component startup by deferring heavy machine-learning dependencies until they are needed.
  • Lightweight audio, image, and video loaders can now be imported without loading the full ML stack.

• Compatibility

  • Existing multimodal exports and factory mappings remain available with unchanged functionality.

• Tests

  • Added coverage confirming lightweight imports avoid unnecessary dependencies while encoder features continue to load correctly.

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Devin Review found 1 potential issue.

🟡 New subprocess test lacks required timeout marker

The test spawns a fresh interpreter with subprocess.run and waits on it (test_lazy_imports.py:25-30) without a @pytest.mark.timeout(...) marker, which the pytest guidelines require for any test that performs subprocess waits.

The repository's .ai/pytest-guidelines.md states: "Always flag any test that runs over 30 seconds or contains time.sleep(), polling loops, network calls, or subprocess waits but lacks a @pytest.mark.timeout(...) marker. This is a required change, not a style suggestion -- a missing timeout can hang CI indefinitely." Every test in this file (three parametrized loader cases plus test_heavy_members_still_import) shells out to a subprocess yet none carries a timeout.

Suggested change

	pytestmark = [pytest.mark.pre_merge, pytest.mark.unit, pytest.mark.gpu_0]
	pytestmark = [
	pytest.mark.pre_merge,
	pytest.mark.unit,
	pytest.mark.gpu_0,
	pytest.mark.timeout(120),
	]

---

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Correct finding, and it was already addressed before this thread got a reply, which is my fault for leaving it silent.

pytestmark in test_lazy_imports.py carries pytest.mark.timeout(60), so all four tests in the file are bounded, and subprocess.run also passes timeout=30 so the child cannot outlive the parent even if the interpreter wedges on an import. Module-level rather than per test, since every test in the file shells out, which is the shape .ai/pytest-guidelines.md asks for.

I used 60 rather than the 120 suggested. The probe is a bare python -c import with no model load and finishes in about a second here, so 60 is already a large multiple; if CI is slower than that I would rather see the timeout than have it pass at two minutes.

Walkthrough

The multimodal package now eagerly imports lightweight media loaders and lazily resolves heavy encoder, embedding-transfer, and factory exports. Subprocess tests verify that loader imports avoid torch while AsyncEncoderCache remains available.

Changes

Multimodal lazy imports

Layer / File(s)	Summary
Lazy export resolution components/src/dynamo/common/multimodal/__init__.py	Lightweight loaders remain eager imports. Heavy exports and factory mappings resolve through module attribute access, cache resolved values, and appear in __dir__.
Import behavior validation components/src/dynamo/common/tests/multimodal/test_lazy_imports.py	Subprocess tests verify that ImageLoader, AudioLoader, and VideoLoader do not import torch, while AsyncEncoderCache does.
Estimated code review effort: 3 (Moderate)	~20 minutes

Merge Risk: 🔵 Low · up to 7118d

The PR preserves the multimodal API while preventing lightweight loader imports from loading torch. The subprocess-based import tests should use a bounded timeout so a stalled import cannot block CI indefinitely; this is a localized follow-up, so the PR is otherwise mergeable with owner awareness.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 7 functions across 2 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly summarizes the primary change: preventing media loader imports from eagerly loading torch.
Description check	✅ Passed	The description explains the problem, implementation, validation, and linked issue, but does not use every template heading.
Linked Issues check	✅ Passed	The changes satisfy issue [#11172] by lazily loading heavy multimodal exports while preserving lightweight loader imports and the public API.
Out of Scope Changes check	✅ Passed	The changes are limited to lazy multimodal imports, preserved public exports, and regression coverage for the linked issue.

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Inline comments:
In `@components/src/dynamo/common/tests/multimodal/test_lazy_imports.py`:
- Around line 25-29: Update the subprocess.run call in the lazy-import probe
test to include an appropriate timeout, ensuring a hung child interpreter
produces a bounded pytest failure while preserving the existing probe arguments
and check behavior.

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Four findings. I deliberately left out the missing timeout= on the subprocess call — Devin and CodeRabbit both raised it and I have nothing to add there.

One of mine lands on a line CodeRabbit already commented on; it is a different claim (swallowed stderr, not the missing timeout) and I have said so inline.

The lazy machinery itself I checked end to end and it holds: the loaders import no torch, the heavy names resolve and cache into globals, both factory dicts rebuild with identical keys and the same class objects, __all__ is byte-identical to base, there is no circular import, and import * yields exactly __all__.

Reviewed at 7118da39.

Going lazy here has two consequences, one local and one package-wide.

Local: EMBEDDING_SENDER_FACTORIES and EMBEDDING_RECEIVER_FACTORIES are not declared in this block, so under mypy they resolve to Any and callers lose the key and value types. The description says the names are re-declared under TYPE_CHECKING so type checkers still resolve them — that holds for the classes, but not for these two.

Package-wide: a module-level __getattr__ tells mypy the package may have any attribute, so from dynamo.common.multimodal import DoesNotExistAtAll now type-checks clean — for every module importing from this package, not just this file. That is a real loss of typo detection, and nothing in the diff shows it.

Declaring both dicts in this block closes the first half. The second needs a .pyi stub, or an if not TYPE_CHECKING: guard around __getattr__ so the checker never sees the widening.

Both halves confirmed and fixed in adde1d5, and the package-wide one is the more serious of the two.

I checked rather than taking it on faith, and my first attempt to check was wrong in a way worth mentioning: I ran mypy with --follow-imports=skip, which makes the module Any regardless and reported no problem. Following imports properly reproduces exactly what you describe.

Before:

probe.py:1: from dynamo.common.multimodal import DoesNotExistAtAll
probe.py:3: reveal_type(mm.EMBEDDING_SENDER_FACTORIES)

probe.py:3: note: Revealed type is "Any"
Success: no issues found in 1 source file

After:

probe.py:1: error: Module "dynamo.common.multimodal" has no attribute "DoesNotExistAtAll"  [attr-defined]
probe.py:3: note: Revealed type is "dict[EmbeddingTransferMode, def () -> AbstractEmbeddingSender]"
Found 1 error in 1 file

For the package-wide half I took the if not TYPE_CHECKING: guard rather than a .pyi. The stub would have to restate every export and then be kept in step with __all__ and _EMBEDDING_TRANSFER_EXPORTS by hand, which is a second source of truth for no extra benefit here; the guard leaves the checker seeing exactly the declarations that are already in the file. Runtime is unaffected, since TYPE_CHECKING is False when it matters, and mm.NoSuchThing still raises AttributeError.

On the factory dicts you are also right that my PR description was wrong. It said the names were re-declared so type checkers still resolve them, which held for the classes and not for those two. They are declared now.

stdout.strip() == "True" treats anything that is not exactly True as False, and the three loader cases assert not _torch_imported_by(...). So any extra line on the child's stdout — a library banner, a warning routed to stdout, a notice from a transitive import — turns a real failure into a pass, and the suite stays green while torch is being imported again. That is the one regression this test exists to catch.

Matching on the last line, or having the probe print a sentinel (RESULT:<bool>) and parsing that, removes the failure mode.

For what it is worth, test_heavy_members_still_import asserts the other direction and would fail loudly in that scenario — so stdout pollution does surface somewhere, just not in the cases that matter.

Answering this in its own thread, because my reply to it went into the capture_output thread by mistake and left this one looking unaddressed.

This was the most valuable comment on the PR, and it is not hypothetical. The three loader cases assert the negative, so stdout noise makes them pass rather than fail, which is the one regression the test exists to catch. dynamo.nixl_connect prints a CuPy fallback notice on this machine, and I had already seen that line in unrelated output in this package without connecting it to this test.

Fixed in adde1d5 with the sentinel you suggested: the probe prints RESULT:<bool> and the helper parses that off its own line, then asserts it saw exactly one, so extra output cannot be mistaken for an answer.

Separate point from the timeout thread on this line.

With capture_output=True and check=True, a child that dies raises CalledProcessError, and the traceback lives in e.stderr, which pytest does not print. If torch or PIL is missing, or the package raises during import, the failure reads as a bare non-zero exit with nothing to go on.

Catching it and re-raising with e.stderr in the message — or pytest.fail(e.stderr) — makes the CI output actionable without changing what the test asserts.

Fixed in adde1d5. This was the most valuable comment on the PR, because the failure mode is silent and points the wrong way: the three loader cases assert the negative, so stdout noise makes them pass rather than fail.

It is not hypothetical either. dynamo.nixl_connect prints a CuPy fallback notice on this machine, and I had already seen it in unrelated output in this package without connecting it to this test. Demonstrated with that exact line:

raw stdout: dynamo.nixl_connect:

This one had not actually been answered, and my earlier reply is the reason it looked like it had. I posted the sentinel explanation into your capture_output thread instead of this one, so both threads read as covered when only the sentinel was.

Answering it properly now, and you were right. Reproduced it first rather than taking the argument on faith. On the code as it stood, a child that dies gives pytest this and nothing else:

FAILED test_probe_child_dies - subprocess.C...
/opt/homebrew/.../subprocess.py:571: CalledProcessError

The ImportError that actually killed the child is in e.stderr, which never reaches the report.

Fixed in 657d248. Dropped check=True and checked the code explicitly, since that keeps stderr in the assertion message without a try/except:

    assert (
        result.returncode == 0
    ), f"probe for {names} exited {result.returncode}\nstderr:\n{result.stderr}"

Same failure now reads:

E       AssertionError: probe for NoSuchSymbolAtAll exited 1
E         stderr:
E         Traceback (most recent call last):
E           File "<string>", line 3, in <module>
E         ImportError: cannot import name 'NoSuchSymbolAtAll' from 'dynamo.common.multimodal'

What the test asserts is unchanged, and the four cases still pass.

sorted(__all__) hides two kinds of name that still import fine:

• the submodules (image_loader, audio_loader, video_loader, async_encoder_cache, embedding_transfer)
• AbstractEmbeddingSender and AbstractEmbeddingReceiver, which __getattr__ resolves through _EMBEDDING_TRANSFER_EXPORTS but which were never in __all__

So dir() and REPL completion now under-report what the package offers. sorted(set(__all__) | set(globals())) keeps the intent of advertising the public names without hiding things that work.

Fixed in b70ad6d. You are right, and this was a regression I introduced: before this PR there was no __dir__ at all, so dir() fell back to globals() and did list the submodules.

I went slightly further than set(__all__) | set(globals()), because that still hides AbstractEmbeddingSender and AbstractEmbeddingReceiver until something touches them. They resolve through _EMBEDDING_TRANSFER_EXPORTS, but a lazily resolved name only lands in globals() after its first access, so on a fresh import they would still be missing from completion. Including the lazy set explicitly covers all three categories:

return sorted(set(__all__) | _EMBEDDING_TRANSFER_EXPORTS | set(globals()))

Verified on a fresh interpreter with nothing accessed first: ImageLoader, AsyncEncoderCache, AbstractEmbeddingSender, AbstractEmbeddingReceiver, EMBEDDING_SENDER_FACTORIES and the image_loader / audio_loader / video_loader submodules all appear.

/ok to test aeb8cd2

The three Multi-GPU failures and backend-status-check are GPU runner scheduling, not this diff. I checked carefully rather than assuming, because this PR changes an __init__ that all three backends import, so it is exactly the shape of change that could break them.

No test ran. All three jobs died before the container started:

##[error]Error: pod failed to come online with error: Error: Pod
  prod-tester-amd-gpu-4-v2-69rw8-runner-xchst-workflow is unhealthy with phase status Pending: {}
##[error]Executing the custom container implementation failed.
  Please contact your self hosted runner administrator.

Same message on all three, same runner set prod-tester-amd-gpu-4-v2-69rw8, three different pods (runner-xchst, runner-cpbs5, runner-mgtpt), all stuck Pending, all within nine seconds of each other at 19:59Z.

Two other pull requests in the same window hit it identically on the same runner set: #12004 on pod runner-22sgk and #13653 on pod runner-bfhgs. Three PRs, six pods, one runner pool.

backend-status-check is the roll-up rather than a separate failure. Its log has no error of its own; it reports failure because the three jobs above did, and it will clear when they do.

Not pushing anything. There is no code fix for a pod that never scheduled, and a speculative commit would only add noise. These need a re-run once the GPU pool has capacity.

Separately, the lychee failure on this PR was real, but it is not from this diff either: a genuine 404 on a link in the SGLang multimodal page that fails every pull request running lychee. #13651 fixes it.

Follow-up with new information rather than a repeat of the diagnosis above: the GPU pool has recovered, so a re-run of the three failed jobs should now pass.

Evidence, from today on the same workflow:

pull-request/13658  run 32585172234
  sglang-runtime / Multi-GPU Test cuda13.0, amd64   success  16:44:29Z
  vllm-runtime   / Multi-GPU Test cuda13.0, amd64   success  17:37:45Z
  trtllm-runtime / Multi-GPU Test cuda13.1, amd64   success  17:41:42Z

Those are the same three job names that failed here, and they scheduled and ran fine, so the prod-tester-amd-gpu-4-v2 capacity problem from yesterday evening has cleared.

Nothing has changed on this PR in the meantime. Run 32518556237 is still the only run on the branch and it is still pinned to aeb8cd2, so it cannot pick up the recovered capacity by itself, and I have no permission to re-run it.

I am deliberately not rebasing to force a fresh run. aeb8cd2 is your own merge of main into this branch and carries your /ok to test, so a force-push would throw away both. A re-run of the three failed jobs is the cheap fix and keeps the vet intact. No new vet needed, since the head is unchanged.

@rmccorm4 flagging that this push moves the head off aeb8cd2, which is the commit you vetted, because that costs you something and you should not have to discover it.

I would not have spent a live vet on a run that could still merge. This one cannot. All four red checks on that run finished on 21 Aug and none of them ran a test:

##[error]Error: pod failed to come online with error: Error: Pod
prod-tester-amd-gpu-4-v2-69rw8-runner-xchst-workflow is unhealthy with phase status Pending: {}
##[error]Executing the custom container implementation failed.

The three Multi-GPU jobs and backend-status-check all fail that way, so it is GPU runner capacity rather than anything in this diff, and it will not clear on its own. The PR needed a fresh commit and a fresh vet either way, so I would rather the next run also carry the review fix than spend a second one later.

What changed is one test helper, in 657d248. @GuanLuo pointed out that check=True hides the child's stderr, and I had replied to a different comment of his in that thread, which left the point looking answered when it was not. Details and the before and after output are in that thread.

No production code moved. test_lazy_imports.py passes 4 of 4 locally and pre-commit is clean on the file.

Disclosure, because I only found this today and it changes who should get credit here.

@waynehacking8 opened #11179 on 2 July against the same issue #11172, on the same two files, using the same PEP 562 lazy-__getattr__ approach. It predates this PR by seven weeks. I did not find it before opening this one, and I should have.

I am not closing this unilaterally, because @GuanLuo has since put real review into it and four of those threads turned into fixes: the EMBEDDING_SENDER_FACTORIES typing, the package-wide Any widening, __dir__ dropping submodules, and the capture_output / stdout-sentinel problem in the test. That work is in this branch and not in #11179, so silently discarding it seemed worse than saying this plainly and letting you choose.

Current state of the two, so the choice is easy to make:

	#11179	this
opened	2 Jul	21 Aug
last updated	9 Jul	28 Aug
CI	1 failing	green
review	none	@GuanLuo, 4 threads resolved

If you would rather take #11179 as the original and have me fold the review fixes into it, say so and I will close this and put them there instead. If you would rather take this one, #11179 should probably be closed pointing here.

Status after attempting the rebase, and a finding worth flagging rather than a push.

This branch is 303 commits behind main. I rebased locally and the PR's own tests went from green to failing, so I have not pushed the rebase. Isolating it:

pre-rebase head 657d248        -> 4 passed
rebased onto current main      -> 4 failed

The cause is that main has grown a new import chain under the media loaders:

common.multimodal -> audio_loader -> common.http -> configuration.groups
  -> runtime_args -> utils.namespace -> utils/__init__ -> endpoint_types
  -> dynamo.llm -> dynamo._core

So importing a loader now reaches dynamo.llm and therefore the compiled bindings. On the base this PR was written against, it did not.

Two things follow, and I would rather you decide which matters:

1. Locally this is unverifiable for me. The test spawns a fresh python -c probe, and in that subprocess the import dies with ImportError: cannot import name 'LoadThresholdConfig' from 'dynamo._core' because the .so on my machine is stale and maturin develop does not complete here. On CI, where the bindings are built, the probe would import and the torch assertion would presumably be reached. So I cannot honestly tell you the rebased branch is green.
2. More interesting: the property this PR is about has narrowed. The point was that the media loaders stay usable without the ML stack. They no longer import cleanly without the compiled bindings, which is a weaker version of the same guarantee. torch may well still be absent, which is what the test actually asserts, but the loaders picking up dynamo.llm through common.http looks like accidental coupling rather than intent.

What I would like from you: if the coupling is unintended, this PR's test is arguably the right place to also pin it, and I will extend it to assert the loaders import without the bindings too, then rebase and let CI be the first real run. If the coupling is deliberate, I will rebase and relax the probe so it tolerates the bindings being present.

Not pushing anything until you say which, since either way I would be guessing at intent. @rmccorm4 @GuanLuo