[rakhmets]

What?

Removed at::cuda::Stream:

• Replaced at::cuda::getStreamFromPool by torch_get_cuda_stream_from_pool.

Why?

To get rid of dependency on specific PyTorch version.

How?

#1793

Summary by CodeRabbit

• Improvements
  • Communication buffers now obtain CUDA streams from a pool.
  • Stream access is exposed as a PyTorch-compatible external stream, and dispatch and combine operations use the buffer’s communication stream directly.
  • These changes provide a consistent stream handle across buffer access and operations.

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• examples/device/ep/csrc/nixl_ep.cpp
• examples/device/ep/csrc/nixl_ep.hpp
• examples/device/ep/nixl_ep/buffer.py

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📝 Walkthrough

Walkthrough

The change adds pooled CUDA stream acquisition and stores the communication stream as a raw CUDA handle. C++ dispatch and combine use that handle as a launch stream when no receive hook is requested. Python wraps the runtime pointer in a torch.cuda.ExternalStream.

Changes

CUDA communication stream handles

Layer / File(s)	Summary
Pooled stream acquisition and buffer storage examples/device/ep/csrc/cuda_stream.*, examples/device/ep/csrc/nixl_ep.hpp, examples/device/ep/csrc/nixl_ep.cpp	Adds a helper that obtains a pooled CUDA stream. Buffer stores the stream as cudaStream_t and returns its raw handle as int64_t.
C++ stream consumers and Python wrapper examples/device/ep/csrc/nixl_ep.cpp, examples/device/ep/nixl_ep/buffer.py	Dispatch and combine use the raw handle as the launch stream when no receive hook is requested. Python constructs a torch.cuda.ExternalStream from the runtime pointer and local device ID.

Priority: ⬇️ Low

Estimated code review effort: 2 (Simple) | ~10 minutes

Change: Refactor

Merge Risk: 🟡 Moderate · up to 53a08

Concurrent buffer destruction may race with holder updates. Confirm that this lifecycle is serialized or address the race before merging.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 4.17% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 24 functions across 9 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Title check	✅ Passed	The title clearly describes the removal of at::cuda stream APIs, which is the main change.
Description check	✅ Passed	The description includes the required What, Why, and How sections. It explains the PyTorch-version compatibility goal and the stream API replacement, but does not mention moving `Tensor::record_stream…

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Inline comments:
In `@examples/device/ep/csrc/record_stream.hpp`:
- Around line 46-52: Update record_stream_impl so the cudaLaunchHostFunc
callback user data remains valid across CUDA graph replays, preserving the
tensor-lifetime guarantee; alternatively, detect and reject graph capture before
enqueueing the callback. Do not leave the callback unqueued without an
equivalent lifetime-preserving mechanism.
- Around line 46-52: Update record_stream_impl so the cudaLaunchHostFunc
callback performs only non-CUDA completion bookkeeping and does not delete the
torch::Tensor holder. Signal callback completion, then reclaim the holder from
host-side code after the callback returns, or use the allocator recordStream
mechanism with host-side release; preserve asynchronous stream-use tracking.

In `@examples/device/ep/nixl_ep/buffer.py`:
- Line 183: Update Buffer’s communication-stream handling so get_comm_stream()
always passes the actual device associated with comm_stream to
torch.cuda.ExternalStream, rather than the uninitialized or later-changing
device_id; capture the stream device when comm_stream is acquired and reuse it
here, or explicitly reject calls before update_memory_buffers() initializes the
buffer.

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• examples/device/ep/csrc/cuda_stream.cpp
• examples/device/ep/csrc/cuda_stream.hpp
• examples/device/ep/csrc/nixl_ep.cpp
• examples/device/ep/csrc/nixl_ep.hpp
• examples/device/ep/csrc/record_stream.hpp
• examples/device/ep/nixl_ep/buffer.py

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Inline comments:
In `@examples/device/ep/csrc/record_stream.hpp`:
- Around line 48-55: Update record_stream_impl to avoid allocating the vector
holder when the target stream is undergoing CUDA graph capture, or otherwise
attach the holder to the graph lifetime and reclaim it on graph destruction.
Ensure the asynchronous paths in get_dispatch_layout, ht_dispatch, and
ht_combine do not permanently retain copied torch::Tensor references for
captured streams.

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Inline comments:
In `@examples/device/ep/csrc/record_stream.hpp`:
- Around line 57-71: Provide an explicit drain operation for the static holders
managed by detail::record_stream_impl, removing and deleting entries whose CUDA
events are ready. Invoke this drain from the owning shutdown or lifecycle path
after relevant CUDA work has completed, so queued holders are reclaimed even
when no later record_stream call occurs.
- Line 57: Protect the function-static holders vector with a single shared mutex
in Buffer::ht_dispatch and every other code path that accesses it. Lock the
mutex while iterating, erasing, or appending, including asynchronous
record_stream handling, so concurrent host threads cannot access holders
unsafely.

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Inline comments:
In `@examples/device/ep/csrc/tensor_holders.cpp`:
- Around line 25-28: Update TensorHolders::clear() to acquire the existing mutex
before traversing, deleting from, or clearing holders, matching the
synchronization used by update().
- Line 42: Update TensorHolders::update and the high-throughput Python wrapper
lifetime handling so returned tensors remain alive through consumer work
enqueued after ht_dispatch or ht_combine returns. Do not use the pre-return
event recorded on compute_stream as that boundary; instead register each tensor
with the consumer stream via torch::Tensor::record_stream, or retain the tensors
until a completion event recorded after consumer work finishes.
- Around line 46-57: Update TensorHolders::update() so completed holders are
reclaimed even when no later record() call occurs, by adding or reusing a
completion/reap path that runs after CUDA event readiness. Keep holders alive
until their asynchronous operations complete; do not delete them immediately
after enqueuing cudaStreamWaitEvent, and preserve Buffer::destroy() cleanup as a
fallback.

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[svc-nixl]

🤖 CI Triage Agent — Run Pre-Commit Hooks · commit f767aaf4

TL;DR: The pre-commit job failed on the mypy hook: the new _record_streams() helper added in this PR uses filter(None, tensors), whose element type mypy cannot infer (torch is untyped in the hook env), producing Need type annotation for "tensor". Replace filter(None, ...) with an explicit is not None check (or annotate the loop vars).

Full analysis

Summary: GitHub Actions "Run Pre-Commit Hooks" (run 35602399213) failed at the pre-commit run --files ... step — the mypy hook exited 1 with one error; every other hook passed.

Root cause: The only Python file in the PR's changed-file set is examples/device/ep/nixl_ep/buffer.py, and the commit under test (f767aaf4, "Record tensor streams via real torch API instead of emulation") added:

def _record_streams(
    tensors: Tuple[Optional[torch.Tensor], ...], streams: List[torch.Stream]
) -> None:
    for tensor, stream in itertools.product(filter(None, tensors), streams):
        tensor.record_stream(stream)

mypy reports examples/device/ep/nixl_ep/buffer.py:47: error: Need type annotation for "tensor" [var-annotated]. The mirrors-mypy hook (.pre-commit-config.yaml:16-21) installs only types-PyYAML and types-tabulate — torch is not available in the hook's isolated venv, so torch.Tensor resolves to Any. typeshed's null-predicate overload filter(function: None, iterable: Iterable[_T | None]) -> filter[_T] cannot solve _T when the element type is Any matched against a union, so the filter/itertools.product result has an unsolved type variable and mypy demands an explicit annotation for the unpacked loop variable. This is a genuine lint failure in the new code, not flaky infra — total step runtime was ~7s with no gaps.

Implicated commit: [REDACTED:Hex High Entropy String] — Raul Akhmetshin, "EXAMPLES/DEVICE/EP: Record tensor streams via real torch API instead of emulation." (2026-09-21)

File: examples/device/ep/nixl_ep/buffer.py:47

Suggested fix: Drop filter(None, ...) so the element type is unambiguous:

def _record_streams(
    tensors: Tuple[Optional[torch.Tensor], ...], streams: List[torch.Stream]
) -> None:
    for tensor, stream in itertools.product(tensors, streams):
        if tensor is not None:
            tensor.record_stream(stream)

Equivalent alternatives if you prefer to keep the product over non-None tensors: itertools.product([t for t in tensors if t is not None], streams), or keep filter(None, ...) and add explicit annotations (tensor: torch.Tensor / stream: torch.Stream) before the loop. Run pre-commit run --files examples/device/ep/nixl_ep/buffer.py locally to confirm before pushing. Longer term, consider adding torch (or a torch stub package) to the mypy hook's additional_dependencies, or excluding examples/device/ep from mypy, so torch-typed code isn't checked against Any-typed torch — the current setup makes such inference failures likely to recur.

Related: PR #2275 (this change); no existing issue tracks this mypy/filter(None, ...) pattern.

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[svc-nixl]

🤖 CI Triage Agent — Run Pre-Commit Hooks · commit f767aaf4

TL;DR: The pre-commit mypy hook failed on the new _record_streams() helper added by this PR — mypy can't infer the loop variable type from filter(None, tensors) because torch isn't installed in the hook env (ignore_missing_imports = true makes torch.Tensor → Any). Replace filter(None, ...) with an explicit if tensor is not None: check (which also avoids a real runtime bool(Tensor) error).

Full analysis

Summary: GitHub Actions "Run Pre-Commit Hooks" job (run 35602396533) failed: mypy hook exit code 1 — examples/device/ep/nixl_ep/buffer.py:47: error: Need type annotation for "tensor" [var-annotated]. All other hooks passed.

Root cause: This PR's commit adds:

def _record_streams(
    tensors: Tuple[Optional[torch.Tensor], ...], streams: List[torch.Stream]
) -> None:
    for tensor, stream in itertools.product(filter(None, tensors), streams):
        tensor.record_stream(stream)

The mypy hook in .pre-commit-config.yaml installs only types-PyYAML, types-tabulate (no torch), and pyproject.toml sets ignore_missing_imports = true, so torch.Tensor resolves to Any. The annotation therefore degrades to Tuple[Any, ...], and the typeshed overload filter.__new__(cls, function: None, iterable: Iterable[_T | None]) -> filter[_T] leaves _T unsolved. mypy cannot type the product(...) element, so it demands an explicit annotation for tensor — a hard error, not a warning, hence the non-zero exit.

Worth noting independently: filter(None, tensors) evaluates truthiness of each tensor, and bool() on a multi-element torch.Tensor raises RuntimeError: Boolean value of Tensor with more than one element is ambiguous. So the line would also fail at runtime for any real activation tensor — fixing the mypy error properly fixes both.

Implicated commit: [REDACTED:Hex High Entropy String] — Raul Akhmetshin, "EXAMPLES/DEVICE/EP: Record tensor streams via real torch API instead of emulation."

File: examples/device/ep/nixl_ep/buffer.py:47

Suggested fix: Drop filter(None, ...) in favour of an explicit None check, which is both type-inferable and runtime-correct:

def _record_streams(
    tensors: Tuple[Optional[torch.Tensor], ...], streams: List[torch.Stream]
) -> None:
    for tensor, stream in itertools.product(tensors, streams):
        if tensor is not None:
            tensor.record_stream(stream)

Or, if you prefer to keep the filtering separate:

    present: Tuple[torch.Tensor, ...] = tuple(t for t in tensors if t is not None)
    for tensor, stream in itertools.product(present, streams):
        tensor.record_stream(stream)

Run pre-commit run mypy --files examples/device/ep/nixl_ep/buffer.py locally before pushing to confirm. (Optional follow-up, not needed for this PR: adding torch to the mypy hook's additional_dependencies would give real torch stubs and let mypy actually check record_stream, which expects a torch.cuda.Stream rather than the broader torch.Stream used in the signature.)

Related: PR #2275 (this change); no prior issue for this error signature.

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[rakhmets]

/build

[svc-nixl]

🤖 CI Triage Agent — Run Pre-Commit Hooks · commit 987a589e

TL;DR: The pre-commit job failed because examples/device/ep/nixl_ep/buffer.py has a Python syntax error — the torch.cuda.Stream( call in get_comm_stream() is never closed — which broke mypy, black and flake8. Add the missing ) (and trailing newline) at line 155.

Full analysis

Summary: Run Pre-Commit Hooks (GHA run 37600478946, PR #2275) failed: mypy, black and flake8 all reported a syntax error in examples/device/ep/nixl_ep/buffer.py.

Root cause: A genuine, unambiguous syntax error introduced by a bad merge resolution. In Buffer.get_comm_stream(), the return torch.cuda.Stream( call opened at line 151 lists three keyword arguments (stream_id, device_index, device_type) and then the block ends at a blank line (155) with no closing parenthesis, so the parser runs straight into the next def get_local_buffer_tensor( at line 156. All three hooks report the same thing:

• mypy: buffer.py:151: error: '(' was never closed [syntax] (exit 2)
• black: cannot format ...: Cannot parse for target version Python 3.13: 156:4: def get_local_buffer_tensor( (exit 123)
• flake8: buffer.py:151:34: E999 SyntaxError: '(' was never closed (exit 1)

This is not flaky and not infra — the file as committed cannot be imported at all.

Implicated commit: [REDACTED:Hex High Entropy String] — Raul Akhmetshin, "Merge branch 'main' into topic/emulate-record-stream". The surrounding get_comm_stream code comes from f767aaf4 ("Record tensor streams via real torch API instead of emulation"); the truncation appears to have been introduced while merging against 7c8d82c1 ("Removed high-throughput kernels").

File: examples/device/ep/nixl_ep/buffer.py:151-155

Suggested fix: Close the call and restore the trailing comma/newline before the next method:

    def get_comm_stream(self) -> torch.Stream:
        ts: torch.Stream = self.runtime.get_comm_stream()
        return torch.cuda.Stream(
            stream_id=ts.stream_id,
            device_index=ts.device_index,
            device_type=ts.device_type,
        )

    def get_local_buffer_tensor(

Then re-run pre-commit run --files examples/device/ep/nixl_ep/buffer.py locally (or python -m compileall examples/device/ep/nixl_ep/buffer.py) before pushing, to confirm mypy/black/flake8 pass. Re-check the rest of the merge diff in this file for other dropped lines from the same conflict resolution.

Related: none found — no existing issue or PR in the repo matches this signature.

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[svc-nixl]

🤖 CI Triage Agent — Run Pre-Commit Hooks · commit 987a589e

TL;DR: The pre-commit job failed because examples/device/ep/nixl_ep/buffer.py has a Python syntax error — the torch.cuda.Stream( call in get_comm_stream() is never closed — which broke mypy, black and flake8. Add the missing ) (and the return of the constructed stream) at the end of that call.

Full analysis

Summary: pre-commit run failed with mypy (exit 2), black (exit 123) and flake8 (exit 1) all reporting the same unterminated-paren syntax error in examples/device/ep/nixl_ep/buffer.py.

Root cause: Genuine Python syntax error, not infra. The log shows three independent parsers agreeing:

• examples/device/ep/nixl_ep/buffer.py:151: error: '(' was never closed [syntax] (mypy)
• cannot format ...buffer.py: Cannot parse for target version Python 3.13: 156:4: def get_local_buffer_tensor( (black)
• buffer.py:151:34: E999 SyntaxError: '(' was never closed (flake8)

Reading the source confirms it — get_comm_stream() opens return torch.cuda.Stream( at line 151, passes stream_id, device_index, device_type on lines 152–154, and then the file jumps straight to def get_local_buffer_tensor( at line 156 with no closing ). The parser therefore swallows the next def as part of the argument list, which is why black points at line 156 while the real defect is at 151. Note flake8/mypy abort after this error, so other hook findings in these files are currently masked.

Implicated commit: 987a589e — Raul Akhmetshin, "Merge branch 'main' into topic/emulate-record-stream" (2026-10-07T09:25:04Z), the HEAD commit of this PR. It merges 7c8d82c1 ("EXAMPLES/DEVICE/EP: Removed high-throughput kernels." #2345) into the topic branch; the truncated call looks like a conflict resolution that dropped the tail of the torch.cuda.Stream(...) expression.

File: examples/device/ep/nixl_ep/buffer.py:151-155

Suggested fix: Close the constructor call in get_comm_stream():

    def get_comm_stream(self) -> torch.Stream:
        ts: torch.Stream = self.runtime.get_comm_stream()
        return torch.cuda.Stream(
            stream_id=ts.stream_id,
            device_index=ts.device_index,
            device_type=ts.device_type,
        )

Then re-run the hooks locally before pushing — pre-commit run --files examples/device/ep/nixl_ep/buffer.py — since mypy and flake8 bailed out early and may surface additional findings in this file once it parses. Re-inspecting the rest of the merge diff for 987a589e is also worth doing, as a resolution that truncates an expression mid-call may have damaged other hunks that the hooks didn't cover (only five files were passed via --files).

Related: PR #2275 (this build); PR #2345 / commit 7c8d82c1, the merge parent. Prior work on this function: f767aaf4 "Record tensor streams via real torch API instead of emulation" and 873c66a1 "Fixed static check" — the same hooks caught a similar issue on this file before.

[itayalroy]

/build

[svc-nixl]

🤖 CI Triage Agent — nixl-ci-dl-gpu-ep · commit 53a089e0

TL;DR: The build failed in the "Allocate DL EP Environment" stage: salloc on the gb200nvl72_cx8 partition sat queued for the full --immediate=3600 window and gave up with Unable to allocate resources: Connection timed out — a slurm capacity/queueing problem, unrelated to the PR's code. Re-run the job, and make the EP allocation request GPUs instead of implicitly taking an exclusive whole node (the same change already made for nixl-ci-gpu-vr in #2352).

Full analysis

Summary: Stage Allocate DL EP Environment (node 164) failed after 3610 s; the slurm allocation for job name nixl-ci-dl-gpu-ep-1543 never got resources.

Root cause: The build compiled cleanly (stage 149 "Compiling NIXL EP Docker Image for DL" succeeded, image nixl-ci-dl-gpu-ep-test:1543 was built and pushed). The failure is entirely in the slurm allocation step:

+ ssh ... svc-nixl@dlcluster.nvidia.com salloc -N 1 -p gb200nvl72_cx8 \
      --job-name=nixl-ci-dl-gpu-ep-1543 --immediate=3600 --time=02:00:00 --no-shell --account=blackwell
salloc: lua: Setting job to exclusive mode as no gpu count was specified
salloc: Pending job allocation 2406328
salloc: job 2406328 queued and waiting for resources
salloc: error: Unable to allocate resources: Connection timed out

The timestamps show a single gap from 11:10:11Z to 12:10:20Z — exactly 3600 s, i.e. the --immediate=3600 deadline, not an unbounded hang. slurm reported the job as queued and waiting for resources the whole time and then returned the --immediate expiry error (slurm renders this as "Connection timed out"). So nothing hung and nothing in the commit under test ran: the gb200nvl72_cx8 partition simply had no free node within the hour.

A contributing factor is visible in the log: because the request specifies no GPU count, the site's job_submit lua plugin promotes it to exclusive mode, so the job must wait for an entire GB200 NVL72 node to drain rather than a couple of GPUs — which makes this timeout far more likely on a busy partition.

Implicated commit: none — the tested commit 53a089e (topic/emulate-record-stream, PR #2275) is not implicated; the failure is in CI infrastructure/scheduling. The relevant CI-side precedent is [REDACTED:Hex High Entropy String] by NirWolfer ("CI: request 2 GPUs instead of a whole node for nixl-ci-gpu-vr", #2352).

File: CI pipeline definition for nixl-ci-dl-gpu-ep — the slurm.allocation call with partition: gb200nvl72_cx8, nodes: 1, immediateTimeout: 3600 (shown verbatim at stage 164, line 2 of the stage log; the file was not readable from the repo snapshot, path likely under .ci/jenkins/pipeline/).

Suggested fix:

1. Immediate action: re-trigger build PLUGINS/UCX: Remove a superfluous using directive. #1543 for PR EXAMPLES/DEVICE/EP/CSRC: Removed at::cuda::*Stream*. #2275 — this failure carries no signal about the change.
2. Reduce exposure: add an explicit GPU request (e.g. --gpus-per-node=<n> / --gres=gpu:<n>) to the extraArgs of the slurm.allocation call for nixl-ci-dl-gpu-ep, so the lua plugin stops promoting the job to exclusive whole-node mode. This mirrors what CI: request 2 GPUs instead of a whole node for nixl-ci-gpu-vr #2352 did for nixl-ci-gpu-vr.
3. Make the failure non-fatal/retryable: wrap the allocation in a Jenkins retry with backoff, or report the --immediate expiry as an infra abort rather than a build failure, so capacity contention on gb200nvl72_cx8 doesn't show up as a red PR check.

Related: PR #2352 "CI: request 2 GPUs instead of a whole node for nixl-ci-gpu-vr" (#2352); PR #1f87bd7b/#2339 "CI: exclude misconfigured Vera Rubin nodes from nixl-ci-gpu-vr" — both address the same class of slurm allocation flakiness on these partitions.

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