fix: bind a CUDA context on the calling thread — the right one, and on both sides of the boundary

[YangXu1990uiuc]

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Affected area

• Python API or bindings; FE OSS kernels or CuTeDSL

Summary

A driver-API launch reads the calling thread's context stack, and a thread that has done no CUDA work has nothing on it. Two fixes, one problem, one on each side of the Python/C++ boundary:

• Python (_device.py, _pygraph.py): ensure_current_context returned as soon as any context was current, so a thread bound to another GPU's context kept it. It now resolves the target instead of accepting the incumbent, and execute() passes the handle's device.
• C++ (cudnn_frontend_shim.h, graph_interface.h): the backend has the same gap. cuDNN's runtime-compiled engines fail with CUDA_ERROR_INVALID_CONTEXT on a genuinely cold thread — measured, 3/3 — and pass as soon as any runtime call touches the thread first. A guard at the C++ execute funnel covers every backend and OSS execute.

This PR has been rebased and rescoped. Everything it originally carried is already on develop; what is left is the one thing nobody fixed, plus the backend-side half it exposed. See the history below.

Why

A context is not interchangeable just because it exists.

stream	wrong context current	what happens
a real stream	it carries its own context	cross-context launch rejected — CUDA_ERROR_INVALID_HANDLE. Loud.
a default-stream handle — 0, CU_STREAM_LEGACY (0x1), CU_STREAM_PER_THREAD (0x2)	carries no context; resolves against whatever is current	work runs on that context's GPU, where the pointers are invalid → async fault at some later sync. Silent.

Measured on parley (5 GPUs, sm89/sm100/sm90/sm80):

torch default stream handle: dev0=0x0  dev1=0x0        <- carries no device information
  cuStreamGetCtx(0)                    with ctx0 -> ctx0   with ctx1 -> ctx1
  cuStreamGetCtx(CU_STREAM_LEGACY)     with ctx0 -> ctx0   with ctx1 -> ctx1
  cuStreamGetCtx(CU_STREAM_PER_THREAD) with ctx0 -> ctx0   with ctx1 -> ctx1
                                                       <- all three follow the thread, not the work

module loaded in ctx0, launched on ctx1's explicit stream:
  cuLaunchKernelEx -> 400 (CUDA_ERROR_INVALID_HANDLE)  <- the loud path
module loaded in ctx0, launched on stream 0 with a cuda:1 pointer:
  cuLaunchKernelEx -> 0 ; cuStreamSynchronize -> 700   <- CUDA_ERROR_ILLEGAL_ADDRESS, and the context is poisoned

torch's default stream is stream 0, so the reachable path is the silent one. Before/after, same scenario (thread holds cuda:0's context, work belongs to cuda:1, stream 0):

  develop today  -> ctx cuda:0   WRONG GPU
  this PR        -> ctx cuda:1   correct

Two further changes fall out of resolving the target:

• execute() passes handle.device.ordinal. #612 made the handle own its device; the function was still asking the CUDA runtime which GPU the thread was on. cudaGetDevice() stays only as the fallback for a caller that cannot name a device — the FE's own path no longer reaches it. (cuda.bindings.runtime is not a new dependency: cuda-python is already required, cuda.bindings.driver is imported at module scope in ~20 files, and this same probe is already reached on this path via frost/buffers.py::current_device_id.)
• The blanket except Exception: pass is gone. Every failure path is now an explicit driver error code, so an out-of-range ordinal no longer turns into "silently no context" and then reappears at the launch wearing a different face. The contract stays best-effort for driver conditions.

Why the backend never needed this, and the python path does

The obvious objection: cuDNN's own FORT engines launch through the driver too, from the same autograd worker threads, and nobody ever wrote this for them. So what is different?

Nothing in the stack establishes it deliberately — the CUDA runtime does, incidentally, and every driver-API launcher rides on that. The cuDNN backend included: it has the same requirement and the same gap, it just never surfaces because something runtime-flavoured almost always touches the thread first.

Measured on a cuDNN graph that lands on the runtime-compiled (FORT) engines — matmul + relu + relu, a driver-API launch, not the <<<>>> precompiled path:

A. genuinely cold thread — cuDNN is the FIRST cuda work on it
   cold #0/#1/#2   ctx 0x0 -> 0x0
                   cuCtxGetLimit returned error invalid device context (201)
                   -> CUDNN_STATUS_NOT_SUPPORTED

B. same thread, ONE torch op first
   warmed #0/#1/#2 ctx 0x44b446b0 -> 0x44b446b0   OK

Deterministic, 3/3 each way. (Allocating the output tensor is enough to warm it, which is why no framework has ever seen this; an earlier version of this experiment had a stray C.zero_() on the worker thread and reported the opposite.)

So this is not "the front end is missing something the backend has". Both need a bound context; the backend gets one for free from the runtime work that always precedes it, and the python-engine path is where the accident finally does not happen — a batch_invariant GDN backward on an autograd worker issues no runtime call before cuTensorMapEncodeTiled. Fixing it at the FE's own execute seam is the same placement torch uses for cuBLAS (CublasHandlePool.cpp::getCurrentCUDABlasHandle, device-aware, warn-once) and Triton uses in its launcher.

The backend cannot be patched retroactively, so the guard is placed in the front end instead — at execute_plan_at_index, which graph_interface.h already documents as the point every execute overload funnels through. Verified by rebuilding the pybind module and re-running the same reproducer:

before (develop's C++)   cold #0/#1/#2   ctx 0x0 -> 0x0          error 201
after                    cold #0/#1/#2   ctx 0x0 -> 0x31275a20   OK

Driver entry points are resolved through the runtime (cudaGetDriverEntryPointByVersion), so the front end still never links libcuda — the approach cu_tensor_map_encode_tiled already uses and documents. (NV_FE_CALL_TO_CU exists in the shim but is unused, and expands to a direct call that would require the link in the non-dynamic-loading build.) Both build configurations compile clean.

Context-independent loading (cuLibrary*/CUkernel) is in there too, and it solves a different problem — a CUfunction is valid only in the context it was loaded into, which is why a ctx0 module launched on a ctx1 stream returns CUDA_ERROR_INVALID_HANDLE. It does not help a thread that has no context at all. cuDNN ships both because they are two problems; the crash that opened this PR was cuTensorMapEncodeTiled → CUDA_ERROR_INVALID_CONTEXT, which is neither a launch nor a load — just a driver call that requires a current context.

So the gap is a boundary artifact, not a missing idea. Every backend op enters through a C entry point that establishes the context on the way in. The python engines never enter libcudnn at all — Python → CuTeDSL/cuTile → driver — so there is no entry point on that path to do it. pygraph.execute's python-engine branch is our cudnnBackendExecute, which is why the call belongs there and not in create_handle (context binding is thread-local, and the handle is created on a different thread than the one that executes) nor in the torch integration (aten/src/ATen/{cudnn,native/cudnn} contains no context code at all — it never needed any; torch writes this pattern for cuBLAS, in CublasHandlePool.cpp::getCurrentCUDABlasHandle, at that library's handle seam, and device-aware exactly as here).

Overhead — measured on both sides, and not visible on either

C++ (every backend execute). The guard probes with one cuCtxGetCurrent and fetches the stream only when a context has to be established. Backend graph.execute() host time, rebuilding the module for each configuration:

	median
develop, no guard	10.805 µs
this PR, probe first	10.498 µs
this PR, probe first (second build)	10.683 µs
this PR, earlier unconditional cudnnGetStream	10.788 µs

The PR measured faster than develop in both builds, so the between-build delta is noise — run-to-run spread alone is ~0.35 µs across the samples, against a guard that costs one cuCtxGetCurrent (~106 ns, ~1%). The unconditional version landed inside the same band, so cudnnGetStream from C++ is nowhere near the ~1.5 µs the Python path costs through pybind; probing first is still the right shape, but it was not buying back a visible regression.

Python (every python-engine execute).

Resolving the target costs a second driver call. I expected that to matter and it does not:

	microbenchmark	in situ
raw cuCtxGetCurrent	106 ns
accept-any (develop today)	235 ns
resolve-target (this PR)	417–526 ns
cost of the whole call inside a real op		59 ns, 0.1%

Measured by no-op'ing the call around a real GDN execute on sm100: 78.80 µs with it, 78.88 µs without, 1.00 call per op. The microbenchmark delta is real and irrelevant — the denominator is 79 µs. No memoization, no thread-local cache; the simple correct version is the one that ships.

History — how the pieces got separated

This PR was opened 2026-08-17 with the function in frost/device.py, the call site in pygraph.execute, deletion of the duplicate ensure_cuda_context in the cuTile LA engines, and a cold-thread test. Since then:

1. First-class cudnn.Handle (create_handle returns an object owning {backend handle, device, stream}) #612 (first-class cudnn.Handle) refactored frost/device.py into shims over a new _device.py, and carried this PR's call site over with the import rewritten to the new home — while the definition stayed on this branch. import cudnn then failed on develop (develop: import cudnn fails - _pygraph imports ensure_current_context which _device no longer defines (#612) #634), and every python test failed at collection.
2. python: add the ensure_current_context #612 imports but never defined #638 restored the missing half by re-deriving the function into _device.py. It is behaviourally identical to what this branch had — including the accept-any early return, which is the bug this PR now fixes. (It also picked up the blanket except from the cuTile ancestor and dropped a != 0 guard.)
3. Align FROST LA with FLA/FI conventions for state layout and fix context and IMA bug #644 absorbed the rest of this PR: the cuTile duplicate deletion and the cold-thread test, both already on develop.

So the accept-any semantics were never anyone's decision — the shape was inherited from linear_attention/cutile/kernels/common.py, hoisted twice, and reviewed as a hotfix each time.

The cuTile runtime is now a declared extra

cuda.tile was not declared anywhere — not an extra, not requirements.txt — so whether the cuTile linear-attention engines run at all depended on the environment happening to have it. That is the other half of the coverage note below: those engines decline in check_support when the import fails, and the test skips, silently.

cutile = ["cuda-tile>=1.4; python_version >= '3.10'"]. Base cuda-tile only: its [tileiras] extra pins cuda-toolkit>=13.2,<13.4, and that upper bound would cap the whole environment's toolkit and shut CUDA 12 out entirely — the same reason nvidia-cutlass-dsl is not pinned to the FROST floor here. Without it, cuda.tile falls back to a system tileiras (_compile.py::_find_pip_tileiras), consistent with this package already leaving GPU wheels to the user.

Resolution checked rather than assumed — .[cutedsl,cutile] together:

Resolved 15 packages in 3.17s
 + cuda-tile==1.5.0

One package added, nothing downgraded, no cuda-toolkit pulled in: base cuda-tile requires only typing-extensions. The python_version marker keeps the extra resolvable on the declared 3.9 floor, which cuda-tile itself does not support.

Note on #644's absorption (checked, and it is structurally correct): the deleted ensure_cuda_context(stream) was the first statement of the cuTile engine's execute(), and the seam call in pygraph.execute runs strictly earlier — before build_plan and before plan.execute — so it dominates. But test_execute_from_a_thread_with_no_cuda_context skips the entire cuTile backend wherever cuda.tile is not installed (no gdn_cutile plan for this graph (offered: ['gdn_frost'])), so that half is only actually exercised on a runner that ships it. That was because cuda.tile was not declared anywhere; the [cutile] extra above fixes the declaration, but whether CI installs it is still worth confirming.

Testing

test/python/test_ensure_current_context.py (new, L0, driver-only — no engine, no torch):

• cold thread ends up bound
• a context on another GPU is replaced, parametrized over all three default-stream handles, each asserting first that cuStreamGetCtx follows the thread (skips below 2 GPUs)
• a real stream's context wins over the named device (skips below 2 GPUs)
• steady state is a no-op — no rebind, no primary-context churn

Run on parley (sm100 for the engine suites, all 5 GPUs for the driver-only tests):

test_ensure_current_context + test_device_info + test_set_stream_cache        14 passed
test_la.py -k no_cuda_context (sm100)                          3 passed, 3 skipped (cuTile: no cuda.tile)
test_la.py + test_ensure_current_context (sm100, -n 4)       386 passed,   0 failed,  488 skipped
default path: test_dispatch + gemm + matmul + the device tests
  (no CUDNN_FRONTEND_ENABLE_FROST_ENGINES, as CI runs it)   5905 passed,   0 failed, 3002 skipped
conv_fuzzer + wgrads + dispatch + gemm + matmul, against the
  rebuilt module (i.e. exercising the C++ change)           1259 passed,   2 failed, 8688 skipped

The two failures are test_render_e5m3_tile_constants[16-8-2] and [32-4-1] — sm107 block-scale TMEM budget arithmetic, pure Python, no CUDA. Confirmed pre-existing by pointing the same test bed at gh/develop's python/cudnn and getting the identical two failures.

The test_la skips are the cuTile half (cuda.tile absent in this test bed) and the arch-gated rows — identical on develop.

CI on 991b9bb (this change before the two review rounds below): pipeline success — 14 jobs green, 0 failed, covering py_test:{rel,dev} on Ampere / Hopper / Blackwell and frost:rel:{gemm,linear}:sm100, frost:rel:sdpa:{sm80,sm100,sm120}. Re-run on the final commit is in flight.

API and compatibility impact

ensure_current_context(stream) gains an optional second parameter device. Existing single-argument calls keep working, and with no device named the behaviour is the previous one. Internal helper; not part of the public cudnn surface.

Review

• codex, P1 — do not override a bound context when no device was named. Valid, and the contract it violates is this repo's own: frost/device.py::ambient_device documents "a bound driver context wins — it is process-wide and authoritative", with the runtime's thread-local slot as the second rung. The device is None branch had inverted those. Now a bound context is left alone when nobody named a GPU, and only a cold thread is given one.
• codex, P2 — unbalanced primary-context retains. Valid: a thread alternating default-stream work across two GPUs re-retained on every transition. _primary_context is now lru_cached, so each ordinal is retained once and held for the process lifetime — which is what the docstring already claimed.
• CodeRabbit, _device.py (Major): CU_STREAM_LEGACY / CU_STREAM_PER_THREAD must be treated as default streams. Confirmed on hardware and fixed — they are 0x1/0x2 and cuStreamGetCtx answers with the calling thread's current context for both, so the accept-any behaviour was re-admitted for exactly those two handles. The fix reads the constants off the binding (no hardcoded 0x1/0x2) and the regression test is parametrized over all three.

Related issues

Supersedes the original scope of this PR (absorbed by #644). Fixes the semantics introduced by #638 / #634.

---

_{note to self: claude::c2a7afc6-a7b4-4a29-a477-370aaaa6adf1 — "Investigate why PR 612 broke test_mhas". cwd /home/scratch.yanxu_libs/cudnn_frontend · worktree /home/scratch.yanxu_gpu/fe-ctxfix · probes /home/scratch.yanxu_gpu/probe638}

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📝 Walkthrough

Walkthrough

The PR updates CUDA context selection for explicit devices, stream-owned contexts, cross-device replacement, and primary-context binding. Graph execution now binds the handle stream context before plan execution. CUDA tests cover direct context setup and cold-thread graph execution.

Changes

CUDA Context Selection

Layer / File(s)	Summary
Context resolution and validation python/cudnn/_device.py, include/cudnn_frontend_shim.h, test/python/test_ensure_current_context.py	ensure_current_context resolves runtime devices, prioritizes non-default stream contexts, replaces mismatched contexts, binds retained primary contexts, and handles unavailable APIs without throwing. Tests cover cold threads, default streams, stream precedence, context preservation, device reuse, and cleanup.
Graph execution context binding python/cudnn/_pygraph.py, include/cudnn_frontend/graph_interface.h, test/python/test_ensure_current_context.py	Graph execution ensures the handle stream context before plan execution. Python-engine execution passes the handle device ordinal, or None when no handle is supplied. The integration test validates execution on a previously unbound worker thread.

Estimated code review effort: 3 (Moderate) | ~25 minutes

Merge Risk: 🟡 Moderate · up to f666e

The change improves device-correct CUDA context binding, but merge readiness is currently limited by a possible runtime exception when CUDA symbols cannot be loaded and by a portability issue in BFLOAT16 test setup. These should be fixed before merging.

Suggested reviewers: anerudhan, yeliu-oss

Sequence Diagram(s)

sequenceDiagram
  participant PythonEngine
  participant Graph
  participant ensure_current_context
  participant CUDA
  PythonEngine->>Graph: execute plan with handle
  Graph->>ensure_current_context: pass handle stream and device
  ensure_current_context->>CUDA: query current, stream, and primary contexts
  CUDA-->>ensure_current_context: bind selected context
  Graph->>CUDA: prepare variant pack and execute plan

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🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 70.59% which is insufficient. The required threshold is 80.00%.	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 summarizes the main change: binding the correct CUDA context on the calling thread across Python and C++ execution paths.
Description check	✅ Passed	The description covers all template sections with detailed scope, rationale, compatibility impact, related issues, and test results.

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Inline comments:
In `@python/cudnn/_device.py`:
- Around line 102-107: Update the stream-selection logic around cuStreamGetCtx
to recognize CU_STREAM_LEGACY and CU_STREAM_PER_THREAD (0x1 and 0x2) as default
streams, skipping the real-stream context branch so normal device selection
remains effective. Add a regression test covering CU_STREAM_PER_THREAD.

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CI triage on `5d914cf` — settled by a retry: the red was flaky, not this change.

Same commit, no code difference, py_test:dev re-run:

first run   Ampere FAILED   Hopper FAILED   Blackwell success
retry       Ampere success  Hopper success

The failures were 480–517 tests each, all carrying one message:

CUDNN_BACKEND_TENSOR_DESCRIPTOR cudnnFinalize failed ptrDesc->finalize()
cudnn_status: CUDNN_STATUS_SUBLIBRARY_LOADING_FAILED

— a cuDNN sublibrary failing to load, raised during C++ graph build. Supporting evidence:

• py_test:rel passed on all three architectures in the same pipeline, running identical code; only the debug-build jobs failed, and Blackwell's debug job passed while Ampere's and Hopper's failed.
• Locally, the exact failing suites pass against a build of this branch: test_conv_fuzzer.py 1040 passed / 0 SUBLIBRARY errors, test_wgrads.py 1 passed.

Correction to what this comment said before: it cited other PRs flip-flopping on py_test:dev as evidence of flakiness. That was wrong — py_test:dev is skipped on most PRs, so it says nothing either way. The retry above is the actual evidence. (The frost:rel:sdpa flip-flopping on unrelated PRs, e.g. #666 green on one commit and red on the next, was checked and does hold.)

analysis:guardwords_scan fails on every ref in the project; the violations it lists are pre-existing files (frost/occupancy.py, tile_dsl/*, ..._dglu_rubin.py, linear_attention/cutile/kernels/kda.py, …), none of them touched here.

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Inline comments:
In `@include/cudnn_frontend_shim.h`:
- Around line 435-447: Update get_driver_entry_point to perform a non-throwing
get_cuda_symbol lookup, validate get_entry_point before invoking it, and return
nullptr when libcudart or the requested symbol cannot be loaded, preventing
exceptions during ensure_current_context initialization.

In `@test/python/test_ensure_current_context.py`:
- Around line 160-174: Update the BFLOAT16 graph test setup before tensor
allocation to skip when the GPU capability or cuDNN backend version does not
support the configuration, not just when CUDA is unavailable. In the
graph-building flow around cudnn.pygraph and g.build, catch
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[YangXu1990uiuc]

Pushed 09c6807fc after the approval — please re-glance before merging.

The C++ guard was calling cudnnGetStream on every execute, though only the cold path needs a stream. It now probes with one cuCtxGetCurrent and fetches the stream only when a context has to be established.

Backend graph.execute() host time, rebuilding the module for each:

develop, no guard                      10.805 us
this PR, probe first                   10.498 us
this PR, probe first (second build)    10.683 us
this PR, earlier unconditional query   10.788 us

The PR measured faster than develop in both builds, so the between-build delta is noise — run-to-run spread alone is ~0.35 us, against a guard costing one cuCtxGetCurrent (~106 ns). Worth noting the unconditional version was also inside that band: cudnnGetStream from C++ is nowhere near the ~1.5 us the Python path costs through pybind, so this was a structural cleanup rather than a regression fix.

Re-verified after the change: cold thread 3/3 ctx 0x0 -> 0x100e0630 OK, warmed 3/3 OK, 15 unit tests passed, and all four build configurations (default / dynamic-loading / disable-exception / both) compile clean.