[Common] Comm+GEMM overlap API updated to support cuBlasMp backend (incl. framework API) by denera · Pull Request #2443 · NVIDIA/TransformerEngine

denera · 2025-12-02T19:47:14Z

Description

This PR adds support for the NVTE cuBlasMp bindings in the Comm+GEMM overlap API.

Type of change

Documentation change (change only to the documentation, either a fix or a new content)
Bug fix (non-breaking change which fixes an issue)
New feature (non-breaking change which adds functionality)
Breaking change (fix or feature that would cause existing functionality to not work as expected)
Infra/Build change
Code refactoring

Checklist:

I have read and followed the contributing guidelines
The functionality is complete
I have commented my code, particularly in hard-to-understand areas
I have made corresponding changes to the documentation
My changes generate no new warnings
I have added tests that prove my fix is effective or that my feature works
New and existing unit tests pass locally with my changes

ptrendx · 2025-12-02T21:35:51Z

@@ -17,6 +18,12 @@

 #define NVTE_COMM_OVERLAP_MAX_STREAMS 3

+/* \brief Check if TE is built with cuBlasMp.


nit: cuBLASMp

ptrendx · 2025-12-02T21:40:28Z

@@ -526,6 +514,11 @@ class CommOverlapHelper : torch::CustomClassHolder {
 ExtComm comm);

 void ub_barrier(ExtComm comm);
+
+ int64_t get_nccl_comm_ptr(std::string comm_name) {
+ NVTE_CHECK(backend_is_nccl, "Cannot get nccComm_t ptr if backend is not NCCL.");


This error message could be more descriptive - e.g. something like "chosen backend for the communication-computation overlap (cuBLASMp) requires NCCL communicator, but the passed ProcessGroup uses a different backend."

greptile-apps · 2025-12-16T23:03:21Z

Greptile Summary

This PR integrates the cuBLASMp backend into the Comm+GEMM overlap API across both the PyTorch and JAX frameworks, adding new constructors, Python bindings, and test infrastructure so callers can opt in via initialize_ub(with_cublasmp=True) or --use-cublasmp flags.

C++ layer: Adds cuBLASMp constructors to CommOverlapCore, CommOverlapBase, and CommOverlapP2PBase that accept a raw ncclComm_t, plus a cublasmp_capture_warmup helper in the PyTorch extension layer that runs a dummy matmul to force cuBLASMp's lazy NCCL window registration and workspace allocation before any CUDA-graph capture occurs. CommOverlapHelper is extended to bootstrap and manage NCCL communicators via ncclCommInitRank when built with NVTE_WITH_CUBLASMP=1.
Python layer (PyTorch): initialize_ub() gains a with_cublasmp flag; module classes (Linear, LayerNormLinear, LayerNormMLP) add runtime guards that disable bulk overlaps and emit warnings for the cuBLASMp path, and the forward/backward functions are updated to read results from the GEMM output tensor rather than the extra-output buffer.
Python layer (JAX): cgemm_helper.cpp is updated to select between Userbuffers and cuBLASMp executors based on CgemmConfig::use_cublasmp, including use_cublasmp in the plan-ID hash for correct executor caching.

Confidence Score: 3/5

Not safe to merge as-is: several correctness issues introduced by this PR remain unaddressed across the C++, Python, and test layers.

The warmup helper leaks up to three device allocations whenever the cuBLASMp matmul or stream-sync throws, which happens inside the object constructor and therefore silently survives into production. The JAX NCCL ID file still has no per-run isolation token, so concurrent test jobs on a shared machine can corrupt each other's communicator bootstrap. The test script's non-FP8 cuBLASMp path never assigns all_outputs2 for the atomic+AG+check-numerics combination, meaning that code path would crash at runtime. The public comm_gemm_overlap.h header now pulls in nccl.h and comm_gemm.h unconditionally, forcing NCCL onto every downstream translation unit regardless of build flags, and test_comm_gemm.cu includes cublasmp.h without a compile-time guard, breaking non-cuBLASMp developer builds.

transformer_engine/pytorch/csrc/extensions/comm_gemm_overlap.cpp (warmup memory leak), transformer_engine/common/include/transformer_engine/comm_gemm_overlap.h (unconditional NCCL headers), transformer_engine/jax/csrc/extensions/cgemm_helper.cpp (NCCL ID file race), tests/pytorch/distributed/run_gemm_with_overlap.py (all_outputs2 undefined), tests/cpp_distributed/test_comm_gemm.cu (unconditional cublasmp.h include)

Important Files Changed

Filename	Overview
transformer_engine/pytorch/csrc/extensions/comm_gemm_overlap.cpp	Adds cuBLASMp bootstrap logic to CommOverlapHelper (ncclCommInitRank, shared_ptr lifetime management) and the cublasmp_capture_warmup helper; CUDA memory is freed with bare cudaFree calls that will leak on any exception thrown by the warmup GEMM or stream sync.
transformer_engine/pytorch/module/linear.py	Forward path gains cuBLASMp-aware output routing and an unconditional out.view() that reshapes the output for every caller (not just cuBLASMp), which changes output shape for existing 3-D input users.
transformer_engine/pytorch/module/base.py	initialize_ub() gains with_cublasmp parameter; correctly bypasses multicast check, disables bulk/external methods, and sets _ub_initialized flag. CommOverlapHelper now always takes two process-group args for the single-domain case.
transformer_engine/pytorch/module/layernorm_linear.py	Bulk-overlap flags are correctly disabled at runtime via using_cublasmp_backend(); cuBLASMp-aware output routing added for forward and backward; ln_out_total aliasing guard prevents premature deallocation.
transformer_engine/pytorch/module/layernorm_mlp.py	any() call now correctly takes a list; bulk flags cleared under cuBLASMp; ln_out_total alias guard added; fc2 wgrad re-gather for cuBLASMp path looks correct.
transformer_engine/jax/csrc/extensions/cgemm_helper.cpp	use_cublasmp is now included in the plan_id hash and the correct tp_rank accessor is used; NCCL ID file path still lacks a per-run isolation token, creating a cross-job race on shared machines.
tests/pytorch/distributed/run_gemm_with_overlap.py	torch.transpose and tuple-unpack bugs fixed; but all_outputs2 is still never assigned for the non-FP8 cuBLASMp path (--quantization none --atomic --comm-type AG --check-numerics), causing a NameError at line 829.
transformer_engine/pytorch/ops/fused/userbuffers_backward_linear.py	cuBLASMp column-parallel backward path: async x gather started before dgrad GEMM+RS, waited before wgrad; FP8/MXFP8 quantizer usage split looks correct.
transformer_engine/common/include/transformer_engine/comm_gemm_overlap.h	Adds cuBLASMp constructors and _with_cublasmp/_cublasmp_ctx members; nccl.h and comm_gemm.h included unconditionally without #ifdef guards, requiring NCCL in all translation units that include this header.
tests/cpp_distributed/test_comm_gemm.cu	cublasmp.h included unconditionally; CMakeLists.txt appends $ENV{CUBLASMP_HOME}/include without guarding, so non-cuBLASMp builds fail to compile this test.

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…ormerEngine into common/tp-overlap-cublasmp

…ublasmp

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denera · 2026-05-27T15:03:29Z

/te-ci

…d non-deterministic failures, removed XLA_FLAGS modifications for TE/JAX tests Signed-off-by: Alp Dener <adener@nvidia.com>

…ormerEngine into common/tp-overlap-cublasmp

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denera · 2026-05-28T00:17:47Z

/te-ci L1

ptrendx · 2026-05-28T22:07:24Z

+ void cublasmp_ag_gemm(const TensorWrapper &A, bool transa, const TensorWrapper &B, bool transb,
+ TensorWrapper &D, TensorWrapper &bias, TensorWrapper &pre_gelu_out,
+ bool grad, bool accumulate, cudaStream_t stream_main);
+
+ void cublasmp_gemm_rs(const TensorWrapper &A, bool transa, const TensorWrapper &B, bool transb,
+ TensorWrapper &D, TensorWrapper &bias, TensorWrapper &pre_gelu_out,
+ bool grad, bool accumulate, cudaStream_t stream_main);
+
+ void cublasmp_gemm_ar(const TensorWrapper &A, bool transa, const TensorWrapper &B, bool transb,
+ TensorWrapper &D, TensorWrapper &bias, TensorWrapper &pre_gelu_out,
+ bool grad, bool accumulate, cudaStream_t stream_main);
+


Why do we need those functions if we already have the nvte_* calls? In general this is supposed to be C API so we should not add and rely on even more C++ things to it.

These exist only because the Userbuffers backend is all C++ and it's just keeping everything together.
I agree with you about the C API issue, but I think that's going to need to be a separate refactor PR entirely.

…tstrapping cuBLASMp Signed-off-by: Alp Dener <adener@nvidia.com>

…ormerEngine into common/tp-overlap-cublasmp

…ublasmp

denera · 2026-06-01T07:11:06Z

/te-ci L1

Signed-off-by: Alp Dener <adener@nvidia.com>

…ublasmp

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denera · 2026-06-01T17:38:42Z

/te-ci L1

Signed-off-by: Alp Dener <adener@nvidia.com>

…ublasmp

denera · 2026-06-02T18:30:03Z

/te-ci L1

denera requested review from jberchtold-nvidia, phu0ngng and ptrendx December 2, 2025 19:47

denera self-assigned this Dec 2, 2025

denera force-pushed the common/tp-overlap-cublasmp branch 2 times, most recently from 908bbc2 to 69cf235Compare December 2, 2025 20:12

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deneraand others added 6 commits December 17, 2025 02:16

cuBlasMp backend logic added to TE/common with connections to framewo…
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added use_cublasmp flags to CollectiveGemm bootstrapping to avoid UB …
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added cuBLASMp backend option to JAX unit tests for CollectiveGEMM
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cublasmp backend support in comm+GEMM overlap extended to fusible ops
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disabling fused attention in TE/PyTorch comm+GEMM layers test to avoi…
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cuichenx mentioned this pull request Jul 9, 2026
[PyTorch] TE 2.17 Userbuffers initialization crashes in CommOverlapP2P with a GIL/refcount error #3199
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test(functional): quarantine MegatronMIMO training smoke NVIDIA-NeMo/Megatron-Bridge#4867
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		@@ -17,6 +18,12 @@

		#define NVTE_COMM_OVERLAP_MAX_STREAMS 3

		/* \brief Check if TE is built with cuBlasMp.

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Confidence Score: 3/5

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