v2.20.2

Transformer Engine v2.20.2 Release Notes

Note

The v2.20.0, v2.20.1 releases were skipped due to issues during the release process.

Key Features and Enhancements

• [Common, JAX, PyTorch] Added an experimental CuTeDSL backend for MXFP8 quantization, enabled at build time with NVTE_WITH_CUTEDSL=1 and selected at runtime with NVTE_ENABLE_CUTEDSL_BACKEND=1, with automatic fallback to CUDA C++ kernels. (#3137)
• [Common, PyTorch] Added fused grouped MXFP8 paths for weighted SwiGLU and clamped-SwiGLU activation with columnwise quantization and for in-place requantization. (#3315) (#3359)
• [Common] Improved low-precision quantization performance with wider NVFP4 stochastic-rounding instructions, columnwise MXFP8 dBias reduction, register-resident MXFP8 cast kernels, and removal of an unused MXFP8 tile fetch. (#3357) (#3439) (#3459) (#3500)
• [Common, PyTorch] Added opt-in Quantile Balancing histogram accumulation to the fused sigmoid router, including two-kernel and fused-atomic modes. (#3395)
• [Common, PyTorch] Added native, row-scaled, and feature-gated fused grouped-MLP support for the SiTU-GLU activation. (#3402)
• [PyTorch] Enabled torch.compile(fullgraph=True) for Linear with supported FP8, MXFP8, and NVFP4 recipes and adopted current opaque custom-class registration where available. (#3053) (#3495)
• [PyTorch] Added per-sequence mask, window, and diagonal-alignment policies to packed THD attention, with automatic padded or grouped dispatch. (#3274)
• [Common, PyTorch] Added a CUDA graph-capturable fused Expert Parallelism prepare-and-dispatch path and reduced eager dispatch/combine CPU overhead. (#3282) (#3380)
• [PyTorch] Added NVTE_PLUGIN support for loading alternate PyTorch backend implementations and overriding FlashAttention and attention-backend selection. (#3401)
• [PyTorch] Reduced grouped-layer CPU overhead by avoiding redundant quantizer validation, reusing grouped-MLP fusion plans, and eliminating repeated device discovery. (#3405) (#3410)
• [PyTorch] Added paged-stashing metadata for saved GroupedLinear activation storage, including unquantized BF16 and FP16 rowwise tensors. (#3423)
• [PyTorch] Reduced CUDA graph memory retention by releasing warmup and capture outputs when consumed and clearing per-callable replay state on reset. (#3427)
• [PyTorch] Added ScaledTanhSReLU, a tanh-soft-clamped squared ReLU with per-row scaling and feature-gated cuDNN fused grouped-MLP support. (#3463)
• [PyTorch] Added experimental cuDNN-backed GatedDeltaProductAttention. (#3539)
• [JAX] Added Hopper BF16 grouped GEMM support. (#3083)
• [JAX] Added sqrtsoftplus scoring to fused MoE routing. (#3448)
• [JAX] Enabled Expert Parallelism to borrow XLA's NCCL communicator when supported, added single-process multi-device execution, and defaulted bootstrap to the AllGather-scan metadata path with fused scan-dispatch support. (#3452) (#3522) (#3561)
• [Build] Improved source-build dependency discovery for CUDA, NVCC, headers, and libraries installed through Python packages. (#3251)
• [Build] Reduced highly parallel source-build time by splitting grouped activation CUDA translation units. (#3430)

Fixed Issues

• [Common] Fixed illegal memory accesses and incorrect results in TMA-based quantization by preserving shared-memory pointer provenance and correcting Grouped MXFP8 work mapping and descriptor synchronization. (#3482) (#3483)
• [Common] Fixed BF16 GEMM precision loss on L40 by excluding cuBLASLt split-K algorithms that store partial reductions in BF16. (#3440)
• [Common, JAX] Fixed race conditions between zeroing and routed gradient stores in Triton permutation kernels. (#3581)
• [PyTorch] Fixed fused grouped-MLP correctness for E5M2 backward gradients, padded single-group SReLU inputs, and unsupported GEGLU or SiTU-GLU configurations on Rubin. (#3352) (#3485) (#3567)
• [PyTorch] Fixed cuDNN grouped-MLP weight-gradient compilation, CUDA graph capture, and FC1/FC2 corruption by retaining separate persistent descriptor workspaces. (#3579)
• [PyTorch] Fixed quantized parameter integration by supporting MXFP8 master-weight casts on ranks with empty shards and preserving quantized-tensor subclasses across detach() and C++ bindings. (#3348) (#3393)
• [PyTorch] Fixed delayed-scaling FP8 activation-recompute metadata pairing for evaluation mode, mode transitions, nested checkpoints, and checkpoint entry under disabled autograd. (#3394)
• [PyTorch] Fixed delayed weight-gradient computation when an external consumer clears an eagerly computed FP8 bias gradient before backward_dw(). (#3400)
• [PyTorch] Fixed FusedAdam with different parameter and gradient dtypes in capturable mode and prevented tensor-handle pool exhaustion for highly fragmented parameter groups. (#3414) (#3418)
• [PyTorch] Fixed Quantile Balancing router capture and replay with mutable bin bounds through an explicit trusted-producer validation handshake. (#3426)
• [PyTorch] Fixed non-fused and BF16 activation paths from failing when activation_recompute_in_mlp=True; unsupported paths now warn and proceed without recomputation. (#3436)
• [PyTorch] Fixed NCCL-EP zero-copy MXFP8 scale offsets for symmetric-memory windows and made offset resolution compatible with older and newer PyTorch APIs. (#3447) (#3466)
• [PyTorch] Made fused grouped-MLP dprob bit-exact when deterministic algorithms are requested and a supporting cuDNN Frontend is installed. (#3407)
• [PyTorch] Replaced deprecated TorchScript fallbacks with eager execution when torch.compile is disabled or unsupported. (#3502)
• [JAX] Fixed fused-attention backward output-gradient sharding, rotated THD metadata handling, and compound DP/FSDP test-data placement. (#3516)
• [PyTorch] Fixed NVFP4 stochastic-quantization corruption and invalid memory reads by retaining the owning RNG tensors through dispatch. (#3533)
• [Common, PyTorch] Fixed distributed Newton-Schulz numerical correctness, workspace sizing and lifetime, and validation of unsupported matrix dimensions. (#3541)
• [Build, PyTorch] Fixed source builds to gate NCCL-EP consistently on targeted GPU architectures and available PyTorch symmetric-memory support. (#3396)
• [Common, JAX, PyTorch] Fixed NCCL-EP synchronization, timeout, and shared-workspace failures and added validated compatibility for builds using NCCL 2.30.5–2.30.x with newer NCCL runtimes through NCCL_DEV_API_JIT. (#3619)

Breaking Changes in This Release

• [Common, JAX, PyTorch] Raised the minimum supported cuDNN version from 9.3 to 9.12.0, added configure-time, build-time, import-time, and runtime enforcement, and deprecated the now-obsolete experimental nvte_get_runtime_num_segments. (#3236)
• [Common, PyTorch] Added MXFP8 support to cuBLASMp communication-overlap GEMMs and raised the minimum cuBLASMp version for NVTE_WITH_CUBLASMP=1 from 0.8.0 to 0.8.1. (#3145)
• [Common, PyTorch] Reduced P2P context-parallel attention forward temporary storage from O(CP) to O(1); callers of experimental nvte_cp_thd_out_correction must pass the new old_lse argument. (#2916)
• [PyTorch] Added attention-logit softcapping to DotProductAttention, MultiheadAttention, and TransformerLayer; callers passing optional arguments after bottom_right_diagonal positionally must switch to keyword arguments. (#3391)
• [PyTorch] Added experimental GatedDeltaNet2Attention and a shared linear-attention base API; experimental GatedDeltaNetAttention no longer accepts name or derives from TransformerEngineBaseModule, and source builds now require nvidia-cudnn-frontend>=1.29.0. (#3521)

Deprecated Features

• [Common, JAX, PyTorch] Added opaque-handle v2 fused-attention C APIs, deprecated the legacy nvte_get_fused_attn_backend, nvte_fused_attn_fwd, and nvte_fused_attn_bwd shims, and removed NVTE_FUSED_ATTN_BACKEND in favor of automatic backend selection. (#2964)

Known Issues in This Release

• [Common] cuBLAS 13.7.0 through 13.8.0 may silently leave grouped-GEMM output tiles unwritten on Blackwell and Rubin when NCCL kernels run concurrently, producing incorrect gradients. No Transformer Engine workaround is available; upgrade to cuBLAS 13.8.1 or later. (#3578)
• [Common, Build] Building or running Transformer Engine with cuSOLVERMp support enabled against CUDA Toolkit 13.4 Update 1 and cuSOLVERMp 0.9.1 or earlier may fail with unresolved cuSOLVER symbols while linking, loading, or using the Transformer Engine library. If affected, use the base CUDA Toolkit 13.4 release instead.