Sync upstream v2.17 to main - #100
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Caozhou1995
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added 6 commits
August 10, 2026 04:14
Resolved all 229 conflicts across P0/P1/P2 priorities: P0 (plugin system): No conflicts - fork's plugin/ directory fully preserved P1 (4 files): Manual merge preserving fork features + upstream additions 1. setup.py: Fork plugin build system + upstream NCCL EP support 2. transformer_engine/__init__.py: Fork plugin patches + upstream NCCL EP version check 3. transformer_engine/common/__init__.py: Fork skip_cuda_build() + plugin import 4. transformer_engine/pytorch/__init__.py: Fork torch_nv + upstream tensor/context utilities P2 (224 files): Accepted upstream version for core library files Upgrade path: v2.14 -> v2.17
- convert_host_pointers_to_tensor - get_device_pointer_for_data_and_scales These bindings were missing in upstream v2.17's pybind.cpp despite the corresponding C++ implementations existing in utils.cpp.
Applied 18 patches across 6 files to replace hardcoded 'cuda' device strings with TE_DEVICE_TYPE constant for multi-backend support. Changes: - utils.py: 4 patches - distributed.py: 3 patches - quantization.py: 6 patches - quantized_tensor.py: 1 patch - cpu_offload.py: 1 patch - jit.py: 3 patches All files passed Python AST syntax validation.
Added missing import for InstallCommand from setuptools.command.install to fix NameError during build.
These P2 files were git-added during Stage 3 merge but still contained conflict markers. Fixed by checking out the upstream v2.17 version for all affected files: - 43 source files (transformer_engine/common, pytorch, jax) - 10 test files - 4 doc/example files - 1 empty artifact removed (quantization_nvfp4.py renamed upstream) 64 files changed, accepting upstream version for all.
These files were generated during the v2.17 sync workflow for debugging and documentation purposes. They should not be part of the final PR.
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- ops.py: Add 15 new API declarations, update 4 existing signatures - cuda backend: Add 15 new methods, update 4 signatures in cuda.py and register_ops.py - All vendor backends (enflame/iluvatar/metax/musa/hygon): Add 19 new methods + OpImpl entries New APIs: splits_to_offsets_multi, copy_data_ptrs_to_device, bulk_allocate, create_empty_quantized_tensor, group_dequantize, get_grouped_gemm_setup_workspace_size, multi_tensor_pad_last_dim, multi_tensor_swizzle_scales_for_gemm_, multi_tensor_transpose_to_bhsd, cusolvermp_ctx_create, cusolvermp_ctx_destroy, newton_schulz, nvfp4_quantize_with_amax, nvfp4_group_quantize_with_amax, swizzle_scales_and_pack_ptrs_for_discrete_weights Modified signatures: group_quantize, bgrad_group_quantize (+tensor_offsets), clamped_swiglu, clamped_dswiglu (+glu_linear_offset)
The Stage 3 merge overwrote transformer_engine/__init__.py with upstream's version, losing the fork-specific TE_DEVICE_TYPE='cuda' default and the te_device_type() helper function. Multiple files depend on these: - transformer_engine/pytorch/utils.py - transformer_engine/pytorch/cpu_offload.py - transformer_engine/pytorch/distributed.py - transformer_engine/pytorch/jit.py - transformer_engine/debug/features/utils/stats_buffer.py - transformer_engine/pytorch/ops/fused/*.py
…Attention - Replace hardcoded device='cuda' with device=te_device_type() (2 locations) - Replace is_cuda checks with device.type == te_device_type() (3 locations) - These changes were lost during v2.17 upstream merge - Restores multi-backend compatibility from commit 4f54860
- triton/permutation.py: 12 te_device_type() calls restored - utils.py: 5 te_device_type() calls restored (_empty_tensor, normalize_device, torch_get_autocast_gpu_dtype)
…or, gemm, optimizers)
…ntion - Fix dot_product_attention.py: use 'from transformer_engine import te_device_type' instead of debug_state import - Add missing import in layernorm_mlp.py - Add missing import in quantization.py All imports now follow main branch convention: 'from transformer_engine import te_device_type'
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