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[ops, ci, docs] fix: validate Qwen3.5 NPU training
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#1919:
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#932
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[ops, ci, docs] fix: validate Qwen3.5 NPU training
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[ops, model] feat: fuse residual-add + RMSNorm fast path for Qwen3
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#1917:
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[ops, dist] feat: add MagiAttention with SM90 and Ulysses support
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[lora, trainer] feat: Add lora support for VLM trainer (#1018)
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#1915:
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[lora, trainer] feat: Add lora support for VLM trainer
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[ops, dist] feat: add MagiAttention with SM90 and Ulysses support
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#1913:
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[ops, dist] feat: add MagiAttention with SM90 and Ulysses support
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#1912:
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[ops, model] feat: fuse residual-add + RMSNorm fast path for Qwen3
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#1911:
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[ops, dist] feat: add MagiAttention with SM90 and Ulysses support
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[perf] fix: correct Qwen2.5-VL window attention FLOPs (#1024)
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#1909:
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[ops, dist] feat: add MagiAttention with SM90 and Ulysses support
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#1908:
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[ops, dist] feat: add MagiAttention with SM90 and Ulysses support
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#1907:
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[ops, model] feat: fuse residual-add + RMSNorm fast path for Qwen3
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#1906:
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[ops, perf, model] refactor: tighten fused-MoE grouped-GEMM max_M launch bound to token count
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#1905:
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[model] feat: Add SDPA fallback for BAGEL NPU attention
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#1904:
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Crystal-jiang:bagel_npu_precision
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[ops, dist] feat: add MagiAttention with SM90 and Ulysses support
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#1903:
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[model] fix: cast q to bfloat16 to use tilelang kernel
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#1902:
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[perf] fix: correct Qwen2.5-VL window attention FLOPs
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#1901:
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#1024
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[lora, trainer] feat: Add lora support for VLM trainer
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#1900:
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[trainer, perf] fix: apply HSDP all-reduce control to custom trainers
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#1898:
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#1026
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WenZheWang:wenzhe/hsdp-ga-allreduce-trainers
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[ops, perf, model] refactor: tighten fused-MoE grouped-GEMM max_M launch bound to token count
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#1897:
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#1025
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[ops, dist] feat: add MagiAttention with SM90 and Ulysses support
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#1896:
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[ops, dist] feat: add MagiAttention with SM90 and Ulysses support
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#1895:
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[ops, dist] feat: add MagiAttention with SM90 and Ulysses support
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#1894:
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