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https://github.com/kvcache-ai/ktransformers.git
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Complete SFT (Supervised Fine-Tuning) backend for MoE models using AMX SIMD: Core C++ implementation: - sft_moe.hpp: Forward/backward with LoRA fused operations (~5500 lines) - moe-sft-tp.hpp: Tensor-parallel wrapper for multi-NUMA - amx/moe-sft-tp.hpp: AMX-specific TP implementation - avx_kernels.hpp: AVX512 SIMD kernels for LoRA GEMM - amx_kernels.hpp: AMX tile kernels for Panel5 rank-outer optimization - worker_pool: RDTSC profiling, Chrome trace output, SFT timer infrastructure - ext_bindings.cpp: SFT MOE pybind bindings (BF16/INT8/INT4 + SkipLoRA variants) Python sft/ submodule (kt_kernel.sft): - base.py: BaseSFTMoEWrapper with buffer management (template method pattern) - amx.py: AMXSFTMoEWrapper (weight loading, C++ task construction) - autograd.py: KTMoEFunction (torch.autograd.Function for distributed training) - layer.py: KTMoELayerWrapper (nn.Module replacing HF MoE layers) - arch.py: MOEArchConfig (Qwen3/DeepSeek/Mixtral architecture detection) - weights.py: Expert weight extraction and checkpoint loading - lora.py: PEFT LoRA adaptation (view buffers, grad buffers, save/load adapter) - wrapper.py: wrap_moe_layers_with_kt_wrapper, load_kt_model, build_kt_device_map - config.py: KTConfig dataclass (DeepSpeed-style opaque config passthrough) - dist_utils.py: Distributed gather/scatter, checkpoint-phase detection Design decisions: - Rank-0-only expert pattern: only rank 0 holds C++ wrapper and expert weights - DeepSpeed-style integration: accelerate keeps only KTransformersPlugin (framework interaction fields), all logic in kt_kernel.sft - Inference isolation: importing kt_kernel does not load sft/ submodule - Old field name compatibility: _get_kt_config() converts kt_xxx→xxx automatically Verified: Qwen3-235B-A22B 4GPU AMXBF16 training, loss converges normally.
83 lines
2.3 KiB
Python
83 lines
2.3 KiB
Python
# SFT (Supervised Fine-Tuning) submodule for kt-kernel
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# SPDX-License-Identifier: Apache-2.0
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"""
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SFT training support for KT-Kernel MoE.
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This submodule adds training capabilities (forward/backward, LoRA, autograd,
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distributed) on top of the inference-only kt_kernel base package.
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Additional dependencies beyond base kt_kernel: torch.nn, torch.distributed, peft (optional).
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"""
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from .config import KTConfig
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from .base import BaseSFTMoEWrapper, KExpertsSFTBuffer
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from .amx import AMXSFTMoEWrapper
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from .arch import (
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MOEArchConfig, get_moe_arch_config, get_moe_module, move_non_experts_to_gpu, get_expert_device,
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KTAMXError, KTAMXNotAvailableError, KTAMXModelNotSupportedError, KTAMXConfigError,
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)
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from .autograd import KTMoEFunction
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from .layer import KTMoELayerWrapper
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from .weights import (
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extract_moe_weights,
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load_experts_from_checkpoint_files,
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load_experts_from_kt_weight_path,
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INT8ExpertWeights,
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)
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from .lora import (
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kt_adapt_peft_lora,
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get_kt_lora_params,
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update_kt_lora_pointers,
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sync_kt_lora_gradients,
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save_lora_experts_to_adapter,
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save_kt_moe_to_adapter,
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load_lora_experts_from_adapter,
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load_kt_moe_from_adapter,
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LoRAExpertMLP,
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LoRAExperts,
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)
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from .wrapper import (
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wrap_moe_layers_with_kt_wrapper,
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build_kt_device_map,
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build_kt_device_map_simplified,
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get_kt_loading_kwargs,
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load_kt_model,
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)
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__all__ = [
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"KTConfig",
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"BaseSFTMoEWrapper",
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"KExpertsSFTBuffer",
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"AMXSFTMoEWrapper",
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"MOEArchConfig",
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"get_moe_arch_config",
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"get_moe_module",
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"move_non_experts_to_gpu",
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"get_expert_device",
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"KTAMXError",
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"KTAMXNotAvailableError",
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"KTAMXModelNotSupportedError",
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"KTAMXConfigError",
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"KTMoEFunction",
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"KTMoELayerWrapper",
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"extract_moe_weights",
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"load_experts_from_checkpoint_files",
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"load_experts_from_kt_weight_path",
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"INT8ExpertWeights",
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"kt_adapt_peft_lora",
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"get_kt_lora_params",
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"update_kt_lora_pointers",
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"sync_kt_lora_gradients",
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"save_lora_experts_to_adapter",
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"save_kt_moe_to_adapter",
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"load_lora_experts_from_adapter",
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"load_kt_moe_from_adapter",
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"LoRAExpertMLP",
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"LoRAExperts",
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"wrap_moe_layers_with_kt_wrapper",
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"build_kt_device_map",
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"build_kt_device_map_simplified",
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"get_kt_loading_kwargs",
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"load_kt_model",
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]
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