mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2026-06-08 23:37:58 +00:00
07fd9328fa
- Create python/sft/ with 11 modules: base, amx, arch, autograd, layer, lora, weights, wrapper, dist_utils, config, __init__ - Move BaseSFTMoEWrapper + buffer management into sft/base.py (template method pattern: subclass provides _make_forward/backward_task) - Move AMXSFTMoEWrapper into sft/amx.py (thinner, no buffer logic) - Move from accelerate kt_moe.py: KTMoEFunction, KTMoELayerWrapper, MOEArchConfig, PEFT LoRA adaptation, weight extraction, wrapping - Add KTConfig dataclass (DeepSpeed pattern: opaque config passthrough) - Add _get_kt_config() with old→new field name compat conversion - Rename forward_sft→forward, submit_forward_sft→submit_forward, sync_forward_sft→sync_forward (Python only, C++ binding names unchanged) - Delete dump utilities from sft_moe.hpp (-526) and moe-sft-tp.hpp (-78) - Delete experts_sft.py and utils/amx_sft.py (moved to sft/) - Remove SFT stubs from BaseMoEWrapper (experts_base.py) - Lazy SFT import in __init__.py and experts.py (inference isolation) - Delete all lifecycle/debug logging (~500 lines) Verified: Qwen3-235B 4GPU AMXBF16 training, 3 steps loss converges.
489 lines
18 KiB
Python
489 lines
18 KiB
Python
# Weight extraction and loading utilities for SFT
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import json
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import logging
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import os
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import time
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from contextlib import nullcontext
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from dataclasses import dataclass
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import torch
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import torch.nn as nn
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from .arch import MOEArchConfig
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from .dist_utils import _maybe_zero3_gathered_parameters
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logger = logging.getLogger(__name__)
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try:
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from safetensors import safe_open
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SAFETENSORS_AVAILABLE = True
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except ImportError:
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SAFETENSORS_AVAILABLE = False
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safe_open = None
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# =============================================================================
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# Weight Extraction
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# =============================================================================
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def extract_moe_weights(
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moe_module: nn.Module, moe_config: MOEArchConfig
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Extract MoE expert weights from the module.
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Returns (gate_proj, up_proj, down_proj) with shape
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[expert_num, out_features, in_features].
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"""
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experts = getattr(moe_module, moe_config.experts_attr)
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gate_name, up_name, down_name = moe_config.weight_names
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gather_params: list[torch.nn.Parameter] = []
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for expert in experts:
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for weight_name in (gate_name, up_name, down_name):
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proj = getattr(expert, weight_name, None)
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if proj is not None and hasattr(proj, "weight"):
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# Handle PEFT LoRA wrapped modules
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weight = proj.weight
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if isinstance(weight, torch.Tensor):
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gather_params.append(weight)
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elif hasattr(weight, "data"):
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gather_params.append(weight.data)
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with _maybe_zero3_gathered_parameters(gather_params):
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gate_weights = []
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up_weights = []
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down_weights = []
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for expert in experts:
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# Handle PEFT LoRA wrapped modules - get weight tensor properly
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gate_proj = getattr(expert, gate_name)
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up_proj_mod = getattr(expert, up_name)
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down_proj_mod = getattr(expert, down_name)
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# Get weight tensors, handling both regular Linear and PEFT LoRA wrapped
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def get_weight_tensor(mod):
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weight = mod.weight
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if isinstance(weight, torch.Tensor):
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return weight.data
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elif hasattr(weight, "data"):
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return weight.data
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else:
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raise ValueError(f"Cannot extract weight from {type(mod)}, weight type={type(weight)}")
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gate_weights.append(get_weight_tensor(gate_proj))
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up_weights.append(get_weight_tensor(up_proj_mod))
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down_weights.append(get_weight_tensor(down_proj_mod))
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gate_proj = torch.stack(gate_weights, dim=0)
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up_proj = torch.stack(up_weights, dim=0)
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down_proj = torch.stack(down_weights, dim=0)
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return gate_proj, up_proj, down_proj
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def _clear_original_expert_weights(moe_module: nn.Module, moe_config: MOEArchConfig) -> None:
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"""
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Clear original expert weights to free memory after KT weights are loaded.
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"""
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experts = getattr(moe_module, moe_config.experts_attr, None)
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if experts is None:
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return
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def _iter_weight_params():
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for expert in experts:
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for weight_name in moe_config.weight_names:
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proj = getattr(expert, weight_name, None)
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if proj is None or not hasattr(proj, "weight"):
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continue
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parametrizations = getattr(proj, "parametrizations", None)
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parametrized_weight = getattr(parametrizations, "weight", None) if parametrizations is not None else None
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if parametrized_weight is not None:
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original = getattr(parametrized_weight, "original", None)
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if isinstance(original, torch.nn.Parameter):
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yield proj, parametrized_weight, "original", original
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continue
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direct_weight = getattr(proj, "_parameters", {}).get("weight")
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if isinstance(direct_weight, torch.nn.Parameter):
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yield proj, proj, "weight", direct_weight
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continue
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# Fallback: `weight` can be a non-settable property (e.g. parametrizations) or a non-Parameter.
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weight_attr = getattr(proj, "weight", None)
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if isinstance(weight_attr, torch.nn.Parameter):
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yield proj, proj, "weight", weight_attr
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gather_params: list[torch.nn.Parameter] = []
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for _, _, _, weight_param in _iter_weight_params():
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gather_params.append(weight_param)
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replaced_count = 0
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with _maybe_zero3_gathered_parameters(gather_params):
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for proj, container, param_name, weight_param in _iter_weight_params():
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original_dtype = weight_param.dtype
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# Create a CPU tensor with the correct shape but NO physical memory.
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# torch.empty(shape, device="cpu") unfortunately touches pages via the
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# allocator, consuming real RSS. Instead, allocate a 1-byte storage and
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# use set_ to give it the original shape with zero strides. The tensor
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# is "valid" (correct dtype, device, shape) so PEFT can discover
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# in/out features, but its storage is essentially zero-cost.
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# NOTE: reading element values from this tensor is undefined -- it is
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# only used for shape/dtype discovery by PEFT.
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tiny_storage = torch.UntypedStorage(1, device="cpu")
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fake_tensor = torch.tensor([], dtype=original_dtype, device="cpu").set_(
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tiny_storage, storage_offset=0, size=weight_param.shape,
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stride=[0] * len(weight_param.shape),
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)
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new_param = nn.Parameter(fake_tensor, requires_grad=False)
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replaced_count += 1
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# Avoid `KeyError: attribute 'weight' already exists` for parametrized modules
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# where `weight` is a property and the real parameter lives elsewhere.
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container_params = getattr(container, "_parameters", {})
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if isinstance(container_params, dict) and param_name in container_params:
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container_params[param_name] = new_param
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continue
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if hasattr(container, param_name):
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logger.debug(
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f"Skipping clearing expert weight {type(proj).__name__}.{param_name}: "
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"attribute exists but is not a registered parameter."
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)
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continue
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try:
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setattr(container, param_name, new_param)
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except Exception as exc:
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logger.warning(
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f"Failed to clear expert weight {type(proj).__name__}.{param_name}: {exc}"
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)
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logger.info(f"Replaced {replaced_count} expert weight params")
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# =============================================================================
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# kt_weight_path Loading Functions
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# =============================================================================
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@dataclass
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class INT8ExpertWeights:
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"""Container for INT8 expert weights with scales."""
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gate_proj: torch.Tensor
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gate_scale: torch.Tensor
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up_proj: torch.Tensor
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up_scale: torch.Tensor
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down_proj: torch.Tensor
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down_scale: torch.Tensor
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def _find_safetensor_files(kt_weight_path: str) -> list[str]:
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if not os.path.isdir(kt_weight_path):
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raise FileNotFoundError(f"kt_weight_path directory not found: {kt_weight_path}")
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safetensor_files = []
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for file in sorted(os.listdir(kt_weight_path)):
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if file.endswith(".safetensors"):
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safetensor_files.append(os.path.join(kt_weight_path, file))
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if not safetensor_files:
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raise FileNotFoundError(f"No safetensors files found in {kt_weight_path}")
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return safetensor_files
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def _load_kt_weight_index(kt_weight_path: str) -> dict[str, str]:
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if not SAFETENSORS_AVAILABLE:
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raise ImportError("safetensors is required for loading kt_weight_path")
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index = {}
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safetensor_files = _find_safetensor_files(kt_weight_path)
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for file_path in safetensor_files:
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with safe_open(file_path, framework="pt") as f:
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for key in f.keys():
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index[key] = file_path
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logger.info(f"Indexed {len(index)} tensors from {len(safetensor_files)} safetensors files")
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return index
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def _dequant_fp8_experts(weights: list[torch.Tensor], scales: list[torch.Tensor | None], block_size: tuple[int, int]) -> torch.Tensor:
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"""Dequantize a list of FP8 expert weights and stack them (batched, vectorized).
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Args:
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weights: list of [out, in] float8_e4m3fn tensors (one per expert)
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scales: list of [out//bs_m, in//bs_n] scale_inv tensors (one per expert, may be None)
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block_size: (bs_m, bs_n)
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Returns:
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Stacked BF16 tensor of shape [num_experts, out, in]
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"""
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has_scales = scales[0] is not None
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if not has_scales:
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return torch.stack(weights, dim=0).to(torch.bfloat16).cpu().contiguous()
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bs_m, bs_n = block_size
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n = len(weights)
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out_features, in_features = weights[0].shape
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# Stack all experts: [N, out, in] fp8 -> reshape to blocks -> bf16
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w = torch.stack(weights, dim=0) # [N, out, in] fp8
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w = w.reshape(n, out_features // bs_m, bs_m, in_features // bs_n, bs_n)
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w = w.to(torch.bfloat16)
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# Stack all scales: [N, out//bs_m, in//bs_n] -> bf16, broadcast multiply
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s = torch.stack(scales, dim=0).to(torch.bfloat16) # [N, out//bs_m, in//bs_n]
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w = w * s[:, :, None, :, None]
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return w.reshape(n, out_features, in_features).contiguous()
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def load_experts_from_checkpoint_files(
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checkpoint_files: list[str],
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sharded_metadata: dict | None,
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layers_prefix: str,
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moe_config: MOEArchConfig,
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layer_idx: int,
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block_size: tuple[int, int] | None = None,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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if not SAFETENSORS_AVAILABLE:
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raise ImportError("safetensors is required for loading experts from checkpoint files")
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if not checkpoint_files:
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raise FileNotFoundError("checkpoint_files is empty")
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t0 = time.time()
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weight_map = None
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base_dir = os.path.dirname(checkpoint_files[0])
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if sharded_metadata is not None:
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weight_map = sharded_metadata.get("weight_map", None)
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gate_name, up_name, down_name = moe_config.weight_names
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keys = []
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for expert_idx in range(moe_config.expert_num):
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base = f"{layers_prefix}.{layer_idx}.{moe_config.moe_layer_attr}.{moe_config.experts_attr}.{expert_idx}"
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keys.append(f"{base}.{gate_name}.weight")
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keys.append(f"{base}.{gate_name}.weight_scale_inv")
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keys.append(f"{base}.{up_name}.weight")
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keys.append(f"{base}.{up_name}.weight_scale_inv")
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keys.append(f"{base}.{down_name}.weight")
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keys.append(f"{base}.{down_name}.weight_scale_inv")
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keys_by_file: dict[str, list[str]] = {}
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mapped_count = 0
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unmapped_count = 0
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for key in keys:
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if weight_map is not None:
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filename = weight_map.get(key)
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if filename is None:
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unmapped_count += 1
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continue
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mapped_count += 1
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file_path = os.path.join(base_dir, filename)
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else:
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file_path = checkpoint_files[0]
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keys_by_file.setdefault(file_path, []).append(key)
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print(
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f"[kt_moe] Layer {layer_idx}: key mapping done in {time.time()-t0:.1f}s — "
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f"total_keys={len(keys)}, mapped={mapped_count}, unmapped={unmapped_count}, "
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f"files_to_open={len(keys_by_file)}",
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flush=True,
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)
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t1 = time.time()
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tensor_map: dict[str, torch.Tensor] = {}
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for file_idx, (file_path, file_keys) in enumerate(keys_by_file.items()):
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with safe_open(file_path, framework="pt") as f:
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available_keys = set(f.keys())
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for key in file_keys:
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if key in available_keys:
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tensor_map[key] = f.get_tensor(key)
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if file_idx == 0:
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print(
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f"[kt_moe] Layer {layer_idx}: first file loaded ({os.path.basename(file_path)}, "
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f"{len(file_keys)} keys) in {time.time()-t1:.1f}s",
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flush=True,
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)
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print(
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f"[kt_moe] Layer {layer_idx}: all files loaded in {time.time()-t1:.1f}s — "
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f"tensor_map has {len(tensor_map)} tensors",
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flush=True,
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)
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gate_weights = []
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up_weights = []
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down_weights = []
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gate_scales = []
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up_scales = []
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down_scales = []
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for expert_idx in range(moe_config.expert_num):
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base = f"{layers_prefix}.{layer_idx}.{moe_config.moe_layer_attr}.{moe_config.experts_attr}.{expert_idx}"
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gate_key = f"{base}.{gate_name}.weight"
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up_key = f"{base}.{up_name}.weight"
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down_key = f"{base}.{down_name}.weight"
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if gate_key not in tensor_map or up_key not in tensor_map or down_key not in tensor_map:
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raise FileNotFoundError(f"Missing expert weights for layer {layer_idx}, expert {expert_idx}")
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gate_weights.append(tensor_map[gate_key])
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up_weights.append(tensor_map[up_key])
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down_weights.append(tensor_map[down_key])
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gate_scales.append(tensor_map.get(f"{base}.{gate_name}.weight_scale_inv"))
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up_scales.append(tensor_map.get(f"{base}.{up_name}.weight_scale_inv"))
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down_scales.append(tensor_map.get(f"{base}.{down_name}.weight_scale_inv"))
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# Check if weights are FP8 and need dequantization
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t2 = time.time()
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is_fp8 = gate_weights[0].dtype == torch.float8_e4m3fn
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if is_fp8:
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if block_size is None:
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block_size = (128, 128)
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print(
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f"[kt_moe] Layer {layer_idx}: FP8 expert weights detected, "
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f"dequantizing with block_size={block_size} "
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f"(has_scales={gate_scales[0] is not None})",
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flush=True,
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)
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gate_proj = _dequant_fp8_experts(gate_weights, gate_scales, block_size)
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up_proj = _dequant_fp8_experts(up_weights, up_scales, block_size)
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down_proj = _dequant_fp8_experts(down_weights, down_scales, block_size)
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else:
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gate_proj = torch.stack(gate_weights, dim=0).cpu().to(torch.bfloat16).contiguous()
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up_proj = torch.stack(up_weights, dim=0).cpu().to(torch.bfloat16).contiguous()
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down_proj = torch.stack(down_weights, dim=0).cpu().to(torch.bfloat16).contiguous()
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print(
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f"[kt_moe] Layer {layer_idx}: done — dtype={gate_proj.dtype}, shape={gate_proj.shape}, "
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f"dequant={time.time()-t2:.1f}s, total={time.time()-t0:.1f}s",
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flush=True,
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)
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return gate_proj, up_proj, down_proj
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def load_experts_from_kt_weight_path(
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kt_weight_path: str,
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layer_idx: int,
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num_experts: int,
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hidden_size: int,
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intermediate_size: int,
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) -> INT8ExpertWeights:
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"""Load INT8 preprocessed expert weights from kt_weight_path for a specific layer."""
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if not SAFETENSORS_AVAILABLE:
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raise ImportError("safetensors is required for loading kt_weight_path")
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index = _load_kt_weight_index(kt_weight_path)
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numa_count = 0
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test_key_prefix = f"blk.{layer_idx}.ffn_gate_exps.0.numa."
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for key in index.keys():
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if key.startswith(test_key_prefix) and key.endswith(".weight"):
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numa_idx = int(key.split("numa.")[1].split(".")[0])
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numa_count = max(numa_count, numa_idx + 1)
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if numa_count == 0:
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raise FileNotFoundError(
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f"No weights found for layer {layer_idx} in {kt_weight_path}. "
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f"Expected keys like 'blk.{layer_idx}.ffn_gate_exps.0.numa.0.weight'"
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)
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logger.info(
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f"Loading INT8 weights for layer {layer_idx}: {num_experts} experts, {numa_count} NUMA partitions"
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)
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gate_weights_list = []
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gate_scales_list = []
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up_weights_list = []
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up_scales_list = []
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down_weights_list = []
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down_scales_list = []
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for expert_idx in range(num_experts):
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gate_w_parts = []
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gate_s_parts = []
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for numa_idx in range(numa_count):
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w_key = f"blk.{layer_idx}.ffn_gate_exps.{expert_idx}.numa.{numa_idx}.weight"
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s_key = f"blk.{layer_idx}.ffn_gate_exps.{expert_idx}.numa.{numa_idx}.scale"
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if w_key not in index:
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raise FileNotFoundError(f"Weight key not found: {w_key}")
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with safe_open(index[w_key], framework="pt") as f:
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gate_w_parts.append(f.get_tensor(w_key))
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gate_s_parts.append(f.get_tensor(s_key))
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gate_w = torch.cat(gate_w_parts, dim=0)
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gate_s = torch.cat(gate_s_parts, dim=0)
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gate_w = gate_w.view(intermediate_size, hidden_size)
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gate_weights_list.append(gate_w)
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gate_scales_list.append(gate_s)
|
|
|
|
up_w_parts = []
|
|
up_s_parts = []
|
|
for numa_idx in range(numa_count):
|
|
w_key = f"blk.{layer_idx}.ffn_up_exps.{expert_idx}.numa.{numa_idx}.weight"
|
|
s_key = f"blk.{layer_idx}.ffn_up_exps.{expert_idx}.numa.{numa_idx}.scale"
|
|
|
|
if w_key not in index:
|
|
raise FileNotFoundError(f"Weight key not found: {w_key}")
|
|
|
|
with safe_open(index[w_key], framework="pt") as f:
|
|
up_w_parts.append(f.get_tensor(w_key))
|
|
up_s_parts.append(f.get_tensor(s_key))
|
|
|
|
up_w = torch.cat(up_w_parts, dim=0)
|
|
up_s = torch.cat(up_s_parts, dim=0)
|
|
up_w = up_w.view(intermediate_size, hidden_size)
|
|
|
|
up_weights_list.append(up_w)
|
|
up_scales_list.append(up_s)
|
|
|
|
down_w_parts = []
|
|
down_s_parts = []
|
|
for numa_idx in range(numa_count):
|
|
w_key = f"blk.{layer_idx}.ffn_down_exps.{expert_idx}.numa.{numa_idx}.weight"
|
|
s_key = f"blk.{layer_idx}.ffn_down_exps.{expert_idx}.numa.{numa_idx}.scale"
|
|
|
|
if w_key not in index:
|
|
raise FileNotFoundError(f"Weight key not found: {w_key}")
|
|
|
|
with safe_open(index[w_key], framework="pt") as f:
|
|
down_w_parts.append(f.get_tensor(w_key))
|
|
down_s_parts.append(f.get_tensor(s_key))
|
|
|
|
down_w = torch.cat(down_w_parts, dim=0)
|
|
down_s = torch.cat(down_s_parts, dim=0)
|
|
down_w = down_w.view(hidden_size, intermediate_size)
|
|
|
|
down_weights_list.append(down_w)
|
|
down_scales_list.append(down_s)
|
|
|
|
gate_proj = torch.stack(gate_weights_list, dim=0)
|
|
gate_scale = torch.stack(gate_scales_list, dim=0)
|
|
up_proj = torch.stack(up_weights_list, dim=0)
|
|
up_scale = torch.stack(up_scales_list, dim=0)
|
|
down_proj = torch.stack(down_weights_list, dim=0)
|
|
down_scale = torch.stack(down_scales_list, dim=0)
|
|
|
|
return INT8ExpertWeights(
|
|
gate_proj=gate_proj,
|
|
gate_scale=gate_scale,
|
|
up_proj=up_proj,
|
|
up_scale=up_scale,
|
|
down_proj=down_proj,
|
|
down_scale=down_scale,
|
|
)
|