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fixes for scattermoe from latest peft upgrade

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Wing Lian
2026-04-21 08:00:16 -04:00
parent 4195605ab2
commit 02e4f2350d
5 changed files with 119 additions and 142 deletions
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40
src/axolotl/integrations/kernels/libs/scattermoe_lora/__init__.py
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@@ -2,17 +2,35 @@
# Copyright (c) Axolotl AI
# Licensed under the Apache License, Version 2.0
from . import layers
from .lora_ops import ParallelExperts
from .parallel_experts import flatten_sort_count, parallel_linear
from .parallel_linear_lora import ScatterMoELoRA, parallel_linear_lora
from .lora_layout import (
peft_down_proj_lora_to_scattermoe,
peft_lora_B_to_scattermoe,
peft_lora_to_scattermoe,
validate_scattermoe_lora_shapes,
)
__all__ = [
"layers",
"ParallelExperts",
"flatten_sort_count",
"parallel_linear",
"ScatterMoELoRA",
"parallel_linear_lora",
"lora_ops",
"peft_down_proj_lora_to_scattermoe",
"peft_lora_B_to_scattermoe",
"peft_lora_to_scattermoe",
"validate_scattermoe_lora_shapes",
]
try:
from . import layers
from .lora_ops import ParallelExperts
from .parallel_experts import flatten_sort_count, parallel_linear
from .parallel_linear_lora import ScatterMoELoRA, parallel_linear_lora
except ModuleNotFoundError as exc:
if exc.name != "triton":
raise
else:
__all__ += [
"layers",
"ParallelExperts",
"flatten_sort_count",
"parallel_linear",
"ScatterMoELoRA",
"parallel_linear_lora",
"lora_ops",
]

86
src/axolotl/integrations/kernels/libs/scattermoe_lora/layers.py
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@@ -35,81 +35,19 @@ import torch
from torch import nn
from torch.nn import functional as F
from .lora_layout import (
peft_down_proj_lora_to_scattermoe,
peft_lora_B_to_scattermoe,
peft_lora_to_scattermoe,
)
from .parallel_experts import flatten_sort_count, parallel_linear
from .parallel_linear_lora import get_lora_params_from_wrapper, parallel_linear_lora
# =============================================================================
# LoRA layout conversion utilities (peft <-> scattermoe)
# =============================================================================
def peft_lora_B_to_scattermoe(peft_B, num_experts, rank):
"""Convert peft rank-major lora_B ``[out, E*r]`` to scattermoe
expert-major ``[N, r*E]``.
peft reshapes B to ``[out, r, E]`` (rank-major).
scattermoe slices B as ``[:, e*r:(e+1)*r]`` (expert-major).
"""
N = peft_B.shape[0]
return (
peft_B.reshape(N, rank, num_experts)
.permute(0, 2, 1)
.contiguous()
.reshape(N, num_experts * rank)
)
def peft_lora_to_scattermoe(peft_A, peft_B, num_experts, rank):
"""Convert peft LoRA weights to scattermoe layout (with A<->B swap).
peft operates on the parameter in its native storage layout ``[E, dim1, dim2]``
where ``in_features=dim1, out_features=dim2``. ScatterMoE transposes the
parameter (``W = param.transpose(2, 1)``) giving ``[E, dim2, dim1]`` with
``K=dim2, N=dim1``. Because of this transposition, peft's A and B roles
are swapped relative to scattermoe's convention.
peft gives:
lora_A ``[r*E, dim1]``, lora_B ``[dim2, r*E]``
scattermoe needs:
lora_A ``[r*E, K=dim2]``, lora_B ``[N=dim1, r*E]``
This function swaps A<->B and converts B from rank-major to expert-major.
Uses vectorized tensor operations (no Python loop over experts).
Works for **both** gate_up_proj and down_proj since the transposition
issue is the same for any parameter.
"""
peft_B_em = peft_lora_B_to_scattermoe(peft_B, num_experts, rank)
dim1 = peft_A.shape[1] # peft in_features -> scattermoe N
dim2 = peft_B_em.shape[0] # peft out_features -> scattermoe K
# smoe_A: per expert, transpose B_e [dim2, r] -> [r, dim2]
# [dim2, E*r] -> [dim2, E, r] -> [E, r, dim2] -> [E*r, dim2]
smoe_A = (
peft_B_em.reshape(dim2, num_experts, rank)
.permute(1, 2, 0)
.contiguous()
.reshape(rank * num_experts, dim2)
)
# smoe_B: per expert, transpose A_e [r, dim1] -> [dim1, r]
# [E*r, dim1] -> [E, r, dim1] -> [dim1, E, r] -> [dim1, E*r]
smoe_B = (
peft_A.reshape(num_experts, rank, dim1)
.permute(2, 0, 1)
.contiguous()
.reshape(dim1, num_experts * rank)
)
return smoe_A, smoe_B
def peft_down_proj_lora_to_scattermoe(peft_A, peft_B, num_experts, rank):
"""Deprecated alias for :func:`peft_lora_to_scattermoe`."""
return peft_lora_to_scattermoe(peft_A, peft_B, num_experts, rank)
__all__ = [
"peft_down_proj_lora_to_scattermoe",
"peft_lora_B_to_scattermoe",
"peft_lora_to_scattermoe",
]
# =============================================================================
# ParamWrapper unwrapping
@@ -199,7 +137,7 @@ def _unwrap_experts_lora(experts_module):
if gup is not None:
num_experts = gup.shape[0]
# Extract gate_up_proj LoRA (needs A<->B swap due to transposition)
# Extract gate_up_proj LoRA
gup_lora = None
gup_wrapper = wrappers.get("gate_up_proj")
if gup_wrapper is not None:
@@ -208,7 +146,7 @@ def _unwrap_experts_lora(experts_module):
rank = lora_A.shape[0] // num_experts
gup_lora = _convert_smoe_lora(lora_A, lora_B, num_experts, rank, scaling)
# Extract down_proj LoRA (needs A<->B swap due to transposition)
# Extract down_proj LoRA
down_lora = None
down_wrapper = wrappers.get("down_proj")
if down_wrapper is not None:

7
src/axolotl/integrations/kernels/libs/scattermoe_lora/parallel_linear_lora.py
View File

@@ -34,6 +34,7 @@ from .kernels.lora_ops import (
scatter2scatter_lora,
scatter2scatter_lora_dX,
)
from .lora_layout import validate_scattermoe_lora_shapes
class ScatterMoELoRA(torch.autograd.Function):
@@ -422,11 +423,6 @@ def get_lora_params_from_wrapper(module) -> tuple:
return lora_A, lora_B, scaling
# =============================================================================
# Drop-in replacement for parallel_linear
# =============================================================================
def parallel_linear_lora(
inputs: torch.Tensor,
expert_weights: torch.Tensor,
@@ -451,6 +447,7 @@ def parallel_linear_lora(
Otherwise falls back to standard scatter2scatter.
"""
if lora_A is not None and lora_B is not None:
validate_scattermoe_lora_shapes(expert_weights, lora_A, lora_B)
return ScatterMoELoRA.apply(
inputs,
expert_weights,