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# Adapted from https://github.com/HazyResearch/fly/tree/master/src/models/layers
import math
import torch
import torch.nn as nn
from einops import rearrange
from .structured_linear import StructuredLinear
from .blockdiag_multiply import blockdiag_multiply
class BlockdiagLinear(StructuredLinear):
def __init__(self, *args, nblocks=4, shuffle=False, **kwargs):
"""shuffle: apply channel_shuffle operation before the matmul as in ShuffleNet
"""
super().__init__(*args, **kwargs)
in_blksz = int(math.ceil(self.in_features / nblocks))
out_blksz = int(math.ceil(self.out_features / nblocks))
self.in_features_extended = in_blksz * nblocks
self.out_features_extended = out_blksz * nblocks
self.shuffle = shuffle
self.weight = nn.Parameter(torch.empty(nblocks, out_blksz, in_blksz))
self.reset_parameters()
def set_weights_from_dense_init(self, dense_init_fn_):
dense_weight = torch.empty(self.out_features_extended, self.in_features_extended,
device=self.weight.device, dtype=self.weight.dtype)
dense_init_fn_(dense_weight)
# Scale by sqrt because the weight is sparse
scaling = math.sqrt(dense_weight.numel() / self.weight.numel())
dense_weight *= scaling
with torch.no_grad():
nblocks = self.weight.shape[0]
self.weight.copy_(rearrange(dense_weight, '(b o) (b1 i) -> b b1 o i',
b=nblocks, b1=nblocks)[0])
@property
def saving(self):
return self.weight.numel() / (self.in_features * self.out_features)
def forward_matmul(self, x):
x = self.preprocess(x)
if self.shuffle:
x = rearrange(x, '... (group c_per_group) -> ... (c_per_group group)',
group=self.weight.shape[0]) # group=nblocks
output = blockdiag_multiply(x, self.weight)
return self.postprocess(output)
class BlockdiagSparsityConfig:
def __init__(self, nblocks, block=32, global_size=0):
"""shuffle: apply channel_shuffle operation before the matmul as in ShuffleNet
"""
self.nblocks = nblocks
self.block = block
self.global_size = global_size
def make_layout(self, out_features, in_features):
assert out_features % self.block == 0 and in_features % self.block == 0
assert out_features % self.nblocks == 0 and in_features % self.nblocks == 0
layout = torch.block_diag(*[torch.ones(out_features // self.nblocks,
in_features // self.nblocks,
dtype=torch.int32)] * self.nblocks)
if self.global_size > 0:
layout[:self.global_size] = 1
layout[:, :self.global_size] = 1
# Convert from (out_features, in_features) mask to
# (out_features // block, in_features // block) mask
layout = rearrange(layout, '(p blksz) (r blksz1) -> p r (blksz blksz1)',
blksz=self.block, blksz1=self.block)
return (layout > 0).any(dim=-1).int() |