File size: 4,395 Bytes
a2ffd07 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | import os
import torch
from jaxtyping import Float
from loguru import logger
from torch import Tensor
from .types import TopK
# NOTE: Avoid this where possible to save memory!
def scatter_topk(topk: TopK, n_latents: int) -> Float[Tensor, "... n_latents"]:
"""
Scatter the k largest latents into a new tensor of shape (..., n_latents).
Args:
topk (TopK): The k largest latents.
n_latents (int): The number of latents.
Returns:
out (Float[Tensor, "... n_latents"]): The k largest latents.
"""
# ... n_latents
buffer = topk.values.new_zeros((*topk.indices.shape[:-1], n_latents))
# ... k -> ... n_latents
return buffer.scatter_(dim=-1, index=topk.indices, src=topk.values)
# Based on https://github.com/EleutherAI/sae/blob/19d95a401e9d17dbf7d6fb0fa7a91081f1b0d01f/sae/utils.py
def decode_triton(topk: TopK, weight: Tensor) -> Tensor:
shape = topk.indices.shape[:-1]
k = topk.indices.shape[-1]
n_inputs, n_latents = weight.shape
indices_flat = topk.indices.view(-1, k)
values_flat = topk.values.view(-1, k)
output: Tensor = TritonDecoderAutograd.apply(indices_flat, values_flat, weight) # type: ignore
return output.view(*shape, n_inputs)
def decode_cuda(topk: TopK, weight: Tensor, chunk_size: int = 1024) -> Tensor:
shape = topk.indices.shape[:-1]
k = topk.indices.shape[-1]
n_inputs, n_latents = weight.shape
indices_flat = topk.indices.view(-1, k)
values_flat = topk.values.view(-1, k)
batch_size = indices_flat.shape[0]
output = torch.zeros(
batch_size, n_inputs, device=topk.values.device, dtype=topk.values.dtype
)
for i in range(0, batch_size, chunk_size):
indices_chunk = indices_flat[i : i + chunk_size]
values_chunk = values_flat[i : i + chunk_size]
chunk_sparse = torch.sparse_coo_tensor(
indices=torch.cat(
[
torch.arange(
indices_chunk.shape[0], device=indices_chunk.device
).repeat_interleave(k),
indices_chunk.flatten(),
]
).view(2, -1),
values=values_chunk.flatten(),
size=(indices_chunk.shape[0], n_latents),
)
chunk_output = torch.sparse.mm(chunk_sparse, weight.t())
output[i : i + chunk_size] = chunk_output
return output.view(*shape, n_inputs)
# NOTE: 'sparse_coo_tensor' isn't supported yet for the MPS backend
def decode_mps(topk: TopK, weight: Tensor, chunk_size: int = 1024) -> Tensor:
shape = topk.indices.shape[:-1]
k = topk.indices.shape[-1]
n_inputs, n_latents = weight.shape
indices_flat = topk.indices.view(-1, k)
values_flat = topk.values.view(-1, k)
batch_size = indices_flat.shape[0]
output = torch.zeros(
batch_size, n_inputs, device=topk.values.device, dtype=topk.values.dtype
)
for i in range(0, batch_size, chunk_size):
indices_chunk = indices_flat[i : i + chunk_size]
values_chunk = values_flat[i : i + chunk_size]
weight_mask = weight[:, indices_chunk.view(-1)].view(
n_inputs, indices_chunk.shape[0], k
)
output_chunk = torch.bmm(
values_chunk.unsqueeze(1), weight_mask.permute(1, 2, 0)
).squeeze(1)
output[i : i + chunk_size] = output_chunk
return output.view(*shape, n_inputs)
def decode(topk: TopK, weight: Tensor) -> Tensor:
"""
Sparse decoder implementation.
Args:
topk (TopK): The k largest latents.
weight (Float[Tensor, "n_inputs n_latents"]): The decoder weight matrix.
Returns:
out (Float[Tensor, "... n_inputs"]): The reconstructions.
"""
...
try:
from .kernels import TritonDecoderAutograd
except ImportError:
logger.info("Triton not found")
if torch.backends.mps.is_available():
logger.info("MPS backend, using 'bmm' decoder")
decode = decode_mps
else:
logger.info("CPU/CUDA backend, using 'sparse_coo_tensor' decoder")
decode = decode_cuda
else:
logger.info("Triton found")
if os.environ.get("USE_TRITON", "1") == "1":
logger.info("Triton enabled, using Triton decoder")
decode = decode_triton
else:
logger.info("Triton disabled, using 'sparse_coo_tensor' decoder")
decode = decode_cuda
|