File size: 6,152 Bytes
3087568 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | """Oasis 2D patch embedding."""
from __future__ import annotations
import torch
import torch.nn as nn
import triton
import triton.language as tl
from ..L1.conv2d import Conv2d
@triton.jit
def _patch_embed_2x2_kernel(
x,
weight,
bias,
out,
N: tl.constexpr,
BM: tl.constexpr,
BN: tl.constexpr,
):
pid_m = tl.program_id(0)
pid_n = tl.program_id(1)
m = pid_m * BM + tl.arange(0, BM)
oc = pid_n * BN + tl.arange(0, BN)
patch = m % 144
batch = m // 144
oh = patch // 16
ow = patch % 16
k = tl.arange(0, 64)
channel = k // 4
pixel = k % 4
ih = oh[:, None] * 2 + pixel[None, :] // 2
iw = ow[:, None] * 2 + pixel[None, :] % 2
x_off = (
batch[:, None] * (16 * 18 * 32)
+ channel[None, :] * (18 * 32)
+ ih * 32
+ iw
)
a = tl.load(x + x_off, mask=m[:, None] < N * 144, other=0.0)
b = tl.load(
weight + oc[None, :] * 64 + k[:, None],
mask=oc[None, :] < 1024,
other=0.0,
)
# Match the round-to-nearest TF32 inputs used by cuDNN on these batches.
a_bits = a.to(tl.int32, bitcast=True)
b_bits = b.to(tl.int32, bitcast=True)
a_bits = (a_bits + 0xFFF + ((a_bits >> 13) & 1)) & -0x2000
b_bits = (b_bits + 0xFFF + ((b_bits >> 13) & 1)) & -0x2000
a_tf32 = a_bits.to(tl.float32, bitcast=True)
b_tf32 = b_bits.to(tl.float32, bitcast=True)
acc = tl.dot(a_tf32, b_tf32, input_precision="tf32")
acc += tl.load(bias + oc, mask=oc < 1024, other=0.0)[None, :]
tl.store(
out + m[:, None] * 1024 + oc[None, :],
acc,
mask=(m[:, None] < N * 144) & (oc[None, :] < 1024),
)
@triton.jit
def _patch_embed_20x20_kernel(
x,
weight,
bias,
out,
BM: tl.constexpr,
BN: tl.constexpr,
BK: tl.constexpr,
):
m = tl.program_id(0) * BM + tl.arange(0, BM)
oc = tl.program_id(1) * BN + tl.arange(0, BN)
oh = m // 32
ow = m % 32
acc = tl.zeros((BM, BN), tl.float32)
for k0 in range(0, 1200, BK):
k = k0 + tl.arange(0, BK)
channel = k // 400
pixel = k % 400
ih = oh[:, None] * 20 + pixel[None, :] // 20
iw = ow[:, None] * 20 + pixel[None, :] % 20
a = tl.load(
x + channel[None, :] * (360 * 640) + ih * 640 + iw,
mask=(m[:, None] < 576) & (k[None, :] < 1200),
other=0.0,
)
b = tl.load(
weight + oc[None, :] * 1200 + k[:, None],
mask=(oc[None, :] < 1024) & (k[:, None] < 1200),
other=0.0,
)
acc = tl.dot(a, b, acc, input_precision="tf32x3")
acc += tl.load(bias + oc, mask=oc < 1024, other=0.0)[None, :]
tl.store(
out + m[:, None] * 1024 + oc[None, :],
acc,
mask=(m[:, None] < 576) & (oc[None, :] < 1024),
)
class OasisPatchEmbed(nn.Module):
def __init__(
self,
img_height: int = 256,
img_width: int = 256,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer=None,
flatten: bool = True,
):
super().__init__()
self.img_size = (img_height, img_width)
self.patch_size = (patch_size, patch_size)
self.grid_size = (img_height // patch_size, img_width // patch_size)
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.flatten = flatten
self.proj = Conv2d(
in_chans,
embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
bias=True,
)
self.norm = norm_layer(embed_dim) if norm_layer else None
def forward(self, x: torch.Tensor, random_sample: bool = False) -> torch.Tensor:
batch, channels, height, width = x.shape
if not random_sample and (height, width) != self.img_size:
raise AssertionError(
f"Input image size ({height}*{width}) doesn't match model {self.img_size}.",
)
if (
x.is_cuda
and x.dtype == torch.float32
and x.is_contiguous()
and self.norm is None
and batch >= 3
and channels == 16
and (height, width) == (18, 32)
and self.patch_size == (2, 2)
and self.proj.out_channels == 1024
):
source = x
if self.flatten:
out = torch.empty(
(batch, 144, 1024), device=x.device, dtype=x.dtype
)
else:
out = torch.empty(
(batch, 9, 16, 1024), device=x.device, dtype=x.dtype
)
_patch_embed_2x2_kernel[
(triton.cdiv(batch * 144, 32), triton.cdiv(1024, 64))
](
x=source,
weight=self.proj.weight,
bias=self.proj.bias,
out=out,
N=batch,
BM=32,
BN=64,
num_warps=4,
num_stages=3,
)
return out
if (
x.is_cuda
and x.dtype == torch.float32
and x.is_contiguous()
and self.norm is None
and batch == 1
and channels == 3
and (height, width) == (360, 640)
and self.patch_size == (20, 20)
and self.proj.out_channels == 1024
):
out = torch.empty((1, 576, 1024), device=x.device, dtype=x.dtype)
_patch_embed_20x20_kernel[
(triton.cdiv(576, 32), triton.cdiv(1024, 128))
](
x,
self.proj.weight,
self.proj.bias,
out,
BM=32,
BN=128,
BK=32,
num_warps=4,
num_stages=3,
)
return out if self.flatten else out.reshape(1, 18, 32, 1024)
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2)
else:
x = x.permute(0, 2, 3, 1)
return self.norm(x) if self.norm is not None else x
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