entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 |
|---|---|---|---|---|---|---|---|---|---|---|
SENet | import torch
import torch.nn as nn
import torch.utils.data
class SENet(nn.Module):
"""support estimation network"""
def __init__(self, input_size: 'int', hidden_size: 'int', output_dims:
'int') ->None:
super(SENet, self).__init__()
self.l_1 = nn.Linear(input_size, hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Weiyuhong-1998/DI-engine | SENet | false | 14,579 | [
"Apache-2.0"
] | 464 | 88658ea358298c6e61e95a454284b8853a3e9484 | https://github.com/Weiyuhong-1998/DI-engine/tree/88658ea358298c6e61e95a454284b8853a3e9484 |
ATOCAttentionUnit | import torch
from typing import Union
import torch.nn as nn
from typing import Dict
import torch.utils.data
class ATOCAttentionUnit(nn.Module):
"""
Overview:
the attention unit of the atoc network. We now implement it as two-layer MLP, same as the original paper
Interface:
__init__, forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Weiyuhong-1998/DI-engine | ATOCAttentionUnit | false | 14,580 | [
"Apache-2.0"
] | 464 | 88658ea358298c6e61e95a454284b8853a3e9484 | https://github.com/Weiyuhong-1998/DI-engine/tree/88658ea358298c6e61e95a454284b8853a3e9484 |
ReverseMaskConv | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasattr(m, 'weight'):
if... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Vious/LBAM_Pytorch | ReverseMaskConv | false | 14,581 | [
"MIT"
] | 112 | b9292440e7a7559c027f48d6fd061dcabc41a6bf | https://github.com/Vious/LBAM_Pytorch/tree/b9292440e7a7559c027f48d6fd061dcabc41a6bf |
AvgPool2dSame | import math
import torch
import numpy as np
from typing import List
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int'):
return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)
def pad_same(x, k: 'List[int]', s: 'List... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import numpy as np
from typing import List
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
assert_... | Weiyuhong-1998/DI-engine | AvgPool2dSame | false | 14,582 | [
"Apache-2.0"
] | 464 | 88658ea358298c6e61e95a454284b8853a3e9484 | https://github.com/Weiyuhong-1998/DI-engine/tree/88658ea358298c6e61e95a454284b8853a3e9484 |
ScaledDotProductAttention | import torch
from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class ScaledDotProductAttention(nn.Module):
"""
Overview:
Implementation of dot product attentionn with scaling.
"""
def __init__(self, d_k: 'int', dropout: 'float'=0.0) ->No... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Weiyuhong-1998/DI-engine | ScaledDotProductAttention | false | 14,583 | [
"Apache-2.0"
] | 464 | 88658ea358298c6e61e95a454284b8853a3e9484 | https://github.com/Weiyuhong-1998/DI-engine/tree/88658ea358298c6e61e95a454284b8853a3e9484 |
BertIntermediate | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | VinodS7/sota-music-tagging-models | BertIntermediate | false | 14,584 | [
"MIT"
] | 199 | 6232abe693ebe6a99ea64a3ea1fe65c34d0a9dd0 | https://github.com/VinodS7/sota-music-tagging-models/tree/6232abe693ebe6a99ea64a3ea1fe65c34d0a9dd0 |
SHR_Block | import torch
import torch.nn as nn
import torch.utils.data
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Vegetebird/MHFormer | SHR_Block | false | 14,585 | [
"MIT"
] | 83 | 68d793414e13c256249431a45ac49949930c8e7f | https://github.com/Vegetebird/MHFormer/tree/68d793414e13c256249431a45ac49949930c8e7f |
MultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class MultiHeadAttention(nn.Module):
def __init__(self, in_dim, out_dim, out_heads, relation_dim=0, residual
=False, projection=True, layer_norm=True):
super().__init__()
self.in_dim = in_dim
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Weiyuhong-1998/DI-engine | MultiHeadAttention | false | 14,586 | [
"Apache-2.0"
] | 464 | 88658ea358298c6e61e95a454284b8853a3e9484 | https://github.com/Weiyuhong-1998/DI-engine/tree/88658ea358298c6e61e95a454284b8853a3e9484 |
CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self, input_size=50, hidden_size=256, dropout=0,
kernel_size=3, padding=1, activation_function=F.relu):
"""
Args:
input_size: dimention of input embedding
kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | WinterSoHot/OpenNRE | CNN | false | 14,587 | [
"MIT"
] | 3,284 | bc58d8fff2a2f42a5349c184f16ab7a8c50ae32b | https://github.com/WinterSoHot/OpenNRE/tree/bc58d8fff2a2f42a5349c184f16ab7a8c50ae32b |
MS_Block | import torch
from torch import nn
from torch.nn.functional import interpolate
class MS_Block(nn.Module):
def __init__(self, in_channel, out_channel, pool_level, txt_length):
super(MS_Block, self).__init__()
self.txt_length = txt_length
pool_kernel = 5 * pool_level, 1
pool_stride =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | WangGodder/deep-cross-modal-hashing | MS_Block | false | 14,588 | [
"MIT"
] | 65 | 9784397c1076c81b43ebd856cb24b8a67cf8f41e | https://github.com/WangGodder/deep-cross-modal-hashing/tree/9784397c1076c81b43ebd856cb24b8a67cf8f41e |
MaxMarginRankingLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class MaxMarginRankingLoss(nn.Module):
def __init__(self, margin=1):
super(MaxMarginRankingLoss, self).__init__()
self.margin = margin
def forward(self, x):
n = x.size()[0]
x1 = torch.diag(x)
x1 = x1.u... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Worm4047/TVR | MaxMarginRankingLoss | false | 14,589 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
BertOutput | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertOutput(nn.Module):
def __init__(self, config):
super(BertOutput, self).__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Worm4047/TVR | BertOutput | false | 14,590 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
StdConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class StdConv2d(nn.Conv2d):
def forward(self, x):
w = self.weight
v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
w = (w - m) / torch.sqrt(v + 1e-05)
return F.conv2d(x, w, self.bias, self.stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Willy0919/progressive-coordinate-transforms | StdConv2d | false | 14,591 | [
"Apache-2.0",
"MIT"
] | 142 | b637fa2541a815d270e162a4c9cd3348b098d48a | https://github.com/Willy0919/progressive-coordinate-transforms/tree/b637fa2541a815d270e162a4c9cd3348b098d48a |
DWConv | import torch
from torch import nn
class BasicConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros',
use_bn=True, use_relu=True, inplace=True):
super().__init__()
self.conv = nn.Conv2d(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | WenmuZhou/crnn.pytorch | DWConv | false | 14,592 | [
"Apache-2.0"
] | 46 | bf7a7c62376eee93943ca7c68e88e3d563c09aa8 | https://github.com/WenmuZhou/crnn.pytorch/tree/bf7a7c62376eee93943ca7c68e88e3d563c09aa8 |
Gradient_Loss | import torch
import numpy as np
import torch.utils.data
import torch.nn as nn
class Gradient_Loss(nn.Module):
def __init__(self, losstype='l2'):
super(Gradient_Loss, self).__init__()
a = np.array([[1, 0, -1], [2, 0, -2], [1, 0, -1]])
conv1 = nn.Conv2d(3, 3, kernel_size=3, stride=1, paddin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | WestCityInstitute/InvDN | Gradient_Loss | false | 14,593 | [
"Apache-2.0"
] | 122 | 3846cf3548ccf6690e58be3aafe1f6d98c56b90d | https://github.com/WestCityInstitute/InvDN/tree/3846cf3548ccf6690e58be3aafe1f6d98c56b90d |
CHI_Block | import torch
import torch.nn as nn
import torch.utils.data
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Vegetebird/MHFormer | CHI_Block | false | 14,594 | [
"MIT"
] | 83 | 68d793414e13c256249431a45ac49949930c8e7f | https://github.com/Vegetebird/MHFormer/tree/68d793414e13c256249431a45ac49949930c8e7f |
FeatureResizer | import torch
from torch import nn
class FeatureResizer(nn.Module):
"""
This class takes as input a set of embeddings of dimension C1 and outputs a set of
embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
"""
def __init__(self, input_feat_size, output_feat_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | XiaoJake/MTTR | FeatureResizer | false | 14,595 | [
"Apache-2.0"
] | 516 | c383c5b151e3c97aeb45cd2fb4bf08719016498b | https://github.com/XiaoJake/MTTR/tree/c383c5b151e3c97aeb45cd2fb4bf08719016498b |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Worm4047/TVR | BertAttention | false | 14,596 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
multi_head_attention_2d | import torch
import torch.nn as nn
class multi_head_attention_2d(torch.nn.Module):
def __init__(self, in_channel, key_filters, value_filters,
output_filters, num_heads, dropout_prob=0.5, layer_type='SAME'):
super().__init__()
"""Multihead scaled-dot-product attention with input/output tra... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Whu-wxy/Non-local-U-Nets-2D-block | multi_head_attention_2d | false | 14,597 | [
"MIT"
] | 117 | 668d0356b9a276f6cfdc69d669da7d47b260c4c0 | https://github.com/Whu-wxy/Non-local-U-Nets-2D-block/tree/668d0356b9a276f6cfdc69d669da7d47b260c4c0 |
MNIST_CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.optim
class MNIST_CNN(nn.Module):
"""
Hand-tuned architecture for MNIST.
Weirdness I've noticed so far with this architecture:
- adding a linear layer after the mean-pool in features hurts
R... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Weixin-Liang/MetaShift | MNIST_CNN | false | 14,598 | [
"MIT"
] | 54 | 84e090a13652437f8f392065f6bebf938e4c7fa3 | https://github.com/Weixin-Liang/MetaShift/tree/84e090a13652437f8f392065f6bebf938e4c7fa3 |
DCHR | import torch
import torch.nn as nn
class DCHR(nn.Module):
def __init__(self, stride):
super(DCHR, self).__init__()
self.pool = nn.AvgPool2d(kernel_size=stride)
def forward(self, x):
pool = self.pool(x)
shape = pool.shape
shape = [i for i in shape]
shape[1] = s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | XiaotaoChen/model-quantization | DCHR | false | 14,599 | [
"BSD-2-Clause"
] | 66 | a745ef691e9329b9c973a2dd795761cd3da8b6ae | https://github.com/XiaotaoChen/model-quantization/tree/a745ef691e9329b9c973a2dd795761cd3da8b6ae |
ESA | import torch
import torch.nn as nn
import torch.nn.functional as F
class ESA(nn.Module):
def __init__(self, channel=64, reduction=4, bias=True):
super(ESA, self).__init__()
self.r_nc = channel // reduction
self.conv1 = nn.Conv2d(channel, self.r_nc, kernel_size=1)
self.conv21 = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | WestCityInstitute/KAIR | ESA | false | 14,600 | [
"MIT"
] | 1,521 | 3eb3cc7776fa8c57e8ed7c71bfa8039beb4c6677 | https://github.com/WestCityInstitute/KAIR/tree/3eb3cc7776fa8c57e8ed7c71bfa8039beb4c6677 |
SEBlock | import torch
from torch import nn
from torch.nn import functional as F
class HardSigmoid(nn.Module):
def __init__(self, slope=0.2, offset=0.5):
super().__init__()
self.slope = slope
self.offset = offset
def forward(self, x):
x = self.slope * x + self.offset
x = F.thre... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | WenmuZhou/crnn.pytorch | SEBlock | false | 14,601 | [
"Apache-2.0"
] | 46 | bf7a7c62376eee93943ca7c68e88e3d563c09aa8 | https://github.com/WenmuZhou/crnn.pytorch/tree/bf7a7c62376eee93943ca7c68e88e3d563c09aa8 |
GlobalAvgPool | import torch
import torch as th
from torch import nn
class GlobalAvgPool(nn.Module):
def __init__(self):
super(GlobalAvgPool, self).__init__()
def forward(self, x):
return th.mean(x, dim=[-2, -1])
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | XudongLinthu/video_feature_extractor | GlobalAvgPool | false | 14,602 | [
"Apache-2.0"
] | 250 | 54bdbeef2e9f4db8d7697b26edef124979625f58 | https://github.com/XudongLinthu/video_feature_extractor/tree/54bdbeef2e9f4db8d7697b26edef124979625f58 |
UNetSeeInDark | import torch
from torch import nn
class UNetSeeInDark(nn.Module):
def __init__(self, in_channels=4, out_channels=3):
super(UNetSeeInDark, self).__init__()
self.conv1_1 = nn.Conv2d(in_channels, 32, kernel_size=3, stride=1,
padding=1)
self.conv1_2 = nn.Conv2d(32, 32, kernel_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Vandermode/ELD | UNetSeeInDark | false | 14,603 | [
"MIT"
] | 258 | aa0edb44a8fc20e01f83c1f6e93ee70d3190e142 | https://github.com/Vandermode/ELD/tree/aa0edb44a8fc20e01f83c1f6e93ee70d3190e142 |
TrainablePositionalEncoding | import torch
import torch.nn as nn
class TrainablePositionalEncoding(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, max_position_embeddings, hidden_size, dropout=0.1):
super(TrainablePositionalEncoding, self).__init__()
self.p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Worm4047/TVR | TrainablePositionalEncoding | false | 14,604 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
EnchanceReLU | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class EnchanceReLU(nn.ReLU):
def __init__(self, args):
super(EnchanceReLU, self).__init__(inplace=True)
self.shift = getattr(args, 'fm_boundary', 0.25)
def forward(self, x):
x = x + self.shift
x ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | XiaotaoChen/model-quantization | EnchanceReLU | false | 14,605 | [
"BSD-2-Clause"
] | 66 | a745ef691e9329b9c973a2dd795761cd3da8b6ae | https://github.com/XiaotaoChen/model-quantization/tree/a745ef691e9329b9c973a2dd795761cd3da8b6ae |
GeM | import torch
import torch.nn as nn
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-06):
super(GeM, self).__init__()
self.p = nn.Parameter(torch.ones(1) * p)
self.eps = eps
def forward(self, x):
return nn.functional.avg_pool2d(x.clamp(min=self.eps).pow(self.p),
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | XiaoJake/MinkLocMultimodal | GeM | false | 14,606 | [
"MIT"
] | 49 | 683ef1aae35ab1b60f13cefccfdd0e3f9cb9ea6e | https://github.com/XiaoJake/MinkLocMultimodal/tree/683ef1aae35ab1b60f13cefccfdd0e3f9cb9ea6e |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Worm4047/TVR | BertSelfAttention | false | 14,607 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
ResBlock | import torch
import torch.utils.data
import torch.nn as nn
class ResBlock(nn.Module):
def __init__(self, channel_in, channel_out):
super(ResBlock, self).__init__()
feature = 64
self.conv1 = nn.Conv2d(channel_in, feature, kernel_size=3, padding=1)
self.relu1 = nn.LeakyReLU(negative... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | WestCityInstitute/InvDN | ResBlock | false | 14,608 | [
"Apache-2.0"
] | 122 | 3846cf3548ccf6690e58be3aafe1f6d98c56b90d | https://github.com/WestCityInstitute/InvDN/tree/3846cf3548ccf6690e58be3aafe1f6d98c56b90d |
GeLU | import torch
import torch.nn as nn
class GeLU(nn.Module):
def forward(self, x):
return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 +
0.044715 * x * x)))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | YJiangcm/Chinese-sentence-pair-modeling | GeLU | false | 14,609 | [
"Apache-2.0"
] | 49 | 90adbc5c121832ce3e4a4057e30417a6ec5e7ebc | https://github.com/YJiangcm/Chinese-sentence-pair-modeling/tree/90adbc5c121832ce3e4a4057e30417a6ec5e7ebc |
PatchMerging | import torch
import torchvision.transforms.functional as F
import torch.nn.functional as F
from torch import nn
class PatchMerging(nn.Module):
""" Patch Merging Layer
Args:
dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | XiaoJake/MTTR | PatchMerging | false | 14,610 | [
"Apache-2.0"
] | 516 | c383c5b151e3c97aeb45cd2fb4bf08719016498b | https://github.com/XiaoJake/MTTR/tree/c383c5b151e3c97aeb45cd2fb4bf08719016498b |
NIN2d | import torch
import torch.nn as nn
from torch.nn import Parameter
def norm(p: 'torch.Tensor', dim: 'int'):
"""Computes the norm over all dimensions except dim"""
if dim is None:
return p.norm()
elif dim == 0:
output_size = (p.size(0),) + (1,) * (p.dim() - 1)
return p.contiguous().v... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | XuezheMax/macow | NIN2d | false | 14,611 | [
"Apache-2.0"
] | 60 | 6de247c09b590a037c9eec2d6b1248845f6efb31 | https://github.com/XuezheMax/macow/tree/6de247c09b590a037c9eec2d6b1248845f6efb31 |
NIN4d | import torch
import torch.nn as nn
from torch.nn import Parameter
def norm(p: 'torch.Tensor', dim: 'int'):
"""Computes the norm over all dimensions except dim"""
if dim is None:
return p.norm()
elif dim == 0:
output_size = (p.size(0),) + (1,) * (p.dim() - 1)
return p.contiguous().v... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | XuezheMax/macow | NIN4d | false | 14,612 | [
"Apache-2.0"
] | 60 | 6de247c09b590a037c9eec2d6b1248845f6efb31 | https://github.com/XuezheMax/macow/tree/6de247c09b590a037c9eec2d6b1248845f6efb31 |
LinearWeightNorm | import torch
import torch.nn as nn
class LinearWeightNorm(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super(LinearWeightNorm, self).__init__()
self.linear = nn.Linear(in_features, out_features, bias=bias)
self.reset_parameters()
def reset_parameters(self):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | XuezheMax/macow | LinearWeightNorm | false | 14,613 | [
"Apache-2.0"
] | 60 | 6de247c09b590a037c9eec2d6b1248845f6efb31 | https://github.com/XuezheMax/macow/tree/6de247c09b590a037c9eec2d6b1248845f6efb31 |
DepthwiseSeparableConv | import torch
import torch.nn as nn
import torch.nn.functional as F
class DepthwiseSeparableConv(nn.Module):
"""
Depth-wise separable convolution uses less parameters to generate output by convolution.
:Examples:
>>> m = DepthwiseSeparableConv(300, 200, 5, dim=1)
>>> input_tensor = torch.ra... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Worm4047/TVR | DepthwiseSeparableConv | false | 14,614 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
McDalNetLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def discrepancy_slice_wasserstein(p1, p2):
s = p1.shape
if s[1] > 1:
proj = torch.randn(s[1], 128)
proj *= torch.rsqrt(torch.sum(torch.mul(proj, proj), 0, keepdim=True))
p1 = torch.matmul(p1, proj)
p2 = torch.ma... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | YBZh/MultiClassDA | McDalNetLoss | false | 14,615 | [
"MIT"
] | 53 | b0f61a5fe82f8b5414a14e8d77753fbf5d4bcb93 | https://github.com/YBZh/MultiClassDA/tree/b0f61a5fe82f8b5414a14e8d77753fbf5d4bcb93 |
TorchAdd | import torch
import torch.nn as nn
class TorchAdd(nn.Module):
"""
TorchAdd Module.
"""
def forward(self, input_list):
return input_list[0] + input_list[1]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Yakings/AIPerf | TorchAdd | false | 14,616 | [
"MIT"
] | 52 | 6e5c50a3b769ab4b1075aaab9841b5554f40bceb | https://github.com/Yakings/AIPerf/tree/6e5c50a3b769ab4b1075aaab9841b5554f40bceb |
GlobalAvgPool1d | import torch
import torch.nn as nn
from abc import abstractmethod
from torch.nn import functional
class AvgPool(nn.Module):
"""
AvgPool Module.
"""
def __init__(self):
super().__init__()
@abstractmethod
def forward(self, input_tensor):
pass
class GlobalAvgPool1d(AvgPool):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from abc import abstractmethod
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = to... | Yakings/AIPerf | GlobalAvgPool1d | false | 14,617 | [
"MIT"
] | 52 | 6e5c50a3b769ab4b1075aaab9841b5554f40bceb | https://github.com/Yakings/AIPerf/tree/6e5c50a3b769ab4b1075aaab9841b5554f40bceb |
Log_Loss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
class Log_Loss(nn.Module):
def __init__(self):
super(Log_Loss, self).__init__()
def forward(self, ytrue, ypred):
delta = ypred - ytrue
return torch.mean(torch.log(torch.cosh(delta)))
def get_inputs(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | YanLu-nyu/transferlearning | Log_Loss | false | 14,618 | [
"MIT"
] | 9,657 | 037806c6eb8b0c12aefbfbf3e35cbf893093cff9 | https://github.com/YanLu-nyu/transferlearning/tree/037806c6eb8b0c12aefbfbf3e35cbf893093cff9 |
GCN | import torch
import torch.nn as nn
import torch.nn.parallel
class GCN(nn.Module):
""" Graph convolution unit (single layer)
"""
def __init__(self, num_state, num_node, bias=False):
super(GCN, self).__init__()
self.conv1 = nn.Conv1d(num_node, num_node, kernel_size=1)
self.relu = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | YJSYJSYJS/GloRe | GCN | false | 14,619 | [
"MIT"
] | 200 | b236dc92bd89f59c2b591c1b1ba5ead134ea75cd | https://github.com/YJSYJSYJS/GloRe/tree/b236dc92bd89f59c2b591c1b1ba5ead134ea75cd |
Sine | import torch
import torch.nn as nn
class Sine(nn.Module):
def __init__(self, w0):
super().__init__()
self.w0 = w0
def forward(self, x):
return torch.sin(self.w0 * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'w0': 4}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | YangChenye/neurecon | Sine | false | 14,620 | [
"MIT"
] | 432 | 972e810ec252cfd16f630b1de6d2802d1b8de59a | https://github.com/YangChenye/neurecon/tree/972e810ec252cfd16f630b1de6d2802d1b8de59a |
Prone | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _quadruple
def conv1x1(in_planes, out_planes, stride=1, args=None, force_fp=False):
"""1x1 convolution"""
if args is not None and hasattr(args, 'keyword'):
return custom_conv(in_planes,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import logging
import torch.nn as nn
import torch.nn.functional as F
from torch.... | XiaotaoChen/model-quantization | Prone | false | 14,621 | [
"BSD-2-Clause"
] | 66 | a745ef691e9329b9c973a2dd795761cd3da8b6ae | https://github.com/XiaotaoChen/model-quantization/tree/a745ef691e9329b9c973a2dd795761cd3da8b6ae |
CFRB | import torch
from collections import OrderedDict
import torch.nn as nn
import torch.nn.functional as F
def sequential(*args):
"""Advanced nn.Sequential.
Args:
nn.Sequential, nn.Module
Returns:
nn.Sequential
"""
if len(args) == 1:
if isinstance(args[0], OrderedDict):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from collections import Order... | WestCityInstitute/KAIR | CFRB | false | 14,622 | [
"MIT"
] | 1,521 | 3eb3cc7776fa8c57e8ed7c71bfa8039beb4c6677 | https://github.com/WestCityInstitute/KAIR/tree/3eb3cc7776fa8c57e8ed7c71bfa8039beb4c6677 |
PatchEmbed3D | import torch
import torchvision.transforms.functional as F
import torch.nn.functional as F
from torch import nn
class PatchEmbed3D(nn.Module):
""" Video to Patch Embedding.
Args:
patch_size (int): Patch token size. Default: (2,4,4).
in_chans (int): Number of input video channels. Default: 3.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | XiaoJake/MTTR | PatchEmbed3D | false | 14,623 | [
"Apache-2.0"
] | 516 | c383c5b151e3c97aeb45cd2fb4bf08719016498b | https://github.com/XiaoJake/MTTR/tree/c383c5b151e3c97aeb45cd2fb4bf08719016498b |
convBlock_basic | import torch
from torch import nn
class convBlock_basic(nn.Module):
def __init__(self, inChannel, outChannel, kernel, stride, pad,
use_batchnorm=False):
super(convBlock_basic, self).__init__()
self.use_batchnorm = use_batchnorm
self.conv = nn.Conv2d(inChannel, outChannel, kernel, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | YacobBY/ICDAR2019-ArT-Recognition-Alchemy | convBlock_basic | false | 14,624 | [
"MIT"
] | 209 | 911c572c2aff4599a74b7974d46ef4cfb17078b9 | https://github.com/YacobBY/ICDAR2019-ArT-Recognition-Alchemy/tree/911c572c2aff4599a74b7974d46ef4cfb17078b9 |
AttentionUnit | import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import init
class AttentionUnit(nn.Module):
def __init__(self, sDim, xDim, attDim):
super(AttentionUnit, self).__init__()
self.sDim = sDim
self.xDim = xDim
self.attDim = attDim
self.sEmbed = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YacobBY/ICDAR2019-ArT-Recognition-Alchemy | AttentionUnit | false | 14,625 | [
"MIT"
] | 209 | 911c572c2aff4599a74b7974d46ef4cfb17078b9 | https://github.com/YacobBY/ICDAR2019-ArT-Recognition-Alchemy/tree/911c572c2aff4599a74b7974d46ef4cfb17078b9 |
SoftmaxAttention | import torch
import torch.nn as nn
def masked_softmax(tensor, mask):
"""
Apply a masked softmax on the last dimension of a tensor.
The input tensor and mask should be of size (batch, *, sequence_length).
Args:
tensor: The tensor on which the softmax function must be applied along
t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YJiangcm/Chinese-sentence-pair-modeling | SoftmaxAttention | false | 14,626 | [
"Apache-2.0"
] | 49 | 90adbc5c121832ce3e4a4057e30417a6ec5e7ebc | https://github.com/YJiangcm/Chinese-sentence-pair-modeling/tree/90adbc5c121832ce3e4a4057e30417a6ec5e7ebc |
OZELoss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
class OZELoss(nn.Module):
"""Custom loss for TRNSys metamodel.
Compute, for temperature and consumptions, the intergral of the squared differences
over time. Sum the log with a coeficient ``alpha``.
.. math::
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
as... | YanLu-nyu/transferlearning | OZELoss | false | 14,627 | [
"MIT"
] | 9,657 | 037806c6eb8b0c12aefbfbf3e35cbf893093cff9 | https://github.com/YanLu-nyu/transferlearning/tree/037806c6eb8b0c12aefbfbf3e35cbf893093cff9 |
AdjustSigmoid | from torch.nn import Module
import torch
from torch import Tensor
from typing import Optional
def KORNIA_CHECK_IS_TENSOR(x, msg: 'Optional[str]'=None):
if not isinstance(x, Tensor):
raise TypeError(f'Not a Tensor type. Got: {type(x)}.\n{msg}')
def adjust_sigmoid(image: 'Tensor', cutoff: 'float'=0.5, gai... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
from torch import Tensor
from typing import O... | YanivHollander/kornia | AdjustSigmoid | false | 14,628 | [
"ECL-2.0",
"Apache-2.0"
] | 418 | ccd258d0956da89b1feca96448eff8e4969d405a | https://github.com/YanivHollander/kornia/tree/ccd258d0956da89b1feca96448eff8e4969d405a |
PositionwiseFeedForward | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
from typing import Optional
class PositionwiseFeedForward(nn.Module):
"""Position-wise Feed Forward Network block from Attention is All You Need.
Apply two linear transformations to each input,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | YanLu-nyu/transferlearning | PositionwiseFeedForward | false | 14,629 | [
"MIT"
] | 9,657 | 037806c6eb8b0c12aefbfbf3e35cbf893093cff9 | https://github.com/YanLu-nyu/transferlearning/tree/037806c6eb8b0c12aefbfbf3e35cbf893093cff9 |
Hflip | import torch
import torch.nn as nn
def hflip(input: 'torch.Tensor') ->torch.Tensor:
"""Horizontally flip a tensor image or a batch of tensor images.
.. image:: _static/img/hflip.png
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input: input tens... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | YanivHollander/kornia | Hflip | false | 14,630 | [
"ECL-2.0",
"Apache-2.0"
] | 418 | ccd258d0956da89b1feca96448eff8e4969d405a | https://github.com/YanivHollander/kornia/tree/ccd258d0956da89b1feca96448eff8e4969d405a |
BinaryFocalLossWithLogits | import torch
import warnings
from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
def binary_focal_loss_with_logits(input: 'torch.Tensor', target:
'torch.Tensor', alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
'str'='none', eps: 'Optional[float]'=None) ->torch.Tensor:
"""... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import warn... | YanivHollander/kornia | BinaryFocalLossWithLogits | false | 14,631 | [
"ECL-2.0",
"Apache-2.0"
] | 418 | ccd258d0956da89b1feca96448eff8e4969d405a | https://github.com/YanivHollander/kornia/tree/ccd258d0956da89b1feca96448eff8e4969d405a |
AdjustLog | from torch.nn import Module
import torch
from torch import Tensor
from typing import Optional
def KORNIA_CHECK_IS_TENSOR(x, msg: 'Optional[str]'=None):
if not isinstance(x, Tensor):
raise TypeError(f'Not a Tensor type. Got: {type(x)}.\n{msg}')
def adjust_log(image: 'Tensor', gain: 'float'=1, inv: 'bool'... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
... | YanivHollander/kornia | AdjustLog | false | 14,632 | [
"ECL-2.0",
"Apache-2.0"
] | 418 | ccd258d0956da89b1feca96448eff8e4969d405a | https://github.com/YanivHollander/kornia/tree/ccd258d0956da89b1feca96448eff8e4969d405a |
FullAttention | from torch.nn import Module
import torch
from torch.nn import Dropout
class FullAttention(Module):
def __init__(self, use_dropout=False, attention_dropout=0.1):
super().__init__()
self.use_dropout = use_dropout
self.dropout = Dropout(attention_dropout)
def forward(self, queries, keys... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YanivHollander/kornia | FullAttention | false | 14,633 | [
"ECL-2.0",
"Apache-2.0"
] | 418 | ccd258d0956da89b1feca96448eff8e4969d405a | https://github.com/YanivHollander/kornia/tree/ccd258d0956da89b1feca96448eff8e4969d405a |
Alignment | from _paritybench_helpers import _mock_config
from torch.nn import Module
import math
import torch
import torch.nn as nn
import torch.nn.functional as f
class Module(nn.Module):
def __init__(self):
super().__init__()
self.summary = {}
def add_summary(self, name, val):
if self.trainin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YJiangcm/Chinese-sentence-pair-modeling | Alignment | false | 14,634 | [
"Apache-2.0"
] | 49 | 90adbc5c121832ce3e4a4057e30417a6ec5e7ebc | https://github.com/YJiangcm/Chinese-sentence-pair-modeling/tree/90adbc5c121832ce3e4a4057e30417a6ec5e7ebc |
PatchEmbed | import torch
from torch import nn
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
num_patches = img_size // patch_size * (img_size // patch_size)
self.img_size = img_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | YangtaoWANG95/TokenCut | PatchEmbed | false | 14,635 | [
"MIT"
] | 97 | ea585c55e631d17c239f875550b2d0b230446b25 | https://github.com/YangtaoWANG95/TokenCut/tree/ea585c55e631d17c239f875550b2d0b230446b25 |
BlobDoG | import torch
from torch import Tensor
from typing import Optional
import torch.nn as nn
from typing import List
def KORNIA_CHECK_IS_TENSOR(x, msg: 'Optional[str]'=None):
if not isinstance(x, Tensor):
raise TypeError(f'Not a Tensor type. Got: {type(x)}.\n{msg}')
def KORNIA_CHECK_SHAPE(x, shape: 'List[str... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import Tensor
from typing import Optional
import torch.nn as nn
from typing import List
assert_size_stride = torch._C._dynamo.gua... | YanivHollander/kornia | BlobDoG | false | 14,636 | [
"ECL-2.0",
"Apache-2.0"
] | 418 | ccd258d0956da89b1feca96448eff8e4969d405a | https://github.com/YanivHollander/kornia/tree/ccd258d0956da89b1feca96448eff8e4969d405a |
EltwiseSubEmbed | import torch
from torch import nn
class EltwiseSubEmbed(nn.Module):
def __init__(self, nonlinearity='square', use_batch_norm=False,
use_classifier=False, num_features=0, num_classes=0):
super(EltwiseSubEmbed, self).__init__()
self.nonlinearity = nonlinearity
if nonlinearity is not... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | YantaoShen/kpm_rw_person_reid | EltwiseSubEmbed | false | 14,637 | [
"MIT"
] | 112 | 01393e024aa1139c9e7e934954cc35826f438a54 | https://github.com/YantaoShen/kpm_rw_person_reid/tree/01393e024aa1139c9e7e934954cc35826f438a54 |
Qux | import torch
import torch.jit
import torch.onnx
import torch.nn
class Qux(torch.nn.Module):
def __init__(self, x):
super(Qux, self).__init__()
self.x = x
def forward(self, a, b):
return a - b - self.x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | Qux | false | 14,638 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleACosModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleACosModule(torch.nn.Module):
def __init__(self):
super(SimpleACosModule, self).__init__()
def forward(self, a):
return torch.acos(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | YaronBenAtar/glow | SimpleACosModule | false | 14,639 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleClampModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleClampModel(torch.nn.Module):
def __init__(self, min, max):
super(SimpleClampModel, self).__init__()
self.min = min
self.max = max
def forward(self, input):
return torch.clamp(input, self.min, self.max... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleClampModel | false | 14,640 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
Homography | import torch
import torch.nn as nn
class Homography(nn.Module):
"""Homography geometric model to be used together with ImageRegistrator
module for the optimization-based image
registration."""
def __init__(self) ->None:
super().__init__()
self.model = nn.Parameter(torch.eye(3))
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | YanivHollander/kornia | Homography | false | 14,641 | [
"ECL-2.0",
"Apache-2.0"
] | 418 | ccd258d0956da89b1feca96448eff8e4969d405a | https://github.com/YanivHollander/kornia/tree/ccd258d0956da89b1feca96448eff8e4969d405a |
RepeatModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class RepeatModule(torch.nn.Module):
def __init__(self, repeats):
super(RepeatModule, self).__init__()
self.repeats = repeats
def forward(self, tensor):
tensor = tensor + tensor
return tensor.repeat(self.repeats)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | RepeatModule | false | 14,642 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleASinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleASinModule(torch.nn.Module):
def __init__(self):
super(SimpleASinModule, self).__init__()
def forward(self, a):
return torch.asin(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | YaronBenAtar/glow | SimpleASinModule | false | 14,643 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleConvTranspose2dModule | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleConvTranspose2dModule(torch.nn.Module):
def __init__(self, stride=1, padding=0, output_padding=0, dilation=1,
groups=1):
super(SimpleConvTranspose2dModule, self).__init__()
self.str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch... | YaronBenAtar/glow | SimpleConvTranspose2dModule | false | 14,644 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
MultiHeadAttention | import torch
import numpy as np
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
from typing import Optional
def generate_local_map_mask(chunk_size: 'int', attention_size: 'int',
mask_future=False, device: 'torch.device'='cpu') ->torch.BoolTensor:
"""Compu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YanLu-nyu/transferlearning | MultiHeadAttention | false | 14,645 | [
"MIT"
] | 9,657 | 037806c6eb8b0c12aefbfbf3e35cbf893093cff9 | https://github.com/YanLu-nyu/transferlearning/tree/037806c6eb8b0c12aefbfbf3e35cbf893093cff9 |
SimpleArgSortModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleArgSortModule(torch.nn.Module):
def __init__(self, descending=True):
super(SimpleArgSortModule, self).__init__()
self.descending = descending
def forward(self, inputs):
return torch.argsort(inputs, dim=-1, de... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleArgSortModule | false | 14,646 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleAvgPool2dModule | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleAvgPool2dModule(torch.nn.Module):
def __init__(self, kernel_size, stride=None, padding=0):
super(SimpleAvgPool2dModule, self).__init__()
self.kernel_size = kernel_size
self.padding ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleAvgPool2dModule | false | 14,647 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleAbsModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleAbsModule(torch.nn.Module):
def __init__(self):
super(SimpleAbsModule, self).__init__()
def forward(self, a):
return torch.abs(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | YaronBenAtar/glow | SimpleAbsModule | false | 14,648 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleATanModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleATanModule(torch.nn.Module):
def __init__(self):
super(SimpleATanModule, self).__init__()
def forward(self, a):
return torch.atan(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | YaronBenAtar/glow | SimpleATanModule | false | 14,649 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
Wide_Conv | import torch
import torch.nn as nn
def match_score(s1, s2, mask1, mask2):
"""
s1, s2: batch_size * seq_len * dim
"""
_batch, seq_len, _dim = s1.shape
s1 = s1 * mask1.eq(0).unsqueeze(2).float()
s2 = s2 * mask2.eq(0).unsqueeze(2).float()
s1 = s1.unsqueeze(2).repeat(1, 1, seq_len, 1)
s2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | YJiangcm/Chinese-sentence-pair-modeling | Wide_Conv | false | 14,650 | [
"Apache-2.0"
] | 49 | 90adbc5c121832ce3e4a4057e30417a6ec5e7ebc | https://github.com/YJiangcm/Chinese-sentence-pair-modeling/tree/90adbc5c121832ce3e4a4057e30417a6ec5e7ebc |
SimpleAddMmModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleAddMmModule(torch.nn.Module):
def __init__(self, alpha=1, beta=1):
super(SimpleAddMmModule, self).__init__()
self.alpha = alpha
self.beta = beta
def forward(self, a, b, c):
return (a + a).addmm(b, c)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | YaronBenAtar/glow | SimpleAddMmModule | false | 14,651 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleBmmModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleBmmModule(torch.nn.Module):
def forward(self, a, b):
return (a + a).bmm(b)
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | YaronBenAtar/glow | SimpleBmmModule | false | 14,652 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleAvgPool1dModule | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleAvgPool1dModule(torch.nn.Module):
def __init__(self, kernel_size, stride=None, padding=0):
super(SimpleAvgPool1dModule, self).__init__()
self.kernel_size = kernel_size
self.padding ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleAvgPool1dModule | false | 14,653 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleClampMinModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleClampMinModel(torch.nn.Module):
def __init__(self, min):
super(SimpleClampMinModel, self).__init__()
self.min = min
def forward(self, input):
return torch.clamp_min(input, self.min)
def get_inputs():
re... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleClampMinModel | false | 14,654 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleCeilModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleCeilModule(torch.nn.Module):
def forward(self, a, b):
c = a + b
return torch.ceil(c)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | YaronBenAtar/glow | SimpleCeilModule | false | 14,655 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleAndModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleAndModule(torch.nn.Module):
def __init__(self):
super(SimpleAndModule, self).__init__()
def forward(self, a, b):
c = torch.logical_and(a, b)
return torch.logical_and(c, c)
def get_inputs():
return [torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleAndModule | false | 14,656 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleNotModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleNotModule(torch.nn.Module):
def __init__(self):
super(SimpleNotModule, self).__init__()
def forward(self, a):
b = torch.logical_not(a)
return torch.logical_not(b)
def get_inputs():
return [torch.rand([4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleNotModule | false | 14,657 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleCosModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleCosModule(torch.nn.Module):
def __init__(self):
super(SimpleCosModule, self).__init__()
def forward(self, a):
return torch.cos(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | YaronBenAtar/glow | SimpleCosModule | false | 14,658 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleFmodModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleFmodModule(torch.nn.Module):
def __init__(self):
super(SimpleFmodModule, self).__init__()
def forward(self, a, b):
if b.size() == torch.Size([]):
c = a.fmod(b.item())
else:
c = a.fmod(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | YaronBenAtar/glow | SimpleFmodModule | false | 14,659 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleLinearModule | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleLinearModule(torch.nn.Module):
def __init__(self):
super(SimpleLinearModule, self).__init__()
def forward(self, input, weight, bias=None):
return F.linear(input + input, weight, bias)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | YaronBenAtar/glow | SimpleLinearModule | false | 14,660 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleFloorModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleFloorModule(torch.nn.Module):
def forward(self, a, b):
c = a + b
return torch.floor(c)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | YaronBenAtar/glow | SimpleFloorModule | false | 14,661 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleExpModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleExpModule(torch.nn.Module):
def forward(self, input):
other = torch.exp(input)
return torch.exp(other)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | YaronBenAtar/glow | SimpleExpModule | false | 14,662 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleCumSumModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleCumSumModule(torch.nn.Module):
def __init__(self, dim):
super(SimpleCumSumModule, self).__init__()
self.dim = dim
def forward(self, tensor):
return torch.cumsum(tensor, self.dim)
def get_inputs():
retur... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleCumSumModule | false | 14,663 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleMinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMinModule(torch.nn.Module):
def __init__(self):
super(SimpleMinModule, self).__init__()
def forward(self, a, b):
return torch.min(a + a, b + b)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleMinModule | false | 14,664 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleGeluModule | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleGeluModule(torch.nn.Module):
def forward(self, tensor):
return F.gelu(tensor + tensor)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | YaronBenAtar/glow | SimpleGeluModule | false | 14,665 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleLogModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleLogModule(torch.nn.Module):
def __init__(self, *dimensions):
super(SimpleLogModule, self).__init__()
def forward(self, a):
b = torch.log(a)
return torch.log(b)
def get_inputs():
return [torch.rand([4, 4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | YaronBenAtar/glow | SimpleLogModule | false | 14,666 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleMatmulModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMatmulModule(torch.nn.Module):
def __init__(self):
super(SimpleMatmulModule, self).__init__()
def forward(self, a, b):
return a.matmul(b + b)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | YaronBenAtar/glow | SimpleMatmulModule | false | 14,667 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleMulModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMulModule(torch.nn.Module):
def __init__(self):
super(SimpleMulModule, self).__init__()
def forward(self, left, right):
other = left.mul(right.item() if right.size() == torch.Size([]) else
right)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleMulModule | false | 14,668 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleModule(torch.nn.Module):
def __init__(self):
super(SimpleModule, self).__init__()
def forward(self, x):
y = x + x
y = y + 2
return y
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleModule | false | 14,669 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleConv2dModule | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleConv2dModule(torch.nn.Module):
def __init__(self, stride=1, padding=0, dilation=1, groups=1):
super(SimpleConv2dModule, self).__init__()
self.stride = stride
self.padding = padding
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch... | YaronBenAtar/glow | SimpleConv2dModule | false | 14,670 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleOrModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleOrModule(torch.nn.Module):
def __init__(self):
super(SimpleOrModule, self).__init__()
def forward(self, a, b):
c = torch.logical_or(a, b)
return torch.logical_or(c, c)
def get_inputs():
return [torch.ra... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleOrModule | false | 14,671 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleLogSoftmaxModel | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleLogSoftmaxModel(torch.nn.Module):
def __init__(self, dimension):
super(SimpleLogSoftmaxModel, self).__init__()
self.dimension = dimension
def forward(self, tensor):
return F.lo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
impor... | YaronBenAtar/glow | SimpleLogSoftmaxModel | false | 14,672 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
Foo | import torch
import torch.jit
import torch.onnx
import torch.nn
class Foo(torch.nn.Module):
def __init__(self):
super(Foo, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 6, 3)
self.relu = torch.nn.ReLU()
self.conv2 = torch.nn.Conv2d(6, 16, 3)
def forward(self, x):
x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch... | YaronBenAtar/glow | Foo | false | 14,673 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleReluModel | import torch
import torch.jit
import torch.nn.functional as F
import torch.onnx
import torch.nn
class SimpleReluModel(torch.nn.Module):
def __init__(self, inplace=False):
super(SimpleReluModel, self).__init__()
self.inplace = inplace
def forward(self, tensor):
other = F.relu(tensor, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleReluModel | false | 14,674 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleLeakyReluModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleLeakyReluModule(torch.nn.Module):
def __init__(self, negative_slope=0.01, inplace=False):
super(SimpleLeakyReluModule, self).__init__()
self.negative_slope = negative_slope
self.inplace = inplace
def forward(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleLeakyReluModule | false | 14,675 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimplePowModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimplePowModule(torch.nn.Module):
def __init__(self, power):
super(SimplePowModule, self).__init__()
self.power = power
def forward(self, tensor):
return torch.pow(tensor, self.power)
def get_inputs():
return... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimplePowModule | false | 14,676 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleMaxModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMaxModule(torch.nn.Module):
def __init__(self):
super(SimpleMaxModule, self).__init__()
def forward(self, a, b):
return torch.max(a + a, b + b)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleMaxModule | false | 14,677 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
SimpleXorModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleXorModule(torch.nn.Module):
def __init__(self):
super(SimpleXorModule, self).__init__()
def forward(self, a, b):
c = torch.logical_xor(a, b)
return torch.logical_xor(c, c)
def get_inputs():
return [torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleXorModule | false | 14,678 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
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