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 |
|---|---|---|---|---|---|---|---|---|---|---|
SpatialSELayer3D | import torch
import torch.nn as nn
import torch.nn.functional as F
class SpatialSELayer3D(nn.Module):
"""
3D extension of SE block -- squeezing spatially and exciting channel-wise described in:
*Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, MICCAI 201... | 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | YilinLiu97/AmygNet-Pytorch | SpatialSELayer3D | false | 18,150 | [
"MIT"
] | 3 | d5bb244fd930791345d38f09870a7ded633f4622 | https://github.com/YilinLiu97/AmygNet-Pytorch/tree/d5bb244fd930791345d38f09870a7ded633f4622 |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
def __init__(self, n_dims, scale=20.0, eps=1e-10):
"""L2 normalization layer.
Args:
n_dims (int): Number of dimensions to be normalized
scale (float, optional): Defaults to 20..
eps (float, optional):... | 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_... | Bin-ze/Food_detection | L2Norm | false | 17,007 | [
"Apache-2.0"
] | 4 | 1c1a067f12644f2b0289e49aec4637d580722f70 | https://github.com/Bin-ze/Food_detection/tree/1c1a067f12644f2b0289e49aec4637d580722f70 |
FFN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | yhgon/Transformer-TTS | FFN | false | 13,138 | [
"MIT"
] | 0 | 5f34945cb5500d484275700c4e393ed125d5e753 | https://github.com/yhgon/Transformer-TTS/tree/5f34945cb5500d484275700c4e393ed125d5e753 |
SpatialSEBlock | import torch
from torch import nn
class SpatialSEBlock(nn.Module):
def __init__(self, channel):
super(SpatialSEBlock, self).__init__()
self.conv = nn.Conv2d(in_channels=channel, out_channels=1,
kernel_size=1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
y = 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | amitkumarj441/TGS_Kaggle | SpatialSEBlock | false | 6,187 | [
"MIT"
] | 1 | a4f613046cc36f3f6dbec28adb35f97a63c2a994 | https://github.com/amitkumarj441/TGS_Kaggle/tree/a4f613046cc36f3f6dbec28adb35f97a63c2a994 |
TensorSigmoid | import torch
class TensorSigmoid(torch.nn.Module):
def __init__(self):
super(TensorSigmoid, self).__init__()
def forward(self, x):
return x.sigmoid()
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | NVIDIA-AI-IOT-private/torch2trt | TensorSigmoid | false | 10,530 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
Advantage_estimate | import torch
import torch.nn as nn
import torch.nn.functional as F
class Advantage_estimate(nn.Module):
def __init__(self, input_shape, output_shape, device, hidden_shape=128):
super(Advantage_estimate, self).__init__()
self.device = device
self.dropout = nn.Dropout(p=0.01)
self.i... | 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | pupupue/Deep-RL-atari | Advantage_estimate | false | 7,490 | [
"MIT"
] | 1 | 9b97157f87826feafcf272761d7eef9693a2b2c4 | https://github.com/pupupue/Deep-RL-atari/tree/9b97157f87826feafcf272761d7eef9693a2b2c4 |
GenNoise | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | GuYuanjie/DeepFusionPrior | GenNoise | false | 5,220 | [
"MIT"
] | 1 | a7126e073ed8c49b6a9a662492b64aaeee56cc01 | https://github.com/GuYuanjie/DeepFusionPrior/tree/a7126e073ed8c49b6a9a662492b64aaeee56cc01 |
StyledConv | import math
import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest_dim, bias.shape[0... | 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
from to... | ozmig77/StyleCLIP-1 | StyledConv | false | 16,226 | [
"MIT"
] | 2,732 | 57b887bba971ef86c107f4805785ce44fca3efef | https://github.com/ozmig77/StyleCLIP-1/tree/57b887bba971ef86c107f4805785ce44fca3efef |
ReLU | import torch
import numpy as np
import torch.nn as nn
from numbers import Number
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))
def _normal_log_pdf(value, mu, stddev):
v... | 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, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import N... | SaumilShah66/dqn_uav | ReLU | false | 9,577 | [
"MIT"
] | 0 | 2bf780369e964b870624aebcff16c0714cad03c1 | https://github.com/SaumilShah66/dqn_uav/tree/2bf780369e964b870624aebcff16c0714cad03c1 |
TransformerDecoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | YiwenShaoStephen/snowfall | TransformerDecoderLayer | false | 14,700 | [
"Apache-2.0"
] | 145 | 949226f35b29c629cb03cae36fa43da5993d27a3 | https://github.com/YiwenShaoStephen/snowfall/tree/949226f35b29c629cb03cae36fa43da5993d27a3 |
CopyChannels | import torch
class CopyChannels(torch.nn.Module):
def __init__(self, multiple=3, dim=1):
super(CopyChannels, self).__init__()
self.multiple = multiple
self.dim = dim
def forward(self, x):
return torch.cat([x for _ in range(self.multiple)], dim=self.dim)
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret... | dianjixz/AutoDL | CopyChannels | false | 15,178 | [
"Apache-2.0"
] | 1,044 | 48db4eb04d55ce69e93d4a3bdc24592bdb34a868 | https://github.com/dianjixz/AutoDL/tree/48db4eb04d55ce69e93d4a3bdc24592bdb34a868 |
ImgLayerNorm | from torch.nn import Module
import torch
import torch.nn
import torch.utils.data
class ImgLayerNorm(Module):
"""
LayerNorm for images with channel axis 1
(this is necessary because PyTorch's LayerNorm operates on the last axis)
"""
def __init__(self, in_dim, eps=1e-05):
super().__init__()... | 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
from torch.nn import Module
import torch.nn
import torch.utils.data
assert_size... | CrhistyanSilva/localbitsback | ImgLayerNorm | false | 13,530 | [
"MIT"
] | 100 | bdf66b41b2120c5b35edac4e4efda0fda3f2db4d | https://github.com/CrhistyanSilva/localbitsback/tree/bdf66b41b2120c5b35edac4e4efda0fda3f2db4d |
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 |
MLP | import torch
import torch.autograd
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, n_in, n_out, dropout=0):
super().__init__()
self.linear = nn.Linear(n_in, n_out)
self.activation = nn.GELU()
self.dropout = nn.Dropout(dropout)
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.triton_helpers import libdevice
import torch.autogr... | yifding/W2NER | MLP | false | 13,139 | [
"MIT"
] | 0 | d13128e45f3930a8b8faa794318939dc90a75974 | https://github.com/yifding/W2NER/tree/d13128e45f3930a8b8faa794318939dc90a75974 |
DiscriminatorLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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 import nn
import torch.utils.data
import torch.nn.init
assert_size_stride = to... | ForrestPi/Unsupervised-Defect-Segmentation | DiscriminatorLoss | false | 8,203 | [
"MIT"
] | 17 | e366ac7c757bb1b45f38ebbc502dfee7ccb72398 | https://github.com/ForrestPi/Unsupervised-Defect-Segmentation/tree/e366ac7c757bb1b45f38ebbc502dfee7ccb72398 |
Biaffine | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | icewing1996/biaffine-parser | Biaffine | false | 6,851 | [
"MIT"
] | 1 | f5a4ece7ba9a087d81b76dd6a8ea6aa7d90c6c82 | https://github.com/icewing1996/biaffine-parser/tree/f5a4ece7ba9a087d81b76dd6a8ea6aa7d90c6c82 |
Hard_Distillation_Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
... | ManojKesani/Transformer-Implementations | Hard_Distillation_Loss | false | 793 | [
"MIT"
] | 0 | faca89d44523da80073790d53e53b4e80bde736f | https://github.com/ManojKesani/Transformer-Implementations/tree/faca89d44523da80073790d53e53b4e80bde736f |
Network | import torch
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self):
super(Network, self).__init__()
self.fc1 = nn.Linear(4, 256)
self.fc2 = nn.Linear(256, 2)
def forward(self, x):
x = F.relu(self.fc1(x))
x = self.fc2(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.nn as nn
assert_... | noureldinalaa/monocular_visual_odometry-_DuckieTown | Network | false | 12,841 | [
"MIT"
] | 0 | 6b65e4fb9918dbf435133a9dd608c58cfb12b44b | https://github.com/noureldinalaa/monocular_visual_odometry-_DuckieTown/tree/6b65e4fb9918dbf435133a9dd608c58cfb12b44b |
Fusion3_MinusFCLayer | import torch
from torch import nn
class Fusion3_MinusFCLayer(nn.Module):
def __init__(self, input_dim):
super(Fusion3_MinusFCLayer, self).__init__()
self._norm_layer1 = nn.Linear(input_dim * 6, input_dim)
def forward(self, input1, input2, input3):
norm_input = self._norm_layer1(torch... | 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... | RUCAIBox/WSDM2022-C2CRS | Fusion3_MinusFCLayer | false | 17,850 | [
"MIT"
] | 4 | 8ef2fa7c44bdba1799ab79f379ae7394bd468c02 | https://github.com/RUCAIBox/WSDM2022-C2CRS/tree/8ef2fa7c44bdba1799ab79f379ae7394bd468c02 |
Bridge | import torch
import torch.nn as nn
import torch.nn.functional as F
class Bridge(nn.Sequential):
def __init__(self, in_channels, out_channels):
super(Bridge, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
padding=1)
self.act1 = nn.LeakyReLU(0.... | 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 ... | aiarjun/Monocular-Depth-Estimation | Bridge | false | 18,247 | [
"MIT"
] | 6 | 5989673f1b6d865f822a342448172b374968c234 | https://github.com/aiarjun/Monocular-Depth-Estimation/tree/5989673f1b6d865f822a342448172b374968c234 |
DecoderBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | jmargutt/automated-building-detection | DecoderBlock | false | 15,718 | [
"MIT"
] | 48 | e1668a470b94252040f27d26098826c293fbb46d | https://github.com/jmargutt/automated-building-detection/tree/e1668a470b94252040f27d26098826c293fbb46d |
MaskedLoss | import torch
import torch.nn as nn
class MaskedLoss(nn.Module):
mse = nn.MSELoss()
def forward(self, pred, target, mask):
pred = torch.log1p(pred).contiguous().view(-1)
target = torch.log1p(target).contiguous().view(-1)
mask = mask.view(-1)
pred = (mask * pred.T).T
ret... | 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... | vegetablejuiceftw/soft-pointer-networks | MaskedLoss | false | 11,075 | [
"MIT"
] | 0 | 9705d9688b6b69db3948172771df4c367165c948 | https://github.com/vegetablejuiceftw/soft-pointer-networks/tree/9705d9688b6b69db3948172771df4c367165c948 |
BartClassificationHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | Clemens123/transformers | BartClassificationHead | false | 11,488 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
Model | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim, output_class_num, **kwargs):
super(Model, self).__init__()
self.linear = nn.Linear(input_dim, output_class_num)
def forward(self, features):
pooled = features.mean(dim=1)
predicted = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | albertvillanova/s3prl | Model | false | 6,157 | [
"MIT"
] | 1 | b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 | https://github.com/albertvillanova/s3prl/tree/b127ade4ed2f80a1027901bbd2f204b4fb1aaf03 |
OELoss | import torch
import torch.nn as nn
class OELoss(nn.Module):
def __init__(self):
super(OELoss, self).__init__()
def forward(self, x):
return -(x.mean(1) - torch.logsumexp(x, dim=1)).mean()
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ChurchChen/SparsityRegularization | OELoss | false | 8,926 | [
"Apache-2.0"
] | 0 | 5c2e050ffe511cf4307a0bcd98360d28b7db8fef | https://github.com/ChurchChen/SparsityRegularization/tree/5c2e050ffe511cf4307a0bcd98360d28b7db8fef |
CIFAR10_Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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_... | nannullna/deep-active-learning | CIFAR10_Net | false | 16,143 | [
"MIT"
] | 465 | c54a995640c63ba4679129c5a1fd5cec9a2858e6 | https://github.com/nannullna/deep-active-learning/tree/c54a995640c63ba4679129c5a1fd5cec9a2858e6 |
SiameseMLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | EE559DeepLearningEPFL/Project1 | SiameseMLP | false | 391 | [
"MIT"
] | 0 | cbafdfee26771ae0ba3cd36375e68d92e9f108b2 | https://github.com/EE559DeepLearningEPFL/Project1/tree/cbafdfee26771ae0ba3cd36375e68d92e9f108b2 |
ConvCompress | import torch
from torch import nn
class ConvCompress(nn.Module):
def __init__(self, dim, ratio=4):
super().__init__()
self.conv = nn.Conv1d(dim, dim, ratio, stride=ratio)
def forward(self, mem):
mem = mem.transpose(1, 2)
compressed_mem = self.conv(mem)
return compress... | 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... | yhgon/cmtf | ConvCompress | false | 13,130 | [
"MIT"
] | 0 | 7a3ffc3a59a7c546a00d3b73be58f7d1c2f1f0cf | https://github.com/yhgon/cmtf/tree/7a3ffc3a59a7c546a00d3b73be58f7d1c2f1f0cf |
PCN1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class PCN1(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, dilation=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=2)
self.conv3 = nn.Conv2d(32, 64... | 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.... | wkdhkr/pytorch-PCN | PCN1 | false | 4,552 | [
"BSD-2-Clause"
] | 0 | 4686c8fcda0b4fe7ecd7488f5554e19e8f6a8f68 | https://github.com/wkdhkr/pytorch-PCN/tree/4686c8fcda0b4fe7ecd7488f5554e19e8f6a8f68 |
SALayer | import torch
import torch.nn as nn
import torch.utils.model_zoo
class SALayer(nn.Module):
def __init__(self, channel, kernel_size=3):
super(SALayer, self).__init__()
self.conv_sa = nn.Conv2d(channel, channel, kernel_size, padding=1,
groups=channel)
def forward(self, x):
y... | 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.nn as nn
import torch.utils.model_zoo
assert_size_stride = torch._C... | iariav/EDSR-PyTorch | SALayer | false | 3,661 | [
"MIT"
] | 0 | c709b3d43adb6c2457cf87c37c1f34a7bcfc48bb | https://github.com/iariav/EDSR-PyTorch/tree/c709b3d43adb6c2457cf87c37c1f34a7bcfc48bb |
GlobalPooling1D | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | HughMun/MultiBench | GlobalPooling1D | false | 13,800 | [
"MIT"
] | 148 | d5712a0815a9486b0e0c76b54cd63c880188fc8e | https://github.com/HughMun/MultiBench/tree/d5712a0815a9486b0e0c76b54cd63c880188fc8e |
BasicModel3 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | Europium248/captum | BasicModel3 | false | 410 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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_... | AlexS28/SABER | QNetwork | false | 16,874 | [
"BSD-3-Clause"
] | 4 | 91f74319a41f473b8e8f9eff6b7d9b604b94c7da | https://github.com/AlexS28/SABER/tree/91f74319a41f473b8e8f9eff6b7d9b604b94c7da |
Conv1dCompression | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.nn import Module
from torch import nn
import torch.utils.data
import ... | techthiyanes/annotated_deep_learning_paper_implementations | Conv1dCompression | false | 16,552 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
TransformerLayer | import torch
import numpy as np
import torch.nn as nn
import torch.distributions
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, n_heads, kq_same=False, bias=True):
super().__init__()
"""
It has projection layer for getting keys, queries and values. Followed by attention.... | 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.... | Yingting-dev/ReChorus | TransformerLayer | false | 3,010 | [
"MIT"
] | 0 | a16bc1e42f3e90e889133d7476c52ada44db573b | https://github.com/Yingting-dev/ReChorus/tree/a16bc1e42f3e90e889133d7476c52ada44db573b |
DenseCrossEntropy | import torch
import torch.nn as nn
import torch.nn.functional as F
class DenseCrossEntropy(nn.Module):
def __init__(self):
super(DenseCrossEntropy, self).__init__()
def forward(self, logits, labels):
logits = logits.float()
labels = labels.float()
logprobs = F.log_softmax(log... | 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
... | Mo5mami/wtfml | DenseCrossEntropy | false | 14,054 | [
"MIT"
] | 283 | afddec88d9c3a94e30ab2897525daf3f5cf8b774 | https://github.com/Mo5mami/wtfml/tree/afddec88d9c3a94e30ab2897525daf3f5cf8b774 |
WeightedFeatureFusion | import torch
import torch.nn as nn
from torchvision.models.resnet import *
import torch.utils.data
class WeightedFeatureFusion(nn.Module):
def __init__(self, layers, weight=False):
super(WeightedFeatureFusion, self).__init__()
self.layers = layers
self.weight = weight
self.n = len... | 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 torchvision.models.resnet import *
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_si... | PanJason/ML_Proj | WeightedFeatureFusion | false | 17,898 | [
"MIT"
] | 4 | 663be12e8eb6e30e3c902a4984ac0db33bfce605 | https://github.com/PanJason/ML_Proj/tree/663be12e8eb6e30e3c902a4984ac0db33bfce605 |
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_... | minjoong507/TVRetrieval | TrainablePositionalEncoding | false | 10,755 | [
"MIT"
] | 0 | 919e1766ab8aa1ef267bd3b80d4f87b06cde09a9 | https://github.com/minjoong507/TVRetrieval/tree/919e1766ab8aa1ef267bd3b80d4f87b06cde09a9 |
NN | import torch
import torch.nn as nn
import torch.nn.functional as F
class NN(nn.Module):
def __init__(self, input_size, num_classes):
super(NN, self).__init__()
self.fc1 = nn.Linear(input_size, 50)
self.fc2 = nn.Linear(50, num_classes)
def forward(self, x):
x = F.relu(self.fc1... | 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_... | AsianZeus/PyTorch-Models | NN | false | 8,842 | [
"Apache-2.0"
] | 0 | 3249a06a5233b22232a8a336c52e8c24d1b55439 | https://github.com/AsianZeus/PyTorch-Models/tree/3249a06a5233b22232a8a336c52e8c24d1b55439 |
SimpleGCN | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.nn
import torch.autograd
class SimpleGCN(nn.Module):
"""A simple graph convolution layer, similar to the one defined in
Kipf et al. https://arxiv.org/abs/1609.02907
"""
... | 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 math
import torch.nn as nn
from torch.nn.parameter import Parameter
from ... | Kh4L/kaolin | SimpleGCN | false | 712 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 83002fedc67e1c9112a8f834ffb4f8a890e6042a | https://github.com/Kh4L/kaolin/tree/83002fedc67e1c9112a8f834ffb4f8a890e6042a |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.fc1 = nn.Linear(state_dim + action_dim, 400)
self.fc2 = nn.Linear(400, 300)
self.fc3 = nn.Linear(300, 1)
... | 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_... | Chris0919/Deep-reinforcement-learning-with-pytorch | Critic | false | 4,999 | [
"MIT"
] | 1 | a4f458dde7659654fcae4635d25f6bd05a5d2d6c | https://github.com/Chris0919/Deep-reinforcement-learning-with-pytorch/tree/a4f458dde7659654fcae4635d25f6bd05a5d2d6c |
down_right_shifted_conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 ... | andiac/pixel-cnn-pp | down_right_shifted_conv2d | false | 6,205 | [
"MIT"
] | 1 | 3ba856320e40208cbb6e9cac3e66a739f148903e | https://github.com/andiac/pixel-cnn-pp/tree/3ba856320e40208cbb6e9cac3e66a739f148903e |
AE_3D_200 | import torch
import torch.nn as nn
import torch.utils.data
class AE_3D_200(nn.Module):
def __init__(self, n_features=4):
super(AE_3D_200, self).__init__()
self.en1 = nn.Linear(n_features, 200)
self.en2 = nn.Linear(200, 100)
self.en3 = nn.Linear(100, 50)
self.en4 = nn.Linea... | 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 ... | gitter-badger/HEPAutoencoders | AE_3D_200 | false | 12,432 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
EdgeCaseModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 typing import Any
import torch.nn as nn
assert_size_stride = torch._C._dyna... | e-dorigatti/torchinfo | EdgeCaseModel | false | 12,327 | [
"MIT"
] | 0 | 9fa0e677fb7002e89afd5b1bb372fe8c1dd813d6 | https://github.com/e-dorigatti/torchinfo/tree/9fa0e677fb7002e89afd5b1bb372fe8c1dd813d6 |
Flatten | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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 itertools import product as product
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | Juggernaut93/InsightFace-v2 | Flatten | false | 674 | [
"Apache-2.0"
] | 0 | 65e9b8d1f285a87472ffb913bec136d4e046798f | https://github.com/Juggernaut93/InsightFace-v2/tree/65e9b8d1f285a87472ffb913bec136d4e046798f |
DupCNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | WillieMaddox/Airbus_SDC_dup | DupCNN | false | 12,007 | [
"MIT"
] | 0 | 09be904cf3c8050086f07538f5e2954282de5d62 | https://github.com/WillieMaddox/Airbus_SDC_dup/tree/09be904cf3c8050086f07538f5e2954282de5d62 |
TransformerLinearXMCHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | cjhsieh/pecos | TransformerLinearXMCHead | false | 3,300 | [
"Apache-2.0",
"BSD-3-Clause"
] | 0 | 22e88ee544d5a5e891a1d23a578881fdf26dfcf7 | https://github.com/cjhsieh/pecos/tree/22e88ee544d5a5e891a1d23a578881fdf26dfcf7 |
LearnedPositionalEmbedding | import torch
import torch.nn as nn
import torch.nn.functional as F
class LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
Padding ids are ignored by either offsetting based on padding_idx
or by setting padding_idx to None and ensuring t... | 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... | leeharry92/esm | LearnedPositionalEmbedding | false | 12,702 | [
"MIT"
] | 0 | 7d0feccf03ebbdeba4e7ba0f21d934099a0223ce | https://github.com/leeharry92/esm/tree/7d0feccf03ebbdeba4e7ba0f21d934099a0223ce |
MeanVarFC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | lingzenan/invertible-resnet | MeanVarFC | false | 7,097 | [
"MIT"
] | 1 | 57b1c0de51a885aed074b77628f3b0c85c548e70 | https://github.com/lingzenan/invertible-resnet/tree/57b1c0de51a885aed074b77628f3b0c85c548e70 |
ContrastiveLoss | import torch
class ContrastiveLoss(torch.nn.Module):
def __init__(self, margin=1.0):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, anchor, positive, negative):
distance_anchor_positive = (anchor - positive).pow(2).sum(1)
distance_anchor_negati... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | DongChengdongHangZhou/Siamese-Network-tiff | ContrastiveLoss | false | 375 | [
"MIT"
] | 0 | aaf923ad59301af1b3237e605964341a90dc414b | https://github.com/DongChengdongHangZhou/Siamese-Network-tiff/tree/aaf923ad59301af1b3237e605964341a90dc414b |
MSELoss | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | 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 functools
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride... | ChHanXiao/mmdetection | MSELoss | false | 9,154 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 |
CDEFunc | import torch
class CDEFunc(torch.nn.Module):
def __init__(self, input_channels, hidden_channels):
super(CDEFunc, self).__init__()
self.input_channels = input_channels
self.hidden_channels = hidden_channels
self.linear1 = torch.nn.Linear(hidden_channels, 128)
self.linear2 =... | 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.... | khaledsaab/NeuralCDE | CDEFunc | false | 12,671 | [
"Apache-2.0"
] | 0 | 559d9d6fdb137afd14965725ea4845cf31e9235c | https://github.com/khaledsaab/NeuralCDE/tree/559d9d6fdb137afd14965725ea4845cf31e9235c |
PositionalEncoding | import torch
from torch import nn
import torch.nn
import torch.optim
class PositionalEncoding(nn.Module):
"""
A special, non-learnable positional encoding for handling variable (possibly longer)
lengths of inputs. We simply add an ordinal number as an additional dimension for
the input embeddings, and... | 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
import t... | ananthsub/ReAgent | PositionalEncoding | false | 6,197 | [
"BSD-3-Clause"
] | 1 | 92f223a135b8fbc0942a217acb117ad0935897a3 | https://github.com/ananthsub/ReAgent/tree/92f223a135b8fbc0942a217acb117ad0935897a3 |
ModuleForDdpCommHook | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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
import torch.utils.data.distributed
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.dat... | woqidaideshi/bagua | ModuleForDdpCommHook | false | 16,724 | [
"MIT"
] | 635 | 0ee96da598685748519d58d24ce983499cb36721 | https://github.com/woqidaideshi/bagua/tree/0ee96da598685748519d58d24ce983499cb36721 |
Minibatch_stddev_layer | import torch
import torch.nn as nn
class Minibatch_stddev_layer(nn.Module):
"""
Minibatch standard deviation layer. (D_stylegan2)
"""
def __init__(self, group_size=4, num_new_features=1):
super().__init__()
self.group_size = group_size
self.num_new_features = num_new_featu... | 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_... | tomguluson92/StyleGAN2_PyTorch | Minibatch_stddev_layer | false | 16,591 | [
"MIT"
] | 89 | 4ab7354c85cb986d2b77f5238c4a18c5efd1db1b | https://github.com/tomguluson92/StyleGAN2_PyTorch/tree/4ab7354c85cb986d2b77f5238c4a18c5efd1db1b |
SageConv | from torch.nn import Module
import torch
import torch.nn as nn
from torch.nn.modules.module import Module
class SageConv(Module):
"""
Simple Graphsage layer
"""
def __init__(self, in_features, out_features, bias=False):
super(SageConv, self).__init__()
self.proj = nn.Linear(in_feature... | 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.nn import Module
import torch.nn as nn
from torch.nn.modules.module i... | yutaoming/Rare-Category-Detection | SageConv | false | 4,688 | [
"MIT"
] | 0 | 76cf023dff44eef3ecc17f0ebf2b11a08cd63a73 | https://github.com/yutaoming/Rare-Category-Detection/tree/76cf023dff44eef3ecc17f0ebf2b11a08cd63a73 |
RegressionModel | import torch
import torch.nn as nn
class RegressionModel(nn.Module):
def __init__(self, num_features_in, num_anchors=1, feature_size=256):
super(RegressionModel, self).__init__()
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3,
padding=1)
self.act1 = nn.ReL... | 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_... | fmrdev/ctracker | RegressionModel | false | 12,622 | [
"Apache-2.0"
] | 0 | 6f5a88d569d0132a9f844cd1e55e60032d32bcba | https://github.com/fmrdev/ctracker/tree/6f5a88d569d0132a9f844cd1e55e60032d32bcba |
TwoLayerNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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
assert_size_stride = torch._C... | Saran-nns/delve | TwoLayerNet | false | 1,015 | [
"MIT"
] | 0 | 3489d8aa13181b392d3c47a19f9d9a47d87f8790 | https://github.com/Saran-nns/delve/tree/3489d8aa13181b392d3c47a19f9d9a47d87f8790 |
Highway | import torch
from torch import nn
import torch.nn.functional as F
class Highway(nn.Module):
def __init__(self, size):
super(Highway, self).__init__()
self.one = nn.Linear(size, size)
self.two = nn.Linear(size, size)
def forward(self, x):
x0 = F.relu(self.one(x))
x1 = ... | 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... | DennisMagnusson/voice2voice | Highway | false | 2,154 | [
"MIT"
] | 0 | cee95b3eda8c2159f6b85e1733652ff8b7a537ce | https://github.com/DennisMagnusson/voice2voice/tree/cee95b3eda8c2159f6b85e1733652ff8b7a537ce |
Foo | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
assert_si... | alexshuang/apex | Foo | false | 1,405 | [
"BSD-3-Clause"
] | 0 | 107f1ff569c40769de2ed8d366126282e63b63ce | https://github.com/alexshuang/apex/tree/107f1ff569c40769de2ed8d366126282e63b63ce |
PerturbationModule | import torch
import torch.utils.data
import torch
import torch.nn as nn
class PerturbationModule(nn.Module):
def __init__(self, T):
super(PerturbationModule, self).__init__()
self.T = T
self.training = False
self.conv_block = None
def forward(self, x):
if not self.tra... | 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.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | jeffkinnison/pytorch-CycleGAN-and-pix2pix | PerturbationModule | false | 10,237 | [
"BSD-3-Clause"
] | 0 | e47041fa4ffa80ad5948d2d1125ec94c34c5947d | https://github.com/jeffkinnison/pytorch-CycleGAN-and-pix2pix/tree/e47041fa4ffa80ad5948d2d1125ec94c34c5947d |
_ASPP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | developfeng/BCM | _ASPP | false | 9,989 | [
"BSD-3-Clause-Attribution"
] | 0 | 8eb5ac950a2d67d10fc707519bb66cd9ea4f14f2 | https://github.com/developfeng/BCM/tree/8eb5ac950a2d67d10fc707519bb66cd9ea4f14f2 |
GramMatrix | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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.fft
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | NejcHirci/material-addon | GramMatrix | false | 17,775 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
DQN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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_... | FranckNdame/drlkit | DQN | false | 8,106 | [
"MIT"
] | 33 | 698f3c182036cc5eed68f2a05b53a3e3670146bf | https://github.com/FranckNdame/drlkit/tree/698f3c182036cc5eed68f2a05b53a3e3670146bf |
CaricatureLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def resize_4d_tensor_by_size(x, height, width):
res = F.interpolate(x, size=(height, width), mode='bilinear')
return res
class CaricatureLoss(nn.Module):
def __init__(self, power=1.0):
super().__init__()
self.power = pow... | 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
import... | Tiamat-Tech/torch-dreams | CaricatureLoss | false | 2,929 | [
"MIT"
] | 0 | e1c1795f0a0007f54293c474de5d2b80ee829ab8 | https://github.com/Tiamat-Tech/torch-dreams/tree/e1c1795f0a0007f54293c474de5d2b80ee829ab8 |
ActivationClamp | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
import torch.nn.parallel
import torch.optim
import torch.utils.data... | ClashLuke/online-normalization | ActivationClamp | false | 13,511 | [
"BSD-3-Clause"
] | 55 | fe08b9f8e288d628eee4f9991e562cdb4f9e997b | https://github.com/ClashLuke/online-normalization/tree/fe08b9f8e288d628eee4f9991e562cdb4f9e997b |
Postnet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | aviasd/Mockingjay-Speech-Representation | Postnet | false | 6,281 | [
"MIT"
] | 1 | c01aef3f98bbb3fd4b0fc1b61e77fb5d02a0e453 | https://github.com/aviasd/Mockingjay-Speech-Representation/tree/c01aef3f98bbb3fd4b0fc1b61e77fb5d02a0e453 |
MNISTFeatures | import torch
import torch.nn.functional as F
import torch.nn as nn
class MNISTFeatures(nn.Module):
"""
A small convnet for extracting features
from MNIST.
"""
def __init__(self):
""" """
super().__init__()
self.conv1 = nn.Conv2d(3, 32, 5, 1)
self.conv2 = 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
import torch.nn as nn
assert_... | KevinMusgrave/pytorch-adapt | MNISTFeatures | false | 13,966 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
ResizeConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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
import torch.utils.data
assert_size_stride = torch._C._dyna... | ELEKTRONN/elektronn3 | ResizeConv | false | 13,629 | [
"MIT"
] | 124 | 19c751855dffc67b744cd43e757aa4a5bd577d9b | https://github.com/ELEKTRONN/elektronn3/tree/19c751855dffc67b744cd43e757aa4a5bd577d9b |
C3D | import random
import torch
import torchvision
import torch.nn.parallel
import torch.optim
from torch import nn
class GroupMultiScaleCrop(object):
def __init__(self, input_size, scales=None, max_distort=1, fix_crop=
True, more_fix_crop=True):
self.scales = scales if scales is not None else [1, 875... | 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 random
import torchvis... | coderSkyChen/Action_Recognition_Zoo | C3D | false | 15,235 | [
"MIT"
] | 240 | 92ec5ec3efeee852aec5c057798298cd3a8e58ae | https://github.com/coderSkyChen/Action_Recognition_Zoo/tree/92ec5ec3efeee852aec5c057798298cd3a8e58ae |
Mean_One | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 ... | KaiQiangSong/joint_parse_summ | Mean_One | false | 8,767 | [
"BSD-3-Clause"
] | 29 | 5d4a40d9a681bc8b06c847643d810846f3867216 | https://github.com/KaiQiangSong/joint_parse_summ/tree/5d4a40d9a681bc8b06c847643d810846f3867216 |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | PeterouZh/SemiNAS | Attention | false | 17,819 | [
"Apache-2.0"
] | 5 | 39731663271b994571160d43d796b2bb93386b3b | https://github.com/PeterouZh/SemiNAS/tree/39731663271b994571160d43d796b2bb93386b3b |
DCENetLoss | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class DCENetLoss(nn.Module):
def __init__(self, config):
super(DCENetLoss, self).__init__()
self.beta = config['beta']
self.pred_seq = config['pred_seq']
def forward(self,... | 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
... | SeongjuLee/DCENet-PyTorch | DCENetLoss | false | 8,757 | [
"MIT"
] | 10 | eb477ce06356ae597c162dd3229285400ebf9168 | https://github.com/SeongjuLee/DCENet-PyTorch/tree/eb477ce06356ae597c162dd3229285400ebf9168 |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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
import torch.... | thilow/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | false | 11,020 | [
"MIT"
] | 0 | 1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 | https://github.com/thilow/onnxruntime/tree/1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv2 = nn.Conv2d(3, 64, 8, 2, 3)
self.conv3 = nn.Conv2d(64, 128, 6, 2, 2)
self.conv4 = nn.Conv2d(128, 256, 4, 2, 1)
self.conv5 = n... | 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.... | eric-yoo/HairNet | Net | false | 12,428 | [
"MIT"
] | 0 | 15725328709f3f0e63d122914f8e55d18c4fa1fa | https://github.com/eric-yoo/HairNet/tree/15725328709f3f0e63d122914f8e55d18c4fa1fa |
Net | import torch
from numpy import *
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
x = self.hid... | 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 numpy import *... | LishudaNoBug/learning_PyTorch | Net | false | 5,549 | [
"MIT"
] | 1 | 1026035a9cb3d70e2fe97363b532e63db3ca136d | https://github.com/LishudaNoBug/learning_PyTorch/tree/1026035a9cb3d70e2fe97363b532e63db3ca136d |
mbr_convex_hull | import torch
import torch.nn as nn
class mbr_convex_hull(nn.Module):
def _init_(self, hull_points_2d):
super(mbr_convex_hull, self)._init_()
self.hull_points_2d = hull_points_2d
return
def forward(ctx, hull_points_2d):
N = hull_points_2d.shape[0]
edges = hull_points_2... | 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.... | hlesmqh/WS3D | mbr_convex_hull | false | 15,548 | [
"MIT"
] | 100 | 6816eeb135923a59de34ee5d94be2d0fd3ec83f9 | https://github.com/hlesmqh/WS3D/tree/6816eeb135923a59de34ee5d94be2d0fd3ec83f9 |
NTN | import torch
import torch.nn as nn
import torch.nn.functional as F
class NTN(nn.Module):
def __init__(self, l_dim, r_dim, k=4, non_linear=F.tanh):
super(NTN, self).__init__()
self.u_R = nn.Linear(k, 1, bias=False)
self.f = non_linear
self.W = nn.Bilinear(l_dim, r_dim, k, bias=True... | 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 ... | jinfenglin/TaxoExpan | NTN | false | 15,694 | [
"Apache-2.0"
] | 55 | 86bd3f805508d03367539f2fdd43889fc0a4f6b2 | https://github.com/jinfenglin/TaxoExpan/tree/86bd3f805508d03367539f2fdd43889fc0a4f6b2 |
SmallMnistNoDropoutWithPassThrough | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | Rohan-Chaudhury/aimet | SmallMnistNoDropoutWithPassThrough | false | 17,970 | [
"BSD-3-Clause"
] | 3 | 1c38cac8cc0fd32dca40ce5e39940805d29f7a4a | https://github.com/Rohan-Chaudhury/aimet/tree/1c38cac8cc0fd32dca40ce5e39940805d29f7a4a |
BinaryCrossEntropyLabelSmooth | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
assert_size... | NehzUx/autodl | BinaryCrossEntropyLabelSmooth | false | 8,580 | [
"Apache-2.0"
] | 25 | c80fdc4b297ed1ec2b9e6911d313f1fe31d83cb9 | https://github.com/NehzUx/autodl/tree/c80fdc4b297ed1ec2b9e6911d313f1fe31d83cb9 |
MaskedLanguageModel | import math
import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
def get_activation_fn(activation):
"""Return an activation function Module according to its name."""
if activation == 'gelu':
fn = GELU()
elif activation == 'relu':
fn = nn.ReLU()
elif activation ... | 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.... | SivilTaram/dialogue-utterance-rewriter-pytorch | MaskedLanguageModel | false | 2,923 | [
"MIT"
] | 0 | 92c2254958b7a1ee9199836f7f2236575270983f | https://github.com/SivilTaram/dialogue-utterance-rewriter-pytorch/tree/92c2254958b7a1ee9199836f7f2236575270983f |
LinearBlock | import torch
import torch.nn as nn
import torch.utils.data
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine ... | 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 ... | AllenPu/mbdg | LinearBlock | false | 7,664 | [
"MIT"
] | 27 | 243f53a57dcf4bfb6e717c0c9f64a839cff8d548 | https://github.com/AllenPu/mbdg/tree/243f53a57dcf4bfb6e717c0c9f64a839cff8d548 |
UpsampleBLock | import torch
import torch.nn as nn
import torch.utils.data
class UpsampleBLock(nn.Module):
def __init__(self, in_channels):
super(UpsampleBLock, self).__init__()
self.conv = nn.Conv2d(in_channels, in_channels * 2 ** 2,
kernel_size=3, padding=1)
self.pixel_shuffle = nn.PixelShu... | 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.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | tomron27/srganus | UpsampleBLock | false | 11,015 | [
"Apache-2.0"
] | 0 | 5dab73540535138375203bf31e31246cd203f3c0 | https://github.com/tomron27/srganus/tree/5dab73540535138375203bf31e31246cd203f3c0 |
EncoderSteenkiste | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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... | jnsrch/disentangling-vae-cwt | EncoderSteenkiste | false | 15,720 | [
"MIT"
] | 581 | 0e927bdcd3d149cadb30aa107331f0c071138c41 | https://github.com/jnsrch/disentangling-vae-cwt/tree/0e927bdcd3d149cadb30aa107331f0c071138c41 |
AdaptiveMaxPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | yifanpu001/PytorchToCaffe | AdaptiveMaxPool2d | false | 4,711 | [
"MIT"
] | 0 | 37c1ebfc3547e93b1c174721036d03c831c60e48 | https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48 |
LanguageModelCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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 torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | ankit1khare/Show_Infer_and_Tell-CIC | LanguageModelCriterion | false | 18,334 | [
"MIT"
] | 5 | 5437cceaaaf1bbcd16cb921650afd769378f4fc4 | https://github.com/ankit1khare/Show_Infer_and_Tell-CIC/tree/5437cceaaaf1bbcd16cb921650afd769378f4fc4 |
MSEGradLoss | import torch
import torch.nn as nn
import torch.utils.data
class MSEGradLoss(nn.Module):
def __init__(self, grad=False):
super(MSEGradLoss, self).__init__()
self.grad = grad
def forward(self, input, target):
err = input - target
loss = err.norm(p=2).pow(2).div(err.numel())
... | 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
import... | JaguAroo/SRResCGAN | MSEGradLoss | false | 626 | [
"MIT"
] | 0 | 9aac612aff631f7fb9142e0a36de9559cfc1a62d | https://github.com/JaguAroo/SRResCGAN/tree/9aac612aff631f7fb9142e0a36de9559cfc1a62d |
RewardModelNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 ... | Hcnaeg/DI-engine | RewardModelNetwork | false | 2,393 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
RegWeightedL1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
... | Ssong24/CenterNet_Custom | RegWeightedL1Loss | false | 9,541 | [
"MIT"
] | 0 | 526ec70f8dfabf9fb9179c9be28ce50fb2a7961c | https://github.com/Ssong24/CenterNet_Custom/tree/526ec70f8dfabf9fb9179c9be28ce50fb2a7961c |
LinearGLUBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Park-Jong-Min/neural_sp | LinearGLUBlock | false | 2,725 | [
"Apache-2.0"
] | 0 | a4f300ae9c16c6e9ea3128292fbc141f68f38081 | https://github.com/Park-Jong-Min/neural_sp/tree/a4f300ae9c16c6e9ea3128292fbc141f68f38081 |
CausalSelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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.... | DQiaole/ZITS | CausalSelfAttention | false | 8,809 | [
"Apache-2.0"
] | 40 | 5f7a060167790789d5e29a3d14d3c2ef8a34e765 | https://github.com/DQiaole/ZITS/tree/5f7a060167790789d5e29a3d14d3c2ef8a34e765 |
MLPEncoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 typ... | HughMun/MultiBench | MLPEncoder | false | 13,811 | [
"MIT"
] | 148 | d5712a0815a9486b0e0c76b54cd63c880188fc8e | https://github.com/HughMun/MultiBench/tree/d5712a0815a9486b0e0c76b54cd63c880188fc8e |
AdaptiveCatAvgMaxPool2d | import torch
import torch.nn as nn
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
def adaptive_catavgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_m... | 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
import torch.utils.data
import torchvision.transforms.functional as... | DifferentSC/pytorch-image-models | AdaptiveCatAvgMaxPool2d | false | 11,611 | [
"Apache-2.0"
] | 0 | ccfb5751abc70d80add4f197464190c4a2637c6c | https://github.com/DifferentSC/pytorch-image-models/tree/ccfb5751abc70d80add4f197464190c4a2637c6c |
FiLM | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class FiLM(nn.Module):
"""
A Feature-wise Linear Modulation Layer from
'FiLM: Visual Reasoning with a General Conditioning Layer'
"""
def forward(self, x, gammas, betas):
ret... | 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
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.... | Dou-Yiming/YouRefIt_ERU | FiLM | false | 7,985 | [
"MIT"
] | 13 | 2a8e849380ed2d253c467b1af744a514bc171372 | https://github.com/Dou-Yiming/YouRefIt_ERU/tree/2a8e849380ed2d253c467b1af744a514bc171372 |
_MCLSTMCell | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from typing import Tuple
class _Gate(nn.Module):
"""Utility class to implement a standard sigmoid gate"""
def __init__(self, in_features: 'int', out_features: 'int'):
super(_Gate, self).__init__()
self.fc = nn.Li... | 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.... | NHoose/neuralhydrology | _MCLSTMCell | false | 1,904 | [
"BSD-3-Clause"
] | 0 | f320b417fe747a923ff8ef685ad33fd8b34effad | https://github.com/NHoose/neuralhydrology/tree/f320b417fe747a923ff8ef685ad33fd8b34effad |
Conv3x3 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 torch.... | minjabenho/image2pcl | Conv3x3 | false | 7,229 | [
"Apache-2.0"
] | 1 | 7e696ee48edae30814d32f32e605ad6cf8bf702c | https://github.com/minjabenho/image2pcl/tree/7e696ee48edae30814d32f32e605ad6cf8bf702c |
InceptionE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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 ... | Galaxies99/inception-cuda | InceptionE | false | 11,476 | [
"MIT"
] | 0 | ed8fdbe3caef415e60b52e671273be90e9423e44 | https://github.com/Galaxies99/inception-cuda/tree/ed8fdbe3caef415e60b52e671273be90e9423e44 |
Delta | import torch
import torch.nn as nn
from torchaudio import transforms
class Delta(nn.Module):
def __init__(self, order=2, **kwargs):
super(Delta, self).__init__()
self.order = order
self.compute_delta = transforms.ComputeDeltas(**kwargs)
def forward(self, x):
feats = [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
import torch.nn as nn
from torchaudio import transforms
assert_size_stride = tor... | czlwang/s3prl | Delta | false | 12,282 | [
"Apache-2.0"
] | 0 | 81d4bb8d051cee20fa87c083b8478999e1766172 | https://github.com/czlwang/s3prl/tree/81d4bb8d051cee20fa87c083b8478999e1766172 |
SoftQNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
def mish(x):
"""
Mish: A Self Regularized Non-Monotonic Neural Activation Function
https://arxiv.org/abs/1908.08681v1
implemented for PyTorch / FastAI by lessw2020
https://github.com/lessw2020/mish
param:
... | 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.... | Crawford-fang/ROS_pytorch_RL | SoftQNetwork | false | 17,175 | [
"Apache-2.0"
] | 10 | 2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f | https://github.com/Crawford-fang/ROS_pytorch_RL/tree/2d3476f15d51aa1f5b5ae9edc5d7f4c776e5de9f |
TLU | import torch
import torch.nn as nn
import torch.utils.data.distributed
class TLU(nn.Module):
""" Thresholded Linear Unit """
def __init__(self, num_features):
super().__init__()
self.num_features = num_features
self.tau = nn.Parameter(torch.zeros(1, num_features, 1, 1))
def forwa... | 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
import torch.utils.data.distributed
assert_size_stride = torch._C._... | derwind/mxfont | TLU | false | 10,127 | [
"MIT"
] | 0 | 0b6d4554a1e2208906230d3121d792d450ed28dd | https://github.com/derwind/mxfont/tree/0b6d4554a1e2208906230d3121d792d450ed28dd |
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