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 |
|---|---|---|---|---|---|---|---|---|---|---|
CoordConv | import torch
import torch.nn as nn
class AddCoords(nn.Module):
def __init__(self, with_r=False):
super().__init__()
self.with_r = with_r
def forward(self, input_tensor):
"""
Args:
input_tensor: shape(batch, channel, x_dim, y_dim)
"""
batch_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | SeunghwanByun/Real-Time-Road-Detection-Network | CoordConv | false | 1,054 | [
"MIT"
] | 0 | bc46615adef0e2b1a9a03dd4951559ca5849e6e1 | https://github.com/SeunghwanByun/Real-Time-Road-Detection-Network/tree/bc46615adef0e2b1a9a03dd4951559ca5849e6e1 |
DivLoss | import torch
import torch.nn as nn
import torch.utils.data
class DivLoss(nn.Module):
"""Diversity loss, which is defined as negative of standard deviation.
"""
def __init__(self):
super(DivLoss, self).__init__()
def forward(self, lam):
mu = lam.mean(0)
std = ((lam - mu) ** 2)... | 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... | XianyuanLiu/Transfer-Learning-Library | DivLoss | false | 10,140 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
GroverAttention | # 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Changgun-Choi/huggingmolecules | GroverAttention | false | 243 | [
"Apache-2.0"
] | 0 | 6d7c5e7d0acfd3d4725eb0deaeb0413dad9cfde8 | https://github.com/Changgun-Choi/huggingmolecules/tree/6d7c5e7d0acfd3d4725eb0deaeb0413dad9cfde8 |
MultiHeadAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0... | 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
from torch._inductor.runtime.... | and-smith/Vac-Scholar-Curb-GAN | MultiHeadAttention | false | 6,213 | [
"MIT"
] | 1 | 142bd70fdf0f1cbc4a1c20c5e58fa5b6a9dbe742 | https://github.com/and-smith/Vac-Scholar-Curb-GAN/tree/142bd70fdf0f1cbc4a1c20c5e58fa5b6a9dbe742 |
ResBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
def get_conv(in_dim, out_dim, kernel_size, stride, padding, zero_bias=True,
zero_weights=False, groups=1):
c = nn.Conv2d(in_dim, out_dim, kernel_size, stride, padding, groups=groups)
if zero_bias:
c.bias.data *= 0.0
if zero_wei... | 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 ... | kpandey008/DiffuseVAE | ResBlock | false | 15,863 | [
"MIT"
] | 90 | b505894668ac1e4ef9a66ec220f5b40f5c83629e | https://github.com/kpandey008/DiffuseVAE/tree/b505894668ac1e4ef9a66ec220f5b40f5c83629e |
Mod | import torch
class Mod(torch.nn.Module):
def __init__(self):
super(Mod, self).__init__()
def forward(self, x, y):
return x % y
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | Ilyabasharov/torch2trt | Mod | false | 2,565 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
NlpCrossEntropy | import torch
import torch.nn as nn
class NlpCrossEntropy(nn.Module):
def __init__(self):
super().__init__()
def forward(self, props, tgt):
tgt_props = props.gather(2, tgt.unsqueeze(2)).squeeze()
mask = (tgt > 0).float()
return -(tgt_props * mask).sum() / mask.sum()
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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | NeilWangziyu/torch_light | NlpCrossEntropy | false | 5,641 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
BinaryReg | import torch
from typing import Optional
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
class BinaryReg(nn.Module):
"""Regularization for encouraging the outputs to be binary.
Args:
pred (torch.Tensor): foreground logits.
mask (Optional[torch.Tensor], optional): weight... | 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.utils.dat... | HarshSulakhe/pytorch_connectomics | BinaryReg | false | 9,852 | [
"MIT"
] | 0 | 73402e654afde69a43a5836cc90a32ef75c75dc2 | https://github.com/HarshSulakhe/pytorch_connectomics/tree/73402e654afde69a43a5836cc90a32ef75c75dc2 |
ShuffleCatChunk | # 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... | akaneko1019/yolact_edge | ShuffleCatChunk | false | 14,767 | [
"MIT"
] | 1,036 | a9a00281b33b3ac90253a4939773308a8f95e21d | https://github.com/akaneko1019/yolact_edge/tree/a9a00281b33b3ac90253a4939773308a8f95e21d |
Feedforward | import torch
class Feedforward(torch.nn.Module):
def __init__(self, input_size, output_size, hidden_size):
super(Feedforward, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.hidden_size = hidden_size
self.fc1 = torch.nn.Linear(self.input_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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | fywu85/eecs206a_project | Feedforward | false | 6,708 | [
"MIT"
] | 1 | 73ea518779da4d187df8bbe4cbe46bca6d1a0714 | https://github.com/fywu85/eecs206a_project/tree/73ea518779da4d187df8bbe4cbe46bca6d1a0714 |
equalized_linear | # 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
from torch.nn.init import normal
import torch.utils.data
a... | mingo-x/pggan-pytorch | equalized_linear | false | 7,234 | [
"MIT"
] | 1 | a1dde73cd4df52476fe7c948d81fa9caea8070a5 | https://github.com/mingo-x/pggan-pytorch/tree/a1dde73cd4df52476fe7c948d81fa9caea8070a5 |
MyGlobalAvgPool2d | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import torch.optim
class MyGlobalAvgPool2d(nn.Module):
def __init__(self, keep_dim=True):
super(MyGlobalAvgPool2d, self).__init__()
self.keep_dim = keep_dim
def forward(self, x):
return x.mean(3, keep... | 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.utils.data
import torch.nn.parallel
import torch.optim
assert_size_stride = torch._C._dynamo.guards.asser... | xmyqsh/once-for-all | MyGlobalAvgPool2d | false | 4,585 | [
"MIT"
] | 0 | 0bca1778b106d33460fc8d0f7d7e6ca4e1e937d9 | https://github.com/xmyqsh/once-for-all/tree/0bca1778b106d33460fc8d0f7d7e6ca4e1e937d9 |
NAE | import torch
import torch.nn as nn
class NAE(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred, gt):
diff = torch.abs(pred - gt)
loss = torch.mean(torch.abs(diff / gt))
return loss
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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | j1a0m0e4sNTU/MachineLearning2019 | NAE | false | 3,677 | [
"MIT"
] | 0 | 44a7a3387837e53134bcf5eb8fcf95daf4dff48d | https://github.com/j1a0m0e4sNTU/MachineLearning2019/tree/44a7a3387837e53134bcf5eb8fcf95daf4dff48d |
period_L1 | # 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
import torc... | flytocc/RAPiD | period_L1 | false | 15,368 | [
"MIT"
] | 142 | 92e6a44b8a0107def055e93c971d78fd548562f8 | https://github.com/flytocc/RAPiD/tree/92e6a44b8a0107def055e93c971d78fd548562f8 |
DistillLoss | # 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
import torch.nn as nn
assert... | Terminator8758/Precise-ICS-master | DistillLoss | false | 18,009 | [
"MIT"
] | 4 | 9f4591fee6ab64d9dd91f551355d29562bf663cb | https://github.com/Terminator8758/Precise-ICS-master/tree/9f4591fee6ab64d9dd91f551355d29562bf663cb |
L2PenaltyConstraintLoss | # 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
from torch import nn
assert_... | ykt345/fairtorch | L2PenaltyConstraintLoss | false | 11,013 | [
"MIT"
] | 0 | fe7e0cfaec3de0fc2b9c92943bb02639acd46bb4 | https://github.com/ykt345/fairtorch/tree/fe7e0cfaec3de0fc2b9c92943bb02639acd46bb4 |
NetworkExtension | import torch
import torch.utils
import torch
import torch.nn as nn
class NetworkExtension(nn.Module):
def __init__(self, orig_num_classes, num_classes, auxiliary):
super(NetworkExtension, self).__init__()
self._auxiliary = auxiliary
self.classifier = nn.Linear(orig_num_classes, num_classe... | 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
import torch
import torch.nn as nn
assert_size_stride = torch... | amelieEmily/RobustDARTS | NetworkExtension | false | 12,207 | [
"Apache-2.0"
] | 0 | b26e127c6e9c330258786f5eb77b17d367f546ff | https://github.com/amelieEmily/RobustDARTS/tree/b26e127c6e9c330258786f5eb77b17d367f546ff |
DummyModelWithSharedSubmodule | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
class DummyDenseWithRelu(nn.Module):
def __init__(self, input_size, output_size, relu=None):
... | 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 ... | Emily0219/distiller | DummyModelWithSharedSubmodule | false | 5,127 | [
"Apache-2.0"
] | 1 | 445ed35b671fb54586acc280b53d951f18bf97ae | https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae |
SoftCrossEntropyLoss2d | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
class SoftCrossEntropyLoss2d(nn.Module):
def __init__(self):
super(SoftCrossEntropyLoss2d, self).__init__()
def forward(self, inputs, targets):
loss = 0
inputs = -F.log_softmax(inputs, dim=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
from torch._inductor.runtime.... | songzijiang/FasterSeg | SoftCrossEntropyLoss2d | false | 16,510 | [
"MIT"
] | 334 | 1a14ef6dd665afd229a16ab43b532b5a406512f8 | https://github.com/songzijiang/FasterSeg/tree/1a14ef6dd665afd229a16ab43b532b5a406512f8 |
Attn | # 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.... | gau820827/AI-writer_Data2Doc | Attn | false | 15,410 | [
"Apache-2.0"
] | 77 | 6be0ee6238158a47aa0fdfa8a34df2a47714835a | https://github.com/gau820827/AI-writer_Data2Doc/tree/6be0ee6238158a47aa0fdfa8a34df2a47714835a |
LayerNorm | # 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
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | hyunwoongko/transformer | LayerNorm | false | 15,558 | [
"Apache-2.0"
] | 233 | 8f7aaa19d37b088c156db0512868127ba9bf1a0f | https://github.com/hyunwoongko/transformer/tree/8f7aaa19d37b088c156db0512868127ba9bf1a0f |
MyKernelTorch | # 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_... | sugatoray/alibi-detect | MyKernelTorch | false | 16,505 | [
"Apache-2.0"
] | 1,227 | 66d7873c248c0be1a1d836e6fe1ef59351b802d9 | https://github.com/sugatoray/alibi-detect/tree/66d7873c248c0be1a1d836e6fe1ef59351b802d9 |
PrimaryCaps | # 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... | esdrascosta/Matrix-Capsules | PrimaryCaps | false | 6,661 | [
"MIT"
] | 1 | ddf35dfa1acfb51a11a3ec27e15fe863a2ff6fa4 | https://github.com/esdrascosta/Matrix-Capsules/tree/ddf35dfa1acfb51a11a3ec27e15fe863a2ff6fa4 |
MetaBilinear | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----
Objects inherited f... | 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
reinterpret_tensor = torch._C._dynamo.guards._reinterp... | RisingStockPrices/multi-shape-siren | MetaBilinear | false | 2,772 | [
"MIT"
] | 0 | f78d6deb94660fd11ef0caf55f88095b74d3e223 | https://github.com/RisingStockPrices/multi-shape-siren/tree/f78d6deb94660fd11ef0caf55f88095b74d3e223 |
RWKV_ChannelMix | # 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, math as tl_math
im... | YUASDS/AI-Writer | RWKV_ChannelMix | false | 6,017 | [
"BSD-3-Clause"
] | 1 | 6ec1e9548802ed5b5a2f1fd297595a52cb605266 | https://github.com/YUASDS/AI-Writer/tree/6ec1e9548802ed5b5a2f1fd297595a52cb605266 |
D | # 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
import torch.nn as nn
assert... | leaderj1001/SimSiam | D | false | 15,874 | [
"MIT"
] | 53 | ed36348d3d5a8621674c78c3ed77c1188bd18e16 | https://github.com/leaderj1001/SimSiam/tree/ed36348d3d5a8621674c78c3ed77c1188bd18e16 |
DiceLoss | import torch
from torch import nn
import torch.backends.cudnn
class DiceLoss(nn.Module):
def __init__(self, smooth=0, eps=1e-07):
super(DiceLoss, self).__init__()
self.smooth = smooth
self.eps = eps
def forward(self, output, target):
return 1 - (2 * torch.sum(output * target)... | 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.backends.cudnn
assert_size_stride = torch._C._dynamo.gu... | cxz/tgs-salt-identification-challenge | DiceLoss | false | 6,503 | [
"MIT"
] | 1 | 859f3d7f2d3184532c42c34444500eec3b03b1c8 | https://github.com/cxz/tgs-salt-identification-challenge/tree/859f3d7f2d3184532c42c34444500eec3b03b1c8 |
LASympNet | from torch.nn import Module
import torch
import torch.nn as nn
class Module(torch.nn.Module):
"""Standard module format.
"""
def __init__(self):
super(Module, self).__init__()
self.activation = None
self.initializer = None
self.__device = None
self.__dtype = None
... | 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
assert_size_stride = torch._C.... | shushu-qin/deeponet | LASympNet | false | 16,461 | [
"Apache-2.0"
] | 140 | 5bbe066279bba055ad80e04c364140363c87634a | https://github.com/shushu-qin/deeponet/tree/5bbe066279bba055ad80e04c364140363c87634a |
DiceLoss | # 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... | Exdenta/torchsat | DiceLoss | false | 13,655 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
Normalize | import torch
import torch.nn as nn
class Normalize(nn.Module):
def __init__(self, features, epsilon=1e-06):
super(Normalize, self).__init__()
self.gain = nn.Parameter(torch.ones(features))
self.bias = nn.Parameter(torch.zeros(features))
self.epsilon = epsilon
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | yuri20198/neurips19-graph-protein-design | Normalize | false | 4,644 | [
"MIT"
] | 0 | 068e8cdfcbba629f996e99d3765cc2f3233f71a3 | https://github.com/yuri20198/neurips19-graph-protein-design/tree/068e8cdfcbba629f996e99d3765cc2f3233f71a3 |
Conv2dSWD | import torch
import torch.utils.data
import torch.nn as nn
import torch
class Conv2dSWD(nn.Module):
def __init__(self, in_channels, out_channels, kernel_radius=2, bias=True):
super(Conv2dSWD, self).__init__()
kernel_size_h = 2 * kernel_radius - 1
self.padding = kernel_radius - 1
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
import torch.utils.data
import torch.nn as nn
import torch
assert_size_stride = ... | FVL2020/MSWSR | Conv2dSWD | false | 8,112 | [
"MIT"
] | 27 | 0844e78ee68fb0465efd5c4a2215ce815980526b | https://github.com/FVL2020/MSWSR/tree/0844e78ee68fb0465efd5c4a2215ce815980526b |
ChannelScale | # 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... | rlmwang/torch-tools | ChannelScale | false | 10,796 | [
"MIT"
] | 0 | 822132534d73414f26045bad38a0a345661b057f | https://github.com/rlmwang/torch-tools/tree/822132534d73414f26045bad38a0a345661b057f |
Res | import torch
from torch import nn
import torch.distributions
class Res(nn.Module):
def __init__(self, H):
super().__init__()
self.u1 = nn.Linear(H, H)
self.u2 = nn.Linear(H, H)
self.v1 = nn.Linear(H, H)
self.v2 = nn.Linear(H, H)
self.w = nn.Linear(H, H)
def fo... | 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... | da03/torch_struct | Res | false | 3,371 | [
"MIT"
] | 0 | 08713b61b0cfe8438e52e82e07c88cf094feb73a | https://github.com/da03/torch_struct/tree/08713b61b0cfe8438e52e82e07c88cf094feb73a |
PACRRConvMax2dModule | # 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... | alpers/FlexNeuART | PACRRConvMax2dModule | false | 12,083 | [
"Apache-2.0"
] | 0 | 2ae263f46b6eb2f1435b9073dad629a2fef23ab9 | https://github.com/alpers/FlexNeuART/tree/2ae263f46b6eb2f1435b9073dad629a2fef23ab9 |
ContrastivePairwiseEmbeddingLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.distributed
class ContrastivePairwiseEmbeddingLoss(nn.Module):
"""
ContrastivePairwiseEmbeddingLos... | 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.... | bbradt/catalyst | ContrastivePairwiseEmbeddingLoss | false | 3,180 | [
"Apache-2.0"
] | 0 | 38a503c8af040906e377b7485d7fe15a7bc1de19 | https://github.com/bbradt/catalyst/tree/38a503c8af040906e377b7485d7fe15a7bc1de19 |
BILM | import torch
import torch.nn as nn
class BILM(nn.Module):
def __init__(self):
super(BILM, self).__init__()
self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
def forward(self, feat):
pos_sig = 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | SeunghwanByun/Real-Time-Road-Detection-Network | BILM | false | 1,046 | [
"MIT"
] | 0 | bc46615adef0e2b1a9a03dd4951559ca5849e6e1 | https://github.com/SeunghwanByun/Real-Time-Road-Detection-Network/tree/bc46615adef0e2b1a9a03dd4951559ca5849e6e1 |
MSBlock | import torch
import torch.nn as nn
class MSBlock(nn.Module):
def __init__(self, c_in, rate=4):
super(MSBlock, self).__init__()
self.rate = rate
self.conv = nn.Conv2d(c_in, 32, 3, stride=1, padding=1)
self.relu = nn.ReLU(inplace=True)
dilation = self.rate * 1 if self.rate >... | 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_... | farkoo/novel-seam-carving-method | MSBlock | false | 3,494 | [
"MIT"
] | 0 | aa3e9a4e3d5e13872eed412444e5be519542f7e5 | https://github.com/farkoo/novel-seam-carving-method/tree/aa3e9a4e3d5e13872eed412444e5be519542f7e5 |
GLU | # 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
import torch.nn.functional
assert_size_stride = torch._C._... | MichalOp/StarTrain | GLU | false | 17,720 | [
"MIT"
] | 7 | e8dddf879f103e18239ad37b373c9b51fbbe093b | https://github.com/MichalOp/StarTrain/tree/e8dddf879f103e18239ad37b373c9b51fbbe093b |
CustomGruCell | # 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 numpy as np
... | kouohhashi/PySyft | CustomGruCell | false | 10,355 | [
"Apache-2.0"
] | 0 | 7415961b459f1d25f762467b346b7b94c1d6943f | https://github.com/kouohhashi/PySyft/tree/7415961b459f1d25f762467b346b7b94c1d6943f |
KL_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 libdevice, math as tl_math
from torch ... | BlakeDai/FedML-test | KL_Loss | false | 9,192 | [
"Apache-2.0"
] | 0 | 3cb9a7234f3f0294f3137e4be572153ba7b62f8f | https://github.com/BlakeDai/FedML-test/tree/3cb9a7234f3f0294f3137e4be572153ba7b62f8f |
FromRGB | # 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 math
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_s... | STomoya/animeface | FromRGB | false | 14,384 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
CustomKLLoss | # 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
from torch.nn.modules.... | WdBlink/AugMix-3DOCUNet-Brats2019 | CustomKLLoss | false | 5,954 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
SelfAttn | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
import torch as th
class SelfAttn(nn.Module):
def __init__(self, hidden_size):
super(SelfAttn, self).__init__()
self.query = nn.Linear(hidden_size, 1)
def forward(self, keys, value... | 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.... | ljw23/ConvLab-2 | SelfAttn | false | 15,937 | [
"Apache-2.0"
] | 339 | 13d48ea0e441701bd66100689b6c25b561f15525 | https://github.com/ljw23/ConvLab-2/tree/13d48ea0e441701bd66100689b6c25b561f15525 |
NN | import torch
from torch import 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
from torch import nn
assert_s... | HaowenWeiJohn/CV_Project | NN | false | 526 | [
"MIT"
] | 0 | 8e2414796f60a8c3fe452f3721e4a6ef7edfdb11 | https://github.com/HaowenWeiJohn/CV_Project/tree/8e2414796f60a8c3fe452f3721e4a6ef7edfdb11 |
SamePad2dStrong | # 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... | IssamLaradji/wisenet | SamePad2dStrong | false | 17,436 | [
"Apache-2.0"
] | 7 | 881457f5168815f5e9d03f110244842d539747a0 | https://github.com/IssamLaradji/wisenet/tree/881457f5168815f5e9d03f110244842d539747a0 |
AnimalStudentNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class AnimalStudentNet(nn.Module):
def __init__(self, num_classes=16):
super(AnimalStudentNet, self).__init__()
self.pool = nn.MaxPool2d(2, 2)
self.dropout = nn.Dropout2d(p=0.1)
self.conv1 = nn.Conv2d(3, 6, 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
import torch.nn as nn
assert_... | elouie/CodeSamples | AnimalStudentNet | false | 10,074 | [
"Apache-2.0"
] | 0 | 3fe9fcf23cbfc82d84a679ea16d69ae41e700f06 | https://github.com/elouie/CodeSamples/tree/3fe9fcf23cbfc82d84a679ea16d69ae41e700f06 |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 1, (5, 5), groups=1)
self.relu1 = nn.ReLU(inplace=True)
self.fc1 = nn.Linear(36, 5)
self.relu2 = nn.ReLU(inplace=True)
def forward(self, 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_... | KarthikGanesan88/stonne | Net | false | 9,270 | [
"MIT"
] | 0 | f228ade67120b9dafac8ea99d201e269b2ad7099 | https://github.com/KarthikGanesan88/stonne/tree/f228ade67120b9dafac8ea99d201e269b2ad7099 |
GraphConvolution | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0.3):
super(GraphConvolution, self).__init__()
self.in_feat... | 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_... | ayyyq/T-LSTM | GraphConvolution | false | 6,292 | [
"MIT"
] | 1 | 36dbc88ac710d3925851cd87c2368ecfc7061b70 | https://github.com/ayyyq/T-LSTM/tree/36dbc88ac710d3925851cd87c2368ecfc7061b70 |
CDAE | # 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 tor... | mmcenta/eye-disease-recognition | CDAE | false | 4,025 | [
"MIT"
] | 0 | 52e1dedbce27514b605b9f8ad976d6042b7e2f14 | https://github.com/mmcenta/eye-disease-recognition/tree/52e1dedbce27514b605b9f8ad976d6042b7e2f14 |
ConvReLUNorm | import torch
import torch.cuda
import torch.distributed
import torch.utils.data
import torch.optim
class ConvReLUNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, dropout=0.0):
super(ConvReLUNorm, self).__init__()
self.conv = torch.nn.Conv1d(in_channels, out_chan... | 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.... | hamjam/NeMo | ConvReLUNorm | false | 15,490 | [
"Apache-2.0"
] | 4,145 | b3484d32e1317666151f931bfa39867d88ed8658 | https://github.com/hamjam/NeMo/tree/b3484d32e1317666151f931bfa39867d88ed8658 |
GlobalAveragePooling | import torch
import torch.nn as nn
class GlobalAveragePooling(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x.mean([2, 3])
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... | HexagonPrime/pixel-nerf | GlobalAveragePooling | false | 2,409 | [
"BSD-2-Clause"
] | 0 | 298aa7a3451c01e6f19f73f0c756672d3de54bf9 | https://github.com/HexagonPrime/pixel-nerf/tree/298aa7a3451c01e6f19f73f0c756672d3de54bf9 |
DilatedResidualLayer | # 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_... | manthan-kodar/Action-seg-experiments | DilatedResidualLayer | false | 12,759 | [
"MIT"
] | 0 | 3515ee64082ab567838782f5600e186bf86473a0 | https://github.com/manthan-kodar/Action-seg-experiments/tree/3515ee64082ab567838782f5600e186bf86473a0 |
TrainablePositionalEncoding | # 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
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 |
DuelingQNetwork | # 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
from co... | nullbyte91/udacity-drl-navigation | DuelingQNetwork | false | 10,610 | [
"MIT"
] | 0 | d981ab906fd3dfc9939d639b2083d004cde0b961 | https://github.com/nullbyte91/udacity-drl-navigation/tree/d981ab906fd3dfc9939d639b2083d004cde0b961 |
PoolingF | # 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
import torch.utils.data
impo... | a11isonliu/contrastive-unpaired-translation | PoolingF | false | 9,846 | [
"BSD-3-Clause"
] | 0 | 67651ed9877cae121d9398f46094ce8dbc678802 | https://github.com/a11isonliu/contrastive-unpaired-translation/tree/67651ed9877cae121d9398f46094ce8dbc678802 |
LogSumExpPool | import torch
from torch import nn
class LogSumExpPool(nn.Module):
def __init__(self, gamma):
super(LogSumExpPool, self).__init__()
self.gamma = gamma
def forward(self, feat_map):
"""
Numerically stable implementation of the operation
Arguments:
feat_map(Te... | 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
from torch import nn
a... | Tarandro/Chexpert | LogSumExpPool | false | 11,927 | [
"Apache-2.0"
] | 0 | 6bc51f899a479f8dbad8a64c92f35ed4632377b3 | https://github.com/Tarandro/Chexpert/tree/6bc51f899a479f8dbad8a64c92f35ed4632377b3 |
RobertaClassificationHead | # 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
from ty... | LaudateCorpus1/text-1 | RobertaClassificationHead | false | 9,267 | [
"BSD-3-Clause"
] | 0 | 8808e7eee5a2df79b9566a4a348889dc2722fcfb | https://github.com/LaudateCorpus1/text-1/tree/8808e7eee5a2df79b9566a4a348889dc2722fcfb |
Custom_dropout | import torch
import torch.nn as nn
import torch.nn.parallel
class Custom_dropout(nn.Module):
"""
An implementation for few , Given a task perform a rowise sum of 2-d
matrix , you get a zero out the contribution of few of rows in the matrix
Given, X a 2-d matrix consisting of row vectors (1-d) x1 , x2 ,..xn.... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | Chahalprincy/deepchem | Custom_dropout | false | 224 | [
"MIT"
] | 0 | 9d1a6a879cc74b065694b3ddb763d52151d57b7a | https://github.com/Chahalprincy/deepchem/tree/9d1a6a879cc74b065694b3ddb763d52151d57b7a |
SphereLoss | # 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.... | modricwang/SphereReID | SphereLoss | false | 10,581 | [
"MIT"
] | 0 | d0c39d2ce52cbc35e4d3adc1e90c0e54585aa492 | https://github.com/modricwang/SphereReID/tree/d0c39d2ce52cbc35e4d3adc1e90c0e54585aa492 |
IndepAnisotropicGaussianUVLoss | # 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
import math... | GOPIKA-0204/Clothing-Detection-and-Recolouring | IndepAnisotropicGaussianUVLoss | false | 9,074 | [
"MIT"
] | 0 | b5d436a981b854228314729b41874f31948a33ba | https://github.com/GOPIKA-0204/Clothing-Detection-and-Recolouring/tree/b5d436a981b854228314729b41874f31948a33ba |
RefModel3d2 | import torch
import torch.nn.functional as F
class RefModel3d2(torch.nn.Module):
"""The 3D reference model."""
def __init__(self):
super().__init__()
self.l1 = torch.nn.Conv3d(2, 2, 3, padding=1, stride=2,
padding_mode='replicate', bias=False)
self.l2 = torch.nn.GroupNorm(... | 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.... | shuohan/pytorch-layers | RefModel3d2 | false | 4,367 | [
"MIT"
] | 0 | 020846fd02d501cf477552179c19ba4b5e9a0695 | https://github.com/shuohan/pytorch-layers/tree/020846fd02d501cf477552179c19ba4b5e9a0695 |
GeLU | # 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.triton_helpers import libdevice
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.a... | HS-YN/PanoAVQA | GeLU | false | 17,356 | [
"MIT"
] | 3 | 657b83421ce64ea18b3e79fb580afc7034403ccc | https://github.com/HS-YN/PanoAVQA/tree/657b83421ce64ea18b3e79fb580afc7034403ccc |
TemporalEmbedding | import math
import torch
import torch.nn as nn
class FixedEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(FixedEmbedding, self).__init__()
w = torch.zeros(c_in, d_model).float()
w.require_grad = False
position = torch.arange(0, c_in).float().unsqueeze(1)
div... | 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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | Xianchao-Wu/informer | TemporalEmbedding | false | 5,991 | [
"Apache-2.0"
] | 1 | bb9cb3c6ff9e7e76c8dbbf3bcc7924df1f18982d | https://github.com/Xianchao-Wu/informer/tree/bb9cb3c6ff9e7e76c8dbbf3bcc7924df1f18982d |
BCELoss | # 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
... | klovbe/UnsupervisedDeepLearning-Pytorch | BCELoss | false | 7,046 | [
"MIT"
] | 1 | 35e8e49cd4024179db173f3dab2e6d1a5d037d35 | https://github.com/klovbe/UnsupervisedDeepLearning-Pytorch/tree/35e8e49cd4024179db173f3dab2e6d1a5d037d35 |
RelPositionMultiHeadedAttention | import math
import torch
from typing import Optional
from typing import Tuple
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
... | 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.... | Honghe/wenet | RelPositionMultiHeadedAttention | false | 5,332 | [
"Apache-2.0"
] | 1 | 4421790bec3778df591816d69f0449930a9be321 | https://github.com/Honghe/wenet/tree/4421790bec3778df591816d69f0449930a9be321 |
ILN | # 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_stride = tor... | STomoya/animeface | ILN | false | 15,296 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
MultinomialCELoss | import torch
import torch.nn as nn
class MultinomialCELoss(nn.Module):
def __init__(self):
super(MultinomialCELoss, self).__init__()
def forward(self, x, y):
x = x + 1e-08
x = torch.log(x)
zlogz = y * x
loss = -zlogz.sum()
loss /= x.shape[0] * x.shape[2] * x.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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | MMujtabaRoohani/FlowerColorizer-PyTorch | MultinomialCELoss | false | 11,668 | [
"MIT"
] | 0 | 4c9c4c954a38babe1f10f816f8406eb4ab998842 | https://github.com/MMujtabaRoohani/FlowerColorizer-PyTorch/tree/4c9c4c954a38babe1f10f816f8406eb4ab998842 |
EcaModule | import math
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
from torch import optim as optim
class EcaModule(nn.Module):
"""Constructs an ECA module.
Args:
channels: Number of channels of the input feature map for use in adaptive kernel sizes
for actual... | 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.utils.data
import torch.nn as nn
import torch.nn.parall... | dumpmemory/NonDeepNetworks | EcaModule | false | 15,250 | [
"BSD-3-Clause"
] | 307 | 5513bf588f4e64c99583440507232675c2e21e34 | https://github.com/dumpmemory/NonDeepNetworks/tree/5513bf588f4e64c99583440507232675c2e21e34 |
GatedLinearUnit | import torch
import torch.nn as nn
class GatedLinearUnit(nn.Module):
def __init__(self, dim: 'int'=-1, nonlinear: 'bool'=True):
super().__init__()
self.dim = dim
self.nonlinear = nonlinear
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
val, gate = torch.chunk(x, 2, dim=... | 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_... | ZhuangweiKang/pytorch-ts | GatedLinearUnit | false | 2,994 | [
"Apache-2.0",
"MIT"
] | 0 | 076d456358fd1bac96becba4f1ba38ec5a5fcf4d | https://github.com/ZhuangweiKang/pytorch-ts/tree/076d456358fd1bac96becba4f1ba38ec5a5fcf4d |
Block | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.nn import functional as F
class RWKV_TimeMix(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
assert config.n_attn % config.n_head == 0
self.layer_id = layer_id
self.ctx... | 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.... | JunnYu/Paddle-AI-Writer | Block | false | 8,822 | [
"BSD-3-Clause"
] | 25 | 8d211f9e60aeed323b6330065668f54350514c70 | https://github.com/JunnYu/Paddle-AI-Writer/tree/8d211f9e60aeed323b6330065668f54350514c70 |
Cosine | # 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
from torch._inductor.runtime.triton_helpers import libdevice
from torch.optim.lr... | anlewy/mt-dnn | Cosine | false | 14,862 | [
"MIT"
] | 2,075 | eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 | https://github.com/anlewy/mt-dnn/tree/eeb6f01ce0630e61a52b8c9c6f7537cd34978e45 |
location_network | # 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.... | felixnon/foveated-visual-attention | location_network | false | 12,371 | [
"MIT"
] | 0 | 7e7d9a5ef24ec42eb76ba72f783bb2227bdb4851 | https://github.com/felixnon/foveated-visual-attention/tree/7e7d9a5ef24ec42eb76ba72f783bb2227bdb4851 |
Decoder | import torch
import torch.nn as nn
from collections import OrderedDict
class Decoder(nn.Module):
def __init__(self, style_dim, class_dim):
super(Decoder, self).__init__()
self.linear_model = nn.Sequential(OrderedDict([('linear_1', nn.
Linear(in_features=style_dim + class_dim, out_feat... | 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 ... | vicissitude1999/multi-level-vae | Decoder | false | 16,684 | [
"MIT"
] | 68 | 83bc98fbe5046c61941298d4fd49b08fd868ee89 | https://github.com/vicissitude1999/multi-level-vae/tree/83bc98fbe5046c61941298d4fd49b08fd868ee89 |
GaussianSample | # 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 import device
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... | ChengF-Lab/scIVA | GaussianSample | false | 8,884 | [
"MIT"
] | 0 | f70a927531dd16236dff30decbe77f0552ad4f2d | https://github.com/ChengF-Lab/scIVA/tree/f70a927531dd16236dff30decbe77f0552ad4f2d |
Contract | # 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... | Alex-Beh/hand_tracking | Contract | false | 11,163 | [
"Apache-2.0"
] | 0 | 40ac39e10ed5815d9400d6a87149015ad6ab9686 | https://github.com/Alex-Beh/hand_tracking/tree/40ac39e10ed5815d9400d6a87149015ad6ab9686 |
Actor | # 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.... | brooky56/DeepRL-UR-motion-planning | Actor | false | 1,583 | [
"MIT"
] | 0 | 0cc523da6d8a55896773f1f57feed1f0c77fea78 | https://github.com/brooky56/DeepRL-UR-motion-planning/tree/0cc523da6d8a55896773f1f57feed1f0c77fea78 |
Network | # 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_... | cheapmouse94/Machine_Learning-Gates-python | Network | false | 1,670 | [
"MIT"
] | 0 | 1e159ccf8f9a5db9104fa3926b85750787676e15 | https://github.com/cheapmouse94/Machine_Learning-Gates-python/tree/1e159ccf8f9a5db9104fa3926b85750787676e15 |
NonLocal | import torch
import torch.nn as nn
import torch.nn.functional as func
import torch.jit
import torch.nn
class NonLocal(nn.Module):
def __init__(self, in_size, attention_size=32, size=None, scale=None):
super(NonLocal, self).__init__()
self.size = size
self.scale = scale
self.attent... | 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.... | ankmathur96/torchsupport | NonLocal | false | 3,178 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
"""
Implements FFN equation (1-D convolution).
"""
def __init__(self, n_hid, dropout=0.1):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Linear(n_hid, n_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 import triton_helpers
import torch.nn as nn
assert_... | acbull/HiCE | PositionwiseFeedForward | false | 14,742 | [
"MIT"
] | 58 | 0a7e3035bc6e1e2ea5d08b0f1fb68656f75df62f | https://github.com/acbull/HiCE/tree/0a7e3035bc6e1e2ea5d08b0f1fb68656f75df62f |
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 |
conv | # 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, math as tl_math
im... | QiuhongAnnaWei/IBRNet | conv | false | 14,258 | [
"Apache-2.0"
] | 254 | 6c8b68e6d95eae04535ff0906387ec7899f5d5ce | https://github.com/QiuhongAnnaWei/IBRNet/tree/6c8b68e6d95eae04535ff0906387ec7899f5d5ce |
LinearEstimator | import torch
import torch.nn as nn
class LinearEstimator(nn.Module):
def __init__(self, in_c, out_c, factor=1.0):
super().__init__()
self.factor = factor
self.net = nn.Linear(in_c, out_c, bias=False)
def forward(self, x):
return self.net(x) * self.factor
def get_inputs():
... | 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... | MLIA/LEADS | LinearEstimator | false | 17,650 | [
"MIT"
] | 6 | 4010f6b6e6a56ee049b4b4a9aec1c24b34730616 | https://github.com/MLIA/LEADS/tree/4010f6b6e6a56ee049b4b4a9aec1c24b34730616 |
Block_MLP | import torch
import torch.nn as nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is... | 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 ... | yifanc96/yifanc-DL | Block_MLP | false | 11,095 | [
"MIT"
] | 0 | 25d56cec776fb151c8f6bcbd997bca94f07f3597 | https://github.com/yifanc96/yifanc-DL/tree/25d56cec776fb151c8f6bcbd997bca94f07f3597 |
MultiheadAttention | # 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.... | CharlesPikachu/mcibi | MultiheadAttention | false | 7,903 | [
"MIT"
] | 41 | 6ce453504741c2eed1d290306055258a377a4094 | https://github.com/CharlesPikachu/mcibi/tree/6ce453504741c2eed1d290306055258a377a4094 |
L2Norm | from torch.nn import Module
import torch
import torch.nn.functional as F
class L2Norm(Module):
def forward(self, input):
return F.normalize(input)
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 libdevice
from torch.nn import Module
... | HAOCHENYE/Silent-Face-Anti-Spoofing-master-yehc | L2Norm | false | 500 | [
"Apache-2.0"
] | 0 | 014c781d4109733f87a50b10d10508ba5e431581 | https://github.com/HAOCHENYE/Silent-Face-Anti-Spoofing-master-yehc/tree/014c781d4109733f87a50b10d10508ba5e431581 |
GraphNet | import torch
import torch.nn as nn
class GraphNet(nn.Module):
def __init__(self, input_size, n_classes, num_neurons=32):
super(GraphNet, self).__init__()
self.fc1 = nn.Linear(input_size, num_neurons)
self.fc2 = nn.Linear(num_neurons, num_neurons)
self.fc3 = nn.Linear(num_neurons, ... | 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.... | adam2392/dldo | GraphNet | false | 12,070 | [
"MIT"
] | 0 | fc57f8700eb048558ab205c2c77a064f1a7cc7f6 | https://github.com/adam2392/dldo/tree/fc57f8700eb048558ab205c2c77a064f1a7cc7f6 |
GramMSELoss | import torch
import torch.nn as nn
import torch.utils.data
class GramMatrix(nn.Module):
def forward(self, input):
b, c, h, w = input.size()
F = input.view(b, c, h * w)
G = torch.bmm(F, F.transpose(1, 2))
G.div_(h * w)
return G
class GramMSELoss(nn.Module):
def 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 ... | Reytuag/non-stationary_texture_syn | GramMSELoss | false | 14,303 | [
"MIT"
] | 351 | 005d3e4ead3dfa2164b14c5b3bf41cdc15fd3b0b | https://github.com/Reytuag/non-stationary_texture_syn/tree/005d3e4ead3dfa2164b14c5b3bf41cdc15fd3b0b |
PyTorchFeedForward | import torch
import torch.nn
import torch.autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch.cuda
class PyTorchFeedForward(nn.Module):
def __init__(self, depth, width, input_size, output_size):
super(PyTorchFeedForward, self).__init__()
self.linears = [... | 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.... | ccoulombe/thinc | PyTorchFeedForward | false | 12,197 | [
"MIT"
] | 0 | 8d891b61ddef3ca00266ca0ec7c47e2d063a3a83 | https://github.com/ccoulombe/thinc/tree/8d891b61ddef3ca00266ca0ec7c47e2d063a3a83 |
activation_quantize_fn | # 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.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | MohammedHAlali/pytorch_DoReFaNet | activation_quantize_fn | false | 847 | [
"MIT"
] | 0 | d208089b9172f02c09cc6633158ed5b5d6cd7f1e | https://github.com/MohammedHAlali/pytorch_DoReFaNet/tree/d208089b9172f02c09cc6633158ed5b5d6cd7f1e |
InverseDepthSmoothnessLoss | import torch
import torch.nn as nn
def _gradient_x(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :, :-1] - img[:, :, :, 1:]
def _gradient_y(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :-1, :] - img[:, :, 1:... | 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... | IEM-Computer-Vision/kornia | InverseDepthSmoothnessLoss | false | 9,297 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | f98bd9a2158a6e59cda076d55d476acf13f4e0af | https://github.com/IEM-Computer-Vision/kornia/tree/f98bd9a2158a6e59cda076d55d476acf13f4e0af |
SoftmaxFocalClassificationLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class SoftmaxFocalClassificationLoss(nn.Module):
"""Criterion that computes Focal loss.
According to [1], the Focal loss is computed as follows:
.. math::
\\text{FL}(p_t) = -\\alpha_t (1 - p_t)^{\\gamma} \\, \\text{log}(p_t)
wh... | 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
... | collector-m/BtcDet | SoftmaxFocalClassificationLoss | false | 15,062 | [
"Apache-2.0"
] | 108 | 80bee34f2f40931600f812a6edbcb27e51cb7ec3 | https://github.com/collector-m/BtcDet/tree/80bee34f2f40931600f812a6edbcb27e51cb7ec3 |
F_conv | # 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 warnings
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guar... | Xenovortex/INN_Embedding_Classification | F_conv | false | 1,245 | [
"MIT"
] | 0 | df31ec3dcf70780cae5140a69ffafdd64f218e5f | https://github.com/Xenovortex/INN_Embedding_Classification/tree/df31ec3dcf70780cae5140a69ffafdd64f218e5f |
StateCritic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
"""Encodes the static & dynamic states using 1d Convolution."""
def __init__(self, input_size, hidden_size):
super(Encoder, self).__init__()
self.conv = nn.Conv1d(input_size, hidden_size, 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
import torch.nn as nn
assert_... | dimichai/City-Metro-Network-Expansion-with-RL | StateCritic | false | 3,444 | [
"MIT"
] | 0 | 54cfec74d89b4e4fc912d480a3025e4c75e3b196 | https://github.com/dimichai/City-Metro-Network-Expansion-with-RL/tree/54cfec74d89b4e4fc912d480a3025e4c75e3b196 |
AddCoords | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | MingSungChao/IPN-hand | AddCoords | false | 14,011 | [
"MIT"
] | 54 | 0b061e4438f159e3e312af4959cb424917b5c367 | https://github.com/MingSungChao/IPN-hand/tree/0b061e4438f159e3e312af4959cb424917b5c367 |
RMSE | # 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
import torc... | d4l3k/crowds | RMSE | false | 12,238 | [
"MIT"
] | 0 | a57eee80d66498474c86cec22dd77be9d627ad97 | https://github.com/d4l3k/crowds/tree/a57eee80d66498474c86cec22dd77be9d627ad97 |
DoubleAttention | import torch
from torch import nn
from torch.nn import init
from torch.nn import functional as F
class DoubleAttention(nn.Module):
def __init__(self, in_channels, c_m, c_n, reconstruct=True):
super().__init__()
self.in_channels = in_channels
self.reconstruct = reconstruct
self.c_m... | 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.... | LeftAttention/Attention-Codebase | DoubleAttention | false | 17,590 | [
"Apache-2.0"
] | 6 | 348ec66233a7c0f95a3cb5f0f11641e2a7a9b9c3 | https://github.com/LeftAttention/Attention-Codebase/tree/348ec66233a7c0f95a3cb5f0f11641e2a7a9b9c3 |
TwoMLPHead | import torch
from torchvision.transforms import functional as F
from torch import nn
import torch.nn.functional as F
import torch.utils.data
class TwoMLPHead(nn.Module):
"""
Standard heads for FPN-based models
Arguments:
in_channels (int): number of input channels
representation_size (int... | 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... | BoChenYS/ROPE | TwoMLPHead | false | 17,301 | [
"BSD-3-Clause"
] | 6 | 3e50f134259b555cf547e4a3ef8b14cf5cda4e00 | https://github.com/BoChenYS/ROPE/tree/3e50f134259b555cf547e4a3ef8b14cf5cda4e00 |
TextProcessor | # 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_... | olipinski/MultimodalGame | TextProcessor | false | 10,634 | [
"BSD-3-Clause"
] | 0 | cfacc66baebfadb6ed6a8b44b3dd71a298285d68 | https://github.com/olipinski/MultimodalGame/tree/cfacc66baebfadb6ed6a8b44b3dd71a298285d68 |
Downsample | # 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.utils.data
import torch
import torch.nn as nn
assert_size_stride = ... | Tiamat-Tech/Image-Super-Resolution-via-Iterative-Refinement | Downsample | false | 14,485 | [
"Apache-2.0"
] | 1,764 | ef9b943b573328d7a5ddb1a0c2abd168b91610dc | https://github.com/Tiamat-Tech/Image-Super-Resolution-via-Iterative-Refinement/tree/ef9b943b573328d7a5ddb1a0c2abd168b91610dc |
KARAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class KARMultiHeadAttention(nn.Module):
def __init__(self, config, hidden_size):
super(KARMultiHeadAttention, self).__init__()
if 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.... | ohadrozen/inferbert | KARAttention | false | 4,137 | [
"Apache-2.0"
] | 0 | 2e450aba894937e5769dcf028e4a8a597991fe43 | https://github.com/ohadrozen/inferbert/tree/2e450aba894937e5769dcf028e4a8a597991fe43 |
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