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 |
|---|---|---|---|---|---|---|---|---|---|---|
TestModel | import torch
import torch.nn as nn
import torch.fx
class TestModel(nn.Module):
def __init__(self):
super().__init__()
self.a = torch.nn.Module()
self.b = torch.nn.Module()
self.a.weights = torch.nn.Parameter(torch.randn(1, 2))
self.b.weights = torch.nn.Parameter(torch.rand... | 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.fx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | NVIDIA/Torch-TensorRT | TestModel | false | 14,086 | [
"BSD-3-Clause"
] | 430 | 1a22204fecec690bc3c2a318dab4f57b98c57f05 | https://github.com/NVIDIA/Torch-TensorRT/tree/1a22204fecec690bc3c2a318dab4f57b98c57f05 |
SquareRoot | import torch
import torch.nn.functional
from torch import nn
class SquareRoot(nn.Module):
def forward(self, x):
return x.sqrt()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.functional
from torch import nn
assert_size_stride = torch._C._... | drivendataorg/DrivenData-2021-Geopose-Solution | SquareRoot | false | 6,595 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
PerturbationModule | # 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.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 |
PositionWiseFF | import torch
from torch import nn
from torch.nn.functional import gelu
import torch.utils.data
import torch.optim
class PositionWiseFF(nn.Module):
"""
Position-wise feed-forward network of Transformer block.
Args:
hidden_size: size of the embeddings in the model, also known as d_model
inn... | 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.... | Oktai15/NeMo | PositionWiseFF | false | 5,681 | [
"Apache-2.0"
] | 1 | 5b6dd3850129898be47cf0d65587897ec45a5b59 | https://github.com/Oktai15/NeMo/tree/5b6dd3850129898be47cf0d65587897ec45a5b59 |
Net1 | # 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.... | SarodYatawatta/federated-pytorch-test | Net1 | false | 8,760 | [
"Apache-2.0"
] | 33 | 42a51ba12a92b32fa19273340d5b61e74e11d8e0 | https://github.com/SarodYatawatta/federated-pytorch-test/tree/42a51ba12a92b32fa19273340d5b61e74e11d8e0 |
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 tnn
assert... | Jittor/Jittor | Net | false | 17,504 | [
"Apache-2.0"
] | 4 | bc945bae94bded917214b0afe12be6bf5b919dbe | https://github.com/Jittor/Jittor/tree/bc945bae94bded917214b0afe12be6bf5b919dbe |
Vol | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | tbright17/audio | Vol | false | 10,927 | [
"BSD-2-Clause"
] | 0 | 00d38203e401b8d9472a8f8394a10e2c309be02c | https://github.com/tbright17/audio/tree/00d38203e401b8d9472a8f8394a10e2c309be02c |
PositionwiseFeedForward | # 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... | pkufool/DaVinci-Speech | PositionwiseFeedForward | false | 7,475 | [
"MIT"
] | 1 | 98602363168476356d492852093adbe65c65ac95 | https://github.com/pkufool/DaVinci-Speech/tree/98602363168476356d492852093adbe65c65ac95 |
BboxHead | # 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 itertools import product as product
import torch.nn as nn
assert_size_strid... | Edward1900/Face-Detector-1MB-with-landmark | BboxHead | false | 13,702 | [
"MIT"
] | 907 | 16c16c4efa74b0264e0fd7fe0ddc0160f540a4bf | https://github.com/Edward1900/Face-Detector-1MB-with-landmark/tree/16c16c4efa74b0264e0fd7fe0ddc0160f540a4bf |
F_fully_connected | import torch
import torch.nn as nn
class F_fully_connected(nn.Module):
"""Fully connected tranformation, not reversible, but used below."""
def __init__(self, size_in, size, internal_size=None, dropout=0.0):
super(F_fully_connected, self).__init__()
if not internal_size:
internal_... | 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_... | Xenovortex/INN_Embedding_Classification | F_fully_connected | false | 1,240 | [
"MIT"
] | 0 | df31ec3dcf70780cae5140a69ffafdd64f218e5f | https://github.com/Xenovortex/INN_Embedding_Classification/tree/df31ec3dcf70780cae5140a69ffafdd64f218e5f |
PointWiseFeedForward | # 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... | RenqinSS/RecSys | PointWiseFeedForward | false | 11,812 | [
"MIT"
] | 0 | 932d54e47af79576b67e28364ae6e97d99ededf4 | https://github.com/RenqinSS/RecSys/tree/932d54e47af79576b67e28364ae6e97d99ededf4 |
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.l1 = nn.Linear(state_dim + action_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, 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
import ... | Barisimre/TD3-Generative | Critic | false | 4,895 | [
"MIT"
] | 1 | 434419b020b88010f09f194c40feac1d420b2086 | https://github.com/Barisimre/TD3-Generative/tree/434419b020b88010f09f194c40feac1d420b2086 |
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.... | asmekal/catalyst | ContrastivePairwiseEmbeddingLoss | false | 12,118 | [
"MIT"
] | 0 | e11365c0a9812649ceaef14e53061cd5117d8684 | https://github.com/asmekal/catalyst/tree/e11365c0a9812649ceaef14e53061cd5117d8684 |
SpacialGatingUnit | import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
from typing import Optional
import torch.autograd
class SpacialGatingUnit(nn.Module):
"""
## Spatial Gating Unit
$$s(Z) = Z_1 \\odot f_{W,b}(Z_2)$$
where $f_{W,b}(Z) = W Z + b$ is a linear transformation along the s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Hadryan/nn | SpacialGatingUnit | false | 9,378 | [
"MIT"
] | 0 | b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d | https://github.com/Hadryan/nn/tree/b10e3dea2c7e1f6569bfdf8e1a48f8d48b5a645d |
Norm | # 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
import... | Manojbhat09/Sane-annotation-shape-complete | Norm | false | 17,698 | [
"Apache-2.0"
] | 9 | 03b298b2c0a187be979ff31ad2a39238b72a6d78 | https://github.com/Manojbhat09/Sane-annotation-shape-complete/tree/03b298b2c0a187be979ff31ad2a39238b72a6d78 |
PredictionConvolutions | import torch
import torch.utils.data
from torch import nn
from itertools import product as product
import torch.optim
class PredictionConvolutions(nn.Module):
"""
Convolutions to predict class scores and bounding boxes using lower and higher-level feature maps.
The bounding boxes (locations) are predicte... | 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
from torch import nn
from itertools import product as pr... | adityag6994/pytorch_ssd_training | PredictionConvolutions | false | 3,410 | [
"MIT"
] | 0 | 404f3cbef815e314337ec2c1b4f06a2403a7ce03 | https://github.com/adityag6994/pytorch_ssd_training/tree/404f3cbef815e314337ec2c1b4f06a2403a7ce03 |
CosModule | import torch
class CosModule(torch.nn.Module):
def __init__(self):
super(CosModule, self).__init__()
def forward(self, x):
return torch.cos(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | mirecta/nncase | CosModule | false | 4,169 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
ProbabilityLinear | import torch
import torch.nn as nn
import torch.nn.functional as F
def normalize_prob(a, dim=-1):
"""Perform 1-norm along the specific dimension."""
return a / a.sum(dim=dim, keepdim=True)
class ProbabilityLinear(nn.Linear):
def __init__(self, in_features, out_features, bias=False, norm=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 import triton_helpers
from torch._inductor.runtime.... | ashutosh1919/neuro-symbolic-sudoku-solver | ProbabilityLinear | false | 14,906 | [
"Apache-2.0"
] | 52 | ecb4274ff66d3b6a86f64584e0a767bf785f107f | https://github.com/ashutosh1919/neuro-symbolic-sudoku-solver/tree/ecb4274ff66d3b6a86f64584e0a767bf785f107f |
TVLoss | import torch
from torch import nn
from torch.nn import functional as F
class TVLoss(nn.Module):
"""L2 total variation loss, as in Mahendran et al."""
def forward(self, input):
input = F.pad(input, (0, 1, 0, 1), 'replicate')
x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]
y_diff = ... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | olaviinha/style-transfer-pytorch | TVLoss | false | 16,200 | [
"MIT"
] | 290 | 9bdb2d932a31b6cf0ac7b651dc38b740c3e37fe8 | https://github.com/olaviinha/style-transfer-pytorch/tree/9bdb2d932a31b6cf0ac7b651dc38b740c3e37fe8 |
FocalLoss2d | import torch
from torch import nn
class FocalLoss2d(nn.Module):
def __init__(self, gamma=2, ignore_index=255):
super().__init__()
self.gamma = gamma
self.ignore_index = ignore_index
def forward(self, outputs: 'torch.Tensor', targets: 'torch.Tensor'):
outputs = outputs.contigu... | 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... | kevinkwshin/kaggle-pneumothorax | FocalLoss2d | false | 15,818 | [
"MIT"
] | 74 | 24b91a9425097023f0cc7781a9380cb247babe22 | https://github.com/kevinkwshin/kaggle-pneumothorax/tree/24b91a9425097023f0cc7781a9380cb247babe22 |
Quaternion | # 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.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | zhuhao-nju/mofanerf | Quaternion | false | 16,824 | [
"MIT"
] | 55 | 0206526e25aab3dd8f0cc789f290c7559642676b | https://github.com/zhuhao-nju/mofanerf/tree/0206526e25aab3dd8f0cc789f290c7559642676b |
TripletLoss_op | import torch
import torch.nn as nn
import torch.nn.functional as F
class TripletLoss_op(nn.Module):
def __init__(self, margin=1.0):
super(TripletLoss_op, self).__init__()
self.margin = margin
def forward(self, op, anchor, positive, negative, size_average=True):
distance_positive = (a... | 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... | awesome-archive/CAIL2019 | TripletLoss_op | false | 14,913 | [
"MIT"
] | 300 | 31e917752676ad77d247a47e04f17a8f9ea68721 | https://github.com/awesome-archive/CAIL2019/tree/31e917752676ad77d247a47e04f17a8f9ea68721 |
Sub | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Ilyabasharov/torch2trt | Sub | false | 2,546 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
LayerNormChan | import torch
from torch import nn
class LayerNormChan(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
var = torch.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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | lucidrains/nuwa-pytorch | LayerNormChan | false | 15,968 | [
"MIT"
] | 310 | bf1f3dc1126ba0a24a280bd7412a8082e5013b46 | https://github.com/lucidrains/nuwa-pytorch/tree/bf1f3dc1126ba0a24a280bd7412a8082e5013b46 |
MIRB3 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, groups=3):
super(ConvBlock, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.groups = gro... | 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 ... | wwjfsfs/wwjyyds | MIRB3 | false | 13,189 | [
"MIT"
] | 0 | 80cd6267fde7cd98838078a0d5178a557ceb7414 | https://github.com/wwjfsfs/wwjyyds/tree/80cd6267fde7cd98838078a0d5178a557ceb7414 |
IOU | import torch
import torch.multiprocessing
def _iou(pred, target, size_average=True):
b = pred.shape[0]
IoU = 0.0
for i in range(0, b):
Iand1 = torch.sum(target[i, :, :, :] * pred[i, :, :, :])
Ior1 = torch.sum(target[i, :, :, :]) + torch.sum(pred[i, :, :, :]
) - Iand1
Io... | 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.multiprocessing
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._e... | intchous/SmartText | IOU | false | 6,886 | [
"MIT"
] | 1 | 81abb84ce135a3859c32257d861c9e87b51f8c3f | https://github.com/intchous/SmartText/tree/81abb84ce135a3859c32257d861c9e87b51f8c3f |
projection_model | # 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
assert_size_stride ... | ShivanshuPurohit/GPT-Neo-visual-grounding | projection_model | false | 17,910 | [
"Apache-2.0"
] | 4 | 9c938257a688ef5ae8bc1b87b61d943aa158e880 | https://github.com/ShivanshuPurohit/GPT-Neo-visual-grounding/tree/9c938257a688ef5ae8bc1b87b61d943aa158e880 |
make_dense | import torch
import torch.nn as nn
import torch.nn.functional as F
class make_dense(nn.Module):
def __init__(self, nChannels, nChannels_, growthRate, kernel_size=3):
super(make_dense, self).__init__()
self.conv = nn.Conv2d(nChannels_, growthRate, kernel_size=
kernel_size, padding=(ker... | 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_... | BJTU-MIMO/Channel_estimation_MRDN | make_dense | false | 127 | [
"MIT"
] | 0 | f41972998a5403c901bc3e5d68d4acd05e9a7f6c | https://github.com/BJTU-MIMO/Channel_estimation_MRDN/tree/f41972998a5403c901bc3e5d68d4acd05e9a7f6c |
SparseDecoder | import torch
import numpy as np
from torch import Tensor
import torch.nn as nn
from scipy.special import erfinv
class SparseDecoder(nn.Module):
def __init__(self, seq_len, alphabet_size, latent_dim, h1_dim=100,
h2_dim=500, n_tiles=4, conv_size=40, scale_mu=0.01, scale_sigma=4.0):
"""
... | 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 import triton_helpers
from... | charlesxu90/DeepSequence-torch | SparseDecoder | false | 10,043 | [
"MIT"
] | 0 | 640db39769a93ef3d5bc11d6ad05aa7f5d761972 | https://github.com/charlesxu90/DeepSequence-torch/tree/640db39769a93ef3d5bc11d6ad05aa7f5d761972 |
TransformerDecoderLayer | import torch
import torch.nn as nn
class TransformerDecoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=16, dropout=0):
super(TransformerDecoderLayer, self).__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.multihead_attn = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | imperial-qore/CAROL | TransformerDecoderLayer | false | 6,874 | [
"BSD-3-Clause"
] | 1 | 57dc42c4ddeb9e75eed43a91ceb336a1ecc9c8b9 | https://github.com/imperial-qore/CAROL/tree/57dc42c4ddeb9e75eed43a91ceb336a1ecc9c8b9 |
GraphConv | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphConv(nn.Module):
def __init__(self, input_dim, output_dim, add_self=False,
normalize_embedding=False, dropout=0.0, bias=True):
super(GraphConv, self).__init__()
self.add_self = add_self
self.dropout = dr... | 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... | Qin-J/Multi-site-transfer-classification-of-major-depressive-disorder | GraphConv | false | 2,744 | [
"Apache-2.0"
] | 0 | f6af292388ec83a9851a2254f38e8d90adfe4e6c | https://github.com/Qin-J/Multi-site-transfer-classification-of-major-depressive-disorder/tree/f6af292388ec83a9851a2254f38e8d90adfe4e6c |
TripletLoss | # 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... | lxy5513/cvToolkit | TripletLoss | false | 15,965 | [
"MIT"
] | 47 | 51586c8016b47f5e7852032f9f3211c89d80f537 | https://github.com/lxy5513/cvToolkit/tree/51586c8016b47f5e7852032f9f3211c89d80f537 |
Temporal_Attention_layer | import torch
import torch.utils.data
import torch.nn.functional as F
import torch.nn as nn
class Temporal_Attention_layer(nn.Module):
def __init__(self, DEVICE, in_channels, num_of_vertices, num_of_timesteps):
super(Temporal_Attention_layer, self).__init__()
self.U1 = nn.Parameter(torch.FloatTens... | 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.... | kevin-xuan/Traffic-Benchmark | Temporal_Attention_layer | false | 15,837 | [
"MIT"
] | 120 | b9f8e40b4df9b58f5ad88432dc070cbbbcdc0228 | https://github.com/kevin-xuan/Traffic-Benchmark/tree/b9f8e40b4df9b58f5ad88432dc070cbbbcdc0228 |
FastRCNNPredictor | # 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... | yokosyun/instance-segmentation | FastRCNNPredictor | false | 4,630 | [
"MIT"
] | 0 | 5779ae864b24c28300b0ddc4c314e63490215606 | https://github.com/yokosyun/instance-segmentation/tree/5779ae864b24c28300b0ddc4c314e63490215606 |
Affine | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Affine(nn.Module):
def __init__(self, dim):
super().__init__()
self.alpha = nn.Parameter(torch.ones((1, 1, dim)))
self.beta = nn.Parameter(torch.zeros((1, 1, dim)))
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | tor4z/pytorch-image-models | Affine | false | 4,450 | [
"Apache-2.0"
] | 0 | d7bab8a6c52a72487d1bed0a28aad41e326d7622 | https://github.com/tor4z/pytorch-image-models/tree/d7bab8a6c52a72487d1bed0a28aad41e326d7622 |
Conv2dSame | import math
import torch
import torch.utils.data
import torch
import torch.nn as nn
class Conv2dSame(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1):
super(Conv2dSame, self).__init__()
self.F = kernel_size
self.S = stride
self.D... | 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... | adityamehta00/HIDeGAN | Conv2dSame | false | 3,027 | [
"BSD-3-Clause"
] | 0 | 91a0674e092ccde2784a82bf927dfefd8673eb4c | https://github.com/adityamehta00/HIDeGAN/tree/91a0674e092ccde2784a82bf927dfefd8673eb4c |
Pow | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | ahangchen/torch2trt | Pow | false | 6,092 | [
"MIT"
] | 1 | 53c663f0e0570ef7ffd6771354ae3478f63bd328 | https://github.com/ahangchen/torch2trt/tree/53c663f0e0570ef7ffd6771354ae3478f63bd328 |
LigthSpeechLoss | import torch
from torch import nn
import torch.utils.data
class LigthSpeechLoss(nn.Module):
""" LigthSpeech Loss """
def __init__(self):
super(LigthSpeechLoss, self).__init__()
def forward(self, mel, padd_predicted, cemb_out, mel_tac2_target, D, cemb):
mel_loss = nn.MSELoss()(mel, mel_ta... | 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
i... | entn-at/LightSpeech | LigthSpeechLoss | false | 6,654 | [
"MIT"
] | 1 | 48250fbcede4b258ba13ab17e3e83afc5fe85a01 | https://github.com/entn-at/LightSpeech/tree/48250fbcede4b258ba13ab17e3e83afc5fe85a01 |
GHMIoU | import torch
import torch.nn.functional as F
import torch.nn as nn
class GHMIoU(nn.Module):
"""GHM IoU prediction loss
Details of the theorem can be viewed in the paper
"Gradient Harmonized Single-stage Detector".
https://arxiv.org/abs/1811.05181
Args:
bins (int): Number of the unit regi... | 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
... | ShegnkaiWu/IoU-aware-single-stage-object-detector-for-accurate-localization | GHMIoU | false | 14,400 | [
"Apache-2.0"
] | 62 | 67b8955eb59137590dbadc6aac45529ae9459e4a | https://github.com/ShegnkaiWu/IoU-aware-single-stage-object-detector-for-accurate-localization/tree/67b8955eb59137590dbadc6aac45529ae9459e4a |
PositionwiseFeedforward | import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedforward(nn.Module):
def __init__(self, hid_dim: 'int', pf_dim: 'int', dropout: 'float'):
super().__init__()
self.hid_dim = hid_dim
self.pf_dim = pf_dim
self.fc_1 = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Art31/pytorch-seq2seq | PositionwiseFeedforward | false | 8,830 | [
"MIT"
] | 0 | 24e0180902a5eadc3390c5fd95634c6c62ef3cc9 | https://github.com/Art31/pytorch-seq2seq/tree/24e0180902a5eadc3390c5fd95634c6c62ef3cc9 |
UGRNNLRCell | import torch
import torch.nn as nn
import torch.onnx
from itertools import product as product
def gen_nonlinearity(A, nonlinearity):
"""
Returns required activation for a tensor based on the inputs
nonlinearity is either a callable or a value in
['tanh', 'sigmoid', 'relu', 'quantTanh', 'quantSigm... | 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 ... | Shenzhen-Cloudatawalk-Technology-Co-Ltd/EdgeML | UGRNNLRCell | false | 14,417 | [
"MIT"
] | 719 | ef9f8a77f096acbdeb941014791f8eda1c1bc35b | https://github.com/Shenzhen-Cloudatawalk-Technology-Co-Ltd/EdgeML/tree/ef9f8a77f096acbdeb941014791f8eda1c1bc35b |
Discriminator | import torch
from torch import nn
class Discriminator(nn.Module):
def __init__(self, in_features=1):
super(Discriminator, self).__init__()
self.conv1 = nn.Conv2d(in_features, 96, kernel_size=7, stride=4)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(96, 64, kernel_size=5, stride=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 import nn
assert_s... | ajdillhoff/simgan-pytorch | Discriminator | false | 3,057 | [
"MIT"
] | 0 | fb61241a85136aae770944e1496f9319df327561 | https://github.com/ajdillhoff/simgan-pytorch/tree/fb61241a85136aae770944e1496f9319df327561 |
cell | import math
import torch
import torch.nn as nn
class cell(nn.Module):
def __init__(self, input_sz: 'int', hidden_sz: 'int', output_sz: 'int'):
super().__init__()
self.weights1 = nn.Parameter(torch.randn(input_sz, hidden_sz) /
math.sqrt(input_sz), requires_grad=True)
self.bias1... | 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.... | Cemu0/Network-of-Neural-Network | cell | false | 11,295 | [
"MIT"
] | 0 | 6a4a097a960fbbec6ea0c5946804666b27c2da0f | https://github.com/Cemu0/Network-of-Neural-Network/tree/6a4a097a960fbbec6ea0c5946804666b27c2da0f |
BayesConv2d | from torch.nn import Module
import math
import torch
from torch.nn import Parameter
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
class _BayesConvNd(Module):
"""
Applies Bayesian Convolution
Arguments:
prior_mu (Float): mean of prior normal distribution.
prior_s... | 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... | anaplasia29/Bayesian-Neural-Network | BayesConv2d | false | 3,110 | [
"MIT"
] | 0 | d98df8039e52cd2505dc8a94ed3cd474c2056d9a | https://github.com/anaplasia29/Bayesian-Neural-Network/tree/d98df8039e52cd2505dc8a94ed3cd474c2056d9a |
ScaledDotProductAttention | # 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.... | AbubakrHassan/attention-is-all-you-need-pytorch | ScaledDotProductAttention | false | 11,198 | [
"MIT"
] | 0 | 2bf9a477dea6271b082556069f3665ffed2745cd | https://github.com/AbubakrHassan/attention-is-all-you-need-pytorch/tree/2bf9a477dea6271b082556069f3665ffed2745cd |
CapsuleLoss | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | leftthomas/CapsNet | CapsuleLoss | false | 15,887 | [
"MIT"
] | 163 | 5de2f45daadbe4377df4ccf8a4d31683d7f397bf | https://github.com/leftthomas/CapsNet/tree/5de2f45daadbe4377df4ccf8a4d31683d7f397bf |
BalancedNet | # 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 ... | liranszlak/causallib | BalancedNet | false | 15,920 | [
"Apache-2.0"
] | 350 | 2636149f6b1e307672aff638a53f8eaf2be56bc9 | https://github.com/liranszlak/causallib/tree/2636149f6b1e307672aff638a53f8eaf2be56bc9 |
InteractingLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import *
class InteractingLayer(nn.Module):
"""A Layer used in AutoInt that model the correlations between different feature fields by multi-head self-attention mechanism.
Input shape
- A 3D tensor with shape... | 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.... | Sunmyunghan/Final_Project | InteractingLayer | false | 1,211 | [
"MIT"
] | 0 | 28cde293dc6d07521b2e1c5613b20444aea91d21 | https://github.com/Sunmyunghan/Final_Project/tree/28cde293dc6d07521b2e1c5613b20444aea91d21 |
VNet | # 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 to... | KtechB/machina | VNet | false | 2,475 | [
"MIT"
] | 0 | 24eca9cc9b89a0e0b9e026282f17c7b9fe2869ab | https://github.com/KtechB/machina/tree/24eca9cc9b89a0e0b9e026282f17c7b9fe2869ab |
ClippedLinearQuantization | 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
def linear_dequantize(input, scale, zero_point, inplace=False):
if inplace:
input.add_(zero_p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | Emily0219/distiller | ClippedLinearQuantization | false | 5,141 | [
"Apache-2.0"
] | 1 | 445ed35b671fb54586acc280b53d951f18bf97ae | https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae |
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.conv1 = nn.Conv2d(3, 32, (3, 3))
self.pool1 = nn.MaxPool2d((2, 2))
self.conv2 = nn.Conv2d(32, 32, (3, 3))
self.pool2 = nn.MaxPool2d... | 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_... | arcada-uas/intro-to-dl | Net | false | 1,481 | [
"MIT"
] | 0 | 675741ad123e88158ecab88f887ea31113c99e7a | https://github.com/arcada-uas/intro-to-dl/tree/675741ad123e88158ecab88f887ea31113c99e7a |
ValueNetwork | import torch
import torch.nn as nn
class ValueNetwork(nn.Module):
def __init__(self):
super(ValueNetwork, self).__init__()
self.relu = nn.ReLU()
self.fc1 = nn.Linear(4, 64)
self.fc2 = nn.Linear(64, 256)
self.fc3 = nn.Linear(256, 1)
def forward(self, x):
x = se... | 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_... | DeepHaeJoong/reinforcement-learning | ValueNetwork | false | 9,022 | [
"MIT"
] | 0 | 63e3053e3209809e67e97d51adaf5f85ce3799ba | https://github.com/DeepHaeJoong/reinforcement-learning/tree/63e3053e3209809e67e97d51adaf5f85ce3799ba |
SpectralConvergence | import torch
import torch.nn as nn
import torch.utils.data
class SpectralConvergence(nn.Module):
def __init__(self):
"""Initilize spectral convergence loss module."""
super().__init__()
def forward(self, predicts_mag, targets_mag):
"""Calculate norm of difference operator.
Args:
... | 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.data
assert_size_stride = torch._C._dy... | SolomidHero/speech-regeneration-enhancer | SpectralConvergence | false | 17,956 | [
"MIT"
] | 8 | eb43907ff085d68a707ff7bc3af14e93ff66fd65 | https://github.com/SolomidHero/speech-regeneration-enhancer/tree/eb43907ff085d68a707ff7bc3af14e93ff66fd65 |
Conv2d_fw | # 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... | RongKaiWeskerMA/INSTA | Conv2d_fw | false | 8,741 | [
"MIT"
] | 22 | 298bec0aeac3c1fde7bbcd4dece72ded1056e478 | https://github.com/RongKaiWeskerMA/INSTA/tree/298bec0aeac3c1fde7bbcd4dece72ded1056e478 |
MyNetwork | # 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.... | Thijsvanede/torch-train | MyNetwork | false | 2,909 | [
"MIT"
] | 0 | e10c64b1d61f9cdfb84b2645a196be4379851a1a | https://github.com/Thijsvanede/torch-train/tree/e10c64b1d61f9cdfb84b2645a196be4379851a1a |
ReconstructionLoss | # 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 functools import reduce
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | BotanAtomic/anomaly-detection | ReconstructionLoss | false | 13,401 | [
"MIT"
] | 179 | 6617880f19a4955d70a34a3bbee83f157eb087f8 | https://github.com/BotanAtomic/anomaly-detection/tree/6617880f19a4955d70a34a3bbee83f157eb087f8 |
RewardCriterion | import torch
from torch import nn
import torch.nn.init
class RewardCriterion(nn.Module):
def __init__(self):
super(RewardCriterion, self).__init__()
def forward(self, input, seq, reward):
input = input.contiguous().view(-1)
reward = reward.contiguous().view(-1)
mask = (seq > ... | 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
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyn... | KunpengLi1994/VSRN | RewardCriterion | false | 13,957 | [
"Apache-2.0"
] | 238 | 777ae74326fdb6abe69dbd3911d0e545322520d1 | https://github.com/KunpengLi1994/VSRN/tree/777ae74326fdb6abe69dbd3911d0e545322520d1 |
HighwayLayer | # 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... | ali-senguel/fairo-explore | HighwayLayer | false | 10,033 | [
"MIT"
] | 0 | 893481da270eed1e6d504c71e483d685ca9218d1 | https://github.com/ali-senguel/fairo-explore/tree/893481da270eed1e6d504c71e483d685ca9218d1 |
SimpleMatmulModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | andreas-hommel/glow | SimpleMatmulModule | false | 3,339 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SEModule | import torch
from torchvision.transforms import functional as F
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
retu... | 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 torchvision.transforms i... | Rick-960123/centermask-mdf-master | SEModule | false | 2,760 | [
"BSD-2-Clause"
] | 0 | 49388b03b9ffb06577cd28b9ddaa68cadb82e926 | https://github.com/Rick-960123/centermask-mdf-master/tree/49388b03b9ffb06577cd28b9ddaa68cadb82e926 |
ComplexActLayer | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | ashishpatel26/source_separation | ComplexActLayer | false | 14,900 | [
"Apache-2.0"
] | 269 | 6f755889654d7207fc89ba03a2f49d9ba92df8ea | https://github.com/ashishpatel26/source_separation/tree/6f755889654d7207fc89ba03a2f49d9ba92df8ea |
FlowHead | import torch
import torch.nn as nn
class FlowHead(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256):
super(FlowHead, self).__init__()
self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
self.relu = nn.ReLU(inp... | 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_... | Etienne-Meunier/FGVC | FlowHead | false | 11,419 | [
"MIT"
] | 0 | a7c6d4b6583ad3a380b0359fde9223dccc8e9c66 | https://github.com/Etienne-Meunier/FGVC/tree/a7c6d4b6583ad3a380b0359fde9223dccc8e9c66 |
PartialBCELoss | # 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... | HKUST-KnowComp/MLMET | PartialBCELoss | false | 8,169 | [
"MIT"
] | 10 | ae1188a929a5ca6a8e087bb091853b328ea2c7e7 | https://github.com/HKUST-KnowComp/MLMET/tree/ae1188a929a5ca6a8e087bb091853b328ea2c7e7 |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, input_size, output_size, seed, f... | 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.... | kangjie-chen/deep-reinforcement-learning | Actor | false | 3,801 | [
"MIT"
] | 0 | 0706f136834ecafc7391f483a6b3c84365a349eb | https://github.com/kangjie-chen/deep-reinforcement-learning/tree/0706f136834ecafc7391f483a6b3c84365a349eb |
PyramidUp | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | masanorihirano/pytorch_extra_mhirano | PyramidUp | false | 7,171 | [
"MIT"
] | 1 | d19e07445567c069793b7ca1a22a846d7cbce58d | https://github.com/masanorihirano/pytorch_extra_mhirano/tree/d19e07445567c069793b7ca1a22a846d7cbce58d |
Fire | import torch
import torch.nn as nn
from torchvision.transforms.functional import *
class Fire(nn.Module):
def __init__(self, inplanes, squeeze_planes, expand1x1_planes,
expand3x3_planes):
super(Fire, self).__init__()
self.inplanes = inplanes
self.squeeze = nn.Conv2d(inplanes, sque... | 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 to... | BiggerBinBin/e3d_handpose_x-master | Fire | false | 634 | [
"Apache-2.0"
] | 0 | 20d091a8a019d85de26c81d02985868f79d5de84 | https://github.com/BiggerBinBin/e3d_handpose_x-master/tree/20d091a8a019d85de26c81d02985868f79d5de84 |
CircleLoss | import torch
from torch import Tensor
from torch import nn
from torchvision.transforms import *
class CircleLoss(nn.Module):
def __init__(self, m: 'float', gamma: 'float') ->None:
super(CircleLoss, self).__init__()
self.m = m
self.gamma = gamma
self.soft_plus = nn.Softplus()
... | 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 ... | uestcwcw/University1652-Baseline | CircleLoss | false | 13,058 | [
"MIT"
] | 0 | fda1e4773fc911cbb43a9b96901d436298dc1284 | https://github.com/uestcwcw/University1652-Baseline/tree/fda1e4773fc911cbb43a9b96901d436298dc1284 |
D_DownBlock | import torch
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | Haabibi/RBPN-PyTorch | D_DownBlock | false | 5,268 | [
"MIT"
] | 1 | 0b04420b384fcc8f78a7b9afeca179fa6c0332c2 | https://github.com/Haabibi/RBPN-PyTorch/tree/0b04420b384fcc8f78a7b9afeca179fa6c0332c2 |
MinibatchDiscrimination1d | # 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.... | shi-weili/torchgan | MinibatchDiscrimination1d | false | 12,974 | [
"MIT"
] | 0 | 28ffd4026b8c0db2217b667d30a222d6758bfc41 | https://github.com/shi-weili/torchgan/tree/28ffd4026b8c0db2217b667d30a222d6758bfc41 |
SeqToSeqAtten | import torch
import torch.utils.data
def masked_softmax(x, m=None, dim=-1):
"""
Softmax with mask
:param x:
:param m:
:param dim:
:return:
"""
if m is not None:
m = m.float()
x = x * m
e_x = torch.exp(x - torch.max(x, dim=dim, keepdim=True)[0])
if m is not 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
from torch._inductor.runtime.... | jamaalhay/Final_Proj | SeqToSeqAtten | false | 15,663 | [
"MIT"
] | 104 | 3f524a90fee5a3cb21466ab76f630d060792045d | https://github.com/jamaalhay/Final_Proj/tree/3f524a90fee5a3cb21466ab76f630d060792045d |
IMul | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr1, xnumel,... | NVIDIA-AI-IOT-private/torch2trt | IMul | false | 10,506 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
FillUpLuminance | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | qway/nerfmeshes | FillUpLuminance | false | 16,299 | [
"MIT"
] | 113 | d983dcbbcfec1337c9f2040969213c6d1ea0c39e | https://github.com/qway/nerfmeshes/tree/d983dcbbcfec1337c9f2040969213c6d1ea0c39e |
SSLoss | import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
import torch
def sum_tensor(inp, axes, keepdim=False):
axes = np.unique(axes).astype(int)
if keepdim:
for ax in axes:
inp = inp.sum(int(ax), keepdim=True)
else:
for ax in sorted(axes, reverse=True):
... | 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
import torch.utils.data
import torch
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | ayanglab/HDL | SSLoss | false | 6,290 | [
"Apache-2.0"
] | 1 | 5ff778d713331671ffa85e9fb63378d8c0a57769 | https://github.com/ayanglab/HDL/tree/5ff778d713331671ffa85e9fb63378d8c0a57769 |
RgbaToBgr | # 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... | connorlee77/kornia | RgbaToBgr | false | 6,478 | [
"ECL-2.0",
"Apache-2.0"
] | 1 | af5b1f76bedf2a7fc0e0da2386b1be3032b6534f | https://github.com/connorlee77/kornia/tree/af5b1f76bedf2a7fc0e0da2386b1be3032b6534f |
AddcmulTestModule | import torch
class AddcmulTestModule(torch.nn.Module):
def __init__(self, value):
super(AddcmulTestModule, self).__init__()
self.value = value
def forward(self, x, y, z):
return torch.addcmul(x, self.value, y, z)
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | jinfagang/torch2trt_dynamic | AddcmulTestModule | false | 15,685 | [
"MIT"
] | 155 | fad7a7845f13cb59c05de25fcb83e7591acb492c | https://github.com/jinfagang/torch2trt_dynamic/tree/fad7a7845f13cb59c05de25fcb83e7591acb492c |
ActNorm | import torch
import torch.utils.data
class ActNorm(torch.nn.Module):
def __init__(self, nsq, data_init=True):
super(ActNorm, self).__init__()
self.initialized = not data_init
self.m = torch.nn.Parameter(torch.zeros(1, nsq, 1))
self.logs = torch.nn.Parameter(torch.zeros(1, nsq, 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.utils.data
assert_size_stride = torch._C._dynamo.guards.asse... | entn-at/blow | ActNorm | false | 15,343 | [
"Apache-2.0"
] | 147 | b597286b24c7ea88c8d9408f9aa35aa8df2ebe11 | https://github.com/entn-at/blow/tree/b597286b24c7ea88c8d9408f9aa35aa8df2ebe11 |
CrossEntropyDiceLoss | # 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 math as tl_math
from typing import Union
from typing import Optional
from typing import I... | MIC-DKFZ/image-time-series | CrossEntropyDiceLoss | false | 5,574 | [
"MIT"
] | 1 | 0480d5cb6936c7d9e839b6741f18c10893d78d8a | https://github.com/MIC-DKFZ/image-time-series/tree/0480d5cb6936c7d9e839b6741f18c10893d78d8a |
LocationLayer | import torch
import torch.utils.data
from torch import nn
class LinearNorm(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
super(LinearNorm, self).__init__()
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
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
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | leijue222/tacotron2 | LocationLayer | false | 15,892 | [
"BSD-3-Clause"
] | 93 | 5950728a91e7a9355f42f658e00db2a2aef94247 | https://github.com/leijue222/tacotron2/tree/5950728a91e7a9355f42f658e00db2a2aef94247 |
Asin | # 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.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | mil-tokyo/webdnn | Asin | false | 16,063 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
MinibatchStd | # 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.nn as nn
import torch.utils.tensorboard
assert_size_stride = torch... | andoleg/stylegan2_pytorch | MinibatchStd | false | 15,327 | [
"MIT"
] | 121 | 27a367d00d35742cf66587f1bd1b1263469a8101 | https://github.com/andoleg/stylegan2_pytorch/tree/27a367d00d35742cf66587f1bd1b1263469a8101 |
SageLayer | # 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_... | OuYangg/GNNs | SageLayer | false | 9,456 | [
"Apache-2.0"
] | 0 | ef5b1944490507684d603de3ae0b2aa7b5168f47 | https://github.com/OuYangg/GNNs/tree/ef5b1944490507684d603de3ae0b2aa7b5168f47 |
RSoftmax | import torch
import torch.nn.functional as F
import torch.nn as nn
class RSoftmax(nn.Module):
"""Radix Softmax module in ``SplitAttentionConv2d``.
Args:
radix (int): Radix of input.
groups (int): Groups of input.
"""
def __init__(self, radix, groups):
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ChengBo5/mask-text-detector | RSoftmax | false | 244 | [
"Apache-2.0"
] | 0 | ce93e45ed1d982ec0ef6ad977c02e49326bf255a | https://github.com/ChengBo5/mask-text-detector/tree/ce93e45ed1d982ec0ef6ad977c02e49326bf255a |
ScoreLayer | import torch
from torchvision.transforms import functional as F
from torch.nn import functional as F
import torch.nn as nn
class ScoreLayer(nn.Module):
def __init__(self, k):
super(ScoreLayer, self).__init__()
self.score = nn.Conv2d(k, 1, 1, 1)
def forward(self, x, x_size=None):
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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | alejodosr/PoolNet | ScoreLayer | false | 12,067 | [
"MIT"
] | 0 | a6a19379933fe02c22f0eb0dd92038fe87cf0bd3 | https://github.com/alejodosr/PoolNet/tree/a6a19379933fe02c22f0eb0dd92038fe87cf0bd3 |
ConvTemporalGraphical | # 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... | Immocat/ACTOR | ConvTemporalGraphical | false | 13,831 | [
"MIT"
] | 164 | c7237e82e333bf2c57f7d8e12f27d0831233befc | https://github.com/Immocat/ACTOR/tree/c7237e82e333bf2c57f7d8e12f27d0831233befc |
SinusoidalPosEmb | import math
import torch
from torch import nn
import torch.utils.data
import torch.optim
import torch.distributions
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
"""
:param x: [B, T]
:return: [B, 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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.utils.data
import torch.optim
import to... | Rexiome/NATSpeech | SinusoidalPosEmb | false | 14,297 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
MeanModule | import torch
import torch.nn as nn
class MeanModule(nn.Module):
def __init__(self, *axis, keepdim=False):
super().__init__()
self.axis = axis
self.keepdim = keepdim
def forward(self, v):
mean = v.mean(self.axis)
if self.keepdim:
dims = list(v.shape)
... | 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... | finalgruntgit/diautils | MeanModule | false | 10,268 | [
"MIT"
] | 0 | b9d7666ed5023700db01a4295430c52721acfc25 | https://github.com/finalgruntgit/diautils/tree/b9d7666ed5023700db01a4295430c52721acfc25 |
BCE_disc_sm_v7 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BCE_disc_sm_v7(nn.Module):
def __init__(self, weight_list=None, lb_sm=0.2):
super(BCE_disc_sm_v7, self).__init__()
self.weight_list = weight_list
self.lb_sm = lb_sm
def forward(self, x, labels):
assert (... | 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... | Sampson-Lee/SIB-Net | BCE_disc_sm_v7 | false | 2,829 | [
"MIT"
] | 0 | 650399082e9237327fa38168ccfc7d48153a1db5 | https://github.com/Sampson-Lee/SIB-Net/tree/650399082e9237327fa38168ccfc7d48153a1db5 |
ReduceMinModule | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | mirecta/nncase | ReduceMinModule | false | 4,179 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
XSigmoidLoss | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | tuantle/regression-losses-pytorch | XSigmoidLoss | false | 16,628 | [
"MIT"
] | 82 | 2893f4439ada5df239e3afd0ec7e781dd61403e9 | https://github.com/tuantle/regression-losses-pytorch/tree/2893f4439ada5df239e3afd0ec7e781dd61403e9 |
BiAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class BiAttention(nn.Module):
def __init__(self, input_size, dropout):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
self.input_linear = nn.Linear(input_size, 1, bias=False)
self.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.... | ChenZhongFu/KOBE | BiAttention | false | 13,473 | [
"MIT"
] | 176 | 710d7556516bdbd9ad971e6ff8b8f625a1a55e5a | https://github.com/ChenZhongFu/KOBE/tree/710d7556516bdbd9ad971e6ff8b8f625a1a55e5a |
FocalLoss1 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
class FocalLoss1(nn.Module):
def __init__(self, gamma):
super(FocalLoss1, self).__init__()
self.gamma = gamma
def forward(self, input, target):
if not target.size() == input.size():
raise... | 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... | OnurUner/DeepSide | FocalLoss1 | false | 17,774 | [
"MIT"
] | 4 | dffb7ddc1d1bde36bbf5abb6eac107d39985c57a | https://github.com/OnurUner/DeepSide/tree/dffb7ddc1d1bde36bbf5abb6eac107d39985c57a |
PAConv | # 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... | Cai631/MBMFN | PAConv | false | 17,072 | [
"Apache-2.0"
] | 6 | 9a48744d7de87a6a7ec08ad87b2d0bd727e1d23c | https://github.com/Cai631/MBMFN/tree/9a48744d7de87a6a7ec08ad87b2d0bd727e1d23c |
eSEModule | import torch
import torch.utils.data
import torch.nn.functional as F
from torch import nn
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | FluteXu/DW-Research | eSEModule | false | 13,704 | [
"Apache-2.0"
] | 780 | 6b559d2d1d440c07e5936a65cd74a3bc657962dc | https://github.com/FluteXu/DW-Research/tree/6b559d2d1d440c07e5936a65cd74a3bc657962dc |
gMLPBlock | import torch
import torch.nn as nn
class SpatialGatingUnit(nn.Module):
def __init__(self, dim_seq, dim_ff):
super().__init__()
self.proj = nn.Linear(dim_seq, dim_seq)
nn.init.zeros_(self.proj.weight)
nn.init.ones_(self.proj.bias)
self.norm = nn.LayerNorm(normalized_shape=d... | 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 ... | nima1999nikkhah/SimSiam_gMLP | gMLPBlock | false | 4,103 | [
"MIT"
] | 0 | 9cccd1092c02267951d39ae77c0fe5a91d735903 | https://github.com/nima1999nikkhah/SimSiam_gMLP/tree/9cccd1092c02267951d39ae77c0fe5a91d735903 |
Conv2dSamePadding | import torch
from torch import nn
import torch.nn.functional as F
def conv2d_same_padding(input, weight, bias=None, stride=1, dilation=1,
groups=1):
input_rows = input.size(2)
filter_rows = weight.size(2)
effective_filter_size_rows = (filter_rows - 1) * dilation[0] + 1
out_rows = (input_rows + str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.functional as F
assert_size_stride = torch.... | Florian-P-Huber/pycrop-yield-prediction | Conv2dSamePadding | false | 9,053 | [
"MIT"
] | 0 | 9c1a000db55589b3480ae3ac2baab8f461947855 | https://github.com/Florian-P-Huber/pycrop-yield-prediction/tree/9c1a000db55589b3480ae3ac2baab8f461947855 |
Encoder | import torch
import torch.nn as nn
class Encoder(nn.Module):
def __init__(self, input_dim, hidden_dim, latent_dim):
super(Encoder, self).__init__()
self.FC_input = nn.Linear(input_dim, hidden_dim)
self.FC_mean = nn.Linear(hidden_dim, latent_dim)
self.FC_var = nn.Linear(hidden_dim,... | 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 import triton_helpers
from... | StefanNa/dtu_mlops | Encoder | false | 5,855 | [
"Apache-2.0"
] | 1 | 148f3427f8d090d39d127857be8a37832f800279 | https://github.com/StefanNa/dtu_mlops/tree/148f3427f8d090d39d127857be8a37832f800279 |
MegatronGelu | # 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.nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.... | thilow/onnxruntime | MegatronGelu | false | 11,009 | [
"MIT"
] | 0 | 1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 | https://github.com/thilow/onnxruntime/tree/1a3ddf0714e1bdf9b807a342eee5f6e160ad1ec9 |
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.... | jisheng047/blinsert | PCN1 | false | 10,290 | [
"BSD-2-Clause"
] | 0 | 923d2ea2af3f2f257c817fa8de02c7db8ec9bcc9 | https://github.com/jisheng047/blinsert/tree/923d2ea2af3f2f257c817fa8de02c7db8ec9bcc9 |
SigmoidFocalLoss | # 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
... | jameslong95/FasterSeg | SigmoidFocalLoss | false | 6,915 | [
"MIT"
] | 1 | 872e04964ea46494a6018d9915cee5476e361c27 | https://github.com/jameslong95/FasterSeg/tree/872e04964ea46494a6018d9915cee5476e361c27 |
SpatialAttentionGate | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class SpatialAttentionGate(nn.Module):
def __init__(self, channel, reduction=16):
super(SpatialAttentionGate, self).__init__()
self.fc1 = nn.Conv2d(channel, reduc... | 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 ... | savan77/nni | SpatialAttentionGate | false | 4,285 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf |
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