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 |
|---|---|---|---|---|---|---|---|---|---|---|
SimpleReciprocalModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleReciprocalModel(torch.nn.Module):
def __init__(self, inplace=False):
super(SimpleReciprocalModel, self).__init__()
self.inplace = inplace
def forward(self, tensor):
other = tensor + tensor
return othe... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | opti-mix/glow | SimpleReciprocalModel | false | 7,410 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
BertPredictionHead | # 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.... | adymaharana/VLCStoryGan | BertPredictionHead | false | 18,257 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
PerceptualLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class PerceptualLoss(nn.Module):
def __init__(self):
super().__init__()
self.tgt_gm = None
def gram_matrix(self, x):
a, b, c, d = x.shape
features = x.view(a * b, c * d)
G = torch.mm(features, features... | 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_... | aadhithya/mobilenet-styletransfer | PerceptualLoss | false | 9,668 | [
"MIT"
] | 0 | 58e2c29020864d82d92d52d01427618bc35773fd | https://github.com/aadhithya/mobilenet-styletransfer/tree/58e2c29020864d82d92d52d01427618bc35773fd |
MeanVoxelFeatureExtractor | import torch
import torch.nn as nn
class VoxelFeatureExtractor(nn.Module):
def __init__(self, **kwargs):
super().__init__()
def get_output_feature_dim(self):
raise NotImplementedError
def forward(self, **kwargs):
raise NotImplementedError
class MeanVoxelFeatureExtractor(VoxelF... | 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... | AndyYuan96/MVF-End-to-End-Multi-View-Fusion-for-3D-Object-Detection-in-LiDAR-Point-Clouds- | MeanVoxelFeatureExtractor | false | 13,250 | [
"Apache-2.0"
] | 55 | cf34897f25353a3f348d0a39c8db5ba15cadb2d7 | https://github.com/AndyYuan96/MVF-End-to-End-Multi-View-Fusion-for-3D-Object-Detection-in-LiDAR-Point-Clouds-/tree/cf34897f25353a3f348d0a39c8db5ba15cadb2d7 |
VGGASPP | import torch
from torch import nn
class FCReLUDrop(nn.Sequential):
def __init__(self, in_ch, out_ch, kernel_size, dilation, padding,
layer_idx, branch_idx):
super(FCReLUDrop, self).__init__()
self.add_module(f'fc{layer_idx}_{branch_idx}', nn.Conv2d(in_ch,
out_ch, kernel_size, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | HAL-42/DeepLabV2YQ | VGGASPP | false | 524 | [
"Apache-2.0"
] | 0 | 96bfcf1055da7adeb4a7c1ed841f6ec29957be59 | https://github.com/HAL-42/DeepLabV2YQ/tree/96bfcf1055da7adeb4a7c1ed841f6ec29957be59 |
ScalarMix | # 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 math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ashim95/parser | ScalarMix | false | 6,240 | [
"MIT"
] | 1 | 61e9cd6bf16dcf1aa2b9d51b3a6c04ed048b3199 | https://github.com/ashim95/parser/tree/61e9cd6bf16dcf1aa2b9d51b3a6c04ed048b3199 |
AnchorFlatten | # 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | AIpakchoi/visualDet3D | AnchorFlatten | false | 4,757 | [
"Apache-2.0"
] | 1 | 920f6f8ea44eac4c1896b7d157c015e039ac39f9 | https://github.com/AIpakchoi/visualDet3D/tree/920f6f8ea44eac4c1896b7d157c015e039ac39f9 |
ResBlock | import torch
import torch.nn as nn
def set_activate_layer(types):
if types == 'relu':
activation = nn.ReLU()
elif types == 'lrelu':
activation = nn.LeakyReLU(0.2)
elif types == 'tanh':
activation = nn.Tanh()
elif types == 'sig':
activation = nn.Sigmoid()
elif types ... | 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.... | innerverz/CodeTemplate | ResBlock | false | 3,681 | [
"MIT"
] | 0 | a20f5d24b0b79871aa39b5cde33e3bb4d2507d13 | https://github.com/innerverz/CodeTemplate/tree/a20f5d24b0b79871aa39b5cde33e3bb4d2507d13 |
L2Norm | # 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
from math import sqrt as sqrt
from itertools import produ... | Atine/pytorch.SSD.handles | L2Norm | false | 8,824 | [
"MIT"
] | 0 | ff57ceacc57f195361adceb92a84d54d155ba1a4 | https://github.com/Atine/pytorch.SSD.handles/tree/ff57ceacc57f195361adceb92a84d54d155ba1a4 |
GRU122 | # 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 ... | smeznar/ProGED | GRU122 | false | 10,805 | [
"BSD-3-Clause"
] | 0 | 191cfd2b7b1fece819109a4b61e3f7533332fd74 | https://github.com/smeznar/ProGED/tree/191cfd2b7b1fece819109a4b61e3f7533332fd74 |
Centered_Grad | import torch
import torch.nn as nn
class Centered_Grad(nn.Module):
def __init__(self):
super(Centered_Grad, self).__init__()
self.x_ker_init = torch.tensor([[[[-0.5, 0, 0.5]]]], dtype=torch.
float, requires_grad=True)
self.y_ker_init = torch.tensor([[[[-0.5], [0], [0.5]]]], dt... | 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... | AmazingAng/pytorch-tvnet | Centered_Grad | false | 7,653 | [
"MIT"
] | 12 | e880d3ce15f55e5d9a11b423cfd1e0461de4fedb | https://github.com/AmazingAng/pytorch-tvnet/tree/e880d3ce15f55e5d9a11b423cfd1e0461de4fedb |
dy_mixprop | # 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.... | kevin-xuan/Traffic-Benchmark | dy_mixprop | false | 15,865 | [
"MIT"
] | 120 | b9f8e40b4df9b58f5ad88432dc070cbbbcdc0228 | https://github.com/kevin-xuan/Traffic-Benchmark/tree/b9f8e40b4df9b58f5ad88432dc070cbbbcdc0228 |
MCDO | # 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... | Daniil-Selikhanovych/bnn-vi | MCDO | false | 17,196 | [
"MIT"
] | 3 | 6788edc1438c66609abca249e33a81da7a0ff1a2 | https://github.com/Daniil-Selikhanovych/bnn-vi/tree/6788edc1438c66609abca249e33a81da7a0ff1a2 |
AELossPureCls | # 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.cuda
assert_size_stride = torch._C._dynamo.guards.asse... | abhithosar/chartocr_cv | AELossPureCls | false | 6,070 | [
"BSD-3-Clause"
] | 1 | 388b95710a02ded0532b021f64c58d8d3e1cc639 | https://github.com/abhithosar/chartocr_cv/tree/388b95710a02ded0532b021f64c58d8d3e1cc639 |
AvgPool2d | from torch.nn import Module
import torch
import torch as th
class AvgPool2d(Module):
"""
This class is the beginning of an exact python port of the torch.nn.AvgPool2d
module. Because PySyft cannot hook into layers which are implemented in C++,
our special functionalities (such as encrypted computation... | 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.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | Rahul-160/PySyft | AvgPool2d | false | 17,833 | [
"Apache-2.0"
] | 7 | 182627db2369d6f93aa0667f5ea2abee5b878d58 | https://github.com/Rahul-160/PySyft/tree/182627db2369d6f93aa0667f5ea2abee5b878d58 |
BasicConvBlock | # 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... | royerloic/aydin | BasicConvBlock | false | 16,345 | [
"BSD-3-Clause"
] | 78 | f9c61a24030891d008c318b250da5faec69fcd7d | https://github.com/royerloic/aydin/tree/f9c61a24030891d008c318b250da5faec69fcd7d |
CRFLoss | # 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Johannes0Horn/mtl-dts | CRFLoss | false | 8,362 | [
"MIT"
] | 19 | ae50253c808bbb77af3b1117f69f08d2268099e9 | https://github.com/Johannes0Horn/mtl-dts/tree/ae50253c808bbb77af3b1117f69f08d2268099e9 |
F_fully_connected | import torch
import torch.nn as nn
import torch.optim
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().__init__()
if not internal_size:
internal_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
import ... | zimmerrol/FrEIA | F_fully_connected | false | 4,668 | [
"MIT"
] | 0 | 73d01ab8c90e0deb5e242d66405bd168db06dc19 | https://github.com/zimmerrol/FrEIA/tree/73d01ab8c90e0deb5e242d66405bd168db06dc19 |
Upscale2d | import torch
from torch import nn
def upscale2d(x, factor=2, gain=1):
assert x.dim() == 4
if gain != 1:
x = x * gain
if factor != 1:
shape = x.shape
x = x.view(shape[0], shape[1], shape[2], 1, shape[3], 1).expand(-1,
-1, -1, factor, -1, factor)
x = x.contiguous(... | 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... | AnimeshKoratana/blurryface | Upscale2d | false | 43 | [
"Apache-2.0"
] | 0 | c6cb5feec02f6d5af3acb1678336800390715d65 | https://github.com/AnimeshKoratana/blurryface/tree/c6cb5feec02f6d5af3acb1678336800390715d65 |
CNN | # 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_... | mswzeus/DeeperHSP | CNN | false | 7,296 | [
"MIT"
] | 1 | 571387f048d3c33fcd78730fdaef57b6c44a27a7 | https://github.com/mswzeus/DeeperHSP/tree/571387f048d3c33fcd78730fdaef57b6c44a27a7 |
BWCEWLoss | # 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 ... | jimthompson5802/ludwig | BWCEWLoss | false | 3,859 | [
"Apache-2.0"
] | 0 | 8a369328a3f839d9cdb3710be315952c7891d7c0 | https://github.com/jimthompson5802/ludwig/tree/8a369328a3f839d9cdb3710be315952c7891d7c0 |
TokenEmbedding | import torch
import torch.nn as nn
class TokenEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(TokenEmbedding, self).__init__()
padding = 1 if torch.__version__ >= '1.5.0' else 2
self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
kernel_size=3, pa... | 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... | MAZiqing/FEDformer | TokenEmbedding | false | 17,645 | [
"MIT"
] | 7 | 7914d39df829494a8172afb9676982c3789d491d | https://github.com/MAZiqing/FEDformer/tree/7914d39df829494a8172afb9676982c3789d491d |
FCLateActionSAQFunction | # 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 numpy as np
import tor... | imatge-upc/pixelcoordEDL | FCLateActionSAQFunction | false | 6,871 | [
"MIT"
] | 1 | 353632feed6ac8c93758c1a2a1b7a477e7ff053c | https://github.com/imatge-upc/pixelcoordEDL/tree/353632feed6ac8c93758c1a2a1b7a477e7ff053c |
RNN | import torch
import torch.nn as nn
from torch.autograd import Variable
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.output_size = output_size
self.layer1 = nn.Linear(input_size, hidd... | 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 ... | Lukx19/TorcsDriver | RNN | false | 789 | [
"MIT"
] | 0 | e6e3dd4b15e8dec487a29465f7592c7d5d2581cc | https://github.com/Lukx19/TorcsDriver/tree/e6e3dd4b15e8dec487a29465f7592c7d5d2581cc |
LayerNormalization | import torch
import torch.nn as nn
class LayerNormalization(nn.Module):
def __init__(self, normal_shape, gamma=True, beta=True, epsilon=1e-10):
"""Layer normalization layer
See: [Layer Normalization](https://arxiv.org/pdf/1607.06450.pdf)
:param normal_shape: The shape of the input tenso... | 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_... | CyberZHG/torch-layer-normalization | LayerNormalization | false | 17,166 | [
"MIT"
] | 9 | 89f405b60f53f85da6f03fe685c190ef394ce50c | https://github.com/CyberZHG/torch-layer-normalization/tree/89f405b60f53f85da6f03fe685c190ef394ce50c |
CharbonnierLoss | import torch
import torch.nn as nn
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=1e-06, mode=None):
super(CharbonnierLoss, self).__init__()
self.eps = eps
self.mode = mode
def forward(self, x, y, mask=None):
N = x.size(1)
dif... | 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... | myeldib/Simple-SR | CharbonnierLoss | false | 12,806 | [
"MIT"
] | 0 | 583456b1f231574d9e0b45c29266cf41603d161d | https://github.com/myeldib/Simple-SR/tree/583456b1f231574d9e0b45c29266cf41603d161d |
CosineDistance | import torch
import torch.utils.data.dataloader
import torch.nn
def dot_product(a: 'torch.Tensor', b: 'torch.Tensor', normalize=False):
"""
Computes dot product for pairs of vectors.
:param normalize: Vectors are normalized (leads to cosine similarity)
:return: Matrix with res[i][j] = dot_product(a[i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | adriensas/flair | CosineDistance | false | 9,751 | [
"MIT"
] | 0 | f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 | https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 |
SoftLarge | import math
import torch
from torch import nn
class SoftCompare(nn.Module):
def __init__(self, alpha=None, beta=None):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1) * (0 if alpha is None else
alpha), requires_grad=True)
self.beta = nn.Parameter(torch.ones(1) * (0 ... | 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 math
from torch import nn
assert_size_stride = torch._C._dynamo.gu... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | SoftLarge | false | 17,144 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
ClassificationCircleLoss | import torch
import torch.nn as nn
import torch.utils.data
from typing import Tuple
from torch.nn.functional import cross_entropy
from itertools import product as product
from math import sqrt as sqrt
class ClassificationCircleLoss(nn.Module):
"""Circle loss for class-level labels as described in the paper
`"... | 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
... | lingtengqiu/LearnableTreeFilterV2 | ClassificationCircleLoss | false | 7,096 | [
"Apache-2.0"
] | 1 | 3814a5a84c0a5c33d6538749eaf5aed4827366de | https://github.com/lingtengqiu/LearnableTreeFilterV2/tree/3814a5a84c0a5c33d6538749eaf5aed4827366de |
PositionWiseFeedForwardNetworks | import torch
from torch import nn
from torch.nn import functional as F
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
class PositionWiseFeedForwardNetworks(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 import nn
assert_s... | L-Zhe/FasySeq | PositionWiseFeedForwardNetworks | false | 8,417 | [
"Apache-2.0"
] | 34 | 2cd2abd290666b1e118d8ad11c973b58ca4f0573 | https://github.com/L-Zhe/FasySeq/tree/2cd2abd290666b1e118d8ad11c973b58ca4f0573 |
CNN64x3 | # 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_... | InExp123/pytorch-self_driving_car | CNN64x3 | false | 2,376 | [
"MIT"
] | 0 | b4e8c8a76079085bf0471dad1820ee9995cffc74 | https://github.com/InExp123/pytorch-self_driving_car/tree/b4e8c8a76079085bf0471dad1820ee9995cffc74 |
SDSDRLoss | # 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... | leoauri/auraloss | SDSDRLoss | false | 15,902 | [
"Apache-2.0"
] | 272 | 0e3362674ae1b53aa61c6a631fb4e6970c5683c1 | https://github.com/leoauri/auraloss/tree/0e3362674ae1b53aa61c6a631fb4e6970c5683c1 |
NormalLoss | import torch
import torch.nn as nn
class NormalLoss(nn.Module):
def __init__(self):
super(NormalLoss, self).__init__()
def forward(self, grad_fake, grad_real):
prod = (grad_fake[:, :, None, :] @ grad_real[:, :, :, None]).squeeze(-1
).squeeze(-1)
fake_norm = torch.sqrt(tor... | 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 ... | d4l3k/crowds | NormalLoss | false | 12,244 | [
"MIT"
] | 0 | a57eee80d66498474c86cec22dd77be9d627ad97 | https://github.com/d4l3k/crowds/tree/a57eee80d66498474c86cec22dd77be9d627ad97 |
AdaptiveInstanceNorm | # 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.utils.... | yuhongherald/pytorch-CycleGAN-and-pix2pix | AdaptiveInstanceNorm | false | 4,640 | [
"BSD-3-Clause"
] | 0 | 48cb3aa46fde39684db9c24586fcec6781138e2a | https://github.com/yuhongherald/pytorch-CycleGAN-and-pix2pix/tree/48cb3aa46fde39684db9c24586fcec6781138e2a |
Conv2d | import torch
import torch.nn as nn
from torch.nn import functional as F
class Conv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2d, self).__init__(in_channels, out_channels, kernel_size,
strid... | 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 ... | JassiGhuman/backgroundSubtraction | Conv2d | false | 11,544 | [
"MIT"
] | 0 | 351a380b34f9d84548bea734a69842227e373e65 | https://github.com/JassiGhuman/backgroundSubtraction/tree/351a380b34f9d84548bea734a69842227e373e65 |
TorchSub | import torch
class TorchSub(torch.nn.Module):
def __init__(self):
super(TorchSub, self).__init__()
def forward(self, x, y):
return torch.sub(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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | ahangchen/torch2trt | TorchSub | false | 6,119 | [
"MIT"
] | 1 | 53c663f0e0570ef7ffd6771354ae3478f63bd328 | https://github.com/ahangchen/torch2trt/tree/53c663f0e0570ef7ffd6771354ae3478f63bd328 |
RepresentationModule | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class RepresentationModule(nn.Module):
def __init__(self, config, task_name, repr_size):
super(RepresentationModule, self).__init__()
self.config = config
self.task_name = task_name
self.repr_size = r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Impavidity/relogic | RepresentationModule | false | 8,792 | [
"MIT"
] | 24 | f647106e143cd603b95b63e06ea530cdd516aefe | https://github.com/Impavidity/relogic/tree/f647106e143cd603b95b63e06ea530cdd516aefe |
HuggingfaceFastGelu | # 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
import torch.utils.checkpoint
assert_size_str... | TingGong1/onnxruntime | HuggingfaceFastGelu | false | 5,890 | [
"MIT"
] | 1 | 435010ab6873974803591fa22262ed8b3e36e44d | https://github.com/TingGong1/onnxruntime/tree/435010ab6873974803591fa22262ed8b3e36e44d |
C1Bilinear | import torch
from torch import nn
class C1Bilinear(nn.Module):
def __init__(self, num_class=150, fc_dim=4096, segSize=384, use_softmax
=False):
super(C1Bilinear, self).__init__()
self.segSize = segSize
self.use_softmax = use_softmax
self.conv_last = nn.Conv2d(fc_dim, num_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
from torch._inductor.runtime.... | PCIHD/Project_Daydream | C1Bilinear | false | 9,762 | [
"MIT"
] | 0 | 94c75ff494e7489a4066e3f9d056a85ff768f40e | https://github.com/PCIHD/Project_Daydream/tree/94c75ff494e7489a4066e3f9d056a85ff768f40e |
RepeatModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | RepeatModule | false | 14,642 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
Block | import math
import torch
import torch.utils.data
import torch
import torch.nn as nn
def gelu(x):
""" Original Implementation of the gelu activation function in Google Bert repo when initialy created.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
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.... | denisleonov/pytorch-CycleGAN-and-pix2pix | Block | false | 12,280 | [
"BSD-3-Clause"
] | 0 | d1a5f0c5911f70ed896f826619b4067ce737a83d | https://github.com/denisleonov/pytorch-CycleGAN-and-pix2pix/tree/d1a5f0c5911f70ed896f826619b4067ce737a83d |
MHAScoresCalculation | # 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.... | JudeDavis1/intel-extension-for-pytorch | MHAScoresCalculation | false | 2,577 | [
"Apache-2.0"
] | 0 | 364e34cb4917a709f5108c07d4005bf82f3d5067 | https://github.com/JudeDavis1/intel-extension-for-pytorch/tree/364e34cb4917a709f5108c07d4005bf82f3d5067 |
ConvGRUCellNd | import torch
import torch.nn as nn
import torch.jit
import torch.nn
class ConvGRUCellNd(nn.Module):
def __init__(self, in_size, out_size, kernel_size, N=1, **kwargs):
super(ConvGRUCellNd, self).__init__()
conv = eval(f'nn.Conv{N}d')
self.conv_ir = conv(in_size, out_size, kernel_size, **kw... | 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 ... | ankmathur96/torchsupport | ConvGRUCellNd | false | 3,189 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
SimpleLoss | # 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... | FranciscoShi/piepline | SimpleLoss | false | 17,276 | [
"MIT"
] | 5 | 6105788339fc18bab39ea07625b5fd26ad687254 | https://github.com/FranciscoShi/piepline/tree/6105788339fc18bab39ea07625b5fd26ad687254 |
L2Norm | # 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_... | ashuk203/face-alignment | L2Norm | false | 6,244 | [
"BSD-3-Clause"
] | 1 | 1f6452ae05ede0db9bbc48331d67d8b239fa9994 | https://github.com/ashuk203/face-alignment/tree/1f6452ae05ede0db9bbc48331d67d8b239fa9994 |
UniformDistributionLoss | import torch
import torch.nn.functional as F
class UniformDistributionLoss(torch.nn.Module):
"""
Implementation of the confusion loss from
[Simultaneous Deep Transfer Across Domains and Tasks](https://arxiv.org/abs/1510.02192).
"""
def forward(self, x, *args):
""""""
probs = F.log... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | KevinMusgrave/pytorch-adapt | UniformDistributionLoss | false | 13,945 | [
"MIT"
] | 131 | ff1491e1bfcc586afb8ee619712c8816ddf10358 | https://github.com/KevinMusgrave/pytorch-adapt/tree/ff1491e1bfcc586afb8ee619712c8816ddf10358 |
MinimalRNNCell | # 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... | tarepan/HiPPO | MinimalRNNCell | false | 16,539 | [
"Apache-2.0"
] | 57 | bc23e2dba13da6c307cb5a4ae248c2d2c56d465f | https://github.com/tarepan/HiPPO/tree/bc23e2dba13da6c307cb5a4ae248c2d2c56d465f |
PositionalEncoder | # 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... | aim-uofa/DyCo3D | PositionalEncoder | false | 14,762 | [
"BSD-2-Clause"
] | 100 | 17d22c2d839c0a1043fb72df301e3935af5ca0e9 | https://github.com/aim-uofa/DyCo3D/tree/17d22c2d839c0a1043fb72df301e3935af5ca0e9 |
TReLU | import torch
import torch.nn as nn
import torch.nn.functional as F
class TReLU(nn.Module):
def __init__(self):
super(TReLU, self).__init__()
self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
self.alpha.data.fill_(0)
def forward(self, x):
x = F.relu(x - self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | HenryOsborne/LearningToPaint | TReLU | false | 9,134 | [
"MIT"
] | 0 | d8fdf41c8d193b91c78f73b7a092897e846e19eb | https://github.com/HenryOsborne/LearningToPaint/tree/d8fdf41c8d193b91c78f73b7a092897e846e19eb |
PredictionHead | # 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.onnx
assert_size_stride = torch._C._dynamo.gu... | danshirron/inference | PredictionHead | false | 10,018 | [
"Apache-2.0"
] | 0 | 31ae9b30ca5b1081a2d35f73ffcde10ae1fdaf41 | https://github.com/danshirron/inference/tree/31ae9b30ca5b1081a2d35f73ffcde10ae1fdaf41 |
ClassAttentionBlock | # 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.... | alhamami/Object-Detection-And-Tracking | ClassAttentionBlock | false | 18,320 | [
"MIT"
] | 5 | a211a1dc103e812c539cd0ee16a2da4251943bed | https://github.com/alhamami/Object-Detection-And-Tracking/tree/a211a1dc103e812c539cd0ee16a2da4251943bed |
PolicyNetwork | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class PolicyNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size, action_range=
1.0, init_w=0.003, log_std_min=-20, log_std_max=2):
super(PolicyNetwo... | 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.... | chagri/SOTA-RL-Algorithms | PolicyNetwork | false | 1,665 | [
"Apache-2.0"
] | 0 | 58b416e7c706d8426dc402482e72ca7283568e71 | https://github.com/chagri/SOTA-RL-Algorithms/tree/58b416e7c706d8426dc402482e72ca7283568e71 |
OverHaulLoss | # 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
... | Lee-Gihun/Micronet_GSJ | OverHaulLoss | false | 8,460 | [
"MIT"
] | 12 | 72289bb66507b6c3b4d14f2e5916dec718a1b198 | https://github.com/Lee-Gihun/Micronet_GSJ/tree/72289bb66507b6c3b4d14f2e5916dec718a1b198 |
ConcatSquashConv2d | import torch
import torch.nn as nn
class ConcatSquashConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatSquashConv2d, self).__init__()
module = nn.ConvTranspose2d if transpose else nn.Conv2d
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | D-hash-code/ffjord-rnode-finalweek-mnist | ConcatSquashConv2d | false | 2,155 | [
"MIT"
] | 0 | 4cabcbadda79c68df53ec25f1f8fe03cfeee78f9 | https://github.com/D-hash-code/ffjord-rnode-finalweek-mnist/tree/4cabcbadda79c68df53ec25f1f8fe03cfeee78f9 |
DirichletLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class DirichletLayer(nn.Module):
def __init__(self, evidence='exp', dim=-1):
super(DirichletLayer, self).__init__()
self.evidence = evidence
self.dim = dim
def evidence_func(self, logit):
if self.evidence == '... | 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
... | Cogito2012/OpenTAL | DirichletLayer | false | 7,904 | [
"BSD-3-Clause"
] | 16 | a7ab938a52b3fb82163eb1ba5403888359eb7e6a | https://github.com/Cogito2012/OpenTAL/tree/a7ab938a52b3fb82163eb1ba5403888359eb7e6a |
Concat3 | # 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.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | mil-tokyo/webdnn | Concat3 | false | 16,061 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
Skew | import torch
import torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class Skew(nn.Module):
def forward(self, X):
A = X.triu(1)
return A - A.transpose(-1, -2)
d... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | Ismail-Mustapha/tutorials | Skew | false | 13,851 | [
"BSD-3-Clause"
] | 6,424 | 0ccfbf0047db855e93e2aadb43c89c92e89f52b8 | https://github.com/Ismail-Mustapha/tutorials/tree/0ccfbf0047db855e93e2aadb43c89c92e89f52b8 |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | qute012/Korean-Speech-Recognition | Attention | false | 7,517 | [
"Apache-2.0"
] | 1 | 0e037fd03df1ad6bf1084ee748781cdf4d428940 | https://github.com/qute012/Korean-Speech-Recognition/tree/0e037fd03df1ad6bf1084ee748781cdf4d428940 |
BasicModel_ConvNet_MaxPool3d | import torch
import torch.nn as nn
class BasicModel_ConvNet_MaxPool3d(nn.Module):
"""Same as above, but with the MaxPool1d replaced
with a MaxPool3d. This is useful because the MaxPool modules
behave differently to other modules from the perspective
of the DeepLift Attributions
"""
def __init... | 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.... | LMdeLiangMi/captum | BasicModel_ConvNet_MaxPool3d | false | 5,508 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
Gradient | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
class Gradient(nn.Module):
def __init__(self):
super(Gradient, self).__init__()
kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]
kernel_h = torch.Fl... | 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... | YDDDDG/3D2Unet | Gradient | false | 6,008 | [
"MIT"
] | 1 | daca056958fb2ae319dc18a350e04b3cefe0d99f | https://github.com/YDDDDG/3D2Unet/tree/daca056958fb2ae319dc18a350e04b3cefe0d99f |
ResNetDownsampleA | # 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... | corypaik/pytorch-lightning-pbt | ResNetDownsampleA | false | 6,480 | [
"Apache-2.0"
] | 1 | ad25e472fe59ca22bc400023d2589f4bedd37e30 | https://github.com/corypaik/pytorch-lightning-pbt/tree/ad25e472fe59ca22bc400023d2589f4bedd37e30 |
LabelSmoothingCrossEntropyBCE | import torch
import torch.nn as nn
import torch.nn.functional as F
class LabelSmoothingCrossEntropyBCE(nn.Module):
def __init__(self, smoothing=0.1):
super(LabelSmoothingCrossEntropyBCE, self).__init__()
assert smoothing < 1.0
self.smoothing = smoothing
self.confidence = 1.0 - smo... | 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... | Diyago/Graph-clasification-by-computer-vision | LabelSmoothingCrossEntropyBCE | false | 17,224 | [
"Apache-2.0"
] | 9 | 703c44b98f9875d7a7b6db1c2b96372e11e256d6 | https://github.com/Diyago/Graph-clasification-by-computer-vision/tree/703c44b98f9875d7a7b6db1c2b96372e11e256d6 |
Alignment | # 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.... | alibaba-edu/simple-effective-text-matching-pytorch | Alignment | false | 14,798 | [
"Apache-2.0"
] | 278 | 05d572e30801b235e989c78c95dd24d5f5d35f74 | https://github.com/alibaba-edu/simple-effective-text-matching-pytorch/tree/05d572e30801b235e989c78c95dd24d5f5d35f74 |
ScaledDotProductAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | 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.... | Blair129/FEAT-master | ScaledDotProductAttention | false | 8,939 | [
"MIT"
] | 0 | 459e05000a8cca5421fafb7d2f33f19418378df7 | https://github.com/Blair129/FEAT-master/tree/459e05000a8cca5421fafb7d2f33f19418378df7 |
TransposeConv2dLayer | # 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 import Parameter
assert_size_stride = torch.... | piggy2303/DeepFillv2_Pytorch | TransposeConv2dLayer | false | 7,468 | [
"MIT"
] | 1 | dd35299f11704f878ed7a33e14ccd51a9d64baaf | https://github.com/piggy2303/DeepFillv2_Pytorch/tree/dd35299f11704f878ed7a33e14ccd51a9d64baaf |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
"""policy-value network module"""
def __init__(self, board_width, board_height):
super(Net, self).__init__()
self.board_width = board_width
self.board_height = board_height
self.conv1 = 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.... | Dryeck/17-18-Reinforcement | Net | false | 8,058 | [
"MIT"
] | 36 | f5a289a96c0139758436ab6a5a589519af1178da | https://github.com/Dryeck/17-18-Reinforcement/tree/f5a289a96c0139758436ab6a5a589519af1178da |
Linear_QNet | # 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... | cheapmouse94/Machine-Learning-tank1990-python | Linear_QNet | false | 3,281 | [
"MIT"
] | 0 | 8b75983289c7bc0831827561cec12d4ad2addee2 | https://github.com/cheapmouse94/Machine-Learning-tank1990-python/tree/8b75983289c7bc0831827561cec12d4ad2addee2 |
ContrastiveLoss | from torch.nn import Module
import torch
import torch.nn as nn
from torch.nn.modules import Module
class ContrastiveLoss(Module):
def __init__(self, margin=3):
super(ContrastiveLoss, self).__init__()
self.margin = margin
self.loss = nn.BCELoss()
def forward(self, output, label):
... | 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.nn import Module
import torch.nn as nn
from torch.nn.modules import Module
ass... | Brain03Yao/M2TGCN | ContrastiveLoss | false | 17,013 | [
"MIT"
] | 6 | 72c65687fa52c618740cd6d1db7366116f68398c | https://github.com/Brain03Yao/M2TGCN/tree/72c65687fa52c618740cd6d1db7366116f68398c |
SoftDiceLossV1 | import torch
import torch.nn as nn
class SoftDiceLossV1(nn.Module):
"""
soft-dice loss, useful in binary segmentation
"""
def __init__(self, p=1, smooth=1, reduction='mean'):
super(SoftDiceLossV1, self).__init__()
self.p = p
self.smooth = smooth
self.reduction = reduct... | 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... | imvladikon/pytorch-loss | SoftDiceLossV1 | false | 6,876 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
Gaussian | import torch
import torch.nn as nn
class Gaussian(nn.Module):
def forward(self, x):
return torch.exp(-x * x / 2.0)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | fmhoward/pysurvival | Gaussian | false | 12,375 | [
"Apache-2.0"
] | 0 | 3fea55f09477e9f0844845e09d6ea60434436e2e | https://github.com/fmhoward/pysurvival/tree/3fea55f09477e9f0844845e09d6ea60434436e2e |
TransformerEncoderLayer | import math
import torch
from torch import nn
import torch.nn.functional as F
def _normalize(tensor, norm_layer):
"""
Broadcast layer norm
"""
size = tensor.size()
return norm_layer(tensor.view(-1, size[-1])).view(size)
class MultiHeadAttention(nn.Module):
def __init__(self, n_heads, dim, 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
from torch._inductor.runtime.... | jinjiren/ParlAI | TransformerEncoderLayer | false | 12,626 | [
"MIT"
] | 0 | 40799aeee69f2a0bb25a1341bb8da0c44861268e | https://github.com/jinjiren/ParlAI/tree/40799aeee69f2a0bb25a1341bb8da0c44861268e |
Smoother | from torch.nn import Module
import torch
from torch import Tensor
from typing import Optional
import torch.nn.functional as F
from torch.nn import Dropout
from torch.nn import LayerNorm
from torch.nn import Conv1d
from torch.nn import MultiheadAttention
class Smoother(Module):
"""Convolutional Transformer Encoder... | 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.... | OlegJakushkin/FragmentVC | Smoother | false | 14,171 | [
"MIT"
] | 136 | 8aa673157b855bf3b67f06fdb6eb4b2a12ed0005 | https://github.com/OlegJakushkin/FragmentVC/tree/8aa673157b855bf3b67f06fdb6eb4b2a12ed0005 |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamma = nn.Param... | 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_... | AntiAegis/PyTorch-GAN | LayerNorm | false | 4,866 | [
"MIT"
] | 1 | 1cb951b3ad3a58b749c1802f84947b85f72c8367 | https://github.com/AntiAegis/PyTorch-GAN/tree/1cb951b3ad3a58b749c1802f84947b85f72c8367 |
VAE | import torch
import torch.nn as nn
import torch.nn.functional as F
class VAE(nn.Module):
def __init__(self, encode_dims, decode_dims, dropout=0.0):
super(VAE, self).__init__()
self.encoder = nn.ModuleDict({f'enc_{i}': nn.Linear(encode_dims[i],
encode_dims[i + 1]) for i in range(len(en... | 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... | Hassan-Lee/FusionModelingOfUser-GeneratedReviewDataOfComplexHeterogeneousTypes | VAE | false | 5,280 | [
"MIT"
] | 1 | b863e3fbf8058ecb06246a843e3fd2568bbbf260 | https://github.com/Hassan-Lee/FusionModelingOfUser-GeneratedReviewDataOfComplexHeterogeneousTypes/tree/b863e3fbf8058ecb06246a843e3fd2568bbbf260 |
ECToCA3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class ECToCA3(nn.Module):
def __init__(self, D_in, D_out):
super(ECToCA3, self).__init__()
self.fc1 = nn.Linear(D_in, 800)
self.fc2 = nn.Linear(800, D_out)
def forward(self, x):
x = F.leaky_relu(self.fc1(x), 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | sachio222/aha4 | ECToCA3 | false | 7,583 | [
"MIT"
] | 1 | ec378fe1bace85e325ad7cb8686b8ba321dc97d0 | https://github.com/sachio222/aha4/tree/ec378fe1bace85e325ad7cb8686b8ba321dc97d0 |
Feedback | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') != -1:
m.weight.data.norm... | 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... | IacoSimoncini/tfvaegan | Feedback | false | 12,630 | [
"MIT"
] | 0 | 157b526d65d0b0d5412f4be6fed02fc7d6325827 | https://github.com/IacoSimoncini/tfvaegan/tree/157b526d65d0b0d5412f4be6fed02fc7d6325827 |
BalancedLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BalancedLoss(nn.Module):
def __init__(self, neg_weight=1.0):
super(BalancedLoss, self).__init__()
self.neg_weight = neg_weight
def forward(self, input, target):
pos_mask = target == 1
neg_mask = 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._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | LIANGKE23/Siamese-FC-KF-CF | BalancedLoss | false | 17,566 | [
"MIT"
] | 10 | 3d9db19c0f39f0588a5061cd182bfbfc37dca76f | https://github.com/LIANGKE23/Siamese-FC-KF-CF/tree/3d9db19c0f39f0588a5061cd182bfbfc37dca76f |
DenseLayer | # 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_... | zzh-tech/RSCD | DenseLayer | false | 16,840 | [
"MIT"
] | 57 | b287b1621121f8ca7ece6b27ebd4e28a5f8e6f5e | https://github.com/zzh-tech/RSCD/tree/b287b1621121f8ca7ece6b27ebd4e28a5f8e6f5e |
SquaredModulus | # 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... | entn-at/leaf-audio-pytorch | SquaredModulus | false | 15,306 | [
"Apache-2.0"
] | 72 | 33f4ba4c8bdf07f125033f8e706d0d0bc6816445 | https://github.com/entn-at/leaf-audio-pytorch/tree/33f4ba4c8bdf07f125033f8e706d0d0bc6816445 |
QuadriLinearScore | # 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.data.dataloader
import torc... | ciaochiaociao/CLNER | QuadriLinearScore | false | 3,379 | [
"MIT"
] | 0 | a31fb1c3bfdaa5d62147dc892489d29a85e6b385 | https://github.com/ciaochiaociao/CLNER/tree/a31fb1c3bfdaa5d62147dc892489d29a85e6b385 |
SimpleCNN | import torch
import torch.nn as nn
from collections import OrderedDict
class SimpleCNN(nn.Module):
def __init__(self, input_dim=3, global_pool=False):
super(SimpleCNN, self).__init__()
self.features = nn.Sequential(OrderedDict([('conv1', nn.Conv2d(
input_dim, 64, kernel_size=3, stride... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | D-X-Y/MSPLD-2018 | SimpleCNN | false | 13,614 | [
"MIT"
] | 63 | 71a6a75830ac84c7a861e63367ad3ace991fae77 | https://github.com/D-X-Y/MSPLD-2018/tree/71a6a75830ac84c7a861e63367ad3ace991fae77 |
PixelWise | import torch
import torch.nn.init
class PixelWise(torch.nn.Module):
""" Implemented - https://arxiv.org/pdf/1710.10196.pdf """
def __init__(self, eps=1e-06):
super(PixelWise, self).__init__()
self.eps = eps
def forward(self, tensor):
return tensor.div(tensor.pow(2).mean(1, 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Johnson-yue/TensorMONK | PixelWise | false | 5,420 | [
"MIT"
] | 1 | 1785132b82c685c3b3fc05b00dec46b1fccfc948 | https://github.com/Johnson-yue/TensorMONK/tree/1785132b82c685c3b3fc05b00dec46b1fccfc948 |
KdLoss | # 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... | CQUlearningsystemgroup/LearningToBinarize | KdLoss | false | 4,949 | [
"MIT"
] | 1 | 1ecad897145af65ff52323bf2ec64a2154dc87d6 | https://github.com/CQUlearningsystemgroup/LearningToBinarize/tree/1ecad897145af65ff52323bf2ec64a2154dc87d6 |
SVIGlobalMeanPool2D | import torch
import torch.nn as nn
class SVIGlobalMeanPool2D(nn.Module):
"""
Expects
:param x: [examples, samples, channels, H, W]
:return: [examples, samples, channels]
"""
def __init__(self):
super(SVIGlobalMeanPool2D, self).__init__()
def forward(self, x):
x = x.mean(4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | RomanShen/radial-bnn | SVIGlobalMeanPool2D | false | 989 | [
"Apache-2.0"
] | 0 | 7c8bc85397c1461a6fd5ea9adf0631f9ade27f6c | https://github.com/RomanShen/radial-bnn/tree/7c8bc85397c1461a6fd5ea9adf0631f9ade27f6c |
Net | import torch
from torch import nn
from torch.nn import functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 5, 1)
self.conv2 = nn.Conv2d(20, 50, 5, 1)
self.fc1 = nn.Linear(5 * 5 * 50, 500)
self.fc2 = nn.Linear(... | 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.... | zwc662/disentangling-vae | Net | false | 11,086 | [
"MIT"
] | 0 | 7eeace2a30f8034e222be6a906f53748b3b2bb6e | https://github.com/zwc662/disentangling-vae/tree/7eeace2a30f8034e222be6a906f53748b3b2bb6e |
LogisticRegression | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | ciubecca/3dunet-cavity | LogisticRegression | false | 1,718 | [
"MIT"
] | 0 | cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | https://github.com/ciubecca/3dunet-cavity/tree/cfcc827773b18a95d221ab86c1afc5e2f7c30ecb |
PadSameConv2d | import math
import torch
import torch.nn.functional as F
class PadSameConv2d(torch.nn.Module):
def __init__(self, kernel_size, stride=1):
"""
Imitates padding_mode="same" from tensorflow.
:param kernel_size: Kernelsize of the convolution, int or tuple/list
:param stride: Stride of... | 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... | fish258/MonoRec | PadSameConv2d | false | 15,354 | [
"MIT"
] | 388 | c0612d2710802004cdd83205e63d0582de543c41 | https://github.com/fish258/MonoRec/tree/c0612d2710802004cdd83205e63d0582de543c41 |
BCE_Dice | # 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... | evilidol/kaggle-Steel-Defect-Detection | BCE_Dice | false | 6,660 | [
"MIT"
] | 1 | 41e3e360f49d706c8c79bcd442342c529648a736 | https://github.com/evilidol/kaggle-Steel-Defect-Detection/tree/41e3e360f49d706c8c79bcd442342c529648a736 |
SingleHeadAttention | # 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.... | JustinLiam/DAN | SingleHeadAttention | false | 7,625 | [
"MIT"
] | 1 | eb29cddad6c93e591854b115ef524643b1cd471c | https://github.com/JustinLiam/DAN/tree/eb29cddad6c93e591854b115ef524643b1cd471c |
ycbcr_to_rgb_jpeg | import torch
import numpy as np
import torch.nn as nn
class ycbcr_to_rgb_jpeg(nn.Module):
""" Converts YCbCr image to RGB JPEG
Input:
image(tensor): batch x height x width x 3
Outpput:
result(tensor): batch x 3 x height x width
"""
def __init__(self):
super(ycbcr_to_rgb_jp... | 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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | mlomnitz/DifferentiableJPEG | ycbcr_to_rgb_jpeg | false | 16,100 | [
"MIT"
] | 86 | a5767feba955a1bcb78600135a09c36a806f6249 | https://github.com/mlomnitz/DifferentiableJPEG/tree/a5767feba955a1bcb78600135a09c36a806f6249 |
BertLayerNorm | # 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... | BIT-ENGD/eeqa | BertLayerNorm | false | 13,366 | [
"MIT"
] | 142 | 2995abbaff1fb47131246a247ee7ed62aa94f4c3 | https://github.com/BIT-ENGD/eeqa/tree/2995abbaff1fb47131246a247ee7ed62aa94f4c3 |
PointWiseConvolution | import torch
from torch import nn as nn
class PointWiseConvolution(nn.Module):
def __init__(self, inChannels, outChannels, stride, expansionFactor,
isNormal):
super(PointWiseConvolution, self).__init__()
if isNormal:
self.layer = nn.Conv2d(in_channels=inChannels * expansionFac... | 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | Pranshu-Bahadur/g2net | PointWiseConvolution | false | 9,485 | [
"MIT"
] | 0 | a117df7699837c9a3ae21ec59a310d7384369601 | https://github.com/Pranshu-Bahadur/g2net/tree/a117df7699837c9a3ae21ec59a310d7384369601 |
GaussianPolicyFunction | import torch
import torch.nn as nn
import torch.nn.functional as F
class GaussianPolicyFunction(nn.Module):
"""fully connected 200x200 hidden layers"""
def __init__(self, state_dim, action_dim):
super(GaussianPolicyFunction, self).__init__()
self.fc1 = nn.Linear(state_dim, 200)
self.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.triton_helpers import libdevice, math as tl_math
im... | himanshusahni/task-biased-url | GaussianPolicyFunction | false | 10,259 | [
"MIT"
] | 0 | 28e4ec318d46d84065b6e197fa9f4100bd4a4c34 | https://github.com/himanshusahni/task-biased-url/tree/28e4ec318d46d84065b6e197fa9f4100bd4a4c34 |
rSoftMax | import torch
import torch.nn as nn
import torch.nn.functional as F
class rSoftMax(nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
if self.radix > 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Exdenta/torchsat | rSoftMax | false | 13,654 | [
"MIT"
] | 316 | 70ea3db758757104fb3ba618ddf7997f0f3a75b4 | https://github.com/Exdenta/torchsat/tree/70ea3db758757104fb3ba618ddf7997f0f3a75b4 |
BasicDeconv | # 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_... | Yuuchuin/C3_V2 | BasicDeconv | false | 6,020 | [
"MIT"
] | 1 | 92a5edbc2c2b3452c5f57e74f928591192293e81 | https://github.com/Yuuchuin/C3_V2/tree/92a5edbc2c2b3452c5f57e74f928591192293e81 |
DiscShiftLoss | # 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... | Jason-Khan/mmediting | DiscShiftLoss | false | 614 | [
"Apache-2.0"
] | 0 | d187f95a675dff3eb975a575bd9278d643b5b645 | https://github.com/Jason-Khan/mmediting/tree/d187f95a675dff3eb975a575bd9278d643b5b645 |
MultiHeadAttention | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
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.... | Hyunseung-Kim/molGCT | MultiHeadAttention | false | 8,249 | [
"Apache-2.0"
] | 10 | 5a2604337cf0a9d3c725295ccb7c8ea4b0144636 | https://github.com/Hyunseung-Kim/molGCT/tree/5a2604337cf0a9d3c725295ccb7c8ea4b0144636 |
GCN | from torch.nn import Module
import math
import torch
from torch.nn.modules import Module
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
Z = f(X, A) = softmax(A` * ReLU(A` * X * W0)* ... | 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.nn import Module
i... | Brain03Yao/M2TGCN | GCN | false | 17,008 | [
"MIT"
] | 6 | 72c65687fa52c618740cd6d1db7366116f68398c | https://github.com/Brain03Yao/M2TGCN/tree/72c65687fa52c618740cd6d1db7366116f68398c |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
"""
Criterion that computes Sørensen-Dice Coefficient loss.
https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
"""
def __init__(self):
super().__init__()
self.smooth = 1.0
def forward(self, input... | 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... | Quentin18/road-segmentation | DiceLoss | false | 965 | [
"MIT"
] | 0 | 9d212c80fa3f6926c431847337d2ca38ec96b614 | https://github.com/Quentin18/road-segmentation/tree/9d212c80fa3f6926c431847337d2ca38ec96b614 |
Upsample | import torch
import torch.nn as nn
class Upsample(nn.Module):
"""PyTorch upsampling implementation.
This module upsamples by inserting <i-1> zeros in between samples in the time
dimension. It does not low pass filter after this and assumes that the filter is a
separate module if desired.
.. see... | 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... | plexixx/rfml | Upsample | false | 16,260 | [
"BSD-3-Clause"
] | 61 | c00633b2c2005d38f991c6b9e3fd855ca25166c4 | https://github.com/plexixx/rfml/tree/c00633b2c2005d38f991c6b9e3fd855ca25166c4 |
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