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 |
|---|---|---|---|---|---|---|---|---|---|---|
InverseSoftplus | # 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asse... | khoehlein/fV-SRN | InverseSoftplus | false | 3,832 | [
"MIT"
] | 0 | 601f3e952b090df92e875c233c2c9ca646523948 | https://github.com/khoehlein/fV-SRN/tree/601f3e952b090df92e875c233c2c9ca646523948 |
Decoder | import torch
import torch.nn as nn
class Decoder(nn.Module):
def __init__(self, h_dim, n_chan, out_dim):
super(Decoder, self).__init__()
self.h_dim = h_dim
self.n_chan = n_chan
self.decoding = nn.Linear(h_dim, out_dim)
def forward(self, x):
x = torch.stack([self.decod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | VDelv/Spatio-Temporal-EEG-Analysis | Decoder | false | 2,926 | [
"BSD-3-Clause"
] | 0 | 7207e642da013e19dfd4316a22b597a93ce59581 | https://github.com/VDelv/Spatio-Temporal-EEG-Analysis/tree/7207e642da013e19dfd4316a22b597a93ce59581 |
NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RyanUnderhill/onnxruntime | NeuralNetMultiplePositionalArgumentsMultiOutputsWithoutDependency | false | 11,823 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
SE | # 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 itertools import chain a... | WoojuLee24/SlowFast | SE | false | 9,601 | [
"Apache-2.0"
] | 0 | 1fa9fda86a83ee09af5d38e11b14d2a2a18e419b | https://github.com/WoojuLee24/SlowFast/tree/1fa9fda86a83ee09af5d38e11b14d2a2a18e419b |
Spike | import torch
import torch.nn as nn
class Spike(nn.Module):
def __init__(self, center=1, width=1):
super(Spike, self).__init__()
self.c = center
self.w = width
self.alpha = torch.nn.Parameter(torch.ones(1))
self.beta = torch.nn.Parameter(torch.ones(1))
def forward(self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | craigxchen/Reinforcement-Learning-Function-Approximation | Spike | false | 6,487 | [
"MIT"
] | 1 | 09c4df1dd44c6a76a3f574bebc959a19b141f3fe | https://github.com/craigxchen/Reinforcement-Learning-Function-Approximation/tree/09c4df1dd44c6a76a3f574bebc959a19b141f3fe |
FakeReLUM | import torch
import torch.nn as nn
import torch.utils.data
class FakeReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input.clamp(min=0)
@staticmethod
def backward(ctx, grad_output):
return grad_output
class FakeReLUM(nn.Module):
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | Ethos-lab/robust-representation-matching | FakeReLUM | false | 17,255 | [
"MIT"
] | 3 | 80d98f11846468c31278146583b9ef4750190211 | https://github.com/Ethos-lab/robust-representation-matching/tree/80d98f11846468c31278146583b9ef4750190211 |
Encoder | import torch
import torch.utils.data
from torch import nn
from torch.nn import functional
class Encoder(nn.Module):
def __init__(self, input_dim, hidden_dim, z_dim):
"""
Args:
input_dim: A integer indicating the size of input.
hidden_dim: A integer indicating 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 import triton_helpers
import torch.utils.data
from ... | MaurizioFD/recsys-challenge-2020-twitter | Encoder | false | 8,518 | [
"Apache-2.0"
] | 44 | 95dc024fb4f8777aa62e1304536daece640428de | https://github.com/MaurizioFD/recsys-challenge-2020-twitter/tree/95dc024fb4f8777aa62e1304536daece640428de |
SAB | # 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.... | karayanni/torch-scae | SAB | false | 10,430 | [
"Apache-2.0"
] | 0 | e044662d8942d8d1923d13d071f375144cf4a1e8 | https://github.com/karayanni/torch-scae/tree/e044662d8942d8d1923d13d071f375144cf4a1e8 |
MultiheadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HughMun/MultiBench | MultiheadAttention | false | 13,804 | [
"MIT"
] | 148 | d5712a0815a9486b0e0c76b54cd63c880188fc8e | https://github.com/HughMun/MultiBench/tree/d5712a0815a9486b0e0c76b54cd63c880188fc8e |
ScaledDotProductAttention | import torch
def masked_softmax(x, m=None, dim=-1):
"""
Softmax with mask (optional)
"""
x = torch.clamp(x, min=-15.0, max=15.0)
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:
e_x = e_x * 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.... | daiki-kimura/commonsense-rl | ScaledDotProductAttention | false | 12,245 | [
"Apache-2.0"
] | 0 | 5513926957b6501ce9cfa46f77f8f2c1c4892fa5 | https://github.com/daiki-kimura/commonsense-rl/tree/5513926957b6501ce9cfa46f77f8f2c1c4892fa5 |
CoralLayer | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | johann-petrak/farm-tools | CoralLayer | false | 3,756 | [
"Apache-2.0"
] | 0 | 7d379bbc5b9b079eedd4a11d7bdb1636c0ad834c | https://github.com/johann-petrak/farm-tools/tree/7d379bbc5b9b079eedd4a11d7bdb1636c0ad834c |
openai_critic | # 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... | FlickerNiko/ai_lib | openai_critic | false | 13,703 | [
"MIT"
] | 99 | 7087d4569c9a827d35dd8735b55a080834d31a82 | https://github.com/FlickerNiko/ai_lib/tree/7087d4569c9a827d35dd8735b55a080834d31a82 |
ClipGlobalAvgPool2d | import torch
from torch import nn
class FastGlobalAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastGlobalAvgPool2d, self).__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
return x.view((in_size[0], i... | 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... | hfyer/NAIC2020_ReID_R1 | ClipGlobalAvgPool2d | false | 6,805 | [
"Apache-2.0"
] | 1 | 240f0c9f65e482e6b0090f01d9f9e3373a337033 | https://github.com/hfyer/NAIC2020_ReID_R1/tree/240f0c9f65e482e6b0090f01d9f9e3373a337033 |
TVLoss | import torch
import torch.nn as nn
class TVLoss(nn.Module):
def __init__(self, strength):
super(TVLoss, self).__init__()
self.strength = strength
def forward(self, input):
self.x_diff = input[:, :, 1:, :] - input[:, :, :-1, :]
self.y_diff = input[:, :, :, 1:] - 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.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | DarekGit/neural_style | TVLoss | false | 8,935 | [
"MIT"
] | 0 | 461f0d791f23e82bbf0adcecf5630854ccac9944 | https://github.com/DarekGit/neural_style/tree/461f0d791f23e82bbf0adcecf5630854ccac9944 |
RmseBceDiceLoss | # 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... | Pandinosaurus/Depth-Estimation-Segmentation | RmseBceDiceLoss | false | 17,795 | [
"MIT"
] | 4 | 2eea883c96bf106774ea94464fc16c6baea86a95 | https://github.com/Pandinosaurus/Depth-Estimation-Segmentation/tree/2eea883c96bf106774ea94464fc16c6baea86a95 |
injective_pad | # 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... | JessieYuW/CrevNet-Traffic4cast | injective_pad | false | 8,326 | [
"Apache-2.0"
] | 13 | 810b2a951de1f99a07bf8cfcbd93e1fc016cce48 | https://github.com/JessieYuW/CrevNet-Traffic4cast/tree/810b2a951de1f99a07bf8cfcbd93e1fc016cce48 |
GlobalAvgPool2d | # 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... | AndreasLTeigen/few_shot_open_world | GlobalAvgPool2d | false | 1,937 | [
"MIT"
] | 0 | 3514824c4233fdff9af9c0b636435b2ff0fa6e09 | https://github.com/AndreasLTeigen/few_shot_open_world/tree/3514824c4233fdff9af9c0b636435b2ff0fa6e09 |
Project3D | import torch
import torch.nn as nn
class Project3D(nn.Module):
"""Layer which projects 3D points into a camera with intrinsics K and at position T
"""
def __init__(self, batch_size, height, width, eps=1e-07):
super(Project3D, self).__init__()
self.batch_size = batch_size
self.heig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | shlomi-amitai/myDIFFNet | Project3D | false | 10,880 | [
"MIT"
] | 0 | 39dead457f10c82caae2a12ea152f2339188014c | https://github.com/shlomi-amitai/myDIFFNet/tree/39dead457f10c82caae2a12ea152f2339188014c |
ReduceSumModule | import torch
class ReduceSumModule(torch.nn.Module):
def __init__(self):
super(ReduceSumModule, self).__init__()
def forward(self, x):
return torch.sum(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 import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | MichaelZhero/nncase | ReduceSumModule | false | 11,935 | [
"Apache-2.0"
] | 0 | 0fae6ce90d7adff386e1a286cd2b42422f4b850a | https://github.com/MichaelZhero/nncase/tree/0fae6ce90d7adff386e1a286cd2b42422f4b850a |
SimpleSumModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleSumModule(torch.nn.Module):
def __init__(self, dtype=None):
super(SimpleSumModule, self).__init__()
self.dtype = dtype
def forward(self, a):
b = a + a
return torch.sum(b, dtype=self.dtype)
def get_i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | briancoutinho/glow | SimpleSumModule | false | 12,593 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
ExampleBackbone | import torch
import torch.nn as nn
class ExampleBackbone(nn.Module):
def __init__(self):
super(ExampleBackbone, self).__init__()
self.conv = nn.Conv2d(3, 3, 3)
def init_weights(self, pretrained=None):
pass
def forward(self, x):
return [self.conv(x)]
def get_inputs():
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ChenDirk/mmrazor | ExampleBackbone | false | 2,087 | [
"Apache-2.0"
] | 0 | 6f262ecd777c15efd4ee2d191cdc567071615421 | https://github.com/ChenDirk/mmrazor/tree/6f262ecd777c15efd4ee2d191cdc567071615421 |
OrthogonalLoss | # 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.... | chrisbyd/ContrastiveVehicleQuant | OrthogonalLoss | false | 9,921 | [
"MIT"
] | 0 | bf471988868cf0cb9713002dd1d6726272ecce7f | https://github.com/chrisbyd/ContrastiveVehicleQuant/tree/bf471988868cf0cb9713002dd1d6726272ecce7f |
LanguageModelCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
target = target[:, :input.size(1)]
mask = mask[:, :input.size(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
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | ankit1khare/Show_Infer_and_Tell-CIC | LanguageModelCriterion | false | 18,334 | [
"MIT"
] | 5 | 5437cceaaaf1bbcd16cb921650afd769378f4fc4 | https://github.com/ankit1khare/Show_Infer_and_Tell-CIC/tree/5437cceaaaf1bbcd16cb921650afd769378f4fc4 |
ConvBlock | import torch
import torch.nn.functional as F
import torch.nn as nn
class ConvBlock(nn.Module):
def __init__(self):
super(ConvBlock, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
def forward(self, x):
x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | coasxu/FedMA | ConvBlock | false | 15,053 | [
"MIT"
] | 254 | 21f4d32338fd2563ebd97c737e3b9f4f470029d9 | https://github.com/coasxu/FedMA/tree/21f4d32338fd2563ebd97c737e3b9f4f470029d9 |
Highway | # 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_... | KinglittleQ/Tacotron | Highway | false | 17,544 | [
"MIT"
] | 6 | d43c0c4e5b91029ffae0f96d69a1d3b3106d49c5 | https://github.com/KinglittleQ/Tacotron/tree/d43c0c4e5b91029ffae0f96d69a1d3b3106d49c5 |
ILN | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.utils.data
class ILN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(ILN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
self.gamma = ... | 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 torch.nn.parameter import Parameter
import torch.uti... | JW9MsjwjnpdRLFw/RMT | ILN | false | 593 | [
"MIT"
] | 0 | a877fd78639a8d4c534d0373b9d0ad023e0fa2dd | https://github.com/JW9MsjwjnpdRLFw/RMT/tree/a877fd78639a8d4c534d0373b9d0ad023e0fa2dd |
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 import triton_helpers
from torch._inductor.runtime.... | Pluriscient/learn-to-learn | Model | false | 11,794 | [
"MIT"
] | 0 | 4aa0143522eb90f6439b83ed424d12b434cb344b | https://github.com/Pluriscient/learn-to-learn/tree/4aa0143522eb90f6439b83ed424d12b434cb344b |
GroupNorm32 | # 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_... | johnpaulbin/glide-text2im | GroupNorm32 | false | 12,625 | [
"MIT"
] | 0 | 4897050c4c540316dfb1ec7e6ff95698bcb20487 | https://github.com/johnpaulbin/glide-text2im/tree/4897050c4c540316dfb1ec7e6ff95698bcb20487 |
NonLinearProbe1 | # 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... | PAL-ML/atari-representation-learning | NonLinearProbe1 | false | 2,803 | [
"MIT"
] | 0 | 11977da174d9ef74c0b2333322b9f0b28e15239e | https://github.com/PAL-ML/atari-representation-learning/tree/11977da174d9ef74c0b2333322b9f0b28e15239e |
MultiHeadAttn | # 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.... | PeganovAnton/transformer-xl | MultiHeadAttn | false | 14,165 | [
"Apache-2.0"
] | 133 | f36428445cc903872fde54d90bc5e61886420a5a | https://github.com/PeganovAnton/transformer-xl/tree/f36428445cc903872fde54d90bc5e61886420a5a |
Sparsemax | # 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.autograd import Function
from torch import nn
assert_size_stride = torch._C._d... | Sologa/awesome-align | Sparsemax | false | 14,430 | [
"BSD-3-Clause"
] | 173 | 62eaae7eac9bac06c10627fac6cc942c07a50e64 | https://github.com/Sologa/awesome-align/tree/62eaae7eac9bac06c10627fac6cc942c07a50e64 |
UNETAdd | # 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.... | quenting44/semantic_segmentation | UNETAdd | false | 10,846 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a |
SSGC | # 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... | EdisonLeeeee/Graphgallery | SSGC | false | 5,123 | [
"MIT"
] | 1 | 8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 | https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8 |
DWT | import torch
import torch.nn as nn
import torch.nn
def dwt_init(x):
x01 = x[:, :, 0::2, :] / 2
x02 = x[:, :, 1::2, :] / 2
x1 = x01[:, :, :, 0::2]
x2 = x02[:, :, :, 0::2]
x3 = x01[:, :, :, 1::2]
x4 = x02[:, :, :, 1::2]
x_LL = x1 + x2 + x3 + x4
x_HL = -x1 - x2 + x3 + x4
x_LH = -x1 + ... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | FanChiMao/HWMNet | DWT | false | 8,103 | [
"Apache-2.0"
] | 13 | 3375f062a7304b06b545fc7eb430555d43cc4075 | https://github.com/FanChiMao/HWMNet/tree/3375f062a7304b06b545fc7eb430555d43cc4075 |
LogSoftMax | import torch
import torch.nn as nn
import torch.nn.functional as F
class LogSoftMax(nn.Module):
def __init__(self):
super(LogSoftMax, self).__init__()
def forward(self, cls):
b, a2, h, w = cls.size()
cls = cls.view(b, 2, a2 // 2, h, w)
cls = cls.permute(0, 2, 3, 4, 1).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._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | mshmoon/siamrpn-lightweight | LogSoftMax | false | 7,291 | [
"MIT"
] | 1 | f6527e34c9eaaeb45817b12babd78ee73b1c7525 | https://github.com/mshmoon/siamrpn-lightweight/tree/f6527e34c9eaaeb45817b12babd78ee73b1c7525 |
Mix2Pooling | import torch
import torch._C
import torch.serialization
from torch import nn
from typing import *
class Mix2Pooling(nn.Module):
def __init__(self, size):
super(Mix2Pooling, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(size)
self.max_pool = nn.AdaptiveMaxPool2d(size)
def forw... | 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._C
import torch.serialization
from torch import nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_siz... | shuaizzZ/mmsegmentation | Mix2Pooling | false | 4,323 | [
"Apache-2.0"
] | 0 | a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c | https://github.com/shuaizzZ/mmsegmentation/tree/a6c6b348dbf8c4a0a39ffbdb832a1e82309c533c |
ScaledDotProductAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
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.... | Sha-Lab/CASTLE | ScaledDotProductAttention | false | 8,752 | [
"MIT"
] | 13 | 212cb7aaad1bfae7041c90143220286bde24db33 | https://github.com/Sha-Lab/CASTLE/tree/212cb7aaad1bfae7041c90143220286bde24db33 |
UniformBoxWarp | import torch
import torch.nn as nn
class UniformBoxWarp(nn.Module):
def __init__(self, sidelength):
super().__init__()
self.scale_factor = 2 / sidelength
def forward(self, coordinates):
return coordinates * self.scale_factor
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | HexagonPrime/pixel-nerf | UniformBoxWarp | false | 2,401 | [
"BSD-2-Clause"
] | 0 | 298aa7a3451c01e6f19f73f0c756672d3de54bf9 | https://github.com/HexagonPrime/pixel-nerf/tree/298aa7a3451c01e6f19f73f0c756672d3de54bf9 |
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.... | howardchenhd/Transformer-pytorch | ScaledDotProductAttention | false | 6,819 | [
"MIT"
] | 1 | ae71ed5767272feb7e717be6d5bfce46f80ec57a | https://github.com/howardchenhd/Transformer-pytorch/tree/ae71ed5767272feb7e717be6d5bfce46f80ec57a |
VAE | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Linear(784, 5000)
self.fc2 = nn.Linear(5000, 5000)
self.fc21 = nn.Linear(5000, 20)
self.fc22 = nn.... | 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... | DanIulian/minigrid_rl | VAE | false | 608 | [
"MIT"
] | 0 | d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa | https://github.com/DanIulian/minigrid_rl/tree/d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa |
DiceLoss | import torch
import warnings
import numpy as np
from torch.nn.modules.loss import _Loss
def one_hot(labels, num_classes):
"""
Converts label image `labels` to a one-hot vector with `num_classes` number of channels as last dimension.
"""
labels = labels % num_classes
y = np.eye(num_classes)
one... | 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
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | JanSellner/MONAI | DiceLoss | false | 9,171 | [
"Apache-2.0"
] | 0 | ff8fa2bae94914030abb1bc0680417fdaa74afd8 | https://github.com/JanSellner/MONAI/tree/ff8fa2bae94914030abb1bc0680417fdaa74afd8 |
Noise_injector | import torch
import torch.nn as nn
def truncated_normal_(tensor, mean=0, std=1):
size = tensor.shape
tmp = tensor.new_empty(size + (4,)).normal_()
valid = (tmp < 2) & (tmp > -2)
ind = valid.max(-1, keepdim=True)[1]
tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1))
tensor.data.mul_(std).add_(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | EliasKassapis/CAR | Noise_injector | false | 8,067 | [
"Apache-2.0"
] | 17 | ff7ec86aab68c4b9ff8aea171244991bd132d487 | https://github.com/EliasKassapis/CAR/tree/ff7ec86aab68c4b9ff8aea171244991bd132d487 |
DiceLoss | import torch
from torch.nn.modules.loss import _Loss
class DiceLoss(_Loss):
def __init__(self):
super(DiceLoss, self).__init__()
self.axis = 2, 3, 4
self.smooth = 1e-07
def forward(self, input, target):
return 1.0 - self.dice_score(input, target)
def dice_score(self, inp... | 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.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | aledelmo/KDCompression | DiceLoss | false | 1,397 | [
"Apache-2.0"
] | 0 | 030e7331f72ac8977964b6adb65d268c23d59130 | https://github.com/aledelmo/KDCompression/tree/030e7331f72ac8977964b6adb65d268c23d59130 |
UsedIndices | import torch
from torch import nn
import torch.onnx
class UsedIndices(nn.Module):
def __init__(self):
super().__init__()
self.mp = nn.MaxPool2d(kernel_size=[3, 3], stride=[2, 2], ceil_mode
=True, return_indices=True)
def forward(self, x):
y, indices = self.mp(x)
r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.asser... | Piteryo/onnx2pytorch | UsedIndices | false | 9,450 | [
"Apache-2.0"
] | 0 | c25b3a5289ee7073d644d280a112c15382b7f690 | https://github.com/Piteryo/onnx2pytorch/tree/c25b3a5289ee7073d644d280a112c15382b7f690 |
ReverseMaskConv | # 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.... | DLwbm123/LBAM_inpainting | ReverseMaskConv | false | 17,213 | [
"MIT"
] | 7 | c809c3cedf09cda7c175e930c7834ac39d8f526f | https://github.com/DLwbm123/LBAM_inpainting/tree/c809c3cedf09cda7c175e930c7834ac39d8f526f |
GlobalMaxPool1d | import torch
from torch import nn
class GlobalMaxPool1d(nn.Module):
"""Performs global max pooling over the entire length of a batched 1D tensor
# Arguments
input: Input tensor
"""
def forward(self, input):
return nn.functional.max_pool1d(input, kernel_size=input.size()[2:]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | AndreasLTeigen/few_shot_open_world | GlobalMaxPool1d | false | 1,943 | [
"MIT"
] | 0 | 3514824c4233fdff9af9c0b636435b2ff0fa6e09 | https://github.com/AndreasLTeigen/few_shot_open_world/tree/3514824c4233fdff9af9c0b636435b2ff0fa6e09 |
Encoder | # 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... | PaParaZz1/DI-engine | Encoder | false | 11,842 | [
"Apache-2.0"
] | 0 | b38144117c1ebc6eb860d8637ec8866dfbcdf2de | https://github.com/PaParaZz1/DI-engine/tree/b38144117c1ebc6eb860d8637ec8866dfbcdf2de |
MatchModule | # 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.... | zfjsail/MatchZoo-py | MatchModule | false | 4,708 | [
"Apache-2.0"
] | 0 | c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2 | https://github.com/zfjsail/MatchZoo-py/tree/c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2 |
GraphConv | import torch
from torch import nn
import torch.nn
import torch.autograd
def sparse_bmm(sparse_matrix, dense_matrix_batch):
"""
Perform torch.bmm on an unbatched sparse matrix and a batched dense matrix.
Args:
sparse_matrix (torch.sparse.FloatTensor): Shape = (m, n)
dense_matrix_batch (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 import nn
import torch.nn
import torch.autograd
assert_size_stride = ... | shumash/kaolin | GraphConv | false | 12,980 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 2158b5ec7a28d57d7df7e606adbb0c693a0145f0 | https://github.com/shumash/kaolin/tree/2158b5ec7a28d57d7df7e606adbb0c693a0145f0 |
WeightNet | import torch
import torch.nn as nn
class WeightNet(nn.Module):
"""WeightNet in Temporal interlace module.
The WeightNet consists of two parts: one convolution layer
and a sigmoid function. Following the convolution layer, the sigmoid
function and rescale module can scale our output to the range (0, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | giahaowjx/mmaction2 | WeightNet | false | 10,336 | [
"Apache-2.0"
] | 0 | 4f95e9b91354acdcae768ce94e01d3821bba0154 | https://github.com/giahaowjx/mmaction2/tree/4f95e9b91354acdcae768ce94e01d3821bba0154 |
ShiftSoftplus | import torch
import numpy as np
from torch.nn import Softplus
class ShiftSoftplus(Softplus):
"""
Shiftsoft plus activation function:
1/beta * (log(1 + exp**(beta * x)) - log(shift))
"""
def __init__(self, beta=1, shift=2, threshold=20):
super().__init__(beta, threshold)
self.s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.nn import Softplus
assert_size_stride = torch._C._d... | jeah-z/BDE-FGCN-DFT | ShiftSoftplus | false | 3,709 | [
"MIT"
] | 0 | 5542544079642a371f08c8c1f356fa235d895194 | https://github.com/jeah-z/BDE-FGCN-DFT/tree/5542544079642a371f08c8c1f356fa235d895194 |
L2NormLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | gfiumara/MSU-LatentAFIS | L2NormLoss | false | 15,419 | [
"MIT"
] | 53 | 682464b0bc4501977f1304c51e2638c0ee89d87c | https://github.com/gfiumara/MSU-LatentAFIS/tree/682464b0bc4501977f1304c51e2638c0ee89d87c |
DistillKL | # 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 ... | Alibaba-MIIL/HeadSharingKD | DistillKL | false | 7,679 | [
"BSD-2-Clause"
] | 15 | 8e2738bf069c7d12ec933f9b9107f267f7b6603a | https://github.com/Alibaba-MIIL/HeadSharingKD/tree/8e2738bf069c7d12ec933f9b9107f267f7b6603a |
CustomClassificationHead | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class CustomClassificationHead(nn.Module):
def __init__(self, config, input_dim, n_labels):
super().__init__()
self.config = config
self.fc1 = nn.Linear(input_dim, 4096)
self.fc2 = nn.Linear(4096, 2048... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | y-kamiya/emotion-classification | CustomClassificationHead | false | 4,712 | [
"MIT"
] | 0 | 8d5b6ab4aafd60607260dc87e5360c04bf149e18 | https://github.com/y-kamiya/emotion-classification/tree/8d5b6ab4aafd60607260dc87e5360c04bf149e18 |
AELossPurePie | # 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 | AELossPurePie | false | 6,063 | [
"BSD-3-Clause"
] | 1 | 388b95710a02ded0532b021f64c58d8d3e1cc639 | https://github.com/abhithosar/chartocr_cv/tree/388b95710a02ded0532b021f64c58d8d3e1cc639 |
ModelNet | import torch
import torch.nn as nn
class ModelNet(nn.Module):
def __init__(self, input_size, num_hidden_layers=1, hidden_layer_size=
512, num_labels=2):
super(ModelNet, self).__init__()
self.model = nn.Sequential()
for i in range(num_hidden_layers):
self.model.add_modu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | patelrajnath/makeesy-deep-learning | ModelNet | false | 7,451 | [
"Apache-2.0"
] | 1 | 172f8a4301d6b60927824a56648d60559ba3f14e | https://github.com/patelrajnath/makeesy-deep-learning/tree/172f8a4301d6b60927824a56648d60559ba3f14e |
TanhDeepLiftModel | # 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_... | aravipati12/captum | TanhDeepLiftModel | false | 10,101 | [
"BSD-3-Clause"
] | 0 | ef3e81d89c8c4404a49c384cf0727f2e7d393f5f | https://github.com/aravipati12/captum/tree/ef3e81d89c8c4404a49c384cf0727f2e7d393f5f |
FPFLU | # 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... | mengzhu0308/PFLU-FPFLU | FPFLU | false | 7,220 | [
"Apache-2.0"
] | 1 | 628cd472db2913e555e902bdf35af834f84a284b | https://github.com/mengzhu0308/PFLU-FPFLU/tree/628cd472db2913e555e902bdf35af834f84a284b |
rmse | import torch
class rmse(torch.nn.Module):
def __init__(self):
super(rmse, self).__init__()
def forward(self, y_true, y_pred):
mse = torch.mean((y_pred - y_true) ** 2, axis=-1)
rmse = torch.sqrt(mse + 1e-07)
return torch.mean(rmse)
def get_inputs():
return [torch.rand([4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | JamesLiao714/FullSubNet | rmse | false | 609 | [
"MIT"
] | 0 | dad740bac35b5d7544c97740ae59101455acdc40 | https://github.com/JamesLiao714/FullSubNet/tree/dad740bac35b5d7544c97740ae59101455acdc40 |
GeM | # 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... | RichardDominik/AIC21-MTMC | GeM | false | 14,311 | [
"MIT"
] | 63 | f69f63f9c40e2dc98e98c7af1cebe3d5605307ee | https://github.com/RichardDominik/AIC21-MTMC/tree/f69f63f9c40e2dc98e98c7af1cebe3d5605307ee |
AdaptiveConcatPool2d | import torch
from torch import nn
from typing import *
from typing import Optional
class AdaptiveConcatPool2d(nn.Module):
"""Layer that concats `AdaptiveAvgPool2d` and `AdaptiveMaxPool2d`"""
def __init__(self, sz: 'Optional[int]'=None):
"""Output will be 2*sz or 2 if sz is None"""
super().__i... | 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
from typing import *
from typing import Optional
assert_size_stride ... | WittmannF/fastai_docs | AdaptiveConcatPool2d | false | 5,978 | [
"Apache-2.0"
] | 1 | 03ecae01557a5e4a196dd858b10a57b224df52cd | https://github.com/WittmannF/fastai_docs/tree/03ecae01557a5e4a196dd858b10a57b224df52cd |
UNet | # 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.functional import ... | samuelpietri/Super-SloMo | UNet | false | 4,417 | [
"MIT"
] | 0 | e20eaa5550c30737be42b61f8e82e731cfd17457 | https://github.com/samuelpietri/Super-SloMo/tree/e20eaa5550c30737be42b61f8e82e731cfd17457 |
OptimizedResidualBlock | # 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_... | ChiragCD/NR-GAN | OptimizedResidualBlock | false | 13,500 | [
"MIT"
] | 54 | fc455c6219b09bc8bf605715504b78b2bb801e48 | https://github.com/ChiragCD/NR-GAN/tree/fc455c6219b09bc8bf605715504b78b2bb801e48 |
UpsampleConv | import torch
import torch.nn as nn
def spectral_norm(layer, n_iters=1):
return torch.nn.utils.spectral_norm(layer, n_power_iterations=n_iters)
class UpsampleConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True,
spec_norm=False):
super().__init__()
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Sriram-Ravula/Inverse_Meta | UpsampleConv | false | 2,851 | [
"MIT"
] | 0 | c6c1e4ae0d670093156249c60d74373b22d61f01 | https://github.com/Sriram-Ravula/Inverse_Meta/tree/c6c1e4ae0d670093156249c60d74373b22d61f01 |
BerHuLoss | # 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... | ezxzeng/FFB6D | BerHuLoss | false | 15,332 | [
"MIT"
] | 145 | fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 | https://github.com/ezxzeng/FFB6D/tree/fd0ea6471532ab1dc68f9a58b52d9a63f8fb76f2 |
DenseNet2D_up_block_concat | import torch
import torch.nn as nn
class DenseNet2D_up_block_concat(nn.Module):
def __init__(self, skip_channels, input_channels, output_channels,
up_stride, dropout=False, prob=0):
super(DenseNet2D_up_block_concat, self).__init__()
self.conv11 = nn.Conv2d(skip_channels + input_channels,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ArmandB/RITnet | DenseNet2D_up_block_concat | false | 91 | [
"MIT"
] | 0 | afe9524fdd795982c7e52761da68af2dfda589ea | https://github.com/ArmandB/RITnet/tree/afe9524fdd795982c7e52761da68af2dfda589ea |
encoder3 | # 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.... | cy-xu/LinearStyleTransfer | encoder3 | false | 6,564 | [
"BSD-2-Clause"
] | 1 | a07ab32db037f60a122e252588d6bd504b7d70d7 | https://github.com/cy-xu/LinearStyleTransfer/tree/a07ab32db037f60a122e252588d6bd504b7d70d7 |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_len).type_a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Fengyee/ASER | Attention | false | 11,431 | [
"MIT"
] | 0 | c284b507ee268a8275456a969b944895cacc54b8 | https://github.com/Fengyee/ASER/tree/c284b507ee268a8275456a969b944895cacc54b8 |
GroupScaling1D | # 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Azerrroth/spacetimeformer | GroupScaling1D | false | 73 | [
"MIT"
] | 0 | e822444a6d696a1edb9e446d6f3482a70681be3c | https://github.com/Azerrroth/spacetimeformer/tree/e822444a6d696a1edb9e446d6f3482a70681be3c |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | ZhaoZhibin/Physionet2020model | DiceLoss | false | 18,195 | [
"BSD-2-Clause",
"MIT"
] | 6 | ea7379bd1e4c145c84fd254faa0d5d1330cd2f6e | https://github.com/ZhaoZhibin/Physionet2020model/tree/ea7379bd1e4c145c84fd254faa0d5d1330cd2f6e |
TorchJaccardLoss | import torch
class TorchJaccardLoss(torch.nn.modules.Module):
def __init__(self):
super(TorchJaccardLoss, self).__init__()
def forward(self, outputs, targets):
eps = 1e-15
jaccard_target = (targets == 1).float()
jaccard_output = torch.sigmoid(outputs)
intersection = (... | 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... | Spiruel/solaris | TorchJaccardLoss | false | 11,889 | [
"Apache-2.0"
] | 0 | eb2ce05265a462d69b01ee2b621a85a3e9082402 | https://github.com/Spiruel/solaris/tree/eb2ce05265a462d69b01ee2b621a85a3e9082402 |
Decoder1 | # 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.... | hologerry/wct_experiment | Decoder1 | false | 6,814 | [
"MIT"
] | 1 | 890d885561dc8df8c4ae732aebd902aa838257e6 | https://github.com/hologerry/wct_experiment/tree/890d885561dc8df8c4ae732aebd902aa838257e6 |
MyModel | # 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_... | Ghazalmg/slimevolleygym | MyModel | false | 2,295 | [
"Apache-2.0"
] | 0 | d880a35625c22bbe0bc10fa0352495f0aea06364 | https://github.com/Ghazalmg/slimevolleygym/tree/d880a35625c22bbe0bc10fa0352495f0aea06364 |
ConvBlock | # 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
from torch im... | royerloic/aydin | ConvBlock | false | 16,354 | [
"BSD-3-Clause"
] | 78 | f9c61a24030891d008c318b250da5faec69fcd7d | https://github.com/royerloic/aydin/tree/f9c61a24030891d008c318b250da5faec69fcd7d |
SpatialAttn | # 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... | wangminjie920705/Part-reid | SpatialAttn | false | 4,520 | [
"MIT"
] | 0 | 34a1e968a2eab692ba810332f309e82b441793f6 | https://github.com/wangminjie920705/Part-reid/tree/34a1e968a2eab692ba810332f309e82b441793f6 |
VectorQuantizer | import torch
import torch.nn as nn
class VectorQuantizer(nn.Module):
"""
Reference:
Taming Transformers for High-Resolution Image Synthesis
https://arxiv.org/pdf/2012.09841.pdf
"""
def __init__(self, n_e, e_dim, beta=1.0):
super().__init__()
self.n_e = n_e
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | PeikeLi/pytorch-vector-quantization | VectorQuantizer | false | 17,814 | [
"MIT"
] | 6 | 48ce6a74ec56b9d8c11dde2cd35b055a925c3070 | https://github.com/PeikeLi/pytorch-vector-quantization/tree/48ce6a74ec56b9d8c11dde2cd35b055a925c3070 |
PosEnc | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchvision.models import *
from torchvision.datasets import *
class PosEnc(nn.Module):
def __init__(self, C, ks):
super().__init__()
self.weight = nn... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchv... | XuelianCheng/ppuda | PosEnc | false | 6,010 | [
"MIT"
] | 1 | d5b89928e430e2d5b976f84b1ea66b4b901e6cda | https://github.com/XuelianCheng/ppuda/tree/d5b89928e430e2d5b976f84b1ea66b4b901e6cda |
ClassBlock | # 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.... | javierrodenas/clearml_javi | ClassBlock | false | 10,374 | [
"Apache-2.0"
] | 0 | b6326104fe6a6f522223c2ac3d87468990a9e6f2 | https://github.com/javierrodenas/clearml_javi/tree/b6326104fe6a6f522223c2ac3d87468990a9e6f2 |
SelfAttention | import torch
from torch import nn
class SelfAttention(nn.Module):
""" Self attention Layer"""
def __init__(self, in_dim, activation):
super(SelfAttention, self).__init__()
self.chanel_in = in_dim
self.activation = activation
self.query_conv = nn.Conv2d(in_channels=in_dim, out_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | PauPerezT/EmoSSpeech | SelfAttention | false | 952 | [
"Apache-2.0"
] | 0 | 168a951a838d0bfb838e7d0e3f6895bff68164a4 | https://github.com/PauPerezT/EmoSSpeech/tree/168a951a838d0bfb838e7d0e3f6895bff68164a4 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import collections
from typing import Optional
from typing import Union
from typing import Callable
from typing import Any
from typing impor... | Irme/MONAI | DiceLoss | false | 17,438 | [
"Apache-2.0"
] | 3 | dc4bf661831b14f4231cb325cc1b15d38c1e406c | https://github.com/Irme/MONAI/tree/dc4bf661831b14f4231cb325cc1b15d38c1e406c |
SpacialGatingUnit | # 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... | techthiyanes/annotated_deep_learning_paper_implementations | SpacialGatingUnit | false | 16,571 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
BCEDiceLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class DiceLoss(nn.Module):
"""Sørensen–Dice coefficient loss to calculate
the mean loss over a batch of data.This loss mainly
calculates the similarity between two samples.
To know more about this loss check this link:
https://en.w... | 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... | g-freire/Brain-Tumor-Segmentation | BCEDiceLoss | false | 15,386 | [
"MIT"
] | 156 | e4f258feb64c11815570e295c58bda78afd21ab9 | https://github.com/g-freire/Brain-Tumor-Segmentation/tree/e4f258feb64c11815570e295c58bda78afd21ab9 |
CmapPafHead | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn
import torch.optim
assert_size_stride = ... | ajsampathk/trt_pose | CmapPafHead | false | 18,233 | [
"MIT"
] | 7 | 592e038cacaf43b6a502b759a035a4e7cae9db9e | https://github.com/ajsampathk/trt_pose/tree/592e038cacaf43b6a502b759a035a4e7cae9db9e |
GlobalConvBlock | import torch
import torch.nn as nn
from math import sqrt
class GlobalConvBlock(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size):
super(GlobalConvBlock, self).__init__()
pad0 = (kernel_size[0] - 1) // 2
pad1 = (kernel_size[1] - 1) // 2
self.conv_l1 = nn.Conv2d(in_dim, o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from math import sqrt
assert_size_stride = torch._C._dynam... | andy091045/SEGANTest | GlobalConvBlock | false | 9,764 | [
"MIT"
] | 0 | 90f626461f021ed76716730f78673bc83196f0af | https://github.com/andy091045/SEGANTest/tree/90f626461f021ed76716730f78673bc83196f0af |
DenseBlock | import torch
from torch import nn
from torch.nn import functional as F
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super(CausalConv1d, self).__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | LittleGuoKe/Entity-Concept-enhanced-Few-shot-Relation-Extraction | DenseBlock | false | 8,447 | [
"MIT"
] | 19 | b41386bdc70a3b84731bdbf700ff1ba4eda6675d | https://github.com/LittleGuoKe/Entity-Concept-enhanced-Few-shot-Relation-Extraction/tree/b41386bdc70a3b84731bdbf700ff1ba4eda6675d |
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... | db-bionlp/CLNER | QuadriLinearScore | false | 15,165 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f |
BPR | # 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
assert_size_stride = torch._... | DanielMorales9/FactorizationPyTorch | BPR | false | 17,179 | [
"MIT"
] | 4 | 50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 | https://github.com/DanielMorales9/FactorizationPyTorch/tree/50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 |
Wide_Conv | import torch
import torch.nn as nn
def match_score(s1, s2, mask1, mask2):
"""
s1, s2: batch_size * seq_len * dim
"""
_batch, seq_len, _dim = s1.shape
s1 = s1 * mask1.eq(0).unsqueeze(2).float()
s2 = s2 * mask2.eq(0).unsqueeze(2).float()
s1 = s1.unsqueeze(2).repeat(1, 1, seq_len, 1)
s2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | YJiangcm/Chinese-sentence-pair-modeling | Wide_Conv | false | 14,650 | [
"Apache-2.0"
] | 49 | 90adbc5c121832ce3e4a4057e30417a6ec5e7ebc | https://github.com/YJiangcm/Chinese-sentence-pair-modeling/tree/90adbc5c121832ce3e4a4057e30417a6ec5e7ebc |
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(1, 32, 5)
self.pool = nn.MaxPool2d(2, 2)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
return x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | bongsang/face-landmark | Net | false | 6,350 | [
"MIT"
] | 1 | bc7644480be1ddf8d35c2875d251bc84c00ccaa7 | https://github.com/bongsang/face-landmark/tree/bc7644480be1ddf8d35c2875d251bc84c00ccaa7 |
DML | # 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... | Capetian/FaceX-Zoo | DML | false | 4,968 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
MemoryDictionary | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import *
class MemoryDictionary(nn.Module):
"""このクラスでは
M_1 -> M_2
という写像を生成します。
この記憶辞書の最もシンプルな場合である、二層の全結合層によって作成されます。
"""
def __init__(self, num_memory: 'int', num_dims: 'int', device:
'torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
from typing import *
asser... | Geson-anko/ThinkingSimulation | MemoryDictionary | false | 11,454 | [
"MIT"
] | 0 | bd4b33c42042a2d8d14e1a9553f19fb4b4bfe8f8 | https://github.com/Geson-anko/ThinkingSimulation/tree/bd4b33c42042a2d8d14e1a9553f19fb4b4bfe8f8 |
ConformerEncoderLayer | # 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.... | PranjaliJain/matchmaker | ConformerEncoderLayer | false | 14,266 | [
"Apache-2.0"
] | 97 | b7e22eb8b70cccabf0729076df7cbab3f4ba4a1f | https://github.com/PranjaliJain/matchmaker/tree/b7e22eb8b70cccabf0729076df7cbab3f4ba4a1f |
fadein_layer | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class fadein_layer(nn.Module):
def __init__(self, config):
super(fadein_layer, self).__init__()
self.alpha = 0.0
def update_alpha(self, delta):
self.alpha = self.alpha + delta
self.alpha = max(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | mikanCan/PG-GAN | fadein_layer | false | 10,651 | [
"MIT"
] | 0 | bc4a1bd2101f836c22a164174381f80b3f5c73c1 | https://github.com/mikanCan/PG-GAN/tree/bc4a1bd2101f836c22a164174381f80b3f5c73c1 |
NotEqual | # 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... | NVIDIA-AI-IOT-private/torch2trt | NotEqual | false | 10,540 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
VDB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libd... | amy12xx/lets-do-irl | VDB | false | 14,831 | [
"MIT"
] | 408 | fd469e9fb7426e41b07c83ce4b87962ac3543b1e | https://github.com/amy12xx/lets-do-irl/tree/fd469e9fb7426e41b07c83ce4b87962ac3543b1e |
CumulativeLinkLoss | import torch
import numpy as np
from torch import nn
from typing import Optional
def _reduction(loss: 'torch.Tensor', reduction: 'str') ->torch.Tensor:
"""
Reduce loss
Parameters
----------
loss : torch.Tensor, [batch_size, num_classes]
Batch losses.
reduction : str
Method for... | 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 numpy as np
fro... | EthanRosenthal/spacecutter | CumulativeLinkLoss | false | 13,638 | [
"MIT"
] | 74 | 37a6f7367905b50e7886dc1ef2bfe1d63220347a | https://github.com/EthanRosenthal/spacecutter/tree/37a6f7367905b50e7886dc1ef2bfe1d63220347a |
DepthNormalizer | # 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... | lingtengqiu/Open-PIFuhd | DepthNormalizer | false | 15,912 | [
"MIT"
] | 191 | 3a66b647bcf5591e818af62735e64a93c4aaef85 | https://github.com/lingtengqiu/Open-PIFuhd/tree/3a66b647bcf5591e818af62735e64a93c4aaef85 |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.utils
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
in_size = inputs.size()
inputs = inp... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | Gaussianer/FasterSeg | GlobalAvgPool2d | false | 17,300 | [
"MIT"
] | 6 | f2e102b433275ac9f3387a8c2ae8439b2687bfda | https://github.com/Gaussianer/FasterSeg/tree/f2e102b433275ac9f3387a8c2ae8439b2687bfda |
L2Norm | import torch
from itertools import product as product
from math import sqrt as sqrt
import torch.nn as nn
import torch.nn.init as init
import torch.utils.data
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.g... | 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 itertools import product as product
from math import sqrt as sqrt
import t... | avisekiit/adversarial_object_detection | L2Norm | false | 14,915 | [
"MIT"
] | 795 | 263f264b3f2bdb0f116ebbb30ec4a805f357b3a6 | https://github.com/avisekiit/adversarial_object_detection/tree/263f264b3f2bdb0f116ebbb30ec4a805f357b3a6 |
MidNet4 | import torch
import torch.nn as nn
class MidNet4(nn.Module):
def forward(self, x_in):
"""Network with dilation rate 4
:param x_in: input convolutional features
:returns: processed convolutional features
:rtype: Tensor
"""
x = self.lrelu(self.conv1(x_in))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | sjmoran/CURL | MidNet4 | false | 16,473 | [
"BSD-3-Clause"
] | 125 | 919e519717b66e14d92ac6fa404c328ee3f254a5 | https://github.com/sjmoran/CURL/tree/919e519717b66e14d92ac6fa404c328ee3f254a5 |
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