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 |
|---|---|---|---|---|---|---|---|---|---|---|
EncoderLayer | import math
import torch
from torch import nn
from torch.nn import functional as F
class LayerNorm(nn.Module):
def __init__(self, d_model, eps=1e-06):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.beta = nn.Parameter(torch.zeros(d_model))
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Sy-Zhang/recurrent-transformer | EncoderLayer | false | 9,749 | [
"MIT"
] | 0 | f66ba49a2c9ec42759d3d00d497b49ffe39e18de | https://github.com/Sy-Zhang/recurrent-transformer/tree/f66ba49a2c9ec42759d3d00d497b49ffe39e18de |
Embed | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
class Embed(nn.Module):
def __init__(self, in_dim, out_dim):
super(Embed, self).__init__()
self.linear = nn.Linear(in_dim, out_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Capetian/FaceX-Zoo | Embed | false | 4,965 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
Conv | import torch
import torch.nn as nn
import torch.utils.data
class Conv(nn.Module):
"""
Convenience class that does padding and convolution for inputs in the format
[batch_size, sequence length, hidden size]
"""
def __init__(self, input_size, output_size, kernel_size, pad_type):
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | alifkurniawan/tesis | Conv | false | 1,412 | [
"MIT"
] | 0 | 6330dba32f5dc12785e956875c94d83344d788a8 | https://github.com/alifkurniawan/tesis/tree/6330dba32f5dc12785e956875c94d83344d788a8 |
PatchEmbed | import torch
import torch.nn as nn
from torch import optim as optim
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
num_patches = img_size // patch_size * (img_size // patch_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch import optim as optim
assert_size_stride = torc... | billpsomas/ibot | PatchEmbed | false | 1,570 | [
"Apache-2.0"
] | 0 | c6fbce7e2a59780f39ad7304ed9a8b1acf038d2d | https://github.com/billpsomas/ibot/tree/c6fbce7e2a59780f39ad7304ed9a8b1acf038d2d |
LRN | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | Richard456/Dann | LRN | false | 5,764 | [
"MIT"
] | 1 | 1971cf1a7b9ecadc17932a8ecb3f0c34609751a3 | https://github.com/Richard456/Dann/tree/1971cf1a7b9ecadc17932a8ecb3f0c34609751a3 |
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... | scenarios/dev | WeightNet | false | 4,454 | [
"Apache-2.0"
] | 0 | 9f91ebc142cea1c31231d233571ad59460ab6fba | https://github.com/scenarios/dev/tree/9f91ebc142cea1c31231d233571ad59460ab6fba |
SE | import torch
import torch.nn as nn
import torch.nn.functional as F
class SE(nn.Module):
"""Squeeze-and-Excitation block."""
def __init__(self, in_planes, se_planes):
super(SE, self).__init__()
self.se1 = nn.Conv2d(in_planes, se_planes, kernel_size=1, bias=True)
self.se2 = nn.Conv2d(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | LIJUNYI95/SuperAdam | SE | false | 8,425 | [
"MIT"
] | 14 | 00fc8a4d90bd037ccb9b871fbc64482818457b93 | https://github.com/LIJUNYI95/SuperAdam/tree/00fc8a4d90bd037ccb9b871fbc64482818457b93 |
GlobalAttention | # 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.... | Fenkail/hgr_v2t | GlobalAttention | false | 13,690 | [
"MIT"
] | 190 | d8cc1c18cdaae54fd1878d6dc7b8e9c60d83fcbb | https://github.com/Fenkail/hgr_v2t/tree/d8cc1c18cdaae54fd1878d6dc7b8e9c60d83fcbb |
CMDS_Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Gustoaxel/Statistical-autoencoder | CMDS_Loss | false | 5,413 | [
"MIT"
] | 1 | f3328f9c2a45ef0f7fe4adf98af4a64d02d34afc | https://github.com/Gustoaxel/Statistical-autoencoder/tree/f3328f9c2a45ef0f7fe4adf98af4a64d02d34afc |
InnerProductDecoder | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.multiprocessing
impor... | LucasAPayne/graph4nlp | InnerProductDecoder | false | 9,698 | [
"Apache-2.0"
] | 0 | 3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 | https://github.com/LucasAPayne/graph4nlp/tree/3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 |
SelfAttentionBatch | # 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.... | Zilize/CRSLab | SelfAttentionBatch | false | 1,327 | [
"MIT"
] | 0 | fb357d0dfb7d2cf7b67b892d98e52032a31ca564 | https://github.com/Zilize/CRSLab/tree/fb357d0dfb7d2cf7b67b892d98e52032a31ca564 |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | IndexFziQ/ASER | PositionwiseFeedForward | false | 2,378 | [
"MIT"
] | 0 | 67dd1a2a25cec175c15675cc1f8a63ca065b447e | https://github.com/IndexFziQ/ASER/tree/67dd1a2a25cec175c15675cc1f8a63ca065b447e |
ConcatELU | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConcatELU(nn.Module):
"""
Activation function that applies ELU in both direction (inverted and plain).
Allows non-linearity while providing strong gradients for any input (important for final convolution)
"""
def forward(sel... | 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_... | onlyrico/lightning-tutorials | ConcatELU | false | 12,854 | [
"Apache-2.0"
] | 0 | b5d5c4015422f8c70411e57734d73bb6c1472999 | https://github.com/onlyrico/lightning-tutorials/tree/b5d5c4015422f8c70411e57734d73bb6c1472999 |
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | dmund95/bcq | Critic | false | 6,594 | [
"MIT"
] | 1 | b1ae39ad7789443f02273aaa1a433c55c6836a5f | https://github.com/dmund95/bcq/tree/b1ae39ad7789443f02273aaa1a433c55c6836a5f |
Linear_Q | from torch.autograd import Function
import torch
import torch.utils.data.distributed
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def quantize(input, nbit):
return Quantizer.apply(input, nbit)
def dorefa_a(input, nbit_a):
return quantize(torch.clamp(0.1 * input, 0, 1), nbit_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jzz24/pytorch_quantization | Linear_Q | false | 13,925 | [
"MIT"
] | 71 | 0c2d93c8ce4f85dd2c34ea6f36c58d14db21bf8e | https://github.com/Jzz24/pytorch_quantization/tree/0c2d93c8ce4f85dd2c34ea6f36c58d14db21bf8e |
Biaffine | import torch
import torch.nn as nn
class Biaffine(nn.Module):
def __init__(self, dim_left, dim_right):
super().__init__()
self.dim_left = dim_left
self.dim_right = dim_right
self.matrix = nn.Parameter(torch.Tensor(dim_left, dim_right))
self.bias = nn.Parameter(torch.Tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | tpimentelms/dep-parser | Biaffine | false | 10,893 | [
"MIT"
] | 0 | be622cdd9a8b0ba85a28c39129ae2cdbfef03901 | https://github.com/tpimentelms/dep-parser/tree/be622cdd9a8b0ba85a28c39129ae2cdbfef03901 |
HeatmapLoss | import torch
import torch.utils.data
class HeatmapLoss(torch.nn.Module):
"""
loss for detection heatmap
"""
def __init__(self):
super(HeatmapLoss, self).__init__()
def forward(self, pred, gt):
l = (pred - gt) ** 2
l = l.mean(dim=3).mean(dim=2).mean(dim=1)
return l... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | NiranthS/pytorch_stacked_hourglass | HeatmapLoss | false | 904 | [
"BSD-3-Clause"
] | 0 | db9838eb13f6848ba3b9db844c1e023eb8688c3c | https://github.com/NiranthS/pytorch_stacked_hourglass/tree/db9838eb13f6848ba3b9db844c1e023eb8688c3c |
SequenceQuantizerSoftEMA | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.cuda
class SequenceQuantizerSoftEMA(nn.Module):
def __init__(self, codebook_size, d_model, l1_cost=1000, entropy_cost=
5e-05, num_samples=10, temp=1.0, epsilon=1e-05, padding_idx=None):
super(SequenceQuantizerSoftEMA,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | brcsomnath/SemAE | SequenceQuantizerSoftEMA | false | 6,385 | [
"MIT"
] | 1 | 8da5de73a5b334c6cb0b22eadaaacc35e98126ed | https://github.com/brcsomnath/SemAE/tree/8da5de73a5b334c6cb0b22eadaaacc35e98126ed |
Upsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Dauriel/weather4cast2021 | Upsample | false | 367 | [
"Apache-2.0"
] | 0 | 29e818c4bcd488ec84b51558bf5392e4a887db70 | https://github.com/Dauriel/weather4cast2021/tree/29e818c4bcd488ec84b51558bf5392e4a887db70 |
GeneralizedDiceLoss | import torch
import warnings
import numpy as np
from typing import Callable
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 =... | 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 numpy as np
from typi... | JanSellner/MONAI | GeneralizedDiceLoss | false | 9,185 | [
"Apache-2.0"
] | 0 | ff8fa2bae94914030abb1bc0680417fdaa74afd8 | https://github.com/JanSellner/MONAI/tree/ff8fa2bae94914030abb1bc0680417fdaa74afd8 |
ConvLayer | import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, norm
='instance'):
super().__init__()
padding_size = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(padding_size)
self.conv_layer = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Gradient-PG/live-nst | ConvLayer | false | 17,320 | [
"MIT"
] | 5 | 02244172646375ff4a4a417bc8220064fadae5a9 | https://github.com/Gradient-PG/live-nst/tree/02244172646375ff4a4a417bc8220064fadae5a9 |
OHEMLoss | import torch
import torch.nn.functional as F
from torch import nn
class OHEMLoss(nn.Module):
def __init__(self, rate=0.8):
super(OHEMLoss, self).__init__()
None
self.rate = rate
def change_rate(self, new_rate):
None
self.rate = new_rate
def forward(self, cls_pred... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | LightnessOfBeing/kaggle-bengali-classification | OHEMLoss | false | 17,577 | [
"MIT"
] | 5 | 342bc2a9bf57f9f03fa25f5271cb178ab8f7b4ff | https://github.com/LightnessOfBeing/kaggle-bengali-classification/tree/342bc2a9bf57f9f03fa25f5271cb178ab8f7b4ff |
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... | ModelTC/EOD | KDLoss | false | 14,080 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 |
MultiHeadAttention | import torch
from torch import Tensor
from typing import Any
import torch.nn.functional as F
from torch import nn
def ifnone(a: 'Any', b: 'Any') ->Any:
"""`a` if `a` is not None, otherwise `b`."""
return b if a is None else a
class MultiHeadAttention(nn.Module):
"""MutiHeadAttention."""
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.... | wegroupwolves/fastai | MultiHeadAttention | false | 4,530 | [
"Apache-2.0"
] | 0 | df40df403e05e132411f0f7abc7ec33c86e58bb9 | https://github.com/wegroupwolves/fastai/tree/df40df403e05e132411f0f7abc7ec33c86e58bb9 |
ConConv | # 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... | anve96/DE_resnet_unet_hyb | ConConv | false | 14,887 | [
"BSD-3-Clause"
] | 45 | f0751854c8707cc4f228bb9d52d93635cc3584ae | https://github.com/anve96/DE_resnet_unet_hyb/tree/f0751854c8707cc4f228bb9d52d93635cc3584ae |
Accuracy | # 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... | ChristophReich1996/Swin-Transformer-V2 | Accuracy | false | 7,922 | [
"MIT"
] | 43 | d71c1b412cd0fe13dc2557ad090cf0f027e54d47 | https://github.com/ChristophReich1996/Swin-Transformer-V2/tree/d71c1b412cd0fe13dc2557ad090cf0f027e54d47 |
MFM2_1 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | x6rulin/TP-GAN | MFM2_1 | false | 4,565 | [
"MIT"
] | 0 | 1716cf06aaff8a6a2cee2548ec662dcdd68c0449 | https://github.com/x6rulin/TP-GAN/tree/1716cf06aaff8a6a2cee2548ec662dcdd68c0449 |
eca_layer | import torch
import torch.nn as nn
class eca_layer(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel, k_size=3):
super(eca_layer, self).__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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | QJYBall/NNDL-Final-Project | eca_layer | false | 2,732 | [
"MIT"
] | 0 | 9906fb59e888b51b33f3c61dd5a0737a1a0f0761 | https://github.com/QJYBall/NNDL-Final-Project/tree/9906fb59e888b51b33f3c61dd5a0737a1a0f0761 |
GatedConv2d | import torch
import torch.nn as nn
class GatedConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, padding):
super(GatedConv2d, self).__init__()
self.in_channels = in_channels
self.conv = nn.Conv2d(in_channels, out_channels * 3, kernel_size=
kernel_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Nintorac/survae_experiments | GatedConv2d | false | 903 | [
"MIT"
] | 0 | d68cc25e2604aab08b53617c1f3ffe4716f166c4 | https://github.com/Nintorac/survae_experiments/tree/d68cc25e2604aab08b53617c1f3ffe4716f166c4 |
TensorExp | import torch
class TensorExp(torch.nn.Module):
def forward(self, input):
return torch.exp(input)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | Minyus/kedex | TensorExp | false | 9,683 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
FermiDiracDecoder | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
from torch.nn.modules.module import Module
im... | RingBDStack/ACE-HGNN | FermiDiracDecoder | false | 17,836 | [
"MIT"
] | 5 | afc610dd838951dcd6c3910795b472566f0c23ca | https://github.com/RingBDStack/ACE-HGNN/tree/afc610dd838951dcd6c3910795b472566f0c23ca |
SoftTree | import torch
import numpy as np
class SoftTree(torch.nn.Module):
"""Soft decision tree."""
def __init__(self, in_features, out_features, depth, projection=
'constant', dropout=0.0):
super(SoftTree, self).__init__()
self.proj = projection
self.depth = depth
self.in_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | alper111/symbol-emergence | SoftTree | false | 1,422 | [
"MIT"
] | 0 | a4abd5d26b6fb36fe1ab3d6304a257df29be8e2c | https://github.com/alper111/symbol-emergence/tree/a4abd5d26b6fb36fe1ab3d6304a257df29be8e2c |
UpBlock | import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torchvision.transforms import *
assert_size_stride ... | RyanMoussouni/iSeeBetter | UpBlock | false | 14,346 | [
"MIT"
] | 327 | af193ae0852f8e477fcd6875dce874eb5092a24a | https://github.com/RyanMoussouni/iSeeBetter/tree/af193ae0852f8e477fcd6875dce874eb5092a24a |
CoordConvSinAct | import torch
import torch.nn as nn
class SinAct(nn.Module):
def __init__(self):
super(SinAct, self).__init__()
def forward(self, x):
return torch.sin(x)
class CoordConvSinAct(nn.Module):
"""
Source: https://github.com/mkocabas/CoordConv-pytorch/blob/master/CoordConv.py
"""
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | justinjohn0306/CIPS-3D | CoordConvSinAct | false | 7,004 | [
"MIT"
] | 1 | 69a910a7841846419a6b5e03182c8cf061a82584 | https://github.com/justinjohn0306/CIPS-3D/tree/69a910a7841846419a6b5e03182c8cf061a82584 |
GatedConv2d | # 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 ... | ShiraLightricks/3d-photo-inpainting | GatedConv2d | false | 1,053 | [
"MIT"
] | 0 | c42ac41576690b765e50f5281ddbfb58439ff36d | https://github.com/ShiraLightricks/3d-photo-inpainting/tree/c42ac41576690b765e50f5281ddbfb58439ff36d |
ResidualBlock | # 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.... | IvoryCandy/neural-style | ResidualBlock | false | 5,397 | [
"Apache-2.0"
] | 1 | d9d73676479e36c1cbd6c9af36d857f80099504b | https://github.com/IvoryCandy/neural-style/tree/d9d73676479e36c1cbd6c9af36d857f80099504b |
Encoder3 | import torch
import torch.nn as nn
class Encoder3(nn.Module):
def __init__(self, model=None, fixed=False):
super(Encoder3, self).__init__()
self.fixed = fixed
self.conv0 = nn.Conv2d(3, 3, 1, 1, 0)
self.conv11 = nn.Conv2d(3, 64, 3, 1, 0)
self.conv12 = nn.Conv2d(64, 64, 3, 1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MingSun-Tse/Collaborative-Distillation | Encoder3 | false | 14,036 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 |
ConvReLUNorm | # 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.... | hamjam/NeMo | ConvReLUNorm | false | 15,490 | [
"Apache-2.0"
] | 4,145 | b3484d32e1317666151f931bfa39867d88ed8658 | https://github.com/hamjam/NeMo/tree/b3484d32e1317666151f931bfa39867d88ed8658 |
ArcMarginProduct_subcenter | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.parallel
class ArcMarginProduct_subcenter(nn.Module):
def __init__(self, in_features, out_features, k=3):
super().__init__()
self.weight = nn.Parameter(torch.FloatTensor(out_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Tanmengxuan/Google-Landmark-Recognition-2020-3rd-Place-Solution | ArcMarginProduct_subcenter | false | 1,135 | [
"Apache-2.0"
] | 0 | 8e2d9056d5c88c6415827086809e73522b336fbb | https://github.com/Tanmengxuan/Google-Landmark-Recognition-2020-3rd-Place-Solution/tree/8e2d9056d5c88c6415827086809e73522b336fbb |
BridgeFeatLoss | # 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... | XianyuanLiu/Transfer-Learning-Library | BridgeFeatLoss | false | 10,139 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
InvHuberLoss | # 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
... | DrSleep/DenseTorch | InvHuberLoss | false | 13,590 | [
"MIT"
] | 69 | f90bef075429d763fc08338dea8222d28b0a4516 | https://github.com/DrSleep/DenseTorch/tree/f90bef075429d763fc08338dea8222d28b0a4516 |
Critic | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Critic(nn.Module):
def __init__(self, num_inputs, args):
super(Critic, self).__init__()
self.fc1 = nn.Linear(num_inputs, args.hidden_size)
self.fc2 = nn.Linear(args.hidden_size, args.hidden_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.triton_helpers import libdevice
import torch.nn as ... | amy12xx/lets-do-irl | Critic | false | 14,832 | [
"MIT"
] | 408 | fd469e9fb7426e41b07c83ce4b87962ac3543b1e | https://github.com/amy12xx/lets-do-irl/tree/fd469e9fb7426e41b07c83ce4b87962ac3543b1e |
C1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from co... | zjgbz/img_cls | C1 | false | 4,664 | [
"MIT"
] | 0 | 513d5ae423d95e008a82a6ffe443db49f8ed9ac2 | https://github.com/zjgbz/img_cls/tree/513d5ae423d95e008a82a6ffe443db49f8ed9ac2 |
TokenClassifier | # 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.... | ngxingyu/Domain-Transfer-for-Punctuation-Retrieval | TokenClassifier | false | 7,332 | [
"Apache-2.0"
] | 1 | f5aa0ea0946c68aaf7fcf49a5085e6c823766a2f | https://github.com/ngxingyu/Domain-Transfer-for-Punctuation-Retrieval/tree/f5aa0ea0946c68aaf7fcf49a5085e6c823766a2f |
WCELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class WCELoss(nn.Module):
def __init__(self):
super(WCELoss, self).__init__()
def forward(self, y_pred, y_true, weights):
y_true = y_true / y_true.sum(2).sum(2, dtype=torch.float).unsqueeze(-1
).unsqueeze(-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
... | PARMAGroup/UNet-Instance-Cell-Segmentation | WCELoss | false | 8,631 | [
"MIT"
] | 30 | 79655a2c5781d2e20c7d5760f631fbb0be392292 | https://github.com/PARMAGroup/UNet-Instance-Cell-Segmentation/tree/79655a2c5781d2e20c7d5760f631fbb0be392292 |
XSigmoid | # 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
assert_size_stride = t... | dattientran/attorch | XSigmoid | false | 12,393 | [
"MIT"
] | 0 | 469b225846c6d8a7d833ebac19d040c7a407a0ff | https://github.com/dattientran/attorch/tree/469b225846c6d8a7d833ebac19d040c7a407a0ff |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, hidden_size, num_inputs, action_space):
super(Critic, self).__init__()
self.action_space = action_space
num_outputs = action_space.shape[0]
self.linear1 = nn.Linear(num_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.... | JieFeng-cse/power-system-rl | Critic | false | 9,186 | [
"MIT"
] | 0 | 8295d14da83a40c755b8e6a14785c53a238f9a64 | https://github.com/JieFeng-cse/power-system-rl/tree/8295d14da83a40c755b8e6a14785c53a238f9a64 |
SigM | # 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.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | anonymous2022ijcai/RGSL | SigM | false | 1,450 | [
"MIT"
] | 0 | 11c38ee50d50127c0f7c2a137bdb21ca5f7f3644 | https://github.com/anonymous2022ijcai/RGSL/tree/11c38ee50d50127c0f7c2a137bdb21ca5f7f3644 |
ToHalf | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stride... | Alwaysproblem/examples-1 | ToHalf | false | 58 | [
"MIT"
] | 0 | 9754fa63ed1931489a21ac1f5b299f945e369a5c | https://github.com/Alwaysproblem/examples-1/tree/9754fa63ed1931489a21ac1f5b299f945e369a5c |
FairLoss | import torch
from torch import nn
class FairLoss(nn.Module):
def __init__(self, lamda):
super(FairLoss, self).__init__()
self.lamda = lamda
def forward(self, rep):
logits = torch.mm(rep, torch.transpose(rep, 0, 1))
logits = logits - torch.diag_embed(torch.diag(logits))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | blackcow/Fair-AT | FairLoss | false | 1,551 | [
"Apache-2.0"
] | 0 | 62fc269fedd4b63c4b48ae390d494b3832e65fa8 | https://github.com/blackcow/Fair-AT/tree/62fc269fedd4b63c4b48ae390d494b3832e65fa8 |
L2Norm | # 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_... | bankbiz/Key.Net | L2Norm | false | 12,150 | [
"BSD-3-Clause-Clear"
] | 0 | 5ba46614821e94be1b36d97721bd6c2e5fff9e20 | https://github.com/bankbiz/Key.Net/tree/5ba46614821e94be1b36d97721bd6c2e5fff9e20 |
SymKlCriterion | # 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.... | johnson7788/mt-dnn | SymKlCriterion | false | 3,905 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
LastLevelMaxPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | Bhaskers-Blu-Org2/arcticseals | LastLevelMaxPool | false | 8,127 | [
"MIT"
] | 16 | 9e2629ca0ce7aadbe63118f39ff2da757d5dbc33 | https://github.com/Bhaskers-Blu-Org2/arcticseals/tree/9e2629ca0ce7aadbe63118f39ff2da757d5dbc33 |
QRNNLayer | import torch
import torch.nn as nn
from torch.optim import *
class ForgetMult(torch.nn.Module):
"""ForgetMult computes a simple recurrent equation:
h_t = f_t * x_t + (1 - f_t) * h_{t-1}
This equation is equivalent to dynamic weighted averaging.
Inputs: X, hidden
- X (seq_len, batch, input_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | boshining/NeuronBlocks | QRNNLayer | false | 14,990 | [
"MIT"
] | 1,257 | 74fbb8658fb3f1cffea5c9bc84b2a1da59c20dd9 | https://github.com/boshining/NeuronBlocks/tree/74fbb8658fb3f1cffea5c9bc84b2a1da59c20dd9 |
SpatialGroupEnhance | import torch
from torch import nn
from torch.nn import init
class SpatialGroupEnhance(nn.Module):
def __init__(self, groups):
super().__init__()
self.groups = groups
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
self.bias ... | 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
from torch.nn import init
assert_size_stride = torch._C._d... | LiChengChen666/DetectDee | SpatialGroupEnhance | false | 9,809 | [
"Apache-2.0"
] | 0 | 1e6aaa0d15b1fc12d1342d8a922004e372b5f437 | https://github.com/LiChengChen666/DetectDee/tree/1e6aaa0d15b1fc12d1342d8a922004e372b5f437 |
RewardCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class RewardCriterion(nn.Module):
def __init__(self):
super(RewardCriterion, self).__init__()
def forward(self, input, seq, reward):
input = input.gather(2, seq.unsqueeze(2)).squeeze(2)
input = input.reshape(-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... | SunZongdi/self-critical.pytorch | RewardCriterion | false | 5,862 | [
"MIT"
] | 1 | 6cecbeb949e68007b72e84198cf74f9fb288aeda | https://github.com/SunZongdi/self-critical.pytorch/tree/6cecbeb949e68007b72e84198cf74f9fb288aeda |
MixtureDensityHead | # 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 ... | Actis92/pytorch_tabular | MixtureDensityHead | false | 7,380 | [
"MIT"
] | 1 | 78dabf5e7b97d8ff24db4bc83d9d0a2273941bbe | https://github.com/Actis92/pytorch_tabular/tree/78dabf5e7b97d8ff24db4bc83d9d0a2273941bbe |
TwoMLPHead | # 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
import t... | littlerain2310/japances_character | TwoMLPHead | false | 15,924 | [
"MIT",
"BSD-3-Clause"
] | 81 | bdca6b30f3058af30462dcd5729eacb69f6fa83b | https://github.com/littlerain2310/japances_character/tree/bdca6b30f3058af30462dcd5729eacb69f6fa83b |
AGELU | import math
import torch
import torch.utils.data
import torch.cuda
import torch.utils.checkpoint
def agelu(x):
SQRT_M2_PI = math.sqrt(2 / math.pi)
COEFF = 0.044715
return 0.5 * x * (1.0 + torch.tanh(SQRT_M2_PI * (x + COEFF * torch.pow(
x, 3))))
class AGELU(torch.nn.Module):
def forward(self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.utils.data
import torch.cuda
import torch.utils.checkp... | quanpn90/NMTGMinor | AGELU | false | 16,302 | [
"MIT"
] | 75 | 0e5f989c8bc01c6c8dc3a8c1ce7c05bfd884b796 | https://github.com/quanpn90/NMTGMinor/tree/0e5f989c8bc01c6c8dc3a8c1ce7c05bfd884b796 |
FocalLoss | import torch
from torch import nn
class FocalLoss(nn.Module):
def __init__(self, gamma=2, eps=1e-07):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.eps = eps
self.ce = nn.CrossEntropyLoss()
def forward(self, input, target):
logp = self.ce(input, 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 math as tl_math
from torch import nn
a... | agikarasugi/Face-Mask-Invariant-End-to-End-Face-Recognition | FocalLoss | false | 6,088 | [
"MIT"
] | 1 | eb274ff98246c1bb8748bd8c8351d3494a87dfce | https://github.com/agikarasugi/Face-Mask-Invariant-End-to-End-Face-Recognition/tree/eb274ff98246c1bb8748bd8c8351d3494a87dfce |
IOULoss | import torch
import torch.nn as nn
def calculate_segmentation_statistics(outputs: 'torch.Tensor', targets:
'torch.Tensor', class_dim: 'int'=1, threshold=None):
"""Compute calculate segmentation statistics.
Args:
outputs: torch.Tensor.
targets: torch.Tensor.
threshold: threshold fo... | 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... | earlbabson/torchflare | IOULoss | false | 6,641 | [
"Apache-2.0"
] | 1 | 15db06d313a53a3ec4640869335ba87730562b28 | https://github.com/earlbabson/torchflare/tree/15db06d313a53a3ec4640869335ba87730562b28 |
MMTensorNorm | # 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_... | amaankhan02/ChaLearn-2021-LAP | MMTensorNorm | false | 18,281 | [
"Apache-2.0",
"MIT"
] | 5 | 73227d642ebd69c3bde4065f22c6ad99b0cbe9f4 | https://github.com/amaankhan02/ChaLearn-2021-LAP/tree/73227d642ebd69c3bde4065f22c6ad99b0cbe9f4 |
BasicModel_ConvNet_MaxPool1d | # 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.... | YNNEKUW/captum | BasicModel_ConvNet_MaxPool1d | false | 12,015 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | Altriaex/d4rl_evaluations | LayerNorm | false | 8,950 | [
"Apache-2.0"
] | 0 | ceb34c04e98af9332c6338a1414c0c2aa5fea68b | https://github.com/Altriaex/d4rl_evaluations/tree/ceb34c04e98af9332c6338a1414c0c2aa5fea68b |
Softmax | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | collector-m/LiDAR-MOS | Softmax | false | 15,066 | [
"MIT"
] | 268 | 7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 | https://github.com/collector-m/LiDAR-MOS/tree/7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 |
HardSigmoid | import torch
import torch.utils.data
import torch.utils.data.distributed
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
def hard_sigmoid(input_, inplace: 'bool'=False):
"""hard sigmoid function"""
if inplace:
return input_.add_(3.0).clamp_(0.0, 6.0).di... | 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.utils.data
import torch.utils.data.distributed
from torch import nn
import t... | Adlik/zen_nas | HardSigmoid | false | 16,863 | [
"Apache-2.0"
] | 7 | d820d5c7d5bbb6fd66a76d5f16513647d6ea7a57 | https://github.com/Adlik/zen_nas/tree/d820d5c7d5bbb6fd66a76d5f16513647d6ea7a57 |
SigmoidCrossEntropyLoss | # 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 ... | carlogrisetti/ludwig | SigmoidCrossEntropyLoss | false | 1,636 | [
"Apache-2.0"
] | 0 | 5c0887f14867e1577e0ddc3806c5cf7a781fb665 | https://github.com/carlogrisetti/ludwig/tree/5c0887f14867e1577e0ddc3806c5cf7a781fb665 |
HardDarkRank | # 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.... | ahu-hpt/AOMD | HardDarkRank | false | 3,049 | [
"Apache-2.0"
] | 0 | 8d99dbb803feaef55fc089bfb3399d2fb21d55d8 | https://github.com/ahu-hpt/AOMD/tree/8d99dbb803feaef55fc089bfb3399d2fb21d55d8 |
MLP | import torch
import torch as th
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, input_size, output_size, hidden=128):
super(MLP, self).__init__()
self.linear1 = nn.Linear(input_size, hidden, bias=False)
self.linear2 = nn.Linear(hidden, output_size, bias=False)
def forw... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ngoby/cherry | MLP | false | 4,194 | [
"Apache-2.0"
] | 0 | ec88bac03bf3ac3fae1010c5db8329db595dc5d6 | https://github.com/ngoby/cherry/tree/ec88bac03bf3ac3fae1010c5db8329db595dc5d6 |
_ImpalaCNN | # 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 typing import Tuple
from... | IBM/vsrl-framework | _ImpalaCNN | false | 8,511 | [
"MIT"
] | 44 | 42e0853bffb5efbb66cd97178aff9e10ad18c5a9 | https://github.com/IBM/vsrl-framework/tree/42e0853bffb5efbb66cd97178aff9e10ad18c5a9 |
MSELoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
class MSELoss(nn.Module):
def __init__(self, ratio=1, size_average=None, reduce=None, reduction=
'mean'):
super(MSELoss, self).__init__()
self.ratio = rat... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data... | ahmad4633/mmfashion | MSELoss | false | 6,115 | [
"Apache-2.0"
] | 1 | ad2c911bf71bb95dce340a963e7f83c477a84824 | https://github.com/ahmad4633/mmfashion/tree/ad2c911bf71bb95dce340a963e7f83c477a84824 |
Decoder | # 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.utils.data
import torch
assert_size_stride = ... | Zoe0123/Diagnostic-Question-Challenge | Decoder | false | 6,029 | [
"MIT"
] | 1 | 49094ba757ac5b6afcf3ebe4d721c637ea4912b1 | https://github.com/Zoe0123/Diagnostic-Question-Challenge/tree/49094ba757ac5b6afcf3ebe4d721c637ea4912b1 |
ScaledDotProductAttention | import torch
import numpy as np
from torch import nn
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries and keys... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Kilichbek/artemis-m2-transformer | ScaledDotProductAttention | false | 17,539 | [
"MIT"
] | 8 | 99f7e797965710bf2565283d6b5028a6fe32664c | https://github.com/Kilichbek/artemis-m2-transformer/tree/99f7e797965710bf2565283d6b5028a6fe32664c |
Affine | import torch
from torch import nn
class Affine(nn.Module):
def __init__(self, channel):
super().__init__()
self.g = nn.Parameter(torch.ones(1, 1, channel))
self.b = nn.Parameter(torch.zeros(1, 1, channel))
def forward(self, x):
return x * self.g + self.b
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Nitin-Mane/External-Attention-pytorch | Affine | false | 14,152 | [
"MIT"
] | 4,466 | 1ceda306c41063af11c956334747763444a4d83f | https://github.com/Nitin-Mane/External-Attention-pytorch/tree/1ceda306c41063af11c956334747763444a4d83f |
FillUpLuminance | import torch
import torch.nn
class FillUpLuminance(torch.nn.Module):
def __init__(self):
super(FillUpLuminance, self).__init__()
def forward(self, color, luminance):
return color + (1 - color) * luminance
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_... | ashwinpn/Computer-Vision | FillUpLuminance | false | 6,254 | [
"MIT"
] | 1 | 9dc3abfe416385171b76e2bad6872e10f36a12b4 | https://github.com/ashwinpn/Computer-Vision/tree/9dc3abfe416385171b76e2bad6872e10f36a12b4 |
Downsampling | # 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 M
assert_s... | SuperbTUM/RAW-image-denoising | Downsampling | false | 17,987 | [
"MIT"
] | 4 | 9f81be8da6a576f641022707d98b8c37f5c599ab | https://github.com/SuperbTUM/RAW-image-denoising/tree/9f81be8da6a576f641022707d98b8c37f5c599ab |
Mlp | import math
import torch
import torch.nn as nn
class DWConv(nn.Module):
def __init__(self, dim=768):
super(DWConv, self).__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
def forward(self, x):
x = self.dwconv(x)
return x
class Mlp(nn.Module):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | LSH9832/MyPythonModules | Mlp | false | 755 | [
"MIT"
] | 0 | 442566a0fbd6ebe2bc20b6914686a1e2663d10c0 | https://github.com/LSH9832/MyPythonModules/tree/442566a0fbd6ebe2bc20b6914686a1e2663d10c0 |
MinibatchStdDev | import torch
import torch.utils.cpp_extension
class MinibatchStdDev(torch.nn.Module):
def __init__(self, group_size, num_channels=1):
super().__init__()
self.group_size = group_size
self.num_channels = num_channels
def forward(self, x):
N, C, H, W = x.shape
G = 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
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.a... | STomoya/animeface | MinibatchStdDev | false | 15,346 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
SpatialRescaler | # 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 functools import partial
import torch.nn as nn
assert_size_stride = torch._C._dynamo... | poliver269/latent-diffusion | SpatialRescaler | false | 12,898 | [
"MIT"
] | 0 | 08e7c987ad423e3f93125b49980c36302ffe3d82 | https://github.com/poliver269/latent-diffusion/tree/08e7c987ad423e3f93125b49980c36302ffe3d82 |
rSoftMax | import torch
from torch import nn
import torch.nn.functional as F
from torch.optim.lr_scheduler import *
from torch.optim import *
class rSoftMax(nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
f... | Challyfilio/NAIC2021 | rSoftMax | false | 239 | [
"MIT"
] | 0 | 11b38a920dcc902f9b798dc43ae360062862e6e4 | https://github.com/Challyfilio/NAIC2021/tree/11b38a920dcc902f9b798dc43ae360062862e6e4 |
L2Normalization | import torch
import torch.nn as nn
class L2Normalization(nn.Module):
def __init__(self):
super(L2Normalization, self).__init__()
def forward(self, x):
div = torch.sqrt(torch.sum(x * x, 1))
x = (x.T / (div + 1e-10)).T
return x
def get_inputs():
return [torch.rand([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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Jiangtong-Li/ZHSIR | L2Normalization | false | 17,502 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 |
AttentionSeq2Vec | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | Tiffany-HONG/easytext | AttentionSeq2Vec | false | 5,901 | [
"MIT"
] | 1 | 9c717d11240d96fab98b0532084ebb5c093d55bd | https://github.com/Tiffany-HONG/easytext/tree/9c717d11240d96fab98b0532084ebb5c093d55bd |
ConvMeanPool | import torch
from torch import nn
class MyConvo2d(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, he_init=True,
stride=1, bias=True):
super(MyConvo2d, self).__init__()
self.he_init = he_init
self.padding = int((kernel_size - 1) / 2)
self.conv = nn.Conv2d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | kolchinski/humanception-score | ConvMeanPool | false | 12,682 | [
"MIT"
] | 0 | da8880eec3be39574718409cfe8ca303f41c64e6 | https://github.com/kolchinski/humanception-score/tree/da8880eec3be39574718409cfe8ca303f41c64e6 |
TSAFusion | import torch
import torch.utils.data
from torch.utils import data as data
import torch.nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
from torch import autograd as autograd
class TSAFusion(nn.Module):
"""Temporal Spatial Attention (TSA) fusion module.
Temporal: Calculate... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | achrefjarray/ESRGANplus-master | TSAFusion | false | 1,409 | [
"Apache-2.0"
] | 0 | ba470ec5c565a6dc8b48575b1e185ef6b796aec6 | https://github.com/achrefjarray/ESRGANplus-master/tree/ba470ec5c565a6dc8b48575b1e185ef6b796aec6 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.multiprocessing
from torch import nn
import torch.optim
import tor... | mbarnig/vits-train | LayerNorm | false | 7,186 | [
"MIT"
] | 1 | cfb8a0fc91daad868fe3d062ebf85d62edbd7506 | https://github.com/mbarnig/vits-train/tree/cfb8a0fc91daad868fe3d062ebf85d62edbd7506 |
HubertFeatureProjection | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Clemens123/transformers | HubertFeatureProjection | false | 13,013 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
ATLoss | # 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
... | fmc123653/DeepKE | ATLoss | false | 15,371 | [
"MIT"
] | 676 | 4d30e51368681c7cb73e2ecacf9b922b441cbe99 | https://github.com/fmc123653/DeepKE/tree/4d30e51368681c7cb73e2ecacf9b922b441cbe99 |
ContextGate | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select the input from the target ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.cuda
assert_size_stride = torch._C._dynamo.gu... | AndrewM1998/MultimodalNMT | ContextGate | false | 7,718 | [
"MIT"
] | 40 | b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b | https://github.com/AndrewM1998/MultimodalNMT/tree/b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b |
SpatialAttention | import torch
import torch.nn as nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_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.nn as nn
assert_... | YeRen123455/Infrared-Small-Target-Detection | SpatialAttention | false | 14,689 | [
"MIT"
] | 62 | 23d84f436afb422d0d0b6cbf65305e1b53aea6db | https://github.com/YeRen123455/Infrared-Small-Target-Detection/tree/23d84f436afb422d0d0b6cbf65305e1b53aea6db |
Upscale_Conv_block | import torch
from torch import nn
class ConvShuffle(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, padding=
'same', upscale_factor=2, padding_mode='zeros'):
super(ConvShuffle, self).__init__()
self.upscale_factor = upscale_factor
self.conv = nn.Conv2d(in_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | GerbenBeintema/deepSI | Upscale_Conv_block | false | 8,176 | [
"BSD-3-Clause"
] | 12 | 580711210398064bb7f01e41d08b7a248a88b35b | https://github.com/GerbenBeintema/deepSI/tree/580711210398064bb7f01e41d08b7a248a88b35b |
CELoss | # 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
... | Karenou/mmfashion | CELoss | false | 9,457 | [
"Apache-2.0"
] | 0 | dfc334232d1700cde18d144f983dd5b0a7f9852a | https://github.com/Karenou/mmfashion/tree/dfc334232d1700cde18d144f983dd5b0a7f9852a |
MixPad2d | # 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... | hhj1897/face_parsing | MixPad2d | false | 15,515 | [
"MIT"
] | 70 | 9cd26b6916f562a2ab356b6b22e9ad93e19f2051 | https://github.com/hhj1897/face_parsing/tree/9cd26b6916f562a2ab356b6b22e9ad93e19f2051 |
AsymmetricLossOptimized | # 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... | imvladikon/pytorch-loss | AsymmetricLossOptimized | false | 6,869 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
GatedResUnit | import torch
from torch import nn
import torch.utils.data
class GatedConv2d(nn.Module):
def __init__(self, input_channels, output_channels, kernel_size, stride,
padding, dilation=1, activation=None):
super(GatedConv2d, self).__init__()
self.activation = activation
self.sigmoid = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | RobertYCXu/vae_vampprior | GatedResUnit | false | 9,589 | [
"MIT"
] | 0 | edcec4f5f7af673172c5b5b9aa2a22f993564fab | https://github.com/RobertYCXu/vae_vampprior/tree/edcec4f5f7af673172c5b5b9aa2a22f993564fab |
MTFullyConnected | import time
import torch
import numpy as np
from torch import nn
from torch import optim
from torch.nn import functional as F
class Base(nn.Module):
""" This class is the base structure for all of classification/regression DNN models.
Mainly, it provides the general methods for training, evaluating model and ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import time
import numpy as n... | naisuu/DrugEx | MTFullyConnected | false | 4,101 | [
"MIT"
] | 0 | 8708c98a137473f11990d70e43a46018806b6f39 | https://github.com/naisuu/DrugEx/tree/8708c98a137473f11990d70e43a46018806b6f39 |
Linear_leaky_relu | # 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... | Armand-Morin/AutoML | Linear_leaky_relu | false | 65 | [
"MIT"
] | 0 | 189867e2c7734d9afb87a9f51fd42bd6cc527a64 | https://github.com/Armand-Morin/AutoML/tree/189867e2c7734d9afb87a9f51fd42bd6cc527a64 |
Policy | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | aranganath/pytorch-trpo | Policy | false | 9,760 | [
"MIT"
] | 0 | a85bc48261eb4ed5833209da706379e9dc84592f | https://github.com/aranganath/pytorch-trpo/tree/a85bc48261eb4ed5833209da706379e9dc84592f |
SimpleReshapeModel | # 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... | briancoutinho/glow | SimpleReshapeModel | false | 12,583 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
SelfAttentionPooling | # 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.... | ana-kuznetsova/s3prl | SelfAttentionPooling | false | 6,204 | [
"Apache-2.0"
] | 1 | 1fd3309f693f9cd765f56b12375ed0e7c41ef093 | https://github.com/ana-kuznetsova/s3prl/tree/1fd3309f693f9cd765f56b12375ed0e7c41ef093 |
BertAttention | # 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.... | kalanile/JDQA | BertAttention | false | 7,017 | [
"MIT"
] | 1 | 68e1d0259d316b3577a1f2fafa773b50f1885762 | https://github.com/kalanile/JDQA/tree/68e1d0259d316b3577a1f2fafa773b50f1885762 |
Subsets and Splits
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