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 |
|---|---|---|---|---|---|---|---|---|---|---|
AnchorFlatten | import torch
import torch.nn as nn
import torch.utils.data
class AnchorFlatten(nn.Module):
"""
Module for anchor-based network outputs,
Init args:
num_output: number of output channel for each anchor.
Forward args:
x: torch.tensor of shape [B, num_anchors * output_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | AIpakchoi/visualDet3D | AnchorFlatten | false | 4,757 | [
"Apache-2.0"
] | 1 | 920f6f8ea44eac4c1896b7d157c015e039ac39f9 | https://github.com/AIpakchoi/visualDet3D/tree/920f6f8ea44eac4c1896b7d157c015e039ac39f9 |
rotate | # 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... | amonod/udvd | rotate | false | 1,429 | [
"MIT"
] | 0 | a1ccb777d205255ac68c40efb93dd3996f562c45 | https://github.com/amonod/udvd/tree/a1ccb777d205255ac68c40efb93dd3996f562c45 |
BinaryCrossEntropyLoss | from torch.nn import Module
import torch
from torch import zeros_like
from torch import ones_like
from torch.nn import Sigmoid
from torch.nn import BCELoss
class BinaryCrossEntropyLoss(Module):
"""This class implements :class:`torch.nn.Module` interface.
"""
def __init__(self):
super().__init__(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | MacOS/torchkge | BinaryCrossEntropyLoss | false | 13,999 | [
"BSD-3-Clause"
] | 248 | 89ed724368f3a5279c0f79c6ba1f948ed2a5696f | https://github.com/MacOS/torchkge/tree/89ed724368f3a5279c0f79c6ba1f948ed2a5696f |
Hsigmoid | # 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.utils.data
import torch.nn.parallel
import torch.optim... | AlbertiPot/once-for-all | Hsigmoid | false | 8,942 | [
"MIT"
] | 0 | 092b9e6184be353383396761ea5ec61d67152645 | https://github.com/AlbertiPot/once-for-all/tree/092b9e6184be353383396761ea5ec61d67152645 |
LN_self | import torch
import torch.nn as nn
class LN_self(nn.Module):
def __init__(self, num_features):
super().__init__()
shape = 1, num_features, 1, 1
self.gamma = nn.Parameter(torch.ones(shape))
self.beta = nn.Parameter(torch.zeros(shape))
def forward(self, X, eps=1e-05):
v... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | EkdeepSLubana/BeyondBatchNorm | LN_self | false | 17,240 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 |
MultiHeadBoxAttention | # 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.... | Maxi-0902/DRAN | MultiHeadBoxAttention | false | 857 | [
"MIT"
] | 0 | c3dbfcbc018446544150dc4e151442d6a9fcd4d9 | https://github.com/Maxi-0902/DRAN/tree/c3dbfcbc018446544150dc4e151442d6a9fcd4d9 |
AFTFull | # 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... | dumpmemory/aft-pytorch | AFTFull | false | 15,274 | [
"MIT"
] | 170 | 9a896966481f4042c2882f544d7bb1381e81dca1 | https://github.com/dumpmemory/aft-pytorch/tree/9a896966481f4042c2882f544d7bb1381e81dca1 |
MultiHeadAttn | import math
import torch
from torch import nn
import torch.nn.functional as F
class MultiHeadAttn(nn.Module):
def __init__(self, d_model, n_head, dropout=0.1, scale=False):
super().__init__()
assert d_model % n_head == 0
self.n_head = n_head
self.qkv_linear = nn.Linear(d_model, 3 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | EmanuelaBoros/stacked-ner | MultiHeadAttn | false | 17,271 | [
"MIT"
] | 4 | b57e4fcf777a5ad2519ffa7223364e383975bf7d | https://github.com/EmanuelaBoros/stacked-ner/tree/b57e4fcf777a5ad2519ffa7223364e383975bf7d |
AdditiveAttention | import torch
import torch.nn.functional as F
import torch.nn as nn
class AdditiveAttention(nn.Module):
def __init__(self, k_size, v_size, hidden_size=None, bias=True):
super(AdditiveAttention, self).__init__()
if hidden_size is None:
hidden_size = v_size
self.W1 = nn.Linear(k_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LindgeW/BiaffineNER | AdditiveAttention | false | 8,467 | [
"Apache-2.0"
] | 13 | 0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf | https://github.com/LindgeW/BiaffineNER/tree/0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf |
SegmentationNet | # 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.... | jinyu-hou/medium-blog-scripts | SegmentationNet | false | 10,278 | [
"MIT"
] | 0 | a645d544a4bd1c937e4ff99dca0d6e98b3abb7f9 | https://github.com/jinyu-hou/medium-blog-scripts/tree/a645d544a4bd1c937e4ff99dca0d6e98b3abb7f9 |
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... | ConstantinSeibold/SGL | C1 | false | 17,120 | [
"MIT"
] | 7 | fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a | https://github.com/ConstantinSeibold/SGL/tree/fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a |
EquivariantLayer | # 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 math
import torch.nn a... | doudoulaile/RL-GAN-Net | EquivariantLayer | false | 15,224 | [
"MIT"
] | 112 | 9c221223d1878bc24f0f39ad34928c1bb2974ae3 | https://github.com/doudoulaile/RL-GAN-Net/tree/9c221223d1878bc24f0f39ad34928c1bb2974ae3 |
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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | AP-EPFL/DA-segmentation-driven-pose | GlobalAvgPool2d | false | 4,767 | [
"MIT"
] | 1 | 451b8ee3619b16db152ac37ba2b64f7ebf5e2832 | https://github.com/AP-EPFL/DA-segmentation-driven-pose/tree/451b8ee3619b16db152ac37ba2b64f7ebf5e2832 |
ContextLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
class ContextLoss(nn.Module):
def __init__(self):
super(ContextLoss, self).__init__()
def forward(self, generated, corrupted, weight_mask):
c_loss = weight_mask * F.l1_loss(generated, corrupted)
c_loss = c_loss.mean(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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | suhongkim/Image-Inpainting | ContextLoss | false | 10,792 | [
"MIT"
] | 0 | 6a3f43b95de2c39aaaf60050211ff03856f24456 | https://github.com/suhongkim/Image-Inpainting/tree/6a3f43b95de2c39aaaf60050211ff03856f24456 |
ResBlock | import torch
import torch.utils.data
import torch
import torch.nn as nn
class ResBlock(nn.Module):
def __init__(self, inFe):
super(ResBlock, self).__init__()
self.conv1 = nn.Conv2d(inFe, inFe, 3, 1, 1)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(inFe, inFe, 3, 1, 1)
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 import triton_helpers
import torch.utils.data
impor... | ZhibingLai/MSFN | ResBlock | false | 2,989 | [
"Apache-2.0"
] | 0 | eb650c351edf27270bc32b50b60842a9fe40308e | https://github.com/ZhibingLai/MSFN/tree/eb650c351edf27270bc32b50b60842a9fe40308e |
convTranspose23DUnit | import torch
import numpy as np
from torch import nn
import torch.utils.data
import torch.nn.init as init
import torch.nn.init
class convTranspose23DUnit(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, groups=1, bias=True, dilation=1, nd=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 numpy as np
from torch import nn
import torch.utils.data
import torch.nn.... | ForrestPi/Unsupervised-Defect-Segmentation | convTranspose23DUnit | false | 8,221 | [
"MIT"
] | 17 | e366ac7c757bb1b45f38ebbc502dfee7ccb72398 | https://github.com/ForrestPi/Unsupervised-Defect-Segmentation/tree/e366ac7c757bb1b45f38ebbc502dfee7ccb72398 |
TracedModule | # 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.quantization
import torch.onnx
import torch.nn.parallel
import tor... | LeeSHa00/PyTorch-tutorials-kr | TracedModule | false | 11,849 | [
"BSD-3-Clause"
] | 0 | 6a25b48b1a6cc96ea4edebeede2e419ef73b96fc | https://github.com/LeeSHa00/PyTorch-tutorials-kr/tree/6a25b48b1a6cc96ea4edebeede2e419ef73b96fc |
DDPGConvBody | import torch
from torch.nn import functional as F
import torch.nn as nn
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class DDPGConvBody(nn.Module):
def __init__(self, in_channels=4):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Marianoetchart/DeepRL | DDPGConvBody | false | 2,651 | [
"Apache-2.0"
] | 0 | 40d4825694c0890440859166de56701fc1f61d5b | https://github.com/Marianoetchart/DeepRL/tree/40d4825694c0890440859166de56701fc1f61d5b |
CrossEntropy | # 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
... | gsaiabhishek/AUTOMATA | CrossEntropy | false | 12,469 | [
"MIT"
] | 0 | e944992a7bf3a50bc8951a303294b3a798822176 | https://github.com/gsaiabhishek/AUTOMATA/tree/e944992a7bf3a50bc8951a303294b3a798822176 |
FocalLoss | # 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... | ValerioB88/self-supervised-relational-reasoning | FocalLoss | false | 9,710 | [
"MIT"
] | 0 | 12692b93d5c8dd3f56a31aa8b790366556e7a621 | https://github.com/ValerioB88/self-supervised-relational-reasoning/tree/12692b93d5c8dd3f56a31aa8b790366556e7a621 |
AttentionLayer | # 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.... | littlekobe/AREL-for-Visual-Storytelling | AttentionLayer | false | 15,921 | [
"MIT"
] | 82 | 7df46be67a2de22a763bad25c70066b702a6afba | https://github.com/littlekobe/AREL-for-Visual-Storytelling/tree/7df46be67a2de22a763bad25c70066b702a6afba |
ActorNetwork | # 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.... | whongyu/MA3C | ActorNetwork | false | 4,534 | [
"MIT"
] | 0 | d3b38cf42a909c0938624ba853119804efaf47eb | https://github.com/whongyu/MA3C/tree/d3b38cf42a909c0938624ba853119804efaf47eb |
Block | # 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... | L597383845/row-col-table-recognition | Block | false | 17,565 | [
"MIT"
] | 7 | 617718751861b3f4e35a4b34dde4c898575e6818 | https://github.com/L597383845/row-col-table-recognition/tree/617718751861b3f4e35a4b34dde4c898575e6818 |
ANNDigitDetect | import torch
import torch.nn as nn
import torch.nn.functional as F
class ANNDigitDetect(nn.Module):
def __init__(self):
super(ANNDigitDetect, self).__init__()
self.fc1 = nn.Linear(32 * 32, 120)
self.fc2 = nn.Linear(120, 32)
self.fc3 = nn.Linear(32, 10)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Quest2GM/timestamp_detection_algorithm | ANNDigitDetect | false | 5,732 | [
"MIT"
] | 1 | 8a5a7fba5a924a37402d7daece90fdf626a6a905 | https://github.com/Quest2GM/timestamp_detection_algorithm/tree/8a5a7fba5a924a37402d7daece90fdf626a6a905 |
SelfAttention | import torch
import torch.nn as nn
import torch.distributed
class SelfAttention(nn.Module):
def __init__(self, model_dim, dropout=0.1):
super(SelfAttention, self).__init__()
self.Va = nn.Linear(model_dim, 1, bias=False)
self.Wa = nn.Linear(model_dim, model_dim)
self.dropout = nn.D... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Eldriann/Master-thesis | SelfAttention | false | 5,129 | [
"MIT"
] | 1 | 9d09d97f4002cc9fc730f10317614e1d0d307353 | https://github.com/Eldriann/Master-thesis/tree/9d09d97f4002cc9fc730f10317614e1d0d307353 |
WRNBottleneck | # 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.utils.data
impor... | HyperGAN/imgclsmob | WRNBottleneck | false | 17,683 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
CustomBatchNormManualModule | import torch
import torch.nn as nn
class CustomBatchNormManualFunction(torch.autograd.Function):
"""
This torch.autograd.Function implements a functional custom version of the batch norm operation for MLPs.
Using torch.autograd.Function allows you to write a custom backward function.
The function will... | 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_... | akashrajkn/sarcastic-gradients | CustomBatchNormManualModule | false | 3,067 | [
"Apache-2.0"
] | 0 | 5a995ab7822dfd49cdc88855c631dcc8f1b0532f | https://github.com/akashrajkn/sarcastic-gradients/tree/5a995ab7822dfd49cdc88855c631dcc8f1b0532f |
RmseBceDiceLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def dice_loss(smooth=1):
"""Create Dice Loss.
Args:
smooth (float, optional): Smoothing value. A larger
smooth value (also known as Laplace smooth, or
Additive smooth) can be used to avoid overfitting.
... | 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 |
Unet | import torch
from torch import nn
from torch.nn import functional as F
class ContractingBlock(nn.Module):
"""
ContractingBlock Class
Performs two convolutions followed by a max pool operation.
Values:
input_channels: the number of channels to expect from a given input
"""
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 import nn
from tor... | akanametov/unet-pytorch | Unet | false | 3,190 | [
"MIT"
] | 0 | 6cf0f70674958356ea4ac36fe61b0415921f72ae | https://github.com/akanametov/unet-pytorch/tree/6cf0f70674958356ea4ac36fe61b0415921f72ae |
CustomLoss | import torch
import torch.nn as nn
class CustomLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(CustomLoss, self).__init__()
def forward(self, outputs, targets):
gamma = 0.5
C4 = 10
gb_hat = outputs[:, :, :34]
rb_hat = outputs[:, :, 34:68]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Le-Xiaohuai-speech/PercepNet | CustomLoss | false | 5,499 | [
"BSD-3-Clause"
] | 1 | df778b5394b96419778cb01fffbc9f16a316d823 | https://github.com/Le-Xiaohuai-speech/PercepNet/tree/df778b5394b96419778cb01fffbc9f16a316d823 |
down_shifted_conv2d | # 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 ... | elahekhodaie/PixelCnnPP | down_shifted_conv2d | false | 10,100 | [
"MIT"
] | 0 | ab1e245ed8c24009364b1f891288eb1a526b0121 | https://github.com/elahekhodaie/PixelCnnPP/tree/ab1e245ed8c24009364b1f891288eb1a526b0121 |
AvgPool2dSame | # 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 math
import numpy as np
from typing import List
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
assert_... | Weiyuhong-1998/DI-engine | AvgPool2dSame | false | 14,582 | [
"Apache-2.0"
] | 464 | 88658ea358298c6e61e95a454284b8853a3e9484 | https://github.com/Weiyuhong-1998/DI-engine/tree/88658ea358298c6e61e95a454284b8853a3e9484 |
Model | import torch
from torch import nn
def depth_to_3d(depth: 'torch.Tensor', xyz: 'torch.Tensor') ->torch.Tensor:
points_depth: 'torch.Tensor' = depth.permute(0, 2, 3, 1)
points_3d: 'torch.Tensor' = xyz * points_depth
return points_3d.permute(0, 3, 1, 2)
class Model(nn.Module):
def forward(self, xyz, 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | crisdeodates/AI-depthai-experiments | Model | false | 6,489 | [
"MIT"
] | 1 | 74b8b84a03cb637d20a7fcd091cce11add78bd2c | https://github.com/crisdeodates/AI-depthai-experiments/tree/74b8b84a03cb637d20a7fcd091cce11add78bd2c |
PositiveLinear | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositiveLinear(nn.Linear):
"""Applies a transformation to the incoming data of the following form: :math:`y_i = xlog(exp(A)+1)^T`
where log and exp are elementwise operations.
Args:
in_features: size of each inpu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | dfioravanti/copula_vae | PositiveLinear | false | 1,838 | [
"MIT"
] | 0 | 4fdadfb9ca65a75367d50df4a5848942de20741f | https://github.com/dfioravanti/copula_vae/tree/4fdadfb9ca65a75367d50df4a5848942de20741f |
PartitionedTransformerEncoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class FeatureDropoutFunction(torch.autograd.function.InplaceFunction):
@staticmethod
def forward(ctx, input, p=0.5, train=False, inplace=False):
if p < 0 or p > 1:
raise ValueError(
'dropout pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | skulick/self-attentive-parser | PartitionedTransformerEncoderLayer | false | 4,379 | [
"MIT"
] | 0 | 04a91e80cc05bcfe8f48145517f58e85f0c8ade6 | https://github.com/skulick/self-attentive-parser/tree/04a91e80cc05bcfe8f48145517f58e85f0c8ade6 |
DarknetMish | import torch
import torch.nn.functional as F
from torch import nn
class darknet_mish(torch.autograd.Function):
"""
We can implement our own custom autograd Functions by subclassing
torch.autograd.Function and implementing the forward and backward passes
which operate on Tensors.
"""
@staticme... | 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.functional as F
from torch import nn
assert_size_stride =... | cooked-sashimi/Yet-Another-YOLOv4-Pytorch | DarknetMish | false | 15,076 | [
"MIT"
] | 133 | c884ef8849987a75b0e17eba1b739c22d3782e90 | https://github.com/cooked-sashimi/Yet-Another-YOLOv4-Pytorch/tree/c884ef8849987a75b0e17eba1b739c22d3782e90 |
JaccardLoss | import torch
import torch.utils.data
import torch.nn as nn
from abc import ABC
class JaccardLoss(nn.Module, ABC):
"""Jaccard loss.
"""
def __init__(self, size_average=True, reduce=True, smooth=1.0):
super(JaccardLoss, self).__init__()
self.smooth = smooth
self.reduce = reduce
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
from abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_stri... | matinraayai/pytorch_connectomics | JaccardLoss | false | 3,985 | [
"MIT"
] | 0 | b11a2f7e71a8d1442fb05f7a6edfaaaa7b0d9205 | https://github.com/matinraayai/pytorch_connectomics/tree/b11a2f7e71a8d1442fb05f7a6edfaaaa7b0d9205 |
FFN | # 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_... | Originofamonia/mcan-vqa | FFN | false | 4,558 | [
"Apache-2.0"
] | 0 | e7e9fdc654d72dbbcbc03e43ae8a59c16b6d10d1 | https://github.com/Originofamonia/mcan-vqa/tree/e7e9fdc654d72dbbcbc03e43ae8a59c16b6d10d1 |
ParallelPolarizedSelfAttention | # 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.... | LeftAttention/Attention-Codebase | ParallelPolarizedSelfAttention | false | 17,661 | [
"Apache-2.0"
] | 6 | 348ec66233a7c0f95a3cb5f0f11641e2a7a9b9c3 | https://github.com/LeftAttention/Attention-Codebase/tree/348ec66233a7c0f95a3cb5f0f11641e2a7a9b9c3 |
MultiHeadDense | import math
import torch
import torch.nn as nn
class MultiHeadDense(nn.Module):
def __init__(self, d):
super(MultiHeadDense, self).__init__()
self.weight = nn.Parameter(torch.Tensor(d, d))
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(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
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | afperezm/DeepGlobe-Road-Extraction-Challenge | MultiHeadDense | false | 1,380 | [
"MIT"
] | 0 | d3e0a8123d64baa3975663ece053edbc4bbdc4e6 | https://github.com/afperezm/DeepGlobe-Road-Extraction-Challenge/tree/d3e0a8123d64baa3975663ece053edbc4bbdc4e6 |
ChannelAttentionGG | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dyn... | dwromero/att_gconvs | ChannelAttentionGG | false | 15,323 | [
"MIT"
] | 53 | 872259cad49763fdcfa3e96e80b6b5c331adf084 | https://github.com/dwromero/att_gconvs/tree/872259cad49763fdcfa3e96e80b6b5c331adf084 |
Actor | import torch
import numpy as np
import torch.nn as nn
def fanin_init(size, fanin=None):
fanin = fanin or size[0]
v = 1.0 / np.sqrt(fanin)
return torch.Tensor(size).uniform_(-v, v)
class Actor(nn.Module):
def __init__(self, nb_states, nb_actions, hidden1=400, hidden2=300,
init_w=0.003):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JackYangzg/pytorch-ddpg | Actor | false | 5,389 | [
"Apache-2.0"
] | 1 | 96838a40dd6992a0a18065a5edafbefc6bb0ac69 | https://github.com/JackYangzg/pytorch-ddpg/tree/96838a40dd6992a0a18065a5edafbefc6bb0ac69 |
Conv1d | # 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... | debasish-mihup/EfficientConformer | Conv1d | false | 10,344 | [
"Apache-2.0"
] | 0 | bddd927cebcde044a999aaa7766fa6d44dc20576 | https://github.com/debasish-mihup/EfficientConformer/tree/bddd927cebcde044a999aaa7766fa6d44dc20576 |
ToRGB | import math
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
from typing import List
import torch.nn.functional
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight"></a>
## Learning-rate Equalized Weights Paramete... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 numpy as np
from torch import nn
import torch.nn.functional a... | techthiyanes/annotated_deep_learning_paper_implementations | ToRGB | false | 16,574 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
_leaky_relu | # 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
import torch.optim
import torch.utils.data
assert_size_stride = torc... | ZephyrII/competitive_colaboration | _leaky_relu | false | 14,714 | [
"MIT"
] | 357 | a557d1e23ef2c0b8e3794f085a79bfffb860f9df | https://github.com/ZephyrII/competitive_colaboration/tree/a557d1e23ef2c0b8e3794f085a79bfffb860f9df |
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 import triton_helpers
import torch.nn as nn
assert_... | ShirelJosef/deep-reinforcement-learning | Policy | false | 11,877 | [
"MIT"
] | 0 | 63979b975c71e730c9d4c66e39efac210260dd18 | https://github.com/ShirelJosef/deep-reinforcement-learning/tree/63979b975c71e730c9d4c66e39efac210260dd18 |
Conv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dooglewoogle/pystiche | Conv | false | 15,203 | [
"BSD-3-Clause"
] | 129 | 14b61123ede2abdb00daaa5b4981de6d7edaf034 | https://github.com/dooglewoogle/pystiche/tree/14b61123ede2abdb00daaa5b4981de6d7edaf034 |
ReSentenceMatrixLayer | import torch
import torch.nn as nn
class ReSentenceMatrixLayer(nn.Module):
def __init__(self, in_size, out_size=1):
super(ReSentenceMatrixLayer, self).__init__()
self.in_size = in_size
self.out_size = out_size
self.a_Asem = nn.Parameter(torch.tensor(0.0))
self.linear = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Yottaxx/T-LSTM | ReSentenceMatrixLayer | false | 18,151 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 |
MSECompositionLoss | # 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 functools
import torch.nn as nn
from torch.nn import functional as F
assert_size_s... | Sardhendu/mmediting | MSECompositionLoss | false | 9,884 | [
"Apache-2.0"
] | 0 | 623b59ac758d856abc9fab7e845beeab61074d8f | https://github.com/Sardhendu/mmediting/tree/623b59ac758d856abc9fab7e845beeab61074d8f |
CustomizedNet | import torch
import torch.nn as nn
import torch.utils.data.distributed
class CustomizedNet(nn.Module):
def __init__(self, dropout, input_size, input_feature_num, hidden_dim,
output_size):
"""
Simply use linear layers for multi-variate single-step forecasting.
"""
super()._... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | cabuliwallah/analytics-zoo | CustomizedNet | false | 6,375 | [
"Apache-2.0"
] | 1 | 5e662bd01c5fc7eed412973119594cf2ecea8b11 | https://github.com/cabuliwallah/analytics-zoo/tree/5e662bd01c5fc7eed412973119594cf2ecea8b11 |
MultiHeadAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class MultiHeadAttention(nn.Module):
def __init__(self, embed_size, num_heads, dropout=0.2, batch_dim=0):
super(MultiHeadAttention, self).__init__()
self.embed_size = embed_size
self.num_heads =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ManojKesani/Transformer-Implementations | MultiHeadAttention | false | 814 | [
"MIT"
] | 0 | faca89d44523da80073790d53e53b4e80bde736f | https://github.com/ManojKesani/Transformer-Implementations/tree/faca89d44523da80073790d53e53b4e80bde736f |
DiscreteCriticNetwork | # 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_... | harwiltz/sac | DiscreteCriticNetwork | false | 3,584 | [
"MIT"
] | 0 | 076e01e63d8933665fbf4038513f163bbfd62800 | https://github.com/harwiltz/sac/tree/076e01e63d8933665fbf4038513f163bbfd62800 |
BatchNorm | # 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, math as tl_math
import numpy as np
from abc import abstractmethod
from torch i... | diana-hep/madminer | BatchNorm | false | 15,185 | [
"MIT"
] | 46 | 3a585d2887a31886cdeadddb0a284f0472146fce | https://github.com/diana-hep/madminer/tree/3a585d2887a31886cdeadddb0a284f0472146fce |
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... | luixiao1223/BSP-NET-pytorch | encoder | false | 3,956 | [
"MIT"
] | 0 | f871c8ce6a9d52ac922e110702c47cd1c89d0a73 | https://github.com/luixiao1223/BSP-NET-pytorch/tree/f871c8ce6a9d52ac922e110702c47cd1c89d0a73 |
MinimaxGeneratorLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def minimax_generator_loss(dgz, nonsaturating=True, reduction='mean'):
if nonsaturating:
target = torch.ones_like(dgz)
return F.binary_cross_entropy_with_logits(dgz, target, reduction=
reduction)
else:
targe... | 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... | shi-weili/torchgan | MinimaxGeneratorLoss | false | 12,978 | [
"MIT"
] | 0 | 28ffd4026b8c0db2217b667d30a222d6758bfc41 | https://github.com/shi-weili/torchgan/tree/28ffd4026b8c0db2217b667d30a222d6758bfc41 |
TokenEmbedding | import torch
import torch.nn as nn
class TokenEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(TokenEmbedding, self).__init__()
padding = 1 if torch.__version__ >= '1.5.0' else 2
self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
kernel_size=3, pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Linan2018/Informer2020 | TokenEmbedding | false | 2,512 | [
"Apache-2.0"
] | 0 | 30e63a7d3ed9310b917b05c4d60b340d2dd0517a | https://github.com/Linan2018/Informer2020/tree/30e63a7d3ed9310b917b05c4d60b340d2dd0517a |
GlobalAttention_text | import torch
import torch.nn as nn
import torch.nn.parallel
class GlobalAttention_text(nn.Module):
def __init__(self, idf, cdf):
super(GlobalAttention_text, self).__init__()
self.conv_context = nn.Conv1d(cdf, idf, kernel_size=1, stride=1,
padding=0)
self.sm = nn.Softmax()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ts170/T2I_CL | GlobalAttention_text | false | 10,909 | [
"MIT"
] | 0 | 8754bea1101aabcbf8108b95e722f7aaeb385869 | https://github.com/ts170/T2I_CL/tree/8754bea1101aabcbf8108b95e722f7aaeb385869 |
TransposeConv2dLayer | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch.... | autocomic/https-github.com-autocomic-DeepFillv2_Pytorch | TransposeConv2dLayer | false | 3,143 | [
"MIT"
] | 0 | 7f6712a9b42dfd827879271f13856f1da5d6a032 | https://github.com/autocomic/https-github.com-autocomic-DeepFillv2_Pytorch/tree/7f6712a9b42dfd827879271f13856f1da5d6a032 |
Hswish | # 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... | Alterith/masters_code | Hswish | false | 4,824 | [
"MIT"
] | 1 | 65d0f2d26698cc8f7a5ffb564936113e2bbec201 | https://github.com/Alterith/masters_code/tree/65d0f2d26698cc8f7a5ffb564936113e2bbec201 |
MaskedMHCA | import math
import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import functional as F
class LayerNorm(nn.Module):
"""
LayerNorm that supports inputs of size B, C, T
"""
def __init__(self, num_channels, eps=1e-05, affine=True, device=None,
dtype=None):
super().__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.... | yjh0410/actionformer_release | MaskedMHCA | false | 16,801 | [
"MIT"
] | 61 | 7a97422111d3e29c8d2e14088c850c6975855ea7 | https://github.com/yjh0410/actionformer_release/tree/7a97422111d3e29c8d2e14088c850c6975855ea7 |
CriticNN | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def init_hidden(layer):
"""
Initialize NN layers
"""
input_size = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(input_size)
return -lim, lim
class CriticNN(nn.Module):
"""
Critic class
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | kaustav1987/Tennis-Collaboration-and-Competition-Continuous-Control | CriticNN | false | 3,826 | [
"MIT"
] | 0 | d724e09d7a5948e2023fb86bf977455f3c507054 | https://github.com/kaustav1987/Tennis-Collaboration-and-Competition-Continuous-Control/tree/d724e09d7a5948e2023fb86bf977455f3c507054 |
CharbonnierLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | myeldib/Simple-SR | CharbonnierLoss | false | 12,806 | [
"MIT"
] | 0 | 583456b1f231574d9e0b45c29266cf41603d161d | https://github.com/myeldib/Simple-SR/tree/583456b1f231574d9e0b45c29266cf41603d161d |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ctoto93/TD3 | Actor | false | 9,965 | [
"MIT"
] | 0 | 88482b9f1fb4441d74426ece60d5da13414aeb77 | https://github.com/ctoto93/TD3/tree/88482b9f1fb4441d74426ece60d5da13414aeb77 |
ReduceLast | import torch
def sequence_length_3D(sequence: 'torch.Tensor') ->torch.Tensor:
used = torch.sign(torch.amax(torch.abs(sequence), dim=2))
length = torch.sum(used, 1)
length = length.int()
return length
class ReduceLast(torch.nn.Module):
def forward(self, inputs, mask=None):
batch_size = 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._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | jimthompson5802/ludwig | ReduceLast | false | 3,863 | [
"Apache-2.0"
] | 0 | 8a369328a3f839d9cdb3710be315952c7891d7c0 | https://github.com/jimthompson5802/ludwig/tree/8a369328a3f839d9cdb3710be315952c7891d7c0 |
DownBlock | import torch
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | EvgeneyZ/RBPN | DownBlock | false | 9,529 | [
"MIT"
] | 0 | acfe636cc48a4fbfea78f934a251c32e53367659 | https://github.com/EvgeneyZ/RBPN/tree/acfe636cc48a4fbfea78f934a251c32e53367659 |
SimpleATanModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleATanModule(torch.nn.Module):
def __init__(self):
super(SimpleATanModule, self).__init__()
def forward(self, a):
return torch.atan(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_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._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | opti-mix/glow | SimpleATanModule | false | 7,382 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
ResBlock | import torch
import torch.nn as nn
from typing import Tuple
def conv3x3(in_channels: 'int', out_channels: 'int', stride: 'int'=1,
padding: 'int'=1) ->nn.Module:
conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=
stride, padding=padding, bias=True)
nn.init.xavier_normal_(conv.weight... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | mdornseif/fastface | ResBlock | false | 16,029 | [
"MIT"
] | 72 | 72772db1fae4af17e829cd5479c4848fe5eb8948 | https://github.com/mdornseif/fastface/tree/72772db1fae4af17e829cd5479c4848fe5eb8948 |
C2 | # 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... | xxchenxx/otdd | C2 | false | 13,128 | [
"MIT"
] | 0 | e63d1d170fed36957052b7bb0a0af1553b980381 | https://github.com/xxchenxx/otdd/tree/e63d1d170fed36957052b7bb0a0af1553b980381 |
Matcher | # 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
assert_size_stride = torch._C._dynamo.guards.a... | nchungvh/pyhgt | Matcher | false | 7,319 | [
"MIT"
] | 1 | 3cb08ea856ca02aaf1664aa7486024a8742c7567 | https://github.com/nchungvh/pyhgt/tree/3cb08ea856ca02aaf1664aa7486024a8742c7567 |
FTest | import torch
import torch.nn as nn
class FTest(nn.Module):
def __init__(self):
super(FTest, self).__init__()
def forward(self, x, y):
x = x - y - 8.3
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | goldbattle/onnx2keras | FTest | false | 12,461 | [
"MIT"
] | 0 | dcf52041299ce4216552d1132ec86eb4debd5303 | https://github.com/goldbattle/onnx2keras/tree/dcf52041299ce4216552d1132ec86eb4debd5303 |
FeatureVolume | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | drixs2050/nglod | FeatureVolume | false | 6,614 | [
"MIT"
] | 1 | 0f3627d3ece82464335b0fab89c2269fcb016308 | https://github.com/drixs2050/nglod/tree/0f3627d3ece82464335b0fab89c2269fcb016308 |
CAM_Module | from torch.nn import Module
import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.nn import Softmax
class C(nn.Module):
"""
This class is for a convolutional layer.
"""
def __init__(self, nIn, nOut, kSize, stride=1):
"""
:param nIn: number of 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ayushmankumar7/pytorch-lanenet | CAM_Module | false | 14,940 | [
"MIT"
] | 160 | db9f116ba3f42dbfabf064e4a89ec068e9da4ee4 | https://github.com/ayushmankumar7/pytorch-lanenet/tree/db9f116ba3f42dbfabf064e4a89ec068e9da4ee4 |
BiLinearSim | from _paritybench_helpers import _mock_config
import torch
from torch.optim.lr_scheduler import *
class BiLinearSim(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.linear = torch.nn.Linear(config.hidden_size, config.
hidden_size, bias=False)
def forward(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.optim.lr_scheduler import *
assert_size_stride = torch._C._dynamo.gua... | johnson7788/mt-dnn | BiLinearSim | false | 3,896 | [
"MIT"
] | 0 | 26e5c4a5bfdbf1a1dd1c903e606db1c070568237 | https://github.com/johnson7788/mt-dnn/tree/26e5c4a5bfdbf1a1dd1c903e606db1c070568237 |
MNIST_CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.optim
class MNIST_CNN(nn.Module):
"""
Hand-tuned architecture for MNIST.
Weirdness I've noticed so far with this architecture:
- adding a linear layer after the mean-pool in features hurts
R... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Weixin-Liang/MetaShift | MNIST_CNN | false | 14,598 | [
"MIT"
] | 54 | 84e090a13652437f8f392065f6bebf938e4c7fa3 | https://github.com/Weixin-Liang/MetaShift/tree/84e090a13652437f8f392065f6bebf938e4c7fa3 |
FCDiscriminator | import torch
from torch import nn
class FCDiscriminator(nn.Module):
def __init__(self, num_classes, ndf=64):
super(FCDiscriminator, self).__init__()
self.conv1 = nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2,
padding=1)
self.conv2 = nn.Conv2d(ndf, ndf * 2, kernel_size=4,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | JDAI-CV/FADA | FCDiscriminator | false | 13,871 | [
"Apache-2.0"
] | 120 | a1c6403963184a3427eda68cc94b03ff6143368a | https://github.com/JDAI-CV/FADA/tree/a1c6403963184a3427eda68cc94b03ff6143368a |
GapAggregator | import torch
import torch.nn as nn
class GapAggregator(nn.Module):
def __init__(self):
super().__init__()
self.pool = nn.AdaptiveAvgPool2d(1)
def forward(self, x):
x = self.pool(x).squeeze(3).squeeze(2)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def ... | 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... | NeverendingNotification/pytorch-xai-analyze | GapAggregator | false | 2,689 | [
"MIT"
] | 0 | fba91bf98c3281ffee5acaa87f2e44191897e0d7 | https://github.com/NeverendingNotification/pytorch-xai-analyze/tree/fba91bf98c3281ffee5acaa87f2e44191897e0d7 |
MultiHeadedAttention | import math
import torch
import numpy as np
from typing import Optional
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer
:param int n_head: the number of head s
:param int n_feat: the number of features
:param float dropout_rate: dropout rate
"""
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 import triton_helpers
from torch._inductor.runtime.... | carankt/FastSpeech2-1 | MultiHeadedAttention | false | 6,393 | [
"Apache-2.0"
] | 1 | 42c06e4fbdf741a0719154d1cb4617b7d3f15a5c | https://github.com/carankt/FastSpeech2-1/tree/42c06e4fbdf741a0719154d1cb4617b7d3f15a5c |
vggUpconv | import torch
import torch.nn as nn
class vggUpconv(nn.Module):
"""Some Information about vggUpconv"""
def __init__(self, in_ch, out_ch, upsample=True):
super(vggUpconv, self).__init__()
if upsample:
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear')
else:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | anudeepsekhar/Lane-Detection-Pytorch | vggUpconv | false | 6,218 | [
"MIT"
] | 1 | cfddda8a0768cf83afd87e29d605fd58aa89df59 | https://github.com/anudeepsekhar/Lane-Detection-Pytorch/tree/cfddda8a0768cf83afd87e29d605fd58aa89df59 |
TargetContextGate | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
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 t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ChenRocks/Distill-BERT-Textgen-ONMT | TargetContextGate | false | 17,099 | [
"MIT"
] | 7 | d83dd1a95af7513cbfae4a2768f6effc2f3a589f | https://github.com/ChenRocks/Distill-BERT-Textgen-ONMT/tree/d83dd1a95af7513cbfae4a2768f6effc2f3a589f |
SharpenedCosineSimilarity | import torch
import torch.nn as nn
import torch.nn.functional as F
def unfold2d(x, kernel_size: 'int', stride: 'int', padding: 'int'):
x = F.pad(x, [padding] * 4)
bs, in_c, h, w = x.size()
ks = kernel_size
strided_x = x.as_strided((bs, in_c, (h - ks) // stride + 1, (w - ks) //
stride + 1, ks, ... | 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
import torch.nn as nn
import torch.nn.functional as F
assert_s... | enzokro/sharpened_cosine_similarity_torch | SharpenedCosineSimilarity | false | 12,349 | [
"MIT"
] | 0 | 150c84f5cf81721baf097abdc0d4ac772fb39fc4 | https://github.com/enzokro/sharpened_cosine_similarity_torch/tree/150c84f5cf81721baf097abdc0d4ac772fb39fc4 |
MinibatchStd | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.tensorboard
assert_size_stride = torch... | allenbao64/jamm-bandit | MinibatchStd | false | 12,411 | [
"Apache-2.0"
] | 0 | 06c9d8815ce907a68b0bc4ecf8bee4a2465c6a9e | https://github.com/allenbao64/jamm-bandit/tree/06c9d8815ce907a68b0bc4ecf8bee4a2465c6a9e |
Upsampler | # 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
import math
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | RyanMoussouni/iSeeBetter | Upsampler | false | 14,345 | [
"MIT"
] | 327 | af193ae0852f8e477fcd6875dce874eb5092a24a | https://github.com/RyanMoussouni/iSeeBetter/tree/af193ae0852f8e477fcd6875dce874eb5092a24a |
Encoder2 | # 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.... | MingSun-Tse/Collaborative-Distillation | Encoder2 | false | 14,040 | [
"MIT"
] | 172 | 915712674af82ff91d926d922c14988cce0430f3 | https://github.com/MingSun-Tse/Collaborative-Distillation/tree/915712674af82ff91d926d922c14988cce0430f3 |
TransformerEncoderLayer | # 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.... | aarora8/icefall | TransformerEncoderLayer | false | 3,020 | [
"Apache-2.0"
] | 0 | 8cb7f712e413fffbcdfdd865be73d6ff43f0ce7a | https://github.com/aarora8/icefall/tree/8cb7f712e413fffbcdfdd865be73d6ff43f0ce7a |
TVLoss | # 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... | juyongjiang/Simple-SR | TVLoss | false | 7,008 | [
"MIT"
] | 1 | 76820511abc04fbe6e4a79d23c67aee97406d563 | https://github.com/juyongjiang/Simple-SR/tree/76820511abc04fbe6e4a79d23c67aee97406d563 |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
from typing import *
from torch import nn
import torch.utils.checkpoint
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if (config.hidden_size % config.num_attention_heads != 0 and not
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | hiaoxui/soft-prompts | BertAttention | false | 15,527 | [
"Apache-2.0"
] | 48 | 214dbedf735fe1c98ab2be3a26066d50ff0a86d8 | https://github.com/hiaoxui/soft-prompts/tree/214dbedf735fe1c98ab2be3a26066d50ff0a86d8 |
PointerAttention | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments
), 'Not all arguments have the same valu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DenDen047/data2text-macro-plan-py | PointerAttention | false | 7,954 | [
"MIT"
] | 20 | bb01ec6e23dab28c1e969f23bd55776b597fb995 | https://github.com/DenDen047/data2text-macro-plan-py/tree/bb01ec6e23dab28c1e969f23bd55776b597fb995 |
CharbonnierCompLoss | # 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 functools
import torc... | Sardhendu/mmediting | CharbonnierCompLoss | false | 9,877 | [
"Apache-2.0"
] | 0 | 623b59ac758d856abc9fab7e845beeab61074d8f | https://github.com/Sardhendu/mmediting/tree/623b59ac758d856abc9fab7e845beeab61074d8f |
KlLoss | import torch
import torch.nn as nn
def kl_div(p: 'torch.Tensor', q: 'torch.Tensor') ->torch.Tensor:
x = p * torch.log(p / q)
return x.abs().mean()
class KlLoss(nn.Module):
def __init__(self) ->None:
super().__init__()
def forward(self, inputs: 'torch.Tensor', targets: 'torch.Tensor'):
... | 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
... | by-liu/RetinalApp | KlLoss | false | 1,621 | [
"MIT"
] | 0 | 53173b2b20dfcf613a3a22d6caa5178771d14225 | https://github.com/by-liu/RetinalApp/tree/53173b2b20dfcf613a3a22d6caa5178771d14225 |
SCS_Cell | # 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.ini... | BoPang1996/Semi-Coupled-Structure-for-visual-sequental-tasks | SCS_Cell | false | 7,805 | [
"Apache-2.0"
] | 13 | c6fe7c77d08928bb30cc8683123f978b0e877394 | https://github.com/BoPang1996/Semi-Coupled-Structure-for-visual-sequental-tasks/tree/c6fe7c77d08928bb30cc8683123f978b0e877394 |
_leaky_relu | import torch
from torch import nn
class _leaky_relu(nn.Module):
def __init__(self):
super(_leaky_relu, self).__init__()
def forward(self, x):
x_neg = 0.1 * x
return torch.max(x_neg, 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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | maxuanquang/SfmLearner-Redesign | _leaky_relu | false | 7,180 | [
"MIT"
] | 1 | 0250a9cc443b5754ba45f69153a03ca26f903a7b | https://github.com/maxuanquang/SfmLearner-Redesign/tree/0250a9cc443b5754ba45f69153a03ca26f903a7b |
GELU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | au55555/classification-pytorch | GELU | false | 6,277 | [
"MIT"
] | 1 | 1937599ae6e688ed7af7470f69964fb6f97241c4 | https://github.com/au55555/classification-pytorch/tree/1937599ae6e688ed7af7470f69964fb6f97241c4 |
SimpleLoss | import torch
class SimpleLoss(torch.nn.Module):
def forward(self, output, target):
return output / target
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | FranciscoShi/piepline | SimpleLoss | false | 17,276 | [
"MIT"
] | 5 | 6105788339fc18bab39ea07625b5fd26ad687254 | https://github.com/FranciscoShi/piepline/tree/6105788339fc18bab39ea07625b5fd26ad687254 |
WeightedCrossEntropyLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class WeightedCrossEntropyLoss(nn.Module):
"""
Transform input to fit the fomation of PyTorch offical cross entropy loss
with anchor-wise weighting.
"""
def __init__(self):
super(WeightedCrossEntropyLoss, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | AhmedMoamen62/OpenPCDet | WeightedCrossEntropyLoss | false | 11,165 | [
"Apache-2.0"
] | 0 | 4d61d099819f40096f795def2c012990d03711cd | https://github.com/AhmedMoamen62/OpenPCDet/tree/4d61d099819f40096f795def2c012990d03711cd |
EncoderLayer | # 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.... | bekirufuk/pointer_summarizer | EncoderLayer | false | 12,169 | [
"Apache-2.0"
] | 0 | 8fc9726f9337b26339848d896a09e7e8f9456bcc | https://github.com/bekirufuk/pointer_summarizer/tree/8fc9726f9337b26339848d896a09e7e8f9456bcc |
ActorNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class ActorNetwork(nn.Module):
def __init__(self, input_size, hidden_size, action_size):
super(ActorNetwork, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, 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 import triton_helpers
from torch._inductor.runtime.... | whongyu/MA3C | ActorNetwork | false | 4,534 | [
"MIT"
] | 0 | d3b38cf42a909c0938624ba853119804efaf47eb | https://github.com/whongyu/MA3C/tree/d3b38cf42a909c0938624ba853119804efaf47eb |
LinearDiag | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | qianrusun1015/E3BM-1 | LinearDiag | false | 7,511 | [
"Apache-2.0"
] | 1 | d2c957bdff66fe28a288f1518f224a1e034d543f | https://github.com/qianrusun1015/E3BM-1/tree/d2c957bdff66fe28a288f1518f224a1e034d543f |
Avg2d | # 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 as T
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stride... | DouglasOrr/Snippets | Avg2d | false | 2,164 | [
"MIT"
] | 0 | 026e15a422b518ee7d9ce4849f971c4403ad9fe8 | https://github.com/DouglasOrr/Snippets/tree/026e15a422b518ee7d9ce4849f971c4403ad9fe8 |
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(4, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LouisCaixuran/gomoku | Net | false | 5,585 | [
"Apache-2.0"
] | 1 | c1b6d508522d9e8c78be827f326bbee54c4dfd8b | https://github.com/LouisCaixuran/gomoku/tree/c1b6d508522d9e8c78be827f326bbee54c4dfd8b |
decoder4 | import torch
from torch import nn
class decoder4(nn.Module):
def __init__(self):
super(decoder4, self).__init__()
self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(512, 256, 3, 1, 0)
self.relu11 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingN... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Holmes-Alan/RefVAE | decoder4 | false | 8,297 | [
"MIT"
] | 13 | 836b8f1168f1b0f923b609a48e202ace7806f79c | https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c |
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