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 |
|---|---|---|---|---|---|---|---|---|---|---|
ExponentialClass | import torch
import torch.cuda
import torch.distributed
from torch.cuda.amp import autocast as autocast
import torch.utils.data
import torch.optim
class ExponentialClass(torch.nn.Module):
def __init__(self):
super(ExponentialClass, self).__init__()
def forward(self, x):
return torch.exp(x)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.cuda
import torch.distributed
from torch.cuda.amp import aut... | MikyasDesta/NeMo | ExponentialClass | false | 2,647 | [
"Apache-2.0"
] | 0 | 4995477e6ce49de55b123723e42021c9eff8e2c0 | https://github.com/MikyasDesta/NeMo/tree/4995477e6ce49de55b123723e42021c9eff8e2c0 |
TD3Critic | # 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
import ... | AkiraHero/rlll | TD3Critic | false | 11,179 | [
"MIT"
] | 0 | f86e1105600629d29b8dca7a7483e7dcb8253056 | https://github.com/AkiraHero/rlll/tree/f86e1105600629d29b8dca7a7483e7dcb8253056 |
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 math as tl_math
import torch.nn as nn
... | FadedFate/TrackerSiamRPN | FocalLoss | false | 5,147 | [
"MIT"
] | 1 | f4156fa4bed9a0ca6c7ac9b653c07e564d8a058d | https://github.com/FadedFate/TrackerSiamRPN/tree/f4156fa4bed9a0ca6c7ac9b653c07e564d8a058d |
SmoothCrossEntropyLoss | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _WeightedLoss
class SmoothCrossEntropyLoss(_WeightedLoss):
def __init__(self, weight=None, reduction='mean', smoothing=0.0):
super().__init__(weight=weight, reduction=reduction)
self.smoothing = smoothing
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.nn.modules.... | cclauss/archai | SmoothCrossEntropyLoss | false | 15,006 | [
"MIT"
] | 344 | a5fb8f937f7f1319e3204120803b2a045e9f768b | https://github.com/cclauss/archai/tree/a5fb8f937f7f1319e3204120803b2a045e9f768b |
eca_channel | import torch
import torch.nn as nn
import torch.utils
class eca_channel(nn.Module):
def __init__(self, channel, k_size=3):
super(eca_channel, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1
) // 2, bi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils
assert_size_stride = torch._C._dynamo.g... | closest-git/QuantumFold | eca_channel | false | 1,725 | [
"Apache-2.0"
] | 0 | 2dc6afa96cdee91b58f66543b440a2d7a8d32a8a | https://github.com/closest-git/QuantumFold/tree/2dc6afa96cdee91b58f66543b440a2d7a8d32a8a |
L2Norm | import torch
from math import sqrt as sqrt
from itertools import product as product
import torch.nn as nn
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self, n_channels, scale):
super(L2Norm, self).__init__()
self.n_channels = n_channels
self.gamma = scale or None
... | 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 math import sqrt as sqrt
from itertools import product as product
import t... | zhujiagang/realtime-neg | L2Norm | false | 13,174 | [
"MIT"
] | 0 | 7e228edc5f2d93d0eee7f3880f0b8473d8c71d27 | https://github.com/zhujiagang/realtime-neg/tree/7e228edc5f2d93d0eee7f3880f0b8473d8c71d27 |
SELayer | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch.backends.cudnn
class SELayer(nn.Module):
def __init__(self, in_channels, reduction):
super(SELayer, self).__init__()
mid_channels = in_channels // reduction
self.fc1 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | CrazyStoneonRoad/pytorch_image_classification | SELayer | false | 17,170 | [
"MIT"
] | 4 | 1dcf6d0ee8f4a102ca93cc6e5e325a2e9153918b | https://github.com/CrazyStoneonRoad/pytorch_image_classification/tree/1dcf6d0ee8f4a102ca93cc6e5e325a2e9153918b |
LinearMask | # 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.optim
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | DMIU-ShELL/deeprl-shell | LinearMask | false | 9,019 | [
"Apache-2.0"
] | 0 | a7845ab1c4967ba2af9486625086c3d0b176d293 | https://github.com/DMIU-ShELL/deeprl-shell/tree/a7845ab1c4967ba2af9486625086c3d0b176d293 |
SpatialSoftArgmax | import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
class SpatialSoftArgmax(nn.Module):
"""Spatial softmax as defined in `1`_.
Concretely, the spatial softmax of each feature map is used to compute a
weighted mean of the pixel locations, effectively performing a so... | 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 Tens... | kevinzakka/torchkit | SpatialSoftArgmax | false | 15,825 | [
"MIT"
] | 144 | 930dba9560d2473406b59b99a474dce1a6621813 | https://github.com/kevinzakka/torchkit/tree/930dba9560d2473406b59b99a474dce1a6621813 |
EncoderLayer | import torch
from abc import ABC
import torch.nn as nn
from torch import matmul
class ScaledDotProductAttention(nn.Module, ABC):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | superMC5657/transformer | EncoderLayer | false | 10,889 | [
"MIT"
] | 0 | b9d9ca3a5f307f6587330a8235e8d5a2a3650510 | https://github.com/superMC5657/transformer/tree/b9d9ca3a5f307f6587330a8235e8d5a2a3650510 |
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.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import N... | collector-m/LiDAR-MOS | ReLU | false | 15,061 | [
"MIT"
] | 268 | 7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 | https://github.com/collector-m/LiDAR-MOS/tree/7ccbb63b4ee7c40195b35dd0dddd71473fae25b1 |
ConvBlock | import torch
import torch.nn as nn
class Conv3x3(nn.Module):
"""Layer to pad and convolve input
"""
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | minjabenho/image2pcl | ConvBlock | false | 7,235 | [
"Apache-2.0"
] | 1 | 7e696ee48edae30814d32f32e605ad6cf8bf702c | https://github.com/minjabenho/image2pcl/tree/7e696ee48edae30814d32f32e605ad6cf8bf702c |
GPT2Layer | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadSelfAttention(nn.Module):
def __init__(self, d_ipt: 'int', n_head: 'int', dropout_p: 'float'=0.1):
super(MultiHeadSelfAttention, self).__init__()
self.qkv_linear = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DunZhang/GPT2SourceCode | GPT2Layer | false | 7,627 | [
"MIT"
] | 1 | d598dbae278c93f88469d45ec025da4cfa7d69ee | https://github.com/DunZhang/GPT2SourceCode/tree/d598dbae278c93f88469d45ec025da4cfa7d69ee |
DotSelector | # 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 as th
from torch... | NagisaZj/RODE | DotSelector | false | 11,125 | [
"Apache-2.0"
] | 0 | f7f6831fee58a7910e1d7c3a8ae19cef82ab8d03 | https://github.com/NagisaZj/RODE/tree/f7f6831fee58a7910e1d7c3a8ae19cef82ab8d03 |
GramMSELoss | import torch
import torch.utils.data
import torch
import torch.nn as nn
class GramMatrix(nn.Module):
def forward(self, input):
b, c, h, w = input.size()
F = input.view(b, c, h * w)
G = torch.bmm(F, F.transpose(1, 2))
G.div_(h * w)
return G
class GramMSELoss(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 import triton_helpers
import torch.utils.data
impor... | ckxy/1d_expan | GramMSELoss | false | 6,456 | [
"MIT"
] | 1 | 29cc294e0314d738e8e041f34c995fd22f9f980b | https://github.com/ckxy/1d_expan/tree/29cc294e0314d738e8e041f34c995fd22f9f980b |
spatial_attn_layer | # 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
import ... | zhanzhibingshang/deblurganv2_mirnet | spatial_attn_layer | false | 11,045 | [
"BSD-3-Clause"
] | 0 | 12fcc94ee0ff33335c557cf46a776a13cae3804b | https://github.com/zhanzhibingshang/deblurganv2_mirnet/tree/12fcc94ee0ff33335c557cf46a776a13cae3804b |
Actor1D | # 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.... | KuangenZhang/StructuredRL | Actor1D | false | 5,458 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a |
CoxPHLoss | # 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, split_scan_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 ... | rohanshad/pycox | CoxPHLoss | false | 16,335 | [
"BSD-2-Clause"
] | 449 | 5483489d21f3441e53f78f9f8898ce607f41c632 | https://github.com/rohanshad/pycox/tree/5483489d21f3441e53f78f9f8898ce607f41c632 |
dnn_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... | Maitreyapatel/speech-conversion-between-different-modalities | dnn_encoder | false | 8,510 | [
"MIT"
] | 23 | f757b487d9e6c20aa4f7d37247ba16f9a967f573 | https://github.com/Maitreyapatel/speech-conversion-between-different-modalities/tree/f757b487d9e6c20aa4f7d37247ba16f9a967f573 |
ANN | # 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.... | GopikrishnanSasikumar/Rita | ANN | false | 8,192 | [
"BSD-3-Clause"
] | 17 | a9537c863140fc8c212f82b51f3d556e683e5f5a | https://github.com/GopikrishnanSasikumar/Rita/tree/a9537c863140fc8c212f82b51f3d556e683e5f5a |
CustomBatchNormManualModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | davide-belli/deep-learning-labs | CustomBatchNormManualModule | false | 1,799 | [
"MIT"
] | 0 | 1acd37a527711dccdc00c1935724cc5de7c10955 | https://github.com/davide-belli/deep-learning-labs/tree/1acd37a527711dccdc00c1935724cc5de7c10955 |
FCUDown | import torch
from torch import nn
class FCUDown(nn.Module):
def __init__(self, c1, c2, dw_stride):
super().__init__()
self.conv_project = nn.Conv2d(c1, c2, 1, 1, 0)
self.sample_pooling = nn.AvgPool2d(dw_stride, dw_stride)
self.ln = nn.LayerNorm(c2)
self.act = nn.GELU()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | sithu31296/image_classification | FCUDown | false | 16,471 | [
"MIT"
] | 57 | 6b8cbce96100225621cee3166a73e852ba216cc3 | https://github.com/sithu31296/image_classification/tree/6b8cbce96100225621cee3166a73e852ba216cc3 |
VGG16 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | candacelax/1-stage-wseg | VGG16 | false | 3,367 | [
"Apache-2.0"
] | 0 | 7a24791a3a78454e6611399ba55a808491551543 | https://github.com/candacelax/1-stage-wseg/tree/7a24791a3a78454e6611399ba55a808491551543 |
BBoxTransform | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class BBoxTransform(nn.Module):
def forward(self, anchors, regression):
"""
decode_box_outputs adapted from https://github.com/google/automl/blob/master/effic... | 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
import torch.nn.parallel
import torch.optim
import ... | NHERI-SimCenter/BRAILS | BBoxTransform | false | 8,579 | [
"BSD-3-Clause"
] | 22 | ec17bcd000b15cb8c2933728fe2fd1fb190cd852 | https://github.com/NHERI-SimCenter/BRAILS/tree/ec17bcd000b15cb8c2933728fe2fd1fb190cd852 |
_Extraction | import torch
from torch import Tensor
import torch.onnx.operators
def create_max_segment_mask(tensor: 'Tensor', max_segment_length):
"""
Create max-segment mask.
Args:
tensor:
:math: (N, T, *) where T is target dimension
Returns:
- max-segment mask:
:math:`(N,... | 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
from torch import Tensor
imp... | godweiyang/ParaGen | _Extraction | false | 15,437 | [
"Apache-2.0"
] | 50 | 9665d1244ea38a41fc06b4e0a7f6411985e2221f | https://github.com/godweiyang/ParaGen/tree/9665d1244ea38a41fc06b4e0a7f6411985e2221f |
Att | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Att(nn.Module):
def __init__(self, args):
super(Att, self).__init__()
self._sigmoid = nn.Sigmoid()
self._ws1 = nn.Linear(args.video_feature_dim, 1, bias=False)
self._init_weights()
def _ini... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | HCShi/IONet | Att | false | 18,380 | [
"MIT"
] | 4 | 42e3c0455a1ecb610f458e814d7310d685b2be7b | https://github.com/HCShi/IONet/tree/42e3c0455a1ecb610f458e814d7310d685b2be7b |
GaussianCriticNet | # 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 numpy as np
... | G-Flor/deeprl | GaussianCriticNet | false | 5,179 | [
"Apache-2.0"
] | 1 | aeae2c5d585e5853dc638968b1f090eb60abd351 | https://github.com/G-Flor/deeprl/tree/aeae2c5d585e5853dc638968b1f090eb60abd351 |
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
import torch.utils.data.distributed
import torch.onnx
def _reduce(x, reduction='elementwise_mean'):
if reduction == 'none':
return x
elif reduction == 'elementwise_mea... | 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... | akshayka/gavel | MSELoss | false | 14,772 | [
"MIT"
] | 67 | 40a22a725f2e70478483e98c9b07c6fc588e0c40 | https://github.com/akshayka/gavel/tree/40a22a725f2e70478483e98c9b07c6fc588e0c40 |
HuberLoss | import torch
import torch.nn as nn
import torch.utils.data
class HuberLoss(nn.Module):
def __init__(self, delta=1):
super().__init__()
self.huber_loss_delta1 = nn.SmoothL1Loss()
self.delta = delta
def forward(self, x, x_hat):
loss = self.huber_loss_delta1(x / self.delta, x_ha... | 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
... | Thibaud-Ardoin/d4rl_evaluations | HuberLoss | false | 14,480 | [
"Apache-2.0"
] | 123 | 135b23d3aecc234aacaeaaa019fbc7101d9b87ec | https://github.com/Thibaud-Ardoin/d4rl_evaluations/tree/135b23d3aecc234aacaeaaa019fbc7101d9b87ec |
MixtureSoftmax | # 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... | anuar12/deep_game_theory | MixtureSoftmax | false | 6,217 | [
"MIT"
] | 1 | 1debe5a498fe5f017f2791965a5e529b0dfb0529 | https://github.com/anuar12/deep_game_theory/tree/1debe5a498fe5f017f2791965a5e529b0dfb0529 |
MmQAHead | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
super(LayerNorm, self).__init__()
self.weight = nn.Paramet... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | MILVLG/rosita | MmQAHead | false | 8,768 | [
"Apache-2.0"
] | 32 | 13f7e68350a64b4b5b2c44b9fa4e7448bbe7420c | https://github.com/MILVLG/rosita/tree/13f7e68350a64b4b5b2c44b9fa4e7448bbe7420c |
MlpAttention | import torch
import torch.nn as nn
class Self_Attn1D(nn.Module):
""" Self attention Layer """
def __init__(self, in_dim, activation, k=8):
super(Self_Attn1D, self).__init__()
self.chanel_in = in_dim
self.activation = activation
self.query_conv = nn.Conv1d(in_channels=in_dim, o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Malta-Lab/IUPE | MlpAttention | false | 8,557 | [
"MIT"
] | 10 | 44ddf119917538f02bb69509fec7a8314eed419f | https://github.com/Malta-Lab/IUPE/tree/44ddf119917538f02bb69509fec7a8314eed419f |
ComposeModel | # 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.... | ycsun2017/simple_transfer | ComposeModel | false | 4,615 | [
"Apache-2.0"
] | 0 | b807f7a9d818c5586c101f616d190fe9968fabbd | https://github.com/ycsun2017/simple_transfer/tree/b807f7a9d818c5586c101f616d190fe9968fabbd |
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
from torch.quantization import QuantStub
from torch.quantization im... | T-head-Semi/tvm | Hsigmoid | false | 17,958 | [
"Apache-2.0"
] | 4 | c1b8e06685c92fb7cacbe989e147b0622aee4503 | https://github.com/T-head-Semi/tvm/tree/c1b8e06685c92fb7cacbe989e147b0622aee4503 |
StddevLayer | import torch
from torch import nn
class StddevLayer(nn.Module):
def __init__(self, group_size=4, num_new_features=1):
super().__init__()
self.group_size = 4
self.num_new_features = 1
def forward(self, x):
b, c, h, w = x.shape
group_size = min(self.group_size, b)
... | 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... | dubtor/EditGAN-Robert | StddevLayer | false | 15,231 | [
"BSD-2-Clause"
] | 110 | 8e6d80e7647c3536827f11cf0a9abf51c42794b2 | https://github.com/dubtor/EditGAN-Robert/tree/8e6d80e7647c3536827f11cf0a9abf51c42794b2 |
BPRLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BPRLoss(nn.Module):
def __init__(self):
super(BPRLoss, self).__init__()
def forward(self, logit):
"""
Args:
logit (BxB): Variable that stores the logits for the items in the mini-batch
... | 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... | Ethan-Yys/GRU4REC-pytorch-master | BPRLoss | false | 2,207 | [
"Apache-2.0"
] | 0 | 175ccb851f881d3506680c459491e76f50aa9898 | https://github.com/Ethan-Yys/GRU4REC-pytorch-master/tree/175ccb851f881d3506680c459491e76f50aa9898 |
Downsample | import torch
import torch.nn as nn
class Downsample(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv3d = nn.Conv3d(in_channels, out_channels, kernel_size=3,
stride=2, padding=1)
self.bn1 = nn.InstanceNorm3d(out_channels, affine=True)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BCV-Uniandes/SAMA | Downsample | false | 116 | [
"BSD-3-Clause"
] | 0 | 4c732c71486af17efed17480e363298cb65c851f | https://github.com/BCV-Uniandes/SAMA/tree/4c732c71486af17efed17480e363298cb65c851f |
FeedForwardBlock | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
def __init__(self, config):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Linear(config.d_model, config.d_f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MSU-MLSys-Lab/CATE | FeedForwardBlock | false | 8,584 | [
"Apache-2.0"
] | 15 | 654c393d7df888d2c3f3b90f9e6752faa061157e | https://github.com/MSU-MLSys-Lab/CATE/tree/654c393d7df888d2c3f3b90f9e6752faa061157e |
Similarity | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.nn import functional as F
class Similarity(nn.Module):
def __init__(self, mem_dim, hidden_dim, num_classes):
super(Similarity, self).__init__()
self.mem_dim = mem_dim
self.hidden_dim = hidden_di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | carol-hsu/relay-bench | Similarity | false | 3,272 | [
"Apache-2.0"
] | 0 | 0facffedb3cbb0d5f110769a84bba68718cff72b | https://github.com/carol-hsu/relay-bench/tree/0facffedb3cbb0d5f110769a84bba68718cff72b |
SegmentationHead | import torch
import torch.nn as nn
import torch.utils.data.dataloader
class SegmentationHead(nn.Module):
def __init__(self, descriptor_dimension, num_classes, **kwargs):
super().__init__()
self.descriptor_dimension = descriptor_dimension
self.classifier = nn.Conv2d(in_channels=descriptor_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.dataloader
assert_size_stride = to... | jamt9000/DVE | SegmentationHead | false | 15,669 | [
"MIT"
] | 72 | 208514419dd1eb0d27ce60876ca836d1ab8c4f4a | https://github.com/jamt9000/DVE/tree/208514419dd1eb0d27ce60876ca836d1ab8c4f4a |
myDecoder | import torch
import torch.nn.functional as F
class myDecoder(torch.nn.Module):
def __init__(self, fomSize, romSize):
super(myDecoder, self).__init__()
self.romSize_ = romSize
self.fomSize_ = fomSize
self.fc1 = torch.nn.Linear(romSize, 64)
self.fc2 = torch.nn.Linear(64, 200... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | Pressio/pressio4py | myDecoder | false | 17,816 | [
"Unlicense",
"BSD-3-Clause"
] | 4 | 36676dbd112a7c7960ccbf302ff14d4376c819ec | https://github.com/Pressio/pressio4py/tree/36676dbd112a7c7960ccbf302ff14d4376c819ec |
ActorDownAction | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLPBase(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(MLPBase, self).__init__()
self.l1 = nn.Linear(num_inputs, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, num_outputs)
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.... | yangfanthu/modular-rl | ActorDownAction | false | 13,140 | [
"BSD-2-Clause"
] | 0 | 25c599bab641a7e732dbaf116cd240fa2358f113 | https://github.com/yangfanthu/modular-rl/tree/25c599bab641a7e732dbaf116cd240fa2358f113 |
L1Loss | # 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 functools
impor... | AtticusJohnson/mmdetection | L1Loss | false | 11,222 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
HardSwish | # 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... | DetectionBLWX/WSDDN.pytorch | HardSwish | false | 17,212 | [
"MIT"
] | 7 | 05020d9d0445af90ba0af3f095aa12b18e3da7d2 | https://github.com/DetectionBLWX/WSDDN.pytorch/tree/05020d9d0445af90ba0af3f095aa12b18e3da7d2 |
VGGBase | import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from itertools import product as product
def decimate(tensor, m):
"""
Decimate a tensor by a factor 'm', i.e. downsample by keeping every 'm'th value.
This is used when we convert FC layers to equivalent Convolutional l... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 torchvision
import tor... | ildoonet/ai-starthon-2019 | VGGBase | false | 15,740 | [
"MIT"
] | 69 | 148855adcb731741938a86545a2d3282287f0a50 | https://github.com/ildoonet/ai-starthon-2019/tree/148855adcb731741938a86545a2d3282287f0a50 |
GlobalAvgPool2DBaseline | import torch
import torch.nn as nn
import torch.optim
class GlobalAvgPool2DBaseline(nn.Module):
def __init__(self):
super(GlobalAvgPool2DBaseline, self).__init__()
def forward(self, x):
x_pool = torch.mean(x.view(x.size(0), x.size(1), x.size(2) * x.size
(3)), dim=2)
x_poo... | 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.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | Bobholamovic/SimpleCV | GlobalAvgPool2DBaseline | false | 7,800 | [
"MIT"
] | 44 | f4edacf088d0155725a469e227de847820bdfa53 | https://github.com/Bobholamovic/SimpleCV/tree/f4edacf088d0155725a469e227de847820bdfa53 |
BCEDiceLoss | import torch
from torch import nn as nn
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
transposed... | 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 ... | ciubecca/3dunet-cavity | BCEDiceLoss | false | 1,717 | [
"MIT"
] | 0 | cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | https://github.com/ciubecca/3dunet-cavity/tree/cfcc827773b18a95d221ab86c1afc5e2f7c30ecb |
LossyRGB | # 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.parallel
import torch.utils.data
from torch import nn
import torch.fft
assert_size_stride = torch._C._dynamo.guards.assert_s... | KazutakaYamanouchi/bachelor-study | LossyRGB | false | 2,625 | [
"Apache-2.0"
] | 0 | a5b8392459e7649cb8a35d09e65bd269d13b5297 | https://github.com/KazutakaYamanouchi/bachelor-study/tree/a5b8392459e7649cb8a35d09e65bd269d13b5297 |
NNSmall | import torch
import torch.nn as nn
class NNSmall(nn.Module):
"""
Sammut et. al. benchmark
"""
def __init__(self, state_dim, output_dim):
super(NNSmall, self).__init__()
self.fc1 = nn.Linear(state_dim, 128)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(128, 128)
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_... | CORE-Robotics-Lab/Personalized_Neural_Trees | NNSmall | false | 17,047 | [
"MIT"
] | 3 | 3e8dd12fe4fc850be65c96c847eb143ef3bcdc2e | https://github.com/CORE-Robotics-Lab/Personalized_Neural_Trees/tree/3e8dd12fe4fc850be65c96c847eb143ef3bcdc2e |
HeatmapLoss | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | rm-rf-me/Study-stacked-hourglass | HeatmapLoss | false | 7,572 | [
"BSD-3-Clause"
] | 1 | 48441f0dd5ae3397470c70db0f50ab5576b9d2f2 | https://github.com/rm-rf-me/Study-stacked-hourglass/tree/48441f0dd5ae3397470c70db0f50ab5576b9d2f2 |
MuSigmaEncoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | benjaminalt/neural-processes | MuSigmaEncoder | false | 14,946 | [
"MIT"
] | 170 | 03d4f921fe0598c77787eecc53cbed23e326a5f5 | https://github.com/benjaminalt/neural-processes/tree/03d4f921fe0598c77787eecc53cbed23e326a5f5 |
TinyConvNet2d | import torch
class TinyConvNet2d(torch.nn.Module):
def __init__(self, in_channels=1, out_channels=1):
super().__init__()
self.conv1 = torch.nn.Conv2d(in_channels, 16, 1)
self.nlin1 = torch.nn.ReLU()
self.conv2 = torch.nn.Conv2d(16, 64, 1)
self.nlin2 = torch.nn.ReLU()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | FynnBe/tiktorch | TinyConvNet2d | false | 11,432 | [
"MIT"
] | 0 | 60c6fa9700e7ff73e44338e8755c56c6e8846f2f | https://github.com/FynnBe/tiktorch/tree/60c6fa9700e7ff73e44338e8755c56c6e8846f2f |
SyntacticGCN | import torch
import torch.nn as nn
import torch.nn.functional as F
class SyntacticGCN(nn.Module):
def __init__(self, input_size, hidden_size, num_labels, bias=True):
super(SyntacticGCN, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_labels = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | NLP-Discourse-SoochowU/TDDiscourseParser | SyntacticGCN | false | 17,732 | [
"Apache-2.0"
] | 9 | 2f9c7cef85c564c47b368ee4935caf1fad7c598d | https://github.com/NLP-Discourse-SoochowU/TDDiscourseParser/tree/2f9c7cef85c564c47b368ee4935caf1fad7c598d |
Scale | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, scale=30):
super(Scale, self).__init__()
self.scale = scale
def forward(self, x):
return x * self.scale
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | MickeyZeng/Data-Visualization | Scale | false | 832 | [
"MIT"
] | 0 | c7005d1096545d7a5eb96dd0c9bc13e874d42fa4 | https://github.com/MickeyZeng/Data-Visualization/tree/c7005d1096545d7a5eb96dd0c9bc13e874d42fa4 |
GradientLoss | import torch
import torch.nn as nn
class GradientLoss(nn.Module):
"""
L1 loss on the gradient of the picture
"""
def __init__(self):
super(GradientLoss, self).__init__()
def forward(self, a):
gradient_a_x = torch.abs(a[:, :, :, :-1] - a[:, :, :, 1:])
gradient_a_y = torch.... | 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... | GuYuanjie/DeepFusionPrior | GradientLoss | false | 5,224 | [
"MIT"
] | 1 | a7126e073ed8c49b6a9a662492b64aaeee56cc01 | https://github.com/GuYuanjie/DeepFusionPrior/tree/a7126e073ed8c49b6a9a662492b64aaeee56cc01 |
LinearActor | # 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... | KuangenZhang/StructuredRL | LinearActor | false | 5,448 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a |
MLP | import torch
import torch.nn as nn
class SharedDropout(nn.Module):
"""
SharedDropout differs from the vanilla dropout strategy in that
the dropout mask is shared across one dimension.
Args:
p (float):
The probability of an element to be zeroed. Default: 0.5.
batch_first (b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ashim95/parser | MLP | false | 6,238 | [
"MIT"
] | 1 | 61e9cd6bf16dcf1aa2b9d51b3a6c04ed048b3199 | https://github.com/ashim95/parser/tree/61e9cd6bf16dcf1aa2b9d51b3a6c04ed048b3199 |
MetaBilinear | import re
import torch
import warnings
from torch import nn
import torch.nn.functional as F
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
----... | 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 re
import warnings
from torch import nn
from collections import OrderedDict
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Timothy102/light-field-networks | MetaBilinear | false | 14,504 | [
"MIT"
] | 95 | 0d2d6099ea1df4332b173fab47e5606d579b4293 | https://github.com/Timothy102/light-field-networks/tree/0d2d6099ea1df4332b173fab47e5606d579b4293 |
SphereMSE | import math
import torch
from numpy import pi
from torch import nn
from torch.nn.parameter import Parameter
import torch as th
from torch.nn import Parameter
from functools import *
class SphereMSE(nn.Module):
def __init__(self, h, w):
super(SphereMSE, self).__init__()
self.h, self.w = h, w
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
from numpy import pi
from torch import nn
from torch.nn.parameter import Para... | JaviBite/TFG | SphereMSE | false | 2,400 | [
"MIT"
] | 0 | e406580697132f53b63a7c983daaa098af45b52c | https://github.com/JaviBite/TFG/tree/e406580697132f53b63a7c983daaa098af45b52c |
ConvNet | import torch
import torch.nn as nn
class ConvNet(nn.Module):
"""Standard convolutional net for baseline
Architecture: 2 convolutional layers, 3 fully connected layers.
"""
def __init__(self):
super(ConvNet, self).__init__()
args = {'stride': 1, 'padding': 1}
self.conv1 = nn.Co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Allen-Z-4230/MoCo-CIFAR10 | ConvNet | false | 4,877 | [
"MIT"
] | 1 | b2ade575b8ed1e05e32e4ec629acdfee55c8ff41 | https://github.com/Allen-Z-4230/MoCo-CIFAR10/tree/b2ade575b8ed1e05e32e4ec629acdfee55c8ff41 |
RMulInt | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | NVIDIA-AI-IOT-private/torch2trt | RMulInt | false | 10,532 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
Normalize | import torch
from torch import nn
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
out = x.div(norm)
return out
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
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Electronicshelf/Few-shot-regularization | Normalize | false | 405 | [
"MIT"
] | 0 | 3fd0fef52684af77a5e574b5d61cfd8dd557b14b | https://github.com/Electronicshelf/Few-shot-regularization/tree/3fd0fef52684af77a5e574b5d61cfd8dd557b14b |
AdaptiveCatAvgMaxPool2d | import torch
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
from torch import optim as optim
import torch.nn.parallel
def adaptive_catavgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_m... | 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 torchvision.transforms.functional as F
import torch.nn as ... | Exir-lxr/crldr-prune-pytorch | AdaptiveCatAvgMaxPool2d | false | 2,688 | [
"Apache-2.0"
] | 0 | adeb5e0b24ce66ff9531d4d947f72412c1b5c033 | https://github.com/Exir-lxr/crldr-prune-pytorch/tree/adeb5e0b24ce66ff9531d4d947f72412c1b5c033 |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | clairecw/TD3 | Critic | false | 1,734 | [
"MIT"
] | 0 | 22840c0ee1620086c2495d859be2c3b7a4c70f3f | https://github.com/clairecw/TD3/tree/22840c0ee1620086c2495d859be2c3b7a4c70f3f |
PosEnc | # 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
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchv... | CrispyHarder/ppuda | PosEnc | false | 806 | [
"MIT"
] | 0 | 15950ba297188163eaadd8ab69268ee7f6ffcf2a | https://github.com/CrispyHarder/ppuda/tree/15950ba297188163eaadd8ab69268ee7f6ffcf2a |
PositionwiseFeedforward | import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedforward(nn.Module):
def __init__(self, hid_dim, pf_dim, dropout):
super().__init__()
self.hid_dim = hid_dim
self.pf_dim = pf_dim
self.fc_1 = nn.Conv1d(hid_dim, pf_dim, 1)
self.fc_2 = 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._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | DingXiangYuanZhiXing/transformerCPI | PositionwiseFeedforward | false | 11,353 | [
"Apache-2.0"
] | 0 | 1fba6b29f6ddba64bdfb264887307c24fdf5c607 | https://github.com/DingXiangYuanZhiXing/transformerCPI/tree/1fba6b29f6ddba64bdfb264887307c24fdf5c607 |
WeightedMCEFocalloss | import torch
import torch.nn.functional
import torch.nn as nn
import torch.nn.functional as F
def centercrop(image, w, h):
_nt, _ct, ht, wt = image.size()
padw, padh = (wt - w) // 2, (ht - h) // 2
if padw > 0 and padh > 0:
image = image[:, :, padh:-padh, padw:-padw]
return image
class Weight... | 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.functi... | HelenGuohx/cv-ferattn-code | WeightedMCEFocalloss | false | 5,298 | [
"MIT"
] | 1 | faa9b7850fe2a0f8c08193bb129b5fec4639d616 | https://github.com/HelenGuohx/cv-ferattn-code/tree/faa9b7850fe2a0f8c08193bb129b5fec4639d616 |
AP | import torch
import torch.nn as nn
class AttentivePooling(nn.Module):
"""
Implementation of Attentive Pooling
"""
def __init__(self, input_dim, **kwargs):
super(AttentivePooling, self).__init__()
self.W_a = nn.Linear(input_dim, input_dim)
self.W = nn.Linear(input_dim, 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.... | gcambara/s3prl | AP | false | 15,411 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
Attention | import math
import torch
from torch import nn
from torch.nn import functional as F
class Attention(nn.Module):
def __init__(self, hidden_size):
super(Attention, self).__init__()
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
self.v = nn.Par... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ronak-44/smiles-transformer | Attention | false | 16,347 | [
"MIT"
] | 154 | 8965ca6211da721a8b708d1b3fa567b1bfd907cf | https://github.com/ronak-44/smiles-transformer/tree/8965ca6211da721a8b708d1b3fa567b1bfd907cf |
PseudoCoord | import torch
import torch.nn as nn
import torch.utils.data
class PseudoCoord(nn.Module):
def __init__(self):
super(PseudoCoord, self).__init__()
def forward(self, b):
"""
Input:
b: bounding box [batch, num_obj, 4] (x1,y1,x2,y2)
Output:
pseudo_coord ... | 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... | KaihuaTang/VQA2.0-Recent-Approachs-2018.pytorch | PseudoCoord | false | 13,933 | [
"MIT"
] | 298 | 52e1ba5a7f3b88c617115ccc755e2e7868e8de2b | https://github.com/KaihuaTang/VQA2.0-Recent-Approachs-2018.pytorch/tree/52e1ba5a7f3b88c617115ccc755e2e7868e8de2b |
DynamicPreHead | # 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.... | huanglf714/COMatchNet | DynamicPreHead | false | 6,838 | [
"Apache-2.0"
] | 1 | 79023f5be65d354eb9bdac026d7e0d73110bc4aa | https://github.com/huanglf714/COMatchNet/tree/79023f5be65d354eb9bdac026d7e0d73110bc4aa |
MaxPoolPad | import torch
import torch.nn as nn
import torch.nn.init
class MaxPoolPad(nn.Module):
def __init__(self):
super(MaxPoolPad, self).__init__()
self.pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.MaxPool2d(3, stride=2, padding=1)
def forward(self, x):
x = self.pad(x)
x =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.a... | dowhilefalse/DeOldify | MaxPoolPad | false | 12,302 | [
"MIT"
] | 0 | 08f012cdbe36e3f8482460f57e1844b361a7fb16 | https://github.com/dowhilefalse/DeOldify/tree/08f012cdbe36e3f8482460f57e1844b361a7fb16 |
VAE | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import functional as F
class VAE(nn.Module):
def __init__(self, n_features=24, z_dim=15):
super(VAE, self).__init__()
self.en1 = nn.Linear(n_features, 200)
self.en2 = nn.Linear(200, 100)
self.en3 = nn.Linear(... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math... | Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow | VAE | false | 4,885 | [
"Apache-2.0"
] | 1 | 772ba547c2b7d5d90e79382bf4d8a50e4d733210 | https://github.com/Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow/tree/772ba547c2b7d5d90e79382bf4d8a50e4d733210 |
HSwish | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data.distributed
class HSwish(nn.Module):
""" Applies the Hard-Swish function element-wise.
`"Searching for MobileNetV3" <https://arxiv.org/pdf/1905.02244.pdf>`_
Examples:
>>> m = Mish()
... | 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.functional
import torch.nn.parallel
import torch.ut... | doansangg/CGAN-PyTorch | HSwish | false | 6,584 | [
"Apache-2.0"
] | 1 | 941f5bd75102bed7f2eccd7feb9af8e6134af0e4 | https://github.com/doansangg/CGAN-PyTorch/tree/941f5bd75102bed7f2eccd7feb9af8e6134af0e4 |
BinResBlock | # 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_... | NeilDG/SGID-PFF | BinResBlock | false | 17,751 | [
"MIT"
] | 8 | e027ac65e63f3c052665290cd0438bb7bdeabf9f | https://github.com/NeilDG/SGID-PFF/tree/e027ac65e63f3c052665290cd0438bb7bdeabf9f |
SoftMaxAvgPoolModel | # 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.cuda
impo... | quic-kyunggeu/aimet | SoftMaxAvgPoolModel | false | 13,208 | [
"BSD-3-Clause"
] | 0 | 877835d5aafcef17cf12864124977d3c128d4aca | https://github.com/quic-kyunggeu/aimet/tree/877835d5aafcef17cf12864124977d3c128d4aca |
ReduceBranch | # 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... | dnddnjs/pytorch-vision | ReduceBranch | false | 15,196 | [
"MIT"
] | 48 | d432b467774f838bef37372d6cff3576c6559803 | https://github.com/dnddnjs/pytorch-vision/tree/d432b467774f838bef37372d6cff3576c6559803 |
Beta | import torch
import torch.nn as nn
import torch.functional as F
import torch.nn.functional as F
class BoundedBeta(torch.distributions.Beta):
def log_prob(self, x):
return super().log_prob((x + 1) / 2)
class Beta(nn.Module):
def __init__(self, action_dim):
super(Beta, 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.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | samarth-robo/apex | Beta | false | 4,254 | [
"MIT"
] | 0 | db24044acacd0fcd006886eb1677eaa2f2beedad | https://github.com/samarth-robo/apex/tree/db24044acacd0fcd006886eb1677eaa2f2beedad |
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
... | CodeLogist/Anti-Spoof_Face_recognitionV2 | L2Norm | false | 2,107 | [
"MIT"
] | 0 | ca2738f3d07442ffca92e76002ea24b26da39517 | https://github.com/CodeLogist/Anti-Spoof_Face_recognitionV2/tree/ca2738f3d07442ffca92e76002ea24b26da39517 |
GlobalAttentionGeneral | # 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.... | Creling/DM-GAN | GlobalAttentionGeneral | false | 2,120 | [
"MIT"
] | 0 | ec2ce6d7fae4cf3ba2099b3db09926e544b2b759 | https://github.com/Creling/DM-GAN/tree/ec2ce6d7fae4cf3ba2099b3db09926e544b2b759 |
EncoderLayer | import torch
from torch import nn
import torch.nn.functional as F
from functools import partial
class FFN(nn.Module):
"""
Feed-Forward Network
"""
def __init__(self, d_inner_hid, d_model, dropout_rate):
super(FFN, self).__init__()
self.dropout_rate = dropout_rate
self.fc1 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DocYard-ai/UCR | EncoderLayer | false | 8,026 | [
"Apache-2.0"
] | 10 | 7618aa336f56e71d9fd8cdc2d591e3d138e3dc68 | https://github.com/DocYard-ai/UCR/tree/7618aa336f56e71d9fd8cdc2d591e3d138e3dc68 |
GatedLinearUnit | import torch
import torch.nn.functional as F
import torch.nn as nn
class GatedLinearUnit(nn.Module):
"""Gated Linear Unit"""
def __init__(self, input_size: 'int', hidden_size: 'int'=None, dropout:
'float'=None):
super().__init__()
if dropout is not None:
self.dropout = 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... | JustinNeumann/pytorch-forecasting | GatedLinearUnit | false | 725 | [
"MIT"
] | 0 | 4f6e449cb3788b856e66c4283398a5db201aa6ff | https://github.com/JustinNeumann/pytorch-forecasting/tree/4f6e449cb3788b856e66c4283398a5db201aa6ff |
AutoEncoder | # 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_... | Xanadu12138/DSCN-superpixels | AutoEncoder | false | 18,097 | [
"MIT"
] | 4 | babe16edde9c61699ef203effbfc9f03246765f3 | https://github.com/Xanadu12138/DSCN-superpixels/tree/babe16edde9c61699ef203effbfc9f03246765f3 |
IrisClassifier | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
class IrisClassifier(nn.Module):
def __init__(self):
super(IrisClassifier, self).__init__()
self.fc1 = nn.Linear(4, 10)
self.fc2 = nn.Linear(10, 10)
self.fc3 = nn.Linear(10, 3)
def forward(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
import ... | abhinavthomas/mlflow | IrisClassifier | false | 12,036 | [
"Apache-2.0"
] | 0 | 1942d788e98e565229615373b4fd6c0899b4026b | https://github.com/abhinavthomas/mlflow/tree/1942d788e98e565229615373b4fd6c0899b4026b |
CCCLoss | import torch
import torch.nn as nn
class CCCLoss(nn.Module):
"""CCC loss for VA regression
"""
def __init__(self, reduction='mean', loss_weight=1.0):
super().__init__()
self.reduction = reduction
self.loss_weight = loss_weight
def get_name(self):
return 'CCC_loss'
... | 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... | youqingxiaozhua/ABAW3 | CCCLoss | false | 11,055 | [
"Apache-2.0"
] | 0 | 51ab58ab311ecd6603a8485a45af0dcc39880e69 | https://github.com/youqingxiaozhua/ABAW3/tree/51ab58ab311ecd6603a8485a45af0dcc39880e69 |
ClassHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from itertools import product as product
import torch.nn as nn
assert_size_strid... | Jung-Jun-Uk/UNPG | ClassHead | false | 17,540 | [
"Apache-2.0"
] | 7 | a6f9c1731a68fc035eb8fe8198f5a5c643825a5b | https://github.com/Jung-Jun-Uk/UNPG/tree/a6f9c1731a68fc035eb8fe8198f5a5c643825a5b |
Sine | # 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... | CGruich/ocp | Sine | false | 11,251 | [
"MIT",
"BSD-3-Clause"
] | 0 | dd97972b39d4a05e37f745e393a5245657ef5f9e | https://github.com/CGruich/ocp/tree/dd97972b39d4a05e37f745e393a5245657ef5f9e |
BertLayer | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.h... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SamarthMM/cs769-assignments | BertLayer | false | 4,736 | [
"MIT"
] | 0 | bac2ad57c50043608276df8e0f21181ef62696c7 | https://github.com/SamarthMM/cs769-assignments/tree/bac2ad57c50043608276df8e0f21181ef62696c7 |
NextSentencePrediction | # 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.... | SivilTaram/dialogue-utterance-rewriter-pytorch | NextSentencePrediction | false | 2,915 | [
"MIT"
] | 0 | 92c2254958b7a1ee9199836f7f2236575270983f | https://github.com/SivilTaram/dialogue-utterance-rewriter-pytorch/tree/92c2254958b7a1ee9199836f7f2236575270983f |
Discriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.weight_norm as weightNorm
class TReLU(nn.Module):
def __init__(self):
super(TReLU, self).__init__()
self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
self.alpha.data.fill_(0)
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | archiroid003/ICCV2019-LearningToPaint | Discriminator | false | 12,134 | [
"MIT"
] | 0 | 4b5fc263e4843c159a61e5956956b3f7812693f8 | https://github.com/archiroid003/ICCV2019-LearningToPaint/tree/4b5fc263e4843c159a61e5956956b3f7812693f8 |
Gaussian | import torch
from torch import Tensor
import torch.utils.tensorboard
import torch.utils.data
class Gaussian(torch.nn.Module):
"""Gaussian activation"""
def forward(self, x: 'Tensor') ->Tensor:
return torch.exp(-x * x)
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 math as tl_math
import torch.utils.tensorboard
import torch.utils.data
assert_size_stride... | cdever01/torchani | Gaussian | false | 4,042 | [
"MIT"
] | 0 | 3f7e1347a06422f50010c04a65219e22f2179bfa | https://github.com/cdever01/torchani/tree/3f7e1347a06422f50010c04a65219e22f2179bfa |
InvertibleLinearFlow | # 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 typing import Dict
from typing import Tuple
import torch.nn as nn
from torc... | juheeuu/flowseq | InvertibleLinearFlow | false | 12,649 | [
"Apache-2.0"
] | 0 | e6e50406656335ff7a2f9ed4bd81d7cc7d1195fb | https://github.com/juheeuu/flowseq/tree/e6e50406656335ff7a2f9ed4bd81d7cc7d1195fb |
MyElementwiseModule | # 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.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
as... | ShuaihuaLu/examples | MyElementwiseModule | false | 5,831 | [
"BSD-3-Clause"
] | 1 | 2639cf050493df9d3cbf065d45e6025733add0f4 | https://github.com/ShuaihuaLu/examples/tree/2639cf050493df9d3cbf065d45e6025733add0f4 |
Conv2dZeroInit | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | tychovdo/RevGAN | Conv2dZeroInit | false | 16,633 | [
"BSD-3-Clause"
] | 79 | 2af25e6a8176eaab3d424db45fb6ee2cfc5dc9a3 | https://github.com/tychovdo/RevGAN/tree/2af25e6a8176eaab3d424db45fb6ee2cfc5dc9a3 |
FCChain | import torch
import torch.utils.data
import torch.nn as nn
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if activation == 'relu':
return nn.ReLU(inplace=True)
if activation ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | sutkarsh/ttools | FCChain | false | 10,935 | [
"MIT"
] | 0 | a2e5fbf308566c0c54ab9d6ad1d9f8bc63f8fe99 | https://github.com/sutkarsh/ttools/tree/a2e5fbf308566c0c54ab9d6ad1d9f8bc63f8fe99 |
Scale | # 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
from torch.nn.parameter import Parameter
from itertools import product as product
assert_size_stride = torch._C._dynam... | DongChengdongHangZhou/caffe-to-pytorch | Scale | false | 2,227 | [
"Apache-2.0"
] | 0 | 5e3104f3aa77d35bad5d2de235b067460c136fd5 | https://github.com/DongChengdongHangZhou/caffe-to-pytorch/tree/5e3104f3aa77d35bad5d2de235b067460c136fd5 |
GCNet | # 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_... | PetarV-/telesign | GCNet | false | 17,805 | [
"MIT"
] | 4 | 05f58162b7c5fbc3993d320fdbc4d5465dd1c71e | https://github.com/PetarV-/telesign/tree/05f58162b7c5fbc3993d320fdbc4d5465dd1c71e |
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
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | nicola-decao/M-NAF-experiments-VAE | GatedConv2d | false | 4,087 | [
"MIT"
] | 0 | b8e127205e84d94ae50618e95734f20d259f7934 | https://github.com/nicola-decao/M-NAF-experiments-VAE/tree/b8e127205e84d94ae50618e95734f20d259f7934 |
PyramidModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torchvision.transforms import *
assert_size_stride = ... | arnon-weinberg/Upscale-interpolate-STARnet | PyramidModule | false | 12,154 | [
"MIT"
] | 0 | d898d38364a36f4633cfba8f914db20d9b900217 | https://github.com/arnon-weinberg/Upscale-interpolate-STARnet/tree/d898d38364a36f4633cfba8f914db20d9b900217 |
PairwiseRankingLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | maksimovVva/SentEval | PairwiseRankingLoss | false | 10,457 | [
"BSD-3-Clause"
] | 0 | d3aa5f24dd84b48ea476e73f4b59a4e1ace7775c | https://github.com/maksimovVva/SentEval/tree/d3aa5f24dd84b48ea476e73f4b59a4e1ace7775c |
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