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 |
|---|---|---|---|---|---|---|---|---|---|---|
EuclideanComparator_1 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from dataclasses import data... | lavis-nlp/irtm | EuclideanComparator_1 | false | 10,407 | [
"MIT"
] | 0 | e6c96519918795cfaa0c09ef2d4164f451265518 | https://github.com/lavis-nlp/irtm/tree/e6c96519918795cfaa0c09ef2d4164f451265518 |
RegLoss | # 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
... | leobean/CenterNet_simple | RegLoss | false | 3,967 | [
"MIT"
] | 0 | 13e2eab2c049563afde5defdf90434a310a32d02 | https://github.com/leobean/CenterNet_simple/tree/13e2eab2c049563afde5defdf90434a310a32d02 |
OutputTransition | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | WdBlink/AugMix-3DOCUNet-Brats2019 | OutputTransition | false | 5,966 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
CuboidPoseHead | # 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... | ALISCIFP/mmpose | CuboidPoseHead | false | 2,037 | [
"Apache-2.0"
] | 0 | 2433e3dbcc44baa2253e2a7c748ba0216937933e | https://github.com/ALISCIFP/mmpose/tree/2433e3dbcc44baa2253e2a7c748ba0216937933e |
HSigmoid | import torch
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data.distributed
class HSigmoid(nn.Module):
""" Applies the Hard-Sigmoid 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... | ardianumam/Vanilla-GAN | HSigmoid | false | 12,104 | [
"Apache-2.0"
] | 0 | 3fce9b60dca4609aad1d4e5eb834a2cc72cf07b3 | https://github.com/ardianumam/Vanilla-GAN/tree/3fce9b60dca4609aad1d4e5eb834a2cc72cf07b3 |
MulScalarNegative | # 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
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
assert_size_stride = torch._C._dyn... | Leslie-Fang/incubator-tvm | MulScalarNegative | false | 9,290 | [
"Apache-2.0"
] | 0 | aa035f4650926f5e714b02cbab6d974f0a17352f | https://github.com/Leslie-Fang/incubator-tvm/tree/aa035f4650926f5e714b02cbab6d974f0a17352f |
SeparableConv2d_same | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | lutbook/pytorch-segmentation-pipeline | SeparableConv2d_same | false | 10,587 | [
"MIT"
] | 0 | eb29d1bf240c158c64d81177e9be93cd958c0026 | https://github.com/lutbook/pytorch-segmentation-pipeline/tree/eb29d1bf240c158c64d81177e9be93cd958c0026 |
InnerProductDecoder | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.modules.loss
class InnerProductDecoder(nn.Module):
"""Decoder for using inner product for prediction."""
def __init__(self, dropout, act=torch.sigmoid):
super(InnerProductDecoder, self).__init__()
self.dropout ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.modules.loss
assert_size_stride = torch._C... | HongyiZhu/EHI | InnerProductDecoder | false | 540 | [
"MIT"
] | 0 | 9fbbc6046546dd7fc6de5d831b4c941bc4404e02 | https://github.com/HongyiZhu/EHI/tree/9fbbc6046546dd7fc6de5d831b4c941bc4404e02 |
MegatronGelu | import torch
import torch.nn
import torch.onnx
class MegatronGelu(torch.nn.Module):
def forward(self, x):
return x * 0.5 * (torch.erf(x / 1.41421) + 1.0)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.... | carefreekk/onnxruntime | MegatronGelu | false | 3,255 | [
"MIT"
] | 0 | 484e9de55c109dadbeb552cd6ede21bbdd63b830 | https://github.com/carefreekk/onnxruntime/tree/484e9de55c109dadbeb552cd6ede21bbdd63b830 |
GramMatrix | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | andreweskeclarke/style-transfer | GramMatrix | false | 1,462 | [
"MIT"
] | 0 | e4b18f4cdb3f473bf946f12cc39447b2f6bb15ca | https://github.com/andreweskeclarke/style-transfer/tree/e4b18f4cdb3f473bf946f12cc39447b2f6bb15ca |
CrossEntropyLoss | # 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
... | AtticusJohnson/mmdetection | CrossEntropyLoss | false | 11,211 | [
"Apache-2.0"
] | 0 | d8d89bafcce13d3b32b1fb3366be3bb9830546c2 | https://github.com/AtticusJohnson/mmdetection/tree/d8d89bafcce13d3b32b1fb3366be3bb9830546c2 |
GCN | from torch.nn import Module
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | d222nguy/gcn_research | GCN | false | 3,372 | [
"MIT"
] | 0 | 83ced4f7d9f7840e48900e62c1eabec0444c5fa2 | https://github.com/d222nguy/gcn_research/tree/83ced4f7d9f7840e48900e62c1eabec0444c5fa2 |
FCLayer | import torch
import torch.nn as nn
class FCLayer(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.0,
use_activation=True):
super(FCLayer, self).__init__()
self.use_activation = use_activation
self.dropout = nn.Dropout(dropout_rate)
self.linear = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Raiselimit/TorchBlocks | FCLayer | false | 5,730 | [
"MIT"
] | 1 | a5baecb9a2470ff175087475630f2b7db3f7ef51 | https://github.com/Raiselimit/TorchBlocks/tree/a5baecb9a2470ff175087475630f2b7db3f7ef51 |
EntropyLoss | # 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
... | NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation | EntropyLoss | false | 17,737 | [
"MIT"
] | 3 | fd0feab42151c0bae60712480301ea26f627a81d | https://github.com/NYCU-MLLab/Strategic-Optimization-for-Worst-case-Augmentation/tree/fd0feab42151c0bae60712480301ea26f627a81d |
Bilinear | import torch
import torch.nn as nn
class Bilinear(nn.Module):
def __init__(self, size):
super(Bilinear, self).__init__()
self.size = size
self.mat = nn.Parameter(torch.FloatTensor(self.size, self.size))
self.reset_parameters()
def reset_parameters(self):
params = [p 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | jalshr21/diora-1 | Bilinear | false | 6,914 | [
"Apache-2.0"
] | 1 | a9b680fde6a840707340e9e8232643b0f0e637bd | https://github.com/jalshr21/diora-1/tree/a9b680fde6a840707340e9e8232643b0f0e637bd |
EqualConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | AaltoVision/balanced-pioneer | EqualConv2d | false | 16,869 | [
"MIT"
] | 5 | 51f58080fd2db3159de3e1ccb47f38e03220faf0 | https://github.com/AaltoVision/balanced-pioneer/tree/51f58080fd2db3159de3e1ccb47f38e03220faf0 |
ResidualBlock | import torch
import torch.nn as nn
from functools import partial
def ncsn_conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1,
init_scale=1.0, padding=1):
"""3x3 convolution with PyTorch initialization. Same as NCSNv1/NCSNv2."""
init_scale = 1e-10 if init_scale == 0 else init_scale
conv = 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.triton_helpers import libdevice
import torch.nn as ... | henryaddison/score_sde_pytorch | ResidualBlock | false | 12,507 | [
"Apache-2.0"
] | 0 | be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 | https://github.com/henryaddison/score_sde_pytorch/tree/be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 |
RMSLELoss | import torch
import torch.nn as nn
class RMSLELoss(nn.Module):
def __init__(self):
super().__init__()
self.mse = nn.MSELoss()
def forward(self, pred, actual):
return torch.sqrt(self.mse(torch.log(pred + 1), torch.log(actual + 1)))
def get_inputs():
return [torch.rand([4, 4, 4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | RosarioAndolina/psychXRF | RMSLELoss | false | 988 | [
"MIT"
] | 0 | e2adadbd17664d7f74c10304f84b3751c571226e | https://github.com/RosarioAndolina/psychXRF/tree/e2adadbd17664d7f74c10304f84b3751c571226e |
TinyCnn | import torch
import torch.nn as nn
class TinyCnn(nn.Module):
def __init__(self, feature_extraction=False) ->None:
super().__init__()
self.feature_extraction = feature_extraction
self.conv1 = nn.Conv2d(3, 3, 5)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(2, 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | YNNEKUW/captum | TinyCnn | false | 12,003 | [
"BSD-3-Clause"
] | 0 | c8b5357b21f2ddf440e5f0ce25635977292aa5d1 | https://github.com/YNNEKUW/captum/tree/c8b5357b21f2ddf440e5f0ce25635977292aa5d1 |
LayerNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | BaiYuhaoSpiceeYJ/SEGAN_denoise | LayerNorm | false | 2,011 | [
"MIT"
] | 0 | 5bf65ae72b9f0a996ae338c53c68c4967e08cd59 | https://github.com/BaiYuhaoSpiceeYJ/SEGAN_denoise/tree/5bf65ae72b9f0a996ae338c53c68c4967e08cd59 |
LinearAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | francescodisalvo05/LoFTR | LinearAttention | false | 12,406 | [
"Apache-2.0"
] | 0 | 66372ebbe1ea97d57fe6cb8b5acf5cd92a87ef8d | https://github.com/francescodisalvo05/LoFTR/tree/66372ebbe1ea97d57fe6cb8b5acf5cd92a87ef8d |
ConvGRUCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | openclimatefix/MetNet | ConvGRUCell | false | 7,388 | [
"MIT"
] | 1 | 06eed550e93da6325641958b0d36c15adde1d928 | https://github.com/openclimatefix/MetNet/tree/06eed550e93da6325641958b0d36c15adde1d928 |
Foo | import torch
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
def add_lowp(a: 'torch.Tensor', b: 'torch.Tensor'):
a, b = a.float(), b.float()
c = a + b
return c.half()
def sigmoid_lowp(x: 'torch.Tensor'):
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
import torch.nn.parallel
import torch.utils.data
import torch.onnx
import torch.fx
import torch.optim
import torch.utils.data.distributed
as... | goytoom/examples | Foo | false | 12,460 | [
"BSD-3-Clause"
] | 0 | 50b2a74dba897a1a98c8276043a3f5c6910c453a | https://github.com/goytoom/examples/tree/50b2a74dba897a1a98c8276043a3f5c6910c453a |
ImageLinearAttention | import torch
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ImageLinearAttention(nn.Module):
def __init__(self, chan, chan_out=None, kernel_size=1, padding=0,
stride=1, key_dim=64, value_dim=64, heads=8):
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
from torch._inductor.runtime.... | CUMLSec/stateformer | ImageLinearAttention | false | 7,916 | [
"MIT"
] | 41 | 87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c | https://github.com/CUMLSec/stateformer/tree/87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c |
SyntacticGCN | # 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_... | NLP-Discourse-SoochowU/TDDiscourseParser | SyntacticGCN | false | 17,732 | [
"Apache-2.0"
] | 9 | 2f9c7cef85c564c47b368ee4935caf1fad7c598d | https://github.com/NLP-Discourse-SoochowU/TDDiscourseParser/tree/2f9c7cef85c564c47b368ee4935caf1fad7c598d |
AvgReducePool1d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | atif93/texar-pytorch | AvgReducePool1d | false | 6,256 | [
"Apache-2.0"
] | 1 | 88163619ec69382e1bbe57fa8bce06260bfc76a2 | https://github.com/atif93/texar-pytorch/tree/88163619ec69382e1bbe57fa8bce06260bfc76a2 |
Pow | import torch
import torch.nn as nn
class Pow(nn.Module):
def __init__(self):
super(Pow, self).__init__()
def forward(self, x):
x = torch.pow(x, 2)
return 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | yifanpu001/PytorchToCaffe | Pow | false | 4,706 | [
"MIT"
] | 0 | 37c1ebfc3547e93b1c174721036d03c831c60e48 | https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48 |
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
from torch import nn as nn
as... | FFTYYY/RoR_relation_extraction | FFN | false | 8,077 | [
"MIT"
] | 25 | a099e98f3708a39debeed4dc522ff57c4f6b960d | https://github.com/FFTYYY/RoR_relation_extraction/tree/a099e98f3708a39debeed4dc522ff57c4f6b960d |
TorchFloorDiv | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | Ilyabasharov/torch2trt | TorchFloorDiv | false | 2,558 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
InformedSender | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.distributions
class InformedSender(nn.Module):
def __init__(self, game_size, feat_size, embedding_size, hidden_size,
vocab_size=100, temp=1.0):
super(InformedSender, se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Slowika/GameBias-EmeCom2020 | InformedSender | false | 17,968 | [
"MIT"
] | 5 | 5b94c47559f8202bca99c26fc1bcb078dd0509a6 | https://github.com/Slowika/GameBias-EmeCom2020/tree/5b94c47559f8202bca99c26fc1bcb078dd0509a6 |
DiceCELoss | import torch
import warnings
from typing import Callable
from typing import Union
from typing import Optional
from enum import Enum
import torch.nn as nn
from torch.nn.modules.loss import _Loss
import torch.multiprocessing
class LossReduction(Enum):
"""
See also:
- :py:class:`monai.losses.dice.DiceLos... | 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 warnings
from t... | elitap/classimbalance | DiceCELoss | false | 6,669 | [
"Apache-2.0"
] | 1 | ae807ec533da5eef18f4180b29383399bc57696a | https://github.com/elitap/classimbalance/tree/ae807ec533da5eef18f4180b29383399bc57696a |
Sub | # 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 | Sub | false | 10,541 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
ResBlock | import torch
import torch.nn as nn
class ResBlock(nn.Module):
def __init__(self, in_c):
super(ResBlock, self).__init__()
self.conv1 = nn.Conv2d(in_c, in_c, kernel_size=3, stride=1, padding
=1, bias=True)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(in_c, in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | MohitLamba94/LLPackNet | ResBlock | false | 8,561 | [
"MIT"
] | 15 | 440e20ac48aed0beca5f473358ec85d24d477575 | https://github.com/MohitLamba94/LLPackNet/tree/440e20ac48aed0beca5f473358ec85d24d477575 |
L2LossWithLogit | import torch
import torch.utils.data
import torch
from torch import nn
class L2LossWithLogit(nn.Module):
def __init__(self):
super(L2LossWithLogit, self).__init__()
self.mse = nn.MSELoss(reduction='sum')
def forward(self, logits, targets):
p = torch.sigmoid(logits)
return sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch
from torch import nn
assert_size_stride = torch._C._... | ucas-vg/TinyBenchmark | L2LossWithLogit | false | 16,638 | [
"MIT"
] | 495 | 36436df3716d842b6148fb6f6bc7715a2fbdfd92 | https://github.com/ucas-vg/TinyBenchmark/tree/36436df3716d842b6148fb6f6bc7715a2fbdfd92 |
SoftArgmax | # 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 as t
impo... | analvikingur/RGAN | SoftArgmax | false | 18,313 | [
"MIT"
] | 8 | b1893c2f53d11c9173c7a30f63f6d93d72232493 | https://github.com/analvikingur/RGAN/tree/b1893c2f53d11c9173c7a30f63f6d93d72232493 |
GaussianSmoothing | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = ... | rakshithShetty/SemanticAdversary | GaussianSmoothing | false | 7,528 | [
"MIT"
] | 1 | e6d50f00af6f7d847cba4210613afea4be773254 | https://github.com/rakshithShetty/SemanticAdversary/tree/e6d50f00af6f7d847cba4210613afea4be773254 |
BERTNextSentence | import torch
import torch.nn as nn
class BERTNextSentence(nn.Module):
def __init__(self, hidden):
super().__init__()
self.linear = nn.Linear(hidden, 2)
self.softmax = nn.LogSoftmax(dim=-1)
def forward(self, x):
return self.softmax(self.linear(x[:, 0]))
def get_inputs():
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Moymix/BERT-pytorch | BERTNextSentence | false | 5,616 | [
"Apache-2.0"
] | 1 | f0b9c3ae53e05c00adcc761e0422e4222d8b5619 | https://github.com/Moymix/BERT-pytorch/tree/f0b9c3ae53e05c00adcc761e0422e4222d8b5619 |
NegativeScaledDotProduct | import torch
import torch.utils.data.dataloader
import torch.nn
def dot_product(a: 'torch.Tensor', b: 'torch.Tensor', normalize=False):
"""
Computes dot product for pairs of vectors.
:param normalize: Vectors are normalized (leads to cosine similarity)
:return: Matrix with res[i][j] = dot_product(a[i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data.dataloader
import torch.nn
assert_size_stride = torch._C... | adriensas/flair | NegativeScaledDotProduct | false | 9,742 | [
"MIT"
] | 0 | f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 | https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21 |
HLoss | import torch
from torch.autograd.gradcheck import *
import torch.nn as nn
import torch.nn
class HLoss(nn.Module):
def __init__(self):
super(HLoss, self).__init__()
def forward(self, x, spacing):
volumeElement = spacing.prod()
b = x * torch.log(x)
b = -1.0 * b.sum() * volumeEl... | 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.autograd.gr... | HastingsGreer/mermaid | HLoss | false | 13,760 | [
"Apache-2.0"
] | 120 | bd13c5fc427eb8cd9054973a8eaaeb302078182d | https://github.com/HastingsGreer/mermaid/tree/bd13c5fc427eb8cd9054973a8eaaeb302078182d |
outconv | import torch
import torch.nn as nn
class outconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(outconv, self).__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1)
self.sig = nn.Sigmoid()
def forward(self, x):
x_conv = self.conv(x)
x = self.sig(x_conv)
ret... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | allen-q/pytorch | outconv | false | 3,085 | [
"MIT"
] | 0 | 76947f8d6f0bcee04425ad69f93b9a5e3a5060ae | https://github.com/allen-q/pytorch/tree/76947f8d6f0bcee04425ad69f93b9a5e3a5060ae |
Tanh2 | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
import torch.optim
class Tanh2(nn.Module):
def __init__(self):
super(Tanh2, self).__init__()
self.tanh = nn.Tanh()
def forward(self, x):
return (self.tanh(x) + 1) / 2
def get_inputs():
return [t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
import t... | csyxwei/FFWM | Tanh2 | false | 15,077 | [
"MIT"
] | 83 | d42c578cabe1b81c6b1bb0c3cb707b190fca3c68 | https://github.com/csyxwei/FFWM/tree/d42c578cabe1b81c6b1bb0c3cb707b190fca3c68 |
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... | chris-price19/ocp | Sine | false | 1,699 | [
"MIT",
"BSD-3-Clause"
] | 0 | 0175c5a11dd3aaccd4f4780c8cb559401f1ca15e | https://github.com/chris-price19/ocp/tree/0175c5a11dd3aaccd4f4780c8cb559401f1ca15e |
Network | # 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.... | gregmbi/polyaxon | Network | false | 3,559 | [
"Apache-2.0"
] | 0 | 8f24089fa9cb5df28fc7b70aec27d6d23ee81e8d | https://github.com/gregmbi/polyaxon/tree/8f24089fa9cb5df28fc7b70aec27d6d23ee81e8d |
TransformerDecoderLayer | # 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.... | cimeister/neural-transducer | TransformerDecoderLayer | false | 1,746 | [
"MIT"
] | 0 | e4dfc718bbcf93254ce23750e5428c5131ddfb98 | https://github.com/cimeister/neural-transducer/tree/e4dfc718bbcf93254ce23750e5428c5131ddfb98 |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | francismontalbo/attention-is-all-you-need-paper | MultiHeadAttention | false | 15,377 | [
"MIT"
] | 167 | 21ba3e48917da0c6808126d183bece6a9969cfd2 | https://github.com/francismontalbo/attention-is-all-you-need-paper/tree/21ba3e48917da0c6808126d183bece6a9969cfd2 |
OneMinusCosThetaByThetaSq | import torch
from torch import cos
from torch import sin
def get_small_and_large_angle_inds(theta: 'torch.Tensor', eps: 'float'=0.001):
"""Returns the indices of small and non-small (large) angles, given
a tensor of angles, and the threshold below (exclusive) which angles
are considered 'small'.
Args... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import cos
from torch import sin
assert_size_stride = torch._C... | darkmatter08/dfa-scales-to-modern-deep-learning | OneMinusCosThetaByThetaSq | false | 6,522 | [
"MIT"
] | 1 | 72bf8a045b4bb7eb81736d8ec1d671c4949fb01e | https://github.com/darkmatter08/dfa-scales-to-modern-deep-learning/tree/72bf8a045b4bb7eb81736d8ec1d671c4949fb01e |
AttDot | import torch
import torch.nn.functional as F
class AttDot(torch.nn.Module):
"""
AttDot: Dot attention that can be used by the Alignment module.
"""
def __init__(self, softmax=True):
super().__init__()
self.softmax = softmax
def forward(self, query, y):
att = torch.bmm(que... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ishine/NISQA | AttDot | false | 15,615 | [
"MIT"
] | 223 | 2c8917f30c4e4bbca3a48e9852301f1e2480a741 | https://github.com/ishine/NISQA/tree/2c8917f30c4e4bbca3a48e9852301f1e2480a741 |
FourierFeatures | # 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... | DeepTitan/v-diffusion-pytorch | FourierFeatures | false | 9,032 | [
"MIT"
] | 0 | 857b6f2a4519973f9a8dc0b6c93f0134cebc3a8d | https://github.com/DeepTitan/v-diffusion-pytorch/tree/857b6f2a4519973f9a8dc0b6c93f0134cebc3a8d |
MultiheadSimilarity | import torch
import torch.utils.data
from torch import nn
class MultiheadSimilarity(nn.Module):
def __init__(self, d_model, num_head, seq_len, in_proj=True):
super().__init__()
self.num_head = num_head
self.seq_len = seq_len
self.d_head = d_model // num_head
self.in_proj =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | bladewaltz1/clipvid-tmp | MultiheadSimilarity | false | 1,564 | [
"MIT"
] | 0 | 8a4a990c318fdfbf6dac443abd3bc16637abba3d | https://github.com/bladewaltz1/clipvid-tmp/tree/8a4a990c318fdfbf6dac443abd3bc16637abba3d |
PredictionHead | import torch
import torch.nn as nn
from torchvision.models.quantization import *
class PredictionHead(nn.Module):
def __init__(self, in_channels, num_classes, num_anchors):
super(PredictionHead, self).__init__()
self.classification = nn.Conv2d(in_channels, num_classes *
num_anchors, 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
import torch.nn as nn
from torchvision.models.quantization import *
assert_size_... | CaoZhongZ/inference | PredictionHead | false | 13,859 | [
"Apache-2.0"
] | 388 | 58025f8fde679ea864d34f96ecc9f14bf70ece53 | https://github.com/CaoZhongZ/inference/tree/58025f8fde679ea864d34f96ecc9f14bf70ece53 |
encoder3 | import torch
import torch.nn as nn
class encoder3(nn.Module):
def __init__(self):
super(encoder3, self).__init__()
self.conv1 = nn.Conv2d(3, 3, 1, 1, 0)
self.reflecPad1 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv2 = nn.Conv2d(3, 64, 3, 1, 0)
self.relu2 = nn.ReLU(inplace=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 import triton_helpers
from torch._inductor.runtime.... | guswl8033/ARtists | encoder3 | false | 3,570 | [
"Apache-2.0"
] | 0 | d353195872c1ef1a1aa68659a32fb47779a416fc | https://github.com/guswl8033/ARtists/tree/d353195872c1ef1a1aa68659a32fb47779a416fc |
SelfAttention | import torch
import torch.nn as nn
class SelfAttention(nn.Module):
"""A simple self-attention solution."""
def __init__(self, data_dim, dim_q):
super(SelfAttention, self).__init__()
self._layers = []
self._fc_q = nn.Linear(data_dim, dim_q)
self._layers.append(self._fc_q)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | daia99/brain-tokyo-workshop | SelfAttention | false | 15,111 | [
"Apache-2.0"
] | 1,097 | cd470255230afddba2b80d99a9641b682f4d0762 | https://github.com/daia99/brain-tokyo-workshop/tree/cd470255230afddba2b80d99a9641b682f4d0762 |
Conv_Blocks | import torch
import torch.nn as nn
class Conv_Blocks(nn.Module):
def __init__(self, input_dim, output_dim, filter_size=3, batch_norm=
False, non_lin='tanh', dropout=0.0, first_block=False, last_block=
False, skip_connection=False):
super(Conv_Blocks, self).__init__()
self.skip_con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HRHLALALA/GoalGAN | Conv_Blocks | false | 9,098 | [
"MIT"
] | 0 | 01443f2a578333a0d5ab3a449bc7da69f5023190 | https://github.com/HRHLALALA/GoalGAN/tree/01443f2a578333a0d5ab3a449bc7da69f5023190 |
ReduceMax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | carlogrisetti/ludwig | ReduceMax | false | 1,632 | [
"Apache-2.0"
] | 0 | 5c0887f14867e1577e0ddc3806c5cf7a781fb665 | https://github.com/carlogrisetti/ludwig/tree/5c0887f14867e1577e0ddc3806c5cf7a781fb665 |
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | JanSoltysik/SimCLR | Encoder | false | 11,553 | [
"MIT"
] | 0 | 34ea6d17a630382b65a00aa445d82876754ee679 | https://github.com/JanSoltysik/SimCLR/tree/34ea6d17a630382b65a00aa445d82876754ee679 |
RPNHead | # 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... | Jaramies/PyTorch-Simple-MaskRCNN | RPNHead | false | 5,371 | [
"MIT"
] | 1 | 21e6c6983b34061800280573ebe705ae17212972 | https://github.com/Jaramies/PyTorch-Simple-MaskRCNN/tree/21e6c6983b34061800280573ebe705ae17212972 |
PixelShuffle | # 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.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | LonglifeHyun/GANda_text-to-image | PixelShuffle | false | 2,567 | [
"MIT"
] | 0 | 095ded617e4df7d7ff7f4954381dde77db6d6883 | https://github.com/LonglifeHyun/GANda_text-to-image/tree/095ded617e4df7d7ff7f4954381dde77db6d6883 |
ResidualBlock | import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.in_channels, self.out_channels = in_channels, out_channels
self.blocks = nn.Identity()
self.shortcut = nn.Identity()
def forward(self, x):... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_add_0(in_ptr0, out_... | Rachneet/amc-app | ResidualBlock | false | 11,796 | [
"MIT"
] | 0 | 20b586608d454a3033333e285f0dbc91e5c6e07f | https://github.com/Rachneet/amc-app/tree/20b586608d454a3033333e285f0dbc91e5c6e07f |
PairwiseCrossCorrelation | import torch
from torch import nn
class PairwiseCrossCorrelation(nn.Module):
def __init__(self, lambd=1):
super().__init__()
self.lambd = lambd
def off_diagonal(self, x):
n, m = x.shape
assert n == m
return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | vzinche/inferno | PairwiseCrossCorrelation | false | 4,512 | [
"Apache-2.0"
] | 0 | 91b22dfcd1b6a9ec415f0bbb6ae66caea42f4034 | https://github.com/vzinche/inferno/tree/91b22dfcd1b6a9ec415f0bbb6ae66caea42f4034 |
SVIGlobalMaxPool2D | import torch
import torch.nn as nn
class SVIGlobalMaxPool2D(nn.Module):
"""
Expects
:param x: [examples, samples, channels, H, W]
:return: [examples, samples, channels]
"""
def __init__(self):
super(SVIGlobalMaxPool2D, self).__init__()
def forward(self, x):
x = x.max(4)[0... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | RomanShen/radial-bnn | SVIGlobalMaxPool2D | false | 992 | [
"Apache-2.0"
] | 0 | 7c8bc85397c1461a6fd5ea9adf0631f9ade27f6c | https://github.com/RomanShen/radial-bnn/tree/7c8bc85397c1461a6fd5ea9adf0631f9ade27f6c |
OneTupleModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class OneTupleModule(torch.nn.Module):
def __init__(self):
super(OneTupleModule, self).__init__()
def forward(self, x):
y = 2 * x
return y,
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
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | opti-mix/glow | OneTupleModule | false | 7,386 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
SSD300 | import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt
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 equi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ildoonet/ai-starthon-2019 | SSD300 | false | 15,742 | [
"MIT"
] | 69 | 148855adcb731741938a86545a2d3282287f0a50 | https://github.com/ildoonet/ai-starthon-2019/tree/148855adcb731741938a86545a2d3282287f0a50 |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | RowitZou/CG-nAR | PositionwiseFeedForward | false | 17,879 | [
"MIT"
] | 8 | 8e2debeb3170045592b3b674ea6f9b56251e71f4 | https://github.com/RowitZou/CG-nAR/tree/8e2debeb3170045592b3b674ea6f9b56251e71f4 |
ResidualDenseBlock | import torch
import torch.nn as nn
class ResidualDenseBlock(nn.Module):
def __init__(self, channels=64, kernel_size=3, growth=32):
super().__init__()
self.conv2d_1 = self.conv2d(channels, growth, kernel_size, growth, 0)
self.conv2d_2 = self.conv2d(channels, growth, kernel_size, growth, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Frognar/Super-Resolution | ResidualDenseBlock | false | 5,187 | [
"MIT"
] | 1 | 406b909d71e156aa11ee589698744e3ad9abfee7 | https://github.com/Frognar/Super-Resolution/tree/406b909d71e156aa11ee589698744e3ad9abfee7 |
PropMaxPool | from _paritybench_helpers import _mock_config
import torch
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
class PropMaxPool(nn.Module):
def __init__(self, cfg):
super(PropMaxPool, self).__init__()
num_layers = cfg.NUM_LAYERS
self.layers ... | 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.parallel
import torch.nn as nn
import torch.utils.data
import torch.backe... | EGO4D/episodic-memory | PropMaxPool | false | 8,812 | [
"MIT"
] | 27 | 2a3464882cd4f665c358c1b05a6397339e33c2e1 | https://github.com/EGO4D/episodic-memory/tree/2a3464882cd4f665c358c1b05a6397339e33c2e1 |
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.... | lingzenan/invertible-resnet | Conv2dZeroInit | false | 7,092 | [
"MIT"
] | 1 | 57b1c0de51a885aed074b77628f3b0c85c548e70 | https://github.com/lingzenan/invertible-resnet/tree/57b1c0de51a885aed074b77628f3b0c85c548e70 |
IoULoss | import torch
import torch.nn as nn
class IoULoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super().__init__()
def forward(self, inputs, targets):
smooth = 1.0
num = targets.size(0)
m1 = inputs.view(num, -1)
m2 = targets.view(num, -1)
inte... | 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... | Luoxd1996/Rank2nuclearSegmentation | IoULoss | false | 17,617 | [
"MIT"
] | 5 | bd85ac13eec7ce18c286efd521a27486483da904 | https://github.com/Luoxd1996/Rank2nuclearSegmentation/tree/bd85ac13eec7ce18c286efd521a27486483da904 |
IrisClassifier | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class IrisClassifier(nn.Module):
def __init__(self):
super(IrisClassifier, self).__init__()
self.fc1 = nn.Linear(4, 100)
self.fc2 = nn.Linear(100, 100)
self.fc3 = nn.Linear(100, 3)
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.... | dbinoy/pytorch-iris-sagemaker | IrisClassifier | false | 9,974 | [
"MIT-0"
] | 0 | afc5bd95f6dd0431338708bc179029fa08724a2f | https://github.com/dbinoy/pytorch-iris-sagemaker/tree/afc5bd95f6dd0431338708bc179029fa08724a2f |
SoftCrossEntropyLossWithOHEM | # 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
from torch import nn
a... | ForrestPi/semanticSegmentation | SoftCrossEntropyLossWithOHEM | false | 17,345 | [
"MIT"
] | 7 | 1e5519279e2a9574f09eaf91439138b74b0f860c | https://github.com/ForrestPi/semanticSegmentation/tree/1e5519279e2a9574f09eaf91439138b74b0f860c |
Normalize | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride =... | a11isonliu/contrastive-unpaired-translation | Normalize | false | 9,850 | [
"BSD-3-Clause"
] | 0 | 67651ed9877cae121d9398f46094ce8dbc678802 | https://github.com/a11isonliu/contrastive-unpaired-translation/tree/67651ed9877cae121d9398f46094ce8dbc678802 |
PredictionConvolutions | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | HFAiLab/ffrecord | PredictionConvolutions | false | 14,023 | [
"MIT"
] | 47 | e916dc715ffa38a304a673ade7c5aa1efff5936d | https://github.com/HFAiLab/ffrecord/tree/e916dc715ffa38a304a673ade7c5aa1efff5936d |
AttentionSeq2Vec | from torch.nn import Module
import torch
from torch.nn import Linear
from typing import Optional
from torch.nn import Tanh
def masked_softmax(vector: 'torch.FloatTensor', mask: 'torch.ByteTensor'):
"""
计算带有 masked 的 softmax
:param vector: shape: (B, seq_len)
:param mask: shape: (B, seq_len),
:retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | Tiffany-HONG/easytext | AttentionSeq2Vec | false | 5,901 | [
"MIT"
] | 1 | 9c717d11240d96fab98b0532084ebb5c093d55bd | https://github.com/Tiffany-HONG/easytext/tree/9c717d11240d96fab98b0532084ebb5c093d55bd |
MAXATTN | import torch
import torch.nn as nn
class MAXATTN(nn.Module):
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None):
super(MAXATTN, self).__init__()
self.attention_layer = nn.MultiheadAttention(embed_dim, 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.... | Hritikbansal/RNNs_SVA_OOD | MAXATTN | false | 17,392 | [
"MIT"
] | 4 | a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | https://github.com/Hritikbansal/RNNs_SVA_OOD/tree/a1c73955342d9d35c49da5fcb7b315e37b0f75d1 |
Conv2d | import torch
import torch.nn as nn
from torch.nn import functional as F
class Conv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2d, self).__init__(in_channels, out_channels, kernel_size,
strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ChuanqiTan/DeepLabv3.pytorch | Conv2d | false | 11,316 | [
"BSD-3-Clause"
] | 0 | 260db5812ae3c85f0aacd5ec9bc0e3d8c5d2d067 | https://github.com/ChuanqiTan/DeepLabv3.pytorch/tree/260db5812ae3c85f0aacd5ec9bc0e3d8c5d2d067 |
FractionProposalModel | # 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.... | alirezakazemipour/Distributional-RL | FractionProposalModel | false | 1,424 | [
"MIT"
] | 0 | a3de3a1707bdd57a420f85c6d64a3fb84fb075af | https://github.com/alirezakazemipour/Distributional-RL/tree/a3de3a1707bdd57a420f85c6d64a3fb84fb075af |
SelfAttention | # 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.... | ahmedelhodaiby/HandMesh | SelfAttention | false | 9,902 | [
"MIT"
] | 0 | d86ec322b7627c5756bd9ae9e152bcd4f2debfa6 | https://github.com/ahmedelhodaiby/HandMesh/tree/d86ec322b7627c5756bd9ae9e152bcd4f2debfa6 |
MeanMaxPooling | import torch
from torch import nn
class MeanMaxPooling(nn.Module):
def __init__(self):
super(MeanMaxPooling, self).__init__()
def forward(self, doc_state, entity_mapping, entity_lens):
"""
:param doc_state: N x L x d
:param entity_mapping: N x E x L
:param entity_le... | 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... | jennybae1024/DFGN-pytorch | MeanMaxPooling | false | 15,681 | [
"MIT"
] | 191 | 056d9317f772cd10bdd215bfafdbac5cbd330026 | https://github.com/jennybae1024/DFGN-pytorch/tree/056d9317f772cd10bdd215bfafdbac5cbd330026 |
EmbeddingsInteraction | # 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... | SUSTechBruce/RL_CTR | EmbeddingsInteraction | false | 2,789 | [
"Apache-2.0"
] | 0 | 817398dc1c117e22f41281830ae3c33bba8062d3 | https://github.com/SUSTechBruce/RL_CTR/tree/817398dc1c117e22f41281830ae3c33bba8062d3 |
InverseDepthSmoothnessLoss | # 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... | JoanFM/kornia | InverseDepthSmoothnessLoss | false | 11,551 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 808898887cde69074ca3e3df9b24dea9682aad90 | https://github.com/JoanFM/kornia/tree/808898887cde69074ca3e3df9b24dea9682aad90 |
PolicyNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class PolicyNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size=256):
super(PolicyNetwork, self).__init__()
self.num_actions = num_actions
self.linear1 = nn.Linear(num_inputs, hidden_size)
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.... | bluebibi/rl_book_codes | PolicyNetwork | false | 3,228 | [
"MIT"
] | 0 | ef7fc9993eb66618e4b4e80e59cc2879a8db3522 | https://github.com/bluebibi/rl_book_codes/tree/ef7fc9993eb66618e4b4e80e59cc2879a8db3522 |
SemiNorm | import torch
import torch.nn as nn
from torch.nn.utils import spectral_norm
import torch.jit
import torch.nn
from torch.nn.utils.spectral_norm import spectral_norm
class SemiNorm(nn.Module):
def __init__(self, in_size, normalization=None):
super().__init__()
normalization = normalization or spect... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ankmathur96/torchsupport | SemiNorm | false | 3,170 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
Head | import torch
import torch.nn as nn
class Conv(nn.Module):
def __init__(self, filters0, filters1, kernel_size, bn, bias=True):
super().__init__()
if bn:
bias = False
self.conv = nn.Conv2d(filters0, filters1, kernel_size, stride=1,
padding=kernel_size // 2, bias=bias... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | IMOKURI/Hungry-Geese | Head | false | 9,336 | [
"MIT"
] | 0 | 5e770b3278452c2ba4006c18a43a16d572c636ac | https://github.com/IMOKURI/Hungry-Geese/tree/5e770b3278452c2ba4006c18a43a16d572c636ac |
DilatedGatedConv1D | # 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... | shinoyuki222/torch-light | DilatedGatedConv1D | false | 16,429 | [
"MIT"
] | 310 | 4799805d9bcae82a9f12a574dcf9fdd838c92ee9 | https://github.com/shinoyuki222/torch-light/tree/4799805d9bcae82a9f12a574dcf9fdd838c92ee9 |
Envelope | import torch
import torch.utils.data
class Envelope(torch.nn.Module):
def __init__(self, exponent):
super(Envelope, self).__init__()
self.p = exponent + 1
self.a = -(self.p + 1) * (self.p + 2) / 2
self.b = self.p * (self.p + 2)
self.c = -self.p * (self.p + 1) / 2
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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | EricAlcaide/pytorch_geometric | Envelope | false | 2,191 | [
"MIT"
] | 0 | 31cef566cfe22602459155fdf91e9b6ce398bfe7 | https://github.com/EricAlcaide/pytorch_geometric/tree/31cef566cfe22602459155fdf91e9b6ce398bfe7 |
ConvRelu | # 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... | BloodAxe/segmentation-networks-benchmark | ConvRelu | false | 7,856 | [
"MIT"
] | 34 | 2e3feb560102230be9369ab442b4a59cc86dff61 | https://github.com/BloodAxe/segmentation-networks-benchmark/tree/2e3feb560102230be9369ab442b4a59cc86dff61 |
PosLinear | import torch
from torch import Tensor
from torch.utils.data import Dataset as Dataset
import torch.nn as nn
import torch.utils.data
class PosLinear(torch.nn.Linear):
def forward(self, x: 'Tensor') ->Tensor:
gain = 1 / x.size(1)
return nn.functional.linear(x, torch.nn.functional.softplus(self.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | JunLi-Galios/CP-Flow | PosLinear | false | 11,595 | [
"MIT"
] | 0 | 69272636c8c644ce3c96bbc4d610591756b8e3ff | https://github.com/JunLi-Galios/CP-Flow/tree/69272636c8c644ce3c96bbc4d610591756b8e3ff |
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.... | JoonHong-Kim/T2I_CL | GlobalAttentionGeneral | false | 8,369 | [
"MIT"
] | 35 | c52aa73da903d6e4174eeef2663e5bc1163785b1 | https://github.com/JoonHong-Kim/T2I_CL/tree/c52aa73da903d6e4174eeef2663e5bc1163785b1 |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
def __init__(self, n_dims, scale=20.0, eps=1e-10):
super(L2Norm, self).__init__()
self.n_dims = n_dims
self.weight = nn.Parameter(torch.Tensor(self.n_dims))
self.eps = eps
self.scale = scale
def forward(self,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | CK-er/mmdet | L2Norm | false | 2,056 | [
"Apache-2.0"
] | 0 | 9bea4068efbcf7bf739dbe41917a68d525c29868 | https://github.com/CK-er/mmdet/tree/9bea4068efbcf7bf739dbe41917a68d525c29868 |
ActorCritic | # 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.... | shinoyuki222/torch-light | ActorCritic | false | 16,425 | [
"MIT"
] | 310 | 4799805d9bcae82a9f12a574dcf9fdd838c92ee9 | https://github.com/shinoyuki222/torch-light/tree/4799805d9bcae82a9f12a574dcf9fdd838c92ee9 |
Scale | import torch
import torch.nn as nn
import torch.utils.data
class Scale(nn.Module):
"""A learnable scale parameter.
This layer scales the input by a learnable factor. It multiplies a
learnable scale parameter of shape (1,) with input of any shape.
Args:
scale (float): Initial value of scale f... | 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 | Scale | false | 4,755 | [
"Apache-2.0"
] | 1 | 920f6f8ea44eac4c1896b7d157c015e039ac39f9 | https://github.com/AIpakchoi/visualDet3D/tree/920f6f8ea44eac4c1896b7d157c015e039ac39f9 |
unrotate | import torch
import torch.nn as nn
class unrotate(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x0, x90, x180, x270 = torch.chunk(x, 4, dim=0)
x90 = x90.transpose(2, 3).flip(2)
x180 = x180.flip(2).flip(3)
x270 = x270.transpose(2, 3).flip(3)
... | 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 | unrotate | false | 1,425 | [
"MIT"
] | 0 | a1ccb777d205255ac68c40efb93dd3996f562c45 | https://github.com/amonod/udvd/tree/a1ccb777d205255ac68c40efb93dd3996f562c45 |
FrameAvgPool | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class FrameAvgPool(nn.Module):
def __init__(self, cfg):
super(FrameAvgPool, self).__init__()
input_size = cfg.INPUT_SIZE
hidden_size = cfg.HIDDEN_SIZE
kernel_size = cfg.KERNEL_SIZE
stride = cf... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | CFM-MSG/Code_LEORN | FrameAvgPool | false | 7,616 | [
"MIT"
] | 1 | fabea1e1ded973a4db692e51e2df442bde55f626 | https://github.com/CFM-MSG/Code_LEORN/tree/fabea1e1ded973a4db692e51e2df442bde55f626 |
Sinc | # 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... | fmhoward/pysurvival | Sinc | false | 12,379 | [
"Apache-2.0"
] | 0 | 3fea55f09477e9f0844845e09d6ea60434436e2e | https://github.com/fmhoward/pysurvival/tree/3fea55f09477e9f0844845e09d6ea60434436e2e |
SPHead | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from inspect import isfunction
def get_activation_layer(activation):
"""
Create activation layer from string/function.
Parameters:
----------
activation : function, or str, or nn.Module
Activation 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
import torch.utils.data
impor... | HyperGAN/imgclsmob | SPHead | false | 17,687 | [
"MIT"
] | 9 | 88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 | https://github.com/HyperGAN/imgclsmob/tree/88b9776a5a927dc9a54e85e31978c4a9ec5ecbf3 |
ToSEG | # 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.autograd import Function
import math
import torch.nn as nn
import tor... | MargauxMasson/semanticGAN_code | ToSEG | false | 2,642 | [
"BSD-2-Clause",
"MIT"
] | 0 | a5b7fbbc505f8ae08c8aab8e199aa6406fffdb07 | https://github.com/MargauxMasson/semanticGAN_code/tree/a5b7fbbc505f8ae08c8aab8e199aa6406fffdb07 |
NBLoss | import torch
import numpy as np
def _nan2inf(x):
return torch.where(torch.isnan(x), torch.zeros_like(x) + np.inf, x)
class NBLoss(torch.nn.Module):
def __init__(self):
super(NBLoss, self).__init__()
def forward(self, yhat, y, eps=1e-08):
"""Negative binomial log-likelihood loss. It ass... | 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
assert_size_stride = torch._C._dynamo.guard... | scottgigante-immunai/CPA | NBLoss | false | 16,367 | [
"MIT"
] | 132 | 9338ede503d36c6163a521bee904aa93d896ef92 | https://github.com/scottgigante-immunai/CPA/tree/9338ede503d36c6163a521bee904aa93d896ef92 |
VNet | import torch
import torch.nn as nn
class VNet(nn.Module):
def __init__(self, input_size, hidden_size, output_size=1):
super(VNet, self).__init__()
self.linear1 = nn.Linear(input_size, hidden_size)
self.relu1 = nn.ReLU(inplace=True)
self.linear2 = nn.Linear(hidden_size, output_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Stranger469/wrench | VNet | false | 5,863 | [
"Apache-2.0"
] | 1 | ab717ac26a76649c8fdb946a28dffe7e682c80ba | https://github.com/Stranger469/wrench/tree/ab717ac26a76649c8fdb946a28dffe7e682c80ba |
AmdimNCELoss | import torch
from torch import nn
def tanh_clip(x, clip_val=10.0):
"""soft clip values to the range [-clip_val, +clip_val]"""
if clip_val is not None:
x_clip = clip_val * torch.tanh(1.0 / clip_val * x)
else:
x_clip = x
return x_clip
class AmdimNCELoss(nn.Module):
"""Compute the 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
from torch._inductor.runtime.... | Benjamin-Etheredge/lightning-bolts | AmdimNCELoss | false | 138 | [
"Apache-2.0"
] | 0 | 1971d6a924729940b98793aa7751bdf769350aca | https://github.com/Benjamin-Etheredge/lightning-bolts/tree/1971d6a924729940b98793aa7751bdf769350aca |
MultiHeadDenseLayer | import torch
import tensorflow as tf
import torch.nn as nn
import torch.nn.functional as F
def get_activation(activ):
if callable(activ):
return activ
if activ is None:
return lambda x: x
if activ == 'tanh':
return F.tanh
elif activ == 'relu':
return F.relu
elif act... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 tensorflow as tf
import torch.nn as nn
import torch.nn.functional as F
as... | ishine/neurst | MultiHeadDenseLayer | false | 16,691 | [
"Apache-2.0"
] | 208 | 2ba322393fcfed4261b33f4a657e12bbe321baaa | https://github.com/ishine/neurst/tree/2ba322393fcfed4261b33f4a657e12bbe321baaa |
FactorizedReduce | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchvision.models import *
from torchvision.datasets import *
def get_norm_layer(norm, C):
if norm in [None, '', 'none']:
norm_layer = nn.Identity()
elif ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | evdcush/ppuda | FactorizedReduce | false | 15,335 | [
"MIT"
] | 262 | 22783ac92207da6730ee618c953af230c5c39f28 | https://github.com/evdcush/ppuda/tree/22783ac92207da6730ee618c953af230c5c39f28 |
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