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 |
|---|---|---|---|---|---|---|---|---|---|---|
AngleSimpleLinear | import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.nn as nn
from torch.nn import Parameter
import torch.nn.functional as F
class AngleSimpleLinear(nn.Module):
"""Computes cos of angles between input vectors and weights vectors"""
def __init__(self, in_fe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | kprokofi/ML_Decoder | AngleSimpleLinear | false | 15,852 | [
"MIT"
] | 99 | c01c50e0165e607afbebd8d615708ef9c084dd5b | https://github.com/kprokofi/ML_Decoder/tree/c01c50e0165e607afbebd8d615708ef9c084dd5b |
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... | ZhitingHu/texar-pytorch | AvgReducePool1d | false | 2,990 | [
"Apache-2.0"
] | 0 | 72ea115013ced8a5a2b004eacf6271184d3572a8 | https://github.com/ZhitingHu/texar-pytorch/tree/72ea115013ced8a5a2b004eacf6271184d3572a8 |
Encoder4 | # 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.... | EndyWon/Texture-Reformer | Encoder4 | false | 8,214 | [
"MIT"
] | 11 | f84f95accb3574c7b759a7f03c0b0b4e150314b5 | https://github.com/EndyWon/Texture-Reformer/tree/f84f95accb3574c7b759a7f03c0b0b4e150314b5 |
DPGRUCell | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
class RNNLinear(nn.Linear):
"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
This module is the... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | anibadde/opacus | DPGRUCell | false | 14,866 | [
"Apache-2.0"
] | 958 | be221231e1b579bdae4ad34c8ae0c7c4928cee25 | https://github.com/anibadde/opacus/tree/be221231e1b579bdae4ad34c8ae0c7c4928cee25 |
CMDS_Loss | import torch
from torch import nn
from sklearn.preprocessing import scale as scale
def Covariance(m, bias=False, rowvar=True, inplace=False):
""" Estimate a covariance matrix given data(tensor).
Covariance indicates the level to which two variables vary together.
If we examine N-dimensional samples, `X = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Gustoaxel/Statistical-autoencoder | CMDS_Loss | false | 5,413 | [
"MIT"
] | 1 | f3328f9c2a45ef0f7fe4adf98af4a64d02d34afc | https://github.com/Gustoaxel/Statistical-autoencoder/tree/f3328f9c2a45ef0f7fe4adf98af4a64d02d34afc |
Attention | # 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.... | pwycl/pytorch_geometric | Attention | false | 10,776 | [
"MIT"
] | 0 | ef7b1add2bb5a36a3a68cae7639c42000f629cac | https://github.com/pwycl/pytorch_geometric/tree/ef7b1add2bb5a36a3a68cae7639c42000f629cac |
NotEqualConst | # 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... | PogChamper/torch2trt | NotEqualConst | false | 14,207 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
SimpleLeakyReluModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleLeakyReluModule | false | 14,675 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
ConvolutionBlock | import torch
class BaseModule(torch.nn.Module):
def __init__(self):
super(BaseModule, self).__init__()
@property
def nparams(self):
return sum(p.numel() for p in self.parameters() if p.requires_grad)
class Conv1dWithInitialization(BaseModule):
def __init__(self, **kwargs):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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_cu... | dodoproptit99/WaveGrad | ConvolutionBlock | false | 10,046 | [
"BSD-3-Clause"
] | 0 | d5e3cb5d8c1c3d115eeb5f1673b87bdbb36f79e0 | https://github.com/dodoproptit99/WaveGrad/tree/d5e3cb5d8c1c3d115eeb5f1673b87bdbb36f79e0 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
def set_init(layers):
for layer in layers:
nn.init.normal_(layer.weight, mean=0.0, std=0.1)
nn.init.constant_(layer.bias, 0.0)
class Net(nn.Module):
def __init__(self, s_dim, a_dim, hidden=16):
super(Net, self).__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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | aivaslab/marlgrid | Net | false | 3,051 | [
"Apache-2.0"
] | 0 | 10b53d27ce224fadeeb5830d6034350a69feb4b4 | https://github.com/aivaslab/marlgrid/tree/10b53d27ce224fadeeb5830d6034350a69feb4b4 |
MultiHeadAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model, dropout=0.1):
super().__init__()
self.d_model = d_model
self.d_k = d_model // heads
self.h = heads
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.... | Chenny0808/tatk | MultiHeadAttention | false | 13,477 | [
"Apache-2.0"
] | 81 | 1c1a3cb557ba84bbfdbd1f6d8b8ea43ed8b9d7c5 | https://github.com/Chenny0808/tatk/tree/1c1a3cb557ba84bbfdbd1f6d8b8ea43ed8b9d7c5 |
Swish | # 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | danielschulz/MONAI | Swish | false | 1,772 | [
"Apache-2.0"
] | 0 | 54ef6e9e700f0de3d50184c0148f953be871a58e | https://github.com/danielschulz/MONAI/tree/54ef6e9e700f0de3d50184c0148f953be871a58e |
AdaptiveAvgMaxPool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch._utils
import torch.optim
def adaptive_avgmax_pool2d(x, output_size=1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_pool2d(x, output_size)
return 0.5 * (x_avg + x_max)
class ... | 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 as F
import torch.nn.parallel
import tor... | Alicegaz/torchok | AdaptiveAvgMaxPool2d | false | 16,899 | [
"Apache-2.0"
] | 8 | 7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 | https://github.com/Alicegaz/torchok/tree/7b8f95df466a25b1ad8ee93bed1a3c7516440cf4 |
ClassificationCircleLoss | import torch
import torch.nn as nn
from typing import Tuple
import torch.utils.data
from torch.nn.functional import cross_entropy
from itertools import product as product
from math import sqrt as sqrt
class ClassificationCircleLoss(nn.Module):
"""Circle loss for class-level labels as described in the paper
`"... | 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
... | WFDetector/WFDetection | ClassificationCircleLoss | false | 2,950 | [
"Apache-2.0"
] | 0 | b16d35b3a3a5de62de9e0bac83eccd21b6358b53 | https://github.com/WFDetector/WFDetection/tree/b16d35b3a3a5de62de9e0bac83eccd21b6358b53 |
WeightedAttention | import torch
from torch import nn
class WeightedAttention(nn.Module):
def __init__(self, dim, eps=1e-08, softmax_dim=1, weighted_mean_dim=2):
super().__init__()
self.norm_input = nn.LayerNorm(dim)
self.norm_context = nn.LayerNorm(dim)
self.to_q = nn.Linear(dim, dim)
self.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.... | ajabri/slot-attention | WeightedAttention | false | 1,387 | [
"MIT"
] | 0 | 32acb6614f1bd511f2dc3c263f852ed2dbe9c213 | https://github.com/ajabri/slot-attention/tree/32acb6614f1bd511f2dc3c263f852ed2dbe9c213 |
LocalVariation | import torch
import torch.nn as nn
class LocalVariation(nn.Module):
"""Layer to compute the LocalVariation of an image
"""
def __init__(self, k_size=5):
super(LocalVariation, self).__init__()
self.mu_x_pool = nn.AvgPool2d(k_size, 1)
self.mu_y_pool = nn.AvgPool2d(k_size, 1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | shlomi-amitai/myDIFFNet | LocalVariation | false | 10,879 | [
"MIT"
] | 0 | 39dead457f10c82caae2a12ea152f2339188014c | https://github.com/shlomi-amitai/myDIFFNet/tree/39dead457f10c82caae2a12ea152f2339188014c |
MLP_G | # 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_... | naraysa/gzsl-od | MLP_G | false | 16,139 | [
"MIT"
] | 50 | be771e12e17a4c02386c70697c4b26e3170a7557 | https://github.com/naraysa/gzsl-od/tree/be771e12e17a4c02386c70697c4b26e3170a7557 |
ParallelDilatedConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | dcrmg/Efficient-Segmentation-Networks | ParallelDilatedConv | false | 3,441 | [
"MIT"
] | 0 | e2f2d90d69e4e9af464678b0f02bc754c28f643d | https://github.com/dcrmg/Efficient-Segmentation-Networks/tree/e2f2d90d69e4e9af464678b0f02bc754c28f643d |
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
from torch.nn impor... | dqawami/openvino_training_extensions | PositionWiseFeedForward | false | 15,233 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad |
NNSmall | # 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_... | CORE-Robotics-Lab/Personalized_Neural_Trees | NNSmall | false | 17,047 | [
"MIT"
] | 3 | 3e8dd12fe4fc850be65c96c847eb143ef3bcdc2e | https://github.com/CORE-Robotics-Lab/Personalized_Neural_Trees/tree/3e8dd12fe4fc850be65c96c847eb143ef3bcdc2e |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | MIMUW-RL/spp-rl | Critic | false | 17,636 | [
"MIT"
] | 7 | 86b96cdd220cc4eae86f7cfd26924c69b498dcc6 | https://github.com/MIMUW-RL/spp-rl/tree/86b96cdd220cc4eae86f7cfd26924c69b498dcc6 |
BBoxTransform | import torch
from torch import nn
import torch.onnx
class BBoxTransform(nn.Module):
def forward(self, anchors, regression):
"""
decode_box_outputs adapted from https://github.com/google/automl/blob/master/efficientdet/anchors.py
Args:
anchors: [batchsize, boxes, (y1, x1, y2, ... | 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 nn
import torch.onnx
assert_size_stride = torch._C._dyn... | Wabinab/eye_of_ml | BBoxTransform | false | 5,949 | [
"Apache-2.0"
] | 1 | 9c475ddf4e56d84bc5a23d871d59169bc6061ab0 | https://github.com/Wabinab/eye_of_ml/tree/9c475ddf4e56d84bc5a23d871d59169bc6061ab0 |
ShuffleCat | # 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... | jjkennedy3/PINTO_model_zoo | ShuffleCat | false | 6,956 | [
"MIT"
] | 1 | a181c3015a6241873798c4ad3eadd4ce97024f70 | https://github.com/jjkennedy3/PINTO_model_zoo/tree/a181c3015a6241873798c4ad3eadd4ce97024f70 |
RAddInt | # 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... | PogChamper/torch2trt | RAddInt | false | 14,208 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
L2LossWithLogit | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch
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 |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.nn.parallel
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
return nn.functional.adaptive_avg_pool... | 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... | HotaekHan/detr_pytorch | GlobalAvgPool2d | false | 547 | [
"MIT"
] | 0 | 730e02db0ac8910ef782234a3990587771ad67f9 | https://github.com/HotaekHan/detr_pytorch/tree/730e02db0ac8910ef782234a3990587771ad67f9 |
MultiHeadAttention | import torch
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
"""
Multi Head Attention module. https://arxiv.org/abs/1706.03762
This version has no normalization module and suppose self-attention
"""
def __init__(self, hidden_dim:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AppleHolic/pytorch_sound | MultiHeadAttention | false | 13,317 | [
"BSD-2-Clause"
] | 86 | 2320516d21d70c406d1dee74927e238972c96106 | https://github.com/AppleHolic/pytorch_sound/tree/2320516d21d70c406d1dee74927e238972c96106 |
Probability | import torch
import torch.nn as nn
class Probability(nn.Module):
"""A layer that predicts the probabilities
"""
def __init__(self, n_primitives, input_channels, make_dense=False):
super(Probability, self).__init__()
self._n_primitives = n_primitives
self._make_dense = make_dense
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ianhuang0630/CSQ | Probability | false | 15,577 | [
"MIT"
] | 98 | 5f1fe99a8d9da73692643b3911d675dce269a03d | https://github.com/ianhuang0630/CSQ/tree/5f1fe99a8d9da73692643b3911d675dce269a03d |
ELU | import torch
import torch.nn as nn
class ActivationFunction(nn.Module):
def __init__(self):
super().__init__()
self.name = self.__class__.__name__
self.config = {'name': self.name}
class ELU(ActivationFunction):
def forward(self, x):
return torch.where(x > 0, x, 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ashutoshml/lightning-tutorials | ELU | false | 6,250 | [
"Apache-2.0"
] | 1 | 898b8b6f9852c0b80f034a3187bc1cd34dd521ce | https://github.com/ashutoshml/lightning-tutorials/tree/898b8b6f9852c0b80f034a3187bc1cd34dd521ce |
SimpleSliceModel | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | geoffberry/glow | SimpleSliceModel | false | 12,412 | [
"Apache-2.0"
] | 0 | 24b2827c830eb58af56a0704e899968026832e9c | https://github.com/geoffberry/glow/tree/24b2827c830eb58af56a0704e899968026832e9c |
SpatialGatingUnit | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | nima1999nikkhah/SimCLR_gMLP | SpatialGatingUnit | false | 4,096 | [
"MIT"
] | 0 | 32cca4764d4266493cb7d141eb9ef01a91f63996 | https://github.com/nima1999nikkhah/SimCLR_gMLP/tree/32cca4764d4266493cb7d141eb9ef01a91f63996 |
FactorizationMachine | from torch.nn import Module
import math
import torch
import numpy as np
from torch.nn import *
from torch.optim import AdamW
from typing import Union
class FactorizationMachine(Module):
"""
[Factorization Machine Recommendation Model]
Learns latent space features to characterize similarity of dataset feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn... | cspades/algorithm-toolkit | FactorizationMachine | false | 12,257 | [
"Apache-2.0"
] | 0 | 8731112162fb60f8ef3ab3c38524456ae96f0c2d | https://github.com/cspades/algorithm-toolkit/tree/8731112162fb60f8ef3ab3c38524456ae96f0c2d |
Conv1d | import torch
import torch.utils.data
from torch import nn
from torch.nn import Conv1d
class Conv1d(nn.Conv1d):
"""
:param in_channels: Scalar
:param out_channels: Scalar
:param kernel_size: Scalar
:param activation_fn: activation function
:param drop_rate: Scalar. dropout rate
:param 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
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | AstraliteHeart/cookietts | Conv1d | false | 7,746 | [
"BSD-3-Clause"
] | 25 | c871f5f7b5790656d5b57bcd9e63946a2da52f0f | https://github.com/AstraliteHeart/cookietts/tree/c871f5f7b5790656d5b57bcd9e63946a2da52f0f |
MinibatchStdDev | # 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_... | cwza/deep_t2i | MinibatchStdDev | false | 1,769 | [
"Apache-2.0"
] | 0 | 22877fdd28ad407984ddc3bc4d57109c54c22fc0 | https://github.com/cwza/deep_t2i/tree/22877fdd28ad407984ddc3bc4d57109c54c22fc0 |
Mlp | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
class GELU(nn.Module):
def __init__(self):
super(GELU, self).__init__()
def forward(self, x):
return 0.5 * x * (1 + F.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class Mlp(nn.M... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | au55555/classification-pytorch | Mlp | false | 6,283 | [
"MIT"
] | 1 | 1937599ae6e688ed7af7470f69964fb6f97241c4 | https://github.com/au55555/classification-pytorch/tree/1937599ae6e688ed7af7470f69964fb6f97241c4 |
ConcatConv2d | import torch
import torch.nn as nn
import torch.utils.data
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatConv2d, self).__init__()
module = nn.ConvTranspose2d if transpose else... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | ClaraBing/ffjord | ConcatConv2d | false | 13,506 | [
"MIT"
] | 518 | a97c34ff546a063316828f53bd041555e663428d | https://github.com/ClaraBing/ffjord/tree/a97c34ff546a063316828f53bd041555e663428d |
C3 | import torch
import torch.nn as nn
from collections import OrderedDict
class C3(nn.Module):
def __init__(self):
super(C3, self).__init__()
self.c3 = nn.Sequential(OrderedDict([('c3', nn.Conv2d(16, 120,
kernel_size=(5, 5))), ('relu3', nn.ReLU())]))
def forward(self, img):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from co... | ConstantinSeibold/SGL | C3 | false | 17,133 | [
"MIT"
] | 7 | fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a | https://github.com/ConstantinSeibold/SGL/tree/fab4d2df515608c2a6a89b2ac8c2655ce8e08b1a |
ConvElement | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvElement(nn.Module):
"""
Residual Core element used inside the NN. Control the number of filters
and batch normalization.
"""
def __init__(self, input_size, num_filters, use_leaky=True, stride=1,
leaky_p=0.2):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | tensormedical/PARIETAL | ConvElement | false | 13,030 | [
"Apache-2.0"
] | 0 | 25bf1cf7828b24d60ccff42efbd0537989aaf160 | https://github.com/tensormedical/PARIETAL/tree/25bf1cf7828b24d60ccff42efbd0537989aaf160 |
SelfAttention2d | import torch
from torch import nn
class SelfAttention2d(nn.Module):
def __init__(self, c_in, n_head=1, dropout_rate=0.1):
super().__init__()
assert c_in % n_head == 0
self.norm = nn.GroupNorm(1, c_in)
self.n_head = n_head
self.qkv_proj = nn.Conv2d(c_in, c_in * 3, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | technillogue/v-diffusion-pytorch | SelfAttention2d | false | 4,418 | [
"MIT"
] | 0 | 3aa8c7f32adbde1d1ea3a9650004ffafabe5221b | https://github.com/technillogue/v-diffusion-pytorch/tree/3aa8c7f32adbde1d1ea3a9650004ffafabe5221b |
MNIST_CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class MNIST_CNN(nn.Module):
"""
Hand-tuned architecture for MNIST.
Weirdness I've noticed so far with this architecture:
- adding a linear layer after the mean-pool in features hurts
RotatedMNIST-100 gen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Luodian/IIB | MNIST_CNN | false | 17,631 | [
"MIT"
] | 3 | a7457e56f4e389bea484e9f9cdbd01485114d6dc | https://github.com/Luodian/IIB/tree/a7457e56f4e389bea484e9f9cdbd01485114d6dc |
BahdanauAttention | # 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.... | cometta/training | BahdanauAttention | false | 10,066 | [
"Apache-2.0"
] | 0 | 2f33c36d5aa2e1c2770fb3bab35afc8c665e01ce | https://github.com/cometta/training/tree/2f33c36d5aa2e1c2770fb3bab35afc8c665e01ce |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_... | NagisaZj/ProMP | LayerNorm | false | 11,727 | [
"MIT"
] | 0 | 539739ae2b7d5fdcad00855da695f643b23df4b3 | https://github.com/NagisaZj/ProMP/tree/539739ae2b7d5fdcad00855da695f643b23df4b3 |
Mean | # 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.nn import Module
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | RL-WWW/ISST | Mean | false | 17,822 | [
"BSD-3-Clause"
] | 5 | 42b656686fa9660794007a0bc00a7177937410e9 | https://github.com/RL-WWW/ISST/tree/42b656686fa9660794007a0bc00a7177937410e9 |
TransposedConvModel | import torch
import torch.nn
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
class TransposedConvModel(torch.nn.Module):
def __init__(self):
super(TransposedConvModel, self).__init__()
self.conv1 = torch.nn.ConvTranspose2d(10, 10, 3)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | arjunsuresh/aimet | TransposedConvModel | false | 12,706 | [
"BSD-3-Clause"
] | 0 | f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 | https://github.com/arjunsuresh/aimet/tree/f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 |
AtteMatchLay | import torch
import torch.nn as nn
from torch.nn.functional import cosine_similarity
def multi_perspective_expand_for_2D(in_tensor, decompose_params):
"""
Return: [batch_size, decompse_dim, dim]
"""
in_tensor = in_tensor.unsqueeze(1)
decompose_params = decompose_params.unsqueeze(0)
return torc... | 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... | NeilWangziyu/torch_light | AtteMatchLay | false | 5,650 | [
"MIT"
] | 1 | daf8fd62f57885cf182f1b3edc3152156d229ef3 | https://github.com/NeilWangziyu/torch_light/tree/daf8fd62f57885cf182f1b3edc3152156d229ef3 |
Pooling | # 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... | hyenal/tensorflow-image-models | Pooling | false | 3,642 | [
"Apache-2.0"
] | 0 | 2012be8ecc7bc23e84dc2488d3e4fe1c80dbfb2c | https://github.com/hyenal/tensorflow-image-models/tree/2012be8ecc7bc23e84dc2488d3e4fe1c80dbfb2c |
PolicyAHG | # 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.... | JimmyMVP/plain_rl | PolicyAHG | false | 17,495 | [
"MIT"
] | 10 | 4780f05fffb62533a339197b49de487cdc9d9954 | https://github.com/JimmyMVP/plain_rl/tree/4780f05fffb62533a339197b49de487cdc9d9954 |
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | NunoEdgarGFlowHub/torchio | Network | false | 5,666 | [
"MIT"
] | 1 | 656e96c8863ecff0bb29bf880af054675bbb30fd | https://github.com/NunoEdgarGFlowHub/torchio/tree/656e96c8863ecff0bb29bf880af054675bbb30fd |
SpatialAttention | import torch
import torch.nn as nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(1, 1, kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | abandonsea/BBS-Net | SpatialAttention | false | 14,736 | [
"MIT"
] | 66 | fd4e60bf3025d0cec745c0594b7104c5746f6d0f | https://github.com/abandonsea/BBS-Net/tree/fd4e60bf3025d0cec745c0594b7104c5746f6d0f |
SEBlock | # 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 ... | COEN-390/YOLOv5-Lite | SEBlock | false | 11,276 | [
"MIT"
] | 0 | 06a53f5d001c5d37729f55f47cbd46cc8eb63f84 | https://github.com/COEN-390/YOLOv5-Lite/tree/06a53f5d001c5d37729f55f47cbd46cc8eb63f84 |
AttentionUnit | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import init
class AttentionUnit(nn.Module):
def __init__(self, sDim, xDim, attDim):
"""
sDim, xDim -> attDim -> 1
Params:
- sDim: decoder的hidden layer dim
- xDim: encoder的output layer dim
- attDim: a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Annihilation7/aster | AttentionUnit | false | 1,951 | [
"MIT"
] | 0 | eab6946eb1f99e395abc56c3446cd05caa90e791 | https://github.com/Annihilation7/aster/tree/eab6946eb1f99e395abc56c3446cd05caa90e791 |
QNet | import torch
import torch as t
import torch.nn as nn
class QNet(nn.Module):
def __init__(self, state_dim, action_num, atom_num=10):
super().__init__()
self.fc1 = nn.Linear(state_dim, 16)
self.fc2 = nn.Linear(16, 16)
self.fc3 = nn.Linear(16, action_num * atom_num)
self.acti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | iffiX/machin | QNet | false | 15,594 | [
"MIT"
] | 287 | 7fa986b1bafdefff117d6ff73d14644a5488de9d | https://github.com/iffiX/machin/tree/7fa986b1bafdefff117d6ff73d14644a5488de9d |
SigmoidFocalLoss | import torch
from torch import nn
class SigmoidFocalLoss(nn.Module):
def __init__(self, gamma, alpha):
super().__init__()
self.gamma = gamma
self.alpha = alpha
def forward(self, out, target):
n_class = out.shape[1]
class_ids = torch.arange(1, n_class + 1, dtype=target... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | YinlinHu/fcos-pytorch | SigmoidFocalLoss | false | 12,008 | [
"MIT"
] | 0 | a0f8b321a7330710e5e8ce5adb92364f381e9e85 | https://github.com/YinlinHu/fcos-pytorch/tree/a0f8b321a7330710e5e8ce5adb92364f381e9e85 |
Mul | # 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... | bunderhi/torch2trt | Mul | false | 1,592 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
ArgMax | import torch
import torch.sparse
import torch.nn as nn
class ArgMax(nn.Module):
def __init__(self, dim=None):
super().__init__()
self.dim = dim
def forward(self, x):
return torch.argmax(x, dim=self.dim)
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 import triton_helpers
import torch.sparse
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.as... | cwerner/deadtrees | ArgMax | false | 6,506 | [
"Apache-2.0"
] | 1 | 15ddfec58c4a40f22f9c1e2424fb535df4d29b03 | https://github.com/cwerner/deadtrees/tree/15ddfec58c4a40f22f9c1e2424fb535df4d29b03 |
Module_CharbonnierLoss | import torch
import torch.nn as nn
class Module_CharbonnierLoss(nn.Module):
def __init__(self, epsilon=0.001):
super(Module_CharbonnierLoss, self).__init__()
self.epsilon = epsilon
def forward(self, output, gt):
return torch.mean(torch.sqrt((output - gt) ** 2 + self.epsilon ** 2))
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Pumpkin123709/LBEC | Module_CharbonnierLoss | false | 944 | [
"MIT"
] | 0 | 18661faa35769f731847e0226ff601754e134668 | https://github.com/Pumpkin123709/LBEC/tree/18661faa35769f731847e0226ff601754e134668 |
Subsample | import torch
import torch.utils.data
import torch.nn as nn
class Subsample(nn.Module):
def __init__(self):
super().__init__()
def forward(self, feats, lengths):
out = feats[:, ::2]
lengths = lengths // 2
return out, lengths
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.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | CoraJung/flexible-input-slu | Subsample | false | 17,134 | [
"Apache-2.0"
] | 7 | 6a1a6bf105f1a0c07e8d483aa6da1df7a554392d | https://github.com/CoraJung/flexible-input-slu/tree/6a1a6bf105f1a0c07e8d483aa6da1df7a554392d |
BertSelfAttention | # 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.... | axiserr/Hetu | BertSelfAttention | false | 14,937 | [
"Apache-2.0"
] | 82 | 0052f727488db0570d6b37f63549b43b0920bc29 | https://github.com/axiserr/Hetu/tree/0052f727488db0570d6b37f63549b43b0920bc29 |
SimpleTypeasModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleTypeasModel(torch.nn.Module):
def __init__(self):
super(SimpleTypeasModel, self).__init__()
def forward(self, tensor, other=None):
other = tensor if other is None else other
if tensor.dtype != torch.bool:
... | 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... | YaronBenAtar/glow | SimpleTypeasModel | false | 14,685 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
BiaffineAttention | # 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.... | LindgeW/DomainAdaption4DependencyParsing | BiaffineAttention | false | 5,542 | [
"Apache-2.0"
] | 1 | 5de136a37d8fe730e4235ed95bf923763fe21ea6 | https://github.com/LindgeW/DomainAdaption4DependencyParsing/tree/5de136a37d8fe730e4235ed95bf923763fe21ea6 |
TransformerLayer | import torch
import torch.nn as nn
import torch.onnx
class TransformerLayer(nn.Module):
def __init__(self, channels, num_heads):
super().__init__()
self.q = nn.Linear(channels, channels, bias=False)
self.k = nn.Linear(channels, channels, bias=False)
self.v = nn.Linear(channels, ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DDGRCF/YOLOX_OBB | TransformerLayer | false | 7,986 | [
"Apache-2.0"
] | 39 | 27b80953306492b8bc83b86b1353d8cee01ef9b6 | https://github.com/DDGRCF/YOLOX_OBB/tree/27b80953306492b8bc83b86b1353d8cee01ef9b6 |
SimpleMinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMinModule(torch.nn.Module):
def __init__(self):
super(SimpleMinModule, self).__init__()
def forward(self, a, b):
return torch.min(a + a, b + b)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | YaronBenAtar/glow | SimpleMinModule | false | 14,664 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
Sine | import torch
import torch.nn as nn
class Sine(nn.Module):
def __init__(self, w0: 'float'=30.0):
super(Sine, self).__init__()
self.w0 = w0
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
return torch.sin(self.w0 * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | Open-Catalyst-Project/baselines | Sine | false | 17,791 | [
"MIT"
] | 10 | 89948582edfb8debb736406d54db9813a5f2c88d | https://github.com/Open-Catalyst-Project/baselines/tree/89948582edfb8debb736406d54db9813a5f2c88d |
Attn | # 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.... | ChrisGeishauser/ConvLab-2 | Attn | false | 2,226 | [
"Apache-2.0"
] | 0 | 8f55d033c6e2453fdc092c4f504be3973a55e7ea | https://github.com/ChrisGeishauser/ConvLab-2/tree/8f55d033c6e2453fdc092c4f504be3973a55e7ea |
GCNLayer | # 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... | onlyrico/lightning-tutorials | GCNLayer | false | 12,855 | [
"Apache-2.0"
] | 0 | b5d5c4015422f8c70411e57734d73bb6c1472999 | https://github.com/onlyrico/lightning-tutorials/tree/b5d5c4015422f8c70411e57734d73bb6c1472999 |
EltwiseSubEmbed | # 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... | YantaoShen/kpm_rw_person_reid | EltwiseSubEmbed | false | 14,637 | [
"MIT"
] | 112 | 01393e024aa1139c9e7e934954cc35826f438a54 | https://github.com/YantaoShen/kpm_rw_person_reid/tree/01393e024aa1139c9e7e934954cc35826f438a54 |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DiegelD/Deep-Reinforcement-Learning-ND | Actor | false | 11,352 | [
"MIT"
] | 0 | 15a91da352414718bb83fdc538d73ac576472cb8 | https://github.com/DiegelD/Deep-Reinforcement-Learning-ND/tree/15a91da352414718bb83fdc538d73ac576472cb8 |
KDLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class KDLoss(nn.Module):
def __init__(self, temp: 'float', reduction: 'str'):
super(KDLoss, self).__init__()
self.temp = temp
self.reduction = reduction
self.kl_loss = nn.KLDivLoss(reduction=reduction)
def for... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | angpo/VKD | KDLoss | false | 14,880 | [
"MIT"
] | 68 | 2a136e00dad4c73612d6efe087675604ac2416eb | https://github.com/angpo/VKD/tree/2a136e00dad4c73612d6efe087675604ac2416eb |
BertAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | vincentlux/TextBrewer | BertAttention | false | 13,075 | [
"Apache-2.0"
] | 0 | 51ffbf390a0b69ee51b6ad6f5045be63e21c98e3 | https://github.com/vincentlux/TextBrewer/tree/51ffbf390a0b69ee51b6ad6f5045be63e21c98e3 |
Entropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | fallcat/synst | Entropy | false | 6,677 | [
"BSD-3-Clause"
] | 1 | 0fa4adffa825af4a62b6e739b59c4125a7b6698e | https://github.com/fallcat/synst/tree/0fa4adffa825af4a62b6e739b59c4125a7b6698e |
BinaryPrimitivesPredefined_v2 | import math
import torch
from torch import nn
def apply_last_dim(model, x):
size = list(x.size())
y = model(x.contiguous().view(-1, size[-1]))
size[-1] = y.size(-1)
y = y.view(torch.Size(size))
return y
def get_int_dim_index(name):
if isinstance(name, int):
return name
name_list ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.a... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | BinaryPrimitivesPredefined_v2 | false | 17,147 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f |
OutputLayer | import torch
import torch.nn as nn
class OutputLayer(nn.Module):
def __init__(self, voxel_size=1.0):
super(OutputLayer, self).__init__()
def forward(self, features_list, index_map_list):
out = []
for feat, index_map in zip(features_list, index_map_list):
out.append(feat[i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | mi-exwzd/Open3D-ML | OutputLayer | false | 16,044 | [
"MIT"
] | 447 | d58b24edd37de7889446360164cd5500e0bde060 | https://github.com/mi-exwzd/Open3D-ML/tree/d58b24edd37de7889446360164cd5500e0bde060 |
ConvLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvLayer(nn.Module):
"""A Convolutional Layer"""
def __init__(self, in_channels=1, out_channels=256, kernel_size=9, stride=1
):
super(ConvLayer, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | VIVelev/capsnets | ConvLayer | false | 1,176 | [
"MIT"
] | 0 | dca4bfcd4007977a6bc3534a4676880326fcf94a | https://github.com/VIVelev/capsnets/tree/dca4bfcd4007977a6bc3534a4676880326fcf94a |
ArcBiaffine | import torch
from torch import nn
import torch.utils.data
import torch.nn.init as init
def initial_parameter(net, initial_method=None):
"""A method used to initialize the weights of PyTorch models.
:param net: a PyTorch model
:param str initial_method: one of the following initializations.
-... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.nn.init as init
assert... | LindaCY/fastNLP | ArcBiaffine | false | 17,619 | [
"Apache-2.0"
] | 4 | 3fa95b6cfc31211453bc21792e3eef87948858da | https://github.com/LindaCY/fastNLP/tree/3fa95b6cfc31211453bc21792e3eef87948858da |
LinearExcitability | import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
def linearExcitability(input, weight, excitability=None, bias=None):
"""
Applies a linear transformation to the incoming data: :math:`y = c(xA^T) + b`.
Shape:
- input: :math:`(N, *, in\\_features)`
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch import nn
from torch.nn.parameter import Parameter
assert... | GMvandeVen/progressive-learning-pytorch | LinearExcitability | false | 17,285 | [
"MIT"
] | 4 | 165645b2d7595d94d036f765c9a311d505e667a3 | https://github.com/GMvandeVen/progressive-learning-pytorch/tree/165645b2d7595d94d036f765c9a311d505e667a3 |
SimpleConvNet | # 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 ... | CorentinChauvin/style-transfer-KD | SimpleConvNet | false | 5,085 | [
"MIT"
] | 1 | 87bcb2963dbb8d09faf94c74a744f358cafe5427 | https://github.com/CorentinChauvin/style-transfer-KD/tree/87bcb2963dbb8d09faf94c74a744f358cafe5427 |
SoftTargetCrossEntropy | # 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.nn.modules.... | ModelTC/EOD | SoftTargetCrossEntropy | false | 14,062 | [
"Apache-2.0"
] | 196 | 164bff80486e9ae6a095a97667b365c46ceabd86 | https://github.com/ModelTC/EOD/tree/164bff80486e9ae6a095a97667b365c46ceabd86 |
SigmoidFocalLoss | import torch
import torch.nn as nn
class SigmoidFocalLoss(nn.Module):
def __init__(self, gamma, alpha):
super().__init__()
self.gamma = gamma
self.alpha = alpha
def forward(self, out, target):
n_class = out.shape[1]
class_ids = torch.arange(1, n_class + 1, dtype=targe... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | berserkrambo/fcos-pytorch | SigmoidFocalLoss | false | 14,947 | [
"MIT"
] | 63 | a064eccf6d45fc85da401151dcefe7a3b01a065b | https://github.com/berserkrambo/fcos-pytorch/tree/a064eccf6d45fc85da401151dcefe7a3b01a065b |
BilinearUpsample | # 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 typing import Union
from typing import List
import torch.nn as nn
import torch.utils... | Hcnaeg/DI-engine | BilinearUpsample | false | 2,371 | [
"Apache-2.0"
] | 0 | aba0c629f87649854091e9e59d948f83962e3e1e | https://github.com/Hcnaeg/DI-engine/tree/aba0c629f87649854091e9e59d948f83962e3e1e |
AdaINConv2dLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | LenKerr/Semantic-Colorization-GAN | AdaINConv2dLayer | false | 5,521 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f |
PetarVGAT | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class BaseModel(nn.Module):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
pass
@classmethod
def build_model_from_args(cls, args):
"""Build a new ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | zxhhh97/cogdl | PetarVGAT | false | 4,718 | [
"MIT"
] | 0 | de21c78d9bbbf0c6cafbc72ff241cda35693ec37 | https://github.com/zxhhh97/cogdl/tree/de21c78d9bbbf0c6cafbc72ff241cda35693ec37 |
Net4 | import torch
from torch import nn
from torch.nn.init import kaiming_normal
from torch.nn.init import normal
def weights_init(m):
if isinstance(m, (nn.Conv1d, nn.Linear)):
kaiming_normal(m.weight.data)
try:
kaiming_normal(m.bias.data)
except ValueError:
normal(m.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
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | moritzschaefer/pavooc | Net4 | false | 7,283 | [
"MIT"
] | 1 | 735f5455f9a95a5734436a24e2aa92cf600c91af | https://github.com/moritzschaefer/pavooc/tree/735f5455f9a95a5734436a24e2aa92cf600c91af |
BasicUNet | # 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.... | royerloic/aydin | BasicUNet | false | 16,420 | [
"BSD-3-Clause"
] | 78 | f9c61a24030891d008c318b250da5faec69fcd7d | https://github.com/royerloic/aydin/tree/f9c61a24030891d008c318b250da5faec69fcd7d |
N3 | import torch
import torch.nn as nn
import torch.utils
from typing import Tuple
from abc import ABC
from abc import abstractmethod
import torch.utils.data
class Regularizer(nn.Module, ABC):
@abstractmethod
def forward(self, factors: 'Tuple[torch.Tensor]'):
pass
class N3(Regularizer):
def __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 math as tl_math
import torch.nn as nn
import torch.utils
from typing import Tuple
from ab... | angusl95/darts-kbc | N3 | false | 1,436 | [
"Apache-2.0"
] | 0 | 85fc6f4bdb7ba73c07d96ce47e96634599b346f9 | https://github.com/angusl95/darts-kbc/tree/85fc6f4bdb7ba73c07d96ce47e96634599b346f9 |
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
from torch._inductor.runtime.... | DocYard-ai/UCR | Encoder | false | 8,025 | [
"Apache-2.0"
] | 10 | 7618aa336f56e71d9fd8cdc2d591e3d138e3dc68 | https://github.com/DocYard-ai/UCR/tree/7618aa336f56e71d9fd8cdc2d591e3d138e3dc68 |
SiamFC | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | Kingzerd/siamfc_pytorch | SiamFC | false | 5,438 | [
"MIT"
] | 1 | fd1dbeb12dd7e2b9190876a1de7ea4b71a7a1166 | https://github.com/Kingzerd/siamfc_pytorch/tree/fd1dbeb12dd7e2b9190876a1de7ea4b71a7a1166 |
FunctionalConv3d | import torch
class FunctionalConv3d(torch.nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
self.conv = torch.nn.Conv3d(*args, **kwargs)
def forward(self, x):
x = torch.nn.functional.conv3d(x, self.conv.weight, self.conv.bias,
self.conv.stride, self.conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
reinterpret_tens... | PogChamper/torch2trt | FunctionalConv3d | false | 14,270 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
TokenEmbedding | # 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | LeeSHa00/PyTorch-tutorials-kr | TokenEmbedding | false | 11,846 | [
"BSD-3-Clause"
] | 0 | 6a25b48b1a6cc96ea4edebeede2e419ef73b96fc | https://github.com/LeeSHa00/PyTorch-tutorials-kr/tree/6a25b48b1a6cc96ea4edebeede2e419ef73b96fc |
DecoderLayer | # 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.... | bsgiovanini/transformer | DecoderLayer | false | 1,619 | [
"Apache-2.0"
] | 0 | e128fa862f1b3d17d7b92df169a2bbee3f08366f | https://github.com/bsgiovanini/transformer/tree/e128fa862f1b3d17d7b92df169a2bbee3f08366f |
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.... | muberraozmen/MrMP | MultiHeadAttention | false | 4,052 | [
"MIT"
] | 0 | da6bcccbad85a682c848ff4aa1121c773d779e57 | https://github.com/muberraozmen/MrMP/tree/da6bcccbad85a682c848ff4aa1121c773d779e57 |
Attention | # 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.... | kad99kev/Image-Captioning | Attention | false | 7,010 | [
"MIT"
] | 1 | a38d7c6469306d7f226d8003bba92f21b3d9a06c | https://github.com/kad99kev/Image-Captioning/tree/a38d7c6469306d7f226d8003bba92f21b3d9a06c |
SinReLU | # 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
... | fmhoward/pysurvival | SinReLU | false | 12,380 | [
"Apache-2.0"
] | 0 | 3fea55f09477e9f0844845e09d6ea60434436e2e | https://github.com/fmhoward/pysurvival/tree/3fea55f09477e9f0844845e09d6ea60434436e2e |
ForegroundDTConsistency | import torch
from typing import Optional
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
class ForegroundDTConsistency(nn.Module):
"""Consistency regularization between the binary foreground mask and
signed distance transform.
Args:
pred1 (to... | 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... | devaansh100/pytorch_connectomics | ForegroundDTConsistency | false | 6,562 | [
"MIT"
] | 1 | b1e4b16b0480546ea806d14876208080815ed964 | https://github.com/devaansh100/pytorch_connectomics/tree/b1e4b16b0480546ea806d14876208080815ed964 |
feedforwardLayer | # 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.... | Yottaxx/T-LSTM | feedforwardLayer | false | 18,168 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 |
GreenBlock | import torch
from torch import nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding=1, stride=1):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias, stride=stride)
class GreenBlock(nn.Module):
"""
green_block(inp, filters, name=None)
--------... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | WdBlink/AugMix-3DOCUNet-Brats2019 | GreenBlock | false | 5,971 | [
"MIT"
] | 1 | 125c6c8682b51a550eeac9173d13d0a211576abc | https://github.com/WdBlink/AugMix-3DOCUNet-Brats2019/tree/125c6c8682b51a550eeac9173d13d0a211576abc |
PSNRLoss | import torch
import torch.nn as nn
from torch.nn.functional import mse_loss as mse
def psnr(input: 'torch.Tensor', target: 'torch.Tensor', max_val: 'float'
) ->torch.Tensor:
"""Creates a function that calculates the PSNR between 2 images.
PSNR is Peek Signal to Noise Ratio, which is similar to mean squar... | 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
from t... | ChristophReich1996/kornia | PSNRLoss | false | 270 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 35f955b46e8015da1cb9faa28c6943ec2b09cc2a | https://github.com/ChristophReich1996/kornia/tree/35f955b46e8015da1cb9faa28c6943ec2b09cc2a |
SmoothL1LossWithIgnore | import torch
import torch.nn.functional
from torch import nn
class SmoothL1LossWithIgnore(nn.Module):
def __init__(self, ignore_value: 'int', fraction: 'float'=1.0):
super().__init__()
self.ignore_value = ignore_value
self.fraction = fraction
def forward(self, output, target):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functi... | drivendataorg/DrivenData-2021-Geopose-Solution | SmoothL1LossWithIgnore | false | 6,607 | [
"MIT"
] | 1 | fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 | https://github.com/drivendataorg/DrivenData-2021-Geopose-Solution/tree/fc1dead0aeb1ade9e9d87b55f56e631c57e966a6 |
BasicBlockWN | # 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.... | ikamensh/machin | BasicBlockWN | false | 6,893 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
ToRGB | from torch.autograd import Function
import math
import torch
from torch import nn
from torch.nn import functional as F
def upsample(in_tens, out_H=64):
in_H = in_tens.shape[2]
scale_factor = 1.0 * out_H / in_H
return nn.Upsample(scale_factor=scale_factor, mode='bilinear',
align_corners=False)(in_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.autograd import Function
import math
from torch import nn
from torch.... | BinahHu/stylegan2-pytorch | ToRGB | false | 183 | [
"MIT",
"BSD-2-Clause",
"Apache-2.0"
] | 0 | 9975707ffd93872fce02f7e3654eb588a09e23e4 | https://github.com/BinahHu/stylegan2-pytorch/tree/9975707ffd93872fce02f7e3654eb588a09e23e4 |
CombinedPooling | # 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.optim
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel... | VisualComputingInstitute/CROWDBOT_perception | CombinedPooling | false | 5,938 | [
"MIT"
] | 1 | df98f3f658c39fb3fa4ac0456f1214f7918009f6 | https://github.com/VisualComputingInstitute/CROWDBOT_perception/tree/df98f3f658c39fb3fa4ac0456f1214f7918009f6 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.