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 |
|---|---|---|---|---|---|---|---|---|---|---|
FcCat | # 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... | huangzsdy/pytorch_basic_learning | FcCat | false | 3,633 | [
"Apache-2.0"
] | 0 | 7880bc3fcee1d38623d93fa2a36482ccde0e335a | https://github.com/huangzsdy/pytorch_basic_learning/tree/7880bc3fcee1d38623d93fa2a36482ccde0e335a |
GatedTanhUnit | import torch
import torch.nn as nn
def gated_tanh(x, dim):
"""Gated Tanh activation."""
x_tanh, x_sigmoid = torch.chunk(x, 2, dim=dim)
return torch.tanh(x_tanh) * torch.sigmoid(x_sigmoid)
class GatedTanhUnit(nn.Module):
"""Gated Tanh activation."""
def __init__(self, dim=-1):
super(Gate... | 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_... | Nintorac/survae_experiments | GatedTanhUnit | false | 899 | [
"MIT"
] | 0 | d68cc25e2604aab08b53617c1f3ffe4716f166c4 | https://github.com/Nintorac/survae_experiments/tree/d68cc25e2604aab08b53617c1f3ffe4716f166c4 |
ClassificationAccuracy | import torch
import torch.nn as nn
class ClassificationAccuracy(nn.Module):
"""
This class implements the classification accuracy computation. No gradients supported.
"""
def __init__(self, threshold: 'float'=0.5) ->None:
"""
Constructor method
:param threshold: (float) Thresh... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ChristophReich1996/Cell-DETR | ClassificationAccuracy | false | 13,491 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
MPJPE | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.functional
assert_size_stride = torch._C.... | miracleyoo/lifting_events_to_3d_hpe | MPJPE | false | 10,599 | [
"Apache-2.0"
] | 0 | dfe734ee055900d6ab90c064bf82db7672830ac7 | https://github.com/miracleyoo/lifting_events_to_3d_hpe/tree/dfe734ee055900d6ab90c064bf82db7672830ac7 |
StyleResidual | # 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
import torch.utils.data
import torch.optim
assert_size_stri... | niklub/NeMo | StyleResidual | false | 7,342 | [
"Apache-2.0"
] | 1 | 4bcb2321cd16835f63afe3dfe993e6d56bcf2c0c | https://github.com/niklub/NeMo/tree/4bcb2321cd16835f63afe3dfe993e6d56bcf2c0c |
DenseSAGEConv | # 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.... | dendisuhubdy/pytorch_geometric | DenseSAGEConv | false | 1,831 | [
"MIT"
] | 0 | a0592f61aef617c0c8ff61b3d822d04901054c22 | https://github.com/dendisuhubdy/pytorch_geometric/tree/a0592f61aef617c0c8ff61b3d822d04901054c22 |
MaskL1Loss | # 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... | DYF-AI/openvino-x | MaskL1Loss | false | 5,034 | [
"Apache-2.0"
] | 1 | 0f18ebb240ea3394f7e461aca34fac158e686d95 | https://github.com/DYF-AI/openvino-x/tree/0f18ebb240ea3394f7e461aca34fac158e686d95 |
GatedConv2d | import torch
import torch.nn as nn
class GatedConv2d(torch.nn.Module):
"""
Gated Convlution layer with activation (default activation:LeakyReLU)
Params: same as conv2d
Input: The feature from last layer "I"
Output:\\phi(f(I))*\\sigmoid(g(I))
"""
def __init__(self, in_channels, out_channel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ShiraLightricks/3d-photo-inpainting | GatedConv2d | false | 1,053 | [
"MIT"
] | 0 | c42ac41576690b765e50f5281ddbfb58439ff36d | https://github.com/ShiraLightricks/3d-photo-inpainting/tree/c42ac41576690b765e50f5281ddbfb58439ff36d |
UnaryBlock | import torch
import torch.utils.data
import torch.nn as nn
from torch.nn.parameter import Parameter
class BatchNormBlock(nn.Module):
def __init__(self, in_dim, use_bn, bn_momentum):
"""
Initialize a batch normalization block. If network does not use batch normalization, replace with biases.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ShengyuH/PredateOverlap | UnaryBlock | false | 14,412 | [
"MIT"
] | 153 | 770c3063399f08b3836935212ab4c84d355b4704 | https://github.com/ShengyuH/PredateOverlap/tree/770c3063399f08b3836935212ab4c84d355b4704 |
PairwiseBCELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from abc im... | MaxDall/flair | PairwiseBCELoss | false | 9,312 | [
"MIT"
] | 0 | fe33be4a63134595c21891edbe00ef9bd6014641 | https://github.com/MaxDall/flair/tree/fe33be4a63134595c21891edbe00ef9bd6014641 |
PixelWise | # 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.utils.data.distributed
import torch.nn.ini... | davidwagnerkc/TensorMONK | PixelWise | false | 1,803 | [
"MIT"
] | 0 | 3607836d3d6bfd0994e044536b2a51bc84b35f31 | https://github.com/davidwagnerkc/TensorMONK/tree/3607836d3d6bfd0994e044536b2a51bc84b35f31 |
LinearBlock | import torch
from functools import partial
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
@dispatcher
def activ_dispatch(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 functools import partial... | derwind/dmfont | LinearBlock | false | 15,173 | [
"MIT"
] | 95 | 17a91a9cc1917d2485eaa8e92b68245578920c76 | https://github.com/derwind/dmfont/tree/17a91a9cc1917d2485eaa8e92b68245578920c76 |
LinearBlock | import torch
class LinearBlock(torch.nn.Module):
def __init__(self, in_features: 'int', out_features: 'int') ->None:
super().__init__()
self.layer_1 = torch.nn.Linear(in_features, out_features)
self.layer_2 = torch.nn.Linear(out_features, out_features)
self.activation = torch.nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | OleguerCanal/transplanter | LinearBlock | false | 5,682 | [
"MIT"
] | 1 | 854fa727747a484dedde9092eeee6884d7d1b44b | https://github.com/OleguerCanal/transplanter/tree/854fa727747a484dedde9092eeee6884d7d1b44b |
ScaleDotProductAttention | import math
import torch
from torch import nn
class ScaleDotProductAttention(nn.Module):
"""
compute scale dot product attention
Query : given sentence that we focused on (decoder)
Key : every sentence to check relationship with Qeury(encoder)
Value : every sentence same with Key (encoder)
""... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 | ScaleDotProductAttention | false | 1,584 | [
"Apache-2.0"
] | 0 | e128fa862f1b3d17d7b92df169a2bbee3f08366f | https://github.com/bsgiovanini/transformer/tree/e128fa862f1b3d17d7b92df169a2bbee3f08366f |
MaskLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | lisadunlap/explainable-nbdt | MaskLoss | false | 7,110 | [
"MIT"
] | 1 | e045bfd0b55b21fd87c9a233b73a0ca77672efff | https://github.com/lisadunlap/explainable-nbdt/tree/e045bfd0b55b21fd87c9a233b73a0ca77672efff |
SelfAttn | # 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.... | etaoxing/crl_alfred | SelfAttn | false | 15,314 | [
"MIT"
] | 148 | cad500cf84f71e47f1191e7810dde0c74d295f08 | https://github.com/etaoxing/crl_alfred/tree/cad500cf84f71e47f1191e7810dde0c74d295f08 |
BCEFocalLoss | import torch
import torch._utils
class BCEFocalLoss(torch.nn.Module):
"""
二分类的Focalloss alpha 固定
"""
def __init__(self, gamma=2, alpha=0.25, reduction='elementwise_mean'):
super().__init__()
self.gamma = gamma
self.alpha = alpha
self.reduction = reduction
def forw... | 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._utils
as... | ilcessadecalcular/segmentation | BCEFocalLoss | false | 10,583 | [
"MIT"
] | 0 | 24ba499a399efdba212ec5e2235b72ed8270cc24 | https://github.com/ilcessadecalcular/segmentation/tree/24ba499a399efdba212ec5e2235b72ed8270cc24 |
DiscriminatorLoss | import torch
import torch.nn as nn
class AdvLoss(nn.Module):
"""BCE for True and False reals"""
def __init__(self, alpha=1):
super().__init__()
self.loss_fn = nn.BCEWithLogitsLoss()
self.alpha = alpha
def forward(self, pred, target):
return self.alpha * self.loss_fn(pred,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | akanametov/SuperResolution | DiscriminatorLoss | false | 6,140 | [
"MIT"
] | 1 | 45313d1309ddb5cdef821aaf5ac7b5ad574b5287 | https://github.com/akanametov/SuperResolution/tree/45313d1309ddb5cdef821aaf5ac7b5ad574b5287 |
OneTupleModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | briancoutinho/glow | OneTupleModule | false | 12,549 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
Aggregation | import torch
from torch import nn
from torch.nn import *
class Aggregation(nn.Module):
"""
Aggregation layer for the Dueling architecture.
https://arxiv.org/abs/1511.06581
This layer computes a Q function by combining
an estimate of V with an estimate of the advantage.
The advantage is normal... | 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
from torch.nn import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._d... | ezelikman/autonomous-learning-library | Aggregation | false | 6,676 | [
"MIT"
] | 1 | b32d059ca8b191afe0b310102d0754796f391aff | https://github.com/ezelikman/autonomous-learning-library/tree/b32d059ca8b191afe0b310102d0754796f391aff |
Standardize | from torch.nn import Module
import torch
from torch.nn import init
from torch.nn.parameter import Parameter
class Standardize(Module):
"""
Applies (element-wise) standardization with trainable translation parameter μ and scale parameter σ, i.e. computes
(x - μ) / σ where '/' is applied element-wise.
... | 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
from torch.nn import init
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.... | SDJustus/Deep-SAD-PyTorch | Standardize | false | 1,009 | [
"MIT"
] | 0 | 4d98e6474a7256329134c075894f885a56f59281 | https://github.com/SDJustus/Deep-SAD-PyTorch/tree/4d98e6474a7256329134c075894f885a56f59281 |
ExtClassifier | # 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.cuda
import torch.distributed
assert_size_str... | eric-zhizu/OpenNMT-kpg-release | ExtClassifier | false | 12,351 | [
"MIT"
] | 0 | 9f15dea6f663425eef2157845c4c8042ad845c11 | https://github.com/eric-zhizu/OpenNMT-kpg-release/tree/9f15dea6f663425eef2157845c4c8042ad845c11 |
LogSoftmaxOutput | import torch
import torch.nn as nn
class Linear(nn.Linear):
"""
Apply linear projection to the last dimention of a tensor.
"""
def forward(self, x):
size = x.size()
return super().forward(x.contiguous().view(-1, size[-1])).view(*
size[:-1], -1)
class LogSoftmaxOutput(nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | aishwaryaprabhat/BRIDGE-Tabular-Semantic-Parsing | LogSoftmaxOutput | false | 9,663 | [
"BSD-3-Clause"
] | 0 | 640858024df444006dfae106a28fdb58f36f687e | https://github.com/aishwaryaprabhat/BRIDGE-Tabular-Semantic-Parsing/tree/640858024df444006dfae106a28fdb58f36f687e |
PartitionedLinear | import torch
import torch.nn as nn
class PartitionedLinear(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super().__init__()
self.linear_c = nn.Linear(in_features // 2, out_features // 2, bias)
self.linear_p = nn.Linear(in_features // 2, out_features // 2, 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... | skulick/self-attentive-parser | PartitionedLinear | false | 4,357 | [
"MIT"
] | 0 | 04a91e80cc05bcfe8f48145517f58e85f0c8ade6 | https://github.com/skulick/self-attentive-parser/tree/04a91e80cc05bcfe8f48145517f58e85f0c8ade6 |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | chakerouari/UNET_segmetation | DiceLoss | false | 6,413 | [
"MIT"
] | 1 | a7d9e9ccd31595d482f620cbf9a625a486f5f0df | https://github.com/chakerouari/UNET_segmetation/tree/a7d9e9ccd31595d482f620cbf9a625a486f5f0df |
MLP_PART | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLP_PART(nn.Module):
def __init__(self, filter_channels, merge_layer=0, res_layers=[], norm=
'group', num_parts=2, last_op=None):
super(MLP_PART, self).__init__()
self.num_parts = num_parts
self.fc_parts_0 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KORguy/PIFu_Part | MLP_PART | false | 9,313 | [
"MIT"
] | 0 | bd199d439a94f8bc8b4036898b0f1ec01e56ab9e | https://github.com/KORguy/PIFu_Part/tree/bd199d439a94f8bc8b4036898b0f1ec01e56ab9e |
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.... | DeepInEvil/kgirnet | Attention | false | 395 | [
"MIT"
] | 0 | 81210907daba7671d3f3fd5814656d1557b7a2b1 | https://github.com/DeepInEvil/kgirnet/tree/81210907daba7671d3f3fd5814656d1557b7a2b1 |
DownSample | # 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 import nn
import t... | techthiyanes/annotated_deep_learning_paper_implementations | DownSample | false | 16,566 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
UnBlock | import torch
import torch.nn as nn
import torch.utils.cpp_extension
def unblock(tensor):
"""blocked tensor back to normal"""
B, M, N, C = tensor.size()
H = W = int(M ** 0.5)
patch_size = int(N ** 0.5)
tensor = tensor.reshape(B, H, W, patch_size, patch_size, C)
tensor = tensor.permute(0, 5, 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
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = ... | STomoya/animeface | UnBlock | false | 14,365 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
MultiHeadAttention | import math
import torch
import torch.utils.checkpoint
from torch import nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
def forward(self, query, key, value, mask=None):
dk = query.size()[-1]
scores = query.matmul(key.transpose(-2, -1)) / math.sqrt(dk)
if ma... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | abedi1/ECLARe | MultiHeadAttention | false | 1,363 | [
"Apache-2.0"
] | 0 | a446b8086404b058923a9b3ce47e75cc40436a58 | https://github.com/abedi1/ECLARe/tree/a446b8086404b058923a9b3ce47e75cc40436a58 |
DiceLoss | # 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... | hikopensource/DAVAR-Lab-OCR | DiceLoss | false | 15,518 | [
"Apache-2.0"
] | 387 | c65285f6668864cca7a12770ae4c8d083ea1cf1b | https://github.com/hikopensource/DAVAR-Lab-OCR/tree/c65285f6668864cca7a12770ae4c8d083ea1cf1b |
ResMLPLayer | # 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... | jaketae/res-mlp | ResMLPLayer | false | 12,597 | [
"MIT"
] | 0 | 6c957e4fe67a2f13d9b4fd3fa36b7eddcf5323fd | https://github.com/jaketae/res-mlp/tree/6c957e4fe67a2f13d9b4fd3fa36b7eddcf5323fd |
Memory | import torch
import torch.nn as nn
import torch.nn.parallel
class Memory(nn.Module):
def __init__(self):
super(Memory, self).__init__()
self.sm = nn.Softmax()
self.mask = None
def applyMask(self, mask):
self.mask = mask
def forward(self, input, context_key, content_value... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ts170/T2I_CL | Memory | false | 10,912 | [
"MIT"
] | 0 | 8754bea1101aabcbf8108b95e722f7aaeb385869 | https://github.com/ts170/T2I_CL/tree/8754bea1101aabcbf8108b95e722f7aaeb385869 |
DQN | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN(nn.Module):
"""
Deep neural network with represents an agent.
"""
def __init__(self, input_size, num_actions):
super(DQN, self).__init__()
self.linear1 = nn.Linear(input_size, 50)
self.head = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Dookas/Robust-Multitask-RL | DQN | false | 13,599 | [
"MIT"
] | 106 | 7970e20cbdf91703c88edcb84568d7354e2525bc | https://github.com/Dookas/Robust-Multitask-RL/tree/7970e20cbdf91703c88edcb84568d7354e2525bc |
F1 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | ChristophReich1996/Cell-DETR | F1 | false | 13,501 | [
"MIT"
] | 55 | 4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea | https://github.com/ChristophReich1996/Cell-DETR/tree/4d0c3a2d3ffd19184c8443e5b3a6dccc053c77ea |
GramMatrix | import torch
import torch.nn as nn
class GramMatrix(nn.Module):
def forward(self, input):
a, b, c, d = input.size()
features = input.view(a, b, c * d)
G = torch.bmm(features, features.transpose(1, 2))
return G.div(b * c * d)
def get_inputs():
return [torch.rand([4, 4, 4, 4])... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Jay2020-01/TextureGAN--Flask | GramMatrix | false | 17,471 | [
"MIT"
] | 5 | cddea505b0d66b58d58fb24435f8bae42fd5a852 | https://github.com/Jay2020-01/TextureGAN--Flask/tree/cddea505b0d66b58d58fb24435f8bae42fd5a852 |
LayerNormChan | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ryok/nuwa-pytorch | LayerNormChan | false | 10,748 | [
"MIT"
] | 0 | 6bde90ee6d87bdce8c9aa52c6bbb2ad15a1f5f54 | https://github.com/ryok/nuwa-pytorch/tree/6bde90ee6d87bdce8c9aa52c6bbb2ad15a1f5f54 |
AUXModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class AUXModule(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.linear = nn.Linear(in_features, out_features)
def forward(self, x):
x = F.adaptive_max_pool2d(x, output_size=(1, 1))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HamzaFarhan/segmentation_models.pytorch | AUXModule | false | 11,473 | [
"MIT"
] | 0 | b7803df1d17027f329e267ba4c55144adfdd4da9 | https://github.com/HamzaFarhan/segmentation_models.pytorch/tree/b7803df1d17027f329e267ba4c55144adfdd4da9 |
pHAbsModel | # 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 math as tl_math
import numpy as np
from torch import nn
assert_size_stride = torch._C._dy... | rokapre/Nonlinear_Regression | pHAbsModel | false | 12,944 | [
"MIT"
] | 0 | d705f6a010fc0bf000531c967ffcf8ed79a5f92e | https://github.com/rokapre/Nonlinear_Regression/tree/d705f6a010fc0bf000531c967ffcf8ed79a5f92e |
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.... | Kilichbek/artemis-m2-transformer | MultiHeadAttention | false | 17,560 | [
"MIT"
] | 8 | 99f7e797965710bf2565283d6b5028a6fe32664c | https://github.com/Kilichbek/artemis-m2-transformer/tree/99f7e797965710bf2565283d6b5028a6fe32664c |
Net1 | # 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... | DPBayes/DP-cross-silo-federated-learning | Net1 | false | 17,207 | [
"Apache-2.0"
] | 8 | 6707db703de5fae48c06116ae8ceee0685c9615d | https://github.com/DPBayes/DP-cross-silo-federated-learning/tree/6707db703de5fae48c06116ae8ceee0685c9615d |
Cos | import torch
import torch.onnx
import torch.nn as nn
class Cos(nn.Module):
def forward(self, x):
return torch.cos(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.onnx
import torch.nn as nn
assert_size_stride = torch._C._dy... | mil-tokyo/webdnn | Cos | false | 16,059 | [
"MIT"
] | 1,967 | 38a60fd3e1a4e72bc01108189a3aa51e0752aecd | https://github.com/mil-tokyo/webdnn/tree/38a60fd3e1a4e72bc01108189a3aa51e0752aecd |
MultiHeadAttn | import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
pre_lnorm=False):
super(MultiHeadAttn, self).__init__()
self.n_head = n_head
self.d_model = d_model
self.d_hea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JingzhaoZhang/transformerxl-noise | MultiHeadAttn | false | 9,201 | [
"Apache-2.0"
] | 0 | 83b91c505217da2a32b6ca592e01b4a1e941937b | https://github.com/JingzhaoZhang/transformerxl-noise/tree/83b91c505217da2a32b6ca592e01b4a1e941937b |
ScaledLeakyReLU | # 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... | Dolorousrtur/style-people | ScaledLeakyReLU | false | 8,015 | [
"MIT"
] | 15 | c48b12b245cc50f8230c0654dffe40016f2a69f1 | https://github.com/Dolorousrtur/style-people/tree/c48b12b245cc50f8230c0654dffe40016f2a69f1 |
Policy | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | tpbarron/pytorch-ppo | Policy | false | 16,616 | [
"MIT"
] | 47 | f73226865e34443f93dbec58939329c9278828e8 | https://github.com/tpbarron/pytorch-ppo/tree/f73226865e34443f93dbec58939329c9278828e8 |
LandmarkHead | import torch
import torch.nn as nn
from itertools import product as product
class LandmarkHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(LandmarkHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=
(1, 1), stride=1, padd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 itertools import product as product
assert_size_strid... | FacePerceiver/facer | LandmarkHead | false | 8,126 | [
"MIT"
] | 12 | cbb01dc457f3713050e89af7b2c9c0d98663842c | https://github.com/FacePerceiver/facer/tree/cbb01dc457f3713050e89af7b2c9c0d98663842c |
InnerProductLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class InnerProductLoss(nn.Module):
"""This is the inner-product loss used in CFKG for optimization.
"""
def __init__(self):
super(InnerProductLoss, self).__init__()
def forward(self, anchor, positive, negative):
pos_s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | BELIEVEfxy/LightSANs | InnerProductLoss | false | 7,760 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
KeypointsMSESmoothLoss | import torch
import torch.utils.data
import torch
import torch.nn as nn
class KeypointsMSESmoothLoss(nn.Module):
def __init__(self, threshold=400):
super().__init__()
self.threshold = threshold
def forward(self, output, target, target_weight):
batch_size = output.size(0)
num_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | yihui-he2020/epipolar-transformers | KeypointsMSESmoothLoss | false | 16,762 | [
"MIT"
] | 360 | 6824f4345b2998500fbacd0f4e30f67f8e3da7b8 | https://github.com/yihui-he2020/epipolar-transformers/tree/6824f4345b2998500fbacd0f4e30f67f8e3da7b8 |
WShift | import torch
import torch.nn as nn
import torch.nn.parallel
class WShift(nn.Module):
def __init__(self, style_dim):
super().__init__()
self.w_shift = nn.Parameter(torch.zeros(1, style_dim))
def forward(self, input):
out = input + self.w_shift
return out
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | AyushExel/GANSketching | WShift | false | 13,362 | [
"MIT"
] | 598 | c72524ac4425de898087af7a4c554b777a4e2218 | https://github.com/AyushExel/GANSketching/tree/c72524ac4425de898087af7a4c554b777a4e2218 |
NodeNetwork | import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
def global_sum_pool(X, batch_mat):
if batch_mat is None or batch_mat.dim() == 1:
return torch.sum(X, dim=0).unsqueeze(0)
else:
return torch.mm(batch_mat, X)
class BasicGraphConvolutionLayer(torch.nn.Module)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | mbrukman/machine-learning-book | NodeNetwork | false | 7,192 | [
"MIT"
] | 1 | f29a0f8aafa63a77081f3bcec68866e33dd41776 | https://github.com/mbrukman/machine-learning-book/tree/f29a0f8aafa63a77081f3bcec68866e33dd41776 |
GuidedBackpropReLUasModule | from torch.autograd import Function
import torch
import torch.cuda
class GuidedBackpropReLU(Function):
@staticmethod
def forward(self, input_img):
positive_mask = (input_img > 0).type_as(input_img)
output = torch.addcmul(torch.zeros(input_img.size()).type_as(
input_img), input_img... | 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.autograd import Function
import torch.cuda
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = t... | TigerKinger/pytorch-grad-cam | GuidedBackpropReLUasModule | false | 11,936 | [
"MIT"
] | 0 | adb3c56e274fde782bf84d2a77454046bd4c5be4 | https://github.com/TigerKinger/pytorch-grad-cam/tree/adb3c56e274fde782bf84d2a77454046bd4c5be4 |
DiceLoss | import torch
import torch.nn as nn
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
def forward(self, input, target):
smooth = 1e-05
input = input.float()
target = target.float()
iflat = input.view(-1)
tflat = target.view(-1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DIAL-RPI/PIPO-FAN | DiceLoss | false | 13,540 | [
"MIT"
] | 53 | 126c17fbdc4c62806a9d249be355542f3990f305 | https://github.com/DIAL-RPI/PIPO-FAN/tree/126c17fbdc4c62806a9d249be355542f3990f305 |
MaxPoolBlock | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DevilMayNotCry/My_curl | MaxPoolBlock | false | 9,122 | [
"BSD-3-Clause"
] | 0 | a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | https://github.com/DevilMayNotCry/My_curl/tree/a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 |
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.... | joao-d-oliveira/CV-Image_Captioning | BahdanauAttention | false | 12,621 | [
"MIT"
] | 0 | 76186c326e4fc44a60da401f4ec71176cba42e87 | https://github.com/joao-d-oliveira/CV-Image_Captioning/tree/76186c326e4fc44a60da401f4ec71176cba42e87 |
PixelWiseModel | # 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | ciubecca/3dunet-cavity | PixelWiseModel | false | 1,704 | [
"MIT"
] | 0 | cfcc827773b18a95d221ab86c1afc5e2f7c30ecb | https://github.com/ciubecca/3dunet-cavity/tree/cfcc827773b18a95d221ab86c1afc5e2f7c30ecb |
LinearScale | import torch
import torch.nn as nn
class LinearScale(nn.Module):
def __init__(self, scale, bias):
super(LinearScale, self).__init__()
self.scale_v = scale
self.bias_v = bias
pass
def forward(self, x):
out = x * self.scale_v + self.bias_v
return out
def __... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | justinjohn0306/CIPS-3D | LinearScale | false | 6,994 | [
"MIT"
] | 1 | 69a910a7841846419a6b5e03182c8cf061a82584 | https://github.com/justinjohn0306/CIPS-3D/tree/69a910a7841846419a6b5e03182c8cf061a82584 |
BackProjection | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | AIpakchoi/visualDet3D | BackProjection | false | 4,772 | [
"Apache-2.0"
] | 1 | 920f6f8ea44eac4c1896b7d157c015e039ac39f9 | https://github.com/AIpakchoi/visualDet3D/tree/920f6f8ea44eac4c1896b7d157c015e039ac39f9 |
ConvLayer | import torch
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.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
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_s... | Bartolo1024/ignite | ConvLayer | false | 4,887 | [
"BSD-3-Clause"
] | 1 | b087fef0bc5f97cda415c1c56f1cd589383c54be | https://github.com/Bartolo1024/ignite/tree/b087fef0bc5f97cda415c1c56f1cd589383c54be |
EqualConvTranspose2d | import torch
import torch.nn as nn
from math import sqrt
import torch.utils.data
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from math import sqrt
import torch.utils.data
assert_size_... | GuiCamargoX/gans_pytorch | EqualConvTranspose2d | false | 9,139 | [
"MIT"
] | 0 | 3103184e54ea0d2922fc664a994a912bf61db426 | https://github.com/GuiCamargoX/gans_pytorch/tree/3103184e54ea0d2922fc664a994a912bf61db426 |
output | # 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... | binzh93/EAST | output | false | 3,229 | [
"MIT"
] | 0 | b5f66ab1a5dd37b6a5134336d494000e1add6da1 | https://github.com/binzh93/EAST/tree/b5f66ab1a5dd37b6a5134336d494000e1add6da1 |
MultiHeadAttn | # 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.... | PiotrDabkowski/NeMo | MultiHeadAttn | false | 11,798 | [
"Apache-2.0"
] | 0 | 7c251e9035b24136cf130f3caf760087e5ccf07c | https://github.com/PiotrDabkowski/NeMo/tree/7c251e9035b24136cf130f3caf760087e5ccf07c |
S2S2Mean | # 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.... | pimdh/lie-vae | S2S2Mean | false | 16,272 | [
"MIT"
] | 83 | 0e0cc4d533c064fcfc405e8a75449f8b2f6cf8cf | https://github.com/pimdh/lie-vae/tree/0e0cc4d533c064fcfc405e8a75449f8b2f6cf8cf |
Denoise_NormalizeLayer | # 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... | Equationliu/GA-Attack | Denoise_NormalizeLayer | false | 17,264 | [
"MIT"
] | 8 | b0280674a211f6451774ec6b1d4cee2fc19a4de6 | https://github.com/Equationliu/GA-Attack/tree/b0280674a211f6451774ec6b1d4cee2fc19a4de6 |
MSE_Loss | import torch
import torch.nn as nn
class MSE_Loss(nn.Module):
def __init__(self, sum_dim=None, sqrt=False, dimension_warn=0):
super().__init__()
self.sum_dim = sum_dim
self.sqrt = sqrt
self.dimension_warn = dimension_warn
def forward(self, x, y):
assert x.shape == y.s... | 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... | WorksApplications/omni_torch | MSE_Loss | false | 1,223 | [
"Apache-2.0"
] | 0 | 10b689d794c8f485e38c765303ef018da17bc641 | https://github.com/WorksApplications/omni_torch/tree/10b689d794c8f485e38c765303ef018da17bc641 |
FactorizedReduce | import torch
import torch.nn as nn
import torch.utils
class FactorizedReduce(nn.Module):
def __init__(self, C_in, C_out, affine=True):
super(FactorizedReduce, self).__init__()
assert C_out % 2 == 0
self.relu = nn.ReLU(inplace=False)
self.conv_1 = nn.Conv3d(C_in, C_out // 2, 1, str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Alison-brie/AutoReg | FactorizedReduce | false | 16,883 | [
"MIT"
] | 10 | a23d45a6f7c6e47f61430e1565dda316452a4418 | https://github.com/Alison-brie/AutoReg/tree/a23d45a6f7c6e47f61430e1565dda316452a4418 |
MultiHeadAttentionWithPooling | # 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.... | BELIEVEfxy/LightSANs | MultiHeadAttentionWithPooling | false | 7,846 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
AsymmetricLossOptimized | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class AsymmetricLossOptimized(nn.Module):
""" Notice - optimized version, minimizes memory allocation and gpu uploading,
favors inplace operations"""
def __init__(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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ckvic3/query2labels | AsymmetricLossOptimized | false | 1,728 | [
"MIT"
] | 0 | e9c30e1b445be773be397a093fa66aef71d54556 | https://github.com/ckvic3/query2labels/tree/e9c30e1b445be773be397a093fa66aef71d54556 |
TdnnAffine | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | fancyliumeng/asv-subtools | TdnnAffine | false | 6,683 | [
"Apache-2.0"
] | 1 | 56a13484472e7ae6eb00d762c00d57e581e78eb4 | https://github.com/fancyliumeng/asv-subtools/tree/56a13484472e7ae6eb00d762c00d57e581e78eb4 |
Generator | import torch
import torch.nn as nn
class Generator(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, size, vocab):
super(Generator, self).__init__()
self.size = size
self.proj = nn.Linear(self.size, vocab)
def forward(self, x):
sliced_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | QuLog1/QuLog | Generator | false | 970 | [
"Apache-2.0"
] | 0 | 121f3a8c6f5ee60cde771c36b9eef823a1b2597a | https://github.com/QuLog1/QuLog/tree/121f3a8c6f5ee60cde771c36b9eef823a1b2597a |
SimpleBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | Aamer98/FeatureNorm | SimpleBlock | false | 12 | [
"MIT"
] | 0 | fbf3d3b4cef81b3351347d272eb51b6cdd9f0cc5 | https://github.com/Aamer98/FeatureNorm/tree/fbf3d3b4cef81b3351347d272eb51b6cdd9f0cc5 |
ResidualBlock | import torch
import numpy as np
import torch.nn as nn
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = int(np.floor(kernel_size / 2))
self.reflection_pad = nn.ReflectionPad2d(reflec... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ImageProcessingCentraleLille2021/fast-neural-style | ResidualBlock | false | 13,846 | [
"MIT"
] | 350 | e77456c35c2a49f90227119d158828a0964c7e13 | https://github.com/ImageProcessingCentraleLille2021/fast-neural-style/tree/e77456c35c2a49f90227119d158828a0964c7e13 |
rSoftMax | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
class rSoftMax(nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = c... | 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
... | Capetian/FaceX-Zoo | rSoftMax | false | 4,979 | [
"Apache-2.0"
] | 1 | 029786c40d8aba15d891d33973de25fcd7e5399a | https://github.com/Capetian/FaceX-Zoo/tree/029786c40d8aba15d891d33973de25fcd7e5399a |
LocalConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class LocalConv2d(nn.Module):
def __init__(self, num_rows, num_feats_in, num_feats_out, kernel=1,
padding=0):
super(LocalConv2d, self).__init__()
self.num_rows = num_rows
self.out_channels = num_feats_out
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | JSharpClone/M3D-RPN- | LocalConv2d | false | 11,522 | [
"Apache-2.0"
] | 0 | 5192b095e921b5c054a66fd0ce948e67aee957be | https://github.com/JSharpClone/M3D-RPN-/tree/5192b095e921b5c054a66fd0ce948e67aee957be |
GradientLoss | # 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_... | GuYuanjie/Deep-Retinex-fusion | GradientLoss | false | 17,340 | [
"MIT"
] | 5 | ffa2a1689fd512c8820fd87cbf665c09bcb142b4 | https://github.com/GuYuanjie/Deep-Retinex-fusion/tree/ffa2a1689fd512c8820fd87cbf665c09bcb142b4 |
FeatureResizer | import torch
import torch.utils.data
import torch
from torch import nn
class FeatureResizer(nn.Module):
"""
This class takes as input a set of embeddings of dimension C1 and outputs a set of
embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
"""
def __init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | Sudhir11292rt/DefVisTR | FeatureResizer | false | 1,089 | [
"Apache-2.0"
] | 0 | d52b2d88c10c6239de1c1ff851a743c58b708b75 | https://github.com/Sudhir11292rt/DefVisTR/tree/d52b2d88c10c6239de1c1ff851a743c58b708b75 |
Attention | import torch
import torch.optim
import torch.utils.data
from torch import nn
class Attention(nn.Module):
"""
Attention Network.
"""
def __init__(self, encoder_dim, decoder_dim, attention_dim):
"""
:param encoder_dim: feature size of encoded images
:param decoder_dim: size of d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Afosado/180b_capstone_xai | Attention | false | 18,447 | [
"MIT"
] | 2 | 808768e8fc73d260845921e8174b69286c066eca | https://github.com/Afosado/180b_capstone_xai/tree/808768e8fc73d260845921e8174b69286c066eca |
DepthNormalizer | import torch
import torch.nn as nn
class DepthNormalizer(nn.Module):
def __init__(self, input_size: 'int'=512, z_size: 'int'=200):
"""
Class about DepthNormalizer
which use to generate depth-information
Parameters:
input_size: the size of image, initially, 512 x 512
... | 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... | lingtengqiu/Open-PIFuhd | DepthNormalizer | false | 15,912 | [
"MIT"
] | 191 | 3a66b647bcf5591e818af62735e64a93c4aaef85 | https://github.com/lingtengqiu/Open-PIFuhd/tree/3a66b647bcf5591e818af62735e64a93c4aaef85 |
EncoderImagePrecomp | # 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.... | jefflai108/VGNSL | EncoderImagePrecomp | false | 6,931 | [
"MIT"
] | 1 | 0edc3db3691abbad2a505b2165bd99e7a62d784f | https://github.com/jefflai108/VGNSL/tree/0edc3db3691abbad2a505b2165bd99e7a62d784f |
Value | import torch
import torch.nn as nn
class Value(nn.Module):
def __init__(self, num_inputs):
super(Value, self).__init__()
self.affine1 = nn.Linear(num_inputs, 64)
self.affine2 = nn.Linear(64, 64)
self.value_head = nn.Linear(64, 1)
self.value_head.weight.data.mul_(0.1)
... | 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.triton_helpers import libdevice
import torch.nn as ... | SaminYeasar/pytorch-trpo | Value | false | 4,127 | [
"MIT"
] | 0 | 653a3357cf0461c175fb741604c0cd4ad1f4b841 | https://github.com/SaminYeasar/pytorch-trpo/tree/653a3357cf0461c175fb741604c0cd4ad1f4b841 |
MarginLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
... | techthiyanes/annotated_deep_learning_paper_implementations | MarginLoss | false | 16,551 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
DirectMultiheadAttention | # 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.... | wukevin/RoseTTAFold | DirectMultiheadAttention | false | 4,560 | [
"MIT"
] | 0 | e3c15dbf4bc1e4f8726e26c63aca1625188da803 | https://github.com/wukevin/RoseTTAFold/tree/e3c15dbf4bc1e4f8726e26c63aca1625188da803 |
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.triton_helpers import libdevice
from torch import n... | puppyapple/tacotron_pytorch | BahdanauAttention | false | 16,292 | [
"MIT"
] | 278 | 800bf8b0538c91f1104e99d8e7c1b645bb6154d3 | https://github.com/puppyapple/tacotron_pytorch/tree/800bf8b0538c91f1104e99d8e7c1b645bb6154d3 |
MultiLayeredConv1d | # 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
assert_size_stride = torch._C... | akreal/end-to-end-slu-espnet | MultiLayeredConv1d | false | 3,073 | [
"Apache-2.0"
] | 0 | 0b16dc8b10b31a4567b3312678a753a94bb200da | https://github.com/akreal/end-to-end-slu-espnet/tree/0b16dc8b10b31a4567b3312678a753a94bb200da |
Conv2dBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | agermanidis/HiDT | Conv2dBlock | false | 18,230 | [
"BSD-3-Clause"
] | 4 | 69192bb26785fc4e05038c45d05f2f880dd362d0 | https://github.com/agermanidis/HiDT/tree/69192bb26785fc4e05038c45d05f2f880dd362d0 |
FeatExemplarAvgBlock | # 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.optim
import torch.nn.parallel
assert_size_st... | Basasuya/FewShotWithoutForgetting | FeatExemplarAvgBlock | false | 2,012 | [
"MIT"
] | 0 | eecc70e416ed82999124ddfca1b145f6dbcd74a6 | https://github.com/Basasuya/FewShotWithoutForgetting/tree/eecc70e416ed82999124ddfca1b145f6dbcd74a6 |
SeqExpandConv | import torch
import torch.nn as nn
from math import sqrt as sqrt
class SeqExpandConv(nn.Module):
def __init__(self, in_channels, out_channels, seq_length):
super(SeqExpandConv, self).__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1,
1), padding=(1, 0, 0), ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 math import sqrt as sqrt
assert_size_stride = torch._... | NTech-Lab/deepfake-detection-challenge | SeqExpandConv | false | 14,081 | [
"Apache-2.0"
] | 98 | 52095ce4a49f298faf075a5eb28391722b9e4103 | https://github.com/NTech-Lab/deepfake-detection-challenge/tree/52095ce4a49f298faf075a5eb28391722b9e4103 |
GRUStep | # 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... | IBM/graph4nlp | GRUStep | false | 8,339 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
ResolutionScalingLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.fft
class ResolutionScalingLayer(nn.Module):
"""Implements the resolution scaling layer.
Basically, this layer can be used to upsample feature maps from spatial domain
with nearest neighbor interpolation.
"""
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
import torch.nn as nn
import torch.fft
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | NejcHirci/material-addon | ResolutionScalingLayer | false | 17,768 | [
"MIT"
] | 4 | c08e2081413c3319b712c2f7193ac8013f601382 | https://github.com/NejcHirci/material-addon/tree/c08e2081413c3319b712c2f7193ac8013f601382 |
GELU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | kwonyos/decision-transformer | GELU | false | 12,691 | [
"MIT"
] | 0 | c3ad7df28a897a016dd24c5337cb871d1f33f456 | https://github.com/kwonyos/decision-transformer/tree/c3ad7df28a897a016dd24c5337cb871d1f33f456 |
PolicyNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | DeepHaeJoong/reinforcement-learning | PolicyNetwork | false | 9,026 | [
"MIT"
] | 0 | 63e3053e3209809e67e97d51adaf5f85ce3799ba | https://github.com/DeepHaeJoong/reinforcement-learning/tree/63e3053e3209809e67e97d51adaf5f85ce3799ba |
ReinforcedReceiver | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
from torch.distributions import Bernoulli
import torch.distributions
class ReinforcedReceiver(nn.Module):
def __init__(self, n_bits, n_hidden):
super(ReinforcedReceiver, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import to... | vengalraoguttha/EGG | ReinforcedReceiver | false | 16,665 | [
"MIT"
] | 254 | e4f8412f197543ec7f1f00cf89b5a364b038dc57 | https://github.com/vengalraoguttha/EGG/tree/e4f8412f197543ec7f1f00cf89b5a364b038dc57 |
Normalize | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
class Normalize(nn.Module):
def __init__(self, p=2):
super(Normalize, self).__init__()
self.p = p
def forward(self, x):
return F.normalize(x, p=self.p, dim=1)
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | JindongGu/SimDis | Normalize | false | 8,352 | [
"MIT"
] | 12 | 0871a217a756acc268f35f802e35b01b12817f0d | https://github.com/JindongGu/SimDis/tree/0871a217a756acc268f35f802e35b01b12817f0d |
CAModule | # 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... | alinstein/X_RAY | CAModule | false | 18,262 | [
"MIT"
] | 4 | 35a39761d3b11ce9e47509025054f25e5f26aab9 | https://github.com/alinstein/X_RAY/tree/35a39761d3b11ce9e47509025054f25e5f26aab9 |
UnpackLayerConv2d | import torch
import torch.nn as nn
class Conv2D(nn.Module):
"""
2D convolution with GroupNorm and ELU
Parameters
----------
in_channels : int
Number of input channels
out_channels : int
Number of output channels
kernel_size : int
Kernel size
stride : int
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING- | UnpackLayerConv2d | false | 2,252 | [
"MIT"
] | 0 | 13fac05601efed16ae8b29989aad487e04cd90a7 | https://github.com/Fatmangh/VIDEO-ACTION-CLASSIFICATION-USING-PRETAINED-SELF-SUPERVISED-DEPTH-AWARE-DENSE-PREDICTIVE-CODING-/tree/13fac05601efed16ae8b29989aad487e04cd90a7 |
PositionwiseFeedForward | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Identity(nn.Module):
def forward(self, input_):
return input_
class LayerNormalization(nn.Module):
""" Layer normalization module """
def __init__(self, d_hid, eps=0.001):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dqawami/openvino_training_extensions | PositionwiseFeedForward | false | 15,234 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad |
ScaledDotProductAttention | import torch
import numpy as np
import torch.utils.data
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Doomski99/MarcCoru2019CropType | ScaledDotProductAttention | false | 11,377 | [
"MIT"
] | 0 | 17db294ef51bdd39fd884e0052141d8092b98b86 | https://github.com/Doomski99/MarcCoru2019CropType/tree/17db294ef51bdd39fd884e0052141d8092b98b86 |
D_UpBlock | # 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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | DengZeshuai/DBPN-Pytorch | D_UpBlock | false | 2,634 | [
"MIT"
] | 0 | a90d241a1c4b07830c6d812ad8389d13e8cf05d1 | https://github.com/DengZeshuai/DBPN-Pytorch/tree/a90d241a1c4b07830c6d812ad8389d13e8cf05d1 |
KLLoss | import torch
import torch.utils.data
from torchvision.transforms import functional as F
import torch.nn as nn
import torch.nn.functional as F
from sklearn import *
class KLLoss(nn.Module):
"""
This criterion is a implemenation of Focal Loss, which is proposed in
Focal Loss for Dense Object Detecti... | 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... | CityU-AIM-Group/SIGMA | KLLoss | false | 17,670 | [
"MIT"
] | 5 | 19f89777db8d42f750a9b87756d3326c7efd18f5 | https://github.com/CityU-AIM-Group/SIGMA/tree/19f89777db8d42f750a9b87756d3326c7efd18f5 |
FRN_self | import torch
import torch.nn as nn
class FRN_self(nn.Module):
def __init__(self, num_features, eps=1e-05, is_eps_learnable=True):
super(FRN_self, self).__init__()
self.num_features = num_features
self.init_eps = eps
self.is_eps_learnable = is_eps_learnable
self.gamma = nn.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | EkdeepSLubana/BeyondBatchNorm | FRN_self | false | 17,244 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 |
BasicBlock | # 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.... | HenryOsborne/LearningToPaint | BasicBlock | false | 9,149 | [
"MIT"
] | 0 | d8fdf41c8d193b91c78f73b7a092897e846e19eb | https://github.com/HenryOsborne/LearningToPaint/tree/d8fdf41c8d193b91c78f73b7a092897e846e19eb |
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