entry_point stringlengths 1 65 | original_triton_code stringlengths 4.5k 619k | python_code stringlengths 208 60.9k | 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
EnDeWithPooling | # 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 numpy as np
import torch.nn as nn
import torch.nn.functional as F
class EnDeWithPooling(nn.Module):
def __init__(self, activation, initType, numChannels, batchnorm=False,
softmax=False):
super(EnDeWithPooling, self).__init__()
self.batchnorm = batchnorm
self.bi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | talsperre/INFER | EnDeWithPooling | false | 16,564 | [
"MIT"
] | 56 | 38fb2356700c5a92991788b7eb9a267c99a07c5b | https://github.com/talsperre/INFER/tree/38fb2356700c5a92991788b7eb9a267c99a07c5b |
SpatialDepthWiseSharedConvolution | # 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... | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class SpatialDepthWiseSharedConvolution(Module):
"""
## Spatial Depth Wise Shared Convolution
We share the same kernel across all channels.
"""
def __init__(self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import nn
import torch.utils.data
import ... | techthiyanes/annotated_deep_learning_paper_implementations | SpatialDepthWiseSharedConvolution | false | 16,565 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
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 import nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
import torch.autograd
class Smooth(nn.Module):
"""
<a id="smooth"></a>
### Smoothing Layer
This layer blurs each channel
"""
def __init__(self):
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
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 |
Smooth | # 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 import nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
import torch.autograd
class Smooth(nn.Module):
"""
<a id="smooth"></a>
### Smoothing Layer
This layer blurs each channel
"""
def __init__(self):
super().__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 import nn
import torch.utils.data
import torch.nn.functional
import t... | techthiyanes/annotated_deep_learning_paper_implementations | Smooth | false | 16,567 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
ATLoss | # 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 import Tensor
import torch.nn as nn
import torch.nn.functional as F
class ATLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits: 'Tensor', labels: 'Tensor') ->float:
"""
Args:
logits: predicted probabilities (shape: bat... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import Tens... | techthiyanes/DeepPavlov | ATLoss | false | 16,568 | [
"Apache-2.0"
] | 5,893 | 08555428388fed3c7b036c0a82a70a25efcabcff | https://github.com/techthiyanes/DeepPavlov/tree/08555428388fed3c7b036c0a82a70a25efcabcff |
SpatialDepthWisePerHeadConvolution | # 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... | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class SpatialDepthWisePerHeadConvolution(Module):
"""
## Spatial Depth Wise Per Head Convolution
"""
def __init__(self, heads: 'int', d_k: 'int', kernel_size: 'int'=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.nn import Module
from torch import nn
import torch.utils.data
import ... | techthiyanes/annotated_deep_learning_paper_implementations | SpatialDepthWisePerHeadConvolution | false | 16,569 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
Squash | # 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... | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional
import torch.autograd
class Squash(Module):
'\n ## Squash\n\n This is **squashing** function from paper, given by equation $(1)$.\n\n $$\\mathbf{v}_j = \x0crac{{\\lVert \\mathbf{s}_j \rVert}^2}{1 + {\\lVert \\math... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
import torch.utils.data
import torch.nn.functional
... | techthiyanes/annotated_deep_learning_paper_implementations | Squash | false | 16,570 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
SpacialGatingUnit | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.utils.data
from typing import Optional
import torch.nn.functional
import torch.autograd
class SpacialGatingUnit(nn.Module):
"""
## Spatial Gating Unit
$$s(Z) = Z_1 \\odot f_{W,b}(Z_2)$$
where $f_{W,b}(Z) = W Z + b$ is a linear transformation along the 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.triton_helpers import libdevice
from torch import n... | techthiyanes/annotated_deep_learning_paper_implementations | SpacialGatingUnit | false | 16,571 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
SpatialDepthWiseConvolution | # 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... | from torch.nn import Module
import math
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class SpatialDepthWiseConvolution(Module):
"""
## Spatial Depth Wise Convolution
This is actually slower
"""
def __init__(self, d_k: 'int', kernel_si... | 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 math
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
assert... | techthiyanes/annotated_deep_learning_paper_implementations | SpatialDepthWiseConvolution | false | 16,572 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
PatchEmbeddings | # 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... | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class PatchEmbeddings(Module):
"""
<a id="PatchEmbeddings"></a>
## Get patch embeddings
The paper splits the image into patches of equal size and do a linear transfo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 import nn
import torch.utils.data
import ... | techthiyanes/annotated_deep_learning_paper_implementations | PatchEmbeddings | false | 16,573 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
ToRGB | # 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 math
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
from typing import List
import torch.nn.functional
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight"></a>
## Learning-rate Equalized Weights Paramete... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import numpy as np
from torch import nn
import torch.nn.functional a... | techthiyanes/annotated_deep_learning_paper_implementations | ToRGB | false | 16,574 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
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 torch.nn as nn
import torch.hub
def dice_loss(input, target):
smooth = 1.0
input = torch.sigmoid(input)
if input.dim() == 4:
B, C, _H, _W = input.size()
iflat = input.view(B * C, -1)
tflat = target.view(B * C, -1)
else:
assert input.dim() == 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.hub
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | thangnx183/kaggle-understanding-clouds | DiceLoss | false | 16,575 | [
"BSD-2-Clause"
] | 207 | 15ad2a9029958262437b899cb00525579da23911 | https://github.com/thangnx183/kaggle-understanding-clouds/tree/15ad2a9029958262437b899cb00525579da23911 |
StyleBlock | # 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 math
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
from typing import Optional
from typing import List
import torch.nn.functional
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight"></a>
## Learning-rat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | techthiyanes/annotated_deep_learning_paper_implementations | StyleBlock | false | 16,576 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
AddTensors | # 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 torch.nn as nn
import torch.hub
class AddTensors(nn.Module):
""" Adds all its inputs together. """
def forward(self, xs):
return sum(xs)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.hub
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | theoway/raster-vision | AddTensors | false | 16,577 | [
"Apache-2.0"
] | 1,577 | dab675517f904771e2ce8c052494f8a6f1ddc026 | https://github.com/theoway/raster-vision/tree/dab675517f904771e2ce8c052494f8a6f1ddc026 |
ACGANDiscriminator | # 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 torch.nn as nn
import torch.nn.utils as utils
import torch.nn.functional as F
from torchvision import utils
def global_pooling(input, pooling='mean'):
if pooling == 'mean':
return input.mean(3).mean(2)
elif pooling == 'sum':
return input.sum(3).sum(2)
else:
rais... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | takuhirok/rGAN | ACGANDiscriminator | false | 16,578 | [
"MIT"
] | 103 | 6f7a092de5814c662fd17224b3d48bebe7e03c2f | https://github.com/takuhirok/rGAN/tree/6f7a092de5814c662fd17224b3d48bebe7e03c2f |
EqualizedLinear | # 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 math
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data
from typing import List
import torch.nn.functional
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight"></a>
## Learning-rate Equalized Weights Paramete... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import numpy as np
from torch import nn
import torch.utils.data
from... | techthiyanes/annotated_deep_learning_paper_implementations | EqualizedLinear | false | 16,579 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
SymmetricBCELoss | # 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 torch.nn as nn
import torch.nn.functional as F
import torch.hub
class SymmetricBCELoss(nn.Module):
def __init__(self, alpha=0.1, beta=0.1):
super().__init__()
self.alpha = alpha
self.beta = beta
def forward(self, input, target):
y_true = target
y_p... | 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... | thangnx183/kaggle-understanding-clouds | SymmetricBCELoss | false | 16,580 | [
"BSD-2-Clause"
] | 207 | 15ad2a9029958262437b899cb00525579da23911 | https://github.com/thangnx183/kaggle-understanding-clouds/tree/15ad2a9029958262437b899cb00525579da23911 |
UpSample | # 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 import nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
import torch.autograd
class Smooth(nn.Module):
"""
<a id="smooth"></a>
### Smoothing Layer
This layer blurs each channel
"""
def __init__(self):
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | techthiyanes/annotated_deep_learning_paper_implementations | UpSample | false | 16,581 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
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... | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
"""
self attention层
原理可看这篇博客: http://jalammar.github.io/illustrated-transformer/
"""
def __init__(self, config):
super(BertSelfAttention, 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | techthiyanes/nlp-notebook | BertAttention | false | 16,582 | [
"MIT"
] | 136 | 0e5f4b75e635128d4056c89a6c65bea60c15e836 | https://github.com/techthiyanes/nlp-notebook/tree/0e5f4b75e635128d4056c89a6c65bea60c15e836 |
moving_avg | # 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 torch.nn as nn
class moving_avg(nn.Module):
"""
Moving average block to highlight the trend of time series
"""
def __init__(self, kernel_size, stride):
super(moving_avg, self).__init__()
self.kernel_size = kernel_size
self.avg = nn.AvgPool1d(kernel_size=ker... | 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... | thuml/Autoformer | moving_avg | false | 16,583 | [
"MIT"
] | 263 | 6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab | https://github.com/thuml/Autoformer/tree/6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab |
GeneratorBlock | # 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 math
import torch
import numpy as np
from torch import nn
from typing import Tuple
import torch.nn.functional as F
import torch.utils.data
from typing import Optional
from typing import List
import torch.nn.functional
import torch.autograd
class EqualizedWeight(nn.Module):
"""
<a id="equalized_weight">... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | techthiyanes/annotated_deep_learning_paper_implementations | GeneratorBlock | false | 16,584 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
BiasAdd | # 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... | from _paritybench_helpers import _mock_config
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class BiasAdd(nn.Module):
def __init__(self, channels, opts, act='linear', alpha=None, gain=None,
lrmul=1):
"""
BiasAdd
"""
super(BiasAdd... | 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... | tomguluson92/StyleGAN2_PyTorch | BiasAdd | false | 16,585 | [
"MIT"
] | 89 | 4ab7354c85cb986d2b77f5238c4a18c5efd1db1b | https://github.com/tomguluson92/StyleGAN2_PyTorch/tree/4ab7354c85cb986d2b77f5238c4a18c5efd1db1b |
GLU | # 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 torch.nn as nn
def initialize_weight(x):
nn.init.xavier_uniform_(x.weight)
if x.bias is not None:
nn.init.constant_(x.bias, 0)
class GLU(nn.Module):
def __init__(self, in_features, dropout_rate):
super(GLU, self).__init__()
self.sigm = nn.Sigmoid()
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | tijsmaas/transformer-pytorch | GLU | false | 16,586 | [
"MIT"
] | 237 | bb517979d62c416f68d66325f51826bbbf4ba1bd | https://github.com/tijsmaas/transformer-pytorch/tree/bb517979d62c416f68d66325f51826bbbf4ba1bd |
SquaredErrorBayesRisk | # 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... | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional
import torch.autograd
class SquaredErrorBayesRisk(Module):
"""
<a id="SquaredErrorBayesRisk"></a>
## Bayes Risk with Squared Error Loss
Here the cost function is squared error,
$$\\sum_{k=1}^K (y_k - p_k)... | 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
import torch.nn.functional
import torch.autograd
assert_size_stride = torch._C._dynamo.g... | techthiyanes/annotated_deep_learning_paper_implementations | SquaredErrorBayesRisk | false | 16,587 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
series_decomp | # 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 torch.nn as nn
class moving_avg(nn.Module):
"""
Moving average block to highlight the trend of time series
"""
def __init__(self, kernel_size, stride):
super(moving_avg, self).__init__()
self.kernel_size = kernel_size
self.avg = nn.AvgPool1d(kernel_size=ker... | 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... | thuml/Autoformer | series_decomp | false | 16,588 | [
"MIT"
] | 263 | 6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab | https://github.com/thuml/Autoformer/tree/6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab |
DilatedNet | # 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 torchvision.transforms.functional as F
from torch.nn import functional as F
from torch import nn
class DilatedNet(nn.Module):
def __init__(self, filters):
super().__init__()
self.filters = filters
self.conv1 = nn.Conv2d(self.filters[-1], self.filters[-1], 3,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | tilacyn/dsb2018_topcoders | DilatedNet | false | 16,589 | [
"MIT"
] | 413 | e0f95ef70bc062d4dea321d2aa73231a9538cd63 | https://github.com/tilacyn/dsb2018_topcoders/tree/e0f95ef70bc062d4dea321d2aa73231a9538cd63 |
my_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 torch.nn as nn
class my_Layernorm(nn.Module):
"""
Special designed layernorm for the seasonal part
"""
def __init__(self, channels):
super(my_Layernorm, self).__init__()
self.layernorm = nn.LayerNorm(channels)
def forward(self, x):
x_hat = self.layerno... | 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_... | thuml/Autoformer | my_Layernorm | false | 16,590 | [
"MIT"
] | 263 | 6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab | https://github.com/thuml/Autoformer/tree/6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab |
Minibatch_stddev_layer | # 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 torch.nn as nn
class Minibatch_stddev_layer(nn.Module):
"""
Minibatch standard deviation layer. (D_stylegan2)
"""
def __init__(self, group_size=4, num_new_features=1):
super().__init__()
self.group_size = group_size
self.num_new_features = num_new_featu... | 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_... | tomguluson92/StyleGAN2_PyTorch | Minibatch_stddev_layer | false | 16,591 | [
"MIT"
] | 89 | 4ab7354c85cb986d2b77f5238c4a18c5efd1db1b | https://github.com/tomguluson92/StyleGAN2_PyTorch/tree/4ab7354c85cb986d2b77f5238c4a18c5efd1db1b |
LearnedPositionalEmbeddings | # 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... | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class LearnedPositionalEmbeddings(Module):
"""
<a id="LearnedPositionalEmbeddings"></a>
## Add parameterized positional encodings
This adds learned positional embedd... | 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 import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
assert_size_stride... | techthiyanes/annotated_deep_learning_paper_implementations | LearnedPositionalEmbeddings | false | 16,592 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
Aggregator | # 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 torchvision.transforms.functional as F
from torch.nn import functional as F
from torch import nn
class Aggregator(nn.Module):
def __init__(self, in_channels, mid_channels, upsample_factor):
super().__init__()
self.upsample = nn.Upsample(scale_factor=2 ** upsample_factor)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | tilacyn/dsb2018_topcoders | Aggregator | false | 16,593 | [
"MIT"
] | 413 | e0f95ef70bc062d4dea321d2aa73231a9538cd63 | https://github.com/tilacyn/dsb2018_topcoders/tree/e0f95ef70bc062d4dea321d2aa73231a9538cd63 |
AdaptiveMaxPool2d | # 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 torch.nn as nn
import torch.nn.functional as F
class _SpikeAdaptiveMaxPoolNd(nn.Module):
def __init__(self, output_size):
super(_SpikeAdaptiveMaxPoolNd, self).__init__()
self.output_size = output_size
self.return_indices = True
def reset_state(self):
pass
... | 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... | tomking/PySNN | AdaptiveMaxPool2d | false | 16,594 | [
"MIT"
] | 175 | c99ba6cd28a518dc07cab765acac9b69ac6fe36b | https://github.com/tomking/PySNN/tree/c99ba6cd28a518dc07cab765acac9b69ac6fe36b |
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 torch.nn as nn
class TokenEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(TokenEmbedding, self).__init__()
padding = 1 if torch.__version__ >= '1.5.0' else 2
self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
kernel_size=3, pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | thuml/Autoformer | TokenEmbedding | false | 16,595 | [
"MIT"
] | 263 | 6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab | https://github.com/thuml/Autoformer/tree/6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab |
ActNorm | # 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 torch.nn as nn
class ActNorm(nn.Module):
"""
ActNorm layer.
[Kingma and Dhariwal, 2018.]
"""
def __init__(self, dim):
super().__init__()
self.dim = dim
self.mu = nn.Parameter(torch.zeros(dim, dtype=torch.float))
self.log_sigma = nn.Parameter(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.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | tonyduan/hybrid-models | ActNorm | false | 16,596 | [
"MIT"
] | 238 | a29bff4756d8306cd24515f2fb825763a71c3d90 | https://github.com/tonyduan/hybrid-models/tree/a29bff4756d8306cd24515f2fb825763a71c3d90 |
GatedMaskedConv2d | # 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 torch.utils.data
from torch import nn
import torch.nn.functional as F
class GatedMaskedConv2d(nn.Module):
def __init__(self, in_dim, out_dim=None, kernel_size=3, mask='B'):
super(GatedMaskedConv2d, self).__init__()
if out_dim is None:
out_dim = in_dim
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | tom-pelsmaeker/vae-lagging-encoder | GatedMaskedConv2d | false | 16,597 | [
"MIT"
] | 173 | b190239019a94c85858d188a0853886eb48ce4be | https://github.com/tom-pelsmaeker/vae-lagging-encoder/tree/b190239019a94c85858d188a0853886eb48ce4be |
MaxPool2d | # 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 torch.nn as nn
import torch.nn.functional as F
class _SpikeMaxPoolNd(nn.Module):
def __init__(self, kernel_size, stride=None, padding=0, dilation=1,
ceil_mode=False):
super(_SpikeMaxPoolNd, self).__init__()
self.kernel_size = kernel_size
self.stride = stride or... | 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... | tomking/PySNN | MaxPool2d | false | 16,598 | [
"MIT"
] | 175 | c99ba6cd28a518dc07cab765acac9b69ac6fe36b | https://github.com/tomking/PySNN/tree/c99ba6cd28a518dc07cab765acac9b69ac6fe36b |
DisAlignFastRCNNOutputLayers | # 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 numpy as np
import torch.nn as nn
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
import torch.nn
def cat(tensors, dim=0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
assert isi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
import torch.utils.data
from itertools ... | tonysy/cvpods | DisAlignFastRCNNOutputLayers | false | 16,599 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
RNN | # 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 torch.nn as nn
from torch.autograd import Variable
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size, all_categories,
n_categories, all_letters, n_letters):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.all_categori... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tom-kuchler/vhive | RNN | false | 16,600 | [
"MIT"
] | 138 | ae1f2f5920e7607e9902ed1060bda62b56e332ac | https://github.com/tom-kuchler/vhive/tree/ae1f2f5920e7607e9902ed1060bda62b56e332ac |
Upsample2d | # 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... | from _paritybench_helpers import _mock_config
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def _setup_kernel(k):
k = np.asarray(k, dtype=np.float32)
if k.ndim == 1:
k = np.outer(k, k)
k /= np.sum(k)
assert k.ndim == 2
assert k.shape[0] == k.shape[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 numpy as np
import tor... | tomguluson92/StyleGAN2_PyTorch | Upsample2d | false | 16,601 | [
"MIT"
] | 89 | 4ab7354c85cb986d2b77f5238c4a18c5efd1db1b | https://github.com/tomguluson92/StyleGAN2_PyTorch/tree/4ab7354c85cb986d2b77f5238c4a18c5efd1db1b |
OptimizedResidualBlock | # 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 torch.nn as nn
import torch.nn.utils as utils
from torchvision import utils
class CustomConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=None, bias=True, spectral_norm=False, residual_init=True):
super(CustomConv2d, 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | takuhirok/rGAN | OptimizedResidualBlock | false | 16,602 | [
"MIT"
] | 103 | 6f7a092de5814c662fd17224b3d48bebe7e03c2f | https://github.com/takuhirok/rGAN/tree/6f7a092de5814c662fd17224b3d48bebe7e03c2f |
WassersteinGeneratorLoss | # 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 torch.nn as nn
def reduce(x, reduction=None):
"""Applies reduction on a torch.Tensor.
Args:
x (torch.Tensor): The tensor on which reduction is to be applied.
reduction (str, optional): The reduction to be applied. If ``mean`` the mean value of the
Tensor is re... | 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... | torchgan/torchgan | WassersteinGeneratorLoss | false | 16,603 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
Buck | # 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 torch.nn
class Buck(torch.nn.Module):
def __init__(self, A=1.0, B=1.0, C=1.0):
super(Buck, self).__init__()
self.A = torch.nn.Parameter(torch.Tensor([A]))
self.B = torch.nn.Parameter(torch.Tensor([B]))
self.C = torch.nn.Parameter(torch.Tensor([C]))
def Buc... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_... | torchmd/mdgrad | Buck | false | 16,604 | [
"MIT"
] | 54 | 77bd7685b74b41acf54a9483546e1e8cb545eb01 | https://github.com/torchmd/mdgrad/tree/77bd7685b74b41acf54a9483546e1e8cb545eb01 |
ParityPonderGRU | # 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... | from torch.nn import Module
import torch
from torch import nn
from typing import Tuple
import torch.utils.data
import torch.nn.functional
import torch.autograd
class ParityPonderGRU(Module):
"""
## PonderNet with GRU for Parity Task
This is a simple model that uses a [GRU Cell](https://pytorch.org/docs/s... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import nn
import... | techthiyanes/annotated_deep_learning_paper_implementations | ParityPonderGRU | false | 16,605 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
WassersteinDiscriminatorLoss | # 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 torch.nn as nn
def reduce(x, reduction=None):
"""Applies reduction on a torch.Tensor.
Args:
x (torch.Tensor): The tensor on which reduction is to be applied.
reduction (str, optional): The reduction to be applied. If ``mean`` the mean value of the
Tensor is re... | 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... | torchgan/torchgan | WassersteinDiscriminatorLoss | false | 16,606 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
NormalizedLinear | # 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 math
import torch
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
import torch.nn
class NormalizedLinear(torch.nn.Module):
"""
A advanced Linear layer which supports weight normalization or cosine normalization.
"""
def __init__(self, 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | tonysy/cvpods | NormalizedLinear | false | 16,607 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
Value | # 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 torch.nn as nn
import torch.nn.functional as F
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_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | tpbarron/pytorch-ppo | Value | false | 16,608 | [
"MIT"
] | 47 | f73226865e34443f93dbec58939329c9278828e8 | https://github.com/tpbarron/pytorch-ppo/tree/f73226865e34443f93dbec58939329c9278828e8 |
MinimaxDiscriminatorLoss | # 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 torch.nn as nn
import torch.nn.functional as F
def minimax_discriminator_loss(dx, dgz, label_smoothing=0.0, reduction='mean'):
target_ones = torch.ones_like(dgz) * (1.0 - label_smoothing)
target_zeros = torch.zeros_like(dx)
loss = F.binary_cross_entropy_with_logits(dx, target_ones, red... | 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... | torchgan/torchgan | MinimaxDiscriminatorLoss | false | 16,609 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
VirtualBatchNorm | # 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 torch.nn as nn
class VirtualBatchNorm(nn.Module):
"""Virtual Batch Normalization Module as proposed in the paper
`"Improved Techniques for Training GANs by Salimans et. al." <https://arxiv.org/abs/1805.08318>`_
Performs Normalizes the features of a batch based on the statistics collec... | 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_... | torchgan/torchgan | VirtualBatchNorm | false | 16,610 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
CosineFastRCNNOutputLayers | # 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 math
import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
import torch.nn
class NormalizedLinear(torch.nn.Module):
"""
A advanced Linear layer which supports weight normalization or cosine normalization.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tonysy/cvpods | CosineFastRCNNOutputLayers | false | 16,611 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
MinibatchDiscrimination1d | # 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 torch.nn as nn
class MinibatchDiscrimination1d(nn.Module):
"""1D Minibatch Discrimination Module as proposed in the paper `"Improved Techniques for
Training GANs by Salimans et. al." <https://arxiv.org/abs/1805.08318>`_
Allows the Discriminator to easily detect mode collapse by augmen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | torchgan/torchgan | MinibatchDiscrimination1d | false | 16,612 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
GaussMembFunc | # 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
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class GaussMembFunc(torch.nn.Module):
"""
Gaussian membership functions, defined by two... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | trituenhantaoio/anfis-pytorch | GaussMembFunc | false | 16,613 | [
"MIT"
] | 66 | 7a6bf123d69b550e46abeddd5b4a776243d43aa6 | https://github.com/trituenhantaoio/anfis-pytorch/tree/7a6bf123d69b550e46abeddd5b4a776243d43aa6 |
DisAlignCosineFastRCNNOutputLayers | # 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 math
import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
import torch.nn
def cat(tensors, dim=0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tonysy/cvpods | DisAlignCosineFastRCNNOutputLayers | false | 16,615 | [
"Apache-2.0"
] | 548 | e322d7842ca0e34b1ef6237ea6d350633efc793a | https://github.com/tonysy/cvpods/tree/e322d7842ca0e34b1ef6237ea6d350633efc793a |
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 copy
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def square(a):
return torch.pow(a, 2.0)
class Policy(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(Policy, self).__init__()
self.affine1 = nn.Linear(num_inputs, 64)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 |
ActorCritic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
class ActorCritic(nn.Module):
def __init__(self, num_inputs, num_outputs, hidden=64):
super(ActorCritic, self).__init__()
self.affine1 = nn.Linear(num_inputs, hidden)
self.affine2 = nn.Linear(hidden, hidden)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 | ActorCritic | false | 16,617 | [
"MIT"
] | 47 | f73226865e34443f93dbec58939329c9278828e8 | https://github.com/tpbarron/pytorch-ppo/tree/f73226865e34443f93dbec58939329c9278828e8 |
BellMembFunc | # 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
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class BellMembFunc(torch.nn.Module):
"""
Generalised Bell membership function; defined ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | trituenhantaoio/anfis-pytorch | BellMembFunc | false | 16,618 | [
"MIT"
] | 66 | 7a6bf123d69b550e46abeddd5b4a776243d43aa6 | https://github.com/trituenhantaoio/anfis-pytorch/tree/7a6bf123d69b550e46abeddd5b4a776243d43aa6 |
DataEmbedding_wo_pos | # 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 math
import torch
import torch.nn as nn
class PositionalEmbedding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEmbedding, self).__init__()
pe = torch.zeros(max_len, d_model).float()
pe.require_grad = False
position = torch.arange(0, max_len).float(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | thuml/Autoformer | DataEmbedding_wo_pos | false | 16,619 | [
"MIT"
] | 263 | 6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab | https://github.com/thuml/Autoformer/tree/6bf300d0bf3e7f3cb4d795dd8ed14ede2000a9ab |
UpsampleConv | # 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 torch.nn.functional as F
import torch.nn as nn
def l2normalize(v, esp=1e-08):
return v / (v.norm() + esp)
def sn_weight(weight, u, height, n_power_iterations):
weight.requires_grad_(False)
for _ in range(n_power_iterations):
v = l2normalize(torch.mv(weight.view(height, -1).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
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch... | tsirif/cortex | UpsampleConv | false | 16,620 | [
"BSD-3-Clause"
] | 109 | 2837b220f9fb73279df3815bb18b274106412c08 | https://github.com/tsirif/cortex/tree/2837b220f9fb73279df3815bb18b274106412c08 |
DQN_RAM | # 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 torch.nn as nn
import torch.nn.functional as F
class DQN_RAM(nn.Module):
def __init__(self, in_features=4, num_actions=18):
"""
Initialize a deep Q-learning network for testing algorithm
in_features: number of features of input.
num_actions: number of 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
import torch.nn as nn
assert_... | transedward/pytoch-dqn | DQN_RAM | false | 16,621 | [
"MIT"
] | 358 | 1ffda6f3724b3bb37c3195b09b651b1682d4d4fd | https://github.com/transedward/pytoch-dqn/tree/1ffda6f3724b3bb37c3195b09b651b1682d4d4fd |
MySimpleNet | # 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 torch.nn.functional as F
from torch import nn
class MySimpleNet(nn.Module):
"""
Very simple 2-layer net, slightly adapted from the docs:
https://skorch.readthedocs.io/en/stable/user/quickstart.html
"""
def __init__(self, num_in, num_feat, num_hidden=10, nonlin=F.re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | trituenhantaoio/anfis-pytorch | MySimpleNet | false | 16,622 | [
"MIT"
] | 66 | 7a6bf123d69b550e46abeddd5b4a776243d43aa6 | https://github.com/trituenhantaoio/anfis-pytorch/tree/7a6bf123d69b550e46abeddd5b4a776243d43aa6 |
MeanPoolConv | # 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 torch.nn.functional as F
import torch.nn as nn
def l2normalize(v, esp=1e-08):
return v / (v.norm() + esp)
def sn_weight(weight, u, height, n_power_iterations):
weight.requires_grad_(False)
for _ in range(n_power_iterations):
v = l2normalize(torch.mv(weight.view(height, -1).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
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch... | tsirif/cortex | MeanPoolConv | false | 16,623 | [
"BSD-3-Clause"
] | 109 | 2837b220f9fb73279df3815bb18b274106412c08 | https://github.com/tsirif/cortex/tree/2837b220f9fb73279df3815bb18b274106412c08 |
ECB | # 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 torch.nn as nn
import torch.nn.functional as F
class SeqConv3x3(nn.Module):
def __init__(self, seq_type, inp_planes, out_planes, depth_multiplier):
super(SeqConv3x3, self).__init__()
self.type = seq_type
self.inp_planes = inp_planes
self.out_planes = out_planes... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | thinkreed/ECBSR | ECB | false | 16,624 | [
"Apache-2.0"
] | 162 | 152b9fef9b9fb61b6e5a93677229143652ef305d | https://github.com/thinkreed/ECBSR/tree/152b9fef9b9fb61b6e5a93677229143652ef305d |
XTanhLoss | # 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
class XTanhLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_t, y_prime_t):
ey_t = y_t - y_prime_t
return torch.mean(ey_t * torch.tanh(ey_t))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_ini... | 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
assert_size_stride = torch._... | tuantle/regression-losses-pytorch | XTanhLoss | false | 16,625 | [
"MIT"
] | 82 | 2893f4439ada5df239e3afd0ec7e781dd61403e9 | https://github.com/tuantle/regression-losses-pytorch/tree/2893f4439ada5df239e3afd0ec7e781dd61403e9 |
BondEnergyModule | # 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 torch.nn
import torch.nn as nn
from itertools import repeat
def gen(src, index, dim=-1, out=None, dim_size=None, fill_value=0):
dim = range(src.dim())[dim]
if index.dim() == 1:
index_size = list(repeat(1, src.dim()))
index_size[dim] = src.size(dim)
index = index.vie... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
import torch.nn as nn
from itertools import repeat
assert_size_... | torchmd/mdgrad | BondEnergyModule | false | 16,626 | [
"MIT"
] | 54 | 77bd7685b74b41acf54a9483546e1e8cb545eb01 | https://github.com/torchmd/mdgrad/tree/77bd7685b74b41acf54a9483546e1e8cb545eb01 |
ConvMeanPool | # 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 torch.nn.functional as F
import torch.nn as nn
def l2normalize(v, esp=1e-08):
return v / (v.norm() + esp)
def sn_weight(weight, u, height, n_power_iterations):
weight.requires_grad_(False)
for _ in range(n_power_iterations):
v = l2normalize(torch.mv(weight.view(height, -1).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
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch... | tsirif/cortex | ConvMeanPool | false | 16,627 | [
"BSD-3-Clause"
] | 109 | 2837b220f9fb73279df3815bb18b274106412c08 | https://github.com/tsirif/cortex/tree/2837b220f9fb73279df3815bb18b274106412c08 |
XSigmoidLoss | # 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
class XSigmoidLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_t, y_prime_t):
ey_t = y_t - y_prime_t
return torch.mean(2 * ey_t * torch.sigmoid(ey_t) - ey_t)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | tuantle/regression-losses-pytorch | XSigmoidLoss | false | 16,628 | [
"MIT"
] | 82 | 2893f4439ada5df239e3afd0ec7e781dd61403e9 | https://github.com/tuantle/regression-losses-pytorch/tree/2893f4439ada5df239e3afd0ec7e781dd61403e9 |
BiAttention | # 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 typing import Optional
import torch.nn as nn
from torch.nn.parameter import Parameter
class BiAttention(nn.Module):
def __init__(self, input_size_encoder: 'int', input_size_decoder: 'int',
num_labels: 'int', biaffine: 'bool'=True, **kwargs) ->None:
super(BiAttention, 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
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_strid... | tucan9389/KLUE-baseline | BiAttention | false | 16,629 | [
"Apache-2.0"
] | 71 | add61158e61f86adfca65087237443828b650090 | https://github.com/tucan9389/KLUE-baseline/tree/add61158e61f86adfca65087237443828b650090 |
AlgebraicLoss | # 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
class AlgebraicLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_t, y_prime_t):
ey_t = y_t - y_prime_t
return torch.mean(ey_t * ey_t / torch.sqrt(1 + ey_t * ey_t))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | tuantle/regression-losses-pytorch | AlgebraicLoss | false | 16,630 | [
"MIT"
] | 82 | 2893f4439ada5df239e3afd0ec7e781dd61403e9 | https://github.com/tuantle/regression-losses-pytorch/tree/2893f4439ada5df239e3afd0ec7e781dd61403e9 |
GPT2Postprocessing | # 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... | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class GPT2Postprocessing(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_f = nn.LayerNorm(config.hidden_size, eps=config.
layer_norm_epsilon)
self.lm_head = nn.Linear(config.hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | tunib-ai/large-scale-lm-tutorials | GPT2Postprocessing | false | 16,631 | [
"Apache-2.0"
] | 128 | ca29ff9f945a59abcc3e3f1000c4d83de97973d4 | https://github.com/tunib-ai/large-scale-lm-tutorials/tree/ca29ff9f945a59abcc3e3f1000c4d83de97973d4 |
KLDivergenceLoss | # 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... | from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional
import torch.autograd
class KLDivergenceLoss(Module):
"""
<a id="KLDivergenceLoss"></a>
## KL Divergence Regularization Loss
This tries to shrink the total evidence to zero if the sample cannot be correctly 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.triton_helpers import libdevice
from torch.nn import Module
import torch.utils.data
import torch.nn.functional
... | techthiyanes/annotated_deep_learning_paper_implementations | KLDivergenceLoss | false | 16,632 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
Conv2dZeroInit | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn
class Conv2dZeroInit(nn.Conv2d):
def __init__(self, channels_in, channels_out, filter_size, stride=1,
padding=0, logscale=3.0):
super().__init__(channels_in, channels_out, filter_size, stride=
stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | tychovdo/RevGAN | Conv2dZeroInit | false | 16,633 | [
"BSD-3-Clause"
] | 79 | 2af25e6a8176eaab3d424db45fb6ee2cfc5dc9a3 | https://github.com/tychovdo/RevGAN/tree/2af25e6a8176eaab3d424db45fb6ee2cfc5dc9a3 |
netmodel | # 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 numpy as np
from torch.nn import Parameter
class netmodel(torch.nn.Module):
def __init__(self):
super(netmodel, self).__init__()
self.w0 = Parameter(torch.Tensor(1))
self.w1 = Parameter(torch.Tensor(1))
self.w0.data.uniform_(-1, 1)
self.w1.data.uniform_... | 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 numpy as np
from torch.nn import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch... | uber-common/safemutations | netmodel | false | 16,634 | [
"MIT"
] | 91 | 40e5fd03a244f89bf157d4bedf79201e706aedc1 | https://github.com/uber-common/safemutations/tree/40e5fd03a244f89bf157d4bedf79201e706aedc1 |
DSC_loss | # 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 torch.nn as nn
class DSC_loss(nn.Module):
def __init__(self):
super(DSC_loss, self).__init__()
self.epsilon = 1e-06
return
def forward(self, pred, target):
batch_num = pred.shape[0]
pred = pred.contiguous().view(batch_num, -1)
target = targ... | 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... | twni2016/OrganSegRSTN_PyTorch | DSC_loss | false | 16,635 | [
"MIT"
] | 100 | bf571320e718c8f138e04d48645e3b4dfe75801d | https://github.com/twni2016/OrganSegRSTN_PyTorch/tree/bf571320e718c8f138e04d48645e3b4dfe75801d |
ConformerConvBlock | # 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 torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.cuda
class ConformerConvBlock(nn.Module):
def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
super(ConformerConvBlock, self).__init__()
assert (kernel_size - 1) % 2 == ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | tuannamnguyen93/NMTGMinor | ConformerConvBlock | false | 16,636 | [
"MIT"
] | 75 | acde3454343bda7060fae541c110d0ad1a8ac4f4 | https://github.com/tuannamnguyen93/NMTGMinor/tree/acde3454343bda7060fae541c110d0ad1a8ac4f4 |
N2 | # 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 typing import Tuple
from abc import ABC
from abc import abstractmethod
from torch import nn
class Regularizer(nn.Module, ABC):
@abstractmethod
def forward(self, factors: 'Tuple[torch.Tensor]'):
pass
class N2(Regularizer):
def __init__(self, weight: 'float'):
super(N2,... | 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 typing import Tuple
from abc import ABC
from abc import abstractmethod
fro... | uclnlp/cqd | N2 | false | 16,637 | [
"MIT"
] | 59 | 36148c110f336415250c98873fc27ca847741a78 | https://github.com/uclnlp/cqd/tree/36148c110f336415250c98873fc27ca847741a78 |
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 torch.utils.data
import torch
from torch import nn
class L2LossWithLogit(nn.Module):
def __init__(self):
super(L2LossWithLogit, self).__init__()
self.mse = nn.MSELoss(reduction='sum')
def forward(self, logits, targets):
p = torch.sigmoid(logits)
return sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch
from torch import nn
assert_size_stride = torch._C._... | ucas-vg/TinyBenchmark | L2LossWithLogit | false | 16,638 | [
"MIT"
] | 495 | 36436df3716d842b6148fb6f6bc7715a2fbdfd92 | https://github.com/ucas-vg/TinyBenchmark/tree/36436df3716d842b6148fb6f6bc7715a2fbdfd92 |
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 torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(1))
self.beta = nn.Parameter(torch.zeros(1))
self.eps = eps
def forward(self, x):
mean = x.me... | 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_... | uber-common/safemutations | LayerNorm | false | 16,639 | [
"MIT"
] | 91 | 40e5fd03a244f89bf157d4bedf79201e706aedc1 | https://github.com/uber-common/safemutations/tree/40e5fd03a244f89bf157d4bedf79201e706aedc1 |
HammingLoss | # 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
class HammingLoss(torch.nn.Module):
def forward(self, suggested, target):
errors = suggested * (1.0 - target) + (1.0 - suggested) * target
return errors.mean(dim=0).sum()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | uclnlp/torch-imle | HammingLoss | false | 16,640 | [
"MIT"
] | 205 | f595cd8d527466f6b5db79276f6ceee01d100a1c | https://github.com/uclnlp/torch-imle/tree/f595cd8d527466f6b5db79276f6ceee01d100a1c |
FCGenerator | # 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... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class FCGenerator(nn.Module):
def __init__(self, options):
"""
The fully connected generator is initialized by creating a chain of
fully connected layers that perform transform... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | unicredit/ganzo | FCGenerator | false | 16,641 | [
"Apache-2.0"
] | 73 | fb1d270f5091073e8f27da76ab508ab24e5d40e9 | https://github.com/unicredit/ganzo/tree/fb1d270f5091073e8f27da76ab508ab24e5d40e9 |
FusedDownsample | # 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 torch.nn.functional as F
from torch import nn
from math import sqrt
class FusedDownsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(out_channel, in_channel, kernel_size, kernel_size)
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 import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | uzielroy/StyleGan_FewShot | FusedDownsample | false | 16,642 | [
"MIT"
] | 76 | 94e4c49dbf39d1c6299f33787afb3e471ece11e3 | https://github.com/uzielroy/StyleGan_FewShot/tree/94e4c49dbf39d1c6299f33787afb3e471ece11e3 |
L1Loss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.functional as F
import torch.onnx
class L1Loss(torch.nn.Module):
"""
L1 loss
"""
def __init__(self, **kwargs):
super(L1Loss, self).__init__()
self.loss_w = kwargs.get('loss_weight', 1)
def forward(self, preds, gts):
return F.l1_loss(preds.view... | 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.onnx
asse... | usutdzxych/CenseoQoE | L1Loss | false | 16,643 | [
"BSD-3-Clause"
] | 75 | 3f653296b223da6190e1e1781e7b9b54ff877102 | https://github.com/usutdzxych/CenseoQoE/tree/3f653296b223da6190e1e1781e7b9b54ff877102 |
Linear | # 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 torch.nn.functional as F
import torch.nn as nn
class Linear(nn.Linear):
def forward(self, x):
weight = self.weight
weight_mean = weight.mean(dim=1, keepdim=True)
weight = weight - weight_mean
std = weight.std(dim=1, keepdim=True) + 1e-05
weight = weight... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | untitled-ai/self_supervised | Linear | false | 16,644 | [
"MIT"
] | 370 | 6d14ca0402ecc13feda9b3a9fdc056fd1ac24473 | https://github.com/untitled-ai/self_supervised/tree/6d14ca0402ecc13feda9b3a9fdc056fd1ac24473 |
FusedUpsample | # 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 torch.nn.functional as F
from torch import nn
from math import sqrt
class FusedUpsample(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, padding=0):
super().__init__()
weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
bias = to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from math import sqrt
assert_size_stride = torch._C._dynamo... | uzielroy/StyleGan_FewShot | FusedUpsample | false | 16,645 | [
"MIT"
] | 76 | 94e4c49dbf39d1c6299f33787afb3e471ece11e3 | https://github.com/uzielroy/StyleGan_FewShot/tree/94e4c49dbf39d1c6299f33787afb3e471ece11e3 |
AlbertAttentionWithoutSkipConnection | # 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... | from _paritybench_helpers import _mock_config
import math
import torch
import torch.utils.checkpoint
from torch import nn
class AlbertAttentionWithoutSkipConnection(nn.Module):
def __init__(self, config):
super().__init__()
if (config.hidden_size % config.num_attention_heads != 0 and not
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | twistedcubic/attention-rank-collapse | AlbertAttentionWithoutSkipConnection | false | 16,646 | [
"Apache-2.0"
] | 118 | 38b5df6dc2add25f6d945e48a6baf96862368c20 | https://github.com/twistedcubic/attention-rank-collapse/tree/38b5df6dc2add25f6d945e48a6baf96862368c20 |
FCDiscriminator | # 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... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class FCDiscriminator(nn.Module):
def __init__(self, options):
"""
The fully connected generator is initialized by creating a chain of
fully connected layers that perform trans... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | unicredit/ganzo | FCDiscriminator | false | 16,647 | [
"Apache-2.0"
] | 73 | fb1d270f5091073e8f27da76ab508ab24e5d40e9 | https://github.com/unicredit/ganzo/tree/fb1d270f5091073e8f27da76ab508ab24e5d40e9 |
Highway | # 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 import nn
import torch.utils.data
class Highway(nn.Module):
def __init__(self, input_dim, dropout):
super(Highway, self).__init__()
self.input_linear = nn.Linear(input_dim, input_dim)
self.relu = nn.ReLU()
self.gate_linear = nn.Linear(input_dim, input_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 import nn
import t... | uwnlp/piqa | Highway | false | 16,648 | [
"Apache-2.0"
] | 89 | e18f2189c93965c94655d5cc943dcecdc2c1ea57 | https://github.com/uwnlp/piqa/tree/e18f2189c93965c94655d5cc943dcecdc2c1ea57 |
Router | # 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... | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class Squash(Module):
'\n ## Squash\n\n This is **squashing** function from paper, given by equation $(1)$.\n\n $$\\mathbf{v}_j = \x0crac{{\\lVert \\mathbf{s}_j \rVert}^2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | techthiyanes/annotated_deep_learning_paper_implementations | Router | false | 16,649 | [
"MIT"
] | 3,714 | 8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 | https://github.com/techthiyanes/annotated_deep_learning_paper_implementations/tree/8af24da2dd39a9a87482a4d18c2dc829bbd3fd47 |
NoiseInjection | # 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 import nn
class NoiseInjection(nn.Module):
def __init__(self, channel):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
def forward(self, image, noise):
return image + self.weight * noise
def get_inputs():
return [torch.rand(... | 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... | uzielroy/StyleGan_FewShot | NoiseInjection | false | 16,650 | [
"MIT"
] | 76 | 94e4c49dbf39d1c6299f33787afb3e471ece11e3 | https://github.com/uzielroy/StyleGan_FewShot/tree/94e4c49dbf39d1c6299f33787afb3e471ece11e3 |
L2Normalize | # 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 torch.nn
import torch.nn.parallel
import torch.backends.cudnn
import torch.distributed
import torch.multiprocessing
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class L2Normalize(nn.Module):
def __init__(self, dim):
super(L2Normalize, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
import torch... | valeoai/obow | L2Normalize | false | 16,651 | [
"Apache-2.0"
] | 84 | 3758504f5e058275725c35ca7faca3731572b911 | https://github.com/valeoai/obow/tree/3758504f5e058275725c35ca7faca3731572b911 |
LDS | # 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 torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class LDS(nn.Module):
def __init__(self):
super(LDS, self).__init__()
self.pool1 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)
self.pool2 = nn.MaxPool2d(kernel_size=(2, 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
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as prod... | vaesl/LRF-Net | LDS | false | 16,653 | [
"MIT"
] | 180 | e44b120dd55288c02852f8e58cda31313525d748 | https://github.com/vaesl/LRF-Net/tree/e44b120dd55288c02852f8e58cda31313525d748 |
conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.autograd import Variable
def spectral_norm(module, name='weight'):
SpectralNorm.apply(module, name)
return module
class SpectralNorm:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(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
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = t... | vandit15/Self-Supervised-Gans-Pytorch | conv2d | false | 16,654 | [
"MIT"
] | 66 | 01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 | https://github.com/vandit15/Self-Supervised-Gans-Pytorch/tree/01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 |
EntropyLossEncap | # 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 import nn
def feature_map_permute(input):
s = input.data.shape
l = len(s)
if l == 2:
x = input
elif l == 3:
x = input.permute(0, 2, 1)
elif l == 4:
x = input.permute(0, 2, 3, 1)
elif l == 5:
x = input.permute(0, 2, 3, 4, 1)
else:
... | 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_... | vartikagpt10/memae-anomaly-detection | EntropyLossEncap | false | 16,655 | [
"MIT"
] | 297 | ceece7714fb241e82ef3f3785d3d1ed86c28113e | https://github.com/vartikagpt10/memae-anomaly-detection/tree/ceece7714fb241e82ef3f3785d3d1ed86c28113e |
deconv2d | # 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 torch.nn as nn
from torch.autograd import Variable
def spectral_norm(module, name='weight'):
SpectralNorm.apply(module, name)
return module
class SpectralNorm:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(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
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = t... | vandit15/Self-Supervised-Gans-Pytorch | deconv2d | false | 16,656 | [
"MIT"
] | 66 | 01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 | https://github.com/vandit15/Self-Supervised-Gans-Pytorch/tree/01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 |
L1RankLoss | # 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 torch.nn.functional as F
import torch.onnx
class L1RankLoss(torch.nn.Module):
"""
L1 loss + Rank loss
"""
def __init__(self, **kwargs):
super(L1RankLoss, self).__init__()
self.l1_w = kwargs.get('l1_w', 1)
self.rank_w = kwargs.get('rank_w', 1)
self.h... | 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.onnx
asse... | usutdzxych/CenseoQoE | L1RankLoss | false | 16,658 | [
"BSD-3-Clause"
] | 75 | 3f653296b223da6190e1e1781e7b9b54ff877102 | https://github.com/usutdzxych/CenseoQoE/tree/3f653296b223da6190e1e1781e7b9b54ff877102 |
Iter_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
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class Iter_Downsample(nn.Module):
def __init__(self):
super(Iter_Downsample, self).__init__()
self.init_ds = nn.Sequential(nn.MaxPool2d(kernel_size=2, stride=2,
padding=0), nn.Max... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as prod... | vaesl/LFIP | Iter_Downsample | false | 16,659 | [
"MIT"
] | 59 | eb9d934616c508c9a9032f170baa1d97fa792822 | https://github.com/vaesl/LFIP/tree/eb9d934616c508c9a9032f170baa1d97fa792822 |
_Residual_Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class _Residual_Block(nn.Module):
def __init__(self):
super(_Residual_Block, self).__init__()
self.conv1 = nn.Conv2d(in_channels=256, out_channels=256,
kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | twtygqyy/pytorch-EDSR | _Residual_Block | false | 16,661 | [
"MIT"
] | 59 | 001031b6563fcc45d4e7edb7e14c41fb9982ce64 | https://github.com/twtygqyy/pytorch-EDSR/tree/001031b6563fcc45d4e7edb7e14c41fb9982ce64 |
Residual_D | # 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 torch.nn as nn
from torch.autograd import Variable
def spectral_norm(module, name='weight'):
SpectralNorm.apply(module, name)
return module
class SpectralNorm:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | vandit15/Self-Supervised-Gans-Pytorch | Residual_D | false | 16,663 | [
"MIT"
] | 66 | 01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 | https://github.com/vandit15/Self-Supervised-Gans-Pytorch/tree/01408fcce3e6cf4795d90c0f9d27e6906d5b59f3 |
GumbelSoftmaxLayer | # 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 torch.nn as nn
from torch.distributions import RelaxedOneHotCategorical
import torch.nn.parallel
import torch.utils.data
import torch.distributions
def gumbel_softmax_sample(logits: 'torch.Tensor', temperature: 'float'=1.0,
training: 'bool'=True, straight_through: 'bool'=False):
size = log... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.distributions import RelaxedOneHotCategorical
import torch.nn.parallel
import torch.utils.data
import torch... | vengalraoguttha/EGG | GumbelSoftmaxLayer | false | 16,664 | [
"MIT"
] | 254 | e4f8412f197543ec7f1f00cf89b5a364b038dc57 | https://github.com/vengalraoguttha/EGG/tree/e4f8412f197543ec7f1f00cf89b5a364b038dc57 |
ReinforcedReceiver | # 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 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 |
EntropyLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class EntropyLoss(nn.Module):
def __init__(self, eps=1e-12):
super(EntropyLoss, self).__init__()
self.eps = eps
def forward(self, x):
b = x * torch.log(x + self.eps)
b = -1.0 * b.sum(dim=1)
b = b.mean()
return b
def get_inpu... | 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_... | vartikagpt10/memae-anomaly-detection | EntropyLoss | false | 16,666 | [
"MIT"
] | 297 | ceece7714fb241e82ef3f3785d3d1ed86c28113e | https://github.com/vartikagpt10/memae-anomaly-detection/tree/ceece7714fb241e82ef3f3785d3d1ed86c28113e |
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
import torch.nn as nn
class BahdanauAttention(nn.Module):
def __init__(self, annot_dim, query_dim, attn_dim):
super(BahdanauAttention, self).__init__()
self.query_layer = nn.Linear(query_dim, attn_dim, bias=True)
self.annot_layer = nn.Linear(annot_dim, attn_dim, bias=True)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | vigilancetrent/chatbot-advanced | BahdanauAttention | false | 16,667 | [
"Apache-2.0"
] | 52 | 2e0c72c4df2e1434da995b7105f8f0414aba6248 | https://github.com/vigilancetrent/chatbot-advanced/tree/2e0c72c4df2e1434da995b7105f8f0414aba6248 |
Interpolate | # 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 torch.nn as nn
import torch.nn.functional as F
class Interpolate(nn.Module):
def __init__(self, scale_factor, mode='bilinear', align_corners=True):
super(Interpolate, self).__init__()
self.scale_factor = scale_factor
self.mode = mode
self.align_corners = align_... | 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... | vietnhatthai/3d-vehicle-tracking | Interpolate | false | 16,668 | [
"BSD-3-Clause"
] | 603 | 8ee189f6792897651bb56bb2950ce07c9629a89d | https://github.com/vietnhatthai/3d-vehicle-tracking/tree/8ee189f6792897651bb56bb2950ce07c9629a89d |
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