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 | pytorch_code stringlengths 200 4.05k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
GlobalMaxPool1d | # 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 GlobalMaxPool1d(nn.Module):
def forward(self, inputs):
return nn.functional.adaptive_max_pool1d(inputs, 1).view(inputs.
size(0), -1)
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | rlmwang/torch-tools | GlobalMaxPool1d | false | 10,801 | [
"MIT"
] | 0 | 822132534d73414f26045bad38a0a345661b057f | https://github.com/rlmwang/torch-tools/tree/822132534d73414f26045bad38a0a345661b057f | import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, inputs):
return nn.functional.adaptive_max_pool1d(inputs, 1).view(inputs.
size(0), -1)
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return []
|
GDeconv1DBlock | # 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.nn.utils.spectral_norm import spectral_norm
def build_norm_layer(norm_type, param=None, num_feats=None):
if norm_type == 'bnorm':
return nn.BatchNorm1d(num_feats)
elif norm_type == 'snorm':
spectral_norm(param)
return None
elif norm_typ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.utils.spectral_norm import spectral_norm
ass... | silvadirceu/segan_pytorch | GDeconv1DBlock | false | 10,802 | [
"MIT"
] | 0 | 2215e711f7223b144e0c4d4fb4ed1d4842b18c5f | https://github.com/silvadirceu/segan_pytorch/tree/2215e711f7223b144e0c4d4fb4ed1d4842b18c5f | import torch
import torch.nn as nn
from torch.nn.utils.spectral_norm import spectral_norm
def build_norm_layer(norm_type, param=None, num_feats=None):
if norm_type == 'bnorm':
return nn.BatchNorm1d(num_feats)
elif norm_type == 'snorm':
spectral_norm(param)
return None
elif norm_typ... |
WeightedAverage | # 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.parallel
import torch.nn.functional as F
def find_local_patch(x, patch_size):
N, _C, H, W = x.shape
x_unfold = F.unfold(x, kernel_size=(patch_size, patch_size), padding=(
patch_size // 2, patch_size // 2), stride=(1, 1))
return x_unfold.view(N, x_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | qiyuqianxai/debvc | WeightedAverage | false | 10,803 | [
"MIT"
] | 0 | 1d919019a3191d1c6a7da9b8f16e47bca6b3aef9 | https://github.com/qiyuqianxai/debvc/tree/1d919019a3191d1c6a7da9b8f16e47bca6b3aef9 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
def find_local_patch(x, patch_size):
N, _C, H, W = x.shape
x_unfold = F.unfold(x, kernel_size=(patch_size, patch_size), padding=(
patch_size // 2, patch_size // 2), stride=(1, 1))
return x_unfold.view(N, x_... |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class MultiHeadAttention(nn.Module):
def __init__(self, channels, out_channels, n_heads, p_dropout=0.0,
window_size=None, heads_share=True, block_length=None,
proximal_bias=False, proximal_i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | roedoejet/vits | MultiHeadAttention | false | 10,804 | [
"MIT"
] | 0 | 982e3632c876562563bc74c37d485eaf53715ecc | https://github.com/roedoejet/vits/tree/982e3632c876562563bc74c37d485eaf53715ecc | import math
import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data
class Model(nn.Module):
def __init__(self, channels, out_channels, n_heads, p_dropout=0.0,
window_size=None, heads_share=True, block_length=None,
proximal_bias=False, proximal_init=False):
... |
GRU122 | # 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 GRU122(nn.Module):
def __init__(self, input_size, hidden_size):
super(GRU122, self).__init__()
self.hidden_size = hidden_size
self.wir = nn.Linear(in_features=input_size, out_features=2 *
hidden_size)
self.whr = nn.Linear(in_fea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | smeznar/ProGED | GRU122 | false | 10,805 | [
"BSD-3-Clause"
] | 0 | 191cfd2b7b1fece819109a4b61e3f7533332fd74 | https://github.com/smeznar/ProGED/tree/191cfd2b7b1fece819109a4b61e3f7533332fd74 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_size, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.wir = nn.Linear(in_features=input_size, out_features=2 *
hidden_size)
self.whr = nn.Linear(in_features=hidden_... |
NeuralModel | # 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 NeuralModel(nn.Module):
def __init__(self, input):
super(NeuralModel, self).__init__()
self.dense1 = nn.Linear(in_features=input, out_features=128)
self.dense2 = nn.Linear(in_features=128, out_features=16)
self.dense3 = nn.Linear(in_feature... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | sumitsharmamanit/Facial-emotion-recognition | NeuralModel | false | 10,806 | [
"Apache-2.0"
] | 0 | f95770c0cfabd46a8f9589eb415ce69eaeaea4c6 | https://github.com/sumitsharmamanit/Facial-emotion-recognition/tree/f95770c0cfabd46a8f9589eb415ce69eaeaea4c6 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input):
super().__init__()
self.dense1 = nn.Linear(in_features=input, out_features=128)
self.dense2 = nn.Linear(in_features=128, out_features=16)
self.dense3 = nn.Linear(in_features=16, out_features=2)
... |
Envelope | # 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
class Envelope(torch.nn.Module):
def __init__(self, exponent):
super(Envelope, self).__init__()
self.p = exponent
self.a = -(self.p + 1) * (self.p + 2) / 2
self.b = self.p * (self.p + 2)
self.c = -self.p * (self.p + 1) / 2
def forw... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | shnhrtkyk/pytorch_geometric | Envelope | false | 10,807 | [
"MIT"
] | 0 | b971fd2ebba10736e6398d6305757be2d81ca681 | https://github.com/shnhrtkyk/pytorch_geometric/tree/b971fd2ebba10736e6398d6305757be2d81ca681 | import torch
import torch.utils.data
class Model(torch.nn.Module):
def __init__(self, exponent):
super().__init__()
self.p = exponent
self.a = -(self.p + 1) * (self.p + 2) / 2
self.b = self.p * (self.p + 2)
self.c = -self.p * (self.p + 1) / 2
def forward(self, x):
... |
GRU221 | # 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 GRU221(nn.Module):
def __init__(self, input_size, hidden_size):
super(GRU221, self).__init__()
self.wir = nn.Linear(in_features=input_size, out_features=hidden_size)
self.whr = nn.Linear(in_features=2 * hidden_size, out_features=
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
import torch.nn as ... | smeznar/ProGED | GRU221 | false | 10,808 | [
"BSD-3-Clause"
] | 0 | 191cfd2b7b1fece819109a4b61e3f7533332fd74 | https://github.com/smeznar/ProGED/tree/191cfd2b7b1fece819109a4b61e3f7533332fd74 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_size, hidden_size):
super().__init__()
self.wir = nn.Linear(in_features=input_size, out_features=hidden_size)
self.whr = nn.Linear(in_features=2 * hidden_size, out_features=
hidden_size)
... |
ContrastiveLoss | # 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 ContrastiveLoss(nn.Module):
def __init__(self, margin=1.0):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, x0, x1, y):
diff = x0 - x1
dist_sq = torch.sum(torch.pow(diff, 2), 1)
dist = torch.sqrt(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | smit25/Siamese-Network-For-Minutiae-Point-Detection | ContrastiveLoss | false | 10,809 | [
"Apache-2.0"
] | 0 | 453e2f91aed7e3d3e5ddb75a53cdfb164d2493d4 | https://github.com/smit25/Siamese-Network-For-Minutiae-Point-Detection/tree/453e2f91aed7e3d3e5ddb75a53cdfb164d2493d4 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, margin=1.0):
super().__init__()
self.margin = margin
def forward(self, x0, x1, y):
diff = x0 - x1
dist_sq = torch.sum(torch.pow(diff, 2), 1)
dist = torch.sqrt(dist_sq)
mdist = self.m... |
DenseGraphConv | # 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
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class DenseGraphConv(torch.nn.Module):
"""See :class:`torch_geometric.nn.conv.GraphConv`.
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch.nn import Parameter
import torch.utils.data
assert_size_s... | shnhrtkyk/pytorch_geometric | DenseGraphConv | false | 10,810 | [
"MIT"
] | 0 | b971fd2ebba10736e6398d6305757be2d81ca681 | https://github.com/shnhrtkyk/pytorch_geometric/tree/b971fd2ebba10736e6398d6305757be2d81ca681 | import math
import torch
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class Model(torch.nn.Module):
"""See :class:`torch_geometric.nn.conv.GraphConv`.
"""
def __... |
ResidualLayer | # 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
from torch import Tensor
from torch.nn import Linear
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if tensor ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import Tensor
from torch.nn import Linear
from torch.nn i... | shnhrtkyk/pytorch_geometric | ResidualLayer | false | 10,811 | [
"MIT"
] | 0 | b971fd2ebba10736e6398d6305757be2d81ca681 | https://github.com/shnhrtkyk/pytorch_geometric/tree/b971fd2ebba10736e6398d6305757be2d81ca681 | import math
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if tensor ... |
FullyCNN | # 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 FullyCNN(nn.Module):
"""UNET Without concatenation during decoding"""
def __init__(self):
super(FullyCNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3,
stride=1, padding=1, padding_mode='reflect')
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | quenting44/semantic_segmentation | FullyCNN | false | 10,812 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a | import torch
from torch import nn
class Model(nn.Module):
"""UNET Without concatenation during decoding"""
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3,
stride=1, padding=1, padding_mode='reflect')
self.relu1 = ... |
_DynamicGates | # 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
class _DynamicGates(nn.Module):
"""Internal class to wrap the dynamic gate parameters into a dedicated PyTorch Module"""
def __init__(self, cfg: 'Config', input_size: 'int'):
super(_DynamicGates, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | rro2q2/transfer-learning-aaai21 | _DynamicGates | false | 10,813 | [
"BSD-3-Clause"
] | 0 | f1960540d0608ce1e4d1d64bb4abd29d953f250f | https://github.com/rro2q2/transfer-learning-aaai21/tree/f1960540d0608ce1e4d1d64bb4abd29d953f250f | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
"""Internal class to wrap the dynamic gate parameters into a dedicated PyTorch Module"""
def __init__(self, cfg: 'Config', input_size: 'int'):
super().__init__()
self.cfg = cfg
sel... |
Net | # 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 Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
self.fc1 = nn.Linear(65536, 10)
self.maxpool = nn.AdaptiveMaxPool2d(32)
def forward(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | surya00060/tvm | Net | false | 10,814 | [
"Zlib",
"Unlicense",
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0"
] | 0 | fd4601514aee1ecf080b74578849c60438f55b0c | https://github.com/surya00060/tvm/tree/fd4601514aee1ecf080b74578849c60438f55b0c | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
self.fc1 = nn.Linear(65536, 10)
self.maxpool = nn.AdaptiveMaxPool2d(32)
def forward(self, x... |
Model | # 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
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(3, 32, kernel_size=6, stride=1, padding=1)
self.conv2 = torch.nn.Conv2d(32, 32, kernel_size=6, stride=1, padding=1
)
self.conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | stepan-krivanek/image-recognition | Model | false | 10,815 | [
"MIT"
] | 0 | 6c421e768e83db489e4caa22989f7dad95519578 | https://github.com/stepan-krivanek/image-recognition/tree/6c421e768e83db489e4caa22989f7dad95519578 | import torch
import torch.nn.functional as F
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(3, 32, kernel_size=6, stride=1, padding=1)
self.conv2 = torch.nn.Conv2d(32, 32, kernel_size=6, stride=1, padding=1
)
self.conv... |
NoiseBlock | # 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.jit
class NoiseBlock(nn.Module):
def __init__(self, sigma):
super(NoiseBlock, self).__init__()
self.sigma = sigma
def forward(self, x):
out = x + self.sigma * torch.randn_like(x)
return out
def set_sigma(self, x):
s... | import torch
from torch import device
import 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.jit
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.ji... | shuj1234/Hopfield-ODE | NoiseBlock | false | 10,816 | [
"MIT"
] | 0 | 2b770c0141082174f394b189df725088308d8bdd | https://github.com/shuj1234/Hopfield-ODE/tree/2b770c0141082174f394b189df725088308d8bdd | import torch
import torch.nn as nn
import torch.jit
class Model(nn.Module):
def __init__(self, sigma):
super().__init__()
self.sigma = sigma
def forward(self, x):
out = x + self.sigma * torch.randn_like(x)
return out
def set_sigma(self, x):
self.sigma = x
... |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class Attention(nn.Module):
def __init__(self, feature_dim, max_seq_len=70):
super().__init__()
self.attention_fc = nn.Linear(feature_dim, 1)
self.bias = nn.Parameter(torch.zeros(1, max_seq_len, 1,
requires_grad=True))
def forward(self, r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | tanreinama/jigsaw_unintendedbiasclassification_validation_model | Attention | false | 10,817 | [
"Apache-2.0"
] | 0 | af1644488e0d0f7d54ce5d8186ae38a8b079b2db | https://github.com/tanreinama/jigsaw_unintendedbiasclassification_validation_model/tree/af1644488e0d0f7d54ce5d8186ae38a8b079b2db | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, feature_dim, max_seq_len=70):
super().__init__()
self.attention_fc = nn.Linear(feature_dim, 1)
self.bias = nn.Parameter(torch.zeros(1, max_seq_len, 1,
requires_grad=True))
def forward(self, rnn_o... |
GateLayer | # 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 GateLayer(nn.Module):
def __init__(self, input_dim):
super(GateLayer, self).__init__()
self._norm_layer1 = nn.Linear(input_dim * 2, input_dim)
self._norm_layer2 = nn.Linear(input_dim, 1)
def forward(self, input1, input2):
norm_input = 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | shubaoyu/CRSLab | GateLayer | false | 10,818 | [
"MIT"
] | 0 | a05730e8b2c03df278587be34923fa818945d4c4 | https://github.com/shubaoyu/CRSLab/tree/a05730e8b2c03df278587be34923fa818945d4c4 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, input_dim):
super().__init__()
self._norm_layer1 = nn.Linear(input_dim * 2, input_dim)
self._norm_layer2 = nn.Linear(input_dim, 1)
def forward(self, input1, input2):
norm_input = self._norm_layer1(to... |
SelfAttentionBatch | # 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.nn.functional as F
class SelfAttentionBatch(nn.Module):
def __init__(self, dim, da, alpha=0.2, dropout=0.5):
super(SelfAttentionBatch, self).__init__()
self.dim = dim
self.da = da
self.alpha = alpha
self.dropout = dropout
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | shubaoyu/CRSLab | SelfAttentionBatch | false | 10,819 | [
"MIT"
] | 0 | a05730e8b2c03df278587be34923fa818945d4c4 | https://github.com/shubaoyu/CRSLab/tree/a05730e8b2c03df278587be34923fa818945d4c4 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, dim, da, alpha=0.2, dropout=0.5):
super().__init__()
self.dim = dim
self.da = da
self.alpha = alpha
self.dropout = dropout
self.a = nn.Parameter(torch.zeros... |
EncoderImageWeightNormPrecomp | # 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 collections import OrderedDict
import torch.nn as nn
import torch.nn.init
from torch.nn.utils.weight_norm import weight_norm
def l2norm(X, dim, eps=1e-08):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
retur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 collections im... | sungjune-p/SCAN | EncoderImageWeightNormPrecomp | false | 10,820 | [
"Apache-2.0"
] | 0 | a3013944a05b48e952141fa295a8132d25da2e97 | https://github.com/sungjune-p/SCAN/tree/a3013944a05b48e952141fa295a8132d25da2e97 | import torch
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
from torch.nn.utils.weight_norm import weight_norm
def l2norm(X, dim, eps=1e-08):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
retur... |
MaskedWordPredictions | # 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
from torch import nn
def gelu(x):
"""Gaussian Error Linear Unitという活性化関数です。
LeLUが0でカクっと不連続なので、そこを連続になるように滑らかにした形のLeLUです。
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class BertLayerNorm(nn.Module):
def __init__(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | Cyndi-Tokyotech/Fin_Text_Analysis_ML | MaskedWordPredictions | false | 10,821 | [
"MIT"
] | 0 | 7f9b6c1ea78f8e6f32c003b2de32809722df88d4 | https://github.com/Cyndi-Tokyotech/Fin_Text_Analysis_ML/tree/7f9b6c1ea78f8e6f32c003b2de32809722df88d4 | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
def gelu(x):
"""Gaussian Error Linear Unitという活性化関数です。
LeLUが0でカクっと不連続なので、そこを連続になるように滑らかにした形のLeLUです。
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class BertLayerNorm(nn.Module):
def __init__(... |
BinaryClassificationHead | # 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
class BinaryClassificationHead(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense = torch.nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = torch.nn.Dropout(conf... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | IgnatovFedor/DeepPavlov | BinaryClassificationHead | false | 10,822 | [
"Apache-2.0"
] | 0 | 02ba9c4b2919384c142c170c7f89c65cf05dd426 | https://github.com/IgnatovFedor/DeepPavlov/tree/02ba9c4b2919384c142c170c7f89c65cf05dd426 | from _paritybench_helpers import _mock_config
import torch
class Model(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense = torch.nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = torch.nn.Dropout(config.hidden_dropout_p... |
ODEfunc_single_conv | # 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.jit
def norm(dim):
return nn.GroupNorm(min(32, dim), dim)
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatConv2d, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | shuj1234/Hopfield-ODE | ODEfunc_single_conv | false | 10,823 | [
"MIT"
] | 0 | 2b770c0141082174f394b189df725088308d8bdd | https://github.com/shuj1234/Hopfield-ODE/tree/2b770c0141082174f394b189df725088308d8bdd | import torch
import torch.nn as nn
import torch.jit
def norm(dim):
return nn.GroupNorm(min(32, dim), dim)
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super().__init__()
module = n... |
UNETWithoutPooling | # 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 UNETWithoutPooling(nn.Module):
"""UNET without pooling"""
def __init__(self):
super(UNETWithoutPooling, self).__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | quenting44/semantic_segmentation | UNETWithoutPooling | false | 10,824 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a | import torch
from torch import nn
class Model(nn.Module):
"""UNET without pooling"""
def __init__(self):
super().__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(in_channels=16, out_channels=16... |
LocalEstimator | # 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 LocalEstimator(nn.Module):
def __init__(self, input_size):
super(LocalEstimator, self).__init__()
self.input2hid = nn.Linear(input_size, 64)
self.hid2hid = nn.Linear(64, 32)
self.hid2out = nn.Linear(32, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | sunqcc/test | LocalEstimator | false | 10,825 | [
"MIT"
] | 0 | f913d2f33a4b85eed571ccf0b9a2d65dca594441 | https://github.com/sunqcc/test/tree/f913d2f33a4b85eed571ccf0b9a2d65dca594441 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_size):
super().__init__()
self.input2hid = nn.Linear(input_size, 64)
self.hid2hid = nn.Linear(64, 32)
self.hid2out = nn.Linear(32, 1)
def forward(self, sptm_s):... |
Net | # 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 Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 128, 3, padding=1)
self.conv2 = nn.Conv2d(128, 64, 3, padding=1)
self.conv3 = nn.Conv2d(64, 3, 3, padding=1)
def ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | suttergustavo/SCC0251_Final_Project | Net | false | 10,826 | [
"MIT"
] | 0 | 81b91ff6ee7675c8bfaedc6ada6bd09baa65d630 | https://github.com/suttergustavo/SCC0251_Final_Project/tree/81b91ff6ee7675c8bfaedc6ada6bd09baa65d630 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 128, 3, padding=1)
self.conv2 = nn.Conv2d(128, 64, 3, padding=1)
self.conv3 = nn.Conv2d(64, 3, 3, padding=1)
def forward... |
EncoderImagePrecomp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
def l2norm(X, dim, eps=1e-08):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
return X
class EncoderImagePrecomp(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | sungjune-p/SCAN | EncoderImagePrecomp | false | 10,827 | [
"Apache-2.0"
] | 0 | a3013944a05b48e952141fa295a8132d25da2e97 | https://github.com/sungjune-p/SCAN/tree/a3013944a05b48e952141fa295a8132d25da2e97 | import torch
import numpy as np
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
def l2norm(X, dim, eps=1e-08):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
return X
class Model(nn.Module):
... |
MLP | # 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 choose_nonlinearity(name):
nl = None
if name == 'tanh':
nl = torch.tanh
elif name == 'relu':
nl = torch.relu
elif name == 'sigmoid':
nl = torch.sigmoid
elif name == 'softplus':
nl = torch.nn.functional.softplus
elif name == 'selu':
nl = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | somu15/hamiltonian-nn | MLP | false | 10,828 | [
"Apache-2.0"
] | 0 | 0c62e92cd50d4bda4b1d0345a4676a6c003aee5e | https://github.com/somu15/hamiltonian-nn/tree/0c62e92cd50d4bda4b1d0345a4676a6c003aee5e | import torch
def choose_nonlinearity(name):
nl = None
if name == 'tanh':
nl = torch.tanh
elif name == 'relu':
nl = torch.relu
elif name == 'sigmoid':
nl = torch.sigmoid
elif name == 'softplus':
nl = torch.nn.functional.softplus
elif name == 'selu':
nl = ... |
UNETMin | # 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 UNETMin(nn.Module):
"""UNET Without concatenation during decoding"""
def __init__(self):
super(UNETMin, self).__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | quenting44/semantic_segmentation | UNETMin | false | 10,829 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a | import torch
from torch import nn
class Model(nn.Module):
"""UNET Without concatenation during decoding"""
def __init__(self):
super().__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(in_channe... |
Concat | # 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.cuda
import torch.nn
import torch.utils.data
import torch.fx
import torch.utils.tensorboard._pytorch_graph
class Concat(torch.nn.Module):
""" Concat module for a functional concat"""
def __init__(self, axis: 'int'=0):
super(Concat, self).__init__()
self.axis = axis
... | 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.cuda
import torch.nn
import torch.utils.data
import torch.fx
import torch.utils.tensorboard._pytorch_graph
assert_size_stride =... | mikeseven/aimet | Concat | false | 10,830 | [
"BSD-3-Clause"
] | 0 | 63211a4f259b6457c58dfae1097c70acb93319fe | https://github.com/mikeseven/aimet/tree/63211a4f259b6457c58dfae1097c70acb93319fe | import torch
import torch.cuda
import torch.nn
import torch.utils.data
import torch.fx
import torch.utils.tensorboard._pytorch_graph
class Model(torch.nn.Module):
""" Concat module for a functional concat"""
def __init__(self, axis: 'int'=0):
super().__init__()
self.axis = axis
def forwa... |
UNETWithoutConcat | # 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 UNETWithoutConcat(nn.Module):
"""UNET Without concatenation during decoding"""
def __init__(self):
super(UNETWithoutConcat, self).__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | quenting44/semantic_segmentation | UNETWithoutConcat | false | 10,831 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a | import torch
from torch import nn
class Model(nn.Module):
"""UNET Without concatenation during decoding"""
def __init__(self):
super().__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(in_channe... |
UNET | # 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 UNET(nn.Module):
def __init__(self):
super(UNET, self).__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(in_channels=16, out_channels=16,
kernel_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | quenting44/semantic_segmentation | UNET | false | 10,832 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a | import torch
from torch import nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(in_channels=16, out_channels=16,
kernel_size=3, st... |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn as nn
from collections import OrderedDict
from torch.nn.functional import relu
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | suulkyy/GPM | BasicBlock | false | 10,833 | [
"MIT"
] | 0 | f094012a6ea6ea145bd100d1481a984783ae14dd | https://github.com/suulkyy/GPM/tree/f094012a6ea6ea145bd100d1481a984783ae14dd | import torch
import torch.utils.data
import torch.nn as nn
from collections import OrderedDict
from torch.nn.functional import relu
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class Model(nn.Module):
expan... |
ODEfunc_double_conv | # 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.jit
def norm(dim):
return nn.GroupNorm(min(32, dim), dim)
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatConv2d, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | shuj1234/Hopfield-ODE | ODEfunc_double_conv | false | 10,834 | [
"MIT"
] | 0 | 2b770c0141082174f394b189df725088308d8bdd | https://github.com/shuj1234/Hopfield-ODE/tree/2b770c0141082174f394b189df725088308d8bdd | import torch
import torch.nn as nn
import torch.jit
def norm(dim):
return nn.GroupNorm(min(32, dim), dim)
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super().__init__()
module = n... |
LocalConv2d | # 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 LocalConv2d(nn.Module):
def __init__(self, num_rows, num_feats_in, num_feats_out, kernel=1,
padding=0):
super(LocalConv2d, self).__init__()
self.num_rows = num_rows
self.out_channels = num_feats_out
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | syKevinPeng/M3D-RPN | LocalConv2d | false | 10,835 | [
"MIT"
] | 0 | ae43248f0d64a83d7deef63308dd5ade25e7b751 | https://github.com/syKevinPeng/M3D-RPN/tree/ae43248f0d64a83d7deef63308dd5ade25e7b751 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_rows, num_feats_in, num_feats_out, kernel=1,
padding=0):
super().__init__()
self.num_rows = num_rows
self.out_channels = num_feats_out
self.kernel = kernel
... |
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
from torch import nn
import torch.utils.data
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
from torch import n... | sbarham/lv-nlm-he-2019 | GatedMaskedConv2d | false | 10,836 | [
"MIT"
] | 0 | 6fd1ce680675759d0a58878ac1fde31122712752 | https://github.com/sbarham/lv-nlm-he-2019/tree/6fd1ce680675759d0a58878ac1fde31122712752 | import torch
from torch import nn
import torch.utils.data
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_dim, out_dim=None, kernel_size=3, mask='B'):
super().__init__()
if out_dim is None:
out_dim = in_dim
self.dim = out_dim
self.size = k... |
ChannelNorm | # 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 ChannelNorm(nn.Module):
def __init__(self, numFeatures, epsilon=1e-05, affine=True):
super(ChannelNorm, self).__init__()
if affine:
self.weight = nn.parameter.Parameter(torch.Tensor(1,
numFeatures, 1))
self.bias = nn... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | raphaelreme/CPC_audio | ChannelNorm | false | 10,837 | [
"MIT"
] | 0 | a2b045d5f03f4a73beaab9b481244e454edacbaa | https://github.com/raphaelreme/CPC_audio/tree/a2b045d5f03f4a73beaab9b481244e454edacbaa | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, numFeatures, epsilon=1e-05, affine=True):
super().__init__()
if affine:
self.weight = nn.parameter.Parameter(torch.Tensor(1,
numFeatures, 1))
self.bias = nn.parameter.Parameter(to... |
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
import torch.nn as nn
class Highway(nn.Module):
def __init__(self, in_size, out_size):
super(Highway, self).__init__()
self.H = nn.Linear(in_size, out_size)
self.H.bias.data.zero_()
self.T = nn.Linear(in_size, out_size)
self.T.bias.data.fill_(-1)
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 import triton_helpers
import torch.nn as nn
assert_... | seo3650/Tacotron-pytorch | Highway | false | 10,838 | [
"MIT"
] | 0 | 223e4f39a3624c409484a1ad55edab1563cf8c87 | https://github.com/seo3650/Tacotron-pytorch/tree/223e4f39a3624c409484a1ad55edab1563cf8c87 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_size, out_size):
super().__init__()
self.H = nn.Linear(in_size, out_size)
self.H.bias.data.zero_()
self.T = nn.Linear(in_size, out_size)
self.T.bias.data.fill_(-1)
self.relu = nn.ReLU(... |
Network | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.init as I
class Network(nn.Module):
"""
Q-network
"""
def __init__(self, state_size, action_size, seed, fc1_units=64,
fc2_units=32):
"""
Build model and Intialize it
Params
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | tae-yeop/Udacity_DRLND_navigation | Network | false | 10,839 | [
"MIT"
] | 0 | dd4a4609c5fe3e00cb4deea3ebd9922dd0772447 | https://github.com/tae-yeop/Udacity_DRLND_navigation/tree/dd4a4609c5fe3e00cb4deea3ebd9922dd0772447 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.init as I
class Model(nn.Module):
"""
Q-network
"""
def __init__(self, state_size, action_size, seed, fc1_units=64,
fc2_units=32):
"""
Build model and Intialize it
Params
... |
AffineGridGen | # 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.nn.functional as F
import torch.nn
from torch.nn.modules.module import Module
class AffineGridGen(Module):
def __init__(self, out_h=240, out_w=240, out_ch=3, use_cuda=True):
super(AffineGridGen, self).__init__()
self.out_h = out_h
self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.nn
from torch.nn.modules.module import Module
assert_size_stride = torch._C._dynamo.guards.assert_s... | sebastian-echeverria/ncnet | AffineGridGen | false | 10,840 | [
"MIT"
] | 0 | c7249fe8f908813bab6443ebfa4590bd362a0dc2 | https://github.com/sebastian-echeverria/ncnet/tree/c7249fe8f908813bab6443ebfa4590bd362a0dc2 | from torch.nn import Module
import torch
import torch.nn.functional as F
import torch.nn
from torch.nn.modules.module import Module
class Model(Module):
def __init__(self, out_h=240, out_w=240, out_ch=3, use_cuda=True):
super().__init__()
self.out_h = out_h
self.out_w = out_w
self... |
BahdanauAttn | # 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 BahdanauAttn(nn.Module):
"""Bahdabau attention mechanism"""
def __init__(self, size):
super(BahdanauAttn, self).__init__()
self.query_layer = nn.Linear(size, size, bias=False)
self.tanh = nn.Tanh()
self.v = nn.Linear(size, 1, bias=False... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | seo3650/Tacotron-pytorch | BahdanauAttn | false | 10,841 | [
"MIT"
] | 0 | 223e4f39a3624c409484a1ad55edab1563cf8c87 | https://github.com/seo3650/Tacotron-pytorch/tree/223e4f39a3624c409484a1ad55edab1563cf8c87 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Bahdabau attention mechanism"""
def __init__(self, size):
super().__init__()
self.query_layer = nn.Linear(size, size, bias=False)
self.tanh = nn.Tanh()
self.v = nn.Linear(size, 1, bias=False)
def forward(self, ... |
StackTime | # 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.onnx
class StackTime(torch.nn.Module):
__constants__ = ['factor']
def __init__(self, factor):
super().__init__()
self.factor = int(factor)
def forward(self, x, x_lens):
seq = [x]
for i in range(1, self.factor):
tmp = torch.zeros_like(... | 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.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | swiftdiaries/inference | StackTime | false | 10,842 | [
"Apache-2.0"
] | 0 | dbb39947d4515449b1a3393cde39ca0dba935b1d | https://github.com/swiftdiaries/inference/tree/dbb39947d4515449b1a3393cde39ca0dba935b1d | import torch
import torch.onnx
class Model(torch.nn.Module):
__constants__ = ['factor']
def __init__(self, factor):
super().__init__()
self.factor = int(factor)
def forward(self, x, x_lens):
seq = [x]
for i in range(1, self.factor):
tmp = torch.zeros_like(x)
... |
ShiftedConv | # 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
from numpy import prod
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from numpy import prod
assert_size_stride = to... | raphaelreme/CPC_audio | ShiftedConv | false | 10,843 | [
"MIT"
] | 0 | a2b045d5f03f4a73beaab9b481244e454edacbaa | https://github.com/raphaelreme/CPC_audio/tree/a2b045d5f03f4a73beaab9b481244e454edacbaa | import math
import torch
import torch.nn as nn
from numpy import prod
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = pro... |
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
class DiceLoss(nn.Module):
def __init__(self, smooth: 'float'=1.0, apply_sigmoid: 'bool'=False):
super().__init__()
self.smooth = smooth
self.apply_sigmoid = apply_sigmoid
def forward(self, y_pred: 'torch.Tensor', y_true: 'torch.Tensor'
) ->... | 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... | tfmoraes/deep_heart_torch | DiceLoss | false | 10,844 | [
"MIT"
] | 0 | 4168ce01d600e69baf82c752a3e57af86861b6ea | https://github.com/tfmoraes/deep_heart_torch/tree/4168ce01d600e69baf82c752a3e57af86861b6ea | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, smooth: 'float'=1.0, apply_sigmoid: 'bool'=False):
super().__init__()
self.smooth = smooth
self.apply_sigmoid = apply_sigmoid
def forward(self, y_pred: 'torch.Tensor', y_true: 'torch.Tensor'
) ->tor... |
NAC | # 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.nn.parameter import Parameter
import torch.nn.functional as F
class NAC(Module):
"""Neural Accumulator: :math:`y = Wx` where :math:`W = \\tanh(\\hat{W}) * \\sigma(\\hat{M})`
Args:
in_features: size of each input sample
out_featur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | tanbur/pytorch-nalu | NAC | false | 10,845 | [
"MIT"
] | 0 | 91cb036230144b166137a8f3533850f2d4123d4f | https://github.com/tanbur/pytorch-nalu/tree/91cb036230144b166137a8f3533850f2d4123d4f | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class Model(Module):
"""Neural Accumulator: :math:`y = Wx` where :math:`W = \\tanh(\\hat{W}) * \\sigma(\\hat{M})`
Args:
in_features: size of each input sample
out_feat... |
UNETAdd | # 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 UNETAdd(nn.Module):
"""UNET Without concatenation during decoding"""
def __init__(self):
super(UNETAdd, self).__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | quenting44/semantic_segmentation | UNETAdd | false | 10,846 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a | import torch
from torch import nn
class Model(nn.Module):
"""UNET Without concatenation during decoding"""
def __init__(self):
super().__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(in_channe... |
NALU | # 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.nn.parameter import Parameter
import torch.nn.functional as F
class NAC(Module):
"""Neural Accumulator: :math:`y = Wx` where :math:`W = \\tanh(\\hat{W}) * \\sigma(\\hat{M})`
Args:
in_features: size of each input sample
out_featur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | tanbur/pytorch-nalu | NALU | false | 10,847 | [
"MIT"
] | 0 | 91cb036230144b166137a8f3533850f2d4123d4f | https://github.com/tanbur/pytorch-nalu/tree/91cb036230144b166137a8f3533850f2d4123d4f | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class NAC(Module):
"""Neural Accumulator: :math:`y = Wx` where :math:`W = \\tanh(\\hat{W}) * \\sigma(\\hat{M})`
Args:
in_features: size of each input sample
out_featur... |
TripletLoss | # 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.nn.modules.distance import PairwiseDistance
class TripletLoss(torch.nn.Module):
def __init__(self, margin):
super(TripletLoss, self).__init__()
self.margin = margin
self.pdist = PairwiseDistance(2)
def forward(self, anchor, positive, negative):
pos_dis... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn.modules.distan... | tobysuwindra/Bird-Similarity | TripletLoss | false | 10,848 | [
"MIT"
] | 0 | 92f182fe89645f6ce6dd4e99f12c1185f52d5d9e | https://github.com/tobysuwindra/Bird-Similarity/tree/92f182fe89645f6ce6dd4e99f12c1185f52d5d9e | import torch
from torch.nn.modules.distance import PairwiseDistance
class Model(torch.nn.Module):
def __init__(self, margin):
super().__init__()
self.margin = margin
self.pdist = PairwiseDistance(2)
def forward(self, anchor, positive, negative):
pos_dist = self.pdist.forward(... |
Net | # 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 Net(nn.Module):
def __init__(self, n_states, n_actions, n_hidden):
super(Net, self).__init__()
self.fc1 = nn.Linear(n_states, n_hidden)
self.fc2 = nn.Linear(n_hidden, n_hidden * 2)
self.fc3 = nn.Linear(n_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 import triton_helpers
import torch.nn as nn
assert_... | tom99763/implement-DQN-on-maze-game | Net | false | 10,849 | [
"BSD-2-Clause"
] | 0 | 24135a06e348b6f8b88a22c58b4a2c930bf7d7b6 | https://github.com/tom99763/implement-DQN-on-maze-game/tree/24135a06e348b6f8b88a22c58b4a2c930bf7d7b6 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, n_states, n_actions, n_hidden):
super().__init__()
self.fc1 = nn.Linear(n_states, n_hidden)
self.fc2 = nn.Linear(n_hidden, n_hidden * 2)
self.fc3 = nn.Linear(n_hidden * 2,... |
SimpleNN | # 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 SimpleNN(nn.Module):
def __init__(self, in_values, out_values):
super().__init__()
self.dense1 = nn.Linear(in_values, 12673)
self.drop1 = nn.Dropout()
self.dense2 = nn.Linear(12673, 4000)
self.drop2 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | sboomi/cp1 | SimpleNN | false | 10,850 | [
"MIT"
] | 0 | 7f7aa96e8ba9cfe00802028a61bfba5e90c999f6 | https://github.com/sboomi/cp1/tree/7f7aa96e8ba9cfe00802028a61bfba5e90c999f6 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_values, out_values):
super().__init__()
self.dense1 = nn.Linear(in_values, 12673)
self.drop1 = nn.Dropout()
self.dense2 = nn.Linear(12673, 4000)
self.drop2 = nn... |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import nn
class GELU(nn.Module):
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x +
0.044715 * torch.pow(x, 3))))
class PositionwiseFeedForward(nn.Module):
def __init__(self, d_model, d_ff, dropout=0.1):
sup... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | tnat410/smiles-transformer | PositionwiseFeedForward | false | 10,851 | [
"MIT"
] | 0 | e64196945ed44cfce529484bcc8b6c77b662cdc8 | https://github.com/tnat410/smiles-transformer/tree/e64196945ed44cfce529484bcc8b6c77b662cdc8 | import math
import torch
from torch import nn
class GELU(nn.Module):
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x +
0.044715 * torch.pow(x, 3))))
class Model(nn.Module):
def __init__(self, d_model, d_ff, dropout=0.1):
super().__init__()
... |
FFN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of input
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | stefantaubert/FastSpeech | FFN | false | 10,852 | [
"MIT"
] | 0 | 4ef8ce2ff8f6a69f9b52ef9bd5b37f8e2783c17e | https://github.com/stefantaubert/FastSpeech/tree/4ef8ce2ff8f6a69f9b52ef9bd5b37f8e2783c17e | import torch
import torch.nn as nn
import torch.utils.data
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of input
... |
DiceBCELoss | # 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 DiceLoss(nn.Module):
def __init__(self, smooth: 'float'=1.0, apply_sigmoid: 'bool'=False):
super().__init__()
self.smooth = smooth
self.apply_sigmoid = apply_sigmoid
def forward(self, y_pred: 'torch.Tensor', y_t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | tfmoraes/deep_heart_torch | DiceBCELoss | false | 10,853 | [
"MIT"
] | 0 | 4168ce01d600e69baf82c752a3e57af86861b6ea | https://github.com/tfmoraes/deep_heart_torch/tree/4168ce01d600e69baf82c752a3e57af86861b6ea | import torch
import torch.nn as nn
import torch.nn.functional as F
class DiceLoss(nn.Module):
def __init__(self, smooth: 'float'=1.0, apply_sigmoid: 'bool'=False):
super().__init__()
self.smooth = smooth
self.apply_sigmoid = apply_sigmoid
def forward(self, y_pred: 'torch.Tensor', y_t... |
WorldNet | # 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
class WorldNet(torch.nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(WorldNet, self).__init__()
self.fc_in = torch.nn.Linear(input_dim, hidden_dim)
self.fc_1 = torch.nn.Linear(hidden_dim, hidden_dim)
self.fc_2 = torch.nn.Linear(hidden_dim, hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | tim-ts-chu/mbpo | WorldNet | false | 10,854 | [
"MIT"
] | 0 | 0d98e6e80499a82812d3361658e0707c0b489fc5 | https://github.com/tim-ts-chu/mbpo/tree/0d98e6e80499a82812d3361658e0707c0b489fc5 | import torch
class Model(torch.nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super().__init__()
self.fc_in = torch.nn.Linear(input_dim, hidden_dim)
self.fc_1 = torch.nn.Linear(hidden_dim, hidden_dim)
self.fc_2 = torch.nn.Linear(hidden_dim, hidden_dim)
... |
ConvLayer | # 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 ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, norm
='instance'):
super(ConvLayer, self).__init__()
padding_size = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(padding_size)
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... | suryawanshishantanu6/Multi-Style-Transfer | ConvLayer | false | 10,855 | [
"MIT"
] | 0 | c5c211847de676596580a8a9afda940ac76abbb1 | https://github.com/suryawanshishantanu6/Multi-Style-Transfer/tree/c5c211847de676596580a8a9afda940ac76abbb1 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, norm
='instance'):
super().__init__()
padding_size = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(padding_size)
self.conv_layer = nn.C... |
DotProductAttention | # 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 DotProductAttention(nn.Module):
"""Dot product attention.
Given a set of vector values, and a vector query, attention is a technique
to compute a weighted sum of the values, dependent on the query.
NOTE: Here we use the terminolo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | tompoek/Listen-Attend-Spell-v2 | DotProductAttention | false | 10,856 | [
"MIT"
] | 0 | aa19543c9d23256a007d6e7a98d9cbc571e89f7f | https://github.com/tompoek/Listen-Attend-Spell-v2/tree/aa19543c9d23256a007d6e7a98d9cbc571e89f7f | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Dot product attention.
Given a set of vector values, and a vector query, attention is a technique
to compute a weighted sum of the values, dependent on the query.
NOTE: Here we use the terminology in Stanford... |
DPRNNCell | # 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
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
class RNNLinear(nn.Linear):
"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
This module is the... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | romovpa/opacus | DPRNNCell | false | 10,857 | [
"Apache-2.0"
] | 0 | 9cda8072e52049a06afba7ab524276bb6613a727 | https://github.com/romovpa/opacus/tree/9cda8072e52049a06afba7ab524276bb6613a727 | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
class RNNLinear(nn.Linear):
"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
This module is the... |
MonotonicMin | # 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 MonotonicMin(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.min(x, dim=1)[0].unsqueeze(1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | tiwalayo/monotonic-mlp | MonotonicMin | false | 10,858 | [
"MIT"
] | 0 | 2f519797a753f7f297fac1365125c6da79f7b890 | https://github.com/tiwalayo/monotonic-mlp/tree/2f519797a753f7f297fac1365125c6da79f7b890 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.min(x, dim=1)[0].unsqueeze(1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
DPGRUCell | # 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
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
class RNNLinear(nn.Linear):
"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
This module is the... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | romovpa/opacus | DPGRUCell | false | 10,859 | [
"Apache-2.0"
] | 0 | 9cda8072e52049a06afba7ab524276bb6613a727 | https://github.com/romovpa/opacus/tree/9cda8072e52049a06afba7ab524276bb6613a727 | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
class RNNLinear(nn.Linear):
"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
This module is the... |
DPLSTMCell | # 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
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
from typing import Tuple
class RNNLinear(nn.Linear):
"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | romovpa/opacus | DPLSTMCell | false | 10,860 | [
"Apache-2.0"
] | 0 | 9cda8072e52049a06afba7ab524276bb6613a727 | https://github.com/romovpa/opacus/tree/9cda8072e52049a06afba7ab524276bb6613a727 | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
from typing import Tuple
class RNNLinear(nn.Linear):
"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b... |
FeatureAssembler | # 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 Optional
import torch.nn as nn
class FeatureAssembler(nn.Module):
def __init__(self, T: 'int', embed_static: 'Optional[FeatureEmbedder]'=
None, embed_dynamic: 'Optional[FeatureEmbedder]'=None) ->None:
super().__init__()
self.T = T
self.embeddings = ... | 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 typing import Optional
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch... | ssmall41/pytorch-ts | FeatureAssembler | false | 10,861 | [
"Apache-2.0",
"MIT"
] | 0 | d0be718d443f8d676640b3aa75a7a154edad5dce | https://github.com/ssmall41/pytorch-ts/tree/d0be718d443f8d676640b3aa75a7a154edad5dce | import torch
from typing import Optional
import torch.nn as nn
class Model(nn.Module):
def __init__(self, T: 'int', embed_static: 'Optional[FeatureEmbedder]'=
None, embed_dynamic: 'Optional[FeatureEmbedder]'=None) ->None:
super().__init__()
self.T = T
self.embeddings = nn.ModuleDi... |
ResidualLayer | # 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 ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, norm
='instance'):
super(ConvLayer, self).__init__()
padding_size = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(padding_size)
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 import triton_helpers
from torch._inductor.runtime.... | suryawanshishantanu6/Multi-Style-Transfer | ResidualLayer | false | 10,862 | [
"MIT"
] | 0 | c5c211847de676596580a8a9afda940ac76abbb1 | https://github.com/suryawanshishantanu6/Multi-Style-Transfer/tree/c5c211847de676596580a8a9afda940ac76abbb1 | import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, norm
='instance'):
super().__init__()
padding_size = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(padding_size)
self.conv_layer = ... |
MonotonicMax | # 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 MonotonicMax(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.cat(tuple(torch.max(i, dim=1)[0].unsqueeze(1) for i in
x), dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | tiwalayo/monotonic-mlp | MonotonicMax | false | 10,863 | [
"MIT"
] | 0 | 2f519797a753f7f297fac1365125c6da79f7b890 | https://github.com/tiwalayo/monotonic-mlp/tree/2f519797a753f7f297fac1365125c6da79f7b890 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.cat(tuple(torch.max(i, dim=1)[0].unsqueeze(1) for i in
x), dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs()... |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
""" VAE encoder """
def __init__(self, img_channels, latent_size):
super(Encoder, self).__init__()
self.latent_size = latent_size
self.img_channels = img_channels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | susanwe/world-models | Encoder | false | 10,864 | [
"MIT"
] | 0 | 0f246a430683e6ab741726df0a97f35830044356 | https://github.com/susanwe/world-models/tree/0f246a430683e6ab741726df0a97f35830044356 | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
""" VAE encoder """
def __init__(self, img_channels, latent_size):
super().__init__()
self.latent_size = latent_size
self.img_channels = img_channels
self.conv1 =... |
LRN | # 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 LRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=True
):
super(LRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:
self.average = nn.AvgPool3d(kernel_size=(local... | 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_... | txsing/dissect | LRN | false | 10,865 | [
"MIT"
] | 0 | 3564605f7be9672c2cfc2ee19ca42225398a6e01 | https://github.com/txsing/dissect/tree/3564605f7be9672c2cfc2ee19ca42225398a6e01 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=True
):
super().__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:
self.average = nn.AvgPool3d(kernel_size=(local_size, ... |
AdaptiveAvgMaxPool2d | # 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 FastGlobalAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastGlobalAvgPool2d, self).__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
return x.view((in_size[0], ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | tenghehan/reid_without_id | AdaptiveAvgMaxPool2d | false | 10,866 | [
"MIT"
] | 0 | d1d0ff273b1ef19fc6da8cbbf210527779b37455 | https://github.com/tenghehan/reid_without_id/tree/d1d0ff273b1ef19fc6da8cbbf210527779b37455 | import torch
import torch.nn as nn
class FastGlobalAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super().__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
return x.view((in_size[0], in_size[1], -1)).mean(dim... |
Decoder | # 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.nn as nn
import torch.nn.functional as F
class Decoder(nn.Module):
""" VAE decoder """
def __init__(self, img_channels, latent_size):
super(Decoder, self).__init__()
self.latent_size = latent_size
self.img_channels = img_channels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | susanwe/world-models | Decoder | false | 10,867 | [
"MIT"
] | 0 | 0f246a430683e6ab741726df0a97f35830044356 | https://github.com/susanwe/world-models/tree/0f246a430683e6ab741726df0a97f35830044356 | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
""" VAE decoder """
def __init__(self, img_channels, latent_size):
super().__init__()
self.latent_size = latent_size
self.img_channels = img_channels
self.fc1 = n... |
GeneralizedMeanPooling | # 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 GeneralizedMeanPooling(nn.Module):
"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
- At p = infinity, one gets Max Pooling
- At p = 1... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | tenghehan/reid_without_id | GeneralizedMeanPooling | false | 10,868 | [
"MIT"
] | 0 | d1d0ff273b1ef19fc6da8cbbf210527779b37455 | https://github.com/tenghehan/reid_without_id/tree/d1d0ff273b1ef19fc6da8cbbf210527779b37455 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
- At p = infinity, one gets Max Pooling
- At p = 1, one gets Averag... |
LearnedPositionalEmbedding | # 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 LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
Padding ids are ignored by either offsetting based on padding_idx
or by setting padding_idx to None and ensuring t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | sohrabi1/esm | LearnedPositionalEmbedding | false | 10,869 | [
"MIT"
] | 0 | e1f60a66b5c351d9d0011926549890b6744903c1 | https://github.com/sohrabi1/esm/tree/e1f60a66b5c351d9d0011926549890b6744903c1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
Padding ids are ignored by either offsetting based on padding_idx
or by setting padding_idx to None and ensuring that the appropriate
... |
LogSTFTMagnitudeLoss | # 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.utils.data
class LogSTFTMagnitudeLoss(torch.nn.Module):
"""Log STFT magnitude loss module."""
def __init__(self):
"""Initilize los STFT magnitude loss module."""
super(LogSTFTMagnitudeLoss, self).__init__()
def forward(self, x_mag... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | tebin/Fre-GAN-pytorch | LogSTFTMagnitudeLoss | false | 10,870 | [
"MIT"
] | 0 | e2f51317ae3953f10b8a0d112fc14991a02ebe91 | https://github.com/tebin/Fre-GAN-pytorch/tree/e2f51317ae3953f10b8a0d112fc14991a02ebe91 | import torch
import torch.nn.functional as F
import torch.utils.data
class Model(torch.nn.Module):
"""Log STFT magnitude loss module."""
def __init__(self):
"""Initilize los STFT magnitude loss module."""
super().__init__()
def forward(self, x_mag, y_mag):
"""Calculate forward pr... |
SpectralConvergengeLoss | # 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
class SpectralConvergengeLoss(torch.nn.Module):
"""Spectral convergence loss module."""
def __init__(self):
"""Initilize spectral convergence loss module."""
super(SpectralConvergengeLoss, self).__init__()
def forward(self, x_mag, y_mag):
"""C... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
asse... | tebin/Fre-GAN-pytorch | SpectralConvergengeLoss | false | 10,871 | [
"MIT"
] | 0 | e2f51317ae3953f10b8a0d112fc14991a02ebe91 | https://github.com/tebin/Fre-GAN-pytorch/tree/e2f51317ae3953f10b8a0d112fc14991a02ebe91 | import torch
import torch.utils.data
class Model(torch.nn.Module):
"""Spectral convergence loss module."""
def __init__(self):
"""Initilize spectral convergence loss module."""
super().__init__()
def forward(self, x_mag, y_mag):
"""Calculate forward propagation.
Args:
... |
ClipGlobalAvgPool2d | # 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 FastGlobalAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastGlobalAvgPool2d, self).__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
return x.view((in_size[0], ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | tenghehan/reid_without_id | ClipGlobalAvgPool2d | false | 10,872 | [
"MIT"
] | 0 | d1d0ff273b1ef19fc6da8cbbf210527779b37455 | https://github.com/tenghehan/reid_without_id/tree/d1d0ff273b1ef19fc6da8cbbf210527779b37455 | import torch
import torch.nn as nn
class FastGlobalAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super().__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
return x.view((in_size[0], in_size[1], -1)).mean(dim... |
AttentionLayer | # 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 AttentionLayer(nn.Module):
def __init__(self, hidden_size):
super(AttentionLayer, self).__init__()
self.hidden_size = hidden_size
def dot_product_attention(self, hidden, encoder_output):
return torch.sum(hidden ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | u7javed/AI-Chatbot | AttentionLayer | false | 10,873 | [
"MIT"
] | 0 | d86916537e7b0b9a45f11d0fe0367fe9f66721e7 | https://github.com/u7javed/AI-Chatbot/tree/d86916537e7b0b9a45f11d0fe0367fe9f66721e7 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.hidden_size = hidden_size
def dot_product_attention(self, hidden, encoder_output):
return torch.sum(hidden * encoder_output, dim=2)
... |
NormScaleFeature | # 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 NormScaleFeature(nn.Module):
def __init__(self, init_value=1):
super().__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
magnitudes = 1e-06 + torch.sqrt(torch.sum(input ** 2, axis=1,
kee... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | uncbiag/FeatureMapICON | NormScaleFeature | false | 10,874 | [
"Apache-2.0"
] | 0 | 04160d0ce4e8f7615e1c59a1be5c6b8340b5b6e5 | https://github.com/uncbiag/FeatureMapICON/tree/04160d0ce4e8f7615e1c59a1be5c6b8340b5b6e5 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, init_value=1):
super().__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
magnitudes = 1e-06 + torch.sqrt(torch.sum(input ** 2, axis=1,
keepdims=True)... |
Scaled_Dot_Product_Attention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Scaled_Dot_Product_Attention(nn.Module):
"""Scaled Dot-Product Attention """
def __init__(self):
super(Scaled_Dot_Product_Attention, self).__init__()
def forward(self, Q, K, V, scale=None):
"""
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | tianjiansmile/Chinese-Text-Classification-Pytorch | Scaled_Dot_Product_Attention | false | 10,875 | [
"MIT"
] | 0 | 05cc211b161f61e6bb32ab185dadcffec2f5b5de | https://github.com/tianjiansmile/Chinese-Text-Classification-Pytorch/tree/05cc211b161f61e6bb32ab185dadcffec2f5b5de | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Scaled Dot-Product Attention """
def __init__(self):
super().__init__()
def forward(self, Q, K, V, scale=None):
"""
Args:
Q: [batch_size, len_Q, dim_Q]
K: [batch_... |
UNETMax | # 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 UNETMax(nn.Module):
"""UNET Without concatenation during decoding"""
def __init__(self):
super(UNETMax, self).__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | quenting44/semantic_segmentation | UNETMax | false | 10,876 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a | import torch
from torch import nn
class Model(nn.Module):
"""UNET Without concatenation during decoding"""
def __init__(self):
super().__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(in_channe... |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from abc import ABC
import torch.nn as nn
class PositionwiseFeedForward(nn.Module, ABC):
def __init__(self, d_in, d_hidden, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hidden)
self.w_2 = nn.Linear(d_hidden, d_in)
self.layer_norm = nn.LayerNorm(d_in, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | superMC5657/transformer | PositionwiseFeedForward | false | 10,877 | [
"MIT"
] | 0 | b9d9ca3a5f307f6587330a8235e8d5a2a3650510 | https://github.com/superMC5657/transformer/tree/b9d9ca3a5f307f6587330a8235e8d5a2a3650510 | import torch
from abc import ABC
import torch.nn as nn
class Model(nn.Module, ABC):
def __init__(self, d_in, d_hidden, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hidden)
self.w_2 = nn.Linear(d_hidden, d_in)
self.layer_norm = nn.LayerNorm(d_in, eps=1e-06)
... |
GCN | # 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.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.modules.loss
class GraphConvolution1(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/16... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | thilinicooray/pygcn | GCN | false | 10,878 | [
"MIT"
] | 0 | a7d4f12f31898a3b386736215a6d5fe5cb857387 | https://github.com/thilinicooray/pygcn/tree/a7d4f12f31898a3b386736215a6d5fe5cb857387 | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.modules.loss
class GraphConvolution1(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/16... |
LocalVariation | # 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 LocalVariation(nn.Module):
"""Layer to compute the LocalVariation of an image
"""
def __init__(self, k_size=5):
super(LocalVariation, self).__init__()
self.mu_x_pool = nn.AvgPool2d(k_size, 1)
self.mu_y_pool = nn.AvgPool2d(k_size, 1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | shlomi-amitai/myDIFFNet | LocalVariation | false | 10,879 | [
"MIT"
] | 0 | 39dead457f10c82caae2a12ea152f2339188014c | https://github.com/shlomi-amitai/myDIFFNet/tree/39dead457f10c82caae2a12ea152f2339188014c | import torch
import torch.nn as nn
class Model(nn.Module):
"""Layer to compute the LocalVariation of an image
"""
def __init__(self, k_size=5):
super().__init__()
self.mu_x_pool = nn.AvgPool2d(k_size, 1)
self.mu_y_pool = nn.AvgPool2d(k_size, 1)
self.sig_x_pool = nn.AvgPoo... |
Project3D | # 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 Project3D(nn.Module):
"""Layer which projects 3D points into a camera with intrinsics K and at position T
"""
def __init__(self, batch_size, height, width, eps=1e-07):
super(Project3D, self).__init__()
self.batch_size = batch_size
self.heig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | shlomi-amitai/myDIFFNet | Project3D | false | 10,880 | [
"MIT"
] | 0 | 39dead457f10c82caae2a12ea152f2339188014c | https://github.com/shlomi-amitai/myDIFFNet/tree/39dead457f10c82caae2a12ea152f2339188014c | import torch
import torch.nn as nn
class Model(nn.Module):
"""Layer which projects 3D points into a camera with intrinsics K and at position T
"""
def __init__(self, batch_size, height, width, eps=1e-07):
super().__init__()
self.batch_size = batch_size
self.height = height
... |
SSIM | # 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 SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
self.sig_x_pool = nn.AvgPool2d(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | shlomi-amitai/myDIFFNet | SSIM | false | 10,881 | [
"MIT"
] | 0 | 39dead457f10c82caae2a12ea152f2339188014c | https://github.com/shlomi-amitai/myDIFFNet/tree/39dead457f10c82caae2a12ea152f2339188014c | import torch
import torch.nn as nn
class Model(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self):
super().__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
self.sig_x_pool = nn.AvgPool2d(3, 1)
... |
Net | # 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.optim
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 16, 3, 1)
self.conv2 = nn.Conv2d(16, 40, 2, 1)
self.fc1 = nn.Linear(3 * 3 * 40, 400)
self.f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | tkhkaeio/PyTorch-GAN | Net | false | 10,882 | [
"MIT"
] | 0 | 565c67cae168a42c6822c787562a1f7a5b35a2ab | https://github.com/tkhkaeio/PyTorch-GAN/tree/565c67cae168a42c6822c787562a1f7a5b35a2ab | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 16, 3, 1)
self.conv2 = nn.Conv2d(16, 40, 2, 1)
self.fc1 = nn.Linear(3 * 3 * 40, 400)
self.fc2 = nn... |
CoAttention | # 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 CoAttention(nn.Module):
"""
CoAttention encoder
in Dynamic Coattention Networks For Question Answering (https://arxiv.org/abs/1611.01604)
check the Figure 2 in paper
* Args:
embed_dim: the number of input embedd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | srlee-ai/claf | CoAttention | false | 10,883 | [
"MIT"
] | 0 | 89b3e5c5ec0486886876ea3bac381508c6a6bf58 | https://github.com/srlee-ai/claf/tree/89b3e5c5ec0486886876ea3bac381508c6a6bf58 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
CoAttention encoder
in Dynamic Coattention Networks For Question Answering (https://arxiv.org/abs/1611.01604)
check the Figure 2 in paper
* Args:
embed_dim: the number of input embedding di... |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class PointwiseConv(nn.Module):
"""
Pointwise Convolution (1x1 Conv)
Convolution 1 Dimension (Faster version)
(cf. https://github.com/huggingface/pytorch-openai-transformer-lm/blob/ eafc28abdfadfa0732f03a0fc65805c5bfb2ffe7/mode... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | srlee-ai/claf | PositionwiseFeedForward | false | 10,884 | [
"MIT"
] | 0 | 89b3e5c5ec0486886876ea3bac381508c6a6bf58 | https://github.com/srlee-ai/claf/tree/89b3e5c5ec0486886876ea3bac381508c6a6bf58 | import torch
import torch.nn as nn
import torch.nn.functional as F
class PointwiseConv(nn.Module):
"""
Pointwise Convolution (1x1 Conv)
Convolution 1 Dimension (Faster version)
(cf. https://github.com/huggingface/pytorch-openai-transformer-lm/blob/ eafc28abdfadfa0732f03a0fc65805c5bfb2ffe7/mode... |
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):
"""
Layer Normalization
(https://arxiv.org/abs/1607.06450)
"""
def __init__(self, normalized_shape, eps=1e-05):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(normalized_shape))
self.bet... | 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_... | srlee-ai/claf | LayerNorm | false | 10,885 | [
"MIT"
] | 0 | 89b3e5c5ec0486886876ea3bac381508c6a6bf58 | https://github.com/srlee-ai/claf/tree/89b3e5c5ec0486886876ea3bac381508c6a6bf58 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Layer Normalization
(https://arxiv.org/abs/1607.06450)
"""
def __init__(self, normalized_shape, eps=1e-05):
super().__init__()
self.gamma = nn.Parameter(torch.ones(normalized_shape))
self.beta = nn.Parameter(to... |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from abc import ABC
import torch.nn as nn
from torch import matmul
class ScaledDotProductAttention(nn.Module, ABC):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | superMC5657/transformer | MultiHeadAttention | false | 10,886 | [
"MIT"
] | 0 | b9d9ca3a5f307f6587330a8235e8d5a2a3650510 | https://github.com/superMC5657/transformer/tree/b9d9ca3a5f307f6587330a8235e8d5a2a3650510 | import torch
from abc import ABC
import torch.nn as nn
from torch import matmul
class ScaledDotProductAttention(nn.Module, ABC):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.... |
Multi_Head_Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Scaled_Dot_Product_Attention(nn.Module):
"""Scaled Dot-Product Attention """
def __init__(self):
super(Scaled_Dot_Product_Attention, self).__init__()
def forward(self, Q, K, V, scale=None):
"""
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | tianjiansmile/Chinese-Text-Classification-Pytorch | Multi_Head_Attention | false | 10,887 | [
"MIT"
] | 0 | 05cc211b161f61e6bb32ab185dadcffec2f5b5de | https://github.com/tianjiansmile/Chinese-Text-Classification-Pytorch/tree/05cc211b161f61e6bb32ab185dadcffec2f5b5de | import torch
import torch.nn as nn
import torch.nn.functional as F
class Scaled_Dot_Product_Attention(nn.Module):
"""Scaled Dot-Product Attention """
def __init__(self):
super().__init__()
def forward(self, Q, K, V, scale=None):
"""
Args:
Q: [batch_size, len_Q, dim_Q]... |
Bilinear | # 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 Bilinear(nn.Module):
def __init__(self, dim_left, dim_right, dim_out):
super().__init__()
self.dim_left = dim_left
self.dim_right = dim_right
self.dim_out = dim_out
self.bilinear = nn.Bilinear(dim_left, dim_right, dim_out)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | tpimentelms/dep-parser | Bilinear | false | 10,888 | [
"MIT"
] | 0 | be622cdd9a8b0ba85a28c39129ae2cdbfef03901 | https://github.com/tpimentelms/dep-parser/tree/be622cdd9a8b0ba85a28c39129ae2cdbfef03901 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dim_left, dim_right, dim_out):
super().__init__()
self.dim_left = dim_left
self.dim_right = dim_right
self.dim_out = dim_out
self.bilinear = nn.Bilinear(dim_left, dim_right, dim_out)
self... |
EncoderLayer | # 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 abc import ABC
import torch.nn as nn
from torch import matmul
class ScaledDotProductAttention(nn.Module, ABC):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | superMC5657/transformer | EncoderLayer | false | 10,889 | [
"MIT"
] | 0 | b9d9ca3a5f307f6587330a8235e8d5a2a3650510 | https://github.com/superMC5657/transformer/tree/b9d9ca3a5f307f6587330a8235e8d5a2a3650510 | import torch
from abc import ABC
import torch.nn as nn
from torch import matmul
class ScaledDotProductAttention(nn.Module, ABC):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.... |
Conv | # 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 Conv(nn.Module):
def __init__(self, chn_in, chn_out, ker_sz=3):
super().__init__()
self.c = nn.Conv2d(chn_in, chn_out, ker_sz, padding=ker_sz // 2,
padding_mode='circular', bias=False)
self.a = nn.ReLU()
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | tuxedcat/A2C | Conv | false | 10,890 | [
"Apache-2.0"
] | 0 | 4a6686af05667f8760f2731f184e1845a2d11c6f | https://github.com/tuxedcat/A2C/tree/4a6686af05667f8760f2731f184e1845a2d11c6f | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, chn_in, chn_out, ker_sz=3):
super().__init__()
self.c = nn.Conv2d(chn_in, chn_out, ker_sz, padding=ker_sz // 2,
padding_mode='circular', bias=False)
self.a = nn.ReLU()
def forward(self, x):
... |
BiAvg | # 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 BiAvg(nn.AvgPool1d):
def forward(self, x):
x = x.transpose(1, 2)
x = super().forward(x)
return x.transpose(1, 2)
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'kernel_size': 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | urchade/urchade-byte_search | BiAvg | false | 10,891 | [
"MIT"
] | 0 | 5155adb1550dcab873db4e9b124c42da24c99b8e | https://github.com/urchade/urchade-byte_search/tree/5155adb1550dcab873db4e9b124c42da24c99b8e | import torch
from torch import nn
class Model(nn.AvgPool1d):
def forward(self, x):
x = x.transpose(1, 2)
x = super().forward(x)
return x.transpose(1, 2)
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [4]
|
DecoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from abc import ABC
import torch.nn as nn
from torch import matmul
class ScaledDotProductAttention(nn.Module, ABC):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | superMC5657/transformer | DecoderLayer | false | 10,892 | [
"MIT"
] | 0 | b9d9ca3a5f307f6587330a8235e8d5a2a3650510 | https://github.com/superMC5657/transformer/tree/b9d9ca3a5f307f6587330a8235e8d5a2a3650510 | import torch
from abc import ABC
import torch.nn as nn
from torch import matmul
class ScaledDotProductAttention(nn.Module, ABC):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.... |
Biaffine | # 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 Biaffine(nn.Module):
def __init__(self, dim_left, dim_right):
super().__init__()
self.dim_left = dim_left
self.dim_right = dim_right
self.matrix = nn.Parameter(torch.Tensor(dim_left, dim_right))
self.bias = nn.Parameter(torch.Tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | tpimentelms/dep-parser | Biaffine | false | 10,893 | [
"MIT"
] | 0 | be622cdd9a8b0ba85a28c39129ae2cdbfef03901 | https://github.com/tpimentelms/dep-parser/tree/be622cdd9a8b0ba85a28c39129ae2cdbfef03901 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dim_left, dim_right):
super().__init__()
self.dim_left = dim_left
self.dim_right = dim_right
self.matrix = nn.Parameter(torch.Tensor(dim_left, dim_right))
self.bias = nn.Parameter(torch.Tensor(1)... |
SelfAttn | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class SelfAttn(nn.Module):
"""
self-attention with learnable parameters
"""
def __init__(self, dhid):
super().__init__()
self.scorer = nn.Linear(dhid, 1)
def forward(self, inp):
scores = F.softmax(self.scor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | uyeongkim/moca | SelfAttn | false | 10,894 | [
"MIT"
] | 0 | 8a5870898b6d59258ce1064bab440b7e8107e9b4 | https://github.com/uyeongkim/moca/tree/8a5870898b6d59258ce1064bab440b7e8107e9b4 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
self-attention with learnable parameters
"""
def __init__(self, dhid):
super().__init__()
self.scorer = nn.Linear(dhid, 1)
def forward(self, inp):
scores = F.softmax(self.scorer(... |
SeqAttnMatch | # 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 SeqAttnMatch(nn.Module):
"""
Given sequences X and Y, match sequence Y to each element in X.
* o_i = sum(alpha_j * y_j) for i in X
* alpha_j = softmax(y_j * x_i)
"""
def __init__(self, embed_dim, identity=False):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | srlee-ai/claf | SeqAttnMatch | false | 10,895 | [
"MIT"
] | 0 | 89b3e5c5ec0486886876ea3bac381508c6a6bf58 | https://github.com/srlee-ai/claf/tree/89b3e5c5ec0486886876ea3bac381508c6a6bf58 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Given sequences X and Y, match sequence Y to each element in X.
* o_i = sum(alpha_j * y_j) for i in X
* alpha_j = softmax(y_j * x_i)
"""
def __init__(self, embed_dim, identity=False):
super(... |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd
class Critic(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Critic, self).__init__()
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | vivekagra/Biplane-Quadrotor | Critic | false | 10,896 | [
"BSD-3-Clause"
] | 0 | afe69216494842f5bfe16cbcc0cdcc6ef0de7769 | https://github.com/vivekagra/Biplane-Quadrotor/tree/afe69216494842f5bfe16cbcc0cdcc6ef0de7769 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd
class Model(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size... |
Simple_nn | # 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
class Simple_nn(torch.nn.Module):
def __init__(self, dims_in, hidden):
super(Simple_nn, self).__init__()
self.linear1 = torch.nn.Linear(dims_in, hidden)
self.linear2 = torch.nn.Linear(hidden, 2)
self.output = torch.nn.LogSoftmax()
def forward(self, x):
hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | urbanriskmap/timeseries-analysis | Simple_nn | false | 10,897 | [
"MIT"
] | 0 | 6b9a8d1a916ff784cb0de93d6997cd072d1ca6ae | https://github.com/urbanriskmap/timeseries-analysis/tree/6b9a8d1a916ff784cb0de93d6997cd072d1ca6ae | import torch
class Model(torch.nn.Module):
def __init__(self, dims_in, hidden):
super().__init__()
self.linear1 = torch.nn.Linear(dims_in, hidden)
self.linear2 = torch.nn.Linear(hidden, 2)
self.output = torch.nn.LogSoftmax()
def forward(self, x):
hidden_activation = s... |
model | # 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 model(nn.Module):
def __init__(self, input_shape=28 * 28, nr_classes=10):
super(model, self).__init__()
self.input_shape = input_shape
self.fc1 = nn.Linear(input_shape, 200)
self.fc2 = nn.Linear(200, nr_classes)
self.relu = nn.ReLU(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | vishal-keshav/pytorch-project-template | model | false | 10,898 | [
"MIT"
] | 0 | 526dd5b1036ed9cf592172301a2c85e8425cd154 | https://github.com/vishal-keshav/pytorch-project-template/tree/526dd5b1036ed9cf592172301a2c85e8425cd154 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_shape=28 * 28, nr_classes=10):
super().__init__()
self.input_shape = input_shape
self.fc1 = nn.Linear(input_shape, 200)
self.fc2 = nn.Linear(200, nr_classes)
self.relu = nn.ReLU()
def ... |
ThreeNet | # 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 ThreeNet(nn.Module):
"""
A network with three layers. This is used for testing a network with more
than one operation. The network has a convolution layer followed by two
fully connected layers.
"""
def __init__(self, input_dim: 'int', conv_dim: 'int',... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | synthara/M-SFV-SyntharaFVcore | ThreeNet | false | 10,899 | [
"Apache-2.0"
] | 0 | b4d2167a110aaecf3df442f58793ca2cb7b028ba | https://github.com/synthara/M-SFV-SyntharaFVcore/tree/b4d2167a110aaecf3df442f58793ca2cb7b028ba | import torch
import torch.nn as nn
class Model(nn.Module):
"""
A network with three layers. This is used for testing a network with more
than one operation. The network has a convolution layer followed by two
fully connected layers.
"""
def __init__(self, input_dim: 'int', conv_dim: 'int', li... |
SmallConvNet | # 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 Tuple
import torch.nn as nn
from numpy import prod
class SmallConvNet(nn.Module):
"""
A network with three conv layers. This is used for testing convolution
layers for activation count.
"""
def __init__(self, input_dim: 'int') ->None:
super(SmallConvNet, se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from typing import Tuple
import torch.nn as nn
from numpy import prod
assert_siz... | synthara/M-SFV-SyntharaFVcore | SmallConvNet | false | 10,900 | [
"Apache-2.0"
] | 0 | b4d2167a110aaecf3df442f58793ca2cb7b028ba | https://github.com/synthara/M-SFV-SyntharaFVcore/tree/b4d2167a110aaecf3df442f58793ca2cb7b028ba | import torch
from typing import Tuple
import torch.nn as nn
from numpy import prod
class Model(nn.Module):
"""
A network with three conv layers. This is used for testing convolution
layers for activation count.
"""
def __init__(self, input_dim: 'int') ->None:
super().__init__()
co... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.