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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
RegL1 | # 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 RegL1(nn.Module):
"""
Run Regression with L1
"""
def __init__(self, n_input, n_output):
super(RegL1, self).__init__()
self.linear = nn.Linear(n_input, n_output, bias=True)
def forward(self, x, training=True):
self.training = traini... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | rmporsch/ML_genetic_risk | RegL1 | false | 4,197 | [
"MIT"
] | 0 | 4e1a0510c94260e69f93639ff4104c5f85080d9f | https://github.com/rmporsch/ML_genetic_risk/tree/4e1a0510c94260e69f93639ff4104c5f85080d9f | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Run Regression with L1
"""
def __init__(self, n_input, n_output):
super().__init__()
self.linear = nn.Linear(n_input, n_output, bias=True)
def forward(self, x, training=True):
self.training = training
... |
DecoderRNN | # 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 DecoderRNN(nn.Module):
def __init__(self, T, d):
super().__init__()
self.T = T
self.d = d
self.W = nn.Linear(d, d)
self.U = nn.Linear(d, d)
self.V = nn.Linear(d, d)
self.b = nn.Paramete... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rish-16/SHA-RNN | DecoderRNN | false | 4,198 | [
"MIT"
] | 0 | 08c701396217f0b645de043963ff8ec4bf27e835 | https://github.com/rish-16/SHA-RNN/tree/08c701396217f0b645de043963ff8ec4bf27e835 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, T, d):
super().__init__()
self.T = T
self.d = d
self.W = nn.Linear(d, d)
self.U = nn.Linear(d, d)
self.V = nn.Linear(d, d)
self.b = nn.Parameter(tor... |
SpatialAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch
import torch.nn as nn
class SpatialAttention(nn.Module):
def __init__(self):
super(SpatialAttention, self).__init__()
self.conv1 = nn.Conv2d(in_channels=2, out_channels=1, kernel_size=3,
padding=1, bias=False)
self.sigmoid = 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
import torch.utils.data
impor... | robvincen/robot_gradet | SpatialAttention | false | 4,199 | [
"BSD-3-Clause"
] | 0 | a39e3c772c72806dfc99e4d24d8787e0d1bdeef5 | https://github.com/robvincen/robot_gradet/tree/a39e3c772c72806dfc99e4d24d8787e0d1bdeef5 | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=2, out_channels=1, kernel_size=3,
padding=1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self,... |
QNet | # 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 QNet(nn.Module):
def __init__(self, in_size: 'int', out_size: 'int'):
super(QNet, self).__init__()
self.fc1 = nn.Linear(in_size, 16)
self.fc_out = nn.Linear(16, out_size)
self.act = nn.LeakyReLU()
def forward(self, x):
o1 = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | rosebin/gymlab | QNet | false | 4,200 | [
"BSD-3-Clause"
] | 0 | de97fc24e0ddf5e328a2aa732cc339b2371d92d1 | https://github.com/rosebin/gymlab/tree/de97fc24e0ddf5e328a2aa732cc339b2371d92d1 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_size: 'int', out_size: 'int'):
super().__init__()
self.fc1 = nn.Linear(in_size, 16)
self.fc_out = nn.Linear(16, out_size)
self.act = nn.LeakyReLU()
def forward(self, x):
o1 = self.act(sel... |
L0Linear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Variable
import logging as lg
def hard_sigmoid(x):
"""Hard Sigmoid function."""
return torch.min(torch.max(x, torch.zeros_like(x)), torch.ones_like(x))
class _L0Norm(nn.Module):
"""L0 no... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | rmporsch/ML_genetic_risk | L0Linear | false | 4,201 | [
"MIT"
] | 0 | 4e1a0510c94260e69f93639ff4104c5f85080d9f | https://github.com/rmporsch/ML_genetic_risk/tree/4e1a0510c94260e69f93639ff4104c5f85080d9f | import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Variable
import logging as lg
def hard_sigmoid(x):
"""Hard Sigmoid function."""
return torch.min(torch.max(x, torch.zeros_like(x)), torch.ones_like(x))
class _L0Norm(nn.Module):
"""L0 no... |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class QNetwork(nn.Module):
def __init__(self, state_size, action_size, seed):
super(QNetwork, self).__init__()
self.seed = torch.manual_seed(seed)
self.fc1 = nn.Linear(state_size, 128)
self.fc2 = nn.Linear(128, 64)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | royveshovda/deep-reinforcement-learning | QNetwork | false | 4,202 | [
"MIT"
] | 0 | 64ba7ef5ab44f095b7e8b29f6c4ff1585025981a | https://github.com/royveshovda/deep-reinforcement-learning/tree/64ba7ef5ab44f095b7e8b29f6c4ff1585025981a | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, state_size, action_size, seed):
super().__init__()
self.seed = torch.manual_seed(seed)
self.fc1 = nn.Linear(state_size, 128)
self.fc2 = nn.Linear(128, 64)
self.fc3... |
Discriminator | # 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 Discriminator(nn.Module):
def __init__(self, state_dim, action_dim):
super(Discriminator, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 500)
self.l2 = nn.Linear(500, 300)
self.l3 = nn.Linear(300, 300)
self.l4 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | rortiz9/meleeml | Discriminator | false | 4,203 | [
"MIT"
] | 0 | 9be4bf53a377dfb46dbb3b51f102f1bffc0124d2 | https://github.com/rortiz9/meleeml/tree/9be4bf53a377dfb46dbb3b51f102f1bffc0124d2 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.l1 = nn.Linear(state_dim + action_dim, 500)
self.l2 = nn.Linear(500, 300)
self.l3 = nn.Linear(300, 300)
self.l4 = nn.Linear(300, 1)
def forwar... |
RelationalTransformerEncoderLayer | # 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 warnings
from torch import Tensor
from torch.nn import TransformerEncoderLayer
from torch.nn.functional import *
from torch.nn.modules.activation import MultiheadAttention
from torch.nn.modules.activation import xavier_uniform_
from torch.nn.modules.activation import xavier_normal_
from torch.nn.mod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | mfk3138/jiant | RelationalTransformerEncoderLayer | false | 4,204 | [
"MIT"
] | 0 | 6e67ff1ecb1bb98533c1019a86af4ad2c04c6a64 | https://github.com/mfk3138/jiant/tree/6e67ff1ecb1bb98533c1019a86af4ad2c04c6a64 | import torch
import warnings
from torch import Tensor
from torch.nn import TransformerEncoderLayer
from torch.nn.functional import *
from torch.nn.modules.activation import MultiheadAttention
from torch.nn.modules.activation import xavier_uniform_
from torch.nn.modules.activation import xavier_normal_
from torch.nn.mod... |
LxmertAttentionOutput | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class LxmertAttentionOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | rsgit95/med_kg_txt_multimodal | LxmertAttentionOutput | false | 4,205 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
self.d... |
Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch as th
from torch import nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
th... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | q5628077/Transformer-in-RL | Block | false | 4,206 | [
"MIT"
] | 0 | 14679656779a372d91d9fbd89bd802b5ff34c200 | https://github.com/q5628077/Transformer-in-RL/tree/14679656779a372d91d9fbd89bd802b5ff34c200 | import torch
import torch as th
from torch import nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
th... |
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, state_dim, action_dim):
super(Net, self).__init__()
fc1_dim = 32
fc2_dim = 64
fc3_dim = 128
self.fc1 = nn.Linear(state_dim, fc1_dim)
self.fc2 = nn.Linear(fc1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | ronekko/study_reinforcement_learning | Net | false | 4,207 | [
"MIT"
] | 0 | ef5201e3eae69c20f29b7f176b5a6de7ecdb856a | https://github.com/ronekko/study_reinforcement_learning/tree/ef5201e3eae69c20f29b7f176b5a6de7ecdb856a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
fc1_dim = 32
fc2_dim = 64
fc3_dim = 128
self.fc1 = nn.Linear(state_dim, fc1_dim)
self.fc2 = nn.Linear(fc1_dim, f... |
IReLU | # 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 math
import torch
class IReLU(torch.nn.Module):
__constants__ = ['negative_slope', 'positive_slope']
negative_slope: 'float'
positive_slope: 'float'
def __init__(self, negative_slope=math.tan(math.pi / 8), positive_slope
=math.tan(3 * math.pi / 8)):
super(IReLU, self).__init__(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | rupumped/DFL | IReLU | false | 4,208 | [
"BSD-3-Clause"
] | 0 | a4e4d96b7ce7522cf7fee3c2cfdbb54eb7a473f2 | https://github.com/rupumped/DFL/tree/a4e4d96b7ce7522cf7fee3c2cfdbb54eb7a473f2 | import math
import torch
class Model(torch.nn.Module):
__constants__ = ['negative_slope', 'positive_slope']
negative_slope: 'float'
positive_slope: 'float'
def __init__(self, negative_slope=math.tan(math.pi / 8), positive_slope
=math.tan(3 * math.pi / 8)):
super().__init__()
s... |
Affine | # 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 Affine(nn.Module):
def __init__(self, channel):
super().__init__()
self.g = nn.Parameter(torch.ones(1, 1, channel))
self.b = nn.Parameter(torch.zeros(1, 1, channel))
def forward(self, x):
return x * self.g + self.b
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | rushirajsherlocked/External-Attention-pytorch | Affine | false | 4,209 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, channel):
super().__init__()
self.g = nn.Parameter(torch.ones(1, 1, channel))
self.b = nn.Parameter(torch.zeros(1, 1, channel))
def forward(self, x):
return x * self.g + self.b
def get_inputs():
... |
ECAAttention | # 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
from torch.nn import init
class ECAAttention(nn.Module):
def __init__(self, kernel_size=3):
super().__init__()
self.gap = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(
kernel_size - 1) // 2)
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 import nn
from torch.nn import init
assert_size_stride = torch._C._dy... | rushirajsherlocked/External-Attention-pytorch | ECAAttention | false | 4,210 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import torch
from torch import nn
from torch.nn import init
class Model(nn.Module):
def __init__(self, kernel_size=3):
super().__init__()
self.gap = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(
kernel_size - 1) // 2)
self.sigmo... |
GTXAttentionOutput | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class GTXAttentionOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | rsgit95/med_kg_txt_multimodal | GTXAttentionOutput | false | 4,211 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
self.d... |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 400)
self.l2 = nn.Linear(400, 200)
self.l3 = nn.Linear(200, action_dim)
def forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | rortiz9/meleeml | Actor | false | 4,212 | [
"MIT"
] | 0 | 9be4bf53a377dfb46dbb3b51f102f1bffc0124d2 | https://github.com/rortiz9/meleeml/tree/9be4bf53a377dfb46dbb3b51f102f1bffc0124d2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.l1 = nn.Linear(state_dim, 400)
self.l2 = nn.Linear(400, 200)
self.l3 = nn.Linear(200, action_dim)
def forward(self, x)... |
PolicyNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.distributions import Normal
class PolicyNetwork(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim, init_w=0.003,
log_std_min=-20, log_std_max=2):
super(PolicyNetwork, self).__init__()
self.lo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | rtharungowda/Soft-Actor-Critic-Pytorch | PolicyNetwork | false | 4,213 | [
"MIT"
] | 0 | 0d2c20c6cfd4e578e0b7cff4525ddf0bc956812f | https://github.com/rtharungowda/Soft-Actor-Critic-Pytorch/tree/0d2c20c6cfd4e578e0b7cff4525ddf0bc956812f | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.distributions import Normal
class Model(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim, init_w=0.003,
log_std_min=-20, log_std_max=2):
super().__init__()
self.log_std_min = log_std_min
... |
Depth_Pointwise_Conv1d | # 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 Depth_Pointwise_Conv1d(nn.Module):
def __init__(self, in_ch, out_ch, k):
super().__init__()
if k == 1:
self.depth_conv = nn.Identity()
else:
self.depth_conv = nn.Conv1d(in_channels=in_ch, out_channels=
in_ch, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | rushirajsherlocked/External-Attention-pytorch | Depth_Pointwise_Conv1d | false | 4,214 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, in_ch, out_ch, k):
super().__init__()
if k == 1:
self.depth_conv = nn.Identity()
else:
self.depth_conv = nn.Conv1d(in_channels=in_ch, out_channels=
in_ch, kernel_size=k, gr... |
ExternalAttention | # 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
from torch.nn import init
class ExternalAttention(nn.Module):
def __init__(self, d_model, S=64):
super().__init__()
self.mk = nn.Linear(d_model, S, bias=False)
self.mv = nn.Linear(S, d_model, bias=False)
self.softmax = nn.Softmax(dim=1)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rushirajsherlocked/External-Attention-pytorch | ExternalAttention | false | 4,215 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import torch
from torch import nn
from torch.nn import init
class Model(nn.Module):
def __init__(self, d_model, S=64):
super().__init__()
self.mk = nn.Linear(d_model, S, bias=False)
self.mv = nn.Linear(S, d_model, bias=False)
self.softmax = nn.Softmax(dim=1)
self.init_weig... |
SpatialAttention | # 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 SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super().__init__()
self.conv = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=
kernel_size // 2)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
max_result,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | rushirajsherlocked/External-Attention-pytorch | SpatialAttention | false | 4,216 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, kernel_size=7):
super().__init__()
self.conv = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=
kernel_size // 2)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
max_result, _ = torch.... |
GTXSelfAttentionLayer | # 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
class GTXAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | rsgit95/med_kg_txt_multimodal | GTXSelfAttentionLayer | false | 4,217 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class GTXAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidde... |
MlpBlock | # 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 MlpBlock(nn.Module):
def __init__(self, input_dim, mlp_dim=512):
super().__init__()
self.fc1 = nn.Linear(input_dim, mlp_dim)
self.gelu = nn.GELU()
self.fc2 = nn.Linear(mlp_dim, input_dim)
def forward(self, x):
return self.fc2(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | rushirajsherlocked/External-Attention-pytorch | MlpBlock | false | 4,218 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, input_dim, mlp_dim=512):
super().__init__()
self.fc1 = nn.Linear(input_dim, mlp_dim)
self.gelu = nn.GELU()
self.fc2 = nn.Linear(mlp_dim, input_dim)
def forward(self, x):
return self.fc2(self.... |
LxmertCrossAttentionLayer | # 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
class LxmertAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The 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.... | rsgit95/med_kg_txt_multimodal | LxmertCrossAttentionLayer | false | 4,219 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class LxmertAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hi... |
GTXCrossAttentionLayer | # 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
class GTXAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | rsgit95/med_kg_txt_multimodal | GTXCrossAttentionLayer | false | 4,220 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class GTXAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidde... |
ConvEncoder | # 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 ConvEncoder(nn.Module):
""" Simple convolutional encoder network.
It consists of 5 convolutional layers, each downsampling the input by a
factor of 2, and a final fully-connected layer projecting the output to
c_dim dimensions.
Args:
c_dim (int): o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | planetceres/differentiable_volumetric_rendering | ConvEncoder | false | 4,221 | [
"MIT"
] | 0 | f2fe46d139244c7642439ced23656db1e7f5c128 | https://github.com/planetceres/differentiable_volumetric_rendering/tree/f2fe46d139244c7642439ced23656db1e7f5c128 | import torch
from torch import nn
class Model(nn.Module):
""" Simple convolutional encoder network.
It consists of 5 convolutional layers, each downsampling the input by a
factor of 2, and a final fully-connected layer projecting the output to
c_dim dimensions.
Args:
c_dim (int): output ... |
DoubleAttention | # 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
from torch.nn import functional as F
from torch.nn import init
class DoubleAttention(nn.Module):
def __init__(self, in_channels, c_m, c_n, reconstruct=True):
super().__init__()
self.in_channels = in_channels
self.reconstruct = reconstruct
self.c_m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rushirajsherlocked/External-Attention-pytorch | DoubleAttention | false | 4,222 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import init
class Model(nn.Module):
def __init__(self, in_channels, c_m, c_n, reconstruct=True):
super().__init__()
self.in_channels = in_channels
self.reconstruct = reconstruct
self.c_m = c_m
... |
LxmertSelfAttentionLayer | # 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
class LxmertAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The 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.... | rsgit95/med_kg_txt_multimodal | LxmertSelfAttentionLayer | false | 4,223 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class LxmertAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hi... |
SimplifiedScaledDotProductAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | rushirajsherlocked/External-Attention-pytorch | SimplifiedScaledDotProductAttention | false | 4,224 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import torch
import numpy as np
from torch import nn
from torch.nn import init
class Model(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries... |
SpatialGroupEnhance | # 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
from torch.nn import init
class SpatialGroupEnhance(nn.Module):
def __init__(self, groups):
super().__init__()
self.groups = groups
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
self.bias ... | 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
from torch.nn import init
assert_size_stride = torch._C._d... | rushirajsherlocked/External-Attention-pytorch | SpatialGroupEnhance | false | 4,225 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import torch
from torch import nn
from torch.nn import init
class Model(nn.Module):
def __init__(self, groups):
super().__init__()
self.groups = groups
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
self.bias = nn.Parameter... |
ScaledDotProductAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from torch import nn
from torch.nn import init
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | rushirajsherlocked/External-Attention-pytorch | ScaledDotProductAttention | false | 4,226 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import torch
import numpy as np
from torch import nn
from torch.nn import init
class Model(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality ... |
AttentionHead | # 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 Tensor
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import Tensor
def scaled_dot_product_attention(query: 'torch.Tensor', key: 'torch.Tensor',
value: 'torch.Tensor') ->Tensor:
temp = query.bmm(key.transpose(1, 2))
scale = query.size(-1) ** 0.5
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | sabernn/vit-pytorch | AttentionHead | false | 4,227 | [
"MIT"
] | 0 | 21a2671aa92adb941a56ae629f6089f550949fb2 | https://github.com/sabernn/vit-pytorch/tree/21a2671aa92adb941a56ae629f6089f550949fb2 | import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import Tensor
def scaled_dot_product_attention(query: 'torch.Tensor', key: 'torch.Tensor',
value: 'torch.Tensor') ->Tensor:
temp = query.bmm(key.transpose(1, 2))
scale = query.size(-1) ** 0.5
... |
SE_Connect | # 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
import torch.nn as nn
class SE_Connect(nn.Module):
def __init__(self, channels, s=4):
super().__init__()
assert channels % s == 0, '{} % {} != 0'.format(channesl, s)
self.linear1 = nn.Linear(channels, channels // s)
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
import torch.... | qlindazm/asv-subtools | SE_Connect | false | 4,228 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 | import torch
import torch.nn.functional as F
import torch.nn
import torch.nn as nn
class Model(nn.Module):
def __init__(self, channels, s=4):
super().__init__()
assert channels % s == 0, '{} % {} != 0'.format(channesl, s)
self.linear1 = nn.Linear(channels, channels // s)
self.line... |
AttentiveStatsPool | # 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
import torch.nn as nn
class AttentiveStatsPool(nn.Module):
def __init__(self, in_dim, bottleneck_dim):
super().__init__()
self.linear1 = nn.Conv1d(in_dim, bottleneck_dim, kernel_size=1)
self.linear2 = nn.Conv1d(bottleneck_dim, in_dim, kernel_size=1)
def 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.... | qlindazm/asv-subtools | AttentiveStatsPool | false | 4,229 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 | import torch
import torch.nn
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_dim, bottleneck_dim):
super().__init__()
self.linear1 = nn.Conv1d(in_dim, bottleneck_dim, kernel_size=1)
self.linear2 = nn.Conv1d(bottleneck_dim, in_dim, kernel_size=1)
def forward(self, ... |
OutlookAttention | # 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
class OutlookAttention(nn.Module):
def __init__(self, dim, num_heads=1, kernel_size=3, padding=1, stride=1,
qkv_bias=False, attn_drop=0.1):
super().__init__()
self.dim = dim
self.num_heads = num_hea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rushirajsherlocked/External-Attention-pytorch | OutlookAttention | false | 4,230 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import math
import torch
from torch import nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, dim, num_heads=1, kernel_size=3, padding=1, stride=1,
qkv_bias=False, attn_drop=0.1):
super().__init__()
self.dim = dim
self.num_heads = num_heads
... |
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
class Critic(nn.Module):
def __init__(self, obs_dim: 'int'):
super().__init__()
self.fc1 = nn.Linear(obs_dim, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 1)
def forward(self, x):
x = torch.tanh(self.fc1(x))
x = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | raznem/rlex | Critic | false | 4,231 | [
"MIT"
] | 0 | d24b964d80067becc81d86f6ce87e5be413b7049 | https://github.com/raznem/rlex/tree/d24b964d80067becc81d86f6ce87e5be413b7049 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, obs_dim: 'int'):
super().__init__()
self.fc1 = nn.Linear(obs_dim, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 1)
def forward(self, x):
x = torch.tanh(self.fc1(x))
x = t... |
TdnnAffine | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | qlindazm/asv-subtools | TdnnAffine | false | 4,232 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 | import torch
import torch.nn.functional as F
import torch.nn
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... |
ChannelAttentionModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | rushirajsherlocked/External-Attention-pytorch | ChannelAttentionModule | false | 4,233 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... |
LDEPooling | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn
class LDEPooling(torch.nn.Module):
"""A novel learnable dictionary encoding layer.
Reference: Weicheng Cai, etc., "A NOVEL LEARNABLE DICTIONARY ENCODING LAYER FOR END-TO-END
LANGUAGE IDENTIFICATION", icassp, 2018
"""
def __init__(self, input_dim, c_num=64,... | 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
assert... | qlindazm/asv-subtools | LDEPooling | false | 4,234 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 | import torch
import torch.nn
class Model(torch.nn.Module):
"""A novel learnable dictionary encoding layer.
Reference: Weicheng Cai, etc., "A NOVEL LEARNABLE DICTIONARY ENCODING LAYER FOR END-TO-END
LANGUAGE IDENTIFICATION", icassp, 2018
"""
def __init__(self, input_dim, c_num=64, eps=... |
SoftmaxAffineLayer | # 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
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | qlindazm/asv-subtools | SoftmaxAffineLayer | false | 4,235 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 | import torch
import torch.nn.functional as F
import torch.nn
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... |
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, Cin, Cout):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(Cin, Cout, (3, 3))
def forward(self, x):
x0 = self.conv1(x)
x1 = self.conv1(x)
z = torch.cat([x0,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | saeta/mlir-npcomp | Net | false | 4,236 | [
"Apache-2.0"
] | 0 | 85898aaf10ea30237ee1d66c977b966cf7fcf6d0 | https://github.com/saeta/mlir-npcomp/tree/85898aaf10ea30237ee1d66c977b966cf7fcf6d0 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, Cin, Cout):
super().__init__()
self.conv1 = nn.Conv2d(Cin, Cout, (3, 3))
def forward(self, x):
x0 = self.conv1(x)
x1 = self.conv1(x)
z = torch.cat([x0, x1])
... |
ChunkSeparationAffine | # 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
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
import torch.nn
assert_size_stride = torch._C._d... | qlindazm/asv-subtools | ChunkSeparationAffine | false | 4,237 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 | import torch
import torch.nn.functional as F
import torch.nn
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... |
BartClassificationHead | # 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.checkpoint
class BartClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, input_dim: 'int', inner_dim: 'int', pooler_dropout:
'float'):
super().__init__()
self.dense = nn.Linear(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.triton_helpers import libdevice
from torch import n... | sajastu/transformers-sent-curr | BartClassificationHead | false | 4,238 | [
"Apache-2.0"
] | 0 | 6dc41545c4ac298a010090fbca4b454c2eaf3dbb | https://github.com/sajastu/transformers-sent-curr/tree/6dc41545c4ac298a010090fbca4b454c2eaf3dbb | import torch
from torch import nn
import torch.utils.checkpoint
class Model(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, input_dim: 'int', inner_dim: 'int', pooler_dropout:
'float'):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
... |
GroupedLinearLayer | # 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.checkpoint
class GroupedLinearLayer(nn.Module):
def __init__(self, input_size, output_size, num_groups):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.num_groups = num_groups
self.gr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.checkpoint
assert_size_stride = torch._C... | sajastu/transformers-sent-curr | GroupedLinearLayer | false | 4,239 | [
"Apache-2.0"
] | 0 | 6dc41545c4ac298a010090fbca4b454c2eaf3dbb | https://github.com/sajastu/transformers-sent-curr/tree/6dc41545c4ac298a010090fbca4b454c2eaf3dbb | import torch
from torch import nn
import torch.utils.checkpoint
class Model(nn.Module):
def __init__(self, input_size, output_size, num_groups):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.num_groups = num_groups
self.group_in_dim = ... |
HubertFeatureProjection | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.utils.checkpoint
class HubertFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.
layer_norm_eps)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | sajastu/transformers-sent-curr | HubertFeatureProjection | false | 4,240 | [
"Apache-2.0"
] | 0 | 6dc41545c4ac298a010090fbca4b454c2eaf3dbb | https://github.com/sajastu/transformers-sent-curr/tree/6dc41545c4ac298a010090fbca4b454c2eaf3dbb | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.utils.checkpoint
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.
layer_norm_eps)
self.projection ... |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.distributions import Categorical
from torch.distributions import Normal
from torch.distributions import Independent
class Actor(nn.Module):
def __init__(self, obs_dim: 'int', ac_lim: 'float', ac_dim: 'int',
discrete: 'bool'=True):
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | raznem/rlex | Actor | false | 4,242 | [
"MIT"
] | 0 | d24b964d80067becc81d86f6ce87e5be413b7049 | https://github.com/raznem/rlex/tree/d24b964d80067becc81d86f6ce87e5be413b7049 | import torch
import torch.nn as nn
from torch.distributions import Categorical
from torch.distributions import Normal
from torch.distributions import Independent
class Model(nn.Module):
def __init__(self, obs_dim: 'int', ac_lim: 'float', ac_dim: 'int',
discrete: 'bool'=True):
super().__init__()
... |
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, weight=None, size_average=True):
super(DiceLoss, self).__init__()
def forward(self, inputs, targets, smooth=1):
inputs = torch.sigmoid(inputs)
inputs = inputs.view(-1)
targets = targets.view(-1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | salem-devloper/COVID-Lung-Segment | DiceLoss | false | 4,243 | [
"MIT"
] | 0 | 6896f6b0c56dac6d32e005afd4a94d59b1917b44 | https://github.com/salem-devloper/COVID-Lung-Segment/tree/6896f6b0c56dac6d32e005afd4a94d59b1917b44 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, weight=None, size_average=True):
super().__init__()
def forward(self, inputs, targets, smooth=1):
inputs = torch.sigmoid(inputs)
inputs = inputs.view(-1)
targets = targets.view(-1)
intersect... |
ImageTransformationNet | # 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 ResidualBlock(nn.Module):
"""
Vanilla convolutional residual block from seminal paper by He et al.
Use of instance normalization suggested by Ulyanov et al. in
https://arxiv.org/pdf/1607.08022.pdf%C2%A0%C2%A0%C2%A0%C2%A0.
""... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | rileypsmith/Fast-Style-Transfer | ImageTransformationNet | false | 4,244 | [
"MIT"
] | 0 | 8b2164f8bc6d63530f914610b6c5c5c1b0f4ffd5 | https://github.com/rileypsmith/Fast-Style-Transfer/tree/8b2164f8bc6d63530f914610b6c5c5c1b0f4ffd5 | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
"""
Vanilla convolutional residual block from seminal paper by He et al.
Use of instance normalization suggested by Ulyanov et al. in
https://arxiv.org/pdf/1607.08022.pdf%C2%A0%C2%A0%C2%A0%C2%A0.
""... |
LayerNormCustom | # 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 LayerNormCustom(nn.Module):
"""A layernorm module in the TF style (epsilon inside the square root)."""
def __init__(self, n_hidden, variance_epsilon=1e-12):
super().__init__()
self.gamma = nn.Parameter(torch.ones(n_hidden))
self.beta = nn.Param... | 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_... | renebidart/pytorch-cifar | LayerNormCustom | false | 4,245 | [
"MIT"
] | 0 | 8f623299c25f7f219bab34bc7df41fe24232b1af | https://github.com/renebidart/pytorch-cifar/tree/8f623299c25f7f219bab34bc7df41fe24232b1af | import torch
import torch.nn as nn
class Model(nn.Module):
"""A layernorm module in the TF style (epsilon inside the square root)."""
def __init__(self, n_hidden, variance_epsilon=1e-12):
super().__init__()
self.gamma = nn.Parameter(torch.ones(n_hidden))
self.beta = nn.Parameter(torch... |
IBertLMHead | # 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
import torch.utils.checkpoint
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class IBertLMHead(nn.Module):
"""I-BERT Head for masked language modelin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | sajastu/transformers-sent-curr | IBertLMHead | false | 4,246 | [
"Apache-2.0"
] | 0 | 6dc41545c4ac298a010090fbca4b454c2eaf3dbb | https://github.com/sajastu/transformers-sent-curr/tree/6dc41545c4ac298a010090fbca4b454c2eaf3dbb | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
import torch.utils.checkpoint
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class Model(nn.Module):
"""I-BERT Head for masked language modeling."""
... |
PatchSequential | # 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 math
import torch
import warnings
from typing import Dict
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import cast
from typing import List
from typing import Union
from torch.distributions import Bernoulli
from itertools import zip_longest... | 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 math
import warnings
from typing import Dict
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.... | rozumden/kornia | PatchSequential | false | 4,247 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | f62f324b201eea50e1e50db3fbf3e968e0a337c5 | https://github.com/rozumden/kornia/tree/f62f324b201eea50e1e50db3fbf3e968e0a337c5 | import math
import torch
import warnings
from typing import Dict
from typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import cast
from typing import List
from typing import Union
from torch.distributions import Bernoulli
from itertools import zip_longest... |
DAModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from torch import nn
from torch.nn import init
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | rushirajsherlocked/External-Attention-pytorch | DAModule | false | 4,248 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 | import torch
import numpy as np
from torch import nn
from torch.nn import init
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
:param ... |
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... | kimihitosugiyama/text_analysis | MaskedWordPredictions | false | 4,249 | [
"Apache-2.0"
] | 0 | 8f51022957928c31e52af1e0fd407daca3addb40 | https://github.com/kimihitosugiyama/text_analysis/tree/8f51022957928c31e52af1e0fd407daca3addb40 | 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__(... |
Conv1dLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn
class Conv1dLinear(torch.nn.Module):
"""Conv1D + Linear for Transformer block.
A variant of MultiLayeredConv1d, which replaces second conv-layer to linear.
"""
def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
"""Initialize Conv1dLinear modu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
assert_size_s... | qlindazm/asv-subtools | Conv1dLinear | false | 4,250 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 | import torch
import torch.nn
class Model(torch.nn.Module):
"""Conv1D + Linear for Transformer block.
A variant of MultiLayeredConv1d, which replaces second conv-layer to linear.
"""
def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
"""Initialize Conv1dLinear module.
... |
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
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function by Hugging Face"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class PositionWiseFeedForward(nn.Module):
""" FeedForward Neural Networks for each position """
def __init__(self, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | renebidart/pytorch-cifar | PositionWiseFeedForward | false | 4,251 | [
"MIT"
] | 0 | 8f623299c25f7f219bab34bc7df41fe24232b1af | https://github.com/renebidart/pytorch-cifar/tree/8f623299c25f7f219bab34bc7df41fe24232b1af | import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function by Hugging Face"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class Model(nn.Module):
""" FeedForward Neural Networks for each position """
def __init__(self, n_hidden):
... |
FC_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.nn as nn
import torch.nn.functional as F
class FC_Decoder(nn.Module):
def __init__(self, embedding_size):
super(FC_Decoder, self).__init__()
self.fc3 = nn.Linear(embedding_size, 1024)
self.fc4 = nn.Linear(1024, 784)
def forward(self, z):
h3 = F.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
import torch.nn as nn
assert_... | saksham36/LangGrounding | FC_Decoder | false | 4,252 | [
"MIT"
] | 0 | 89ee9e5b8090e61e6bf7bf2b3e1dd45edf9664b7 | https://github.com/saksham36/LangGrounding/tree/89ee9e5b8090e61e6bf7bf2b3e1dd45edf9664b7 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, embedding_size):
super().__init__()
self.fc3 = nn.Linear(embedding_size, 1024)
self.fc4 = nn.Linear(1024, 784)
def forward(self, z):
h3 = F.relu(self.fc3(z))
... |
Word2Vec | # 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 Word2Vec(nn.Module):
def __init__(self, features, embedding_size):
super().__init__()
0.5 / embedding_size
self.fc1 = nn.Linear(features, embedding_size)
self.fc2 = nn.Linear(embedding_size, features)
def forward(self, one_hot):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | salmanedhi/NNTI-WS2021-NLP-Project | Word2Vec | false | 4,253 | [
"MIT"
] | 0 | 5b0a8f1258ef4e835a6e647082a8286078a0bdd6 | https://github.com/salmanedhi/NNTI-WS2021-NLP-Project/tree/5b0a8f1258ef4e835a6e647082a8286078a0bdd6 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, features, embedding_size):
super().__init__()
0.5 / embedding_size
self.fc1 = nn.Linear(features, embedding_size)
self.fc2 = nn.Linear(embedding_size, features)
def forward(self, one_hot):
x ... |
Beta | # 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.functional as F
import torch.nn.functional as F
class BoundedBeta(torch.distributions.Beta):
def log_prob(self, x):
return super().log_prob((x + 1) / 2)
class Beta(nn.Module):
def __init__(self, action_dim):
super(Beta, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | samarth-robo/apex | Beta | false | 4,254 | [
"MIT"
] | 0 | db24044acacd0fcd006886eb1677eaa2f2beedad | https://github.com/samarth-robo/apex/tree/db24044acacd0fcd006886eb1677eaa2f2beedad | import torch
import torch.nn as nn
import torch.functional as F
import torch.nn.functional as F
class BoundedBeta(torch.distributions.Beta):
def log_prob(self, x):
return super().log_prob((x + 1) / 2)
class Model(nn.Module):
def __init__(self, action_dim):
super().__init__()
self.a... |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Actor(nn.Module):
def __init__(self, nb_states, nb_actions, hidden1=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | saman-aghazadeh/distiller | Actor | false | 4,255 | [
"Apache-2.0"
] | 0 | 7e8d3e6193c807f7c55d8453f64e1bc3c02eee30 | https://github.com/saman-aghazadeh/distiller/tree/7e8d3e6193c807f7c55d8453f64e1bc3c02eee30 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Model(nn.Module):
def __init__(self, nb_states, nb_actions, hidden1=... |
Beta2 | # 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 numpy as np
import torch.nn as nn
class BoundedBeta(torch.distributions.Beta):
def log_prob(self, x):
return super().log_prob((x + 1) / 2)
class Beta2(nn.Module):
def __init__(self, action_dim, init_std=0.25, learn_std=False):
super(Beta2, self).__init__()
asser... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._d... | samarth-robo/apex | Beta2 | false | 4,256 | [
"MIT"
] | 0 | db24044acacd0fcd006886eb1677eaa2f2beedad | https://github.com/samarth-robo/apex/tree/db24044acacd0fcd006886eb1677eaa2f2beedad | import torch
import numpy as np
import torch.nn as nn
class BoundedBeta(torch.distributions.Beta):
def log_prob(self, x):
return super().log_prob((x + 1) / 2)
class Model(nn.Module):
def __init__(self, action_dim, init_std=0.25, learn_std=False):
super().__init__()
assert init_std ... |
BertPooler2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.nn.parallel
import torch.optim
from torch.utils.data import *
import torch.nn.functional
class BertPooler2(nn.Module):
def __init__(self, config):
super(BertPooler2, self).__init__()
self.dense = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | samuelyu2002/PACS | BertPooler2 | false | 4,257 | [
"MIT"
] | 0 | 5010b2f0d20933b0647e3d6230d673e1830249ec | https://github.com/samuelyu2002/PACS/tree/5010b2f0d20933b0647e3d6230d673e1830249ec | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.nn.parallel
import torch.optim
from torch.utils.data import *
import torch.nn.functional
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, c... |
ModelWithDuplicates | # 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.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class ModelWithDuplicates(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.... | saman-aghazadeh/distiller | ModelWithDuplicates | false | 4,258 | [
"Apache-2.0"
] | 0 | 7e8d3e6193c807f7c55d8453f64e1bc3c02eee30 | https://github.com/saman-aghazadeh/distiller/tree/7e8d3e6193c807f7c55d8453f64e1bc3c02eee30 | import torch
from collections import OrderedDict
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
import torch.onnx
import torch.testing
class Model(nn.Module):
def __init__(s... |
MySimpleNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class MySimpleNet(nn.Module):
"""
Very simple 2-layer net, slightly adapted from the docs:
https://skorch.readthedocs.io/en/stable/user/quickstart.html
"""
def __init__(self, num_in, num_feat, num_hidden=10, nonlin=F.re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | samxu0823/anfis-pytorch | MySimpleNet | false | 4,259 | [
"MIT"
] | 0 | b4ec3f0e8259963800e9e0a2904a580d1e56cc1c | https://github.com/samxu0823/anfis-pytorch/tree/b4ec3f0e8259963800e9e0a2904a580d1e56cc1c | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
Very simple 2-layer net, slightly adapted from the docs:
https://skorch.readthedocs.io/en/stable/user/quickstart.html
"""
def __init__(self, num_in, num_feat, num_hidden=10, nonlin=F.relu):
... |
BahdanauAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
from torch.nn.parameter import Parameter
import torch.onnx
import torch.testing
class EltwiseAdd(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.... | saman-aghazadeh/distiller | BahdanauAttention | false | 4,260 | [
"Apache-2.0"
] | 0 | 7e8d3e6193c807f7c55d8453f64e1bc3c02eee30 | https://github.com/saman-aghazadeh/distiller/tree/7e8d3e6193c807f7c55d8453f64e1bc3c02eee30 | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.quantization
from torch.nn.parameter import Parameter
import torch.onnx
import torch.testing
class EltwiseAdd(nn.Module... |
GaussMembFunc | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class GaussMembFunc(torch.nn.Module):
"""
Gaussian membership functions, defined by two... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | samxu0823/anfis-pytorch | GaussMembFunc | false | 4,261 | [
"MIT"
] | 0 | b4ec3f0e8259963800e9e0a2904a580d1e56cc1c | https://github.com/samxu0823/anfis-pytorch/tree/b4ec3f0e8259963800e9e0a2904a580d1e56cc1c | import torch
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class Model(torch.nn.Module):
"""
Gaussian membership functions, defined by two paramet... |
qy | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class qy(nn.Module):
def __init__(self, d_dim, x_dim, y_dim, z_dim):
super(qy, self).__init__()
self.fc1 = nn.Linear(z_dim, y_dim)
torch.nn.init.xavier_uniform_(self.fc1.weight)
self.fc1.bias.data.zero_()
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_... | sautami26/DIVA | qy | false | 4,262 | [
"MIT"
] | 0 | 52af683db216cb6e2ac777597fd9ec744ce7c8f2 | https://github.com/sautami26/DIVA/tree/52af683db216cb6e2ac777597fd9ec744ce7c8f2 | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, d_dim, x_dim, y_dim, z_dim):
super().__init__()
self.fc1 = nn.Linear(z_dim, y_dim)
torch.nn.init.xavier_uniform_(self.fc1.weight)
self.fc1.bias.data.zero_()
def forwa... |
BertAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if (config.hidden_size % config.num_attention_heads != 0 and not
hasattr(config... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Hzfinfdu/Black-Box-Tuning | BertAttention | false | 4,263 | [
"MIT"
] | 0 | 64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 | https://github.com/Hzfinfdu/Black-Box-Tuning/tree/64eb5505875dc1b242c6f0a2a2f07e4000c24cb4 | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if (config.hidden_size % config.num_attention_heads != 0 and not
hasattr(config... |
down | # 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.functional import F
import torch.nn as nn
import torch.nn.functional as F
class down(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Leaky ReLU --> Convolution + Leaky ReLU
This is used in the UNet Class t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | samuelpietri/Super-SloMo | down | false | 4,264 | [
"MIT"
] | 0 | e20eaa5550c30737be42b61f8e82e731cfd17457 | https://github.com/samuelpietri/Super-SloMo/tree/e20eaa5550c30737be42b61f8e82e731cfd17457 | import torch
from torch.functional import F
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Leaky ReLU --> Convolution + Leaky ReLU
This is used in the UNet Class ... |
BellMembFunc | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class BellMembFunc(torch.nn.Module):
"""
Generalised Bell membership function; defined ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | samxu0823/anfis-pytorch | BellMembFunc | false | 4,265 | [
"MIT"
] | 0 | b4ec3f0e8259963800e9e0a2904a580d1e56cc1c | https://github.com/samxu0823/anfis-pytorch/tree/b4ec3f0e8259963800e9e0a2904a580d1e56cc1c | import torch
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class Model(torch.nn.Module):
"""
Generalised Bell membership function; defined by thre... |
qd | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class qd(nn.Module):
def __init__(self, d_dim, x_dim, y_dim, z_dim):
super(qd, self).__init__()
self.fc1 = nn.Linear(z_dim, d_dim)
torch.nn.init.xavier_uniform_(self.fc1.weight)
self.fc1.bias.data.zero_()
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_... | sautami26/DIVA | qd | false | 4,266 | [
"MIT"
] | 0 | 52af683db216cb6e2ac777597fd9ec744ce7c8f2 | https://github.com/sautami26/DIVA/tree/52af683db216cb6e2ac777597fd9ec744ce7c8f2 | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, d_dim, x_dim, y_dim, z_dim):
super().__init__()
self.fc1 = nn.Linear(z_dim, d_dim)
torch.nn.init.xavier_uniform_(self.fc1.weight)
self.fc1.bias.data.zero_()
def forwa... |
ConditionalBatchNorm2d | # 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 import Parameter
def l2normalize(v, eps=0.0001):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super(SpectralNorm, self).__init__()
self.module = module
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | samuelemarro/anne | ConditionalBatchNorm2d | false | 4,267 | [
"MIT"
] | 0 | 918022eb029a46fbfd1589369e9817f570d5651c | https://github.com/samuelemarro/anne/tree/918022eb029a46fbfd1589369e9817f570d5651c | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=0.0001):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super().__init__()
self.module = module
self.name = name
... |
GlobalAvgPool1d | # 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.optim
import torch.utils.data
from abc import abstractmethod
from torch.nn import functional
class AvgPool(nn.Module):
"""
AvgPool Module.
"""
def __init__(self):
super().__init__()
@abstractmethod
def forward(s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from abc import abstractmethod
assert_size_stride ... | savan77/nni | GlobalAvgPool1d | false | 4,268 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from abc import abstractmethod
from torch.nn import functional
class AvgPool(nn.Module):
"""
AvgPool Module.
"""
def __init__(self):
super().__init__()
@abstractmethod
def forward(s... |
DeepQNetwork | # 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 DeepQNetwork(nn.Module):
def __init__(self, imagesize, num_input_frames, num_actions, **kwargs):
super(DeepQNetwork, self).__init__()
self.conv1 = nn.Conv2d(in_channels=num_input_frames, out_channels=
32, kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | sanmusane/AIGames | DeepQNetwork | false | 4,269 | [
"MIT"
] | 0 | 3f4eecdd02089911d1989e40e2b336e13b800e55 | https://github.com/sanmusane/AIGames/tree/3f4eecdd02089911d1989e40e2b336e13b800e55 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, imagesize, num_input_frames, num_actions, **kwargs):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=num_input_frames, out_channels=
32, kernel_size=9, stride=4, padding... |
Mask | # 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.optim
import torch.utils.data
class Mask(nn.Module):
def forward(self, seq, mask):
seq_mask = torch.unsqueeze(mask, 2)
seq_mask = torch.transpose(seq_mask.repeat(1, 1, seq.size()[1]), 1, 2)
return seq.where(torch.eq(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | savan77/nni | Mask | false | 4,270 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class Model(nn.Module):
def forward(self, seq, mask):
seq_mask = torch.unsqueeze(mask, 2)
seq_mask = torch.transpose(seq_mask.repeat(1, 1, seq.size()[1]), 1, 2)
return seq.where(torch.eq... |
Pooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class ReLUConvBN(nn.Module):
"""
Parameters
---
C_in: int
the number of input channels
C_out: int
the number of output channels
stride: int
stride of the convolution
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | savan77/nni | Pooling | false | 4,271 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class ReLUConvBN(nn.Module):
"""
Parameters
---
C_in: int
the number of input channels
C_out: int
the number of output channels
stride: int
stride of the convolution
... |
BackboneModel1 | # 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.parallel
import torch.optim
import torch.utils.data
class BackboneModel1(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 1, 1, 1)
def forward(self, x):
return self.conv1(x)
def get_inputs():
return ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.u... | savan77/nni | BackboneModel1 | false | 4,272 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 1, 1, 1)
def forward(self, x):
return self.conv1(x)
def get_inputs():
return [torch.ra... |
InteractiveKLLoss | # 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.optim
import torch.utils.data
import torch.nn.functional as F
class InteractiveKLLoss(nn.Module):
def __init__(self, temperature):
super().__init__()
self.temperature = temperature
self.kl_loss = nn.KLDivLoss()
... | 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... | savan77/nni | InteractiveKLLoss | false | 4,273 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, temperature):
super().__init__()
self.temperature = temperature
self.kl_loss = nn.KLDivLoss()
def forward(... |
GAT | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class GraphAttention(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttention, 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
from torch._inductor.runtime.... | NightmareNyx/pygcn | GAT | false | 4,274 | [
"MIT"
] | 0 | 3972f167ce7fcc41cb21284d75816dfd9a15f7ef | https://github.com/NightmareNyx/pygcn/tree/3972f167ce7fcc41cb21284d75816dfd9a15f7ef | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class GraphAttention(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super().__init__()
s... |
Auto_Encoder_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
import torch.nn.functional as F
class Auto_Encoder_Model(nn.Module):
def __init__(self):
super(Auto_Encoder_Model, self).__init__()
self.conv1 = nn.Conv2d(1, 64, padding=1, kernel_size=3)
self.max_pool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(64... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | sarahESL/MICCAI19-MedVQA | Auto_Encoder_Model | false | 4,275 | [
"MIT"
] | 0 | aa751cb905f79cd356ad5746f8a0640f1d81b5d2 | https://github.com/sarahESL/MICCAI19-MedVQA/tree/aa751cb905f79cd356ad5746f8a0640f1d81b5d2 | 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(1, 64, padding=1, kernel_size=3)
self.max_pool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(64, 32, padding=1, kernel_size=3)
... |
ZeroLayer | # 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.optim
import torch.utils.data
class ZeroLayer(nn.Module):
def __init__(self, stride):
super(ZeroLayer, self).__init__()
self.stride = stride
def forward(self, x):
"""n, c, h, w = x.size()
h //= self.stri... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | savan77/nni | ZeroLayer | false | 4,276 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class Model(nn.Module):
def __init__(self, stride):
super().__init__()
self.stride = stride
def forward(self, x):
"""n, c, h, w = x.size()
h //= self.stride
w //= se... |
FCNet | # 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.parallel
import torch.optim
import torch.utils.data
class FCNet(nn.Module):
def __init__(self, input_size, output_size):
super().__init__()
self.l1 = nn.Linear(input_size, 5)
self.relu = nn.ReLU()
self.l2 = nn.Linear(5, output_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | savan77/nni | FCNet | false | 4,277 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class Model(nn.Module):
def __init__(self, input_size, output_size):
super().__init__()
self.l1 = nn.Linear(input_size, 5)
self.relu = nn.ReLU()
self.l2 = nn.Linear(5, output_siz... |
LinearCombine | # 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.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class LinearCombine(nn.Module):
def __init__(self, layers_num, trainable=True, input_aware=False,
word_level=False):
super(LinearCombine, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import ... | savan77/nni | LinearCombine | false | 4,278 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, layers_num, trainable=True, input_aware=False,
word_level=False):
super().__init__()
self.input_aware = input_a... |
TorchAdd | # 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.optim
import torch.utils.data
class TorchAdd(nn.Module):
"""
TorchAdd Module.
"""
def forward(self, input_list):
return input_list[0] + input_list[1]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | savan77/nni | TorchAdd | false | 4,279 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class Model(nn.Module):
"""
TorchAdd Module.
"""
def forward(self, input_list):
return input_list[0] + input_list[1]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_... |
ActorCritic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class ActorCritic(nn.Module):
def __init__(self, num_states, num_actions, hidden_size):
super(ActorCritic, self).__init__()
self.num_actions = num_actions
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | savan77/nni | ActorCritic | false | 4,280 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_states, num_actions, hidden_size):
super().__init__()
self.num_actions = num_actions
self.fc = nn.Linear(nu... |
LipschitzCube | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
import torch.utils.data.distributed
class LipschitzCube(nn.Module):
def forward(self, x):
return (x >= 1) * (x - 2 / 3) + (x <= -1) * (x + 2 / 3) + (x > -1) * (x
< 1) * x ** 3 / 3
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_input... | 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
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda ... | rh-ia/color-information | LipschitzCube | false | 4,281 | [
"MIT"
] | 0 | e912a1667e4fffb339dbc574c85020ec6cf78b02 | https://github.com/rh-ia/color-information/tree/e912a1667e4fffb339dbc574c85020ec6cf78b02 | import torch
from torch import nn
import torch.utils.data.distributed
class Model(nn.Module):
def forward(self, x):
return (x >= 1) * (x - 2 / 3) + (x <= -1) * (x + 2 / 3) + (x > -1) * (x
< 1) * x ** 3 / 3
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... |
ExtendedModel | # 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 ExtendedModel(nn.Module):
def __init__(self, D_in, H, D_out):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
"""
super(ExtendedModel, self).__init__()
self.linear1 = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | sauyon/BentoML | ExtendedModel | false | 4,282 | [
"Apache-2.0"
] | 0 | ff702f1fc1ee7cc4cf7aab2e67d1e27512858fe4 | https://github.com/sauyon/BentoML/tree/ff702f1fc1ee7cc4cf7aab2e67d1e27512858fe4 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, D_in, H, D_out):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
"""
super().__init__()
self.linear1 = nn.Linear(D_in, H)
self.lin... |
FullSort | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
import torch.utils.data.distributed
class FullSort(nn.Module):
def forward(self, x):
return torch.sort(x, 1)[0]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.utils.data.distributed
assert_size_stride = torch._C._d... | rh-ia/color-information | FullSort | false | 4,283 | [
"MIT"
] | 0 | e912a1667e4fffb339dbc574c85020ec6cf78b02 | https://github.com/rh-ia/color-information/tree/e912a1667e4fffb339dbc574c85020ec6cf78b02 | import torch
from torch import nn
import torch.utils.data.distributed
class Model(nn.Module):
def forward(self, x):
return torch.sort(x, 1)[0]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
Clamp | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
import torch.utils.data
class Clamp(nn.Module):
def __init__(self, min_out=-3, max_out=3):
super().__init__()
self.min_out = min_out
self.max_out = max_out
def forward(self, input):
return input.clamp(self.min_out, self.max_out)
def get_inp... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards... | sbuschjaeger/Pysembles | Clamp | false | 4,284 | [
"MIT"
] | 0 | 7e69b0975a7d4373242c7026ade6c5fdbad4fe67 | https://github.com/sbuschjaeger/Pysembles/tree/7e69b0975a7d4373242c7026ade6c5fdbad4fe67 | import torch
from torch import nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, min_out=-3, max_out=3):
super().__init__()
self.min_out = min_out
self.max_out = max_out
def forward(self, input):
return input.clamp(self.min_out, self.max_out)
def get_inp... |
SpatialAttentionGate | # 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.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class SpatialAttentionGate(nn.Module):
def __init__(self, channel, reduction=16):
super(SpatialAttentionGate, self).__init__()
self.fc1 = nn.Conv2d(channel, reduc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | savan77/nni | SpatialAttentionGate | false | 4,285 | [
"MIT"
] | 0 | 510213393d9cae58c5a8cccd21f322f7bba4e0cf | https://github.com/savan77/nni/tree/510213393d9cae58c5a8cccd21f322f7bba4e0cf | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, channel, reduction=16):
super().__init__()
self.fc1 = nn.Conv2d(channel, reduction, kernel_size=1, padding=0)
s... |
FlexibleDropout | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
from torch.distributions import Bernoulli
class FlexibleDropout(nn.Module):
"""FlexibleDropout disconnects the sampling step from the masking step of dropout.
There are two important differences between FlexibleDropout and nn.Dropout. First, FlexibleDropout exposes a
sa... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.distributions import Bernoulli
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_stride... | scfrank/deep-generative-lm | FlexibleDropout | false | 4,286 | [
"MIT"
] | 0 | 70067fcda82aa035bba805ce6c2709097166a7a4 | https://github.com/scfrank/deep-generative-lm/tree/70067fcda82aa035bba805ce6c2709097166a7a4 | import torch
import torch.nn as nn
from torch.distributions import Bernoulli
class Model(nn.Module):
"""FlexibleDropout disconnects the sampling step from the masking step of dropout.
There are two important differences between FlexibleDropout and nn.Dropout. First, FlexibleDropout exposes a
sample_mask ... |
BertImagePooler | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.multiprocessing
class BertImagePooler(nn.Module):
def __init__(self, config):
super(BertImagePooler, self).__init__()
self.dense = nn.Linear(config.v_hidden_size, config.bi_hidden_size)
self.acti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ayushjain1144/vilbert-multi-task | BertImagePooler | false | 4,287 | [
"MIT"
] | 0 | cf30feee9617dd92bb030f380f8b59388b7054f6 | https://github.com/ayushjain1144/vilbert-multi-task/tree/cf30feee9617dd92bb030f380f8b59388b7054f6 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.multiprocessing
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.v_hidden_size, config.bi_hidden_size)
self.activation = nn.ReLU()
def for... |
LipNormConv2d | # 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
import torch.utils.data.distributed
def _max_except_dim(input, dim):
maxed = input
for axis in range(input.ndimension() - 1, dim, -1):
maxed, _ = maxed.max(axis, keepdim=True)
for axis in range(dim - 1, -1, -1):
maxed, _ = m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | rh-ia/color-information | LipNormConv2d | false | 4,288 | [
"MIT"
] | 0 | e912a1667e4fffb339dbc574c85020ec6cf78b02 | https://github.com/rh-ia/color-information/tree/e912a1667e4fffb339dbc574c85020ec6cf78b02 | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data.distributed
def _max_except_dim(input, dim):
maxed = input
for axis in range(input.ndimension() - 1, dim, -1):
maxed, _ = maxed.max(axis, keepdim=True)
for axis in range(dim - 1, -1, -1):
maxed, _ = m... |
RelevanceVector | # 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 RelevanceVector(nn.Module):
def __init__(self, z_dim):
super(RelevanceVector, self).__init__()
self.rvlogit = nn.Parameter(0.001 * torch.randn(z_dim))
def forward(self):
rv = torch.sigmoid(self.rvlogit)
return self.rvlogit, rv
def ge... | 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... | seqam-lab/rfvae | RelevanceVector | false | 4,289 | [
"MIT"
] | 0 | 07089e2cca6d51f305731750c2c67b83a42df12a | https://github.com/seqam-lab/rfvae/tree/07089e2cca6d51f305731750c2c67b83a42df12a | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, z_dim):
super().__init__()
self.rvlogit = nn.Parameter(0.001 * torch.randn(z_dim))
def forward(self):
rv = torch.sigmoid(self.rvlogit)
return self.rvlogit, rv
def get_inputs():
return []
def... |
QNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class QNetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=64, fc2=128):
"""Initialize parameters and build model.
Params
======
state_size (int): Dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | schottkey7/deep-reinforcement-learning | QNetwork | false | 4,290 | [
"MIT"
] | 0 | 92c97fadbb5b95caa3fd3813a0757debc2c2747a | https://github.com/schottkey7/deep-reinforcement-learning/tree/92c97fadbb5b95caa3fd3813a0757debc2c2747a | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=64, fc2=128):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimens... |
LipNormLinear | # 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
import torch.utils.data.distributed
def _max_except_dim(input, dim):
maxed = input
for axis in range(input.ndimension() - 1, dim, -1):
maxed, _ = maxed.max(axis, keepdim=True)
for axis in range(dim - 1, -1, -1):
maxed, _ = m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | rh-ia/color-information | LipNormLinear | false | 4,291 | [
"MIT"
] | 0 | e912a1667e4fffb339dbc574c85020ec6cf78b02 | https://github.com/rh-ia/color-information/tree/e912a1667e4fffb339dbc574c85020ec6cf78b02 | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data.distributed
def _max_except_dim(input, dim):
maxed = input
for axis in range(input.ndimension() - 1, dim, -1):
maxed, _ = maxed.max(axis, keepdim=True)
for axis in range(dim - 1, -1, -1):
maxed, _ = m... |
Net1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class Net1(nn.Module):
def __init__(self):
super(Net1, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | sermolin/amazon-sagemaker-examples | Net1 | false | 4,292 | [
"Apache-2.0"
] | 0 | 3e6083d1b53cb718893a04c46513a9482a17bd6b | https://github.com/sermolin/amazon-sagemaker-examples/tree/3e6083d1b53cb718893a04c46513a9482a17bd6b | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Con... |
DemodulatedConv2d | # 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
from torchvision.transforms import functional as F
import torch.nn as nn
from torch.nn import functional as F
class DemodulatedConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size=3, stride=1,
padding=0, bias=False, dilation=1):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | seawee1/ForkGAN-pytorch | DemodulatedConv2d | false | 4,293 | [
"BSD-3-Clause"
] | 0 | 02d721875d47e4a1e96a14cc4770edcb6b68a5d0 | https://github.com/seawee1/ForkGAN-pytorch/tree/02d721875d47e4a1e96a14cc4770edcb6b68a5d0 | import torch
import torch.utils.data
import torch
from torchvision.transforms import functional as F
import torch.nn as nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size=3, stride=1,
padding=0, bias=False, dilation=1):
super().... |
ATLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class ATLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits, labels):
th_label = torch.zeros_like(labels, dtype=torch.float)
th_label[:, 0] = 1.0
labels[:, 0] = 0.0
p_mask ... | 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
... | seanswyi/R-BERT | ATLoss | false | 4,294 | [
"Apache-2.0"
] | 0 | 4a4aeab3a9314307ce4458bd2b943d94aaf4a706 | https://github.com/seanswyi/R-BERT/tree/4a4aeab3a9314307ce4458bd2b943d94aaf4a706 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, logits, labels):
th_label = torch.zeros_like(labels, dtype=torch.float)
th_label[:, 0] = 1.0
labels[:, 0] = 0.0
p_mask =... |
Planar | # 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 PlanarStep(nn.Module):
def __init__(self):
super(PlanarStep, self).__init__()
self.h = nn.Tanh()
self.softplus = nn.Softplus()
def _der_h(self, x):
"""Derivative of activation function h."""
return self._der_tanh(x)
def _d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | scfrank/deep-generative-lm | Planar | false | 4,295 | [
"MIT"
] | 0 | 70067fcda82aa035bba805ce6c2709097166a7a4 | https://github.com/scfrank/deep-generative-lm/tree/70067fcda82aa035bba805ce6c2709097166a7a4 | import torch
import torch.nn as nn
class PlanarStep(nn.Module):
def __init__(self):
super().__init__()
self.h = nn.Tanh()
self.softplus = nn.Softplus()
def _der_h(self, x):
"""Derivative of activation function h."""
return self._der_tanh(x)
def _der_tanh(self, x)... |
Decoder_h | # 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.distributions as dist
import torch.nn as nn
class Decoder_h(nn.Module):
def __init__(self, B, H_dim):
super().__init__()
self.B = B
self.H_dim = H_dim
self.make_parameters()
def make_parameters(self):
self.mu = nn.Linear(self.H_dim, self.B, b... | 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.distributions as dist
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda ... | shaabhishek/pp_lvm | Decoder_h | false | 4,296 | [
"Apache-2.0"
] | 0 | 0fcceb7f004ab01da7c5508b576983b9d4af36c8 | https://github.com/shaabhishek/pp_lvm/tree/0fcceb7f004ab01da7c5508b576983b9d4af36c8 | import torch
import torch.distributions as dist
import torch.nn as nn
class Model(nn.Module):
def __init__(self, B, H_dim):
super().__init__()
self.B = B
self.H_dim = H_dim
self.make_parameters()
def make_parameters(self):
self.mu = nn.Linear(self.H_dim, self.B, bias=... |
VDB | # 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 VDB(nn.Module):
def __init__(self, num_inputs, args):
super(VDB, self).__init__()
self.fc1 = nn.Linear(num_inputs, args.hidden_size)
self.fc2 = nn.Linear(args.hidden_size, args.z_size)
self.fc3 ... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libd... | sgrimbly/lets-do-irl | VDB | false | 4,297 | [
"MIT"
] | 0 | 4233e238342394feef6a7bd495cc6b700d435b00 | https://github.com/sgrimbly/lets-do-irl/tree/4233e238342394feef6a7bd495cc6b700d435b00 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_inputs, args):
super().__init__()
self.fc1 = nn.Linear(num_inputs, args.hidden_size)
self.fc2 = nn.Linear(args.hidden_size, args.z_size)
self.fc3 = nn.Li... |
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