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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
MarginCosineProduct | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
import torch.utils.data
import torch.optim
def cosine_sim(x1, x2, dim=1, eps=1e-08):
ip = torch.mm(x1, x2.t())
w1 = torch.norm(x1, 2, dim)
w2 = torch.norm(x2, 2, dim)
return ip / torch.ger(w1, w2).clamp(min=eps)
class MarginCosineProd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | lindsey98/CosFace_pytorch | MarginCosineProduct | false | 10,397 | [
"MIT"
] | 0 | 39bddf763e06c7ccd21fbf45d0c7f1f4a9d8d24d | https://github.com/lindsey98/CosFace_pytorch/tree/39bddf763e06c7ccd21fbf45d0c7f1f4a9d8d24d | import torch
import torch.nn as nn
from torch.nn import Parameter
import torch.utils.data
import torch.optim
def cosine_sim(x1, x2, dim=1, eps=1e-08):
ip = torch.mm(x1, x2.t())
w1 = torch.norm(x1, 2, dim)
w2 = torch.norm(x2, 2, dim)
return ip / torch.ger(w1, w2).clamp(min=eps)
class Model(nn.Module)... |
SplitDim | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 as nn
import torch.utils.data
class SplitDim(nn.Module):
def __init__(self, nonlin_col=1, nonlin_type=torch.nn.functional.
softplus, correction=True):
super(SplitDim, self).__init__()
self.nonlinearity = nonlin_type
self.col = nonlin_col
i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn as nn
import torch.utils.data
assert_size... | junmokane/rlkit_jm | SplitDim | false | 10,398 | [
"MIT"
] | 0 | 34a1bcf47706d4c98e9ce3b7edfd96fee6f2dd70 | https://github.com/junmokane/rlkit_jm/tree/34a1bcf47706d4c98e9ce3b7edfd96fee6f2dd70 | import torch
from torch import nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, nonlin_col=1, nonlin_type=torch.nn.functional.
softplus, correction=True):
super().__init__()
self.nonlinearity = nonlin_type
self.col = nonlin_col
if correction:
... |
StyledConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.autograd import Function
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.functional import leaky_relu
def fused_leaky_relu(input_, bias, negative_slope=0.2, scale=2 ** 0.5):
return scale * leaky_relu(input_ + bias[:input_.shape[1]],
negative_slop... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.autograd... | jchetboun/anycost-gan | StyledConv | false | 10,399 | [
"MIT"
] | 0 | 7e0005e50b915e2dfeb90fe7a9846c5df38d7c06 | https://github.com/jchetboun/anycost-gan/tree/7e0005e50b915e2dfeb90fe7a9846c5df38d7c06 | from torch.autograd import Function
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.functional import leaky_relu
def fused_leaky_relu(input_, bias, negative_slope=0.2, scale=2 ** 0.5):
return scale * leaky_relu(input_ + bias[:input_.shape[1]],
negative_slop... |
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
from torch import nn
from torch.autograd import Variable
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | joowlim/pytorch-3dunet | DiceLoss | false | 10,400 | [
"MIT"
] | 0 | d08049f60b619627521efd0fb171247e1536b262 | https://github.com/joowlim/pytorch-3dunet/tree/d08049f60b619627521efd0fb171247e1536b262 | import torch
from torch import nn
from torch.autograd import Variable
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2,... |
InferenceNetLSTMCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 InferenceNetLSTMCell(nn.Module):
def __init__(self, z_dim: 'int', input_dim: 'int', hidden_hat_dim:
'int', hidden_dim: 'int'):
super(InferenceNetLSTMCell, self).__init__()
self.w_hh = nn.Linear(hidden_hat_dim, z_dim)
self.w_hx = nn.Linear(h... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | kingofpigeon/hypernlp | InferenceNetLSTMCell | false | 10,401 | [
"MIT"
] | 0 | 1270ae318e698775160a6299db35752823fda7c7 | https://github.com/kingofpigeon/hypernlp/tree/1270ae318e698775160a6299db35752823fda7c7 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, z_dim: 'int', input_dim: 'int', hidden_hat_dim:
'int', hidden_dim: 'int'):
super().__init__()
self.w_hh = nn.Linear(hidden_hat_dim, z_dim)
self.w_hx = nn.Linear(hidden_hat_dim, z_dim)
self.w_hb =... |
MinMaxNorm | # 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 MinMaxNorm(nn.Module):
def __init__(self, min, max, a=0, b=1):
super(MinMaxNorm, self).__init__()
self.min, self.max = min, max
self.a, self.b = a, b
def forward(self, x):
return self.a + (x - self.min) * (self.b - self.a) / (self.max ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | iclementine/speedyspeech | MinMaxNorm | false | 10,402 | [
"BSD-3-Clause"
] | 0 | db527587a3699b71082d61c9e9fad7ed795d1980 | https://github.com/iclementine/speedyspeech/tree/db527587a3699b71082d61c9e9fad7ed795d1980 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, min, max, a=0, b=1):
super().__init__()
self.min, self.max = min, max
self.a, self.b = a, b
def forward(self, x):
return self.a + (x - self.min) * (self.b - self.a) / (self.max -
self.mi... |
CCAMDec | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import Softmax
from torch.nn.parameter import Parameter
class CCAMDec(Module):
"""
CCAM decoding module
"""
def __init__(self):
super(CCAMDec, self).__init__()
self.softmax = Softmax(dim=-1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | bfjei2825401/siamban | CCAMDec | false | 10,403 | [
"Apache-2.0"
] | 0 | c41d58742b146dfc8960053453227c6e9fec1bac | https://github.com/bfjei2825401/siamban/tree/c41d58742b146dfc8960053453227c6e9fec1bac | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import Softmax
from torch.nn.parameter import Parameter
class Model(Module):
"""
CCAM decoding module
"""
def __init__(self):
super().__init__()
self.softmax = Softmax(dim=-1)
self.scale = Pa... |
PAM_Module | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import Parameter
from torch.nn import Softmax
from torch.nn.parameter import Parameter
class PAM_Module(Module):
""" Position attention module"""
def __init__(self, in_dim):
super(PAM_Module, self).__init__()
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | bfjei2825401/siamban | PAM_Module | false | 10,404 | [
"Apache-2.0"
] | 0 | c41d58742b146dfc8960053453227c6e9fec1bac | https://github.com/bfjei2825401/siamban/tree/c41d58742b146dfc8960053453227c6e9fec1bac | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import Parameter
from torch.nn import Softmax
from torch.nn.parameter import Parameter
class Model(Module):
""" Position attention module"""
def __init__(self, in_dim):
super().__init__()
self.channel_in = in_d... |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding=1):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding=1):
"""
Create a lis... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | joowlim/pytorch-3dunet | Encoder | false | 10,405 | [
"MIT"
] | 0 | d08049f60b619627521efd0fb171247e1536b262 | https://github.com/joowlim/pytorch-3dunet/tree/d08049f60b619627521efd0fb171247e1536b262 | import torch
from torch import nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding=1):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding=1):
"""
Create a lis... |
StandardNorm | # 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 StandardNorm(nn.Module):
def __init__(self, mean, std):
super(StandardNorm, self).__init__()
self.mean = mean
self.std = std
def forward(self, x):
return (x - self.mean) / self.std
def inverse(self, x):
return x * self.std... | 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... | iclementine/speedyspeech | StandardNorm | false | 10,406 | [
"BSD-3-Clause"
] | 0 | db527587a3699b71082d61c9e9fad7ed795d1980 | https://github.com/iclementine/speedyspeech/tree/db527587a3699b71082d61c9e9fad7ed795d1980 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, mean, std):
super().__init__()
self.mean = mean
self.std = std
def forward(self, x):
return (x - self.mean) / self.std
def inverse(self, x):
return x * self.std + self.mean
def get_in... |
EuclideanComparator_1 | # 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 dataclasses import dataclass
from collections import defaultdict
import torch.optim
from torch import nn
class Base(nn.Module):
registered = defaultdict(dict)
@dataclass
class Config:
pass
@property
def config(self):
return self._config
def __init__(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from dataclasses import data... | lavis-nlp/irtm | EuclideanComparator_1 | false | 10,407 | [
"MIT"
] | 0 | e6c96519918795cfaa0c09ef2d4164f451265518 | https://github.com/lavis-nlp/irtm/tree/e6c96519918795cfaa0c09ef2d4164f451265518 | import torch
from dataclasses import dataclass
from collections import defaultdict
import torch.optim
from torch import nn
class Base(nn.Module):
registered = defaultdict(dict)
@dataclass
class Config:
pass
@property
def config(self):
return self._config
def __init__(self, ... |
AffineConstantFlow | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
from torch import nn
class FlowBlock(nn.Module):
"""
Abstract base class for any flow blocks.
"""
def __init__(self, dimension):
super(FlowBlock, self).__init__()
self.dimension = dimension
def forward(self, x: 'Tensor') ->(Tensor, Tensor):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import Tensor
from torch import nn
assert_size_stride = torch.... | lleonart1984/generative_modeling | AffineConstantFlow | false | 10,408 | [
"MIT"
] | 0 | d47c53d34b9eb704b6e8b2c334262b53fe7f4f32 | https://github.com/lleonart1984/generative_modeling/tree/d47c53d34b9eb704b6e8b2c334262b53fe7f4f32 | import torch
from torch import Tensor
from torch import nn
class FlowBlock(nn.Module):
"""
Abstract base class for any flow blocks.
"""
def __init__(self, dimension):
super().__init__()
self.dimension = dimension
def forward(self, x: 'Tensor') ->(Tensor, Tensor):
"""
... |
MaxPoolingAggregator_1 | # 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 dataclasses import dataclass
from collections import defaultdict
import torch.optim
from torch import nn
class Base(nn.Module):
registered = defaultdict(dict)
@dataclass
class Config:
pass
@property
def config(self):
return self._config
def __init__(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from dataclasses import dataclass
from collections import defaultdict
import torch.optim
... | lavis-nlp/irtm | MaxPoolingAggregator_1 | false | 10,409 | [
"MIT"
] | 0 | e6c96519918795cfaa0c09ef2d4164f451265518 | https://github.com/lavis-nlp/irtm/tree/e6c96519918795cfaa0c09ef2d4164f451265518 | import torch
from dataclasses import dataclass
from collections import defaultdict
import torch.optim
from torch import nn
class Base(nn.Module):
registered = defaultdict(dict)
@dataclass
class Config:
pass
@property
def config(self):
return self._config
def __init__(self, ... |
CPAMDec | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import Parameter
from torch.nn import Softmax
from torch.nn import Linear
from torch.nn.parameter import Parameter
class CPAMDec(Module):
"""
CPAM decoding module
"""
def __init__(self, in_channels):
super(CPAM... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | bfjei2825401/siamban | CPAMDec | false | 10,410 | [
"Apache-2.0"
] | 0 | c41d58742b146dfc8960053453227c6e9fec1bac | https://github.com/bfjei2825401/siamban/tree/c41d58742b146dfc8960053453227c6e9fec1bac | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import Parameter
from torch.nn import Softmax
from torch.nn import Linear
from torch.nn.parameter import Parameter
class Model(Module):
"""
CPAM decoding module
"""
def __init__(self, in_channels):
super().__in... |
LearnedPositionalEncoding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 LayerNorm(nn.Module):
"""A layernorm module in the TF style (epsilon inside the square root)."""
def __init__(self, d_model, variance_epsilon=1e-12):
super().__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.beta = nn.Parameter(torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | longnsl1998/vietocr | LearnedPositionalEncoding | false | 10,411 | [
"Apache-2.0"
] | 0 | 686dd6c9d897e0401c20e7dcadb07a07c1dbc284 | https://github.com/longnsl1998/vietocr/tree/686dd6c9d897e0401c20e7dcadb07a07c1dbc284 | import torch
from torch import nn
class LayerNorm(nn.Module):
"""A layernorm module in the TF style (epsilon inside the square root)."""
def __init__(self, d_model, variance_epsilon=1e-12):
super().__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.beta = nn.Parameter(torc... |
CrossNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 sklearn.metrics import *
import torch.onnx
import torch as torch
class CrossNet(nn.Module):
"""The Cross Network part of Deep&Cross Network model,
which leans both low and high degree cross feature.
Input shape
- 2D tensor with shape: ``(batch_size, units)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 sklearn.metrics import *
import torch.onnx
import tor... | dulvqingyunLT/DeepCTR-Torch | CrossNet | false | 10,412 | [
"Apache-2.0"
] | 0 | f40cf08f3469aa471f9ca69e44c5de51180341cc | https://github.com/dulvqingyunLT/DeepCTR-Torch/tree/f40cf08f3469aa471f9ca69e44c5de51180341cc | import torch
import torch.nn as nn
from sklearn.metrics import *
import torch.onnx
import torch as torch
class Model(nn.Module):
"""The Cross Network part of Deep&Cross Network model,
which leans both low and high degree cross feature.
Input shape
- 2D tensor with shape: ``(batch_size, units)``.... |
ExtResNetBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
def conv3d(in_channels, out_channels, kernel_size, bias, padding=1):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding=1):
"""
Create a lis... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | joowlim/pytorch-3dunet | ExtResNetBlock | false | 10,413 | [
"MIT"
] | 0 | d08049f60b619627521efd0fb171247e1536b262 | https://github.com/joowlim/pytorch-3dunet/tree/d08049f60b619627521efd0fb171247e1536b262 | import torch
from torch import nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding=1):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding=1):
"""
Create a lis... |
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=296,
fc2_units=296):
"""Initialize parameters and build model.
Params
======
state_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | luiz-rocha94/navigation | QNetwork | false | 10,414 | [
"MIT"
] | 0 | fd5e00d8b9051e82dfe15793e53f8d1f86e8ecbe | https://github.com/luiz-rocha94/navigation/tree/fd5e00d8b9051e82dfe15793e53f8d1f86e8ecbe | 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=296,
fc2_units=296):
"""Initialize parameters and build model.
Params
======
state_siz... |
Coskx | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class Coskx(nn.Module):
def __init__(self, k=50):
super(Coskx, self).__init__()
self.k = k
def forward(self, input):
return torch.cos(input * self.k)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | jiaj15/SAIL | Coskx | false | 10,415 | [
"MIT"
] | 0 | 734be06a2b0ae70801f59c191b86332592da97cf | https://github.com/jiaj15/SAIL/tree/734be06a2b0ae70801f59c191b86332592da97cf | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, k=50):
super().__init__()
self.k = k
def forward(self, input):
return torch.cos(input * self.k)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
GroupNorm32 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 GroupNorm32(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-05):
super().__init__(num_groups=num_groups, num_channels=num_channels,
eps=eps)
self.swish = swish
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | litevxx/glid-3 | GroupNorm32 | false | 10,416 | [
"MIT"
] | 0 | d7bd53e671d642b0cbc8af81197170b585c7e624 | https://github.com/litevxx/glid-3/tree/d7bd53e671d642b0cbc8af81197170b585c7e624 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-05):
super().__init__(num_groups=num_groups, num_channels=num_channels,
eps=eps)
self.swish = swish
def forward(self, x):
... |
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 random
import torch
import torch.nn as nn
import torch.nn.functional as F
class Qnet(nn.Module):
def __init__(self):
super(Qnet, self).__init__()
self.fc1 = nn.Linear(4, 128)
self.fc2 = nn.Linear(128, 128)
self.fc3 = nn.Linear(128, 2)
def forward(self, 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 import triton_helpers
import random
import torch.nn... | linklab/link_rl_book_codes | Qnet | false | 10,417 | [
"MIT"
] | 0 | b272b46d5ecd2802f34648440ff53641c68cbbf0 | https://github.com/linklab/link_rl_book_codes/tree/b272b46d5ecd2802f34648440ff53641c68cbbf0 | import random
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 128)
self.fc2 = nn.Linear(128, 128)
self.fc3 = nn.Linear(128, 2)
def forward(self, x):
x = F.relu(se... |
ScaledDotAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 LayerNorm
def scaled_dot_attention(q, k, v, mask=None, noise=0, dropout=lambda x: x):
"""
:param q: queries, (batch, time1, channels1)
:param k: keys, (batch, time2, channels1)
:param v: values, (batch, time2, channels2)
:param mask: boolean ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | iclementine/speedyspeech | ScaledDotAttention | false | 10,418 | [
"BSD-3-Clause"
] | 0 | db527587a3699b71082d61c9e9fad7ed795d1980 | https://github.com/iclementine/speedyspeech/tree/db527587a3699b71082d61c9e9fad7ed795d1980 | import torch
import torch.nn as nn
from torch.nn import LayerNorm
def scaled_dot_attention(q, k, v, mask=None, noise=0, dropout=lambda x: x):
"""
:param q: queries, (batch, time1, channels1)
:param k: keys, (batch, time2, channels1)
:param v: values, (batch, time2, channels2)
:param mask: boolean ... |
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.functional as F
from torch import nn
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class Decoder(torch.nn.Module):
def __init__(self, input_dim, out_dim, hidden_size=128):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | jiaj15/SAIL | Decoder | false | 10,419 | [
"MIT"
] | 0 | 734be06a2b0ae70801f59c191b86332592da97cf | https://github.com/jiaj15/SAIL/tree/734be06a2b0ae70801f59c191b86332592da97cf | import torch
import torch.nn.functional as F
from torch import nn
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class Model(torch.nn.Module):
def __init__(self, input_dim, out_dim, hidden_size=128):
... |
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
import torch.nn.functional as F
class PolicyNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size=256):
super(PolicyNetwork, self).__init__()
self.num_actions = num_actions
self.linear1 = nn.Linear(num_inputs, hidden_size)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | linklab/link_rl_book_codes | PolicyNetwork | false | 10,420 | [
"MIT"
] | 0 | b272b46d5ecd2802f34648440ff53641c68cbbf0 | https://github.com/linklab/link_rl_book_codes/tree/b272b46d5ecd2802f34648440ff53641c68cbbf0 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size=256):
super().__init__()
self.num_actions = num_actions
self.linear1 = nn.Linear(num_inputs, hidden_size)
self.linear2 = nn.Linear(hid... |
ActorCriticNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 ActorCriticNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size=256):
super(ActorCriticNetwork, self).__init__()
self.num_actions = num_actions
self.critic_linear1 = nn.Linear(num_inputs, 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.... | linklab/link_rl_book_codes | ActorCriticNetwork | false | 10,421 | [
"MIT"
] | 0 | b272b46d5ecd2802f34648440ff53641c68cbbf0 | https://github.com/linklab/link_rl_book_codes/tree/b272b46d5ecd2802f34648440ff53641c68cbbf0 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size=256):
super().__init__()
self.num_actions = num_actions
self.critic_linear1 = nn.Linear(num_inputs, hidden_size)
self.critic_linear2 =... |
SE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
def swish(x):
return x * x.sigmoid()
class SE(nn.Module):
"""Squeeze-and-Excitation block with Swish."""
def __init__(self, in_planes, se_planes):
super(SE, self).__init__()
self.se1 = nn.Conv2d(in_planes, se_planes, ker... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | liormagram/pytorch-cifar | SE | false | 10,422 | [
"MIT"
] | 0 | 2ed0fabe6cbd4a468c5c4d155fb76c5b9ad4a764 | https://github.com/liormagram/pytorch-cifar/tree/2ed0fabe6cbd4a468c5c4d155fb76c5b9ad4a764 | import torch
import torch.nn as nn
import torch.nn.functional as F
def swish(x):
return x * x.sigmoid()
class Model(nn.Module):
"""Squeeze-and-Excitation block with Swish."""
def __init__(self, in_planes, se_planes):
super().__init__()
self.se1 = nn.Conv2d(in_planes, se_planes, kernel_s... |
MultiHeadQKVAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | karayanni/torch-scae | MultiHeadQKVAttention | false | 10,423 | [
"Apache-2.0"
] | 0 | e044662d8942d8d1923d13d071f375144cf4a1e8 | https://github.com/karayanni/torch-scae/tree/e044662d8942d8d1923d13d071f375144cf4a1e8 | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... |
AFMLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import *
import torch.onnx
import torch as torch
class AFMLayer(nn.Module):
"""Attentonal Factorization Machine models pairwise (order-2) feature
interactions without linear term and bias.
Input shap... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | dulvqingyunLT/DeepCTR-Torch | AFMLayer | false | 10,424 | [
"Apache-2.0"
] | 0 | f40cf08f3469aa471f9ca69e44c5de51180341cc | https://github.com/dulvqingyunLT/DeepCTR-Torch/tree/f40cf08f3469aa471f9ca69e44c5de51180341cc | import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import *
import torch.onnx
import torch as torch
class Model(nn.Module):
"""Attentonal Factorization Machine models pairwise (order-2) feature
interactions without linear term and bias.
Input shape
... |
DRRN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 math import sqrt
class DRRN(nn.Module):
def __init__(self):
super(DRRN, self).__init__()
self.input = nn.Conv2d(in_channels=1, out_channels=128, kernel_size
=3, stride=1, padding=1, bias=False)
self.conv1 = nn.Conv2d(in_channels=128, out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ma... | loyo1990/DRRN-pytorch | DRRN | false | 10,425 | [
"MIT"
] | 0 | 63d7dfd4c6bcb4f7b668fc2f5b4e2031cbba6619 | https://github.com/loyo1990/DRRN-pytorch/tree/63d7dfd4c6bcb4f7b668fc2f5b4e2031cbba6619 | import torch
import torch.nn as nn
from math import sqrt
class Model(nn.Module):
def __init__(self):
super().__init__()
self.input = nn.Conv2d(in_channels=1, out_channels=128, kernel_size
=3, stride=1, padding=1, bias=False)
self.conv1 = nn.Conv2d(in_channels=128, out_channels... |
UpSampleX2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 torchvision.transforms import *
class DeconvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=4, stride=2,
padding=1, bias=True, activation='prelu', norm=None):
super(DeconvBlock, self).__init__()
self.deconv = torch.nn.ConvTranspose2d(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 torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | lizatish/My_CNN | UpSampleX2 | false | 10,426 | [
"MIT"
] | 0 | b13818bcce2f8a3697d20e34157e3dce53f953ee | https://github.com/lizatish/My_CNN/tree/b13818bcce2f8a3697d20e34157e3dce53f953ee | import torch
from torchvision.transforms import *
class DeconvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=4, stride=2,
padding=1, bias=True, activation='prelu', norm=None):
super().__init__()
self.deconv = torch.nn.ConvTranspose2d(input_size, output_siz... |
InteractingLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
from sklearn.metrics import *
import torch.onnx
import torch as torch
class InteractingLayer(nn.Module):
"""A Layer used in AutoInt that model the correlations between different feature fields by multi-head self-attention mechanism.
Input sh... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | dulvqingyunLT/DeepCTR-Torch | InteractingLayer | false | 10,427 | [
"Apache-2.0"
] | 0 | f40cf08f3469aa471f9ca69e44c5de51180341cc | https://github.com/dulvqingyunLT/DeepCTR-Torch/tree/f40cf08f3469aa471f9ca69e44c5de51180341cc | import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import *
import torch.onnx
import torch as torch
class Model(nn.Module):
"""A Layer used in AutoInt that model the correlations between different feature fields by multi-head self-attention mechanism.
Input shape
... |
CriticMlp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
def init_weights(layer, gain):
for p in layer.parameters():
if len(p.data.shape) >= 2:
nn.init.orthogonal_(p, gain=gain)
else:
p.data.zero_()
def all_init_weights(m, gain=2 ** 0.5):
init_weights(m, gai... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | heavenlysf/thesis | CriticMlp | false | 10,428 | [
"MIT"
] | 0 | 646553c45860f337c91a48ab7f666a174784472f | https://github.com/heavenlysf/thesis/tree/646553c45860f337c91a48ab7f666a174784472f | import torch
import torch.nn as nn
import torch.nn.functional as F
def init_weights(layer, gain):
for p in layer.parameters():
if len(p.data.shape) >= 2:
nn.init.orthogonal_(p, gain=gain)
else:
p.data.zero_()
def all_init_weights(m, gain=2 ** 0.5):
init_weights(m, gai... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.nn import Parameter
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
... | 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
from torch.nn import Parameter
assert_size_stride = torch... | kangzhiq/DeepFillv2_Pytorch | LayerNorm | false | 10,429 | [
"MIT"
] | 0 | 9c7ed61b25bb995713f89108b712490737abe1b1 | https://github.com/kangzhiq/DeepFillv2_Pytorch/tree/9c7ed61b25bb995713f89108b712490737abe1b1 | import torch
import torch.nn as nn
from torch.nn import Parameter
class Model(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamm... |
SAB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | karayanni/torch-scae | SAB | false | 10,430 | [
"Apache-2.0"
] | 0 | e044662d8942d8d1923d13d071f375144cf4a1e8 | https://github.com/karayanni/torch-scae/tree/e044662d8942d8d1923d13d071f375144cf4a1e8 | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... |
MAB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | karayanni/torch-scae | MAB | false | 10,431 | [
"Apache-2.0"
] | 0 | e044662d8942d8d1923d13d071f375144cf4a1e8 | https://github.com/karayanni/torch-scae/tree/e044662d8942d8d1923d13d071f375144cf4a1e8 | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... |
DiscriminatorHingeLoss | # 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 DiscriminatorHingeLoss(nn.Module):
def __init__(self, reduction='mean'):
super(DiscriminatorHingeLoss, self).__init__()
if reduction not in ['mean', 'sum']:
raise ValueError(
'Valid values for the reduction param are `mean`, `su... | 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... | kpandey008/SAGAN | DiscriminatorHingeLoss | false | 10,432 | [
"MIT"
] | 0 | 8e673d2ccabeb0450faf30dcb347b9ff2d710ae2 | https://github.com/kpandey008/SAGAN/tree/8e673d2ccabeb0450faf30dcb347b9ff2d710ae2 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, reduction='mean'):
super().__init__()
if reduction not in ['mean', 'sum']:
raise ValueError(
'Valid values for the reduction param are `mean`, `sum`')
self.reduction = reduction
... |
TransposeConv2dLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch.... | kangzhiq/DeepFillv2_Pytorch | TransposeConv2dLayer | false | 10,433 | [
"MIT"
] | 0 | 9c7ed61b25bb995713f89108b712490737abe1b1 | https://github.com/kangzhiq/DeepFillv2_Pytorch/tree/9c7ed61b25bb995713f89108b712490737abe1b1 | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super().__init__()
self.num_features = num_... |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 50, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(50, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | lykasbongbongbong/Pytorch | Net | false | 10,434 | [
"MIT"
] | 0 | f01d89fb51ac939f5a110f5ab6190c11917e66fc | https://github.com/lykasbongbongbong/Pytorch/tree/f01d89fb51ac939f5a110f5ab6190c11917e66fc | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 50, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(50, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
... |
Max_AvgPool | # 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 itertools import product as product
class Max_AvgPool(nn.Module):
def __init__(self, kernel_size=(3, 3), stride=2, padding=1, dim=128):
super(Max_AvgPool, self).__init__()
self.Maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride,
paddi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from itertools import product as product
assert_size_stride = torch... | kooBH/EXTD_Pytorch | Max_AvgPool | false | 10,435 | [
"MIT"
] | 0 | e93b196c87054684cc6c757e1dfd26f8b7dc57cf | https://github.com/kooBH/EXTD_Pytorch/tree/e93b196c87054684cc6c757e1dfd26f8b7dc57cf | import torch
import torch.nn as nn
from itertools import product as product
class Model(nn.Module):
def __init__(self, kernel_size=(3, 3), stride=2, padding=1, dim=128):
super().__init__()
self.Maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride,
padding=padding)
sel... |
my_AvgPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
class my_AvgPool2d(Module):
def __init__(self, kernel_size, stride=None, padding=0, ceil_mode=False,
count_include_pad=True):
super(my_AvgPool2d, self).__init__()
self.kerne... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch.nn.modules.module import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | likun97/Low_quality_classification_with_mobilenetv3 | my_AvgPool2d | false | 10,436 | [
"Apache-2.0"
] | 0 | a9e6f66caad937fc7c8e101cddb76f116219b255 | https://github.com/likun97/Low_quality_classification_with_mobilenetv3/tree/a9e6f66caad937fc7c8e101cddb76f116219b255 | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
class Model(Module):
def __init__(self, kernel_size, stride=None, padding=0, ceil_mode=False,
count_include_pad=True):
super().__init__()
self.kernel_size = kernel_size
... |
Conv2dLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
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 ... | kangzhiq/DeepFillv2_Pytorch | Conv2dLayer | false | 10,437 | [
"MIT"
] | 0 | 9c7ed61b25bb995713f89108b712490737abe1b1 | https://github.com/kangzhiq/DeepFillv2_Pytorch/tree/9c7ed61b25bb995713f89108b712490737abe1b1 | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine... |
GatedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
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, math as tl_math
im... | kangzhiq/DeepFillv2_Pytorch | GatedConv2d | false | 10,438 | [
"MIT"
] | 0 | 9c7ed61b25bb995713f89108b712490737abe1b1 | https://github.com/kangzhiq/DeepFillv2_Pytorch/tree/9c7ed61b25bb995713f89108b712490737abe1b1 | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine... |
my_MaxPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.modules.utils import _pair
class my_MaxPool2d(Module):
def __init__(self, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False):
supe... | 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.nn import Module
from torch.nn.modules.module import Module
from torch.nn.modu... | likun97/Low_quality_classification_with_mobilenetv3 | my_MaxPool2d | false | 10,439 | [
"Apache-2.0"
] | 0 | a9e6f66caad937fc7c8e101cddb76f116219b255 | https://github.com/likun97/Low_quality_classification_with_mobilenetv3/tree/a9e6f66caad937fc7c8e101cddb76f116219b255 | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.modules.utils import _pair
class Model(Module):
def __init__(self, kernel_size, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False):
super().__i... |
BaselineActor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
from torch.functional import F
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.distributions
class BaselineActor(nn.Module):
def __init__(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.... | greenstar1151/pytorch-benchmark | BaselineActor | false | 10,440 | [
"BSD-3-Clause"
] | 0 | 8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | https://github.com/greenstar1151/pytorch-benchmark/tree/8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import F
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.distributions
class Model(nn.Module):
def __init__(self, stat... |
PMA | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | karayanni/torch-scae | PMA | false | 10,441 | [
"Apache-2.0"
] | 0 | e044662d8942d8d1923d13d071f375144cf4a1e8 | https://github.com/karayanni/torch-scae/tree/e044662d8942d8d1923d13d071f375144cf4a1e8 | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... |
ISAB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | karayanni/torch-scae | ISAB | false | 10,442 | [
"Apache-2.0"
] | 0 | e044662d8942d8d1923d13d071f375144cf4a1e8 | https://github.com/karayanni/torch-scae/tree/e044662d8942d8d1923d13d071f375144cf4a1e8 | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor... |
adder2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.autograd import Function
import math
import torch
import torch.nn as nn
def adder2d_function(X, W, stride=1, padding=0):
n_filters, _d_filter, h_filter, w_filter = W.size()
n_x, _d_x, h_x, w_x = X.size()
h_out = (h_x - h_filter + 2 * padding) / stride + 1
w_out = (w_x - w_filter + 2 * paddi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.autograd import Function
import math
import torch.nn as nn
ass... | mark531593296/AdderNet | adder2d | false | 10,443 | [
"BSD-3-Clause"
] | 0 | 2936728f537c0cceb8a47727630e5723af86df61 | https://github.com/mark531593296/AdderNet/tree/2936728f537c0cceb8a47727630e5723af86df61 | from torch.autograd import Function
import math
import torch
import torch.nn as nn
def adder2d_function(X, W, stride=1, padding=0):
n_filters, _d_filter, h_filter, w_filter = W.size()
n_x, _d_x, h_x, w_x = X.size()
h_out = (h_x - h_filter + 2 * padding) / stride + 1
w_out = (w_x - w_filter + 2 * paddi... |
BaselineDiscreteCritic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
from torch.functional import F
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.distributions
class BaselineDiscreteCritic(nn.Module):
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
import ... | greenstar1151/pytorch-benchmark | BaselineDiscreteCritic | false | 10,444 | [
"BSD-3-Clause"
] | 0 | 8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | https://github.com/greenstar1151/pytorch-benchmark/tree/8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import F
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.distributions
class Model(nn.Module):
def __init__(self, obs_... |
TransposeGatedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | kangzhiq/DeepFillv2_Pytorch | TransposeGatedConv2d | false | 10,445 | [
"MIT"
] | 0 | 9c7ed61b25bb995713f89108b712490737abe1b1 | https://github.com/kangzhiq/DeepFillv2_Pytorch/tree/9c7ed61b25bb995713f89108b712490737abe1b1 | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super().__init__()
self.num_features = num_... |
NextSentencePrediction | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.distributions
class NextSentencePrediction(nn.Module):
"""
2-class classification model : is_next, is_not_next
"""
def __init__(self, hidden):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | greenstar1151/pytorch-benchmark | NextSentencePrediction | false | 10,446 | [
"BSD-3-Clause"
] | 0 | 8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | https://github.com/greenstar1151/pytorch-benchmark/tree/8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | 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.distributions
class Model(nn.Module):
"""
2-class classification model : is_next, is_not_next
"""
def __init__(self, hidden):
"""
:pa... |
AttentionPool2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
import torch as th
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | litevxx/glid-3 | AttentionPool2d | false | 10,447 | [
"MIT"
] | 0 | d7bd53e671d642b0cbc8af81197170b585c7e624 | https://github.com/litevxx/glid-3/tree/d7bd53e671d642b0cbc8af81197170b585c7e624 | import math
import torch
from torch import nn
import torch as th
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
retu... |
ConcreteDenseMixture | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
class ConcreteDropout(nn.Module):
def __init__(self, weight_regularizer=1e-06, dropout_regularizer=1e-05,
init_min=0.1, init_max=0.1):
super(ConcreteDropout, self).__init__()
self.weight_regularizer = weight_regularizer
self.dro... | 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... | jiwoncpark/fast-forward | ConcreteDenseMixture | false | 10,448 | [
"MIT"
] | 0 | 640a521241a8756be2a0d42282e88d56a2290fca | https://github.com/jiwoncpark/fast-forward/tree/640a521241a8756be2a0d42282e88d56a2290fca | import torch
import numpy as np
from torch import nn
class ConcreteDropout(nn.Module):
def __init__(self, weight_regularizer=1e-06, dropout_regularizer=1e-05,
init_min=0.1, init_max=0.1):
super().__init__()
self.weight_regularizer = weight_regularizer
self.dropout_regularizer = dr... |
FirstBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
class BatchNormLayer(nn.Module):
"""Implements batch normalization layer."""
def __init__(self, channels, gamma=False, beta=True, decay=0.9, epsilon
=1e-05):
"""Initializes with basic settings.
Args:
channels: Number of channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | lelechen63/idinvert_pytorch | FirstBlock | false | 10,449 | [
"MIT"
] | 0 | 0469e1e5460ee4dd626c05bd35a83d52f9dc2cac | https://github.com/lelechen63/idinvert_pytorch/tree/0469e1e5460ee4dd626c05bd35a83d52f9dc2cac | import torch
import numpy as np
import torch.nn as nn
class BatchNormLayer(nn.Module):
"""Implements batch normalization layer."""
def __init__(self, channels, gamma=False, beta=True, decay=0.9, epsilon
=1e-05):
"""Initializes with basic settings.
Args:
channels: Number of channels... |
LastBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
class BatchNormLayer(nn.Module):
"""Implements batch normalization layer."""
def __init__(self, channels, gamma=False, beta=True, decay=0.9, epsilon
=1e-05):
"""Initializes with basic settings.
Args:
channels: Number of channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | lelechen63/idinvert_pytorch | LastBlock | false | 10,450 | [
"MIT"
] | 0 | 0469e1e5460ee4dd626c05bd35a83d52f9dc2cac | https://github.com/lelechen63/idinvert_pytorch/tree/0469e1e5460ee4dd626c05bd35a83d52f9dc2cac | import torch
import numpy as np
import torch.nn as nn
class BatchNormLayer(nn.Module):
"""Implements batch normalization layer."""
def __init__(self, channels, gamma=False, beta=True, decay=0.9, epsilon
=1e-05):
"""Initializes with basic settings.
Args:
channels: Number of channels... |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLP(nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.fc1 = nn.Linear(28 * 28, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 64)
self.fc4 = nn.Linear(64, 10)
sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | lynfi/Classification | MLP | false | 10,451 | [
"MIT"
] | 0 | 691731629c6577432c8c9eee70b67911011a07b7 | https://github.com/lynfi/Classification/tree/691731629c6577432c8c9eee70b67911011a07b7 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(28 * 28, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 64)
self.fc4 = nn.Linear(64, 10)
self.dropo... |
CausalConv1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 CausalConv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1, groups=1, bias=True):
super(CausalConv1d, self).__init__(in_channels, out_channels,
kernel_size, stride=st... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | marc-moreaux/pytorch_text_generator | CausalConv1d | false | 10,452 | [
"MIT"
] | 0 | 99dd11c67d89f8a09faa28b7032fcc66f90672c0 | https://github.com/marc-moreaux/pytorch_text_generator/tree/99dd11c67d89f8a09faa28b7032fcc66f90672c0 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1, groups=1, bias=True):
super().__init__(in_channels, out_channels,
kernel_size, stride=stride, padding=0, dilation... |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import F
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.distributions
class PositionwiseFeedForward(nn.Module):
"""Imp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | greenstar1151/pytorch-benchmark | PositionwiseFeedForward | false | 10,453 | [
"BSD-3-Clause"
] | 0 | 8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | https://github.com/greenstar1151/pytorch-benchmark/tree/8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import F
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.distributions
class Model(nn.Module):
"""Implements position-w... |
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
class Decoder(nn.Module):
def __init__(self, latent_size, m):
super(Decoder, self).__init__()
self.latent_size = latent_size
self.fc = nn.Linear(latent_size, m)
self.deconv1 = nn.ConvTranspose2d(m, 128, 5, stride=2)
self.deconv2 = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | lshoek/creative-evo-controller | Decoder | false | 10,454 | [
"MIT"
] | 0 | a5f1742c172255cca2338b76ae1c5b4db277bb0d | https://github.com/lshoek/creative-evo-controller/tree/a5f1742c172255cca2338b76ae1c5b4db277bb0d | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, latent_size, m):
super().__init__()
self.latent_size = latent_size
self.fc = nn.Linear(latent_size, m)
self.deconv1 = nn.ConvTranspose2d(m, 128, 5, stride=2)
self.deconv2 = nn.ConvTranspose2d(128... |
TemporalConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 TemporalConv(nn.Module):
"""Temporal convolution block applied to nodes in the STGCN Layer
For details see: `"Spatio-Temporal Graph Convolutional Networks:
A Deep Learning Framework for Traffic Forecasting"
<https://arxiv.org/ab... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | marcdemers/pytorch_geometric_temporal | TemporalConv | false | 10,455 | [
"MIT"
] | 0 | 446aadcd890158bade2e9974f9840ed5a7bba827 | https://github.com/marcdemers/pytorch_geometric_temporal/tree/446aadcd890158bade2e9974f9840ed5a7bba827 | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
"""Temporal convolution block applied to nodes in the STGCN Layer
For details see: `"Spatio-Temporal Graph Convolutional Networks:
A Deep Learning Framework for Traffic Forecasting"
<https://arxiv.org/abs/1709.... |
ResBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
from torch.nn import init as init
class conv_relu(nn.Module):
"""docstring for conv_relu"""
def __init__(self, in_channels, out_channels, **kwargs):
super(conv_relu, self).__init__()
self.conv = nn.Conv2d(in_channels, out_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | llpspark/PytorchToCaffe | ResBlock | false | 10,456 | [
"MIT"
] | 0 | 01f6fb2cfd42e2c06ae5d46a7a91f7fd6d40d5d1 | https://github.com/llpspark/PytorchToCaffe/tree/01f6fb2cfd42e2c06ae5d46a7a91f7fd6d40d5d1 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init as init
class conv_relu(nn.Module):
"""docstring for conv_relu"""
def __init__(self, in_channels, out_channels, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=Fals... |
PairwiseRankingLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class PairwiseRankingLoss(nn.Module):
"""
Pairwise ranking loss
"""
def __init__(self, margin):
super(PairwiseRankingLoss, self).__init__()
self.margin = margin
def forward(self, anchor1, anchor2, img_sentc, sent_imgc):
cost_sent = torch.... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | maksimovVva/SentEval | PairwiseRankingLoss | false | 10,457 | [
"BSD-3-Clause"
] | 0 | d3aa5f24dd84b48ea476e73f4b59a4e1ace7775c | https://github.com/maksimovVva/SentEval/tree/d3aa5f24dd84b48ea476e73f4b59a4e1ace7775c | import torch
from torch import nn
class Model(nn.Module):
"""
Pairwise ranking loss
"""
def __init__(self, margin):
super().__init__()
self.margin = margin
def forward(self, anchor1, anchor2, img_sentc, sent_imgc):
cost_sent = torch.clamp(self.margin - anchor1 + img_sentc... |
DiscreteNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
def set_init(layers):
for layer in layers:
nn.init.normal_(layer.weight, mean=0.0, std=0.1)
nn.init.constant_(layer.bias, 0.0)
class DiscreteNet(nn.Module):
def __init__(self, s_dim, a_dim):
super(DiscreteNet, self).... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | lws803/pytorch-A3C | DiscreteNet | false | 10,458 | [
"MIT"
] | 0 | 944e7f42a8fa54b7d6efbe169d8a3467b20a0f7f | https://github.com/lws803/pytorch-A3C/tree/944e7f42a8fa54b7d6efbe169d8a3467b20a0f7f | import torch
import torch.nn as nn
import torch.nn.functional as F
def set_init(layers):
for layer in layers:
nn.init.normal_(layer.weight, mean=0.0, std=0.1)
nn.init.constant_(layer.bias, 0.0)
class Model(nn.Module):
def __init__(self, s_dim, a_dim):
super().__init__()
self... |
NoiseLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 NoiseLayer(nn.Module):
"""adds noise. noise is per pixel (constant over channels) with per-channel weight"""
def __init__(self, channels):
super().__init__()
self.weight = nn.Parameter(torch.zeros(channels))
self.noise = None
def forward(s... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C... | justinpinkney/ganspace | NoiseLayer | false | 10,459 | [
"Apache-2.0"
] | 0 | 7dc76d1d2ddad21d946a7ceb375efe5d5316fb3f | https://github.com/justinpinkney/ganspace/tree/7dc76d1d2ddad21d946a7ceb375efe5d5316fb3f | import torch
import torch.nn as nn
class Model(nn.Module):
"""adds noise. noise is per pixel (constant over channels) with per-channel weight"""
def __init__(self, channels):
super().__init__()
self.weight = nn.Parameter(torch.zeros(channels))
self.noise = None
def forward(self, ... |
mlp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class mlp(nn.Module):
def __init__(self, seq_len):
super(mlp, self).__init__()
self.lin1 = nn.Linear(seq_len, 2048)
self.lin2 = nn.Linear(2048, 2048)
self.lin3 = nn.Linear(2048, seq_len)
self.relu = nn.ReLU()
def forward(self, input_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | liuziyang1106/sodeep | mlp | false | 10,460 | [
"BSD-3-Clause-Clear"
] | 0 | 47f8a5cbe5b8405624877efc81cb28f104f1e2d7 | https://github.com/liuziyang1106/sodeep/tree/47f8a5cbe5b8405624877efc81cb28f104f1e2d7 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, seq_len):
super().__init__()
self.lin1 = nn.Linear(seq_len, 2048)
self.lin2 = nn.Linear(2048, 2048)
self.lin3 = nn.Linear(2048, seq_len)
self.relu = nn.ReLU()
def forward(self, input_):
... |
GetSegPred | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data.dataset
class GetSegPred(torch.nn.Module):
def __init__(self, scale):
super(GetSegPred, self).__init__()
self.scale = scale // 2
def forward(self, segs, ptcloud):
temp_cloud = torch.round((ptcloud + 1) * self.scale - 0.501).long()
temp_clo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data.dataset
assert_size_stride = torch._C._dynamo.guards.as... | melisataspinar/Concurrent-Completion-and-Part-Segmentation-for-3D-Missing-Point-Clouds-viaSynergistic-Feature-Mappi | GetSegPred | false | 10,461 | [
"MIT"
] | 0 | 3b03f3c167d9927a660d798ffcd8ecc0f5cbaf89 | https://github.com/melisataspinar/Concurrent-Completion-and-Part-Segmentation-for-3D-Missing-Point-Clouds-viaSynergistic-Feature-Mappi/tree/3b03f3c167d9927a660d798ffcd8ecc0f5cbaf89 | import torch
import torch.utils.data.dataset
class Model(torch.nn.Module):
def __init__(self, scale):
super().__init__()
self.scale = scale // 2
def forward(self, segs, ptcloud):
temp_cloud = torch.round((ptcloud + 1) * self.scale - 0.501).long()
temp_cloud[temp_cloud == -1] ... |
StyleMod | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
False, lrmul=1, bias=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
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | justinpinkney/ganspace | StyleMod | false | 10,462 | [
"Apache-2.0"
] | 0 | 7dc76d1d2ddad21d946a7ceb375efe5d5316fb3f | https://github.com/justinpinkney/ganspace/tree/7dc76d1d2ddad21d946a7ceb375efe5d5316fb3f | import torch
import torch.nn as nn
import torch.nn.functional as F
class MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
False, lrmul=1, bias=True):
super().__init_... |
MultiHeadedAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 typing import Optional
import torch.nn.functional as F
from torch import nn
def attention(query, key, value, mask=None, dropout=None):
"""Compute 'Scaled Dot Product Attention'
"""
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | malhotraa/transformer-experiments | MultiHeadedAttention | false | 10,463 | [
"MIT"
] | 0 | 82931b89b14d26dbd6e4ffef8d6f2fd8b7279c0f | https://github.com/malhotraa/transformer-experiments/tree/82931b89b14d26dbd6e4ffef8d6f2fd8b7279c0f | import math
import torch
from typing import Optional
import torch.nn.functional as F
from torch import nn
def attention(query, key, value, mask=None, dropout=None):
"""Compute 'Scaled Dot Product Attention'
"""
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
... |
GE2ELoss | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
from torch.functional import F
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.distributions
class GE2ELoss(nn.Module):
def __init__(self, i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | greenstar1151/pytorch-benchmark | GE2ELoss | false | 10,464 | [
"BSD-3-Clause"
] | 0 | 8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | https://github.com/greenstar1151/pytorch-benchmark/tree/8b7808d3be6b7ca1d57f1812e35fd2df5e470f8b | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import F
from torch.nn import functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.distributions
class Model(nn.Module):
def __init__(self, init... |
MyLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 MyLinear(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
False, lrmul=1, bias=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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | justinpinkney/ganspace | MyLinear | false | 10,465 | [
"Apache-2.0"
] | 0 | 7dc76d1d2ddad21d946a7ceb375efe5d5316fb3f | https://github.com/justinpinkney/ganspace/tree/7dc76d1d2ddad21d946a7ceb375efe5d5316fb3f | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Linear layer with equalized learning rate and custom learning rate multiplier."""
def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
False, lrmul=1, bias=True):
super().__init__()... |
VAE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Linear(784, 500)
self.fc21 = nn.Linear(500, 5)
self.fc22 = nn.Linear(500, 5)
self.fc3 = nn.Linear(... | 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 import triton_helpers
from... | mcabbott/Avalon.jl | VAE | false | 10,466 | [
"MIT"
] | 0 | 6885bcc8204952a2396e762ce51432d9969c4138 | https://github.com/mcabbott/Avalon.jl/tree/6885bcc8204952a2396e762ce51432d9969c4138 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(784, 500)
self.fc21 = nn.Linear(500, 5)
self.fc22 = nn.Linear(500, 5)
self.fc3 = nn.Linear(5, 500)... |
ContinuousNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.functional as F
def set_init(layers):
for layer in layers:
nn.init.normal_(layer.weight, mean=0.0, std=0.1)
nn.init.constant_(layer.bias, 0.0)
class ContinuousNet(nn.Module):
def __init__(self, s_dim, a_dim):
super(Conti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | lws803/pytorch-A3C | ContinuousNet | false | 10,467 | [
"MIT"
] | 0 | 944e7f42a8fa54b7d6efbe169d8a3467b20a0f7f | https://github.com/lws803/pytorch-A3C/tree/944e7f42a8fa54b7d6efbe169d8a3467b20a0f7f | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def set_init(layers):
for layer in layers:
nn.init.normal_(layer.weight, mean=0.0, std=0.1)
nn.init.constant_(layer.bias, 0.0)
class Model(nn.Module):
def __init__(self, s_dim, a_dim):
super().__init__()
... |
FullAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | from torch.nn import Module
import torch
from torch.nn import Dropout
class FullAttention(Module):
def __init__(self, use_dropout=False, attention_dropout=0.1):
super().__init__()
self.use_dropout = use_dropout
self.dropout = Dropout(attention_dropout)
def forward(self, queries, keys... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | lee-vius/LoFTR | FullAttention | false | 10,468 | [
"Apache-2.0"
] | 0 | dd9add373a20696fb6f020f4fda38bca7a91cdd9 | https://github.com/lee-vius/LoFTR/tree/dd9add373a20696fb6f020f4fda38bca7a91cdd9 | from torch.nn import Module
import torch
from torch.nn import Dropout
class Model(Module):
def __init__(self, use_dropout=False, attention_dropout=0.1):
super().__init__()
self.use_dropout = use_dropout
self.dropout = Dropout(attention_dropout)
def forward(self, queries, keys, values... |
LRN | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class LRN(nn.Module):
def __init__(self, local_size=1, alpha=0.0001, beta=0.75,
ACROSS_CHANNELS=False):
super(LRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if self.ACROSS_CHANNELS:
self.average = nn.AvgPool3d(kernel_siz... | 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_... | melster1010/VIAME | LRN | false | 10,469 | [
"BSD-3-Clause"
] | 0 | 0062265088aae65effbfcd130bfb874c343c785f | https://github.com/melster1010/VIAME/tree/0062265088aae65effbfcd130bfb874c343c785f | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, local_size=1, alpha=0.0001, beta=0.75,
ACROSS_CHANNELS=False):
super().__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if self.ACROSS_CHANNELS:
self.average = nn.AvgPool3d(kernel_size=(loca... |
LinearAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | from torch.nn import Module
import torch
def elu_feature_map(x):
return torch.nn.functional.elu(x) + 1
class LinearAttention(Module):
def __init__(self, eps=1e-06):
super().__init__()
self.feature_map = elu_feature_map
self.eps = eps
def forward(self, queries, keys, values, q_m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | lee-vius/LoFTR | LinearAttention | false | 10,470 | [
"Apache-2.0"
] | 0 | dd9add373a20696fb6f020f4fda38bca7a91cdd9 | https://github.com/lee-vius/LoFTR/tree/dd9add373a20696fb6f020f4fda38bca7a91cdd9 | from torch.nn import Module
import torch
def elu_feature_map(x):
return torch.nn.functional.elu(x) + 1
class Model(Module):
def __init__(self, eps=1e-06):
super().__init__()
self.feature_map = elu_feature_map
self.eps = eps
def forward(self, queries, keys, values, q_mask=None, ... |
Descendant | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 Descendant(nn.Module):
"""Descendant descendantEncoder model for ADDA."""
def __init__(self):
"""Init Descendant descendantEncoder."""
super(Descendant, self).__init__()
self.restored = False
self.conv1 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | lindagaw/Kadara | Descendant | false | 10,471 | [
"MIT"
] | 0 | f1059b69a581344ca460c8df02ac3f73f3fbcba1 | https://github.com/lindagaw/Kadara/tree/f1059b69a581344ca460c8df02ac3f73f3fbcba1 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
"""Descendant descendantEncoder model for ADDA."""
def __init__(self):
"""Init Descendant descendantEncoder."""
super().__init__()
self.restored = False
self.conv1 = nn.Conv2d(1, 20, kern... |
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 math
import torch
from typing import Optional
import torch.nn.functional as F
from torch import nn
def attention(query, key, value, mask=None, dropout=None):
"""Compute 'Scaled Dot Product Attention'
"""
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | malhotraa/transformer-experiments | Block | false | 10,472 | [
"MIT"
] | 0 | 82931b89b14d26dbd6e4ffef8d6f2fd8b7279c0f | https://github.com/malhotraa/transformer-experiments/tree/82931b89b14d26dbd6e4ffef8d6f2fd8b7279c0f | import math
import torch
from typing import Optional
import torch.nn.functional as F
from torch import nn
def attention(query, key, value, mask=None, dropout=None):
"""Compute 'Scaled Dot Product Attention'
"""
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
... |
BinaryLoss | # 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 BinaryLoss(nn.Module):
def __init__(self):
super(BinaryLoss, self).__init__()
def forward(self, pos_score, neg_score):
pos_loss = -F.log_softmax(pos_score)[:, 1]
neg_loss = -F.log_softmax(neg_score)[:, 0]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | melster1010/VIAME | BinaryLoss | false | 10,473 | [
"BSD-3-Clause"
] | 0 | 0062265088aae65effbfcd130bfb874c343c785f | https://github.com/melster1010/VIAME/tree/0062265088aae65effbfcd130bfb874c343c785f | 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, pos_score, neg_score):
pos_loss = -F.log_softmax(pos_score)[:, 1]
neg_loss = -F.log_softmax(neg_score)[:, 0]
loss = (pos_loss.su... |
Successor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 Successor(nn.Module):
"""Successor successorEncoder model for ADDA."""
def __init__(self):
"""Init Successor successorEncoder."""
super(Successor, self).__init__()
self.restored = False
self.conv1 = nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | lindagaw/Kadara | Successor | false | 10,474 | [
"MIT"
] | 0 | f1059b69a581344ca460c8df02ac3f73f3fbcba1 | https://github.com/lindagaw/Kadara/tree/f1059b69a581344ca460c8df02ac3f73f3fbcba1 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
"""Successor successorEncoder model for ADDA."""
def __init__(self):
"""Init Successor successorEncoder."""
super().__init__()
self.restored = False
self.conv1 = nn.Conv2d(1, 20, kernel_s... |
MHSA | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 MHSA(nn.Module):
def __init__(self, height, width, dim, head):
super(MHSA, self).__init__()
self.head = head
self.r_h = nn.Parameter(data=torch.randn(1, head, dim // head, 1,
height), requires_grad=True)
self.r_w = nn.Parameter(d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | lzu-zhanghr/vision-transformer-zoo | MHSA | false | 10,475 | [
"MIT"
] | 0 | 2cc6e3551c39816acc95ade040bbf9bd115a6b03 | https://github.com/lzu-zhanghr/vision-transformer-zoo/tree/2cc6e3551c39816acc95ade040bbf9bd115a6b03 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, height, width, dim, head):
super().__init__()
self.head = head
self.r_h = nn.Parameter(data=torch.randn(1, head, dim // head, 1,
height), requires_grad=True)
self.r_w = nn.Parameter(data=torch... |
IdentityPadding | # 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 IdentityPadding(nn.Module):
def __init__(self, in_channels, out_channels, stride):
super(IdentityPadding, self).__init__()
self.pooling = nn.MaxPool2d(1, stride=stride)
self.add_channels = out_channels - in_channels
... | 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... | moerashidi/deep_ensemble | IdentityPadding | false | 10,476 | [
"MIT"
] | 0 | 51cd890643b0f01849583e6585eef241776b0ef4 | https://github.com/moerashidi/deep_ensemble/tree/51cd890643b0f01849583e6585eef241776b0ef4 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_channels, out_channels, stride):
super().__init__()
self.pooling = nn.MaxPool2d(1, stride=stride)
self.add_channels = out_channels - in_channels
def forward(self, x):
... |
MutliClassNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 MutliClassNN(nn.Module):
def __init__(self, num_features, num_labels):
super(MutliClassNN, self).__init__()
self.fc1 = torch.nn.Linear(num_features, 1000)
self.fc3 = torch.nn.Linear(1000, num_labels)
def forward(self, x):
x = torch.relu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | mhagenow01/ECE532ClassifierComparison | MutliClassNN | false | 10,477 | [
"MIT"
] | 0 | 5066931d97aae2c25c8b9451fe3d12021f5748a1 | https://github.com/mhagenow01/ECE532ClassifierComparison/tree/5066931d97aae2c25c8b9451fe3d12021f5748a1 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, num_features, num_labels):
super().__init__()
self.fc1 = torch.nn.Linear(num_features, 1000)
self.fc3 = torch.nn.Linear(1000, num_labels)
def forward(self, x):
x = torch.relu(self.fc1(x))
x =... |
SymKlCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | mahartmann/mt-dnn | SymKlCriterion | false | 10,479 | [
"MIT"
] | 0 | c9aa3379dc255fd8fc40f24b6cd508f6a645b32f | https://github.com/mahartmann/mt-dnn/tree/c9aa3379dc255fd8fc40f24b6cd508f6a645b32f | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... |
KlCriterion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | mahartmann/mt-dnn | KlCriterion | false | 10,480 | [
"MIT"
] | 0 | c9aa3379dc255fd8fc40f24b6cd508f6a645b32f | https://github.com/mahartmann/mt-dnn/tree/c9aa3379dc255fd8fc40f24b6cd508f6a645b32f | import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
... |
FlowHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 FlowHead(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256):
super(FlowHead, self).__init__()
self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
self.relu = nn.ReLU(inp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | luyu94/RAFT | FlowHead | false | 10,481 | [
"BSD-3-Clause"
] | 0 | d0a37db031af49a5d0d9b524d214acc989becf5b | https://github.com/luyu94/RAFT/tree/d0a37db031af49a5d0d9b524d214acc989becf5b | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256):
super().__init__()
self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
self.relu = nn.ReLU(inplace=True)
d... |
DPLSTMCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Tuple
class LSTMLinear(nn.Linear):
"""
This function is the same as a nn.Linear layer, except that in the backward pass
the grad_samples get accumulated (i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | madhavajay/opacus | DPLSTMCell | false | 10,482 | [
"Apache-2.0"
] | 0 | 7ae098764b4cf2388c66e263dd8d56bca0a290d0 | https://github.com/madhavajay/opacus/tree/7ae098764b4cf2388c66e263dd8d56bca0a290d0 | import math
import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Tuple
class LSTMLinear(nn.Linear):
"""
This function is the same as a nn.Linear layer, except that in the backward pass
the grad_samples get accumulated (i... |
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... | from _paritybench_helpers import _mock_config
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 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | FengMingquan-sjtu/pytorchic-bert | PositionWiseFeedForward | false | 10,483 | [
"Apache-2.0"
] | 0 | 83d616fb9c7e1d5c3646f9b6267ca912e2616d65 | https://github.com/FengMingquan-sjtu/pytorchic-bert/tree/83d616fb9c7e1d5c3646f9b6267ca912e2616d65 | from _paritybench_helpers import _mock_config
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 ... |
AttentionUnit | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
from torch.nn import init
class AttentionUnit(nn.Module):
def __init__(self, sDim, xDim, attDim):
super(AttentionUnit, self).__init__()
self.sDim = sDim
self.xDim = xDim
self.attDim = attDim
self.sEmbed = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | lohzhunyewcs/aster.pytorch | AttentionUnit | false | 10,484 | [
"MIT"
] | 0 | 9441d386135a73b1baa3ec8c505f5eba99c26905 | https://github.com/lohzhunyewcs/aster.pytorch/tree/9441d386135a73b1baa3ec8c505f5eba99c26905 | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import init
class Model(nn.Module):
def __init__(self, sDim, xDim, attDim):
super().__init__()
self.sDim = sDim
self.xDim = xDim
self.attDim = attDim
self.sEmbed = nn.Linear(sDim, attDim)
... |
FeatureResizer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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
import torch.optim
import torch.utils
from torch import nn
import torch.distributed
class FeatureResizer(nn.Module):
"""
This class takes as input a set of embeddings of dimension C1 and outputs a set of
embedding of dimension C2, after a l... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | mmaaz60/mdetr | FeatureResizer | false | 10,485 | [
"Apache-2.0"
] | 0 | fe1394c67e76a6c7e521bbda77d8294714038a3a | https://github.com/mmaaz60/mdetr/tree/fe1394c67e76a6c7e521bbda77d8294714038a3a | import torch
import torch.utils.data
import torch
import torch.nn
import torch.optim
import torch.utils
from torch import nn
import torch.distributed
class Model(nn.Module):
"""
This class takes as input a set of embeddings of dimension C1 and outputs a set of
embedding of dimension C2, after a linear tra... |
PairwiseRankerModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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.onnx
import torch.nn as nn
class PairwiseRankerModel(nn.Module):
def __init__(self, embedding_size):
super(PairwiseRankerModel, self).__init__()
self.query_doc_transform = torch.nn.Linear(in_features=
embedding_size * 2, out_features=embedding_size)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.onnx
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | mikhail-tsir/vespa-exloration | PairwiseRankerModel | false | 10,486 | [
"Apache-2.0"
] | 0 | 9bebc00acb43021fa60c6e144fe4f1fa1d7719fc | https://github.com/mikhail-tsir/vespa-exloration/tree/9bebc00acb43021fa60c6e144fe4f1fa1d7719fc | import torch
import torch.onnx
import torch.nn as nn
class Model(nn.Module):
def __init__(self, embedding_size):
super().__init__()
self.query_doc_transform = torch.nn.Linear(in_features=
embedding_size * 2, out_features=embedding_size)
self.compare_transform = torch.nn.Linear... |
DNN_Classifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 DNN_Classifier(torch.nn.Module):
def __init__(self, input_dim, nb_categories, hidden_dim=100):
super(DNN_Classifier, self).__init__()
self.fc_1 = nn.Linear(input_dim, hidden_dim)
self.fc_2 = nn.Linear(hidden_dim, nb_categories)
self.softmax ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | mleila/AGNews_Document_Classifcation | DNN_Classifier | false | 10,487 | [
"MIT"
] | 0 | 1ff44edf1fcaaee582b79141a419d61df62da56e | https://github.com/mleila/AGNews_Document_Classifcation/tree/1ff44edf1fcaaee582b79141a419d61df62da56e | import torch
from torch import nn
class Model(torch.nn.Module):
def __init__(self, input_dim, nb_categories, hidden_dim=100):
super().__init__()
self.fc_1 = nn.Linear(input_dim, hidden_dim)
self.fc_2 = nn.Linear(hidden_dim, nb_categories)
self.softmax = nn.Softmax(dim=1)
def ... |
PositionEmbs | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 PositionEmbs(nn.Module):
def __init__(self, num_patches, emb_dim, dropout_rate=0.1):
super(PositionEmbs, self).__init__()
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1,
emb_dim))
if dropout_rate > 0:
self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | longxianlei/UtilsTools | PositionEmbs | false | 10,488 | [
"MIT"
] | 0 | f45c648eb679ed59bb512b61a1af52938e326ac3 | https://github.com/longxianlei/UtilsTools/tree/f45c648eb679ed59bb512b61a1af52938e326ac3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_patches, emb_dim, dropout_rate=0.1):
super().__init__()
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1,
emb_dim))
if dropout_rate > 0:
self.dropout = nn.Dropout(drop... |
FPNHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, 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 FPNHead(nn.Module):
def __init__(self, num_in, num_mid, num_out):
super().__init__()
self.block0 = nn.Conv2d(num_in, num_mid, kernel_size=3, padding=1,
bias=False)
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | lePossum/DeblurGANv2 | FPNHead | false | 10,489 | [
"BSD-2-Clause"
] | 0 | b02c86de98f98604e2416a3a6121110ede7a2de9 | https://github.com/lePossum/DeblurGANv2/tree/b02c86de98f98604e2416a3a6121110ede7a2de9 | 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, num_in, num_mid, num_out):
super().__init__()
self.block0 = nn.Conv2d(num_in, num_mid, kernel_size=3, padding=1,
bias=False)
self.b... |
ClipLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
def clip_data(data, max_norm):
norms = torch.norm(data.reshape(data.shape[0], -1), dim=-1)
scale = (max_norm / norms).clamp(max=1.0)
data *= scale.reshape(-1, 1, 1, 1)
return data
class ClipLayer(nn.Module):
def __init__(self, max_norm):
super(ClipLaye... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | lxuechen/Handcrafted-DP | ClipLayer | false | 10,490 | [
"MIT"
] | 0 | 64ca4759238027e307d8e88215a0a86fc8f3b395 | https://github.com/lxuechen/Handcrafted-DP/tree/64ca4759238027e307d8e88215a0a86fc8f3b395 | import torch
import torch.nn as nn
def clip_data(data, max_norm):
norms = torch.norm(data.reshape(data.shape[0], -1), dim=-1)
scale = (max_norm / norms).clamp(max=1.0)
data *= scale.reshape(-1, 1, 1, 1)
return data
class Model(nn.Module):
def __init__(self, max_norm):
super().__init__()... |
PolynomialEnvelope | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class PolynomialEnvelope(torch.nn.Module):
"""
Polynomial envelope function that ensures a smooth cutoff.
Parameters
----------
exponent: int
Exponent of the envelope function.
"""
def __init__(self, exponent):
super().__init__()
assert expone... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | krylea/ocp | PolynomialEnvelope | false | 10,491 | [
"MIT"
] | 0 | 00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | https://github.com/krylea/ocp/tree/00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | import torch
class Model(torch.nn.Module):
"""
Polynomial envelope function that ensures a smooth cutoff.
Parameters
----------
exponent: int
Exponent of the envelope function.
"""
def __init__(self, exponent):
super().__init__()
assert exponent > 0
... |
ScaledSiLU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class ScaledSiLU(torch.nn.Module):
def __init__(self):
super().__init__()
self.scale_factor = 1 / 0.6
self._activation = torch.nn.SiLU()
def forward(self, x):
return self._activation(x) * self.scale_factor
def get_inputs():
return [torch.rand([4, 4, 4, 4])]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | krylea/ocp | ScaledSiLU | false | 10,492 | [
"MIT"
] | 0 | 00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | https://github.com/krylea/ocp/tree/00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.scale_factor = 1 / 0.6
self._activation = torch.nn.SiLU()
def forward(self, x):
return self._activation(x) * self.scale_factor
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
de... |
SiQU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class SiQU(torch.nn.Module):
def __init__(self):
super().__init__()
self._activation = torch.nn.SiLU()
def forward(self, x):
return x * self._activation(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | krylea/ocp | SiQU | false | 10,493 | [
"MIT"
] | 0 | 00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | https://github.com/krylea/ocp/tree/00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self._activation = torch.nn.SiLU()
def forward(self, x):
return x * self._activation(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
DPSLTMAdapter | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Tuple
from typing import List
from typing import Optional
from typing import Dict
from typing import Union
from torch.nn.modules.module import _... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | madhavajay/opacus | DPSLTMAdapter | false | 10,494 | [
"Apache-2.0"
] | 0 | 7ae098764b4cf2388c66e263dd8d56bca0a290d0 | https://github.com/madhavajay/opacus/tree/7ae098764b4cf2388c66e263dd8d56bca0a290d0 | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Tuple
from typing import List
from typing import Optional
from typing import Dict
from typing import Union
from torch.nn.modules.module import _... |
CombineSlices | # 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
import torch.utils.data.distributed
import torch.optim
class CombineSlices(nn.Module):
def __init__(self, slice_dim=2):
super().__init__()
self.slice_dim = slice_dim
def forward(self, x):
return torch.index_select(x, dim=self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
assert_size_stride = torch._C._dynamo.gu... | kapoor1992/fastMRI | CombineSlices | false | 10,495 | [
"MIT"
] | 0 | 6b0af94663faa55a2dd901a6a5cbb7d7b5f4cf6d | https://github.com/kapoor1992/fastMRI/tree/6b0af94663faa55a2dd901a6a5cbb7d7b5f4cf6d | import torch
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
class Model(nn.Module):
def __init__(self, slice_dim=2):
super().__init__()
self.slice_dim = slice_dim
def forward(self, x):
return torch.index_select(x, dim=self.slice_di... |
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, n_h):
super(Discriminator, self).__init__()
self.f_k = nn.Bilinear(n_h, n_h, 1)
for m in self.modules():
self.weights_init(m)
def weights_init(self, m):
if isinstance(m, nn.Bilin... | 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... | mess-clarifai/DGI | Discriminator | false | 10,496 | [
"MIT"
] | 0 | 3a7c96d59991d448b84d709916d1d5f256e5b9be | https://github.com/mess-clarifai/DGI/tree/3a7c96d59991d448b84d709916d1d5f256e5b9be | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_h):
super().__init__()
self.f_k = nn.Bilinear(n_h, n_h, 1)
for m in self.modules():
self.weights_init(m)
def weights_init(self, m):
if isinstance(m, nn.Bilinear):
torch.nn.... |
SphericalBesselBasis | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import numpy as np
class SphericalBesselBasis(torch.nn.Module):
"""
1D spherical Bessel basis
Parameters
----------
num_radial: int
Controls maximum frequency.
cutoff: float
Cutoff distance in Angstrom.
"""
def __init__(self, num_radial: 'int'... | 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 math
import numpy as np
assert_size_stride = torch._C._dynamo.guar... | krylea/ocp | SphericalBesselBasis | false | 10,497 | [
"MIT"
] | 0 | 00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | https://github.com/krylea/ocp/tree/00fc1df29731d70ff1b5cf8e9323d1d2f1f8e540 | import math
import torch
import numpy as np
class Model(torch.nn.Module):
"""
1D spherical Bessel basis
Parameters
----------
num_radial: int
Controls maximum frequency.
cutoff: float
Cutoff distance in Angstrom.
"""
def __init__(self, num_radial: 'int', cutoff: 'floa... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.