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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import numpy as np
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super(ScaledDotProductAttention, self).__init__()
self.temperature = temperature
self.dropout = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Xlinford/TDNet | ScaledDotProductAttention | false | 2,968 | [
"MIT"
] | 0 | e7cb59c40b8751b6dab9691d26ad224fd61c24d1 | https://github.com/Xlinford/TDNet/tree/e7cb59c40b8751b6dab9691d26ad224fd61c24d1 | import torch
import numpy as np
import torch.nn as nn
class Model(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
self.softmax = nn.Sof... |
ConvCompress | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 ConvCompress(nn.Module):
def __init__(self, d_model, ratio=4, groups=1):
super().__init__()
self.conv = nn.Conv1d(d_model, d_model, ratio, stride=ratio, groups
=groups)
def forward(self, mem):
mem = mem.transpose(1, 2)
compr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Xinxinatg/DM-Count | ConvCompress | false | 2,969 | [
"MIT"
] | 0 | 9ac3327e26c0ede219bd44cb5a4ae6db9fded045 | https://github.com/Xinxinatg/DM-Count/tree/9ac3327e26c0ede219bd44cb5a4ae6db9fded045 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, d_model, ratio=4, groups=1):
super().__init__()
self.conv = nn.Conv1d(d_model, d_model, ratio, stride=ratio, groups
=groups)
def forward(self, mem):
mem = mem.transpose(1, 2)
compressed_m... |
NICEMLPBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 LinearWeightNorm(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super(LinearWeightNorm, self).__init__()
self.linear = nn.Linear(in_features, out_features, bias=bias)
self.reset_parameters()
def reset_parameters(self):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | TRUMANCFY/wolf | NICEMLPBlock | false | 2,970 | [
"Apache-2.0"
] | 0 | 1a21479256e4f51885e2d2fdd449b1faa61277a6 | https://github.com/TRUMANCFY/wolf/tree/1a21479256e4f51885e2d2fdd449b1faa61277a6 | import torch
import torch.nn as nn
class LinearWeightNorm(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super().__init__()
self.linear = nn.Linear(in_features, out_features, bias=bias)
self.reset_parameters()
def reset_parameters(self):
nn.init.normal_... |
SummaryNet_large | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 SummaryNet_large(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv1d(in_channels=2, out_channels=20, kernel_size=
3, padding=2)
self.pool = nn.MaxPool1d(kernel_size=5, stride=5)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Wrede/BNN-LFI | SummaryNet_large | false | 2,971 | [
"MIT"
] | 0 | 8c5094f01c1eef286bdd84613c7259d534d2eb7e | https://github.com/Wrede/BNN-LFI/tree/8c5094f01c1eef286bdd84613c7259d534d2eb7e | 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.Conv1d(in_channels=2, out_channels=20, kernel_size=
3, padding=2)
self.pool = nn.MaxPool1d(kernel_size=5, stride=5)
self.fc... |
ResidualBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 ResidualBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.conv0 = nn.Conv2d(in_channels=channels, out_channels=channels,
kernel_size=3, padding=1)
self.conv1 = nn.Conv2d(in_channels=channels, out_channels=channels,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Thaigun/Griddly | ResidualBlock | false | 2,972 | [
"MIT"
] | 0 | de5972a608a2928172510a0ac81a977c48af6b1f | https://github.com/Thaigun/Griddly/tree/de5972a608a2928172510a0ac81a977c48af6b1f | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, channels):
super().__init__()
self.conv0 = nn.Conv2d(in_channels=channels, out_channels=channels,
kernel_size=3, padding=1)
self.conv1 = nn.Conv2d(in_channels=channels, out_channels=channels,
... |
FPNOutput | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 ConvBNReLU(nn.Module):
def __init__(self, in_chan, out_chan, ks=1, stride=1, padding=0,
norm_layer=None, bias=True, *args, **kwargs):
super(ConvBNReLU, self).__init__()
self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, 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... | Xlinford/TDNet | FPNOutput | false | 2,974 | [
"MIT"
] | 0 | e7cb59c40b8751b6dab9691d26ad224fd61c24d1 | https://github.com/Xlinford/TDNet/tree/e7cb59c40b8751b6dab9691d26ad224fd61c24d1 | import torch
import torch.nn as nn
class ConvBNReLU(nn.Module):
def __init__(self, in_chan, out_chan, ks=1, stride=1, padding=0,
norm_layer=None, bias=True, *args, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=
stride, padding=pa... |
Net_Tran | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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
class Net_Tran(nn.Module):
def __init__(self):
super(Net_Tran, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 3, stride=2, padding=1)
self.deconv1 = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.u... | YibinXie/Pose_Estimation | Net_Tran | false | 2,975 | [
"MIT"
] | 0 | 5849140bf842bf3aeaad75827f5e7b7f2999c9ee | https://github.com/YibinXie/Pose_Estimation/tree/5849140bf842bf3aeaad75827f5e7b7f2999c9ee | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, 3, stride=2, padding=1)
self.deconv1 = nn.ConvTranspose2d... |
FlatCat | # 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 FlatCat(torch.nn.Module):
def __init__(self):
super(FlatCat, self).__init__()
def forward(self, x, y):
x = x.view(x.shape[0], -1, 1, 1)
y = y.view(y.shape[0], -1, 1, 1)
return torch.cat([x, y], 1)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | YirongMao/torch2trt | FlatCat | false | 2,976 | [
"MIT"
] | 0 | 7635051998a9cd6b9483da1569814031c04a1b52 | https://github.com/YirongMao/torch2trt/tree/7635051998a9cd6b9483da1569814031c04a1b52 | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
x = x.view(x.shape[0], -1, 1, 1)
y = y.view(y.shape[0], -1, 1, 1)
return torch.cat([x, y], 1)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4,... |
MagnitudeTestModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
def fill_bias(module, value):
module.bias.data.fill_(value)
def fill_conv_weight(conv, value):
conv.weight.data... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torchvision import models as models
import torch.nn.pa... | JinYAnGHe/openvino_training_extensions | MagnitudeTestModel | false | 2,977 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
def fill_bias(module, value):
module.bias.data.fill_(value)
def fill_conv_weight(conv, value):
conv.weight.data... |
ResidualBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
from torch import nn
class ResidualBlock(nn.Module):
def __init__(self, in_channels, hidden, out_channels):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=hidden,
kernel_size=3, stride=1, padding=1)
self.rel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | YigitGunduc/self-driving-car | ResidualBlock | false | 2,978 | [
"MIT"
] | 0 | 2be31f6473c911cf004236ce0874cb2da8fe8ad1 | https://github.com/YigitGunduc/self-driving-car/tree/2be31f6473c911cf004236ce0874cb2da8fe8ad1 | import torch
import torch.utils.data
from torch import nn
class Model(nn.Module):
def __init__(self, in_channels, hidden, out_channels):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=hidden,
kernel_size=3, stride=1, padding=1)
self.relu1 = nn.... |
FusedLeakyReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
return F.leaky_relu(input + bias.view(1, bias.shape[0], *rest_dim),
negative_slope=negative_slop... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda... | YotamNitzan/pixel2style2pixel | FusedLeakyReLU | false | 2,979 | [
"MIT"
] | 0 | b943f9e6de046a54b901eea1d8714cb02a71605f | https://github.com/YotamNitzan/pixel2style2pixel/tree/b943f9e6de046a54b901eea1d8714cb02a71605f | import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
return F.leaky_relu(input + bias.view(1, bias.shape[0], *rest_dim),
negative_slope=negative_slop... |
GAT | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, self).__init__(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | XiangwenNing/pyGAT | GAT | false | 2,980 | [
"MIT"
] | 0 | c4bd8e2be044c6c7481d484875b3c318271cca9c | https://github.com/XiangwenNing/pyGAT/tree/c4bd8e2be044c6c7481d484875b3c318271cca9c | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super().__init__()
self.dropout = ... |
GraphLevelAttentionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 GraphLevelAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features):
super(GraphLevelAttentionLayer, self).__init__()
self.in_f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Yonggie/HsCTRD | GraphLevelAttentionLayer | false | 2,981 | [
"Apache-2.0"
] | 0 | d4541f13630a6abd0e17b116ad6aeeab74f54f1c | https://github.com/Yonggie/HsCTRD/tree/d4541f13630a6abd0e17b116ad6aeeab74f54f1c | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features):
super().__init__()
self.in_features = in_features
self.out_features =... |
UpsampleBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 PixelShuffle1d(nn.Module):
def __init__(self, upscale_factor):
super().__init__()
self.upscale_factor = upscale_factor
def forward(self, x):
batch_size = x.shape[0]
short_channel_len = x.shape[1]
short_width = x.shape[2]
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Yotsuyubi/drumgan | UpsampleBlock | false | 2,982 | [
"MIT"
] | 0 | eb6a9aa8b5c0d64bad65e4dbd14d444b7a859a29 | https://github.com/Yotsuyubi/drumgan/tree/eb6a9aa8b5c0d64bad65e4dbd14d444b7a859a29 | import torch
import torch.nn as nn
class PixelShuffle1d(nn.Module):
def __init__(self, upscale_factor):
super().__init__()
self.upscale_factor = upscale_factor
def forward(self, x):
batch_size = x.shape[0]
short_channel_len = x.shape[1]
short_width = x.shape[2]
... |
Generator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 Generator(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, hidden_size, vocab_size):
super(Generator, self).__init__()
self.proj = nn.Linear(hidden_size, vocab_size, bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Yixuan-Lee/yixuan-lee.github.io | Generator | false | 2,983 | [
"MIT"
] | 0 | 139dd141544302ca1802a6104f7db7aeb1ace825 | https://github.com/Yixuan-Lee/yixuan-lee.github.io/tree/139dd141544302ca1802a6104f7db7aeb1ace825 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, hidden_size, vocab_size):
super().__init__()
self.proj = nn.Linear(hidden_size, vocab_size, bias=False)
def forward(self, ... |
SamePaddingConv1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 SamePaddingConv1d(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size):
super().__init__()
self.conv = nn.Conv1d(in_dim, out_dim, kernel_size, padding=int((
kernel_size - 1) / 2))
def forward(self, x):
return self.conv(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Yotsuyubi/drumgan | SamePaddingConv1d | false | 2,984 | [
"MIT"
] | 0 | eb6a9aa8b5c0d64bad65e4dbd14d444b7a859a29 | https://github.com/Yotsuyubi/drumgan/tree/eb6a9aa8b5c0d64bad65e4dbd14d444b7a859a29 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_dim, out_dim, kernel_size):
super().__init__()
self.conv = nn.Conv1d(in_dim, out_dim, kernel_size, padding=int((
kernel_size - 1) / 2))
def forward(self, x):
return self.conv(x)
def get_inp... |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.distributions
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, n_heads, kq_same=False, bias=True):
super().__init__()
"""
It has projection layer for getting keys, queries and values. Followed by attention.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Yingting-dev/ReChorus | MultiHeadAttention | false | 2,985 | [
"MIT"
] | 0 | a16bc1e42f3e90e889133d7476c52ada44db573b | https://github.com/Yingting-dev/ReChorus/tree/a16bc1e42f3e90e889133d7476c52ada44db573b | import torch
import numpy as np
import torch.nn as nn
import torch.distributions
class Model(nn.Module):
def __init__(self, d_model, n_heads, kq_same=False, bias=True):
super().__init__()
"""
It has projection layer for getting keys, queries and values. Followed by attention.
"""
... |
LNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.functional as F
import torch.utils.data
class LNN(torch.nn.Module):
"""
A pytorch implementation of LNN layer
Input shape
- A 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
Output shape
- 2D tensor with shape:``(batch_size,LNN... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ZEKAICHEN/RecSys | LNN | false | 2,986 | [
"MIT"
] | 0 | 7ab66b4a6cee620cc4baeb00f916ff329834f903 | https://github.com/ZEKAICHEN/RecSys/tree/7ab66b4a6cee620cc4baeb00f916ff329834f903 | import math
import torch
import torch.nn.functional as F
import torch.utils.data
class Model(torch.nn.Module):
"""
A pytorch implementation of LNN layer
Input shape
- A 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
Output shape
- 2D tensor with shape:``(batch_size,L... |
FirstStage | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
padding=0, b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | YibinXie/Pose_Estimation | FirstStage | false | 2,987 | [
"MIT"
] | 0 | 5849140bf842bf3aeaad75827f5e7b7f2999c9ee | https://github.com/YibinXie/Pose_Estimation/tree/5849140bf842bf3aeaad75827f5e7b7f2999c9ee | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
padding=0, b... |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YufengJin/deep-reinforcement-learning | Actor | false | 2,988 | [
"MIT"
] | 0 | 141cf00f169b46aa492c9e7520429bfdaab0117d | https://github.com/YufengJin/deep-reinforcement-learning/tree/141cf00f169b46aa492c9e7520429bfdaab0117d | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Model(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... |
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.utils.data
import torch
import torch.nn as nn
class ResBlock(nn.Module):
def __init__(self, inFe):
super(ResBlock, self).__init__()
self.conv1 = nn.Conv2d(inFe, inFe, 3, 1, 1)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(inFe, inFe, 3, 1, 1)
def forw... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | ZhibingLai/MSFN | ResBlock | false | 2,989 | [
"Apache-2.0"
] | 0 | eb650c351edf27270bc32b50b60842a9fe40308e | https://github.com/ZhibingLai/MSFN/tree/eb650c351edf27270bc32b50b60842a9fe40308e | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, inFe):
super().__init__()
self.conv1 = nn.Conv2d(inFe, inFe, 3, 1, 1)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(inFe, inFe, 3, 1, 1)
def forward(self, x):
... |
AvgReducePool1d | # 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 AvgReducePool1d(nn.Module):
"""A subclass of :torch_nn:`Module`.
Avg Pool layer for 1D inputs. The same as :torch_nn:`AvgPool1d` except that
the pooling dimension is entirely reduced (i.e., `pool_size=input_length`).
"""
def forward(self, input: 'torch.Tens... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | ZhitingHu/texar-pytorch | AvgReducePool1d | false | 2,990 | [
"Apache-2.0"
] | 0 | 72ea115013ced8a5a2b004eacf6271184d3572a8 | https://github.com/ZhitingHu/texar-pytorch/tree/72ea115013ced8a5a2b004eacf6271184d3572a8 | import torch
from torch import nn
class Model(nn.Module):
"""A subclass of :torch_nn:`Module`.
Avg Pool layer for 1D inputs. The same as :torch_nn:`AvgPool1d` except that
the pooling dimension is entirely reduced (i.e., `pool_size=input_length`).
"""
def forward(self, input: 'torch.Tensor') ->tor... |
FeatureAssembler | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from typing import Optional
import torch.nn as nn
class FeatureAssembler(nn.Module):
def __init__(self, T: 'int', embed_static: 'Optional[FeatureEmbedder]'=
None, embed_dynamic: 'Optional[FeatureEmbedder]'=None) ->None:
super().__init__()
self.T = T
self.embeddings = ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from typing import Optional
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch... | ZhuangweiKang/pytorch-ts | FeatureAssembler | false | 2,991 | [
"Apache-2.0",
"MIT"
] | 0 | 076d456358fd1bac96becba4f1ba38ec5a5fcf4d | https://github.com/ZhuangweiKang/pytorch-ts/tree/076d456358fd1bac96becba4f1ba38ec5a5fcf4d | import torch
from typing import Optional
import torch.nn as nn
class Model(nn.Module):
def __init__(self, T: 'int', embed_static: 'Optional[FeatureEmbedder]'=
None, embed_dynamic: 'Optional[FeatureEmbedder]'=None) ->None:
super().__init__()
self.T = T
self.embeddings = nn.ModuleDi... |
TimeIntervalMultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.distributions
class TimeIntervalMultiHeadAttention(nn.Module):
def __init__(self, d_model, n_heads, kq_same=False, bias=True):
super().__init__()
"""
It also needs position and interaction (time interval) key/value input.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Yingting-dev/ReChorus | TimeIntervalMultiHeadAttention | false | 2,992 | [
"MIT"
] | 0 | a16bc1e42f3e90e889133d7476c52ada44db573b | https://github.com/Yingting-dev/ReChorus/tree/a16bc1e42f3e90e889133d7476c52ada44db573b | import torch
import numpy as np
import torch.nn as nn
import torch.distributions
class Model(nn.Module):
def __init__(self, d_model, n_heads, kq_same=False, bias=True):
super().__init__()
"""
It also needs position and interaction (time interval) key/value input.
"""
self.... |
CondUpsampler | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 CondUpsampler(nn.Module):
def __init__(self, cond_length, target_dim):
super().__init__()
self.linear1 = nn.Linear(cond_length, target_dim // 2)
self.linear2 = nn.Linear(target_dim // 2, target_dim)
def forward(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ZhuangweiKang/pytorch-ts | CondUpsampler | false | 2,993 | [
"Apache-2.0",
"MIT"
] | 0 | 076d456358fd1bac96becba4f1ba38ec5a5fcf4d | https://github.com/ZhuangweiKang/pytorch-ts/tree/076d456358fd1bac96becba4f1ba38ec5a5fcf4d | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, cond_length, target_dim):
super().__init__()
self.linear1 = nn.Linear(cond_length, target_dim // 2)
self.linear2 = nn.Linear(target_dim // 2, target_dim)
def forward(self, x)... |
GatedLinearUnit | # 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 GatedLinearUnit(nn.Module):
def __init__(self, dim: 'int'=-1, nonlinear: 'bool'=True):
super().__init__()
self.dim = dim
self.nonlinear = nonlinear
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
val, gate = torch.chunk(x, 2, dim=... | 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_... | ZhuangweiKang/pytorch-ts | GatedLinearUnit | false | 2,994 | [
"Apache-2.0",
"MIT"
] | 0 | 076d456358fd1bac96becba4f1ba38ec5a5fcf4d | https://github.com/ZhuangweiKang/pytorch-ts/tree/076d456358fd1bac96becba4f1ba38ec5a5fcf4d | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dim: 'int'=-1, nonlinear: 'bool'=True):
super().__init__()
self.dim = dim
self.nonlinear = nonlinear
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
val, gate = torch.chunk(x, 2, dim=self.dim)
... |
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... | import math
import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
return F.leaky_relu(input + bias.view(1, bias.shape[0], *rest_dim),
negative_slope=n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | YotamNitzan/pixel2style2pixel | StyledConv | false | 2,995 | [
"MIT"
] | 0 | b943f9e6de046a54b901eea1d8714cb02a71605f | https://github.com/YotamNitzan/pixel2style2pixel/tree/b943f9e6de046a54b901eea1d8714cb02a71605f | import math
import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
return F.leaky_relu(input + bias.view(1, bias.shape[0], *rest_dim),
negative_slope=n... |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | YufengJin/deep-reinforcement-learning | Critic | false | 2,996 | [
"MIT"
] | 0 | 141cf00f169b46aa492c9e7520429bfdaab0117d | https://github.com/YufengJin/deep-reinforcement-learning/tree/141cf00f169b46aa492c9e7520429bfdaab0117d | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Model(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, f... |
TimeIntervalTransformerLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.distributions
class TimeIntervalMultiHeadAttention(nn.Module):
def __init__(self, d_model, n_heads, kq_same=False, bias=True):
super().__init__()
"""
It also needs position and interaction (time interval) key/value input.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Yingting-dev/ReChorus | TimeIntervalTransformerLayer | false | 2,997 | [
"MIT"
] | 0 | a16bc1e42f3e90e889133d7476c52ada44db573b | https://github.com/Yingting-dev/ReChorus/tree/a16bc1e42f3e90e889133d7476c52ada44db573b | import torch
import numpy as np
import torch.nn as nn
import torch.distributions
class TimeIntervalMultiHeadAttention(nn.Module):
def __init__(self, d_model, n_heads, kq_same=False, bias=True):
super().__init__()
"""
It also needs position and interaction (time interval) key/value input.
... |
GeneralizedMeanPooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torchvision.transforms import *
from torch import nn
class GeneralizedMeanPooling(nn.Module):
"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
- At p = infinity, one... | 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 torchvision.transforms ... | ZoRoronoa/Camera-Aware-Proxy | GeneralizedMeanPooling | false | 2,998 | [
"Apache-2.0"
] | 0 | 352f900bbae330f18c2bfe2b3f2516fb4e31adea | https://github.com/ZoRoronoa/Camera-Aware-Proxy/tree/352f900bbae330f18c2bfe2b3f2516fb4e31adea | import torch
from torchvision.transforms import *
from torch import nn
class Model(nn.Module):
"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
- At p = infinity, one gets Max Pooling... |
SpatialGather_Module | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._utils
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
O... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Zhoushanglin100/Cityscape-model | SpatialGather_Module | false | 2,999 | [
"BSD-3-Clause"
] | 0 | 62b3d25712f16f01d951d5168d0f11e3133cd06b | https://github.com/Zhoushanglin100/Cityscape-model/tree/62b3d25712f16f01d951d5168d0f11e3133cd06b | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._utils
class Model(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
Output:
... |
ToLongTensor | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import Tensor
from typing import List
import torch.nn as nn
class ToLongTensor(nn.Module):
"""Convert a list of integers to long tensor
"""
def __init__(self):
super(ToLongTensor, self).__init__()
def forward(self, tokens: 'List[List[int]]') ->Tensor:
return t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ZongHR/text | ToLongTensor | false | 3,000 | [
"BSD-3-Clause"
] | 0 | 856607154be7c784505869f10ae578346868b121 | https://github.com/ZongHR/text/tree/856607154be7c784505869f10ae578346868b121 | import torch
from torch import Tensor
from typing import List
import torch.nn as nn
class Model(nn.Module):
"""Convert a list of integers to long tensor
"""
def __init__(self):
super().__init__()
def forward(self, tokens: 'List[List[int]]') ->Tensor:
return torch.tensor(tokens)
def... |
SirenLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
class Sine(nn.Module):
def __init__(self, w0=30.0):
super().__init__()
self.w0 = w0
def forward(self, x):
return torch.sin(self.w0 * x)
class SirenLayer(nn.Module):
def __init__(self, input_dim, hidden_dim, use_bias=True, w0=1.0,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
i... | ZixiuHuang/nex-code | SirenLayer | false | 3,001 | [
"MIT"
] | 0 | c9432fb675914391b4de4786220351a0dc35aecb | https://github.com/ZixiuHuang/nex-code/tree/c9432fb675914391b4de4786220351a0dc35aecb | import math
import torch
import torch.nn as nn
class Sine(nn.Module):
def __init__(self, w0=30.0):
super().__init__()
self.w0 = w0
def forward(self, x):
return torch.sin(self.w0 * x)
class Model(nn.Module):
def __init__(self, input_dim, hidden_dim, use_bias=True, w0=1.0,
... |
Biaffine | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from typing import Callable
from typing import Optional
from torch import nn
class Biaffine(nn.Module):
def __init__(self, in1_features: 'int', in2_features: 'int',
out_features: 'int', init_func: 'Optional[Callable]'=None) ->None:
super(Biaffine, self).__init__()
self.in1_fe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from typing import Callable
from typing import Optional
from torch import nn
ass... | Zzoay/dependency_representations | Biaffine | false | 3,002 | [
"Apache-2.0"
] | 0 | 7f4726629878aaf9bfee645fe1b11032df05c82e | https://github.com/Zzoay/dependency_representations/tree/7f4726629878aaf9bfee645fe1b11032df05c82e | import torch
from typing import Callable
from typing import Optional
from torch import nn
class Model(nn.Module):
def __init__(self, in1_features: 'int', in2_features: 'int',
out_features: 'int', init_func: 'Optional[Callable]'=None) ->None:
super().__init__()
self.in1_features = in1_feat... |
RobertaClassificationHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 typing import Optional
class RobertaClassificationHead(nn.Module):
def __init__(self, num_classes, input_dim, inner_dim: 'Optional[int]'=
None, dropout: 'float'=0.1, activation=nn.ReLU):
super().__init__()
if not inner_dim:
inner_dim = 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
import torch.nn as nn
from ty... | ZongHR/text | RobertaClassificationHead | false | 3,003 | [
"BSD-3-Clause"
] | 0 | 856607154be7c784505869f10ae578346868b121 | https://github.com/ZongHR/text/tree/856607154be7c784505869f10ae578346868b121 | import torch
import torch.nn as nn
from typing import Optional
class Model(nn.Module):
def __init__(self, num_classes, input_dim, inner_dim: 'Optional[int]'=
None, dropout: 'float'=0.1, activation=nn.ReLU):
super().__init__()
if not inner_dim:
inner_dim = input_dim
sel... |
RobertaMaskLeanerHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class RobertaMaskLeanerHead(nn.Module):
"""
Head for mask leaner.
input: (batch, src_lens, embed_dim)
output: (batch, src_lens，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.... | a1600012888/fairseq | RobertaMaskLeanerHead | false | 3,004 | [
"MIT"
] | 0 | dbd2cd08fc396f919d2e737513095fcb966896c0 | https://github.com/a1600012888/fairseq/tree/dbd2cd08fc396f919d2e737513095fcb966896c0 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Model(nn.Module):
"""
Head for mask leaner.
input: (batch, src_lens, embed_dim)
output: (batch, src_lens，1)
"""
def _... |
Block | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm(nn.Module):
""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
shape (batch_size, height, width, c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ZhijieXiao-0624/CNXA | Block | false | 3,005 | [
"MIT"
] | 0 | a63b3561010cf87f696a005f8ea252e7cdaa7ca2 | https://github.com/ZhijieXiao-0624/CNXA/tree/a63b3561010cf87f696a005f8ea252e7cdaa7ca2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm(nn.Module):
""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
shape (batch_size, height, width, c... |
LinearWeightNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 LinearWeightNorm(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super(LinearWeightNorm, self).__init__()
self.linear = nn.Linear(in_features, out_features, bias=bias)
self.reset_parameters()
def reset_parameters(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 ... | TRUMANCFY/wolf | LinearWeightNorm | false | 3,006 | [
"Apache-2.0"
] | 0 | 1a21479256e4f51885e2d2fdd449b1faa61277a6 | https://github.com/TRUMANCFY/wolf/tree/1a21479256e4f51885e2d2fdd449b1faa61277a6 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super().__init__()
self.linear = nn.Linear(in_features, out_features, bias=bias)
self.reset_parameters()
def reset_parameters(self):
nn.init.normal_(self.linea... |
ConvBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 *
import torch.nn.functional as F
class ConvBlock(nn.Module):
def __init__(self):
super(ConvBlock, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
def forward(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from ma... | a8252525/NIID-Bench | ConvBlock | false | 3,007 | [
"MIT"
] | 0 | 33df8d3a7b941884eec3c7bd52adb8a9476eb282 | https://github.com/a8252525/NIID-Bench/tree/33df8d3a7b941884eec3c7bd52adb8a9476eb282 | import torch
import torch.nn as nn
from math import *
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
def forward(self, x):
x... |
Unet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, dropout=False, norm=
'batch', residual=True, activation='leakyrelu', transpose=False):
super(ConvBlock, self).__init__()
self.dropout = dropout
self.residual = residual
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | XinweiYu/noise2self | Unet | false | 3,008 | [
"MIT"
] | 0 | 04e0379a67e1cb0c807abd3f8d4fd1666db5a793 | https://github.com/XinweiYu/noise2self/tree/04e0379a67e1cb0c807abd3f8d4fd1666db5a793 | import torch
import torch.nn as nn
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, dropout=False, norm=
'batch', residual=True, activation='leakyrelu', transpose=False):
super().__init__()
self.dropout = dropout
self.residual = residual
self.activ... |
ResNetV2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 collections import OrderedDict
import torch.nn.functional as F
def conv1x1(cin, cout, stride=1, bias=False):
return StdConv2d(cin, cout, kernel_size=1, stride=stride, padding=0,
bias=bias)
def conv3x3(cin, cout, stride=1, groups=1, bias=False):
return StdConv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | HodaEb/ViT-pytorch | ResNetV2 | false | 3,009 | [
"MIT"
] | 0 | 2643740b1d846ae666635bb0f5a71bceba208675 | https://github.com/HodaEb/ViT-pytorch/tree/2643740b1d846ae666635bb0f5a71bceba208675 | import torch
import torch.nn as nn
from collections import OrderedDict
import torch.nn.functional as F
def conv1x1(cin, cout, stride=1, bias=False):
return StdConv2d(cin, cout, kernel_size=1, stride=stride, padding=0,
bias=bias)
def conv3x3(cin, cout, stride=1, groups=1, bias=False):
return StdConv2... |
TransformerLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.distributions
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, n_heads, kq_same=False, bias=True):
super().__init__()
"""
It has projection layer for getting keys, queries and values. Followed by attention.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Yingting-dev/ReChorus | TransformerLayer | false | 3,010 | [
"MIT"
] | 0 | a16bc1e42f3e90e889133d7476c52ada44db573b | https://github.com/Yingting-dev/ReChorus/tree/a16bc1e42f3e90e889133d7476c52ada44db573b | import torch
import numpy as np
import torch.nn as nn
import torch.distributions
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, n_heads, kq_same=False, bias=True):
super().__init__()
"""
It has projection layer for getting keys, queries and values. Followed by attention.... |
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):
super().__init__()
self.conv1 = nn.Conv2d(1, 64, kernel_size=5, padding=2)
self.activation1 = nn.Tanh()
self.maxpool1 = nn.MaxPool2d(kernel_size=(2, 2))
self.conv2 = nn.Conv2d(64, 128, 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.... | Ziems/pytorch-dcgan | Discriminator | false | 3,011 | [
"MIT"
] | 0 | 1a251a330b9b0df6061a10463bce8057f1230797 | https://github.com/Ziems/pytorch-dcgan/tree/1a251a330b9b0df6061a10463bce8057f1230797 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 64, kernel_size=5, padding=2)
self.activation1 = nn.Tanh()
self.maxpool1 = nn.MaxPool2d(kernel_size=(2, 2))
self.conv2 = nn.Conv2d(64, 128, kernel_si... |
SelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
from torch.autograd import *
class SelfAttention(nn.Module):
dim_in: 'int'
dim_k: 'int'
dim_v: 'int'
def __init__(self, dim_in, dim_k, dim_v):
super(SelfAttention, self).__init__()
self.dim_in = dim_in
self.dim_k = dim_k
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.... | YapingZ/News-image-caption | SelfAttention | false | 3,012 | [
"Apache-2.0"
] | 0 | fcccf51bbe5607adbf71c1da8ecdc6693555993f | https://github.com/YapingZ/News-image-caption/tree/fcccf51bbe5607adbf71c1da8ecdc6693555993f | import math
import torch
import torch.nn as nn
from torch.autograd import *
class Model(nn.Module):
dim_in: 'int'
dim_k: 'int'
dim_v: 'int'
def __init__(self, dim_in, dim_k, dim_v):
super().__init__()
self.dim_in = dim_in
self.dim_k = dim_k
self.dim_v = dim_v
s... |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn as nn
def conv3x3(in_planes, out_planes, dilation=1, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=int(dilation * (3 - 1) / 2), dilation=dilation, bias=False)
class Basi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | ZurMaD/DAIN | BasicBlock | false | 3,013 | [
"MIT"
] | 0 | 22570e51e84f7dfd48ba4f88e6ee7c9ff1b0b123 | https://github.com/ZurMaD/DAIN/tree/22570e51e84f7dfd48ba4f88e6ee7c9ff1b0b123 | import torch
import torch.utils.data
import torch.nn as nn
def conv3x3(in_planes, out_planes, dilation=1, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=int(dilation * (3 - 1) / 2), dilation=dilation, bias=False)
class Mode... |
SimpleNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
class SimpleNet(nn.Module):
def __init__(self):
super(SimpleNet, self).__init__()
self.fc1 = nn.Linear(12288, 84)
self.fc2 = nn.Linear(84, 50)
self.fc3 = nn.Linear(50, 2)
def forward(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
import torch.nn as nn
import ... | aakgun/pytorch-VideoDataset | SimpleNet | false | 3,014 | [
"MIT"
] | 0 | 619e385f37b99bfabca0b814673825ed902242b2 | https://github.com/aakgun/pytorch-VideoDataset/tree/619e385f37b99bfabca0b814673825ed902242b2 | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(12288, 84)
self.fc2 = nn.Linear(84, 50)
self.fc3 = nn.Linear(50, 2)
def forward(self, x):
x =... |
WineLoss | # 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 WineLoss(nn.Module):
def __init__(self):
super(WineLoss, self).__init__()
self.smoothl1 = nn.SmoothL1Loss()
def forward(self, pred, label):
loss = self.smoothl1(pred, label)
return loss
def get_inputs():
return [torch.rand([4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ZhongyuanW/foundation_wines | WineLoss | false | 3,015 | [
"Apache-2.0"
] | 0 | 92b9ae0ece46ecd291a9101ebef9e9421ead92d6 | https://github.com/ZhongyuanW/foundation_wines/tree/92b9ae0ece46ecd291a9101ebef9e9421ead92d6 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.smoothl1 = nn.SmoothL1Loss()
def forward(self, pred, label):
loss = self.smoothl1(pred, label)
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.ra... |
SA | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 SA(nn.Module):
def __init__(self, kernel_size=7):
super(SA, self).__init__()
assert kernel_size in (3, 7)
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=padding)
self.sigmoid = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | ZhijieXiao-0624/CNXA | SA | false | 3,016 | [
"MIT"
] | 0 | a63b3561010cf87f696a005f8ea252e7cdaa7ca2 | https://github.com/ZhijieXiao-0624/CNXA/tree/a63b3561010cf87f696a005f8ea252e7cdaa7ca2 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, kernel_size=7):
super().__init__()
assert kernel_size in (3, 7)
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=padding)
self.sigmoid = nn.Sig... |
GELU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class GELU(nn.Module):
def forward(self, input):
return F.gelu(input)
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 libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | ag8/mrl | GELU | false | 3,017 | [
"MIT"
] | 0 | f05b00347f88020cbeb216c7e4764a4d2523b67e | https://github.com/ag8/mrl/tree/f05b00347f88020cbeb216c7e4764a4d2523b67e | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, input):
return F.gelu(input)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
TransformerDecoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from typing import Optional
from torch import nn
def _get_activation_fn(activation: 'str'):
if activation == 'relu':
return nn.functional.relu
elif activation == 'gelu':
return nn.functional.gelu
raise RuntimeError('activation should be relu/gelu, not {}'.format(
activ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | aarora8/icefall | TransformerDecoderLayer | false | 3,018 | [
"Apache-2.0"
] | 0 | 8cb7f712e413fffbcdfdd865be73d6ff43f0ce7a | https://github.com/aarora8/icefall/tree/8cb7f712e413fffbcdfdd865be73d6ff43f0ce7a | import torch
from typing import Optional
from torch import nn
def _get_activation_fn(activation: 'str'):
if activation == 'relu':
return nn.functional.relu
elif activation == 'gelu':
return nn.functional.gelu
raise RuntimeError('activation should be relu/gelu, not {}'.format(
activ... |
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... | adarsh-kr/CVModelsBenchmark | SE | false | 3,019 | [
"MIT"
] | 0 | 85aeb76c7c796d86baa97e1272aea83d734665b5 | https://github.com/adarsh-kr/CVModelsBenchmark/tree/85aeb76c7c796d86baa97e1272aea83d734665b5 | 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... |
TransformerEncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from typing import Optional
from torch import nn
def _get_activation_fn(activation: 'str'):
if activation == 'relu':
return nn.functional.relu
elif activation == 'gelu':
return nn.functional.gelu
raise RuntimeError('activation should be relu/gelu, not {}'.format(
activ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | aarora8/icefall | TransformerEncoderLayer | false | 3,020 | [
"Apache-2.0"
] | 0 | 8cb7f712e413fffbcdfdd865be73d6ff43f0ce7a | https://github.com/aarora8/icefall/tree/8cb7f712e413fffbcdfdd865be73d6ff43f0ce7a | import torch
from typing import Optional
from torch import nn
def _get_activation_fn(activation: 'str'):
if activation == 'relu':
return nn.functional.relu
elif activation == 'gelu':
return nn.functional.gelu
raise RuntimeError('activation should be relu/gelu, not {}'.format(
activ... |
SimpleFC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.onnx
class SimpleFC(nn.Module):
def __init__(self, input_size, num_classes, name='SimpleFC'):
super(SimpleFC, self).__init__()
self.FC = nn.Parameter(torch.randn([input_size, num_classes]))
self.FCbias = nn.Parameter(torch.randn([num_classes... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.onnx
assert_size_stride = torch._C._dynamo.gu... | adityakusupati/EdgeML | SimpleFC | false | 3,021 | [
"MIT"
] | 0 | 65933a6fdfc38945f4311043a62e120784b2b0bf | https://github.com/adityakusupati/EdgeML/tree/65933a6fdfc38945f4311043a62e120784b2b0bf | import torch
import torch.nn as nn
import torch.onnx
class Model(nn.Module):
def __init__(self, input_size, num_classes, name='SimpleFC'):
super().__init__()
self.FC = nn.Parameter(torch.randn([input_size, num_classes]))
self.FCbias = nn.Parameter(torch.randn([num_classes]))
def forw... |
DummyAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 DummyAttention(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.linear1 = nn.Linear(in_channels, 32)
self.linear2 = nn.Linear(32, 1)
pass
def forward(self, net):
value = net
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | aaalgo/health_outcome_challenge | DummyAttention | false | 3,022 | [
"BSD-3-Clause"
] | 0 | 6d0cb660123ac6fb4939c1d50a8f1914e8e3e165 | https://github.com/aaalgo/health_outcome_challenge/tree/6d0cb660123ac6fb4939c1d50a8f1914e8e3e165 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.linear1 = nn.Linear(in_channels, 32)
self.linear2 = nn.Linear(32, 1)
pass
def forward(self, net):
value = net
ne... |
ProtoNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.onnx
class ProtoNN(nn.Module):
def __init__(self, inputDimension, projectionDimension, numPrototypes,
numOutputLabels, gamma, W=None, B=None, Z=None):
"""
Forward computation graph for ProtoNN.
inputDimension: 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.triton_helpers import math as tl_math
import numpy ... | adityakusupati/EdgeML | ProtoNN | false | 3,023 | [
"MIT"
] | 0 | 65933a6fdfc38945f4311043a62e120784b2b0bf | https://github.com/adityakusupati/EdgeML/tree/65933a6fdfc38945f4311043a62e120784b2b0bf | import torch
import numpy as np
import torch.nn as nn
import torch.onnx
class Model(nn.Module):
def __init__(self, inputDimension, projectionDimension, numPrototypes,
numOutputLabels, gamma, W=None, B=None, Z=None):
"""
Forward computation graph for ProtoNN.
inputDimension: Input... |
ReferenceWeightBinarizationModule | # 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 torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
class ReferenceDOREFABinarize(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
norm = x.abs(... | 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
from torchvision import models as models
import torc... | JinYAnGHe/openvino_training_extensions | ReferenceWeightBinarizationModule | false | 3,024 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
class ReferenceDOREFABinarize(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
norm = x.abs(... |
StateInitZero | # 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 torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
class StateInitZero(nn.Module):
def __init__(self, hidden_size, num_layers=1, batch_first=False):
super(Stat... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import tor... | JinYAnGHe/openvino_training_extensions | StateInitZero | false | 3,025 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
class Model(nn.Module):
def __init__(self, hidden_size, num_layers=1, batch_first=False):
super().__init__()... |
FakeReLUM | # 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 typing import *
import torch.utils.data
class FakeReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input.clamp(min=0)
@staticmethod
def backward(ctx, grad_output):
return grad_output
class FakeReLUM(nn.Module):
... | 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 typing import *
import torch.utils.data
assert_size_stride = t... | agarwalsiddhant10/blackbox-smoothing | FakeReLUM | false | 3,026 | [
"MIT"
] | 0 | cf18a9dc45f807494955d0cf19a3d1dd4315b54f | https://github.com/agarwalsiddhant10/blackbox-smoothing/tree/cf18a9dc45f807494955d0cf19a3d1dd4315b54f | import torch
import torch.nn as nn
from typing import *
import torch.utils.data
class FakeReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input.clamp(min=0)
@staticmethod
def backward(ctx, grad_output):
return grad_output
class Model(nn.Module):
de... |
Conv2dSame | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.utils.data
import torch
import torch.nn as nn
class Conv2dSame(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1):
super(Conv2dSame, self).__init__()
self.F = kernel_size
self.S = stride
self.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
import torch.utils.data
impor... | adityamehta00/HIDeGAN | Conv2dSame | false | 3,027 | [
"BSD-3-Clause"
] | 0 | 91a0674e092ccde2784a82bf927dfefd8673eb4c | https://github.com/adityamehta00/HIDeGAN/tree/91a0674e092ccde2784a82bf927dfefd8673eb4c | import math
import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1):
super().__init__()
self.F = kernel_size
self.S = stride
self.D = dilation
s... |
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.utils.data
import torch.nn.init as init
def kaiming_init(m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
init.kaiming_normal(m.weight)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
... | 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... | aasensio/neural_hinode | VAE | false | 3,028 | [
"MIT"
] | 0 | 63ec076d920f82343618ce67669c73a3b5209957 | https://github.com/aasensio/neural_hinode/tree/63ec076d920f82343618ce67669c73a3b5209957 | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.init as init
def kaiming_init(m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
init.kaiming_normal(m.weight)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
... |
UGRNNLRCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.onnx
def gen_nonlinearity(A, nonlinearity):
"""
Returns required activation for a tensor based on the inputs
nonlinearity is either a callable or a value in
['tanh', 'sigmoid', 'relu', 'quantTanh', 'quantSigm', 'quantSigm4']
"""
if nonlinear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | adityakusupati/EdgeML | UGRNNLRCell | false | 3,029 | [
"MIT"
] | 0 | 65933a6fdfc38945f4311043a62e120784b2b0bf | https://github.com/adityakusupati/EdgeML/tree/65933a6fdfc38945f4311043a62e120784b2b0bf | import torch
import torch.nn as nn
import torch.onnx
def gen_nonlinearity(A, nonlinearity):
"""
Returns required activation for a tensor based on the inputs
nonlinearity is either a callable or a value in
['tanh', 'sigmoid', 'relu', 'quantTanh', 'quantSigm', 'quantSigm4']
"""
if nonlinear... |
LSTMPredictor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 LSTMPredictor(nn.Module):
def __init__(self, look_back, target_days):
super(LSTMPredictor, self).__init__()
self.layer_a = nn.Linear(look_back, 32)
self.relu = nn.ReLU()
self.output = nn.Linear(32, target_days)
def predict(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_... | Yu-Hao-88/NN_stock_prediction | LSTMPredictor | false | 3,030 | [
"MIT"
] | 0 | bb84a3f4450d95af317d60d83dcd53ad4f3d350d | https://github.com/Yu-Hao-88/NN_stock_prediction/tree/bb84a3f4450d95af317d60d83dcd53ad4f3d350d | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, look_back, target_days):
super().__init__()
self.layer_a = nn.Linear(look_back, 32)
self.relu = nn.ReLU()
self.output = nn.Linear(32, target_days)
def predict(self, input):
with torch.no_gra... |
ReconstructionBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.utils.data
import torch
import torch.nn as nn
class Conv2dSame(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1):
super(Conv2dSame, self).__init__()
self.F = kernel_size
self.S = stride
self.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
import math
import torch.util... | adityamehta00/HIDeGAN | ReconstructionBlock | false | 3,031 | [
"BSD-3-Clause"
] | 0 | 91a0674e092ccde2784a82bf927dfefd8673eb4c | https://github.com/adityamehta00/HIDeGAN/tree/91a0674e092ccde2784a82bf927dfefd8673eb4c | import math
import torch
import torch.utils.data
import torch
import torch.nn as nn
class Conv2dSame(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1):
super().__init__()
self.F = kernel_size
self.S = stride
self.D = dilation
... |
FastRNNCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.onnx
def gen_nonlinearity(A, nonlinearity):
"""
Returns required activation for a tensor based on the inputs
nonlinearity is either a callable or a value in
['tanh', 'sigmoid', 'relu', 'quantTanh', 'quantSigm', 'quantSigm4']
"""
if nonlinear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | adityakusupati/EdgeML | FastRNNCell | false | 3,032 | [
"MIT"
] | 0 | 65933a6fdfc38945f4311043a62e120784b2b0bf | https://github.com/adityakusupati/EdgeML/tree/65933a6fdfc38945f4311043a62e120784b2b0bf | import torch
import torch.nn as nn
import torch.onnx
def gen_nonlinearity(A, nonlinearity):
"""
Returns required activation for a tensor based on the inputs
nonlinearity is either a callable or a value in
['tanh', 'sigmoid', 'relu', 'quantTanh', 'quantSigm', 'quantSigm4']
"""
if nonlinear... |
LocallyConnectedLayer1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.onnx
class LocallyConnectedLayer1d(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, stride, padding=
True, bias=False):
"""
Defines one locally connected layer for one dimensional vector input.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | adityayedetore/LCRNN | LocallyConnectedLayer1d | false | 3,033 | [
"MIT"
] | 0 | 7b6afaf6098fed584b90fe0196cfd26aa6a190c5 | https://github.com/adityayedetore/LCRNN/tree/7b6afaf6098fed584b90fe0196cfd26aa6a190c5 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
class Model(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, stride, padding=
True, bias=False):
"""
Defines one locally connected layer for one dimensional vector input.
NOTE: This ... |
AugCNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 apply_init_(modules):
"""
Initialize NN modules
"""
for m in modules:
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | agarwl/auto-drac | AugCNN | false | 3,034 | [
"MIT"
] | 0 | d86c480b51929e6e4ec0ae1adba84d9f78e91705 | https://github.com/agarwl/auto-drac/tree/d86c480b51929e6e4ec0ae1adba84d9f78e91705 | import torch
import torch.nn as nn
import torch.nn.functional as F
def apply_init_(modules):
"""
Initialize NN modules
"""
for m in modules:
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0... |
GRULRCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.onnx
def gen_nonlinearity(A, nonlinearity):
"""
Returns required activation for a tensor based on the inputs
nonlinearity is either a callable or a value in
['tanh', 'sigmoid', 'relu', 'quantTanh', 'quantSigm', 'quantSigm4']
"""
if nonlinear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | adityakusupati/EdgeML | GRULRCell | false | 3,035 | [
"MIT"
] | 0 | 65933a6fdfc38945f4311043a62e120784b2b0bf | https://github.com/adityakusupati/EdgeML/tree/65933a6fdfc38945f4311043a62e120784b2b0bf | import torch
import torch.nn as nn
import torch.onnx
def gen_nonlinearity(A, nonlinearity):
"""
Returns required activation for a tensor based on the inputs
nonlinearity is either a callable or a value in
['tanh', 'sigmoid', 'relu', 'quantTanh', 'quantSigm', 'quantSigm4']
"""
if nonlinear... |
RGBDiff | # 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 torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
class RGBDiff(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forw... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import tor... | JinYAnGHe/openvino_training_extensions | RGBDiff | false | 3,036 | [
"Apache-2.0"
] | 0 | a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | https://github.com/JinYAnGHe/openvino_training_extensions/tree/a0b4456a3c9fe6c1b7eabc9d5eb4e74d01453dee | import torch
from torch import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
class Model(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forwar... |
BiaffineScorer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 BiaffineScorer(nn.Module):
def __init__(self, input1_size, input2_size, output_size):
super().__init__()
self.W_bilin = nn.Bilinear(input1_size + 1, input2_size + 1,
output_size)
self.W_bilin.weight.data.zero_()
self.W_bilin.bia... | 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... | WEYAI/PhoNLP | BiaffineScorer | false | 3,037 | [
"Apache-2.0"
] | 0 | 8fefe49965dc6346c224a5636d9333a7ddf55a2c | https://github.com/WEYAI/PhoNLP/tree/8fefe49965dc6346c224a5636d9333a7ddf55a2c | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input1_size, input2_size, output_size):
super().__init__()
self.W_bilin = nn.Bilinear(input1_size + 1, input2_size + 1,
output_size)
self.W_bilin.weight.data.zero_()
self.W_bilin.bias.data.ze... |
DHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 *
class DHead(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(256, 1, 4)
def forward(self, x):
output = torch.sigmoid(self.conv(x))
return output
def get_inputs():
return [torch.rand([4, 256, 6... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from math import *
assert_size_stride = torch._C._dynamo.g... | a8252525/NIID-Bench | DHead | false | 3,038 | [
"MIT"
] | 0 | 33df8d3a7b941884eec3c7bd52adb8a9476eb282 | https://github.com/a8252525/NIID-Bench/tree/33df8d3a7b941884eec3c7bd52adb8a9476eb282 | import torch
import torch.nn as nn
from math import *
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(256, 1, 4)
def forward(self, x):
output = torch.sigmoid(self.conv(x))
return output
def get_inputs():
return [torch.rand([4, 256, 6... |
FeatureExtractionBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.utils.data
import torch
import torch.nn as nn
class Conv2dSame(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1):
super(Conv2dSame, self).__init__()
self.F = kernel_size
self.S = stride
self.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
import math
import torch.util... | adityamehta00/HIDeGAN | FeatureExtractionBlock | false | 3,039 | [
"BSD-3-Clause"
] | 0 | 91a0674e092ccde2784a82bf927dfefd8673eb4c | https://github.com/adityamehta00/HIDeGAN/tree/91a0674e092ccde2784a82bf927dfefd8673eb4c | import math
import torch
import torch.utils.data
import torch
import torch.nn as nn
class Conv2dSame(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1):
super().__init__()
self.F = kernel_size
self.S = stride
self.D = dilation
... |
FastGRNNCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.onnx
def gen_nonlinearity(A, nonlinearity):
"""
Returns required activation for a tensor based on the inputs
nonlinearity is either a callable or a value in
['tanh', 'sigmoid', 'relu', 'quantTanh', 'quantSigm', 'quantSigm4']
"""
if nonlinear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | adityakusupati/EdgeML | FastGRNNCell | false | 3,040 | [
"MIT"
] | 0 | 65933a6fdfc38945f4311043a62e120784b2b0bf | https://github.com/adityakusupati/EdgeML/tree/65933a6fdfc38945f4311043a62e120784b2b0bf | import torch
import torch.nn as nn
import torch.onnx
def gen_nonlinearity(A, nonlinearity):
"""
Returns required activation for a tensor based on the inputs
nonlinearity is either a callable or a value in
['tanh', 'sigmoid', 'relu', 'quantTanh', 'quantSigm', 'quantSigm4']
"""
if nonlinear... |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ahgit2021/deep-reinforcement-learning | Actor | false | 3,041 | [
"MIT"
] | 0 | 081464ba45f803663e841e1635d829aa00cce870 | https://github.com/ahgit2021/deep-reinforcement-learning/tree/081464ba45f803663e841e1635d829aa00cce870 | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Model(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... |
FitNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 FitNet(nn.Module):
def __init__(self, in_feature, out_feature):
super().__init__()
self.in_feature = in_feature
self.out_feature = out_feature
self.transform = nn.Conv2d(in_feature, out_feature, 1, bias=False)
self.transform.weight.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ahu-hpt/AOMD | FitNet | false | 3,043 | [
"Apache-2.0"
] | 0 | 8d99dbb803feaef55fc089bfb3399d2fb21d55d8 | https://github.com/ahu-hpt/AOMD/tree/8d99dbb803feaef55fc089bfb3399d2fb21d55d8 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_feature, out_feature):
super().__init__()
self.in_feature = in_feature
self.out_feature = out_feature
self.transform = nn.Conv2d(in_feature, out_feature, 1, bias=False)
self.transform.weight.d... |
AE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class AE(nn.Module):
def __init__(self, num_channels):
super(AE, self).__init__()
self.enc1 = nn.Conv2d(num_channels, 64, kernel_size=3, padding=1)
self.enc2 = nn.Conv2d(64, 32, kernel_size=3, padding=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ahanagemini/Phd_final_year_old_sr | AE | false | 3,045 | [
"BSD-2-Clause"
] | 0 | 62be9d1294acdb724a2fe424789b657a44e2cd7d | https://github.com/ahanagemini/Phd_final_year_old_sr/tree/62be9d1294acdb724a2fe424789b657a44e2cd7d | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class Model(nn.Module):
def __init__(self, num_channels):
super().__init__()
self.enc1 = nn.Conv2d(num_channels, 64, kernel_size=3, padding=1)
self.enc2 = nn.Conv2d(64, 32, kernel_size=3, padding=1)
... |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ahgit2021/deep-reinforcement-learning | Critic | false | 3,046 | [
"MIT"
] | 0 | 081464ba45f803663e841e1635d829aa00cce870 | https://github.com/ahgit2021/deep-reinforcement-learning/tree/081464ba45f803663e841e1635d829aa00cce870 | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Model(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, f... |
Network | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Network(nn.Module):
def __init__(self, number_of_inputs, number_of_outputs):
super(Network, self).__init__()
self.l1 = nn.Linear(number_of_inputs, number_of_inputs)
self.l2 = nn.Linear(number_of_inputs, number_of_inputs)
self.l3 = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ajthinking/migration-analysis | Network | false | 3,047 | [
"MIT"
] | 0 | c48312c5bf513a37e23eaaf8aa245921992f8e7f | https://github.com/ajthinking/migration-analysis/tree/c48312c5bf513a37e23eaaf8aa245921992f8e7f | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, number_of_inputs, number_of_outputs):
super().__init__()
self.l1 = nn.Linear(number_of_inputs, number_of_inputs)
self.l2 = nn.Linear(number_of_inputs, number_of_inputs)
self.l3 = nn.Linear(number_of_inpu... |
AttentionTransfer | # 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 AttentionTransfer(nn.Module):
def forward(self, student, teacher):
s_attention = F.normalize(student.pow(2).mean(1).view(student.size(
0), -1))
with torch.no_grad():
t_attention = F.normalize(teacher.... | 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... | ahu-hpt/AOMD | AttentionTransfer | false | 3,048 | [
"Apache-2.0"
] | 0 | 8d99dbb803feaef55fc089bfb3399d2fb21d55d8 | https://github.com/ahu-hpt/AOMD/tree/8d99dbb803feaef55fc089bfb3399d2fb21d55d8 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, student, teacher):
s_attention = F.normalize(student.pow(2).mean(1).view(student.size(
0), -1))
with torch.no_grad():
t_attention = F.normalize(teacher.pow(2).mean(... |
HardDarkRank | # 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 pdist(e, squared=False, eps=1e-12):
e_square = e.pow(2).sum(dim=1)
prod = e @ e.t()
res = (e_square.unsqueeze(1) + e_square.unsqueeze(0) - 2 * prod).clamp(min
=eps)
if not squared:
res = res.sqrt()
res = res.clone()
res[range(len(e)), rang... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ahu-hpt/AOMD | HardDarkRank | false | 3,049 | [
"Apache-2.0"
] | 0 | 8d99dbb803feaef55fc089bfb3399d2fb21d55d8 | https://github.com/ahu-hpt/AOMD/tree/8d99dbb803feaef55fc089bfb3399d2fb21d55d8 | import torch
import torch.nn as nn
def pdist(e, squared=False, eps=1e-12):
e_square = e.pow(2).sum(dim=1)
prod = e @ e.t()
res = (e_square.unsqueeze(1) + e_square.unsqueeze(0) - 2 * prod).clamp(min
=eps)
if not squared:
res = res.sqrt()
res = res.clone()
res[range(len(e)), rang... |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self, num_channels, embed_size, dropout=True):
"""Stacked attention Module
"""
super(Attention, self).__init__()
self.ff_image = nn.Linear(embed_size, num_channels)
self.ff_questions = nn.Linear(em... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | ahmed563/Group14_Project_DLSpring2021 | Attention | false | 3,050 | [
"MIT"
] | 0 | d2b03555cadb483ae472b613f107173f89c07d9b | https://github.com/ahmed563/Group14_Project_DLSpring2021/tree/d2b03555cadb483ae472b613f107173f89c07d9b | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_channels, embed_size, dropout=True):
"""Stacked attention Module
"""
super().__init__()
self.ff_image = nn.Linear(embed_size, num_channels)
self.ff_questions = nn.Linear(embed_size, num_chann... |
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
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 Net(nn.Module):
def __init__(self, s_dim, a_dim, hidden=16):
super(Net, self).__ini... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | aivaslab/marlgrid | Net | false | 3,051 | [
"Apache-2.0"
] | 0 | 10b53d27ce224fadeeb5830d6034350a69feb4b4 | https://github.com/aivaslab/marlgrid/tree/10b53d27ce224fadeeb5830d6034350a69feb4b4 | 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, hidden=16):
super().__init__()
... |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
from torch import nn
from torch.nn import functional as F
class Actor(nn.Module):
def __init__(self, input_dim, output_dim):
super(Actor, self).__init__()
self.layer1 = nn.Linear(input_dim, output_dim, bias=True)
def _format(self, state):
x = 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
from torch._inductor.runtime.... | ajy8456/active-recognition | Actor | false | 3,052 | [
"MIT"
] | 0 | 7d3a4bbfceaf5fb32cd43f62636f36a10ab63807 | https://github.com/ajy8456/active-recognition/tree/7d3a4bbfceaf5fb32cd43f62636f36a10ab63807 | import torch
import numpy as np
from torch import nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, input_dim, output_dim):
super().__init__()
self.layer1 = nn.Linear(input_dim, output_dim, bias=True)
def _format(self, state):
x = state
if not... |
ContractingBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import functional as F
class ContractingBlock(nn.Module):
"""
ContractingBlock Class
Performs two convolutions followed by a max pool operation.
Values:
input_channels: the number of channels to expect from a given input
"""
def __init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | akanametov/unet-pytorch | ContractingBlock | false | 3,054 | [
"MIT"
] | 0 | 6cf0f70674958356ea4ac36fe61b0415921f72ae | https://github.com/akanametov/unet-pytorch/tree/6cf0f70674958356ea4ac36fe61b0415921f72ae | import torch
from torch import nn
from torch.nn import functional as F
class Model(nn.Module):
"""
ContractingBlock Class
Performs two convolutions followed by a max pool operation.
Values:
input_channels: the number of channels to expect from a given input
"""
def __init__(self, in_c... |
SqueezeExcitation | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 make_divisible(v, divisor=8, min_value=None):
"""
The channel number of each layer should be divisable by 8.
The function is taken from
github.com/rwightman/pytorch-image-models/master/timm/models/layers/helpers.py
"""
min_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | akashAD98/EfficientNetv2-with-Detectron2 | SqueezeExcitation | false | 3,055 | [
"Apache-2.0"
] | 0 | 1ba7f32cda31550ed4a040c15271612fa3f73d74 | https://github.com/akashAD98/EfficientNetv2-with-Detectron2/tree/1ba7f32cda31550ed4a040c15271612fa3f73d74 | import torch
import torch.nn as nn
import torch.nn.functional as F
def make_divisible(v, divisor=8, min_value=None):
"""
The channel number of each layer should be divisable by 8.
The function is taken from
github.com/rwightman/pytorch-image-models/master/timm/models/layers/helpers.py
"""
min_... |
SimpleGCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.nn
import torch.autograd
class SimpleGCN(nn.Module):
"""A simple graph convolution layer, similar to the one defined in
Kipf et al. https://arxiv.org/abs/1609.02907
.. note:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
from torch.nn.parameter import Parameter
from ... | akashgokul/kaolin | SimpleGCN | false | 3,056 | [
"ECL-2.0",
"Apache-2.0"
] | 0 | 6360c4f2bcdd81f461dfb4d96267e79d89d5e112 | https://github.com/akashgokul/kaolin/tree/6360c4f2bcdd81f461dfb4d96267e79d89d5e112 | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.nn
import torch.autograd
class Model(nn.Module):
"""A simple graph convolution layer, similar to the one defined in
Kipf et al. https://arxiv.org/abs/1609.02907
.. note::
... |
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
from torch import nn
class Discriminator(nn.Module):
def __init__(self, in_features=1):
super(Discriminator, self).__init__()
self.conv1 = nn.Conv2d(in_features, 96, kernel_size=7, stride=4)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(96, 64, kernel_size=5, stride=2)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | ajdillhoff/simgan-pytorch | Discriminator | false | 3,057 | [
"MIT"
] | 0 | fb61241a85136aae770944e1496f9319df327561 | https://github.com/ajdillhoff/simgan-pytorch/tree/fb61241a85136aae770944e1496f9319df327561 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, in_features=1):
super().__init__()
self.conv1 = nn.Conv2d(in_features, 96, kernel_size=7, stride=4)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(96, 64, kernel_size=5, stride=2)
self.max_pool = nn... |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self, input_shape, output_shape):
super(Encoder, self).__init__()
self.input_shape = input_shape
self.encoder_out_shape = output_shape
self.linear_one = nn.Linear(self.input_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | akaprasanga/AutoEncoder | Encoder | false | 3,058 | [
"MIT"
] | 0 | a1562a7a720c199b717796e469b9957eb958264a | https://github.com/akaprasanga/AutoEncoder/tree/a1562a7a720c199b717796e469b9957eb958264a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_shape, output_shape):
super().__init__()
self.input_shape = input_shape
self.encoder_out_shape = output_shape
self.linear_one = nn.Linear(self.input_shape, 400)
... |
Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Decoder(nn.Module):
def __init__(self, input_shape, output_shape):
super(Decoder, self).__init__()
self.input_shape = input_shape
self.decoder_out_shape = output_shape
self.linear_one = nn.Linear(self.input_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | akaprasanga/AutoEncoder | Decoder | false | 3,059 | [
"MIT"
] | 0 | a1562a7a720c199b717796e469b9957eb958264a | https://github.com/akaprasanga/AutoEncoder/tree/a1562a7a720c199b717796e469b9957eb958264a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_shape, output_shape):
super().__init__()
self.input_shape = input_shape
self.decoder_out_shape = output_shape
self.linear_one = nn.Linear(self.input_shape, 100)
... |
Model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input, hidden, output):
super(Model, self).__init__()
self.l1 = nn.Linear(input, hidden)
self.l2 = nn.Linear(hidden, hidden)
self.l3 = nn.Linear(hidden, 2)
def forward... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | akapoorx00/machinelearning-stuff | Model | false | 3,060 | [
"Apache-2.0"
] | 0 | 53184019b77d3387fd15b13d3bfa75529b8ed003 | https://github.com/akapoorx00/machinelearning-stuff/tree/53184019b77d3387fd15b13d3bfa75529b8ed003 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input, hidden, output):
super(Model, self).__init__()
self.l1 = nn.Linear(input, hidden)
self.l2 = nn.Linear(hidden, hidden)
self.l3 = nn.Linear(hidden, 2)
def forward... |
DiscShiftLoss | # 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 DiscShiftLoss(nn.Module):
"""Disc shift loss.
Args:
loss_weight (float, optional): Loss weight. Defaults to 1.0.
"""
def __init__(self, loss_weight=0.1):
super().__init__()
self.loss_weight = loss_weight
def forward(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | akimotty877/mmediting | DiscShiftLoss | false | 3,061 | [
"Apache-2.0"
] | 0 | cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | https://github.com/akimotty877/mmediting/tree/cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Disc shift loss.
Args:
loss_weight (float, optional): Loss weight. Defaults to 1.0.
"""
def __init__(self, loss_weight=0.1):
super().__init__()
self.loss_weight = loss_weight
def forward(self, x):
... |
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
from torch import nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, input, hidden, output):
super(Net, self).__init__()
self.l1 = nn.Linear(input, hidden)
self.l2 = nn.Linear(hidden, hidden)
self.l3 = nn.Linear(hidden, hidden)
self.l4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | akapoorx00/machinelearning-stuff | Net | false | 3,062 | [
"Apache-2.0"
] | 0 | 53184019b77d3387fd15b13d3bfa75529b8ed003 | https://github.com/akapoorx00/machinelearning-stuff/tree/53184019b77d3387fd15b13d3bfa75529b8ed003 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input, hidden, output):
super().__init__()
self.l1 = nn.Linear(input, hidden)
self.l2 = nn.Linear(hidden, hidden)
self.l3 = nn.Linear(hidden, hidden)
self.l4 = nn.L... |
CharbonnierCompLoss | # 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 functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... | 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 functools
import torc... | akimotty877/mmediting | CharbonnierCompLoss | false | 3,063 | [
"Apache-2.0"
] | 0 | cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | https://github.com/akimotty877/mmediting/tree/cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... |
BboxHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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
class BboxHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(BboxHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(
1, 1), stride=1, padding=0)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn
assert_size_stride = torch._C._dynamo.guard... | ZongqingHou/Pytorch_Retinaface | BboxHead | false | 3,064 | [
"MIT"
] | 0 | 6284b7158a0d9d3d4a2cc267a393c21863a1b938 | https://github.com/ZongqingHou/Pytorch_Retinaface/tree/6284b7158a0d9d3d4a2cc267a393c21863a1b938 | import torch
from torch import nn
import torch.nn
class Model(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super().__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(
1, 1), stride=1, padding=0)
def forward(self, x):
out = self... |
MSECompositionLoss | # 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 functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... | 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 functools
import torch.nn as nn
from torch.nn import functional as F
assert_size_s... | akimotty877/mmediting | MSECompositionLoss | false | 3,065 | [
"Apache-2.0"
] | 0 | cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | https://github.com/akimotty877/mmediting/tree/cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... |
L1CompositionLoss | # 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 functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... | 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 functools
impor... | akimotty877/mmediting | L1CompositionLoss | false | 3,066 | [
"Apache-2.0"
] | 0 | cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | https://github.com/akimotty877/mmediting/tree/cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... |
CustomBatchNormManualModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 CustomBatchNormManualFunction(torch.autograd.Function):
"""
This torch.autograd.Function implements a functional custom version of the batch norm operation for MLPs.
Using torch.autograd.Function allows you to write a custom backward function.
The function will... | 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_... | akashrajkn/sarcastic-gradients | CustomBatchNormManualModule | false | 3,067 | [
"Apache-2.0"
] | 0 | 5a995ab7822dfd49cdc88855c631dcc8f1b0532f | https://github.com/akashrajkn/sarcastic-gradients/tree/5a995ab7822dfd49cdc88855c631dcc8f1b0532f | import torch
import torch.nn as nn
class CustomBatchNormManualFunction(torch.autograd.Function):
"""
This torch.autograd.Function implements a functional custom version of the batch norm operation for MLPs.
Using torch.autograd.Function allows you to write a custom backward function.
The function will... |
EqualLinearActModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 copy import deepcopy
from functools import partial
from torch.nn.init import _calculate_correct_fan
def equalized_lr(module, name='weight', gain=2 ** 0.5, mode='fan_in',
lr_mul=1.0):
"""Equalized Learning Rate.
This trick is proposed in:
Progressive Growing of ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 copy import deepcopy
from functools import partial
fr... | akimotty877/mmediting | EqualLinearActModule | false | 3,068 | [
"Apache-2.0"
] | 0 | cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | https://github.com/akimotty877/mmediting/tree/cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | import torch
import torch.nn as nn
from copy import deepcopy
from functools import partial
from torch.nn.init import _calculate_correct_fan
def equalized_lr(module, name='weight', gain=2 ** 0.5, mode='fan_in',
lr_mul=1.0):
"""Equalized Learning Rate.
This trick is proposed in:
Progressive Growing of ... |
CustomBatchNormAutograd | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 CustomBatchNormAutograd(nn.Module):
"""
This nn.module implements a custom version of the batch norm operation for MLPs.
The operations called in self.forward track the history if the input tensors have the
flag requires_grad set to True. The backward pass does... | 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_... | akashrajkn/sarcastic-gradients | CustomBatchNormAutograd | false | 3,069 | [
"Apache-2.0"
] | 0 | 5a995ab7822dfd49cdc88855c631dcc8f1b0532f | https://github.com/akashrajkn/sarcastic-gradients/tree/5a995ab7822dfd49cdc88855c631dcc8f1b0532f | import torch
import torch.nn as nn
class Model(nn.Module):
"""
This nn.module implements a custom version of the batch norm operation for MLPs.
The operations called in self.forward track the history if the input tensors have the
flag requires_grad set to True. The backward pass does not need to be im... |
MaxFeature | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 MaxFeature(nn.Module):
"""Conv2d or Linear layer with max feature selector
Generate feature maps with double channels, split them and select the max
feature.
Args:
in_channels (int): Channel number of inputs.
out_channels (int): Channel nu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | akimotty877/mmediting | MaxFeature | false | 3,070 | [
"Apache-2.0"
] | 0 | cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | https://github.com/akimotty877/mmediting/tree/cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Conv2d or Linear layer with max feature selector
Generate feature maps with double channels, split them and select the max
feature.
Args:
in_channels (int): Channel number of inputs.
out_channels (int): Channel number ... |
PlainRefiner | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 PlainRefiner(nn.Module):
"""Simple refiner from Deep Image Matting.
Args:
conv_channels (int): Number of channels produced by the three main
convolutional layer.
loss_refine (dict): Config of the loss of the refiner. Default: None.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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_... | akimotty877/mmediting | PlainRefiner | false | 3,071 | [
"Apache-2.0"
] | 0 | cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | https://github.com/akimotty877/mmediting/tree/cae872d6f3e867ba144c7c0dbc29a0ee1a29e5a6 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Simple refiner from Deep Image Matting.
Args:
conv_channels (int): Number of channels produced by the three main
convolutional layer.
loss_refine (dict): Config of the loss of the refiner. Default: None.
pretrai... |
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