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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
InputInjection | # 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._C
import torch.serialization
class InputInjection(nn.Module):
"""Downsampling module for CGNet."""
def __init__(self, num_downsampling):
super(InputInjection, self).__init__()
self.pool = nn.ModuleList()
for i in range(num_downsampling)... | 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._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | Jun-jieChen/real-time-segmentation | InputInjection | false | 5,415 | [
"Apache-2.0"
] | 1 | 22d0cb1a8a0dfa3b38f25bcd05db15f345be291a | https://github.com/Jun-jieChen/real-time-segmentation/tree/22d0cb1a8a0dfa3b38f25bcd05db15f345be291a | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class Model(nn.Module):
"""Downsampling module for CGNet."""
def __init__(self, num_downsampling):
super().__init__()
self.pool = nn.ModuleList()
for i in range(num_downsampling):
self.pool.appen... |
FCN8VGG16 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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
import torch.utils.model_zoo as model_zoo
def conv3x3(in_planes, out_planes, stride=1, padding=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=(3, 3), stride=(
stride, stride), padding=(padding, padding))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
from torch... | DoranLyong/DeepFish | FCN8VGG16 | false | 5,416 | [
"MIT"
] | 1 | 3ea3e13653f708d4a8dcb54b990dcc2997edf4e9 | https://github.com/DoranLyong/DeepFish/tree/3ea3e13653f708d4a8dcb54b990dcc2997edf4e9 | import torch
import numpy as np
from torch import nn
import torch.utils.model_zoo as model_zoo
def conv3x3(in_planes, out_planes, stride=1, padding=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=(3, 3), stride=(
stride, stride), padding=(padding, padding))
... |
CyclicShift | # 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 to_2tuple(value):
return value, value
class CyclicShift(nn.Module):
def __init__(self, displacement):
super().__init__()
if isinstance(displacement, int):
self.displacement = to_2tuple(displacement)
else:
self.displaceme... | 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... | Justin900429/vision-transformer | CyclicShift | false | 5,417 | [
"MIT"
] | 1 | e149092efbb83c166449944137db0ee5200f9325 | https://github.com/Justin900429/vision-transformer/tree/e149092efbb83c166449944137db0ee5200f9325 | import torch
import torch.nn as nn
def to_2tuple(value):
return value, value
class Model(nn.Module):
def __init__(self, displacement):
super().__init__()
if isinstance(displacement, int):
self.displacement = to_2tuple(displacement)
else:
self.displacement = d... |
AttentionPool2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class AttentionPool2d(nn.Module):
def __init__(self, spacial_dim: 'int', embed_dim: 'int', num_heads:
'int', output_dim: 'int'=None):
super().__init__()
self.positional_embedding = nn.Parameter(torch.randn(spacial_dim **
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Jinsu-L/KELIP | AttentionPool2d | false | 5,418 | [
"Apache-2.0"
] | 1 | d3261cbb9ba3c3ad474dd560a5add8b69ed78477 | https://github.com/Jinsu-L/KELIP/tree/d3261cbb9ba3c3ad474dd560a5add8b69ed78477 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def __init__(self, spacial_dim: 'int', embed_dim: 'int', num_heads:
'int', output_dim: 'int'=None):
super().__init__()
self.positional_embedding = nn.Parameter(torch.randn(spacial_dim **
... |
TracedModule | # 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class TracedModule(torch.nn.Module):
def forward(self, x):
x = x.type(torch.float32)
return torch.floor(torch.sqrt(x) / 5.0)
def get_i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.quantization
import torch.onnx
import torch.nn.parallel
import tor... | Justin-A/PyTorch-tutorials-kr | TracedModule | false | 5,419 | [
"BSD-3-Clause"
] | 1 | 0d8e407523e5e75de0081becf800b82b37eb912f | https://github.com/Justin-A/PyTorch-tutorials-kr/tree/0d8e407523e5e75de0081becf800b82b37eb912f | import torch
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Model(torch.nn.Module):
def forward(self, x):
x = x.type(torch.float32)
return torch.floor(torch.sqrt(x) / 5.0)
def get_inputs()... |
PixelWise | # 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.init
class PixelWise(torch.nn.Module):
""" Implemented - https://arxiv.org/pdf/1710.10196.pdf """
def __init__(self, eps=1e-06):
super(PixelWise, self).__init__()
self.eps = eps
def forward(self, tensor):
return tensor.div(tensor.pow(2).mean(1, True).... | 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.init
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Johnson-yue/TensorMONK | PixelWise | false | 5,420 | [
"MIT"
] | 1 | 1785132b82c685c3b3fc05b00dec46b1fccfc948 | https://github.com/Johnson-yue/TensorMONK/tree/1785132b82c685c3b3fc05b00dec46b1fccfc948 | import torch
import torch.nn.init
class Model(torch.nn.Module):
""" Implemented - https://arxiv.org/pdf/1710.10196.pdf """
def __init__(self, eps=1e-06):
super().__init__()
self.eps = eps
def forward(self, tensor):
return tensor.div(tensor.pow(2).mean(1, True).pow(0.5).add(self.e... |
Connect2Model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Connect2Model(nn.Module):
def __init__(self, board_size, action_size, device):
super(Connect2Model, self).__init__()
self.device = device
self.size = board_size
self.action_size = action_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JoshVarty/ConnectX | Connect2Model | false | 5,421 | [
"MIT"
] | 1 | 05478e250a149df46bf93a6b85282ded34afadc3 | https://github.com/JoshVarty/ConnectX/tree/05478e250a149df46bf93a6b85282ded34afadc3 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, board_size, action_size, device):
super().__init__()
self.device = device
self.size = board_size
self.action_size = action_size
self.fc1 = nn.Li... |
RON | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class RON(nn.Module):
def __init__(self, lat_inC, top_inC, outC):
super(RON, self).__init__()
self.latlayer = nn.Conv2d(lat_inC, outC, 3, 1, padding=1)
self.toplayer = nn.ConvTranspos... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 sqrt as sqrt
from itertools import produc... | KaiOtter/pytorch_DSOD_variants | RON | false | 5,422 | [
"MIT"
] | 1 | f29088b13b24f24e2cf20e9a2dc800cd6dbde145 | https://github.com/KaiOtter/pytorch_DSOD_variants/tree/f29088b13b24f24e2cf20e9a2dc800cd6dbde145 | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class Model(nn.Module):
def __init__(self, lat_inC, top_inC, outC):
super().__init__()
self.latlayer = nn.Conv2d(lat_inC, outC, 3, 1, padding=1)
self.toplayer = nn.ConvTranspose2d(top... |
PairwiseRankingLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.utils.data
class PairwiseRankingLoss(nn.Module):
"""
Pairwise ranking loss
"""
def __init__(self, margin):
super(PairwiseRankingLoss, self).__init__()
self.margin = margin
def forward(self, anchor1, anchor2, img_sentc, sent_imgc):
... | 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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | KarmaJun/nlp | PairwiseRankingLoss | false | 5,423 | [
"MIT"
] | 1 | ef14634f45483415205d2738b4e11594a380f082 | https://github.com/KarmaJun/nlp/tree/ef14634f45483415205d2738b4e11594a380f082 | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""
Pairwise ranking loss
"""
def __init__(self, margin):
super().__init__()
self.margin = margin
def forward(self, anchor1, anchor2, img_sentc, sent_imgc):
cost_sent = torch.clamp(self.mar... |
PatchEmbedding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 PatchEmbedding(nn.Module):
"""
small patches embedding
image(B, C, H, W) -> projection(B, emb_dims, H/P, W/P) -> flatten & transpose(B, {(H/P) * (W/P)}, embed_dims)
"""
def __init__(self, image_size=224, patch_size=16, in_channels=3,
embed_dims=768... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Junhojuno/vision-transformer | PatchEmbedding | false | 5,424 | [
"MIT"
] | 1 | 38f8a17967e91e98f767c8e5754081ee8bcd72b4 | https://github.com/Junhojuno/vision-transformer/tree/38f8a17967e91e98f767c8e5754081ee8bcd72b4 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
small patches embedding
image(B, C, H, W) -> projection(B, emb_dims, H/P, W/P) -> flatten & transpose(B, {(H/P) * (W/P)}, embed_dims)
"""
def __init__(self, image_size=224, patch_size=16, in_channels=3,
embed_dims=768, norm_la... |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, n_obs, n_actions, hidden_size, init_w=0.003):
super(Actor, self).__init__()
self.linear1 = nn.Linear(n_obs, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | KOWKO1/reinforcement-learning-tutorials | Actor | false | 5,425 | [
"MIT"
] | 1 | 5f29d6eba8b580041f3e82d88dc3e1cd8e4cae10 | https://github.com/KOWKO1/reinforcement-learning-tutorials/tree/5f29d6eba8b580041f3e82d88dc3e1cd8e4cae10 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, n_obs, n_actions, hidden_size, init_w=0.003):
super().__init__()
self.linear1 = nn.Linear(n_obs, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
self.linea... |
ResidualAttentionBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from collections import OrderedDict
from torch import nn
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: 'torch.Tensor'):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
cla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jinsu-L/KELIP | ResidualAttentionBlock | false | 5,426 | [
"Apache-2.0"
] | 1 | d3261cbb9ba3c3ad474dd560a5add8b69ed78477 | https://github.com/Jinsu-L/KELIP/tree/d3261cbb9ba3c3ad474dd560a5add8b69ed78477 | import torch
from collections import OrderedDict
from torch import nn
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: 'torch.Tensor'):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
cla... |
Mnist_CNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Mnist_CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Justin-A/PyTorch-tutorials-kr | Mnist_CNN | false | 5,427 | [
"BSD-3-Clause"
] | 1 | 0d8e407523e5e75de0081becf800b82b37eb912f | https://github.com/Justin-A/PyTorch-tutorials-kr/tree/0d8e407523e5e75de0081becf800b82b37eb912f | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.quantization
import torch.onnx
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.Con... |
FPN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
import torch.nn.functional as F
class FPN(nn.Module):
def __init__(self, lat_inC, top_inC, outC, mode='nearest'):
super(FPN, self).__init__()
assert mode in ['nearest', 'bilinear']
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from math import sqrt as sqrt
from itertools import produc... | KaiOtter/pytorch_DSOD_variants | FPN | false | 5,428 | [
"MIT"
] | 1 | f29088b13b24f24e2cf20e9a2dc800cd6dbde145 | https://github.com/KaiOtter/pytorch_DSOD_variants/tree/f29088b13b24f24e2cf20e9a2dc800cd6dbde145 | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, lat_inC, top_inC, outC, mode='nearest'):
super().__init__()
assert mode in ['nearest', 'bilinear']
self.latl... |
StandardizedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 StandardizedConv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(StandardizedConv2d, self).__init__(in_channels, out_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.triton_helpers import libdevice
import torch.nn as ... | KKallidromitis/vissl | StandardizedConv2d | false | 5,429 | [
"MIT"
] | 1 | c553e7f6b13c5fa951e3f989beb129899eb8cc80 | https://github.com/KKallidromitis/vissl/tree/c553e7f6b13c5fa951e3f989beb129899eb8cc80 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super().__init__(in_channels, out_channels,
kernel_size, stride, padding, dilati... |
SameBlock2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class SameBlock2d(nn.Module):
"""
Simple block, preserve spatial resolution.
"""
def __init__(self, in_features, out_features, groups=1, kernel_size=3,
padding=1):
super(SameBlock2d, self).__init__()
self.conv =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | KangweiiLiu/Thin-Plate-Spline-Motion-Model | SameBlock2d | false | 5,430 | [
"MIT"
] | 1 | 0ec14f6c06f5beeef159340142ec5182a1be9bc7 | https://github.com/KangweiiLiu/Thin-Plate-Spline-Motion-Model/tree/0ec14f6c06f5beeef159340142ec5182a1be9bc7 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
Simple block, preserve spatial resolution.
"""
def __init__(self, in_features, out_features, groups=1, kernel_size=3,
padding=1):
super().__init__()
self.conv = nn.Conv2d(in_channels=... |
NeuralNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
class NeuralNetwork(torch.nn.Module):
"""
Neural network class of fully connected layers
Args:
n_input_feature : int
number of input features
n_output : int
number of output classes
"""
def __init__(self, n_input_feature, n_output):
su... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | Kani712/CMSI-535 | NeuralNetwork | false | 5,431 | [
"MIT"
] | 1 | 605e7812ee0e5294b6bf3ecb8fadaed4e85a7dd3 | https://github.com/Kani712/CMSI-535/tree/605e7812ee0e5294b6bf3ecb8fadaed4e85a7dd3 | import torch
class Model(torch.nn.Module):
"""
Neural network class of fully connected layers
Args:
n_input_feature : int
number of input features
n_output : int
number of output classes
"""
def __init__(self, n_input_feature, n_output):
super().__... |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLP(nn.Module):
""" 全连接网络"""
def __init__(self, state_dim):
super(MLP, self).__init__()
self.fc1 = nn.Linear(state_dim, 36)
self.fc2 = nn.Linear(36, 36)
self.fc3 = nn.Linear(36, 1)
def forward(self, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | KOWKO1/reinforcement-learning-tutorials | MLP | false | 5,432 | [
"MIT"
] | 1 | 5f29d6eba8b580041f3e82d88dc3e1cd8e4cae10 | https://github.com/KOWKO1/reinforcement-learning-tutorials/tree/5f29d6eba8b580041f3e82d88dc3e1cd8e4cae10 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
""" 全连接网络"""
def __init__(self, state_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim, 36)
self.fc2 = nn.Linear(36, 36)
self.fc3 = nn.Linear(36, 1)
def forward(self, x):
... |
UpBlock2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class UpBlock2d(nn.Module):
"""
Upsampling block for use in decoder.
"""
def __init__(self, in_features, out_features, kernel_size=3, padding=1,
groups=1):
super(UpBlock2d, self).__init__()
self.conv = nn.Conv2d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KangweiiLiu/Thin-Plate-Spline-Motion-Model | UpBlock2d | false | 5,433 | [
"MIT"
] | 1 | 0ec14f6c06f5beeef159340142ec5182a1be9bc7 | https://github.com/KangweiiLiu/Thin-Plate-Spline-Motion-Model/tree/0ec14f6c06f5beeef159340142ec5182a1be9bc7 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
Upsampling block for use in decoder.
"""
def __init__(self, in_features, out_features, kernel_size=3, padding=1,
groups=1):
super().__init__()
self.conv = nn.Conv2d(in_channels=in_fea... |
FCNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn as nn
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
self.drop_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | KaihuaTang/scene-graph-benchmark.pytorch | FCNet | false | 5,434 | [
"MIT"
] | 1 | 45cd54f7465b81d3154e94fcab2b554a09637f6f | https://github.com/KaihuaTang/scene-graph-benchmark.pytorch/tree/45cd54f7465b81d3154e94fcab2b554a09637f6f | import torch
import torch.utils.data
import torch.nn as nn
from torch.nn.utils import weight_norm
class Model(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super().__init__()
self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
self.drop_value = dro... |
DownBlock2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class DownBlock2d(nn.Module):
"""
Downsampling block for use in encoder.
"""
def __init__(self, in_features, out_features, kernel_size=3, padding=1,
groups=1):
super(DownBlock2d, self).__init__()
self.conv = 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.... | KangweiiLiu/Thin-Plate-Spline-Motion-Model | DownBlock2d | false | 5,435 | [
"MIT"
] | 1 | 0ec14f6c06f5beeef159340142ec5182a1be9bc7 | https://github.com/KangweiiLiu/Thin-Plate-Spline-Motion-Model/tree/0ec14f6c06f5beeef159340142ec5182a1be9bc7 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
Downsampling block for use in encoder.
"""
def __init__(self, in_features, out_features, kernel_size=3, padding=1,
groups=1):
super().__init__()
self.conv = nn.Conv2d(in_channels=in_f... |
GateContextSelectionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 GateContextSelectionLayer(nn.Module):
def __init__(self, dim_model, dim_ff, prob_dropout):
super(GateContextSelectionLayer, self).__init__()
self.source = nn.Linear(dim_model, dim_model)
self.context = nn.Linear(dim_model, dim_model)
def forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | KirkGuo/HCN | GateContextSelectionLayer | false | 5,437 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dim_model, dim_ff, prob_dropout):
super().__init__()
self.source = nn.Linear(dim_model, dim_model)
self.context = nn.Linear(dim_model, dim_model)
def forward(self, x_1, x_2, *args):
update = torch.s... |
SiamFC | # 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 SiamFC(nn.Module):
def __init__(self, out_scale=0.001):
super(SiamFC, self).__init__()
self.out_scale = out_scale
def forward(self, z, x):
return self._fast_xcorr(z, x) * self.out_scale
def _fast_xcorr(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
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | Kingzerd/siamfc_pytorch | SiamFC | false | 5,438 | [
"MIT"
] | 1 | fd1dbeb12dd7e2b9190876a1de7ea4b71a7a1166 | https://github.com/Kingzerd/siamfc_pytorch/tree/fd1dbeb12dd7e2b9190876a1de7ea4b71a7a1166 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, out_scale=0.001):
super().__init__()
self.out_scale = out_scale
def forward(self, z, x):
return self._fast_xcorr(z, x) * self.out_scale
def _fast_xcorr(self, z, x):
... |
BalancedLoss | # 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 BalancedLoss(nn.Module):
def __init__(self, neg_weight=1.0):
super(BalancedLoss, self).__init__()
self.neg_weight = neg_weight
def forward(self, input, target):
pos_mask = target == 1
neg_mask = target =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Kingzerd/siamfc_pytorch | BalancedLoss | false | 5,439 | [
"MIT"
] | 1 | fd1dbeb12dd7e2b9190876a1de7ea4b71a7a1166 | https://github.com/Kingzerd/siamfc_pytorch/tree/fd1dbeb12dd7e2b9190876a1de7ea4b71a7a1166 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, neg_weight=1.0):
super().__init__()
self.neg_weight = neg_weight
def forward(self, input, target):
pos_mask = target == 1
neg_mask = target == 0
pos_num = pos... |
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
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Junhojuno/vision-transformer | Block | false | 5,440 | [
"MIT"
] | 1 | 38f8a17967e91e98f767c8e5754081ee8bcd72b4 | https://github.com/Junhojuno/vision-transformer/tree/38f8a17967e91e98f767c8e5754081ee8bcd72b4 | import torch
import torch.nn as nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is ... |
ConcatFusionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ConcatFusionLayer(nn.Module):
def __init__(self, dim_model, voc_size, dout_p):
super(ConcatFusionLayer, self).__init__()
self.linear = nn.Linear(dim_model, voc_size)
self.dropout = nn.Dropout(dout_p)
self.lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | KirkGuo/HCN | ConcatFusionLayer | false | 5,441 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, dim_model, voc_size, dout_p):
super().__init__()
self.linear = nn.Linear(dim_model, voc_size)
self.dropout = nn.Dropout(dout_p)
self.linear2 = nn.Linear(voc_size, voc_size... |
FeatureEmbeddingLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
class FeatureEmbeddingLayer(nn.Module):
def __init__(self, dim_feature, dim_model):
super(FeatureEmbeddingLayer, self).__init__()
self.dim_model = dim_model
self.embed = nn.Linear(dim_feature, dim_model)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | KirkGuo/HCN | FeatureEmbeddingLayer | false | 5,442 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 | import torch
import numpy as np
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dim_feature, dim_model):
super().__init__()
self.dim_model = dim_model
self.embed = nn.Linear(dim_feature, dim_model)
def forward(self, x):
out = self.embed(x)
out = out *... |
BiAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torchvision.transforms import functional as F
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, 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.... | KaihuaTang/scene-graph-benchmark.pytorch | BiAttention | false | 5,443 | [
"MIT"
] | 1 | 45cd54f7465b81d3154e94fcab2b554a09637f6f | https://github.com/KaihuaTang/scene-graph-benchmark.pytorch/tree/45cd54f7465b81d3154e94fcab2b554a09637f6f | import torch
from torchvision.transforms import functional as F
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super().__init__()
self.lin ... |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torchvision.transforms import functional as F
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | KaihuaTang/scene-graph-benchmark.pytorch | PositionwiseFeedForward | false | 5,444 | [
"MIT"
] | 1 | 45cd54f7465b81d3154e94fcab2b554a09637f6f | https://github.com/KaihuaTang/scene-graph-benchmark.pytorch/tree/45cd54f7465b81d3154e94fcab2b554a09637f6f | import torch
from torchvision.transforms import functional as F
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Conv1d(d... |
PatchEmbed | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 PatchEmbed(nn.Module):
def __init__(self, img_size: 'int'=224, patch_size: 'int'=16, stride:
'int'=None, in_channels: 'int'=3, embed_dim: 'int'=768, multi_conv:
'bool'=False, norm_layer: 'Optional'=nn.LayerNorm):
super(P... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Justin900429/vision-transformer | PatchEmbed | false | 5,446 | [
"MIT"
] | 1 | e149092efbb83c166449944137db0ee5200f9325 | https://github.com/Justin900429/vision-transformer/tree/e149092efbb83c166449944137db0ee5200f9325 | import torch
import torch.nn as nn
from typing import Optional
class Model(nn.Module):
def __init__(self, img_size: 'int'=224, patch_size: 'int'=16, stride:
'int'=None, in_channels: 'int'=3, embed_dim: 'int'=768, multi_conv:
'bool'=False, norm_layer: 'Optional'=nn.LayerNorm):
super().__in... |
AvgPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | from torch.nn import Module
import torch
import torch as th
class AvgPool2d(Module):
"""
This class is the beginning of an exact python port of the torch.nn.AvgPool2d
module. Because PySyft cannot hook into layers which are implemented in C++,
our special functionalities (such as encrypted computation... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._em... | Kritikalcoder/PySyft | AvgPool2d | false | 5,447 | [
"Apache-2.0"
] | 1 | 4c418084607de74cac7b7795f91168992c555f50 | https://github.com/Kritikalcoder/PySyft/tree/4c418084607de74cac7b7795f91168992c555f50 | from torch.nn import Module
import torch
import torch as th
class Model(Module):
"""
This class is the beginning of an exact python port of the torch.nn.AvgPool2d
module. Because PySyft cannot hook into layers which are implemented in C++,
our special functionalities (such as encrypted computation) do... |
LinearActor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 LinearActor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(LinearActor, self).__init__()
self.l1 = nn.Linear(state_dim, action_dim)
self.max_action = max_action
def forward(self, x):
return self.max_action * t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | KuangenZhang/StructuredRL | LinearActor | false | 5,448 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super().__init__()
self.l1 = nn.Linear(state_dim, action_dim)
self.max_action = max_action
def forward(self, x):
return self.max_action * torch.sigmoid(self.l1(x)... |
SelfGating | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 as th
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.cuda
class SelfGating(nn.Module):
def __init__(self, input_dim):
super(SelfGating, self).__init__()
self.fc = nn.Linear(input_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import to... | KoDohwan/MIL-NCE_HowTo100M | SelfGating | false | 5,449 | [
"Apache-2.0"
] | 1 | 459f32b40aeb6f00da1315f957d02cd0c82f9307 | https://github.com/KoDohwan/MIL-NCE_HowTo100M/tree/459f32b40aeb6f00da1315f957d02cd0c82f9307 | import torch
import torch.nn as nn
import torch.utils.data
import torch as th
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.cuda
class Model(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.fc = nn.Linear(input_dim, input_dim)
... |
GateGRUSelectionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 GateGRUSelectionLayer(nn.Module):
def __init__(self, dim_model, dim_ff, prob_dropout):
super(GateGRUSelectionLayer, self).__init__()
self.reset = nn.Linear(dim_model * 2, dim_model)
self.update = nn.Linear(dim_model * 2, dim_model)
self.pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | KirkGuo/HCN | GateGRUSelectionLayer | false | 5,450 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dim_model, dim_ff, prob_dropout):
super().__init__()
self.reset = nn.Linear(dim_model * 2, dim_model)
self.update = nn.Linear(dim_model * 2, dim_model)
self.proposal = nn.Linear(dim_model * 2, dim_model)... |
PAM_Module | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import Parameter
from torch.nn import Softmax
class PAM_Module(Module):
""" Position attention module"""
def __init__(self, in_dim):
super(PAM_Module, self).__init__()
self.chanel_in = in_dim
self.query... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KonarkPaul/COVID_Adv_attack_vulnerability_study | PAM_Module | false | 5,452 | [
"MIT"
] | 1 | f0d1256d0d57a933dd86ccd5fe12d83f9f79ca9c | https://github.com/KonarkPaul/COVID_Adv_attack_vulnerability_study/tree/f0d1256d0d57a933dd86ccd5fe12d83f9f79ca9c | from torch.nn import Module
import torch
from torch.nn import Conv2d
from torch.nn import Parameter
from torch.nn import Softmax
class Model(Module):
""" Position attention module"""
def __init__(self, in_dim):
super().__init__()
self.chanel_in = in_dim
self.query_conv = Conv2d(in_cha... |
ActionMapper | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ActionMapper(nn.Module):
def __init__(self, feature_dim, action_dim, max_action):
super(ActionMapper, self).__init__()
self.l1 = nn.Linear(feature_dim, 300)
self.l2 = nn.Linear(300, action_dim)
self.max_actio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KuangenZhang/StructuredRL | ActionMapper | false | 5,453 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, feature_dim, action_dim, max_action):
super().__init__()
self.l1 = nn.Linear(feature_dim, 300)
self.l2 = nn.Linear(300, action_dim)
self.max_action = max_action
def f... |
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
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of
residual blocks).
"""
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KKallidromitis/vissl | Block | false | 5,454 | [
"MIT"
] | 1 | c553e7f6b13c5fa951e3f989beb129899eb8cc80 | https://github.com/KKallidromitis/vissl/tree/c553e7f6b13c5fa951e3f989beb129899eb8cc80 | import torch
import torch.nn as nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of
residual blocks).
"""
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[... |
SA_Module | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class SA_Module(nn.Module):
""" Self attention Layer"""
def __init__(self, in_dim, activation):
super(SA_Module, self).__init__()
self.chanel_in = in_dim
self.activation = activation
self.query_conv = nn.Conv2d(in_channels=in_dim, out_channel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KonarkPaul/COVID_Adv_attack_vulnerability_study | SA_Module | false | 5,455 | [
"MIT"
] | 1 | f0d1256d0d57a933dd86ccd5fe12d83f9f79ca9c | https://github.com/KonarkPaul/COVID_Adv_attack_vulnerability_study/tree/f0d1256d0d57a933dd86ccd5fe12d83f9f79ca9c | import torch
import torch.nn as nn
class Model(nn.Module):
""" Self attention Layer"""
def __init__(self, in_dim, activation):
super().__init__()
self.chanel_in = in_dim
self.activation = activation
self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim //
... |
CAM_Module | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import Softmax
class CAM_Module(Module):
""" Channel attention module"""
def __init__(self, in_dim):
super(CAM_Module, self).__init__()
self.chanel_in = in_dim
self.gamma = Parameter(torch.zeros(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.... | KonarkPaul/COVID_Adv_attack_vulnerability_study | CAM_Module | false | 5,456 | [
"MIT"
] | 1 | f0d1256d0d57a933dd86ccd5fe12d83f9f79ca9c | https://github.com/KonarkPaul/COVID_Adv_attack_vulnerability_study/tree/f0d1256d0d57a933dd86ccd5fe12d83f9f79ca9c | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import Softmax
class Model(Module):
""" Channel attention module"""
def __init__(self, in_dim):
super().__init__()
self.chanel_in = in_dim
self.gamma = Parameter(torch.zeros(1))
self.softmax ... |
ANN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ANN(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim, use_tanh=True):
super(ANN, self).__init__()
self.l1 = nn.Linear(input_dim, hidden_dim)
self.l2 = nn.Linear(hidden_dim, output_dim)
self.us... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KuangenZhang/StructuredRL | ANN | false | 5,457 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim, use_tanh=True):
super().__init__()
self.l1 = nn.Linear(input_dim, hidden_dim)
self.l2 = nn.Linear(hidden_dim, output_dim)
self.use_tanh ... |
Actor1D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 Actor1D(nn.Module):
def __init__(self, state_dim, action_dim, max_action, option_num=3):
super(Actor1D, self).__init__()
"""
Input size: (batch_num, channel = state_dim * option_num, length = 1)
"""
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.... | KuangenZhang/StructuredRL | Actor1D | false | 5,458 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim, max_action, option_num=3):
super().__init__()
"""
Input size: (batch_num, channel = state_dim * option_num, length = 1)
"""
self.conv1 = nn.... |
BasicModel | # 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 BasicModel(nn.Module):
def __init__(self) ->None:
super().__init__()
def forward(self, input):
input = 1 - F.relu(1 - input)
return input
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | LMdeLiangMi/captum | BasicModel | false | 5,459 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self) ->None:
super().__init__()
def forward(self, input):
input = 1 - F.relu(1 - input)
return input
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_input... |
MultiHeadedAttentionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadedAttentionLayer(nn.Module):
def __init__(self, dim_model, dim_k, dim_v, h):
super(MultiHeadedAttentionLayer, self).__init__()
self.dim_model = dim_model
self.dim_k = dim_k
self.di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KirkGuo/HCN | MultiHeadedAttentionLayer | false | 5,460 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, dim_model, dim_k, dim_v, h):
super().__init__()
self.dim_model = dim_model
self.dim_k = dim_k
self.dim_v = dim_v
self.h = h
self.Q_linea... |
ActorSigmoid | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ActorSigmoid(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(ActorSigmoid, self).__init__()
self.l3 = nn.Linear(state_dim, action_dim)
self.max_action = max_action
def forward(self, x):
x = self.max_action * to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | KuangenZhang/StructuredRL | ActorSigmoid | false | 5,461 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super().__init__()
self.l3 = nn.Linear(state_dim, action_dim)
self.max_action = max_action
def forward(self, x):
x = self.max_action * torch.sigmoid(self.l3(x))
... |
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, state_dim, action_dim):
super(Encoder, self).__init__()
self.encoder_1 = nn.Linear(state_dim, 400)
self.encoder_2 = nn.Linear(400, 300)
self.encoder_3 = nn.Linear(300, 2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | KuangenZhang/StructuredRL | Encoder | false | 5,462 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.encoder_1 = nn.Linear(state_dim, 400)
self.encoder_2 = nn.Linear(400, 300)
self.encoder_3 = nn.Linear(300, 2 * action_dim)
... |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, 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
assert_... | KuangenZhang/StructuredRL | Critic | false | 5,463 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, 1)
self.l4 = nn.... |
DenseGraphConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class DenseGraphConv(torch.nn.Module):
"""See :class:`torch_geometric.nn.conv.GraphConv`.
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch.nn import Parameter
import torch.utils.data
assert_size_s... | KuangenZhang/pytorch_geometric | DenseGraphConv | false | 5,464 | [
"MIT"
] | 1 | 0bfc79a5eaccfcd16a82395e8578a90c5e44759f | https://github.com/KuangenZhang/pytorch_geometric/tree/0bfc79a5eaccfcd16a82395e8578a90c5e44759f | import math
import torch
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class Model(torch.nn.Module):
"""See :class:`torch_geometric.nn.conv.GraphConv`.
"""
def __... |
ActorDeep | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ActorDeep(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(ActorDeep, self).__init__()
self.l1 = nn.Linear(state_dim, 300)
self.l2 = nn.Linear(300, 300)
self.l3 = nn.Linear(300, 300)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KuangenZhang/StructuredRL | ActorDeep | false | 5,465 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super().__init__()
self.l1 = nn.Linear(state_dim, 300)
self.l2 = nn.Linear(300, 300)
self.l3 = nn.Linear(300, 300)
self.l4 = nn... |
ReLUDeepLiftModel | # 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 ReLUDeepLiftModel(nn.Module):
"""
https://www.youtube.com/watch?v=f_iAM0NPwnM
"""
def __init__(self) ->None:
super().__init__()
self.relu1 = nn.ReLU()
self.relu2 = nn.ReLU()
def forward(self, x1, x2, x3=2):
return 2 * 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... | LMdeLiangMi/captum | ReLUDeepLiftModel | false | 5,466 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
https://www.youtube.com/watch?v=f_iAM0NPwnM
"""
def __init__(self) ->None:
super().__init__()
self.relu1 = nn.ReLU()
self.relu2 = nn.ReLU()
def forward(self, x1, x2, x3=2):
return 2 * self.relu1(x1) + ... |
Addition_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
class Addition_Module(nn.Module):
"""Custom addition module that uses multiple inputs to assure correct relevance
propagation. Any addition in a forward function needs to be replaced with the
module before using LRP."""
def __init__(self) ->None:
super().__i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | LMdeLiangMi/captum | Addition_Module | false | 5,467 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Custom addition module that uses multiple inputs to assure correct relevance
propagation. Any addition in a forward function needs to be replaced with the
module before using LRP."""
def __init__(self) ->None:
super().__init__()
... |
FeatureNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 FeatureNet(nn.Module):
def __init__(self, state_dim, feature_dim):
super(FeatureNet, self).__init__()
self.l1 = nn.Linear(state_dim, 300)
self.l2 = nn.Linear(300, feature_dim)
def forward(self, x):
x = 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
import torch.nn as nn
assert_... | KuangenZhang/StructuredRL | FeatureNet | false | 5,468 | [
"MIT"
] | 1 | 9b05e5034ff0e045aabf83786efb0859f08e989a | https://github.com/KuangenZhang/StructuredRL/tree/9b05e5034ff0e045aabf83786efb0859f08e989a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, feature_dim):
super().__init__()
self.l1 = nn.Linear(state_dim, 300)
self.l2 = nn.Linear(300, feature_dim)
def forward(self, x):
x = F.relu(self.l1(x))
... |
BasicModel2 | # 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 BasicModel2(nn.Module):
"""
Example model one from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1) - 1 - ReLU(x2))
"""
def __init__(self) ->None:
super().__init__()
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... | LMdeLiangMi/captum | BasicModel2 | false | 5,469 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Example model one from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1) - 1 - ReLU(x2))
"""
def __init__(self) ->None:
super().__init__()
def forward(self, inpu... |
BasicModel5_MultiArgs | # 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 BasicModel5_MultiArgs(nn.Module):
"""
Slightly modified example model from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) * x3[0] - ReLU(x2) * x3[1])
"""
def __init__(self) ->None:
s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | LMdeLiangMi/captum | BasicModel5_MultiArgs | false | 5,470 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Slightly modified example model from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) * x3[0] - ReLU(x2) * x3[1])
"""
def __init__(self) ->None:
super().__init__(... |
BasicModel4_MultiArgs | # 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 BasicModel4_MultiArgs(nn.Module):
"""
Slightly modified example model from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) - ReLU(x2) / x3)
"""
def __init__(self) ->None:
super().__in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | LMdeLiangMi/captum | BasicModel4_MultiArgs | false | 5,471 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Slightly modified example model from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) - ReLU(x2) / x3)
"""
def __init__(self) ->None:
super().__init__()
def ... |
NormLayer | # 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 NormLayer(nn.Module):
def __init__(self, mean, std, n=None, eps=1e-08) ->None:
super().__init__()
self.mean = mean
self.std = std
self.eps = eps
def forward(self, x):
return (x - self.mean) / (self.std + self.eps)
def get_inp... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | LMdeLiangMi/captum | NormLayer | false | 5,472 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, mean, std, n=None, eps=1e-08) ->None:
super().__init__()
self.mean = mean
self.std = std
self.eps = eps
def forward(self, x):
return (x - self.mean) / (self.std + self.eps)
def get_inputs(... |
BasicModel3 | # 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 BasicModel3(nn.Module):
"""
Example model two from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) - ReLU(x2))
"""
def __init__(self) ->None:
super().__init__()
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... | LMdeLiangMi/captum | BasicModel3 | false | 5,473 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Example model two from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1 - 1) - ReLU(x2))
"""
def __init__(self) ->None:
super().__init__()
def forward(self, inpu... |
TanhDeepLiftModel | # 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 TanhDeepLiftModel(nn.Module):
"""
Same as the ReLUDeepLiftModel, but with activations
that can have negative outputs
"""
def __init__(self) ->None:
super().__init__()
self.tanh1 = nn.Tanh()
self.tanh2 = nn.Tanh()
def forward(se... | 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_... | LMdeLiangMi/captum | TanhDeepLiftModel | false | 5,474 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Same as the ReLUDeepLiftModel, but with activations
that can have negative outputs
"""
def __init__(self) ->None:
super().__init__()
self.tanh1 = nn.Tanh()
self.tanh2 = nn.Tanh()
def forward(self, x1, x2):... |
MultiRelu | # 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 Tuple
import torch.nn as nn
from typing import no_type_check
class MultiRelu(nn.Module):
def __init__(self, inplace: 'bool'=False) ->None:
super().__init__()
self.relu1 = nn.ReLU(inplace=inplace)
self.relu2 = nn.ReLU(inplace=inplace... | 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... | LMdeLiangMi/captum | MultiRelu | false | 5,475 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
from torch import Tensor
from typing import Tuple
import torch.nn as nn
from typing import no_type_check
class Model(nn.Module):
def __init__(self, inplace: 'bool'=False) ->None:
super().__init__()
self.relu1 = nn.ReLU(inplace=inplace)
self.relu2 = nn.ReLU(inplace=inplace)
... |
SoftmaxModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SoftmaxModel(nn.Module):
"""
Model architecture from:
https://adventuresinmachinelearning.com/pytorch-tutorial-deep-learning/
"""
def __init__(self, num_in, num_hidden, num_out, inplace=False) ->None:
super().__init__()
self.num_in = num_in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LMdeLiangMi/captum | SoftmaxModel | false | 5,476 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Model architecture from:
https://adventuresinmachinelearning.com/pytorch-tutorial-deep-learning/
"""
def __init__(self, num_in, num_hidden, num_out, inplace=False) ->None:
super().__init__()
self.num_in = num_in
... |
BasicModel_MaxPool_ReLU | # 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 BasicModel_MaxPool_ReLU(nn.Module):
def __init__(self, inplace=False) ->None:
super().__init__()
self.maxpool = nn.MaxPool1d(3)
self.relu = nn.ReLU(inplace=inplace)
def forward(self, x):
return self.relu(self.maxpool(x)).sum(dim=1)
d... | 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... | LMdeLiangMi/captum | BasicModel_MaxPool_ReLU | false | 5,477 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, inplace=False) ->None:
super().__init__()
self.maxpool = nn.MaxPool1d(3)
self.relu = nn.ReLU(inplace=inplace)
def forward(self, x):
return self.relu(self.maxpool(x)).sum(dim=1)
def get_inputs():
... |
BasicModel6_MultiTensor | # 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 BasicModel6_MultiTensor(nn.Module):
def __init__(self) ->None:
super().__init__()
def forward(self, input1, input2):
input = input1 + input2
return 1 - F.relu(1 - input)[:, 1]
def get_inputs():
return [tor... | 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... | LMdeLiangMi/captum | BasicModel6_MultiTensor | false | 5,478 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self) ->None:
super().__init__()
def forward(self, input1, input2):
input = input1 + input2
return 1 - F.relu(1 - input)[:, 1]
def get_inputs():
return [torch.rand([4, 4, 4, ... |
CosineSimilarityLoss | # 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 CosineSimilarityLoss(nn.Module):
def __init__(self, dim=1, eps=1e-08):
super(CosineSimilarityLoss, self).__init__()
self.cos = nn.CosineSimilarity(dim=dim, eps=eps)
self.eps = eps
def forward(self, inputs, target):
scores = self.cos(in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | LTTM/LSR | CosineSimilarityLoss | false | 5,479 | [
"Apache-2.0"
] | 1 | ab204895a86160a5d278fe3cee14c11532251218 | https://github.com/LTTM/LSR/tree/ab204895a86160a5d278fe3cee14c11532251218 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dim=1, eps=1e-08):
super().__init__()
self.cos = nn.CosineSimilarity(dim=dim, eps=eps)
self.eps = eps
def forward(self, inputs, target):
scores = self.cos(inputs, target)
return 1.0 - torch.... |
SigmoidDeepLiftModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SigmoidDeepLiftModel(nn.Module):
"""
Model architecture from:
https://medium.com/coinmonks/create-a-neural-network-in
-pytorch-and-make-your-life-simpler-ec5367895199
"""
def __init__(self, num_in, num_hidden, num_out) ->None:
super().__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 import triton_helpers
import torch.nn as nn
assert_... | LMdeLiangMi/captum | SigmoidDeepLiftModel | false | 5,480 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Model architecture from:
https://medium.com/coinmonks/create-a-neural-network-in
-pytorch-and-make-your-life-simpler-ec5367895199
"""
def __init__(self, num_in, num_hidden, num_out) ->None:
super().__init__()
s... |
BackwardCrossAttentionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class ResidualConnectionLayer(nn.Module):
def __init__(self, dim_model, prob_dropout=0.1, add_sublayer=True):
super(ResidualConnectionLayer, self).__init__()
self.add_sublayer = add_sublayer
self.norm = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KirkGuo/HCN | BackwardCrossAttentionLayer | false | 5,481 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class ResidualConnectionLayer(nn.Module):
def __init__(self, dim_model, prob_dropout=0.1, add_sublayer=True):
super().__init__()
self.add_sublayer = add_sublayer
self.norm = nn.LayerNorm(dim_model)
... |
EnsembleFC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
class EnsembleFC(nn.Module):
__constants__ = ['in_features', 'out_features']
in_features: 'int'
out_features: 'int'
ensemble_size: 'int'
weight: 'torch.Tensor'
def __init__(self, in_features: 'int', out_features: 'int',
ensemb... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.utils.data
assert_size_stride = torch._C._dyn... | L-Net-1992/DI-engine | EnsembleFC | false | 5,482 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
__constants__ = ['in_features', 'out_features']
in_features: 'int'
out_features: 'int'
ensemble_size: 'int'
weight: 'torch.Tensor'
def __init__(self, in_features: 'int', out_features: 'int',
ensemble_si... |
LinearMaxPoolLinearModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 LinearMaxPoolLinearModel(nn.Module):
def __init__(self) ->None:
super().__init__()
self.lin1 = nn.Linear(4, 4, bias=False)
self.lin1.weight = nn.Parameter(torch.eye(4, 4))
self.pool1 = nn.MaxPool1d(4)
self.lin2 = nn.Linear(1, 1, bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | LMdeLiangMi/captum | LinearMaxPoolLinearModel | false | 5,483 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self) ->None:
super().__init__()
self.lin1 = nn.Linear(4, 4, bias=False)
self.lin1.weight = nn.Parameter(torch.eye(4, 4))
self.pool1 = nn.MaxPool1d(4)
self.lin2 = nn.Linear(1, 1, bias=False)
se... |
TinyCnn | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 TinyCnn(nn.Module):
def __init__(self, feature_extraction=False) ->None:
super().__init__()
self.feature_extraction = feature_extraction
self.conv1 = nn.Conv2d(3, 3, 5)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(2, 2)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | LMdeLiangMi/captum | TinyCnn | false | 5,484 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, feature_extraction=False) ->None:
super().__init__()
self.feature_extraction = feature_extraction
self.conv1 = nn.Conv2d(3, 3, 5)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(2, 2)
if... |
BilinearUpsample | # 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 List
from typing import Union
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class BilinearUpsample(nn.Module):
"""
Overview:
Upsamples the input to the given member varible scale_factor using mode biliner
Interface:
forward
... | 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 typing import List
from typing import Union
import torch.nn as nn
import torch.utils... | L-Net-1992/DI-engine | BilinearUpsample | false | 5,485 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f | import torch
from typing import List
from typing import Union
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""
Overview:
Upsamples the input to the given member varible scale_factor using mode biliner
Interface:
forward
"""
... |
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.utils.data
class Conv(nn.Module):
def __init__(self, filters0, filters1, kernel_size, bn, bias=True):
super().__init__()
if bn:
bias = False
self.conv = nn.Conv2d(filters0, filters1, kernel_size, stride=1,
padding=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
import torch.utils.data
assert_size_stride = torch._C._dyn... | L-Net-1992/DI-engine | Encoder | false | 5,486 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f | import torch
import torch.nn as nn
import torch.utils.data
class Conv(nn.Module):
def __init__(self, filters0, filters1, kernel_size, bn, bias=True):
super().__init__()
if bn:
bias = False
self.conv = nn.Conv2d(filters0, filters1, kernel_size, stride=1,
padding=ker... |
BasicAttentionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class ResidualConnectionLayer(nn.Module):
def __init__(self, dim_model, prob_dropout=0.1, add_sublayer=True):
super(ResidualConnectionLayer, self).__init__()
self.add_sublayer = add_sublayer
self.norm = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KirkGuo/HCN | BasicAttentionLayer | false | 5,487 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class ResidualConnectionLayer(nn.Module):
def __init__(self, dim_model, prob_dropout=0.1, add_sublayer=True):
super().__init__()
self.add_sublayer = add_sublayer
self.norm = nn.LayerNorm(dim_model)
... |
ATOCAttentionUnit | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 Dict
from typing import Union
import torch.nn as nn
import torch.utils.data
class ATOCAttentionUnit(nn.Module):
"""
Overview:
the attention unit of the atoc network. We now implement it as two-layer MLP, same as the original paper
Interface:
__init__, forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | L-Net-1992/DI-engine | ATOCAttentionUnit | false | 5,488 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f | import torch
from typing import Dict
from typing import Union
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""
Overview:
the attention unit of the atoc network. We now implement it as two-layer MLP, same as the original paper
Interface:
__init__, forward
.. n... |
BasicModel_ConvNet_One_Conv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import Tensor
from typing import Optional
import torch.nn as nn
from typing import no_type_check
class BasicModel_ConvNet_One_Conv(nn.Module):
def __init__(self, inplace: 'bool'=False) ->None:
super().__init__()
self.conv1 = nn.Conv2d(1, 2, 3, 1)
self.relu1 = nn.Re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | LMdeLiangMi/captum | BasicModel_ConvNet_One_Conv | false | 5,489 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
from torch import Tensor
from typing import Optional
import torch.nn as nn
from typing import no_type_check
class Model(nn.Module):
def __init__(self, inplace: 'bool'=False) ->None:
super().__init__()
self.conv1 = nn.Conv2d(1, 2, 3, 1)
self.relu1 = nn.ReLU(inplace=inplace)
... |
ResidualBlock | # 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.utils.data
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, activation='relu'):
super().__init__()
self.in_channels, self.out_channels, self.activation = (in_channels,
out_channels, activation)
self.b... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | L-Net-1992/DI-engine | ResidualBlock | false | 5,490 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, in_channels, out_channels, activation='relu'):
super().__init__()
self.in_channels, self.out_channels, self.activation = (in_channels,
out_channels, activation)
self.blocks = ... |
GRUGatingUnit | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
class GRUGatingUnit(torch.nn.Module):
"""
Overview:
GRU Gating Unit used in GTrXL.
"""
def __init__(self, input_dim: 'int', bg: 'float'=2.0):
"""
Arguments:
- input_dim: (:obj:`int`): dimension of input.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | L-Net-1992/DI-engine | GRUGatingUnit | false | 5,491 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f | import torch
import torch.nn as nn
import torch.utils.data
class Model(torch.nn.Module):
"""
Overview:
GRU Gating Unit used in GTrXL.
"""
def __init__(self, input_dim: 'int', bg: 'float'=2.0):
"""
Arguments:
- input_dim: (:obj:`int`): dimension of input.
... |
ForwardCrossAttentionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class ResidualConnectionLayer(nn.Module):
def __init__(self, dim_model, prob_dropout=0.1, add_sublayer=True):
super(ResidualConnectionLayer, self).__init__()
self.add_sublayer = add_sublayer
self.norm = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KirkGuo/HCN | ForwardCrossAttentionLayer | false | 5,492 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class ResidualConnectionLayer(nn.Module):
def __init__(self, dim_model, prob_dropout=0.1, add_sublayer=True):
super().__init__()
self.add_sublayer = add_sublayer
self.norm = nn.LayerNorm(dim_model)
... |
DoubleForwardCrossAttentionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class ResidualConnectionLayer(nn.Module):
def __init__(self, dim_model, prob_dropout=0.1, add_sublayer=True):
super(ResidualConnectionLayer, self).__init__()
self.add_sublayer = add_sublayer
self.norm = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | KirkGuo/HCN | DoubleForwardCrossAttentionLayer | false | 5,493 | [
"MIT"
] | 1 | 7d8020c8d76413b6ca3a359fb2e9b34652949e17 | https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17 | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class ResidualConnectionLayer(nn.Module):
def __init__(self, dim_model, prob_dropout=0.1, add_sublayer=True):
super().__init__()
self.add_sublayer = add_sublayer
self.norm = nn.LayerNorm(dim_model)
... |
LabelSmoothCELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
def one_hot(val: 'torch.LongTensor', num: 'int', num_first: 'bool'=False
) ->torch.FloatTensor:
"""
Overview:
Convert a ``torch.LongTensor`` to one hot encoding.
This implementation can be slightly f... | 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
... | L-Net-1992/DI-engine | LabelSmoothCELoss | false | 5,494 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
def one_hot(val: 'torch.LongTensor', num: 'int', num_first: 'bool'=False
) ->torch.FloatTensor:
"""
Overview:
Convert a ``torch.LongTensor`` to one hot encoding.
This implementation can be slightly f... |
RewardModelNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
class RewardModelNetwork(nn.Module):
def __init__(self, input_size: 'int', hidden_size: 'int', output_size:
'int') ->None:
super(RewardModelNetwork, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = 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 torch.nn as ... | L-Net-1992/DI-engine | RewardModelNetwork | false | 5,495 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, input_size: 'int', hidden_size: 'int', output_size:
'int') ->None:
super().__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = nn.Linear(hidden_size, output_size)
... |
Conv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of input
:param out_channel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Labmem-Zhouyx/Attentron_FastSpeech2 | Conv | false | 5,496 | [
"MIT"
] | 1 | ac1c0bd0d23e8314eb2518f3d5abffc9b4b9f5cb | https://github.com/Labmem-Zhouyx/Attentron_FastSpeech2/tree/ac1c0bd0d23e8314eb2518f3d5abffc9b4b9f5cb | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Convolution Module
"""
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=True, w_init='linear'):
"""
:param in_channels: dimension of input
:param out_channe... |
AdaptiveInstanceNorm2d | # 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 AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, eps=1e-08):
super(AdaptiveInstanceNorm2d, self).__init__()
self.eps = eps
def IN_noWeight(self, x):
N, C = x.size(0), x.size(1)
mean = x.contiguous().view(N, C, -1).mean(2).contiguo... | 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_... | LenKerr/Semantic-Colorization-GAN | AdaptiveInstanceNorm2d | false | 5,497 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, eps=1e-08):
super().__init__()
self.eps = eps
def IN_noWeight(self, x):
N, C = x.size(0), x.size(1)
mean = x.contiguous().view(N, C, -1).mean(2).contiguous().view(N, C,
1, 1)
x =... |
TripletLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class TripletLoss(nn.Module):
"""
Triplet loss
Takes embeddings of an anchor sample, a positive sample and a negative sample
"""
def __init__(self, margin):
super(TripletLoss, self).__init__()
self.margin = margin
... | 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... | Leo-xxx/lighttrack | TripletLoss | false | 5,498 | [
"MIT"
] | 1 | bc12f53c621c42038066a1af7499838b571b0c76 | https://github.com/Leo-xxx/lighttrack/tree/bc12f53c621c42038066a1af7499838b571b0c76 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Triplet loss
Takes embeddings of an anchor sample, a positive sample and a negative sample
"""
def __init__(self, margin):
super().__init__()
self.margin = margin
def forward(self, ... |
CustomLoss | # 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 CustomLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(CustomLoss, self).__init__()
def forward(self, outputs, targets):
gamma = 0.5
C4 = 10
gb_hat = outputs[:, :, :34]
rb_hat = outputs[:, :, 34:68]
... | 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... | Le-Xiaohuai-speech/PercepNet | CustomLoss | false | 5,499 | [
"BSD-3-Clause"
] | 1 | df778b5394b96419778cb01fffbc9f16a316d823 | https://github.com/Le-Xiaohuai-speech/PercepNet/tree/df778b5394b96419778cb01fffbc9f16a316d823 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, weight=None, size_average=True):
super().__init__()
def forward(self, outputs, targets):
gamma = 0.5
C4 = 10
gb_hat = outputs[:, :, :34]
rb_hat = outputs[:, :, 34:68]
gb = targets[:,... |
GatedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | LenKerr/Semantic-Colorization-GAN | GatedConv2d | false | 5,500 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine... |
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 torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class MultiHeadAttention(nn.Module):
def __init__(self, in_dim, out_dim, out_heads, relation_dim=0, residual
=False, projection=True, layer_norm=True):
super().__init__()
self.in_dim = in_dim
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | L-Net-1992/DI-engine | MultiHeadAttention | false | 5,501 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, in_dim, out_dim, out_heads, relation_dim=0, residual
=False, projection=True, layer_norm=True):
super().__init__()
self.in_dim = in_dim
self.out_di... |
BasicModel_ConvNet_MaxPool1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import Tensor
import torch.nn as nn
from typing import no_type_check
class BasicModel_ConvNet_MaxPool1d(nn.Module):
"""Same as above, but with the MaxPool2d replaced
with a MaxPool1d. This is useful because the MaxPool modules
behave differently to other modules from the perspectiv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LMdeLiangMi/captum | BasicModel_ConvNet_MaxPool1d | false | 5,502 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
from torch import Tensor
import torch.nn as nn
from typing import no_type_check
class Model(nn.Module):
"""Same as above, but with the MaxPool2d replaced
with a MaxPool1d. This is useful because the MaxPool modules
behave differently to other modules from the perspective
of the DeepLift A... |
Upsample | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Upsample(nn.Module):
""" nn.Upsample is deprecated """
def __init__(self, scale_factor, mode='linear'):
super(Upsample, self).__init__()
self.scale_factor = scale_factor
self.mode = mode
def forward(self, x)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Leo-xxx/lighttrack | Upsample | false | 5,503 | [
"MIT"
] | 1 | bc12f53c621c42038066a1af7499838b571b0c76 | https://github.com/Leo-xxx/lighttrack/tree/bc12f53c621c42038066a1af7499838b571b0c76 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
""" nn.Upsample is deprecated """
def __init__(self, scale_factor, mode='linear'):
super().__init__()
self.scale_factor = scale_factor
self.mode = mode
def forward(self, x):
x = F.i... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.nn import Parameter
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch... | LenKerr/Semantic-Colorization-GAN | LayerNorm | false | 5,504 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | import torch
import torch.nn as nn
from torch.nn import Parameter
class Model(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamm... |
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
from typing import Optional
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class ScaledDotProductAttention(nn.Module):
"""
Overview:
Implementation of dot product attentionn with scaling.
"""
def __init__(self, d_k: 'int', dropout: 'float'=0.0) ->No... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | L-Net-1992/DI-engine | ScaledDotProductAttention | false | 5,505 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f | import torch
from typing import Optional
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""
Overview:
Implementation of dot product attentionn with scaling.
"""
def __init__(self, d_k: 'int', dropout: 'float'=0.0) ->None:
super().... |
CoefficientRegularization | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch
import torch.nn as nn
class CoefficientRegularization(nn.Module):
def __init__(self):
super(CoefficientRegularization, self).__init__()
def forward(self, input):
return torch.sum(input ** 2)
def get_inputs():
return [torch.rand([4, 4, 4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C.... | LeoniusChen/AudioDVP | CoefficientRegularization | false | 5,506 | [
"MIT"
] | 1 | c3829b9f1056827e2fe8b2d1fc9083c8cba93984 | https://github.com/LeoniusChen/AudioDVP/tree/c3829b9f1056827e2fe8b2d1fc9083c8cba93984 | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return torch.sum(input ** 2)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
BasicModel_ConvNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import Tensor
import torch.nn as nn
from typing import no_type_check
class BasicModel_ConvNet(nn.Module):
def __init__(self) ->None:
super().__init__()
self.conv1 = nn.Conv2d(1, 2, 3, 1)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(2)
self.conv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LMdeLiangMi/captum | BasicModel_ConvNet | false | 5,507 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
from torch import Tensor
import torch.nn as nn
from typing import no_type_check
class Model(nn.Module):
def __init__(self) ->None:
super().__init__()
self.conv1 = nn.Conv2d(1, 2, 3, 1)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(... |
BasicModel_ConvNet_MaxPool3d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 BasicModel_ConvNet_MaxPool3d(nn.Module):
"""Same as above, but with the MaxPool1d replaced
with a MaxPool3d. This is useful because the MaxPool modules
behave differently to other modules from the perspective
of the DeepLift Attributions
"""
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._inductor.runtime.... | LMdeLiangMi/captum | BasicModel_ConvNet_MaxPool3d | false | 5,508 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Same as above, but with the MaxPool1d replaced
with a MaxPool3d. This is useful because the MaxPool modules
behave differently to other modules from the perspective
of the DeepLift Attributions
"""
def __init__(self) ->None:
... |
BiasAdd | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class BiasAdd(nn.Module):
def __init__(self, num_features):
super(BiasAdd, self).__init__()
self.bias = torch.nn.Parameter(torch.Tensor(num_features))
de... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_st... | LeoMaximal/RepVGG | BiasAdd | false | 5,509 | [
"MIT"
] | 1 | 1e2e7bde551860a1453601424294f25fa7bcaa76 | https://github.com/LeoMaximal/RepVGG/tree/1e2e7bde551860a1453601424294f25fa7bcaa76 | 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, num_features):
super().__init__()
self.bias = torch.nn.Parameter(torch.Tensor(num_features))
def forward(self,... |
GLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
class GLU(nn.Module):
"""
Overview:
Gating Linear Unit.
This class does a thing like this:
.. code:: python
# Inputs: input, context, output_size
# The gate value is a learnt function of the 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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | L-Net-1992/DI-engine | GLU | false | 5,510 | [
"Apache-2.0"
] | 1 | 06803b4e18fa64bbed0fd1d44952242c0c063b0f | https://github.com/L-Net-1992/DI-engine/tree/06803b4e18fa64bbed0fd1d44952242c0c063b0f | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""
Overview:
Gating Linear Unit.
This class does a thing like this:
.. code:: python
# Inputs: input, context, output_size
# The gate value is a learnt function of the input.
... |
C3D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
def get_10x_lr_params(model):
"""
This generator returns all the parameters for the fc layer of the net.
"""
b = [model.linear]
for j in range(len(b)):
for k in b[j].parameters():
if k.requires_grad:
yield k
def get_1x_lr_para... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | HuaizhengZhang/autovideo | C3D | false | 5,511 | [
"MIT"
] | 1 | 58817a6e5973efaabae8e9d749a5cf0f3ff5d13b | https://github.com/HuaizhengZhang/autovideo/tree/58817a6e5973efaabae8e9d749a5cf0f3ff5d13b | import torch
from torch import nn
def get_10x_lr_params(model):
"""
This generator returns all the parameters for the fc layer of the net.
"""
b = [model.linear]
for j in range(len(b)):
for k in b[j].parameters():
if k.requires_grad:
yield k
def get_1x_lr_para... |
Skew | # 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class Skew(nn.Module):
def forward(self, X):
A = X.triu(1)
return A - A.transpose(-1, -2)
d... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import to... | Lezcano/tutorials | Skew | false | 5,512 | [
"BSD-3-Clause"
] | 1 | 24946b2e6d3d825afed6b35c1c4d618a70a88be8 | https://github.com/Lezcano/tutorials/tree/24946b2e6d3d825afed6b35c1c4d618a70a88be8 | import torch
import torch.nn as nn
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class Model(nn.Module):
def forward(self, X):
A = X.triu(1)
return A - A.transpose(-1, -2)
... |
Conv2dLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | LenKerr/Semantic-Colorization-GAN | Conv2dLayer | false | 5,513 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine... |
DummyNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 DummyNet(nn.Module):
def __init__(self):
super(DummyNet, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=5, padding=2)
self.conv2 = nn.Conv2d(10, 5, kernel_size=5, padding=2)
self.softmax = nn.Soft... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LendelTheGreat/weak-segmentation | DummyNet | false | 5,514 | [
"MIT"
] | 1 | 0ff6015f1af741cfb50ef8fb6f55cea822f68f7a | https://github.com/LendelTheGreat/weak-segmentation/tree/0ff6015f1af741cfb50ef8fb6f55cea822f68f7a | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=5, padding=2)
self.conv2 = nn.Conv2d(10, 5, kernel_size=5, padding=2)
self.softmax = nn.Softmax2d()
def ... |
Polynomial3 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class Polynomial3(torch.nn.Module):
def __init__(self):
"""
In the constructor we instantiate four parameters and assi... | 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.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.op... | Lezcano/tutorials | Polynomial3 | false | 5,515 | [
"BSD-3-Clause"
] | 1 | 24946b2e6d3d825afed6b35c1c4d618a70a88be8 | https://github.com/Lezcano/tutorials/tree/24946b2e6d3d825afed6b35c1c4d618a70a88be8 | import torch
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.utils.data
import torch.fx
import torch.nn
import torch.optim
import torch.profiler
class Model(torch.nn.Module):
def __init__(self):
"""
In the constructor we instantiate four parameters and assign the... |
LinearLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LenKerr/Semantic-Colorization-GAN | LinearLayer | false | 5,516 | [
"MIT"
] | 1 | 2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | https://github.com/LenKerr/Semantic-Colorization-GAN/tree/2ce52406ca6fc92e69692b451b1c9ae66ba3b76f | import torch
import torch.nn as nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super().__init__()
self.num_features = num_features
self.affine = affine... |
SEBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class SEBlock(nn.Module):
def __init__(self, input_channels, internal_neurons):
super(SEBlock, self).__init__()
self.down = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | LeoMaximal/RepVGG | SEBlock | false | 5,517 | [
"MIT"
] | 1 | 1e2e7bde551860a1453601424294f25fa7bcaa76 | https://github.com/LeoMaximal/RepVGG/tree/1e2e7bde551860a1453601424294f25fa7bcaa76 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_channels, internal_neurons):
super().__init__()
self.down = nn.Conv2d(in_chan... |
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