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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
LogitCond | # 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 LogitCond(nn.Module):
"""
from the softmax outputs, decides whether the samples are above or below threshold.
"""
def __init__(self, thres=1.0):
super(LogitCond, self).__init__()
self.thres = thres
self.softmax = nn.Softmax(dim=1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Lee-Gihun/Micronet_GSJ | LogitCond | false | 8,451 | [
"MIT"
] | 12 | 72289bb66507b6c3b4d14f2e5916dec718a1b198 | https://github.com/Lee-Gihun/Micronet_GSJ/tree/72289bb66507b6c3b4d14f2e5916dec718a1b198 |
softCrossEntropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
from torch.nn import functional as F
class softCrossEntropy(nn.Module):
def __init__(self, reduce=True):
super(softCrossEntropy, self).__init__()
self.reduce = reduce
return
def forward(self, inputs, target):
"""
:param inputs: predic... | 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
from torch import nn
a... | Lingkai-Kong/Calibrated-BERT-Fine-Tuning | softCrossEntropy | false | 8,452 | [
"Apache-2.0"
] | 29 | 34b8dbf1bfb0d1e466621f149622933bfeab1555 | https://github.com/Lingkai-Kong/Calibrated-BERT-Fine-Tuning/tree/34b8dbf1bfb0d1e466621f149622933bfeab1555 |
DropBlock_Ske | # 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 DropBlock_Ske(nn.Module):
def __init__(self, num_point=25, keep_prob=0.9):
super(DropBlock_Ske, self).__init__()
self.keep_prob = keep_prob
self.num_point = num_point
def forward(self, input, mask):
n, _c, _t, _v = input.size()
... | 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... | Levigty/AimCLR | DropBlock_Ske | false | 8,453 | [
"MIT"
] | 25 | 6cd73767f17748792508647355fa324fa63e235d | https://github.com/Levigty/AimCLR/tree/6cd73767f17748792508647355fa324fa63e235d |
ImageEncoderV4 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
class ImageEncoderV4(nn.Module):
"""
Outputs a 5 x 5 x 32 feature map that preserves spatial information.
"""
def __init__(self, input_channels=3, init_scale=1.0, no_weight_init=
False, init_method='ortho', activation='relu'):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | KH-Kyle/rmp_nav | ImageEncoderV4 | false | 8,454 | [
"MIT"
] | 30 | d598fe70664a4cdc0e9b9dd4b52e84aa3de1b551 | https://github.com/KH-Kyle/rmp_nav/tree/d598fe70664a4cdc0e9b9dd4b52e84aa3de1b551 |
FocalLoss | # 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 FocalLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(FocalLoss, self).__init__()
def forward(self, inputs: 'torch.Tensor', targets: 'torch.Tensor',
alpha: 'float'=0.5, gamma: 'float'=0.5, smoo... | 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... | Latterlig96/DCUnet | FocalLoss | false | 8,455 | [
"MIT"
] | 11 | 87d1c137a60177d6daf1dfff0483678d5580fda0 | https://github.com/Latterlig96/DCUnet/tree/87d1c137a60177d6daf1dfff0483678d5580fda0 |
DiceBCELoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
class DiceBCELoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceBCELoss, self).__init__()
def forward(self, inputs: 'torch.Tensor', targets: 'torch.Tensor',
smooth: 'int'=1):
inputs = input... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | Latterlig96/DCUnet | DiceBCELoss | false | 8,456 | [
"MIT"
] | 11 | 87d1c137a60177d6daf1dfff0483678d5580fda0 | https://github.com/Latterlig96/DCUnet/tree/87d1c137a60177d6daf1dfff0483678d5580fda0 |
ConvTranspose | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 Union
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class ConvTranspose(nn.Module):
def __init__(self, input_channels: 'int... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from typing import Union
import torch.nn as nn
from typing import Tuple
assert_s... | Latterlig96/DCUnet | ConvTranspose | false | 8,457 | [
"MIT"
] | 11 | 87d1c137a60177d6daf1dfff0483678d5580fda0 | https://github.com/Latterlig96/DCUnet/tree/87d1c137a60177d6daf1dfff0483678d5580fda0 |
EqualLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._dynam... | KwonGihyun/DiagonalGAN | EqualLinear | false | 8,458 | [
"MIT"
] | 13 | 9e401c00e741d700f85df2c715ee11c1e66e1d1c | https://github.com/KwonGihyun/DiagonalGAN/tree/9e401c00e741d700f85df2c715ee11c1e66e1d1c |
AdaptiveBilinear | # 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
class AdaptiveBilinear(nn.Module):
def __init__(self):
super(AdaptiveBilinear, self).__init__()
def forward(self, x1, x2):
"""
:param x1: (b, l1, dim1)
:param x2: (b, l2, dim2)
:return:
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LindgeW/BiaffineNER | AdaptiveBilinear | false | 8,459 | [
"Apache-2.0"
] | 13 | 0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf | https://github.com/LindgeW/BiaffineNER/tree/0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf |
OverHaulLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
from torch.nn import functional as F
class LabelSmoothingLoss(nn.Module):
def __init__(self, classes, smoothing=0.0, dim=-1):
super(LabelSmoothingLoss, self).__init__()
self.confidence = 1.0 - smoothing
self.smoothing = smoothing
self.cls = class... | 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
... | Lee-Gihun/Micronet_GSJ | OverHaulLoss | false | 8,460 | [
"MIT"
] | 12 | 72289bb66507b6c3b4d14f2e5916dec718a1b198 | https://github.com/Lee-Gihun/Micronet_GSJ/tree/72289bb66507b6c3b4d14f2e5916dec718a1b198 |
length_evolution | # 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 length_evolution(nn.Module):
"""
calcaulate the length of evolution curve by the gradient
"""
def __init__(self, func='l1'):
super(length_evolution, self).__init__()
self.func = func
def forward(self, mask_score, class_weight):
gra... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | LiWentomng/boxlevelset | length_evolution | false | 8,461 | [
"Apache-2.0"
] | 25 | 8cc40bf6ae4a343c482c676c72259cc12c29d31c | https://github.com/LiWentomng/boxlevelset/tree/8cc40bf6ae4a343c482c676c72259cc12c29d31c |
evolution_area | # 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 evolution_area(nn.Module):
"""
calcaulate the area of evolution curve
"""
def __init__(self):
super(evolution_area, self).__init__()
def forward(self, mask_score, class_weight):
curve_area = torch.sum(class_weight * mask_score)
ret... | 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... | LiWentomng/boxlevelset | evolution_area | false | 8,462 | [
"Apache-2.0"
] | 25 | 8cc40bf6ae4a343c482c676c72259cc12c29d31c | https://github.com/LiWentomng/boxlevelset/tree/8cc40bf6ae4a343c482c676c72259cc12c29d31c |
DotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.functional as F
import torch.nn as nn
class DotProductAttention(nn.Module):
def __init__(self, k_dim):
super(DotProductAttention, self).__init__()
self.scale = 1.0 / k_dim ** 0.5
def forward(self, hn, enc_out, mask=None):
"""
:param hn: query - rn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LindgeW/BiaffineNER | DotProductAttention | false | 8,463 | [
"Apache-2.0"
] | 13 | 0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf | https://github.com/LindgeW/BiaffineNER/tree/0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf |
Bilinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Bilinear(nn.Module):
def __init__(self, in_dim1, in_dim2, label_dim=1, use_input_bias=False):
super(Bilinear, self).__init__()
self.label_dim = label_dim
self.use_input_bias = use_input_bias
if self.use_input_bias:
in_dim1 += 1
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | LindgeW/BiaffineNER | Bilinear | false | 8,464 | [
"Apache-2.0"
] | 13 | 0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf | https://github.com/LindgeW/BiaffineNER/tree/0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf |
MaxPooling | # 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 Union
import torch.nn as nn
from typing import Tuple
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class MaxPooling(nn.Module):
def __init__(self, input_channels: 'int', kernel_size:
'Tuple[int,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from typing import Union
import torch.nn as nn
from typing import Tuple
assert_size_strid... | Latterlig96/DCUnet | MaxPooling | false | 8,465 | [
"MIT"
] | 11 | 87d1c137a60177d6daf1dfff0483678d5580fda0 | https://github.com/Latterlig96/DCUnet/tree/87d1c137a60177d6daf1dfff0483678d5580fda0 |
FocalTverskyLoss | # 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 FocalTverskyLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(FocalTverskyLoss, self).__init__()
def forward(self, inputs: 'torch.Tensor', targets: 'torch.Tensor',
smooth: 'int'=1, alpha: 'float'=0.5, beta: 'float'=0.5, gamma:... | 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... | Latterlig96/DCUnet | FocalTverskyLoss | false | 8,466 | [
"MIT"
] | 11 | 87d1c137a60177d6daf1dfff0483678d5580fda0 | https://github.com/Latterlig96/DCUnet/tree/87d1c137a60177d6daf1dfff0483678d5580fda0 |
AdditiveAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class AdditiveAttention(nn.Module):
def __init__(self, k_size, v_size, hidden_size=None, bias=True):
super(AdditiveAttention, self).__init__()
if hidden_size is None:
hidden_size = v_size
self.W1 = nn.Linear(k_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LindgeW/BiaffineNER | AdditiveAttention | false | 8,467 | [
"Apache-2.0"
] | 13 | 0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf | https://github.com/LindgeW/BiaffineNER/tree/0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf |
DilatedCircularConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 DilatedCircularConv(nn.Module):
def __init__(self, state_dim, out_state_dim=None, n_adj=4, dilation=1):
super(DilatedCircularConv, self).__init__()
self.n_adj = n_adj
self.dilation = dilation
out_state_dim = state_dim if out_state_dim is 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | LiWentomng/boxlevelset | DilatedCircularConv | false | 8,468 | [
"Apache-2.0"
] | 25 | 8cc40bf6ae4a343c482c676c72259cc12c29d31c | https://github.com/LiWentomng/boxlevelset/tree/8cc40bf6ae4a343c482c676c72259cc12c29d31c |
CrossEntropyLoss | # AOT ID: ['1_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
def mask_cross_entropy(pred, target, label):
num_rois = pred.size()[0]
inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
pred_slice = pred[inds, label].squeeze(1)
return F.binary_cross_entropy_with_logits(pred_slic... | 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
... | LiWentomng/boxlevelset | CrossEntropyLoss | false | 8,469 | [
"Apache-2.0"
] | 25 | 8cc40bf6ae4a343c482c676c72259cc12c29d31c | https://github.com/LiWentomng/boxlevelset/tree/8cc40bf6ae4a343c482c676c72259cc12c29d31c |
SmoothL1Loss | # AOT ID: ['1_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
def smooth_l1_loss(pred, target, beta=1.0, reduction='mean'):
assert beta > 0
assert pred.size() == target.size() and target.numel() > 0
diff = torch.abs(pred - target)
loss = torch.where(diff < beta, 0.5 * diff * diff / beta, diff - 0... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | LiWentomng/boxlevelset | SmoothL1Loss | false | 8,470 | [
"Apache-2.0"
] | 25 | 8cc40bf6ae4a343c482c676c72259cc12c29d31c | https://github.com/LiWentomng/boxlevelset/tree/8cc40bf6ae4a343c482c676c72259cc12c29d31c |
Biaffine | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Biaffine(nn.Module):
def __init__(self, in_features, out_features=1, bias=(True, True)):
super(Biaffine, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.bias = bias
self.linear_input_size = in_f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | LindgeW/BiaffineNER | Biaffine | false | 8,471 | [
"Apache-2.0"
] | 13 | 0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf | https://github.com/LindgeW/BiaffineNER/tree/0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf |
BiaffineScorer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
def timestep_dropout(inputs, p=0.5, batch_first=True):
"""
:param inputs: (bz, time_step, feature_size)
:param p: probability p mask out output nodes
:param batch_first: default True
:return:
"""
if not batch_first:
inputs = inputs.transpose(0, 1)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | LindgeW/BiaffineNER | BiaffineScorer | false | 8,472 | [
"Apache-2.0"
] | 13 | 0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf | https://github.com/LindgeW/BiaffineNER/tree/0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf |
ILN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.utils.data.distributed
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class ILN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(ILN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.utils.data.distributed
import torch
import... | Lornatang/UGATIT_PyTorch | ILN | false | 8,473 | [
"Apache-2.0"
] | 25 | 03519e4829b85ceee67c031a28d5a9318ac932b5 | https://github.com/Lornatang/UGATIT_PyTorch/tree/03519e4829b85ceee67c031a28d5a9318ac932b5 |
MedianPool2d | # 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
from torch.nn.modules.utils import _pair
from torch.nn.modules.utils import _quadruple
class MedianPool2d(nn.Module):
"""Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kernel, int or 2-... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn.modules.utils import _pair
from torch... | LuckMonkeys/ATSPrivacy | MedianPool2d | false | 8,474 | [
"MIT"
] | 14 | 6b580942c6b98b6348d313f2bf90202ec19cefce | https://github.com/LuckMonkeys/ATSPrivacy/tree/6b580942c6b98b6348d313f2bf90202ec19cefce |
MaskedLanguageModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.optim.lr_scheduler
import torch.nn as nn
import torch.optim
import torch.onnx.operators
class MaskedLanguageModel(nn.Module):
"""
predicting origin token from masked input sequence
n-class classification problem, n-class = vocab_size
"""
def __init__(self, hidden, vocab_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LogIntelligence/LogADEmpirical | MaskedLanguageModel | false | 8,475 | [
"MIT"
] | 11 | 48458aee65c1c84466b04dd4092fae79a7f341fd | https://github.com/LogIntelligence/LogADEmpirical/tree/48458aee65c1c84466b04dd4092fae79a7f341fd |
ToRGB | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.autograd import Function
import abc
import math
import torch
from torch import nn
import torch.nn.functional as F
from collections import abc
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import abc
import math
from torch import nn
... | LizhenWangT/FaceVerse | ToRGB | false | 8,476 | [
"BSD-2-Clause",
"MIT"
] | 20 | bb4a5d3e52fb10b34bbe94f055ff637095bf9152 | https://github.com/LizhenWangT/FaceVerse/tree/bb4a5d3e52fb10b34bbe94f055ff637095bf9152 |
HausdorffLoss | # 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 HausdorffLoss(nn.Module):
def __init__(self, loss_weight=1.0):
super(HausdorffLoss, self).__init__()
self.weight = loss_weight
def forward(self, set1, set2):
"""
Compute the Averaged Hausdorff Distance function
between two unor... | 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... | LiWentomng/boxlevelset | HausdorffLoss | false | 8,477 | [
"Apache-2.0"
] | 25 | 8cc40bf6ae4a343c482c676c72259cc12c29d31c | https://github.com/LiWentomng/boxlevelset/tree/8cc40bf6ae4a343c482c676c72259cc12c29d31c |
Generator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.optim.lr_scheduler
import torch.nn as nn
import torch.optim
import torch.onnx.operators
def masked_softmax(vector: 'torch.Tensor', mask: 'torch.Tensor', dim: 'int'
=-1, memory_efficient: 'bool'=False, mask_fill_value: 'float'=-1e+32
) ->torch.Tensor:
"""
``torch.nn.functional... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LogIntelligence/LogADEmpirical | Generator | false | 8,478 | [
"MIT"
] | 11 | 48458aee65c1c84466b04dd4092fae79a7f341fd | https://github.com/LogIntelligence/LogADEmpirical/tree/48458aee65c1c84466b04dd4092fae79a7f341fd |
NextSentencePrediction | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.optim.lr_scheduler
import torch.nn as nn
import torch.optim
import torch.onnx.operators
class NextSentencePrediction(nn.Module):
"""
2-class classification model : is_next, is_not_next
"""
def __init__(self, hidden):
"""
:param hidden: BERT model output 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.... | LogIntelligence/LogADEmpirical | NextSentencePrediction | false | 8,479 | [
"MIT"
] | 11 | 48458aee65c1c84466b04dd4092fae79a7f341fd | https://github.com/LogIntelligence/LogADEmpirical/tree/48458aee65c1c84466b04dd4092fae79a7f341fd |
FCLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class FCLayer(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0.0,
use_activation=True):
super(FCLayer, self).__init__()
self.use_activation = use_activation
self.dropout = nn.Dropout(dropout_rate)
self.linear = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | LostCow/KLUE | FCLayer | false | 8,480 | [
"MIT"
] | 18 | 73b1b0526cf6b1b6f5ef535b9527d8abe6ca1a77 | https://github.com/LostCow/KLUE/tree/73b1b0526cf6b1b6f5ef535b9527d8abe6ca1a77 |
psi | # 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 psi(nn.Module):
def __init__(self, block_size):
super(psi, self).__init__()
self.block_size = block_size
self.block_size_sq = block_size * block_size
def inverse(self, input):
output = input.permute(0, 2, 3, 1)
batch_size, d_he... | 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... | LuckMonkeys/ATSPrivacy | psi | false | 8,481 | [
"MIT"
] | 14 | 6b580942c6b98b6348d313f2bf90202ec19cefce | https://github.com/LuckMonkeys/ATSPrivacy/tree/6b580942c6b98b6348d313f2bf90202ec19cefce |
Conv_Blocks | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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_Blocks(nn.Module):
def __init__(self, input_dim, output_dim, filter_size=3, batch_norm=
False, non_lin='tanh', dropout=0.0, first_block=False, last_block=
False, skip_connection=False):
super(Conv_Blocks, self).__init__()
self.skip_con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LuigiFilippoChiara/GoalGAN | Conv_Blocks | false | 8,482 | [
"MIT"
] | 36 | 11ac7448af7ac8934e6eb47a06c51d92f04dec8c | https://github.com/LuigiFilippoChiara/GoalGAN/tree/11ac7448af7ac8934e6eb47a06c51d92f04dec8c |
UpConv_Blocks | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 UpConv_Blocks(nn.Module):
def __init__(self, input_dim, output_dim, filter=4, padding=1,
first_block=False, last_block=False, batch_norm=False, non_lin=
'relu', dropout=0, skip_connection=False):
super(UpConv_Blocks, self).__init__()
self.B... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | LuigiFilippoChiara/GoalGAN | UpConv_Blocks | false | 8,483 | [
"MIT"
] | 36 | 11ac7448af7ac8934e6eb47a06c51d92f04dec8c | https://github.com/LuigiFilippoChiara/GoalGAN/tree/11ac7448af7ac8934e6eb47a06c51d92f04dec8c |
ScaleDotProductAttention | # 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
class ScaleDotProductAttention(nn.Module):
def __init__(self, k_dim, dropout=0.1):
super(ScaleDotProductAttention, self).__init__()
self.scale = 1.0 / k_dim ** 0.5
self.dropout = dropout
def forward(self, q, k, v, mas... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LindgeW/BiaffineNER | ScaleDotProductAttention | false | 8,484 | [
"Apache-2.0"
] | 13 | 0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf | https://github.com/LindgeW/BiaffineNER/tree/0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf |
GRU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 GRU(nn.Module):
def __init__(self, outfea):
super(GRU, self).__init__()
self.ff = nn.Linear(2 * outfea, 2 * outfea)
self.zff = nn.Linear(2 * outfea, outfea)
self.outfea = outfea
def forward(self, x, xh):
r, u = torch.split(torc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | LMissher/STGNN | GRU | false | 8,485 | [
"MIT"
] | 26 | 9c35d994738ad768ca4385273235bd30e994b746 | https://github.com/LMissher/STGNN/tree/9c35d994738ad768ca4385273235bd30e994b746 |
VanillaGenerativeAdversarialLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
class VanillaGenerativeAdversarialLoss(nn.Module):
"""
Loss for `Vanilla Generative Adversarial Network <https://arxiv.org/abs/1406.2661>`_
Args:
reduction (str, optional): Spec... | 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... | Liuhong99/CST | VanillaGenerativeAdversarialLoss | false | 8,486 | [
"MIT"
] | 20 | f6653a4ee7968fa3ba875a182670636f648be783 | https://github.com/Liuhong99/CST/tree/f6653a4ee7968fa3ba875a182670636f648be783 |
SAC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SAC(nn.Module):
def __init__(self, input_channel, out_channel):
super(SAC, self).__init__()
self.conv_1 = nn.Conv3d(input_channel, out_channel, kernel_size=3,
stride=1, padding=1)
self.conv_3 = nn.Conv3d(input_channel, out_channel, kern... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Luoxd1996/SCPM-Net | SAC | false | 8,487 | [
"MIT"
] | 26 | 2039ea5253ec831dcae79c2f0caa6e5d2641a1f9 | https://github.com/Luoxd1996/SCPM-Net/tree/2039ea5253ec831dcae79c2f0caa6e5d2641a1f9 |
GaussianKernel | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
from typing import Optional
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
class GaussianKernel(nn.Module):
"""Gaussian Kernel Matrix
Gaussian Kernel k is defined by
.. math::
k(x_1, x_2) = \\exp \\left( - \\dfrac{\\| x_1 -... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Liuhong99/CST | GaussianKernel | false | 8,488 | [
"MIT"
] | 20 | f6653a4ee7968fa3ba875a182670636f648be783 | https://github.com/Liuhong99/CST/tree/f6653a4ee7968fa3ba875a182670636f648be783 |
IoULoss | # 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 IoULoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(IoULoss, self).__init__()
def forward(self, inputs: 'torch.Tensor', targets: 'torch.Tensor',
smooth: 'int'=1):
inputs = torch.sigmoid(inputs)
inputs = inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Latterlig96/DCUnet | IoULoss | false | 8,489 | [
"MIT"
] | 11 | 87d1c137a60177d6daf1dfff0483678d5580fda0 | https://github.com/Latterlig96/DCUnet/tree/87d1c137a60177d6daf1dfff0483678d5580fda0 |
AdaILN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.utils.data.distributed
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class AdaILN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(AdaILN, self).__init__()
self.eps = eps
self.rho = Parameter(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.triton_helpers import libdevice
import torch.utils.data
import torch.utils.data.distributed
import torch
import... | Lornatang/UGATIT_PyTorch | AdaILN | false | 8,490 | [
"Apache-2.0"
] | 25 | 03519e4829b85ceee67c031a28d5a9318ac932b5 | https://github.com/Lornatang/UGATIT_PyTorch/tree/03519e4829b85ceee67c031a28d5a9318ac932b5 |
AttentionHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import Tensor
import torch.optim.lr_scheduler
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch.onnx.operators
def scaled_dot_product_attention(query: 'Tensor', key: 'Tensor', value:
'Tensor') ->Tensor:
temp = query.bmm(key.transpose(1, 2))
scale... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | LogIntelligence/LogADEmpirical | AttentionHead | false | 8,491 | [
"MIT"
] | 11 | 48458aee65c1c84466b04dd4092fae79a7f341fd | https://github.com/LogIntelligence/LogADEmpirical/tree/48458aee65c1c84466b04dd4092fae79a7f341fd |
PatchToPatchEdgeConvolution | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.sparse as sp
class PatchToPatchEdgeConvolution(nn.Module):
def __init__(self, in_features, out_features):
super(PatchToPatchEdgeConvolution, self).__init__()
self.weight = nn.parameter.Parameter(torch.FloatTensor(in_features,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | Lujian-123321/gcn- | PatchToPatchEdgeConvolution | false | 8,492 | [
"MIT"
] | 12 | 8f3a0a1d979bc7f075352e194e1e39687f0b12ab | https://github.com/Lujian-123321/gcn-/tree/8f3a0a1d979bc7f075352e194e1e39687f0b12ab |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.optim.lr_scheduler
import torch.nn as nn
import torch.optim
import torch.onnx.operators
class GELU(nn.Module):
"""
Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
"""
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | LogIntelligence/LogADEmpirical | PositionwiseFeedForward | false | 8,493 | [
"MIT"
] | 11 | 48458aee65c1c84466b04dd4092fae79a7f341fd | https://github.com/LogIntelligence/LogADEmpirical/tree/48458aee65c1c84466b04dd4092fae79a7f341fd |
LeastSquaresGenerativeAdversarialLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
class LeastSquaresGenerativeAdversarialLoss(nn.Module):
"""
Loss for `Least Squares Generative Adversarial Network (LSGAN) <https://arxiv.org/abs/1611.04076>`_
Args:
reduction (... | 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.nn.parallel
import torch.utils.data
import torch.utils... | Liuhong99/CST | LeastSquaresGenerativeAdversarialLoss | false | 8,494 | [
"MIT"
] | 20 | f6653a4ee7968fa3ba875a182670636f648be783 | https://github.com/Liuhong99/CST/tree/f6653a4ee7968fa3ba875a182670636f648be783 |
FastGuidedFilter | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torchvision.transforms import functional as F
from torch import nn
from torch.nn import functional as F
class BoxFilter(nn.Module):
def __init__(self, r):
super(BoxFilter, self).__init__()
self.r = r
def forward(self, x):
kernel_size = 2 * self.r + 1
kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torchvision.transforms i... | LightTwist/RobustVideoMatting | FastGuidedFilter | false | 8,495 | [
"Apache-2.0"
] | 11 | 79eb143fef3a4c58b4857c1a5a927a318f528093 | https://github.com/LightTwist/RobustVideoMatting/tree/79eb143fef3a4c58b4857c1a5a927a318f528093 |
AdaptiveFeatureNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
class AdaptiveFeatureNorm(nn.Module):
"""
The `Stepwise Adaptive Feature Norm loss (ICCV 2019) <https://arxiv.org/pdf/1811.07456v2.pdf>`_
Instead of using restrictive scalar R to match ... | 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
import torch.nn.parallel
import torch.utils.data
import t... | Liuhong99/CST | AdaptiveFeatureNorm | false | 8,496 | [
"MIT"
] | 20 | f6653a4ee7968fa3ba875a182670636f648be783 | https://github.com/Liuhong99/CST/tree/f6653a4ee7968fa3ba875a182670636f648be783 |
FeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 FeedForward(nn.Module):
def __init__(self, d_model, d_ff=512, dropout=0.5):
super().__init__()
self.linear_1 = nn.Linear(d_model, d_ff)
self.dropout = nn.Dropout(dropout)
self.linear_2 = nn.Linear(d_ff, d_mod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | MadanMl/PyTorch-Transformer-for-RUL-Prediction | FeedForward | false | 8,497 | [
"Apache-2.0"
] | 25 | 5bf0a4739abdecbbc88118ea413393997bdc1e24 | https://github.com/MadanMl/PyTorch-Transformer-for-RUL-Prediction/tree/5bf0a4739abdecbbc88118ea413393997bdc1e24 |
UpsampleConvLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 UpsampleConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(UpsampleConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_paddin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | MKFMIKU/PFFNet | UpsampleConvLayer | false | 8,498 | [
"MIT"
] | 41 | e506010a7cf00a32e77681845bdaf78ba88b027d | https://github.com/MKFMIKU/PFFNet/tree/e506010a7cf00a32e77681845bdaf78ba88b027d |
MeanPoolConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class IWConv2d(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, he_init=True,
stride=1, bias=True):
super(IWConv2d, self).__init__()
self.he_init = he_init
self.padding = int((kernel_size - 1) / 2)
self.conv = nn.Conv2d(i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | MIC-DKFZ/mood | MeanPoolConv | false | 8,499 | [
"Apache-2.0"
] | 42 | a01303adb4256653b133e2f7cd4741d366b681f7 | https://github.com/MIC-DKFZ/mood/tree/a01303adb4256653b133e2f7cd4741d366b681f7 |
Sobelxy | # 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 Sobelxy(nn.Module):
def __init__(self):
super(Sobelxy, self).__init__()
kernelx = [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]
kernely = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]
kernelx = torch.FloatTensor(kernelx).unsqu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | Linfeng-Tang/SeAFusion | Sobelxy | false | 8,500 | [
"MIT"
] | 18 | 54cf7ee116da3f726941560279bf12fedd0d434d | https://github.com/Linfeng-Tang/SeAFusion/tree/54cf7ee116da3f726941560279bf12fedd0d434d |
ConvMeanPool | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class IWConv2d(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, he_init=True,
stride=1, bias=True):
super(IWConv2d, self).__init__()
self.he_init = he_init
self.padding = int((kernel_size - 1) / 2)
self.conv = nn.Conv2d(i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | MIC-DKFZ/mood | ConvMeanPool | false | 8,501 | [
"Apache-2.0"
] | 42 | a01303adb4256653b133e2f7cd4741d366b681f7 | https://github.com/MIC-DKFZ/mood/tree/a01303adb4256653b133e2f7cd4741d366b681f7 |
Theta | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from torch.autograd import Function
import torch
import torch.nn as nn
from typing import Tuple
from typing import Optional
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
from typing import Any
class GradientReverseFunction(Function):
@staticmethod
def forward(ctx: 'Any'... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import torch.nn as nn
from typing import Tup... | Liuhong99/CST | Theta | false | 8,502 | [
"MIT"
] | 20 | f6653a4ee7968fa3ba875a182670636f648be783 | https://github.com/Liuhong99/CST/tree/f6653a4ee7968fa3ba875a182670636f648be783 |
ResidualBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(reflection_padding)
self.conv2d = 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.triton_helpers import math as tl_math
import torch.... | MKFMIKU/PFFNet | ResidualBlock | false | 8,503 | [
"MIT"
] | 41 | e506010a7cf00a32e77681845bdaf78ba88b027d | https://github.com/MKFMIKU/PFFNet/tree/e506010a7cf00a32e77681845bdaf78ba88b027d |
Spatial_Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Spatial_Attention(nn.Module):
def __init__(self, input_dim):
super(Spatial_Attention, self).__init__()
self.att_conv1 = nn.Conv2d(input_dim, 1, kernel_size=(1, 1),
padding=0, stride=1, bias=False)
self.att_act2 = nn.Softplus(beta=1, thr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | MCC-WH/Token | Spatial_Attention | false | 8,504 | [
"MIT"
] | 30 | eadc301f2df9e1851633be1b63c273659af0da49 | https://github.com/MCC-WH/Token/tree/eadc301f2df9e1851633be1b63c273659af0da49 |
region_levelset | # 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 region_levelset(nn.Module):
"""
the mian of leveset function
"""
def __init__(self):
super(region_levelset, self).__init__()
def forward(self, mask_score, norm_img, class_weight):
"""
mask_score: predcited mask scores tensor:(N,C,W... | 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... | LiWentomng/boxlevelset | region_levelset | false | 8,505 | [
"Apache-2.0"
] | 25 | 8cc40bf6ae4a343c482c676c72259cc12c29d31c | https://github.com/LiWentomng/boxlevelset/tree/8cc40bf6ae4a343c482c676c72259cc12c29d31c |
DenseBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ConvLeakyRelu2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, padding=1,
stride=1, dilation=1, groups=1):
super(ConvLeakyRelu2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch... | Linfeng-Tang/SeAFusion | DenseBlock | false | 8,506 | [
"MIT"
] | 18 | 54cf7ee116da3f726941560279bf12fedd0d434d | https://github.com/Linfeng-Tang/SeAFusion/tree/54cf7ee116da3f726941560279bf12fedd0d434d |
UpSampleConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class IWConv2d(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, he_init=True,
stride=1, bias=True):
super(IWConv2d, self).__init__()
self.he_init = he_init
self.padding = int((kernel_size - 1) / 2)
self.conv = nn.Conv2d(i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | MIC-DKFZ/mood | UpSampleConv | false | 8,507 | [
"Apache-2.0"
] | 42 | a01303adb4256653b133e2f7cd4741d366b681f7 | https://github.com/MIC-DKFZ/mood/tree/a01303adb4256653b133e2f7cd4741d366b681f7 |
BoxFilter | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torchvision.transforms import functional as F
from torch import nn
from torch.nn import functional as F
class BoxFilter(nn.Module):
def __init__(self, r):
super(BoxFilter, self).__init__()
self.r = r
def forward(self, x):
kernel_size = 2 * self.r + 1
kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | LightTwist/RobustVideoMatting | BoxFilter | false | 8,508 | [
"Apache-2.0"
] | 11 | 79eb143fef3a4c58b4857c1a5a927a318f528093 | https://github.com/LightTwist/RobustVideoMatting/tree/79eb143fef3a4c58b4857c1a5a927a318f528093 |
SingleHiddenLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SingleHiddenLayer(torch.nn.Module):
def __init__(self, input_channels, hidden_channels):
super(SingleHiddenLayer, self).__init__()
self.input_channels = input_channels
self.hidden_channels = hidden_channels
self.linear1 = torch.nn.Linear(hidden_channels, 128)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | MLforHealth/state_representations_for_RLinHealth | SingleHiddenLayer | false | 8,509 | [
"MIT"
] | 24 | aa8dbb7d56caa95bf4380e3e745e134996291b66 | https://github.com/MLforHealth/state_representations_for_RLinHealth/tree/aa8dbb7d56caa95bf4380e3e745e134996291b66 |
dnn_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 dnn_encoder(nn.Module):
def __init__(self, G_in, G_out, w1, w2, w3):
super(dnn_encoder, self).__init__()
self.fc1 = nn.Linear(G_in, w1)
self.fc2 = nn.Linear(w1, w2)
self.fc3 = nn.Linear(w2, w3)
self.o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Maitreyapatel/speech-conversion-between-different-modalities | dnn_encoder | false | 8,510 | [
"MIT"
] | 23 | f757b487d9e6c20aa4f7d37247ba16f9a967f573 | https://github.com/Maitreyapatel/speech-conversion-between-different-modalities/tree/f757b487d9e6c20aa4f7d37247ba16f9a967f573 |
_ImpalaCNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from typing import Tuple
from torch import nn
class _ImpalaResBlock(nn.Module):
def __init__(self, n_channels: 'int'):
super().__init__()
self.n_channels = n_channels
kernel_size = 3
padding = 1
self.relu = nn.ReLU()
self.relu_inplace = nn.ReLU()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from typing import Tuple
from... | IBM/vsrl-framework | _ImpalaCNN | false | 8,511 | [
"MIT"
] | 44 | 42e0853bffb5efbb66cd97178aff9e10ad18c5a9 | https://github.com/IBM/vsrl-framework/tree/42e0853bffb5efbb66cd97178aff9e10ad18c5a9 |
FinalTanh | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 FinalTanh(torch.nn.Module):
def __init__(self, input_channels, hidden_channels,
hidden_hidden_channels, num_hidden_layers):
super(FinalTanh, self).__init__()
self.input_channels = input_channels
self.hidden_channels = hidden_channels
self.hidden_hidden_c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MLforHealth/state_representations_for_RLinHealth | FinalTanh | false | 8,512 | [
"MIT"
] | 24 | aa8dbb7d56caa95bf4380e3e745e134996291b66 | https://github.com/MLforHealth/state_representations_for_RLinHealth/tree/aa8dbb7d56caa95bf4380e3e745e134996291b66 |
Simple224Upsample | # 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 Simple224Upsample(nn.Module):
def __init__(self, arch=''):
super(Simple224Upsample, self).__init__()
self.upsample = nn.Upsample(mode='nearest', scale_factor=7)
self.arch = arch
def forward(self, x):
return self.upsample(x)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | MadryLab/smoothed-vit | Simple224Upsample | false | 8,513 | [
"MIT"
] | 16 | a4327542e519e010764821716b64b944d458d1c1 | https://github.com/MadryLab/smoothed-vit/tree/a4327542e519e010764821716b64b944d458d1c1 |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = F.softmax(scores, dim=-1)
if dropout is not... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MadanMl/PyTorch-Transformer-for-RUL-Prediction | MultiHeadAttention | false | 8,514 | [
"Apache-2.0"
] | 25 | 5bf0a4739abdecbbc88118ea413393997bdc1e24 | https://github.com/MadanMl/PyTorch-Transformer-for-RUL-Prediction/tree/5bf0a4739abdecbbc88118ea413393997bdc1e24 |
DDM_Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
weight_shape = list(m.weight.data.size())
fan_in = np.prod(weight_shape[1:4])
fan_ou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | MLforHealth/state_representations_for_RLinHealth | DDM_Decoder | false | 8,515 | [
"MIT"
] | 24 | aa8dbb7d56caa95bf4380e3e745e134996291b66 | https://github.com/MLforHealth/state_representations_for_RLinHealth/tree/aa8dbb7d56caa95bf4380e3e745e134996291b66 |
_GRU_ODE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 _GRU_ODE(torch.nn.Module):
def __init__(self, input_channels, hidden_channels):
super(_GRU_ODE, self).__init__()
self.input_channels = input_channels
self.hidden_channels = hidden_channels
self.W_r = torch.nn.Linear(input_channels, hidden_channels, bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | MLforHealth/state_representations_for_RLinHealth | _GRU_ODE | false | 8,516 | [
"MIT"
] | 24 | aa8dbb7d56caa95bf4380e3e745e134996291b66 | https://github.com/MLforHealth/state_representations_for_RLinHealth/tree/aa8dbb7d56caa95bf4380e3e745e134996291b66 |
L2Conv2D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch.utils.data
class L2Conv2D(nn.Module):
"""
Convolutional layer that computes the squared L2 distance instead of the conventional inner product.
"""
def __init__(self, num_prototypes, num_features, w_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.... | M-Nauta/ProtoTree | L2Conv2D | false | 8,517 | [
"MIT"
] | 35 | 72cad5e42b0eb05c1312e5496f36b842726e081a | https://github.com/M-Nauta/ProtoTree/tree/72cad5e42b0eb05c1312e5496f36b842726e081a |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
from torch import nn
from torch.nn import functional
class Encoder(nn.Module):
def __init__(self, input_dim, hidden_dim, z_dim):
"""
Args:
input_dim: A integer indicating the size of input.
hidden_dim: A integer indicating the s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | MaurizioFD/recsys-challenge-2020-twitter | Encoder | false | 8,518 | [
"Apache-2.0"
] | 44 | 95dc024fb4f8777aa62e1304536daece640428de | https://github.com/MaurizioFD/recsys-challenge-2020-twitter/tree/95dc024fb4f8777aa62e1304536daece640428de |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
"""Basic residual block class"""
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv1d(in_planes, planes, kernel_size=3, strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Maosef/easy-to-hard | BasicBlock | false | 8,519 | [
"MIT"
] | 44 | 711ec0965229444a6c51b1b06a4e2cad3e32d02e | https://github.com/Maosef/easy-to-hard/tree/711ec0965229444a6c51b1b06a4e2cad3e32d02e |
FC_Q | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class FC_Q(nn.Module):
def __init__(self, state_dim, num_actions, num_nodes=128):
super(FC_Q, self).__init__()
self.q1 = nn.Linear(state_dim, num_nodes)
self.q2 = nn.Linear(num_nodes, num_nodes)
self.q3 = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MLforHealth/state_representations_for_RLinHealth | FC_Q | false | 8,520 | [
"MIT"
] | 24 | aa8dbb7d56caa95bf4380e3e745e134996291b66 | https://github.com/MLforHealth/state_representations_for_RLinHealth/tree/aa8dbb7d56caa95bf4380e3e745e134996291b66 |
gem | # 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 gem(nn.Module):
def __init__(self, p=3.0, eps=1e-06):
super(gem, self).__init__()
self.p = p
self.eps = eps
def forward(self, x):
return F.avg_pool2d(x.clamp(min=self.eps).pow(self.p), (x.size(-2),
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | MCC-WH/Token | gem | false | 8,521 | [
"MIT"
] | 30 | eadc301f2df9e1851633be1b63c273659af0da49 | https://github.com/MCC-WH/Token/tree/eadc301f2df9e1851633be1b63c273659af0da49 |
FFNN1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
from torch import nn
class FFNN1(nn.Module):
def __init__(self, input_size, hidden_size, hidden_dropout_prob):
super(FFNN1, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.hidden_dropout_prob = hidden_dropout_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
import torch.utils.data
from ... | MaurizioFD/recsys-challenge-2020-twitter | FFNN1 | false | 8,522 | [
"Apache-2.0"
] | 44 | 95dc024fb4f8777aa62e1304536daece640428de | https://github.com/MaurizioFD/recsys-challenge-2020-twitter/tree/95dc024fb4f8777aa62e1304536daece640428de |
DDM_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 numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
weight_shape = list(m.weight.data.size())
fan_in = np.prod(weight_shape[1:4])
fan_ou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | MLforHealth/state_representations_for_RLinHealth | DDM_Encoder | false | 8,523 | [
"MIT"
] | 24 | aa8dbb7d56caa95bf4380e3e745e134996291b66 | https://github.com/MLforHealth/state_representations_for_RLinHealth/tree/aa8dbb7d56caa95bf4380e3e745e134996291b66 |
FFNNDual | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
from torch import nn
class FFNNDual(nn.Module):
def __init__(self, input_size, hidden_size_1, hidden_size_2,
hidden_dropout_prob_1, hidden_dropout_prob_2):
super(FFNNDual, self).__init__()
self.input_size = input_size
self.hidden_size_1 = hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | MaurizioFD/recsys-challenge-2020-twitter | FFNNDual | false | 8,524 | [
"Apache-2.0"
] | 44 | 95dc024fb4f8777aa62e1304536daece640428de | https://github.com/MaurizioFD/recsys-challenge-2020-twitter/tree/95dc024fb4f8777aa62e1304536daece640428de |
FFNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 FFNet(nn.Module):
"""Modified ResidualNetworkSegment model class"""
def __init__(self, block, num_blocks, width, depth):
super(FFNet, self).__init__()
assert (depth - 4
) % 4 == 0, 'Depth not compatible with ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Maosef/easy-to-hard | FFNet | false | 8,525 | [
"MIT"
] | 44 | 711ec0965229444a6c51b1b06a4e2cad3e32d02e | https://github.com/Maosef/easy-to-hard/tree/711ec0965229444a6c51b1b06a4e2cad3e32d02e |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils
class Net(nn.Module):
def __init__(self, n_inputs, n_units=50):
super(Net, self).__init__()
self.fc = nn.Linear(n_inputs, n_units)
self.out = nn.Linear(n_units, 1)
def forward(self, x):
x = torch.tanh(self.fc(x))
r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | MSU-MLSys-Lab/CATE | Net | false | 8,526 | [
"Apache-2.0"
] | 15 | 654c393d7df888d2c3f3b90f9e6752faa061157e | https://github.com/MSU-MLSys-Lab/CATE/tree/654c393d7df888d2c3f3b90f9e6752faa061157e |
VGGOutputBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 VGGDense(nn.Module):
def __init__(self, in_channels, out_channels):
super(VGGDense, self).__init__()
self.fc = nn.Linear(in_features=in_channels, out_features=out_channels)
self.activ = nn.ReLU(inplace=True)
self.dropout = nn.Dropout(p=0.5)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | MarioMZhang/HAP-tryout | VGGOutputBlock | false | 8,527 | [
"MIT"
] | 24 | 9a423f35b50766533a0d2cab8069316ccb21954b | https://github.com/MarioMZhang/HAP-tryout/tree/9a423f35b50766533a0d2cab8069316ccb21954b |
GlobalAttentionGeneral | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.onnx
def conv1x1(in_planes, out_planes, bias=False):
"""1x1 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1,
padding=0, bias=bias)
class GlobalAttentionGeneral(nn.Module):
def __... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | MaxyLee/Style-AttnGAN | GlobalAttentionGeneral | false | 8,528 | [
"MIT"
] | 36 | d33d0df061c94b75ad4af5c750b8d6f37ee1a35a | https://github.com/MaxyLee/Style-AttnGAN/tree/d33d0df061c94b75ad4af5c750b8d6f37ee1a35a |
FFModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 swish(x):
return x * torch.sigmoid(x)
class FFModule(nn.Module):
def __init__(self, d_model, h_size, dropout=0.2):
super(FFModule, self).__init__()
self.layer_norm = nn.LayerNorm(d_model)
self.layer1 = nn.Linear(d_model, h_size)
self.sw... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Masao-Someki/Conformer | FFModule | false | 8,529 | [
"MIT"
] | 18 | 866da9ae05a6d07304775c592caac8d516f67c92 | https://github.com/Masao-Someki/Conformer/tree/866da9ae05a6d07304775c592caac8d516f67c92 |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from abc import ABC
import torch.utils.data
import torch.nn.functional as F
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module, ABC):
expansion = 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
from ab... | Mattdl/RehearsalRevealed | BasicBlock | false | 8,530 | [
"MIT"
] | 12 | f9cd2548f6c6d3ff119b40fecdb0df6fcd1525f6 | https://github.com/Mattdl/RehearsalRevealed/tree/f9cd2548f6c6d3ff119b40fecdb0df6fcd1525f6 |
EncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = F.softmax(scores, dim=-1)
if dropout is not... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MadanMl/PyTorch-Transformer-for-RUL-Prediction | EncoderLayer | false | 8,531 | [
"Apache-2.0"
] | 25 | 5bf0a4739abdecbbc88118ea413393997bdc1e24 | https://github.com/MadanMl/PyTorch-Transformer-for-RUL-Prediction/tree/5bf0a4739abdecbbc88118ea413393997bdc1e24 |
MultiHeadedAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from typing import Optional
from typing import Tuple
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Mashiro083/wenet-onnx | MultiHeadedAttention | false | 8,532 | [
"Apache-2.0"
] | 18 | ae8f8451d73fa9ceac6f7738194543e83959ca86 | https://github.com/Mashiro083/wenet-onnx/tree/ae8f8451d73fa9ceac6f7738194543e83959ca86 |
SMAPE | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class SMAPE(torch.nn.Module):
"""Symmetric Mean Absolute error.
:math:`\\frac{|x - y|} {|x| + |y| + \\epsilon}`
Args:
eps(float): small number to avoid division by 0.
"""
def __init__(self, eps=0.01):
super(SMAPE, self).__init__()
self.eps = eps
def forwa... | 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
assert_size_stride = t... | Mephisto405/WCMC-Public | SMAPE | false | 8,533 | [
"BSD-2-Clause"
] | 19 | bd54f218d5239db84f404fbe1b465f9497bcf9e4 | https://github.com/Mephisto405/WCMC-Public/tree/bd54f218d5239db84f404fbe1b465f9497bcf9e4 |
baseRNN_predict | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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.init as init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
weight_shape = list(m.weight.data.size())
fan_in = np.prod(weight_shape[1:4])
fan_out = np.prod(weight_shape[2:4]) *... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | MLforHealth/state_representations_for_RLinHealth | baseRNN_predict | false | 8,534 | [
"MIT"
] | 24 | aa8dbb7d56caa95bf4380e3e745e134996291b66 | https://github.com/MLforHealth/state_representations_for_RLinHealth/tree/aa8dbb7d56caa95bf4380e3e745e134996291b66 |
LocalStatisticsNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 LocalStatisticsNetwork(nn.Module):
def __init__(self, img_feature_channels: 'int'):
"""Local statistique nerwork
Args:
img_feature_channels (int): [Number of input channels]
"""
super().__init__()
self.conv1 = 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
import torch.nn as nn
assert_... | MehdiZouitine/Learning-Disentangled-Representations-via-Mutual-Information-Estimation | LocalStatisticsNetwork | false | 8,535 | [
"MIT"
] | 25 | 52952aff647a33b749b709cd7f0c3cd059c66b54 | https://github.com/MehdiZouitine/Learning-Disentangled-Representations-via-Mutual-Information-Estimation/tree/52952aff647a33b749b709cd7f0c3cd059c66b54 |
AdaFM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
class AdaFM(nn.Module):
def __init__(self, in_channel, out_channel, style_dim=0):
super().__init__()
self.style_gama = nn.Parameter(torch.ones(in_channel, out_channel,
1, 1))
self.st... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | MiaoyunZhao/GANTransferLimitedData | AdaFM | false | 8,536 | [
"MIT"
] | 41 | 5545bc37a1d7d4f28a9c3588aaa12a616bbddd88 | https://github.com/MiaoyunZhao/GANTransferLimitedData/tree/5545bc37a1d7d4f28a9c3588aaa12a616bbddd88 |
Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
from torch import nn
from torch.nn import functional
class Decoder(nn.Module):
def __init__(self, z_dim, hidden_dim, output_dim):
"""
Args:
z_dim: A integer indicating the latent size.
hidden_dim: A integer indicating the size o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | MaurizioFD/recsys-challenge-2020-twitter | Decoder | false | 8,537 | [
"Apache-2.0"
] | 44 | 95dc024fb4f8777aa62e1304536daece640428de | https://github.com/MaurizioFD/recsys-challenge-2020-twitter/tree/95dc024fb4f8777aa62e1304536daece640428de |
ClampModule | # 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 as th
class ClampModule(th.nn.Module):
"""Why is this not a thing in the main library?"""
def __init__(self, min_v, max_v):
super().__init__()
self.min_v = min_v
self.max_v = max_v
def forward(self, x):
return th.clamp(x, self.min_v, self.max_v)
... | 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 as th
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_... | Miffyli/policy-supervectors | ClampModule | false | 8,538 | [
"MIT"
] | 17 | 358284805e5bc96b95cae15e9741571e46d84bc9 | https://github.com/Miffyli/policy-supervectors/tree/358284805e5bc96b95cae15e9741571e46d84bc9 |
ResnetBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.utils.data.distributed
def actvn(x):
out = F.leaky_relu(x, 0.2)
return out
class ResnetBlock(nn.Module):
def __init__(self, fin, fout, fhidden=None, is_bias=True):
super().__init__()
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.functional as F
import torch.utils.data
imp... | MiaoyunZhao/GANTransferLimitedData | ResnetBlock | false | 8,539 | [
"MIT"
] | 41 | 5545bc37a1d7d4f28a9c3588aaa12a616bbddd88 | https://github.com/MiaoyunZhao/GANTransferLimitedData/tree/5545bc37a1d7d4f28a9c3588aaa12a616bbddd88 |
RelativeMSE | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
class RelativeMSE(torch.nn.Module):
"""Relative Mean-Squared Error.
:math:`0.5 * \\frac{(x - y)^2}{y^2 + \\epsilon}`
Args:
eps(float): small number to avoid division by 0.
"""
def __init__(self, eps=0.01):
super(RelativeMSE, self).__init__()
self.eps = eps
... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Mephisto405/WCMC-Public | RelativeMSE | false | 8,540 | [
"BSD-2-Clause"
] | 19 | bd54f218d5239db84f404fbe1b465f9497bcf9e4 | https://github.com/Mephisto405/WCMC-Public/tree/bd54f218d5239db84f404fbe1b465f9497bcf9e4 |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, in_dim, out_dim):
super(MLP, self).__init__()
out = max(8, in_dim * 2)
self.input = nn.Linear(in_dim, out)
self.fc = nn.Linear(out, out)
self.fc2 = nn.Linear(out, out)
self.output = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Malta-Lab/IUPE | MLP | false | 8,541 | [
"MIT"
] | 10 | 44ddf119917538f02bb69509fec7a8314eed419f | https://github.com/Malta-Lab/IUPE/tree/44ddf119917538f02bb69509fec7a8314eed419f |
FFChessNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 FFChessNet(nn.Module):
"""Modified ResidualNetworkSegment model class"""
def __init__(self, block, num_blocks, width, depth):
super(FFChessNet, self).__init__()
assert (depth - 4
) % 4 == 0, 'Depth not compat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Maosef/easy-to-hard | FFChessNet | false | 8,542 | [
"MIT"
] | 44 | 711ec0965229444a6c51b1b06a4e2cad3e32d02e | https://github.com/Maosef/easy-to-hard/tree/711ec0965229444a6c51b1b06a4e2cad3e32d02e |
RelPositionMultiHeadedAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 numpy
import torch
import torch.nn as nn
class RelPositionMultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer with relative position encoding.
This class is aquired from
https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/attention.py
(Ap... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Masao-Someki/Conformer | RelPositionMultiHeadedAttention | false | 8,543 | [
"MIT"
] | 18 | 866da9ae05a6d07304775c592caac8d516f67c92 | https://github.com/Masao-Someki/Conformer/tree/866da9ae05a6d07304775c592caac8d516f67c92 |
SuperPointNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 SuperPointNet(torch.nn.Module):
""" Pytorch definition of SuperPoint Network. """
def __init__(self):
super(SuperPointNet, self).__init__()
self.relu = torch.nn.ReLU(inplace=True)
self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2)
self.numberOfClasses =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MamonaAwan/UnsupervisedLandmarks | SuperPointNet | false | 8,544 | [
"MIT"
] | 26 | 89180755b891fd28e0199560d628dc8b0d2b3e68 | https://github.com/MamonaAwan/UnsupervisedLandmarks/tree/89180755b891fd28e0199560d628dc8b0d2b3e68 |
TonemappedRelativeMSE | # 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
def _tonemap(im):
"""Helper Reinhards tonemapper.
Args:
im(torch.Tensor): image to tonemap.
Returns:
(torch.Tensor) tonemaped image.
"""
im = torch.clamp(im, min=0)
return im / (1 + im)
class TonemappedRelativeMSE(torch.nn.Module):
"""Relative mean-squared er... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Mephisto405/WCMC-Public | TonemappedRelativeMSE | false | 8,545 | [
"BSD-2-Clause"
] | 19 | bd54f218d5239db84f404fbe1b465f9497bcf9e4 | https://github.com/Mephisto405/WCMC-Public/tree/bd54f218d5239db84f404fbe1b465f9497bcf9e4 |
RelPositionMultiHeadedAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from typing import Optional
from typing import Tuple
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Mashiro083/wenet-onnx | RelPositionMultiHeadedAttention | false | 8,546 | [
"Apache-2.0"
] | 18 | ae8f8451d73fa9ceac6f7738194543e83959ca86 | https://github.com/Mashiro083/wenet-onnx/tree/ae8f8451d73fa9ceac6f7738194543e83959ca86 |
IRW_L1_Loss | # 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 IRW_L1_Loss(nn.Module):
def __init__(self, threshold):
super(IRW_L1_Loss, self).__init__()
self.threshold = threshold
def forward(self, x, y, beta):
beta = beta.view(len(x), 1, 1, 1)
beta = torch.nn... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | Mid-Push/IrwGAN | IRW_L1_Loss | false | 8,547 | [
"BSD-3-Clause"
] | 31 | f56e7274cf7de3362459549dd807b66b93dc5e89 | https://github.com/Mid-Push/IrwGAN/tree/f56e7274cf7de3362459549dd807b66b93dc5e89 |
Attention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
class Atte... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MichiganCOG/Video-Grounding | Attention | false | 8,548 | [
"MIT"
] | 41 | 3e0ec0b69578a59be583911590354fe77d357cab | https://github.com/MichiganCOG/Video-Grounding/tree/3e0ec0b69578a59be583911590354fe77d357cab |
MultiHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
class Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | MichiganCOG/Video-Grounding | MultiHead | false | 8,549 | [
"MIT"
] | 41 | 3e0ec0b69578a59be583911590354fe77d357cab | https://github.com/MichiganCOG/Video-Grounding/tree/3e0ec0b69578a59be583911590354fe77d357cab |
FeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
class Linear(nn.Linear):
def forward(self, x):
size = x.size()
return super().forward(x.contiguous().view(-1, size[-1])).view(*
size[:-1], -1)
class FeedForward(nn.Module):
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | MichiganCOG/Video-Grounding | FeedForward | false | 8,550 | [
"MIT"
] | 41 | 3e0ec0b69578a59be583911590354fe77d357cab | https://github.com/MichiganCOG/Video-Grounding/tree/3e0ec0b69578a59be583911590354fe77d357cab |
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