entry_point stringlengths 1 65 | original_triton_code stringlengths 4.5k 619k | python_code stringlengths 208 60.9k | triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 | pytorch_code stringlengths 200 4.05k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
MultiheadConvAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.data
from torch.nn import Parameter
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class MultiheadConvAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | amaurySabran/fairseq | MultiheadConvAttention | false | 18,291 | [
"BSD-3-Clause"
] | 4 | e6d5dd36678224e8b06aa0e97749f7a1c20a9949 | https://github.com/amaurySabran/fairseq/tree/e6d5dd36678224e8b06aa0e97749f7a1c20a9949 | import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.data
from torch.nn import Parameter
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Model(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
... |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class DiceLoss(nn.Module):
"""
DICE loss function
Args:
alpha (default: int=10): Coefficient in exp of sigmoid function
smooth (default: int=1): To prevent zero in nominator
"""
def __init__(self, alpha=10, smooth=1):
super().__init__()... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | akanametov/pathgan | DiceLoss | false | 18,292 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d | import torch
from torch import nn
class Model(nn.Module):
"""
DICE loss function
Args:
alpha (default: int=10): Coefficient in exp of sigmoid function
smooth (default: int=1): To prevent zero in nominator
"""
def __init__(self, alpha=10, smooth=1):
super().__init__()
... |
KLDivergence | # 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
def kl_divergence(px, py):
eps = 1e-08
kl_div = px * (torch.log(px + eps) - torch.log(py + eps))
return kl_div
class KLDivergence(nn.Module):
"""
Kullback–Leibler divergence
Args:
- None -
"""
def __init__(self):
super().__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | akanametov/pathgan | KLDivergence | false | 18,293 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d | import torch
from torch import nn
def kl_divergence(px, py):
eps = 1e-08
kl_div = px * (torch.log(px + eps) - torch.log(py + eps))
return kl_div
class Model(nn.Module):
"""
Kullback–Leibler divergence
Args:
- None -
"""
def __init__(self):
super().__init__()
de... |
BiDAFAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
def masked_softmax(logits, mask, dim=-1, log_softmax=False):
"""Take the softmax of `logits` over given dimension, and set
entries to 0 wherever `mask` is 0.
Args:
logits (torch.Tensor): Inputs to the softmax function.
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.... | amankhullar/MMBiDAF | BiDAFAttention | false | 18,294 | [
"MIT"
] | 4 | 510a0c4f3bdeb7a84fb1554d8daee6b3fada3d61 | https://github.com/amankhullar/MMBiDAF/tree/510a0c4f3bdeb7a84fb1554d8daee6b3fada3d61 | import torch
import torch.nn as nn
import torch.nn.functional as F
def masked_softmax(logits, mask, dim=-1, log_softmax=False):
"""Take the softmax of `logits` over given dimension, and set
entries to 0 wherever `mask` is 0.
Args:
logits (torch.Tensor): Inputs to the softmax function.
mas... |
PixelwiseLossMSE | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class PixelwiseLossMSE(nn.Module):
"""
MSE loss function
Args:
alpha (default: int=20): Coefficient by which loss will be multiplied
"""
def __init__(self, alpha=20):
super().__init__()
self.alpha = alpha
def forward(self, fake, real... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | akanametov/pathgan | PixelwiseLossMSE | false | 18,295 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d | import torch
from torch import nn
class Model(nn.Module):
"""
MSE loss function
Args:
alpha (default: int=20): Coefficient by which loss will be multiplied
"""
def __init__(self, alpha=20):
super().__init__()
self.alpha = alpha
def forward(self, fake, real):
... |
DiscriminatorLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class DiscriminatorLoss(nn.Module):
"""
Discriminator (BCE) loss function
Args:
- None -
"""
def __init__(self):
super().__init__()
self.adv_criterion = nn.BCEWithLogitsLoss()
def forward(self, fake_pred, real_pred):
fake_tar... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | akanametov/pathgan | DiscriminatorLoss | false | 18,296 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d | import torch
from torch import nn
class Model(nn.Module):
"""
Discriminator (BCE) loss function
Args:
- None -
"""
def __init__(self):
super().__init__()
self.adv_criterion = nn.BCEWithLogitsLoss()
def forward(self, fake_pred, real_pred):
fake_target = torch.... |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as f
from torch import nn
class Critic(nn.Module):
def __init__(self, input_dim):
super(Critic, self).__init__()
self._input_dim = input_dim
self.dense1 = nn.Linear(self._input_dim, self._input_dim)
self.dense2 = nn.Linear(self._input_dim, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | amirarsalan90/TabFairGAN | Critic | false | 18,297 | [
"MIT"
] | 5 | 402c434e0aa7a335fda652a67e72b132edb5f663 | https://github.com/amirarsalan90/TabFairGAN/tree/402c434e0aa7a335fda652a67e72b132edb5f663 | import torch
import torch.nn.functional as f
from torch import nn
class Model(nn.Module):
def __init__(self, input_dim):
super().__init__()
self._input_dim = input_dim
self.dense1 = nn.Linear(self._input_dim, self._input_dim)
self.dense2 = nn.Linear(self._input_dim, self._input_di... |
TimeEncode | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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
class TimeEncode(torch.nn.Module):
def __init__(self, dim):
super(TimeEncode, self).__init__()
self.dim = dim
self.w = torch.nn.Linear(1, dim)
self.w.weight = torch.nn.Parameter(torch.from_numpy(1 / 10 ** np.
linspace(0, 9, dim, dtype=np... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | amazon-research/tgl | TimeEncode | false | 18,298 | [
"Apache-2.0"
] | 9 | 5d852b8ae643b64b591a10dfbe8a1d10f696b200 | https://github.com/amazon-research/tgl/tree/5d852b8ae643b64b591a10dfbe8a1d10f696b200 | import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
self.w = torch.nn.Linear(1, dim)
self.w.weight = torch.nn.Parameter(torch.from_numpy(1 / 10 ** np.
linspace(0, 9, dim, dtype=np.float32)).reshape(di... |
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
from torch import nn
class GaussianKernel(nn.Module):
"""
Gaussian kernel module.
:param mu: Float, mean of the kernel.
:param sigma: Float, sigma of the kernel.
Examples:
>>> import torch
>>> kernel = GaussianKernel()
>>> x = torch.randn(4, 5, 10)
>>... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | amberhuang01/LearningFromFactCheckers | GaussianKernel | false | 18,299 | [
"MIT"
] | 9 | 3c21684709bf5e331c4585c7d62596960dd44732 | https://github.com/amberhuang01/LearningFromFactCheckers/tree/3c21684709bf5e331c4585c7d62596960dd44732 | import torch
from torch import nn
class Model(nn.Module):
"""
Gaussian kernel module.
:param mu: Float, mean of the kernel.
:param sigma: Float, sigma of the kernel.
Examples:
>>> import torch
>>> kernel = GaussianKernel()
>>> x = torch.randn(4, 5, 10)
>>> x.shape... |
IoUnionLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class IoUnionLoss(nn.Module):
"""
Intersection over Union loss function
Args:
alpha (default: int=10): Coefficient in exp of sigmoid function
smooth (default: int=1): To prevent zero in nominator
"""
def __init__(self, alpha=10, smooth=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
from torch import nn
a... | akanametov/pathgan | IoUnionLoss | false | 18,300 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d | import torch
from torch import nn
class Model(nn.Module):
"""
Intersection over Union loss function
Args:
alpha (default: int=10): Coefficient in exp of sigmoid function
smooth (default: int=1): To prevent zero in nominator
"""
def __init__(self, alpha=10, smooth=1):
su... |
Discriminator | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, n_hidden):
super(Discriminator, self).__init__()
self.weight = nn.Parameter(torch.Tensor(n_hidden, n_hidden))
self.reset_parameters()
def uniform(self, size, tensor):
bound = 1.0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | amazon-research/panrep | Discriminator | false | 18,301 | [
"Apache-2.0"
] | 10 | 57e6f71bb70c0908f3db28be97af0d818a863e19 | https://github.com/amazon-research/panrep/tree/57e6f71bb70c0908f3db28be97af0d818a863e19 | import math
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_hidden):
super().__init__()
self.weight = nn.Parameter(torch.Tensor(n_hidden, n_hidden))
self.reset_parameters()
def uniform(self, size, tensor):
bound = 1.0 / math.sqrt(size)
... |
EdgePredictor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 EdgePredictor(torch.nn.Module):
def __init__(self, dim_in):
super(EdgePredictor, self).__init__()
self.dim_in = dim_in
self.src_fc = torch.nn.Linear(dim_in, dim_in)
self.dst_fc = torch.nn.Linear(dim_in, dim_in)
self.out_fc = torch.nn.Linear(dim_in, 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
assert_size_stride = torch._C... | amazon-research/tgl | EdgePredictor | false | 18,302 | [
"Apache-2.0"
] | 9 | 5d852b8ae643b64b591a10dfbe8a1d10f696b200 | https://github.com/amazon-research/tgl/tree/5d852b8ae643b64b591a10dfbe8a1d10f696b200 | import torch
class Model(torch.nn.Module):
def __init__(self, dim_in):
super().__init__()
self.dim_in = dim_in
self.src_fc = torch.nn.Linear(dim_in, dim_in)
self.dst_fc = torch.nn.Linear(dim_in, dim_in)
self.out_fc = torch.nn.Linear(dim_in, 1)
def forward(self, h, neg... |
MSE_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.nn as nn
import torch.utils.data
import torch.optim
class MSE_loss(nn.Module):
def __init__(self):
super(MSE_loss, self).__init__()
def forward(self, prediction, gt, epoch=0):
err = prediction[:, 0:1] - gt
mask = (gt > 0).detach()
mse_loss = torch.me... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | alopezgit/project-adapt | MSE_loss | false | 18,303 | [
"MIT"
] | 8 | e93ab350344a5504f76f4e460002e0163996f88a | https://github.com/alopezgit/project-adapt/tree/e93ab350344a5504f76f4e460002e0163996f88a | import torch
import torch.nn as nn
import torch.utils.data
import torch.optim
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, prediction, gt, epoch=0):
err = prediction[:, 0:1] - gt
mask = (gt > 0).detach()
mse_loss = torch.mean(err[mask] ** 2... |
PixelwiseLossL1 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class PixelwiseLossL1(nn.Module):
"""
L1 loss function
Args:
alpha (default: int=1): Coefficient by which loss will be multiplied
"""
def __init__(self, alpha=1):
super().__init__()
self.alpha = alpha
self.criterion = nn.L1Loss()
... | 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... | akanametov/pathgan | PixelwiseLossL1 | false | 18,304 | [
"MIT"
] | 8 | d93464a9c2490532afdf7bbc0f60decdf2d0767d | https://github.com/akanametov/pathgan/tree/d93464a9c2490532afdf7bbc0f60decdf2d0767d | import torch
from torch import nn
class Model(nn.Module):
"""
L1 loss function
Args:
alpha (default: int=1): Coefficient by which loss will be multiplied
"""
def __init__(self, alpha=1):
super().__init__()
self.alpha = alpha
self.criterion = nn.L1Loss()
def f... |
RankingLoss | # 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 RankingLoss(nn.Module):
def __init__(self, margin_lambda: 'float'=0.01) ->None:
super(RankingLoss, self).__init__()
self.margin_lambda = margin_lambda
def forward(self, candidates_scores: 'torch.Tensor', summary_scores:... | 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... | andrejmiscic/simcls-pytorch | RankingLoss | false | 18,305 | [
"MIT"
] | 5 | 516315c4b35955e4201677fc838f5f38a6e8fd54 | https://github.com/andrejmiscic/simcls-pytorch/tree/516315c4b35955e4201677fc838f5f38a6e8fd54 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, margin_lambda: 'float'=0.01) ->None:
super().__init__()
self.margin_lambda = margin_lambda
def forward(self, candidates_scores: 'torch.Tensor', summary_scores:
'torch.Tensor'... |
RankCrossEntropyLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.functional as F
from torch import nn
class RankCrossEntropyLoss(nn.Module):
"""Creates a criterion that measures rank cross entropy loss."""
__constants__ = ['num_neg']
def __init__(self, num_neg: 'int'=1):
"""
:class:`RankCrossEntropyLoss` constructor.
... | 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... | amberhuang01/LearningFromFactCheckers | RankCrossEntropyLoss | false | 18,306 | [
"MIT"
] | 9 | 3c21684709bf5e331c4585c7d62596960dd44732 | https://github.com/amberhuang01/LearningFromFactCheckers/tree/3c21684709bf5e331c4585c7d62596960dd44732 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""Creates a criterion that measures rank cross entropy loss."""
__constants__ = ['num_neg']
def __init__(self, num_neg: 'int'=1):
"""
:class:`RankCrossEntropyLoss` constructor.
:param num_n... |
DecoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.2):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | alipay/Pyraformer | DecoderLayer | false | 18,307 | [
"Apache-2.0"
] | 7 | 84af4dbd93b7b96975b5034f0dde412005260123 | https://github.com/alipay/Pyraformer/tree/84af4dbd93b7b96975b5034f0dde412005260123 | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.2):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... |
F | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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
from torch.autograd import Variable
class F(nn.Module):
def __init__(self, input_size, hidden_size, output_size, learning_rate=
0.001):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | amolk/AGI-experiments | F | false | 18,309 | [
"MIT"
] | 5 | ddb352c884d513ff4d9a843d0901699acb9e39b9 | https://github.com/amolk/AGI-experiments/tree/ddb352c884d513ff4d9a843d0901699acb9e39b9 | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
class Model(nn.Module):
def __init__(self, input_size, hidden_size, output_size, learning_rate=
0.001):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_... |
LayerNormalization | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 LayerNormalization(nn.Module):
def __init__(self, hidden_size, eps=1e-05):
super(LayerNormalization, self).__init__()
self.eps = eps
self.hidden_size = hidden_size
self.a2 = nn.Parameter(torch.ones(hidden_size), requires_grad=True)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | analvikingur/RGAN | LayerNormalization | false | 18,310 | [
"MIT"
] | 8 | b1893c2f53d11c9173c7a30f63f6d93d72232493 | https://github.com/analvikingur/RGAN/tree/b1893c2f53d11c9173c7a30f63f6d93d72232493 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, hidden_size, eps=1e-05):
super().__init__()
self.eps = eps
self.hidden_size = hidden_size
self.a2 = nn.Parameter(torch.ones(hidden_size), requires_grad=True)
self.b2 = nn.Parameter(torch.zeros(hi... |
AdaIN | # 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.optim
class AdaIN(torch.nn.Module):
def __init__(self, epsilon: 'float'=1e-05):
super(AdaIN, self).__init__()
self.epsilon = epsilon
def calc_vector_mean_std(self, x):
std = torch.sqrt(torch.var(x, dim=1) + self.epsilon)
mean = torch.mean(x, 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.triton_helpers import libdevice
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_str... | ai-in-motion/moai | AdaIN | false | 18,311 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class Model(torch.nn.Module):
def __init__(self, epsilon: 'float'=1e-05):
super().__init__()
self.epsilon = epsilon
def calc_vector_mean_std(self, x):
std = torch.sqrt(torch.var(x, dim=1) + self.epsilon)
mean = torch.mean(x, dim=1)
retu... |
Downsample2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import functools
import torch
import torch.optim
class Downsample2d(torch.nn.Module):
def __init__(self, scale: 'float'=0.5, mode: 'str'='bilinear'):
super(Downsample2d, self).__init__()
self.downsample = functools.partial(torch.nn.functional.interpolate,
scale_factor=scale, mode=mode... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import functools
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_s... | ai-in-motion/moai | Downsample2d | false | 18,312 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import functools
import torch
import torch.optim
class Model(torch.nn.Module):
def __init__(self, scale: 'float'=0.5, mode: 'str'='bilinear'):
super().__init__()
self.downsample = functools.partial(torch.nn.functional.interpolate,
scale_factor=scale, mode=mode)
def forward(self, ... |
SoftArgmax | # 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 t
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class SoftArgmax(nn.Module):
def __init__(self, temperature=0.001):
super(SoftArgmax, self).__init__()
self.temperature = temperature
def forward(self, input, sampling=Fal... | 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 as t
impo... | analvikingur/RGAN | SoftArgmax | false | 18,313 | [
"MIT"
] | 8 | b1893c2f53d11c9173c7a30f63f6d93d72232493 | https://github.com/analvikingur/RGAN/tree/b1893c2f53d11c9173c7a30f63f6d93d72232493 | import torch
import torch as t
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class Model(nn.Module):
def __init__(self, temperature=0.001):
super().__init__()
self.temperature = temperature
def forward(self, input, sampling=False):
size = i... |
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
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.2):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | alipay/Pyraformer | EncoderLayer | false | 18,314 | [
"Apache-2.0"
] | 7 | 84af4dbd93b7b96975b5034f0dde412005260123 | https://github.com/alipay/Pyraformer/tree/84af4dbd93b7b96975b5034f0dde412005260123 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.2):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropou... |
MinusOne | # 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.optim
class MinusOne(torch.nn.Module):
def __init__(self):
super(MinusOne, self).__init__()
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
return x - 1.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | MinusOne | false | 18,315 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
return x - 1.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
KL | # 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.optim
class KL(torch.nn.KLDivLoss):
def __init__(self, is_input_log: 'bool'=False, is_target_log: 'bool'=False
):
super(KL, self).__init__(reduction='none', log_target=is_target_log)
self.is_input_log = is_input_log
def forward(self, gt: 'torch.Tensor', pred... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.optim
assert_size_stride = torch._C._dynamo.guard... | ai-in-motion/moai | KL | false | 18,316 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class Model(torch.nn.KLDivLoss):
def __init__(self, is_input_log: 'bool'=False, is_target_log: 'bool'=False
):
super().__init__(reduction='none', log_target=is_target_log)
self.is_input_log = is_input_log
def forward(self, gt: 'torch.Tensor', pred: 'to... |
GemanMcClure | # 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.optim
class L2(torch.nn.Module):
def __init__(self):
super(L2, self).__init__()
def forward(self, pred: 'torch.Tensor', gt: 'torch.Tensor'=None,
weights: 'torch.Tensor'=None, mask: 'torch.Tensor'=None
) ->torch.Tensor:
l2 = (gt - pred) ** 2 if gt is ... | 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.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | GemanMcClure | false | 18,317 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class L2(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred: 'torch.Tensor', gt: 'torch.Tensor'=None,
weights: 'torch.Tensor'=None, mask: 'torch.Tensor'=None
) ->torch.Tensor:
l2 = (gt - pred) ** 2 if gt is not None... |
SplitAndConcat | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.quantization.quantize_fx
import torch.utils.data
class SplitAndConcat(nn.Module):
"""Split the data from split_dim and concatenate in concat_dim.
@param split_dim from which axis the data will be chunk
@param concat_dim to which axis the data will be concat... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.quantization.quantize_fx
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | ananthsub/d2go | SplitAndConcat | false | 18,318 | [
"Apache-2.0"
] | 3 | 8c3618d9e73518d32350ab4e6d0fb6509c9e08b6 | https://github.com/ananthsub/d2go/tree/8c3618d9e73518d32350ab4e6d0fb6509c9e08b6 | import torch
import torch.nn as nn
import torch.quantization.quantize_fx
import torch.utils.data
class Model(nn.Module):
"""Split the data from split_dim and concatenate in concat_dim.
@param split_dim from which axis the data will be chunk
@param concat_dim to which axis the data will be concatenated
... |
MatchModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class MatchModule(nn.Module):
"""
Computing the match representation for Match LSTM.
:param hidden_size: Size of hidden vectors.
:param dropout_rate: Dropout rate of the projection layer. Defaults to 0.
Examples:
>>> impor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | amberhuang01/LearningFromFactCheckers | MatchModule | false | 18,319 | [
"MIT"
] | 9 | 3c21684709bf5e331c4585c7d62596960dd44732 | https://github.com/amberhuang01/LearningFromFactCheckers/tree/3c21684709bf5e331c4585c7d62596960dd44732 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
Computing the match representation for Match LSTM.
:param hidden_size: Size of hidden vectors.
:param dropout_rate: Dropout rate of the projection layer. Defaults to 0.
Examples:
>>> import torc... |
ClassAttentionBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import Tensor
from torch import nn
class MLP(nn.Module):
def __init__(self, dim, hidden_dim, out_dim=None) ->None:
super().__init__()
out_dim = out_dim or dim
self.fc1 = nn.Linear(dim, hidden_dim)
self.act = nn.GELU()
self.fc2 = nn.Linear(hidden_dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | alhamami/Object-Detection-And-Tracking | ClassAttentionBlock | false | 18,320 | [
"MIT"
] | 5 | a211a1dc103e812c539cd0ee16a2da4251943bed | https://github.com/alhamami/Object-Detection-And-Tracking/tree/a211a1dc103e812c539cd0ee16a2da4251943bed | import torch
from torch import Tensor
from torch import nn
class MLP(nn.Module):
def __init__(self, dim, hidden_dim, out_dim=None) ->None:
super().__init__()
out_dim = out_dim or dim
self.fc1 = nn.Linear(dim, hidden_dim)
self.act = nn.GELU()
self.fc2 = nn.Linear(hidden_dim... |
Clamp | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.optim
class Clamp(torch.nn.Module):
min_value: 'float'
max_value: 'float'
def __init__(self, min_value: 'float'=0.0, max_value: 'float'=1.0):
super(Clamp, self).__init__()
self.min_value = min_value
self.max_value = max_value
def forward(self, x: 'to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_... | ai-in-motion/moai | Clamp | false | 18,321 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class Model(torch.nn.Module):
min_value: 'float'
max_value: 'float'
def __init__(self, min_value: 'float'=0.0, max_value: 'float'=1.0):
super().__init__()
self.min_value = min_value
self.max_value = max_value
def forward(self, x: 'torch.Tensor'... |
MAE_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.nn as nn
import torch.utils.data
import torch.optim
class MAE_loss(nn.Module):
def __init__(self):
super(MAE_loss, self).__init__()
def forward(self, prediction, gt, epoch=0):
prediction = prediction[:, 0:1]
abs_err = torch.abs(prediction - gt)
mask ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.data
import torch.optim
assert_s... | alopezgit/project-adapt | MAE_loss | false | 18,322 | [
"MIT"
] | 8 | e93ab350344a5504f76f4e460002e0163996f88a | https://github.com/alopezgit/project-adapt/tree/e93ab350344a5504f76f4e460002e0163996f88a | import torch
import torch.nn as nn
import torch.utils.data
import torch.optim
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, prediction, gt, epoch=0):
prediction = prediction[:, 0:1]
abs_err = torch.abs(prediction - gt)
mask = (gt > 0).detach... |
CosineDistance | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import numpy as np
import torch.optim
def _acos_safe(x: 'torch.Tensor', eps: 'float'=0.0001):
slope = np.arccos(1.0 - eps) / eps
buf = torch.empty_like(x)
good = torch.abs(x) <= 1.0 - eps
bad = ~good
sign = torch.sign(x[bad])
buf[good] = torch.acos(x[good])
buf[bad] = torch.ac... | 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 numpy as np
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | ai-in-motion/moai | CosineDistance | false | 18,323 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import numpy as np
import torch.optim
def _acos_safe(x: 'torch.Tensor', eps: 'float'=0.0001):
slope = np.arccos(1.0 - eps) / eps
buf = torch.empty_like(x)
good = torch.abs(x) <= 1.0 - eps
bad = ~good
sign = torch.sign(x[bad])
buf[good] = torch.acos(x[good])
buf[bad] = torch.ac... |
Discriminator2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
import torch
class Discriminator2d(nn.Module):
def __init__(self, ngpu, wd, nc_d):
super(Discriminator2d, self).__init__()
self.ngpu = ngpu
self.conv0 = nn.Conv2d(nc_d, 2 ** (wd - 4), 4, 2, 1)
self.conv1 = nn.Conv2d(2 ** (... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | amirDahari1/SuperRes | Discriminator2d | false | 18,324 | [
"MIT"
] | 6 | 6e7500b803136d6a60d1571630b16e81bec5f888 | https://github.com/amirDahari1/SuperRes/tree/6e7500b803136d6a60d1571630b16e81bec5f888 | import torch
import torch.nn as nn
import torch.utils.data
import torch
class Model(nn.Module):
def __init__(self, ngpu, wd, nc_d):
super().__init__()
self.ngpu = ngpu
self.conv0 = nn.Conv2d(nc_d, 2 ** (wd - 4), 4, 2, 1)
self.conv1 = nn.Conv2d(2 ** (wd - 4), 2 ** (wd - 3), 4, 2, 1... |
Lambda | # 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.optim
class KL(torch.nn.KLDivLoss):
def __init__(self, is_input_log: 'bool'=False, is_target_log: 'bool'=False
):
super(KL, self).__init__(reduction='none', log_target=is_target_log)
self.is_input_log = is_input_log
def forward(self, gt: 'torch.Tensor', pred... | 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.optim
assert_size_stride = torch._C._dynamo.guards.assert_si... | ai-in-motion/moai | Lambda | false | 18,325 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class KL(torch.nn.KLDivLoss):
def __init__(self, is_input_log: 'bool'=False, is_target_log: 'bool'=False
):
super().__init__(reduction='none', log_target=is_target_log)
self.is_input_log = is_input_log
def forward(self, gt: 'torch.Tensor', pred: 'torch... |
Dense | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
from string import ascii_lowercase
import torch.optim
class Dense(nn.Module):
def __init__(self, input_features, output_features=None):
super(Dense, self).__init__()
self.input_features = input_features
self.output_features = (input_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
import math
import torch.nn as nn
from string import ascii_lowercase
import torc... | andrew-xu-monash/UMM-Modified | Dense | false | 18,326 | [
"Apache-2.0"
] | 4 | 18729dc34733c203e8cd3873fec2b9f7d0b56dba | https://github.com/andrew-xu-monash/UMM-Modified/tree/18729dc34733c203e8cd3873fec2b9f7d0b56dba | import math
import torch
import torch.nn as nn
from string import ascii_lowercase
import torch.optim
class Model(nn.Module):
def __init__(self, input_features, output_features=None):
super().__init__()
self.input_features = input_features
self.output_features = (input_features if output_f... |
MSE_log_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.nn as nn
import torch.utils.data
import torch.optim
class MSE_log_loss(nn.Module):
def __init__(self):
super(MSE_log_loss, self).__init__()
def forward(self, prediction, gt):
prediction = torch.clamp(prediction, min=0)
err = torch.log(prediction + 1e-06) - t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | alopezgit/project-adapt | MSE_log_loss | false | 18,327 | [
"MIT"
] | 8 | e93ab350344a5504f76f4e460002e0163996f88a | https://github.com/alopezgit/project-adapt/tree/e93ab350344a5504f76f4e460002e0163996f88a | import torch
import torch.nn as nn
import torch.utils.data
import torch.optim
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, prediction, gt):
prediction = torch.clamp(prediction, min=0)
err = torch.log(prediction + 1e-06) - torch.log(gt + 1e-06)
... |
AngleError | # 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.optim
def _angular_error(gt: 'torch.Tensor', pred: 'torch.Tensor', radians: 'bool'):
relative = gt @ torch.transpose(pred, -2, -1)
trace = relative[:, 0, 0] + relative[:, 1, 1] + relative[:, 2, 2]
trace = torch.clamp(trace, -1.0, 3.0)
phi = 0.5 * (trace - 1.0)
return phi.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ai-in-motion/moai | AngleError | false | 18,328 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
def _angular_error(gt: 'torch.Tensor', pred: 'torch.Tensor', radians: 'bool'):
relative = gt @ torch.transpose(pred, -2, -1)
trace = relative[:, 0, 0] + relative[:, 1, 1] + relative[:, 2, 2]
trace = torch.clamp(trace, -1.0, 3.0)
phi = 0.5 * (trace - 1.0)
return phi.... |
VisibilityFOV | # 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.optim
class VisibilityFOV(torch.nn.Module):
def __init__(self, width: 'int'=1, height: 'int'=1, coord_type: 'str'=
'coord'):
super(VisibilityFOV, self).__init__()
self.coord_type = coord_type
self.width = width
self.height = height
def forwar... | 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.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | VisibilityFOV | false | 18,329 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class Model(torch.nn.Module):
def __init__(self, width: 'int'=1, height: 'int'=1, coord_type: 'str'=
'coord'):
super().__init__()
self.coord_type = coord_type
self.width = width
self.height = height
def forward(self, coords: 'torch.Tens... |
Upsample2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import functools
import torch
import typing
import torch.optim
class Upsample2d(torch.nn.Module):
def __init__(self, resolution: 'typing.Sequence[int]'=None, scale:
'float'=2.0, mode: 'str'='bilinear'):
super(Upsample2d, self).__init__()
if resolution:
self.upsample = functool... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import functools
import typing
import torch.optim
assert_size_stride = torch._C._dynamo.g... | ai-in-motion/moai | Upsample2d | false | 18,330 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import functools
import torch
import typing
import torch.optim
class Model(torch.nn.Module):
def __init__(self, resolution: 'typing.Sequence[int]'=None, scale:
'float'=2.0, mode: 'str'='bilinear'):
super().__init__()
if resolution:
self.upsample = functools.partial(torch.nn.fu... |
Collapse | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 string import ascii_lowercase
import torch.optim
class Collapse(nn.Module):
def __init__(self, size):
super(Collapse, self).__init__()
self.weight = nn.Parameter(torch.Tensor(size), requires_grad=True)
self.weight.data.zero_()
self.p_avg_l =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from string import ascii_lowercase
import torch.optim
assert_size_s... | andrew-xu-monash/UMM-Modified | Collapse | false | 18,331 | [
"Apache-2.0"
] | 4 | 18729dc34733c203e8cd3873fec2b9f7d0b56dba | https://github.com/andrew-xu-monash/UMM-Modified/tree/18729dc34733c203e8cd3873fec2b9f7d0b56dba | import torch
import torch.nn as nn
from string import ascii_lowercase
import torch.optim
class Model(nn.Module):
def __init__(self, size):
super().__init__()
self.weight = nn.Parameter(torch.Tensor(size), requires_grad=True)
self.weight.data.zero_()
self.p_avg_l = nn.AdaptiveAvgPo... |
DownsampleB | # 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 DownsampleB(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleB, self).__init__()
self.avg = nn.AvgPool2d(stride)
self.expand_ratio = nOut // nIn
def forward(self, x):
x = self.avg(x)
return torch.cat([x] + [x.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | andyqmongo/InstAParam | DownsampleB | false | 18,332 | [
"MIT"
] | 3 | 00494d5367ec32b4ce90d01778cba9d4f1166833 | https://github.com/andyqmongo/InstAParam/tree/00494d5367ec32b4ce90d01778cba9d4f1166833 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, nIn, nOut, stride):
super().__init__()
self.avg = nn.AvgPool2d(stride)
self.expand_ratio = nOut // nIn
def forward(self, x):
x = self.avg(x)
return torch.cat([x] + [x.mul(0)] * (self.expand_... |
InstanceNormFC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 InstanceNormFC(nn.Module):
def __init__(self, _unused=0, affine=True):
super().__init__()
self.norm = nn.InstanceNorm1d(1, affine=affine)
def forward(self, x):
return self.norm(x.unsqueeze(1)).squeeze(1)
def get_inputs():
return [torch.ra... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ankitkv/pylego | InstanceNormFC | false | 18,333 | [
"MIT"
] | 4 | 38d4a8fe8497d748b22c58313cbfd187efb8326e | https://github.com/ankitkv/pylego/tree/38d4a8fe8497d748b22c58313cbfd187efb8326e | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, _unused=0, affine=True):
super().__init__()
self.norm = nn.InstanceNorm1d(1, affine=affine)
def forward(self, x):
return self.norm(x.unsqueeze(1)).squeeze(1)
def get_inputs():
return [torch.rand([4, 4]... |
LanguageModelCriterion | # 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.autograd import *
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
target = target[:, :input.size(1)]
mask = mask[:, :input.size(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
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | ankit1khare/Show_Infer_and_Tell-CIC | LanguageModelCriterion | false | 18,334 | [
"MIT"
] | 5 | 5437cceaaaf1bbcd16cb921650afd769378f4fc4 | https://github.com/ankit1khare/Show_Infer_and_Tell-CIC/tree/5437cceaaaf1bbcd16cb921650afd769378f4fc4 | import torch
import torch.nn as nn
from torch.autograd import *
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target, mask):
target = target[:, :input.size(1)]
mask = mask[:, :input.size(1)]
output = -input.gather(2, target.unsqueeze(... |
MutualInformationDiscriminatorHomo | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, n_hidden):
super(Discriminator, self).__init__()
self.weight = nn.Parameter(torch.Tensor(n_hidden, n_hidden))
self.reset_parameters()
def uniform(self, size, tensor):
bound = 1.0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | amazon-research/panrep | MutualInformationDiscriminatorHomo | false | 18,335 | [
"Apache-2.0"
] | 10 | 57e6f71bb70c0908f3db28be97af0d818a863e19 | https://github.com/amazon-research/panrep/tree/57e6f71bb70c0908f3db28be97af0d818a863e19 | import math
import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, n_hidden):
super().__init__()
self.weight = nn.Parameter(torch.Tensor(n_hidden, n_hidden))
self.reset_parameters()
def uniform(self, size, tensor):
bound = 1.0 / math.sqrt(size)
... |
Bottleneck | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.norm1 = nn.GroupNor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | andyqmongo/InstAParam | Bottleneck | false | 18,336 | [
"MIT"
] | 3 | 00494d5367ec32b4ce90d01778cba9d4f1166833 | https://github.com/andyqmongo/InstAParam/tree/00494d5367ec32b4ce90d01778cba9d4f1166833 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1):
super().__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.norm1 = nn.GroupNorm(2, planes)
... |
PlusOne | # 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.optim
class PlusOne(torch.nn.Module):
def __init__(self):
super(PlusOne, self).__init__()
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
return x + 1.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | PlusOne | false | 18,337 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
return x + 1.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
ResBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_planes, out_planes, stride=1, groups=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False, groups=groups)
class ResBlock(nn.Module):
exp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | andyqmongo/InstAParam | ResBlock | false | 18,338 | [
"MIT"
] | 3 | 00494d5367ec32b4ce90d01778cba9d4f1166833 | https://github.com/andyqmongo/InstAParam/tree/00494d5367ec32b4ce90d01778cba9d4f1166833 | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_planes, out_planes, stride=1, groups=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False, groups=groups)
class Model(nn.Module):
expans... |
Adaptive | # 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.optim
def dims(tensor: 'torch.Tensor', start_index: 'int'=1) ->torch.Tensor:
return torch.Tensor([tensor.size()[start_index:]]).squeeze()
class Adaptive(torch.nn.Module):
def __init__(self, scale_factor: 'float'=2.0, mode: 'str'='max', dims:
'int'=2):
super(Adaptiv... | 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.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_... | ai-in-motion/moai | Adaptive | false | 18,339 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
def dims(tensor: 'torch.Tensor', start_index: 'int'=1) ->torch.Tensor:
return torch.Tensor([tensor.size()[start_index:]]).squeeze()
class Model(torch.nn.Module):
def __init__(self, scale_factor: 'float'=2.0, mode: 'str'='max', dims:
'int'=2):
super().__init__... |
NormalizedPositionError | # 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.optim
def _normalised_position_error(gt: 'torch.Tensor', pred: 'torch.Tensor'):
l2_norm = torch.linalg.norm(gt - pred, ord=2, dim=-1)
return l2_norm / (torch.linalg.norm(gt, ord=2, dim=-1) + 1e-07)
class NormalizedPositionError(torch.nn.Module):
def __init__(self):
sup... | 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.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_str... | ai-in-motion/moai | NormalizedPositionError | false | 18,340 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
def _normalised_position_error(gt: 'torch.Tensor', pred: 'torch.Tensor'):
l2_norm = torch.linalg.norm(gt - pred, ord=2, dim=-1)
return l2_norm / (torch.linalg.norm(gt, ord=2, dim=-1) + 1e-07)
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
... |
Ones | # 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.optim
class Ones(torch.nn.Module):
def __init__(self):
super(Ones, self).__init__()
def forward(self, tensor: 'torch.Tensor') ->torch.Tensor:
return torch.ones(1, *tensor.shape[1:], dtype=tensor.dtype, device=
tensor.device).expand_as(tensor
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | Ones | false | 18,341 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, tensor: 'torch.Tensor') ->torch.Tensor:
return torch.ones(1, *tensor.shape[1:], dtype=tensor.dtype, device=
tensor.device).expand_as(tensor
) if tens... |
Binary | # 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.optim
class Binary(torch.nn.Module):
def __init__(self):
super(Binary, self).__init__()
def forward(self, tensor: 'torch.Tensor') ->torch.Tensor:
return (tensor != 0.0).bool()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
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
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | Binary | false | 18,342 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, tensor: 'torch.Tensor') ->torch.Tensor:
return (tensor != 0.0).bool()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
SpatialSoftmax | # 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.optim
def flatten_spatial_dims(tensor: 'torch.Tensor', spatial_start_index: 'int'=2
) ->torch.Tensor:
dims = [*tensor.shape[:spatial_start_index]] + [-1]
return tensor.view(*dims)
def dims(tensor: 'torch.Tensor', start_index: 'int'=1) ->torch.Tensor:
return torch.Tensor([te... | 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.optim
ass... | ai-in-motion/moai | SpatialSoftmax | false | 18,343 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
def flatten_spatial_dims(tensor: 'torch.Tensor', spatial_start_index: 'int'=2
) ->torch.Tensor:
dims = [*tensor.shape[:spatial_start_index]] + [-1]
return tensor.view(*dims)
def dims(tensor: 'torch.Tensor', start_index: 'int'=1) ->torch.Tensor:
return torch.Tensor([te... |
Zeros | # 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.optim
class Zeros(torch.nn.Module):
def __init__(self):
super(Zeros, self).__init__()
def forward(self, tensor: 'torch.Tensor') ->torch.Tensor:
return torch.zeros(1, *tensor.shape[1:], dtype=tensor.dtype, device
=tensor.device).expand_as(tensor)
def ge... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | Zeros | false | 18,344 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, tensor: 'torch.Tensor') ->torch.Tensor:
return torch.zeros(1, *tensor.shape[1:], dtype=tensor.dtype, device
=tensor.device).expand_as(tensor)
def get_inputs():... |
Znorm | # 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 typing
import torch.optim
def dims(tensor: 'torch.Tensor', start_index: 'int'=1) ->torch.Tensor:
return torch.Tensor([tensor.size()[start_index:]]).squeeze()
class Znorm(torch.nn.Module):
def __init__(self, dims: 'typing.Sequence[int]'):
super(Znorm, self).__init__()
sel... | 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 typing
import torch.optim
assert_size_stride = torch._C._dynamo.guards.a... | ai-in-motion/moai | Znorm | false | 18,345 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import typing
import torch.optim
def dims(tensor: 'torch.Tensor', start_index: 'int'=1) ->torch.Tensor:
return torch.Tensor([tensor.size()[start_index:]]).squeeze()
class Model(torch.nn.Module):
def __init__(self, dims: 'typing.Sequence[int]'):
super().__init__()
self.dims = di... |
Snake | # 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.optim
class Snake(torch.nn.Module):
def __init__(self, alpha: 'float'=1.0):
super(Snake, self).__init__()
self.alpha = alpha
self.one_over_alpha = 1.0 / alpha
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
s = torch.sin(self.alpha * x)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_si... | ai-in-motion/moai | Snake | false | 18,346 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class Model(torch.nn.Module):
def __init__(self, alpha: 'float'=1.0):
super().__init__()
self.alpha = alpha
self.one_over_alpha = 1.0 / alpha
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
s = torch.sin(self.alpha * x)
return x + ... |
LayerNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class LayerNorm(nn.Module):
def __init__(self, eps=0.0001):
super(LayerNorm, self).__init__()
self.eps = eps
def forward(self, x):
x_shape = x.sh... | 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.optim
import torch.... | amazon-research/network-deconvolution-pp | LayerNorm | false | 18,347 | [
"Apache-2.0"
] | 6 | 99e27ecec7d27c7c4c3fb230e96005bdcbf6f2ce | https://github.com/amazon-research/network-deconvolution-pp/tree/99e27ecec7d27c7c4c3fb230e96005bdcbf6f2ce | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Model(nn.Module):
def __init__(self, eps=0.0001):
super().__init__()
self.eps = eps
def forward(self, x):
x_shape = x.shape
x = x.r... |
Threshold | # 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.optim
class Threshold(torch.nn.Module):
CAST_OPS = {'float': lambda t: t.float(), 'byte': lambda t: t.byte()}
def __init__(self, value: 'float', comparison: 'str'='lower', dtype:
'str'='float'):
super(Threshold, self).__init__()
self.threshold = value
... | 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.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | Threshold | false | 18,348 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf | import torch
import torch.optim
class Model(torch.nn.Module):
CAST_OPS = {'float': lambda t: t.float(), 'byte': lambda t: t.byte()}
def __init__(self, value: 'float', comparison: 'str'='lower', dtype:
'str'='float'):
super().__init__()
self.threshold = value
self.comp_op = (to... |
Classifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import functional as F
class Classifier(nn.Module):
def __init__(self, input_size, hidden_size, n_classes):
super().__init__()
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, n_classes)
def forwar... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | ankitkv/pylego | Classifier | false | 18,349 | [
"MIT"
] | 4 | 38d4a8fe8497d748b22c58313cbfd187efb8326e | https://github.com/ankitkv/pylego/tree/38d4a8fe8497d748b22c58313cbfd187efb8326e | import torch
from torch import nn
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, input_size, hidden_size, n_classes):
super().__init__()
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, n_classes)
def forward(sel... |
Conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 Conv2d(nn.Module):
"""docstring for Conv2d
Attributes
----------
bn : TYPE
Description
conv : TYPE
Description
relu : TYPE
Description
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | anhlt/yolo-pytorch | Conv2d | false | 18,350 | [
"MIT"
] | 4 | 6e01146a93cbad3207c070536dffb26aef1d9c0f | https://github.com/anhlt/yolo-pytorch/tree/6e01146a93cbad3207c070536dffb26aef1d9c0f | import torch
from torch import nn
class Model(nn.Module):
"""docstring for Conv2d
Attributes
----------
bn : TYPE
Description
conv : TYPE
Description
relu : TYPE
Description
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
relu=... |
BERTIntermediate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Chriskuei/FedMatch | BERTIntermediate | false | 18,351 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... |
BERTLhuc | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class BERTLhuc(nn.Module):
def __init__(self, config):
super(BERTLhuc, self).__init__()
self.lhuc = Parameter(torch.zeros(config.hidden_size))
def forward(self, hidden_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
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | Chriskuei/FedMatch | BERTLhuc | false | 18,352 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.lhuc = Parameter(torch.zeros(config.hidden_size))
def forward(self, hidden_states):
hi... |
LeNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision.transforms import functional as F
from torch.nn import functional as F
class LeNet(nn.Module):
def __init__(self, num_classes... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | amazon-research/network-deconvolution-pp | LeNet | false | 18,353 | [
"Apache-2.0"
] | 6 | 99e27ecec7d27c7c4c3fb230e96005bdcbf6f2ce | https://github.com/amazon-research/network-deconvolution-pp/tree/99e27ecec7d27c7c4c3fb230e96005bdcbf6f2ce | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision.transforms import functional as F
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self, num_classes... |
ReceptiveFieldNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision.transforms import functional as F
from torch.nn import functional as F
def box_filter(x, k):
if k % 2 == 0:
k = k + 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 libdevice
import torch.nn as nn
import... | amazon-research/network-deconvolution-pp | ReceptiveFieldNorm | false | 18,354 | [
"Apache-2.0"
] | 6 | 99e27ecec7d27c7c4c3fb230e96005bdcbf6f2ce | https://github.com/amazon-research/network-deconvolution-pp/tree/99e27ecec7d27c7c4c3fb230e96005bdcbf6f2ce | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torchvision.transforms import functional as F
from torch.nn import functional as F
def box_filter(x, k):
if k % 2 == 0:
k = k + 1
... |
Network | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.l1 = nn.Linear(self.config['in_feature'], 500)
self.l2 = nn.Linear(50... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | AutuanLiu/PyTorch-ML | Network | false | 18,355 | [
"MIT"
] | 9 | 884c7723843d9ffb4da09d95eb97886b2cc38f28 | https://github.com/AutuanLiu/PyTorch-ML/tree/884c7723843d9ffb4da09d95eb97886b2cc38f28 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.l1 = nn.Linear(self.config['in_feature'], 500)
self.l2 = nn.Linear(500,... |
BERTMultSelfOutput | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BERTLayerNorm(nn.Module):
def __init__(self, config, multi_params=None, variance_epsilon=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BERTLayerNorm... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Chriskuei/FedMatch | BERTMultSelfOutput | false | 18,356 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BERTLayerNorm(nn.Module):
def __init__(self, config, multi_params=None, variance_epsilon=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super().__init__()
... |
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... | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class Conv1D(nn.Module):
def __init__(self, nf, nx):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EMBEDDIA/tnt_kid | MLP | false | 18,357 | [
"MIT"
] | 4 | 7a8c095de9581a641129939d950ae99ab1593456 | https://github.com/EMBEDDIA/tnt_kid/tree/7a8c095de9581a641129939d950ae99ab1593456 | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class Conv1D(nn.Module):
def __init__(self, nf, nx):
... |
BertImageSelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertImageSelfAttention(nn.Module):
def __init__(self, config):
super(BertImageSelfAttention, self).__init__()
if config.v_hidden_size % config.v_num_attention_heads != 0:
raise ValueErro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | IMNearth/Curriculum-Learning-For-VLN | BertImageSelfAttention | false | 18,358 | [
"MIT"
] | 8 | d2fe1286eb295dc8c63a0c886b35883f32481d85 | https://github.com/IMNearth/Curriculum-Learning-For-VLN/tree/d2fe1286eb295dc8c63a0c886b35883f32481d85 | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
if config.v_hidden_size % config.v_num_attention_heads != 0:
raise ValueError(
'The hidden size (%d) is n... |
Wav2Vec2ClassificationHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Wav2Vec2ClassificationHead(nn.Module):
"""Head for classification tasks
Layers:
- dropout
- dense layer (default xlsr hidden size = 1024)
- relu
- dropout
- classificiation layer ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | HLasse/wav2vec_finetune | Wav2Vec2ClassificationHead | false | 18,359 | [
"MIT"
] | 6 | 084ab432ba4acbf5ce81267e2791fb36a0b70daa | https://github.com/HLasse/wav2vec_finetune/tree/084ab432ba4acbf5ce81267e2791fb36a0b70daa | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
"""Head for classification tasks
Layers:
- dropout
- dense layer (default xlsr hidden size = 1024)
- relu
- dropout
- classificiation layer of size num_labels
... |
LogitsSelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class LogitsSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.cuda
import torch.distributed
assert_size_str... | KaijuML/dtt-multi-branch | LogitsSelfAttention | false | 18,360 | [
"Apache-2.0"
] | 8 | a49850a95034e58d387b9d48c647cfc2b83c45b5 | https://github.com/KaijuML/dtt-multi-branch/tree/a49850a95034e58d387b9d48c647cfc2b83c45b5 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class Model(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... |
G_t | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class G_t(nn.Module):
def __init__(self, args):
super(G_t, self).__init__()
self._relu = nn.ReLU()
self._ws1 = nn.Linear(args.image_feature_dim, args.
Vt_middle_feature_dim, bias=False)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HCShi/IONet | G_t | false | 18,361 | [
"MIT"
] | 4 | 42e3c0455a1ecb610f458e814d7310d685b2be7b | https://github.com/HCShi/IONet/tree/42e3c0455a1ecb610f458e814d7310d685b2be7b | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, args):
super().__init__()
self._relu = nn.ReLU()
self._ws1 = nn.Linear(args.image_feature_dim, args.
Vt_middle_feature_dim, bias=False)
self._ws2... |
G_u | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class G_u(nn.Module):
def __init__(self, args):
super(G_u, self).__init__()
self._relu = nn.ReLU()
self._ws1 = nn.Linear(args.video_feature_dim, args.
Vu_middle_feature_dim, bias=False)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HCShi/IONet | G_u | false | 18,362 | [
"MIT"
] | 4 | 42e3c0455a1ecb610f458e814d7310d685b2be7b | https://github.com/HCShi/IONet/tree/42e3c0455a1ecb610f458e814d7310d685b2be7b | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, args):
super().__init__()
self._relu = nn.ReLU()
self._ws1 = nn.Linear(args.video_feature_dim, args.
Vu_middle_feature_dim, bias=False)
self._ws2... |
BERTOutput | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import copy
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.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.triton_helpers import libdevice
import copy
import ... | Chriskuei/FedMatch | BERTOutput | false | 18,363 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 | from _paritybench_helpers import _mock_config
import copy
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.s... |
D_V | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class D_V(nn.Module):
def __init__(self, args):
super(D_V, self).__init__()
self._relu = nn.ReLU()
self._ws1 = nn.Linear(args.video_feature_dim, args.
DV_middle_feature_dim, bias=False)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HCShi/IONet | D_V | false | 18,364 | [
"MIT"
] | 4 | 42e3c0455a1ecb610f458e814d7310d685b2be7b | https://github.com/HCShi/IONet/tree/42e3c0455a1ecb610f458e814d7310d685b2be7b | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, args):
super().__init__()
self._relu = nn.ReLU()
self._ws1 = nn.Linear(args.video_feature_dim, args.
DV_middle_feature_dim, bias=False)
self._ws2... |
BertSelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidden size (... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DerryHub/the-TaobaoLive-Commodity-Identify-Competition | BertSelfAttention | false | 18,365 | [
"MIT"
] | 4 | 7e5e5c4fbddd9949fe01810d58bd7994889c007c | https://github.com/DerryHub/the-TaobaoLive-Commodity-Identify-Competition/tree/7e5e5c4fbddd9949fe01810d58bd7994889c007c | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidden size (%d) is not a... |
GreedySearch | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
def cuda():
return torch.cuda.is_available()
def get_device():
return torch.device('cuda' if cuda() else 'cpu')
class Search(nn.Module):
"""Base search class."""
def __init__(self, *args, **kwargs):
super().__init__()
self.device = get_device()
... | 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... | PaccMann/paccmann_chemistry | GreedySearch | false | 18,366 | [
"MIT"
] | 9 | f7e9735aafb936f837c38b5055c654be178f385f | https://github.com/PaccMann/paccmann_chemistry/tree/f7e9735aafb936f837c38b5055c654be178f385f | import torch
import torch.nn as nn
def cuda():
return torch.cuda.is_available()
def get_device():
return torch.device('cuda' if cuda() else 'cpu')
class Search(nn.Module):
"""Base search class."""
def __init__(self, *args, **kwargs):
super().__init__()
self.device = get_device()
... |
SamplingSearch | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
def cuda():
return torch.cuda.is_available()
def get_device():
return torch.device('cuda' if cuda() else 'cpu')
class Search(nn.Module):
"""Base search class."""
def __init__(self, *args, **kwargs):
super().__init__()
self.device = get_device()
... | 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
... | PaccMann/paccmann_chemistry | SamplingSearch | false | 18,367 | [
"MIT"
] | 9 | f7e9735aafb936f837c38b5055c654be178f385f | https://github.com/PaccMann/paccmann_chemistry/tree/f7e9735aafb936f837c38b5055c654be178f385f | import torch
import torch.nn as nn
def cuda():
return torch.cuda.is_available()
def get_device():
return torch.device('cuda' if cuda() else 'cpu')
class Search(nn.Module):
"""Base search class."""
def __init__(self, *args, **kwargs):
super().__init__()
self.device = get_device()
... |
BertTextPooler | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertTextPooler(nn.Module):
def __init__(self, config):
super(BertTextPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.bi_hidden_size)
self.activation = nn.ReLU()
def forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | IMNearth/Curriculum-Learning-For-VLN | BertTextPooler | false | 18,368 | [
"MIT"
] | 8 | d2fe1286eb295dc8c63a0c886b35883f32481d85 | https://github.com/IMNearth/Curriculum-Learning-For-VLN/tree/d2fe1286eb295dc8c63a0c886b35883f32481d85 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.bi_hidden_size)
self.activation = nn.ReLU()
def forward(self, hidden_states):
... |
CNNCifar | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
from torch import nn
class CNNCifar(nn.Module):
def __init__(self, args):
super(CNNCifar, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ITSEG-MQ/Chain-PPFL | CNNCifar | false | 18,369 | [
"MIT"
] | 8 | 21d4fafcd8e118cc4eaa35348f1204fecce78138 | https://github.com/ITSEG-MQ/Chain-PPFL/tree/21d4fafcd8e118cc4eaa35348f1204fecce78138 | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def __init__(self, args):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
... |
BERTLayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BERTLayerNorm(nn.Module):
def __init__(self, config, multi_params=None, variance_epsilon=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BERTLayerNorm... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Chriskuei/FedMatch | BERTLayerNorm | false | 18,370 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, config, multi_params=None, variance_epsilon=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super().__init__()
... |
BertMLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.dense_layer = nn.Linear(config.hidden_size, config.hidden_size)
self.dense_to_labels_layer = nn.Linear(config.hidden_size, config.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | JunnYu/GlyceBert_tokenizer | BertMLP | false | 18,371 | [
"MIT"
] | 7 | 27ded9d20421e274ec2e7139e9c79da56d8ad42f | https://github.com/JunnYu/GlyceBert_tokenizer/tree/27ded9d20421e274ec2e7139e9c79da56d8ad42f | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.dense_layer = nn.Linear(config.hidden_size, config.hidden_size)
self.dense_to_labels_layer = nn.Linear(config.hidden_size, config.
... |
AdapterLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Chriskuei/FedMatch | AdapterLayer | false | 18,372 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... |
CentralV_Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class CentralV_Critic(nn.Module):
def __init__(self, input_shape, args):
super(CentralV_Critic, self).__init__()
self.args = args
self.fc1 = nn.Linear(input_shape, 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
import torch.nn as nn
assert_... | OkYongChoi/smac | CentralV_Critic | false | 18,373 | [
"Apache-2.0"
] | 8 | 5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | https://github.com/OkYongChoi/smac/tree/5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_shape, args):
super().__init__()
self.args = args
self.fc1 = nn.Linear(input_shape, 128)
self.fc2 = nn.Linear(128, 128)... |
BERTLowRank | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Chriskuei/FedMatch | BERTLowRank | false | 18,374 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, opts):
super(Critic, self).__init__()
self.l1 = nn.Linear(opts.state_dim + opts.action_dim, 256)
self.l2 = nn.Linear(256, 256)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Jiang-HB/AC_CDQ | Critic | false | 18,375 | [
"MIT"
] | 7 | 4b4ec2d611c4481ad0b99cf7ea79eb23014a0325 | https://github.com/Jiang-HB/AC_CDQ/tree/4b4ec2d611c4481ad0b99cf7ea79eb23014a0325 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, opts):
super().__init__()
self.l1 = nn.Linear(opts.state_dim + opts.action_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = ... |
BertSelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, model_config):
super().__init__()
if model_config.hidden_size % model_config.num_attention_heads != 0:
raise ValueError(
'... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HS-YN/PanoAVQA | BertSelfAttention | false | 18,376 | [
"MIT"
] | 3 | 657b83421ce64ea18b3e79fb580afc7034403ccc | https://github.com/HS-YN/PanoAVQA/tree/657b83421ce64ea18b3e79fb580afc7034403ccc | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class Model(nn.Module):
def __init__(self, model_config):
super().__init__()
if model_config.hidden_size % model_config.num_attention_heads != 0:
raise ValueError(
'The hidden s... |
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 torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.optim
class non_bottleneck_1d(nn.Module):
def __init__(self, chann, dropprob, dilated):
super().__init__()
self.conv3x1_1 = nn.Conv2d(chann, chann,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | alopezgit/project-adapt | Decoder | false | 18,377 | [
"MIT"
] | 8 | e93ab350344a5504f76f4e460002e0163996f88a | https://github.com/alopezgit/project-adapt/tree/e93ab350344a5504f76f4e460002e0163996f88a | import torch
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.optim
class non_bottleneck_1d(nn.Module):
def __init__(self, chann, dropprob, dilated):
super().__init__()
self.conv3x1_1 = nn.Conv2d(chann, chann,... |
Alignment | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
from torch.nn import Module
import math
import torch
import torch.nn as nn
import torch.nn.functional as f
class Module(nn.Module):
def __init__(self):
super().__init__()
self.summary = {}
def add_summary(self, name, val):
if self.trainin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Chriskuei/FedMatch | Alignment | false | 18,378 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 | from _paritybench_helpers import _mock_config
from torch.nn import Module
import math
import torch
import torch.nn as nn
import torch.nn.functional as f
class Module(nn.Module):
def __init__(self):
super().__init__()
self.summary = {}
def add_summary(self, name, val):
if self.trainin... |
RNNAgent | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
import torch.nn as nn
class RNNAgent(nn.Module):
def __init__(self, input_shape, args):
super(RNNAgent, self).__init__()
self.args = args
self.fc1 = nn.Linear(input_shape, args.rnn_hidden_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Sud0x67/mrmix | RNNAgent | false | 18,379 | [
"Apache-2.0"
] | 4 | 4f4784e421c768509bd007e21b4455b56edc7cd2 | https://github.com/Sud0x67/mrmix/tree/4f4784e421c768509bd007e21b4455b56edc7cd2 | from _paritybench_helpers import _mock_config
import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_shape, args):
super().__init__()
self.args = args
self.fc1 = nn.Linear(input_shape, args.rnn_hidden_dim)
self.rnn = nn.... |
Att | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Att(nn.Module):
def __init__(self, args):
super(Att, self).__init__()
self._sigmoid = nn.Sigmoid()
self._ws1 = nn.Linear(args.video_feature_dim, 1, bias=False)
self._init_weights()
def _ini... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | HCShi/IONet | Att | false | 18,380 | [
"MIT"
] | 4 | 42e3c0455a1ecb610f458e814d7310d685b2be7b | https://github.com/HCShi/IONet/tree/42e3c0455a1ecb610f458e814d7310d685b2be7b | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, args):
super().__init__()
self._sigmoid = nn.Sigmoid()
self._ws1 = nn.Linear(args.video_feature_dim, 1, bias=False)
self._init_weights()
def _init_weigh... |
FusionConcat | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.utils.data
from torch import nn
class _NewEmptyTensorOp(torch.autograd.Function):
@staticmethod
def forward(ctx, x, new_shape):
ctx.shape = x.shape
return x.new_empty(new_shape)
@staticmethod
def backward(ctx, gr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyna... | Singingkettle/SAF-FCOS | FusionConcat | false | 18,381 | [
"BSD-2-Clause"
] | 10 | 5d00b83d659552940025923460d02bb2db7d29e8 | https://github.com/Singingkettle/SAF-FCOS/tree/5d00b83d659552940025923460d02bb2db7d29e8 | from _paritybench_helpers import _mock_config
import torch
import torch.utils.data
from torch import nn
class _NewEmptyTensorOp(torch.autograd.Function):
@staticmethod
def forward(ctx, x, new_shape):
ctx.shape = x.shape
return x.new_empty(new_shape)
@staticmethod
def backward(ctx, gr... |
BERTAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import copy
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Chriskuei/FedMatch | BERTAttention | false | 18,382 | [
"Apache-2.0"
] | 4 | 305e8c4bbb398712b00c883a986dfec17b500f76 | https://github.com/Chriskuei/FedMatch/tree/305e8c4bbb398712b00c883a986dfec17b500f76 | from _paritybench_helpers import _mock_config
import copy
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.s... |
DotRole | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch as th
import torch.nn as nn
class DotRole(nn.Module):
def __init__(self, args):
super(DotRole, self).__init__()
self.args = args
self.n_actions = args.n_actions
self.q_fc = nn.Linear(args.rnn_hidden_dim, args.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 as th
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | OkYongChoi/smac | DotRole | false | 18,383 | [
"Apache-2.0"
] | 8 | 5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | https://github.com/OkYongChoi/smac/tree/5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | from _paritybench_helpers import _mock_config
import torch
import torch as th
import torch.nn as nn
class Model(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.n_actions = args.n_actions
self.q_fc = nn.Linear(args.rnn_hidden_dim, args.action_latent_d... |
BertAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, model_config):
super().__init__()
if model_config.hidden_size % model_config.num_attention_heads != 0:
raise ValueError(
'... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HS-YN/PanoAVQA | BertAttention | false | 18,384 | [
"MIT"
] | 3 | 657b83421ce64ea18b3e79fb580afc7034403ccc | https://github.com/HS-YN/PanoAVQA/tree/657b83421ce64ea18b3e79fb580afc7034403ccc | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, model_config):
super().__init__()
if model_config.hidden_size % model_config.num_attention_heads != 0:
raise ValueError(
'... |
RobertaClassificationHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super(RobertaClassificationHead, self).__init__()
self.dense = nn.Linear(config.hidden_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | INK-USC/expl-refinement | RobertaClassificationHead | false | 18,385 | [
"MIT"
] | 7 | 815a7892a8d4c42fb429856746212a44f67d2547 | https://github.com/INK-USC/expl-refinement/tree/815a7892a8d4c42fb429856746212a44f67d2547 | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class Model(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.D... |
DotSelector | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch as th
from torch.distributions import Categorical
import torch.nn as nn
import torch.nn.functional as F
class DotSelector(nn.Module):
def __init__(self, input_shape, args):
super(DotSelector, self).__init__()
self.args = args... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
from torch... | OkYongChoi/smac | DotSelector | false | 18,386 | [
"Apache-2.0"
] | 8 | 5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | https://github.com/OkYongChoi/smac/tree/5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | from _paritybench_helpers import _mock_config
import torch
import torch as th
from torch.distributions import Categorical
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_shape, args):
super().__init__()
self.args = args
self.epsilon_s... |
PositionWiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class PositionWiseFeedForward(nn.Module):
def __init__(self, args):
super(PositionWiseFeedForward, self).__init__()
self.fc1 = 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 libdevice
import math
import ... | DannielSilva/MMBERT | PositionWiseFeedForward | false | 18,387 | [
"MIT"
] | 4 | 2c9069b59b66b8f3fec6de2e68ec42b489a3a437 | https://github.com/DannielSilva/MMBERT/tree/2c9069b59b66b8f3fec6de2e68ec42b489a3a437 | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class Model(nn.Module):
def __init__(self, args):
super().__init__()
self.fc1 = nn.Linear(args.hidden_size, args.hidden_size * 4)
... |
FusionMul | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | from _paritybench_helpers import _mock_config
import torch
import torch.utils.data
from torch import nn
class FusionMul(nn.Module):
def __init__(self, input_channels, cfg):
super(FusionMul, self).__init__()
def forward(self, im_x, ra_x):
x = torch.mul(im_x, ra_x)
return 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.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | Singingkettle/SAF-FCOS | FusionMul | false | 18,388 | [
"BSD-2-Clause"
] | 10 | 5d00b83d659552940025923460d02bb2db7d29e8 | https://github.com/Singingkettle/SAF-FCOS/tree/5d00b83d659552940025923460d02bb2db7d29e8 | from _paritybench_helpers import _mock_config
import torch
import torch.utils.data
from torch import nn
class Model(nn.Module):
def __init__(self, input_channels, cfg):
super().__init__()
def forward(self, im_x, ra_x):
x = torch.mul(im_x, ra_x)
return x
def get_inputs():
return... |
CriticNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.parallel
class CriticNet(nn.Module):
def __init__(self, args):
super(CriticNet, self).__init__()
state_dim = args.state_dim
action_dim =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Manojbhat09/Sane-annotation-shape-complete | CriticNet | false | 18,389 | [
"Apache-2.0"
] | 9 | 03b298b2c0a187be979ff31ad2a39238b72a6d78 | https://github.com/Manojbhat09/Sane-annotation-shape-complete/tree/03b298b2c0a187be979ff31ad2a39238b72a6d78 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.parallel
class Model(nn.Module):
def __init__(self, args):
super().__init__()
state_dim = args.state_dim
action_dim = args.z_dim
... |
BertCrossAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, model_config):
super().__init__()
if model_config.hidden_size % model_config.num_attention_heads != 0:
raise ValueError(
'... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HS-YN/PanoAVQA | BertCrossAttention | false | 18,390 | [
"MIT"
] | 3 | 657b83421ce64ea18b3e79fb580afc7034403ccc | https://github.com/HS-YN/PanoAVQA/tree/657b83421ce64ea18b3e79fb580afc7034403ccc | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, model_config):
super().__init__()
if model_config.hidden_size % model_config.num_attention_heads != 0:
raise ValueError(
'... |
BertOutput | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class BertOutput(nn.Module):
def __init__(self, model_config):
super().__init__()
self.dense = nn.Linear(model_config.intermediate_size, model_config
.hidden_size)
self.LayerNorm = nn.LayerNorm(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.triton_helpers import libdevice
from torch import n... | HS-YN/PanoAVQA | BertOutput | false | 18,391 | [
"MIT"
] | 3 | 657b83421ce64ea18b3e79fb580afc7034403ccc | https://github.com/HS-YN/PanoAVQA/tree/657b83421ce64ea18b3e79fb580afc7034403ccc | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class Model(nn.Module):
def __init__(self, model_config):
super().__init__()
self.dense = nn.Linear(model_config.intermediate_size, model_config
.hidden_size)
self.LayerNorm = nn.LayerNorm(model_co... |
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