entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_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 |
|---|---|---|---|---|---|---|---|---|---|---|
LinearAttentionLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class LinearAttentionLayer(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.linear = nn.Linear(input_dim, 1)
def forward(self, question, question_mask):
qtn = question.view(-1, question.shape[-1])
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HuyTu7/dl_optimizers | LinearAttentionLayer | false | 9,136 | [
"MIT"
] | 0 | 245242718324cebcabe657bdbc704aa54ad0b8d2 | https://github.com/HuyTu7/dl_optimizers/tree/245242718324cebcabe657bdbc704aa54ad0b8d2 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, hidden_size, num_inputs, action_space):
super(Actor, self).__init__()
self.action_space = action_space
num_outputs = action_space.shape[0]
self.linear1 = nn.Linear(num_inp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gntoni/pytorch-ddpg-naf | Actor | false | 12,471 | [
"MIT"
] | 0 | d208d0c0c38a9d2d2041f1e7e95695359eba430e | https://github.com/gntoni/pytorch-ddpg-naf/tree/d208d0c0c38a9d2d2041f1e7e95695359eba430e |
InputInjection | import torch
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class InputInjection(nn.Module):
"""Downsampling module for CGNet."""
def __init__(self, num_downsampling):
super(InputInjection, self).__init__()
self.pool = nn.ModuleList()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
assert_size_stride = torch._C._dynamo.guar... | Atten4Vis/DemystifyLocalViT | InputInjection | false | 13,337 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
LandmarkHead | import torch
import torch.nn as nn
from itertools import product as product
class LandmarkHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(LandmarkHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=
(1, 1), stride=1, padd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 itertools import product as product
assert_size_strid... | Akshobhya2018eeb1137/Attendance_System_Using_Face_Recognition | LandmarkHead | false | 18,446 | [
"MIT"
] | 2 | a52ca53e15332ab706f6ed23045b38ea6d38dfd9 | https://github.com/Akshobhya2018eeb1137/Attendance_System_Using_Face_Recognition/tree/a52ca53e15332ab706f6ed23045b38ea6d38dfd9 |
GaussianKernel | import torch
import torch.nn as nn
from typing import Optional
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
class GaussianKernel(nn.Module):
"""Gaussian Kernel Matrix
Gaussian Kernel k is defined by
.. math::
k(x_1, x_2) = \\exp \\left( - \\dfrac{\\| x_1 -... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Liuhong99/CST | GaussianKernel | false | 8,488 | [
"MIT"
] | 20 | f6653a4ee7968fa3ba875a182670636f648be783 | https://github.com/Liuhong99/CST/tree/f6653a4ee7968fa3ba875a182670636f648be783 |
LinearPotential | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import Parameter
assert_size_stride = torch._... | minhnhat93/didyprog | LinearPotential | false | 16,091 | [
"MIT"
] | 57 | 78886ed939d269b9b2bcb192bf849aa34082880c | https://github.com/minhnhat93/didyprog/tree/78886ed939d269b9b2bcb192bf849aa34082880c |
Model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | jizongFox/adversarial-robustness-toolbox | Model | false | 3,758 | [
"MIT"
] | 0 | 0649fe44d42bc7ba39a4b1a2ff95a31320fd1ae5 | https://github.com/jizongFox/adversarial-robustness-toolbox/tree/0649fe44d42bc7ba39a4b1a2ff95a31320fd1ae5 |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
def __init__(self, actor_in, actor_out, seed, fc1_units=256, fc2_units=128
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | davidhtf/drlnd | Actor | false | 6,530 | [
"MIT"
] | 1 | 221601f38659055824763ce41c6d9edd3d476fd4 | https://github.com/davidhtf/drlnd/tree/221601f38659055824763ce41c6d9edd3d476fd4 |
Normalize | import torch
import torch.nn as nn
class Normalize(nn.Module):
"""
Scale Audio to be between -1 and 1
"""
def __init__(self):
super(Normalize, self).__init__()
def forward(self, audio: 'torch.Tensor'):
if len(audio.shape) != 2:
raise ValueError('Audio should be 2D: [... | 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... | CiscoDevNet/vo-id | Normalize | false | 17,092 | [
"MIT"
] | 7 | 9a01f866c7539a9cd095d9627ba4f65ad540ea6b | https://github.com/CiscoDevNet/vo-id/tree/9a01f866c7539a9cd095d9627ba4f65ad540ea6b |
GroupNorm | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class GroupNorm(nn.Module):
def __init__(self, num_groups, embed_dim, eps=1e-05, affine=True):
super().__init__()
self.gn = nn.GroupNorm(num_groups, embed_dim, eps, affine)
def forward(self, x):
B, 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.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_s... | dumpmemory/TokenLabeling | GroupNorm | false | 15,259 | [
"Apache-2.0"
] | 367 | 9dbfd59aedecfe83f6f3253db4e99b82359d48ac | https://github.com/dumpmemory/TokenLabeling/tree/9dbfd59aedecfe83f6f3253db4e99b82359d48ac |
ClassificationLogSoftmax | import torch
import torch.nn as nn
class ClassificationLogSoftmax(nn.Module):
"""
Classifier on top of the hidden representation of the first token, which
is usually [CLS] token in BERT-like architectures.
"""
def __init__(self, hidden_size, num_classes):
super().__init__()
self.d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | awesome-archive/NeMo | ClassificationLogSoftmax | false | 9,771 | [
"Apache-2.0"
] | 0 | 0e566e62f0d102b725d3839564e51f7f40fa41b5 | https://github.com/awesome-archive/NeMo/tree/0e566e62f0d102b725d3839564e51f7f40fa41b5 |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dustasa/senior_software_HW | Net | false | 3,446 | [
"Apache-2.0"
] | 0 | 767d1d7bbd5e7d7414c17fa14b92b942e53d84ed | https://github.com/dustasa/senior_software_HW/tree/767d1d7bbd5e7d7414c17fa14b92b942e53d84ed |
MLP | 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 ... | VayerMaking/gpt-2-Pytorch | MLP | false | 1,902 | [
"MIT"
] | 0 | 7bc35f3c1d6c87d1ac306c0f789282b9df59182a | https://github.com/VayerMaking/gpt-2-Pytorch/tree/7bc35f3c1d6c87d1ac306c0f789282b9df59182a |
VAELoss | # 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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | jlrussin/RL_project | VAELoss | false | 10,275 | [
"Apache-2.0"
] | 0 | a8562b4797afdf5944dba768a88d779056e8506a | https://github.com/jlrussin/RL_project/tree/a8562b4797afdf5944dba768a88d779056e8506a |
ODEfunc_single_conv | import torch
import torch.nn as nn
import torch.jit
def norm(dim):
return nn.GroupNorm(min(32, dim), dim)
class ConcatConv2d(nn.Module):
def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
dilation=1, groups=1, bias=True, transpose=False):
super(ConcatConv2d, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | shuj1234/Hopfield-ODE | ODEfunc_single_conv | false | 10,823 | [
"MIT"
] | 0 | 2b770c0141082174f394b189df725088308d8bdd | https://github.com/shuj1234/Hopfield-ODE/tree/2b770c0141082174f394b189df725088308d8bdd |
ActorCritic | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
class ActorCritic(nn.Module):
def __init__(self, num_states, num_actions, hidden_size):
super(ActorCritic, self).__init__()
self.num_actions ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | rmfan/nni | ActorCritic | false | 10,969 | [
"MIT"
] | 0 | 727ee1ce47e070061fe3dab8a2da5d3cd5e55546 | https://github.com/rmfan/nni/tree/727ee1ce47e070061fe3dab8a2da5d3cd5e55546 |
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Maosef/easy-to-hard | BasicBlock | false | 8,519 | [
"MIT"
] | 44 | 711ec0965229444a6c51b1b06a4e2cad3e32d02e | https://github.com/Maosef/easy-to-hard/tree/711ec0965229444a6c51b1b06a4e2cad3e32d02e |
LayerTanh | # 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 triton
import triton.language 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_... | dawnclaude/onnx2keras | LayerTanh | false | 15,147 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 |
FC_Q | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | lysuk96/rl_representations | FC_Q | false | 15,989 | [
"MIT"
] | 438 | 19de69305e40c9b3a1d746a7af26d232c9fb3f6f | https://github.com/lysuk96/rl_representations/tree/19de69305e40c9b3a1d746a7af26d232c9fb3f6f |
MultiheadAttention | import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch.jit
import torch.jit.quantized
import torch.onnx.operators
def combine_heads(X):
"""
Combine heads (the inverse of split heads):
1) Transpose X from (batch size, nheads, sequence length, d_head) ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jeffyrao/translate | MultiheadAttention | false | 2,422 | [
"BSD-3-Clause"
] | 0 | ab928e0b692f476c0a43ee7f9d0fbd3ecbada2b4 | https://github.com/Jeffyrao/translate/tree/ab928e0b692f476c0a43ee7f9d0fbd3ecbada2b4 |
NotNorm | # 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 triton
import triton.language 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.jit
import torch.nn
assert_size_stride = tor... | ankmathur96/torchsupport | NotNorm | false | 3,162 | [
"MIT"
] | 0 | 77bf4a90b8770a408665e2604428808c3ed2f979 | https://github.com/ankmathur96/torchsupport/tree/77bf4a90b8770a408665e2604428808c3ed2f979 |
GlobalAvgPool | # 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 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... | DaShi-Git/simsg | GlobalAvgPool | false | 13,536 | [
"Apache-2.0"
] | 58 | 31df608cd04facb2b8b546cc6f53d84716117bdf | https://github.com/DaShi-Git/simsg/tree/31df608cd04facb2b8b546cc6f53d84716117bdf |
SERF | import torch
import torch.nn as nn
class SERF(nn.Module):
def __init__(self, thresh=50):
super().__init__()
self.thresh = thresh
None
def forward(self, x):
return self.serf_log1pexp(x)
def serf(self, x):
return x * torch.erf(torch.log(1 + torch.exp(x)))
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | DannielSilva/MMBERT | SERF | false | 17,202 | [
"MIT"
] | 4 | 2c9069b59b66b8f3fec6de2e68ec42b489a3a437 | https://github.com/DannielSilva/MMBERT/tree/2c9069b59b66b8f3fec6de2e68ec42b489a3a437 |
QRNNLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | boshining/NeuronBlocks | QRNNLayer | false | 14,990 | [
"MIT"
] | 1,257 | 74fbb8658fb3f1cffea5c9bc84b2a1da59c20dd9 | https://github.com/boshining/NeuronBlocks/tree/74fbb8658fb3f1cffea5c9bc84b2a1da59c20dd9 |
EntropyLoss | # 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 triton
import triton.language 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
import torch.utils.data
assert_size_stride = torch._... | Joshua-Schroijen/deepproblog | EntropyLoss | false | 672 | [
"Apache-2.0"
] | 0 | 4ae56f1e860010b7857b29d5bd76fb1555d5e19d | https://github.com/Joshua-Schroijen/deepproblog/tree/4ae56f1e860010b7857b29d5bd76fb1555d5e19d |
EntityLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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._inductor.select_algorithm import extern_kernels
import 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... | TraianVidrascu/DGAT | EntityLayer | false | 2,908 | [
"Apache-2.0"
] | 0 | 8855634d6262dec867512880442429918a9ee4b4 | https://github.com/TraianVidrascu/DGAT/tree/8855634d6262dec867512880442429918a9ee4b4 |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ella77/tacotron2_multispeaker_pytorch | MultiHeadAttention | false | 5,133 | [
"BSD-3-Clause"
] | 1 | 859eab0a8e3bd7545e623ce47fe1563702d38442 | https://github.com/Ella77/tacotron2_multispeaker_pytorch/tree/859eab0a8e3bd7545e623ce47fe1563702d38442 |
MiniBatchStdDev | # 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 triton
import triton.language 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
import torch.utils.data
import torch.nn.functional
import ... | Aarsh2001/annotated_deep_learning_paper_implementations | MiniBatchStdDev | false | 4,774 | [
"MIT"
] | 1 | ff0d5c065da1a46769f5f66fddc252c178f8fa37 | https://github.com/Aarsh2001/annotated_deep_learning_paper_implementations/tree/ff0d5c065da1a46769f5f66fddc252c178f8fa37 |
minibatch_std_concat_layer | # 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 triton
import triton.language 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_... | mikanCan/PG-GAN | minibatch_std_concat_layer | false | 10,648 | [
"MIT"
] | 0 | bc4a1bd2101f836c22a164174381f80b3f5c73c1 | https://github.com/mikanCan/PG-GAN/tree/bc4a1bd2101f836c22a164174381f80b3f5c73c1 |
Hardswish | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Hardswish(nn.Module):
@staticmethod
def forward(x):
return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0
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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | Arui66/FPSAutomaticAiming | Hardswish | false | 13,294 | [
"Apache-2.0"
] | 129 | 87674385d42b065b984b38a2ff59e7f2d4f07dc9 | https://github.com/Arui66/FPSAutomaticAiming/tree/87674385d42b065b984b38a2ff59e7f2d4f07dc9 |
TorchAdd | import torch
import torch.nn as nn
class TorchAdd(nn.Module):
"""
TorchAdd Module.
"""
def forward(self, input_list):
return input_list[0] + input_list[1]
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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Yakings/AIPerf | TorchAdd | false | 14,616 | [
"MIT"
] | 52 | 6e5c50a3b769ab4b1075aaab9841b5554f40bceb | https://github.com/Yakings/AIPerf/tree/6e5c50a3b769ab4b1075aaab9841b5554f40bceb |
RMSE_log | # 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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | d4l3k/crowds | RMSE_log | false | 12,237 | [
"MIT"
] | 0 | a57eee80d66498474c86cec22dd77be9d627ad97 | https://github.com/d4l3k/crowds/tree/a57eee80d66498474c86cec22dd77be9d627ad97 |
C3 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 co... | devillove084/DeepSignal | C3 | false | 12,260 | [
"MIT"
] | 0 | 1fe122b32752b11e10ca4bef3d07ddd7de4348b5 | https://github.com/devillove084/DeepSignal/tree/1fe122b32752b11e10ca4bef3d07ddd7de4348b5 |
Attention_Decoder | import torch
import torch.nn as nn
import torch._utils
class Attention_Decoder(nn.Module):
def __init__(self, dim, num_heads=1, qkv_bias=False, qk_scale=None,
attn_drop=0.0, proj_drop=0.0):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scal... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | GhadeerElmkaiel/Trans2Seg | Attention_Decoder | false | 491 | [
"Apache-2.0"
] | 0 | 6717db602205cbed494ae1913ac7cbbca8e83463 | https://github.com/GhadeerElmkaiel/Trans2Seg/tree/6717db602205cbed494ae1913ac7cbbca8e83463 |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, n_obs, output_dim, hidden_size, init_w=0.003):
super().__init__()
self.linear1 = nn.Linear(n_obs + output_dim, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Crazyalltnt/RL-Alogorithms-Implement | Critic | false | 322 | [
"MIT"
] | 0 | 27905f1c1890b1aff907564230b4ec0c22e60ba0 | https://github.com/Crazyalltnt/RL-Alogorithms-Implement/tree/27905f1c1890b1aff907564230b4ec0c22e60ba0 |
PrefModel | import torch
import torch.nn as nn
class PrefModel(nn.Module):
def __init__(self, input_dim):
super(PrefModel, self).__init__()
self.combination = nn.Linear(input_dim, 2)
self.softmax = nn.Softmax(1)
def forward(self, features):
h = self.combination(features)
out = se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | UKPLab/ijcai2019-relis | PrefModel | false | 18,015 | [
"MIT"
] | 5 | 8a40762dcfa90c075a4f6591cbdceb468026ef17 | https://github.com/UKPLab/ijcai2019-relis/tree/8a40762dcfa90c075a4f6591cbdceb468026ef17 |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
import t... | KilianRuiz2B/demucs | Encoder | false | 13,949 | [
"MIT"
] | 3,013 | a6fbf3806b018634f68563887feaee64c5e36600 | https://github.com/KilianRuiz2B/demucs/tree/a6fbf3806b018634f68563887feaee64c5e36600 |
Wide | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guard... | FlyingWing/pytorch-widedeep | Wide | false | 2,260 | [
"MIT"
] | 0 | 91a255d08bc9bdd5a05669465b7cf0313849ec9c | https://github.com/FlyingWing/pytorch-widedeep/tree/91a255d08bc9bdd5a05669465b7cf0313849ec9c |
ToeplitzBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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._inductor.select_algorithm import extern_kernels
import 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... | daemon/toepl.it.z | ToeplitzBlock | false | 9,967 | [
"MIT"
] | 0 | b16754b11f03f33bbfa05cf8544ef0dca3574ed4 | https://github.com/daemon/toepl.it.z/tree/b16754b11f03f33bbfa05cf8544ef0dca3574ed4 |
MaxPool2dStaticSamePadding | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MaxPool2dStaticSamePadding(nn.Module):
"""
自定义的padding、最终效果为，高宽减半，通道数不变
"""
def __init__(self, *args, **kwargs):
super().__init__()
self.pool = nn.MaxPool2d(*args, **kwargs)
self.stride = self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | CYHYCY/EfficientDet | MaxPool2dStaticSamePadding | false | 4,958 | [
"Apache-2.0"
] | 1 | e749c29d31d611250ba63ff4dec443847dc08572 | https://github.com/CYHYCY/EfficientDet/tree/e749c29d31d611250ba63ff4dec443847dc08572 |
SameModule | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class SameModule(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv = nn.Conv2d(dim + 1, 1, kernel_size=(1, 1))
torch.nn.init.kaiming_normal_(self.conv.weight)
self.dim = dim
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SpyrosMouselinos/DeltaFormers | SameModule | false | 5,876 | [
"Apache-2.0"
] | 1 | 38508fa9b85f2c50aa0031b67e7e8feff1a75b27 | https://github.com/SpyrosMouselinos/DeltaFormers/tree/38508fa9b85f2c50aa0031b67e7e8feff1a75b27 |
OneSideInterModalityUpdate | import torch
import torch.nn as nn
import torch.nn.functional as F
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = nn.Linear(in_size, out_size)
self.drop_value = drop
self.drop = nn.Dropout(drop)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Ruiver/CTCNet | OneSideInterModalityUpdate | false | 17,906 | [
"Apache-2.0"
] | 6 | 539e55ec9fed06028379d35dfd5cd4074755ffd8 | https://github.com/Ruiver/CTCNet/tree/539e55ec9fed06028379d35dfd5cd4074755ffd8 |
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... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 |
LqLoss | import torch
from torch import nn
def lq_loss(y_pred, y_true, q):
eps = 1e-07
loss = y_pred * y_true
loss = (1 - (loss + eps) ** q) / q
return loss.mean()
class LqLoss(nn.Module):
def __init__(self, q=0.5):
super().__init__()
self.q = q
def forward(self, output, target):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | Vanova/argus-freesound | LqLoss | false | 11,946 | [
"MIT"
] | 0 | 55f6e1b5ca1fd95c985f88a3e3fb0c81f8317b9d | https://github.com/Vanova/argus-freesound/tree/55f6e1b5ca1fd95c985f88a3e3fb0c81f8317b9d |
EqualLinear | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class EqualLinear(nn.Module):
def __init__(self, in_dim, out_dim, lr_mul=1, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim))
if bias:
self.bias = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | GuiCamargoX/gans_pytorch | EqualLinear | false | 9,142 | [
"MIT"
] | 0 | 3103184e54ea0d2922fc664a994a912bf61db426 | https://github.com/GuiCamargoX/gans_pytorch/tree/3103184e54ea0d2922fc664a994a912bf61db426 |
SoftDiceLossV1 | # 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 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... | imvladikon/pytorch-loss | SoftDiceLossV1 | false | 6,876 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
SingleHiddenLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | lysuk96/rl_representations | SingleHiddenLayer | false | 15,985 | [
"MIT"
] | 438 | 19de69305e40c9b3a1d746a7af26d232c9fb3f6f | https://github.com/lysuk96/rl_representations/tree/19de69305e40c9b3a1d746a7af26d232c9fb3f6f |
Model | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_actions, input_len):
super(Model, self).__init__()
self.fc1 = nn.Linear(input_len, 100)
self.fc2 = nn.Linear(100, 100)
self.out_policy = nn.Linear(100, n_actions)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ChengUVa/ptan | Model | false | 13,475 | [
"MIT"
] | 492 | f9b3ef2680ff64fad52e600d73ff2bf42eee310d | https://github.com/ChengUVa/ptan/tree/f9b3ef2680ff64fad52e600d73ff2bf42eee310d |
Combiner | import torch
import numpy as np
import torch.nn as nn
def FC(shape=None, init=None):
if init is None:
K = shape[-2]
init = [torch.rand(shape) * 2 - 1]
shape_bias = shape.copy()
shape_bias[-2] = 1
init.append(torch.rand(shape_bias) * 2 - 1)
else:
K = init[0].shap... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | BaharAzari/EquiGenDyna | Combiner | false | 11,252 | [
"MIT"
] | 0 | 1f71d9f7bf278880c61ceacec705bbb23852227c | https://github.com/BaharAzari/EquiGenDyna/tree/1f71d9f7bf278880c61ceacec705bbb23852227c |
BinaryFocalLossWithLogits | import torch
import warnings
from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
def binary_focal_loss_with_logits(input: 'torch.Tensor', target:
'torch.Tensor', alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
'str'='none', eps: 'Optional[float]'=None)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import warn... | shubham-gupta-iitr/mmmlX | BinaryFocalLossWithLogits | false | 4,329 | [
"Apache-2.0"
] | 0 | 3485e6191e0e45bf1c8168e4e928a36ab9264d22 | https://github.com/shubham-gupta-iitr/mmmlX/tree/3485e6191e0e45bf1c8168e4e928a36ab9264d22 |
InceptionB | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=True, **kwargs)
def forward(self, x):
x ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Galaxies99/inception-cuda | InceptionB | false | 11,445 | [
"MIT"
] | 0 | ed8fdbe3caef415e60b52e671273be90e9423e44 | https://github.com/Galaxies99/inception-cuda/tree/ed8fdbe3caef415e60b52e671273be90e9423e44 |
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... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 numpy import *... | LishudaNoBug/learning_PyTorch | Net | false | 5,549 | [
"MIT"
] | 1 | 1026035a9cb3d70e2fe97363b532e63db3ca136d | https://github.com/LishudaNoBug/learning_PyTorch/tree/1026035a9cb3d70e2fe97363b532e63db3ca136d |
Softmax_Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
class Softmax_Policy(nn.Module):
"""
Simple neural network with softmax action selection
"""
def __init__(self, num_inputs, hidden_size, action_space):
super(Softmax_Policy, self).__init__()
num_outputs = action_space
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | NadeemWard/pytorch_simple_policy_gradients | Softmax_Policy | false | 17,734 | [
"MIT"
] | 5 | d0ae66b46860504a077fdffdac45b5077c12c480 | https://github.com/NadeemWard/pytorch_simple_policy_gradients/tree/d0ae66b46860504a077fdffdac45b5077c12c480 |
KLDLoss | import torch
import torch.nn as nn
import torch.utils.data
class KLDLoss(nn.Module):
def forward(self, mu, logvar):
return -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | SebyakinAndrei/MichiGAN | KLDLoss | false | 1,047 | [
"MIT"
] | 0 | 6584c9a106b33096f38e8f5b11d0320f7065fd26 | https://github.com/SebyakinAndrei/MichiGAN/tree/6584c9a106b33096f38e8f5b11d0320f7065fd26 |
SimpleReluModel | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleReluModel(torch.nn.Module):
def __init__(self, inplace=False):
super(SimpleReluModel, self).__init__()
self.inplace = inplace
def forward(self, tensor):
other = F.relu(tensor, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | opti-mix/glow | SimpleReluModel | false | 7,411 | [
"Apache-2.0"
] | 1 | 4ba074df5da9822986a23a6679ab592c22660f6d | https://github.com/opti-mix/glow/tree/4ba074df5da9822986a23a6679ab592c22660f6d |
FEM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from ma... | fuankarion/FaceDetection-DSFD | FEM | false | 12,422 | [
"Apache-2.0"
] | 0 | f1e464ec5c9d95c2fe73edf44e4d414a464839b1 | https://github.com/fuankarion/FaceDetection-DSFD/tree/f1e464ec5c9d95c2fe73edf44e4d414a464839b1 |
mlp_model | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | st186/complementary_label_learning | mlp_model | false | 4,380 | [
"MIT"
] | 0 | 5d22ea638e9e6c087cc5bba7797c1c201679ba12 | https://github.com/st186/complementary_label_learning/tree/5d22ea638e9e6c087cc5bba7797c1c201679ba12 |
EncoderLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
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.... | AbubakrHassan/attention-is-all-you-need-pytorch | EncoderLayer | false | 11,182 | [
"MIT"
] | 0 | 2bf9a477dea6271b082556069f3665ffed2745cd | https://github.com/AbubakrHassan/attention-is-all-you-need-pytorch/tree/2bf9a477dea6271b082556069f3665ffed2745cd |
SimpleBmmModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleBmmModule(torch.nn.Module):
def forward(self, a, b):
return (a + a).bmm(b)
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | andreas-hommel/glow | SimpleBmmModule | false | 3,327 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | leduchuy225/HairNet | Net | false | 4,012 | [
"MIT"
] | 0 | 2d3f0b82a686d2ccc7fee4429ef5925ffabd8982 | https://github.com/leduchuy225/HairNet/tree/2d3f0b82a686d2ccc7fee4429ef5925ffabd8982 |
LxmertAttentionOutput | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | IsmaelElsharkawi/new_pororo_repo | LxmertAttentionOutput | false | 8,335 | [
"MIT"
] | 19 | 4617083b420615b8a3eb0f44d02e4e91a8f407f7 | https://github.com/IsmaelElsharkawi/new_pororo_repo/tree/4617083b420615b8a3eb0f44d02e4e91a8f407f7 |
TransformerEncoderLayer | from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn import Dropout
from torch.nn import LayerNorm
from torch.nn import Identity
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""
Obtained from: github.com:r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RongKaiWeskerMA/INSTA | TransformerEncoderLayer | false | 8,745 | [
"MIT"
] | 22 | 298bec0aeac3c1fde7bbcd4dece72ded1056e478 | https://github.com/RongKaiWeskerMA/INSTA/tree/298bec0aeac3c1fde7bbcd4dece72ded1056e478 |
ConcatSquashLinear | from torch.nn import Module
import torch
from torch.nn import Linear
import torch.utils.tensorboard
class ConcatSquashLinear(Module):
def __init__(self, dim_in, dim_out, dim_ctx):
super(ConcatSquashLinear, self).__init__()
self._layer = Linear(dim_in, dim_out)
self._hyper_bias = Linear(di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch.nn import Linear
import torch.utils.tenso... | entc-17-fyp-05/diffusion-point-cloud | ConcatSquashLinear | false | 16,271 | [
"MIT"
] | 138 | cde2e501855dea31496ddffad16f40aa588e3af8 | https://github.com/entc-17-fyp-05/diffusion-point-cloud/tree/cde2e501855dea31496ddffad16f40aa588e3af8 |
PLU | import torch
import torch.nn as nn
class PLU(nn.Module):
"""
y = max(alpha*(x+c)−c, min(alpha*(x−c)+c, x))
from PLU: The Piecewise Linear Unit Activation Function
"""
def __init__(self, alpha=0.1, c=1):
super().__init__()
self.alpha = alpha
self.c = c
def forward(self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | hilman-dayo/ObjectDetection-OneStageDet | PLU | false | 15,534 | [
"MIT"
] | 331 | 44054ad335e24e99a98fdad0d18b9bf3a80c941c | https://github.com/hilman-dayo/ObjectDetection-OneStageDet/tree/44054ad335e24e99a98fdad0d18b9bf3a80c941c |
MeanStd | import torch
import torch.nn as nn
class MeanStd(nn.Module):
def __init__(self):
super(MeanStd, self).__init__()
def forward(self, x):
x = x.view(x.size(0), x.size(1), -1)
mean_x = torch.mean(x, dim=2)
var_x = torch.mean(x ** 2, dim=2) - mean_x * mean_x
return torch.c... | 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... | jwen307/pytorch_GAN_zoo | MeanStd | false | 10,366 | [
"BSD-3-Clause"
] | 0 | b1e538a2f03fda42bd7a12872238b770ea5e0f23 | https://github.com/jwen307/pytorch_GAN_zoo/tree/b1e538a2f03fda42bd7a12872238b770ea5e0f23 |
Transformer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | QLSong/cv-classify | Transformer | false | 2,766 | [
"Apache-2.0"
] | 0 | 02f53d03868f299a08b5c97a266b50a7fdcd3f2b | https://github.com/QLSong/cv-classify/tree/02f53d03868f299a08b5c97a266b50a7fdcd3f2b |
ResizeTransform | # 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 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... | mariakesa/ZebraFishRegistrationPipeline | ResizeTransform | false | 7,160 | [
"MIT"
] | 1 | 4955044eb69dc04c579f59ccb24e02e4451aebcc | https://github.com/mariakesa/ZebraFishRegistrationPipeline/tree/4955044eb69dc04c579f59ccb24e02e4451aebcc |
DeContraster | # 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 triton
import triton.language as tl
from 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.distributions
import torch.utils.data
assert_size_stride = torch._C._dynamo.... | AlexMeinke/Provable-OOD-Detection | DeContraster | false | 7,671 | [
"MIT"
] | 21 | 9a132aec994ff718c96b81885736ab866df60d87 | https://github.com/AlexMeinke/Provable-OOD-Detection/tree/9a132aec994ff718c96b81885736ab866df60d87 |
TwoPartSimpleModel | import torch
import torch.nn as nn
import torch.utils.data
class SimpleModel(nn.Module):
def forward(self, x):
return 2 * x
def prepare_for_export(self, cfg, inputs, predictor_type):
return PredictorExportConfig(model=self, data_generator=lambda x: (x,))
class TwoPartSimpleModel(nn.Module)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | tsubauaaa/d2go | TwoPartSimpleModel | false | 4,459 | [
"Apache-2.0"
] | 0 | 9f746159ebf78ce79f644c405ca8695bc29d1075 | https://github.com/tsubauaaa/d2go/tree/9f746159ebf78ce79f644c405ca8695bc29d1075 |
Nloss_GD | import torch
import numpy as np
from torch import nn
class Nloss_GD(nn.Module):
def __init__(self, dim):
super(Nloss_GD, self).__init__()
self.dim = dim
torch.manual_seed(0)
def get_likelihoods(self, X, Y, Beta, eps=1e-06):
inv_det = Beta.prod(dim=1)
if (inv_det < eps... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
from torch import nn
assert_size_stride = torch._C._dy... | JavierAntoran/tiger-costume | Nloss_GD | false | 17,463 | [
"MIT"
] | 10 | 975661dfab2c435281f74c6be86529b16881ebcb | https://github.com/JavierAntoran/tiger-costume/tree/975661dfab2c435281f74c6be86529b16881ebcb |
ConvBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | HalleyJiang/UniFuse-Unidirectional-Fusion | ConvBlock | false | 8,222 | [
"MIT"
] | 30 | 27a4441fe3d3031d1c9f3eb2d72a3624407d19fc | https://github.com/HalleyJiang/UniFuse-Unidirectional-Fusion/tree/27a4441fe3d3031d1c9f3eb2d72a3624407d19fc |
MSELoss2d | import torch
from torch import nn
class MSELoss2d(nn.Module):
def __init__(self, size_average=None, reduce=None, reduction='mean',
ignore_index=255):
super(MSELoss2d, self).__init__()
self.MSE = nn.MSELoss(size_average=size_average, reduce=reduce,
reduction=reduction)
def... | 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... | giannifranchi/deeplabv3-superpixelmix | MSELoss2d | false | 6,742 | [
"MIT"
] | 1 | db52bf83b3b242af05bde5e39ee3de896e44c264 | https://github.com/giannifranchi/deeplabv3-superpixelmix/tree/db52bf83b3b242af05bde5e39ee3de896e44c264 |
LocationNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class LocationNetwork(nn.Module):
"""The location network.
Uses the internal state `h_t` of the core network to
produce the location coordinates `l_t` for the next
time step.
Concretely, fee... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SmirnovKol/recurrent-visual-attention | LocationNetwork | false | 14,436 | [
"MIT"
] | 463 | 4cb8d9e768ae35f38439278bb8a7b4d6b253a537 | https://github.com/SmirnovKol/recurrent-visual-attention/tree/4cb8d9e768ae35f38439278bb8a7b4d6b253a537 |
AvgPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | THAKAORI/SalsaNext | AvgPool2d | false | 11,920 | [
"MIT"
] | 0 | 855cd7e9ebb83ee62538ba4753a011ada7bbfb6c | https://github.com/THAKAORI/SalsaNext/tree/855cd7e9ebb83ee62538ba4753a011ada7bbfb6c |
DownBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guard... | DengZeshuai/DBPN-Pytorch | DownBlock | false | 2,521 | [
"MIT"
] | 0 | a90d241a1c4b07830c6d812ad8389d13e8cf05d1 | https://github.com/DengZeshuai/DBPN-Pytorch/tree/a90d241a1c4b07830c6d812ad8389d13e8cf05d1 |
GlobalAvgPool1d | # 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 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 abc import abstractmethod
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = to... | Yakings/AIPerf | GlobalAvgPool1d | false | 14,617 | [
"MIT"
] | 52 | 6e5c50a3b769ab4b1075aaab9841b5554f40bceb | https://github.com/Yakings/AIPerf/tree/6e5c50a3b769ab4b1075aaab9841b5554f40bceb |
IcosahedronUnpool | import math
import torch
from torch import nn
import torch.nn.functional as F
class IcosahedronUnpool(nn.Module):
"""Isocahedron Unpooling, consists in adding 1 values to match the desired un pooling size
"""
def forward(self, x):
"""Forward calculates the subset of pixels that will result from 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | phil-hawkins/deepsphere-pytorch | IcosahedronUnpool | false | 16,235 | [
"MIT"
] | 99 | f23c531445b3ddf234c7e98cdadb010163051e6d | https://github.com/phil-hawkins/deepsphere-pytorch/tree/f23c531445b3ddf234c7e98cdadb010163051e6d |
LastLevelMaxPool | import torch
import torch.utils.data
from torchvision.transforms import functional as F
from torch import nn
import torch.nn.functional as F
class LastLevelMaxPool(nn.Module):
def forward(self, x):
return [F.max_pool2d(x, 1, 2, 0)]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | CV-Rookie/EmbedMask | LastLevelMaxPool | false | 5,191 | [
"MIT"
] | 1 | 3b4d9fb4e0b6112dc501708184ff684dfb45f3f0 | https://github.com/CV-Rookie/EmbedMask/tree/3b4d9fb4e0b6112dc501708184ff684dfb45f3f0 |
ConditionTime | import torch
from torch import nn
def condition_time(x, i=0, size=(12, 16), seq_len=15):
"""create one hot encoded time image-layers, i in [1, seq_len]"""
assert i < seq_len
times = torch.eye(seq_len, dtype=x.dtype, device=x.device)[i].unsqueeze(-1
).unsqueeze(-1)
ones = torch.ones(1, *size, d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | lukeleeai/metnet | ConditionTime | false | 12,743 | [
"MIT"
] | 0 | 1dc0bf11780f413f3d55207866e0fa921b8aa60d | https://github.com/lukeleeai/metnet/tree/1dc0bf11780f413f3d55207866e0fa921b8aa60d |
Norm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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 triton
import triton.language 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_... | Aalanli/MusicGeneration | Norm | false | 5 | [
"MIT"
] | 0 | 7d268322d692013d8ac6e70be31741cea519fa28 | https://github.com/Aalanli/MusicGeneration/tree/7d268322d692013d8ac6e70be31741cea519fa28 |
Policy | import torch
import torch.nn as nn
class Policy(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(Policy, self).__init__()
self.affine1 = nn.Linear(num_inputs, 64)
self.affine2 = nn.Linear(64, 64)
self.action_mean = nn.Linear(64, num_outputs)
self.action_mean.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
im... | aranganath/pytorch-trpo | Policy | false | 9,760 | [
"MIT"
] | 0 | a85bc48261eb4ed5833209da706379e9dc84592f | https://github.com/aranganath/pytorch-trpo/tree/a85bc48261eb4ed5833209da706379e9dc84592f |
Network | import torch
import torch.nn.functional as F
from torch import nn
class Network(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=128,
fc2_units=128):
"""Initialize parameters and build model.
Params
======
state_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | gray-li/HalfRainbowDQN | Network | false | 12,473 | [
"MIT"
] | 0 | 43e2b12945c14e0e39eea3bbf56c7af785c48720 | https://github.com/gray-li/HalfRainbowDQN/tree/43e2b12945c14e0e39eea3bbf56c7af785c48720 |
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
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | qbhan/pathembed | ResBlock | false | 7,508 | [
"MIT"
] | 1 | c21823529840593bf606e10696f5879e5adb51b2 | https://github.com/qbhan/pathembed/tree/c21823529840593bf606e10696f5879e5adb51b2 |
EqualLinear | import torch
import torch.nn.functional as F
from torch import nn
class EqualLinear(nn.Module):
def __init__(self, in_dim, out_dim, lr_mul=1, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim))
if bias:
self.bias = nn.Parameter(torch.zeros(o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | ericguizzo/stylegan2-pytorch | EqualLinear | false | 12,355 | [
"MIT"
] | 0 | d6e5cf4e30247e12d330537676f9ba63867cfaa0 | https://github.com/ericguizzo/stylegan2-pytorch/tree/d6e5cf4e30247e12d330537676f9ba63867cfaa0 |
RegressionHead | import abc
import torch
import torch.nn as nn
import torch.utils.data.dataset
class BaseHead(nn.Module, metaclass=abc.ABCMeta):
"""Absract class for task heads"""
@abc.abstractmethod
def __init__(self):
super().__init__()
class RegressionHead(BaseHead):
def __init__(self, task, 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 abc
import t... | dumpmemory/jiant | RegressionHead | false | 15,269 | [
"MIT"
] | 1,108 | f9e0e7c9ecf88da0c26559c5f903aef0338c7bd9 | https://github.com/dumpmemory/jiant/tree/f9e0e7c9ecf88da0c26559c5f903aef0338c7bd9 |
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
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | icml2019-anonymous-author/Action-Robust-Reinforcement-Learning | Critic | false | 6,852 | [
"MIT"
] | 1 | 03f0a1dd5f4a0fc5230c0ad0b41f63161bae862b | https://github.com/icml2019-anonymous-author/Action-Robust-Reinforcement-Learning/tree/03f0a1dd5f4a0fc5230c0ad0b41f63161bae862b |
ResBlock | import torch
from torch import nn
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, dilation=1,
bias=False):
super(CausalConv1d, self).__init__()
self.padding = padding = (kernel_size - 1) * dilation
self.conv = nn.Conv1d(in_channels, out_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | oleges1/TTS | ResBlock | false | 7,375 | [
"MIT"
] | 1 | 19b389714078729fae29faf9c23112bdbe4c8dec | https://github.com/oleges1/TTS/tree/19b389714078729fae29faf9c23112bdbe4c8dec |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | myeldib/Simple-SR | Attention | false | 12,836 | [
"MIT"
] | 0 | 583456b1f231574d9e0b45c29266cf41603d161d | https://github.com/myeldib/Simple-SR/tree/583456b1f231574d9e0b45c29266cf41603d161d |
GlobalWeightedAvgPool2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | dong03/DogNoseLandmarks | GlobalWeightedAvgPool2d | false | 6,599 | [
"MIT"
] | 1 | ac5d1e0436e9e0835a6939f8d125f1d36007bc62 | https://github.com/dong03/DogNoseLandmarks/tree/ac5d1e0436e9e0835a6939f8d125f1d36007bc62 |
Conv2dBlock | import torch
import torch.nn.functional as F
from torch import nn
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = mome... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.functional as... | MattAlexMiracle/SmartPatch | Conv2dBlock | false | 17,690 | [
"MIT"
] | 7 | c485cb433d8e085d6eae10a335ee19f5e6c1a41c | https://github.com/MattAlexMiracle/SmartPatch/tree/c485cb433d8e085d6eae10a335ee19f5e6c1a41c |
AngleWiseRKD | import torch
from torch import nn
import torch.nn.functional as F
def angle(pred):
"""Calculate the angle-wise relational potential which measures the angle
formed by the three examples in the output representation space.
Args:
pred (torch.Tensor): The prediction of the teacher or student with
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HIT-cwh/mmrazor | AngleWiseRKD | false | 13,749 | [
"Apache-2.0"
] | 553 | 2dad24044d7f1dad88f20221f8fc071dd40fdd4f | https://github.com/HIT-cwh/mmrazor/tree/2dad24044d7f1dad88f20221f8fc071dd40fdd4f |
ReinforcedReceiver | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import to... | ptigas/EGG | ReinforcedReceiver | false | 7,498 | [
"MIT"
] | 1 | 5319cc9de2c17bc72de717737cfbb5be2285c59b | https://github.com/ptigas/EGG/tree/5319cc9de2c17bc72de717737cfbb5be2285c59b |
RPNHead | import torch
import torch.nn.functional as F
from torch import nn
class RPNHead(nn.Module):
def __init__(self, in_channels, num_anchors):
super().__init__()
self.conv = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
self.cls_logits = nn.Conv2d(in_channels, num_anchors, 1)
self.bbox_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Okery/PyTorch-Simple-MaskRCNN | RPNHead | false | 14,136 | [
"MIT"
] | 147 | 5e57a353f211c7130bfcf1d55cacd80057d81423 | https://github.com/Okery/PyTorch-Simple-MaskRCNN/tree/5e57a353f211c7130bfcf1d55cacd80057d81423 |
BinaryAdd | # 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 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 abc
import inspect
import warnings
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from typ... | Johnsonms/NNI_master | BinaryAdd | false | 11,589 | [
"MIT"
] | 0 | e5e5c7aed89cf3189cffe1056464833c15eb54ff | https://github.com/Johnsonms/NNI_master/tree/e5e5c7aed89cf3189cffe1056464833c15eb54ff |
DepthwiseSeparableConv | import torch
import torch.nn as nn
import torch.nn.functional as F
class DepthwiseSeparableConv(nn.Module):
"""
Depth-wise separable convolution uses less parameters to generate output by convolution.
:Examples:
>>> m = DepthwiseSeparableConv(300, 200, 5, dim=1)
>>> input_tensor = torch.ra... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Worm4047/TVR | DepthwiseSeparableConv | false | 14,614 | [
"MIT"
] | 106 | 2a8ce2edbdc0966aef3b84c28872267039f01700 | https://github.com/Worm4047/TVR/tree/2a8ce2edbdc0966aef3b84c28872267039f01700 |
SmoothL1Loss | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | CityU-AIM-Group/HTD | SmoothL1Loss | false | 17,115 | [
"MIT"
] | 5 | 0be9fd844118c275abc6053b3cbd5ffb589e62ee | https://github.com/CityU-AIM-Group/HTD/tree/0be9fd844118c275abc6053b3cbd5ffb589e62ee |
MultConst | import torch
import torch.nn as nn
class MultConst(nn.Module):
def forward(self, input):
return 255 * input
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | globz-eu/PyTorch-Multi-Style-Transfer | MultConst | false | 3,549 | [
"MIT"
] | 0 | d00ca44ffee6a4eb4b517f3f1a6eabf72db2a3d2 | https://github.com/globz-eu/PyTorch-Multi-Style-Transfer/tree/d00ca44ffee6a4eb4b517f3f1a6eabf72db2a3d2 |
ActorNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class ActorNetwork(nn.Module):
def __init__(self, state_size, action_size, seed):
super(ActorNetwork, self).__init__()
torch.manual_seed(seed)
hidden1 = 64
hidden2 = 64
self.fc1 = nn.Linear(state_size, hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | aishikawa/drl-impl | ActorNetwork | false | 9,694 | [
"MIT"
] | 0 | 1afe7426494cd94990cb4dae247486a25dfe37bf | https://github.com/aishikawa/drl-impl/tree/1afe7426494cd94990cb4dae247486a25dfe37bf |
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
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | benedictquartey/softgym_wm | Decoder | false | 12,170 | [
"BSD-3-Clause"
] | 0 | 0aef75fed207b11029f6052c656a679c105b4677 | https://github.com/benedictquartey/softgym_wm/tree/0aef75fed207b11029f6052c656a679c105b4677 |
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