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 |
|---|---|---|---|---|---|---|---|---|---|---|
IBertLMHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 math
from to... | Clemens123/transformers | IBertLMHead | false | 13,220 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
AttentivePooling | import torch
import torch.nn as nn
class AttentivePooling(nn.Module):
"""
Implementation of Attentive Pooling
"""
def __init__(self, input_dim, **kwargs):
super(AttentivePooling, self).__init__()
self.W_a = nn.Linear(input_dim, input_dim)
self.W = nn.Linear(input_dim, 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.... | AyushExel/s3prl | AttentivePooling | false | 1,985 | [
"MIT"
] | 0 | 6531904e9621a778978b9cfef3ba9f582e56639a | https://github.com/AyushExel/s3prl/tree/6531904e9621a778978b9cfef3ba9f582e56639a |
RMulFloat | # 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | PogChamper/torch2trt | RMulFloat | false | 14,213 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
TimeIntervalMultiHeadAttention | import torch
import numpy as np
import torch.nn as nn
import torch.distributions
class TimeIntervalMultiHeadAttention(nn.Module):
def __init__(self, d_model, n_heads, kq_same=False, bias=True):
super().__init__()
"""
It also needs position and interaction (time interval) key/value input.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | nmrenyi/ReChorus | TimeIntervalMultiHeadAttention | false | 16,197 | [
"MIT"
] | 314 | 9ab632579d0464b0aaf365539f87b04866920b66 | https://github.com/nmrenyi/ReChorus/tree/9ab632579d0464b0aaf365539f87b04866920b66 |
_TestNetStrided | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.... | arjunsuresh/aimet | _TestNetStrided | false | 12,325 | [
"BSD-3-Clause"
] | 0 | f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 | https://github.com/arjunsuresh/aimet/tree/f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 |
Encoding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | ImportPaddle/APCNet | Encoding | false | 2,367 | [
"MIT"
] | 0 | 68ade1f83827b4cdd60ee4b6ac25454397100316 | https://github.com/ImportPaddle/APCNet/tree/68ade1f83827b4cdd60ee4b6ac25454397100316 |
InnerProductLayer | # 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 sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = tor... | chenkkkk/DeepCTR-PyTorch | InnerProductLayer | false | 6,432 | [
"Apache-2.0"
] | 1 | a10a3ace4ad79171e7fb182407b3e4d22bf753e7 | https://github.com/chenkkkk/DeepCTR-PyTorch/tree/a10a3ace4ad79171e7fb182407b3e4d22bf753e7 |
TargetContextGate | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | BradLin0819/kg2text | TargetContextGate | false | 13,407 | [
"Apache-2.0"
] | 86 | e586eb2027c0d85db9826cbe1d9e14f2d26fc93f | https://github.com/BradLin0819/kg2text/tree/e586eb2027c0d85db9826cbe1d9e14f2d26fc93f |
Multi_Head_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.triton_helpers import libdevice, math as tl_math
im... | Ch4ndelier/Transformer_Zero_Velocity_classification | Multi_Head_Attention | false | 17,087 | [
"MIT"
] | 6 | 857efb66189c503e983c11bd7dde16ad19c51ada | https://github.com/Ch4ndelier/Transformer_Zero_Velocity_classification/tree/857efb66189c503e983c11bd7dde16ad19c51ada |
SineODE | import math
import torch
class SineODE(torch.nn.Module):
def forward(self, t, y):
return 2 * y / t + t ** 4 * torch.sin(2 * t) - t ** 2 + 4 * t ** 3
def y_exact(self, t):
return -0.5 * t ** 4 * torch.cos(2 * t) + 0.5 * t ** 3 * torch.sin(
2 * t) + 0.25 * t ** 2 * torch.cos(2 * 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 math as tl_math
import math
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | TylerChoi1224/torchdiffeq | SineODE | false | 1,157 | [
"MIT"
] | 0 | 72f74d9651a58ab11cdadd60682f1b61e625ef53 | https://github.com/TylerChoi1224/torchdiffeq/tree/72f74d9651a58ab11cdadd60682f1b61e625ef53 |
DiceLoss_TRDP | import torch
from torch.nn.modules.loss import _Loss
class DiceLoss_TRDP(_Loss):
def __init__(self, per_image=False):
super(DiceLoss_TRDP, self).__init__()
self.per_image = per_image
def forward(self, y_pred, y_true):
"""
:param y_pred: NxCxHxW
:param y_true: NxCxHxW
... | 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.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.asse... | sebasmos/Spacenet7TRDP | DiceLoss_TRDP | false | 12,957 | [
"Apache-2.0"
] | 0 | 03b5819321108017f8f8c2d359264c8e18d9e38a | https://github.com/sebasmos/Spacenet7TRDP/tree/03b5819321108017f8f8c2d359264c8e18d9e38a |
LocationEncoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.... | sibeiyang/sgmn | LocationEncoder | false | 16,444 | [
"MIT"
] | 130 | 00731b4f2202246d40a36d2a6727c599e6e649aa | https://github.com/sibeiyang/sgmn/tree/00731b4f2202246d40a36d2a6727c599e6e649aa |
Mean | # 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 torchvision.datasets import *
import torch.nn as nn
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.a... | JJavierga/PyTorch-Encoding | Mean | false | 9,455 | [
"MIT"
] | 0 | 207254b2a60276a31ffa24b76ae84df27c6ebf94 | https://github.com/JJavierga/PyTorch-Encoding/tree/207254b2a60276a31ffa24b76ae84df27c6ebf94 |
GeneratorLoss | # 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 import nn
import torch.utils.data
import torch
assert_size_stride = torch._C._... | LiFH/MySR | GeneratorLoss | false | 768 | [
"MIT"
] | 0 | f6075f8711853aba6f0aae9cef18c5da84abb78c | https://github.com/LiFH/MySR/tree/f6075f8711853aba6f0aae9cef18c5da84abb78c |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
class PointwiseConv(nn.Module):
"""
Pointwise Convolution (1x1 Conv)
Convolution 1 Dimension (Faster version)
(cf. https://github.com/huggingface/pytorch-openai-transformer-lm/blob/ eafc28abdfadfa0732f03a0fc65805c5bfb2ffe7/mode... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | DongjunLee/claf | PositionwiseFeedForward | false | 13,588 | [
"MIT"
] | 225 | ef548dda27c9aac8ce4db09774c8a1459d25bde1 | https://github.com/DongjunLee/claf/tree/ef548dda27c9aac8ce4db09774c8a1459d25bde1 |
ClassHead | import torch
import torch.nn as nn
class ClassHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=3):
super(ClassHead, self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2,
kernel_size=(1, 1), stride=1, padding=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.... | lurenjia307/RetinaPedestrian_Pytorch | ClassHead | false | 7,173 | [
"MIT"
] | 1 | 59c4aa50f3ef2ecb1113ad3b9950e8bbbff1206f | https://github.com/lurenjia307/RetinaPedestrian_Pytorch/tree/59c4aa50f3ef2ecb1113ad3b9950e8bbbff1206f |
decoder5 | import torch
from torch import nn
class decoder5(nn.Module):
def __init__(self):
super(decoder5, self).__init__()
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu15 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingN... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Holmes-Alan/RefVAE | decoder5 | false | 8,334 | [
"MIT"
] | 13 | 836b8f1168f1b0f923b609a48e202ace7806f79c | https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c |
L2_DistanceAttention | # 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._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | hk19960522/2018-DL-Final | L2_DistanceAttention | false | 3,596 | [
"MIT"
] | 0 | cbc70260aa22d7df366a1d28bee472f1fc5b82c7 | https://github.com/hk19960522/2018-DL-Final/tree/cbc70260aa22d7df366a1d28bee472f1fc5b82c7 |
AdaptiveAvgPool3dOutSize1 | import torch
from typing import Tuple
import torch.nn as nn
from abc import abstractmethod
import torch.utils.data
import torch.nn
class EfficientBlockBase(nn.Module):
"""
PyTorchVideo/accelerator provides a set of efficient blocks
that have optimal efficiency for each target hardware device.
Each ef... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from typing import Tuple
import torch.nn as nn
from abc import abstractmethod
import torch.utils.data
import torch.nn
assert_size_stride = t... | zijian-hu/pytorchvideo | AdaptiveAvgPool3dOutSize1 | false | 4,701 | [
"Apache-2.0"
] | 0 | 51589b100437af2285c56ce2ccc7ccecb7f9b18b | https://github.com/zijian-hu/pytorchvideo/tree/51589b100437af2285c56ce2ccc7ccecb7f9b18b |
SimpleFC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.onnx
assert_size_stride = torch._C._dynamo.gu... | adityakusupati/EdgeML | SimpleFC | false | 3,021 | [
"MIT"
] | 0 | 65933a6fdfc38945f4311043a62e120784b2b0bf | https://github.com/adityakusupati/EdgeML/tree/65933a6fdfc38945f4311043a62e120784b2b0bf |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
"""Initialize parameters and build model.
An nn.Module contains layers, and a method
forward(input)that returns the output.
Weights (learnable params) are inherently defined here.
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
from torch._inductor.runtime.... | sofya-pugach/spot_mini_mini | Actor | false | 16,481 | [
"MIT"
] | 323 | 42770145e91ed2625ccc7e4f4d7016ce14a61464 | https://github.com/sofya-pugach/spot_mini_mini/tree/42770145e91ed2625ccc7e4f4d7016ce14a61464 |
BahdanauAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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, math as tl_math
im... | Fei00Wu/espresso | BahdanauAttention | false | 2,406 | [
"MIT"
] | 0 | 4e8e6e2f9151a87448845c5142611c103dd4580c | https://github.com/Fei00Wu/espresso/tree/4e8e6e2f9151a87448845c5142611c103dd4580c |
Quantization | import torch
import torch.nn as nn
import torch.utils.data
class Quantization(nn.Module):
@staticmethod
def forward(input):
return torch.round(input)
@staticmethod
def backward(grad_output):
grad_input = grad_output.clone()
return grad_input
def get_inputs():
return [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.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | Geunwoo-Jeon/iclr_17_compression | Quantization | false | 13,710 | [
"MIT"
] | 56 | a28746b1f1c518d91125d8f289d9511cde488c77 | https://github.com/Geunwoo-Jeon/iclr_17_compression/tree/a28746b1f1c518d91125d8f289d9511cde488c77 |
MyCustomFunctionReluModel | import torch
import torch.nn
import torch.onnx
class MyCustomFunctionReluModel(torch.nn.Module):
def __init__(self):
super().__init__()
class MyReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_siz... | RyanUnderhill/onnxruntime | MyCustomFunctionReluModel | false | 11,830 | [
"MIT"
] | 0 | 6df4e293ffbb47d739d2dedfbb87fa6234b8c37c | https://github.com/RyanUnderhill/onnxruntime/tree/6df4e293ffbb47d739d2dedfbb87fa6234b8c37c |
Stoplinear | import torch
from collections import OrderedDict
import torch.nn as nn
class Linear(nn.Module):
"""
Linear Module
"""
def __init__(self, in_dim, out_dim, bias=True, w_init='linear'):
"""
:param in_dim: dimension of input
:param out_dim: dimension of output
:param bias:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 collections import Order... | Munna-Manoj/Team7_TTS | Stoplinear | false | 11,729 | [
"MIT"
] | 0 | 5e2d473a2afe429023876bcc51c2ac966a4938b8 | https://github.com/Munna-Manoj/Team7_TTS/tree/5e2d473a2afe429023876bcc51c2ac966a4938b8 |
SupportEncoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.... | RussellMcGrady/Multi-head-attention-based-MetaR | SupportEncoder | false | 2,788 | [
"Apache-2.0"
] | 0 | 4e47546da35bd57ff7ab16d0fed19be31c063563 | https://github.com/RussellMcGrady/Multi-head-attention-based-MetaR/tree/4e47546da35bd57ff7ab16d0fed19be31c063563 |
NALUCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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, math as tl_math
im... | mikomel/machine-number-sense | NALUCell | false | 7,225 | [
"MIT"
] | 1 | 173b67e4f25bd8249ba4a41904d4cd4af26bae05 | https://github.com/mikomel/machine-number-sense/tree/173b67e4f25bd8249ba4a41904d4cd4af26bae05 |
Project3D | # 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._inductor.select_algorithm import extern_kernels
import triton
import 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 functools import *
assert_size_stride = torch._C._dyna... | JaviBite/TFG | Project3D | false | 2,407 | [
"MIT"
] | 0 | e406580697132f53b63a7c983daaa098af45b52c | https://github.com/JaviBite/TFG/tree/e406580697132f53b63a7c983daaa098af45b52c |
KeypointRCNNPredictorNoUpscale | import torch
import torch.nn as nn
import torch.utils.data
class KeypointRCNNPredictorNoUpscale(nn.Module):
def __init__(self, in_channels, num_keypoints):
super(KeypointRCNNPredictorNoUpscale, self).__init__()
input_features = in_channels
deconv_kernel = 4
self.kps_score_lowres =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | newstzpz/d2go | KeypointRCNNPredictorNoUpscale | false | 12,827 | [
"Apache-2.0"
] | 0 | fcd511714ec4e34040d35379cb0382b70fb58c70 | https://github.com/newstzpz/d2go/tree/fcd511714ec4e34040d35379cb0382b70fb58c70 |
WSLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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
import torch.utils.data
import torch.nn
assert_size_stride ... | shimon-c/Machine-Learning-Collection | WSLinear | false | 16,408 | [
"MIT"
] | 3,094 | ac5dcd03a40a08a8af7e1a67ade37f28cf88db43 | https://github.com/shimon-c/Machine-Learning-Collection/tree/ac5dcd03a40a08a8af7e1a67ade37f28cf88db43 |
TSA_Fusion | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.utils.data
impor... | WenlongZhang0724/mmsr | TSA_Fusion | false | 12,004 | [
"Apache-2.0"
] | 0 | 375ce9207c2b8586101406577faea285885b8009 | https://github.com/WenlongZhang0724/mmsr/tree/375ce9207c2b8586101406577faea285885b8009 |
qd | import torch
import torch.nn.functional as F
import torch.nn as nn
class qd(nn.Module):
def __init__(self, d_dim, x_dim, y_dim, z_dim):
super(qd, self).__init__()
self.fc1 = nn.Linear(z_dim, d_dim)
torch.nn.init.xavier_uniform_(self.fc1.weight)
self.fc1.bias.data.zero_()
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
import torch.nn as nn
assert_... | sautami26/DIVA | qd | false | 4,266 | [
"MIT"
] | 0 | 52af683db216cb6e2ac777597fd9ec744ce7c8f2 | https://github.com/sautami26/DIVA/tree/52af683db216cb6e2ac777597fd9ec744ce7c8f2 |
LinearNormalGamma | import torch
from torch import nn
class LinearNormalGamma(nn.Module):
def __init__(self, in_chanels, out_channels):
super().__init__()
self.linear = nn.Linear(in_chanels, out_channels * 4)
def evidence(self, x):
return torch.log(torch.exp(x) + 1)
def forward(self, x):
pr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 im... | wanzysky/evidential-deep-learning | LinearNormalGamma | false | 4,524 | [
"Apache-2.0"
] | 0 | 71ebd59ab3a4b66c38d919e8aa9ad3711a416796 | https://github.com/wanzysky/evidential-deep-learning/tree/71ebd59ab3a4b66c38d919e8aa9ad3711a416796 |
CrossEntropyLossWithOHEM | import torch
from torch import nn
def _ohem_mask(loss, ohem_ratio):
with torch.no_grad():
values, _ = torch.topk(loss.reshape(-1), int(loss.nelement() *
ohem_ratio))
mask = loss >= values[-1]
return mask.float()
class CrossEntropyLossWithOHEM(nn.Module):
def __init__(self, o... | 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... | ForrestPi/semanticSegmentation | CrossEntropyLossWithOHEM | false | 17,344 | [
"MIT"
] | 7 | 1e5519279e2a9574f09eaf91439138b74b0f860c | https://github.com/ForrestPi/semanticSegmentation/tree/1e5519279e2a9574f09eaf91439138b74b0f860c |
MaxMap | # 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.nn as nn
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.... | LLNL/fastcam | MaxMap | false | 8,428 | [
"BSD-3-Clause"
] | 25 | 99cefe37528014247319468cf05f54fef259d3bf | https://github.com/LLNL/fastcam/tree/99cefe37528014247319468cf05f54fef259d3bf |
CoordConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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... | apyrros/HCC-comorbidities | CoordConv | false | 1,465 | [
"MIT"
] | 0 | fd74fb2f1438bc741cfe6728c5cb64737bc99d68 | https://github.com/apyrros/HCC-comorbidities/tree/fd74fb2f1438bc741cfe6728c5cb64737bc99d68 |
CDilated | import torch
import torch.nn as nn
class CDilated(nn.Module):
"""
This class defines the dilated convolution.
空洞卷积
"""
def __init__(self, nIn, nOut, kSize, stride=1, d=1):
"""
:param nIn: number of input channels
:param nOut: number of output channels
:param kSize:... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | IRLSCU/siamban | CDilated | false | 2,431 | [
"Apache-2.0"
] | 0 | abb12d028e93aaee74efc5042a5bb305c7805053 | https://github.com/IRLSCU/siamban/tree/abb12d028e93aaee74efc5042a5bb305c7805053 |
SimpleFmodModule | # 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | YaronBenAtar/glow | SimpleFmodModule | false | 14,659 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
BothContextGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 ... | ESCM-summarization/ESCM-summary-evaluation | BothContextGate | false | 9,117 | [
"MIT"
] | 0 | 3780b51f0ed44cbbea3f163a871d875f1e5e9393 | https://github.com/ESCM-summarization/ESCM-summary-evaluation/tree/3780b51f0ed44cbbea3f163a871d875f1e5e9393 |
BertAttention | 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 and not
hasattr(config, 'embedding_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.... | hongyuntw/Col-KBERT | BertAttention | false | 6,828 | [
"MIT"
] | 1 | e77ce2585d228a783bf83cc1de53583aff70f7b4 | https://github.com/hongyuntw/Col-KBERT/tree/e77ce2585d228a783bf83cc1de53583aff70f7b4 |
ATLoss | import torch
import torch.nn as nn
def multilabel_categorical_crossentropy(y_pred, y_true):
y_pred = (1 - 2 * y_true) * y_pred
y_pred_neg = y_pred - y_true * 1000000000000.0
y_pred_pos = y_pred - (1 - y_true) * 1000000000000.0
zeros = torch.zeros_like(y_pred[..., :1])
y_pred_neg = torch.cat([y_pre... | 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
... | fmc123653/DeepKE | ATLoss | false | 15,371 | [
"MIT"
] | 676 | 4d30e51368681c7cb73e2ecacf9b922b441cbe99 | https://github.com/fmc123653/DeepKE/tree/4d30e51368681c7cb73e2ecacf9b922b441cbe99 |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | CFengFeng/face-nn | GlobalAvgPool2d | false | 4,918 | [
"MIT"
] | 1 | a76a689774b5101959d3c5b8a04898ae82c7bfc2 | https://github.com/CFengFeng/face-nn/tree/a76a689774b5101959d3c5b8a04898ae82c7bfc2 |
SoftTargetCrossEntropy | # 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 math as tl_math
import torch.nn as nn
... | dumpmemory/TokenLabeling | SoftTargetCrossEntropy | false | 15,272 | [
"Apache-2.0"
] | 367 | 9dbfd59aedecfe83f6f3253db4e99b82359d48ac | https://github.com/dumpmemory/TokenLabeling/tree/9dbfd59aedecfe83f6f3253db4e99b82359d48ac |
_nms | # 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.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C.... | donnyyou/centerX | _nms | false | 15,204 | [
"Apache-2.0"
] | 350 | 6e381cb669a6014d02e31a43915271237690531c | https://github.com/donnyyou/centerX/tree/6e381cb669a6014d02e31a43915271237690531c |
FinalPool | import torch
import torch.utils.data
class FinalPool(torch.nn.Module):
def __init__(self):
super(FinalPool, self).__init__()
def forward(self, input):
"""
input : Tensor of shape (batch size, T, Cin)
Outputs a Tensor of shape (batch size, Cin).
"""
return input.max(dim=1)[0]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | CoraJung/flexible-input-slu | FinalPool | false | 17,146 | [
"Apache-2.0"
] | 7 | 6a1a6bf105f1a0c07e8d483aa6da1df7a554392d | https://github.com/CoraJung/flexible-input-slu/tree/6a1a6bf105f1a0c07e8d483aa6da1df7a554392d |
HLoss | # 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... | wengong-jin/chemprop | HLoss | false | 16,702 | [
"MIT"
] | 77 | 3ad3577367d8a53f28aade0be41b56b1f25b6125 | https://github.com/wengong-jin/chemprop/tree/3ad3577367d8a53f28aade0be41b56b1f25b6125 |
LinearNormalize | # 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | DSciLab/eye_datasets | LinearNormalize | false | 2,114 | [
"MIT"
] | 0 | 4733ce8a272fef37aa9a3dab779254ab010e97b5 | https://github.com/DSciLab/eye_datasets/tree/4733ce8a272fef37aa9a3dab779254ab010e97b5 |
GeneralizedMeanPooling | import torch
from torch import Tensor
import torch.nn as nn
from torch.functional import Tensor
import torch.nn.functional as F
from torch import Tensor
from torch.nn.parameter import Parameter
def gem(x: 'Tensor', p: 'Parameter', eps: 'float'=1e-06, clamp=True) ->Tensor:
if clamp:
x = x.clamp(min=eps)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | YuxinZou/mmclassification | GeneralizedMeanPooling | false | 14,722 | [
"Apache-2.0"
] | 1,190 | 2037260ea6c98a3b115e97727e1151a1c2c32f7a | https://github.com/YuxinZou/mmclassification/tree/2037260ea6c98a3b115e97727e1151a1c2c32f7a |
Hardswish | import torch
import torch.nn as nn
import torch.nn.functional as F
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | AsakusaRinne/tensorrt_yolov5_tracker | Hardswish | false | 7,727 | [
"MIT"
] | 22 | b9a3a6fc94710e8291d6a614ed2b04cbc4c56599 | https://github.com/AsakusaRinne/tensorrt_yolov5_tracker/tree/b9a3a6fc94710e8291d6a614ed2b04cbc4c56599 |
ScaledDotProductAttentionMemory | import torch
import numpy as np
from torch import nn
class ScaledDotProductAttentionMemory(nn.Module):
"""
Scaled dot-product attention with memory
"""
def __init__(self, d_model, d_k, d_v, h, m):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionalit... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jmhessel/meshed-memory-transformer | ScaledDotProductAttentionMemory | false | 10,334 | [
"BSD-3-Clause"
] | 0 | b502da2522f2e25d602fba547ed6ebf7968857a9 | https://github.com/jmhessel/meshed-memory-transformer/tree/b502da2522f2e25d602fba547ed6ebf7968857a9 |
PatchEmbed | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._dy... | javierrodenas/clearml_javi | PatchEmbed | false | 10,364 | [
"Apache-2.0"
] | 0 | b6326104fe6a6f522223c2ac3d87468990a9e6f2 | https://github.com/javierrodenas/clearml_javi/tree/b6326104fe6a6f522223c2ac3d87468990a9e6f2 |
WSDiceLoss | # 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.utils.data
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | Atharva-Peshkar/pytorch_connectomics | WSDiceLoss | false | 13,336 | [
"MIT"
] | 99 | 8eccd9640a9a454d4df095a3529a030e58f882f5 | https://github.com/Atharva-Peshkar/pytorch_connectomics/tree/8eccd9640a9a454d4df095a3529a030e58f882f5 |
InterWeightedBCEWithLogits | # 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
from torch ... | CAMP-eXplain-AI/imba-explain | InterWeightedBCEWithLogits | false | 2,052 | [
"MIT"
] | 0 | e41b4ca5de63955cb0e925aad9599f38c5a3e973 | https://github.com/CAMP-eXplain-AI/imba-explain/tree/e41b4ca5de63955cb0e925aad9599f38c5a3e973 |
ChannelAttentionModule | import torch
import torch.nn.functional as F
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, scale=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
"""forward"""
def forward(self, x):
return x * self.scale
cl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SegmentationBLWX/sssegmentation | ChannelAttentionModule | false | 14,390 | [
"MIT"
] | 411 | 0b2e3ff5abd7b97e15ac8daf63ea214688c26541 | https://github.com/SegmentationBLWX/sssegmentation/tree/0b2e3ff5abd7b97e15ac8daf63ea214688c26541 |
Perplexity | import torch
import torch as t
import torch.nn as nn
import torch.nn.functional as F
class Perplexity(nn.Module):
def __init__(self):
super(Perplexity, self).__init__()
def forward(self, logits, target):
"""
:param logits: tensor with shape of [batch_size, seq_len, input_size]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | kefirski/contiguous-succotash | Perplexity | false | 15,804 | [
"MIT"
] | 57 | 7497efd1392693248ed98805dcdbbf5dc125afc2 | https://github.com/kefirski/contiguous-succotash/tree/7497efd1392693248ed98805dcdbbf5dc125afc2 |
DepthwiseSeparableConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.cuda
import torc... | andy840314/QANet-pytorch- | DepthwiseSeparableConv | false | 14,851 | [
"MIT"
] | 92 | 3c11e2d7139e040eee90dd24b673eb1039957cae | https://github.com/andy840314/QANet-pytorch-/tree/3c11e2d7139e040eee90dd24b673eb1039957cae |
LearnedPositionalEmbeddings | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class LearnedPositionalEmbeddings(Module):
"""
<a id="LearnedPositionalEmbeddings">
## Add parameterized positional encodings
</a>
This adds learned positional em... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
assert_size_stride... | ppvalluri09/annotated_deep_learning_paper_implementations | LearnedPositionalEmbeddings | false | 11,064 | [
"MIT"
] | 0 | 387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 | https://github.com/ppvalluri09/annotated_deep_learning_paper_implementations/tree/387b6dfd1ef1f6d295e9394c24b5798071d9a3e4 |
AttLayer | import torch
import torch.nn as nn
import torch.nn.functional as fn
class AttLayer(nn.Module):
"""Calculate the attention signal(weight) according the input tensor.
Args:
infeatures (torch.FloatTensor): A 3D input tensor with shape of[batch_size, M, embed_dim].
Returns:
torch.FloatTensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Ahren09/RecBole | AttLayer | false | 1,928 | [
"MIT"
] | 0 | b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 | https://github.com/Ahren09/RecBole/tree/b3921818dfbc1b81f9eda8d5e9f05bc9d9114089 |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
super(BertLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Vitvicky/mrc-for-flat-nested-ner | BertAttention | false | 18,055 | [
"Apache-2.0"
] | 9 | 37099625e3002c334884fe982a6476e2c783da63 | https://github.com/Vitvicky/mrc-for-flat-nested-ner/tree/37099625e3002c334884fe982a6476e2c783da63 |
ShuffleCatChunk | # 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... | tony23545/yolact_edge | ShuffleCatChunk | false | 10,916 | [
"MIT"
] | 0 | 11840512ab46f22dce6aea37a7823110175adffa | https://github.com/tony23545/yolact_edge/tree/11840512ab46f22dce6aea37a7823110175adffa |
ConstractiveThresholdHingeLoss | # 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
import torch.nn as nn
assert... | tommy90191/Find_Tiny_but_Important_Image_Changes | ConstractiveThresholdHingeLoss | false | 4,448 | [
"MIT"
] | 0 | 429d679606f96f32db4cddf167a9cfb963d3df26 | https://github.com/tommy90191/Find_Tiny_but_Important_Image_Changes/tree/429d679606f96f32db4cddf167a9cfb963d3df26 |
SpatialGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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_... | BJTU-MIMO/Channel_estimation_MRDN | SpatialGate | false | 139 | [
"MIT"
] | 0 | f41972998a5403c901bc3e5d68d4acd05e9a7f6c | https://github.com/BJTU-MIMO/Channel_estimation_MRDN/tree/f41972998a5403c901bc3e5d68d4acd05e9a7f6c |
SimpleNormModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleNormModule(torch.nn.Module):
def __init__(self, *args, **kwargs):
super(SimpleNormModule, self).__init__()
self.args = args
self.kwargs = kwargs
def forward(self, tensor):
return torch.norm(tensor, *s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torc... | andreas-hommel/glow | SimpleNormModule | false | 3,345 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
Accuracy | import torch
import torch.nn as nn
def accuracy(logits, labels, ignore_index: 'int'=-100):
with torch.no_grad():
valid_mask = labels != ignore_index
predictions = logits.float().argmax(-1)
correct = (predictions == labels) * valid_mask
return correct.sum().float() / valid_mask.sum(... | 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... | StephanHeijl/tape | Accuracy | false | 2,865 | [
"BSD-3-Clause"
] | 0 | ec631ca53217686605477cf31af4fb8846ff660f | https://github.com/StephanHeijl/tape/tree/ec631ca53217686605477cf31af4fb8846ff660f |
PatchEmbed | import torch
import torch.nn as nn
from typing import Optional
class PatchEmbed(nn.Module):
def __init__(self, img_size: 'int'=224, patch_size: 'int'=16, stride:
'int'=None, in_channels: 'int'=3, embed_dim: 'int'=768, multi_conv:
'bool'=False, norm_layer: 'Optional'=nn.LayerNorm):
super(P... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Justin900429/vision-transformer | PatchEmbed | false | 5,446 | [
"MIT"
] | 1 | e149092efbb83c166449944137db0ee5200f9325 | https://github.com/Justin900429/vision-transformer/tree/e149092efbb83c166449944137db0ee5200f9325 |
Attention | import torch
from torch import nn
from torch import einsum
class Attention(nn.Module):
def __init__(self, dim_in, dim_out, dim_inner, causal=False):
super().__init__()
self.scale = dim_inner ** -0.5
self.causal = causal
self.to_qkv = nn.Linear(dim_in, dim_inner * 3, bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | cpmolnar/gMLP-Disaster-Tweets | Attention | false | 9,914 | [
"MIT"
] | 0 | 7b13651c2260bc112d706a99466c069fb9348205 | https://github.com/cpmolnar/gMLP-Disaster-Tweets/tree/7b13651c2260bc112d706a99466c069fb9348205 |
ClippedLinearQuantization | import torch
from torch.optim.lr_scheduler import *
import torch.optim
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.onnx
def linear_dequantize(input, scale_factor, inplace=False):
if inplace:
input.div_(scale_factor)
return input
return input / scale_fact... | 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.optim.lr_schedule... | Chih-Ling-Hsu/distiller | ClippedLinearQuantization | false | 13,512 | [
"Apache-2.0"
] | 94 | 33d1697298c6e3a7f7bfa615741fd0cda61d2794 | https://github.com/Chih-Ling-Hsu/distiller/tree/33d1697298c6e3a7f7bfa615741fd0cda61d2794 |
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
from torch._inductor.runtime.... | MiscellaneousStuff/tlol-py | Model | false | 17,723 | [
"MIT"
] | 4 | 60477b4f794daa12930d7bbec4cf692bab426a33 | https://github.com/MiscellaneousStuff/tlol-py/tree/60477b4f794daa12930d7bbec4cf692bab426a33 |
SeparableConv2d | import torch
class SeparableConv2d(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, dilation=1, bias=False):
super(SeparableConv2d, self).__init__()
self.conv1 = torch.nn.Conv2d(in_channels, in_channels, kernel_size,
stride, p... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
de... | Pumpkin123709/LBEC | SeparableConv2d | false | 950 | [
"MIT"
] | 0 | 18661faa35769f731847e0226ff601754e134668 | https://github.com/Pumpkin123709/LBEC/tree/18661faa35769f731847e0226ff601754e134668 |
Vec2ArousalNet | import torch
import torch.utils.data
class Vec2ArousalNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super(Vec2ArousalNet, self).__init__()
self.layer_1 = torch.nn.Linear(D_in, H)
self.layer_2 = torch.nn.Linear(H, D_out)
def forward(self, x):
h = self.layer_1(x).cl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
asser... | jackvandrunen/hackuci18 | Vec2ArousalNet | false | 6,909 | [
"BSD-2-Clause"
] | 1 | fff3fd7d116a6a83f19229a17377b84922145ebd | https://github.com/jackvandrunen/hackuci18/tree/fff3fd7d116a6a83f19229a17377b84922145ebd |
PreNet | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.optim
import torch.distributions
class PreNet(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Rexiome/NATSpeech | PreNet | false | 14,296 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
DecoderBlock | import torch
from torch import nn
class DecoderBlock(nn.Module):
"""
A block in decoder that makes use of sentence representation
TODO: block is a boring name; there gotta be a more creative name for this step
"""
def __init__(self, d_model, dropout=0.1, mode='add_attn'):
super().__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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | Lev-etd/rtg_streamlit | DecoderBlock | false | 770 | [
"Apache-2.0"
] | 0 | 7cab50e80f424601dbed0b14e1e121144581244c | https://github.com/Lev-etd/rtg_streamlit/tree/7cab50e80f424601dbed0b14e1e121144581244c |
GroupSort | import torch
from torch import nn
def process_group_size(x, group_size, axis=-1):
size = list(x.size())
num_channels = size[axis]
if num_channels % group_size:
raise ValueError(
'number of features({}) is not a multiple of group_size({})'.
format(num_channels, num_units))
... | 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... | dattientran/attorch | GroupSort | false | 12,390 | [
"MIT"
] | 0 | 469b225846c6d8a7d833ebac19d040c7a407a0ff | https://github.com/dattientran/attorch/tree/469b225846c6d8a7d833ebac19d040c7a407a0ff |
PaddedMaxPool2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class PaddedMaxPool2d(nn.Module):
""" Maxpool layer with a replicating padding.
Args:
kernel_size (int or tuple): Kernel size for maxpooling
stride (int or tuple, optional): The stride of the window; Default ``kernel_size``
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | IrisDinge/YoloV3_DOTA | PaddedMaxPool2d | false | 5,359 | [
"MIT"
] | 1 | cdfe6375a2323e9ee162e50a46478d8a66529e6c | https://github.com/IrisDinge/YoloV3_DOTA/tree/cdfe6375a2323e9ee162e50a46478d8a66529e6c |
ThreeLayerCNN | import torch
import torch.utils.data
class ThreeLayerCNN(torch.nn.Module):
"""
Input: 128x128 face image (eye aligned).
Output: 1-D tensor with 2 elements. Used for binary classification.
Parameters:
Number of conv layers: 3
Number of fully connected layers: 2
"""
def __init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
asser... | Bhaskers-Blu-Org1/Trusted-ML-Pipelines | ThreeLayerCNN | false | 7,783 | [
"Apache-2.0"
] | 13 | 3805a2e72f73cef318e1992eee70aeb319b06d1a | https://github.com/Bhaskers-Blu-Org1/Trusted-ML-Pipelines/tree/3805a2e72f73cef318e1992eee70aeb319b06d1a |
TransformerEncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dongyan007/Pretrained-IPT-main-master | TransformerEncoderLayer | false | 1,875 | [
"Apache-2.0"
] | 0 | 7ed47002373e11bd57b7904f6935acdfba1e44ff | https://github.com/dongyan007/Pretrained-IPT-main-master/tree/7ed47002373e11bd57b7904f6935acdfba1e44ff |
SpatialPyramidPooling | import torch
import torch.nn as nn
class SpatialPyramidPooling(nn.Module):
def __init__(self, pool_sizes=[5, 9, 13]):
super(SpatialPyramidPooling, self).__init__()
self.maxpools = nn.ModuleList([nn.MaxPool2d(pool_size, 1, pool_size //
2) for pool_size in pool_sizes])
def forward(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Arcofcosmos/MyYolov4_Pytorch | SpatialPyramidPooling | false | 11,240 | [
"MIT"
] | 0 | 14c445503d0fc69b8a8b64ecdc87256ac4c1fce1 | https://github.com/Arcofcosmos/MyYolov4_Pytorch/tree/14c445503d0fc69b8a8b64ecdc87256ac4c1fce1 |
Down2d | import torch
import torch.nn as nn
class Down2d(nn.Module):
"""docstring for Down2d."""
def __init__(self, in_channel, out_channel, kernel, stride, padding):
super(Down2d, self).__init__()
self.c1 = nn.Conv2d(in_channel, out_channel, kernel_size=kernel,
stride=stride, padding=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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Shimamura-Lab-SU/SGV | Down2d | false | 2,830 | [
"MIT"
] | 0 | 8df3c314532528b8597c5dbb28bdfb23155bee82 | https://github.com/Shimamura-Lab-SU/SGV/tree/8df3c314532528b8597c5dbb28bdfb23155bee82 |
MultiHeadedAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | hengwei-chan/protein_transformer | MultiHeadedAttention | false | 15,521 | [
"BSD-3-Clause"
] | 77 | 988bb0fcbb94b37e5a02071bd345ea073ad605f8 | https://github.com/hengwei-chan/protein_transformer/tree/988bb0fcbb94b37e5a02071bd345ea073ad605f8 |
BoundSqrt | from _paritybench_helpers import _mock_config
import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from numbers import Number
from torch.nn import MSELoss
def isnan(x):
if isinstance(x, Patches):
return False
return torch.isnan(x).any()
class Perturbation... | 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 math
import numpy as np
import torch.nn as nn
import torch.nn.functional... | Mahoumaru/auto_LiRPA | BoundSqrt | false | 13,224 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 |
BCE_disc_sm_v4 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BCE_disc_sm_v4(nn.Module):
def __init__(self, weight_list=None, lb_sm=0.2):
super(BCE_disc_sm_v4, self).__init__()
self.weight_list = weight_list
self.lb_sm = lb_sm
def forward(self, x, labels):
assert (... | 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... | Sampson-Lee/SIB-Net | BCE_disc_sm_v4 | false | 2,833 | [
"MIT"
] | 0 | 650399082e9237327fa38168ccfc7d48153a1db5 | https://github.com/Sampson-Lee/SIB-Net/tree/650399082e9237327fa38168ccfc7d48153a1db5 |
FeatureAttentionLayer | import torch
import torch.nn as nn
class FeatureAttentionLayer(nn.Module):
"""Single Graph Feature/Spatial Attention Layer
:param n_features: Number of input features/nodes
:param window_size: length of the input sequence
:param dropout: percentage of nodes to dropout
:param alpha: negative slope ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | lawson-source/mtad-gat-pytorch | FeatureAttentionLayer | false | 15,883 | [
"MIT"
] | 93 | 9e671ea99dedd82ac55f53e53af1d1b56c13ebff | https://github.com/lawson-source/mtad-gat-pytorch/tree/9e671ea99dedd82ac55f53e53af1d1b56c13ebff |
PCBActiv | import math
import torch
import torch.nn as nn
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasattr(m, 'weight'):
if init_type == 'gaussian':
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | marcelsan/Deep-HdrReconstruction | PCBActiv | false | 16,015 | [
"BSD-3-Clause"
] | 80 | 7cb0d93938baa6fbe029116451a661c18dfba49e | https://github.com/marcelsan/Deep-HdrReconstruction/tree/7cb0d93938baa6fbe029116451a661c18dfba49e |
PositionalEncoding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | hankyul2/ImageClassification | PositionalEncoding | false | 6,782 | [
"Apache-2.0"
] | 1 | c4df6bf3dc1ee804f9885d586aa581ebb4d7ca05 | https://github.com/hankyul2/ImageClassification/tree/c4df6bf3dc1ee804f9885d586aa581ebb4d7ca05 |
HardSwish | import torch
import torch.nn as nn
class HardSwish(nn.Module):
def __init__(self, inplace=False):
super(HardSwish, self).__init__()
self.act = nn.ReLU6(inplace)
"""forward"""
def forward(self, x):
return x * self.act(x + 3) / 6
def get_inputs():
return [torch.rand([4, 4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DetectionBLWX/WSDDN.pytorch | HardSwish | false | 17,212 | [
"MIT"
] | 7 | 05020d9d0445af90ba0af3f095aa12b18e3da7d2 | https://github.com/DetectionBLWX/WSDDN.pytorch/tree/05020d9d0445af90ba0af3f095aa12b18e3da7d2 |
RadialPredictionLayer | # 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_... | Monkso/RPL-Softmax_RoadSigns | RadialPredictionLayer | false | 851 | [
"MIT"
] | 0 | 3df929d779ff02ec796e717659943bb46311ba0f | https://github.com/Monkso/RPL-Softmax_RoadSigns/tree/3df929d779ff02ec796e717659943bb46311ba0f |
LocalContextNorm | import math
import torch
import torch.utils.data
from torchvision.transforms import functional as F
from torch import nn
from torch.nn import functional as F
class LocalContextNorm(nn.Module):
def __init__(self, num_features, channels_per_group=2, window_size=(227,
227), eps=1e-05):
super(LocalCo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dyn... | pjh4993/FCOS | LocalContextNorm | false | 4,159 | [
"BSD-2-Clause"
] | 0 | 27f79e3fd3f5043796450b9a2201b42c744fd3df | https://github.com/pjh4993/FCOS/tree/27f79e3fd3f5043796450b9a2201b42c744fd3df |
NumericLabelEncoder | # 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 abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | hannahaih/hummingbird | NumericLabelEncoder | false | 6,781 | [
"MIT"
] | 1 | b8ec670b3c90ec7e87d3ae4a2b268075bd5eae65 | https://github.com/hannahaih/hummingbird/tree/b8ec670b3c90ec7e87d3ae4a2b268075bd5eae65 |
CosineBasisLinear | import torch
import numpy as np
from torch import nn
def cosine_basis_functions(x, n_basis_functions=64):
"""Cosine basis functions used to embed quantile thresholds.
Args:
x (torch.Tensor): Input.
n_basis_functions (int): Number of cosine basis functions.
Returns:
ndarray: Embed... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | lin826/pfrl | CosineBasisLinear | false | 12,722 | [
"MIT"
] | 0 | 62d7f13b854f1879211a386fd870a7db982cc8ec | https://github.com/lin826/pfrl/tree/62d7f13b854f1879211a386fd870a7db982cc8ec |
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
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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/super-res | Discriminator2d | false | 6,199 | [
"MIT"
] | 1 | 2a93a20d65c570a5398caef65957fb612c3581c8 | https://github.com/amirDahari1/super-res/tree/2a93a20d65c570a5398caef65957fb612c3581c8 |
PA_UP | import torch
import torch.nn as nn
import torch.nn.functional as F
class PA(nn.Module):
"""PA is pixel attention"""
def __init__(self, nf):
super(PA, self).__init__()
self.conv = nn.Conv2d(nf, nf, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
y = self.conv(x)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | YingqiLiulll/scrips_for_SR | PA_UP | false | 1,288 | [
"MIT"
] | 0 | 04fa6fdaf157e913d3e2521cd80315a10a2ccedc | https://github.com/YingqiLiulll/scrips_for_SR/tree/04fa6fdaf157e913d3e2521cd80315a10a2ccedc |
GNNExplainerProbe | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 math as tl_math
import math
assert_size_stride = torch._C._dynamo.guards.assert_size_stri... | djz233/GraphMask | GNNExplainerProbe | false | 12,291 | [
"MIT"
] | 0 | 4b699a1685f0d26973bb90cd75b09d74726cdc2f | https://github.com/djz233/GraphMask/tree/4b699a1685f0d26973bb90cd75b09d74726cdc2f |
DynamicsModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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
assert_size_stride ... | numahha/wmopo | DynamicsModel | false | 7,360 | [
"MIT"
] | 1 | 1557dab2e8168c1f2e53ffbc435b4000680f1d28 | https://github.com/numahha/wmopo/tree/1557dab2e8168c1f2e53ffbc435b4000680f1d28 |
Mean_One | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class Linear(nn.Module):
def __init__(self, options, weights=None):
super(Linear, self).__init__()
self.n_in = options['n_in']
self.n_out = options['n_out']
self.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.triton_helpers import libdevice
import torch.nn as ... | KaiQiangSong/joint_parse_summ | Mean_One | false | 8,767 | [
"BSD-3-Clause"
] | 29 | 5d4a40d9a681bc8b06c847643d810846f3867216 | https://github.com/KaiQiangSong/joint_parse_summ/tree/5d4a40d9a681bc8b06c847643d810846f3867216 |
TwoLayerCNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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_... | Changxi-Liu/EditDistance | TwoLayerCNN | false | 11,288 | [
"MIT"
] | 0 | 925f43c3cf0bd6fdd8f5f0e919ac49916a020459 | https://github.com/Changxi-Liu/EditDistance/tree/925f43c3cf0bd6fdd8f5f0e919ac49916a020459 |
CRF | # 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | jbogensperger/DRUG_CROSSNER | CRF | false | 10,224 | [
"MIT"
] | 0 | c82fc4ce6fd6229b48d28bafffe38f5ea3dcd6aa | https://github.com/jbogensperger/DRUG_CROSSNER/tree/c82fc4ce6fd6229b48d28bafffe38f5ea3dcd6aa |
RMSE | # 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
from torch.optim import *
assert_size_stride = torch._C._... | kakaxi314/GuideNet | RMSE | false | 15,778 | [
"MIT"
] | 142 | 9f53b4086d707e94d48a47bbac7dd87aaba9fdea | https://github.com/kakaxi314/GuideNet/tree/9f53b4086d707e94d48a47bbac7dd87aaba9fdea |
MetaLayerNorm | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----
Objects inherited f... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | RisingStockPrices/multi-shape-siren | MetaLayerNorm | false | 2,771 | [
"MIT"
] | 0 | f78d6deb94660fd11ef0caf55f88095b74d3e223 | https://github.com/RisingStockPrices/multi-shape-siren/tree/f78d6deb94660fd11ef0caf55f88095b74d3e223 |
GCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.nn import Module
import math
import numpy as np
import torch.nn as nn... | cjx96/CDRIB | GCN | false | 6,459 | [
"MIT"
] | 1 | e0d2d2b70ec195a76b479b94fb7758d286350c39 | https://github.com/cjx96/CDRIB/tree/e0d2d2b70ec195a76b479b94fb7758d286350c39 |
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 import triton_helpers
import torch.nn as nn
import ... | Altriaex/d4rl_evaluations | Critic | false | 8,956 | [
"Apache-2.0"
] | 0 | ceb34c04e98af9332c6338a1414c0c2aa5fea68b | https://github.com/Altriaex/d4rl_evaluations/tree/ceb34c04e98af9332c6338a1414c0c2aa5fea68b |
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