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 |
|---|---|---|---|---|---|---|---|---|---|---|
DecoderLayer | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0... | 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.... | macg0406/Transformer | DecoderLayer | false | 12,763 | [
"Apache-2.0"
] | 0 | 8c747a6e9f108c63ecc600bf14cde6827b438172 | https://github.com/macg0406/Transformer/tree/8c747a6e9f108c63ecc600bf14cde6827b438172 |
Invertible1x1Conv | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import torch.utils.data
import torch.nn
class Invertible1x1Conv(torch.nn.Module):
"""
The layer outputs both the convolution, and the log determinant
of its weight matrix. If reverse=True it does convolution with
inverse... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.functional as F
from torch.autograd import Variable
import torch... | malithj/TensorRT | Invertible1x1Conv | false | 12,764 | [
"Apache-2.0"
] | 0 | 48605d4b5673df89110cf41249ad007259d7c34a | https://github.com/malithj/TensorRT/tree/48605d4b5673df89110cf41249ad007259d7c34a |
ConcatAttention | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class ConcatAttention(nn.Module):
"""
Concatenate attention layer.
"""
def __init__(self, input_size_encoder, input_size_decoder, hidden_size,
num_labels, **kwargs):
"""
Args:
input_size_e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | krishnamrith12/DCST | ConcatAttention | false | 12,765 | [
"MIT"
] | 0 | 7ba956d7e648aaeb25816ccfc709106db9293270 | https://github.com/krishnamrith12/DCST/tree/7ba956d7e648aaeb25816ccfc709106db9293270 |
VectorQuantizer | import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
class VectorQuantizer(nn.Module):
"""
Reference:
[1] https://github.com/deepmind/sonnet/blob/v2/sonnet/src/nets/vqvae.py
"""
def __init__(self, num_embeddings: 'int', embedding_dim: 'int', beta:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | mateoIdemia/PyTorch-VAE | VectorQuantizer | false | 12,766 | [
"Apache-2.0"
] | 0 | b485924182e62843aae1955fcaf0886ac8492295 | https://github.com/mateoIdemia/PyTorch-VAE/tree/b485924182e62843aae1955fcaf0886ac8492295 |
folder | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class folder(nn.Module):
def __init__(self):
super().__init__()
def forward(self, feature_map):
N, _, H, W = feature_map.size()
feature_map = F.unfold(feature_map, kernel_size=3, padding=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 import nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | memesoo99/AdelaiDet | folder | false | 12,767 | [
"BSD-2-Clause"
] | 0 | 1e9cdfee3d1c35dcb6b4e04fdcc966115f34c71f | https://github.com/memesoo99/AdelaiDet/tree/1e9cdfee3d1c35dcb6b4e04fdcc966115f34c71f |
PredictionConvolutions | import torch
from torch import nn
from itertools import product as product
import torch.optim
import torch.utils.data
class PredictionConvolutions(nn.Module):
"""
Convolutions to predict class scores and bounding boxes using lower and higher-level feature maps.
The bounding boxes (locations) are predicte... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 itertools import product as product
import torch.optim... | gigajet/ICDAR-2019-SROIE | PredictionConvolutions | false | 12,768 | [
"MIT"
] | 0 | 62dd3ecc90600c0bdf8ceece796fc4e555d3bd16 | https://github.com/gigajet/ICDAR-2019-SROIE/tree/62dd3ecc90600c0bdf8ceece796fc4e555d3bd16 |
LinearBlock | import torch
from torch import nn
from scipy.stats import truncnorm
def truncated_normal_(tensor, mean=0.0, std=1.0):
values = truncnorm.rvs(-2, 2, size=tensor.shape)
values = mean + std * values
tensor.copy_(torch.from_numpy(values))
return tensor
def fc_init_(module):
if hasattr(module, 'weigh... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | joemzhao/learn2learn | LinearBlock | false | 12,769 | [
"MIT"
] | 0 | e161e0a9e0de513d64315c4ceaf2d8608e4cef4d | https://github.com/joemzhao/learn2learn/tree/e161e0a9e0de513d64315c4ceaf2d8608e4cef4d |
DebertaSelfOutput | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.utils.checkpoint
def get_mask(input, local_context):
if not isinstance(local_context, DropoutContext):
dropout = local_context
mask = None
else:
dropout = local_context.dropout
dropout ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Clemens123/transformers | DebertaSelfOutput | false | 12,770 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self, embed_size, hidden_size):
super(CNN, self).__init__()
self.hidden_size = hidden_size
self.conv2d = nn.Conv2d(embed_size, hidden_size, (1, 5), bias=True)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | melaniezhang/cs224n-final-proj | CNN | false | 12,771 | [
"MIT"
] | 0 | a012759e8caf4d585421d78c07125fa3696fda4e | https://github.com/melaniezhang/cs224n-final-proj/tree/a012759e8caf4d585421d78c07125fa3696fda4e |
MultiHeadSelfAttention | import torch
import torch.nn as nn
class MultiHeadSelfAttention(nn.Module):
def __init__(self, input_size, num_heads, drop_prob=0.1):
super(MultiHeadSelfAttention, self).__init__()
self.drop_prob = drop_prob
self.multihead_attention = nn.MultiheadAttention(input_size, num_heads)
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.... | melaniezhang/cs224n-final-proj | MultiHeadSelfAttention | false | 12,772 | [
"MIT"
] | 0 | a012759e8caf4d585421d78c07125fa3696fda4e | https://github.com/melaniezhang/cs224n-final-proj/tree/a012759e8caf4d585421d78c07125fa3696fda4e |
GAT | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, 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.... | markheimann/fgc | GAT | false | 12,773 | [
"MIT"
] | 0 | 909d4f0a84c9b61a8030f9f3f50b17f143576007 | https://github.com/markheimann/fgc/tree/909d4f0a84c9b61a8030f9f3f50b17f143576007 |
MyNeural | import torch
import torch.nn
import torch.nn.functional as Functional
class MyNeural(torch.nn.Module):
def __init__(self, columns):
super(MyNeural, self).__init__()
self.f1 = torch.nn.Linear(columns, 32)
self.f2 = torch.nn.Linear(32, 16)
self.f3 = torch.nn.Linear(16, 1)
def f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
assert_size_s... | med-boubekri/Covid-Fact-Checker | MyNeural | false | 12,774 | [
"MIT"
] | 0 | 7869bcd830f33aefe4afeb5b75808f479e8094f2 | https://github.com/med-boubekri/Covid-Fact-Checker/tree/7869bcd830f33aefe4afeb5b75808f479e8094f2 |
BeitSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
import torch.utils.checkpoint
class BeitRelativePositionBias(nn.Module):
def __init__(self, config, window_size):
super().__init__()
self.window_size = window_size
self.num_relative_distance = (2 *... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Clemens123/transformers | BeitSelfAttention | false | 12,775 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
EncoderLayer | import math
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data.distributed
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
class FeedF... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | matatabinoneko/densecap | EncoderLayer | false | 12,776 | [
"BSD-3-Clause"
] | 0 | 723d9c2cfd3f16b2eb7584cc7cb0aaef973854dd | https://github.com/matatabinoneko/densecap/tree/723d9c2cfd3f16b2eb7584cc7cb0aaef973854dd |
SE | import torch
from torch import nn
class SE(nn.Module):
def __init__(self, channels, se_ratio):
super(SE, self).__init__()
inter_channels = max(1, int(channels * se_ratio))
self.conv1 = nn.Conv2d(channels, inter_channels, (1, 1))
self.silu = nn.SiLU(inplace=True)
self.conv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | mengzhu0308/EfficientNetV2-PyTorch | SE | false | 12,777 | [
"Apache-2.0"
] | 0 | b9946a4372849d9231a044dcbf697ae17008b467 | https://github.com/mengzhu0308/EfficientNetV2-PyTorch/tree/b9946a4372849d9231a044dcbf697ae17008b467 |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
"""
Fully-connected classifier for MNIST.
"""
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(28 * 28, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 10)
def forward(self, x):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | mateuszjurewicz/Copilot | Net | false | 12,778 | [
"MIT"
] | 0 | ccb3eb2755c7cbb5bb035567aa7e73c1d767147a | https://github.com/mateuszjurewicz/Copilot/tree/ccb3eb2755c7cbb5bb035567aa7e73c1d767147a |
TreeCRF | import torch
import numpy as np
import torch.nn as nn
from torch.nn.parameter import Parameter
def logdet(x):
"""
Args:
x: 2D positive semidefinite matrix.
Returns: log determinant of x
"""
None
None
u_chol = x.potrf()
return torch.sum(torch.log(u_chol.diag())) * 2
class B... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_strid... | krishnamrith12/DCST | TreeCRF | false | 12,779 | [
"MIT"
] | 0 | 7ba956d7e648aaeb25816ccfc709106db9293270 | https://github.com/krishnamrith12/DCST/tree/7ba956d7e648aaeb25816ccfc709106db9293270 |
SpatialAttentionModule | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.onnx
def conv1x1(in_planes, out_planes, bias=False):
"""1x1 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1,
padding=0, bias=bias)
class SpatialAttentionModule(nn.Module):
def __... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | mattrent/AttnGAN | SpatialAttentionModule | false | 12,780 | [
"MIT"
] | 0 | 913a34d1324508a09c18875d41c76baec47cbc6d | https://github.com/mattrent/AttnGAN/tree/913a34d1324508a09c18875d41c76baec47cbc6d |
Swish | import torch
import torch.nn as nn
class Swish(nn.Module):
def forward(self, x):
return x.mul_(torch.sigmoid(x))
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
@triton.jit
def triton_poi_fused_mul_sigmoid_0(in_pt... | minhduc0711/labelImg | Swish | false | 12,781 | [
"MIT"
] | 0 | 5030721bb6a59424bfed1d7c09b56e01d08662a1 | https://github.com/minhduc0711/labelImg/tree/5030721bb6a59424bfed1d7c09b56e01d08662a1 |
Mish | import torch
import torch.nn.functional as F
import torch.nn as nn
class Mish(nn.Module):
def forward(self, x):
return x.mul_(F.softplus(x).tanh())
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.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | minhduc0711/labelImg | Mish | false | 12,782 | [
"MIT"
] | 0 | 5030721bb6a59424bfed1d7c09b56e01d08662a1 | https://github.com/minhduc0711/labelImg/tree/5030721bb6a59424bfed1d7c09b56e01d08662a1 |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | mcao516/SSKD-TinyBERT | BertAttention | false | 12,783 | [
"Apache-2.0"
] | 0 | d862002e03df5cb54a80657e41a77f1b6f7732d9 | https://github.com/mcao516/SSKD-TinyBERT/tree/d862002e03df5cb54a80657e41a77f1b6f7732d9 |
ScaledL2Norm | import torch
import torch.onnx
import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledL2Norm(nn.Module):
def __init__(self, in_channels, initial_scale):
super(ScaledL2Norm, self).__init__()
self.in_channels = in_channels
self.scale = nn.Parameter(torch.Tensor(in_ch... | 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.onnx
import tor... | mirecta/pytorch-ssd | ScaledL2Norm | false | 12,784 | [
"MIT"
] | 0 | 360f31bfff12f2954c9166dc78df038334a01c53 | https://github.com/mirecta/pytorch-ssd/tree/360f31bfff12f2954c9166dc78df038334a01c53 |
NRelu | import torch
import torch.utils.data
import torch.nn as nn
import torch.optim
import torch.backends.cudnn
import torch.nn.functional as F
class NRelu(nn.Module):
"""
-max(-x,0)
Parameters
----------
Input shape: (N, C, W, H)
Output shape: (N, C * W * H)
"""
def __init__(self, inplace)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
import torch.optim
import torch.backends.cu... | minhtannguyen/pytorch_shake_shake | NRelu | false | 12,785 | [
"MIT"
] | 0 | d7f245d8d8b9e81a6020aadb438ffeae6d5593c2 | https://github.com/minhtannguyen/pytorch_shake_shake/tree/d7f245d8d8b9e81a6020aadb438ffeae6d5593c2 |
BiDAFSelfAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
def masked_softmax(logits, mask, dim=-1, log_softmax=False):
"""Take the softmax of `logits` over given dimension, and set
entries to 0 wherever `mask` is 0.
Args:
logits (torch.Tensor): Inputs to the softmax function.
mas... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | melaniezhang/cs224n-final-proj | BiDAFSelfAttention | false | 12,786 | [
"MIT"
] | 0 | a012759e8caf4d585421d78c07125fa3696fda4e | https://github.com/melaniezhang/cs224n-final-proj/tree/a012759e8caf4d585421d78c07125fa3696fda4e |
MultiHead | import math
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data.distributed
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
class Atten... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | matatabinoneko/densecap | MultiHead | false | 12,787 | [
"BSD-3-Clause"
] | 0 | 723d9c2cfd3f16b2eb7584cc7cb0aaef973854dd | https://github.com/matatabinoneko/densecap/tree/723d9c2cfd3f16b2eb7584cc7cb0aaef973854dd |
Learned_Aggregation_Layer | import torch
import torch.nn as nn
import torch.utils.checkpoint
class Learned_Aggregation_Layer(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... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | mengxinpku/deit | Learned_Aggregation_Layer | false | 12,788 | [
"Apache-2.0"
] | 0 | 5b61a1ec0a4e73579f41ebdc3d34f319e5d19d14 | https://github.com/mengxinpku/deit/tree/5b61a1ec0a4e73579f41ebdc3d34f319e5d19d14 |
MCRMSE | import torch
from torch import nn
class MCRMSE(nn.Module):
def __init__(self, num_scored=3, eps=1e-08):
super().__init__()
self.mse = nn.MSELoss()
self.num_scored = num_scored
self.eps = eps
def forward(self, outputs, targets):
score = 0
for idx in range(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.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | mohsinkhn/standford-covid-vaccine-kaggle | MCRMSE | false | 12,789 | [
"MIT"
] | 0 | fc1e160a6ee67d1ca21dfec3da4dc4863e6bbdba | https://github.com/mohsinkhn/standford-covid-vaccine-kaggle/tree/fc1e160a6ee67d1ca21dfec3da4dc4863e6bbdba |
BiAAttention | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class BiAAttention(nn.Module):
"""
Bi-Affine attention layer.
"""
def __init__(self, input_size_encoder, input_size_decoder, num_labels,
biaffine=True, **kwargs):
"""
Args:
input_size_enco... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_strid... | krishnamrith12/DCST | BiAAttention | false | 12,790 | [
"MIT"
] | 0 | 7ba956d7e648aaeb25816ccfc709106db9293270 | https://github.com/krishnamrith12/DCST/tree/7ba956d7e648aaeb25816ccfc709106db9293270 |
FCMinibatchStd | import math
import torch
from torch import nn
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest_dim, bias.shape[0]),
ne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | mkleshchenok/dlcourse_2021_p1_final_project | FCMinibatchStd | false | 12,791 | [
"MIT"
] | 0 | 1dd4f2e3dccc4604aa98982bf9377273ab4783c1 | https://github.com/mkleshchenok/dlcourse_2021_p1_final_project/tree/1dd4f2e3dccc4604aa98982bf9377273ab4783c1 |
BertPooler | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class BertPooler(nn.Module):
def __init__(self, config, recurs=None):
super(BertPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | mcao516/SSKD-TinyBERT | BertPooler | false | 12,792 | [
"Apache-2.0"
] | 0 | d862002e03df5cb54a80657e41a77f1b6f7732d9 | https://github.com/mcao516/SSKD-TinyBERT/tree/d862002e03df5cb54a80657e41a77f1b6f7732d9 |
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):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | monimoyd/project_deep_reinforcement_learning_collaboration_competition | Actor | false | 12,793 | [
"MIT"
] | 0 | 3782abb839b671ea53ece1435a4d481d7871cd39 | https://github.com/monimoyd/project_deep_reinforcement_learning_collaboration_competition/tree/3782abb839b671ea53ece1435a4d481d7871cd39 |
Transition | import torch
import torch.nn as nn
class Transition(nn.Module):
def __init__(self, z_dim, hidden_dim):
super(Transition, self).__init__()
self.z_to_hidden = nn.Linear(z_dim, hidden_dim)
self.hidden_to_hidden = nn.Linear(hidden_dim, hidden_dim)
self.hidden_to_loc = nn.Linear(hidden... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | morimo27182/DeepKalmanFilter | Transition | false | 12,794 | [
"MIT"
] | 0 | 5d78d2e700fdc24f2a5cfa2877ecdcfc8218c8b7 | https://github.com/morimo27182/DeepKalmanFilter/tree/5d78d2e700fdc24f2a5cfa2877ecdcfc8218c8b7 |
BinaryNLLEntropy | import torch
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
from torch.nn.modules.loss import _Loss
class BinaryNLLEntropy(_Loss):
def __init__(self, size_average=True):
super(BinaryNLLEntropy, self).__init__()
self.size_average = size_average
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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | msft-shahins/ConvLab-2 | BinaryNLLEntropy | false | 12,795 | [
"Apache-2.0"
] | 0 | ad74c0e9e021916f9330af11e046ed72914b7740 | https://github.com/msft-shahins/ConvLab-2/tree/ad74c0e9e021916f9330af11e046ed72914b7740 |
Critic | 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 Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, full_state_size, full_action_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | monimoyd/project_deep_reinforcement_learning_collaboration_competition | Critic | false | 12,796 | [
"MIT"
] | 0 | 3782abb839b671ea53ece1435a4d481d7871cd39 | https://github.com/monimoyd/project_deep_reinforcement_learning_collaboration_competition/tree/3782abb839b671ea53ece1435a4d481d7871cd39 |
InResBlock | import math
import torch
from torch import nn
from torch.nn import functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0,
pad_x1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | mkleshchenok/dlcourse_2021_p1_final_project | InResBlock | false | 12,797 | [
"MIT"
] | 0 | 1dd4f2e3dccc4604aa98982bf9377273ab4783c1 | https://github.com/mkleshchenok/dlcourse_2021_p1_final_project/tree/1dd4f2e3dccc4604aa98982bf9377273ab4783c1 |
Generator | import torch
import torch.nn as nn
class Generator(nn.Module):
def __init__(self, input_length: 'int'):
super(Generator, self).__init__()
self.dense_layer = nn.Linear(int(input_length), int(input_length))
self.activation = nn.Sigmoid()
def forward(self, x):
return self.activa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | msank00/ganTutorial | Generator | false | 12,798 | [
"MIT"
] | 0 | 7657ff8cbb0cd66c98b5fd91bf19677e467aac68 | https://github.com/msank00/ganTutorial/tree/7657ff8cbb0cd66c98b5fd91bf19677e467aac68 |
Posterior | import torch
import torch.nn as nn
class Posterior(nn.Module):
def __init__(self, z_dim, hidden_dim, obs_dim):
super(Posterior, self).__init__()
self.z_obs_to_hidden = nn.Linear(2 * z_dim + obs_dim, hidden_dim)
self.hidden_to_hidden = nn.Linear(hidden_dim, hidden_dim)
self.hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | morimo27182/DeepKalmanFilter | Posterior | false | 12,799 | [
"MIT"
] | 0 | 5d78d2e700fdc24f2a5cfa2877ecdcfc8218c8b7 | https://github.com/morimo27182/DeepKalmanFilter/tree/5d78d2e700fdc24f2a5cfa2877ecdcfc8218c8b7 |
MultiHeadAttentionLayer | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class Layer(nn.Module):
def __init__(self, name):
super(Layer, self).__init__()
self.name = name
class MultiHeadAttentionLayer(Layer):
def __init__(self, n_heads, d_src, d_tgt, dropout, name='None'):
sup... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | mmwebster/DeepRL-Grounding | MultiHeadAttentionLayer | false | 12,800 | [
"MIT"
] | 0 | aa7fa63fbc26e8b0fa3fe289a5fe5a00ef3e6278 | https://github.com/mmwebster/DeepRL-Grounding/tree/aa7fa63fbc26e8b0fa3fe289a5fe5a00ef3e6278 |
BilinearWithBias | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
import torch.nn.functional as F
from torch.nn.modules import Module
class BilinearWithBias(Module):
def __init__(self, in1_features, in2_features, out_features):
super(BilinearWithBias, 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.nn import Module
import math
from torch.nn.parameter import Parameter... | masashi-y/myccg | BilinearWithBias | false | 12,801 | [
"MIT"
] | 0 | 263fd0afa7a619626fc2d506016625b6068bb27b | https://github.com/masashi-y/myccg/tree/263fd0afa7a619626fc2d506016625b6068bb27b |
Norm | import torch
import torch.nn as nn
class Norm(nn.Module):
def __init__(self, d_model, eps=1e-06):
super().__init__()
self.size = d_model
self.alpha = nn.Parameter(torch.ones(self.size))
self.bias = nn.Parameter(torch.zeros(self.size))
self.eps = eps
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | msank00/miniTransformer | Norm | false | 12,802 | [
"MIT"
] | 0 | a264f30982d9e2dbf8c796d495f7a237c0dd53ef | https://github.com/msank00/miniTransformer/tree/a264f30982d9e2dbf8c796d495f7a237c0dd53ef |
MaxPool | import torch
import torch.nn as nn
class MaxPool(nn.Module):
def __init__(self, kernel_size, stride=1, padding=1, zero_pad=False):
super(MaxPool, self).__init__()
self.is_zero_padded = zero_pad
self.zero_pad = nn.ZeroPad2d((1, 0, 1, 0))
self.pool = nn.MaxPool2d(kernel_size, stride... | 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... | mruberry/pnas_torch | MaxPool | false | 12,803 | [
"BSD-3-Clause"
] | 0 | e6471f900f28698fe0ebca158fec059337acee2c | https://github.com/mruberry/pnas_torch/tree/e6471f900f28698fe0ebca158fec059337acee2c |
SelfAttn | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
import torch as th
class SelfAttn(nn.Module):
def __init__(self, hidden_size):
super(SelfAttn, self).__init__()
self.query = nn.Linear(hidden_size, 1)
def forward(self, keys, value... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | msft-shahins/ConvLab-2 | SelfAttn | false | 12,804 | [
"Apache-2.0"
] | 0 | ad74c0e9e021916f9330af11e046ed72914b7740 | https://github.com/msft-shahins/ConvLab-2/tree/ad74c0e9e021916f9330af11e046ed72914b7740 |
NormKLLoss | import torch
import torch.utils.data
import torch.nn.init
import torch as th
from torch.nn.modules.loss import _Loss
class NormKLLoss(_Loss):
def __init__(self, unit_average=False):
super(NormKLLoss, self).__init__()
self.unit_average = unit_average
def forward(self, recog_mu, recog_logvar, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.data
import torch.nn.init
from torch.nn.modules.loss i... | msft-shahins/ConvLab-2 | NormKLLoss | false | 12,805 | [
"Apache-2.0"
] | 0 | ad74c0e9e021916f9330af11e046ed72914b7740 | https://github.com/msft-shahins/ConvLab-2/tree/ad74c0e9e021916f9330af11e046ed72914b7740 |
CharbonnierLoss | import torch
import torch.nn as nn
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=1e-06, mode=None):
super(CharbonnierLoss, self).__init__()
self.eps = eps
self.mode = mode
def forward(self, x, y, mask=None):
N = x.size(1)
dif... | 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... | myeldib/Simple-SR | CharbonnierLoss | false | 12,806 | [
"MIT"
] | 0 | 583456b1f231574d9e0b45c29266cf41603d161d | https://github.com/myeldib/Simple-SR/tree/583456b1f231574d9e0b45c29266cf41603d161d |
AddNorm | import torch
import torch.nn as nn
class Norm(nn.Module):
def __init__(self, d_model, eps=1e-06):
super().__init__()
self.size = d_model
self.alpha = nn.Parameter(torch.ones(self.size))
self.bias = nn.Parameter(torch.zeros(self.size))
self.eps = eps
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | msank00/miniTransformer | AddNorm | false | 12,807 | [
"MIT"
] | 0 | a264f30982d9e2dbf8c796d495f7a237c0dd53ef | https://github.com/msank00/miniTransformer/tree/a264f30982d9e2dbf8c796d495f7a237c0dd53ef |
TVLoss | import torch
import torch.nn as nn
class TVLoss(nn.Module):
def __init__(self, weight=1.0):
super(TVLoss, self).__init__()
self.weight = weight
self.l1 = nn.L1Loss(reduction='mean')
def forward(self, out, gt):
grad_out_x = out[:, :, :, 1:] - out[:, :, :, :-1]
grad_out... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | myeldib/Simple-SR | TVLoss | false | 12,808 | [
"MIT"
] | 0 | 583456b1f231574d9e0b45c29266cf41603d161d | https://github.com/myeldib/Simple-SR/tree/583456b1f231574d9e0b45c29266cf41603d161d |
TorchFCNModel | import torch
class TorchFCNModel(torch.nn.Module):
def __init__(self, inputD, outputD, hiddenC=2, hiddenD=36):
super(TorchFCNModel, self).__init__()
self.device = torch.device('cuda:0' if torch.cuda.is_available() else
'cpu')
self.inputD, self.outputD = inputD, outputD
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | muratcancicek/pointer_head | TorchFCNModel | false | 12,809 | [
"MIT"
] | 0 | b2a357f0183d5ced82b6dc7f6f12e0391bdc7380 | https://github.com/muratcancicek/pointer_head/tree/b2a357f0183d5ced82b6dc7f6f12e0391bdc7380 |
Hidden2Discrete | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
class Hidden2Discrete(nn.Module):
def __init__(self, input_size, y_size, k_size, is_lstm=False, has_bias=True
):
super(Hidden2Discrete, self).__init__()
self.y_size = y_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.... | msft-shahins/ConvLab-2 | Hidden2Discrete | false | 12,810 | [
"Apache-2.0"
] | 0 | ad74c0e9e021916f9330af11e046ed72914b7740 | https://github.com/msft-shahins/ConvLab-2/tree/ad74c0e9e021916f9330af11e046ed72914b7740 |
StyledResBlock | import math
import torch
from torch import nn
from torch.nn import functional as F
def make_kernel(k):
k = torch.tensor(k, dtype=torch.float32)
if k.ndim == 1:
k = k[None, :] * k[:, None]
k /= k.sum()
return k
def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0,
pad_x1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | mkleshchenok/dlcourse_2021_p1_final_project | StyledResBlock | false | 12,811 | [
"MIT"
] | 0 | 1dd4f2e3dccc4604aa98982bf9377273ab4783c1 | https://github.com/mkleshchenok/dlcourse_2021_p1_final_project/tree/1dd4f2e3dccc4604aa98982bf9377273ab4783c1 |
DynamicConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class DynamicConv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, sr_in_list=(1.0,),
sr_out_list=None):
self.sr_idx, self.sr_in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn
assert_size_stride = torch._C._dynamo.guar... | naili-xing/singa-easy | DynamicConv2d | false | 12,812 | [
"Apache-2.0"
] | 0 | ed94cd8b6b77dc1e86c670000eae06d06f81926b | https://github.com/naili-xing/singa-easy/tree/ed94cd8b6b77dc1e86c670000eae06d06f81926b |
MultiAccuracy | import torch
class MultiAccuracy(torch.nn.Module):
"""Calculates accuracy for multiclass inputs (batchsize, feature length) by determining the most likely class
using argmax -> (batchsize,) and then comparing with targets which are also (batchsize,)
"""
def __init__(self):
super(MultiAccuracy... | 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... | namiyousef/ml-utils | MultiAccuracy | false | 12,813 | [
"MIT"
] | 0 | b67611e9e112f8bbc004a083ce4c9fcd8c1949fa | https://github.com/namiyousef/ml-utils/tree/b67611e9e112f8bbc004a083ce4c9fcd8c1949fa |
Attention | import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self, src_size, trg_size):
super().__init__()
self.W = nn.Bilinear(src_size, trg_size, 1)
self.softmax = nn.Softmax(dim=-1)
def forward(self, src, trg, attention_mask=None):
"""
src: [src_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 math as tl_math
import torch.... | myunghakLee/GainParallel | Attention | false | 12,814 | [
"MIT"
] | 0 | 63112bd996591ad898cbb88fdb839992227a5b74 | https://github.com/myunghakLee/GainParallel/tree/63112bd996591ad898cbb88fdb839992227a5b74 |
MlpLite | import torch
from torch import nn
class MlpLite(nn.Module):
def __init__(self, H, W, in_features, hidden_features=None,
out_features=None, act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | likelyzhao/dino | MlpLite | false | 12,815 | [
"Apache-2.0"
] | 0 | ad019889b0e4c103f0471d085f79bba42c817d1b | https://github.com/likelyzhao/dino/tree/ad019889b0e4c103f0471d085f79bba42c817d1b |
BinaryFocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryFocalLoss(nn.Module):
"""
This is a implementation of Focal Loss with smooth label cross entropy supported which is proposed in
'Focal Loss for Dense Object Detection. (https://arxiv.org/abs/1708.02002)'
Focal_Loss= -1*... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | naivepig1998/brain_met_3d_cnn | BinaryFocalLoss | false | 12,816 | [
"MIT"
] | 0 | 6abd783a6e0185c72d64a89713fdaa3bee68a65f | https://github.com/naivepig1998/brain_met_3d_cnn/tree/6abd783a6e0185c72d64a89713fdaa3bee68a65f |
SimpleModel | import torch
import torch.cuda
class SimpleModel(torch.nn.Module):
def __init__(self, hidden_dim, empty_grad=False):
super(SimpleModel, self).__init__()
self.linear = torch.nn.Linear(hidden_dim, hidden_dim)
if empty_grad:
self.layers2 = torch.nn.ModuleList([torch.nn.Linear(hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | mbeacom/DeepSpeed | SimpleModel | false | 12,817 | [
"MIT"
] | 0 | 012d91df67a9ddd66df847c7608481af027cace9 | https://github.com/mbeacom/DeepSpeed/tree/012d91df67a9ddd66df847c7608481af027cace9 |
KeyValueAttention | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.utils.data
import torch.nn.init
class KeyValueAttention(nn.Module):
def __init__(self, query_size, key_size, value_size, hid_size, init_range):
super(KeyValueAttention, 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.... | msft-shahins/ConvLab-2 | KeyValueAttention | false | 12,818 | [
"Apache-2.0"
] | 0 | ad74c0e9e021916f9330af11e046ed72914b7740 | https://github.com/msft-shahins/ConvLab-2/tree/ad74c0e9e021916f9330af11e046ed72914b7740 |
KLLoss | import torch
import torch.nn as nn
class KLLoss(nn.Module):
def forward(self, mu: 'torch.Tensor', sigma: 'torch.Tensor', target_mu:
'torch.Tensor', target_std: 'torch.Tensor'):
std1 = target_std
std2 = sigma
mean1 = target_mu
mean2 = mu
kl = torch.log(torch.abs(std... | 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
... | ncduy0303/wmt21-qe-task | KLLoss | false | 12,819 | [
"Apache-2.0"
] | 0 | 93082afd0c56fb8d60101457082116c79adeac50 | https://github.com/ncduy0303/wmt21-qe-task/tree/93082afd0c56fb8d60101457082116c79adeac50 |
D_GCN | import math
import torch
from torch import nn
import torch.nn.functional as F
class D_GCN(nn.Module):
"""
Neural network block that applies a diffusion graph convolution to sampled location
"""
def __init__(self, in_channels, out_channels, orders, activation='relu'):
"""
:param in_cha... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 math
from torch import... | mpourhoma/PWWB-London | D_GCN | false | 12,820 | [
"MIT"
] | 0 | cfe7a6e3d92ff6b1f18bb5d5bc6a86334e9509d8 | https://github.com/mpourhoma/PWWB-London/tree/cfe7a6e3d92ff6b1f18bb5d5bc6a86334e9509d8 |
Layer4NN | import torch
import torch.nn
import torch.cuda
class Layer4NN(torch.nn.Module):
def __init__(self, inputSize, numClasses, channels=3):
super(Layer4NN, self).__init__()
self.cnn_layer1 = torch.nn.Conv2d(channels, 32, kernel_size=3,
stride=1, padding=1)
self.cnn_layer2 = torch.n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn
import torch.... | naruarjun/SADAM-reproducibility | Layer4NN | false | 12,821 | [
"MIT"
] | 0 | 1654804268ae984f49abc3ab2495c350dc09a3e2 | https://github.com/naruarjun/SADAM-reproducibility/tree/1654804268ae984f49abc3ab2495c350dc09a3e2 |
TemporalFusion | import torch
import torch.nn as nn
class TemporalFusion(nn.Module):
def __init__(self, nf, n_frame):
super(TemporalFusion, self).__init__()
self.n_frame = n_frame
self.ref_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.nbr_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | myeldib/Simple-SR | TemporalFusion | false | 12,822 | [
"MIT"
] | 0 | 583456b1f231574d9e0b45c29266cf41603d161d | https://github.com/myeldib/Simple-SR/tree/583456b1f231574d9e0b45c29266cf41603d161d |
SplitAndConcat | import torch
import torch.nn as nn
import torch.utils.data
class SplitAndConcat(nn.Module):
"""Split the data from split_dim and concatenate in concat_dim.
@param split_dim from which axis the data will be chunk
@param concat_dim to which axis the data will be concatenated
@param chunk size of the da... | 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.... | newstzpz/d2go | SplitAndConcat | false | 12,823 | [
"Apache-2.0"
] | 0 | fcd511714ec4e34040d35379cb0382b70fb58c70 | https://github.com/newstzpz/d2go/tree/fcd511714ec4e34040d35379cb0382b70fb58c70 |
VarianceLoss | import torch
import torch.nn as nn
class VarianceLoss(nn.Module):
def forward(self, mu: 'torch.Tensor', std: 'torch.Tensor', target:
'torch.Tensor'):
sigma = std ** 2
log1 = 0.5 * torch.neg(torch.log(sigma)).exp()
mse = (target - mu) ** 2
log2 = 0.5 * torch.log(sigma)
... | 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
... | ncduy0303/wmt21-qe-task | VarianceLoss | false | 12,824 | [
"Apache-2.0"
] | 0 | 93082afd0c56fb8d60101457082116c79adeac50 | https://github.com/ncduy0303/wmt21-qe-task/tree/93082afd0c56fb8d60101457082116c79adeac50 |
T5LayerNorm | import torch
import torch.nn as nn
import torch.utils.checkpoint
class T5LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-06):
"""
Construct a layernorm module in the T5 style No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter... | 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.checkpoint
assert_size_stride = torch.... | longquan0609/bert_seq2seq | T5LayerNorm | false | 12,825 | [
"Apache-2.0"
] | 0 | 3aaeb2ea76cd435d53ebcfedd2a080d0c37c1976 | https://github.com/longquan0609/bert_seq2seq/tree/3aaeb2ea76cd435d53ebcfedd2a080d0c37c1976 |
EncoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0... | 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.... | msank00/miniTransformer | EncoderLayer | false | 12,826 | [
"MIT"
] | 0 | a264f30982d9e2dbf8c796d495f7a237c0dd53ef | https://github.com/msank00/miniTransformer/tree/a264f30982d9e2dbf8c796d495f7a237c0dd53ef |
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 |
ResidualBlock_noBN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaimin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | myeldib/Simple-SR | ResidualBlock_noBN | false | 12,828 | [
"MIT"
] | 0 | 583456b1f231574d9e0b45c29266cf41603d161d | https://github.com/myeldib/Simple-SR/tree/583456b1f231574d9e0b45c29266cf41603d161d |
DiagLinear | import math
import torch
from torch import Tensor
from torch import nn
class DiagLinear(nn.Module):
"""Applies a diagonal linear transformation to the incoming data: :math:`y = xD^T + b`"""
__constants__ = ['features']
def __init__(self, features, bias=True):
super(DiagLinear, self).__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 math
from torch import Tensor
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda... | nihaarshah/behavenet | DiagLinear | false | 12,829 | [
"MIT"
] | 0 | 35bf5360e136075ca5ec30b3f98a2112a53e992c | https://github.com/nihaarshah/behavenet/tree/35bf5360e136075ca5ec30b3f98a2112a53e992c |
conv_head_pooling | import torch
import torch.nn as nn
import torch.utils.data
class conv_head_pooling(nn.Module):
def __init__(self, in_feature, out_feature, stride, conv_type,
padding_mode='zeros', dilation=1):
super(conv_head_pooling, self).__init__()
if conv_type == 'depthwise':
_groups = in_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | newstzpz/d2go | conv_head_pooling | false | 12,830 | [
"Apache-2.0"
] | 0 | fcd511714ec4e34040d35379cb0382b70fb58c70 | https://github.com/newstzpz/d2go/tree/fcd511714ec4e34040d35379cb0382b70fb58c70 |
DecoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0... | 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.... | msank00/miniTransformer | DecoderLayer | false | 12,831 | [
"MIT"
] | 0 | a264f30982d9e2dbf8c796d495f7a237c0dd53ef | https://github.com/msank00/miniTransformer/tree/a264f30982d9e2dbf8c796d495f7a237c0dd53ef |
SelfGating | import torch
from torch import nn
import torch as th
import torch.hub
import torch.utils.data
class SelfGating(nn.Module):
def __init__(self, input_dim):
super(SelfGating, self).__init__()
self.fc = nn.Linear(input_dim, input_dim)
def forward(self, input_tensor):
"""Feature gating as... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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.hub
import torch.utils.data
assert_size_stride... | nicholasneo78/wav2vec-demo | SelfGating | false | 12,832 | [
"MIT"
] | 0 | c37db7b8211458dc810a85d4262ef41e3e3e4f12 | https://github.com/nicholasneo78/wav2vec-demo/tree/c37db7b8211458dc810a85d4262ef41e3e3e4f12 |
SpatialGatingUnit | import torch
import torch.nn as nn
class SpatialGatingUnit(nn.Module):
def __init__(self, dim_seq, dim_ff):
super().__init__()
self.proj = nn.Linear(dim_seq, dim_seq)
nn.init.zeros_(self.proj.weight)
nn.init.ones_(self.proj.bias)
self.norm = nn.LayerNorm(normalized_shape=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.triton_helpers import libdevice
import torch.nn as ... | nima1999nikkhah/gMLP | SpatialGatingUnit | false | 12,833 | [
"MIT"
] | 0 | 6e04a173bdb137680695fe55753d8b2284f03fa4 | https://github.com/nima1999nikkhah/gMLP/tree/6e04a173bdb137680695fe55753d8b2284f03fa4 |
SelfAttentionFuseLayer | import torch
from torch import nn
class SelfAttentionFuseLayer(nn.Module):
def __init__(self, dim):
super(SelfAttentionFuseLayer, self).__init__()
self.W_7 = nn.Linear(dim, dim)
self.w_8 = nn.Linear(dim, 1)
self.activation = nn.Tanh()
def forward(self, hidden_states):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | nju-websoft/Jeeves | SelfAttentionFuseLayer | false | 12,834 | [
"Apache-2.0"
] | 0 | 6c817ed9e9c36a27c1c10a0a3c863ca0e5fdb5c1 | https://github.com/nju-websoft/Jeeves/tree/6c817ed9e9c36a27c1c10a0a3c863ca0e5fdb5c1 |
gMLPBlock | import torch
import torch.nn as nn
class SpatialGatingUnit(nn.Module):
def __init__(self, dim_seq, dim_ff):
super().__init__()
self.proj = nn.Linear(dim_seq, dim_seq)
nn.init.zeros_(self.proj.weight)
nn.init.ones_(self.proj.bias)
self.norm = nn.LayerNorm(normalized_shape=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.triton_helpers import libdevice
import torch.nn as ... | nima1999nikkhah/gMLP | gMLPBlock | false | 12,835 | [
"MIT"
] | 0 | 6e04a173bdb137680695fe55753d8b2284f03fa4 | https://github.com/nima1999nikkhah/gMLP/tree/6e04a173bdb137680695fe55753d8b2284f03fa4 |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, nf=64):
super(Attention, self).__init__()
self.sAtt_1 = nn.Conv2d(nf, nf, 1, 1, bias=True)
self.max_pool = nn.MaxPool2d(3, stride=2, padding=1)
self.avg_pool = nn.AvgP... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 |
ExpanderConv2d | import torch
import torch.nn as nn
class ExpanderConv2d(nn.Module):
def __init__(self, indim, outdim, kernel_size, expandSize, stride=1,
padding=0, inDil=1, groups=1, mode='random'):
super(ExpanderConv2d, self).__init__()
self.conStride = stride
self.conPad = padding
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | noonespecial009/resnet-variations | ExpanderConv2d | false | 12,837 | [
"MIT"
] | 0 | 11ee33d1855c292b15930a2a2c1d757d1ac85699 | https://github.com/noonespecial009/resnet-variations/tree/11ee33d1855c292b15930a2a2c1d757d1ac85699 |
DPDALayear | import torch
from torch import nn
class DPDALayear(nn.Module):
def __init__(self, dim):
super(DPDALayear, self).__init__()
self.W_p = nn.Linear(2 * dim, dim)
self.W_q = nn.Linear(2 * dim, dim)
def forward(self, P, Q, p_mask=None, q_mask=None):
P_ori = P
Q_ori = Q
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | nju-websoft/Jeeves | DPDALayear | false | 12,838 | [
"Apache-2.0"
] | 0 | 6c817ed9e9c36a27c1c10a0a3c863ca0e5fdb5c1 | https://github.com/nju-websoft/Jeeves/tree/6c817ed9e9c36a27c1c10a0a3c863ca0e5fdb5c1 |
C3D | import torch
import torch.nn as nn
class C3D(nn.Module):
"""
The C3D network.
"""
def __init__(self, num_classes, pretrained=False):
super(C3D, self).__init__()
self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.pool1 = nn.MaxPool3d(kernel_size=(1, 2,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | gramuah/gui4lola | C3D | false | 12,839 | [
"MIT"
] | 0 | 6924d681db3b14f9b10a53b115640a749a33e774 | https://github.com/gramuah/gui4lola/tree/6924d681db3b14f9b10a53b115640a749a33e774 |
WavePool | import torch
import numpy as np
import torch.nn as nn
def get_wav(in_channels, pool=True):
"""wavelet decomposition using conv2d"""
harr_wav_L = 1 / np.sqrt(2) * np.ones((1, 2))
harr_wav_H = 1 / np.sqrt(2) * np.ones((1, 2))
harr_wav_H[0, 0] = -1 * harr_wav_H[0, 0]
harr_wav_LL = np.transpose(harr_w... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | noapadan/WCT2 | WavePool | false | 12,840 | [
"MIT"
] | 0 | 56c819bebb9f023e9eb8603f1f56a37650231730 | https://github.com/noapadan/WCT2/tree/56c819bebb9f023e9eb8603f1f56a37650231730 |
Network | import torch
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self):
super(Network, self).__init__()
self.fc1 = nn.Linear(4, 256)
self.fc2 = nn.Linear(256, 2)
def forward(self, x):
x = F.relu(self.fc1(x))
x = self.fc2(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
assert_... | noureldinalaa/monocular_visual_odometry-_DuckieTown | Network | false | 12,841 | [
"MIT"
] | 0 | 6b65e4fb9918dbf435133a9dd608c58cfb12b44b | https://github.com/noureldinalaa/monocular_visual_odometry-_DuckieTown/tree/6b65e4fb9918dbf435133a9dd608c58cfb12b44b |
SoftmaxAttention | import torch
import torch.nn as nn
def masked_softmax(tensor, mask):
"""
Apply a masked softmax on the last dimension of a tensor.
The input tensor and mask should be of size (batch, *, sequence_length).
Args:
tensor: The tensor on which the softmax function must be applied along
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | nlpcl-lab/msent-cred-predictor | SoftmaxAttention | false | 12,842 | [
"Apache-2.0"
] | 0 | 1ac75953583e427dd37717a522a1aaa5b2d1a6a9 | https://github.com/nlpcl-lab/msent-cred-predictor/tree/1ac75953583e427dd37717a522a1aaa5b2d1a6a9 |
MLP | import torch
import torch.nn as nn
from collections import OrderedDict
class MLP(nn.Module):
def __init__(self, input_dims, n_hiddens, n_class):
super(MLP, self).__init__()
assert isinstance(input_dims, int), 'Please provide int for input_dims'
self.input_dims = input_dims
current... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | luk1684tw/Precision-Reduction | MLP | false | 12,843 | [
"MIT"
] | 0 | c782e9a121ed176b12eb9a081aa1960fabd40019 | https://github.com/luk1684tw/Precision-Reduction/tree/c782e9a121ed176b12eb9a081aa1960fabd40019 |
CRFLayer | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.checkpoint
class CRFLayer(nn.Module):
"""
"""
def __init__(self, output_dim):
super(CRFLayer, self).__init__()
self.output_dim = output_dim
self.trans = nn.Parameter(torch.Tensor(output_dim, outp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | longquan0609/bert_seq2seq | CRFLayer | false | 12,845 | [
"Apache-2.0"
] | 0 | 3aaeb2ea76cd435d53ebcfedd2a080d0c37c1976 | https://github.com/longquan0609/bert_seq2seq/tree/3aaeb2ea76cd435d53ebcfedd2a080d0c37c1976 |
SeparableConv1D | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.utils.checkpoint
class SeparableConv1D(nn.Module):
"""This class implements separable convolution, i.e. a depthwise and a pointwise layer"""
def __init__(self, config, input_filters, output_filters, kernel_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 import nn
import torch.utils.checkpoint
assert_size_stride = torch._C... | Clemens123/transformers | SeparableConv1D | false | 12,847 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
RMSNorm | import torch
import torch.nn as nn
class RMSNorm(nn.Module):
def __init__(self, d):
super().__init__()
self.dd = d ** (-1.0 / 2)
self.weight = nn.Parameter(torch.ones(d))
def forward(self, x):
norm_x = x.norm(2, dim=-1, keepdim=True)
x_normed = x / (norm_x * self.dd +... | 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_... | ofooo/AI-Writer | RMSNorm | false | 12,849 | [
"BSD-3-Clause"
] | 0 | 1ba84894c15c9e5605d3c6cd7521d5c6dab6eb6d | https://github.com/ofooo/AI-Writer/tree/1ba84894c15c9e5605d3c6cd7521d5c6dab6eb6d |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, 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.... | noble6emc2/MoCo-SSPT | BertAttention | false | 12,850 | [
"MIT"
] | 0 | e6d7cf3f0a3b5a467318dfc32096e4929adbe646 | https://github.com/noble6emc2/MoCo-SSPT/tree/e6d7cf3f0a3b5a467318dfc32096e4929adbe646 |
FocalLoss | import torch
import torch.nn as nn
class FocalLoss(nn.Module):
"""
Softmax and sigmoid focal loss
https://github.com/lonePatient/TorchBlocks/blob/master/torchblocks/losses/focal_loss.py
"""
def __init__(self, num_labels, gamma=2.0, alpha=0.25, epsilon=1e-09,
reduction='mean', activation_t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | okcd00/CDPrototype | FocalLoss | false | 12,851 | [
"MIT"
] | 0 | 5a05b144e3e4b341c1a67fe455f94c01899539d8 | https://github.com/okcd00/CDPrototype/tree/5a05b144e3e4b341c1a67fe455f94c01899539d8 |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | noble6emc2/MoCo-SSPT | BertSelfAttention | false | 12,852 | [
"MIT"
] | 0 | e6d7cf3f0a3b5a467318dfc32096e4929adbe646 | https://github.com/noble6emc2/MoCo-SSPT/tree/e6d7cf3f0a3b5a467318dfc32096e4929adbe646 |
RandomShiftsAug | import torch
import torch.nn as nn
import torch.nn.functional as F
class RandomShiftsAug(nn.Module):
def __init__(self, pad):
super().__init__()
self.pad = pad
def forward(self, x):
n, _c, h, w = x.size()
assert h == w
padding = tuple([self.pad] * 4)
x = F.pad... | import torch
from torch import device
import triton
import triton.language 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._d... | nsortur/drqv2 | RandomShiftsAug | false | 12,853 | [
"MIT"
] | 0 | 2443f93feeb5cace855d16bfa31152d63a2d66aa | https://github.com/nsortur/drqv2/tree/2443f93feeb5cace855d16bfa31152d63a2d66aa |
ConcatELU | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConcatELU(nn.Module):
"""
Activation function that applies ELU in both direction (inverted and plain).
Allows non-linearity while providing strong gradients for any input (important for final convolution)
"""
def forward(sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | onlyrico/lightning-tutorials | ConcatELU | false | 12,854 | [
"Apache-2.0"
] | 0 | b5d5c4015422f8c70411e57734d73bb6c1472999 | https://github.com/onlyrico/lightning-tutorials/tree/b5d5c4015422f8c70411e57734d73bb6c1472999 |
GCNLayer | import torch
import torch.nn as nn
class GCNLayer(nn.Module):
def __init__(self, c_in, c_out):
super().__init__()
self.projection = nn.Linear(c_in, c_out)
def forward(self, node_feats, adj_matrix):
"""
Inputs:
node_feats - Tensor with node features of shape [batch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | onlyrico/lightning-tutorials | GCNLayer | false | 12,855 | [
"Apache-2.0"
] | 0 | b5d5c4015422f8c70411e57734d73bb6c1472999 | https://github.com/onlyrico/lightning-tutorials/tree/b5d5c4015422f8c70411e57734d73bb6c1472999 |
LayerNormChannels | import torch
import torch.nn as nn
class LayerNormChannels(nn.Module):
def __init__(self, c_in):
"""
This module applies layer norm across channels in an image. Has been shown to work well with ResNet connections.
Inputs:
c_in - Number of channels of the 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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | onlyrico/lightning-tutorials | LayerNormChannels | false | 12,856 | [
"Apache-2.0"
] | 0 | b5d5c4015422f8c70411e57734d73bb6c1472999 | https://github.com/onlyrico/lightning-tutorials/tree/b5d5c4015422f8c70411e57734d73bb6c1472999 |
Decoder | import torch
import torch.nn.functional as F
from torch import nn
class Decoder(torch.nn.Module):
def __init__(self, Z_dim, h_dim, X_dim):
super(Decoder, self).__init__()
self.hidden1 = torch.nn.Linear(Z_dim, int(h_dim / 4))
self.hidden2 = torch.nn.Linear(int(h_dim / 4), int(h_dim / 2))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | onimaru/Generative_models | Decoder | false | 12,858 | [
"Apache-2.0"
] | 0 | 915750066996aa3d4dce6ae605778b4eee3f0f3d | https://github.com/onimaru/Generative_models/tree/915750066996aa3d4dce6ae605778b4eee3f0f3d |
UpBlock | import torch
import torch.nn as nn
class UpBlock(nn.Module):
def __init__(self, in_f, out_f, stride=2, add_blur=False):
super(UpBlock, self).__init__()
self.shuffle = nn.ConvTranspose2d(in_f, out_f, kernel_size=3,
stride=stride, padding=0)
self.has_blur = add_blur
if s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | parth-shettiwar/Image-Toonification | UpBlock | false | 12,859 | [
"MIT"
] | 0 | a24d76fa9737558ac38a2fdf23469376f25c0abd | https://github.com/parth-shettiwar/Image-Toonification/tree/a24d76fa9737558ac38a2fdf23469376f25c0abd |
MLP | import random
import torch
import numpy as np
from torch import nn
class MLP(nn.Module):
def __init__(self, kernels, num_features, num_hiddens, normalize=True,
num_updates=3000, batch_size=128, weight_decay=0.0001, soft_preds=False
):
super().__init__()
self.kernels = kernels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | openmynet/tract | MLP | false | 12,860 | [
"ECL-2.0",
"Apache-2.0",
"MIT-0",
"MIT"
] | 0 | a9aba6edcfeacd34f781f08717ae374bfbaba80e | https://github.com/openmynet/tract/tree/a9aba6edcfeacd34f781f08717ae374bfbaba80e |
RWKV_TimeMix | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class RWKV_TimeMix(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
assert config.n_attn % config.n_head == 0
self.layer_id = layer_id
self.ctx_len = config.ctx_len
self.n_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ofooo/AI-Writer | RWKV_TimeMix | false | 12,861 | [
"BSD-3-Clause"
] | 0 | 1ba84894c15c9e5605d3c6cd7521d5c6dab6eb6d | https://github.com/ofooo/AI-Writer/tree/1ba84894c15c9e5605d3c6cd7521d5c6dab6eb6d |
Encoder | import torch
import torch.nn.functional as F
from torch import nn
class Encoder(torch.nn.Module):
def __init__(self, X_dim, h_dim, Z_dim):
super(Encoder, self).__init__()
self.hidden1 = torch.nn.Linear(X_dim, X_dim)
self.hidden2 = torch.nn.Linear(X_dim, h_dim)
self.hidden3 = 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.triton_helpers import libdevice
assert_size_stride ... | onimaru/Generative_models | Encoder | false | 12,862 | [
"Apache-2.0"
] | 0 | 915750066996aa3d4dce6ae605778b4eee3f0f3d | https://github.com/onimaru/Generative_models/tree/915750066996aa3d4dce6ae605778b4eee3f0f3d |
Upconv | import math
import torch
import torch.nn.functional as F
from torch.nn import Conv2d
from torch.nn import Upsample
class PadSameConv2d(torch.nn.Module):
def __init__(self, kernel_size, stride=1):
"""
Imitates padding_mode="same" from tensorflow.
:param kernel_size: Kernelsize of the convo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn.functional as F
from torch.nn import Conv2d
from tor... | pc2005/MonoRec | Upconv | false | 12,863 | [
"MIT"
] | 0 | 6e1628eeef9987b1acce3e5e8bb6a6a324fc8d2c | https://github.com/pc2005/MonoRec/tree/6e1628eeef9987b1acce3e5e8bb6a6a324fc8d2c |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self, r0, c0):
super(Net, self).__init__()
self.r = nn.Parameter(torch.FloatTensor([r0]))
self.c = nn.Parameter(torch.FloatTensor([c0]))
def forward(self):
cube_r = -3 * self.c * self.c * self.r + self.r * ... | 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... | pbloem/python-stuff | Net | false | 12,864 | [
"MIT"
] | 0 | db50fc52bcd59245c826013f196eb63319b326bc | https://github.com/pbloem/python-stuff/tree/db50fc52bcd59245c826013f196eb63319b326bc |
RGBBlock | import torch
from torch import nn
import torch.nn.functional as F
class Conv2DMod(nn.Module):
def __init__(self, in_chan, out_chan, kernel, demod=True, stride=1,
dilation=1, **kwargs):
super().__init__()
self.filters = out_chan
self.demod = demod
self.kernel = kernel
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | p0werHu/unet-stylegan2 | RGBBlock | false | 12,865 | [
"MIT"
] | 0 | 9978025e2932d5962fcb724cbd0313b85292f0d3 | https://github.com/p0werHu/unet-stylegan2/tree/9978025e2932d5962fcb724cbd0313b85292f0d3 |
DQN_RAM | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN_RAM(nn.Module):
def __init__(self, in_features=4, num_actions=18):
"""
Initialize a deep Q-learning network for testing algorithm
in_features: number of features of input.
num_actions: number of a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | paulesta55/pytorch-dqn | DQN_RAM | false | 12,866 | [
"MIT"
] | 0 | 0c1345952c8f99b2f74ec357867262fae6d928ec | https://github.com/paulesta55/pytorch-dqn/tree/0c1345952c8f99b2f74ec357867262fae6d928ec |
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