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MaxPoolStride1
|
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
class MaxPoolStride1(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1, self).__init__()
self.kernel_size = kernel_size
self.pad = kernel_size - 1
def forward(self, x):
padding = int(self.pad / 2)
padded_x = F.pad(x, (padding, padding, padding, padding), mode=
'constant', value=0)
pooled_x = nn.MaxPool2d(self.kernel_size, 1)(padded_x)
return pooled_x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'kernel_size': 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
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_0(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 144
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 3 % 3
x0 = xindex % 3
x2 = xindex // 9
x4 = xindex
tmp0 = -1 + x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = -1 + x0
tmp6 = tmp5 >= tmp1
tmp7 = tmp5 < tmp3
tmp8 = tmp2 & tmp4
tmp9 = tmp8 & tmp6
tmp10 = tmp9 & tmp7
tmp11 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask,
other=0.0)
tmp12 = x0
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp8 & tmp13
tmp16 = tmp15 & tmp14
tmp17 = tl.load(in_ptr0 + (-4 + x0 + 4 * x1 + 16 * x2), tmp16 & xmask,
other=0.0)
tmp18 = triton_helpers.maximum(tmp17, tmp11)
tmp19 = 1 + x0
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp8 & tmp20
tmp23 = tmp22 & tmp21
tmp24 = tl.load(in_ptr0 + (-3 + x0 + 4 * x1 + 16 * x2), tmp23 & xmask,
other=0.0)
tmp25 = triton_helpers.maximum(tmp24, tmp18)
tmp26 = 2 + x0
tmp27 = tmp26 >= tmp1
tmp28 = tmp26 < tmp3
tmp29 = tmp8 & tmp27
tmp30 = tmp29 & tmp28
tmp31 = tl.load(in_ptr0 + (-2 + x0 + 4 * x1 + 16 * x2), tmp30 & xmask,
other=0.0)
tmp32 = triton_helpers.maximum(tmp31, tmp25)
tmp33 = x1
tmp34 = tmp33 >= tmp1
tmp35 = tmp33 < tmp3
tmp36 = tmp34 & tmp35
tmp37 = tmp36 & tmp6
tmp38 = tmp37 & tmp7
tmp39 = tl.load(in_ptr0 + (-1 + x0 + 4 * x1 + 16 * x2), tmp38 & xmask,
other=0.0)
tmp40 = triton_helpers.maximum(tmp39, tmp32)
tmp41 = tmp36 & tmp13
tmp42 = tmp41 & tmp14
tmp43 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp42 & xmask, other=0.0
)
tmp44 = triton_helpers.maximum(tmp43, tmp40)
tmp45 = tmp36 & tmp20
tmp46 = tmp45 & tmp21
tmp47 = tl.load(in_ptr0 + (1 + x0 + 4 * x1 + 16 * x2), tmp46 & xmask,
other=0.0)
tmp48 = triton_helpers.maximum(tmp47, tmp44)
tmp49 = tmp36 & tmp27
tmp50 = tmp49 & tmp28
tmp51 = tl.load(in_ptr0 + (2 + x0 + 4 * x1 + 16 * x2), tmp50 & xmask,
other=0.0)
tmp52 = triton_helpers.maximum(tmp51, tmp48)
tmp53 = 1 + x1
tmp54 = tmp53 >= tmp1
tmp55 = tmp53 < tmp3
tmp56 = tmp54 & tmp55
tmp57 = tmp56 & tmp6
tmp58 = tmp57 & tmp7
tmp59 = tl.load(in_ptr0 + (3 + x0 + 4 * x1 + 16 * x2), tmp58 & xmask,
other=0.0)
tmp60 = triton_helpers.maximum(tmp59, tmp52)
tmp61 = tmp56 & tmp13
tmp62 = tmp61 & tmp14
tmp63 = tl.load(in_ptr0 + (4 + x0 + 4 * x1 + 16 * x2), tmp62 & xmask,
other=0.0)
tmp64 = triton_helpers.maximum(tmp63, tmp60)
tmp65 = tmp56 & tmp20
tmp66 = tmp65 & tmp21
tmp67 = tl.load(in_ptr0 + (5 + x0 + 4 * x1 + 16 * x2), tmp66 & xmask,
other=0.0)
tmp68 = triton_helpers.maximum(tmp67, tmp64)
tmp69 = tmp56 & tmp27
tmp70 = tmp69 & tmp28
tmp71 = tl.load(in_ptr0 + (6 + x0 + 4 * x1 + 16 * x2), tmp70 & xmask,
other=0.0)
tmp72 = triton_helpers.maximum(tmp71, tmp68)
tmp73 = 2 + x1
tmp74 = tmp73 >= tmp1
tmp75 = tmp73 < tmp3
tmp76 = tmp74 & tmp75
tmp77 = tmp76 & tmp6
tmp78 = tmp77 & tmp7
tmp79 = tl.load(in_ptr0 + (7 + x0 + 4 * x1 + 16 * x2), tmp78 & xmask,
other=0.0)
tmp80 = triton_helpers.maximum(tmp79, tmp72)
tmp81 = tmp76 & tmp13
tmp82 = tmp81 & tmp14
tmp83 = tl.load(in_ptr0 + (8 + x0 + 4 * x1 + 16 * x2), tmp82 & xmask,
other=0.0)
tmp84 = triton_helpers.maximum(tmp83, tmp80)
tmp85 = tmp76 & tmp20
tmp86 = tmp85 & tmp21
tmp87 = tl.load(in_ptr0 + (9 + x0 + 4 * x1 + 16 * x2), tmp86 & xmask,
other=0.0)
tmp88 = triton_helpers.maximum(tmp87, tmp84)
tmp89 = tmp76 & tmp27
tmp90 = tmp89 & tmp28
tmp91 = tl.load(in_ptr0 + (10 + x0 + 4 * x1 + 16 * x2), tmp90 & xmask,
other=0.0)
tmp92 = triton_helpers.maximum(tmp91, tmp88)
tl.store(out_ptr0 + x4, tmp92, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_max_pool2d_with_indices_0[grid(144)](arg0_1, buf0,
144, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class MaxPoolStride1New(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1New, self).__init__()
self.kernel_size = kernel_size
self.pad = kernel_size - 1
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Dazz993/AlphaPose
|
MaxPoolStride1
| false
| 5,049
|
[
"Apache-2.0"
] | 1
|
d4b9a3af5f590fa21bd033b4a19e98b5748ae683
|
https://github.com/Dazz993/AlphaPose/tree/d4b9a3af5f590fa21bd033b4a19e98b5748ae683
|
RSoftmax
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class RSoftmax(nn.Module):
"""Radix Softmax module in ``SplitAttentionConv2d``.
Args:
radix (int): Radix of input.
groups (int): Groups of input.
"""
def __init__(self, radix, groups):
super().__init__()
self.radix = radix
self.groups = groups
def forward(self, x):
batch = x.size(0)
if self.radix > 1:
x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2)
x = F.softmax(x, dim=1)
x = x.reshape(batch, -1)
else:
x = torch.sigmoid(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'radix': 4, 'groups': 1}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x3, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x3, tmp8, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 16), (64, 16, 256, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused__softmax_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del arg0_1
buf1 = empty_strided_cuda((4, 4, 1, 16), (64, 16, 16, 1), torch.float32
)
triton_poi_fused__softmax_1[grid(256)](buf0, buf1, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf0
return reinterpret_tensor(buf1, (4, 64), (64, 1), 0),
class RSoftmaxNew(nn.Module):
"""Radix Softmax module in ``SplitAttentionConv2d``.
Args:
radix (int): Radix of input.
groups (int): Groups of input.
"""
def __init__(self, radix, groups):
super().__init__()
self.radix = radix
self.groups = groups
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
David-19940718/mmclassification
|
RSoftmax
| false
| 5,050
|
[
"Apache-2.0"
] | 1
|
987dd45457e38c4787237ea468799849dce11ada
|
https://github.com/David-19940718/mmclassification/tree/987dd45457e38c4787237ea468799849dce11ada
|
ConvRelu
|
import torch
import torch.utils.data
import torch.nn as nn
import torch.onnx
import torch.autograd
import torch.backends.cudnn
class ConvRelu(nn.Module):
"""3x3 convolution followed by ReLU activation building block."""
def __init__(self, num_in, num_out):
super().__init__()
self.block = nn.Conv2d(num_in, num_out, kernel_size=3, padding=1,
bias=False)
def forward(self, x):
return nn.functional.relu(self.block(x), inplace=True)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_in': 4, 'num_out': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
import torch.onnx
import torch.autograd
import torch.backends.cudnn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(in_out_ptr0 + x0, tmp2, xmask)
tl.store(out_ptr0 + x0, tmp4, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = buf0
del buf0
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1, buf2,
256, XBLOCK=128, num_warps=4, num_stages=1)
return buf1, primals_1, primals_2, buf2
class ConvReluNew(nn.Module):
"""3x3 convolution followed by ReLU activation building block."""
def __init__(self, num_in, num_out):
super().__init__()
self.block = nn.Conv2d(num_in, num_out, kernel_size=3, padding=1,
bias=False)
def forward(self, input_0):
primals_1 = self.block.weight
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
CorentinLemaitre/robosat.pink
|
ConvRelu
| false
| 5,051
|
[
"MIT"
] | 1
|
6ec29a4dd4c0cbf953e73818d7338ee68b2451d3
|
https://github.com/CorentinLemaitre/robosat.pink/tree/6ec29a4dd4c0cbf953e73818d7338ee68b2451d3
|
FocalLoss
|
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
reduction_enum = F._Reduction.get_enum(reduction)
if reduction_enum == 0:
return loss
elif reduction_enum == 1:
return loss.mean()
elif reduction_enum == 2:
return loss.sum()
def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
"""Apply element-wise weight and reduce loss.
Args:
loss (Tensor): Element-wise loss.
weight (Tensor): Element-wise weights.
reduction (str): Same as built-in losses of PyTorch.
avg_factor (float): Average factor when computing the mean of losses.
Returns:
Tensor: Processed loss values.
"""
if weight is not None:
loss = loss * weight
if avg_factor is None:
loss = reduce_loss(loss, reduction)
elif reduction == 'mean':
loss = loss.sum() / avg_factor
elif reduction != 'none':
raise ValueError('avg_factor can not be used with reduction="sum"')
return loss
def convert_to_one_hot(targets: 'torch.Tensor', classes) ->torch.Tensor:
"""This function converts target class indices to one-hot vectors, given
the number of classes.
Args:
targets (Tensor): The ground truth label of the prediction
with shape (N, 1)
classes (int): the number of classes.
Returns:
Tensor: Processed loss values.
"""
assert torch.max(targets).item(
) < classes, 'Class Index must be less than number of classes'
one_hot_targets = torch.zeros((targets.shape[0], classes), dtype=torch.
long, device=targets.device)
one_hot_targets.scatter_(1, targets.long(), 1)
return one_hot_targets
def sigmoid_focal_loss(pred, target, weight=None, gamma=2.0, alpha=0.25,
reduction='mean', avg_factor=None):
"""Sigmoid focal loss.
Args:
pred (torch.Tensor): The prediction with shape (N, \\*).
target (torch.Tensor): The ground truth label of the prediction with
shape (N, \\*).
weight (torch.Tensor, optional): Sample-wise loss weight with shape
(N, ). Defaults to None.
gamma (float): The gamma for calculating the modulating factor.
Defaults to 2.0.
alpha (float): A balanced form for Focal Loss. Defaults to 0.25.
reduction (str): The method used to reduce the loss.
Options are "none", "mean" and "sum". If reduction is 'none' ,
loss is same shape as pred and label. Defaults to 'mean'.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
Returns:
torch.Tensor: Loss.
"""
assert pred.shape == target.shape, 'pred and target should be in the same shape.'
pred_sigmoid = pred.sigmoid()
target = target.type_as(pred)
pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
focal_weight = (alpha * target + (1 - alpha) * (1 - target)) * pt.pow(gamma
)
loss = F.binary_cross_entropy_with_logits(pred, target, reduction='none'
) * focal_weight
if weight is not None:
assert weight.dim() == 1
weight = weight.float()
if pred.dim() > 1:
weight = weight.reshape(-1, 1)
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
return loss
class FocalLoss(nn.Module):
"""Focal loss.
Args:
gamma (float): Focusing parameter in focal loss.
Defaults to 2.0.
alpha (float): The parameter in balanced form of focal
loss. Defaults to 0.25.
reduction (str): The method used to reduce the loss into
a scalar. Options are "none" and "mean". Defaults to 'mean'.
loss_weight (float): Weight of loss. Defaults to 1.0.
"""
def __init__(self, gamma=2.0, alpha=0.25, reduction='mean', loss_weight=1.0
):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self, pred, target, weight=None, avg_factor=None,
reduction_override=None):
"""Sigmoid focal loss.
Args:
pred (torch.Tensor): The prediction with shape (N, \\*).
target (torch.Tensor): The ground truth label of the prediction
with shape (N, \\*), N or (N,1).
weight (torch.Tensor, optional): Sample-wise loss weight with shape
(N, \\*). Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The method used to reduce the
loss into a scalar. Options are "none", "mean" and "sum".
Defaults to None.
Returns:
torch.Tensor: Loss.
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (reduction_override if reduction_override else self.
reduction)
if target.dim() == 1 or target.dim() == 2 and target.shape[1] == 1:
target = convert_to_one_hot(target.view(-1, 1), pred.shape[-1])
loss_cls = self.loss_weight * sigmoid_focal_loss(pred, target,
weight, gamma=self.gamma, alpha=self.alpha, reduction=reduction,
avg_factor=avg_factor)
return loss_cls
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_0(
in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp3 = tl.load(in_ptr1 + r0, None)
tmp1 = 1.0
tmp2 = tmp1 - tmp0
tmp4 = tmp2 * tmp3
tmp5 = 0.0
tmp6 = triton_helpers.minimum(tmp5, tmp3)
tmp7 = tl_math.abs(tmp3)
tmp8 = -tmp7
tmp9 = tl_math.exp(tmp8)
tmp10 = libdevice.log1p(tmp9)
tmp11 = tmp6 - tmp10
tmp12 = tmp4 - tmp11
tmp13 = 0.25
tmp14 = tmp0 * tmp13
tmp15 = 0.75
tmp16 = tmp2 * tmp15
tmp17 = tmp14 + tmp16
tmp18 = tl.sigmoid(tmp3)
tmp19 = tmp1 - tmp18
tmp20 = tmp19 * tmp0
tmp21 = tmp18 * tmp2
tmp22 = tmp20 + tmp21
tmp23 = tmp22 * tmp22
tmp24 = tmp17 * tmp23
tmp25 = tmp12 * tmp24
tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
tmp29 = 256.0
tmp30 = tmp28 / tmp29
tmp31 = tmp30 * tmp1
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp31, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_0[
grid(1)](buf1, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
reduction_enum = F._Reduction.get_enum(reduction)
if reduction_enum == 0:
return loss
elif reduction_enum == 1:
return loss.mean()
elif reduction_enum == 2:
return loss.sum()
def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
"""Apply element-wise weight and reduce loss.
Args:
loss (Tensor): Element-wise loss.
weight (Tensor): Element-wise weights.
reduction (str): Same as built-in losses of PyTorch.
avg_factor (float): Average factor when computing the mean of losses.
Returns:
Tensor: Processed loss values.
"""
if weight is not None:
loss = loss * weight
if avg_factor is None:
loss = reduce_loss(loss, reduction)
elif reduction == 'mean':
loss = loss.sum() / avg_factor
elif reduction != 'none':
raise ValueError('avg_factor can not be used with reduction="sum"')
return loss
def convert_to_one_hot(targets: 'torch.Tensor', classes) ->torch.Tensor:
"""This function converts target class indices to one-hot vectors, given
the number of classes.
Args:
targets (Tensor): The ground truth label of the prediction
with shape (N, 1)
classes (int): the number of classes.
Returns:
Tensor: Processed loss values.
"""
assert torch.max(targets).item(
) < classes, 'Class Index must be less than number of classes'
one_hot_targets = torch.zeros((targets.shape[0], classes), dtype=torch.
long, device=targets.device)
one_hot_targets.scatter_(1, targets.long(), 1)
return one_hot_targets
def sigmoid_focal_loss(pred, target, weight=None, gamma=2.0, alpha=0.25,
reduction='mean', avg_factor=None):
"""Sigmoid focal loss.
Args:
pred (torch.Tensor): The prediction with shape (N, \\*).
target (torch.Tensor): The ground truth label of the prediction with
shape (N, \\*).
weight (torch.Tensor, optional): Sample-wise loss weight with shape
(N, ). Defaults to None.
gamma (float): The gamma for calculating the modulating factor.
Defaults to 2.0.
alpha (float): A balanced form for Focal Loss. Defaults to 0.25.
reduction (str): The method used to reduce the loss.
Options are "none", "mean" and "sum". If reduction is 'none' ,
loss is same shape as pred and label. Defaults to 'mean'.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
Returns:
torch.Tensor: Loss.
"""
assert pred.shape == target.shape, 'pred and target should be in the same shape.'
pred_sigmoid = pred.sigmoid()
target = target.type_as(pred)
pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
focal_weight = (alpha * target + (1 - alpha) * (1 - target)) * pt.pow(gamma
)
loss = F.binary_cross_entropy_with_logits(pred, target, reduction='none'
) * focal_weight
if weight is not None:
assert weight.dim() == 1
weight = weight.float()
if pred.dim() > 1:
weight = weight.reshape(-1, 1)
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
return loss
class FocalLossNew(nn.Module):
"""Focal loss.
Args:
gamma (float): Focusing parameter in focal loss.
Defaults to 2.0.
alpha (float): The parameter in balanced form of focal
loss. Defaults to 0.25.
reduction (str): The method used to reduce the loss into
a scalar. Options are "none" and "mean". Defaults to 'mean'.
loss_weight (float): Weight of loss. Defaults to 1.0.
"""
def __init__(self, gamma=2.0, alpha=0.25, reduction='mean', loss_weight=1.0
):
super(FocalLossNew, self).__init__()
self.gamma = gamma
self.alpha = alpha
self.reduction = reduction
self.loss_weight = loss_weight
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
David-19940718/mmclassification
|
FocalLoss
| false
| 5,052
|
[
"Apache-2.0"
] | 1
|
987dd45457e38c4787237ea468799849dce11ada
|
https://github.com/David-19940718/mmclassification/tree/987dd45457e38c4787237ea468799849dce11ada
|
FocalTverskyLoss
|
import torch
from torch import nn
class FocalTverskyLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(FocalTverskyLoss, self).__init__()
def forward(self, inputs, targets, smooth=1, alpha=0.3, beta=0.7, gamma=2):
inputs = inputs.view(-1)
targets = targets.view(-1)
TP = (inputs * targets).sum()
FP = ((1 - targets) * inputs).sum()
FN = (targets * (1 - inputs)).sum()
Tversky = (TP + smooth) / (TP + alpha * FP + beta * FN + smooth)
FocalTversky = (1 - Tversky) ** gamma
return FocalTversky
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_div_mul_pow_rsub_sum_0(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
tmp6 = 1.0
tmp7 = tmp6 - tmp1
tmp8 = tmp7 * tmp0
tmp9 = tl.broadcast_to(tmp8, [RBLOCK])
tmp11 = triton_helpers.promote_to_tensor(tl.sum(tmp9, 0))
tmp12 = tmp6 - tmp0
tmp13 = tmp1 * tmp12
tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
tmp17 = tmp5 + tmp6
tmp18 = 0.3
tmp19 = tmp11 * tmp18
tmp20 = tmp5 + tmp19
tmp21 = 0.7
tmp22 = tmp16 * tmp21
tmp23 = tmp20 + tmp22
tmp24 = tmp23 + tmp6
tmp25 = tmp17 / tmp24
tmp26 = tmp6 - tmp25
tmp27 = tmp26 * tmp26
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp27, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf3 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_div_mul_pow_rsub_sum_0[grid(1)](buf3, arg0_1,
arg1_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf3,
class FocalTverskyLossNew(nn.Module):
def __init__(self, weight=None, size_average=True):
super(FocalTverskyLossNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
DeVriesMatt/cellshape-voxel
|
FocalTverskyLoss
| false
| 5,053
|
[
"BSD-3-Clause"
] | 1
|
64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1
|
https://github.com/DeVriesMatt/cellshape-voxel/tree/64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1
|
SplAtConv2d
|
from torch.nn import Module
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn.modules.utils import _pair
class DropBlock2D(object):
def __init__(self, *args, **kwargs):
raise NotImplementedError
class rSoftMax(nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
if self.radix > 1:
x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
x = F.softmax(x, dim=1)
x = x.reshape(batch, -1)
else:
x = torch.sigmoid(x)
return x
class SplAtConv2d(Module):
"""Split-Attention Conv2d
"""
def __init__(self, in_channels, channels, kernel_size, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), groups=1, bias=True, radix=2,
reduction_factor=4, rectify=False, rectify_avg=False, norm_layer=
None, dropblock_prob=0.0, **kwargs):
super(SplAtConv2d, self).__init__()
padding = _pair(padding)
self.rectify = rectify and (padding[0] > 0 or padding[1] > 0)
self.rectify_avg = rectify_avg
inter_channels = max(in_channels * radix // reduction_factor, 32)
self.radix = radix
self.cardinality = groups
self.channels = channels
self.dropblock_prob = dropblock_prob
if self.rectify:
self.conv = RFConv2d(in_channels, channels * radix, kernel_size,
stride, padding, dilation, groups=groups * radix, bias=bias,
average_mode=rectify_avg, **kwargs)
else:
self.conv = Conv2d(in_channels, channels * radix, kernel_size,
stride, padding, dilation, groups=groups * radix, bias=bias,
**kwargs)
self.use_bn = norm_layer is not None
if self.use_bn:
self.bn0 = norm_layer(channels * radix)
self.relu = ReLU(inplace=True)
self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)
if self.use_bn:
self.bn1 = norm_layer(inter_channels)
self.fc2 = Conv2d(inter_channels, channels * radix, 1, groups=self.
cardinality)
if dropblock_prob > 0.0:
self.dropblock = DropBlock2D(dropblock_prob, 3)
self.rsoftmax = rSoftMax(radix, groups)
def forward(self, x):
x = self.conv(x)
if self.use_bn:
x = self.bn0(x)
if self.dropblock_prob > 0.0:
x = self.dropblock(x)
x = self.relu(x)
batch, rchannel = x.shape[:2]
if self.radix > 1:
if torch.__version__ < '1.5':
splited = torch.split(x, int(rchannel // self.radix), dim=1)
else:
splited = torch.split(x, rchannel // self.radix, dim=1)
gap = sum(splited)
else:
gap = x
gap = F.adaptive_avg_pool2d(gap, 1)
gap = self.fc1(gap)
if self.use_bn:
gap = self.bn1(gap)
gap = self.relu(gap)
atten = self.fc2(gap)
atten = self.rsoftmax(atten).view(batch, -1, 1, 1)
if self.radix > 1:
if torch.__version__ < '1.5':
attens = torch.split(atten, int(rchannel // self.radix), dim=1)
else:
attens = torch.split(atten, rchannel // self.radix, dim=1)
out = sum([(att * split) for att, split in zip(attens, splited)])
else:
out = atten * x
return out.contiguous()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'channels': 4, 'kernel_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
from torch import nn
import torch.nn.functional as F
from torch.nn import Conv2d
from torch.nn import ReLU
from torch.nn.modules.utils import _pair
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_0(in_out_ptr0,
in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 8
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_mean_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
tmp1 = 0.0
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp5 = 1.0
tmp6 = tmp4 / tmp5
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 32
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 8
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x2, tmp2, xmask)
@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 4
x2 = xindex // 8
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 8 * x2), xmask, eviction_policy='evict_last'
)
tmp2 = tl.load(in_ptr0 + (4 + x0 + 8 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp4 = tmp0 - tmp3
tmp5 = tl_math.exp(tmp4)
tmp6 = tmp1 - tmp3
tmp7 = tl_math.exp(tmp6)
tmp8 = tmp2 - tmp3
tmp9 = tl_math.exp(tmp8)
tmp10 = tmp7 + tmp9
tmp11 = tmp5 / tmp10
tl.store(out_ptr0 + x3, tmp11, xmask)
@triton.jit
def triton_poi_fused_add_mul_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
tmp1 = tl.load(in_ptr1 + (x0 + 8 * x1), xmask)
tmp5 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
tmp6 = tl.load(in_ptr1 + (4 + x0 + 8 * x1), xmask)
tmp2 = tmp0 * tmp1
tmp3 = 0.0
tmp4 = tmp2 + tmp3
tmp7 = tmp5 * tmp6
tmp8 = tmp4 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (8, 2, 4, 4), (32, 16, 4, 1))
assert_size_stride(primals_2, (8,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (32, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_5, (32,), (1,))
assert_size_stride(primals_6, (8, 32, 1, 1), (32, 1, 1, 1))
assert_size_stride(primals_7, (8,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=2, bias=None)
assert_size_stride(buf0, (4, 8, 1, 1), (8, 1, 1, 1))
buf1 = reinterpret_tensor(buf0, (4, 8, 1, 1), (8, 1, 32, 32), 0)
del buf0
buf9 = empty_strided_cuda((4, 8, 1, 1), (8, 1, 1, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_convolution_relu_threshold_backward_0[grid(32)](buf1,
primals_2, buf9, 32, XBLOCK=32, num_warps=1, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
triton_poi_fused_add_mean_1[grid(16)](buf1, buf2, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 32, 1, 1), (32, 1, 1, 1))
buf4 = buf3
del buf3
triton_poi_fused_convolution_relu_2[grid(128)](buf4, primals_5, 128,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 8, 1, 1), (8, 1, 1, 1))
buf6 = buf5
del buf5
triton_poi_fused_convolution_3[grid(32)](buf6, primals_7, 32,
XBLOCK=32, num_warps=1, num_stages=1)
del primals_7
buf7 = empty_strided_cuda((4, 2, 1, 4), (8, 4, 4, 1), torch.float32)
triton_poi_fused__softmax_4[grid(32)](buf6, buf7, 32, XBLOCK=32,
num_warps=1, num_stages=1)
buf8 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
triton_poi_fused_add_mul_5[grid(16)](buf7, buf1, buf8, 16, XBLOCK=
16, num_warps=1, num_stages=1)
return (buf8, primals_1, primals_3, primals_4, primals_6,
reinterpret_tensor(buf1, (4, 4, 1, 1), (8, 1, 1, 1), 0),
reinterpret_tensor(buf1, (4, 4, 1, 1), (8, 1, 1, 1), 4), buf2, buf4,
buf6, reinterpret_tensor(buf7, (4, 4, 1, 1), (8, 1, 1, 1), 0),
reinterpret_tensor(buf7, (4, 4, 1, 1), (8, 1, 1, 1), 4), buf9)
class DropBlock2D(object):
def __init__(self, *args, **kwargs):
raise NotImplementedError
class rSoftMax(nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
if self.radix > 1:
x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
x = F.softmax(x, dim=1)
x = x.reshape(batch, -1)
else:
x = torch.sigmoid(x)
return x
class SplAtConv2dNew(Module):
"""Split-Attention Conv2d
"""
def __init__(self, in_channels, channels, kernel_size, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), groups=1, bias=True, radix=2,
reduction_factor=4, rectify=False, rectify_avg=False, norm_layer=
None, dropblock_prob=0.0, **kwargs):
super(SplAtConv2dNew, self).__init__()
padding = _pair(padding)
self.rectify = rectify and (padding[0] > 0 or padding[1] > 0)
self.rectify_avg = rectify_avg
inter_channels = max(in_channels * radix // reduction_factor, 32)
self.radix = radix
self.cardinality = groups
self.channels = channels
self.dropblock_prob = dropblock_prob
if self.rectify:
self.conv = RFConv2d(in_channels, channels * radix, kernel_size,
stride, padding, dilation, groups=groups * radix, bias=bias,
average_mode=rectify_avg, **kwargs)
else:
self.conv = Conv2d(in_channels, channels * radix, kernel_size,
stride, padding, dilation, groups=groups * radix, bias=bias,
**kwargs)
self.use_bn = norm_layer is not None
if self.use_bn:
self.bn0 = norm_layer(channels * radix)
self.relu = ReLU(inplace=True)
self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)
if self.use_bn:
self.bn1 = norm_layer(inter_channels)
self.fc2 = Conv2d(inter_channels, channels * radix, 1, groups=self.
cardinality)
if dropblock_prob > 0.0:
self.dropblock = DropBlock2D(dropblock_prob, 3)
self.rsoftmax = rSoftMax(radix, groups)
def forward(self, input_0):
primals_1 = self.conv.weight
primals_2 = self.conv.bias
primals_4 = self.fc1.weight
primals_5 = self.fc1.bias
primals_6 = self.fc2.weight
primals_7 = self.fc2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
DYF-AI/openvino-x
|
SplAtConv2d
| false
| 5,054
|
[
"Apache-2.0"
] | 1
|
0f18ebb240ea3394f7e461aca34fac158e686d95
|
https://github.com/DYF-AI/openvino-x/tree/0f18ebb240ea3394f7e461aca34fac158e686d95
|
SpatialCrossMapLRN
|
import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.utils.data
import torch.backends.cudnn
class SpatialCrossMapLRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
ACROSS_CHANNELS=True):
super(SpatialCrossMapLRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:
self.average = nn.AvgPool3d(kernel_size=(local_size, 1, 1),
stride=1, padding=(int((local_size - 1.0) / 2), 0, 0))
else:
self.average = nn.AvgPool2d(kernel_size=local_size, stride=1,
padding=int((local_size - 1.0) / 2))
self.alpha = alpha
self.beta = beta
self.k = k
def forward(self, x):
if self.ACROSS_CHANNELS:
div = x.pow(2).unsqueeze(1)
div = self.average(div).squeeze(1)
div = div.mul(self.alpha).add(self.k).pow(self.beta)
else:
div = x.pow(2)
div = self.average(div)
div = div.mul(self.alpha).add(self.k).pow(self.beta)
x = x.div(div)
return 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data.dataloader
import torch.utils.data
import torch.backends.cudnn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_div_mul_pow_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tmp0 * tmp0
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = tmp3 * tmp2
tmp5 = tmp4 + tmp2
tmp6 = 0.75
tmp7 = libdevice.pow(tmp5, tmp6)
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x0, tmp8, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_div_mul_pow_0[grid(256)](arg0_1, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class SpatialCrossMapLRNNew(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
ACROSS_CHANNELS=True):
super(SpatialCrossMapLRNNew, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:
self.average = nn.AvgPool3d(kernel_size=(local_size, 1, 1),
stride=1, padding=(int((local_size - 1.0) / 2), 0, 0))
else:
self.average = nn.AvgPool2d(kernel_size=local_size, stride=1,
padding=int((local_size - 1.0) / 2))
self.alpha = alpha
self.beta = beta
self.k = k
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
DeepBrainsMe/PyDoctor_Final
|
SpatialCrossMapLRN
| false
| 5,055
|
[
"MIT"
] | 1
|
49ecfc64b2a2866e7f37cc79c1f32a817975f064
|
https://github.com/DeepBrainsMe/PyDoctor_Final/tree/49ecfc64b2a2866e7f37cc79c1f32a817975f064
|
StyleAdaptiveLayerNorm
|
import torch
import torch.nn
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
class AffineLinear(nn.Module):
def __init__(self, in_dim, out_dim):
super(AffineLinear, self).__init__()
affine = nn.Linear(in_dim, out_dim)
self.affine = affine
def forward(self, input):
return self.affine(input)
class StyleAdaptiveLayerNorm(nn.Module):
def __init__(self, in_channel, style_dim):
super(StyleAdaptiveLayerNorm, self).__init__()
self.in_channel = in_channel
self.norm = nn.LayerNorm(in_channel, elementwise_affine=False)
self.style = AffineLinear(style_dim, in_channel * 2)
self.style.affine.bias.data[:in_channel] = 1
self.style.affine.bias.data[in_channel:] = 0
def forward(self, input, style_code):
style = self.style(style_code).unsqueeze(1)
gamma, beta = style.chunk(2, dim=-1)
out = self.norm(input)
out = gamma * out + beta
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channel': 4, 'style_dim': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_add_mul_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 16
x3 = xindex // 256
x4 = xindex % 256
x5 = xindex // 4 % 64
x7 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1 + 128 * x3), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr3 + x5, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr4 + x5, xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (4 + x0 + 8 * x1 + 128 * x3), xmask,
eviction_policy='evict_last')
tmp10 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 - tmp4
tmp7 = tmp5 * tmp6
tmp8 = tmp2 * tmp7
tmp11 = tmp9 + tmp10
tmp12 = tmp8 + tmp11
tl.store(out_ptr0 + x7, tmp12, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (8, 4), (4, 1))
assert_size_stride(primals_2, (8,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(64)](primals_4, buf1,
buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf3 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
torch.float32)
triton_poi_fused_add_mul_native_layer_norm_1[grid(1024)](buf0,
primals_2, primals_4, buf1, buf2, buf3, 1024, XBLOCK=256,
num_warps=4, num_stages=1)
del buf0
del buf1
del buf2
del primals_2
return buf3, primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0)
class AffineLinear(nn.Module):
def __init__(self, in_dim, out_dim):
super(AffineLinear, self).__init__()
affine = nn.Linear(in_dim, out_dim)
self.affine = affine
def forward(self, input):
return self.affine(input)
class StyleAdaptiveLayerNormNew(nn.Module):
def __init__(self, in_channel, style_dim):
super(StyleAdaptiveLayerNormNew, self).__init__()
self.in_channel = in_channel
self.norm = nn.LayerNorm(in_channel, elementwise_affine=False)
self.style = AffineLinear(style_dim, in_channel * 2)
self.style.affine.bias.data[:in_channel] = 1
self.style.affine.bias.data[in_channel:] = 0
def forward(self, input_0, input_1):
primals_1 = self.style.affine.weight
primals_2 = self.style.affine.bias
primals_3 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
DanielLin94144/StyleSpeech
|
StyleAdaptiveLayerNorm
| false
| 5,056
|
[
"MIT"
] | 1
|
809e8ead55bea2c63f714fdc19bf24d80f0f546c
|
https://github.com/DanielLin94144/StyleSpeech/tree/809e8ead55bea2c63f714fdc19bf24d80f0f546c
|
ATLoss
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class ATLoss(nn.Module):
"""
Module for calculating AT Loss
:param norm_type (int): Norm to be used in calculating loss
"""
def __init__(self, norm_type=2):
super(ATLoss, self).__init__()
self.p = norm_type
def forward(self, teacher_output, student_output):
"""
Forward function
:param teacher_output (torch.FloatTensor): Prediction made by the teacher model
:param student_output (torch.FloatTensor): Prediction made by the student model
"""
A_t = teacher_output[1:]
A_s = student_output[1:]
loss = 0.0
for layerT, layerS in zip(A_t, A_s):
xT = self.single_at_loss(layerT)
xS = self.single_at_loss(layerS)
loss += (xS - xT).pow(self.p).mean()
return loss
def single_at_loss(self, activation):
"""
Function for calculating single attention loss
"""
return F.normalize(activation.pow(self.p).mean(1).view(activation.
size(0), -1))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_linalg_vector_norm_mean_pow_view_0(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (64 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (68 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr0 + (72 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp8 = tl.load(in_ptr0 + (76 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp14 = tl.load(in_ptr0 + (65 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp16 = tl.load(in_ptr0 + (69 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp19 = tl.load(in_ptr0 + (73 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp22 = tl.load(in_ptr0 + (77 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp28 = tl.load(in_ptr0 + (66 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp30 = tl.load(in_ptr0 + (70 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp33 = tl.load(in_ptr0 + (74 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp36 = tl.load(in_ptr0 + (78 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp42 = tl.load(in_ptr0 + (67 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp44 = tl.load(in_ptr0 + (71 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp47 = tl.load(in_ptr0 + (75 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp50 = tl.load(in_ptr0 + (79 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp1 = tmp0 * tmp0
tmp3 = tmp2 * tmp2
tmp4 = tmp1 + tmp3
tmp6 = tmp5 * tmp5
tmp7 = tmp4 + tmp6
tmp9 = tmp8 * tmp8
tmp10 = tmp7 + tmp9
tmp11 = 4.0
tmp12 = tmp10 / tmp11
tmp13 = tmp12 * tmp12
tmp15 = tmp14 * tmp14
tmp17 = tmp16 * tmp16
tmp18 = tmp15 + tmp17
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp24 / tmp11
tmp26 = tmp25 * tmp25
tmp27 = tmp13 + tmp26
tmp29 = tmp28 * tmp28
tmp31 = tmp30 * tmp30
tmp32 = tmp29 + tmp31
tmp34 = tmp33 * tmp33
tmp35 = tmp32 + tmp34
tmp37 = tmp36 * tmp36
tmp38 = tmp35 + tmp37
tmp39 = tmp38 / tmp11
tmp40 = tmp39 * tmp39
tmp41 = tmp27 + tmp40
tmp43 = tmp42 * tmp42
tmp45 = tmp44 * tmp44
tmp46 = tmp43 + tmp45
tmp48 = tmp47 * tmp47
tmp49 = tmp46 + tmp48
tmp51 = tmp50 * tmp50
tmp52 = tmp49 + tmp51
tmp53 = tmp52 / tmp11
tmp54 = tmp53 * tmp53
tmp55 = tmp41 + tmp54
tl.store(out_ptr0 + x0, tmp55, xmask)
@triton.jit
def triton_poi_fused_linalg_vector_norm_mean_pow_view_1(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (128 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (132 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr0 + (136 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp8 = tl.load(in_ptr0 + (140 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp14 = tl.load(in_ptr0 + (129 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp16 = tl.load(in_ptr0 + (133 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp19 = tl.load(in_ptr0 + (137 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp22 = tl.load(in_ptr0 + (141 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp28 = tl.load(in_ptr0 + (130 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp30 = tl.load(in_ptr0 + (134 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp33 = tl.load(in_ptr0 + (138 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp36 = tl.load(in_ptr0 + (142 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp42 = tl.load(in_ptr0 + (131 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp44 = tl.load(in_ptr0 + (135 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp47 = tl.load(in_ptr0 + (139 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp50 = tl.load(in_ptr0 + (143 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp1 = tmp0 * tmp0
tmp3 = tmp2 * tmp2
tmp4 = tmp1 + tmp3
tmp6 = tmp5 * tmp5
tmp7 = tmp4 + tmp6
tmp9 = tmp8 * tmp8
tmp10 = tmp7 + tmp9
tmp11 = 4.0
tmp12 = tmp10 / tmp11
tmp13 = tmp12 * tmp12
tmp15 = tmp14 * tmp14
tmp17 = tmp16 * tmp16
tmp18 = tmp15 + tmp17
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp24 / tmp11
tmp26 = tmp25 * tmp25
tmp27 = tmp13 + tmp26
tmp29 = tmp28 * tmp28
tmp31 = tmp30 * tmp30
tmp32 = tmp29 + tmp31
tmp34 = tmp33 * tmp33
tmp35 = tmp32 + tmp34
tmp37 = tmp36 * tmp36
tmp38 = tmp35 + tmp37
tmp39 = tmp38 / tmp11
tmp40 = tmp39 * tmp39
tmp41 = tmp27 + tmp40
tmp43 = tmp42 * tmp42
tmp45 = tmp44 * tmp44
tmp46 = tmp43 + tmp45
tmp48 = tmp47 * tmp47
tmp49 = tmp46 + tmp48
tmp51 = tmp50 * tmp50
tmp52 = tmp49 + tmp51
tmp53 = tmp52 / tmp11
tmp54 = tmp53 * tmp53
tmp55 = tmp41 + tmp54
tl.store(out_ptr0 + x0, tmp55, xmask)
@triton.jit
def triton_poi_fused_linalg_vector_norm_mean_pow_view_2(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (192 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (196 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr0 + (200 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp8 = tl.load(in_ptr0 + (204 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp14 = tl.load(in_ptr0 + (193 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp16 = tl.load(in_ptr0 + (197 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp19 = tl.load(in_ptr0 + (201 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp22 = tl.load(in_ptr0 + (205 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp28 = tl.load(in_ptr0 + (194 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp30 = tl.load(in_ptr0 + (198 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp33 = tl.load(in_ptr0 + (202 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp36 = tl.load(in_ptr0 + (206 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp42 = tl.load(in_ptr0 + (195 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp44 = tl.load(in_ptr0 + (199 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp47 = tl.load(in_ptr0 + (203 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp50 = tl.load(in_ptr0 + (207 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp1 = tmp0 * tmp0
tmp3 = tmp2 * tmp2
tmp4 = tmp1 + tmp3
tmp6 = tmp5 * tmp5
tmp7 = tmp4 + tmp6
tmp9 = tmp8 * tmp8
tmp10 = tmp7 + tmp9
tmp11 = 4.0
tmp12 = tmp10 / tmp11
tmp13 = tmp12 * tmp12
tmp15 = tmp14 * tmp14
tmp17 = tmp16 * tmp16
tmp18 = tmp15 + tmp17
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp24 / tmp11
tmp26 = tmp25 * tmp25
tmp27 = tmp13 + tmp26
tmp29 = tmp28 * tmp28
tmp31 = tmp30 * tmp30
tmp32 = tmp29 + tmp31
tmp34 = tmp33 * tmp33
tmp35 = tmp32 + tmp34
tmp37 = tmp36 * tmp36
tmp38 = tmp35 + tmp37
tmp39 = tmp38 / tmp11
tmp40 = tmp39 * tmp39
tmp41 = tmp27 + tmp40
tmp43 = tmp42 * tmp42
tmp45 = tmp44 * tmp44
tmp46 = tmp43 + tmp45
tmp48 = tmp47 * tmp47
tmp49 = tmp46 + tmp48
tmp51 = tmp50 * tmp50
tmp52 = tmp49 + tmp51
tmp53 = tmp52 / tmp11
tmp54 = tmp53 * tmp53
tmp55 = tmp41 + tmp54
tl.store(out_ptr0 + x0, tmp55, xmask)
@triton.jit
def triton_per_fused_add_div_mean_pow_sub_view_3(in_out_ptr0, in_ptr0,
in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, xnumel,
rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex % 4
r1 = rindex // 4
tmp0 = tl.load(in_ptr0 + (64 + r0 + 16 * r1), None)
tmp2 = tl.load(in_ptr0 + (68 + r0 + 16 * r1), None)
tmp5 = tl.load(in_ptr0 + (72 + r0 + 16 * r1), None)
tmp8 = tl.load(in_ptr0 + (76 + r0 + 16 * r1), None)
tmp13 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
tmp18 = tl.load(in_ptr2 + (64 + r0 + 16 * r1), None)
tmp20 = tl.load(in_ptr2 + (68 + r0 + 16 * r1), None)
tmp23 = tl.load(in_ptr2 + (72 + r0 + 16 * r1), None)
tmp26 = tl.load(in_ptr2 + (76 + r0 + 16 * r1), None)
tmp30 = tl.load(in_ptr3 + r1, None, eviction_policy='evict_last')
tmp39 = tl.load(in_ptr0 + (128 + r0 + 16 * r1), None)
tmp41 = tl.load(in_ptr0 + (132 + r0 + 16 * r1), None)
tmp44 = tl.load(in_ptr0 + (136 + r0 + 16 * r1), None)
tmp47 = tl.load(in_ptr0 + (140 + r0 + 16 * r1), None)
tmp51 = tl.load(in_ptr4 + r1, None, eviction_policy='evict_last')
tmp55 = tl.load(in_ptr2 + (128 + r0 + 16 * r1), None)
tmp57 = tl.load(in_ptr2 + (132 + r0 + 16 * r1), None)
tmp60 = tl.load(in_ptr2 + (136 + r0 + 16 * r1), None)
tmp63 = tl.load(in_ptr2 + (140 + r0 + 16 * r1), None)
tmp67 = tl.load(in_ptr5 + r1, None, eviction_policy='evict_last')
tmp76 = tl.load(in_ptr0 + (192 + r0 + 16 * r1), None)
tmp78 = tl.load(in_ptr0 + (196 + r0 + 16 * r1), None)
tmp81 = tl.load(in_ptr0 + (200 + r0 + 16 * r1), None)
tmp84 = tl.load(in_ptr0 + (204 + r0 + 16 * r1), None)
tmp88 = tl.load(in_ptr6 + r1, None, eviction_policy='evict_last')
tmp92 = tl.load(in_ptr2 + (192 + r0 + 16 * r1), None)
tmp94 = tl.load(in_ptr2 + (196 + r0 + 16 * r1), None)
tmp97 = tl.load(in_ptr2 + (200 + r0 + 16 * r1), None)
tmp100 = tl.load(in_ptr2 + (204 + r0 + 16 * r1), None)
tmp104 = tl.load(in_ptr7 + r1, None, eviction_policy='evict_last')
tmp1 = tmp0 * tmp0
tmp3 = tmp2 * tmp2
tmp4 = tmp1 + tmp3
tmp6 = tmp5 * tmp5
tmp7 = tmp4 + tmp6
tmp9 = tmp8 * tmp8
tmp10 = tmp7 + tmp9
tmp11 = 4.0
tmp12 = tmp10 / tmp11
tmp14 = libdevice.sqrt(tmp13)
tmp15 = 1e-12
tmp16 = triton_helpers.maximum(tmp14, tmp15)
tmp17 = tmp12 / tmp16
tmp19 = tmp18 * tmp18
tmp21 = tmp20 * tmp20
tmp22 = tmp19 + tmp21
tmp24 = tmp23 * tmp23
tmp25 = tmp22 + tmp24
tmp27 = tmp26 * tmp26
tmp28 = tmp25 + tmp27
tmp29 = tmp28 / tmp11
tmp31 = libdevice.sqrt(tmp30)
tmp32 = triton_helpers.maximum(tmp31, tmp15)
tmp33 = tmp29 / tmp32
tmp34 = tmp17 - tmp33
tmp35 = tmp34 * tmp34
tmp36 = tl.broadcast_to(tmp35, [XBLOCK, RBLOCK])
tmp38 = tl.sum(tmp36, 1)[:, None]
tmp40 = tmp39 * tmp39
tmp42 = tmp41 * tmp41
tmp43 = tmp40 + tmp42
tmp45 = tmp44 * tmp44
tmp46 = tmp43 + tmp45
tmp48 = tmp47 * tmp47
tmp49 = tmp46 + tmp48
tmp50 = tmp49 / tmp11
tmp52 = libdevice.sqrt(tmp51)
tmp53 = triton_helpers.maximum(tmp52, tmp15)
tmp54 = tmp50 / tmp53
tmp56 = tmp55 * tmp55
tmp58 = tmp57 * tmp57
tmp59 = tmp56 + tmp58
tmp61 = tmp60 * tmp60
tmp62 = tmp59 + tmp61
tmp64 = tmp63 * tmp63
tmp65 = tmp62 + tmp64
tmp66 = tmp65 / tmp11
tmp68 = libdevice.sqrt(tmp67)
tmp69 = triton_helpers.maximum(tmp68, tmp15)
tmp70 = tmp66 / tmp69
tmp71 = tmp54 - tmp70
tmp72 = tmp71 * tmp71
tmp73 = tl.broadcast_to(tmp72, [XBLOCK, RBLOCK])
tmp75 = tl.sum(tmp73, 1)[:, None]
tmp77 = tmp76 * tmp76
tmp79 = tmp78 * tmp78
tmp80 = tmp77 + tmp79
tmp82 = tmp81 * tmp81
tmp83 = tmp80 + tmp82
tmp85 = tmp84 * tmp84
tmp86 = tmp83 + tmp85
tmp87 = tmp86 / tmp11
tmp89 = libdevice.sqrt(tmp88)
tmp90 = triton_helpers.maximum(tmp89, tmp15)
tmp91 = tmp87 / tmp90
tmp93 = tmp92 * tmp92
tmp95 = tmp94 * tmp94
tmp96 = tmp93 + tmp95
tmp98 = tmp97 * tmp97
tmp99 = tmp96 + tmp98
tmp101 = tmp100 * tmp100
tmp102 = tmp99 + tmp101
tmp103 = tmp102 / tmp11
tmp105 = libdevice.sqrt(tmp104)
tmp106 = triton_helpers.maximum(tmp105, tmp15)
tmp107 = tmp103 / tmp106
tmp108 = tmp91 - tmp107
tmp109 = tmp108 * tmp108
tmp110 = tl.broadcast_to(tmp109, [XBLOCK, RBLOCK])
tmp112 = tl.sum(tmp110, 1)[:, None]
tmp113 = 16.0
tmp114 = tmp38 / tmp113
tmp115 = 0.0
tmp116 = tmp114 + tmp115
tmp117 = tmp75 / tmp113
tmp118 = tmp116 + tmp117
tmp119 = tmp112 / tmp113
tmp120 = tmp118 + tmp119
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp120, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
get_raw_stream(0)
triton_poi_fused_linalg_vector_norm_mean_pow_view_0[grid(4)](arg1_1,
buf0, 4, XBLOCK=4, num_warps=1, num_stages=1)
buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
triton_poi_fused_linalg_vector_norm_mean_pow_view_0[grid(4)](arg0_1,
buf1, 4, XBLOCK=4, num_warps=1, num_stages=1)
buf4 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
triton_poi_fused_linalg_vector_norm_mean_pow_view_1[grid(4)](arg1_1,
buf4, 4, XBLOCK=4, num_warps=1, num_stages=1)
buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
triton_poi_fused_linalg_vector_norm_mean_pow_view_1[grid(4)](arg0_1,
buf5, 4, XBLOCK=4, num_warps=1, num_stages=1)
buf8 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
triton_poi_fused_linalg_vector_norm_mean_pow_view_2[grid(4)](arg1_1,
buf8, 4, XBLOCK=4, num_warps=1, num_stages=1)
buf9 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
triton_poi_fused_linalg_vector_norm_mean_pow_view_2[grid(4)](arg0_1,
buf9, 4, XBLOCK=4, num_warps=1, num_stages=1)
buf11 = empty_strided_cuda((), (), torch.float32)
buf12 = buf11
del buf11
triton_per_fused_add_div_mean_pow_sub_view_3[grid(1)](buf12, arg1_1,
buf0, arg0_1, buf1, buf4, buf5, buf8, buf9, 1, 16, XBLOCK=1,
num_warps=2, num_stages=1)
del arg0_1
del arg1_1
del buf0
del buf1
del buf4
del buf5
del buf8
del buf9
return buf12,
class ATLossNew(nn.Module):
"""
Module for calculating AT Loss
:param norm_type (int): Norm to be used in calculating loss
"""
def __init__(self, norm_type=2):
super(ATLossNew, self).__init__()
self.p = norm_type
def single_at_loss(self, activation):
"""
Function for calculating single attention loss
"""
return F.normalize(activation.pow(self.p).mean(1).view(activation.
size(0), -1))
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
DA-southampton/KD_Lib
|
ATLoss
| false
| 5,057
|
[
"MIT"
] | 1
|
bd4a9b93b9674607ecf467d280d5cab1c516bdc6
|
https://github.com/DA-southampton/KD_Lib/tree/bd4a9b93b9674607ecf467d280d5cab1c516bdc6
|
DiceBCELoss
|
import torch
from torch import nn
import torch.nn.functional as F
class DiceBCELoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceBCELoss, self).__init__()
def forward(self, inputs, targets, smooth=1):
inputs = inputs.view(-1)
targets = targets.view(-1)
intersection = (inputs * targets).sum()
dice_loss = 1 - (2.0 * intersection + smooth) / (inputs.sum() +
targets.sum() + smooth)
BCE = F.binary_cross_entropy(inputs, targets, reduction='mean')
Dice_BCE = BCE + dice_loss
return Dice_BCE
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_binary_cross_entropy_div_mul_rsub_sum_0(in_out_ptr0,
in_ptr0, in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp3 = tl.load(in_ptr1 + r0, None)
tmp1 = 1.0
tmp2 = tmp0 - tmp1
tmp4 = -tmp3
tmp5 = libdevice.log1p(tmp4)
tmp6 = -100.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp2 * tmp7
tmp9 = tl_math.log(tmp3)
tmp10 = triton_helpers.maximum(tmp9, tmp6)
tmp11 = tmp0 * tmp10
tmp12 = tmp8 - tmp11
tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
tmp16 = tmp3 * tmp0
tmp17 = tl.broadcast_to(tmp16, [RBLOCK])
tmp19 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
tmp20 = tl.broadcast_to(tmp3, [RBLOCK])
tmp22 = triton_helpers.promote_to_tensor(tl.sum(tmp20, 0))
tmp23 = tl.broadcast_to(tmp0, [RBLOCK])
tmp25 = triton_helpers.promote_to_tensor(tl.sum(tmp23, 0))
tmp26 = 256.0
tmp27 = tmp15 / tmp26
tmp28 = 2.0
tmp29 = tmp19 * tmp28
tmp30 = tmp29 + tmp1
tmp31 = tmp22 + tmp25
tmp32 = tmp31 + tmp1
tmp33 = tmp30 / tmp32
tmp34 = tmp1 - tmp33
tmp35 = tmp27 + tmp34
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp35, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf4 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_binary_cross_entropy_div_mul_rsub_sum_0[grid(1)](
buf4, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf4,
class DiceBCELossNew(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceBCELossNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
DeVriesMatt/cellshape-voxel
|
DiceBCELoss
| false
| 5,058
|
[
"BSD-3-Clause"
] | 1
|
64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1
|
https://github.com/DeVriesMatt/cellshape-voxel/tree/64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1
|
TverskyLoss
|
import torch
from torch import nn
class TverskyLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(TverskyLoss, self).__init__()
def forward(self, inputs, targets, smooth=1, alpha=0.3, beta=0.7):
inputs = inputs.view(-1)
targets = targets.view(-1)
TP = (inputs * targets).sum()
FP = ((1 - targets) * inputs).sum()
FN = (targets * (1 - inputs)).sum()
Tversky = (TP + smooth) / (TP + alpha * FP + beta * FN + smooth)
return 1 - Tversky
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_div_mul_rsub_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
tmp6 = 1.0
tmp7 = tmp6 - tmp1
tmp8 = tmp7 * tmp0
tmp9 = tl.broadcast_to(tmp8, [RBLOCK])
tmp11 = triton_helpers.promote_to_tensor(tl.sum(tmp9, 0))
tmp12 = tmp6 - tmp0
tmp13 = tmp1 * tmp12
tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
tmp17 = tmp5 + tmp6
tmp18 = 0.3
tmp19 = tmp11 * tmp18
tmp20 = tmp5 + tmp19
tmp21 = 0.7
tmp22 = tmp16 * tmp21
tmp23 = tmp20 + tmp22
tmp24 = tmp23 + tmp6
tmp25 = tmp17 / tmp24
tmp26 = tmp6 - tmp25
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp26, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf3 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_div_mul_rsub_sum_0[grid(1)](buf3, arg0_1,
arg1_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf3,
class TverskyLossNew(nn.Module):
def __init__(self, weight=None, size_average=True):
super(TverskyLossNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
DeVriesMatt/cellshape-voxel
|
TverskyLoss
| false
| 5,059
|
[
"BSD-3-Clause"
] | 1
|
64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1
|
https://github.com/DeVriesMatt/cellshape-voxel/tree/64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1
|
EuclideanDistLoss
|
import torch
from torch import nn
class EuclideanDistLoss(nn.Module):
def __init__(self):
super(EuclideanDistLoss, self).__init__()
def forward(self, inputs, inputs_rot):
dist = torch.dist(inputs, inputs_rot, p=2.0)
return dist
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_dist_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = libdevice.sqrt(tmp6)
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp7, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_dist_0[grid(1)](buf1, arg1_1, arg0_1, 1, 256,
num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class EuclideanDistLossNew(nn.Module):
def __init__(self):
super(EuclideanDistLossNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
DeVriesMatt/cellshape-voxel
|
EuclideanDistLoss
| false
| 5,060
|
[
"BSD-3-Clause"
] | 1
|
64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1
|
https://github.com/DeVriesMatt/cellshape-voxel/tree/64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1
|
MaskedMSELoss
|
import torch
import torch.utils.data
from torch import nn
class MaskedMSELoss(nn.Module):
def __init__(self):
super(MaskedMSELoss, self).__init__()
def forward(self, pred, target, output_lengths):
squared_error = (target - pred) ** 2
loss = (squared_error.mean(1).sum(1) / output_lengths).mean()
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_mean_pow_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp5 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
tmp9 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp10 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
tmp14 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp15 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
tmp21 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
tmp22 = tl.load(in_ptr1 + (4 + x0 + 64 * x1), xmask)
tmp25 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
tmp26 = tl.load(in_ptr1 + (20 + x0 + 64 * x1), xmask)
tmp30 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
tmp31 = tl.load(in_ptr1 + (36 + x0 + 64 * x1), xmask)
tmp35 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
tmp36 = tl.load(in_ptr1 + (52 + x0 + 64 * x1), xmask)
tmp42 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
tmp43 = tl.load(in_ptr1 + (8 + x0 + 64 * x1), xmask)
tmp46 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
tmp47 = tl.load(in_ptr1 + (24 + x0 + 64 * x1), xmask)
tmp51 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
tmp52 = tl.load(in_ptr1 + (40 + x0 + 64 * x1), xmask)
tmp56 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
tmp57 = tl.load(in_ptr1 + (56 + x0 + 64 * x1), xmask)
tmp63 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
tmp64 = tl.load(in_ptr1 + (12 + x0 + 64 * x1), xmask)
tmp67 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
tmp68 = tl.load(in_ptr1 + (28 + x0 + 64 * x1), xmask)
tmp72 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
tmp73 = tl.load(in_ptr1 + (44 + x0 + 64 * x1), xmask)
tmp77 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
tmp78 = tl.load(in_ptr1 + (60 + x0 + 64 * x1), xmask)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp6 = tmp4 - tmp5
tmp7 = tmp6 * tmp6
tmp8 = tmp3 + tmp7
tmp11 = tmp9 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tmp8 + tmp12
tmp16 = tmp14 - tmp15
tmp17 = tmp16 * tmp16
tmp18 = tmp13 + tmp17
tmp19 = 4.0
tmp20 = tmp18 / tmp19
tmp23 = tmp21 - tmp22
tmp24 = tmp23 * tmp23
tmp27 = tmp25 - tmp26
tmp28 = tmp27 * tmp27
tmp29 = tmp24 + tmp28
tmp32 = tmp30 - tmp31
tmp33 = tmp32 * tmp32
tmp34 = tmp29 + tmp33
tmp37 = tmp35 - tmp36
tmp38 = tmp37 * tmp37
tmp39 = tmp34 + tmp38
tmp40 = tmp39 / tmp19
tmp41 = tmp20 + tmp40
tmp44 = tmp42 - tmp43
tmp45 = tmp44 * tmp44
tmp48 = tmp46 - tmp47
tmp49 = tmp48 * tmp48
tmp50 = tmp45 + tmp49
tmp53 = tmp51 - tmp52
tmp54 = tmp53 * tmp53
tmp55 = tmp50 + tmp54
tmp58 = tmp56 - tmp57
tmp59 = tmp58 * tmp58
tmp60 = tmp55 + tmp59
tmp61 = tmp60 / tmp19
tmp62 = tmp41 + tmp61
tmp65 = tmp63 - tmp64
tmp66 = tmp65 * tmp65
tmp69 = tmp67 - tmp68
tmp70 = tmp69 * tmp69
tmp71 = tmp66 + tmp70
tmp74 = tmp72 - tmp73
tmp75 = tmp74 * tmp74
tmp76 = tmp71 + tmp75
tmp79 = tmp77 - tmp78
tmp80 = tmp79 * tmp79
tmp81 = tmp76 + tmp80
tmp82 = tmp81 / tmp19
tmp83 = tmp62 + tmp82
tl.store(out_ptr0 + x2, tmp83, xmask)
@triton.jit
def triton_per_fused_div_mean_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex % 16
r2 = rindex
tmp0 = tl.load(in_ptr0 + r0, None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + r2, None)
tmp2 = tmp0 / tmp1
tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
tmp6 = 256.0
tmp7 = tmp5 / tmp6
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp7, None)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mean_pow_sub_sum_0[grid(16)](arg0_1, arg1_1, buf0,
16, XBLOCK=16, num_warps=1, num_stages=1)
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = buf1
del buf1
triton_per_fused_div_mean_1[grid(1)](buf2, buf0, arg2_1, 1, 256,
num_warps=2, num_stages=1)
del arg2_1
del buf0
return buf2,
class MaskedMSELossNew(nn.Module):
def __init__(self):
super(MaskedMSELossNew, self).__init__()
def forward(self, input_0, input_1, input_2):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
output = call([arg0_1, arg1_1, arg2_1])
return output[0]
|
DashaSerdyuk/tacotron2
|
MaskedMSELoss
| false
| 5,061
|
[
"BSD-3-Clause"
] | 1
|
1a88669670750f8b0e1aff76abc8b1b15300e1dc
|
https://github.com/DashaSerdyuk/tacotron2/tree/1a88669670750f8b0e1aff76abc8b1b15300e1dc
|
FocalLoss
|
import torch
from torch import nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(FocalLoss, self).__init__()
def forward(self, inputs, targets, alpha=0.8, gamma=2, smooth=1):
inputs = inputs.view(-1)
targets = targets.view(-1)
BCE = F.binary_cross_entropy(inputs, targets, reduction='mean')
BCE_EXP = torch.exp(-BCE)
focal_loss = alpha * (1 - BCE_EXP) ** gamma * BCE
return focal_loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_binary_cross_entropy_exp_mul_neg_pow_rsub_0(in_out_ptr0,
in_ptr0, in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp3 = tl.load(in_ptr1 + r0, None)
tmp1 = 1.0
tmp2 = tmp0 - tmp1
tmp4 = -tmp3
tmp5 = libdevice.log1p(tmp4)
tmp6 = -100.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp2 * tmp7
tmp9 = tl_math.log(tmp3)
tmp10 = triton_helpers.maximum(tmp9, tmp6)
tmp11 = tmp0 * tmp10
tmp12 = tmp8 - tmp11
tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
tmp16 = 256.0
tmp17 = tmp15 / tmp16
tmp18 = -tmp17
tmp19 = tl_math.exp(tmp18)
tmp20 = tmp1 - tmp19
tmp21 = tmp20 * tmp20
tmp22 = 0.8
tmp23 = tmp21 * tmp22
tmp24 = tmp23 * tmp17
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp24, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_binary_cross_entropy_exp_mul_neg_pow_rsub_0[grid(1)](
buf1, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class FocalLossNew(nn.Module):
def __init__(self, weight=None, size_average=True):
super(FocalLossNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
DeVriesMatt/cellshape-voxel
|
FocalLoss
| false
| 5,062
|
[
"BSD-3-Clause"
] | 1
|
64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1
|
https://github.com/DeVriesMatt/cellshape-voxel/tree/64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1
|
h_swish
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.dataloader
import torch.utils.data
import torch.backends.cudnn
class h_swish(nn.Module):
def __init__(self, inplace=True):
super(h_swish, self).__init__()
self.inplace = inplace
def forward(self, x):
out = F.relu6(x + 3.0, self.inplace) / 6.0
return out * 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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data.dataloader
import torch.utils.data
import torch.backends.cudnn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_div_hardtanh_mul_0(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 3.0
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 6.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tmp7 = 0.16666666666666666
tmp8 = tmp6 * tmp7
tmp9 = tmp8 * tmp0
tl.store(out_ptr0 + x0, tmp9, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_div_hardtanh_mul_0[grid(256)](arg0_1, buf0,
256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class h_swishNew(nn.Module):
def __init__(self, inplace=True):
super(h_swishNew, self).__init__()
self.inplace = inplace
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
DeepBrainsMe/PyDoctor_Final
|
h_swish
| false
| 5,063
|
[
"MIT"
] | 1
|
49ecfc64b2a2866e7f37cc79c1f32a817975f064
|
https://github.com/DeepBrainsMe/PyDoctor_Final/tree/49ecfc64b2a2866e7f37cc79c1f32a817975f064
|
ReconstructLoss
|
import torch
import torch.nn as nn
class ReconstructLoss(nn.Module):
def __init__(self):
super(ReconstructLoss, self).__init__()
self.criterion = nn.L1Loss()
def forward(self, x, y):
loss = self.criterion(x, y)
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_abs_mean_sub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = 256.0
tmp8 = tmp6 / tmp7
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp8, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_abs_mean_sub_0[grid(1)](buf1, arg1_1, arg0_1, 1,
256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class ReconstructLossNew(nn.Module):
def __init__(self):
super(ReconstructLossNew, self).__init__()
self.criterion = nn.L1Loss()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
DevKiHyun/SRNTT.pytorch
|
ReconstructLoss
| false
| 5,064
|
[
"MIT"
] | 1
|
d7540921983cf42ea2a7eef544862a95318e6a35
|
https://github.com/DevKiHyun/SRNTT.pytorch/tree/d7540921983cf42ea2a7eef544862a95318e6a35
|
SinActv
|
import torch
import torch.nn as nn
class SinActv(nn.Module):
"""The sin activation function.
"""
def __init__(self):
"""Initializer method.
"""
super().__init__()
def forward(self, input_):
return torch.sin(input_)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_sin_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl_math.sin(tmp0)
tl.store(out_ptr0 + x0, tmp1, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_sin_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class SinActvNew(nn.Module):
"""The sin activation function.
"""
def __init__(self):
"""Initializer method.
"""
super().__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
DiffEqML/neurodiffeq
|
SinActv
| false
| 5,065
|
[
"MIT"
] | 1
|
c5e7404c47a4729578ee2149f289be0a8909d775
|
https://github.com/DiffEqML/neurodiffeq/tree/c5e7404c47a4729578ee2149f289be0a8909d775
|
AvgSpacial
|
import torch
import torch.utils.data
import torch.nn as nn
import torch.utils.checkpoint
class AvgSpacial(nn.Module):
def forward(self, inp):
return inp.view(inp.size(0), inp.size(1), -1).mean(-1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 16.0
tmp6 = tmp4 / tmp5
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp6, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_mean_0[grid(16)](buf1, arg0_1, 16, 16, XBLOCK=8,
num_warps=2, num_stages=1)
del arg0_1
return buf1,
class AvgSpacialNew(nn.Module):
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
CNNs4QSPR/se3cnn
|
AvgSpacial
| false
| 5,066
|
[
"MIT"
] | 1
|
513f5f827c4c511bdc96e3c6ea663c8fbce60f57
|
https://github.com/CNNs4QSPR/se3cnn/tree/513f5f827c4c511bdc96e3c6ea663c8fbce60f57
|
DiceLoss
|
import torch
from torch import nn
class DiceLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceLoss, self).__init__()
def forward(self, inputs, targets, smooth=1):
inputs = inputs.view(-1)
targets = targets.view(-1)
intersection = (inputs * targets).sum()
dice = (2.0 * intersection + smooth) / (inputs.sum() + targets.sum(
) + smooth)
return 1 - dice
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_div_mul_rsub_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
tmp6 = tl.broadcast_to(tmp0, [RBLOCK])
tmp8 = triton_helpers.promote_to_tensor(tl.sum(tmp6, 0))
tmp9 = tl.broadcast_to(tmp1, [RBLOCK])
tmp11 = triton_helpers.promote_to_tensor(tl.sum(tmp9, 0))
tmp12 = 2.0
tmp13 = tmp5 * tmp12
tmp14 = 1.0
tmp15 = tmp13 + tmp14
tmp16 = tmp8 + tmp11
tmp17 = tmp16 + tmp14
tmp18 = tmp15 / tmp17
tmp19 = tmp14 - tmp18
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp19, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf3 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_div_mul_rsub_sum_0[grid(1)](buf3, arg0_1,
arg1_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf3,
class DiceLossNew(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceLossNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
DeVriesMatt/cellshape-voxel
|
DiceLoss
| false
| 5,067
|
[
"BSD-3-Clause"
] | 1
|
64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1
|
https://github.com/DeVriesMatt/cellshape-voxel/tree/64c2c57cc8b8ebe7f6ba1934caaaa3aaa1d6a0c1
|
maxout
|
import torch
import torch.nn as nn
import torch.utils.data
class maxout(nn.Module):
def __init__(self, in_feature, out_feature, pool_size):
super(maxout, self).__init__()
self.in_feature = in_feature
self.out_feature = out_feature
self.pool_size = pool_size
self.linear = nn.Linear(in_feature, out_feature * pool_size)
def forward(self, x):
output = self.linear(x)
output = output.view(-1, self.out_feature, self.pool_size)
output = output.max(2)[0]
return output
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_feature': 4, 'out_feature': 4, 'pool_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_max_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp6 = triton_helpers.maximum(tmp4, tmp5)
tmp7 = tmp0 > tmp1
tmp8 = tmp0 == tmp1
tmp9 = tmp0 != tmp0
tmp10 = tmp1 != tmp1
tmp11 = tmp9 > tmp10
tmp12 = tmp7 | tmp11
tmp13 = tmp9 & tmp10
tmp14 = tmp8 | tmp13
tmp15 = tl.full([1], 0, tl.int64)
tmp16 = tl.full([1], 1, tl.int64)
tmp17 = tmp15 < tmp16
tmp18 = tmp14 & tmp17
tmp19 = tmp12 | tmp18
tmp20 = tl.where(tmp19, tmp0, tmp1)
tmp21 = tl.where(tmp19, tmp15, tmp16)
tmp22 = tmp20 > tmp3
tmp23 = tmp20 == tmp3
tmp24 = tmp20 != tmp20
tmp25 = tmp3 != tmp3
tmp26 = tmp24 > tmp25
tmp27 = tmp22 | tmp26
tmp28 = tmp24 & tmp25
tmp29 = tmp23 | tmp28
tmp30 = tl.full([1], 2, tl.int64)
tmp31 = tmp21 < tmp30
tmp32 = tmp29 & tmp31
tmp33 = tmp27 | tmp32
tmp34 = tl.where(tmp33, tmp20, tmp3)
tmp35 = tl.where(tmp33, tmp21, tmp30)
tmp36 = tmp34 > tmp5
tmp37 = tmp34 == tmp5
tmp38 = tmp34 != tmp34
tmp39 = tmp5 != tmp5
tmp40 = tmp38 > tmp39
tmp41 = tmp36 | tmp40
tmp42 = tmp38 & tmp39
tmp43 = tmp37 | tmp42
tmp44 = tl.full([1], 3, tl.int64)
tmp45 = tmp35 < tmp44
tmp46 = tmp43 & tmp45
tmp47 = tmp41 | tmp46
tl.where(tmp47, tmp34, tmp5)
tmp49 = tl.where(tmp47, tmp35, tmp44)
tl.store(out_ptr0 + x0, tmp6, xmask)
tl.store(out_ptr1 + x0, tmp49, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (16, 4), (4, 1))
assert_size_stride(primals_2, (16,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 16), (1, 4),
0), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.int64)
get_raw_stream(0)
triton_poi_fused_max_0[grid(256)](buf0, buf1, buf2, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf0
return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf2, (64, 4, 1), (4, 1, 1), 0)
class maxoutNew(nn.Module):
def __init__(self, in_feature, out_feature, pool_size):
super(maxoutNew, self).__init__()
self.in_feature = in_feature
self.out_feature = out_feature
self.pool_size = pool_size
self.linear = nn.Linear(in_feature, out_feature * pool_size)
def forward(self, input_0):
primals_1 = self.linear.weight
primals_2 = self.linear.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Diego999/Global-Encoding
|
maxout
| false
| 5,068
|
[
"MIT"
] | 1
|
d3a4af9459ac3192686c94de6f2693afd6083638
|
https://github.com/Diego999/Global-Encoding/tree/d3a4af9459ac3192686c94de6f2693afd6083638
|
MonomialNN
|
import torch
import torch.nn as nn
from warnings import warn
class MonomialNN(nn.Module):
"""A network that expands its input to a given list of monomials.
Its output shape will be (n_samples, n_input_units * n_degrees)
:param degrees: max degree to be included, or a list of degrees that will be used
:type degrees: int or list[int] or tuple[int]
"""
def __init__(self, degrees):
super(MonomialNN, self).__init__()
if isinstance(degrees, int):
degrees = [d for d in range(1, degrees + 1)]
self.degrees = tuple(degrees)
if len(self.degrees) == 0:
raise ValueError('No degrees used, check `degrees` argument again')
if 0 in degrees:
warn(
'One of the degrees is 0 which might introduce redundant features'
)
if len(set(self.degrees)) < len(self.degrees):
warn(f'Duplicate degrees found: {self.degrees}')
def forward(self, x):
return torch.cat([(x ** d) for d in self.degrees], dim=1)
def __repr__(self):
return f'{self.__class__.__name__}(degrees={self.degrees})'
def __str__(self):
return self.__repr__()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'degrees': 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
import torch.nn as nn
from warnings import warn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16 % 16
x0 = xindex % 16
x2 = xindex // 256
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp9 & xmask,
other=0.0)
tmp11 = tmp10 * tmp10
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp9, tmp11, tmp12)
tmp14 = tmp0 >= tmp7
tmp15 = tl.full([1], 12, tl.int64)
tmp16 = tmp0 < tmp15
tmp17 = tmp14 & tmp16
tmp18 = tl.load(in_ptr0 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp17 &
xmask, other=0.0)
tmp19 = tmp18 * tmp18
tmp20 = tmp19 * tmp18
tmp21 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp22 = tl.where(tmp17, tmp20, tmp21)
tmp23 = tmp0 >= tmp15
tl.full([1], 16, tl.int64)
tmp26 = tl.load(in_ptr0 + (x0 + 16 * (-12 + x1) + 64 * x2), tmp23 &
xmask, other=0.0)
tmp27 = tmp26 * tmp26
tmp28 = tmp27 * tmp27
tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
tmp30 = tl.where(tmp23, tmp28, tmp29)
tmp31 = tl.where(tmp17, tmp22, tmp30)
tmp32 = tl.where(tmp9, tmp13, tmp31)
tmp33 = tl.where(tmp4, tmp5, tmp32)
tl.store(out_ptr0 + x3, tmp33, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32
)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(1024)](arg0_1, buf0, 1024, XBLOCK=128,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class MonomialNNNew(nn.Module):
"""A network that expands its input to a given list of monomials.
Its output shape will be (n_samples, n_input_units * n_degrees)
:param degrees: max degree to be included, or a list of degrees that will be used
:type degrees: int or list[int] or tuple[int]
"""
def __init__(self, degrees):
super(MonomialNNNew, self).__init__()
if isinstance(degrees, int):
degrees = [d for d in range(1, degrees + 1)]
self.degrees = tuple(degrees)
if len(self.degrees) == 0:
raise ValueError('No degrees used, check `degrees` argument again')
if 0 in degrees:
warn(
'One of the degrees is 0 which might introduce redundant features'
)
if len(set(self.degrees)) < len(self.degrees):
warn(f'Duplicate degrees found: {self.degrees}')
def __repr__(self):
return f'{self.__class__.__name__}(degrees={self.degrees})'
def __str__(self):
return self.__repr__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
DiffEqML/neurodiffeq
|
MonomialNN
| false
| 5,069
|
[
"MIT"
] | 1
|
c5e7404c47a4729578ee2149f289be0a8909d775
|
https://github.com/DiffEqML/neurodiffeq/tree/c5e7404c47a4729578ee2149f289be0a8909d775
|
SigSoftmaxV1
|
import torch
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
def logsigsoftmax_v1(logits, dim=1):
"""
Computes sigsoftmax from the paper - https://arxiv.org/pdf/1805.10829.pdf
"""
max_values = torch.max(logits, dim, keepdim=True)[0]
exp_logits_sigmoided = torch.exp(logits - max_values) * torch.sigmoid(
logits)
sum_exp_logits_sigmoided = exp_logits_sigmoided.sum(1, keepdim=True)
log_probs = logits - max_values + torch.log(torch.sigmoid(logits)
) - torch.log(sum_exp_logits_sigmoided)
return log_probs
class SigSoftmaxV1(nn.Module):
"""
Sigmoid 加上 softmax的实现
"""
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, logits):
return logsigsoftmax_v1(logits, dim=self.dim)
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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_exp_log_max_mul_sigmoid_sub_sum_0(in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl.sigmoid(tmp0)
tmp10 = tl_math.log(tmp9)
tmp11 = tmp8 + tmp10
tmp12 = tmp1 - tmp7
tmp13 = tl_math.exp(tmp12)
tmp14 = tl.sigmoid(tmp1)
tmp15 = tmp13 * tmp14
tmp16 = tmp2 - tmp7
tmp17 = tl_math.exp(tmp16)
tmp18 = tl.sigmoid(tmp2)
tmp19 = tmp17 * tmp18
tmp20 = tmp15 + tmp19
tmp21 = tmp4 - tmp7
tmp22 = tl_math.exp(tmp21)
tmp23 = tl.sigmoid(tmp4)
tmp24 = tmp22 * tmp23
tmp25 = tmp20 + tmp24
tmp26 = tmp6 - tmp7
tmp27 = tl_math.exp(tmp26)
tmp28 = tl.sigmoid(tmp6)
tmp29 = tmp27 * tmp28
tmp30 = tmp25 + tmp29
tmp31 = tl_math.log(tmp30)
tmp32 = tmp11 - tmp31
tl.store(out_ptr0 + x3, tmp32, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_exp_log_max_mul_sigmoid_sub_sum_0[grid(256)](
arg0_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
def logsigsoftmax_v1(logits, dim=1):
"""
Computes sigsoftmax from the paper - https://arxiv.org/pdf/1805.10829.pdf
"""
max_values = torch.max(logits, dim, keepdim=True)[0]
exp_logits_sigmoided = torch.exp(logits - max_values) * torch.sigmoid(
logits)
sum_exp_logits_sigmoided = exp_logits_sigmoided.sum(1, keepdim=True)
log_probs = logits - max_values + torch.log(torch.sigmoid(logits)
) - torch.log(sum_exp_logits_sigmoided)
return log_probs
class SigSoftmaxV1New(nn.Module):
"""
Sigmoid 加上 softmax的实现
"""
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
DingYuan0118/DeepEMD
|
SigSoftmaxV1
| false
| 5,070
|
[
"MIT"
] | 1
|
a91f77c3da16fecefa62b14aa8b2f195b0e49b84
|
https://github.com/DingYuan0118/DeepEMD/tree/a91f77c3da16fecefa62b14aa8b2f195b0e49b84
|
IoULoss
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class IoULoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(IoULoss, self).__init__()
def forward(self, inputs, targets, smooth=1):
inputs = F.sigmoid(inputs)
inputs = inputs.view(-1)
targets = targets.view(-1)
intersection = (inputs * targets).sum()
total = (inputs + targets).sum()
union = total - intersection
IoU = (intersection + smooth) / (union + smooth)
return 1 - IoU
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_div_mul_rsub_sub_sum_0(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp2 = tl.load(in_ptr1 + r0, None)
tmp1 = tl.sigmoid(tmp0)
tmp3 = tmp1 * tmp2
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = tmp1 + tmp2
tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
tmp11 = 1.0
tmp12 = tmp6 + tmp11
tmp13 = tmp10 - tmp6
tmp14 = tmp13 + tmp11
tmp15 = tmp12 / tmp14
tmp16 = tmp11 - tmp15
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp16, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf2 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_div_mul_rsub_sub_sum_0[grid(1)](buf2, arg0_1,
arg1_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf2,
class IoULossNew(nn.Module):
def __init__(self, weight=None, size_average=True):
super(IoULossNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
DoggyLiu0116/MamboNet
|
IoULoss
| false
| 5,071
|
[
"MIT"
] | 1
|
3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
ContrastiveDistanceLoss
|
import torch
import torch.nn as nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
class ContrastiveDistanceLoss(nn.Module):
"""The Contrastive distance loss.
@TODO: Docs. Contribution is welcome.
"""
def __init__(self, margin=1.0, reduction='mean'):
"""
Args:
margin: margin parameter
reduction: criterion reduction type
"""
super().__init__()
self.margin = margin
self.reduction = reduction or 'none'
def forward(self, distance_pred, distance_true) ->torch.Tensor:
"""Forward propagation method for the contrastive loss.
Args:
distance_pred: predicted distances
distance_true: true distances
Returns:
torch.Tensor: loss
"""
bs = len(distance_true)
margin_distance = self.margin - distance_pred
margin_distance = torch.clamp(margin_distance, min=0.0)
loss = (1 - distance_true) * torch.pow(distance_pred, 2
) + distance_true * torch.pow(margin_distance, 2)
if self.reduction == 'mean':
loss = torch.sum(loss) / 2.0 / bs
elif self.reduction == 'sum':
loss = torch.sum(loss)
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_clamp_div_mul_pow_rsub_sum_0(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp3 = tl.load(in_ptr1 + r0, None)
tmp1 = 1.0
tmp2 = tmp1 - tmp0
tmp4 = tmp3 * tmp3
tmp5 = tmp2 * tmp4
tmp6 = tmp1 - tmp3
tmp7 = 0.0
tmp8 = triton_helpers.maximum(tmp6, tmp7)
tmp9 = tmp8 * tmp8
tmp10 = tmp0 * tmp9
tmp11 = tmp5 + tmp10
tmp12 = tl.broadcast_to(tmp11, [RBLOCK])
tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp12, 0))
tmp15 = 0.5
tmp16 = tmp14 * tmp15
tmp17 = 0.25
tmp18 = tmp16 * tmp17
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp18, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_clamp_div_mul_pow_rsub_sum_0[grid(1)](buf1,
arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class ContrastiveDistanceLossNew(nn.Module):
"""The Contrastive distance loss.
@TODO: Docs. Contribution is welcome.
"""
def __init__(self, margin=1.0, reduction='mean'):
"""
Args:
margin: margin parameter
reduction: criterion reduction type
"""
super().__init__()
self.margin = margin
self.reduction = reduction or 'none'
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
Ditwoo/catalyst
|
ContrastiveDistanceLoss
| false
| 5,072
|
[
"Apache-2.0"
] | 1
|
3126390f9f679ebcfedbe01707b416678a2732ac
|
https://github.com/Ditwoo/catalyst/tree/3126390f9f679ebcfedbe01707b416678a2732ac
|
AsymLoss
|
import torch
import numpy as np
import torch.nn as nn
def sum_tensor(inp, axes, keepdim=False):
axes = np.unique(axes).astype(int)
if keepdim:
for ax in axes:
inp = inp.sum(int(ax), keepdim=True)
else:
for ax in sorted(axes, reverse=True):
inp = inp.sum(int(ax))
return inp
def get_tp_fp_fn(net_output, gt, axes=None, mask=None, square=False):
"""
net_output must be (b, c, x, y(, z)))
gt must be a label map (shape (b, 1, x, y(, z)) OR shape (b, x, y(, z))) or one hot encoding (b, c, x, y(, z))
if mask is provided it must have shape (b, 1, x, y(, z)))
:param net_output:
:param gt:
:param axes:
:param mask: mask must be 1 for valid pixels and 0 for invalid pixels
:param square: if True then fp, tp and fn will be squared before summation
:return:
"""
if axes is None:
axes = tuple(range(2, len(net_output.size())))
shp_x = net_output.shape
shp_y = gt.shape
with torch.no_grad():
if len(shp_x) != len(shp_y):
gt = gt.view((shp_y[0], 1, *shp_y[1:]))
if all([(i == j) for i, j in zip(net_output.shape, gt.shape)]):
y_onehot = gt
else:
gt = gt.long()
y_onehot = torch.zeros(shp_x)
if net_output.device.type == 'cuda':
y_onehot = y_onehot
y_onehot.scatter_(1, gt, 1)
tp = net_output * y_onehot
fp = net_output * (1 - y_onehot)
fn = (1 - net_output) * y_onehot
if mask is not None:
tp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(tp,
dim=1)), dim=1)
fp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fp,
dim=1)), dim=1)
fn = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fn,
dim=1)), dim=1)
if square:
tp = tp ** 2
fp = fp ** 2
fn = fn ** 2
tp = sum_tensor(tp, axes, keepdim=False)
fp = sum_tensor(fp, axes, keepdim=False)
fn = sum_tensor(fn, axes, keepdim=False)
return tp, fp, fn
class AsymLoss(nn.Module):
def __init__(self, apply_nonlin=None, batch_dice=False, do_bg=True,
smooth=1.0, square=False):
"""
paper: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8573779
"""
super(AsymLoss, self).__init__()
self.square = square
self.do_bg = do_bg
self.batch_dice = batch_dice
self.apply_nonlin = apply_nonlin
self.smooth = smooth
self.beta = 1.5
def forward(self, x, y, loss_mask=None):
shp_x = x.shape
if self.batch_dice:
axes = [0] + list(range(2, len(shp_x)))
else:
axes = list(range(2, len(shp_x)))
if self.apply_nonlin is not None:
x = self.apply_nonlin(x)
tp, fp, fn = get_tp_fp_fn(x, y, axes, loss_mask, self.square)
weight = self.beta ** 2 / (1 + self.beta ** 2)
asym = (tp + self.smooth) / (tp + weight * fn + (1 - weight) * fp +
self.smooth)
if not self.do_bg:
if self.batch_dice:
asym = asym[1:]
else:
asym = asym[:, 1:]
asym = asym.mean()
return -asym
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
out_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask)
tmp2 = tmp0 * tmp1
tmp3 = 1.0
tmp4 = tmp3 - tmp1
tmp5 = tmp0 * tmp4
tmp6 = tmp3 - tmp0
tmp7 = tmp6 * tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
tl.store(out_ptr1 + x0, tmp5, xmask)
tl.store(out_ptr2 + x0, tmp7, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_rsub_0[grid(256)](arg0_1, arg1_1, buf0, buf1,
buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
return buf0, buf1, buf2
def sum_tensor(inp, axes, keepdim=False):
axes = np.unique(axes).astype(int)
if keepdim:
for ax in axes:
inp = inp.sum(int(ax), keepdim=True)
else:
for ax in sorted(axes, reverse=True):
inp = inp.sum(int(ax))
return inp
def get_tp_fp_fn(net_output, gt, axes=None, mask=None, square=False):
"""
net_output must be (b, c, x, y(, z)))
gt must be a label map (shape (b, 1, x, y(, z)) OR shape (b, x, y(, z))) or one hot encoding (b, c, x, y(, z))
if mask is provided it must have shape (b, 1, x, y(, z)))
:param net_output:
:param gt:
:param axes:
:param mask: mask must be 1 for valid pixels and 0 for invalid pixels
:param square: if True then fp, tp and fn will be squared before summation
:return:
"""
if axes is None:
axes = tuple(range(2, len(net_output.size())))
shp_x = net_output.shape
shp_y = gt.shape
with torch.no_grad():
if len(shp_x) != len(shp_y):
gt = gt.view((shp_y[0], 1, *shp_y[1:]))
if all([(i == j) for i, j in zip(net_output.shape, gt.shape)]):
y_onehot = gt
else:
gt = gt.long()
y_onehot = torch.zeros(shp_x)
if net_output.device.type == 'cuda':
y_onehot = y_onehot
y_onehot.scatter_(1, gt, 1)
tp = net_output * y_onehot
fp = net_output * (1 - y_onehot)
fn = (1 - net_output) * y_onehot
if mask is not None:
tp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(tp,
dim=1)), dim=1)
fp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fp,
dim=1)), dim=1)
fn = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fn,
dim=1)), dim=1)
if square:
tp = tp ** 2
fp = fp ** 2
fn = fn ** 2
tp = sum_tensor(tp, axes, keepdim=False)
fp = sum_tensor(fp, axes, keepdim=False)
fn = sum_tensor(fn, axes, keepdim=False)
return tp, fp, fn
class AsymLossNew(nn.Module):
def __init__(self, apply_nonlin=None, batch_dice=False, do_bg=True,
smooth=1.0, square=False):
"""
paper: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8573779
"""
super(AsymLossNew, self).__init__()
self.square = square
self.do_bg = do_bg
self.batch_dice = batch_dice
self.apply_nonlin = apply_nonlin
self.smooth = smooth
self.beta = 1.5
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
DoggyLiu0116/MamboNet
|
AsymLoss
| false
| 5,073
|
[
"MIT"
] | 1
|
3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
LeakyReLU
|
import torch
import numpy as np
import torch.nn as nn
from numbers import Number
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))
def _normal_log_pdf(value, mu, stddev):
var = stddev ** 2
log_scale = np.log(stddev) if isinstance(stddev, Number) else torch.log(
stddev)
return -(value - mu) ** 2 / (2.0 * var) - log_scale - np.log(np.sqrt(
2.0 * np.pi))
def normpdf(value, mu=0.0, stddev=1.0):
return torch.exp(_normal_log_pdf(value, mu, stddev))
def keep_variance_fn(x):
return x + 0.001
class LeakyReLU(nn.Module):
def __init__(self, negative_slope=0.01, keep_variance_fn=None):
super(LeakyReLU, self).__init__()
self._keep_variance_fn = keep_variance_fn
self._negative_slope = negative_slope
def forward(self, features_mean, features_variance):
features_stddev = torch.sqrt(features_variance)
div = features_mean / features_stddev
pdf = normpdf(div)
cdf = normcdf(div)
negative_cdf = 1.0 - cdf
mu_cdf = features_mean * cdf
stddev_pdf = features_stddev * pdf
squared_mean_variance = features_mean ** 2 + features_variance
mean_stddev_pdf = features_mean * stddev_pdf
mean_r = mu_cdf + stddev_pdf
variance_r = (squared_mean_variance * cdf + mean_stddev_pdf -
mean_r ** 2)
mean_n = -features_mean * negative_cdf + stddev_pdf
variance_n = (squared_mean_variance * negative_cdf -
mean_stddev_pdf - mean_n ** 2)
covxy = -mean_r * mean_n
outputs_mean = mean_r - self._negative_slope * mean_n
outputs_variance = (variance_r + self._negative_slope * self.
_negative_slope * variance_n - 2.0 * self._negative_slope * covxy)
if self._keep_variance_fn is not None:
outputs_variance = self._keep_variance_fn(outputs_variance)
return outputs_mean, outputs_variance
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import Number
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_rsub_sqrt_sub_0(
in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask)
tmp2 = libdevice.sqrt(tmp1)
tmp3 = tmp0 / tmp2
tmp4 = 0.0
tmp5 = tmp3 - tmp4
tmp6 = 1.0
tmp7 = tmp5 * tmp6
tmp8 = 1.414213562373095
tmp9 = tmp7 / tmp8
tmp10 = libdevice.erf(tmp9)
tmp11 = tmp10 + tmp6
tmp12 = 0.5
tmp13 = tmp11 * tmp12
tmp14 = tmp0 * tmp13
tmp15 = tmp5 * tmp5
tmp16 = -tmp15
tmp17 = tmp16 * tmp12
tmp18 = 0.9189385332046727
tmp19 = tmp17 - tmp18
tmp20 = tl_math.exp(tmp19)
tmp21 = tmp2 * tmp20
tmp22 = tmp14 + tmp21
tmp23 = -tmp0
tmp24 = tmp6 - tmp13
tmp25 = tmp23 * tmp24
tmp26 = tmp25 + tmp21
tmp27 = 0.01
tmp28 = tmp26 * tmp27
tmp29 = tmp22 - tmp28
tmp30 = tmp0 * tmp0
tmp31 = tmp30 + tmp1
tmp32 = tmp31 * tmp13
tmp33 = tmp0 * tmp21
tmp34 = tmp32 + tmp33
tmp35 = tmp22 * tmp22
tmp36 = tmp34 - tmp35
tmp37 = tmp31 * tmp24
tmp38 = tmp37 - tmp33
tmp39 = tmp26 * tmp26
tmp40 = tmp38 - tmp39
tmp41 = -tmp22
tmp42 = tmp41 * tmp26
tmp43 = 0.0001
tmp44 = tmp40 * tmp43
tmp45 = tmp36 + tmp44
tmp46 = 0.02
tmp47 = tmp42 * tmp46
tmp48 = tmp45 - tmp47
tl.store(out_ptr0 + x0, tmp29, xmask)
tl.store(in_out_ptr0 + x0, tmp48, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf4 = buf1
del buf1
get_raw_stream(0)
triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_rsub_sqrt_sub_0[grid
(256)](buf4, arg1_1, arg0_1, buf0, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del arg0_1
del arg1_1
return buf0, buf4
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))
def _normal_log_pdf(value, mu, stddev):
var = stddev ** 2
log_scale = np.log(stddev) if isinstance(stddev, Number) else torch.log(
stddev)
return -(value - mu) ** 2 / (2.0 * var) - log_scale - np.log(np.sqrt(
2.0 * np.pi))
def normpdf(value, mu=0.0, stddev=1.0):
return torch.exp(_normal_log_pdf(value, mu, stddev))
def keep_variance_fn(x):
return x + 0.001
class LeakyReLUNew(nn.Module):
def __init__(self, negative_slope=0.01, keep_variance_fn=None):
super(LeakyReLUNew, self).__init__()
self._keep_variance_fn = keep_variance_fn
self._negative_slope = negative_slope
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0], output[1]
|
DoggyLiu0116/MamboNet
|
LeakyReLU
| false
| 5,074
|
[
"MIT"
] | 1
|
3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
LayerScale
|
import torch
from torch import nn
class LayerScale(nn.Module):
"""Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
This rescales diagonaly residual outputs close to 0 initially, then learnt.
"""
def __init__(self, channels: 'int', init: 'float'=0):
super().__init__()
self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))
self.scale.data[:] = init
def forward(self, x):
return self.scale[:, None] * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'channels': 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 4
x3 = xindex
tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x3, xmask)
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x3, tmp2, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4,), (1,))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_0[grid(256)](primals_1, primals_2, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_1
return buf0, primals_2
class LayerScaleNew(nn.Module):
"""Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
This rescales diagonaly residual outputs close to 0 initially, then learnt.
"""
def __init__(self, channels: 'int', init: 'float'=0):
super().__init__()
self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))
self.scale.data[:] = init
def forward(self, input_0):
primals_1 = self.scale
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
DilwoarH/demucs
|
LayerScale
| false
| 5,075
|
[
"MIT"
] | 1
|
32d21592dfa015468aa117cace52b21e7af79d71
|
https://github.com/DilwoarH/demucs/tree/32d21592dfa015468aa117cace52b21e7af79d71
|
ContrastiveEmbeddingLoss
|
import torch
import torch.nn as nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
class ContrastiveEmbeddingLoss(nn.Module):
"""The Contrastive embedding loss.
It has been proposed in `Dimensionality Reduction
by Learning an Invariant Mapping`_.
.. _Dimensionality Reduction by Learning an Invariant Mapping:
http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=1.0, reduction='mean'):
"""
Args:
margin: margin parameter
reduction: criterion reduction type
"""
super().__init__()
self.margin = margin
self.reduction = reduction or 'none'
def forward(self, embeddings_left: 'torch.Tensor', embeddings_right:
'torch.Tensor', distance_true) ->torch.Tensor:
"""Forward propagation method for the contrastive loss.
Args:
embeddings_left: left objects embeddings
embeddings_right: right objects embeddings
distance_true: true distances
Returns:
torch.Tensor: loss
"""
diff = embeddings_left - embeddings_right
distance_pred = torch.sqrt(torch.sum(torch.pow(diff, 2), 1))
bs = len(distance_true)
margin_distance = self.margin - distance_pred
margin_distance = torch.clamp(margin_distance, min=0.0)
loss = (1 - distance_true) * torch.pow(distance_pred, 2
) + distance_true * torch.pow(margin_distance, 2)
if self.reduction == 'mean':
loss = torch.sum(loss) / 2.0 / bs
elif self.reduction == 'sum':
loss = torch.sum(loss)
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_pow_sqrt_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp5 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
tmp9 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp10 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
tmp14 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp15 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp6 = tmp4 - tmp5
tmp7 = tmp6 * tmp6
tmp8 = tmp3 + tmp7
tmp11 = tmp9 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tmp8 + tmp12
tmp16 = tmp14 - tmp15
tmp17 = tmp16 * tmp16
tmp18 = tmp13 + tmp17
tmp19 = libdevice.sqrt(tmp18)
tl.store(out_ptr0 + x2, tmp19, xmask)
@triton.jit
def triton_per_fused_add_clamp_div_mul_pow_rsub_sum_1(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r2 = rindex
r0 = rindex % 64
tmp0 = tl.load(in_ptr0 + r2, None)
tmp3 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
tmp1 = 1.0
tmp2 = tmp1 - tmp0
tmp4 = tmp3 * tmp3
tmp5 = tmp2 * tmp4
tmp6 = tmp1 - tmp3
tmp7 = 0.0
tmp8 = triton_helpers.maximum(tmp6, tmp7)
tmp9 = tmp8 * tmp8
tmp10 = tmp0 * tmp9
tmp11 = tmp5 + tmp10
tmp12 = tl.broadcast_to(tmp11, [RBLOCK])
tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp12, 0))
tmp15 = 0.5
tmp16 = tmp14 * tmp15
tmp17 = 0.25
tmp18 = tmp16 * tmp17
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp18, None)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_pow_sqrt_sub_sum_0[grid(64)](arg0_1, arg1_1, buf0,
64, XBLOCK=64, num_warps=1, num_stages=1)
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = buf1
del buf1
triton_per_fused_add_clamp_div_mul_pow_rsub_sum_1[grid(1)](buf2,
arg2_1, buf0, 1, 256, num_warps=2, num_stages=1)
del arg2_1
del buf0
return buf2,
class ContrastiveEmbeddingLossNew(nn.Module):
"""The Contrastive embedding loss.
It has been proposed in `Dimensionality Reduction
by Learning an Invariant Mapping`_.
.. _Dimensionality Reduction by Learning an Invariant Mapping:
http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=1.0, reduction='mean'):
"""
Args:
margin: margin parameter
reduction: criterion reduction type
"""
super().__init__()
self.margin = margin
self.reduction = reduction or 'none'
def forward(self, input_0, input_1, input_2):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
output = call([arg0_1, arg1_1, arg2_1])
return output[0]
|
Ditwoo/catalyst
|
ContrastiveEmbeddingLoss
| false
| 5,076
|
[
"Apache-2.0"
] | 1
|
3126390f9f679ebcfedbe01707b416678a2732ac
|
https://github.com/Ditwoo/catalyst/tree/3126390f9f679ebcfedbe01707b416678a2732ac
|
SigSoftmaxV2
|
import torch
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
def logsigsoftmax_v2(logits, dim=1):
"""
v 1与 v2 差别在于 pytorch 计算softmax时有一个中心化的过程,v1 与 v2 实质上应该等同
"""
sigmoid_logits = logits.sigmoid().log()
sigsoftmax_logits = logits + sigmoid_logits
return sigsoftmax_logits.log_softmax(dim=dim)
class SigSoftmaxV2(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, logits):
return logsigsoftmax_v2(logits, dim=self.dim)
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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused__log_softmax_add_log_sigmoid_0(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp4 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp8 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp13 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp18 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp1 = tl.sigmoid(tmp0)
tmp2 = tl_math.log(tmp1)
tmp3 = tmp0 + tmp2
tmp5 = tl.sigmoid(tmp4)
tmp6 = tl_math.log(tmp5)
tmp7 = tmp4 + tmp6
tmp9 = tl.sigmoid(tmp8)
tmp10 = tl_math.log(tmp9)
tmp11 = tmp8 + tmp10
tmp12 = triton_helpers.maximum(tmp7, tmp11)
tmp14 = tl.sigmoid(tmp13)
tmp15 = tl_math.log(tmp14)
tmp16 = tmp13 + tmp15
tmp17 = triton_helpers.maximum(tmp12, tmp16)
tmp19 = tl.sigmoid(tmp18)
tmp20 = tl_math.log(tmp19)
tmp21 = tmp18 + tmp20
tmp22 = triton_helpers.maximum(tmp17, tmp21)
tmp23 = tmp3 - tmp22
tl.store(out_ptr0 + x3, tmp23, xmask)
@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl_math.exp(tmp1)
tmp4 = tl_math.exp(tmp3)
tmp5 = tmp2 + tmp4
tmp7 = tl_math.exp(tmp6)
tmp8 = tmp5 + tmp7
tmp10 = tl_math.exp(tmp9)
tmp11 = tmp8 + tmp10
tmp12 = tl_math.log(tmp11)
tmp13 = tmp0 - tmp12
tl.store(out_ptr0 + x3, tmp13, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__log_softmax_add_log_sigmoid_0[grid(256)](arg0_1,
buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__log_softmax_1[grid(256)](buf0, buf1, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del buf0
return buf1,
def logsigsoftmax_v2(logits, dim=1):
"""
v 1与 v2 差别在于 pytorch 计算softmax时有一个中心化的过程,v1 与 v2 实质上应该等同
"""
sigmoid_logits = logits.sigmoid().log()
sigsoftmax_logits = logits + sigmoid_logits
return sigsoftmax_logits.log_softmax(dim=dim)
class SigSoftmaxV2New(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
DingYuan0118/DeepEMD
|
SigSoftmaxV2
| false
| 5,077
|
[
"MIT"
] | 1
|
a91f77c3da16fecefa62b14aa8b2f195b0e49b84
|
https://github.com/DingYuan0118/DeepEMD/tree/a91f77c3da16fecefa62b14aa8b2f195b0e49b84
|
SimpleCNN
|
import torch
import torch.nn.functional as F
class Model(torch.nn.Module):
def __init__(self):
pass
class SimpleCNN(Model):
def __init__(self):
super(Model, self).__init__()
self.conv1 = torch.nn.Conv2d(in_channels=1, out_channels=64,
kernel_size=3, stride=1, padding=1)
self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.conv2 = torch.nn.Conv2d(64, 128, kernel_size=3, stride=1,
padding=1)
self.pool2 = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.conv3 = torch.nn.Conv2d(128, 256, kernel_size=3, stride=1,
padding=1)
self.pool3 = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.fc1 = torch.nn.Linear(256 * 3 * 3, 2048)
self.fc2 = torch.nn.Linear(2048, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool(x)
x = F.relu(self.conv2(x))
x = self.pool2(x)
x = F.relu(self.conv3(x))
x = self.pool3(x)
x = x.view(-1, 256 * 3 * 3)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
def get_inputs():
return [torch.rand([4, 1, 24, 24])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 64
y1 = yindex // 64
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 128
y1 = yindex // 128
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_ptr0, in_ptr1, out_ptr0, ynumel,
xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 256
xnumel = 576
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 64
y1 = yindex // 64
tmp0 = tl.load(in_ptr0 + (x2 + 576 * y3), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1, 1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (y0 + 64 * x2 + 36864 * y1), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 64
x1 = xindex // 64 % 12
x2 = xindex // 768
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1 + 3072 * x2), None)
tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 3072 * x2), None)
tmp3 = tl.load(in_ptr0 + (1536 + x0 + 128 * x1 + 3072 * x2), None)
tmp5 = tl.load(in_ptr0 + (1600 + x0 + 128 * x1 + 3072 * x2), None)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x3, tmp6, None)
tl.store(out_ptr1 + x3, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 128
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 128
x1 = xindex // 128 % 6
x2 = xindex // 768
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1 + 3072 * x2), None)
tmp1 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 3072 * x2), None)
tmp3 = tl.load(in_ptr0 + (1536 + x0 + 256 * x1 + 3072 * x2), None)
tmp5 = tl.load(in_ptr0 + (1664 + x0 + 256 * x1 + 3072 * x2), None)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x3, tmp6, None)
tl.store(out_ptr1 + x3, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 256
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_7(in_ptr0, out_ptr0, out_ptr1,
ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 36
xnumel = 256
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 3
y1 = yindex // 3
y5 = yindex
y4 = yindex // 9
y6 = yindex % 9
tmp0 = tl.load(in_ptr0 + (x2 + 512 * y0 + 3072 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (256 + x2 + 512 * y0 + 3072 * y1), xmask &
ymask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (1536 + x2 + 512 * y0 + 3072 * y1), xmask &
ymask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (1792 + x2 + 512 * y0 + 3072 * y1), xmask &
ymask, eviction_policy='evict_last')
tmp2 = tmp1 > tmp0
tmp3 = tl.full([1, 1], 1, tl.int8)
tmp4 = tl.full([1, 1], 0, tl.int8)
tmp5 = tl.where(tmp2, tmp3, tmp4)
tmp6 = triton_helpers.maximum(tmp1, tmp0)
tmp8 = tmp7 > tmp6
tmp9 = tl.full([1, 1], 2, tl.int8)
tmp10 = tl.where(tmp8, tmp9, tmp5)
tmp11 = triton_helpers.maximum(tmp7, tmp6)
tmp13 = tmp12 > tmp11
tmp14 = tl.full([1, 1], 3, tl.int8)
tmp15 = tl.where(tmp13, tmp14, tmp10)
tmp16 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x2 + 256 * y5), tmp15, xmask & ymask)
tl.store(out_ptr1 + (y6 + 9 * x2 + 2304 * y4), tmp16, xmask & ymask)
@triton.jit
def triton_poi_fused_relu_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 2048
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (64, 1, 3, 3), (9, 9, 3, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 1, 24, 24), (576, 576, 24, 1))
assert_size_stride(primals_4, (128, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (128,), (1,))
assert_size_stride(primals_6, (256, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_7, (256,), (1,))
assert_size_stride(primals_8, (2048, 2304), (2304, 1))
assert_size_stride(primals_9, (2048,), (1,))
assert_size_stride(primals_10, (10, 2048), (2048, 1))
assert_size_stride(primals_11, (10,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch
.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(8192, 9)](primals_4, buf0, 8192, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_4
buf1 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128),
torch.float32)
triton_poi_fused_1[grid(32768, 9)](primals_6, buf1, 32768, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_6
buf2 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 64, 24, 24), (36864, 576, 24, 1))
buf3 = empty_strided_cuda((4, 64, 24, 24), (36864, 1, 1536, 64),
torch.float32)
triton_poi_fused_convolution_relu_2[grid(256, 576)](buf2, primals_2,
buf3, 256, 576, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
del buf2
del primals_2
buf4 = empty_strided_cuda((4, 64, 12, 12), (9216, 1, 768, 64),
torch.float32)
buf5 = empty_strided_cuda((4, 64, 12, 12), (9216, 1, 768, 64),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_3[grid(36864)](buf3, buf4,
buf5, 36864, XBLOCK=512, num_warps=4, num_stages=1)
buf6 = extern_kernels.convolution(buf4, buf0, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 128, 12, 12), (18432, 1, 1536, 128))
buf7 = buf6
del buf6
triton_poi_fused_convolution_relu_4[grid(73728)](buf7, primals_5,
73728, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_5
buf8 = empty_strided_cuda((4, 128, 6, 6), (4608, 1, 768, 128),
torch.float32)
buf9 = empty_strided_cuda((4, 128, 6, 6), (4608, 1, 768, 128),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_5[grid(18432)](buf7, buf8,
buf9, 18432, XBLOCK=256, num_warps=4, num_stages=1)
buf10 = extern_kernels.convolution(buf8, buf1, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 256, 6, 6), (9216, 1, 1536, 256))
buf11 = buf10
del buf10
triton_poi_fused_convolution_relu_6[grid(36864)](buf11, primals_7,
36864, XBLOCK=512, num_warps=4, num_stages=1)
del primals_7
buf12 = empty_strided_cuda((4, 256, 3, 3), (2304, 1, 768, 256),
torch.int8)
buf13 = empty_strided_cuda((4, 256, 3, 3), (2304, 9, 3, 1), torch.
float32)
triton_poi_fused_max_pool2d_with_indices_7[grid(36, 256)](buf11,
buf12, buf13, 36, 256, XBLOCK=4, YBLOCK=64, num_warps=4,
num_stages=1)
buf14 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf13, (4, 2304), (2304, 1), 0
), reinterpret_tensor(primals_8, (2304, 2048), (1, 2304), 0),
out=buf14)
buf15 = buf14
del buf14
triton_poi_fused_relu_8[grid(8192)](buf15, primals_9, 8192, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_9
buf16 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_11, buf15, reinterpret_tensor(
primals_10, (2048, 10), (1, 2048), 0), alpha=1, beta=1, out=buf16)
del primals_11
return (buf16, primals_1, primals_3, buf0, buf1, buf3, buf4, buf5, buf7,
buf8, buf9, buf11, buf12, reinterpret_tensor(buf13, (4, 2304), (
2304, 1), 0), buf15, primals_10, primals_8)
class Model(torch.nn.Module):
def __init__(self):
pass
class SimpleCNNNew(Model):
def __init__(self):
super(Model, self).__init__()
self.conv1 = torch.nn.Conv2d(in_channels=1, out_channels=64,
kernel_size=3, stride=1, padding=1)
self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.conv2 = torch.nn.Conv2d(64, 128, kernel_size=3, stride=1,
padding=1)
self.pool2 = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.conv3 = torch.nn.Conv2d(128, 256, kernel_size=3, stride=1,
padding=1)
self.pool3 = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.fc1 = torch.nn.Linear(256 * 3 * 3, 2048)
self.fc2 = torch.nn.Linear(2048, 10)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.conv3.weight
primals_7 = self.conv3.bias
primals_8 = self.fc1.weight
primals_9 = self.fc1.bias
primals_10 = self.fc2.weight
primals_11 = self.fc2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0]
|
Cuilie/Collect-feature-maps
|
SimpleCNN
| false
| 5,078
|
[
"MIT"
] | 1
|
32e8ac59690837f2a299ab6d4c11b98f5d3d721a
|
https://github.com/Cuilie/Collect-feature-maps/tree/32e8ac59690837f2a299ab6d4c11b98f5d3d721a
|
ContrastivePairwiseEmbeddingLoss
|
import torch
import torch.nn as nn
import torch.distributed
import torch.nn.functional as F
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
class ContrastivePairwiseEmbeddingLoss(nn.Module):
"""ContrastivePairwiseEmbeddingLoss – proof of concept criterion.
Still work in progress.
@TODO: Docs. Contribution is welcome.
"""
def __init__(self, margin=1.0, reduction='mean'):
"""
Args:
margin: margin parameter
reduction: criterion reduction type
"""
super().__init__()
self.margin = margin
self.reduction = reduction or 'none'
def forward(self, embeddings_pred, embeddings_true) ->torch.Tensor:
"""Forward propagation method for the contrastive loss.
Work in progress.
Args:
embeddings_pred: predicted embeddings
embeddings_true: true embeddings
Returns:
torch.Tensor: loss
"""
device = embeddings_pred.device
pairwise_similarity = torch.einsum('se,ae->sa', embeddings_pred,
embeddings_true)
bs = embeddings_pred.shape[0]
batch_idx = torch.arange(bs, device=device)
loss = F.cross_entropy(pairwise_similarity, batch_idx, reduction=
self.reduction)
return loss
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_per_fused_arange_nll_loss_forward_1(in_out_ptr0, in_ptr0, xnumel,
rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 4
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp6 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
tmp0 = r0
tmp1 = tl.full([1, 1], -100, tl.int64)
tmp2 = tmp0 != tmp1
tmp3 = tl.full([1, 1], 0, tl.int64)
tmp4 = tl.where(tmp2, tmp0, tmp3)
tmp5 = tl.load(in_ptr0 + (tmp4 + 4 * r0), None, eviction_policy=
'evict_last')
tmp7 = tl_math.exp(tmp6)
tmp9 = tl_math.exp(tmp8)
tmp10 = tmp7 + tmp9
tmp12 = tl_math.exp(tmp11)
tmp13 = tmp10 + tmp12
tmp15 = tl_math.exp(tmp14)
tmp16 = tmp13 + tmp15
tmp17 = tl_math.log(tmp16)
tmp18 = tmp5 - tmp17
tmp19 = -tmp18
tmp20 = 0.0
tmp21 = tl.where(tmp2, tmp19, tmp20)
tmp22 = tl.broadcast_to(tmp21, [XBLOCK, RBLOCK])
tmp24 = tl.sum(tmp22, 1)[:, None]
tmp25 = tmp2.to(tl.int64)
tmp26 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
tmp28 = tl.sum(tmp26, 1)[:, None]
tmp29 = tmp28.to(tl.float32)
tmp30 = tmp24 / tmp29
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp30, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4), (4, 1))
assert_size_stride(arg1_1, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((1, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(arg0_1, (1, 4, 4), (16, 4, 1),
0), reinterpret_tensor(arg1_1, (1, 4, 4), (0, 1, 4), 0), out=buf0)
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__log_softmax_0[grid(16)](buf0, buf1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf0
buf2 = empty_strided_cuda((), (), torch.float32)
buf4 = buf2
del buf2
triton_per_fused_arange_nll_loss_forward_1[grid(1)](buf4, buf1, 1,
4, XBLOCK=1, num_warps=2, num_stages=1)
del buf1
return buf4,
class ContrastivePairwiseEmbeddingLossNew(nn.Module):
"""ContrastivePairwiseEmbeddingLoss – proof of concept criterion.
Still work in progress.
@TODO: Docs. Contribution is welcome.
"""
def __init__(self, margin=1.0, reduction='mean'):
"""
Args:
margin: margin parameter
reduction: criterion reduction type
"""
super().__init__()
self.margin = margin
self.reduction = reduction or 'none'
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
Ditwoo/catalyst
|
ContrastivePairwiseEmbeddingLoss
| false
| 5,079
|
[
"Apache-2.0"
] | 1
|
3126390f9f679ebcfedbe01707b416678a2732ac
|
https://github.com/Ditwoo/catalyst/tree/3126390f9f679ebcfedbe01707b416678a2732ac
|
ShallowConvNet
|
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
import torch.onnx
class ShallowConvNet(nn.Module):
def __init__(self, hidden=1000):
super(ShallowConvNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, hidden)
self.fc2 = nn.Linear(hidden, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
def get_inputs():
return [torch.rand([4, 3, 32, 32])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 18816
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 784 % 6
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 4704
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 14
x3 = xindex // 14
x2 = xindex // 1176
x4 = xindex % 1176
tmp0 = tl.load(in_ptr0 + (2 * x0 + 56 * x3), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 56 * x3), xmask, eviction_policy
='evict_last')
tmp3 = tl.load(in_ptr0 + (28 + 2 * x0 + 56 * x3), xmask,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (29 + 2 * x0 + 56 * x3), xmask,
eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + (x4 + 1184 * x2), tmp6, xmask)
tl.store(out_ptr1 + (x4 + 1280 * x2), tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 6400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 100 % 16
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 5
x1 = xindex // 5
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 20 * x1), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 20 * x1), xmask, eviction_policy
='evict_last')
tmp7 = tl.load(in_ptr0 + (10 + 2 * x0 + 20 * x1), xmask,
eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (11 + 2 * x0 + 20 * x1), xmask,
eviction_policy='evict_last')
tmp2 = tmp1 > tmp0
tmp3 = tl.full([1], 1, tl.int8)
tmp4 = tl.full([1], 0, tl.int8)
tmp5 = tl.where(tmp2, tmp3, tmp4)
tmp6 = triton_helpers.maximum(tmp1, tmp0)
tmp8 = tmp7 > tmp6
tmp9 = tl.full([1], 2, tl.int8)
tmp10 = tl.where(tmp8, tmp9, tmp5)
tmp11 = triton_helpers.maximum(tmp7, tmp6)
tmp13 = tmp12 > tmp11
tmp14 = tl.full([1], 3, tl.int8)
tmp15 = tl.where(tmp13, tmp14, tmp10)
tmp16 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x2, tmp15, xmask)
tl.store(out_ptr1 + x2, tmp16, xmask)
@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 4000
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 1000
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9) = args
args.clear()
assert_size_stride(primals_1, (6, 3, 5, 5), (75, 25, 5, 1))
assert_size_stride(primals_2, (6,), (1,))
assert_size_stride(primals_3, (4, 3, 32, 32), (3072, 1024, 32, 1))
assert_size_stride(primals_4, (16, 6, 5, 5), (150, 25, 5, 1))
assert_size_stride(primals_5, (16,), (1,))
assert_size_stride(primals_6, (1000, 400), (400, 1))
assert_size_stride(primals_7, (1000,), (1,))
assert_size_stride(primals_8, (10, 1000), (1000, 1))
assert_size_stride(primals_9, (10,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 6, 28, 28), (4704, 784, 28, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(18816)](buf1, primals_2,
18816, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((4, 6, 14, 14), (1184, 196, 14, 1), torch
.float32)
buf3 = empty_strided_cuda((4, 6, 14, 14), (1280, 196, 14, 1), torch
.int8)
triton_poi_fused_max_pool2d_with_indices_1[grid(4704)](buf1, buf2,
buf3, 4704, XBLOCK=128, num_warps=4, num_stages=1)
buf4 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 16, 10, 10), (1600, 100, 10, 1))
buf5 = buf4
del buf4
triton_poi_fused_convolution_relu_2[grid(6400)](buf5, primals_5,
6400, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf6 = empty_strided_cuda((4, 16, 5, 5), (400, 25, 5, 1), torch.int8)
buf7 = empty_strided_cuda((4, 16, 5, 5), (400, 25, 5, 1), torch.float32
)
triton_poi_fused_max_pool2d_with_indices_3[grid(1600)](buf5, buf6,
buf7, 1600, XBLOCK=256, num_warps=4, num_stages=1)
buf8 = empty_strided_cuda((4, 1000), (1000, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf7, (4, 400), (400, 1), 0),
reinterpret_tensor(primals_6, (400, 1000), (1, 400), 0), out=buf8)
buf9 = buf8
del buf8
triton_poi_fused_relu_4[grid(4000)](buf9, primals_7, 4000, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_7
buf10 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_9, buf9, reinterpret_tensor(primals_8,
(1000, 10), (1, 1000), 0), alpha=1, beta=1, out=buf10)
del primals_9
return (buf10, primals_1, primals_3, primals_4, buf1, buf2, buf3, buf5,
buf6, reinterpret_tensor(buf7, (4, 400), (400, 1), 0), buf9,
primals_8, primals_6)
class ShallowConvNetNew(nn.Module):
def __init__(self, hidden=1000):
super(ShallowConvNetNew, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, hidden)
self.fc2 = nn.Linear(hidden, 10)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.fc1.weight
primals_7 = self.fc1.bias
primals_8 = self.fc2.weight
primals_9 = self.fc2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9])
return output[0]
|
CorentinChauvin/style-transfer-KD
|
ShallowConvNet
| false
| 5,080
|
[
"MIT"
] | 1
|
87bcb2963dbb8d09faf94c74a744f358cafe5427
|
https://github.com/CorentinChauvin/style-transfer-KD/tree/87bcb2963dbb8d09faf94c74a744f358cafe5427
|
ReLU
|
import torch
import numpy as np
import torch.nn as nn
from numbers import Number
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))
def _normal_log_pdf(value, mu, stddev):
var = stddev ** 2
log_scale = np.log(stddev) if isinstance(stddev, Number) else torch.log(
stddev)
return -(value - mu) ** 2 / (2.0 * var) - log_scale - np.log(np.sqrt(
2.0 * np.pi))
def normpdf(value, mu=0.0, stddev=1.0):
return torch.exp(_normal_log_pdf(value, mu, stddev))
def keep_variance_fn(x):
return x + 0.001
class ReLU(nn.Module):
def __init__(self, keep_variance_fn=None):
super(ReLU, self).__init__()
self._keep_variance_fn = keep_variance_fn
def forward(self, features_mean, features_variance):
features_stddev = torch.sqrt(features_variance)
div = features_mean / features_stddev
pdf = normpdf(div)
cdf = normcdf(div)
outputs_mean = features_mean * cdf + features_stddev * pdf
outputs_variance = (features_mean ** 2 + features_variance
) * cdf + features_mean * features_stddev * pdf - outputs_mean ** 2
if self._keep_variance_fn is not None:
outputs_variance = self._keep_variance_fn(outputs_variance)
return outputs_mean, outputs_variance
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import Number
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_sqrt_sub_0(in_ptr0,
in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask)
tmp2 = libdevice.sqrt(tmp1)
tmp3 = tmp0 / tmp2
tmp4 = 0.0
tmp5 = tmp3 - tmp4
tmp6 = 1.0
tmp7 = tmp5 * tmp6
tmp8 = 1.414213562373095
tmp9 = tmp7 / tmp8
tmp10 = libdevice.erf(tmp9)
tmp11 = tmp10 + tmp6
tmp12 = 0.5
tmp13 = tmp11 * tmp12
tmp14 = tmp0 * tmp13
tmp15 = tmp5 * tmp5
tmp16 = -tmp15
tmp17 = tmp16 * tmp12
tmp18 = 0.9189385332046727
tmp19 = tmp17 - tmp18
tmp20 = tl_math.exp(tmp19)
tmp21 = tmp2 * tmp20
tmp22 = tmp14 + tmp21
tmp23 = tmp0 * tmp0
tmp24 = tmp23 + tmp1
tmp25 = tmp24 * tmp13
tmp26 = tmp0 * tmp2
tmp27 = tmp26 * tmp20
tmp28 = tmp25 + tmp27
tmp29 = tmp22 * tmp22
tmp30 = tmp28 - tmp29
tl.store(out_ptr0 + x0, tmp22, xmask)
tl.store(out_ptr1 + x0, tmp30, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_sqrt_sub_0[grid(256)](
arg1_1, arg0_1, buf0, buf1, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del arg0_1
del arg1_1
return buf0, buf1
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))
def _normal_log_pdf(value, mu, stddev):
var = stddev ** 2
log_scale = np.log(stddev) if isinstance(stddev, Number) else torch.log(
stddev)
return -(value - mu) ** 2 / (2.0 * var) - log_scale - np.log(np.sqrt(
2.0 * np.pi))
def normpdf(value, mu=0.0, stddev=1.0):
return torch.exp(_normal_log_pdf(value, mu, stddev))
def keep_variance_fn(x):
return x + 0.001
class ReLUNew(nn.Module):
def __init__(self, keep_variance_fn=None):
super(ReLUNew, self).__init__()
self._keep_variance_fn = keep_variance_fn
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0], output[1]
|
DoggyLiu0116/MamboNet
|
ReLU
| false
| 5,081
|
[
"MIT"
] | 1
|
3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
JointHeatmapLoss
|
import torch
import torch.utils.data
import torch.nn as nn
class JointHeatmapLoss(nn.Module):
def __ini__(self):
super(JointHeatmapLoss, self).__init__()
def forward(self, joint_out, joint_gt, joint_valid):
loss = (joint_out - joint_gt) ** 2 * joint_valid[:, :, None, None, None
]
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_mul_pow_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex % 256
x0 = xindex % 16
x2 = xindex // 256
x4 = xindex
tmp0 = tl.load(in_ptr0 + x3, None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x3, None, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr2 + (x0 + 16 * x2), None, eviction_policy='evict_last'
)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp5 = tmp3 * tmp4
tl.store(out_ptr0 + x4, tmp5, None)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 4, 4, 4, 4), (1024, 256, 256,
64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_pow_sub_0[grid(4096)](arg0_1, arg1_1, arg2_1,
buf0, 4096, XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
del arg2_1
return buf0,
class JointHeatmapLossNew(nn.Module):
def __ini__(self):
super(JointHeatmapLossNew, self).__init__()
def forward(self, input_0, input_1, input_2):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
output = call([arg0_1, arg1_1, arg2_1])
return output[0]
|
DuinoDu/InterHand2.6M.pl
|
JointHeatmapLoss
| false
| 5,082
|
[
"MIT"
] | 1
|
2d216960cf95b066a197a9b49795840b1ecfd0c1
|
https://github.com/DuinoDu/InterHand2.6M.pl/tree/2d216960cf95b066a197a9b49795840b1ecfd0c1
|
RegressionModel
|
import torch
from torch import nn
class RegressionModel(nn.Module):
def __init__(self, num_features_in, num_anchors=9, feature_size=256):
super(RegressionModel, self).__init__()
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3,
padding=1)
self.act1 = nn.ReLU()
self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3,
padding=1)
self.act2 = nn.ReLU()
self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3,
padding=1)
self.act3 = nn.ReLU()
self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3,
padding=1)
self.act4 = nn.ReLU()
self.output = nn.Conv2d(feature_size, num_anchors * 4, kernel_size=
3, padding=1)
def forward(self, x):
out = self.conv1(x)
out = self.act1(out)
out = self.conv2(out)
out = self.act2(out)
out = self.conv3(out)
out = self.act3(out)
out = self.conv4(out)
out = self.act4(out)
out = self.output(out)
out = out.permute(0, 2, 3, 1)
return out.contiguous().view(out.shape[0], -1, 4)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_features_in': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 16 % 256
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_clone_view_1(in_out_ptr0, in_ptr0, in_ptr1, ynumel,
xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 64
xnumel = 36
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 16
y1 = yindex // 16
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 576 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.debug_barrier()
tl.store(in_out_ptr0 + (x2 + 36 * y3), tmp2, xmask & ymask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (256, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (256,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_5, (256,), (1,))
assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_7, (256,), (1,))
assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_9, (256,), (1,))
assert_size_stride(primals_10, (36, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_11, (36,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 256, 4, 4), (4096, 16, 4, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(16384)](buf1, primals_2,
16384, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 256, 4, 4), (4096, 16, 4, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_relu_0[grid(16384)](buf3, primals_5,
16384, XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 256, 4, 4), (4096, 16, 4, 1))
buf5 = buf4
del buf4
triton_poi_fused_convolution_relu_0[grid(16384)](buf5, primals_7,
16384, XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 256, 4, 4), (4096, 16, 4, 1))
buf7 = buf6
del buf6
triton_poi_fused_convolution_relu_0[grid(16384)](buf7, primals_9,
16384, XBLOCK=128, num_warps=4, num_stages=1)
del primals_9
buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 36, 4, 4), (576, 16, 4, 1))
buf9 = empty_strided_cuda((4, 4, 4, 36), (576, 144, 36, 1), torch.
float32)
buf10 = reinterpret_tensor(buf9, (4, 144, 4), (576, 4, 1), 0)
del buf9
triton_poi_fused_clone_view_1[grid(64, 36)](buf10, buf8, primals_11,
64, 36, XBLOCK=64, YBLOCK=4, num_warps=4, num_stages=1)
del buf8
del primals_11
return (buf10, primals_1, primals_3, primals_4, primals_6, primals_8,
primals_10, buf1, buf3, buf5, buf7)
class RegressionModelNew(nn.Module):
def __init__(self, num_features_in, num_anchors=9, feature_size=256):
super(RegressionModelNew, self).__init__()
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3,
padding=1)
self.act1 = nn.ReLU()
self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3,
padding=1)
self.act2 = nn.ReLU()
self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3,
padding=1)
self.act3 = nn.ReLU()
self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3,
padding=1)
self.act4 = nn.ReLU()
self.output = nn.Conv2d(feature_size, num_anchors * 4, kernel_size=
3, padding=1)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.conv3.weight
primals_7 = self.conv3.bias
primals_8 = self.conv4.weight
primals_9 = self.conv4.bias
primals_10 = self.output.weight
primals_11 = self.output.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0]
|
DerekGloudemans/temporary-repo
|
RegressionModel
| false
| 5,083
|
[
"MIT"
] | 1
|
f278e9c7c9c7c1f362a64aec492ddb8fb1f984ad
|
https://github.com/DerekGloudemans/temporary-repo/tree/f278e9c7c9c7c1f362a64aec492ddb8fb1f984ad
|
AvgPool2d
|
import torch
import torch.nn as nn
import torch.nn.functional as F
def keep_variance_fn(x):
return x + 0.001
class AvgPool2d(nn.Module):
def __init__(self, keep_variance_fn=None, kernel_size=2):
super(AvgPool2d, self).__init__()
self._keep_variance_fn = keep_variance_fn
self.kernel_size = kernel_size
def forward(self, inputs_mean, inputs_variance):
outputs_mean = F.avg_pool2d(inputs_mean, self.kernel_size, stride=2,
padding=1)
outputs_variance = F.avg_pool2d(inputs_variance, self.kernel_size,
stride=2, padding=1)
outputs_variance = outputs_variance / (inputs_mean.size(2) *
inputs_mean.size(3))
if self._keep_variance_fn is not None:
outputs_variance = self._keep_variance_fn(outputs_variance)
return outputs_mean, outputs_variance / (inputs_mean.shape[2] *
inputs_mean.shape[3])
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_avg_pool2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 144
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 3 % 3
x0 = xindex % 3
x2 = xindex // 9
x4 = xindex
tmp0 = -1 + 2 * x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = -1 + 2 * x0
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = tmp5 & tmp9
tmp11 = tl.load(in_ptr0 + (-5 + 2 * x0 + 8 * x1 + 16 * x2), tmp10 &
xmask, eviction_policy='evict_last', other=0.0)
tmp12 = 2 * x0
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp13 & tmp14
tmp16 = tmp5 & tmp15
tmp17 = tl.load(in_ptr0 + (-4 + 2 * x0 + 8 * x1 + 16 * x2), tmp16 &
xmask, eviction_policy='evict_last', other=0.0)
tmp18 = tmp17 + tmp11
tmp19 = 2 * x1
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp20 & tmp21
tmp23 = tmp22 & tmp9
tmp24 = tl.load(in_ptr0 + (-1 + 2 * x0 + 8 * x1 + 16 * x2), tmp23 &
xmask, eviction_policy='evict_last', other=0.0)
tmp25 = tmp24 + tmp18
tmp26 = tmp22 & tmp15
tmp27 = tl.load(in_ptr0 + (2 * x0 + 8 * x1 + 16 * x2), tmp26 & xmask,
eviction_policy='evict_last', other=0.0)
tmp28 = tmp27 + tmp25
tmp29 = 1 + -2 * x0 + -2 * x1 + (5 * (5 <= 1 + 2 * x0) + (1 + 2 * x0) *
(1 + 2 * x0 < 5)) * (5 * (5 <= 1 + 2 * x1) + (1 + 2 * x1) * (1 + 2 *
x1 < 5)) + -2 * x0 * (5 * (5 <= 1 + 2 * x1) + (1 + 2 * x1) * (1 + 2 *
x1 < 5)) + -2 * x1 * (5 * (5 <= 1 + 2 * x0) + (1 + 2 * x0) * (1 + 2 *
x0 < 5)) + 4 * x0 * x1 + (5 * (5 <= 1 + 2 * x0) + (1 + 2 * x0) * (1 +
2 * x0 < 5)) + (5 * (5 <= 1 + 2 * x1) + (1 + 2 * x1) * (1 + 2 * x1 < 5)
)
tmp30 = tmp28 / tmp29
tl.store(out_ptr0 + x4, tmp30, xmask)
@triton.jit
def triton_poi_fused_avg_pool2d_div_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 144
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 3 % 3
x0 = xindex % 3
x2 = xindex // 9
x3 = xindex
tmp0 = -1 + 2 * x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = -1 + 2 * x0
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = tmp5 & tmp9
tmp11 = tl.load(in_ptr0 + (-5 + 2 * x0 + 8 * x1 + 16 * x2), tmp10 &
xmask, eviction_policy='evict_last', other=0.0)
tmp12 = 2 * x0
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp13 & tmp14
tmp16 = tmp5 & tmp15
tmp17 = tl.load(in_ptr0 + (-4 + 2 * x0 + 8 * x1 + 16 * x2), tmp16 &
xmask, eviction_policy='evict_last', other=0.0)
tmp18 = tmp17 + tmp11
tmp19 = 2 * x1
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp20 & tmp21
tmp23 = tmp22 & tmp9
tmp24 = tl.load(in_ptr0 + (-1 + 2 * x0 + 8 * x1 + 16 * x2), tmp23 &
xmask, eviction_policy='evict_last', other=0.0)
tmp25 = tmp24 + tmp18
tmp26 = tmp22 & tmp15
tmp27 = tl.load(in_ptr0 + (2 * x0 + 8 * x1 + 16 * x2), tmp26 & xmask,
eviction_policy='evict_last', other=0.0)
tmp28 = tmp27 + tmp25
tmp29 = 1 + -2 * x0 + -2 * x1 + (5 * (5 <= 1 + 2 * x0) + (1 + 2 * x0) *
(1 + 2 * x0 < 5)) * (5 * (5 <= 1 + 2 * x1) + (1 + 2 * x1) * (1 + 2 *
x1 < 5)) + -2 * x0 * (5 * (5 <= 1 + 2 * x1) + (1 + 2 * x1) * (1 + 2 *
x1 < 5)) + -2 * x1 * (5 * (5 <= 1 + 2 * x0) + (1 + 2 * x0) * (1 + 2 *
x0 < 5)) + 4 * x0 * x1 + (5 * (5 <= 1 + 2 * x0) + (1 + 2 * x0) * (1 +
2 * x0 < 5)) + (5 * (5 <= 1 + 2 * x1) + (1 + 2 * x1) * (1 + 2 * x1 < 5)
)
tmp30 = tmp28 / tmp29
tmp31 = 0.0625
tmp32 = tmp30 * tmp31
tmp33 = tmp32 * tmp31
tl.store(in_out_ptr0 + x3, tmp33, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_avg_pool2d_0[grid(144)](arg0_1, buf0, 144, XBLOCK=
128, num_warps=4, num_stages=1)
del arg0_1
buf1 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
buf2 = buf1
del buf1
triton_poi_fused_avg_pool2d_div_1[grid(144)](buf2, arg1_1, 144,
XBLOCK=256, num_warps=4, num_stages=1)
del arg1_1
return buf0, buf2
def keep_variance_fn(x):
return x + 0.001
class AvgPool2dNew(nn.Module):
def __init__(self, keep_variance_fn=None, kernel_size=2):
super(AvgPool2dNew, self).__init__()
self._keep_variance_fn = keep_variance_fn
self.kernel_size = kernel_size
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0], output[1]
|
DoggyLiu0116/MamboNet
|
AvgPool2d
| false
| 5,084
|
[
"MIT"
] | 1
|
3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
SimpleConvNet
|
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
import torch.onnx
class SimpleConvNet(nn.Module):
def __init__(self, hidden=1000):
super(SimpleConvNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, hidden)
self.fc2 = nn.Linear(hidden, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def get_inputs():
return [torch.rand([4, 3, 32, 32])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 18816
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 784 % 6
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 4704
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 14
x3 = xindex // 14
x2 = xindex // 1176
x4 = xindex % 1176
tmp0 = tl.load(in_ptr0 + (2 * x0 + 56 * x3), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 56 * x3), xmask, eviction_policy
='evict_last')
tmp3 = tl.load(in_ptr0 + (28 + 2 * x0 + 56 * x3), xmask,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (29 + 2 * x0 + 56 * x3), xmask,
eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + (x4 + 1184 * x2), tmp6, xmask)
tl.store(out_ptr1 + (x4 + 1280 * x2), tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 6400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 100 % 16
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 5
x1 = xindex // 5
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 20 * x1), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 20 * x1), xmask, eviction_policy
='evict_last')
tmp7 = tl.load(in_ptr0 + (10 + 2 * x0 + 20 * x1), xmask,
eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (11 + 2 * x0 + 20 * x1), xmask,
eviction_policy='evict_last')
tmp2 = tmp1 > tmp0
tmp3 = tl.full([1], 1, tl.int8)
tmp4 = tl.full([1], 0, tl.int8)
tmp5 = tl.where(tmp2, tmp3, tmp4)
tmp6 = triton_helpers.maximum(tmp1, tmp0)
tmp8 = tmp7 > tmp6
tmp9 = tl.full([1], 2, tl.int8)
tmp10 = tl.where(tmp8, tmp9, tmp5)
tmp11 = triton_helpers.maximum(tmp7, tmp6)
tmp13 = tmp12 > tmp11
tmp14 = tl.full([1], 3, tl.int8)
tmp15 = tl.where(tmp13, tmp14, tmp10)
tmp16 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x2, tmp15, xmask)
tl.store(out_ptr1 + x2, tmp16, xmask)
@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 4000
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 1000
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 336
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 84
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (6, 3, 5, 5), (75, 25, 5, 1))
assert_size_stride(primals_2, (6,), (1,))
assert_size_stride(primals_3, (4, 3, 32, 32), (3072, 1024, 32, 1))
assert_size_stride(primals_4, (16, 6, 5, 5), (150, 25, 5, 1))
assert_size_stride(primals_5, (16,), (1,))
assert_size_stride(primals_6, (1000, 400), (400, 1))
assert_size_stride(primals_7, (1000,), (1,))
assert_size_stride(primals_8, (84, 1000), (1000, 1))
assert_size_stride(primals_9, (84,), (1,))
assert_size_stride(primals_10, (10, 84), (84, 1))
assert_size_stride(primals_11, (10,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 6, 28, 28), (4704, 784, 28, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(18816)](buf1, primals_2,
18816, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((4, 6, 14, 14), (1184, 196, 14, 1), torch
.float32)
buf3 = empty_strided_cuda((4, 6, 14, 14), (1280, 196, 14, 1), torch
.int8)
triton_poi_fused_max_pool2d_with_indices_1[grid(4704)](buf1, buf2,
buf3, 4704, XBLOCK=128, num_warps=4, num_stages=1)
buf4 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 16, 10, 10), (1600, 100, 10, 1))
buf5 = buf4
del buf4
triton_poi_fused_convolution_relu_2[grid(6400)](buf5, primals_5,
6400, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf6 = empty_strided_cuda((4, 16, 5, 5), (400, 25, 5, 1), torch.int8)
buf7 = empty_strided_cuda((4, 16, 5, 5), (400, 25, 5, 1), torch.float32
)
triton_poi_fused_max_pool2d_with_indices_3[grid(1600)](buf5, buf6,
buf7, 1600, XBLOCK=256, num_warps=4, num_stages=1)
buf8 = empty_strided_cuda((4, 1000), (1000, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf7, (4, 400), (400, 1), 0),
reinterpret_tensor(primals_6, (400, 1000), (1, 400), 0), out=buf8)
buf9 = buf8
del buf8
triton_poi_fused_relu_4[grid(4000)](buf9, primals_7, 4000, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_7
buf10 = empty_strided_cuda((4, 84), (84, 1), torch.float32)
extern_kernels.mm(buf9, reinterpret_tensor(primals_8, (1000, 84), (
1, 1000), 0), out=buf10)
buf11 = buf10
del buf10
triton_poi_fused_relu_5[grid(336)](buf11, primals_9, 336, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_9
buf12 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_11, buf11, reinterpret_tensor(
primals_10, (84, 10), (1, 84), 0), alpha=1, beta=1, out=buf12)
del primals_11
return (buf12, primals_1, primals_3, primals_4, buf1, buf2, buf3, buf5,
buf6, reinterpret_tensor(buf7, (4, 400), (400, 1), 0), buf9, buf11,
primals_10, primals_8, primals_6)
class SimpleConvNetNew(nn.Module):
def __init__(self, hidden=1000):
super(SimpleConvNetNew, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, hidden)
self.fc2 = nn.Linear(hidden, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.fc1.weight
primals_7 = self.fc1.bias
primals_8 = self.fc2.weight
primals_9 = self.fc2.bias
primals_10 = self.fc3.weight
primals_11 = self.fc3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0]
|
CorentinChauvin/style-transfer-KD
|
SimpleConvNet
| false
| 5,085
|
[
"MIT"
] | 1
|
87bcb2963dbb8d09faf94c74a744f358cafe5427
|
https://github.com/CorentinChauvin/style-transfer-KD/tree/87bcb2963dbb8d09faf94c74a744f358cafe5427
|
RelRootDepthLoss
|
import torch
import torch.utils.data
import torch.nn as nn
class RelRootDepthLoss(nn.Module):
def __init__(self):
super(RelRootDepthLoss, self).__init__()
def forward(self, root_depth_out, root_depth_gt, root_valid):
loss = torch.abs(root_depth_out - root_depth_gt) * root_valid
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_abs_mul_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask)
tmp4 = tl.load(in_ptr2 + x0, xmask)
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp5 = tmp3 * tmp4
tl.store(out_ptr0 + x0, tmp5, xmask)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_abs_mul_sub_0[grid(256)](arg0_1, arg1_1, arg2_1,
buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
del arg2_1
return buf0,
class RelRootDepthLossNew(nn.Module):
def __init__(self):
super(RelRootDepthLossNew, self).__init__()
def forward(self, input_0, input_1, input_2):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
output = call([arg0_1, arg1_1, arg2_1])
return output[0]
|
DuinoDu/InterHand2.6M.pl
|
RelRootDepthLoss
| false
| 5,086
|
[
"MIT"
] | 1
|
2d216960cf95b066a197a9b49795840b1ecfd0c1
|
https://github.com/DuinoDu/InterHand2.6M.pl/tree/2d216960cf95b066a197a9b49795840b1ecfd0c1
|
Linear
|
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
def keep_variance_fn(x):
return x + 0.001
class Linear(nn.Module):
def __init__(self, in_features, out_features, bias=True,
keep_variance_fn=None):
super(Linear, self).__init__()
self._keep_variance_fn = keep_variance_fn
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.Tensor(out_features, in_features))
if bias:
self.bias = Parameter(torch.Tensor(out_features))
else:
self.register_parameter('bias', None)
def forward(self, inputs_mean, inputs_variance):
outputs_mean = F.linear(inputs_mean, self.weight, self.bias)
outputs_variance = F.linear(inputs_variance, self.weight ** 2, None)
if self._keep_variance_fn is not None:
outputs_variance = self._keep_variance_fn(outputs_variance)
return outputs_mean, outputs_variance
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_pow_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tmp0 * tmp0
tl.store(out_ptr0 + x0, tmp1, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf0)
del primals_2
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_pow_0[grid(16)](primals_1, buf1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2)
del buf1
return reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
), primals_1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(primals_4, (64, 4), (4, 1), 0)
def keep_variance_fn(x):
return x + 0.001
class LinearNew(nn.Module):
def __init__(self, in_features, out_features, bias=True,
keep_variance_fn=None):
super(LinearNew, self).__init__()
self._keep_variance_fn = keep_variance_fn
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.Tensor(out_features, in_features))
if bias:
self.bias = Parameter(torch.Tensor(out_features))
else:
self.register_parameter('bias', None)
def forward(self, input_0, input_1):
primals_1 = self.weight
primals_2 = self.bias
primals_3 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0], output[1]
|
DoggyLiu0116/MamboNet
|
Linear
| false
| 5,087
|
[
"MIT"
] | 1
|
3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
Softmax
|
import torch
import torch.nn as nn
def keep_variance_fn(x):
return x + 0.001
class Softmax(nn.Module):
def __init__(self, dim=1, keep_variance_fn=None):
super(Softmax, self).__init__()
self.dim = dim
self._keep_variance_fn = keep_variance_fn
def forward(self, features_mean, features_variance, eps=1e-05):
"""Softmax function applied to a multivariate Gaussian distribution.
It works under the assumption that features_mean and features_variance
are the parameters of a the indepent gaussians that contribute to the
multivariate gaussian.
Mean and variance of the log-normal distribution are computed following
https://en.wikipedia.org/wiki/Log-normal_distribution."""
log_gaussian_mean = features_mean + 0.5 * features_variance
log_gaussian_variance = 2 * log_gaussian_mean
log_gaussian_mean = torch.exp(log_gaussian_mean)
log_gaussian_variance = torch.exp(log_gaussian_variance)
log_gaussian_variance = log_gaussian_variance * (torch.exp(
features_variance) - 1)
constant = torch.sum(log_gaussian_mean, dim=self.dim) + eps
constant = constant.unsqueeze(self.dim)
outputs_mean = log_gaussian_mean / constant
outputs_variance = log_gaussian_variance / constant ** 2
if self._keep_variance_fn is not None:
outputs_variance = self._keep_variance_fn(outputs_variance)
return outputs_mean, outputs_variance
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_exp_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
tmp6 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp7 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
tmp12 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp13 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
tmp18 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp19 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp0 + tmp3
tmp5 = tl_math.exp(tmp4)
tmp8 = tmp7 * tmp2
tmp9 = tmp6 + tmp8
tmp10 = tl_math.exp(tmp9)
tmp11 = tmp5 + tmp10
tmp14 = tmp13 * tmp2
tmp15 = tmp12 + tmp14
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp11 + tmp16
tmp20 = tmp19 * tmp2
tmp21 = tmp18 + tmp20
tmp22 = tl_math.exp(tmp21)
tmp23 = tmp17 + tmp22
tmp24 = 1e-05
tmp25 = tmp23 + tmp24
tl.store(out_ptr0 + x2, tmp25, xmask)
@triton.jit
def triton_poi_fused_add_div_exp_mul_pow_sub_1(in_ptr0, in_ptr1, in_ptr2,
out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x3, xmask)
tmp6 = tl.load(in_ptr2 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp0 + tmp3
tmp5 = tl_math.exp(tmp4)
tmp7 = tmp5 / tmp6
tmp8 = 2.0
tmp9 = tmp4 * tmp8
tmp10 = tl_math.exp(tmp9)
tmp11 = tl_math.exp(tmp1)
tmp12 = 1.0
tmp13 = tmp11 - tmp12
tmp14 = tmp10 * tmp13
tmp15 = tmp6 * tmp6
tmp16 = tmp14 / tmp15
tl.store(out_ptr0 + x3, tmp7, xmask)
tl.store(out_ptr1 + x3, tmp16, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_exp_mul_sum_0[grid(64)](arg1_1, arg0_1, buf0,
64, XBLOCK=64, num_warps=1, num_stages=1)
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_div_exp_mul_pow_sub_1[grid(256)](arg1_1,
arg0_1, buf0, buf1, buf2, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del arg0_1
del arg1_1
del buf0
return buf1, buf2
def keep_variance_fn(x):
return x + 0.001
class SoftmaxNew(nn.Module):
def __init__(self, dim=1, keep_variance_fn=None):
super(SoftmaxNew, self).__init__()
self.dim = dim
self._keep_variance_fn = keep_variance_fn
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0], output[1]
|
DoggyLiu0116/MamboNet
|
Softmax
| false
| 5,088
|
[
"MIT"
] | 1
|
3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
Conv2d
|
import torch
import torch.nn.functional as F
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pair
def keep_variance_fn(x):
return x + 0.001
class Conv2d(_ConvNd):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, keep_variance_fn=None,
padding_mode='zeros'):
self._keep_variance_fn = keep_variance_fn
kernel_size = _pair(kernel_size)
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
super(Conv2d, self).__init__(in_channels, out_channels, kernel_size,
stride, padding, dilation, False, _pair(0), groups, bias,
padding_mode)
def forward(self, inputs_mean, inputs_variance):
outputs_mean = F.conv2d(inputs_mean, self.weight, self.bias, self.
stride, self.padding, self.dilation, self.groups)
outputs_variance = F.conv2d(inputs_variance, self.weight ** 2, None,
self.stride, self.padding, self.dilation, self.groups)
if self._keep_variance_fn is not None:
outputs_variance = self._keep_variance_fn(outputs_variance)
return outputs_mean, outputs_variance
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pair
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x2, tmp2, xmask)
@triton.jit
def triton_poi_fused_pow_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tmp0 * tmp0
tl.store(out_ptr0 + x0, tmp1, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(16)](buf1, primals_2, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_pow_1[grid(256)](primals_1, buf2, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf3 = extern_kernels.convolution(primals_4, buf2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 1, 1), (4, 1, 1, 1))
return buf1, buf3, primals_1, primals_3, primals_4, buf2
def keep_variance_fn(x):
return x + 0.001
class Conv2dNew(_ConvNd):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, keep_variance_fn=None,
padding_mode='zeros'):
self._keep_variance_fn = keep_variance_fn
kernel_size = _pair(kernel_size)
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
super(Conv2dNew, self).__init__(in_channels, out_channels,
kernel_size, stride, padding, dilation, False, _pair(0), groups,
bias, padding_mode)
def forward(self, input_0, input_1):
primals_1 = self.weight
primals_2 = self.bias
primals_3 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0], output[1]
|
DoggyLiu0116/MamboNet
|
Conv2d
| false
| 5,089
|
[
"MIT"
] | 1
|
3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
NN
|
import torch
import torch.nn as nn
class NN(nn.Module):
def __init__(self, input_size, num_classes):
super(NN, self).__init__()
self.fc1 = nn.Linear(in_features=input_size, out_features=50)
self.activation1 = nn.ReLU()
self.fc2 = nn.Linear(in_features=50, out_features=num_classes)
def forward(self, x):
x = self.fc1(x)
x = self.activation1(x)
x = self.fc2(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'num_classes': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 3200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 50
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (50, 4), (4, 1))
assert_size_stride(primals_2, (50,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 50), (50, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 50), (50, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 50), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 50), (800, 200, 50, 1), 0)
del buf0
buf3 = empty_strided_cuda((4, 4, 4, 50), (800, 200, 50, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(3200)](buf1,
primals_2, buf3, 3200, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 50),
(50, 1), 0), reinterpret_tensor(primals_4, (50, 4), (1, 50), 0),
alpha=1, beta=1, out=buf2)
del primals_5
return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 50), (50, 1), 0), primals_4, buf3
class NNNew(nn.Module):
def __init__(self, input_size, num_classes):
super(NNNew, self).__init__()
self.fc1 = nn.Linear(in_features=input_size, out_features=50)
self.activation1 = nn.ReLU()
self.fc2 = nn.Linear(in_features=50, out_features=num_classes)
def forward(self, input_0):
primals_1 = self.fc1.weight
primals_2 = self.fc1.bias
primals_4 = self.fc2.weight
primals_5 = self.fc2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
Dutta-SD/Python_Programs
|
NN
| false
| 5,090
|
[
"MIT"
] | 1
|
f002dbd49c979a6d8b156f88003a79f364ff01da
|
https://github.com/Dutta-SD/Python_Programs/tree/f002dbd49c979a6d8b156f88003a79f364ff01da
|
BiDAFAttention
|
import torch
import torch.nn.functional as F
import torch.nn as nn
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.
mask (torch.Tensor): Same shape as `logits`, with 0 indicating
positions that should be assigned 0 probability in the output.
dim (int): Dimension over which to take softmax.
log_softmax (bool): Take log-softmax rather than regular softmax.
E.g., some PyTorch functions such as `F.nll_loss` expect log-softmax.
Returns:
probs (torch.Tensor): Result of taking masked softmax over the logits.
"""
mask = mask.type(torch.float32)
masked_logits = mask * logits + (1 - mask) * -1e+30
softmax_fn = F.log_softmax if log_softmax else F.softmax
probs = softmax_fn(masked_logits, dim)
return probs
class BiDAFAttention(nn.Module):
"""Bidirectional attention originally used by BiDAF.
Bidirectional attention computes attention in two directions:
The context attends to the query and the query attends to the context.
The output of this layer is the concatenation of [context, c2q_attention,
context * c2q_attention, context * q2c_attention]. This concatenation allows
the attention vector at each timestep, along with the embeddings from
previous layers, to flow through the attention layer to the modeling layer.
The output has shape (batch_size, context_len, 8 * hidden_size).
Args:
hidden_size (int): Size of hidden activations.
drop_prob (float): Probability of zero-ing out activations.
"""
def __init__(self, hidden_size, drop_prob=0.1):
super(BiDAFAttention, self).__init__()
self.drop_prob = drop_prob
self.c_weight = nn.Parameter(torch.zeros(hidden_size, 1))
self.q_weight = nn.Parameter(torch.zeros(hidden_size, 1))
self.cq_weight = nn.Parameter(torch.zeros(1, 1, hidden_size))
for weight in (self.c_weight, self.q_weight, self.cq_weight):
nn.init.xavier_uniform_(weight)
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, c, q, c_mask, q_mask):
batch_size, c_len, _ = c.size()
q_len = q.size(1)
s = self.get_similarity_matrix(c, q)
c_mask = c_mask.view(batch_size, c_len, 1)
q_mask = q_mask.view(batch_size, 1, q_len)
s1 = masked_softmax(s, q_mask, dim=2)
s2 = masked_softmax(s, c_mask, dim=1)
a = torch.bmm(s1, q)
b = torch.bmm(torch.bmm(s1, s2.transpose(1, 2)), c)
x = torch.cat([c, a, c * a, c * b], dim=2)
return x
def get_similarity_matrix(self, c, q):
"""Get the "similarity matrix" between context and query (using the
terminology of the BiDAF paper).
A naive implementation as described in BiDAF would concatenate the
three vectors then project the result with a single weight matrix. This
method is a more memory-efficient implementation of the same operation.
See Also:
Equation 1 in https://arxiv.org/abs/1611.01603
"""
c_len, q_len = c.size(1), q.size(1)
c = F.dropout(c, self.drop_prob, self.training)
q = F.dropout(q, self.drop_prob, self.training)
s0 = torch.matmul(c, self.c_weight).expand([-1, -1, q_len])
s1 = torch.matmul(q, self.q_weight).transpose(1, 2).expand([-1,
c_len, -1])
s2 = torch.matmul(c * self.cq_weight, q.transpose(1, 2))
s = s0 + s1 + s2 + self.bias
return s
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4,
1]), torch.rand([4, 1, 4])]
def get_init_inputs():
return [[], {'hidden_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x2, tmp2, xmask)
@triton.jit
def triton_poi_fused_add_mul_rsub_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, in_ptr5, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex // 16
x3 = xindex // 4
x4 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp1 = tl.load(in_ptr1 + x3, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp4 = tl.load(in_ptr3 + x4, xmask)
tmp6 = tl.load(in_ptr4 + 0)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp15 = tl.load(in_ptr5 + x3, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp8 = tmp5 + tmp7
tmp9 = tmp0 * tmp8
tmp10 = 1.0
tmp11 = tmp10 - tmp0
tmp12 = -1e+30
tmp13 = tmp11 * tmp12
tmp14 = tmp9 + tmp13
tmp16 = tmp15 * tmp8
tmp17 = tmp10 - tmp15
tmp18 = tmp17 * tmp12
tmp19 = tmp16 + tmp18
tl.store(out_ptr0 + x4, tmp14, xmask)
tl.store(out_ptr1 + x4, tmp19, xmask)
@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 4
x2 = xindex // 16
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x3, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 4
x2 = xindex // 16
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x3, tmp8, xmask)
@triton.jit
def triton_poi_fused_cat_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x1 = xindex // 16
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = tmp0 >= tmp7
tmp12 = tl.full([1], 12, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tmp11 & tmp13
tmp15 = tl.load(in_ptr0 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
eviction_policy='evict_last', other=0.0)
tmp16 = tl.load(in_ptr1 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
eviction_policy='evict_last', other=0.0)
tmp17 = tmp15 * tmp16
tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
tmp19 = tl.where(tmp14, tmp17, tmp18)
tmp20 = tmp0 >= tmp12
tl.full([1], 16, tl.int64)
tmp23 = tl.load(in_ptr0 + (4 * x1 + (-12 + x0)), tmp20 & xmask,
eviction_policy='evict_last', other=0.0)
tmp24 = tl.load(in_ptr2 + (4 * x1 + (-12 + x0)), tmp20 & xmask,
eviction_policy='evict_last', other=0.0)
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp20, tmp25, tmp26)
tmp28 = tl.where(tmp14, tmp19, tmp27)
tmp29 = tl.where(tmp9, tmp10, tmp28)
tmp30 = tl.where(tmp4, tmp5, tmp29)
tl.store(out_ptr0 + x2, tmp30, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, 1), (1, 1))
assert_size_stride(primals_4, (4, 1), (1, 1))
assert_size_stride(primals_5, (1, 1, 4), (4, 4, 1))
assert_size_stride(primals_6, (1,), (1,))
assert_size_stride(primals_7, (4, 4, 1), (4, 1, 1))
assert_size_stride(primals_8, (4, 1, 4), (4, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
primals_3, out=buf0)
del primals_3
buf1 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
primals_4, out=buf1)
del primals_4
buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_0[grid(64)](primals_1, primals_5, buf2, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_5
buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(buf2, reinterpret_tensor(primals_2, (4, 4, 4), (
16, 1, 4), 0), out=buf3)
buf4 = buf2
del buf2
buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_add_mul_rsub_1[grid(64)](primals_8, buf0, buf1,
buf3, primals_6, primals_7, buf4, buf7, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del buf0
del buf1
del primals_6
buf5 = buf3
del buf3
triton_poi_fused__softmax_2[grid(64)](buf4, buf5, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf6 = buf4
del buf4
triton_poi_fused__softmax_3[grid(64)](buf5, buf6, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf8 = buf5
del buf5
triton_poi_fused__softmax_4[grid(64)](buf7, buf8, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf9 = buf7
del buf7
triton_poi_fused__softmax_5[grid(64)](buf8, buf9, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf10 = buf8
del buf8
extern_kernels.bmm(buf6, primals_2, out=buf10)
buf11 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(buf6, reinterpret_tensor(buf9, (4, 4, 4), (16, 1,
4), 0), out=buf11)
buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(buf11, primals_1, out=buf12)
del buf11
buf13 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
triton_poi_fused_cat_6[grid(256)](primals_1, buf10, buf12, buf13,
256, XBLOCK=256, num_warps=4, num_stages=1)
del buf10
del buf12
return buf13, primals_1, primals_2, primals_7, primals_8, buf6, buf9
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.
mask (torch.Tensor): Same shape as `logits`, with 0 indicating
positions that should be assigned 0 probability in the output.
dim (int): Dimension over which to take softmax.
log_softmax (bool): Take log-softmax rather than regular softmax.
E.g., some PyTorch functions such as `F.nll_loss` expect log-softmax.
Returns:
probs (torch.Tensor): Result of taking masked softmax over the logits.
"""
mask = mask.type(torch.float32)
masked_logits = mask * logits + (1 - mask) * -1e+30
softmax_fn = F.log_softmax if log_softmax else F.softmax
probs = softmax_fn(masked_logits, dim)
return probs
class BiDAFAttentionNew(nn.Module):
"""Bidirectional attention originally used by BiDAF.
Bidirectional attention computes attention in two directions:
The context attends to the query and the query attends to the context.
The output of this layer is the concatenation of [context, c2q_attention,
context * c2q_attention, context * q2c_attention]. This concatenation allows
the attention vector at each timestep, along with the embeddings from
previous layers, to flow through the attention layer to the modeling layer.
The output has shape (batch_size, context_len, 8 * hidden_size).
Args:
hidden_size (int): Size of hidden activations.
drop_prob (float): Probability of zero-ing out activations.
"""
def __init__(self, hidden_size, drop_prob=0.1):
super(BiDAFAttentionNew, self).__init__()
self.drop_prob = drop_prob
self.c_weight = nn.Parameter(torch.zeros(hidden_size, 1))
self.q_weight = nn.Parameter(torch.zeros(hidden_size, 1))
self.cq_weight = nn.Parameter(torch.zeros(1, 1, hidden_size))
for weight in (self.c_weight, self.q_weight, self.cq_weight):
nn.init.xavier_uniform_(weight)
self.bias = nn.Parameter(torch.zeros(1))
def get_similarity_matrix(self, c, q):
"""Get the "similarity matrix" between context and query (using the
terminology of the BiDAF paper).
A naive implementation as described in BiDAF would concatenate the
three vectors then project the result with a single weight matrix. This
method is a more memory-efficient implementation of the same operation.
See Also:
Equation 1 in https://arxiv.org/abs/1611.01603
"""
c_len, q_len = c.size(1), q.size(1)
c = F.dropout(c, self.drop_prob, self.training)
q = F.dropout(q, self.drop_prob, self.training)
s0 = torch.matmul(c, self.c_weight).expand([-1, -1, q_len])
s1 = torch.matmul(q, self.q_weight).transpose(1, 2).expand([-1,
c_len, -1])
s2 = torch.matmul(c * self.cq_weight, q.transpose(1, 2))
s = s0 + s1 + s2 + self.bias
return s
def forward(self, input_0, input_1, input_2, input_3):
primals_3 = self.c_weight
primals_4 = self.q_weight
primals_5 = self.cq_weight
primals_6 = self.bias
primals_1 = input_0
primals_2 = input_1
primals_7 = input_2
primals_8 = input_3
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
Derek318/Adversarial-Squad-CS224N
|
BiDAFAttention
| false
| 5,091
|
[
"MIT"
] | 1
|
9b4a5da2a262f4de9b9b05d7b67dc48b2b857e46
|
https://github.com/Derek318/Adversarial-Squad-CS224N/tree/9b4a5da2a262f4de9b9b05d7b67dc48b2b857e46
|
MinusRbfHSIC
|
import torch
import torch.nn as nn
import torch.utils.data
class HSIC(nn.Module):
"""Base class for the finite sample estimator of Hilbert-Schmidt Independence Criterion (HSIC)
..math:: HSIC (X, Y) := || C_{x, y} ||^2_{HS}, where HSIC (X, Y) = 0 iif X and Y are independent.
Empirically, we use the finite sample estimator of HSIC (with m observations) by,
(1) biased estimator (HSIC_0)
Gretton, Arthur, et al. "Measuring statistical dependence with Hilbert-Schmidt norms." 2005.
:math: (m - 1)^2 tr KHLH.
where K_{ij} = kernel_x (x_i, x_j), L_{ij} = kernel_y (y_i, y_j), H = 1 - m^{-1} 1 1 (Hence, K, L, H are m by m matrices).
(2) unbiased estimator (HSIC_1)
Song, Le, et al. "Feature selection via dependence maximization." 2012.
:math: rac{1}{m (m - 3)} igg[ tr ( ilde K ilde L) + rac{1^ op ilde K 1 1^ op ilde L 1}{(m-1)(m-2)} - rac{2}{m-2} 1^ op ilde K ilde L 1 igg].
where ilde K and ilde L are related to K and L by the diagonal entries of ilde K_{ij} and ilde L_{ij} are set to zero.
Parameters
----------
sigma_x : float
the kernel size of the kernel function for X.
sigma_y : float
the kernel size of the kernel function for Y.
algorithm: str ('unbiased' / 'biased')
the algorithm for the finite sample estimator. 'unbiased' is used for our paper.
reduction: not used (for compatibility with other losses).
"""
def __init__(self, sigma_x, sigma_y=None, algorithm='unbiased',
reduction=None):
super(HSIC, self).__init__()
if sigma_y is None:
sigma_y = sigma_x
self.sigma_x = sigma_x
self.sigma_y = sigma_y
if algorithm == 'biased':
self.estimator = self.biased_estimator
elif algorithm == 'unbiased':
self.estimator = self.unbiased_estimator
else:
raise ValueError('invalid estimator: {}'.format(algorithm))
def _kernel_x(self, X):
raise NotImplementedError
def _kernel_y(self, Y):
raise NotImplementedError
def biased_estimator(self, input1, input2):
"""Biased estimator of Hilbert-Schmidt Independence Criterion
Gretton, Arthur, et al. "Measuring statistical dependence with Hilbert-Schmidt norms." 2005.
"""
K = self._kernel_x(input1)
L = self._kernel_y(input2)
KH = K - K.mean(0, keepdim=True)
LH = L - L.mean(0, keepdim=True)
N = len(input1)
return torch.trace(KH @ LH / (N - 1) ** 2)
def unbiased_estimator(self, input1, input2):
"""Unbiased estimator of Hilbert-Schmidt Independence Criterion
Song, Le, et al. "Feature selection via dependence maximization." 2012.
"""
kernel_XX = self._kernel_x(input1)
kernel_YY = self._kernel_y(input2)
tK = kernel_XX - torch.diag(kernel_XX)
tL = kernel_YY - torch.diag(kernel_YY)
N = len(input1)
hsic = torch.trace(tK @ tL) + torch.sum(tK) * torch.sum(tL) / (N - 1
) / (N - 2) - 2 * torch.sum(tK, 0).dot(torch.sum(tL, 0)) / (N - 2)
return hsic / (N * (N - 3))
def forward(self, input1, input2, **kwargs):
return self.estimator(input1, input2)
class RbfHSIC(HSIC):
"""Radial Basis Function (RBF) kernel HSIC implementation.
"""
def _kernel(self, X, sigma):
X = X.view(len(X), -1)
XX = X @ X.t()
X_sqnorms = torch.diag(XX)
X_L2 = -2 * XX + X_sqnorms.unsqueeze(1) + X_sqnorms.unsqueeze(0)
gamma = 1 / (2 * sigma ** 2)
kernel_XX = torch.exp(-gamma * X_L2)
return kernel_XX
def _kernel_x(self, X):
return self._kernel(X, self.sigma_x)
def _kernel_y(self, Y):
return self._kernel(Y, self.sigma_y)
class MinusRbfHSIC(RbfHSIC):
"""``Minus'' RbfHSIC for the ``max'' optimization.
"""
def forward(self, input1, input2, **kwargs):
return -self.estimator(input1, input2)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'sigma_x': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_add_diagonal_copy_exp_mul_sub_sum_0(in_ptr0, out_ptr0,
out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
r1 = rindex // 4
r0 = rindex % 4
tmp0 = tl.load(in_ptr0 + r2, None)
tmp3 = tl.load(in_ptr0 + 5 * r1, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + 5 * r0, None, eviction_policy='evict_last')
tmp1 = -2.0
tmp2 = tmp0 * tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = -0.03125
tmp8 = tmp6 * tmp7
tmp9 = tl_math.exp(tmp8)
tmp10 = tmp5 * tmp1
tmp11 = tmp10 + tmp5
tmp12 = tmp11 + tmp5
tmp13 = tmp12 * tmp7
tmp14 = tl_math.exp(tmp13)
tmp15 = tmp9 - tmp14
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp18 = tl.sum(tmp16, 1)[:, None]
tl.store(out_ptr0 + tl.broadcast_to(r2, [XBLOCK, RBLOCK]), tmp15, None)
tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp18, None)
@triton.jit
def triton_per_fused_add_div_dot_mul_neg_sub_sum_trace_1(in_out_ptr0,
in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, xnumel, rnumel, XBLOCK: tl
.constexpr):
RBLOCK: tl.constexpr = 4
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + 5 * r0, None, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + r0, None)
tmp5 = tl.load(in_ptr1 + (4 + r0), None)
tmp7 = tl.load(in_ptr1 + (8 + r0), None)
tmp9 = tl.load(in_ptr1 + (12 + r0), None)
tmp11 = tl.load(in_ptr2 + r0, None)
tmp12 = tl.load(in_ptr2 + (4 + r0), None)
tmp14 = tl.load(in_ptr2 + (8 + r0), None)
tmp16 = tl.load(in_ptr2 + (12 + r0), None)
tmp22 = tl.load(in_ptr3 + 0)
tmp23 = tl.broadcast_to(tmp22, [XBLOCK, 1])
tmp24 = tl.load(in_ptr4 + 0)
tmp25 = tl.broadcast_to(tmp24, [XBLOCK, 1])
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.sum(tmp1, 1)[:, None]
tmp6 = tmp4 + tmp5
tmp8 = tmp6 + tmp7
tmp10 = tmp8 + tmp9
tmp13 = tmp11 + tmp12
tmp15 = tmp13 + tmp14
tmp17 = tmp15 + tmp16
tmp18 = tmp10 * tmp17
tmp19 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
tmp21 = tl.sum(tmp19, 1)[:, None]
tmp26 = tmp23 * tmp25
tmp27 = 0.3333333333333333
tmp28 = tmp26 * tmp27
tmp29 = 0.5
tmp30 = tmp28 * tmp29
tmp31 = tmp3 + tmp30
tmp32 = 2.0
tmp33 = tmp21 * tmp32
tmp34 = tmp33 * tmp29
tmp35 = tmp31 - tmp34
tmp36 = 0.25
tmp37 = tmp35 * tmp36
tmp38 = -tmp37
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp38, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 64), (64, 1), 0),
reinterpret_tensor(arg0_1, (64, 4), (1, 64), 0), out=buf0)
del arg0_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf6 = empty_strided_cuda((), (), torch.float32)
get_raw_stream(0)
triton_per_fused_add_diagonal_copy_exp_mul_sub_sum_0[grid(1)](buf0,
buf1, buf6, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
buf2 = buf0
del buf0
extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 64), (64, 1), 0),
reinterpret_tensor(arg1_1, (64, 4), (1, 64), 0), out=buf2)
del arg1_1
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf7 = empty_strided_cuda((), (), torch.float32)
triton_per_fused_add_diagonal_copy_exp_mul_sub_sum_0[grid(1)](buf2,
buf3, buf7, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
buf4 = buf2
del buf2
extern_kernels.mm(buf1, buf3, out=buf4)
buf5 = empty_strided_cuda((), (), torch.float32)
buf9 = buf5
del buf5
triton_per_fused_add_div_dot_mul_neg_sub_sum_trace_1[grid(1)](buf9,
buf4, buf1, buf3, buf6, buf7, 1, 4, XBLOCK=1, num_warps=2,
num_stages=1)
del buf1
del buf3
del buf4
del buf6
del buf7
return buf9,
class HSIC(nn.Module):
"""Base class for the finite sample estimator of Hilbert-Schmidt Independence Criterion (HSIC)
..math:: HSIC (X, Y) := || C_{x, y} ||^2_{HS}, where HSIC (X, Y) = 0 iif X and Y are independent.
Empirically, we use the finite sample estimator of HSIC (with m observations) by,
(1) biased estimator (HSIC_0)
Gretton, Arthur, et al. "Measuring statistical dependence with Hilbert-Schmidt norms." 2005.
:math: (m - 1)^2 tr KHLH.
where K_{ij} = kernel_x (x_i, x_j), L_{ij} = kernel_y (y_i, y_j), H = 1 - m^{-1} 1 1 (Hence, K, L, H are m by m matrices).
(2) unbiased estimator (HSIC_1)
Song, Le, et al. "Feature selection via dependence maximization." 2012.
:math: rac{1}{m (m - 3)} igg[ tr ( ilde K ilde L) + rac{1^ op ilde K 1 1^ op ilde L 1}{(m-1)(m-2)} - rac{2}{m-2} 1^ op ilde K ilde L 1 igg].
where ilde K and ilde L are related to K and L by the diagonal entries of ilde K_{ij} and ilde L_{ij} are set to zero.
Parameters
----------
sigma_x : float
the kernel size of the kernel function for X.
sigma_y : float
the kernel size of the kernel function for Y.
algorithm: str ('unbiased' / 'biased')
the algorithm for the finite sample estimator. 'unbiased' is used for our paper.
reduction: not used (for compatibility with other losses).
"""
def __init__(self, sigma_x, sigma_y=None, algorithm='unbiased',
reduction=None):
super(HSIC, self).__init__()
if sigma_y is None:
sigma_y = sigma_x
self.sigma_x = sigma_x
self.sigma_y = sigma_y
if algorithm == 'biased':
self.estimator = self.biased_estimator
elif algorithm == 'unbiased':
self.estimator = self.unbiased_estimator
else:
raise ValueError('invalid estimator: {}'.format(algorithm))
def _kernel_x(self, X):
raise NotImplementedError
def _kernel_y(self, Y):
raise NotImplementedError
def biased_estimator(self, input1, input2):
"""Biased estimator of Hilbert-Schmidt Independence Criterion
Gretton, Arthur, et al. "Measuring statistical dependence with Hilbert-Schmidt norms." 2005.
"""
K = self._kernel_x(input1)
L = self._kernel_y(input2)
KH = K - K.mean(0, keepdim=True)
LH = L - L.mean(0, keepdim=True)
N = len(input1)
return torch.trace(KH @ LH / (N - 1) ** 2)
def unbiased_estimator(self, input1, input2):
"""Unbiased estimator of Hilbert-Schmidt Independence Criterion
Song, Le, et al. "Feature selection via dependence maximization." 2012.
"""
kernel_XX = self._kernel_x(input1)
kernel_YY = self._kernel_y(input2)
tK = kernel_XX - torch.diag(kernel_XX)
tL = kernel_YY - torch.diag(kernel_YY)
N = len(input1)
hsic = torch.trace(tK @ tL) + torch.sum(tK) * torch.sum(tL) / (N - 1
) / (N - 2) - 2 * torch.sum(tK, 0).dot(torch.sum(tL, 0)) / (N - 2)
return hsic / (N * (N - 3))
def forward(self, input1, input2, **kwargs):
return self.estimator(input1, input2)
class RbfHSIC(HSIC):
"""Radial Basis Function (RBF) kernel HSIC implementation.
"""
def _kernel(self, X, sigma):
X = X.view(len(X), -1)
XX = X @ X.t()
X_sqnorms = torch.diag(XX)
X_L2 = -2 * XX + X_sqnorms.unsqueeze(1) + X_sqnorms.unsqueeze(0)
gamma = 1 / (2 * sigma ** 2)
kernel_XX = torch.exp(-gamma * X_L2)
return kernel_XX
def _kernel_x(self, X):
return self._kernel(X, self.sigma_x)
def _kernel_y(self, Y):
return self._kernel(Y, self.sigma_y)
class MinusRbfHSICNew(RbfHSIC):
"""``Minus'' RbfHSIC for the ``max'' optimization.
"""
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
EIDOSlab/bridging-debiasing-privacy-deep-learning
|
MinusRbfHSIC
| false
| 5,092
|
[
"MIT"
] | 1
|
b30ab798d5ffd7d44a6d7136523400c14a4d08f5
|
https://github.com/EIDOSlab/bridging-debiasing-privacy-deep-learning/tree/b30ab798d5ffd7d44a6d7136523400c14a4d08f5
|
HandTypeLoss
|
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class HandTypeLoss(nn.Module):
def __init__(self):
super(HandTypeLoss, self).__init__()
def forward(self, hand_type_out, hand_type_gt, hand_type_valid):
loss = F.binary_cross_entropy(hand_type_out, hand_type_gt,
reduction='none')
loss = loss.mean(1)
loss = loss * hand_type_valid
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_binary_cross_entropy_mean_0(in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tmp3 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
tmp13 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp15 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
tmp25 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp27 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
tmp37 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp39 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
tmp1 = 1.0
tmp2 = tmp0 - tmp1
tmp4 = -tmp3
tmp5 = libdevice.log1p(tmp4)
tmp6 = -100.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp2 * tmp7
tmp9 = tl_math.log(tmp3)
tmp10 = triton_helpers.maximum(tmp9, tmp6)
tmp11 = tmp0 * tmp10
tmp12 = tmp8 - tmp11
tmp14 = tmp13 - tmp1
tmp16 = -tmp15
tmp17 = libdevice.log1p(tmp16)
tmp18 = triton_helpers.maximum(tmp17, tmp6)
tmp19 = tmp14 * tmp18
tmp20 = tl_math.log(tmp15)
tmp21 = triton_helpers.maximum(tmp20, tmp6)
tmp22 = tmp13 * tmp21
tmp23 = tmp19 - tmp22
tmp24 = tmp12 + tmp23
tmp26 = tmp25 - tmp1
tmp28 = -tmp27
tmp29 = libdevice.log1p(tmp28)
tmp30 = triton_helpers.maximum(tmp29, tmp6)
tmp31 = tmp26 * tmp30
tmp32 = tl_math.log(tmp27)
tmp33 = triton_helpers.maximum(tmp32, tmp6)
tmp34 = tmp25 * tmp33
tmp35 = tmp31 - tmp34
tmp36 = tmp24 + tmp35
tmp38 = tmp37 - tmp1
tmp40 = -tmp39
tmp41 = libdevice.log1p(tmp40)
tmp42 = triton_helpers.maximum(tmp41, tmp6)
tmp43 = tmp38 * tmp42
tmp44 = tl_math.log(tmp39)
tmp45 = triton_helpers.maximum(tmp44, tmp6)
tmp46 = tmp37 * tmp45
tmp47 = tmp43 - tmp46
tmp48 = tmp36 + tmp47
tmp49 = 4.0
tmp50 = tmp48 / tmp49
tl.store(out_ptr0 + x2, tmp50, xmask)
@triton.jit
def triton_poi_fused_mul_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 64
x2 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x2, tmp2, xmask)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_binary_cross_entropy_mean_0[grid(64)](arg0_1,
arg1_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_mul_1[grid(256)](buf0, arg2_1, buf1, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del arg2_1
del buf0
return buf1,
class HandTypeLossNew(nn.Module):
def __init__(self):
super(HandTypeLossNew, self).__init__()
def forward(self, input_0, input_1, input_2):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
output = call([arg0_1, arg1_1, arg2_1])
return output[0]
|
DuinoDu/InterHand2.6M.pl
|
HandTypeLoss
| false
| 5,093
|
[
"MIT"
] | 1
|
2d216960cf95b066a197a9b49795840b1ecfd0c1
|
https://github.com/DuinoDu/InterHand2.6M.pl/tree/2d216960cf95b066a197a9b49795840b1ecfd0c1
|
ScaledDotProductAttention
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
def forward(self, q, k, v, mask=None):
attn = torch.matmul(q, k.transpose(2, 3)) / self.temperature
if mask is not None:
attn = attn.masked_fill(mask is True, -1000000000.0)
attn = self.dropout(F.softmax(attn, dim=-1))
output = torch.matmul(attn, v)
return output, attn
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4])]
def get_init_inputs():
return [[], {'temperature': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tmp4 = tmp3 * tmp1
tmp6 = tmp5 * tmp1
tmp7 = triton_helpers.maximum(tmp4, tmp6)
tmp9 = tmp8 * tmp1
tmp10 = triton_helpers.maximum(tmp7, tmp9)
tmp12 = tmp11 * tmp1
tmp13 = triton_helpers.maximum(tmp10, tmp12)
tmp14 = tmp2 - tmp13
tmp15 = 0.25
tmp16 = tmp14 * tmp15
tmp17 = tl_math.exp(tmp16)
tl.store(out_ptr0 + x2, tmp17, xmask)
@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(arg0_1, (16, 4, 4), (16, 1, 4), 0),
out=buf0)
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__softmax_0[grid(256)](buf0, buf1, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
triton_poi_fused__softmax_1[grid(256)](buf1, buf2, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf3 = reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0)
del buf1
extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(arg2_1, (16, 4, 4), (16, 4, 1), 0), out=buf3
)
del arg2_1
return reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf2
class ScaledDotProductAttentionNew(nn.Module):
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
def forward(self, input_0, input_1, input_2):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
output = call([arg0_1, arg1_1, arg2_1])
return output[0], output[1]
|
Eddie-Hwang/Co-Eye_Motion_Generation
|
ScaledDotProductAttention
| false
| 5,094
|
[
"MIT"
] | 1
|
8e244680115fb63bc26018cb6b53bcfbd04e9683
|
https://github.com/Eddie-Hwang/Co-Eye_Motion_Generation/tree/8e244680115fb63bc26018cb6b53bcfbd04e9683
|
StableBCELoss
|
import torch
class StableBCELoss(torch.nn.modules.Module):
def __init__(self):
super(StableBCELoss, self).__init__()
def forward(self, input, target):
neg_abs = -input.abs()
loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
return loss.mean()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_abs_add_clamp_exp_log_mean_mul_neg_sub_0(in_out_ptr0,
in_ptr0, in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp3 = tl.load(in_ptr1 + r0, None)
tmp1 = 0.0
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp4 = tmp0 * tmp3
tmp5 = tmp2 - tmp4
tmp6 = tl_math.abs(tmp0)
tmp7 = -tmp6
tmp8 = tl_math.exp(tmp7)
tmp9 = 1.0
tmp10 = tmp8 + tmp9
tmp11 = tl_math.log(tmp10)
tmp12 = tmp5 + tmp11
tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
tmp16 = 256.0
tmp17 = tmp15 / tmp16
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp17, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_abs_add_clamp_exp_log_mean_mul_neg_sub_0[grid(1)](buf1
, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class StableBCELossNew(torch.nn.modules.Module):
def __init__(self):
super(StableBCELossNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
EastGit0/JITNet_segmentation
|
StableBCELoss
| false
| 5,095
|
[
"MIT"
] | 1
|
7f6598a38b39dafbe6def90385e342b12982143e
|
https://github.com/EastGit0/JITNet_segmentation/tree/7f6598a38b39dafbe6def90385e342b12982143e
|
MaxPool2d
|
import torch
import numpy as np
import torch.nn as nn
from numbers import Number
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))
def _normal_log_pdf(value, mu, stddev):
var = stddev ** 2
log_scale = np.log(stddev) if isinstance(stddev, Number) else torch.log(
stddev)
return -(value - mu) ** 2 / (2.0 * var) - log_scale - np.log(np.sqrt(
2.0 * np.pi))
def normpdf(value, mu=0.0, stddev=1.0):
return torch.exp(_normal_log_pdf(value, mu, stddev))
def keep_variance_fn(x):
return x + 0.001
class MaxPool2d(nn.Module):
def __init__(self, keep_variance_fn=None):
super(MaxPool2d, self).__init__()
self._keep_variance_fn = keep_variance_fn
def _max_pool_internal(self, mu_a, mu_b, var_a, var_b):
stddev = torch.sqrt(var_a + var_b)
ab = mu_a - mu_b
alpha = ab / stddev
pdf = normpdf(alpha)
cdf = normcdf(alpha)
z_mu = stddev * pdf + ab * cdf + mu_b
z_var = (mu_a + mu_b) * stddev * pdf + (mu_a ** 2 + var_a) * cdf + (
mu_b ** 2 + var_b) * (1.0 - cdf) - z_mu ** 2
if self._keep_variance_fn is not None:
z_var = self._keep_variance_fn(z_var)
return z_mu, z_var
def _max_pool_1x2(self, inputs_mean, inputs_variance):
mu_a = inputs_mean[:, :, :, 0::2]
mu_b = inputs_mean[:, :, :, 1::2]
var_a = inputs_variance[:, :, :, 0::2]
var_b = inputs_variance[:, :, :, 1::2]
outputs_mean, outputs_variance = self._max_pool_internal(mu_a, mu_b,
var_a, var_b)
return outputs_mean, outputs_variance
def _max_pool_2x1(self, inputs_mean, inputs_variance):
mu_a = inputs_mean[:, :, 0::2, :]
mu_b = inputs_mean[:, :, 1::2, :]
var_a = inputs_variance[:, :, 0::2, :]
var_b = inputs_variance[:, :, 1::2, :]
outputs_mean, outputs_variance = self._max_pool_internal(mu_a, mu_b,
var_a, var_b)
return outputs_mean, outputs_variance
def forward(self, inputs_mean, inputs_variance):
z_mean, z_variance = self._max_pool_1x2(inputs_mean, inputs_variance)
outputs_mean, outputs_variance = self._max_pool_2x1(z_mean, z_variance)
return outputs_mean, outputs_variance
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import Number
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_rsub_sqrt_sub_0(
in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 2 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + 2 * x0, xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 2 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = libdevice.sqrt(tmp5)
tmp7 = tmp2 * tmp6
tmp8 = tmp0 - tmp1
tmp9 = tmp8 / tmp6
tmp10 = 0.0
tmp11 = tmp9 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = -tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 0.9189385332046727
tmp17 = tmp15 - tmp16
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp7 * tmp18
tmp20 = tmp0 * tmp0
tmp21 = tmp20 + tmp3
tmp22 = 1.0
tmp23 = tmp11 * tmp22
tmp24 = 1.414213562373095
tmp25 = tmp23 / tmp24
tmp26 = libdevice.erf(tmp25)
tmp27 = tmp26 + tmp22
tmp28 = tmp27 * tmp14
tmp29 = tmp21 * tmp28
tmp30 = tmp19 + tmp29
tmp31 = tmp6 * tmp18
tmp32 = tmp8 * tmp28
tmp33 = tmp31 + tmp32
tmp34 = tmp33 + tmp1
tmp35 = tmp34 * tmp34
tmp36 = tmp1 * tmp1
tmp37 = tmp36 + tmp4
tmp38 = tmp22 - tmp28
tmp39 = tmp37 * tmp38
tmp40 = tmp30 + tmp39
tmp41 = tmp40 - tmp35
tl.store(in_out_ptr0 + x0, tmp41, xmask)
@triton.jit
def triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_rsub_sqrt_sub_1(
in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr2, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 2
x1 = xindex // 2
x2 = xindex
tmp0 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr1 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr1 + (5 + 2 * x0 + 8 * x1), xmask, eviction_policy=
'evict_last')
tmp29 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy=
'evict_last')
tmp30 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy
='evict_last')
tmp33 = tl.load(in_ptr1 + (2 * x0 + 8 * x1), xmask, eviction_policy=
'evict_last')
tmp34 = tl.load(in_ptr1 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy
='evict_last')
tmp54 = tl.load(in_ptr2 + (x0 + 4 * x1), xmask)
tmp55 = tl.load(in_ptr2 + (2 + x0 + 4 * x1), xmask)
tmp2 = tmp0 + tmp1
tmp3 = libdevice.sqrt(tmp2)
tmp6 = tmp4 - tmp5
tmp7 = tmp6 / tmp3
tmp8 = 0.0
tmp9 = tmp7 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = -tmp10
tmp12 = 0.5
tmp13 = tmp11 * tmp12
tmp14 = 0.9189385332046727
tmp15 = tmp13 - tmp14
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp3 * tmp16
tmp18 = 1.0
tmp19 = tmp9 * tmp18
tmp20 = 1.414213562373095
tmp21 = tmp19 / tmp20
tmp22 = libdevice.erf(tmp21)
tmp23 = tmp22 + tmp18
tmp24 = tmp23 * tmp12
tmp25 = tmp6 * tmp24
tmp26 = tmp17 + tmp25
tmp27 = tmp26 + tmp5
tmp28 = tmp27 * tmp27
tmp31 = tmp29 + tmp30
tmp32 = libdevice.sqrt(tmp31)
tmp35 = tmp33 - tmp34
tmp36 = tmp35 / tmp32
tmp37 = tmp36 - tmp8
tmp38 = tmp37 * tmp37
tmp39 = -tmp38
tmp40 = tmp39 * tmp12
tmp41 = tmp40 - tmp14
tmp42 = tl_math.exp(tmp41)
tmp43 = tmp32 * tmp42
tmp44 = tmp37 * tmp18
tmp45 = tmp44 / tmp20
tmp46 = libdevice.erf(tmp45)
tmp47 = tmp46 + tmp18
tmp48 = tmp47 * tmp12
tmp49 = tmp35 * tmp48
tmp50 = tmp43 + tmp49
tmp51 = tmp50 + tmp34
tmp52 = tmp51 + tmp27
tmp53 = tmp51 - tmp27
tmp56 = tmp54 + tmp55
tmp57 = libdevice.sqrt(tmp56)
tmp58 = tmp53 / tmp57
tmp59 = tmp58 - tmp8
tmp60 = tmp59 * tmp59
tmp61 = -tmp60
tmp62 = tmp61 * tmp12
tmp63 = tmp62 - tmp14
tmp64 = tl_math.exp(tmp63)
tmp65 = tmp57 * tmp64
tmp66 = tmp59 * tmp18
tmp67 = tmp66 / tmp20
tmp68 = libdevice.erf(tmp67)
tmp69 = tmp68 + tmp18
tmp70 = tmp69 * tmp12
tmp71 = tmp53 * tmp70
tmp72 = tmp65 + tmp71
tmp73 = tmp72 + tmp27
tmp74 = tmp51 * tmp51
tmp75 = tmp52 * tmp57
tmp76 = tmp75 * tmp64
tmp77 = tmp74 + tmp54
tmp78 = tmp77 * tmp70
tmp79 = tmp76 + tmp78
tmp80 = tmp28 + tmp55
tmp81 = tmp18 - tmp70
tmp82 = tmp80 * tmp81
tmp83 = tmp79 + tmp82
tmp84 = tmp73 * tmp73
tmp85 = tmp83 - tmp84
tl.store(out_ptr2 + x2, tmp73, xmask)
tl.store(in_out_ptr0 + x2, tmp85, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf1 = empty_strided_cuda((4, 4, 4, 2), (32, 8, 2, 1), torch.float32)
buf3 = buf1
del buf1
get_raw_stream(0)
triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_rsub_sqrt_sub_0[grid
(128)](buf3, arg0_1, arg1_1, 128, XBLOCK=128, num_warps=4,
num_stages=1)
buf0 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
buf8 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
buf6 = buf0
del buf0
buf9 = buf6
del buf6
triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_rsub_sqrt_sub_1[grid
(64)](buf9, arg1_1, arg0_1, buf3, buf8, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del arg0_1
del arg1_1
del buf3
return buf8, buf9
def normcdf(value, mu=0.0, stddev=1.0):
sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))
def _normal_log_pdf(value, mu, stddev):
var = stddev ** 2
log_scale = np.log(stddev) if isinstance(stddev, Number) else torch.log(
stddev)
return -(value - mu) ** 2 / (2.0 * var) - log_scale - np.log(np.sqrt(
2.0 * np.pi))
def normpdf(value, mu=0.0, stddev=1.0):
return torch.exp(_normal_log_pdf(value, mu, stddev))
def keep_variance_fn(x):
return x + 0.001
class MaxPool2dNew(nn.Module):
def __init__(self, keep_variance_fn=None):
super(MaxPool2dNew, self).__init__()
self._keep_variance_fn = keep_variance_fn
def _max_pool_internal(self, mu_a, mu_b, var_a, var_b):
stddev = torch.sqrt(var_a + var_b)
ab = mu_a - mu_b
alpha = ab / stddev
pdf = normpdf(alpha)
cdf = normcdf(alpha)
z_mu = stddev * pdf + ab * cdf + mu_b
z_var = (mu_a + mu_b) * stddev * pdf + (mu_a ** 2 + var_a) * cdf + (
mu_b ** 2 + var_b) * (1.0 - cdf) - z_mu ** 2
if self._keep_variance_fn is not None:
z_var = self._keep_variance_fn(z_var)
return z_mu, z_var
def _max_pool_1x2(self, inputs_mean, inputs_variance):
mu_a = inputs_mean[:, :, :, 0::2]
mu_b = inputs_mean[:, :, :, 1::2]
var_a = inputs_variance[:, :, :, 0::2]
var_b = inputs_variance[:, :, :, 1::2]
outputs_mean, outputs_variance = self._max_pool_internal(mu_a, mu_b,
var_a, var_b)
return outputs_mean, outputs_variance
def _max_pool_2x1(self, inputs_mean, inputs_variance):
mu_a = inputs_mean[:, :, 0::2, :]
mu_b = inputs_mean[:, :, 1::2, :]
var_a = inputs_variance[:, :, 0::2, :]
var_b = inputs_variance[:, :, 1::2, :]
outputs_mean, outputs_variance = self._max_pool_internal(mu_a, mu_b,
var_a, var_b)
return outputs_mean, outputs_variance
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0], output[1]
|
DoggyLiu0116/MamboNet
|
MaxPool2d
| false
| 5,096
|
[
"MIT"
] | 1
|
3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
ClassWisePool
|
import torch
from torch import nn
class ClassWisePool(nn.Module):
def __init__(self, num_maps):
super(ClassWisePool, self).__init__()
self.num_maps = num_maps
def forward(self, input):
batch_size, num_channels, s = input.size()
num_outputs = int(num_channels / self.num_maps)
x = input.view(batch_size, num_outputs, self.num_maps, s)
output = torch.sum(x, 2)
return output.view(batch_size, num_outputs, s) / self.num_maps
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'num_maps': 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_div_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 0.25
tmp8 = tmp6 * tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_div_sum_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del arg0_1
return buf0,
class ClassWisePoolNew(nn.Module):
def __init__(self, num_maps):
super(ClassWisePoolNew, self).__init__()
self.num_maps = num_maps
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Ecocytus/Roberta-ZeroShot-Label
|
ClassWisePool
| false
| 5,097
|
[
"MIT"
] | 1
|
8a6d74187a0e2fd5b1b75549cfb724f54269c5a5
|
https://github.com/Ecocytus/Roberta-ZeroShot-Label/tree/8a6d74187a0e2fd5b1b75549cfb724f54269c5a5
|
SimpleArch
|
import torch
import torch.nn as nn
class SimpleArch(nn.Module):
def __init__(self, input_size, dropout=0.1, hidden_layer_size=10,
output_neurons=1):
"""
A simple architecture wrapper -- build with intuitive Sklearn-like API.
"""
super(SimpleArch, self).__init__()
self.h1 = nn.Linear(input_size, hidden_layer_size)
self.h2 = nn.Linear(hidden_layer_size, hidden_layer_size)
self.h3 = nn.Linear(hidden_layer_size, 16)
self.h4 = nn.Linear(16, output_neurons)
self.drop = nn.Dropout(dropout)
self.act = nn.ELU()
self.sigma = nn.Sigmoid()
def forward(self, x):
"""
The standard forward pass. See the original paper for the formal description of this part of the DRMs.
"""
out = self.h1(x)
out = self.drop(out)
out = self.act(out)
out = self.h2(out)
out = self.drop(out)
out = self.act(out)
out = self.h3(out)
out = self.drop(out)
out = self.act(out)
out = self.h4(out)
out = self.sigma(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_elu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 640
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tl.store(out_ptr0 + x0, tmp7, xmask)
@triton.jit
def triton_poi_fused_elu_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tl.store(out_ptr0 + x0, tmp7, xmask)
@triton.jit
def triton_poi_fused_sigmoid_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tmp4 = tl.sigmoid(tmp3)
tl.store(in_out_ptr0 + x0, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9) = args
args.clear()
assert_size_stride(primals_1, (10, 4), (4, 1))
assert_size_stride(primals_2, (10,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (10, 10), (10, 1))
assert_size_stride(primals_5, (10,), (1,))
assert_size_stride(primals_6, (16, 10), (10, 1))
assert_size_stride(primals_7, (16,), (1,))
assert_size_stride(primals_8, (1, 16), (16, 1))
assert_size_stride(primals_9, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 10), (1, 4),
0), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4, 4, 10), (160, 40, 10, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused_elu_0[grid(640)](buf0, buf1, 640, XBLOCK=256,
num_warps=4, num_stages=1)
buf2 = empty_strided_cuda((64, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 10),
(10, 1), 0), reinterpret_tensor(primals_4, (10, 10), (1, 10), 0
), alpha=1, beta=1, out=buf2)
del primals_5
buf3 = empty_strided_cuda((4, 4, 4, 10), (160, 40, 10, 1), torch.
float32)
triton_poi_fused_elu_0[grid(640)](buf2, buf3, 640, XBLOCK=256,
num_warps=4, num_stages=1)
buf4 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 10),
(10, 1), 0), reinterpret_tensor(primals_6, (10, 16), (1, 10), 0
), alpha=1, beta=1, out=buf4)
del primals_7
buf5 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
float32)
triton_poi_fused_elu_1[grid(1024)](buf4, buf5, 1024, XBLOCK=128,
num_warps=4, num_stages=1)
buf6 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf5, (64, 16), (16, 1), 0),
reinterpret_tensor(primals_8, (16, 1), (1, 16), 0), out=buf6)
buf7 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf6
triton_poi_fused_sigmoid_2[grid(64)](buf7, primals_9, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_9
return buf7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf0, reinterpret_tensor(buf1, (64, 10), (10, 1), 0
), buf2, reinterpret_tensor(buf3, (64, 10), (10, 1), 0
), buf4, reinterpret_tensor(buf5, (64, 16), (16, 1), 0
), buf7, primals_8, primals_6, primals_4
class SimpleArchNew(nn.Module):
def __init__(self, input_size, dropout=0.1, hidden_layer_size=10,
output_neurons=1):
"""
A simple architecture wrapper -- build with intuitive Sklearn-like API.
"""
super(SimpleArchNew, self).__init__()
self.h1 = nn.Linear(input_size, hidden_layer_size)
self.h2 = nn.Linear(hidden_layer_size, hidden_layer_size)
self.h3 = nn.Linear(hidden_layer_size, 16)
self.h4 = nn.Linear(16, output_neurons)
self.drop = nn.Dropout(dropout)
self.act = nn.ELU()
self.sigma = nn.Sigmoid()
def forward(self, input_0):
primals_1 = self.h1.weight
primals_2 = self.h1.bias
primals_4 = self.h2.weight
primals_5 = self.h2.bias
primals_6 = self.h3.weight
primals_7 = self.h3.bias
primals_8 = self.h4.weight
primals_9 = self.h4.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9])
return output[0]
|
EMBEDDIA/PropStar
|
SimpleArch
| false
| 5,098
|
[
"BSD-3-Clause"
] | 1
|
987be390775130893f2c3440a5f1f94025309e4d
|
https://github.com/EMBEDDIA/PropStar/tree/987be390775130893f2c3440a5f1f94025309e4d
|
APPNProp
|
import torch
import torch.nn.functional as F
import torch.nn as nn
class SparseDropout(nn.Module):
def __init__(self, p=0.5):
super().__init__()
self.p = p
def forward(self, x):
if not self.training:
return x
x_coal = x.coalesce()
drop_val = F.dropout(x_coal._values(), self.p, self.training)
return torch.sparse.FloatTensor(x_coal._indices(), drop_val, x.shape)
class MixedDropout(nn.Module):
def __init__(self, p=0.5):
super().__init__()
self.dense_dropout = nn.Dropout(p)
self.sparse_dropout = SparseDropout(p)
def forward(self, x):
if x.is_sparse:
return self.sparse_dropout(x)
else:
return self.dense_dropout(x)
class APPNProp(nn.Module):
def __init__(self, alpha: 'float'=0.1, K: 'int'=10, dropout: 'float'=0.0):
super().__init__()
self.alpha = alpha
self.K = K
if not dropout:
self.dropout = lambda x: x
else:
self.dropout = MixedDropout(dropout)
def forward(self, x, adj):
h = x
for _ in range(self.K):
A_drop = self.dropout(adj)
h = (1 - self.alpha) * A_drop.mm(h) + self.alpha * x
return h
def __repr__(self):
return (
f'{self.__class__.__name__}(alpha={self.alpha}, K={self.K}, dropout={self.dropout})'
)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp3 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.9
tmp2 = tmp0 * tmp1
tmp4 = 0.1
tmp5 = tmp3 * tmp4
tmp6 = tmp2 + tmp5
tl.store(in_out_ptr0 + x0, tmp6, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4), (4, 1))
assert_size_stride(arg1_1, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, arg0_1, out=buf0)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_add_mul_0[grid(16)](buf1, arg0_1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf1, out=buf2)
buf3 = buf2
del buf2
triton_poi_fused_add_mul_0[grid(16)](buf3, arg0_1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf4 = buf1
del buf1
extern_kernels.mm(arg1_1, buf3, out=buf4)
buf5 = buf4
del buf4
triton_poi_fused_add_mul_0[grid(16)](buf5, arg0_1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf6 = buf3
del buf3
extern_kernels.mm(arg1_1, buf5, out=buf6)
buf7 = buf6
del buf6
triton_poi_fused_add_mul_0[grid(16)](buf7, arg0_1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf8 = buf5
del buf5
extern_kernels.mm(arg1_1, buf7, out=buf8)
buf9 = buf8
del buf8
triton_poi_fused_add_mul_0[grid(16)](buf9, arg0_1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf10 = buf7
del buf7
extern_kernels.mm(arg1_1, buf9, out=buf10)
buf11 = buf10
del buf10
triton_poi_fused_add_mul_0[grid(16)](buf11, arg0_1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf12 = buf9
del buf9
extern_kernels.mm(arg1_1, buf11, out=buf12)
buf13 = buf12
del buf12
triton_poi_fused_add_mul_0[grid(16)](buf13, arg0_1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf14 = buf11
del buf11
extern_kernels.mm(arg1_1, buf13, out=buf14)
buf15 = buf14
del buf14
triton_poi_fused_add_mul_0[grid(16)](buf15, arg0_1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf16 = buf13
del buf13
extern_kernels.mm(arg1_1, buf15, out=buf16)
buf17 = buf16
del buf16
triton_poi_fused_add_mul_0[grid(16)](buf17, arg0_1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf18 = buf15
del buf15
extern_kernels.mm(arg1_1, buf17, out=buf18)
del arg1_1
del buf17
buf19 = buf18
del buf18
triton_poi_fused_add_mul_0[grid(16)](buf19, arg0_1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del arg0_1
return buf19,
class SparseDropout(nn.Module):
def __init__(self, p=0.5):
super().__init__()
self.p = p
def forward(self, x):
if not self.training:
return x
x_coal = x.coalesce()
drop_val = F.dropout(x_coal._values(), self.p, self.training)
return torch.sparse.FloatTensor(x_coal._indices(), drop_val, x.shape)
class MixedDropout(nn.Module):
def __init__(self, p=0.5):
super().__init__()
self.dense_dropout = nn.Dropout(p)
self.sparse_dropout = SparseDropout(p)
def forward(self, x):
if x.is_sparse:
return self.sparse_dropout(x)
else:
return self.dense_dropout(x)
class APPNPropNew(nn.Module):
def __init__(self, alpha: 'float'=0.1, K: 'int'=10, dropout: 'float'=0.0):
super().__init__()
self.alpha = alpha
self.K = K
if not dropout:
self.dropout = lambda x: x
else:
self.dropout = MixedDropout(dropout)
def __repr__(self):
return (
f'{self.__class__.__name__}(alpha={self.alpha}, K={self.K}, dropout={self.dropout})'
)
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
EdisonLeeeee/Graphgallery
|
APPNProp
| false
| 5,099
|
[
"MIT"
] | 1
|
8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
NormedConv2d
|
import torch
from torch import nn
import torch.onnx
class NormedConv2d(nn.Conv2d):
"""Normalized Conv2d Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divisor to
keep numerical stability. Default to 1e-6.
norm_over_kernel (bool, optional): Normalize over kernel.
Default to False.
"""
def __init__(self, *args, tempearture=20, power=1.0, eps=1e-06,
norm_over_kernel=False, **kwargs):
super(NormedConv2d, self).__init__(*args, **kwargs)
self.tempearture = tempearture
self.power = power
self.norm_over_kernel = norm_over_kernel
self.eps = eps
def forward(self, x):
if not self.norm_over_kernel:
weight_ = self.weight / (self.weight.norm(dim=1, keepdim=True).
pow(self.power) + self.eps)
else:
weight_ = self.weight / (self.weight.view(self.weight.size(0),
-1).norm(dim=1, keepdim=True).pow(self.power)[..., None,
None] + self.eps)
x_ = x / (x.norm(dim=1, keepdim=True).pow(self.power) + self.eps)
x_ = x_ * self.tempearture
if hasattr(self, 'conv2d_forward'):
x_ = self.conv2d_forward(x_, weight_)
elif torch.__version__ >= '1.8':
x_ = self._conv_forward(x_, weight_, self.bias)
else:
x_ = self._conv_forward(x_, weight_)
return x_
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_div_linalg_vector_norm_pow_0(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-06
tmp14 = tmp12 + tmp13
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + x3, tmp15, xmask)
@triton.jit
def triton_poi_fused_add_div_linalg_vector_norm_mul_pow_1(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-06
tmp14 = tmp12 + tmp13
tmp15 = tmp0 / tmp14
tmp16 = 20.0
tmp17 = tmp15 * tmp16
tl.store(out_ptr0 + x3, tmp17, xmask)
@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x2, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_div_linalg_vector_norm_pow_0[grid(256)](primals_1,
buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_div_linalg_vector_norm_mul_pow_1[grid(256)](
primals_2, buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 4, 1, 1), (4, 1, 1, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_2[grid(16)](buf3, primals_3, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del primals_3
return buf3, primals_1, buf0, buf1
class NormedConv2dNew(nn.Conv2d):
"""Normalized Conv2d Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divisor to
keep numerical stability. Default to 1e-6.
norm_over_kernel (bool, optional): Normalize over kernel.
Default to False.
"""
def __init__(self, *args, tempearture=20, power=1.0, eps=1e-06,
norm_over_kernel=False, **kwargs):
super(NormedConv2dNew, self).__init__(*args, **kwargs)
self.tempearture = tempearture
self.power = power
self.norm_over_kernel = norm_over_kernel
self.eps = eps
def forward(self, input_0):
primals_1 = self.weight
primals_3 = self.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
ENOT-AutoDL/mmdetection-enot
|
NormedConv2d
| false
| 5,100
|
[
"Apache-2.0"
] | 1
|
f541749554436e3327bac00eee89b84f66c03551
|
https://github.com/ENOT-AutoDL/mmdetection-enot/tree/f541749554436e3327bac00eee89b84f66c03551
|
ClassificationModel
|
import torch
from torch import nn
class ClassificationModel(nn.Module):
def __init__(self, num_features_in, num_anchors=9, num_classes=80,
prior=0.01, feature_size=256):
super(ClassificationModel, self).__init__()
self.num_classes = num_classes
self.num_anchors = num_anchors
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3,
padding=1)
self.act1 = nn.ReLU()
self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3,
padding=1)
self.act2 = nn.ReLU()
self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3,
padding=1)
self.act3 = nn.ReLU()
self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3,
padding=1)
self.act4 = nn.ReLU()
self.output = nn.Conv2d(feature_size, num_anchors * num_classes,
kernel_size=3, padding=1)
self.output_act = nn.Sigmoid()
def forward(self, x):
out = self.conv1(x)
out = self.act1(out)
out = self.conv2(out)
out = self.act2(out)
out = self.conv3(out)
out = self.act3(out)
out = self.conv4(out)
out = self.act4(out)
out = self.output(out)
out = self.output_act(out)
out1 = out.permute(0, 2, 3, 1)
batch_size, width, height, _channels = out1.shape
out2 = out1.view(batch_size, width, height, self.num_anchors, self.
num_classes)
return out2.contiguous().view(x.shape[0], -1, self.num_classes)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_features_in': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 4
y1 = yindex // 4
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 4 * x2 + 36 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 4
y1 = yindex // 4
tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 256
y1 = yindex // 256
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 256
y1 = yindex // 256
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 256
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_clone_convolution_5(in_out_ptr0, in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 46080
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 720
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.sigmoid(tmp2)
tl.store(in_out_ptr0 + x2, tmp2, xmask)
tl.store(out_ptr0 + x2, tmp3, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (256, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (256,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_5, (256,), (1,))
assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_7, (256,), (1,))
assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_9, (256,), (1,))
assert_size_stride(primals_10, (720, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_11, (720,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((256, 4, 3, 3), (36, 1, 12, 4), torch.float32
)
get_raw_stream(0)
triton_poi_fused_0[grid(1024, 9)](primals_1, buf0, 1024, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
triton_poi_fused_1[grid(16, 16)](primals_3, buf1, 16, 16, XBLOCK=16,
YBLOCK=16, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_2[grid(65536, 9)](primals_4, buf2, 65536, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_2[grid(65536, 9)](primals_6, buf3, 65536, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_6
buf4 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_2[grid(65536, 9)](primals_8, buf4, 65536, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_8
buf5 = empty_strided_cuda((720, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_3[grid(184320, 9)](primals_10, buf5, 184320, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_10
buf6 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf7 = buf6
del buf6
triton_poi_fused_convolution_relu_4[grid(16384)](buf7, primals_2,
16384, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf8 = extern_kernels.convolution(buf7, buf2, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf9 = buf8
del buf8
triton_poi_fused_convolution_relu_4[grid(16384)](buf9, primals_5,
16384, XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf10 = extern_kernels.convolution(buf9, buf3, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf11 = buf10
del buf10
triton_poi_fused_convolution_relu_4[grid(16384)](buf11, primals_7,
16384, XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
buf12 = extern_kernels.convolution(buf11, buf4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf12, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf13 = buf12
del buf12
triton_poi_fused_convolution_relu_4[grid(16384)](buf13, primals_9,
16384, XBLOCK=128, num_warps=4, num_stages=1)
del primals_9
buf14 = extern_kernels.convolution(buf13, buf5, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf14, (4, 720, 4, 4), (11520, 1, 2880, 720))
buf15 = buf14
del buf14
buf16 = empty_strided_cuda((4, 4, 4, 9, 80), (11520, 2880, 720, 80,
1), torch.float32)
triton_poi_fused_clone_convolution_5[grid(46080)](buf15, primals_11,
buf16, 46080, XBLOCK=256, num_warps=4, num_stages=1)
del primals_11
return reinterpret_tensor(buf16, (4, 144, 80), (11520, 80, 1), 0
), buf0, buf1, buf2, buf3, buf4, buf5, buf7, buf9, buf11, buf13, buf15
class ClassificationModelNew(nn.Module):
def __init__(self, num_features_in, num_anchors=9, num_classes=80,
prior=0.01, feature_size=256):
super(ClassificationModelNew, self).__init__()
self.num_classes = num_classes
self.num_anchors = num_anchors
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3,
padding=1)
self.act1 = nn.ReLU()
self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3,
padding=1)
self.act2 = nn.ReLU()
self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3,
padding=1)
self.act3 = nn.ReLU()
self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3,
padding=1)
self.act4 = nn.ReLU()
self.output = nn.Conv2d(feature_size, num_anchors * num_classes,
kernel_size=3, padding=1)
self.output_act = nn.Sigmoid()
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.conv3.weight
primals_7 = self.conv3.bias
primals_8 = self.conv4.weight
primals_9 = self.conv4.bias
primals_10 = self.output.weight
primals_11 = self.output.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0]
|
DerekGloudemans/temporary-repo
|
ClassificationModel
| false
| 5,101
|
[
"MIT"
] | 1
|
f278e9c7c9c7c1f362a64aec492ddb8fb1f984ad
|
https://github.com/DerekGloudemans/temporary-repo/tree/f278e9c7c9c7c1f362a64aec492ddb8fb1f984ad
|
Merge
|
import torch
import torch.utils.data
import torch.nn as nn
import torch.utils.checkpoint
class Merge(nn.Module):
def forward(self, x1, x2):
return torch.cat([x1, x2], dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16 % 8
x0 = xindex % 16
x2 = xindex // 128
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x3, tmp10, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(512)](arg0_1, arg1_1, buf0, 512, XBLOCK
=256, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
return buf0,
class MergeNew(nn.Module):
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
CNNs4QSPR/se3cnn
|
Merge
| false
| 5,102
|
[
"MIT"
] | 1
|
513f5f827c4c511bdc96e3c6ea663c8fbce60f57
|
https://github.com/CNNs4QSPR/se3cnn/tree/513f5f827c4c511bdc96e3c6ea663c8fbce60f57
|
GaussionConvF
|
import torch
import torch.nn.functional as F
import torch.nn as nn
class GaussionConvF(nn.Module):
"""The first layer in `RobustGCN` that conver node features to distribution (mean, var)"""
def __init__(self, in_features, out_features, bias=False, gamma=1.0):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.w = nn.Linear(in_features, out_features, bias=bias)
self.gamma = gamma
def reset_parameters(self):
self.w.reset_parameters()
def forward(self, x, adj_mean, adj_var):
h = self.w(x)
mean = F.elu(h)
var = F.relu(h)
attention = torch.exp(-self.gamma * var)
mean = adj_mean.mm(mean * attention)
var = adj_var.mm(var * attention * attention)
return mean, var
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features})'
)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_elu_exp_mul_relu_0(in_ptr0, out_ptr0, out_ptr1,
out_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tmp8 = tl.full([1], 0, tl.int32)
tmp9 = triton_helpers.maximum(tmp8, tmp0)
tmp10 = -1.0
tmp11 = tmp9 * tmp10
tmp12 = tl_math.exp(tmp11)
tmp13 = tmp7 * tmp12
tmp14 = tmp9 * tmp12
tmp15 = tmp14 * tmp12
tl.store(out_ptr0 + x0, tmp13, xmask)
tl.store(out_ptr1 + x0, tmp14, xmask)
tl.store(out_ptr2 + x0, tmp15, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_2, reinterpret_tensor(primals_1, (4, 4),
(1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_elu_exp_mul_relu_0[grid(16)](buf0, buf1, buf3,
buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf1, out=buf2)
buf5 = buf1
del buf1
extern_kernels.mm(primals_4, buf4, out=buf5)
del buf4
return buf2, buf5, primals_2, buf0, buf3, reinterpret_tensor(primals_4,
(4, 4), (1, 4), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0)
class GaussionConvFNew(nn.Module):
"""The first layer in `RobustGCN` that conver node features to distribution (mean, var)"""
def __init__(self, in_features, out_features, bias=False, gamma=1.0):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.w = nn.Linear(in_features, out_features, bias=bias)
self.gamma = gamma
def reset_parameters(self):
self.w.reset_parameters()
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features})'
)
def forward(self, input_0, input_1, input_2):
primals_1 = self.w.weight
primals_2 = input_0
primals_3 = input_1
primals_4 = input_2
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0], output[1]
|
EdisonLeeeee/Graphgallery
|
GaussionConvF
| false
| 5,103
|
[
"MIT"
] | 1
|
8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
SSGConv
|
from torch.nn import Module
import torch
class SSGConv(Module):
def __init__(self, K=16, alpha=0.1, **kwargs):
super().__init__()
assert K > 0
self.K = K
self.alpha = alpha
def forward(self, x, adj):
x_in = x
x_out = torch.zeros_like(x)
for _ in range(self.K):
x = torch.spmm(adj, x)
x_out += (1 - self.alpha) * x
x_out /= self.K
x_out += self.alpha * x_in
return x_out
def reset_parameters(self):
pass
def extra_repr(self):
return f'K={self.K}, alpha={self.alpha}'
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_div_mul_0(in_out_ptr0, in_out_ptr1, in_ptr0,
in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8,
in_ptr9, in_ptr10, in_ptr11, in_ptr12, in_ptr13, in_ptr14, xnumel,
XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp3 = tl.load(in_ptr0 + x0, xmask)
tmp6 = tl.load(in_ptr1 + x0, xmask)
tmp9 = tl.load(in_ptr2 + x0, xmask)
tmp12 = tl.load(in_ptr3 + x0, xmask)
tmp15 = tl.load(in_ptr4 + x0, xmask)
tmp18 = tl.load(in_ptr5 + x0, xmask)
tmp21 = tl.load(in_ptr6 + x0, xmask)
tmp24 = tl.load(in_ptr7 + x0, xmask)
tmp27 = tl.load(in_out_ptr1 + x0, xmask)
tmp30 = tl.load(in_ptr8 + x0, xmask)
tmp33 = tl.load(in_ptr9 + x0, xmask)
tmp36 = tl.load(in_ptr10 + x0, xmask)
tmp39 = tl.load(in_ptr11 + x0, xmask)
tmp42 = tl.load(in_ptr12 + x0, xmask)
tmp45 = tl.load(in_ptr13 + x0, xmask)
tmp50 = tl.load(in_ptr14 + x0, xmask)
tmp1 = 0.9
tmp2 = tmp0 * tmp1
tmp4 = tmp3 * tmp1
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp1
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp1
tmp11 = tmp8 + tmp10
tmp13 = tmp12 * tmp1
tmp14 = tmp11 + tmp13
tmp16 = tmp15 * tmp1
tmp17 = tmp14 + tmp16
tmp19 = tmp18 * tmp1
tmp20 = tmp17 + tmp19
tmp22 = tmp21 * tmp1
tmp23 = tmp20 + tmp22
tmp25 = tmp24 * tmp1
tmp26 = tmp23 + tmp25
tmp28 = tmp27 * tmp1
tmp29 = tmp26 + tmp28
tmp31 = tmp30 * tmp1
tmp32 = tmp29 + tmp31
tmp34 = tmp33 * tmp1
tmp35 = tmp32 + tmp34
tmp37 = tmp36 * tmp1
tmp38 = tmp35 + tmp37
tmp40 = tmp39 * tmp1
tmp41 = tmp38 + tmp40
tmp43 = tmp42 * tmp1
tmp44 = tmp41 + tmp43
tmp46 = tmp45 * tmp1
tmp47 = tmp44 + tmp46
tmp48 = 0.0625
tmp49 = tmp47 * tmp48
tmp51 = 0.1
tmp52 = tmp50 * tmp51
tmp53 = tmp49 + tmp52
tl.store(in_out_ptr1 + x0, tmp53, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4), (4, 1))
assert_size_stride(arg1_1, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, arg0_1, out=buf0)
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf0, out=buf1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf1, out=buf2)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf2, out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf3, out=buf4)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf4, out=buf5)
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf5, out=buf6)
buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf6, out=buf7)
buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf7, out=buf8)
buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf8, out=buf10)
buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf10, out=buf11)
buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf11, out=buf12)
buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf12, out=buf13)
buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf13, out=buf14)
buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf14, out=buf15)
buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg1_1, buf15, out=buf16)
del arg1_1
buf9 = buf0
del buf0
buf17 = buf10
del buf10
get_raw_stream(0)
triton_poi_fused_add_div_mul_0[grid(16)](buf9, buf17, buf1, buf2,
buf3, buf4, buf5, buf6, buf7, buf8, buf11, buf12, buf13, buf14,
buf15, buf16, arg0_1, 16, XBLOCK=16, num_warps=1, num_stages=1)
del arg0_1
del buf1
del buf11
del buf12
del buf13
del buf14
del buf15
del buf16
del buf2
del buf3
del buf4
del buf5
del buf6
del buf7
del buf8
del buf9
return buf17,
class SSGConvNew(Module):
def __init__(self, K=16, alpha=0.1, **kwargs):
super().__init__()
assert K > 0
self.K = K
self.alpha = alpha
def reset_parameters(self):
pass
def extra_repr(self):
return f'K={self.K}, alpha={self.alpha}'
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
EdisonLeeeee/Graphgallery
|
SSGConv
| false
| 5,104
|
[
"MIT"
] | 1
|
8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
GaussionConvD
|
import torch
import torch.nn.functional as F
import torch.nn as nn
class GaussionConvD(nn.Module):
"""The subsequent layer in `RobustGCN` that takes node distribution (mean, var) as input"""
def __init__(self, in_features, out_features, bias=False, gamma=1.0):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.w_mean = nn.Linear(in_features, out_features, bias=bias)
self.w_var = nn.Linear(in_features, out_features, bias=bias)
self.gamma = gamma
def reset_parameters(self):
self.w_mean.reset_parameters()
self.w_var.reset_parameters()
def forward(self, mean, var, adj_mean, adj_var):
mean = F.elu(self.w_mean(mean))
var = F.relu(self.w_var(var))
attention = torch.exp(-self.gamma * var)
mean = adj_mean.mm(mean * attention)
var = adj_var.mm(var * attention * attention)
return mean, var
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features})'
)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4]), torch.rand([4, 4]),
torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_elu_exp_mul_relu_0(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp8 = tl.load(in_ptr1 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tmp11 = -1.0
tmp12 = tmp10 * tmp11
tmp13 = tl_math.exp(tmp12)
tmp14 = tmp7 * tmp13
tmp15 = tmp10 * tmp13
tmp16 = tmp15 * tmp13
tl.store(out_ptr0 + x0, tmp14, xmask)
tl.store(out_ptr1 + x0, tmp15, xmask)
tl.store(out_ptr2 + x0, tmp16, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_2, reinterpret_tensor(primals_1, (4, 4),
(1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_4, reinterpret_tensor(primals_3, (4, 4),
(1, 4), 0), out=buf1)
del primals_3
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_elu_exp_mul_relu_0[grid(16)](buf0, buf1, buf2,
buf4, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_5, buf2, out=buf3)
buf6 = buf2
del buf2
extern_kernels.mm(primals_6, buf5, out=buf6)
del buf5
return (buf3, buf6, primals_2, primals_4, buf0, buf1, buf4,
reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
reinterpret_tensor(primals_5, (4, 4), (1, 4), 0))
class GaussionConvDNew(nn.Module):
"""The subsequent layer in `RobustGCN` that takes node distribution (mean, var) as input"""
def __init__(self, in_features, out_features, bias=False, gamma=1.0):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.w_mean = nn.Linear(in_features, out_features, bias=bias)
self.w_var = nn.Linear(in_features, out_features, bias=bias)
self.gamma = gamma
def reset_parameters(self):
self.w_mean.reset_parameters()
self.w_var.reset_parameters()
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features})'
)
def forward(self, input_0, input_1, input_2, input_3):
primals_1 = self.w_mean.weight
primals_2 = self.w_var.weight
primals_3 = input_0
primals_4 = input_1
primals_5 = input_2
primals_6 = input_3
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0], output[1]
|
EdisonLeeeee/Graphgallery
|
GaussionConvD
| false
| 5,105
|
[
"MIT"
] | 1
|
8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
PositionwiseFeedForward
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w1 = nn.Linear(d_in, d_hid)
self.w2 = nn.Linear(d_hid, d_in)
self.layer_norm = nn.LayerNorm(d_in, eps=1e-06)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
residual = x
x = self.layer_norm(x)
x = self.w2(F.relu(self.w1(x)))
x = self.dropout(x)
x += residual
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'d_in': 4, 'd_hid': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-06
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(64)](primals_1, buf0,
buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(256)](primals_1, buf0,
buf1, primals_2, primals_3, buf2, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del buf0
del buf1
del primals_2
del primals_3
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
buf4 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf3
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_2[grid(256)](buf4,
primals_5, buf7, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf4, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf5)
buf6 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused_add_3[grid(256)](buf6, primals_7, primals_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
return buf6, primals_1, reinterpret_tensor(buf2, (64, 4), (4, 1), 0
), reinterpret_tensor(buf4, (64, 4), (4, 1), 0
), primals_6, buf7, primals_4
class PositionwiseFeedForwardNew(nn.Module):
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w1 = nn.Linear(d_in, d_hid)
self.w2 = nn.Linear(d_hid, d_in)
self.layer_norm = nn.LayerNorm(d_in, eps=1e-06)
self.dropout = nn.Dropout(dropout)
def forward(self, input_0):
primals_4 = self.w1.weight
primals_2 = self.w1.bias
primals_6 = self.w2.weight
primals_3 = self.w2.bias
primals_5 = self.layer_norm.weight
primals_7 = self.layer_norm.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
Eddie-Hwang/Co-Eye_Motion_Generation
|
PositionwiseFeedForward
| false
| 5,106
|
[
"MIT"
] | 1
|
8e244680115fb63bc26018cb6b53bcfbd04e9683
|
https://github.com/Eddie-Hwang/Co-Eye_Motion_Generation/tree/8e244680115fb63bc26018cb6b53bcfbd04e9683
|
MultiHeadSelfAttention
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadSelfAttention(nn.Module):
def __init__(self, d_ipt: 'int', n_head: 'int', dropout_p: 'float'=0.1):
super(MultiHeadSelfAttention, self).__init__()
self.qkv_linear = nn.Linear(d_ipt, d_ipt * 3, True)
self.n_head = n_head
self.output_linear = nn.Linear(d_ipt, d_ipt, True)
self.dropout = nn.Dropout(dropout_p)
def forward(self, src: 'torch.FloatTensor', attn_mask: 'torch.FloatTensor'
) ->torch.FloatTensor:
if attn_mask.dim() == 2:
attn_mask = attn_mask.unsqueeze(0)
if attn_mask.dim() == 3:
attn_mask = attn_mask.unsqueeze(1)
q, k, v = self.qkv_linear(src).chunk(3, dim=-1)
q = q.contiguous().view(src.shape[0], src.shape[1], self.n_head,
src.shape[2] // self.n_head).permute(0, 2, 1, 3)
k = k.contiguous().view(src.shape[0], src.shape[1], self.n_head,
src.shape[2] // self.n_head).permute(0, 2, 3, 1)
v = v.contiguous().view(src.shape[0], src.shape[1], self.n_head,
src.shape[2] // self.n_head).permute(0, 2, 1, 3)
attn_weights = torch.matmul(q, k)
attn_weights = attn_weights * float(src.shape[2] // self.n_head
) ** -0.5
attn_weights = attn_weights * attn_mask + (attn_mask - 1) * 10000.0
attn_weights = F.softmax(attn_weights, dim=-1)
attn_weights = self.dropout(attn_weights)
attn_output = torch.matmul(attn_weights, v)
attn_output = attn_output.permute(0, 2, 1, 3).contiguous().view(src
.shape)
attn_output = self.output_linear(attn_output)
return attn_output
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'d_ipt': 4, 'n_head': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_clone_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 12 * x2 + 48 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (4 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (4 + y0), ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused__softmax_add_mul_sub_2(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 4
x2 = xindex // 16
tmp0 = tl.load(in_ptr0 + 4 * x3, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (4 * x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr0 + (1 + 4 * x3), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr1 + (1 + 4 * x0 + 16 * x2), xmask,
eviction_policy='evict_last')
tmp17 = tl.load(in_ptr0 + (2 + 4 * x3), xmask, eviction_policy='evict_last'
)
tmp19 = tl.load(in_ptr1 + (2 + 4 * x0 + 16 * x2), xmask,
eviction_policy='evict_last')
tmp25 = tl.load(in_ptr0 + (3 + 4 * x3), xmask, eviction_policy='evict_last'
)
tmp27 = tl.load(in_ptr1 + (3 + 4 * x0 + 16 * x2), xmask,
eviction_policy='evict_last')
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tmp4 = tmp2 * tmp3
tmp5 = tmp3 - tmp1
tmp6 = 10000.0
tmp7 = tmp5 * tmp6
tmp8 = tmp4 + tmp7
tmp10 = tmp9 * tmp1
tmp12 = tmp10 * tmp11
tmp13 = tmp11 - tmp1
tmp14 = tmp13 * tmp6
tmp15 = tmp12 + tmp14
tmp16 = triton_helpers.maximum(tmp8, tmp15)
tmp18 = tmp17 * tmp1
tmp20 = tmp18 * tmp19
tmp21 = tmp19 - tmp1
tmp22 = tmp21 * tmp6
tmp23 = tmp20 + tmp22
tmp24 = triton_helpers.maximum(tmp16, tmp23)
tmp26 = tmp25 * tmp1
tmp28 = tmp26 * tmp27
tmp29 = tmp27 - tmp1
tmp30 = tmp29 * tmp6
tmp31 = tmp28 + tmp30
tmp32 = triton_helpers.maximum(tmp24, tmp31)
tmp33 = tmp8 - tmp32
tmp34 = tl_math.exp(tmp33)
tmp35 = tmp15 - tmp32
tmp36 = tl_math.exp(tmp35)
tmp37 = tmp34 + tmp36
tmp38 = tmp23 - tmp32
tmp39 = tl_math.exp(tmp38)
tmp40 = tmp37 + tmp39
tmp41 = tmp31 - tmp32
tmp42 = tl_math.exp(tmp41)
tmp43 = tmp40 + tmp42
tl.store(out_ptr0 + x3, tmp32, xmask)
tl.store(out_ptr1 + x3, tmp43, xmask)
@triton.jit
def triton_poi_fused__softmax_add_mul_sub_3(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x4 = xindex
x3 = xindex // 64
x5 = xindex % 16
x6 = xindex // 4
tmp0 = tl.load(in_out_ptr0 + x4, xmask)
tmp3 = tl.load(in_ptr0 + (x5 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr1 + x6, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr2 + x6, xmask, eviction_policy='evict_last')
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tmp4 = tmp2 * tmp3
tmp5 = tmp3 - tmp1
tmp6 = 10000.0
tmp7 = tmp5 * tmp6
tmp8 = tmp4 + tmp7
tmp10 = tmp8 - tmp9
tmp11 = tl_math.exp(tmp10)
tmp13 = tmp11 / tmp12
tl.store(in_out_ptr0 + x4, tmp13, xmask)
@triton.jit
def triton_poi_fused_clone_4(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (8 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (8 + y0), ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (12, 4), (4, 1))
assert_size_stride(primals_3, (12,), (1,))
assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 12), (1, 4), 0), out=buf0)
del primals_2
buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(16, 4)](buf0, primals_3, buf1, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
triton_poi_fused_clone_1[grid(16, 4)](buf0, primals_3, buf2, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf2, (16, 1, 4), (4, 0, 1), 0), out=buf3)
buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
triton_poi_fused__softmax_add_mul_sub_2[grid(64)](buf3, primals_1,
buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf6 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf3
triton_poi_fused__softmax_add_mul_sub_3[grid(256)](buf6, primals_1,
buf4, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
buf7 = reinterpret_tensor(buf5, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf5
triton_poi_fused_clone_4[grid(16, 4)](buf0, primals_3, buf7, 16, 4,
XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
del buf0
del primals_3
buf8 = reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 1), 0)
del buf4
extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf7, (16, 4, 1), (4, 1, 0), 0), out=buf8)
buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
triton_poi_fused_clone_5[grid(16, 4)](buf8, buf9, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf10 = reinterpret_tensor(buf8, (16, 4), (4, 1), 0)
del buf8
extern_kernels.addmm(primals_6, reinterpret_tensor(buf9, (16, 4), (
4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf10)
del primals_6
return reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0
), primals_1, reinterpret_tensor(primals_4, (16, 4), (4, 1), 0
), buf6, reinterpret_tensor(buf9, (16, 4), (4, 1), 0
), primals_5, reinterpret_tensor(buf7, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf1, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 4), 0)
class MultiHeadSelfAttentionNew(nn.Module):
def __init__(self, d_ipt: 'int', n_head: 'int', dropout_p: 'float'=0.1):
super(MultiHeadSelfAttentionNew, self).__init__()
self.qkv_linear = nn.Linear(d_ipt, d_ipt * 3, True)
self.n_head = n_head
self.output_linear = nn.Linear(d_ipt, d_ipt, True)
self.dropout = nn.Dropout(dropout_p)
def forward(self, input_0, input_1):
primals_2 = self.qkv_linear.weight
primals_3 = self.qkv_linear.bias
primals_5 = self.output_linear.weight
primals_6 = self.output_linear.bias
primals_1 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
DunZhang/GPT2SourceCode
|
MultiHeadSelfAttention
| false
| 5,107
|
[
"MIT"
] | 1
|
d598dbae278c93f88469d45ec025da4cfa7d69ee
|
https://github.com/DunZhang/GPT2SourceCode/tree/d598dbae278c93f88469d45ec025da4cfa7d69ee
|
LocalState
|
import math
import torch
from torch import nn
class LocalState(nn.Module):
"""Local state allows to have attention based only on data (no positional embedding),
but while setting a constraint on the time window (e.g. decaying penalty term).
Also a failed experiments with trying to provide some frequency based attention.
"""
def __init__(self, channels: 'int', heads: 'int'=4, nfreqs: 'int'=0,
ndecay: 'int'=4):
super().__init__()
assert channels % heads == 0, (channels, heads)
self.heads = heads
self.nfreqs = nfreqs
self.ndecay = ndecay
self.content = nn.Conv1d(channels, channels, 1)
self.query = nn.Conv1d(channels, channels, 1)
self.key = nn.Conv1d(channels, channels, 1)
if nfreqs:
self.query_freqs = nn.Conv1d(channels, heads * nfreqs, 1)
if ndecay:
self.query_decay = nn.Conv1d(channels, heads * ndecay, 1)
self.query_decay.weight.data *= 0.01
assert self.query_decay.bias is not None
self.query_decay.bias.data[:] = -2
self.proj = nn.Conv1d(channels + heads * nfreqs, channels, 1)
def forward(self, x):
B, _C, T = x.shape
heads = self.heads
indexes = torch.arange(T, device=x.device, dtype=x.dtype)
delta = indexes[:, None] - indexes[None, :]
queries = self.query(x).view(B, heads, -1, T)
keys = self.key(x).view(B, heads, -1, T)
dots = torch.einsum('bhct,bhcs->bhts', keys, queries)
dots /= keys.shape[2] ** 0.5
if self.nfreqs:
periods = torch.arange(1, self.nfreqs + 1, device=x.device,
dtype=x.dtype)
freq_kernel = torch.cos(2 * math.pi * delta / periods.view(-1,
1, 1))
freq_q = self.query_freqs(x).view(B, heads, -1, T
) / self.nfreqs ** 0.5
dots += torch.einsum('fts,bhfs->bhts', freq_kernel, freq_q)
if self.ndecay:
decays = torch.arange(1, self.ndecay + 1, device=x.device,
dtype=x.dtype)
decay_q = self.query_decay(x).view(B, heads, -1, T)
decay_q = torch.sigmoid(decay_q) / 2
decay_kernel = -decays.view(-1, 1, 1) * delta.abs(
) / self.ndecay ** 0.5
dots += torch.einsum('fts,bhfs->bhts', decay_kernel, decay_q)
dots.masked_fill_(torch.eye(T, device=dots.device, dtype=torch.bool
), -100)
weights = torch.softmax(dots, dim=2)
content = self.content(x).view(B, heads, -1, T)
result = torch.einsum('bhts,bhct->bhcs', weights, content)
if self.nfreqs:
time_sig = torch.einsum('bhts,fts->bhfs', weights, freq_kernel)
result = torch.cat([result, time_sig], 2)
result = result.reshape(B, -1, T)
return x + self.proj(result)
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 4 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_convolution_div_sigmoid_1(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 4 % 16
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.sigmoid(tmp2)
tmp4 = 0.5
tmp5 = tmp3 * tmp4
tl.store(in_out_ptr0 + x3, tmp2, xmask)
tl.store(out_ptr0 + x3, tmp5, xmask)
@triton.jit
def triton_poi_fused_abs_div_mul_neg_sub_2(out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
x3 = xindex
tmp0 = -1 * tl_math.abs(x0 + -1 * x1) + -1 * x2 * tl_math.abs(x0 + -1 * x1)
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tl.store(out_ptr0 + x3, tmp3, xmask)
@triton.jit
def triton_poi_fused_arange_eye_3(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4
x0 = xindex % 4
x2 = xindex
tmp0 = x1
tmp1 = x0
tmp2 = tmp0 == tmp1
tl.store(out_ptr0 + x2, tmp2, xmask)
@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 4
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
y0 = yindex
x1 = xindex
tmp0 = tl.load(in_ptr0 + y0, ymask, eviction_policy='evict_last').to(tl
.int1)
tmp1 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + (y0 + 4 * x1), xmask & ymask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr3 + (x1 + 64 * y0), xmask & ymask, eviction_policy
='evict_last')
tmp10 = tl.load(in_ptr0 + (4 + y0), ymask, eviction_policy='evict_last'
).to(tl.int1)
tmp11 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp14 = tl.load(in_ptr3 + (16 + x1 + 64 * y0), xmask & ymask,
eviction_policy='evict_last')
tmp18 = tl.load(in_ptr0 + (8 + y0), ymask, eviction_policy='evict_last'
).to(tl.int1)
tmp19 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp22 = tl.load(in_ptr3 + (32 + x1 + 64 * y0), xmask & ymask,
eviction_policy='evict_last')
tmp26 = tl.load(in_ptr0 + (12 + y0), ymask, eviction_policy='evict_last'
).to(tl.int1)
tmp27 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp30 = tl.load(in_ptr3 + (48 + x1 + 64 * y0), xmask & ymask,
eviction_policy='evict_last')
tmp3 = tmp1 * tmp2
tmp4 = 1.0
tmp5 = tmp3 * tmp4
tmp7 = tmp5 + tmp6
tmp8 = -100.0
tmp9 = tl.where(tmp0, tmp8, tmp7)
tmp12 = tmp11 * tmp2
tmp13 = tmp12 * tmp4
tmp15 = tmp13 + tmp14
tmp16 = tl.where(tmp10, tmp8, tmp15)
tmp17 = triton_helpers.maximum(tmp9, tmp16)
tmp20 = tmp19 * tmp2
tmp21 = tmp20 * tmp4
tmp23 = tmp21 + tmp22
tmp24 = tl.where(tmp18, tmp8, tmp23)
tmp25 = triton_helpers.maximum(tmp17, tmp24)
tmp28 = tmp27 * tmp2
tmp29 = tmp28 * tmp4
tmp31 = tmp29 + tmp30
tmp32 = tl.where(tmp26, tmp8, tmp31)
tmp33 = triton_helpers.maximum(tmp25, tmp32)
tl.store(out_ptr0 + (x1 + 16 * y0), tmp33, xmask & ymask)
@triton.jit
def triton_poi_fused__softmax_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x3 = xindex
x2 = xindex // 4
y0 = yindex
x1 = xindex % 4
tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp1 = tl.load(in_ptr1 + (x2 + 4 * y0), xmask & ymask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr2 + (x1 + 4 * y0), xmask & ymask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr3 + (y0 + 16 * x2 + 64 * x1), xmask & ymask)
tmp10 = tl.load(in_ptr4 + (y0 + 16 * x1), xmask & ymask,
eviction_policy='evict_last')
tmp3 = tmp1 * tmp2
tmp4 = 1.0
tmp5 = tmp3 * tmp4
tmp7 = tmp5 + tmp6
tmp8 = -100.0
tmp9 = tl.where(tmp0, tmp8, tmp7)
tmp11 = tmp9 - tmp10
tmp12 = tl_math.exp(tmp11)
tl.store(out_ptr0 + (x3 + 16 * y0), tmp12, xmask & ymask)
@triton.jit
def triton_poi_fused__softmax_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 4
x2 = xindex // 16
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x3, tmp8, xmask)
@triton.jit
def triton_poi_fused_add_convolution_7(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 4 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_out_ptr0 + x3, xmask)
tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + x3, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 1), (4, 1, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (16, 4, 1), (4, 1, 1))
assert_size_stride(primals_7, (16,), (1,))
assert_size_stride(primals_8, (4, 4, 1), (4, 1, 1))
assert_size_stride(primals_9, (4,), (1,))
assert_size_stride(primals_10, (4, 4, 1), (4, 1, 1))
assert_size_stride(primals_11, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4), (16, 4, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(64)](buf1, primals_3, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_3
buf2 = extern_kernels.convolution(primals_1, primals_4, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf2, (4, 4, 4), (16, 4, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_0[grid(64)](buf3, primals_5, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_5
buf4 = extern_kernels.convolution(primals_1, primals_6, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf4, (4, 16, 4), (64, 4, 1))
buf5 = buf4
del buf4
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_convolution_div_sigmoid_1[grid(256)](buf5,
primals_7, buf7, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_abs_div_mul_neg_sub_2[grid(64)](buf6, 64, XBLOCK=
64, num_warps=1, num_stages=1)
buf8 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf6, (4, 4, 4), (1, 4, 16),
0), reinterpret_tensor(buf7, (4, 4, 16), (1, 4, 16), 0), out=buf8)
buf9 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_arange_eye_3[grid(16)](buf9, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf10 = empty_strided_cuda((4, 4, 1, 4), (4, 1, 64, 16), torch.float32)
triton_poi_fused__softmax_4[grid(4, 16)](buf9, buf3, buf1, buf8,
buf10, 4, 16, XBLOCK=16, YBLOCK=4, num_warps=1, num_stages=1)
buf11 = buf7
del buf7
triton_poi_fused__softmax_5[grid(16, 16)](buf9, buf3, buf1, buf8,
buf10, buf11, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4,
num_stages=1)
buf12 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf8
triton_poi_fused__softmax_6[grid(256)](buf11, buf12, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del buf11
buf13 = extern_kernels.convolution(primals_1, primals_8, stride=(1,
), padding=(0,), dilation=(1,), transposed=False,
output_padding=(0,), groups=1, bias=None)
assert_size_stride(buf13, (4, 4, 4), (16, 4, 1))
buf14 = buf13
del buf13
triton_poi_fused_convolution_0[grid(64)](buf14, primals_9, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_9
buf15 = reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 1), 0)
del buf10
extern_kernels.bmm(reinterpret_tensor(buf12, (16, 4, 4), (16, 1, 4),
0), reinterpret_tensor(buf14, (16, 4, 1), (4, 1, 0), 0), out=buf15)
buf16 = extern_kernels.convolution(reinterpret_tensor(buf15, (4, 4,
4), (16, 4, 1), 0), primals_10, stride=(1,), padding=(0,),
dilation=(1,), transposed=False, output_padding=(0,), groups=1,
bias=None)
assert_size_stride(buf16, (4, 4, 4), (16, 4, 1))
buf17 = buf16
del buf16
triton_poi_fused_add_convolution_7[grid(64)](buf17, primals_1,
primals_11, 64, XBLOCK=64, num_warps=1, num_stages=1)
del primals_11
return (buf17, primals_1, primals_2, primals_4, primals_6, primals_8,
primals_10, buf1, buf3, buf5, buf9, buf12, reinterpret_tensor(buf15,
(4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf14, (16, 1, 4), (4,
4, 1), 0), reinterpret_tensor(buf6, (4, 4, 4), (1, 16, 4), 0))
class LocalStateNew(nn.Module):
"""Local state allows to have attention based only on data (no positional embedding),
but while setting a constraint on the time window (e.g. decaying penalty term).
Also a failed experiments with trying to provide some frequency based attention.
"""
def __init__(self, channels: 'int', heads: 'int'=4, nfreqs: 'int'=0,
ndecay: 'int'=4):
super().__init__()
assert channels % heads == 0, (channels, heads)
self.heads = heads
self.nfreqs = nfreqs
self.ndecay = ndecay
self.content = nn.Conv1d(channels, channels, 1)
self.query = nn.Conv1d(channels, channels, 1)
self.key = nn.Conv1d(channels, channels, 1)
if nfreqs:
self.query_freqs = nn.Conv1d(channels, heads * nfreqs, 1)
if ndecay:
self.query_decay = nn.Conv1d(channels, heads * ndecay, 1)
self.query_decay.weight.data *= 0.01
assert self.query_decay.bias is not None
self.query_decay.bias.data[:] = -2
self.proj = nn.Conv1d(channels + heads * nfreqs, channels, 1)
def forward(self, input_0):
primals_2 = self.content.weight
primals_3 = self.content.bias
primals_4 = self.query.weight
primals_5 = self.query.bias
primals_8 = self.key.weight
primals_9 = self.key.bias
primals_6 = self.query_decay.weight
primals_7 = self.query_decay.bias
primals_10 = self.proj.weight
primals_11 = self.proj.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0]
|
DilwoarH/demucs
|
LocalState
| false
| 5,108
|
[
"MIT"
] | 1
|
32d21592dfa015468aa117cace52b21e7af79d71
|
https://github.com/DilwoarH/demucs/tree/32d21592dfa015468aa117cace52b21e7af79d71
|
SAGEAggregator
|
import torch
import torch.nn as nn
class SAGEAggregator(nn.Module):
def __init__(self, in_features, out_features, agg_method='mean', concat
=False, bias=False):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.concat = concat
self.agg_method = agg_method
self.aggregator = {'mean': torch.mean, 'sum': torch.sum}[agg_method]
self.lin_l = nn.Linear(in_features, out_features, bias=bias)
self.lin_r = nn.Linear(in_features, out_features, bias=bias)
def reset_parameters(self):
self.lin_l.reset_parameters()
self.lin_r.reset_parameters()
def forward(self, x, neigh_x):
if not isinstance(x, torch.Tensor):
x = torch.cat(x, dim=0)
if not isinstance(neigh_x, torch.Tensor):
neigh_x = torch.cat([self.aggregator(h, dim=1) for h in neigh_x
], dim=0)
else:
neigh_x = self.aggregator(neigh_x, dim=1)
neigh_x = self.lin_r(neigh_x)
x = self.lin_l(x)
out = torch.cat([x, neigh_x], dim=1) if self.concat else x + neigh_x
return out
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features})'
)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_mean_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x2, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mean_0[grid(64)](primals_2, buf0, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_2
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
del primals_3
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
del primals_4
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf2
triton_poi_fused_add_1[grid(256)](buf3, buf1, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf1
return buf3, reinterpret_tensor(buf0, (16, 4), (4, 1), 0
), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0)
class SAGEAggregatorNew(nn.Module):
def __init__(self, in_features, out_features, agg_method='mean', concat
=False, bias=False):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.concat = concat
self.agg_method = agg_method
self.aggregator = {'mean': torch.mean, 'sum': torch.sum}[agg_method]
self.lin_l = nn.Linear(in_features, out_features, bias=bias)
self.lin_r = nn.Linear(in_features, out_features, bias=bias)
def reset_parameters(self):
self.lin_l.reset_parameters()
self.lin_r.reset_parameters()
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features})'
)
def forward(self, input_0, input_1):
primals_3 = self.lin_l.weight
primals_4 = self.lin_r.weight
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
EdisonLeeeee/Graphgallery
|
SAGEAggregator
| false
| 5,109
|
[
"MIT"
] | 1
|
8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
TransformerNet2
|
import torch
class TransformerNet2(torch.nn.Module):
def __init__(self):
super(TransformerNet2, self).__init__()
self.tanh = torch.nn.Tanh()
self.a = 10
def forward(self, r, p):
m = -0.5 * self.tanh(self.a * (p - 2 * r)) + 0.5 * self.tanh(self.a *
(p - 2 * (1 - r)))
return m
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_mul_rsub_sub_tanh_0(in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask)
tmp2 = 2.0
tmp3 = tmp1 * tmp2
tmp4 = tmp0 - tmp3
tmp5 = 10.0
tmp6 = tmp4 * tmp5
tmp7 = libdevice.tanh(tmp6)
tmp8 = -0.5
tmp9 = tmp7 * tmp8
tmp10 = 1.0
tmp11 = tmp10 - tmp1
tmp12 = tmp11 * tmp2
tmp13 = tmp0 - tmp12
tmp14 = tmp13 * tmp5
tmp15 = libdevice.tanh(tmp14)
tmp16 = 0.5
tmp17 = tmp15 * tmp16
tmp18 = tmp9 + tmp17
tl.store(out_ptr0 + x0, tmp18, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_mul_rsub_sub_tanh_0[grid(256)](arg1_1, arg0_1,
buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
return buf0,
class TransformerNet2New(torch.nn.Module):
def __init__(self):
super(TransformerNet2New, self).__init__()
self.tanh = torch.nn.Tanh()
self.a = 10
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
Ekko-zn/StegoAdv
|
TransformerNet2
| false
| 5,110
|
[
"MIT"
] | 1
|
2852dbc85d66f30efb7127695c0d75806bf4aa4c
|
https://github.com/Ekko-zn/StegoAdv/tree/2852dbc85d66f30efb7127695c0d75806bf4aa4c
|
NormedLinear
|
import torch
import torch.nn.functional as F
from torch import nn
import torch.onnx
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divisor to
keep numerical stability. Default to 1e-6.
"""
def __init__(self, *args, tempearture=20, power=1.0, eps=1e-06, **kwargs):
super(NormedLinear, self).__init__(*args, **kwargs)
self.tempearture = tempearture
self.power = power
self.eps = eps
self.init_weights()
def init_weights(self):
nn.init.normal_(self.weight, mean=0, std=0.01)
if self.bias is not None:
nn.init.constant_(self.bias, 0)
def forward(self, x):
weight_ = self.weight / (self.weight.norm(dim=1, keepdim=True).pow(
self.power) + self.eps)
x_ = x / (x.norm(dim=1, keepdim=True).pow(self.power) + self.eps)
x_ = x_ * self.tempearture
return F.linear(x_, weight_, self.bias)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_div_linalg_vector_norm_mul_pow_0(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-06
tmp14 = tmp12 + tmp13
tmp15 = tmp0 / tmp14
tmp16 = 20.0
tmp17 = tmp15 * tmp16
tl.store(out_ptr0 + x3, tmp17, xmask)
@triton.jit
def triton_poi_fused_add_div_linalg_vector_norm_pow_1(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-06
tmp14 = tmp12 + tmp13
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + x2, tmp15, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_div_linalg_vector_norm_mul_pow_0[grid(256)](
primals_2, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_add_div_linalg_vector_norm_pow_1[grid(16)](primals_1,
buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (64, 4), (
4, 1), 0), reinterpret_tensor(buf1, (4, 4), (1, 4), 0), alpha=1,
beta=1, out=buf2)
del buf1
del primals_3
return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
), primals_1, reinterpret_tensor(buf0, (64, 4), (4, 1), 0)
class NormedLinearNew(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. Default to 20.
power (int, optional): Power term. Default to 1.0.
eps (float, optional): The minimal value of divisor to
keep numerical stability. Default to 1e-6.
"""
def __init__(self, *args, tempearture=20, power=1.0, eps=1e-06, **kwargs):
super(NormedLinearNew, self).__init__(*args, **kwargs)
self.tempearture = tempearture
self.power = power
self.eps = eps
self.init_weights()
def init_weights(self):
nn.init.normal_(self.weight, mean=0, std=0.01)
if self.bias is not None:
nn.init.constant_(self.bias, 0)
def forward(self, input_0):
primals_1 = self.weight
primals_3 = self.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
ENOT-AutoDL/mmdetection-enot
|
NormedLinear
| false
| 5,111
|
[
"Apache-2.0"
] | 1
|
f541749554436e3327bac00eee89b84f66c03551
|
https://github.com/ENOT-AutoDL/mmdetection-enot/tree/f541749554436e3327bac00eee89b84f66c03551
|
WaveletConv
|
import torch
import torch.nn as nn
class WaveletConv(nn.Module):
def __init__(self, in_features, out_features, num_nodes, bias=False):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.w = nn.Linear(in_features, out_features, bias=bias)
self.filter = nn.Parameter(torch.ones(num_nodes, 1))
def reset_parameters(self):
self.w.reset_parameters()
self.filter.data.fill_(1.0)
def forward(self, x, wavelet, inverse_wavelet):
h = self.w(x)
h = inverse_wavelet.mm(h)
h = self.filter * h
out = wavelet.mm(h)
return out
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features})'
)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4, 'num_nodes': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x2, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4, 1), (1, 1))
assert_size_stride(primals_5, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_2, reinterpret_tensor(primals_1, (4, 4),
(1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf0, out=buf1)
buf2 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_mul_0[grid(16)](primals_4, buf1, buf2, 16, XBLOCK=
16, num_warps=1, num_stages=1)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_5, buf2, out=buf3)
del buf2
return buf3, primals_2, primals_4, buf1, reinterpret_tensor(primals_5,
(4, 4), (1, 4), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0)
class WaveletConvNew(nn.Module):
def __init__(self, in_features, out_features, num_nodes, bias=False):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.w = nn.Linear(in_features, out_features, bias=bias)
self.filter = nn.Parameter(torch.ones(num_nodes, 1))
def reset_parameters(self):
self.w.reset_parameters()
self.filter.data.fill_(1.0)
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features})'
)
def forward(self, input_0, input_1, input_2):
primals_4 = self.filter
primals_1 = self.w.weight
primals_2 = input_0
primals_3 = input_1
primals_5 = input_2
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
EdisonLeeeee/Graphgallery
|
WaveletConv
| false
| 5,112
|
[
"MIT"
] | 1
|
8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
ASPP
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class ASPP(nn.Module):
def __init__(self, in_channel=256, depth=256):
super(ASPP, self).__init__()
self.mean = nn.AdaptiveAvgPool2d((1, 1))
self.conv = nn.Conv2d(in_channel, depth, 1, 1)
self.atrous_block1 = nn.Conv2d(in_channel, depth, 1, 1)
self.atrous_block6 = nn.Conv2d(in_channel, depth, 3, 1, padding=6,
dilation=6)
self.atrous_block12 = nn.Conv2d(in_channel, depth, 3, 1, padding=12,
dilation=12)
self.atrous_block18 = nn.Conv2d(in_channel, depth, 3, 1, padding=18,
dilation=18)
self.conv_1x1_output = nn.Conv2d(depth * 5, depth, 1, 1)
def forward(self, x):
size = x.shape[2:]
image_features = self.mean(x)
image_features = self.conv(image_features)
image_features = F.interpolate(image_features, size=size, mode=
'bilinear', align_corners=True)
atrous_block1 = self.atrous_block1(x)
atrous_block6 = self.atrous_block6(x)
atrous_block12 = self.atrous_block12(x)
atrous_block18 = self.atrous_block18(x)
net = self.conv_1x1_output(torch.cat([image_features, atrous_block1,
atrous_block6, atrous_block12, atrous_block18], dim=1))
return net
def get_inputs():
return [torch.rand([4, 256, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, rnumel,
XBLOCK: tl.constexpr):
xnumel = 1024
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
x3 = xindex
x0 = xindex % 256
x1 = xindex // 256
tmp0 = tl.load(in_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 16.0
tmp6 = tmp4 / tmp5
tl.store(out_ptr0 + (x0 + 256 * r2 + 4096 * x1), tmp0, xmask)
tl.debug_barrier()
tl.store(in_out_ptr0 + x3, tmp6, xmask)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 256
y1 = yindex // 256
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused__to_copy_2(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.full([1], 0, tl.int64)
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_3(out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = 0.0
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_4(in_ptr0,
in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 4 % 4
x0 = xindex % 4
x5 = xindex // 16
x2 = xindex // 16 % 256
x6 = xindex
tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr2 + x5, None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
tmp17 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 1, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tl.where(tmp7, tmp6, tmp5)
tmp11 = tmp9 + tmp10
tmp13 = tmp12 + tmp1
tmp14 = tmp12 < 0
tl.where(tmp14, tmp13, tmp12)
tmp16 = tmp11 - tmp11
tmp18 = tmp16 * tmp17
tmp19 = tmp11 + tmp18
tl.store(out_ptr0 + x6, tmp19, None)
@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, in_ptr10, in_ptr11,
in_ptr12, in_ptr13, in_ptr14, in_ptr15, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 1280
x3 = xindex // 20480
x4 = xindex // 1280 % 16
x2 = xindex // 5120 % 4
x1 = xindex // 1280 % 4
x5 = xindex // 1280
x6 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 256, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x4 + 16 * x0 + 4096 * x3), tmp4,
eviction_policy='evict_last', other=0.0)
tmp6 = tl.load(in_ptr1 + x2, tmp4, eviction_policy='evict_last', other=0.0)
tmp7 = tl.full([XBLOCK], 1, tl.int32)
tmp8 = tmp6 + tmp7
tmp9 = tmp6 < 0
tl.where(tmp9, tmp8, tmp6)
tmp11 = tl.load(in_ptr2 + x1, tmp4, eviction_policy='evict_last', other=0.0
)
tmp12 = tmp11 + tmp7
tmp13 = tmp11 < 0
tl.where(tmp13, tmp12, tmp11)
tmp15 = tl.load(in_ptr3 + (256 * x3 + x0), tmp4, eviction_policy=
'evict_last', other=0.0)
tmp16 = tl.load(in_ptr4 + x0, tmp4, eviction_policy='evict_last', other=0.0
)
tmp17 = tmp15 + tmp16
tmp18 = tl.load(in_ptr5 + x1, tmp4, eviction_policy='evict_last', other=0.0
)
tmp19 = tmp18 + tmp7
tmp20 = tmp18 < 0
tl.where(tmp20, tmp19, tmp18)
tmp22 = tmp17 - tmp17
tmp23 = tl.load(in_ptr6 + x1, tmp4, eviction_policy='evict_last', other=0.0
)
tmp24 = tmp22 * tmp23
tmp25 = tmp17 + tmp24
tmp26 = tmp25 - tmp5
tmp27 = tl.load(in_ptr7 + x2, tmp4, eviction_policy='evict_last', other=0.0
)
tmp28 = tmp26 * tmp27
tmp29 = tmp5 + tmp28
tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
tmp31 = tl.where(tmp4, tmp29, tmp30)
tmp32 = tmp0 >= tmp3
tmp33 = tl.full([1], 512, tl.int64)
tmp34 = tmp0 < tmp33
tmp35 = tmp32 & tmp34
tmp36 = tl.load(in_ptr8 + (256 * x5 + (-256 + x0)), tmp35,
eviction_policy='evict_last', other=0.0)
tmp37 = tl.load(in_ptr9 + (-256 + x0), tmp35, eviction_policy=
'evict_last', other=0.0)
tmp38 = tmp36 + tmp37
tmp39 = tl.full(tmp38.shape, 0.0, tmp38.dtype)
tmp40 = tl.where(tmp35, tmp38, tmp39)
tmp41 = tmp0 >= tmp33
tmp42 = tl.full([1], 768, tl.int64)
tmp43 = tmp0 < tmp42
tmp44 = tmp41 & tmp43
tmp45 = tl.load(in_ptr10 + (256 * x5 + (-512 + x0)), tmp44,
eviction_policy='evict_last', other=0.0)
tmp46 = tl.load(in_ptr11 + (-512 + x0), tmp44, eviction_policy=
'evict_last', other=0.0)
tmp47 = tmp45 + tmp46
tmp48 = tl.full(tmp47.shape, 0.0, tmp47.dtype)
tmp49 = tl.where(tmp44, tmp47, tmp48)
tmp50 = tmp0 >= tmp42
tmp51 = tl.full([1], 1024, tl.int64)
tmp52 = tmp0 < tmp51
tmp53 = tmp50 & tmp52
tmp54 = tl.load(in_ptr12 + (256 * x5 + (-768 + x0)), tmp53,
eviction_policy='evict_last', other=0.0)
tmp55 = tl.load(in_ptr13 + (-768 + x0), tmp53, eviction_policy=
'evict_last', other=0.0)
tmp56 = tmp54 + tmp55
tmp57 = tl.full(tmp56.shape, 0.0, tmp56.dtype)
tmp58 = tl.where(tmp53, tmp56, tmp57)
tmp59 = tmp0 >= tmp51
tl.full([1], 1280, tl.int64)
tmp62 = tl.load(in_ptr14 + (256 * x5 + (-1024 + x0)), tmp59,
eviction_policy='evict_last', other=0.0)
tmp63 = tl.load(in_ptr15 + (-1024 + x0), tmp59, eviction_policy=
'evict_last', other=0.0)
tmp64 = tmp62 + tmp63
tmp65 = tl.full(tmp64.shape, 0.0, tmp64.dtype)
tmp66 = tl.where(tmp59, tmp64, tmp65)
tmp67 = tl.where(tmp53, tmp58, tmp66)
tmp68 = tl.where(tmp44, tmp49, tmp67)
tmp69 = tl.where(tmp35, tmp40, tmp68)
tmp70 = tl.where(tmp4, tmp31, tmp69)
tl.store(out_ptr0 + x6, tmp70, None)
@triton.jit
def triton_poi_fused_convolution_6(in_ptr0, in_ptr1, out_ptr0, ynumel,
xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 256
y1 = yindex // 256
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 256 * x2 + 4096 * y1), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13) = args
args.clear()
assert_size_stride(primals_1, (4, 256, 4, 4), (4096, 16, 4, 1))
assert_size_stride(primals_2, (256, 256, 1, 1), (256, 1, 1, 1))
assert_size_stride(primals_3, (256,), (1,))
assert_size_stride(primals_4, (256, 256, 1, 1), (256, 1, 1, 1))
assert_size_stride(primals_5, (256,), (1,))
assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_7, (256,), (1,))
assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_9, (256,), (1,))
assert_size_stride(primals_10, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_11, (256,), (1,))
assert_size_stride(primals_12, (256, 1280, 1, 1), (1280, 1, 1, 1))
assert_size_stride(primals_13, (256,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256),
torch.float32)
buf4 = empty_strided_cuda((4, 256, 1, 1), (256, 1, 1024, 1024),
torch.float32)
buf5 = reinterpret_tensor(buf4, (4, 256, 1, 1), (256, 1, 256, 256), 0)
del buf4
get_raw_stream(0)
triton_per_fused_mean_0[grid(1024)](buf5, primals_1, buf0, 1024, 16,
XBLOCK=32, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_1[grid(65536, 9)](primals_6, buf1, 65536, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_6
buf2 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_1[grid(65536, 9)](primals_8, buf2, 65536, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_8
buf3 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_1[grid(65536, 9)](primals_10, buf3, 65536, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_10
buf6 = extern_kernels.convolution(buf5, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 256, 1, 1), (256, 1, 256, 256))
buf7 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
triton_poi_fused__to_copy_2[grid(4)](buf7, 4, XBLOCK=4, num_warps=1,
num_stages=1)
buf8 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
triton_poi_fused__to_copy_2[grid(4)](buf8, 4, XBLOCK=4, num_warps=1,
num_stages=1)
buf9 = empty_strided_cuda((4,), (1,), torch.int64)
triton_poi_fused__to_copy_2[grid(4)](buf9, 4, XBLOCK=4, num_warps=1,
num_stages=1)
buf10 = empty_strided_cuda((4,), (1,), torch.int64)
triton_poi_fused__to_copy_2[grid(4)](buf10, 4, XBLOCK=4, num_warps=
1, num_stages=1)
buf11 = empty_strided_cuda((4,), (1,), torch.float32)
triton_poi_fused__to_copy_arange_clamp_mul_sub_3[grid(4)](buf11, 4,
XBLOCK=4, num_warps=1, num_stages=1)
buf12 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
float32)
triton_poi_fused__unsafe_index_add_convolution_mul_sub_4[grid(16384)](
buf7, buf9, buf6, primals_3, buf10, buf11, buf12, 16384, XBLOCK
=256, num_warps=4, num_stages=1)
buf13 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
triton_poi_fused__to_copy_arange_clamp_mul_sub_3[grid(4)](buf13, 4,
XBLOCK=4, num_warps=1, num_stages=1)
buf14 = extern_kernels.convolution(buf0, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf14, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf15 = extern_kernels.convolution(buf0, buf1, stride=(1, 1),
padding=(6, 6), dilation=(6, 6), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf15, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf16 = extern_kernels.convolution(buf0, buf2, stride=(1, 1),
padding=(12, 12), dilation=(12, 12), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf16, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf17 = extern_kernels.convolution(buf0, buf3, stride=(1, 1),
padding=(18, 18), dilation=(18, 18), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf17, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf18 = empty_strided_cuda((4, 1280, 4, 4), (20480, 1, 5120, 1280),
torch.float32)
triton_poi_fused_cat_5[grid(81920)](buf12, buf8, buf9, buf6,
primals_3, buf10, buf11, buf13, buf14, primals_5, buf15,
primals_7, buf16, primals_9, buf17, primals_11, buf18, 81920,
XBLOCK=512, num_warps=8, num_stages=1)
del buf12
del buf14
del buf15
del buf16
del buf6
del primals_11
del primals_3
del primals_5
del primals_7
del primals_9
buf19 = extern_kernels.convolution(buf18, primals_12, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf19, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf20 = reinterpret_tensor(buf17, (4, 256, 4, 4), (4096, 16, 4, 1), 0)
del buf17
triton_poi_fused_convolution_6[grid(1024, 16)](buf19, primals_13,
buf20, 1024, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
del buf19
del primals_13
return (buf20, buf0, primals_2, primals_4, buf1, buf2, buf3, primals_12,
buf5, buf7, buf8, buf9, buf10, buf11, buf13, buf18)
class ASPPNew(nn.Module):
def __init__(self, in_channel=256, depth=256):
super(ASPPNew, self).__init__()
self.mean = nn.AdaptiveAvgPool2d((1, 1))
self.conv = nn.Conv2d(in_channel, depth, 1, 1)
self.atrous_block1 = nn.Conv2d(in_channel, depth, 1, 1)
self.atrous_block6 = nn.Conv2d(in_channel, depth, 3, 1, padding=6,
dilation=6)
self.atrous_block12 = nn.Conv2d(in_channel, depth, 3, 1, padding=12,
dilation=12)
self.atrous_block18 = nn.Conv2d(in_channel, depth, 3, 1, padding=18,
dilation=18)
self.conv_1x1_output = nn.Conv2d(depth * 5, depth, 1, 1)
def forward(self, input_0):
primals_2 = self.conv.weight
primals_3 = self.conv.bias
primals_4 = self.atrous_block1.weight
primals_5 = self.atrous_block1.bias
primals_6 = self.atrous_block6.weight
primals_7 = self.atrous_block6.bias
primals_8 = self.atrous_block12.weight
primals_9 = self.atrous_block12.bias
primals_10 = self.atrous_block18.weight
primals_11 = self.atrous_block18.bias
primals_12 = self.conv_1x1_output.weight
primals_13 = self.conv_1x1_output.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13])
return output[0]
|
DoggyLiu0116/MamboNet
|
ASPP
| false
| 5,113
|
[
"MIT"
] | 1
|
3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
https://github.com/DoggyLiu0116/MamboNet/tree/3b708091422491f660c4bd5eb12b06ce3b8a5f79
|
TAGConv
|
import torch
import torch.nn as nn
class TAGConv(nn.Module):
def __init__(self, in_features, out_features, K=3, bias=True):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.K = K
self.w = nn.Linear(in_features * (self.K + 1), out_features, bias=bias)
def reset_parameters(self):
self.w.reset_parameters()
def forward(self, x, adj):
out = x
xs = [x]
for _ in range(self.K):
out = adj.mm(out)
xs.append(out)
out = self.w(torch.cat(xs, dim=-1))
return out
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features}, K={self.K})'
)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tl.store(out_ptr0 + (x0 + 16 * x1), tmp0, xmask)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tl.store(out_ptr0 + (x0 + 16 * x1), tmp0, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 16), (16, 1))
assert_size_stride(primals_4, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_2, primals_1, out=buf0)
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_2, buf0, out=buf1)
buf6 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
buf2 = reinterpret_tensor(buf6, (4, 4), (16, 1), 12)
extern_kernels.mm(primals_2, buf1, out=buf2)
del primals_2
buf3 = reinterpret_tensor(buf6, (4, 4), (16, 1), 0)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(16)](primals_1, buf3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_1
buf4 = reinterpret_tensor(buf6, (4, 4), (16, 1), 4)
triton_poi_fused_cat_1[grid(16)](buf0, buf4, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf0
buf5 = reinterpret_tensor(buf6, (4, 4), (16, 1), 8)
triton_poi_fused_cat_1[grid(16)](buf1, buf5, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf7 = buf1
del buf1
extern_kernels.addmm(primals_4, buf6, reinterpret_tensor(primals_3,
(16, 4), (1, 16), 0), alpha=1, beta=1, out=buf7)
del primals_3
del primals_4
return buf7, buf6
class TAGConvNew(nn.Module):
def __init__(self, in_features, out_features, K=3, bias=True):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.K = K
self.w = nn.Linear(in_features * (self.K + 1), out_features, bias=bias)
def reset_parameters(self):
self.w.reset_parameters()
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features}, K={self.K})'
)
def forward(self, input_0, input_1):
primals_3 = self.w.weight
primals_4 = self.w.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
EdisonLeeeee/Graphgallery
|
TAGConv
| false
| 5,114
|
[
"MIT"
] | 1
|
8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
DAGNNConv
|
import torch
import torch.nn as nn
class DAGNNConv(nn.Module):
def __init__(self, in_features, out_features=1, K=10, bias=False):
super().__init__()
assert out_features == 1, "'out_features' must be 1"
self.in_features = in_features
self.out_features = out_features
self.lin = nn.Linear(in_features, out_features, bias=bias)
self.K = K
self.act = nn.Sigmoid()
def reset_parameters(self):
self.lin.reset_parameters()
def forward(self, x, adj):
propagations = [x]
for _ in range(self.K):
x = adj.mm(x)
propagations.append(x)
h = torch.stack(propagations, dim=1)
retain_score = self.act(self.lin(h)).permute(0, 2, 1).contiguous()
out = (retain_score @ h).squeeze(1)
return out
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features}, K={self.K})'
)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_stack_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tl.store(out_ptr0 + (x0 + 44 * x1), tmp0, xmask)
@triton.jit
def triton_poi_fused_stack_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tl.store(out_ptr0 + (x0 + 44 * x1), tmp0, xmask)
@triton.jit
def triton_poi_fused_sigmoid_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 44
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.sigmoid(tmp0)
tl.store(out_ptr0 + x0, tmp1, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (1, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, primals_2, out=buf0)
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, buf0, out=buf1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, buf1, out=buf2)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, buf2, out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, buf3, out=buf4)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, buf4, out=buf5)
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, buf5, out=buf6)
buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, buf6, out=buf7)
buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, buf7, out=buf8)
buf20 = empty_strided_cuda((4, 44), (44, 1), torch.float32)
buf9 = reinterpret_tensor(buf20, (4, 4), (44, 1), 40)
extern_kernels.mm(primals_1, buf8, out=buf9)
del primals_1
buf10 = reinterpret_tensor(buf20, (4, 4), (44, 1), 0)
get_raw_stream(0)
triton_poi_fused_stack_0[grid(16)](primals_2, buf10, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_2
buf11 = reinterpret_tensor(buf20, (4, 4), (44, 1), 4)
triton_poi_fused_stack_1[grid(16)](buf0, buf11, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf0
buf12 = reinterpret_tensor(buf20, (4, 4), (44, 1), 8)
triton_poi_fused_stack_1[grid(16)](buf1, buf12, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf1
buf13 = reinterpret_tensor(buf20, (4, 4), (44, 1), 12)
triton_poi_fused_stack_1[grid(16)](buf2, buf13, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf2
buf14 = reinterpret_tensor(buf20, (4, 4), (44, 1), 16)
triton_poi_fused_stack_0[grid(16)](buf3, buf14, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf3
buf15 = reinterpret_tensor(buf20, (4, 4), (44, 1), 20)
triton_poi_fused_stack_1[grid(16)](buf4, buf15, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf4
buf16 = reinterpret_tensor(buf20, (4, 4), (44, 1), 24)
triton_poi_fused_stack_1[grid(16)](buf5, buf16, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf5
buf17 = reinterpret_tensor(buf20, (4, 4), (44, 1), 28)
triton_poi_fused_stack_1[grid(16)](buf6, buf17, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf6
buf18 = reinterpret_tensor(buf20, (4, 4), (44, 1), 32)
triton_poi_fused_stack_0[grid(16)](buf7, buf18, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf7
buf19 = reinterpret_tensor(buf20, (4, 4), (44, 1), 36)
triton_poi_fused_stack_1[grid(16)](buf8, buf19, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf21 = empty_strided_cuda((44, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf20, (44, 4), (4, 1), 0),
reinterpret_tensor(primals_3, (4, 1), (1, 4), 0), out=buf21)
del primals_3
buf22 = empty_strided_cuda((4, 11, 1), (11, 1, 1), torch.float32)
triton_poi_fused_sigmoid_2[grid(44)](buf21, buf22, 44, XBLOCK=64,
num_warps=1, num_stages=1)
buf23 = reinterpret_tensor(buf8, (4, 1, 4), (4, 4, 1), 0)
del buf8
extern_kernels.bmm(reinterpret_tensor(buf22, (4, 1, 11), (11, 0, 1),
0), reinterpret_tensor(buf20, (4, 11, 4), (44, 4, 1), 0), out=buf23
)
del buf22
return reinterpret_tensor(buf23, (4, 4), (4, 1), 0), buf20, buf21
class DAGNNConvNew(nn.Module):
def __init__(self, in_features, out_features=1, K=10, bias=False):
super().__init__()
assert out_features == 1, "'out_features' must be 1"
self.in_features = in_features
self.out_features = out_features
self.lin = nn.Linear(in_features, out_features, bias=bias)
self.K = K
self.act = nn.Sigmoid()
def reset_parameters(self):
self.lin.reset_parameters()
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features}, K={self.K})'
)
def forward(self, input_0, input_1):
primals_3 = self.lin.weight
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3])
return output[0]
|
EdisonLeeeee/Graphgallery
|
DAGNNConv
| false
| 5,115
|
[
"MIT"
] | 1
|
8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
Attn
|
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Attn(nn.Module):
def __init__(self, hidden):
super().__init__()
self.hidden = hidden
self.attn = nn.Linear(self.hidden * 2, hidden)
self.v = nn.Parameter(torch.rand(hidden))
stdv = 1.0 / math.sqrt(self.v.size(0))
self.v.data.normal_(mean=0, std=stdv)
def forward(self, hidden, enc_out):
l = enc_out.size(0)
enc_out.size(1)
H = hidden.repeat(l, 1, 1).transpose(0, 1)
enc_out = enc_out.transpose(0, 1)
attn_score = self.score(H, enc_out)
return F.softmax(attn_score, dim=1).unsqueeze(1)
def score(self, hidden, enc_out):
"""
concat score function
score(s_t, h_i) = vT_a tanh(Wa[s_t; h_i])
"""
energy = torch.tanh(self.attn(torch.cat([hidden, enc_out], 2)))
energy = energy.transpose(2, 1)
v = self.v.repeat(enc_out.data.shape[0], 1).unsqueeze(1)
energy = torch.bmm(v, energy)
return energy.squeeze(1)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'hidden': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x2 = xindex // 32
x1 = xindex // 8 % 4
x3 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x2 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x2 + 16 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x3, tmp10, xmask)
@triton.jit
def triton_poi_fused_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, xmask)
@triton.jit
def triton_poi_fused_repeat_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x2, tmp0, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 8), (8, 1))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(128)](primals_2, primals_1, buf0, 128,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
del primals_2
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 8), (8, 1), 0),
reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf1)
del primals_3
buf2 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
del buf1
triton_poi_fused_tanh_1[grid(64)](buf2, primals_4, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_repeat_2[grid(16)](primals_5, buf3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf3, (4, 1, 4), (4, 0, 1), 0
), reinterpret_tensor(buf2, (4, 4, 4), (16, 1, 4), 0), out=buf4)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused__softmax_3[grid(16)](buf4, buf5, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf6 = reinterpret_tensor(buf4, (4, 4), (4, 1), 0)
del buf4
triton_poi_fused__softmax_4[grid(16)](buf5, buf6, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf5
return reinterpret_tensor(buf6, (4, 1, 4), (4, 4, 1), 0
), reinterpret_tensor(buf0, (16, 8), (8, 1), 0
), buf2, buf6, reinterpret_tensor(buf3, (4, 4, 1), (4, 1, 4), 0)
class AttnNew(nn.Module):
def __init__(self, hidden):
super().__init__()
self.hidden = hidden
self.attn = nn.Linear(self.hidden * 2, hidden)
self.v = nn.Parameter(torch.rand(hidden))
stdv = 1.0 / math.sqrt(self.v.size(0))
self.v.data.normal_(mean=0, std=stdv)
def score(self, hidden, enc_out):
"""
concat score function
score(s_t, h_i) = vT_a tanh(Wa[s_t; h_i])
"""
energy = torch.tanh(self.attn(torch.cat([hidden, enc_out], 2)))
energy = energy.transpose(2, 1)
v = self.v.repeat(enc_out.data.shape[0], 1).unsqueeze(1)
energy = torch.bmm(v, energy)
return energy.squeeze(1)
def forward(self, input_0, input_1):
primals_4 = self.v
primals_3 = self.attn.weight
primals_5 = self.attn.bias
primals_2 = input_0
primals_1 = input_1
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
Eddie-Hwang/Co-Eye_Motion_Generation
|
Attn
| false
| 5,116
|
[
"MIT"
] | 1
|
8e244680115fb63bc26018cb6b53bcfbd04e9683
|
https://github.com/Eddie-Hwang/Co-Eye_Motion_Generation/tree/8e244680115fb63bc26018cb6b53bcfbd04e9683
|
SpectralEigenConv
|
import torch
import torch.nn as nn
class SpectralEigenConv(nn.Module):
def __init__(self, in_features, out_features, bias=False, K=10, alpha=
0.1, **kwargs):
super().__init__()
assert K > 0
self.K = K
self.alpha = alpha
self.in_features = in_features
self.out_features = out_features
self.w = nn.Linear(in_features, out_features, bias=bias)
def forward(self, x, U, V=None):
"""
x: node attribute matrix
if `V=None`:
U: (N, N) adjacency matrix
else:
U: (N, k) eigenvector matrix
V: (k,) eigenvalue
"""
x = self.w(x)
if V is not None:
V_pow = torch.ones_like(V)
V_out = torch.zeros_like(V)
for _ in range(self.K):
V_pow *= V
V_out += (1 - self.alpha) * V_pow
V_out = V_out / self.K
x_out = U * V_out @ (U.t() @ x) + self.alpha * x
else:
adj = U
x_in = x
x_out = torch.zeros_like(x)
for _ in range(self.K):
x = torch.spmm(adj, x)
x_out += (1 - self.alpha) * x
x_out /= self.K
x_out += self.alpha * x_in
return x_out
def reset_parameters(self):
self.w.reset_parameters()
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features}, K={self.K}, alpha={self.alpha})'
)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_div_mul_0(in_out_ptr0, in_out_ptr1, in_ptr0,
in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8,
xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp3 = tl.load(in_ptr0 + x0, xmask)
tmp6 = tl.load(in_ptr1 + x0, xmask)
tmp9 = tl.load(in_ptr2 + x0, xmask)
tmp12 = tl.load(in_ptr3 + x0, xmask)
tmp15 = tl.load(in_ptr4 + x0, xmask)
tmp18 = tl.load(in_ptr5 + x0, xmask)
tmp21 = tl.load(in_ptr6 + x0, xmask)
tmp24 = tl.load(in_ptr7 + x0, xmask)
tmp27 = tl.load(in_ptr8 + x0, xmask)
tmp32 = tl.load(in_out_ptr1 + x0, xmask)
tmp1 = 0.9
tmp2 = tmp0 * tmp1
tmp4 = tmp3 * tmp1
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp1
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp1
tmp11 = tmp8 + tmp10
tmp13 = tmp12 * tmp1
tmp14 = tmp11 + tmp13
tmp16 = tmp15 * tmp1
tmp17 = tmp14 + tmp16
tmp19 = tmp18 * tmp1
tmp20 = tmp17 + tmp19
tmp22 = tmp21 * tmp1
tmp23 = tmp20 + tmp22
tmp25 = tmp24 * tmp1
tmp26 = tmp23 + tmp25
tmp28 = tmp27 * tmp1
tmp29 = tmp26 + tmp28
tmp30 = 0.1
tmp31 = tmp29 * tmp30
tmp33 = tmp32 * tmp30
tmp34 = tmp31 + tmp33
tl.store(in_out_ptr1 + x0, tmp34, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_2, reinterpret_tensor(primals_1, (4, 4),
(1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf0, out=buf1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf1, out=buf2)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf2, out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf3, out=buf4)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf4, out=buf5)
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf5, out=buf6)
buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf6, out=buf7)
buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf7, out=buf8)
buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf8, out=buf9)
buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf9, out=buf11)
buf10 = buf1
del buf1
buf12 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_add_div_mul_0[grid(16)](buf10, buf12, buf2, buf3,
buf4, buf5, buf6, buf7, buf8, buf9, buf11, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf10
del buf11
del buf2
del buf3
del buf4
del buf5
del buf6
del buf7
del buf8
del buf9
return buf12, primals_2, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0)
class SpectralEigenConvNew(nn.Module):
def __init__(self, in_features, out_features, bias=False, K=10, alpha=
0.1, **kwargs):
super().__init__()
assert K > 0
self.K = K
self.alpha = alpha
self.in_features = in_features
self.out_features = out_features
self.w = nn.Linear(in_features, out_features, bias=bias)
def reset_parameters(self):
self.w.reset_parameters()
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features}, K={self.K}, alpha={self.alpha})'
)
def forward(self, input_0, input_1):
primals_1 = self.w.weight
primals_2 = input_0
primals_3 = input_1
output = call([primals_1, primals_2, primals_3])
return output[0]
|
EdisonLeeeee/Graphgallery
|
SpectralEigenConv
| false
| 5,117
|
[
"MIT"
] | 1
|
8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
CO2Regularizer
|
import torch
class MemoryBankModule(torch.nn.Module):
"""Memory bank implementation
This is a parent class to all loss functions implemented by the lightly
Python package. This way, any loss can be used with a memory bank if
desired.
Attributes:
size:
Number of keys the memory bank can store. If set to 0,
memory bank is not used.
Examples:
>>> class MyLossFunction(MemoryBankModule):
>>>
>>> def __init__(self, memory_bank_size: int = 2 ** 16):
>>> super(MyLossFunction, self).__init__(memory_bank_size)
>>>
>>> def forward(self, output: torch.Tensor,
>>> labels: torch.Tensor = None):
>>>
>>> output, negatives = super(
>>> MyLossFunction, self).forward(output)
>>>
>>> if negatives is not None:
>>> # evaluate loss with negative samples
>>> else:
>>> # evaluate loss without negative samples
"""
def __init__(self, size: 'int'=2 ** 16):
super(MemoryBankModule, self).__init__()
if size < 0:
msg = f'Illegal memory bank size {size}, must be non-negative.'
raise ValueError(msg)
self.size = size
self.bank = None
self.bank_ptr = None
@torch.no_grad()
def _init_memory_bank(self, dim: 'int'):
"""Initialize the memory bank if it's empty
Args:
dim:
The dimension of the which are stored in the bank.
"""
self.bank = torch.randn(dim, self.size)
self.bank = torch.nn.functional.normalize(self.bank, dim=0)
self.bank_ptr = torch.LongTensor([0])
@torch.no_grad()
def _dequeue_and_enqueue(self, batch: 'torch.Tensor'):
"""Dequeue the oldest batch and add the latest one
Args:
batch:
The latest batch of keys to add to the memory bank.
"""
batch_size = batch.shape[0]
ptr = int(self.bank_ptr)
if ptr + batch_size >= self.size:
self.bank[:, ptr:] = batch[:self.size - ptr].T.detach()
self.bank_ptr[0] = 0
else:
self.bank[:, ptr:ptr + batch_size] = batch.T.detach()
self.bank_ptr[0] = ptr + batch_size
def forward(self, output: 'torch.Tensor', labels: 'torch.Tensor'=None,
update: 'bool'=False):
"""Query memory bank for additional negative samples
Args:
output:
The output of the model.
labels:
Should always be None, will be ignored.
Returns:
The output if the memory bank is of size 0, otherwise the output
and the entries from the memory bank.
"""
if self.size == 0:
return output, None
_, dim = output.shape
if self.bank is None:
self._init_memory_bank(dim)
bank = self.bank.clone().detach()
if update:
self._dequeue_and_enqueue(output)
return output, bank
class CO2Regularizer(MemoryBankModule):
"""Implementation of the CO2 regularizer [0] for self-supervised learning.
[0] CO2, 2021, https://arxiv.org/abs/2010.02217
Attributes:
alpha:
Weight of the regularization term.
t_consistency:
Temperature used during softmax calculations.
memory_bank_size:
Number of negative samples to store in the memory bank.
Use 0 to use the second batch for negative samples.
Examples:
>>> # initialize loss function for MoCo
>>> loss_fn = NTXentLoss(memory_bank_size=4096)
>>>
>>> # initialize CO2 regularizer
>>> co2 = CO2Regularizer(alpha=1.0, memory_bank_size=4096)
>>>
>>> # generate two random trasnforms of images
>>> t0 = transforms(images)
>>> t1 = transforms(images)
>>>
>>> # feed through the MoCo model
>>> out0, out1 = model(t0, t1)
>>>
>>> # calculate loss and apply regularizer
>>> loss = loss_fn(out0, out1) + co2(out0, out1)
"""
def __init__(self, alpha: 'float'=1, t_consistency: 'float'=0.05,
memory_bank_size: 'int'=0):
super(CO2Regularizer, self).__init__(size=memory_bank_size)
self.log_target = True
try:
self.kl_div = torch.nn.KLDivLoss(reduction='batchmean',
log_target=True)
except TypeError:
self.log_target = False
self.kl_div = torch.nn.KLDivLoss(reduction='batchmean')
self.t_consistency = t_consistency
self.alpha = alpha
def _get_pseudo_labels(self, out0: 'torch.Tensor', out1: 'torch.Tensor',
negatives: 'torch.Tensor'=None):
"""Computes the soft pseudo labels across negative samples.
Args:
out0:
Output projections of the first set of transformed images (query).
Shape: bsz x n_ftrs
out1:
Output projections of the second set of transformed images (positive sample).
Shape: bsz x n_ftrs
negatives:
Negative samples to compare against. If this is None, the second
batch of images will be used as negative samples.
Shape: memory_bank_size x n_ftrs
Returns:
Log probability that a positive samples will classify each negative
sample as the positive sample.
Shape: bsz x (bsz - 1) or bsz x memory_bank_size
"""
batch_size, _ = out0.shape
if negatives is None:
l_pos = torch.einsum('nc,nc->n', [out0, out1]).unsqueeze(-1)
l_neg = torch.einsum('nc,ck->nk', [out0, out1.t()])
l_neg = l_neg.masked_select(~torch.eye(batch_size, dtype=bool,
device=l_neg.device)).view(batch_size, batch_size - 1)
else:
negatives = negatives
l_pos = torch.einsum('nc,nc->n', [out0, out1]).unsqueeze(-1)
l_neg = torch.einsum('nc,ck->nk', [out0, negatives.clone().
detach()])
logits = torch.cat([l_pos, l_neg], dim=1)
logits = logits / self.t_consistency
return torch.nn.functional.log_softmax(logits, dim=-1)
def forward(self, out0: 'torch.Tensor', out1: 'torch.Tensor'):
"""Computes the CO2 regularization term for two model outputs.
Args:
out0:
Output projections of the first set of transformed images.
out1:
Output projections of the second set of transformed images.
Returns:
The regularization term multiplied by the weight factor alpha.
"""
out0 = torch.nn.functional.normalize(out0, dim=1)
out1 = torch.nn.functional.normalize(out1, dim=1)
out1, negatives = super(CO2Regularizer, self).forward(out1, update=True
)
p = self._get_pseudo_labels(out0, out1, negatives)
q = self._get_pseudo_labels(out1, out0, negatives)
if self.log_target:
div = self.kl_div(p, q) + self.kl_div(q, p)
else:
div = self.kl_div(p, torch.exp(q)) + self.kl_div(q, torch.exp(p))
return self.alpha * 0.5 * div
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-12
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + x2, tmp15, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4), (4, 1))
assert_size_stride(arg1_1, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_div_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del arg0_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_div_0[grid(16)](arg1_1, buf1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del arg1_1
return buf0, buf1
class MemoryBankModule(torch.nn.Module):
"""Memory bank implementation
This is a parent class to all loss functions implemented by the lightly
Python package. This way, any loss can be used with a memory bank if
desired.
Attributes:
size:
Number of keys the memory bank can store. If set to 0,
memory bank is not used.
Examples:
>>> class MyLossFunction(MemoryBankModule):
>>>
>>> def __init__(self, memory_bank_size: int = 2 ** 16):
>>> super(MyLossFunction, self).__init__(memory_bank_size)
>>>
>>> def forward(self, output: torch.Tensor,
>>> labels: torch.Tensor = None):
>>>
>>> output, negatives = super(
>>> MyLossFunction, self).forward(output)
>>>
>>> if negatives is not None:
>>> # evaluate loss with negative samples
>>> else:
>>> # evaluate loss without negative samples
"""
def __init__(self, size: 'int'=2 ** 16):
super(MemoryBankModule, self).__init__()
if size < 0:
msg = f'Illegal memory bank size {size}, must be non-negative.'
raise ValueError(msg)
self.size = size
self.bank = None
self.bank_ptr = None
@torch.no_grad()
def _init_memory_bank(self, dim: 'int'):
"""Initialize the memory bank if it's empty
Args:
dim:
The dimension of the which are stored in the bank.
"""
self.bank = torch.randn(dim, self.size)
self.bank = torch.nn.functional.normalize(self.bank, dim=0)
self.bank_ptr = torch.LongTensor([0])
@torch.no_grad()
def _dequeue_and_enqueue(self, batch: 'torch.Tensor'):
"""Dequeue the oldest batch and add the latest one
Args:
batch:
The latest batch of keys to add to the memory bank.
"""
batch_size = batch.shape[0]
ptr = int(self.bank_ptr)
if ptr + batch_size >= self.size:
self.bank[:, ptr:] = batch[:self.size - ptr].T.detach()
self.bank_ptr[0] = 0
else:
self.bank[:, ptr:ptr + batch_size] = batch.T.detach()
self.bank_ptr[0] = ptr + batch_size
def forward(self, output: 'torch.Tensor', labels: 'torch.Tensor'=None,
update: 'bool'=False):
"""Query memory bank for additional negative samples
Args:
output:
The output of the model.
labels:
Should always be None, will be ignored.
Returns:
The output if the memory bank is of size 0, otherwise the output
and the entries from the memory bank.
"""
if self.size == 0:
return output, None
_, dim = output.shape
if self.bank is None:
self._init_memory_bank(dim)
bank = self.bank.clone().detach()
if update:
self._dequeue_and_enqueue(output)
return output, bank
class CO2RegularizerNew(MemoryBankModule):
"""Implementation of the CO2 regularizer [0] for self-supervised learning.
[0] CO2, 2021, https://arxiv.org/abs/2010.02217
Attributes:
alpha:
Weight of the regularization term.
t_consistency:
Temperature used during softmax calculations.
memory_bank_size:
Number of negative samples to store in the memory bank.
Use 0 to use the second batch for negative samples.
Examples:
>>> # initialize loss function for MoCo
>>> loss_fn = NTXentLoss(memory_bank_size=4096)
>>>
>>> # initialize CO2 regularizer
>>> co2 = CO2Regularizer(alpha=1.0, memory_bank_size=4096)
>>>
>>> # generate two random trasnforms of images
>>> t0 = transforms(images)
>>> t1 = transforms(images)
>>>
>>> # feed through the MoCo model
>>> out0, out1 = model(t0, t1)
>>>
>>> # calculate loss and apply regularizer
>>> loss = loss_fn(out0, out1) + co2(out0, out1)
"""
def __init__(self, alpha: 'float'=1, t_consistency: 'float'=0.05,
memory_bank_size: 'int'=0):
super(CO2RegularizerNew, self).__init__(size=memory_bank_size)
self.log_target = True
try:
self.kl_div = torch.nn.KLDivLoss(reduction='batchmean',
log_target=True)
except TypeError:
self.log_target = False
self.kl_div = torch.nn.KLDivLoss(reduction='batchmean')
self.t_consistency = t_consistency
self.alpha = alpha
def _get_pseudo_labels(self, out0: 'torch.Tensor', out1: 'torch.Tensor',
negatives: 'torch.Tensor'=None):
"""Computes the soft pseudo labels across negative samples.
Args:
out0:
Output projections of the first set of transformed images (query).
Shape: bsz x n_ftrs
out1:
Output projections of the second set of transformed images (positive sample).
Shape: bsz x n_ftrs
negatives:
Negative samples to compare against. If this is None, the second
batch of images will be used as negative samples.
Shape: memory_bank_size x n_ftrs
Returns:
Log probability that a positive samples will classify each negative
sample as the positive sample.
Shape: bsz x (bsz - 1) or bsz x memory_bank_size
"""
batch_size, _ = out0.shape
if negatives is None:
l_pos = torch.einsum('nc,nc->n', [out0, out1]).unsqueeze(-1)
l_neg = torch.einsum('nc,ck->nk', [out0, out1.t()])
l_neg = l_neg.masked_select(~torch.eye(batch_size, dtype=bool,
device=l_neg.device)).view(batch_size, batch_size - 1)
else:
negatives = negatives
l_pos = torch.einsum('nc,nc->n', [out0, out1]).unsqueeze(-1)
l_neg = torch.einsum('nc,ck->nk', [out0, negatives.clone().
detach()])
logits = torch.cat([l_pos, l_neg], dim=1)
logits = logits / self.t_consistency
return torch.nn.functional.log_softmax(logits, dim=-1)
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
EelcoHoogendoorn/lightly
|
CO2Regularizer
| false
| 5,118
|
[
"MIT"
] | 1
|
98e0148967738404fa7f45196ec5eabfe00cd22e
|
https://github.com/EelcoHoogendoorn/lightly/tree/98e0148967738404fa7f45196ec5eabfe00cd22e
|
SmallTransformerNet
|
import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.onnx
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv2d(in_channels, out_channels,
kernel_size, stride)
def forward(self, x):
out = self.reflection_pad(x)
out = self.conv2d(out)
return out
class ResidualBlock(torch.nn.Module):
"""ResidualBlock
introduced in: https://arxiv.org/abs/1512.03385
recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
"""
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
self.relu = torch.nn.ReLU()
def forward(self, x):
residual = x
out = self.relu(self.in1(self.conv1(x)))
out = self.in2(self.conv2(out))
out = out + residual
return out
class UpsampleConvLayer(torch.nn.Module):
"""UpsampleConvLayer
Upsamples the input and then does a convolution. This method gives better results
compared to ConvTranspose2d.
ref: http://distill.pub/2016/deconv-checkerboard/
"""
def __init__(self, in_channels, out_channels, kernel_size, stride,
upsample=None):
super(UpsampleConvLayer, self).__init__()
self.upsample = upsample
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv2d(in_channels, out_channels,
kernel_size, stride)
def forward(self, x):
x_in = x
if self.upsample:
x_in = torch.nn.functional.interpolate(x_in, mode='nearest',
scale_factor=self.upsample)
out = self.reflection_pad(x_in)
out = self.conv2d(out)
return out
class SmallTransformerNet(torch.nn.Module):
def __init__(self):
super(SmallTransformerNet, self).__init__()
self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
self.res6 = ResidualBlock(64)
self.res7 = ResidualBlock(64)
self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1,
upsample=2)
self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
self.relu = torch.nn.ReLU()
def forward(self, X):
y = self.relu(self.in1(self.conv1(X)))
y = self.relu(self.in2(self.conv2(y)))
y = self.res6(y)
y = self.res7(y)
y = self.relu(self.in5(self.deconv2(y)))
y = self.deconv3(y)
return y
def get_inputs():
return [torch.rand([4, 3, 64, 64])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 62208
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 72
x1 = xindex // 72 % 72
x2 = xindex // 5184
x3 = xindex
tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-4 +
x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-4 + x1)) + 4096 * x2),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_red_fused__native_batch_norm_legit_convolution_1(in_out_ptr0,
in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr,
RBLOCK: tl.constexpr):
xnumel = 128
rnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rbase = tl.arange(0, RBLOCK)[None, :]
x3 = xindex
x0 = xindex % 32
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = rindex
tmp0 = tl.load(in_out_ptr0 + (r2 + 4096 * x3), rmask & xmask,
eviction_policy='evict_first', other=0.0)
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = (triton_helpers.
welford_reduce(tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0)
)
tmp4_mean = tl.where(rmask & xmask, tmp4_mean_next, tmp4_mean)
tmp4_m2 = tl.where(rmask & xmask, tmp4_m2_next, tmp4_m2)
tmp4_weight = tl.where(rmask & xmask, tmp4_weight_next, tmp4_weight)
tl.store(in_out_ptr0 + (r2 + 4096 * x3), tmp2, rmask & xmask)
tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(tmp4_mean,
tmp4_m2, tmp4_weight, 1)
tmp4 = tmp4_tmp[:, None]
tmp5 = tmp5_tmp[:, None]
tmp6_tmp[:, None]
tl.store(out_ptr0 + x3, tmp4, xmask)
tmp7 = 4096.0
tmp8 = tmp5 / tmp7
tmp9 = 1e-05
tmp10 = tmp8 + tmp9
tmp11 = libdevice.rsqrt(tmp10)
tl.debug_barrier()
tl.store(in_out_ptr1 + x3, tmp11, xmask)
@triton.jit
def triton_poi_fused_repeat_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0 % 32, xmask)
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused_reflection_pad2d_relu_3(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 557568
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 66
x1 = xindex // 66 % 66
x2 = xindex // 4356
x3 = xindex
tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-1 + x1)) + 4096 * x2),
xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tl.store(out_ptr0 + x3, tmp10, xmask)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_4(
in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
out_ptr2, out_ptr3, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
x0 = xindex
r3 = rindex
x1 = xindex % 64
tmp0 = tl.load(in_ptr0 + x0 % 64, None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x0 % 64, None, eviction_policy='evict_last')
tmp2 = tl.load(in_out_ptr0 + (r3 + 1024 * x0), None)
tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
tmp4 = tmp2 + tmp3
tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
tmp7 = tl.broadcast_to(tmp5, [RBLOCK])
tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
tmp10 = tl.full([1], 1024, tl.int32)
tmp11 = tmp10.to(tl.float32)
tmp12 = tmp9 / tmp11
tmp13 = tmp5 - tmp12
tmp14 = tmp13 * tmp13
tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
tmp17 = triton_helpers.promote_to_tensor(tl.sum(tmp15, 0))
tmp18 = 1024.0
tmp19 = tmp17 / tmp18
tmp20 = 1e-05
tmp21 = tmp19 + tmp20
tmp22 = libdevice.rsqrt(tmp21)
tmp23 = tmp4 - tmp12
tmp24 = tmp23 * tmp22
tmp25 = tmp24 * tmp0
tmp26 = tmp25 + tmp1
tmp27 = tl.full([1], 0, tl.int32)
tmp28 = triton_helpers.maximum(tmp27, tmp26)
tl.store(out_ptr0 + x0, tmp0, None)
tl.store(out_ptr1 + x0, tmp1, None)
tl.store(in_out_ptr0 + (r3 + 1024 * x0), tmp4, None)
tl.debug_barrier()
tl.store(in_out_ptr1 + x0, tmp22, None)
tl.store(out_ptr3 + (r3 + 1024 * x0), tmp28, None)
tl.store(out_ptr2 + x0, tmp12, None)
@triton.jit
def triton_poi_fused_reflection_pad2d_5(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 295936
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 34
x1 = xindex // 34 % 34
x2 = xindex // 1156
x3 = xindex
tmp0 = tl.load(in_ptr0 + (1023 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
x0)) + -32 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 1024 * x2),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_6(in_out_ptr0,
in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r2 = rindex
x3 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + (r2 + 1024 * x3), None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
tmp8 = tl.full([1], 1024, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp3 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
tmp16 = 1024.0
tmp17 = tmp15 / tmp16
tmp18 = 1e-05
tmp19 = tmp17 + tmp18
tmp20 = libdevice.rsqrt(tmp19)
tl.store(in_out_ptr0 + (r2 + 1024 * x3), tmp2, None)
tl.debug_barrier()
tl.store(in_out_ptr1 + x3, tmp20, None)
tl.store(out_ptr0 + x3, tmp10, None)
@triton.jit
def triton_poi_fused_repeat_7(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0 % 64, xmask)
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused_reflection_pad2d_relu_8(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 295936
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 34
x1 = xindex // 34 % 34
x2 = xindex // 1156
x3 = xindex
tmp0 = tl.load(in_ptr0 + (1023 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
x0)) + -32 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 1024 * x2),
xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tl.store(out_ptr0 + x3, tmp10, xmask)
@triton.jit
def triton_per_fused__native_batch_norm_legit_add_convolution_repeat_9(
in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
out_ptr3, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
x0 = xindex
r3 = rindex
x1 = xindex % 64
tmp0 = tl.load(in_ptr0 + x0 % 64, None, eviction_policy='evict_last')
tmp1 = tl.load(in_out_ptr0 + (r3 + 1024 * x0), None)
tmp2 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
tmp27 = tl.load(in_out_ptr1 + (r3 + 1024 * x0), None)
tmp3 = tmp1 + tmp2
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = tl.broadcast_to(tmp4, [RBLOCK])
tmp8 = triton_helpers.promote_to_tensor(tl.sum(tmp6, 0))
tmp9 = tl.full([1], 1024, tl.int32)
tmp10 = tmp9.to(tl.float32)
tmp11 = tmp8 / tmp10
tmp12 = tmp4 - tmp11
tmp13 = tmp12 * tmp12
tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
tmp17 = tmp3 - tmp11
tmp18 = 1024.0
tmp19 = tmp16 / tmp18
tmp20 = 1e-05
tmp21 = tmp19 + tmp20
tmp22 = libdevice.rsqrt(tmp21)
tmp23 = tmp17 * tmp22
tmp24 = tmp23 * tmp0
tmp26 = tmp24 + tmp25
tmp28 = tmp26 + tmp27
tl.store(out_ptr0 + x0, tmp0, None)
tl.store(in_out_ptr0 + (r3 + 1024 * x0), tmp3, None)
tl.store(in_out_ptr1 + (r3 + 1024 * x0), tmp28, None)
tl.store(out_ptr3 + x0, tmp22, None)
tl.store(out_ptr1 + x0, tmp11, None)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_10(in_out_ptr0,
in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r2 = rindex
x3 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + (r2 + 1024 * x3), None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
tmp8 = tl.full([1], 1024, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp3 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
tmp16 = 1024.0
tmp17 = tmp15 / tmp16
tmp18 = 1e-05
tmp19 = tmp17 + tmp18
tmp20 = libdevice.rsqrt(tmp19)
tl.store(in_out_ptr0 + (r2 + 1024 * x3), tmp2, None)
tl.store(out_ptr2 + x3, tmp20, None)
tl.store(out_ptr0 + x3, tmp10, None)
tl.store(out_ptr1 + x3, tmp15, None)
@triton.jit
def triton_poi_fused_arange_11(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_12(out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_reflection_pad2d_13(in_ptr0, in_ptr1,
in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 1115136
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 66 % 66
x0 = xindex % 66
x4 = xindex // 4356
x2 = xindex // 4356 % 64
x7 = xindex
tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
x1))), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
x0))), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
tmp19 = tl.load(in_ptr4 + x4, xmask, eviction_policy='evict_last')
tmp21 = tl.load(in_ptr5 + x2, xmask, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 32, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + 32 * tmp4 + 1024 * x4), xmask,
eviction_policy='evict_last')
tmp11 = tmp9 - tmp10
tmp13 = 1024.0
tmp14 = tmp12 / tmp13
tmp15 = 1e-05
tmp16 = tmp14 + tmp15
tmp17 = libdevice.rsqrt(tmp16)
tmp18 = tmp11 * tmp17
tmp20 = tmp18 * tmp19
tmp22 = tmp20 + tmp21
tmp23 = tl.load(in_ptr6 + (tmp8 + 32 * tmp4 + 1024 * x4), xmask,
eviction_policy='evict_last')
tmp24 = tmp22 + tmp23
tl.store(out_ptr0 + x7, tmp24, xmask)
@triton.jit
def triton_poi_fused_reflection_pad2d_relu_14(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 72
x1 = xindex // 72 % 72
x2 = xindex // 5184
x3 = xindex
tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-4 +
x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-4 + x1)) + 4096 * x2),
None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tl.store(out_ptr0 + x3, tmp10, None)
@triton.jit
def triton_poi_fused_convolution_15(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 4096 % 3
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, None)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17,
primals_18, primals_19, primals_20, primals_21, primals_22,
primals_23, primals_24, primals_25, primals_26, primals_27,
primals_28, primals_29, primals_30, primals_31) = args
args.clear()
assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_2, (32, 3, 9, 9), (243, 81, 9, 1))
assert_size_stride(primals_3, (32,), (1,))
assert_size_stride(primals_4, (32,), (1,))
assert_size_stride(primals_5, (32,), (1,))
assert_size_stride(primals_6, (64, 32, 3, 3), (288, 9, 3, 1))
assert_size_stride(primals_7, (64,), (1,))
assert_size_stride(primals_8, (64,), (1,))
assert_size_stride(primals_9, (64,), (1,))
assert_size_stride(primals_10, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_11, (64,), (1,))
assert_size_stride(primals_12, (64,), (1,))
assert_size_stride(primals_13, (64,), (1,))
assert_size_stride(primals_14, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_15, (64,), (1,))
assert_size_stride(primals_16, (64,), (1,))
assert_size_stride(primals_17, (64,), (1,))
assert_size_stride(primals_18, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_19, (64,), (1,))
assert_size_stride(primals_20, (64,), (1,))
assert_size_stride(primals_21, (64,), (1,))
assert_size_stride(primals_22, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_23, (64,), (1,))
assert_size_stride(primals_24, (64,), (1,))
assert_size_stride(primals_25, (64,), (1,))
assert_size_stride(primals_26, (32, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_27, (32,), (1,))
assert_size_stride(primals_28, (32,), (1,))
assert_size_stride(primals_29, (32,), (1,))
assert_size_stride(primals_30, (3, 32, 9, 9), (2592, 81, 9, 1))
assert_size_stride(primals_31, (3,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 3, 72, 72), (15552, 5184, 72, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(62208)](primals_1, buf0,
62208, XBLOCK=256, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 32, 64, 64), (131072, 4096, 64, 1))
buf2 = buf1
del buf1
buf5 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 1, 1), torch.float32
)
buf6 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch
.float32)
buf8 = reinterpret_tensor(buf6, (1, 128, 1, 1), (128, 1, 1, 1), 0)
del buf6
triton_red_fused__native_batch_norm_legit_convolution_1[grid(128)](buf2
, buf8, primals_3, buf5, 128, 4096, XBLOCK=1, RBLOCK=2048,
num_warps=16, num_stages=1)
del primals_3
buf3 = empty_strided_cuda((128,), (1,), torch.float32)
triton_poi_fused_repeat_2[grid(128)](primals_4, buf3, 128, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_4
buf4 = empty_strided_cuda((128,), (1,), torch.float32)
triton_poi_fused_repeat_2[grid(128)](primals_5, buf4, 128, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_5
buf9 = empty_strided_cuda((4, 32, 66, 66), (139392, 4356, 66, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_relu_3[grid(557568)](buf2, buf5,
buf8, buf3, buf4, buf9, 557568, XBLOCK=512, num_warps=8,
num_stages=1)
buf10 = extern_kernels.convolution(buf9, primals_6, stride=(2, 2),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf12 = empty_strided_cuda((256,), (1,), torch.float32)
buf13 = empty_strided_cuda((256,), (1,), torch.float32)
buf11 = buf10
del buf10
buf14 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 1, 1), torch.
float32)
buf15 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
torch.float32)
buf17 = reinterpret_tensor(buf15, (1, 256, 1, 1), (256, 1, 1, 1), 0)
del buf15
buf18 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.float32)
triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_4[
grid(256)](buf11, buf17, primals_8, primals_9, primals_7, buf12,
buf13, buf14, buf18, 256, 1024, num_warps=8, num_stages=1)
del primals_7
del primals_8
del primals_9
buf19 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_5[grid(295936)](buf18, buf19,
295936, XBLOCK=1024, num_warps=4, num_stages=1)
buf20 = extern_kernels.convolution(buf19, primals_10, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf20, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf21 = buf20
del buf20
buf24 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 1, 1), torch.
float32)
buf25 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
torch.float32)
buf27 = reinterpret_tensor(buf25, (1, 256, 1, 1), (256, 1, 1, 1), 0)
del buf25
triton_per_fused__native_batch_norm_legit_convolution_6[grid(256)](
buf21, buf27, primals_11, buf24, 256, 1024, num_warps=8,
num_stages=1)
del primals_11
buf22 = empty_strided_cuda((256,), (1,), torch.float32)
triton_poi_fused_repeat_7[grid(256)](primals_12, buf22, 256, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_12
buf23 = empty_strided_cuda((256,), (1,), torch.float32)
triton_poi_fused_repeat_7[grid(256)](primals_13, buf23, 256, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_13
buf28 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_relu_8[grid(295936)](buf21, buf24,
buf27, buf22, buf23, buf28, 295936, XBLOCK=1024, num_warps=4,
num_stages=1)
buf29 = extern_kernels.convolution(buf28, primals_14, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf29, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf31 = empty_strided_cuda((256,), (1,), torch.float32)
buf30 = buf29
del buf29
buf32 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
torch.float32)
buf36 = buf18
del buf18
buf35 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
torch.float32)
triton_per_fused__native_batch_norm_legit_add_convolution_repeat_9[grid
(256)](buf30, buf36, primals_16, primals_15, primals_17, buf31,
buf32, buf35, 256, 1024, num_warps=8, num_stages=1)
del primals_15
del primals_16
del primals_17
buf37 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_5[grid(295936)](buf36, buf37,
295936, XBLOCK=1024, num_warps=4, num_stages=1)
buf38 = extern_kernels.convolution(buf37, primals_18, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf38, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf39 = buf38
del buf38
buf42 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 1, 1), torch.
float32)
buf43 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
torch.float32)
buf45 = reinterpret_tensor(buf43, (1, 256, 1, 1), (256, 1, 1, 1), 0)
del buf43
triton_per_fused__native_batch_norm_legit_convolution_6[grid(256)](
buf39, buf45, primals_19, buf42, 256, 1024, num_warps=8,
num_stages=1)
del primals_19
buf40 = empty_strided_cuda((256,), (1,), torch.float32)
triton_poi_fused_repeat_7[grid(256)](primals_20, buf40, 256, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_20
buf41 = empty_strided_cuda((256,), (1,), torch.float32)
triton_poi_fused_repeat_7[grid(256)](primals_21, buf41, 256, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_21
buf46 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_relu_8[grid(295936)](buf39, buf42,
buf45, buf40, buf41, buf46, 295936, XBLOCK=1024, num_warps=4,
num_stages=1)
buf47 = extern_kernels.convolution(buf46, primals_22, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf47, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf48 = buf47
del buf47
buf50 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
torch.float32)
buf51 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
torch.float32)
buf53 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
torch.float32)
triton_per_fused__native_batch_norm_legit_convolution_10[grid(256)](
buf48, primals_23, buf50, buf51, buf53, 256, 1024, num_warps=8,
num_stages=1)
del primals_23
buf49 = empty_strided_cuda((256,), (1,), torch.float32)
triton_poi_fused_repeat_7[grid(256)](primals_24, buf49, 256, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_24
buf54 = empty_strided_cuda((64,), (1,), torch.int64)
triton_poi_fused_arange_11[grid(64)](buf54, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf55 = empty_strided_cuda((64,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_12[grid(64)](buf55, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf56 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1),
torch.float32)
triton_poi_fused__unsafe_index_add_reflection_pad2d_13[grid(1115136)](
buf55, buf48, buf50, buf51, buf49, primals_25, buf36, buf56,
1115136, XBLOCK=512, num_warps=8, num_stages=1)
del buf36
del buf51
del primals_25
buf57 = extern_kernels.convolution(buf56, primals_26, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf57, (4, 32, 64, 64), (131072, 4096, 64, 1))
buf58 = buf57
del buf57
buf61 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 1, 1), torch.
float32)
buf62 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
torch.float32)
buf64 = reinterpret_tensor(buf62, (1, 128, 1, 1), (128, 1, 1, 1), 0)
del buf62
triton_red_fused__native_batch_norm_legit_convolution_1[grid(128)](
buf58, buf64, primals_27, buf61, 128, 4096, XBLOCK=1, RBLOCK=
2048, num_warps=16, num_stages=1)
del primals_27
buf59 = empty_strided_cuda((128,), (1,), torch.float32)
triton_poi_fused_repeat_2[grid(128)](primals_28, buf59, 128, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_28
buf60 = empty_strided_cuda((128,), (1,), torch.float32)
triton_poi_fused_repeat_2[grid(128)](primals_29, buf60, 128, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_29
buf65 = empty_strided_cuda((4, 32, 72, 72), (165888, 5184, 72, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_relu_14[grid(663552)](buf58,
buf61, buf64, buf59, buf60, buf65, 663552, XBLOCK=512,
num_warps=8, num_stages=1)
buf66 = extern_kernels.convolution(buf65, primals_30, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf66, (4, 3, 64, 64), (12288, 4096, 64, 1))
buf67 = buf66
del buf66
triton_poi_fused_convolution_15[grid(49152)](buf67, primals_31,
49152, XBLOCK=256, num_warps=4, num_stages=1)
del primals_31
return (buf67, primals_2, primals_6, primals_10, primals_14, primals_18,
primals_22, primals_26, primals_30, buf0, buf2, buf3, buf4, buf5,
buf8, buf9, buf11, buf12, buf13, buf14, buf17, buf19, buf21, buf22,
buf23, buf24, buf27, buf28, buf30, buf31, reinterpret_tensor(buf35,
(256,), (1,), 0), buf37, buf39, buf40, buf41, buf42, buf45, buf46,
buf48, buf49, reinterpret_tensor(buf53, (256,), (1,), 0), buf54,
buf55, buf56, buf58, buf59, buf60, buf61, buf64, buf65,
reinterpret_tensor(buf50, (1, 256, 1, 1), (256, 1, 1, 1), 0),
reinterpret_tensor(buf32, (1, 256, 1, 1), (256, 1, 1, 1), 0))
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv2d(in_channels, out_channels,
kernel_size, stride)
def forward(self, x):
out = self.reflection_pad(x)
out = self.conv2d(out)
return out
class ResidualBlock(torch.nn.Module):
"""ResidualBlock
introduced in: https://arxiv.org/abs/1512.03385
recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
"""
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
self.relu = torch.nn.ReLU()
def forward(self, x):
residual = x
out = self.relu(self.in1(self.conv1(x)))
out = self.in2(self.conv2(out))
out = out + residual
return out
class UpsampleConvLayer(torch.nn.Module):
"""UpsampleConvLayer
Upsamples the input and then does a convolution. This method gives better results
compared to ConvTranspose2d.
ref: http://distill.pub/2016/deconv-checkerboard/
"""
def __init__(self, in_channels, out_channels, kernel_size, stride,
upsample=None):
super(UpsampleConvLayer, self).__init__()
self.upsample = upsample
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv2d(in_channels, out_channels,
kernel_size, stride)
def forward(self, x):
x_in = x
if self.upsample:
x_in = torch.nn.functional.interpolate(x_in, mode='nearest',
scale_factor=self.upsample)
out = self.reflection_pad(x_in)
out = self.conv2d(out)
return out
class SmallTransformerNetNew(torch.nn.Module):
def __init__(self):
super(SmallTransformerNetNew, self).__init__()
self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
self.res6 = ResidualBlock(64)
self.res7 = ResidualBlock(64)
self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1,
upsample=2)
self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
self.relu = torch.nn.ReLU()
def forward(self, input_0):
primals_2 = self.conv1.conv2d.weight
primals_3 = self.conv1.conv2d.bias
primals_4 = self.in1.weight
primals_5 = self.in1.bias
primals_6 = self.conv2.conv2d.weight
primals_7 = self.conv2.conv2d.bias
primals_8 = self.in2.weight
primals_9 = self.in2.bias
primals_10 = self.res6.conv1.conv2d.weight
primals_11 = self.res6.conv1.conv2d.bias
primals_12 = self.res6.in1.weight
primals_13 = self.res6.in1.bias
primals_14 = self.res6.conv2.conv2d.weight
primals_15 = self.res6.conv2.conv2d.bias
primals_16 = self.res6.in2.weight
primals_17 = self.res6.in2.bias
primals_18 = self.res7.conv1.conv2d.weight
primals_19 = self.res7.conv1.conv2d.bias
primals_20 = self.res7.in1.weight
primals_21 = self.res7.in1.bias
primals_22 = self.res7.conv2.conv2d.weight
primals_23 = self.res7.conv2.conv2d.bias
primals_24 = self.res7.in2.weight
primals_25 = self.res7.in2.bias
primals_26 = self.deconv2.conv2d.weight
primals_27 = self.deconv2.conv2d.bias
primals_28 = self.in5.weight
primals_29 = self.in5.bias
primals_30 = self.deconv3.conv2d.weight
primals_31 = self.deconv3.conv2d.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17, primals_18, primals_19,
primals_20, primals_21, primals_22, primals_23, primals_24,
primals_25, primals_26, primals_27, primals_28, primals_29,
primals_30, primals_31])
return output[0]
|
CorentinChauvin/style-transfer-KD
|
SmallTransformerNet
| false
| 5,119
|
[
"MIT"
] | 1
|
87bcb2963dbb8d09faf94c74a744f358cafe5427
|
https://github.com/CorentinChauvin/style-transfer-KD/tree/87bcb2963dbb8d09faf94c74a744f358cafe5427
|
LabelPropagation
|
import torch
import torch.nn.functional as F
import torch.nn as nn
class LabelPropagation(nn.Module):
"""label propagation model adapted from https://github.com/CUAI/CorrectAndSmooth
`"Learning from Labeled and
Unlabeled Datawith Label Propagation"
<http://mlg.eng.cam.ac.uk/zoubin/papers/CMU-CALD-02-107.pdf>`_ paper
"""
def __init__(self, num_layers=50, alpha=0.5, residual=True):
super().__init__()
self.num_layers = num_layers
self.alpha = alpha
self.residual = residual
@torch.no_grad()
def forward(self, y, adj, mask=None):
if y.dtype == torch.long:
y = F.one_hot(y.view(-1)).float()
out = y
if mask is not None:
out = torch.zeros_like(y)
out[mask] = y[mask]
if self.residual:
res = (1 - self.alpha) * out
else:
res = out.clone()
for _ in range(self.num_layers):
out = self.alpha * (adj @ out) + res
out = torch.clamp(out, 0, 1)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_clamp_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp3 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp4 = tmp3 * tmp1
tmp5 = tmp2 + tmp4
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = 1.0
tmp9 = triton_helpers.minimum(tmp7, tmp8)
tl.store(in_out_ptr0 + x0, tmp9, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(arg0_1, (16, 4, 4), (16, 4, 1), 0),
out=buf0)
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_add_clamp_mul_0[grid(256)](buf1, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf2 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0), out
=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf2
triton_poi_fused_add_clamp_mul_0[grid(256)](buf3, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf4 = reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0)
del buf1
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), out
=buf4)
buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf4
triton_poi_fused_add_clamp_mul_0[grid(256)](buf5, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf6 = reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0)
del buf3
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), out
=buf6)
buf7 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf6
triton_poi_fused_add_clamp_mul_0[grid(256)](buf7, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf8 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0)
del buf5
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), out
=buf8)
buf9 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf8
triton_poi_fused_add_clamp_mul_0[grid(256)](buf9, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf10 = reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0)
del buf7
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1), 0), out
=buf10)
buf11 = reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf10
triton_poi_fused_add_clamp_mul_0[grid(256)](buf11, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf12 = reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1), 0)
del buf9
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1), 0),
out=buf12)
buf13 = reinterpret_tensor(buf12, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf12
triton_poi_fused_add_clamp_mul_0[grid(256)](buf13, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf14 = reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1), 0)
del buf11
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf13, (16, 4, 4), (16, 4, 1), 0),
out=buf14)
buf15 = reinterpret_tensor(buf14, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf14
triton_poi_fused_add_clamp_mul_0[grid(256)](buf15, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf16 = reinterpret_tensor(buf13, (16, 4, 4), (16, 4, 1), 0)
del buf13
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf15, (16, 4, 4), (16, 4, 1), 0),
out=buf16)
buf17 = reinterpret_tensor(buf16, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf16
triton_poi_fused_add_clamp_mul_0[grid(256)](buf17, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf18 = reinterpret_tensor(buf15, (16, 4, 4), (16, 4, 1), 0)
del buf15
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf17, (16, 4, 4), (16, 4, 1), 0),
out=buf18)
buf19 = reinterpret_tensor(buf18, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf18
triton_poi_fused_add_clamp_mul_0[grid(256)](buf19, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf20 = reinterpret_tensor(buf17, (16, 4, 4), (16, 4, 1), 0)
del buf17
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf19, (16, 4, 4), (16, 4, 1), 0),
out=buf20)
buf21 = reinterpret_tensor(buf20, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf20
triton_poi_fused_add_clamp_mul_0[grid(256)](buf21, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf22 = reinterpret_tensor(buf19, (16, 4, 4), (16, 4, 1), 0)
del buf19
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf21, (16, 4, 4), (16, 4, 1), 0),
out=buf22)
buf23 = reinterpret_tensor(buf22, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf22
triton_poi_fused_add_clamp_mul_0[grid(256)](buf23, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf24 = reinterpret_tensor(buf21, (16, 4, 4), (16, 4, 1), 0)
del buf21
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf23, (16, 4, 4), (16, 4, 1), 0),
out=buf24)
buf25 = reinterpret_tensor(buf24, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf24
triton_poi_fused_add_clamp_mul_0[grid(256)](buf25, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf26 = reinterpret_tensor(buf23, (16, 4, 4), (16, 4, 1), 0)
del buf23
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf25, (16, 4, 4), (16, 4, 1), 0),
out=buf26)
buf27 = reinterpret_tensor(buf26, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf26
triton_poi_fused_add_clamp_mul_0[grid(256)](buf27, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf28 = reinterpret_tensor(buf25, (16, 4, 4), (16, 4, 1), 0)
del buf25
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf27, (16, 4, 4), (16, 4, 1), 0),
out=buf28)
buf29 = reinterpret_tensor(buf28, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf28
triton_poi_fused_add_clamp_mul_0[grid(256)](buf29, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf30 = reinterpret_tensor(buf27, (16, 4, 4), (16, 4, 1), 0)
del buf27
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf29, (16, 4, 4), (16, 4, 1), 0),
out=buf30)
buf31 = reinterpret_tensor(buf30, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf30
triton_poi_fused_add_clamp_mul_0[grid(256)](buf31, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf32 = reinterpret_tensor(buf29, (16, 4, 4), (16, 4, 1), 0)
del buf29
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf31, (16, 4, 4), (16, 4, 1), 0),
out=buf32)
buf33 = reinterpret_tensor(buf32, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf32
triton_poi_fused_add_clamp_mul_0[grid(256)](buf33, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf34 = reinterpret_tensor(buf31, (16, 4, 4), (16, 4, 1), 0)
del buf31
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf33, (16, 4, 4), (16, 4, 1), 0),
out=buf34)
buf35 = reinterpret_tensor(buf34, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf34
triton_poi_fused_add_clamp_mul_0[grid(256)](buf35, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf36 = reinterpret_tensor(buf33, (16, 4, 4), (16, 4, 1), 0)
del buf33
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf35, (16, 4, 4), (16, 4, 1), 0),
out=buf36)
buf37 = reinterpret_tensor(buf36, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf36
triton_poi_fused_add_clamp_mul_0[grid(256)](buf37, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf38 = reinterpret_tensor(buf35, (16, 4, 4), (16, 4, 1), 0)
del buf35
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf37, (16, 4, 4), (16, 4, 1), 0),
out=buf38)
buf39 = reinterpret_tensor(buf38, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf38
triton_poi_fused_add_clamp_mul_0[grid(256)](buf39, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf40 = reinterpret_tensor(buf37, (16, 4, 4), (16, 4, 1), 0)
del buf37
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf39, (16, 4, 4), (16, 4, 1), 0),
out=buf40)
buf41 = reinterpret_tensor(buf40, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf40
triton_poi_fused_add_clamp_mul_0[grid(256)](buf41, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf42 = reinterpret_tensor(buf39, (16, 4, 4), (16, 4, 1), 0)
del buf39
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf41, (16, 4, 4), (16, 4, 1), 0),
out=buf42)
buf43 = reinterpret_tensor(buf42, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf42
triton_poi_fused_add_clamp_mul_0[grid(256)](buf43, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf44 = reinterpret_tensor(buf41, (16, 4, 4), (16, 4, 1), 0)
del buf41
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf43, (16, 4, 4), (16, 4, 1), 0),
out=buf44)
buf45 = reinterpret_tensor(buf44, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf44
triton_poi_fused_add_clamp_mul_0[grid(256)](buf45, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf46 = reinterpret_tensor(buf43, (16, 4, 4), (16, 4, 1), 0)
del buf43
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf45, (16, 4, 4), (16, 4, 1), 0),
out=buf46)
buf47 = reinterpret_tensor(buf46, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf46
triton_poi_fused_add_clamp_mul_0[grid(256)](buf47, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf48 = reinterpret_tensor(buf45, (16, 4, 4), (16, 4, 1), 0)
del buf45
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf47, (16, 4, 4), (16, 4, 1), 0),
out=buf48)
buf49 = reinterpret_tensor(buf48, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf48
triton_poi_fused_add_clamp_mul_0[grid(256)](buf49, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf50 = reinterpret_tensor(buf47, (16, 4, 4), (16, 4, 1), 0)
del buf47
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf49, (16, 4, 4), (16, 4, 1), 0),
out=buf50)
buf51 = reinterpret_tensor(buf50, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf50
triton_poi_fused_add_clamp_mul_0[grid(256)](buf51, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf52 = reinterpret_tensor(buf49, (16, 4, 4), (16, 4, 1), 0)
del buf49
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf51, (16, 4, 4), (16, 4, 1), 0),
out=buf52)
buf53 = reinterpret_tensor(buf52, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf52
triton_poi_fused_add_clamp_mul_0[grid(256)](buf53, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf54 = reinterpret_tensor(buf51, (16, 4, 4), (16, 4, 1), 0)
del buf51
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf53, (16, 4, 4), (16, 4, 1), 0),
out=buf54)
buf55 = reinterpret_tensor(buf54, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf54
triton_poi_fused_add_clamp_mul_0[grid(256)](buf55, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf56 = reinterpret_tensor(buf53, (16, 4, 4), (16, 4, 1), 0)
del buf53
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf55, (16, 4, 4), (16, 4, 1), 0),
out=buf56)
buf57 = reinterpret_tensor(buf56, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf56
triton_poi_fused_add_clamp_mul_0[grid(256)](buf57, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf58 = reinterpret_tensor(buf55, (16, 4, 4), (16, 4, 1), 0)
del buf55
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf57, (16, 4, 4), (16, 4, 1), 0),
out=buf58)
buf59 = reinterpret_tensor(buf58, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf58
triton_poi_fused_add_clamp_mul_0[grid(256)](buf59, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf60 = reinterpret_tensor(buf57, (16, 4, 4), (16, 4, 1), 0)
del buf57
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf59, (16, 4, 4), (16, 4, 1), 0),
out=buf60)
buf61 = reinterpret_tensor(buf60, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf60
triton_poi_fused_add_clamp_mul_0[grid(256)](buf61, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf62 = reinterpret_tensor(buf59, (16, 4, 4), (16, 4, 1), 0)
del buf59
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf61, (16, 4, 4), (16, 4, 1), 0),
out=buf62)
buf63 = reinterpret_tensor(buf62, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf62
triton_poi_fused_add_clamp_mul_0[grid(256)](buf63, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf64 = reinterpret_tensor(buf61, (16, 4, 4), (16, 4, 1), 0)
del buf61
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf63, (16, 4, 4), (16, 4, 1), 0),
out=buf64)
buf65 = reinterpret_tensor(buf64, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf64
triton_poi_fused_add_clamp_mul_0[grid(256)](buf65, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf66 = reinterpret_tensor(buf63, (16, 4, 4), (16, 4, 1), 0)
del buf63
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf65, (16, 4, 4), (16, 4, 1), 0),
out=buf66)
buf67 = reinterpret_tensor(buf66, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf66
triton_poi_fused_add_clamp_mul_0[grid(256)](buf67, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf68 = reinterpret_tensor(buf65, (16, 4, 4), (16, 4, 1), 0)
del buf65
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf67, (16, 4, 4), (16, 4, 1), 0),
out=buf68)
buf69 = reinterpret_tensor(buf68, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf68
triton_poi_fused_add_clamp_mul_0[grid(256)](buf69, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf70 = reinterpret_tensor(buf67, (16, 4, 4), (16, 4, 1), 0)
del buf67
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf69, (16, 4, 4), (16, 4, 1), 0),
out=buf70)
buf71 = reinterpret_tensor(buf70, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf70
triton_poi_fused_add_clamp_mul_0[grid(256)](buf71, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf72 = reinterpret_tensor(buf69, (16, 4, 4), (16, 4, 1), 0)
del buf69
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf71, (16, 4, 4), (16, 4, 1), 0),
out=buf72)
buf73 = reinterpret_tensor(buf72, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf72
triton_poi_fused_add_clamp_mul_0[grid(256)](buf73, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf74 = reinterpret_tensor(buf71, (16, 4, 4), (16, 4, 1), 0)
del buf71
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf73, (16, 4, 4), (16, 4, 1), 0),
out=buf74)
buf75 = reinterpret_tensor(buf74, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf74
triton_poi_fused_add_clamp_mul_0[grid(256)](buf75, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf76 = reinterpret_tensor(buf73, (16, 4, 4), (16, 4, 1), 0)
del buf73
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf75, (16, 4, 4), (16, 4, 1), 0),
out=buf76)
buf77 = reinterpret_tensor(buf76, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf76
triton_poi_fused_add_clamp_mul_0[grid(256)](buf77, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf78 = reinterpret_tensor(buf75, (16, 4, 4), (16, 4, 1), 0)
del buf75
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf77, (16, 4, 4), (16, 4, 1), 0),
out=buf78)
buf79 = reinterpret_tensor(buf78, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf78
triton_poi_fused_add_clamp_mul_0[grid(256)](buf79, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf80 = reinterpret_tensor(buf77, (16, 4, 4), (16, 4, 1), 0)
del buf77
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf79, (16, 4, 4), (16, 4, 1), 0),
out=buf80)
buf81 = reinterpret_tensor(buf80, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf80
triton_poi_fused_add_clamp_mul_0[grid(256)](buf81, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf82 = reinterpret_tensor(buf79, (16, 4, 4), (16, 4, 1), 0)
del buf79
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf81, (16, 4, 4), (16, 4, 1), 0),
out=buf82)
buf83 = reinterpret_tensor(buf82, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf82
triton_poi_fused_add_clamp_mul_0[grid(256)](buf83, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf84 = reinterpret_tensor(buf81, (16, 4, 4), (16, 4, 1), 0)
del buf81
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf83, (16, 4, 4), (16, 4, 1), 0),
out=buf84)
buf85 = reinterpret_tensor(buf84, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf84
triton_poi_fused_add_clamp_mul_0[grid(256)](buf85, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf86 = reinterpret_tensor(buf83, (16, 4, 4), (16, 4, 1), 0)
del buf83
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf85, (16, 4, 4), (16, 4, 1), 0),
out=buf86)
buf87 = reinterpret_tensor(buf86, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf86
triton_poi_fused_add_clamp_mul_0[grid(256)](buf87, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf88 = reinterpret_tensor(buf85, (16, 4, 4), (16, 4, 1), 0)
del buf85
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf87, (16, 4, 4), (16, 4, 1), 0),
out=buf88)
buf89 = reinterpret_tensor(buf88, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf88
triton_poi_fused_add_clamp_mul_0[grid(256)](buf89, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf90 = reinterpret_tensor(buf87, (16, 4, 4), (16, 4, 1), 0)
del buf87
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf89, (16, 4, 4), (16, 4, 1), 0),
out=buf90)
buf91 = reinterpret_tensor(buf90, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf90
triton_poi_fused_add_clamp_mul_0[grid(256)](buf91, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf92 = reinterpret_tensor(buf89, (16, 4, 4), (16, 4, 1), 0)
del buf89
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf91, (16, 4, 4), (16, 4, 1), 0),
out=buf92)
buf93 = reinterpret_tensor(buf92, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf92
triton_poi_fused_add_clamp_mul_0[grid(256)](buf93, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf94 = reinterpret_tensor(buf91, (16, 4, 4), (16, 4, 1), 0)
del buf91
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf93, (16, 4, 4), (16, 4, 1), 0),
out=buf94)
buf95 = reinterpret_tensor(buf94, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf94
triton_poi_fused_add_clamp_mul_0[grid(256)](buf95, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf96 = reinterpret_tensor(buf93, (16, 4, 4), (16, 4, 1), 0)
del buf93
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf95, (16, 4, 4), (16, 4, 1), 0),
out=buf96)
buf97 = reinterpret_tensor(buf96, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf96
triton_poi_fused_add_clamp_mul_0[grid(256)](buf97, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf98 = reinterpret_tensor(buf95, (16, 4, 4), (16, 4, 1), 0)
del buf95
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(buf97, (16, 4, 4), (16, 4, 1), 0),
out=buf98)
del arg1_1
del buf97
buf99 = reinterpret_tensor(buf98, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf98
triton_poi_fused_add_clamp_mul_0[grid(256)](buf99, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return buf99,
class LabelPropagationNew(nn.Module):
"""label propagation model adapted from https://github.com/CUAI/CorrectAndSmooth
`"Learning from Labeled and
Unlabeled Datawith Label Propagation"
<http://mlg.eng.cam.ac.uk/zoubin/papers/CMU-CALD-02-107.pdf>`_ paper
"""
def __init__(self, num_layers=50, alpha=0.5, residual=True):
super().__init__()
self.num_layers = num_layers
self.alpha = alpha
self.residual = residual
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
EdisonLeeeee/Graphgallery
|
LabelPropagation
| false
| 5,120
|
[
"MIT"
] | 1
|
8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
Foo
|
import torch
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
class Foo(torch.nn.Module):
def __init__(self, size):
super(Foo, self).__init__()
self.n = torch.nn.Parameter(torch.ones(size))
self.m = torch.nn.Parameter(torch.ones(size))
def forward(self, input):
return self.n * input + self.m
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'size': 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
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 * tmp1
tmp4 = tmp2 + tmp3
tl.store(out_ptr0 + x2, tmp4, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4,), (1,))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_mul_0[grid(256)](primals_1, primals_2,
primals_3, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
del primals_3
return buf0, primals_2
class FooNew(torch.nn.Module):
def __init__(self, size):
super(FooNew, self).__init__()
self.n = torch.nn.Parameter(torch.ones(size))
self.m = torch.nn.Parameter(torch.ones(size))
def forward(self, input_0):
primals_1 = self.n
primals_3 = self.m
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Ella77/tacotron2_multispeaker_pytorch
|
Foo
| false
| 5,121
|
[
"BSD-3-Clause"
] | 1
|
859eab0a8e3bd7545e623ce47fe1563702d38442
|
https://github.com/Ella77/tacotron2_multispeaker_pytorch/tree/859eab0a8e3bd7545e623ce47fe1563702d38442
|
LinearZeros
|
import torch
import torch.nn as nn
class LinearZeros(nn.Linear):
def __init__(self, in_features, out_features, bias=True,
logscale_factor=3.0):
"""
Linear layer with zero initialization
:param in_features: size of each input sample
:type in_features: int
:param out_features: size of each output sample
:type out_features: int
:param bias: whether to learn an additive bias.
:type bias: bool
:param logscale_factor: factor of logscale
:type logscale_factor: float
"""
super().__init__(in_features, out_features, bias)
self.logscale_factor = logscale_factor
self.weight.data.zero_()
self.bias.data.zero_()
self.register_parameter('logs', nn.Parameter(torch.zeros(out_features))
)
def forward(self, x):
"""
Forward linear zero layer
:param x: input
:type x: torch.Tensor
:return: output
:rtype: torch.Tensor
"""
output = super().forward(x)
output *= torch.exp(self.logs * self.logscale_factor)
return output
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_exp_mul_view_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x4 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x4, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = 3.0
tmp3 = tmp1 * tmp2
tmp4 = tl_math.exp(tmp3)
tmp5 = tmp0 * tmp4
tl.store(in_out_ptr0 + x4, tmp5, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf2 = buf1
del buf1
get_raw_stream(0)
triton_poi_fused_exp_mul_view_0[grid(256)](buf2, buf0, primals_4,
256, XBLOCK=256, num_warps=4, num_stages=1)
return buf2, primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf0
class LinearZerosNew(nn.Linear):
def __init__(self, in_features, out_features, bias=True,
logscale_factor=3.0):
"""
Linear layer with zero initialization
:param in_features: size of each input sample
:type in_features: int
:param out_features: size of each output sample
:type out_features: int
:param bias: whether to learn an additive bias.
:type bias: bool
:param logscale_factor: factor of logscale
:type logscale_factor: float
"""
super().__init__(in_features, out_features, bias)
self.logscale_factor = logscale_factor
self.weight.data.zero_()
self.bias.data.zero_()
self.register_parameter('logs', nn.Parameter(torch.zeros(out_features))
)
def forward(self, input_0):
primals_1 = self.weight
primals_2 = self.bias
primals_4 = self.logs
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
Eladhi/VI_Glow
|
LinearZeros
| false
| 5,122
|
[
"MIT"
] | 1
|
9c48fbf8fa10c81fc2354a07fcc2837a77d06cef
|
https://github.com/Eladhi/VI_Glow/tree/9c48fbf8fa10c81fc2354a07fcc2837a77d06cef
|
SSGC
|
import torch
import torch.nn as nn
class SpectralEigenConv(nn.Module):
def __init__(self, in_features, out_features, bias=False, K=10, alpha=
0.1, **kwargs):
super().__init__()
assert K > 0
self.K = K
self.alpha = alpha
self.in_features = in_features
self.out_features = out_features
self.w = nn.Linear(in_features, out_features, bias=bias)
def forward(self, x, U, V=None):
"""
x: node attribute matrix
if `V=None`:
U: (N, N) adjacency matrix
else:
U: (N, k) eigenvector matrix
V: (k,) eigenvalue
"""
x = self.w(x)
if V is not None:
V_pow = torch.ones_like(V)
V_out = torch.zeros_like(V)
for _ in range(self.K):
V_pow *= V
V_out += (1 - self.alpha) * V_pow
V_out = V_out / self.K
x_out = U * V_out @ (U.t() @ x) + self.alpha * x
else:
adj = U
x_in = x
x_out = torch.zeros_like(x)
for _ in range(self.K):
x = torch.spmm(adj, x)
x_out += (1 - self.alpha) * x
x_out /= self.K
x_out += self.alpha * x_in
return x_out
def reset_parameters(self):
self.w.reset_parameters()
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features}, K={self.K}, alpha={self.alpha})'
)
class SSGC(nn.Module):
def __init__(self, in_features, out_features, K=10, alpha=0.1, hids=[],
acts=[], dropout=0.5, bias=False):
super().__init__()
lin = []
lin.append(nn.Dropout(dropout))
for hid, act in zip(hids, acts):
lin.append(nn.Linear(in_features, hid, bias=bias))
lin.append(activations.get(act))
lin.append(nn.Dropout(dropout))
in_features = hid
lin = nn.Sequential(*lin)
conv = SpectralEigenConv(in_features, out_features, bias=bias, K=K,
alpha=alpha)
self.lin = lin
self.conv = conv
def forward(self, x, U, V=None):
"""
x: feature matrix
if `V=None`:
U: (N, N) adjacency matrix
else:
U: (N, k) eigenvector matrix
V: (k,) eigenvalue
"""
x = self.lin(x)
x = self.conv(x, U, V)
return x
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_div_mul_0(in_out_ptr0, in_out_ptr1, in_ptr0,
in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8,
xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp3 = tl.load(in_ptr0 + x0, xmask)
tmp6 = tl.load(in_ptr1 + x0, xmask)
tmp9 = tl.load(in_ptr2 + x0, xmask)
tmp12 = tl.load(in_ptr3 + x0, xmask)
tmp15 = tl.load(in_ptr4 + x0, xmask)
tmp18 = tl.load(in_ptr5 + x0, xmask)
tmp21 = tl.load(in_ptr6 + x0, xmask)
tmp24 = tl.load(in_ptr7 + x0, xmask)
tmp27 = tl.load(in_ptr8 + x0, xmask)
tmp32 = tl.load(in_out_ptr1 + x0, xmask)
tmp1 = 0.9
tmp2 = tmp0 * tmp1
tmp4 = tmp3 * tmp1
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp1
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp1
tmp11 = tmp8 + tmp10
tmp13 = tmp12 * tmp1
tmp14 = tmp11 + tmp13
tmp16 = tmp15 * tmp1
tmp17 = tmp14 + tmp16
tmp19 = tmp18 * tmp1
tmp20 = tmp17 + tmp19
tmp22 = tmp21 * tmp1
tmp23 = tmp20 + tmp22
tmp25 = tmp24 * tmp1
tmp26 = tmp23 + tmp25
tmp28 = tmp27 * tmp1
tmp29 = tmp26 + tmp28
tmp30 = 0.1
tmp31 = tmp29 * tmp30
tmp33 = tmp32 * tmp30
tmp34 = tmp31 + tmp33
tl.store(in_out_ptr1 + x0, tmp34, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
(1, 4), 0), out=buf0)
del primals_2
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf0, out=buf1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf1, out=buf2)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf2, out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf3, out=buf4)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf4, out=buf5)
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf5, out=buf6)
buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf6, out=buf7)
buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf7, out=buf8)
buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf8, out=buf9)
buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf9, out=buf11)
buf10 = buf1
del buf1
buf12 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_add_div_mul_0[grid(16)](buf10, buf12, buf2, buf3,
buf4, buf5, buf6, buf7, buf8, buf9, buf11, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf10
del buf11
del buf2
del buf3
del buf4
del buf5
del buf6
del buf7
del buf8
del buf9
return buf12, primals_1, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0)
class SpectralEigenConv(nn.Module):
def __init__(self, in_features, out_features, bias=False, K=10, alpha=
0.1, **kwargs):
super().__init__()
assert K > 0
self.K = K
self.alpha = alpha
self.in_features = in_features
self.out_features = out_features
self.w = nn.Linear(in_features, out_features, bias=bias)
def forward(self, x, U, V=None):
"""
x: node attribute matrix
if `V=None`:
U: (N, N) adjacency matrix
else:
U: (N, k) eigenvector matrix
V: (k,) eigenvalue
"""
x = self.w(x)
if V is not None:
V_pow = torch.ones_like(V)
V_out = torch.zeros_like(V)
for _ in range(self.K):
V_pow *= V
V_out += (1 - self.alpha) * V_pow
V_out = V_out / self.K
x_out = U * V_out @ (U.t() @ x) + self.alpha * x
else:
adj = U
x_in = x
x_out = torch.zeros_like(x)
for _ in range(self.K):
x = torch.spmm(adj, x)
x_out += (1 - self.alpha) * x
x_out /= self.K
x_out += self.alpha * x_in
return x_out
def reset_parameters(self):
self.w.reset_parameters()
def __repr__(self):
return (
f'{self.__class__.__name__}({self.in_features}, {self.out_features}, K={self.K}, alpha={self.alpha})'
)
class SSGCNew(nn.Module):
def __init__(self, in_features, out_features, K=10, alpha=0.1, hids=[],
acts=[], dropout=0.5, bias=False):
super().__init__()
lin = []
lin.append(nn.Dropout(dropout))
for hid, act in zip(hids, acts):
lin.append(nn.Linear(in_features, hid, bias=bias))
lin.append(activations.get(act))
lin.append(nn.Dropout(dropout))
in_features = hid
lin = nn.Sequential(*lin)
conv = SpectralEigenConv(in_features, out_features, bias=bias, K=K,
alpha=alpha)
self.lin = lin
self.conv = conv
def forward(self, input_0, input_1):
primals_1 = self.conv.w.weight
primals_2 = input_0
primals_3 = input_1
output = call([primals_1, primals_2, primals_3])
return output[0]
|
EdisonLeeeee/Graphgallery
|
SSGC
| false
| 5,123
|
[
"MIT"
] | 1
|
8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
https://github.com/EdisonLeeeee/Graphgallery/tree/8ae9ef57d44f073d0ceaf3f33a3a998546f960a8
|
Conv2dZeros
|
import torch
import torch.nn as nn
class ActNorm(nn.Module):
def __init__(self, num_channels, scale=1.0, logscale_factor=3.0,
batch_variance=False):
"""
Activation normalization layer
:param num_channels: number of channels
:type num_channels: int
:param scale: scale
:type scale: float
:param logscale_factor: factor for logscale
:type logscale_factor: float
:param batch_variance: use batch variance
:type batch_variance: bool
"""
super().__init__()
self.num_channels = num_channels
self.scale = scale
self.logscale_factor = logscale_factor
self.batch_variance = batch_variance
self.bias_inited = False
self.logs_inited = False
self.register_parameter('bias', nn.Parameter(torch.zeros(1, self.
num_channels, 1, 1)))
self.register_parameter('logs', nn.Parameter(torch.zeros(1, self.
num_channels, 1, 1)))
def actnorm_center(self, x, reverse=False):
"""
center operation of activation normalization
:param x: input
:type x: torch.Tensor
:param reverse: whether to reverse bias
:type reverse: bool
:return: centered input
:rtype: torch.Tensor
"""
if not self.bias_inited:
self.initialize_bias(x)
if not reverse:
return x + self.bias
else:
return x - self.bias
def actnorm_scale(self, x, logdet, reverse=False):
"""
scale operation of activation normalization
:param x: input
:type x: torch.Tensor
:param logdet: log determinant
:type logdet:
:param reverse: whether to reverse bias
:type reverse: bool
:return: centered input and logdet
:rtype: tuple(torch.Tensor, torch.Tensor)
"""
if not self.logs_inited:
self.initialize_logs(x)
logs = self.logs * self.logscale_factor
if not reverse:
x *= torch.exp(logs)
else:
x *= torch.exp(-logs)
if logdet is not None:
logdet_factor = ops.count_pixels(x)
dlogdet = torch.sum(logs) * logdet_factor
if reverse:
dlogdet *= -1
logdet += dlogdet
return x, logdet
def initialize_bias(self, x):
"""
Initialize bias
:param x: input
:type x: torch.Tensor
"""
if not self.training:
return
with torch.no_grad():
x_mean = -1.0 * ops.reduce_mean(x, dim=[0, 2, 3], keepdim=True)
self.bias.data.copy_(x_mean.data)
self.bias_inited = True
def initialize_logs(self, x):
"""
Initialize logs
:param x: input
:type x: torch.Tensor
"""
if not self.training:
return
with torch.no_grad():
if self.batch_variance:
x_var = ops.reduce_mean(x ** 2, keepdim=True)
else:
x_var = ops.reduce_mean(x ** 2, dim=[0, 2, 3], keepdim=True)
logs = torch.log(self.scale / (torch.sqrt(x_var) + 1e-06)
) / self.logscale_factor
self.logs.data.copy_(logs.data)
self.logs_inited = True
def forward(self, x, logdet=None, reverse=False):
"""
Forward activation normalization layer
:param x: input
:type x: torch.Tensor
:param logdet: log determinant
:type logdet:
:param reverse: whether to reverse bias
:type reverse: bool
:return: normalized input and logdet
:rtype: tuple(torch.Tensor, torch.Tensor)
"""
assert len(x.shape) == 4
assert x.shape[1
] == self.num_channels, 'Input shape should be NxCxHxW, however channels are {} instead of {}'.format(
x.shape[1], self.num_channels)
assert x.device == self.bias.device and x.device == self.logs.device, 'Expect input device {} instead of {}'.format(
self.bias.device, x.device)
if not reverse:
x = self.actnorm_center(x, reverse=False)
x, logdet = self.actnorm_scale(x, logdet, reverse=False)
else:
x, logdet = self.actnorm_scale(x, logdet, reverse=True)
x = self.actnorm_center(x, reverse=True)
return x, logdet
class Conv2d(nn.Conv2d):
@staticmethod
def get_padding(padding_type, kernel_size, stride):
"""
Get padding size.
mentioned in https://github.com/pytorch/pytorch/issues/3867#issuecomment-361775080
behaves as 'SAME' padding in TensorFlow
independent on input size when stride is 1
:param padding_type: type of padding in ['SAME', 'VALID']
:type padding_type: str
:param kernel_size: kernel size
:type kernel_size: tuple(int) or int
:param stride: stride
:type stride: int
:return: padding size
:rtype: tuple(int)
"""
assert padding_type in ['SAME', 'VALID'
], 'Unsupported padding type: {}'.format(padding_type)
if isinstance(kernel_size, int):
kernel_size = [kernel_size, kernel_size]
if padding_type == 'SAME':
assert stride == 1, "'SAME' padding only supports stride=1"
return tuple((k - 1) // 2 for k in kernel_size)
return tuple(0 for _ in kernel_size)
def __init__(self, in_channels, out_channels, kernel_size=(3, 3),
stride=1, padding_type='SAME', do_weightnorm=False, do_actnorm=True,
dilation=1, groups=1):
"""
Wrapper of nn.Conv2d with weight normalization and activation normalization
:param padding_type: type of padding in ['SAME', 'VALID']
:type padding_type: str
:param do_weightnorm: whether to do weight normalization after convolution
:type do_weightnorm: bool
:param do_actnorm: whether to do activation normalization after convolution
:type do_actnorm: bool
"""
padding = self.get_padding(padding_type, kernel_size, stride)
super().__init__(in_channels, out_channels, kernel_size, stride,
padding, dilation, groups, bias=not do_actnorm)
self.do_weight_norm = do_weightnorm
self.do_actnorm = do_actnorm
self.weight.data.normal_(mean=0.0, std=0.05)
if self.do_actnorm:
self.actnorm = ActNorm(out_channels)
else:
self.bias.data.zero_()
def forward(self, x):
"""
Forward wrapped Conv2d layer
:param x: input
:type x: torch.Tensor
:return: output
:rtype: torch.Tensor
"""
x = super().forward(x)
if self.do_actnorm:
x, _ = self.actnorm(x)
return x
class Conv2dZeros(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size=(3, 3),
stride=1, padding_type='SAME', logscale_factor=3, dilation=1,
groups=1, bias=True):
"""
Wrapper of nn.Conv2d with zero initialization and logs
:param padding_type: type of padding in ['SAME', 'VALID']
:type padding_type: str
:param logscale_factor: factor for logscale
:type logscale_factor: float
"""
padding = Conv2d.get_padding(padding_type, kernel_size, stride)
super().__init__(in_channels, out_channels, kernel_size, stride,
padding, dilation, groups, bias)
self.logscale_factor = logscale_factor
self.bias.data.zero_()
self.weight.data.zero_()
self.register_parameter('logs', nn.Parameter(torch.zeros(
out_channels, 1, 1)))
def forward(self, x):
"""
Forward wrapped Conv2d layer
:param x: input
:type x: torch.Tensor
:return: output
:rtype: torch.Tensor
"""
x = super().forward(x)
x *= torch.exp(self.logs * self.logscale_factor)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_exp_mul_0(in_out_ptr0, in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = 3.0
tmp5 = tmp3 * tmp4
tmp6 = tl_math.exp(tmp5)
tmp7 = tmp2 * tmp6
tl.store(in_out_ptr0 + x3, tmp2, xmask)
tl.store(out_ptr0 + x3, tmp7, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 1, 1), (1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = buf0
del buf0
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_exp_mul_0[grid(256)](buf1, primals_2,
primals_4, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
return buf2, primals_1, primals_3, primals_4, buf1
class ActNorm(nn.Module):
def __init__(self, num_channels, scale=1.0, logscale_factor=3.0,
batch_variance=False):
"""
Activation normalization layer
:param num_channels: number of channels
:type num_channels: int
:param scale: scale
:type scale: float
:param logscale_factor: factor for logscale
:type logscale_factor: float
:param batch_variance: use batch variance
:type batch_variance: bool
"""
super().__init__()
self.num_channels = num_channels
self.scale = scale
self.logscale_factor = logscale_factor
self.batch_variance = batch_variance
self.bias_inited = False
self.logs_inited = False
self.register_parameter('bias', nn.Parameter(torch.zeros(1, self.
num_channels, 1, 1)))
self.register_parameter('logs', nn.Parameter(torch.zeros(1, self.
num_channels, 1, 1)))
def actnorm_center(self, x, reverse=False):
"""
center operation of activation normalization
:param x: input
:type x: torch.Tensor
:param reverse: whether to reverse bias
:type reverse: bool
:return: centered input
:rtype: torch.Tensor
"""
if not self.bias_inited:
self.initialize_bias(x)
if not reverse:
return x + self.bias
else:
return x - self.bias
def actnorm_scale(self, x, logdet, reverse=False):
"""
scale operation of activation normalization
:param x: input
:type x: torch.Tensor
:param logdet: log determinant
:type logdet:
:param reverse: whether to reverse bias
:type reverse: bool
:return: centered input and logdet
:rtype: tuple(torch.Tensor, torch.Tensor)
"""
if not self.logs_inited:
self.initialize_logs(x)
logs = self.logs * self.logscale_factor
if not reverse:
x *= torch.exp(logs)
else:
x *= torch.exp(-logs)
if logdet is not None:
logdet_factor = ops.count_pixels(x)
dlogdet = torch.sum(logs) * logdet_factor
if reverse:
dlogdet *= -1
logdet += dlogdet
return x, logdet
def initialize_bias(self, x):
"""
Initialize bias
:param x: input
:type x: torch.Tensor
"""
if not self.training:
return
with torch.no_grad():
x_mean = -1.0 * ops.reduce_mean(x, dim=[0, 2, 3], keepdim=True)
self.bias.data.copy_(x_mean.data)
self.bias_inited = True
def initialize_logs(self, x):
"""
Initialize logs
:param x: input
:type x: torch.Tensor
"""
if not self.training:
return
with torch.no_grad():
if self.batch_variance:
x_var = ops.reduce_mean(x ** 2, keepdim=True)
else:
x_var = ops.reduce_mean(x ** 2, dim=[0, 2, 3], keepdim=True)
logs = torch.log(self.scale / (torch.sqrt(x_var) + 1e-06)
) / self.logscale_factor
self.logs.data.copy_(logs.data)
self.logs_inited = True
def forward(self, x, logdet=None, reverse=False):
"""
Forward activation normalization layer
:param x: input
:type x: torch.Tensor
:param logdet: log determinant
:type logdet:
:param reverse: whether to reverse bias
:type reverse: bool
:return: normalized input and logdet
:rtype: tuple(torch.Tensor, torch.Tensor)
"""
assert len(x.shape) == 4
assert x.shape[1
] == self.num_channels, 'Input shape should be NxCxHxW, however channels are {} instead of {}'.format(
x.shape[1], self.num_channels)
assert x.device == self.bias.device and x.device == self.logs.device, 'Expect input device {} instead of {}'.format(
self.bias.device, x.device)
if not reverse:
x = self.actnorm_center(x, reverse=False)
x, logdet = self.actnorm_scale(x, logdet, reverse=False)
else:
x, logdet = self.actnorm_scale(x, logdet, reverse=True)
x = self.actnorm_center(x, reverse=True)
return x, logdet
class Conv2d(nn.Conv2d):
@staticmethod
def get_padding(padding_type, kernel_size, stride):
"""
Get padding size.
mentioned in https://github.com/pytorch/pytorch/issues/3867#issuecomment-361775080
behaves as 'SAME' padding in TensorFlow
independent on input size when stride is 1
:param padding_type: type of padding in ['SAME', 'VALID']
:type padding_type: str
:param kernel_size: kernel size
:type kernel_size: tuple(int) or int
:param stride: stride
:type stride: int
:return: padding size
:rtype: tuple(int)
"""
assert padding_type in ['SAME', 'VALID'
], 'Unsupported padding type: {}'.format(padding_type)
if isinstance(kernel_size, int):
kernel_size = [kernel_size, kernel_size]
if padding_type == 'SAME':
assert stride == 1, "'SAME' padding only supports stride=1"
return tuple((k - 1) // 2 for k in kernel_size)
return tuple(0 for _ in kernel_size)
def __init__(self, in_channels, out_channels, kernel_size=(3, 3),
stride=1, padding_type='SAME', do_weightnorm=False, do_actnorm=True,
dilation=1, groups=1):
"""
Wrapper of nn.Conv2d with weight normalization and activation normalization
:param padding_type: type of padding in ['SAME', 'VALID']
:type padding_type: str
:param do_weightnorm: whether to do weight normalization after convolution
:type do_weightnorm: bool
:param do_actnorm: whether to do activation normalization after convolution
:type do_actnorm: bool
"""
padding = self.get_padding(padding_type, kernel_size, stride)
super().__init__(in_channels, out_channels, kernel_size, stride,
padding, dilation, groups, bias=not do_actnorm)
self.do_weight_norm = do_weightnorm
self.do_actnorm = do_actnorm
self.weight.data.normal_(mean=0.0, std=0.05)
if self.do_actnorm:
self.actnorm = ActNorm(out_channels)
else:
self.bias.data.zero_()
def forward(self, x):
"""
Forward wrapped Conv2d layer
:param x: input
:type x: torch.Tensor
:return: output
:rtype: torch.Tensor
"""
x = super().forward(x)
if self.do_actnorm:
x, _ = self.actnorm(x)
return x
class Conv2dZerosNew(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size=(3, 3),
stride=1, padding_type='SAME', logscale_factor=3, dilation=1,
groups=1, bias=True):
"""
Wrapper of nn.Conv2d with zero initialization and logs
:param padding_type: type of padding in ['SAME', 'VALID']
:type padding_type: str
:param logscale_factor: factor for logscale
:type logscale_factor: float
"""
padding = Conv2d.get_padding(padding_type, kernel_size, stride)
super().__init__(in_channels, out_channels, kernel_size, stride,
padding, dilation, groups, bias)
self.logscale_factor = logscale_factor
self.bias.data.zero_()
self.weight.data.zero_()
self.register_parameter('logs', nn.Parameter(torch.zeros(
out_channels, 1, 1)))
def forward(self, input_0):
primals_1 = self.weight
primals_2 = self.bias
primals_4 = self.logs
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
Eladhi/VI_Glow
|
Conv2dZeros
| false
| 5,124
|
[
"MIT"
] | 1
|
9c48fbf8fa10c81fc2354a07fcc2837a77d06cef
|
https://github.com/Eladhi/VI_Glow/tree/9c48fbf8fa10c81fc2354a07fcc2837a77d06cef
|
PositionwiseFeedForward
|
import torch
import torch.nn as nn
import torch.distributed
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
Args:
d_model (int): the size of input for the first-layer of the FFN.
d_ff (int): the hidden layer size of the second-layer
of the FNN.
dropout (float): dropout probability(0-1.0).
"""
def __init__(self, d_model, d_ff, dropout=0.1):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Linear(d_model, d_ff)
self.w_2 = nn.Linear(d_ff, d_model)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)
self.dropout_1 = nn.Dropout(dropout)
self.relu = nn.ReLU()
self.dropout_2 = nn.Dropout(dropout)
def forward(self, x):
"""
Layer definition.
Args:
input: [ batch_size, input_len, model_dim ]
Returns:
output: [ batch_size, input_len, model_dim ]
"""
inter = self.dropout_1(self.relu(self.w_1(self.layer_norm(x))))
output = self.dropout_2(self.w_2(inter))
return output + x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'d_model': 4, 'd_ff': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-06
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (4,), (1,))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(64)](primals_3, buf0,
buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(256)](primals_3, buf0,
buf1, primals_1, primals_2, buf2, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del buf0
del buf1
del primals_1
del primals_2
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
buf4 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf3
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_2[grid(256)](buf4,
primals_5, buf7, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf4, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf5)
buf6 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused_add_3[grid(256)](buf6, primals_7, primals_3, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
return buf6, primals_3, reinterpret_tensor(buf2, (64, 4), (4, 1), 0
), reinterpret_tensor(buf4, (64, 4), (4, 1), 0
), primals_6, buf7, primals_4
class PositionwiseFeedForwardNew(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
Args:
d_model (int): the size of input for the first-layer of the FFN.
d_ff (int): the hidden layer size of the second-layer
of the FNN.
dropout (float): dropout probability(0-1.0).
"""
def __init__(self, d_model, d_ff, dropout=0.1):
super(PositionwiseFeedForwardNew, self).__init__()
self.w_1 = nn.Linear(d_model, d_ff)
self.w_2 = nn.Linear(d_ff, d_model)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)
self.dropout_1 = nn.Dropout(dropout)
self.relu = nn.ReLU()
self.dropout_2 = nn.Dropout(dropout)
def forward(self, input_0):
primals_4 = self.w_1.weight
primals_1 = self.w_1.bias
primals_6 = self.w_2.weight
primals_2 = self.w_2.bias
primals_5 = self.layer_norm.weight
primals_7 = self.layer_norm.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
Eldriann/Master-thesis
|
PositionwiseFeedForward
| false
| 5,125
|
[
"MIT"
] | 1
|
9d09d97f4002cc9fc730f10317614e1d0d307353
|
https://github.com/Eldriann/Master-thesis/tree/9d09d97f4002cc9fc730f10317614e1d0d307353
|
BartClassificationHead
|
import torch
import torch.nn as nn
import torch.utils.checkpoint
class BartClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, input_dim: 'int', inner_dim: 'int', num_classes:
'int', pooler_dropout: 'float'):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, hidden_states: 'torch.Tensor'):
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense(hidden_states)
hidden_states = torch.tanh(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.out_proj(hidden_states)
return hidden_states
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_dim': 4, 'inner_dim': 4, 'num_classes': 4,
'pooler_dropout': 0.5}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
del primals_2
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_tanh_0[grid(256)](buf1, primals_3, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf2)
del primals_5
return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf1, primals_4
class BartClassificationHeadNew(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, input_dim: 'int', inner_dim: 'int', num_classes:
'int', pooler_dropout: 'float'):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, input_0):
primals_2 = self.dense.weight
primals_3 = self.dense.bias
primals_4 = self.out_proj.weight
primals_5 = self.out_proj.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
Elvisambition/bert_seq2seq
|
BartClassificationHead
| false
| 5,126
|
[
"Apache-2.0"
] | 1
|
643ac537c16872f0d13200de06001d8201a54fbb
|
https://github.com/Elvisambition/bert_seq2seq/tree/643ac537c16872f0d13200de06001d8201a54fbb
|
DummyModelWithSharedSubmodule
|
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
class DummyDenseWithRelu(nn.Module):
def __init__(self, input_size, output_size, relu=None):
super(DummyDenseWithRelu, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.relu = relu or nn.ReLU()
self.linear = nn.Linear(input_size, output_size)
def forward(self, x):
return self.relu(self.linear(x))
class DummyModelWithSharedSubmodule(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(DummyModelWithSharedSubmodule, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.dense1 = DummyDenseWithRelu(input_size, hidden_size)
self.dense2 = DummyDenseWithRelu(hidden_size, output_size, self.
dense1.relu)
def forward(self, x):
x = self.dense1(x)
x = self.dense2(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'hidden_size': 4, 'output_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
primals_2, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf2
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf3,
primals_5, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
return buf3, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf4, primals_4, buf5
class DummyDenseWithRelu(nn.Module):
def __init__(self, input_size, output_size, relu=None):
super(DummyDenseWithRelu, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.relu = relu or nn.ReLU()
self.linear = nn.Linear(input_size, output_size)
def forward(self, x):
return self.relu(self.linear(x))
class DummyModelWithSharedSubmoduleNew(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(DummyModelWithSharedSubmoduleNew, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.dense1 = DummyDenseWithRelu(input_size, hidden_size)
self.dense2 = DummyDenseWithRelu(hidden_size, output_size, self.
dense1.relu)
def forward(self, input_0):
primals_1 = self.dense1.linear.weight
primals_2 = self.dense1.linear.bias
primals_4 = self.dense2.linear.weight
primals_5 = self.dense2.linear.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
Emily0219/distiller
|
DummyModelWithSharedSubmodule
| false
| 5,127
|
[
"Apache-2.0"
] | 1
|
445ed35b671fb54586acc280b53d951f18bf97ae
|
https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae
|
DummyDenseWithRelu
|
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
class DummyDenseWithRelu(nn.Module):
def __init__(self, input_size, output_size, relu=None):
super(DummyDenseWithRelu, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.relu = relu or nn.ReLU()
self.linear = nn.Linear(input_size, output_size)
def forward(self, x):
return self.relu(self.linear(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'output_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
primals_2, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2
class DummyDenseWithReluNew(nn.Module):
def __init__(self, input_size, output_size, relu=None):
super(DummyDenseWithReluNew, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.relu = relu or nn.ReLU()
self.linear = nn.Linear(input_size, output_size)
def forward(self, input_0):
primals_1 = self.linear.weight
primals_2 = self.linear.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Emily0219/distiller
|
DummyDenseWithRelu
| false
| 5,128
|
[
"Apache-2.0"
] | 1
|
445ed35b671fb54586acc280b53d951f18bf97ae
|
https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae
|
SelfAttention
|
import torch
import torch.nn as nn
import torch.distributed
class SelfAttention(nn.Module):
def __init__(self, model_dim, dropout=0.1):
super(SelfAttention, self).__init__()
self.Va = nn.Linear(model_dim, 1, bias=False)
self.Wa = nn.Linear(model_dim, model_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, x, mask=None):
b, t, n = x.size()
proj = torch.tanh(self.Wa(x.view(b * t, n).contiguous()))
scores = self.Va(proj)
scores = scores.view(b, t).contiguous()
if mask is not None:
scores = scores.masked_fill(mask, -1e+18)
attn = torch.softmax(scores, -1)
drop_attn = self.dropout(attn)
context = torch.sum(drop_attn.unsqueeze(-1) * x, -2)
return context, attn
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'model_dim': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, xmask)
@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_mul_sum_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4
x0 = xindex % 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x0 + 16 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (4 + x0 + 16 * x1), xmask)
tmp7 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (8 + x0 + 16 * x1), xmask)
tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (12 + x0 + 16 * x1), xmask)
tmp2 = tmp0 * tmp1
tmp5 = tmp3 * tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 * tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 * tmp12
tmp14 = tmp10 + tmp13
tl.store(out_ptr0 + x2, tmp14, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (1, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
del primals_2
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_tanh_0[grid(64)](buf1, primals_3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 1), (1, 4
), 0), out=buf2)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused__softmax_1[grid(16)](buf2, buf3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf4 = reinterpret_tensor(buf2, (4, 4), (4, 1), 0)
del buf2
triton_poi_fused__softmax_2[grid(16)](buf3, buf4, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf5 = buf3
del buf3
triton_poi_fused_mul_sum_3[grid(16)](buf4, primals_1, buf5, 16,
XBLOCK=16, num_warps=1, num_stages=1)
return buf5, buf4, primals_1, buf1, buf4, primals_4
class SelfAttentionNew(nn.Module):
def __init__(self, model_dim, dropout=0.1):
super(SelfAttentionNew, self).__init__()
self.Va = nn.Linear(model_dim, 1, bias=False)
self.Wa = nn.Linear(model_dim, model_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, input_0):
primals_4 = self.Va.weight
primals_2 = self.Wa.weight
primals_3 = self.Wa.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0], output[1]
|
Eldriann/Master-thesis
|
SelfAttention
| false
| 5,129
|
[
"MIT"
] | 1
|
9d09d97f4002cc9fc730f10317614e1d0d307353
|
https://github.com/Eldriann/Master-thesis/tree/9d09d97f4002cc9fc730f10317614e1d0d307353
|
ContentLoss
|
import torch
from torch import nn
class ContentLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return 0.5 * torch.sum((x - y) ** 2)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = 0.5
tmp8 = tmp6 * tmp7
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp8, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_mul_pow_sub_sum_0[grid(1)](buf1, arg0_1, arg1_1, 1,
256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class ContentLossNew(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
Enigmatisms/NeuralStyle
|
ContentLoss
| false
| 5,130
|
[
"Apache-2.0"
] | 1
|
27b435b5c51b41427e9f465793a0b81ad7248ab8
|
https://github.com/Enigmatisms/NeuralStyle/tree/27b435b5c51b41427e9f465793a0b81ad7248ab8
|
BahdanauAttention
|
import torch
from torch import nn
class BahdanauAttention(nn.Module):
def __init__(self, dim):
super(BahdanauAttention, self).__init__()
self.query_layer = nn.Linear(dim, dim, bias=False)
self.tanh = nn.Tanh()
self.v = nn.Linear(dim, 1, bias=False)
def forward(self, query, processed_memory):
"""
Args:
query: (batch, 1, dim) or (batch, dim)
processed_memory: (batch, max_time, dim)
"""
if query.dim() == 2:
query = query.unsqueeze(1)
processed_query = self.query_layer(query)
alignment = self.v(self.tanh(processed_query + processed_memory))
return alignment.squeeze(-1)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'dim': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask)
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tl.store(in_out_ptr0 + x0, tmp3, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (1, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
del primals_2
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_add_tanh_0[grid(256)](buf1, primals_3, 256, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), out=buf2)
return reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf1, primals_4
class BahdanauAttentionNew(nn.Module):
def __init__(self, dim):
super(BahdanauAttentionNew, self).__init__()
self.query_layer = nn.Linear(dim, dim, bias=False)
self.tanh = nn.Tanh()
self.v = nn.Linear(dim, 1, bias=False)
def forward(self, input_0, input_1):
primals_2 = self.query_layer.weight
primals_4 = self.v.weight
primals_1 = input_0
primals_3 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
Emotional-Text-to-Speech/tacotron_pytorch
|
BahdanauAttention
| false
| 5,131
|
[
"MIT"
] | 1
|
e6b1a3907afb01fe31bcbd77c677667adf6733f5
|
https://github.com/Emotional-Text-to-Speech/tacotron_pytorch/tree/e6b1a3907afb01fe31bcbd77c677667adf6733f5
|
HILL
|
import torch
import torch.nn as nn
class HILL(nn.Module):
def __init__(self, img_size):
super(HILL, self).__init__()
self.img_size = img_size
self.pad_3 = nn.ReplicationPad2d(3)
self.pad = nn.ReplicationPad2d(7)
self.conv1 = nn.Conv2d(1, 1, 3, 1, padding=1, bias=False)
self.avepool1 = nn.AvgPool2d(3, stride=1, padding=1)
self.avepool2 = nn.AvgPool2d(15, stride=1)
self.eps = 1e-10
self.res()
def res(self):
self.conv1.weight.data = torch.tensor([[-1, 2, -1], [2, -4, 2], [-1,
2, -1]], dtype=torch.float).view(1, 1, 3, 3)
def forward(self, x):
t1 = self.pad_3(x)
t2 = self.conv1(t1)
t3 = self.avepool1(torch.abs(t2))
t4 = 1 / (t3[:, :, 3:self.img_size + 3, 3:self.img_size + 3] + self.eps
)
t5 = self.avepool2(self.pad(t4))
return t5
def get_inputs():
return [torch.rand([4, 1, 4, 4])]
def get_init_inputs():
return [[], {'img_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 10
x1 = xindex // 10 % 10
x2 = xindex // 100
x3 = xindex
tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= 0 * (0 >= -3 + x1) + (-3 + x1) *
(-3 + x1 > 0)) + (0 * (0 >= -3 + x1) + (-3 + x1) * (-3 + x1 > 0)) *
(0 * (0 >= -3 + x1) + (-3 + x1) * (-3 + x1 > 0) < 3)) + 16 * x2 + (
3 * (3 <= 0 * (0 >= -3 + x0) + (-3 + x0) * (-3 + x0 > 0)) + (0 * (0 >=
-3 + x0) + (-3 + x0) * (-3 + x0 > 0)) * (0 * (0 >= -3 + x0) + (-3 +
x0) * (-3 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_abs_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl_math.abs(tmp0)
tl.store(out_ptr0 + x0, tmp1, xmask)
@triton.jit
def triton_poi_fused_avg_pool2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 10 % 10
x0 = xindex % 10
x4 = xindex
tmp0 = -1 + x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 10, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = -1 + x0
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = tmp5 & tmp9
tmp11 = tl.load(in_ptr0 + (-11 + x4), tmp10 & xmask, other=0.0)
tmp12 = x0
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp13 & tmp14
tmp16 = tmp5 & tmp15
tmp17 = tl.load(in_ptr0 + (-10 + x4), tmp16 & xmask, other=0.0)
tmp18 = tmp17 + tmp11
tmp19 = 1 + x0
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp20 & tmp21
tmp23 = tmp5 & tmp22
tmp24 = tl.load(in_ptr0 + (-9 + x4), tmp23 & xmask, other=0.0)
tmp25 = tmp24 + tmp18
tmp26 = x1
tmp27 = tmp26 >= tmp1
tmp28 = tmp26 < tmp3
tmp29 = tmp27 & tmp28
tmp30 = tmp29 & tmp9
tmp31 = tl.load(in_ptr0 + (-1 + x4), tmp30 & xmask, other=0.0)
tmp32 = tmp31 + tmp25
tmp33 = tmp29 & tmp15
tmp34 = tl.load(in_ptr0 + x4, tmp33 & xmask, other=0.0)
tmp35 = tmp34 + tmp32
tmp36 = tmp29 & tmp22
tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=0.0)
tmp38 = tmp37 + tmp35
tmp39 = 1 + x1
tmp40 = tmp39 >= tmp1
tmp41 = tmp39 < tmp3
tmp42 = tmp40 & tmp41
tmp43 = tmp42 & tmp9
tmp44 = tl.load(in_ptr0 + (9 + x4), tmp43 & xmask, other=0.0)
tmp45 = tmp44 + tmp38
tmp46 = tmp42 & tmp15
tmp47 = tl.load(in_ptr0 + (10 + x4), tmp46 & xmask, other=0.0)
tmp48 = tmp47 + tmp45
tmp49 = tmp42 & tmp22
tmp50 = tl.load(in_ptr0 + (11 + x4), tmp49 & xmask, other=0.0)
tmp51 = tmp50 + tmp48
tmp52 = 1 + -1 * x0 + -1 * x1 + x0 * x1 + (11 * (11 <= 2 + x0) + (2 +
x0) * (2 + x0 < 11)) * (11 * (11 <= 2 + x1) + (2 + x1) * (2 + x1 < 11)
) + -1 * x0 * (11 * (11 <= 2 + x1) + (2 + x1) * (2 + x1 < 11)
) + -1 * x1 * (11 * (11 <= 2 + x0) + (2 + x0) * (2 + x0 < 11)) + (
11 * (11 <= 2 + x0) + (2 + x0) * (2 + x0 < 11)) + (11 * (11 <= 2 +
x1) + (2 + x1) * (2 + x1 < 11))
tmp53 = tmp51 / tmp52
tl.store(out_ptr0 + x4, tmp53, xmask)
@triton.jit
def triton_poi_fused_add_mul_reciprocal_3(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (33 + x0 + 10 * x1 + 100 * x2), xmask)
tmp1 = 1e-10
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 1, tl.int32)
tmp4 = tmp3 / tmp2
tmp5 = 1.0
tmp6 = tmp4 * tmp5
tmp7 = tmp4 * tmp4
tl.store(out_ptr0 + x3, tmp6, xmask)
tl.store(out_ptr1 + x3, tmp7, xmask)
@triton.jit
def triton_poi_fused_replication_pad2d_4(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 1296
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 18
x1 = xindex // 18 % 18
x2 = xindex // 324
x3 = xindex
tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= 0 * (0 >= -7 + x1) + (-7 + x1) *
(-7 + x1 > 0)) + (0 * (0 >= -7 + x1) + (-7 + x1) * (-7 + x1 > 0)) *
(0 * (0 >= -7 + x1) + (-7 + x1) * (-7 + x1 > 0) < 3)) + 16 * x2 + (
3 * (3 <= 0 * (0 >= -7 + x0) + (-7 + x0) * (-7 + x0 > 0)) + (0 * (0 >=
-7 + x0) + (-7 + x0) * (-7 + x0 > 0)) * (0 * (0 >= -7 + x0) + (-7 +
x0) * (-7 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 1, 4, 4), (16, 16, 4, 1))
assert_size_stride(primals_2, (1, 1, 3, 3), (9, 9, 3, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 10, 10), (100, 100, 10, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused_replication_pad2d_0[grid(400)](primals_1, buf0,
400, XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 1, 10, 10), (100, 100, 10, 1))
buf2 = empty_strided_cuda((4, 1, 10, 10), (100, 100, 10, 1), torch.
float32)
triton_poi_fused_abs_1[grid(400)](buf1, buf2, 400, XBLOCK=256,
num_warps=4, num_stages=1)
buf3 = empty_strided_cuda((4, 1, 10, 10), (100, 400, 10, 1), torch.
float32)
triton_poi_fused_avg_pool2d_2[grid(400)](buf2, buf3, 400, XBLOCK=
128, num_warps=4, num_stages=1)
buf4 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.float32)
buf8 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.float32)
triton_poi_fused_add_mul_reciprocal_3[grid(64)](buf3, buf4, buf8,
64, XBLOCK=64, num_warps=1, num_stages=1)
del buf3
buf5 = empty_strided_cuda((4, 1, 18, 18), (324, 324, 18, 1), torch.
float32)
triton_poi_fused_replication_pad2d_4[grid(1296)](buf4, buf5, 1296,
XBLOCK=128, num_warps=4, num_stages=1)
buf6 = torch.ops.aten.avg_pool2d.default(buf5, [15, 15], [1, 1], [0,
0], False, True, None)
buf7 = buf6
del buf6
return buf7, primals_2, buf0, buf1, buf2, buf4, buf5, buf8
class HILLNew(nn.Module):
def __init__(self, img_size):
super(HILLNew, self).__init__()
self.img_size = img_size
self.pad_3 = nn.ReplicationPad2d(3)
self.pad = nn.ReplicationPad2d(7)
self.conv1 = nn.Conv2d(1, 1, 3, 1, padding=1, bias=False)
self.avepool1 = nn.AvgPool2d(3, stride=1, padding=1)
self.avepool2 = nn.AvgPool2d(15, stride=1)
self.eps = 1e-10
self.res()
def res(self):
self.conv1.weight.data = torch.tensor([[-1, 2, -1], [2, -4, 2], [-1,
2, -1]], dtype=torch.float).view(1, 1, 3, 3)
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_1 = input_0
output = call([primals_1, primals_2])
return output[0]
|
Ekko-zn/StegoAdv
|
HILL
| false
| 5,132
|
[
"MIT"
] | 1
|
2852dbc85d66f30efb7127695c0d75806bf4aa4c
|
https://github.com/Ekko-zn/StegoAdv/tree/2852dbc85d66f30efb7127695c0d75806bf4aa4c
|
MultiHeadAttention
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
class MultiHeadAttention(nn.Module):
"""
input:
query [N, T_q, query_dim]
key [N, T_k, key_dim]
output:
out [N, T_q, num_units]
"""
def __init__(self, query_dim, key_dim, num_units, num_heads):
super(MultiHeadAttention, self).__init__()
if key_dim % num_heads != 0:
raise ValueError(
'Key depth {} must be divisible by the number of attention heads {}'
.format(key_dim, num_heads))
self.num_units = num_units
self.num_heads = num_heads
self.query_scale = (key_dim // num_heads) ** -0.5
self.query_linear = nn.Linear(query_dim, num_units, bias=False)
self.key_linear = nn.Linear(key_dim, num_units, bias=False)
self.value_linear = nn.Linear(key_dim, num_units, bias=False)
def _split_heads(self, x):
"""
Split x such to add an extra num_heads dimension
Input:
x: a Tensor with shape [batch_size, seq_len, depth]
Returns:
A Tensor with shape [batch_size, num_heads, seq_len, depth / num_heads]
"""
if len(x.shape) != 3:
raise ValueError('x must have rank 3')
shape = x.shape
return x.view(shape[0], shape[1], self.num_heads, shape[2] // self.
num_heads).permute(0, 2, 1, 3)
def _merge_heads(self, x):
"""
Merge the extra num_heads into the last dimension
Input:
x: a Tensor with shape [batch_size, num_heads, seq_len, depth/num_heads]
Output:
A Tensor with shape [batch_size, seq_len, depth]
"""
if len(x.shape) != 4:
raise ValueError('x must have rank 4')
shape = x.shape
return x.permute(0, 2, 1, 3).contiguous().view(shape[0], shape[2],
shape[3] * self.num_heads)
def forward(self, queries, keys, values):
queries = self.query_linear(queries)
keys = self.key_linear(keys)
values = self.value_linear(values)
queries = self._split_heads(queries)
keys = self._split_heads(keys)
values = self._split_heads(values)
queries *= self.query_scale
logits = torch.matmul(queries, keys.permute(0, 1, 3, 2))
scores = F.softmax(logits, dim=-1)
contexts = torch.matmul(scores, values)
contexts = self._merge_heads(contexts)
return contexts
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4])
]
def get_init_inputs():
return [[], {'query_dim': 4, 'key_dim': 4, 'num_units': 4, 'num_heads': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
del primals_3
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
del primals_5
buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(16, 4)](buf0, buf3, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf0
triton_poi_fused_clone_1[grid(16, 4)](buf1, buf4, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf7 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused__softmax_3[grid(256)](buf6, buf7, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf6
buf8 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf1
triton_poi_fused_clone_1[grid(16, 4)](buf2, buf8, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf9 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
del buf2
extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9)
buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
triton_poi_fused_clone_1[grid(16, 4)](buf9, buf10, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
del buf9
return reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
), reinterpret_tensor(primals_4, (16, 4), (4, 1), 0
), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0
), buf7, reinterpret_tensor(buf8, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0)
class MultiHeadAttentionNew(nn.Module):
"""
input:
query [N, T_q, query_dim]
key [N, T_k, key_dim]
output:
out [N, T_q, num_units]
"""
def __init__(self, query_dim, key_dim, num_units, num_heads):
super(MultiHeadAttentionNew, self).__init__()
if key_dim % num_heads != 0:
raise ValueError(
'Key depth {} must be divisible by the number of attention heads {}'
.format(key_dim, num_heads))
self.num_units = num_units
self.num_heads = num_heads
self.query_scale = (key_dim // num_heads) ** -0.5
self.query_linear = nn.Linear(query_dim, num_units, bias=False)
self.key_linear = nn.Linear(key_dim, num_units, bias=False)
self.value_linear = nn.Linear(key_dim, num_units, bias=False)
def _split_heads(self, x):
"""
Split x such to add an extra num_heads dimension
Input:
x: a Tensor with shape [batch_size, seq_len, depth]
Returns:
A Tensor with shape [batch_size, num_heads, seq_len, depth / num_heads]
"""
if len(x.shape) != 3:
raise ValueError('x must have rank 3')
shape = x.shape
return x.view(shape[0], shape[1], self.num_heads, shape[2] // self.
num_heads).permute(0, 2, 1, 3)
def _merge_heads(self, x):
"""
Merge the extra num_heads into the last dimension
Input:
x: a Tensor with shape [batch_size, num_heads, seq_len, depth/num_heads]
Output:
A Tensor with shape [batch_size, seq_len, depth]
"""
if len(x.shape) != 4:
raise ValueError('x must have rank 4')
shape = x.shape
return x.permute(0, 2, 1, 3).contiguous().view(shape[0], shape[2],
shape[3] * self.num_heads)
def forward(self, input_0, input_1, input_2):
primals_1 = self.query_linear.weight
primals_3 = self.key_linear.weight
primals_5 = self.value_linear.weight
primals_2 = input_0
primals_4 = input_1
primals_6 = input_2
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
Ella77/tacotron2_multispeaker_pytorch
|
MultiHeadAttention
| false
| 5,133
|
[
"BSD-3-Clause"
] | 1
|
859eab0a8e3bd7545e623ce47fe1563702d38442
|
https://github.com/Ella77/tacotron2_multispeaker_pytorch/tree/859eab0a8e3bd7545e623ce47fe1563702d38442
|
Actor
|
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
class Actor(nn.Module):
def __init__(self, nb_states, nb_actions, hidden1=400, hidden2=300):
super(Actor, self).__init__()
self.fc1 = nn.Linear(nb_states, hidden1)
self.fc2 = nn.Linear(hidden1, hidden2)
self.fc3 = nn.Linear(hidden2, nb_actions)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
out = self.relu(out)
out = self.fc3(out)
out = self.sigmoid(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'nb_states': 4, 'nb_actions': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 25600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x4 = xindex
x0 = xindex % 400
x2 = xindex % 1600
x3 = xindex // 1600
tmp0 = tl.load(in_out_ptr0 + x4, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x4, tmp4, xmask)
tl.store(out_ptr0 + (x2 + 1664 * x3), tmp6, xmask)
@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 19200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x4 = xindex
x0 = xindex % 300
x2 = xindex // 1200
x3 = xindex % 1200
tmp0 = tl.load(in_ptr0 + x4, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3 + 1216 * x2), tmp4, xmask)
tl.store(out_ptr1 + (x3 + 1280 * x2), tmp6, xmask)
@triton.jit
def triton_poi_fused_relu_view_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 19200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 300
x1 = xindex // 300
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 300 * (x1 % 4) + 1216 * (x1 // 4)), xmask)
tl.store(out_ptr0 + x2, tmp0, xmask)
@triton.jit
def triton_poi_fused_sigmoid_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.sigmoid(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (400, 4), (4, 1))
assert_size_stride(primals_2, (400,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (300, 400), (400, 1))
assert_size_stride(primals_5, (300,), (1,))
assert_size_stride(primals_6, (4, 300), (300, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 400), (400, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 400), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 400), (6400, 1600, 400, 1), 0
)
del buf0
buf8 = empty_strided_cuda((4, 4, 4, 400), (6656, 1664, 400, 1),
torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(25600)](buf1,
primals_2, buf8, 25600, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 400), (400, 1), 0),
reinterpret_tensor(primals_4, (400, 300), (1, 400), 0), out=buf2)
buf3 = empty_strided_cuda((4, 4, 4, 300), (4864, 1216, 300, 1),
torch.float32)
buf7 = empty_strided_cuda((4, 4, 4, 300), (5120, 1280, 300, 1),
torch.bool)
triton_poi_fused_relu_threshold_backward_1[grid(19200)](buf2,
primals_5, buf3, buf7, 19200, XBLOCK=256, num_warps=4, num_stages=1
)
del primals_5
buf4 = buf2
del buf2
triton_poi_fused_relu_view_2[grid(19200)](buf3, buf4, 19200, XBLOCK
=128, num_warps=4, num_stages=1)
del buf3
buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(buf4, reinterpret_tensor(primals_6, (300, 4), (1,
300), 0), out=buf5)
buf6 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused_sigmoid_3[grid(256)](buf6, primals_7, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_7
return buf6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 400), (400, 1), 0
), buf4, buf6, primals_6, buf7, primals_4, buf8
class ActorNew(nn.Module):
def __init__(self, nb_states, nb_actions, hidden1=400, hidden2=300):
super(ActorNew, self).__init__()
self.fc1 = nn.Linear(nb_states, hidden1)
self.fc2 = nn.Linear(hidden1, hidden2)
self.fc3 = nn.Linear(hidden2, nb_actions)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def forward(self, input_0):
primals_1 = self.fc1.weight
primals_2 = self.fc1.bias
primals_4 = self.fc2.weight
primals_5 = self.fc2.bias
primals_6 = self.fc3.weight
primals_7 = self.fc3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
Emily0219/distiller
|
Actor
| false
| 5,134
|
[
"Apache-2.0"
] | 1
|
445ed35b671fb54586acc280b53d951f18bf97ae
|
https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae
|
RBFExpansion
|
import torch
import numpy as np
import torch.nn as nn
class RBFExpansion(nn.Module):
"""Expand distances between nodes by radial basis functions.
.. math::
\\exp(- \\gamma * ||d - \\mu||^2)
where :math:`d` is the distance between two nodes and :math:`\\mu` helps centralizes
the distances. We use multiple centers evenly distributed in the range of
:math:`[\\text{low}, \\text{high}]` with the difference between two adjacent centers
being :math:`gap`.
The number of centers is decided by :math:`(\\text{high} - \\text{low}) / \\text{gap}`.
Choosing fewer centers corresponds to reducing the resolution of the filter.
Parameters
----------
low : float
Smallest center. Default to 0.
high : float
Largest center. Default to 30.
gap : float
Difference between two adjacent centers. :math:`\\gamma` will be computed as the
reciprocal of gap. Default to 0.1.
"""
def __init__(self, low=0.0, high=30.0, gap=0.1):
super(RBFExpansion, self).__init__()
num_centers = int(np.ceil((high - low) / gap))
self.centers = np.linspace(low, high, num_centers)
self.centers = nn.Parameter(torch.tensor(self.centers).float(),
requires_grad=False)
self.gamma = 1 / gap
def reset_parameters(self):
"""Reinitialize model parameters."""
self.centers = nn.Parameter(torch.tensor(self.centers).float(),
requires_grad=False)
def forward(self, edge_dists):
"""Expand distances.
Parameters
----------
edge_dists : float32 tensor of shape (E, 1)
Distances between end nodes of edges, E for the number of edges.
Returns
-------
float32 tensor of shape (E, len(self.centers))
Expanded distances.
"""
radial = edge_dists - self.centers
coef = -self.gamma
return torch.exp(coef * radial ** 2)
def get_inputs():
return [torch.rand([4, 4, 4, 300])]
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 math as tl_math
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_exp_mul_pow_sub_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 19200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 300
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = -10.0
tmp5 = tmp3 * tmp4
tmp6 = tl_math.exp(tmp5)
tl.store(out_ptr0 + x2, tmp6, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (300,), (1,))
assert_size_stride(arg1_1, (4, 4, 4, 300), (4800, 1200, 300, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 300), (4800, 1200, 300, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_exp_mul_pow_sub_0[grid(19200)](arg1_1, arg0_1,
buf0, 19200, XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
return buf0,
class RBFExpansionNew(nn.Module):
"""Expand distances between nodes by radial basis functions.
.. math::
\\exp(- \\gamma * ||d - \\mu||^2)
where :math:`d` is the distance between two nodes and :math:`\\mu` helps centralizes
the distances. We use multiple centers evenly distributed in the range of
:math:`[\\text{low}, \\text{high}]` with the difference between two adjacent centers
being :math:`gap`.
The number of centers is decided by :math:`(\\text{high} - \\text{low}) / \\text{gap}`.
Choosing fewer centers corresponds to reducing the resolution of the filter.
Parameters
----------
low : float
Smallest center. Default to 0.
high : float
Largest center. Default to 30.
gap : float
Difference between two adjacent centers. :math:`\\gamma` will be computed as the
reciprocal of gap. Default to 0.1.
"""
def __init__(self, low=0.0, high=30.0, gap=0.1):
super(RBFExpansionNew, self).__init__()
num_centers = int(np.ceil((high - low) / gap))
self.centers = np.linspace(low, high, num_centers)
self.centers = nn.Parameter(torch.tensor(self.centers).float(),
requires_grad=False)
self.gamma = 1 / gap
def reset_parameters(self):
"""Reinitialize model parameters."""
self.centers = nn.Parameter(torch.tensor(self.centers).float(),
requires_grad=False)
def forward(self, input_0):
arg0_1 = self.centers
arg1_1 = input_0
output = call([arg0_1, arg1_1])
return output[0]
|
Erfaan-Rostami/dgl-lifesci
|
RBFExpansion
| false
| 5,135
|
[
"Apache-2.0"
] | 1
|
08fc317f634fbaee4a8d074c332e871357845e4f
|
https://github.com/Erfaan-Rostami/dgl-lifesci/tree/08fc317f634fbaee4a8d074c332e871357845e4f
|
Highway
|
import torch
from torch import nn
class Highway(nn.Module):
def __init__(self, in_size, out_size):
super(Highway, self).__init__()
self.H = nn.Linear(in_size, out_size)
self.H.bias.data.zero_()
self.T = nn.Linear(in_size, out_size)
self.T.bias.data.fill_(-1)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def forward(self, inputs):
H = self.relu(self.H(inputs))
T = self.sigmoid(self.T(inputs))
return H * T + inputs * (1.0 - T)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_size': 4, 'out_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_mul_relu_rsub_sigmoid_0(in_ptr0, in_ptr1, in_ptr2,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp3 = tl.load(in_ptr1 + x0, xmask)
tmp6 = tl.load(in_ptr2 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = tl.sigmoid(tmp3)
tmp5 = tmp2 * tmp4
tmp7 = 1.0
tmp8 = tmp7 - tmp4
tmp9 = tmp6 * tmp8
tmp10 = tmp5 + tmp9
tl.store(out_ptr0 + x0, tmp10, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf1)
del primals_4
del primals_5
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_mul_relu_rsub_sigmoid_0[grid(256)](buf0, buf1,
primals_3, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
return buf2, primals_3, buf0, buf1
class HighwayNew(nn.Module):
def __init__(self, in_size, out_size):
super(HighwayNew, self).__init__()
self.H = nn.Linear(in_size, out_size)
self.H.bias.data.zero_()
self.T = nn.Linear(in_size, out_size)
self.T.bias.data.fill_(-1)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def forward(self, input_0):
primals_1 = self.H.weight
primals_2 = self.H.bias
primals_4 = self.T.weight
primals_5 = self.T.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
Emotional-Text-to-Speech/tacotron_pytorch
|
Highway
| false
| 5,136
|
[
"MIT"
] | 1
|
e6b1a3907afb01fe31bcbd77c677667adf6733f5
|
https://github.com/Emotional-Text-to-Speech/tacotron_pytorch/tree/e6b1a3907afb01fe31bcbd77c677667adf6733f5
|
Norm
|
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
class Norm(nn.Module):
"""
A module wrapper for vector/matrix norm
"""
def __init__(self, p='fro', dim=None, keepdim=False):
super(Norm, self).__init__()
self.p = p
self.dim = dim
self.keepdim = keepdim
def forward(self, x: 'torch.Tensor'):
return torch.norm(x, p=self.p, dim=self.dim, keepdim=self.keepdim)
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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_linalg_vector_norm_0(in_out_ptr0, in_ptr0, xnumel, rnumel
):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
tmp4 = triton_helpers.promote_to_tensor(tl.sum(tmp2, 0))
tmp5 = libdevice.sqrt(tmp4)
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp5, None)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_linalg_vector_norm_0[grid(1)](buf1, arg0_1, 1, 256,
num_warps=2, num_stages=1)
del arg0_1
return buf1,
class NormNew(nn.Module):
"""
A module wrapper for vector/matrix norm
"""
def __init__(self, p='fro', dim=None, keepdim=False):
super(NormNew, self).__init__()
self.p = p
self.dim = dim
self.keepdim = keepdim
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Emily0219/distiller
|
Norm
| false
| 5,137
|
[
"Apache-2.0"
] | 1
|
445ed35b671fb54586acc280b53d951f18bf97ae
|
https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae
|
ModelWithDuplicates
|
import torch
from collections import OrderedDict
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
class ModelWithDuplicates(nn.Module):
def __init__(self):
super(ModelWithDuplicates, self).__init__()
self.conv1 = nn.Conv2d(3, 10, 5)
self.post_conv1 = nn.ModuleList([nn.ReLU(), nn.Tanh()])
self.conv2 = nn.Conv2d(10, 20, 3)
self.post_conv2 = self.post_conv1
self.expected_mlist_to_dmlist = OrderedDict([('post_conv1', [
'post_conv1']), ('post_conv2', ['post_conv2'])])
self.expected_list_contents_name_changes = OrderedDict([(
'post_conv1.0', 'post_conv1_0'), ('post_conv1.1',
'post_conv1_1'), ('post_conv2.0', 'post_conv2_0'), (
'post_conv2.1', 'post_conv2_1')])
def forward(self, x):
x = self.conv1(x)
for m in self.post_conv1:
x = m(x)
x = self.conv2(x)
for m in self.post_conv2:
x = m(x)
return x
def get_inputs():
return [torch.rand([4, 3, 64, 64])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from collections import OrderedDict
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_relu_tanh_threshold_backward_0(in_ptr0,
in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 144000
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 3600 % 10
x0 = xindex % 3600
x4 = xindex // 3600
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = libdevice.tanh(tmp4)
tmp6 = 0.0
tmp7 = tmp4 <= tmp6
tl.store(out_ptr0 + x3, tmp5, xmask)
tl.store(out_ptr1 + (x0 + 3712 * x4), tmp7, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_tanh_threshold_backward_1(in_ptr0,
in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 269120
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 3364 % 20
x0 = xindex % 3364
x4 = xindex // 3364
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = libdevice.tanh(tmp4)
tmp6 = 0.0
tmp7 = tmp4 <= tmp6
tl.store(out_ptr0 + x3, tmp5, xmask)
tl.store(out_ptr1 + (x0 + 3456 * x4), tmp7, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (10, 3, 5, 5), (75, 25, 5, 1))
assert_size_stride(primals_2, (10,), (1,))
assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_4, (20, 10, 3, 3), (90, 9, 3, 1))
assert_size_stride(primals_5, (20,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 10, 60, 60), (36000, 3600, 60, 1))
buf1 = empty_strided_cuda((4, 10, 60, 60), (36000, 3600, 60, 1),
torch.float32)
buf5 = empty_strided_cuda((4, 10, 60, 60), (37120, 3712, 60, 1),
torch.bool)
get_raw_stream(0)
triton_poi_fused_convolution_relu_tanh_threshold_backward_0[grid(
144000)](buf0, primals_2, buf1, buf5, 144000, XBLOCK=512,
num_warps=8, num_stages=1)
del buf0
del primals_2
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 20, 58, 58), (67280, 3364, 58, 1))
buf3 = empty_strided_cuda((4, 20, 58, 58), (67280, 3364, 58, 1),
torch.float32)
buf4 = empty_strided_cuda((4, 20, 58, 58), (69120, 3456, 58, 1),
torch.bool)
triton_poi_fused_convolution_relu_tanh_threshold_backward_1[grid(
269120)](buf2, primals_5, buf3, buf4, 269120, XBLOCK=512,
num_warps=8, num_stages=1)
del buf2
del primals_5
return buf3, primals_1, primals_3, primals_4, buf1, buf3, buf4, buf5
class ModelWithDuplicatesNew(nn.Module):
def __init__(self):
super(ModelWithDuplicatesNew, self).__init__()
self.conv1 = nn.Conv2d(3, 10, 5)
self.post_conv1 = nn.ModuleList([nn.ReLU(), nn.Tanh()])
self.conv2 = nn.Conv2d(10, 20, 3)
self.post_conv2 = self.post_conv1
self.expected_mlist_to_dmlist = OrderedDict([('post_conv1', [
'post_conv1']), ('post_conv2', ['post_conv2'])])
self.expected_list_contents_name_changes = OrderedDict([(
'post_conv1.0', 'post_conv1_0'), ('post_conv1.1',
'post_conv1_1'), ('post_conv2.0', 'post_conv2_0'), (
'post_conv2.1', 'post_conv2_1')])
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
Emily0219/distiller
|
ModelWithDuplicates
| false
| 5,138
|
[
"Apache-2.0"
] | 1
|
445ed35b671fb54586acc280b53d951f18bf97ae
|
https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae
|
Mean
|
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
class Mean(nn.Module):
def __init__(self, *args, **kwargs):
super(Mean, self).__init__()
self.args = args
self.kwargs = kwargs
def forward(self, x: 'torch.Tensor'):
return torch.mean(x, *self.args, **self.kwargs)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
tmp4 = 256.0
tmp5 = tmp3 / tmp4
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp5, None)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_mean_0[grid(1)](buf1, arg0_1, 1, 256, num_warps=2,
num_stages=1)
del arg0_1
return buf1,
class MeanNew(nn.Module):
def __init__(self, *args, **kwargs):
super(MeanNew, self).__init__()
self.args = args
self.kwargs = kwargs
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Emily0219/distiller
|
Mean
| false
| 5,139
|
[
"Apache-2.0"
] | 1
|
445ed35b671fb54586acc280b53d951f18bf97ae
|
https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae
|
upsample
|
import torch
import torch.nn as nn
class upsample(nn.Module):
def __init__(self):
super(upsample, self).__init__()
self.upsample = torch.nn.UpsamplingBilinear2d([256, 256])
def forward(self, input):
return (self.upsample(input) + 1.0) / 2
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
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_div_mul_sub_0(
in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 256 % 256
x0 = xindex % 256
x2 = xindex // 65536
x3 = xindex
tmp0 = x1
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.011764705882352941
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tmp7 = tl.full([1], 1, tl.int64)
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 3, tl.int64)
tmp10 = triton_helpers.minimum(tmp8, tmp9)
tmp11 = x0
tmp12 = tmp11.to(tl.float32)
tmp13 = tmp12 * tmp2
tmp14 = triton_helpers.maximum(tmp13, tmp4)
tmp15 = tmp14.to(tl.int32)
tmp16 = tl.load(in_ptr0 + (tmp15 + 4 * tmp10 + 16 * x2), None,
eviction_policy='evict_last')
tmp17 = tmp15 + tmp7
tmp18 = triton_helpers.minimum(tmp17, tmp9)
tmp19 = tl.load(in_ptr0 + (tmp18 + 4 * tmp10 + 16 * x2), None,
eviction_policy='evict_last')
tmp20 = tmp19 - tmp16
tmp21 = tmp15.to(tl.float32)
tmp22 = tmp14 - tmp21
tmp23 = triton_helpers.maximum(tmp22, tmp4)
tmp24 = 1.0
tmp25 = triton_helpers.minimum(tmp23, tmp24)
tmp26 = tmp20 * tmp25
tmp27 = tmp16 + tmp26
tmp28 = tl.load(in_ptr0 + (tmp15 + 4 * tmp6 + 16 * x2), None,
eviction_policy='evict_last')
tmp29 = tl.load(in_ptr0 + (tmp18 + 4 * tmp6 + 16 * x2), None,
eviction_policy='evict_last')
tmp30 = tmp29 - tmp28
tmp31 = tmp30 * tmp25
tmp32 = tmp28 + tmp31
tmp33 = tmp27 - tmp32
tmp34 = tmp6.to(tl.float32)
tmp35 = tmp5 - tmp34
tmp36 = triton_helpers.maximum(tmp35, tmp4)
tmp37 = triton_helpers.minimum(tmp36, tmp24)
tmp38 = tmp33 * tmp37
tmp39 = tmp32 + tmp38
tmp40 = tmp39 + tmp24
tmp41 = 0.5
tmp42 = tmp40 * tmp41
tl.store(in_out_ptr0 + x3, tmp42, None)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 256, 256), (262144, 65536, 256, 1),
torch.float32)
buf1 = buf0
del buf0
buf2 = buf1
del buf1
get_raw_stream(0)
triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_div_mul_sub_0[
grid(1048576)](buf2, arg0_1, 1048576, XBLOCK=1024, num_warps=4,
num_stages=1)
del arg0_1
return buf2,
class upsampleNew(nn.Module):
def __init__(self):
super(upsampleNew, self).__init__()
self.upsample = torch.nn.UpsamplingBilinear2d([256, 256])
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Euiyeon-Kim/SuperFAN-Pytorch
|
upsample
| false
| 5,140
|
[
"MIT"
] | 1
|
4a18e559c4b91d0d422b66e63509aeea8a7dc8f2
|
https://github.com/Euiyeon-Kim/SuperFAN-Pytorch/tree/4a18e559c4b91d0d422b66e63509aeea8a7dc8f2
|
ClippedLinearQuantization
|
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
def linear_dequantize(input, scale, zero_point, inplace=False):
if inplace:
input.add_(zero_point).div_(scale)
return input
return (input + zero_point) / scale
def linear_quantize(input, scale, zero_point, inplace=False):
if inplace:
input.mul_(scale).sub_(zero_point).round_()
return input
return torch.round(scale * input - zero_point)
def _prep_saturation_val_tensor(sat_val):
is_scalar = not isinstance(sat_val, torch.Tensor)
out = torch.tensor(sat_val) if is_scalar else sat_val.clone().detach()
if not out.is_floating_point():
out = out
if out.dim() == 0:
out = out.unsqueeze(0)
return is_scalar, out
def asymmetric_linear_quantization_params(num_bits, saturation_min,
saturation_max, integral_zero_point=True, signed=False):
scalar_min, sat_min = _prep_saturation_val_tensor(saturation_min)
scalar_max, sat_max = _prep_saturation_val_tensor(saturation_max)
is_scalar = scalar_min and scalar_max
if scalar_max and not scalar_min:
sat_max = sat_max
elif scalar_min and not scalar_max:
sat_min = sat_min
if any(sat_min > sat_max):
raise ValueError('saturation_min must be smaller than saturation_max')
n = 2 ** num_bits - 1
sat_min = torch.min(sat_min, torch.zeros_like(sat_min))
sat_max = torch.max(sat_max, torch.zeros_like(sat_max))
diff = sat_max - sat_min
diff[diff == 0] = n
scale = n / diff
zero_point = scale * sat_min
if integral_zero_point:
zero_point = zero_point.round()
if signed:
zero_point += 2 ** (num_bits - 1)
if is_scalar:
return scale.item(), zero_point.item()
return scale, zero_point
def clamp(input, min, max, inplace=False):
if inplace:
input.clamp_(min, max)
return input
return torch.clamp(input, min, max)
class LinearQuantizeSTE(torch.autograd.Function):
@staticmethod
def forward(ctx, input, scale, zero_point, dequantize, inplace):
if inplace:
ctx.mark_dirty(input)
output = linear_quantize(input, scale, zero_point, inplace)
if dequantize:
output = linear_dequantize(output, scale, zero_point, inplace)
return output
@staticmethod
def backward(ctx, grad_output):
return grad_output, None, None, None, None
class ClippedLinearQuantization(nn.Module):
def __init__(self, num_bits, clip_val, dequantize=True, inplace=False):
super(ClippedLinearQuantization, self).__init__()
self.num_bits = num_bits
self.clip_val = clip_val
self.scale, self.zero_point = asymmetric_linear_quantization_params(
num_bits, 0, clip_val, signed=False)
self.dequantize = dequantize
self.inplace = inplace
def forward(self, input):
input = clamp(input, 0, self.clip_val, self.inplace)
input = LinearQuantizeSTE.apply(input, self.scale, self.zero_point,
self.dequantize, self.inplace)
return input
def __repr__(self):
inplace_str = ', inplace' if self.inplace else ''
return '{0}(num_bits={1}, clip_val={2}{3})'.format(self.__class__.
__name__, self.num_bits, self.clip_val, inplace_str)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_bits': 4, 'clip_val': 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
import torch.onnx
import torch.testing
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_clamp_div_mul_round_sub_0(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp3 = 4.0
tmp4 = triton_helpers.minimum(tmp2, tmp3)
tmp5 = 3.75
tmp6 = tmp4 * tmp5
tmp7 = tmp6 - tmp1
tmp8 = libdevice.nearbyint(tmp7)
tmp9 = tmp8 + tmp1
tmp10 = 0.26666666666666666
tmp11 = tmp9 * tmp10
tl.store(out_ptr0 + x0, tmp11, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_clamp_div_mul_round_sub_0[grid(256)](arg0_1,
buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
return buf0,
def linear_dequantize(input, scale, zero_point, inplace=False):
if inplace:
input.add_(zero_point).div_(scale)
return input
return (input + zero_point) / scale
def linear_quantize(input, scale, zero_point, inplace=False):
if inplace:
input.mul_(scale).sub_(zero_point).round_()
return input
return torch.round(scale * input - zero_point)
def _prep_saturation_val_tensor(sat_val):
is_scalar = not isinstance(sat_val, torch.Tensor)
out = torch.tensor(sat_val) if is_scalar else sat_val.clone().detach()
if not out.is_floating_point():
out = out
if out.dim() == 0:
out = out.unsqueeze(0)
return is_scalar, out
def asymmetric_linear_quantization_params(num_bits, saturation_min,
saturation_max, integral_zero_point=True, signed=False):
scalar_min, sat_min = _prep_saturation_val_tensor(saturation_min)
scalar_max, sat_max = _prep_saturation_val_tensor(saturation_max)
is_scalar = scalar_min and scalar_max
if scalar_max and not scalar_min:
sat_max = sat_max
elif scalar_min and not scalar_max:
sat_min = sat_min
if any(sat_min > sat_max):
raise ValueError('saturation_min must be smaller than saturation_max')
n = 2 ** num_bits - 1
sat_min = torch.min(sat_min, torch.zeros_like(sat_min))
sat_max = torch.max(sat_max, torch.zeros_like(sat_max))
diff = sat_max - sat_min
diff[diff == 0] = n
scale = n / diff
zero_point = scale * sat_min
if integral_zero_point:
zero_point = zero_point.round()
if signed:
zero_point += 2 ** (num_bits - 1)
if is_scalar:
return scale.item(), zero_point.item()
return scale, zero_point
def clamp(input, min, max, inplace=False):
if inplace:
input.clamp_(min, max)
return input
return torch.clamp(input, min, max)
class LinearQuantizeSTE(torch.autograd.Function):
@staticmethod
def forward(ctx, input, scale, zero_point, dequantize, inplace):
if inplace:
ctx.mark_dirty(input)
output = linear_quantize(input, scale, zero_point, inplace)
if dequantize:
output = linear_dequantize(output, scale, zero_point, inplace)
return output
@staticmethod
def backward(ctx, grad_output):
return grad_output, None, None, None, None
class ClippedLinearQuantizationNew(nn.Module):
def __init__(self, num_bits, clip_val, dequantize=True, inplace=False):
super(ClippedLinearQuantizationNew, self).__init__()
self.num_bits = num_bits
self.clip_val = clip_val
self.scale, self.zero_point = asymmetric_linear_quantization_params(
num_bits, 0, clip_val, signed=False)
self.dequantize = dequantize
self.inplace = inplace
def __repr__(self):
inplace_str = ', inplace' if self.inplace else ''
return '{0}(num_bits={1}, clip_val={2}{3})'.format(self.__class__.
__name__, self.num_bits, self.clip_val, inplace_str)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Emily0219/distiller
|
ClippedLinearQuantization
| false
| 5,141
|
[
"Apache-2.0"
] | 1
|
445ed35b671fb54586acc280b53d951f18bf97ae
|
https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae
|
BahdanauAttention
|
import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
from torch.nn.parameter import Parameter
import torch.onnx
import torch.testing
class EltwiseAdd(nn.Module):
def __init__(self, inplace=False):
super(EltwiseAdd, self).__init__()
self.inplace = inplace
def forward(self, *input):
res = input[0]
if self.inplace:
for t in input[1:]:
res += t
else:
for t in input[1:]:
res = res + t
return res
class EltwiseMult(nn.Module):
def __init__(self, inplace=False):
super(EltwiseMult, self).__init__()
self.inplace = inplace
def forward(self, *input):
res = input[0]
if self.inplace:
for t in input[1:]:
res *= t
else:
for t in input[1:]:
res = res * t
return res
class Matmul(nn.Module):
"""
A wrapper module for matmul operation between 2 tensors.
"""
def __init__(self):
super(Matmul, self).__init__()
def forward(self, a: 'torch.Tensor', b: 'torch.Tensor'):
return a.matmul(b)
class BatchMatmul(nn.Module):
"""
A wrapper module for torch.bmm operation between 2 tensors.
"""
def __init__(self):
super(BatchMatmul, self).__init__()
def forward(self, a: 'torch.Tensor', b: 'torch.Tensor'):
return torch.bmm(a, b)
class BahdanauAttention(nn.Module):
"""
It should be very similar to tf.contrib.seq2seq.BahdanauAttention
"""
def __init__(self, query_size, key_size, num_units, normalize=False,
dropout=0, batch_first=False):
super(BahdanauAttention, self).__init__()
self.normalize = normalize
self.batch_first = batch_first
self.num_units = num_units
self.linear_q = nn.Linear(query_size, num_units, bias=False)
self.linear_k = nn.Linear(key_size, num_units, bias=False)
self.linear_att = Parameter(torch.Tensor(num_units))
self.dropout = nn.Dropout(dropout)
self.mask = None
self.eltwiseadd_qk = EltwiseAdd()
self.eltwiseadd_norm_bias = EltwiseAdd()
self.eltwisemul_norm_scaler = EltwiseMult()
self.tanh = nn.Tanh()
self.matmul_score = Matmul()
self.softmax_att = nn.Softmax(dim=-1)
self.context_matmul = BatchMatmul()
if self.normalize:
self.normalize_scalar = Parameter(torch.Tensor(1))
self.normalize_bias = Parameter(torch.Tensor(num_units))
else:
self.register_parameter('normalize_scalar', None)
self.register_parameter('normalize_bias', None)
self.reset_parameters()
def reset_parameters(self):
stdv = 1.0 / math.sqrt(self.num_units)
self.linear_att.data.uniform_(-stdv, stdv)
if self.normalize:
self.normalize_scalar.data.fill_(stdv)
self.normalize_bias.data.zero_()
def set_mask(self, context_len, context):
"""
sets self.mask which is applied before softmax
ones for inactive context fields, zeros for active context fields
:param context_len: b
:param context: if batch_first: (b x t_k x n) else: (t_k x b x n)
self.mask: (b x t_k)
"""
if self.batch_first:
max_len = context.size(1)
else:
max_len = context.size(0)
indices = torch.arange(0, max_len, dtype=torch.int64, device=
context.device)
self.mask = indices >= context_len.unsqueeze(1)
def calc_score(self, att_query, att_keys):
"""
Calculate Bahdanau score
:param att_query: b x t_q x n
:param att_keys: b x t_k x n
return b x t_q x t_k scores
"""
b, t_k, n = att_keys.size()
t_q = att_query.size(1)
att_query = att_query.unsqueeze(2).expand(b, t_q, t_k, n)
att_keys = att_keys.unsqueeze(1).expand(b, t_q, t_k, n)
sum_qk = self.eltwiseadd_qk(att_query, att_keys)
if self.normalize:
sum_qk = self.eltwiseadd_norm_bias(sum_qk, self.normalize_bias)
tmp = self.linear_att
linear_att = tmp / tmp.norm()
linear_att = linear_att
linear_att = self.eltwisemul_norm_scaler(linear_att, self.
normalize_scalar)
else:
linear_att = self.linear_att
out = self.matmul_score(self.tanh(sum_qk), linear_att)
return out
def forward(self, query, keys):
"""
:param query: if batch_first: (b x t_q x n) else: (t_q x b x n)
:param keys: if batch_first: (b x t_k x n) else (t_k x b x n)
:returns: (context, scores_normalized)
context: if batch_first: (b x t_q x n) else (t_q x b x n)
scores_normalized: if batch_first (b x t_q x t_k) else (t_q x b x t_k)
"""
if not self.batch_first:
keys = keys.transpose(0, 1)
if query.dim() == 3:
query = query.transpose(0, 1)
if query.dim() == 2:
single_query = True
query = query.unsqueeze(1)
else:
single_query = False
b = query.size(0)
t_k = keys.size(1)
t_q = query.size(1)
processed_query = self.linear_q(query)
processed_key = self.linear_k(keys)
scores = self.calc_score(processed_query, processed_key)
if self.mask is not None:
mask = self.mask.unsqueeze(1).expand(b, t_q, t_k)
scores.data.masked_fill_(mask, -65504.0)
scores_normalized = self.softmax_att(scores)
scores_normalized = self.dropout(scores_normalized)
context = self.context_matmul(scores_normalized, keys)
if single_query:
context = context.squeeze(1)
scores_normalized = scores_normalized.squeeze(1)
elif not self.batch_first:
context = context.transpose(0, 1)
scores_normalized = scores_normalized.transpose(0, 1)
return context, scores_normalized
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'query_size': 4, 'key_size': 4, 'num_units': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
from torch.optim.lr_scheduler import *
import torch.optim.lr_scheduler
from torch.nn.parameter import Parameter
import torch.onnx
import torch.testing
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_mv_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * (x0 // 4), xmask, eviction_policy='evict_last'
)
tmp1 = tl.load(in_ptr1 + (4 * (x0 % 4) + 16 * (x0 // 16)), xmask,
eviction_policy='evict_last')
tmp4 = tl.load(in_ptr2 + 0)
tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
tmp7 = tl.load(in_ptr0 + (1 + 4 * (x0 // 4)), xmask, eviction_policy=
'evict_last')
tmp8 = tl.load(in_ptr1 + (1 + 4 * (x0 % 4) + 16 * (x0 // 16)), xmask,
eviction_policy='evict_last')
tmp11 = tl.load(in_ptr2 + 1)
tmp12 = tl.broadcast_to(tmp11, [XBLOCK])
tmp15 = tl.load(in_ptr0 + (2 + 4 * (x0 // 4)), xmask, eviction_policy=
'evict_last')
tmp16 = tl.load(in_ptr1 + (2 + 4 * (x0 % 4) + 16 * (x0 // 16)), xmask,
eviction_policy='evict_last')
tmp19 = tl.load(in_ptr2 + 2)
tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
tmp23 = tl.load(in_ptr0 + (3 + 4 * (x0 // 4)), xmask, eviction_policy=
'evict_last')
tmp24 = tl.load(in_ptr1 + (3 + 4 * (x0 % 4) + 16 * (x0 // 16)), xmask,
eviction_policy='evict_last')
tmp27 = tl.load(in_ptr2 + 3)
tmp28 = tl.broadcast_to(tmp27, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tmp6 = tmp3 * tmp5
tmp9 = tmp7 + tmp8
tmp10 = libdevice.tanh(tmp9)
tmp13 = tmp10 * tmp12
tmp14 = tmp6 + tmp13
tmp17 = tmp15 + tmp16
tmp18 = libdevice.tanh(tmp17)
tmp21 = tmp18 * tmp20
tmp22 = tmp14 + tmp21
tmp25 = tmp23 + tmp24
tmp26 = libdevice.tanh(tmp25)
tmp29 = tmp26 * tmp28
tmp30 = tmp22 + tmp29
tl.store(out_ptr0 + x0, tmp30, xmask)
@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(64)](primals_2, buf0, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_2
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
del primals_3
buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_clone_0[grid(64)](primals_1, buf2, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
del primals_4
buf4 = empty_strided_cuda((64,), (1,), torch.float32)
triton_poi_fused_mv_1[grid(64)](buf1, buf3, primals_5, buf4, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__softmax_2[grid(64)](buf4, buf5, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf6 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0)
del buf4
triton_poi_fused__softmax_3[grid(64)](buf5, buf6, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf7 = buf5
del buf5
extern_kernels.bmm(buf6, reinterpret_tensor(primals_1, (4, 4, 4), (
4, 16, 1), 0), out=buf7)
return reinterpret_tensor(buf7, (4, 4, 4), (4, 16, 1), 0
), reinterpret_tensor(buf6, (4, 4, 4), (4, 16, 1), 0
), primals_5, reinterpret_tensor(buf0, (16, 4), (4, 1), 0
), buf1, reinterpret_tensor(buf2, (16, 4), (4, 1), 0
), buf3, buf6, reinterpret_tensor(primals_1, (4, 4, 4), (4, 1, 16), 0)
class EltwiseAdd(nn.Module):
def __init__(self, inplace=False):
super(EltwiseAdd, self).__init__()
self.inplace = inplace
def forward(self, *input):
res = input[0]
if self.inplace:
for t in input[1:]:
res += t
else:
for t in input[1:]:
res = res + t
return res
class EltwiseMult(nn.Module):
def __init__(self, inplace=False):
super(EltwiseMult, self).__init__()
self.inplace = inplace
def forward(self, *input):
res = input[0]
if self.inplace:
for t in input[1:]:
res *= t
else:
for t in input[1:]:
res = res * t
return res
class Matmul(nn.Module):
"""
A wrapper module for matmul operation between 2 tensors.
"""
def __init__(self):
super(Matmul, self).__init__()
def forward(self, a: 'torch.Tensor', b: 'torch.Tensor'):
return a.matmul(b)
class BatchMatmul(nn.Module):
"""
A wrapper module for torch.bmm operation between 2 tensors.
"""
def __init__(self):
super(BatchMatmul, self).__init__()
def forward(self, a: 'torch.Tensor', b: 'torch.Tensor'):
return torch.bmm(a, b)
class BahdanauAttentionNew(nn.Module):
"""
It should be very similar to tf.contrib.seq2seq.BahdanauAttention
"""
def __init__(self, query_size, key_size, num_units, normalize=False,
dropout=0, batch_first=False):
super(BahdanauAttentionNew, self).__init__()
self.normalize = normalize
self.batch_first = batch_first
self.num_units = num_units
self.linear_q = nn.Linear(query_size, num_units, bias=False)
self.linear_k = nn.Linear(key_size, num_units, bias=False)
self.linear_att = Parameter(torch.Tensor(num_units))
self.dropout = nn.Dropout(dropout)
self.mask = None
self.eltwiseadd_qk = EltwiseAdd()
self.eltwiseadd_norm_bias = EltwiseAdd()
self.eltwisemul_norm_scaler = EltwiseMult()
self.tanh = nn.Tanh()
self.matmul_score = Matmul()
self.softmax_att = nn.Softmax(dim=-1)
self.context_matmul = BatchMatmul()
if self.normalize:
self.normalize_scalar = Parameter(torch.Tensor(1))
self.normalize_bias = Parameter(torch.Tensor(num_units))
else:
self.register_parameter('normalize_scalar', None)
self.register_parameter('normalize_bias', None)
self.reset_parameters()
def reset_parameters(self):
stdv = 1.0 / math.sqrt(self.num_units)
self.linear_att.data.uniform_(-stdv, stdv)
if self.normalize:
self.normalize_scalar.data.fill_(stdv)
self.normalize_bias.data.zero_()
def set_mask(self, context_len, context):
"""
sets self.mask which is applied before softmax
ones for inactive context fields, zeros for active context fields
:param context_len: b
:param context: if batch_first: (b x t_k x n) else: (t_k x b x n)
self.mask: (b x t_k)
"""
if self.batch_first:
max_len = context.size(1)
else:
max_len = context.size(0)
indices = torch.arange(0, max_len, dtype=torch.int64, device=
context.device)
self.mask = indices >= context_len.unsqueeze(1)
def calc_score(self, att_query, att_keys):
"""
Calculate Bahdanau score
:param att_query: b x t_q x n
:param att_keys: b x t_k x n
return b x t_q x t_k scores
"""
b, t_k, n = att_keys.size()
t_q = att_query.size(1)
att_query = att_query.unsqueeze(2).expand(b, t_q, t_k, n)
att_keys = att_keys.unsqueeze(1).expand(b, t_q, t_k, n)
sum_qk = self.eltwiseadd_qk(att_query, att_keys)
if self.normalize:
sum_qk = self.eltwiseadd_norm_bias(sum_qk, self.normalize_bias)
tmp = self.linear_att
linear_att = tmp / tmp.norm()
linear_att = linear_att
linear_att = self.eltwisemul_norm_scaler(linear_att, self.
normalize_scalar)
else:
linear_att = self.linear_att
out = self.matmul_score(self.tanh(sum_qk), linear_att)
return out
def forward(self, input_0, input_1):
primals_5 = self.linear_att
primals_3 = self.linear_q.weight
primals_4 = self.linear_k.weight
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0], output[1]
|
Emily0219/distiller
|
BahdanauAttention
| false
| 5,142
|
[
"Apache-2.0"
] | 1
|
445ed35b671fb54586acc280b53d951f18bf97ae
|
https://github.com/Emily0219/distiller/tree/445ed35b671fb54586acc280b53d951f18bf97ae
|
SelfAttention
|
import torch
import torch.nn.functional as F
from torch import nn
class SelfAttention(nn.Module):
def __init__(self, embedding_dimension, num_heads):
super().__init__()
assert embedding_dimension % num_heads == 0, f'embedding dimension must be divisible by number of heads, got embedding_dimension={embedding_dimension!r}, num_heads={num_heads!r}'
self.num_heads = num_heads
k = embedding_dimension
self.to_keys = nn.Linear(k, k * num_heads, bias=False)
self.to_queries = nn.Linear(k, k * num_heads, bias=False)
self.to_values = nn.Linear(k, k * num_heads, bias=False)
self.unify_heads = nn.Linear(num_heads * k, k)
def forward(self, x):
b, t, k = x.size()
h = self.num_heads
keys = self.to_keys(x).view(b, t, h, k)
queries = self.to_queries(x).view(b, t, h, k)
values = self.to_values(x).view(b, t, h, k)
keys = keys.transpose(1, 2).contiguous().view(b * h, t, k)
queries = queries.transpose(1, 2).contiguous().view(b * h, t, k)
values = values.transpose(1, 2).contiguous().view(b * h, t, k)
dot = torch.bmm(queries, keys.transpose(1, 2))
dot = dot / k ** (1 / 2)
dot = F.softmax(dot, dim=2)
out = torch.bmm(dot, values).view(b, h, t, k)
out = out.transpose(1, 2).contiguous().view(b, t, h * k)
return self.unify_heads(out)
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'embedding_dimension': 4, 'num_heads': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16 % 4
x3 = xindex // 64
x4 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
tl.store(out_ptr0 + x4, tmp0, xmask)
@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tmp4 = tmp3 * tmp1
tmp6 = tmp5 * tmp1
tmp7 = triton_helpers.maximum(tmp4, tmp6)
tmp9 = tmp8 * tmp1
tmp10 = triton_helpers.maximum(tmp7, tmp9)
tmp12 = tmp11 * tmp1
tmp13 = triton_helpers.maximum(tmp10, tmp12)
tmp14 = tmp2 - tmp13
tmp15 = 0.5
tmp16 = tmp14 * tmp15
tmp17 = tl_math.exp(tmp16)
tl.store(out_ptr0 + x2, tmp17, xmask)
@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (16, 4), (4, 1))
assert_size_stride(primals_3, (16, 4), (4, 1))
assert_size_stride(primals_4, (16, 4), (4, 1))
assert_size_stride(primals_5, (4, 16), (16, 1))
assert_size_stride(primals_6, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 16), (1, 4), 0), out=buf0)
del primals_2
buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_3, (4, 16), (1, 4), 0), out=buf1)
del primals_3
buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf2)
del primals_4
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(256)](buf1, buf3, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf4 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf1
triton_poi_fused_clone_0[grid(256)](buf0, buf4, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf5 = reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0)
del buf0
extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf4, (16, 4, 4), (16, 1, 4), 0), out=buf5)
buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__softmax_1[grid(256)](buf5, buf6, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf7 = buf5
del buf5
triton_poi_fused__softmax_2[grid(256)](buf6, buf7, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf8 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf6
triton_poi_fused_clone_0[grid(256)](buf2, buf8, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf9 = reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0)
del buf2
extern_kernels.bmm(buf7, reinterpret_tensor(buf8, (16, 4, 4), (16,
4, 1), 0), out=buf9)
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_0[grid(256)](buf9, buf10, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf9
buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_6, reinterpret_tensor(buf10, (16, 16),
(16, 1), 0), reinterpret_tensor(primals_5, (16, 4), (1, 16), 0),
alpha=1, beta=1, out=buf11)
del primals_6
return reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
), buf7, reinterpret_tensor(buf10, (16, 16), (16, 1), 0
), primals_5, reinterpret_tensor(buf8, (16, 4, 4), (16, 1, 4), 0
), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0
), reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0)
class SelfAttentionNew(nn.Module):
def __init__(self, embedding_dimension, num_heads):
super().__init__()
assert embedding_dimension % num_heads == 0, f'embedding dimension must be divisible by number of heads, got embedding_dimension={embedding_dimension!r}, num_heads={num_heads!r}'
self.num_heads = num_heads
k = embedding_dimension
self.to_keys = nn.Linear(k, k * num_heads, bias=False)
self.to_queries = nn.Linear(k, k * num_heads, bias=False)
self.to_values = nn.Linear(k, k * num_heads, bias=False)
self.unify_heads = nn.Linear(num_heads * k, k)
def forward(self, input_0):
primals_2 = self.to_keys.weight
primals_3 = self.to_queries.weight
primals_4 = self.to_values.weight
primals_5 = self.unify_heads.weight
primals_6 = self.unify_heads.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
Ensembl/gene_pcp
|
SelfAttention
| false
| 5,143
|
[
"Apache-2.0"
] | 1
|
121be9895d414da3f13b5c8ec7588754e03336e1
|
https://github.com/Ensembl/gene_pcp/tree/121be9895d414da3f13b5c8ec7588754e03336e1
|
AffineChannel2d
|
import torch
import torch.nn as nn
class AffineChannel2d(nn.Module):
""" A simple channel-wise affine transformation operation """
def __init__(self, num_features):
super().__init__()
self.num_features = num_features
self.weight = nn.Parameter(torch.Tensor(num_features))
self.bias = nn.Parameter(torch.Tensor(num_features))
self.weight.data.uniform_()
self.bias.data.zero_()
def forward(self, x):
return x * self.weight.view(1, self.num_features, 1, 1
) + self.bias.view(1, self.num_features, 1, 1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_features': 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 * tmp1
tmp4 = tmp2 + tmp3
tl.store(out_ptr0 + x3, tmp4, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4,), (1,))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_mul_0[grid(256)](primals_2, primals_1,
primals_3, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
del primals_3
return buf0, primals_2
class AffineChannel2dNew(nn.Module):
""" A simple channel-wise affine transformation operation """
def __init__(self, num_features):
super().__init__()
self.num_features = num_features
self.weight = nn.Parameter(torch.Tensor(num_features))
self.bias = nn.Parameter(torch.Tensor(num_features))
self.weight.data.uniform_()
self.bias.data.zero_()
def forward(self, input_0):
primals_1 = self.weight
primals_3 = self.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
FVL2020/2DImage2BMI
|
AffineChannel2d
| false
| 5,144
|
[
"MIT"
] | 1
|
90783bcb6fce0b91fb5ab70f62f595e3cfff39d0
|
https://github.com/FVL2020/2DImage2BMI/tree/90783bcb6fce0b91fb5ab70f62f595e3cfff39d0
|
FactorizationMachine
|
import torch
import torch.utils.data
class FactorizationMachine(torch.nn.Module):
def __init__(self, reduce_sum=True):
super().__init__()
self.reduce_sum = reduce_sum
def forward(self, x):
"""
:param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
"""
square_of_sum = torch.sum(x, dim=1) ** 2
sum_of_square = torch.sum(x ** 2, dim=1)
ix = square_of_sum - sum_of_square
if self.reduce_sum:
ix = torch.sum(ix, dim=1, keepdim=True)
return 0.5 * ix
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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp16 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
tmp17 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
tmp19 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
tmp21 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
tmp33 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
tmp34 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
tmp36 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
tmp38 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
tmp50 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
tmp51 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
tmp53 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
tmp55 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = tmp6 * tmp6
tmp8 = tmp0 * tmp0
tmp9 = tmp1 * tmp1
tmp10 = tmp8 + tmp9
tmp11 = tmp3 * tmp3
tmp12 = tmp10 + tmp11
tmp13 = tmp5 * tmp5
tmp14 = tmp12 + tmp13
tmp15 = tmp7 - tmp14
tmp18 = tmp16 + tmp17
tmp20 = tmp18 + tmp19
tmp22 = tmp20 + tmp21
tmp23 = tmp22 * tmp22
tmp24 = tmp16 * tmp16
tmp25 = tmp17 * tmp17
tmp26 = tmp24 + tmp25
tmp27 = tmp19 * tmp19
tmp28 = tmp26 + tmp27
tmp29 = tmp21 * tmp21
tmp30 = tmp28 + tmp29
tmp31 = tmp23 - tmp30
tmp32 = tmp15 + tmp31
tmp35 = tmp33 + tmp34
tmp37 = tmp35 + tmp36
tmp39 = tmp37 + tmp38
tmp40 = tmp39 * tmp39
tmp41 = tmp33 * tmp33
tmp42 = tmp34 * tmp34
tmp43 = tmp41 + tmp42
tmp44 = tmp36 * tmp36
tmp45 = tmp43 + tmp44
tmp46 = tmp38 * tmp38
tmp47 = tmp45 + tmp46
tmp48 = tmp40 - tmp47
tmp49 = tmp32 + tmp48
tmp52 = tmp50 + tmp51
tmp54 = tmp52 + tmp53
tmp56 = tmp54 + tmp55
tmp57 = tmp56 * tmp56
tmp58 = tmp50 * tmp50
tmp59 = tmp51 * tmp51
tmp60 = tmp58 + tmp59
tmp61 = tmp53 * tmp53
tmp62 = tmp60 + tmp61
tmp63 = tmp55 * tmp55
tmp64 = tmp62 + tmp63
tmp65 = tmp57 - tmp64
tmp66 = tmp49 + tmp65
tmp67 = 0.5
tmp68 = tmp66 * tmp67
tl.store(in_out_ptr0 + x2, tmp68, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 4), (4, 16, 1), torch.float32)
buf1 = reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_mul_pow_sub_sum_0[grid(16)](buf1, arg0_1, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del arg0_1
return buf1,
class FactorizationMachineNew(torch.nn.Module):
def __init__(self, reduce_sum=True):
super().__init__()
self.reduce_sum = reduce_sum
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Fanxingye/Autotabular
|
FactorizationMachine
| false
| 5,145
|
[
"Apache-2.0"
] | 1
|
d630c78290a52f8c73885afb16884e18135c34f6
|
https://github.com/Fanxingye/Autotabular/tree/d630c78290a52f8c73885afb16884e18135c34f6
|
AGELU
|
import math
import torch
import torch.utils.data
import torch.cuda
import torch.utils.checkpoint
def agelu(x):
SQRT_M2_PI = math.sqrt(2 / math.pi)
COEFF = 0.044715
return 0.5 * x * (1.0 + torch.tanh(SQRT_M2_PI * (x + COEFF * torch.pow(
x, 3))))
class AGELU(torch.nn.Module):
def forward(self, input):
return agelu(input)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.utils.data
import torch.cuda
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_mul_pow_tanh_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = tmp0 * tmp0
tmp4 = tmp3 * tmp0
tmp5 = 0.044715
tmp6 = tmp4 * tmp5
tmp7 = tmp0 + tmp6
tmp8 = 0.7978845608028654
tmp9 = tmp7 * tmp8
tmp10 = libdevice.tanh(tmp9)
tmp11 = 1.0
tmp12 = tmp10 + tmp11
tmp13 = tmp2 * tmp12
tl.store(out_ptr0 + x0, tmp13, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_mul_pow_tanh_0[grid(256)](arg0_1, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
return buf0,
def agelu(x):
SQRT_M2_PI = math.sqrt(2 / math.pi)
COEFF = 0.044715
return 0.5 * x * (1.0 + torch.tanh(SQRT_M2_PI * (x + COEFF * torch.pow(
x, 3))))
class AGELUNew(torch.nn.Module):
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Dan-hbd/NMTGMinor
|
AGELU
| false
| 5,146
|
[
"MIT"
] | 1
|
84e59ac8391ee78852d7c71afc60c3c8b8e3d44d
|
https://github.com/Dan-hbd/NMTGMinor/tree/84e59ac8391ee78852d7c71afc60c3c8b8e3d44d
|
FocalLoss
|
import torch
import torch.nn as nn
def log_minus_sigmoid(x):
return torch.clamp(-x, max=0) - torch.log(1 + torch.exp(-torch.abs(x))
) + 0.5 * torch.clamp(x, min=0, max=0)
def log_sigmoid(x):
return torch.clamp(x, max=0) - torch.log(1 + torch.exp(-torch.abs(x))
) + 0.5 * torch.clamp(x, min=0, max=0)
class FocalLoss(nn.Module):
def __init__(self, gamma=2):
super(FocalLoss, self).__init__()
self.gamma = gamma
def forward(self, input, target):
pos_log_sig = log_sigmoid(input)
neg_log_sig = log_minus_sigmoid(input)
prob = torch.sigmoid(input)
pos_weight = torch.pow(1 - prob, self.gamma)
neg_weight = torch.pow(prob, self.gamma)
loss = -(target * pos_weight * pos_log_sig + (1 - target) *
neg_weight * neg_log_sig)
avg_weight = target * pos_weight + (1 - target) * neg_weight
loss /= avg_weight.mean()
return loss.mean()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_abs_add_clamp_div_exp_log_mean_mul_neg_pow_rsub_sigmoid_sub_0(
in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tl.sigmoid(tmp1)
tmp3 = 1.0
tmp4 = tmp3 - tmp2
tmp5 = tmp4 * tmp4
tmp6 = tmp0 * tmp5
tmp7 = 0.0
tmp8 = triton_helpers.minimum(tmp1, tmp7)
tmp9 = tl_math.abs(tmp1)
tmp10 = -tmp9
tmp11 = tl_math.exp(tmp10)
tmp12 = tmp11 + tmp3
tmp13 = tl_math.log(tmp12)
tmp14 = tmp8 - tmp13
tmp15 = triton_helpers.maximum(tmp1, tmp7)
tmp16 = triton_helpers.minimum(tmp15, tmp7)
tmp17 = 0.5
tmp18 = tmp16 * tmp17
tmp19 = tmp14 + tmp18
tmp20 = tmp6 * tmp19
tmp21 = tmp3 - tmp0
tmp22 = tmp2 * tmp2
tmp23 = tmp21 * tmp22
tmp24 = -tmp1
tmp25 = triton_helpers.minimum(tmp24, tmp7)
tmp26 = tmp25 - tmp13
tmp27 = tmp26 + tmp18
tmp28 = tmp23 * tmp27
tmp29 = tmp20 + tmp28
tmp30 = -tmp29
tmp31 = tmp6 + tmp23
tmp32 = tl.broadcast_to(tmp31, [RBLOCK])
tmp34 = triton_helpers.promote_to_tensor(tl.sum(tmp32, 0))
tmp35 = 256.0
tmp36 = tmp34 / tmp35
tmp37 = tmp30 / tmp36
tmp38 = tl.broadcast_to(tmp37, [RBLOCK])
tmp40 = triton_helpers.promote_to_tensor(tl.sum(tmp38, 0))
tmp41 = tmp40 / tmp35
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp41, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = buf1
del buf1
buf3 = buf2
del buf2
get_raw_stream(0)
triton_per_fused_abs_add_clamp_div_exp_log_mean_mul_neg_pow_rsub_sigmoid_sub_0[
grid(1)](buf3, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf3,
def log_minus_sigmoid(x):
return torch.clamp(-x, max=0) - torch.log(1 + torch.exp(-torch.abs(x))
) + 0.5 * torch.clamp(x, min=0, max=0)
def log_sigmoid(x):
return torch.clamp(x, max=0) - torch.log(1 + torch.exp(-torch.abs(x))
) + 0.5 * torch.clamp(x, min=0, max=0)
class FocalLossNew(nn.Module):
def __init__(self, gamma=2):
super(FocalLossNew, self).__init__()
self.gamma = gamma
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
FadedFate/TrackerSiamRPN
|
FocalLoss
| false
| 5,147
|
[
"MIT"
] | 1
|
f4156fa4bed9a0ca6c7ac9b653c07e564d8a058d
|
https://github.com/FadedFate/TrackerSiamRPN/tree/f4156fa4bed9a0ca6c7ac9b653c07e564d8a058d
|
ReLUDropout
|
import torch
import torch.utils.data
import torch.cuda
import torch.utils.checkpoint
def relu_dropout(x, p=0, training=False, variational=False, batch_first=False):
if not training or p == 0:
return x.clamp_(min=0)
p1m = 1 - p
if variational:
if batch_first:
mask = torch.rand_like(x[:, 0, :]) > p1m
mask = mask.unsqueeze(1).repeat(1, x.size(1), 1)
else:
mask = torch.rand_like(x[0]) > p1m
mask = mask.unsqueeze(0).repeat(x.size(0), 1, 1)
else:
mask = torch.rand_like(x) > p1m
mask |= x < 0
return x.masked_fill_(mask, 0).div_(p1m)
class ReLUDropout(torch.nn.Dropout):
def __init__(self, p=0.5, variational=False, batch_first=False, inplace
=False):
super().__init__(p, inplace=True)
self.variational = variational
self.batch_first = batch_first
def forward(self, input):
return relu_dropout(input, p=self.p, training=self.training,
variational=self.variational, batch_first=self.batch_first)
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.utils.data
import torch.cuda
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_clamp_0(in_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tl.store(out_ptr1 + x0, tmp2, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
get_raw_stream(0)
triton_poi_fused_clamp_0[grid(256)](arg0_1, arg0_1, 256, XBLOCK=128,
num_warps=4, num_stages=1)
return arg0_1,
def relu_dropout(x, p=0, training=False, variational=False, batch_first=False):
if not training or p == 0:
return x.clamp_(min=0)
p1m = 1 - p
if variational:
if batch_first:
mask = torch.rand_like(x[:, 0, :]) > p1m
mask = mask.unsqueeze(1).repeat(1, x.size(1), 1)
else:
mask = torch.rand_like(x[0]) > p1m
mask = mask.unsqueeze(0).repeat(x.size(0), 1, 1)
else:
mask = torch.rand_like(x) > p1m
mask |= x < 0
return x.masked_fill_(mask, 0).div_(p1m)
class ReLUDropoutNew(torch.nn.Dropout):
def __init__(self, p=0.5, variational=False, batch_first=False, inplace
=False):
super().__init__(p, inplace=True)
self.variational = variational
self.batch_first = batch_first
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Dan-hbd/NMTGMinor
|
ReLUDropout
| false
| 5,148
|
[
"MIT"
] | 1
|
84e59ac8391ee78852d7c71afc60c3c8b8e3d44d
|
https://github.com/Dan-hbd/NMTGMinor/tree/84e59ac8391ee78852d7c71afc60c3c8b8e3d44d
|
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