entry_point
stringlengths 1
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| original_triton_python_code
stringlengths 208
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| optimised_triton_code
stringlengths 1.15k
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| repo_name
stringlengths 7
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| module_name
stringlengths 1
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class | uuid
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listlengths 1
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stringlengths 40
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stringlengths 72
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Residential
|
import torch
class Convlayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1):
super().__init__()
padding = kernel_size // 2
self.refl = torch.nn.ReflectionPad2d(padding)
self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size,
stride)
def forward(self, x):
x = self.refl(x)
return self.conv(x)
class Residential(torch.nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv1 = Convlayer(in_channels=in_channels, out_channels=
in_channels, kernel_size=3)
self.inst1 = torch.nn.InstanceNorm2d(in_channels, affine=True)
self.conv2 = Convlayer(in_channels=in_channels, out_channels=
in_channels, kernel_size=3)
self.inst2 = torch.nn.InstanceNorm2d(in_channels, affine=True)
self.relu = torch.nn.ReLU()
def forward(self, x):
resident = x
x = self.relu(self.inst1(self.conv1(x)))
x = self.inst2(self.conv2(x))
return resident + x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_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 libdevice, math as tl_math
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 = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_1(in_out_ptr0,
in_out_ptr1, in_ptr0, out_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)
r2 = rindex
x3 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tl.where(xmask, tmp3, 0)
tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp8 = tl.where(xmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp11 = tmp10.to(tl.float32)
tmp12 = tmp9 / tmp11
tmp13 = tmp3 - tmp12
tmp14 = tmp13 * tmp13
tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
tmp17 = tl.where(xmask, tmp15, 0)
tmp18 = tl.sum(tmp17, 1)[:, None]
tmp19 = 16.0
tmp20 = tmp18 / tmp19
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + x3, tmp23, xmask)
tl.store(out_ptr0 + x3, tmp12, 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
tmp0 = tl.load(in_ptr0 + x0 % 4, 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 = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * 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_4(
in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1,
out_ptr3, out_ptr4, 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)
x0 = xindex
r3 = rindex
x1 = xindex % 4
tmp0 = tl.load(in_ptr0 + x0 % 4, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_out_ptr0 + (r3 + 16 * x0), xmask, other=0.0)
tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp20 = tl.load(in_ptr2 + (r3 + 16 * x0), xmask, other=0.0)
tmp29 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tl.where(xmask, tmp4, 0)
tmp7 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
tmp9 = tl.where(xmask, tmp7, 0)
tmp10 = tl.sum(tmp9, 1)[:, None]
tmp11 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp12 = tmp11.to(tl.float32)
tmp13 = tmp10 / tmp12
tmp14 = tmp4 - tmp13
tmp15 = tmp14 * tmp14
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp18 = tl.where(xmask, tmp16, 0)
tmp19 = tl.sum(tmp18, 1)[:, None]
tmp21 = tmp3 - tmp13
tmp22 = 16.0
tmp23 = tmp19 / tmp22
tmp24 = 1e-05
tmp25 = tmp23 + tmp24
tmp26 = libdevice.rsqrt(tmp25)
tmp27 = tmp21 * tmp26
tmp28 = tmp27 * tmp0
tmp30 = tmp28 + tmp29
tmp31 = tmp20 + tmp30
tl.store(out_ptr0 + x0, tmp0, xmask)
tl.store(in_out_ptr0 + (r3 + 16 * x0), tmp3, xmask)
tl.store(out_ptr3 + (r3 + 16 * x0), tmp31, xmask)
tl.store(out_ptr4 + x0, tmp26, xmask)
tl.store(out_ptr1 + x0, tmp13, 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, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(576)](primals_1, buf0, 576,
XBLOCK=256, num_warps=4, num_stages=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, 4, 4, 4), (64, 16, 4, 1))
buf2 = buf1
del buf1
buf5 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
)
buf8 = reinterpret_tensor(buf6, (1, 16, 1, 1), (16, 1, 1, 1), 0)
del buf6
triton_per_fused__native_batch_norm_legit_convolution_1[grid(16)](buf2,
buf8, primals_3, buf5, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
del primals_3
buf3 = empty_strided_cuda((16,), (1,), torch.float32)
triton_poi_fused_repeat_2[grid(16)](primals_4, buf3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_4
buf4 = empty_strided_cuda((16,), (1,), torch.float32)
triton_poi_fused_repeat_2[grid(16)](primals_5, buf4, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_5
buf9 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
triton_poi_fused_reflection_pad2d_relu_3[grid(576)](buf2, buf5,
buf8, buf3, buf4, buf9, 576, XBLOCK=256, num_warps=4, num_stages=1)
buf10 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 4, 4, 4), (64, 16, 4, 1))
buf12 = empty_strided_cuda((16,), (1,), torch.float32)
buf11 = buf10
del buf10
buf13 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
float32)
buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf16 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
float32)
triton_per_fused__native_batch_norm_legit_add_convolution_repeat_4[grid
(16)](buf11, primals_8, primals_7, primals_1, primals_9, buf12,
buf13, buf17, buf16, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
del primals_1
del primals_7
del primals_8
del primals_9
return (buf17, primals_2, primals_6, buf0, buf2, buf3, buf4, buf5, buf8,
buf9, buf11, buf12, reinterpret_tensor(buf16, (16,), (1,), 0),
reinterpret_tensor(buf13, (1, 16, 1, 1), (16, 1, 1, 1), 0))
class Convlayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1):
super().__init__()
padding = kernel_size // 2
self.refl = torch.nn.ReflectionPad2d(padding)
self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size,
stride)
def forward(self, x):
x = self.refl(x)
return self.conv(x)
class ResidentialNew(torch.nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv1 = Convlayer(in_channels=in_channels, out_channels=
in_channels, kernel_size=3)
self.inst1 = torch.nn.InstanceNorm2d(in_channels, affine=True)
self.conv2 = Convlayer(in_channels=in_channels, out_channels=
in_channels, kernel_size=3)
self.inst2 = torch.nn.InstanceNorm2d(in_channels, affine=True)
self.relu = torch.nn.ReLU()
def forward(self, input_0):
primals_2 = self.conv1.conv.weight
primals_3 = self.conv1.conv.bias
primals_4 = self.inst1.weight
primals_5 = self.inst1.bias
primals_6 = self.conv2.conv.weight
primals_7 = self.conv2.conv.bias
primals_8 = self.inst2.weight
primals_9 = self.inst2.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])
return output[0]
|
bruchano/ImageStyler
|
Residential
| false
| 9,928
|
[
"MIT"
] | 0
|
7bde13bc954566088c477065adb5c4e4214c28bb
|
https://github.com/bruchano/ImageStyler/tree/7bde13bc954566088c477065adb5c4e4214c28bb
|
FC_Block
|
import math
import torch
import torch.nn as nn
import torch.optim
import torch.multiprocessing
from torch.nn.parameter import Parameter
class FullyConnected(nn.Module):
def __init__(self, in_features, out_features, bias=True):
"""
Fully connected layer of learnable weights with learnable bias
:param self:
:param in_features: number neurons in
:param out_features: num neurons out
:param bias: to use bias (boole)
:return:
"""
super(FullyConnected, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.FloatTensor(in_features, out_features))
if bias:
self.bias = Parameter(torch.FloatTensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
stdv = 1.0 / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward(self, input):
output = torch.matmul(input, self.weight)
if self.bias is not None:
return output + self.bias
else:
return output
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
class FC_Block(nn.Module):
def __init__(self, in_features, out_features, activation=nn.LeakyReLU(
0.1), batch_norm=False, p_dropout=0.0, bias=True):
"""
Define a fully connected block
"""
super(FC_Block, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.act_f = activation
self.batch_norm = batch_norm
self.p_dropout = p_dropout
self.do = nn.Dropout(p_dropout)
self.fc = FullyConnected(self.in_features, self.out_features, bias=bias
)
if self.batch_norm:
self.bn = nn.BatchNorm1d(out_features)
def forward(self, x):
y = self.fc(x)
y = self.act_f(y)
if self.batch_norm:
b, f = y.shape
y = self.bn(y.view(b, -1)).view(b, f)
y = self.do(y)
return y
def __repr__(self):
representation = self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')' + ', dropout: ' + str(
self.p_dropout)
if self.batch_norm:
representation = representation + ', batch norm'
representation = representation + ', act_fn: {0}'.format(self.act_f)
return representation
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
import math
import torch.nn as nn
import torch.optim
import torch.multiprocessing
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_add_leaky_relu_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
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
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.1
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr1 + x2, tmp7, 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((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
primals_1, out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_leaky_relu_0[grid(256)](buf0, primals_3, buf1,
buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
del buf0
del primals_3
return buf2, buf1, reinterpret_tensor(primals_2, (4, 64), (1, 4), 0)
class FullyConnected(nn.Module):
def __init__(self, in_features, out_features, bias=True):
"""
Fully connected layer of learnable weights with learnable bias
:param self:
:param in_features: number neurons in
:param out_features: num neurons out
:param bias: to use bias (boole)
:return:
"""
super(FullyConnected, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.FloatTensor(in_features, out_features))
if bias:
self.bias = Parameter(torch.FloatTensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
stdv = 1.0 / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward(self, input):
output = torch.matmul(input, self.weight)
if self.bias is not None:
return output + self.bias
else:
return output
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
class FC_BlockNew(nn.Module):
def __init__(self, in_features, out_features, activation=nn.LeakyReLU(
0.1), batch_norm=False, p_dropout=0.0, bias=True):
"""
Define a fully connected block
"""
super(FC_BlockNew, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.act_f = activation
self.batch_norm = batch_norm
self.p_dropout = p_dropout
self.do = nn.Dropout(p_dropout)
self.fc = FullyConnected(self.in_features, self.out_features, bias=bias
)
if self.batch_norm:
self.bn = nn.BatchNorm1d(out_features)
def __repr__(self):
representation = self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')' + ', dropout: ' + str(
self.p_dropout)
if self.batch_norm:
representation = representation + ', batch norm'
representation = representation + ', act_fn: {0}'.format(self.act_f)
return representation
def forward(self, input_0):
primals_1 = self.fc.weight
primals_3 = self.fc.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
bouracha/Gen_Motion
|
FC_Block
| false
| 9,929
|
[
"MIT"
] | 0
|
873caa496d14c9a9723581cdf1464f44db4cf358
|
https://github.com/bouracha/Gen_Motion/tree/873caa496d14c9a9723581cdf1464f44db4cf358
|
_BoundaryRefineModule
|
import torch
from torch import nn
class _BoundaryRefineModule(nn.Module):
def __init__(self, dim):
super(_BoundaryRefineModule, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
def forward(self, x):
residual = self.conv1(x)
residual = self.relu(residual)
residual = self.conv2(residual)
out = x + residual
return out
def get_inputs():
return [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 import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_convolution_relu_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
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')
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_add_convolution_1(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
x3 = xindex
x1 = xindex // 16 % 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 = 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, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4,), (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
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(256)](buf1, primals_2, 256,
XBLOCK=256, 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, 4, 4, 4), (64, 16, 4, 1))
buf3 = buf2
del buf2
triton_poi_fused_add_convolution_1[grid(256)](buf3, primals_3,
primals_5, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
return buf3, primals_1, primals_3, primals_4, buf1
class _BoundaryRefineModuleNew(nn.Module):
def __init__(self, dim):
super(_BoundaryRefineModuleNew, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(dim, dim, 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_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
adynathos/pytorch-semantic-segmentation
|
_BoundaryRefineModule
| false
| 9,930
|
[
"MIT"
] | 0
|
44d1784984cfd0926821c3fdbc20d371bb074296
|
https://github.com/adynathos/pytorch-semantic-segmentation/tree/44d1784984cfd0926821c3fdbc20d371bb074296
|
GraphGaussianBlock
|
import math
import torch
import torch.nn as nn
import torch.optim
import torch.multiprocessing
from torch.nn.parameter import Parameter
class GraphConvolution(nn.Module):
def __init__(self, in_features, out_features, bias=True, node_n=48,
out_node_n=None):
super(GraphConvolution, self).__init__()
if out_node_n is None:
out_node_n = node_n
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.FloatTensor(in_features, out_features))
self.att = Parameter(torch.FloatTensor(out_node_n, node_n))
if bias:
self.bias = Parameter(torch.FloatTensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
stdv = 1.0 / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
self.att.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward(self, input):
support = torch.matmul(input, self.weight)
output = torch.matmul(self.att, support)
if self.bias is not None:
return output + self.bias
else:
return output
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
class GraphGaussianBlock(nn.Module):
def __init__(self, in_nodes, in_features, n_z_nodes, n_z_features):
"""
:param input_feature: num of input feature
:param n_z: dim of distribution
"""
super(GraphGaussianBlock, self).__init__()
self.in_features = in_features
self.in_nodes = in_nodes
self.n_z_features = n_z_features
self.n_z_nodes = n_z_nodes
self.z_mu_graphconv = GraphConvolution(in_features, n_z_features,
bias=True, node_n=in_nodes, out_node_n=n_z_nodes)
self.z_log_var_graphconv = GraphConvolution(in_features,
n_z_features, bias=True, node_n=in_nodes, out_node_n=n_z_nodes)
def forward(self, x):
y = x
mu = self.z_mu_graphconv(y)
log_var = self.z_log_var_graphconv(y)
log_var = torch.clamp(log_var, min=-20.0, max=3.0)
return mu, log_var
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_nodes': 4, 'in_features': 4, 'n_z_nodes': 4,
'n_z_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
import math
import torch.nn as nn
import torch.optim
import torch.multiprocessing
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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 64
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_add_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 64
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 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused_add_clamp_clone_2(in_ptr0, in_ptr1, out_ptr0, ynumel,
xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 64
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 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = -20.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 3.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tl.store(out_ptr0 + (x2 + 4 * y3), tmp6, xmask & ymask)
@triton.jit
def triton_poi_fused_add_clone_ge_le_logical_and_3(in_ptr0, in_ptr1,
out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 64
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
y3 = yindex
y0 = yindex % 4
y1 = yindex // 4
tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = -20.0
tmp4 = tmp2 >= tmp3
tmp5 = 3.0
tmp6 = tmp2 <= tmp5
tmp7 = tmp4 & tmp6
tl.store(out_ptr0 + (y0 + 4 * x2 + 16 * y1), tmp7, xmask & ymask)
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, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (4, 4), (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((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
primals_2, out=buf0)
del primals_2
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(64, 4)](buf0, buf1, 64, 4, XBLOCK=4,
YBLOCK=32, num_warps=4, num_stages=1)
buf2 = buf0
del buf0
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf2)
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_clone_1[grid(64, 4)](buf2, primals_4, buf3, 64,
4, XBLOCK=4, YBLOCK=64, num_warps=4, num_stages=1)
del primals_4
buf4 = buf2
del buf2
extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
primals_5, out=buf4)
del primals_5
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_0[grid(64, 4)](buf4, buf5, 64, 4, XBLOCK=4,
YBLOCK=32, num_warps=4, num_stages=1)
buf6 = buf4
del buf4
extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf6)
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_clamp_clone_2[grid(64, 4)](buf6, primals_7,
buf7, 64, 4, XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_add_clone_ge_le_logical_and_3[grid(64, 4)](buf6,
primals_7, buf8, 64, 4, XBLOCK=4, YBLOCK=64, num_warps=4,
num_stages=1)
del buf6
del primals_7
return buf3, buf7, reinterpret_tensor(buf1, (64, 4), (4, 1), 0
), reinterpret_tensor(buf5, (64, 4), (4, 1), 0
), buf8, primals_6, reinterpret_tensor(primals_1, (4, 64), (1, 4), 0
), primals_3
class GraphConvolution(nn.Module):
def __init__(self, in_features, out_features, bias=True, node_n=48,
out_node_n=None):
super(GraphConvolution, self).__init__()
if out_node_n is None:
out_node_n = node_n
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.FloatTensor(in_features, out_features))
self.att = Parameter(torch.FloatTensor(out_node_n, node_n))
if bias:
self.bias = Parameter(torch.FloatTensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
stdv = 1.0 / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
self.att.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward(self, input):
support = torch.matmul(input, self.weight)
output = torch.matmul(self.att, support)
if self.bias is not None:
return output + self.bias
else:
return output
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
class GraphGaussianBlockNew(nn.Module):
def __init__(self, in_nodes, in_features, n_z_nodes, n_z_features):
"""
:param input_feature: num of input feature
:param n_z: dim of distribution
"""
super(GraphGaussianBlockNew, self).__init__()
self.in_features = in_features
self.in_nodes = in_nodes
self.n_z_features = n_z_features
self.n_z_nodes = n_z_nodes
self.z_mu_graphconv = GraphConvolution(in_features, n_z_features,
bias=True, node_n=in_nodes, out_node_n=n_z_nodes)
self.z_log_var_graphconv = GraphConvolution(in_features,
n_z_features, bias=True, node_n=in_nodes, out_node_n=n_z_nodes)
def forward(self, input_0):
primals_2 = self.z_mu_graphconv.weight
primals_3 = self.z_mu_graphconv.att
primals_4 = self.z_mu_graphconv.bias
primals_5 = self.z_log_var_graphconv.weight
primals_6 = self.z_log_var_graphconv.att
primals_7 = self.z_log_var_graphconv.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0], output[1]
|
bouracha/Gen_Motion
|
GraphGaussianBlock
| false
| 9,931
|
[
"MIT"
] | 0
|
873caa496d14c9a9723581cdf1464f44db4cf358
|
https://github.com/bouracha/Gen_Motion/tree/873caa496d14c9a9723581cdf1464f44db4cf358
|
SpatialAttention2d
|
import torch
import torch.nn as nn
class SpatialAttention2d(nn.Module):
def __init__(self, channel):
super(SpatialAttention2d, self).__init__()
self.squeeze = nn.Conv2d(channel, 1, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
z = self.squeeze(x)
z = self.sigmoid(z)
return x * z
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'channel': 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
@triton.jit
def triton_poi_fused_mul_sigmoid_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
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.sigmoid(tmp1)
tmp3 = tmp0 * tmp2
tl.store(out_ptr0 + x3, tmp3, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (1, 4, 1, 1), (4, 1, 1, 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=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 1, 4, 4), (16, 16, 4, 1))
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_sigmoid_0[grid(256)](primals_2, buf0, buf1,
256, XBLOCK=128, num_warps=4, num_stages=1)
return buf1, primals_1, primals_2, buf0
class SpatialAttention2dNew(nn.Module):
def __init__(self, channel):
super(SpatialAttention2dNew, self).__init__()
self.squeeze = nn.Conv2d(channel, 1, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, input_0):
primals_1 = self.squeeze.weight
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
advian123/kaggle-birdsong-recognition
|
SpatialAttention2d
| false
| 9,932
|
[
"MIT"
] | 0
|
a4ca8ab81e166b919452fb5d6ca4c2912c65e904
|
https://github.com/advian123/kaggle-birdsong-recognition/tree/a4ca8ab81e166b919452fb5d6ca4c2912c65e904
|
LSoftLoss
|
import torch
import torch.nn.functional as F
import torch.nn as nn
class LSoftLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, y_pred, y_true, beta):
with torch.no_grad():
y_true_updated = beta * y_true + (1 - beta) * y_pred
return F.binary_cross_entropy(y_pred, y_true_updated, reduction='none')
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.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_binary_cross_entropy_mul_rsub_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)
tmp5 = tl.load(in_ptr2 + x0, xmask)
tmp2 = tmp0 * tmp1
tmp3 = 1.0
tmp4 = tmp3 - tmp0
tmp6 = tmp4 * tmp5
tmp7 = tmp2 + tmp6
tmp8 = tmp7 - tmp3
tmp9 = -tmp5
tmp10 = libdevice.log1p(tmp9)
tmp11 = -100.0
tmp12 = triton_helpers.maximum(tmp10, tmp11)
tmp13 = tmp8 * tmp12
tmp14 = tl_math.log(tmp5)
tmp15 = triton_helpers.maximum(tmp14, tmp11)
tmp16 = tmp7 * tmp15
tmp17 = tmp13 - tmp16
tl.store(out_ptr0 + x0, tmp17, 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_add_binary_cross_entropy_mul_rsub_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 LSoftLossNew(nn.Module):
def __init__(self):
super().__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]
|
advian123/kaggle-birdsong-recognition
|
LSoftLoss
| false
| 9,933
|
[
"MIT"
] | 0
|
a4ca8ab81e166b919452fb5d6ca4c2912c65e904
|
https://github.com/advian123/kaggle-birdsong-recognition/tree/a4ca8ab81e166b919452fb5d6ca4c2912c65e904
|
LanguageModelCriterion
|
import torch
import torch.nn as nn
from torch.autograd import *
def to_contiguous(tensor):
if tensor.is_contiguous():
return tensor
else:
return tensor.contiguous()
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
target = target[:, :input.size(1)]
mask = mask[:, :input.size(1)]
input = to_contiguous(input).view(-1, input.size(2))
target = to_contiguous(target).view(-1, 1)
mask = to_contiguous(mask).view(-1, 1)
output = -input.gather(1, target) * mask
output = torch.sum(output) / torch.sum(mask)
return output
def get_inputs():
return [torch.ones([4, 4, 4], dtype=torch.int64), torch.ones([4, 4],
dtype=torch.int64), 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
import torch.nn as nn
from torch.autograd import *
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_div_gather_mul_neg_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, 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
tmp0 = tl.load(in_ptr0 + r0, None)
tmp9 = tl.load(in_ptr2 + r0, None)
tmp1 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tl.device_assert((0 <= tmp4) & (tmp4 < 4),
'index out of bounds: 0 <= tmp4 < 4')
tmp6 = tl.load(in_ptr1 + (tmp4 + 4 * r0), None, eviction_policy=
'evict_last')
tmp7 = -tmp6
tmp8 = tmp7.to(tl.float32)
tmp10 = tmp8 * tmp9
tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
tmp13 = tl.sum(tmp11, 1)[:, None]
tmp14 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
tmp16 = tl.sum(tmp14, 1)[:, None]
tmp17 = tmp13 / tmp16
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp17, None)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(arg1_1, (4, 4), (4, 1))
assert_size_stride(arg2_1, (4, 4), (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_div_gather_mul_neg_sum_0[grid(1)](buf2, arg1_1,
arg0_1, arg2_1, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
del arg2_1
return buf2,
def to_contiguous(tensor):
if tensor.is_contiguous():
return tensor
else:
return tensor.contiguous()
class LanguageModelCriterionNew(nn.Module):
def __init__(self):
super(LanguageModelCriterionNew, 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]
|
curlG0/videotime
|
LanguageModelCriterion
| false
| 9,934
|
[
"MIT"
] | 0
|
4eba44d148ba2d11f9bf2e9ba3ea9a3ecac70721
|
https://github.com/curlG0/videotime/tree/4eba44d148ba2d11f9bf2e9ba3ea9a3ecac70721
|
DownsampleA
|
import torch
import torch.nn as nn
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
def forward(self, x):
x = self.avg(x)
return torch.cat((x, x.mul(0)), 1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'nIn': 4, 'nOut': 4, 'stride': 1}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
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 = 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 = 1.0
tmp7 = tmp5 * tmp6
tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
tmp9 = tl.where(tmp4, tmp7, tmp8)
tmp10 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp13 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp10 &
xmask, other=0.0)
tmp14 = tmp13 * tmp6
tmp15 = 0.0
tmp16 = tmp14 * tmp15
tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
tmp18 = tl.where(tmp10, tmp16, tmp17)
tmp19 = tl.where(tmp4, tmp9, tmp18)
tl.store(out_ptr0 + x3, tmp19, 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, 8, 4, 4), (128, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(512)](arg0_1, buf0, 512, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class DownsampleANew(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleANew, self).__init__()
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
code-inter/leak
|
DownsampleA
| false
| 9,935
|
[
"MIT"
] | 0
|
0e2b12a42f5fbaac4c5fa68627a21aa9a2f3d1d6
|
https://github.com/code-inter/leak/tree/0e2b12a42f5fbaac4c5fa68627a21aa9a2f3d1d6
|
VGG19Decoder2
|
import torch
import torch.nn as nn
from collections import OrderedDict
class VGG19Decoder2(nn.Module):
def __init__(self):
super(VGG19Decoder2, self).__init__()
self.blocks = OrderedDict([('pad2_1', nn.ReflectionPad2d(1)), (
'conv2_1', nn.Conv2d(128, 64, 3, 1, 0)), ('relu2_1', nn.ReLU(
inplace=True)), ('unpool1', nn.Upsample(scale_factor=2)), (
'pad1_2', nn.ReflectionPad2d(1)), ('conv1_2', nn.Conv2d(64, 64,
3, 1, 0)), ('relu1_2', nn.ReLU(inplace=True)), ('pad1_1', nn.
ReflectionPad2d(1)), ('conv1_1', nn.Conv2d(64, 3, 3, 1, 0))])
self.seq = nn.Sequential(self.blocks)
def forward(self, x, targets=None):
return self.seq(x)
def get_inputs():
return [torch.rand([4, 128, 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
from collections import OrderedDict
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_reflection_pad2d_0(in_ptr0, 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 % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), None,
eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 8
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_convolution_reflection_pad2d_relu_2(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 25600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 10 % 10
x0 = xindex % 10
x4 = xindex // 100
x2 = xindex // 100 % 64
x7 = xindex
tmp0 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x1
))), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0
))), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 4, 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 + 4 * tmp4 + 16 * x4), xmask,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x7, tmp13, xmask)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 25600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 10
x1 = xindex // 10 % 10
x4 = xindex // 100
x2 = xindex // 100 % 64
x5 = xindex
tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-7 + tl_math.abs(-1 +
x0)) + -8 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 64 * x4),
xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, xmask)
@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 768
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 64 % 3
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_relu_threshold_backward_5(in_ptr0, in_ptr1,
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
x1 = xindex // 64 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, 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, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_6(in_ptr0, in_ptr1,
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
x1 = xindex // 16 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, 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, tmp6, None)
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, 128, 4, 4), (2048, 16, 4, 1))
assert_size_stride(primals_2, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_3, (64,), (1,))
assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (3, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_7, (3,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 128, 6, 6), (4608, 36, 6, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(18432)](primals_1, buf0,
18432, 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, 64, 4, 4), (1024, 16, 4, 1))
buf2 = empty_strided_cuda((8,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_1[grid(8)](buf2, 8, XBLOCK
=8, num_warps=1, num_stages=1)
buf3 = empty_strided_cuda((4, 64, 10, 10), (6400, 100, 10, 1),
torch.float32)
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2[grid
(25600)](buf2, buf1, primals_3, buf3, 25600, XBLOCK=256,
num_warps=4, num_stages=1)
buf4 = extern_kernels.convolution(buf3, 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, 64, 8, 8), (4096, 64, 8, 1))
buf5 = empty_strided_cuda((4, 64, 10, 10), (6400, 100, 10, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(25600)](buf4,
primals_5, buf5, 25600, XBLOCK=256, num_warps=4, num_stages=1)
buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 3, 8, 8), (192, 64, 8, 1))
buf7 = buf6
del buf6
triton_poi_fused_convolution_4[grid(768)](buf7, primals_7, 768,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_7
buf8 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_5[grid(16384)](
buf4, primals_5, buf8, 16384, XBLOCK=256, num_warps=4, num_stages=1
)
del buf4
del primals_5
buf9 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_6[grid(4096)](buf1
, primals_3, buf9, 4096, XBLOCK=256, num_warps=4, num_stages=1)
del buf1
del primals_3
return (buf7, primals_2, primals_4, primals_6, buf0, buf2, buf3, buf5,
buf8, buf9)
class VGG19Decoder2New(nn.Module):
def __init__(self):
super(VGG19Decoder2New, self).__init__()
self.blocks = OrderedDict([('pad2_1', nn.ReflectionPad2d(1)), (
'conv2_1', nn.Conv2d(128, 64, 3, 1, 0)), ('relu2_1', nn.ReLU(
inplace=True)), ('unpool1', nn.Upsample(scale_factor=2)), (
'pad1_2', nn.ReflectionPad2d(1)), ('conv1_2', nn.Conv2d(64, 64,
3, 1, 0)), ('relu1_2', nn.ReLU(inplace=True)), ('pad1_1', nn.
ReflectionPad2d(1)), ('conv1_1', nn.Conv2d(64, 3, 3, 1, 0))])
self.seq = nn.Sequential(self.blocks)
def forward(self, input_0):
primals_2 = self.seq.conv2_1.weight
primals_3 = self.seq.conv2_1.bias
primals_4 = self.seq.conv1_2.weight
primals_5 = self.seq.conv1_2.bias
primals_6 = self.seq.conv1_1.weight
primals_7 = self.seq.conv1_1.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
chenhsiu48/PytorchWCT
|
VGG19Decoder2
| false
| 9,936
|
[
"MIT"
] | 0
|
c3346ebaec95358ad1d4d5a519d5d0e7de73bc75
|
https://github.com/chenhsiu48/PytorchWCT/tree/c3346ebaec95358ad1d4d5a519d5d0e7de73bc75
|
SCse
|
import torch
import torch.nn as nn
class SpatialAttention2d(nn.Module):
def __init__(self, channel):
super(SpatialAttention2d, self).__init__()
self.squeeze = nn.Conv2d(channel, 1, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
z = self.squeeze(x)
z = self.sigmoid(z)
return x * z
class GAB(nn.Module):
def __init__(self, input_dim, reduction=4):
super(GAB, self).__init__()
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(input_dim, input_dim // reduction,
kernel_size=1, stride=1)
self.conv2 = nn.Conv2d(input_dim // reduction, input_dim,
kernel_size=1, stride=1)
self.relu = nn.ReLU(inplace=True)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
z = self.global_avgpool(x)
z = self.relu(self.conv1(z))
z = self.sigmoid(self.conv2(z))
return x * z
class SCse(nn.Module):
def __init__(self, dim):
super(SCse, self).__init__()
self.satt = SpatialAttention2d(dim)
self.catt = GAB(dim)
def forward(self, x):
return self.satt(x) + self.catt(x)
def get_inputs():
return [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 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_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)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_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_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tmp4 = tl.full([1], 0, tl.int32)
tmp5 = triton_helpers.maximum(tmp4, tmp3)
tl.store(in_out_ptr0 + x0, tmp5, 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)
@triton.jit
def triton_poi_fused_add_mul_sigmoid_3(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
x0 = xindex % 16
x2 = xindex // 64
x4 = xindex // 16
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
tmp2 = tl.sigmoid(tmp1)
tmp3 = tmp0 * tmp2
tmp5 = tl.sigmoid(tmp4)
tmp6 = tmp0 * tmp5
tmp7 = tmp3 + tmp6
tl.store(out_ptr0 + x3, tmp7, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_4, (1,), (1,))
assert_size_stride(primals_5, (4, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_6, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_2, 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, 1, 4, 4), (16, 16, 4, 1))
buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf2 = reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0)
del buf1
get_raw_stream(0)
triton_per_fused_mean_0[grid(16)](buf2, primals_2, 16, 16, XBLOCK=8,
num_warps=2, num_stages=1)
buf3 = extern_kernels.convolution(buf2, primals_3, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 1, 1, 1), (1, 1, 1, 1))
buf4 = buf3
del buf3
triton_poi_fused_convolution_relu_1[grid(4)](buf4, primals_4, 4,
XBLOCK=4, num_warps=1, num_stages=1)
del primals_4
buf5 = extern_kernels.convolution(buf4, primals_5, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 4, 1, 1), (4, 1, 1, 1))
buf6 = buf5
del buf5
triton_poi_fused_convolution_2[grid(16)](buf6, primals_6, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del primals_6
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_mul_sigmoid_3[grid(256)](primals_2, buf0, buf6,
buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
return (buf7, primals_1, primals_2, primals_3, primals_5, buf0, buf2,
buf4, buf6)
class SpatialAttention2d(nn.Module):
def __init__(self, channel):
super(SpatialAttention2d, self).__init__()
self.squeeze = nn.Conv2d(channel, 1, kernel_size=1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
z = self.squeeze(x)
z = self.sigmoid(z)
return x * z
class GAB(nn.Module):
def __init__(self, input_dim, reduction=4):
super(GAB, self).__init__()
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(input_dim, input_dim // reduction,
kernel_size=1, stride=1)
self.conv2 = nn.Conv2d(input_dim // reduction, input_dim,
kernel_size=1, stride=1)
self.relu = nn.ReLU(inplace=True)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
z = self.global_avgpool(x)
z = self.relu(self.conv1(z))
z = self.sigmoid(self.conv2(z))
return x * z
class SCseNew(nn.Module):
def __init__(self, dim):
super(SCseNew, self).__init__()
self.satt = SpatialAttention2d(dim)
self.catt = GAB(dim)
def forward(self, input_0):
primals_1 = self.satt.squeeze.weight
primals_3 = self.catt.conv1.weight
primals_4 = self.catt.conv1.bias
primals_5 = self.catt.conv2.weight
primals_6 = self.catt.conv2.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
advian123/kaggle-birdsong-recognition
|
SCse
| false
| 9,937
|
[
"MIT"
] | 0
|
a4ca8ab81e166b919452fb5d6ca4c2912c65e904
|
https://github.com/advian123/kaggle-birdsong-recognition/tree/a4ca8ab81e166b919452fb5d6ca4c2912c65e904
|
Model
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.linear1 = nn.Linear(28 * 28, 32)
self.linear2 = nn.Linear(32, 10)
def forward(self, inputs):
x = inputs.view(-1, 28 * 28)
x = F.relu(self.linear1(x))
x = self.linear2(x)
return F.log_softmax(x)
def get_inputs():
return [torch.rand([4, 784])]
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
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_0(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_per_fused__log_softmax_1(in_ptr0, out_ptr2, xnumel, rnumel,
XBLOCK: tl.constexpr):
xnumel = 4
rnumel = 10
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, :]
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 10 * x0), rmask & xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(rmask & xmask, tmp1, float('-inf'))
tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
tmp5 = tmp0 - tmp4
tmp6 = tl_math.exp(tmp5)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp9 = tl.where(rmask & xmask, tmp7, 0)
tmp10 = tl.sum(tmp9, 1)[:, None]
tmp11 = tl_math.log(tmp10)
tmp12 = tmp5 - tmp11
tl.store(out_ptr2 + (r1 + 10 * x0), tmp12, rmask & xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 784), (784, 1))
assert_size_stride(primals_2, (32, 784), (784, 1))
assert_size_stride(primals_3, (32,), (1,))
assert_size_stride(primals_4, (10, 32), (32, 1))
assert_size_stride(primals_5, (10,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (784, 32
), (1, 784), 0), out=buf0)
del primals_2
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_relu_0[grid(128)](buf1, primals_3, 128, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
(32, 10), (1, 32), 0), alpha=1, beta=1, out=buf2)
del primals_5
buf5 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
triton_per_fused__log_softmax_1[grid(4)](buf2, buf5, 4, 10, XBLOCK=
1, num_warps=2, num_stages=1)
del buf2
return buf5, primals_1, buf1, buf5, primals_4
class ModelNew(nn.Module):
def __init__(self):
super(ModelNew, self).__init__()
self.linear1 = nn.Linear(28 * 28, 32)
self.linear2 = nn.Linear(32, 10)
def forward(self, input_0):
primals_2 = self.linear1.weight
primals_3 = self.linear1.bias
primals_4 = self.linear2.weight
primals_5 = self.linear2.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
codeislife99/pytorch-meta-optimizer
|
Model
| false
| 9,938
|
[
"MIT"
] | 0
|
24f00be05e6e173efa67fe953e466bdf1dcb50e9
|
https://github.com/codeislife99/pytorch-meta-optimizer/tree/24f00be05e6e173efa67fe953e466bdf1dcb50e9
|
CatRepr
|
import torch
import torch.nn as nn
class CatRepr(nn.Module):
def __init__(self):
super().__init__()
def forward(self, data_list):
cat_regions = [torch.cat([hidden[0], torch.mean(hidden, dim=0),
hidden[-1]], dim=-1).view(1, -1) for hidden in data_list]
cat_out = torch.cat(cat_regions, dim=0)
return cat_out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 48
x2 = xindex
x0 = xindex % 48
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = x2 % 12
tmp7 = tl.full([1], 4, tl.int64)
tmp8 = tmp5 < tmp7
tmp9 = tmp8 & tmp4
tmp10 = tl.load(in_ptr0 + (4 * (x0 // 12) + x0 % 12), tmp9 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = tmp5 >= tmp7
tmp12 = tl.full([1], 8, tl.int64)
tmp13 = tmp5 < tmp12
tmp14 = tmp11 & tmp13
tmp15 = tmp14 & tmp4
tmp16 = tl.load(in_ptr0 + (4 * (x0 // 12) + (-4 + x0 % 12)), tmp15 &
xmask, eviction_policy='evict_last', other=0.0)
tmp17 = tl.load(in_ptr0 + (16 + 4 * (x0 // 12) + (-4 + x0 % 12)), tmp15 &
xmask, eviction_policy='evict_last', other=0.0)
tmp18 = tmp16 + tmp17
tmp19 = tl.load(in_ptr0 + (32 + 4 * (x0 // 12) + (-4 + x0 % 12)), tmp15 &
xmask, eviction_policy='evict_last', other=0.0)
tmp20 = tmp18 + tmp19
tmp21 = tl.load(in_ptr0 + (48 + 4 * (x0 // 12) + (-4 + x0 % 12)), tmp15 &
xmask, eviction_policy='evict_last', other=0.0)
tmp22 = tmp20 + tmp21
tmp23 = 4.0
tmp24 = tmp22 / tmp23
tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
tmp26 = tl.where(tmp15, tmp24, tmp25)
tmp27 = tmp5 >= tmp12
tl.full([1], 12, tl.int64)
tmp30 = tmp27 & tmp4
tmp31 = tl.load(in_ptr0 + (48 + 4 * (x0 // 12) + (-8 + x0 % 12)), tmp30 &
xmask, eviction_policy='evict_last', other=0.0)
tmp32 = tl.where(tmp14, tmp26, tmp31)
tmp33 = tl.where(tmp8, tmp10, tmp32)
tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
tmp35 = tl.where(tmp4, tmp33, tmp34)
tmp36 = tmp0 >= tmp3
tmp37 = tl.full([1], 2, tl.int64)
tmp38 = tmp0 < tmp37
tmp39 = tmp36 & tmp38
tmp40 = tmp8 & tmp39
tmp41 = tl.load(in_ptr0 + (64 + 4 * (x0 // 12) + x0 % 12), tmp40 &
xmask, eviction_policy='evict_last', other=0.0)
tmp42 = tmp14 & tmp39
tmp43 = tl.load(in_ptr0 + (64 + 4 * (x0 // 12) + (-4 + x0 % 12)), tmp42 &
xmask, eviction_policy='evict_last', other=0.0)
tmp44 = tl.load(in_ptr0 + (80 + 4 * (x0 // 12) + (-4 + x0 % 12)), tmp42 &
xmask, eviction_policy='evict_last', other=0.0)
tmp45 = tmp43 + tmp44
tmp46 = tl.load(in_ptr0 + (96 + 4 * (x0 // 12) + (-4 + x0 % 12)), tmp42 &
xmask, eviction_policy='evict_last', other=0.0)
tmp47 = tmp45 + tmp46
tmp48 = tl.load(in_ptr0 + (112 + 4 * (x0 // 12) + (-4 + x0 % 12)),
tmp42 & xmask, eviction_policy='evict_last', other=0.0)
tmp49 = tmp47 + tmp48
tmp50 = tmp49 / tmp23
tmp51 = tl.full(tmp50.shape, 0.0, tmp50.dtype)
tmp52 = tl.where(tmp42, tmp50, tmp51)
tmp53 = tmp27 & tmp39
tmp54 = tl.load(in_ptr0 + (112 + 4 * (x0 // 12) + (-8 + x0 % 12)),
tmp53 & xmask, eviction_policy='evict_last', other=0.0)
tmp55 = tl.where(tmp14, tmp52, tmp54)
tmp56 = tl.where(tmp8, tmp41, tmp55)
tmp57 = tl.full(tmp56.shape, 0.0, tmp56.dtype)
tmp58 = tl.where(tmp39, tmp56, tmp57)
tmp59 = tmp0 >= tmp37
tmp60 = tl.full([1], 3, tl.int64)
tmp61 = tmp0 < tmp60
tmp62 = tmp59 & tmp61
tmp63 = tmp8 & tmp62
tmp64 = tl.load(in_ptr0 + (128 + 4 * (x0 // 12) + x0 % 12), tmp63 &
xmask, eviction_policy='evict_last', other=0.0)
tmp65 = tmp14 & tmp62
tmp66 = tl.load(in_ptr0 + (128 + 4 * (x0 // 12) + (-4 + x0 % 12)),
tmp65 & xmask, eviction_policy='evict_last', other=0.0)
tmp67 = tl.load(in_ptr0 + (144 + 4 * (x0 // 12) + (-4 + x0 % 12)),
tmp65 & xmask, eviction_policy='evict_last', other=0.0)
tmp68 = tmp66 + tmp67
tmp69 = tl.load(in_ptr0 + (160 + 4 * (x0 // 12) + (-4 + x0 % 12)),
tmp65 & xmask, eviction_policy='evict_last', other=0.0)
tmp70 = tmp68 + tmp69
tmp71 = tl.load(in_ptr0 + (176 + 4 * (x0 // 12) + (-4 + x0 % 12)),
tmp65 & xmask, eviction_policy='evict_last', other=0.0)
tmp72 = tmp70 + tmp71
tmp73 = tmp72 / tmp23
tmp74 = tl.full(tmp73.shape, 0.0, tmp73.dtype)
tmp75 = tl.where(tmp65, tmp73, tmp74)
tmp76 = tmp27 & tmp62
tmp77 = tl.load(in_ptr0 + (176 + 4 * (x0 // 12) + (-8 + x0 % 12)),
tmp76 & xmask, eviction_policy='evict_last', other=0.0)
tmp78 = tl.where(tmp14, tmp75, tmp77)
tmp79 = tl.where(tmp8, tmp64, tmp78)
tmp80 = tl.full(tmp79.shape, 0.0, tmp79.dtype)
tmp81 = tl.where(tmp62, tmp79, tmp80)
tmp82 = tmp0 >= tmp60
tmp84 = tmp8 & tmp82
tmp85 = tl.load(in_ptr0 + (192 + 4 * (x0 // 12) + x0 % 12), tmp84 &
xmask, eviction_policy='evict_last', other=0.0)
tmp86 = tmp14 & tmp82
tmp87 = tl.load(in_ptr0 + (192 + 4 * (x0 // 12) + (-4 + x0 % 12)),
tmp86 & xmask, eviction_policy='evict_last', other=0.0)
tmp88 = tl.load(in_ptr0 + (208 + 4 * (x0 // 12) + (-4 + x0 % 12)),
tmp86 & xmask, eviction_policy='evict_last', other=0.0)
tmp89 = tmp87 + tmp88
tmp90 = tl.load(in_ptr0 + (224 + 4 * (x0 // 12) + (-4 + x0 % 12)),
tmp86 & xmask, eviction_policy='evict_last', other=0.0)
tmp91 = tmp89 + tmp90
tmp92 = tl.load(in_ptr0 + (240 + 4 * (x0 // 12) + (-4 + x0 % 12)),
tmp86 & xmask, eviction_policy='evict_last', other=0.0)
tmp93 = tmp91 + tmp92
tmp94 = tmp93 / tmp23
tmp95 = tl.full(tmp94.shape, 0.0, tmp94.dtype)
tmp96 = tl.where(tmp86, tmp94, tmp95)
tmp97 = tmp27 & tmp82
tmp98 = tl.load(in_ptr0 + (240 + 4 * (x0 // 12) + (-8 + x0 % 12)),
tmp97 & xmask, eviction_policy='evict_last', other=0.0)
tmp99 = tl.where(tmp14, tmp96, tmp98)
tmp100 = tl.where(tmp8, tmp85, tmp99)
tmp101 = tl.full(tmp100.shape, 0.0, tmp100.dtype)
tmp102 = tl.where(tmp82, tmp100, tmp101)
tmp103 = tl.where(tmp62, tmp81, tmp102)
tmp104 = tl.where(tmp39, tmp58, tmp103)
tmp105 = tl.where(tmp4, tmp35, tmp104)
tl.store(out_ptr0 + x2, tmp105, 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, 48), (48, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(192)](arg0_1, buf0, 192, XBLOCK=128,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class CatReprNew(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
csJd/CRANN
|
CatRepr
| false
| 9,939
|
[
"MIT"
] | 0
|
8139b19b84ec11eff3c801185e4bfa974766d599
|
https://github.com/csJd/CRANN/tree/8139b19b84ec11eff3c801185e4bfa974766d599
|
Residual_module
|
import torch
import torch.nn as nn
class Residual_module(nn.Module):
def __init__(self, in_ch):
super(Residual_module, self).__init__()
self.prelu1 = nn.PReLU(in_ch, 0)
self.prelu2 = nn.PReLU(in_ch, 0)
self.conv1_1by1 = nn.Conv2d(in_channels=in_ch, out_channels=in_ch,
kernel_size=1)
self.conv2_1by1 = nn.Conv2d(in_channels=in_ch, out_channels=in_ch,
kernel_size=1)
def forward(self, input):
output_residual = self.conv1_1by1(input)
output_residual = self.prelu1(output_residual)
output_residual = self.conv2_1by1(output_residual)
output = torch.mean(torch.stack([input, output_residual]), dim=0)
output = self.prelu2(output)
return output
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_ch': 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
@triton.jit
def triton_poi_fused__prelu_kernel_convolution_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')
tmp5 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp6 = tmp5 * tmp2
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(in_out_ptr0 + x3, tmp2, xmask)
tl.store(out_ptr0 + x3, tmp7, xmask)
@triton.jit
def triton_poi_fused__prelu_kernel_mean_1(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, 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
x2 = xindex // 64
x3 = xindex % 64
x1 = xindex // 16 % 4
x4 = xindex
tmp32 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp0 = x2
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x3 + 64 * x2), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (x3 + 64 * (-4 + x2)), tmp6 & xmask, other=0.0)
tmp10 = tl.load(in_ptr2 + x1, tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp11 = tmp9 + tmp10
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp6, tmp11, tmp12)
tmp14 = tl.where(tmp4, tmp5, tmp13)
tmp15 = 4 + x2
tmp17 = tmp15 < tmp3
tmp18 = tl.load(in_ptr0 + (x3 + 64 * (4 + x2)), tmp17 & xmask, other=0.0)
tmp19 = tmp15 >= tmp3
tmp21 = tl.load(in_ptr1 + (x3 + 64 * x2), tmp19 & xmask, other=0.0)
tmp22 = tl.load(in_ptr2 + x1, tmp19 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp23 = tmp21 + tmp22
tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
tmp25 = tl.where(tmp19, tmp23, tmp24)
tmp26 = tl.where(tmp17, tmp18, tmp25)
tmp27 = tmp14 + tmp26
tmp28 = 2.0
tmp29 = tmp27 / tmp28
tmp30 = 0.0
tmp31 = tmp29 > tmp30
tmp33 = tmp32 * tmp29
tmp34 = tl.where(tmp31, tmp29, tmp33)
tl.store(out_ptr0 + x4, tmp29, xmask)
tl.store(out_ptr1 + x4, tmp34, 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, 1, 1), (4, 1, 1, 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,))
assert_size_stride(primals_5, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (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, 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__prelu_kernel_convolution_0[grid(256)](buf1,
primals_2, primals_4, buf2, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del primals_2
buf3 = extern_kernels.convolution(buf2, primals_5, 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, 4, 4), (64, 16, 4, 1))
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__prelu_kernel_mean_1[grid(256)](primals_3, buf3,
primals_6, primals_7, buf4, buf5, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del buf3
del primals_6
return (buf5, primals_1, primals_3, primals_4, primals_5, primals_7,
buf1, buf2, buf4)
class Residual_moduleNew(nn.Module):
def __init__(self, in_ch):
super(Residual_moduleNew, self).__init__()
self.prelu1 = nn.PReLU(in_ch, 0)
self.prelu2 = nn.PReLU(in_ch, 0)
self.conv1_1by1 = nn.Conv2d(in_channels=in_ch, out_channels=in_ch,
kernel_size=1)
self.conv2_1by1 = nn.Conv2d(in_channels=in_ch, out_channels=in_ch,
kernel_size=1)
def forward(self, input_0):
primals_2 = self.prelu1.weight
primals_4 = self.prelu2.weight
primals_1 = self.conv1_1by1.weight
primals_6 = self.conv1_1by1.bias
primals_5 = self.conv2_1by1.weight
primals_7 = self.conv2_1by1.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
csm9493/FC-AIDE-Pytorch
|
Residual_module
| false
| 9,940
|
[
"MIT"
] | 0
|
8ac7e4ee675824af002419650428948e60930712
|
https://github.com/csm9493/FC-AIDE-Pytorch/tree/8ac7e4ee675824af002419650428948e60930712
|
LayerNorm1D
|
import torch
import torch.nn as nn
class LayerNorm1D(nn.Module):
def __init__(self, num_outputs, eps=1e-05, affine=True):
super(LayerNorm1D, self).__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(1, num_outputs))
self.bias = nn.Parameter(torch.zeros(1, num_outputs))
def forward(self, inputs):
input_mean = inputs.mean(1, keepdim=True).expand_as(inputs)
input_std = inputs.std(1, keepdim=True).expand_as(inputs)
x = (inputs - input_mean) / (input_std + self.eps)
return x * self.weight.expand_as(x) + self.bias.expand_as(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_outputs': 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.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
@triton.jit
def triton_poi_fused_add_div_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
x4 = xindex
x3 = xindex // 64
x5 = xindex % 16
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x4, xmask)
tmp1 = tl.load(in_ptr0 + (x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp28 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp30 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = 4.0
tmp9 = tmp7 / tmp8
tmp10 = tmp0 - tmp9
tmp11 = tmp1 - tmp9
tmp12 = tmp11 * tmp11
tmp13 = tmp2 - tmp9
tmp14 = tmp13 * tmp13
tmp15 = tmp12 + tmp14
tmp16 = tmp4 - tmp9
tmp17 = tmp16 * tmp16
tmp18 = tmp15 + tmp17
tmp19 = tmp6 - tmp9
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = 3.0
tmp23 = tmp21 / tmp22
tmp24 = libdevice.sqrt(tmp23)
tmp25 = 1e-05
tmp26 = tmp24 + tmp25
tmp27 = tmp10 / tmp26
tmp29 = tmp27 * tmp28
tmp31 = tmp29 + tmp30
tl.store(out_ptr0 + x4, tmp31, 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, (1, 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, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_div_mul_sub_0[grid(256)](primals_1, primals_2,
primals_3, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
del primals_3
return buf0, primals_1
class LayerNorm1DNew(nn.Module):
def __init__(self, num_outputs, eps=1e-05, affine=True):
super(LayerNorm1DNew, self).__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(1, num_outputs))
self.bias = nn.Parameter(torch.zeros(1, num_outputs))
def forward(self, input_0):
primals_2 = self.weight
primals_3 = self.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
codeislife99/pytorch-meta-optimizer
|
LayerNorm1D
| false
| 9,941
|
[
"MIT"
] | 0
|
24f00be05e6e173efa67fe953e466bdf1dcb50e9
|
https://github.com/codeislife99/pytorch-meta-optimizer/tree/24f00be05e6e173efa67fe953e466bdf1dcb50e9
|
PhonyLanguageModel
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class PhonyLanguageModel(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
lm_x = x.clone().detach().float() * 0
return F.log_softmax(lm_x, 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
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_poi_fused_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)
tmp3 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp8 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp11 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), 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 = 0.0
tmp16 = tmp14 * tmp15
tl.store(out_ptr0 + x3, tmp16, 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_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
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,
class PhonyLanguageModelNew(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
daemon/vivi
|
PhonyLanguageModel
| false
| 9,942
|
[
"MIT"
] | 0
|
6b7819006c944a756bf8a7b6d8beed92d19eb51a
|
https://github.com/daemon/vivi/tree/6b7819006c944a756bf8a7b6d8beed92d19eb51a
|
CDCM
|
import torch
import torch.nn as nn
class CDCM(nn.Module):
"""
Compact Dilation Convolution based Module
"""
def __init__(self, in_channels, out_channels):
super(CDCM, self).__init__()
self.relu1 = nn.ReLU()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1,
padding=0)
self.conv2_1 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
dilation=5, padding=5, bias=False)
self.conv2_2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
dilation=7, padding=7, bias=False)
self.conv2_3 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
dilation=9, padding=9, bias=False)
self.conv2_4 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
dilation=11, padding=11, bias=False)
nn.init.constant_(self.conv1.bias, 0)
def forward(self, x):
x = self.relu1(x)
x = self.conv1(x)
x1 = self.conv2_1(x)
x2 = self.conv2_2(x)
x3 = self.conv2_3(x)
x4 = self.conv2_4(x)
return x1 + x2 + x3 + x4
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 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_relu_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.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_convolution_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
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')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_add_2(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
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask)
tmp3 = tl.load(in_ptr1 + x0, xmask)
tmp5 = tl.load(in_ptr2 + x0, xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tl.store(in_out_ptr0 + x0, tmp6, 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, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_7, (4, 4, 3, 3), (36, 9, 3, 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_relu_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
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, 4, 4, 4), (64, 16, 4, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_1[grid(256)](buf2, primals_3, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_3
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(5, 5), dilation=(5, 5), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = extern_kernels.convolution(buf2, primals_5, stride=(1, 1),
padding=(7, 7), dilation=(7, 7), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
buf5 = extern_kernels.convolution(buf2, primals_6, stride=(1, 1),
padding=(9, 9), dilation=(9, 9), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
buf6 = extern_kernels.convolution(buf2, primals_7, stride=(1, 1),
padding=(11, 11), dilation=(11, 11), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
buf7 = buf3
del buf3
triton_poi_fused_add_2[grid(256)](buf7, buf4, buf5, buf6, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del buf4
del buf5
del buf6
return (buf7, primals_2, primals_4, primals_5, primals_6, primals_7,
buf0, buf2)
class CDCMNew(nn.Module):
"""
Compact Dilation Convolution based Module
"""
def __init__(self, in_channels, out_channels):
super(CDCMNew, self).__init__()
self.relu1 = nn.ReLU()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1,
padding=0)
self.conv2_1 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
dilation=5, padding=5, bias=False)
self.conv2_2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
dilation=7, padding=7, bias=False)
self.conv2_3 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
dilation=9, padding=9, bias=False)
self.conv2_4 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
dilation=11, padding=11, bias=False)
nn.init.constant_(self.conv1.bias, 0)
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = self.conv1.bias
primals_4 = self.conv2_1.weight
primals_5 = self.conv2_2.weight
primals_6 = self.conv2_3.weight
primals_7 = self.conv2_4.weight
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
arkel23/mmgeneration
|
CDCM
| false
| 9,943
|
[
"Apache-2.0"
] | 0
|
41a30e2972f2037f6aac60ed761bed3fe47bfe4d
|
https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d
|
DenoisingDownsample
|
import torch
import torch.nn as nn
class DenoisingDownsample(nn.Module):
"""Downsampling operation used in the denoising network. Support average
pooling and convolution for downsample operation.
Args:
in_channels (int): Number of channels of the input feature map to be
downsampled.
with_conv (bool, optional): Whether use convolution operation for
downsampling. Defaults to `True`.
"""
def __init__(self, in_channels, with_conv=True):
super().__init__()
if with_conv:
self.downsample = nn.Conv2d(in_channels, in_channels, 3, 2, 1)
else:
self.downsample = nn.AvgPool2d(stride=2)
def forward(self, x):
"""Forward function for downsampling operation.
Args:
x (torch.Tensor): Feature map to downsample.
Returns:
torch.Tensor: Feature map after downsampling.
"""
return self.downsample(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@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)
def call(args):
primals_1, primals_2, primals_3 = 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))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2,
2), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(64)](buf1, primals_2, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_2
return buf1, primals_1, primals_3
class DenoisingDownsampleNew(nn.Module):
"""Downsampling operation used in the denoising network. Support average
pooling and convolution for downsample operation.
Args:
in_channels (int): Number of channels of the input feature map to be
downsampled.
with_conv (bool, optional): Whether use convolution operation for
downsampling. Defaults to `True`.
"""
def __init__(self, in_channels, with_conv=True):
super().__init__()
if with_conv:
self.downsample = nn.Conv2d(in_channels, in_channels, 3, 2, 1)
else:
self.downsample = nn.AvgPool2d(stride=2)
def forward(self, input_0):
primals_1 = self.downsample.weight
primals_2 = self.downsample.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
arkel23/mmgeneration
|
DenoisingDownsample
| false
| 9,944
|
[
"Apache-2.0"
] | 0
|
41a30e2972f2037f6aac60ed761bed3fe47bfe4d
|
https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d
|
Transformer
|
import torch
class Convlayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1):
super().__init__()
padding = kernel_size // 2
self.refl = torch.nn.ReflectionPad2d(padding)
self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size,
stride)
def forward(self, x):
x = self.refl(x)
return self.conv(x)
class Residential(torch.nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv1 = Convlayer(in_channels=in_channels, out_channels=
in_channels, kernel_size=3)
self.inst1 = torch.nn.InstanceNorm2d(in_channels, affine=True)
self.conv2 = Convlayer(in_channels=in_channels, out_channels=
in_channels, kernel_size=3)
self.inst2 = torch.nn.InstanceNorm2d(in_channels, affine=True)
self.relu = torch.nn.ReLU()
def forward(self, x):
resident = x
x = self.relu(self.inst1(self.conv1(x)))
x = self.inst2(self.conv2(x))
return resident + x
class UpsampleConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride,
upsample=None):
super().__init__()
self.upsample = upsample
reflectpad = kernel_size // 2
self.reflectionpad = torch.nn.ReflectionPad2d(reflectpad)
self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size,
stride)
self.relu = torch.nn.ReLU()
def forward(self, x):
if self.upsample:
x = torch.nn.functional.interpolate(x, scale_factor=self.
upsample, mode='nearest')
x = self.reflectionpad(x)
return self.relu(self.conv(x))
class Transformer(torch.nn.Module):
"""
the min input size is [1, 3, 16, 16] as the kernel size and stride reduce the height and width
otherwise exception might caused as the input_size != output_size
"""
def __init__(self):
super().__init__()
self.conv1 = Convlayer(in_channels=3, out_channels=32, kernel_size=
9, stride=1)
self.inst1 = torch.nn.InstanceNorm2d(num_features=32, affine=True)
self.conv2 = Convlayer(in_channels=32, out_channels=64, kernel_size
=3, stride=2)
self.inst2 = torch.nn.InstanceNorm2d(num_features=64, affine=True)
self.conv3 = Convlayer(in_channels=64, out_channels=128,
kernel_size=3, stride=2)
self.inst3 = torch.nn.InstanceNorm2d(num_features=128, affine=True)
self.res1 = Residential(128)
self.res2 = Residential(128)
self.res3 = Residential(128)
self.res4 = Residential(128)
self.res5 = Residential(128)
self.upsample1 = UpsampleConvLayer(in_channels=128, out_channels=64,
kernel_size=3, stride=1, upsample=2)
self.upsample2 = UpsampleConvLayer(in_channels=64, out_channels=32,
kernel_size=3, stride=1, upsample=2)
self.upsample3 = UpsampleConvLayer(in_channels=32, out_channels=3,
kernel_size=9, stride=1)
self.relu = torch.nn.ReLU()
def forward(self, x):
x = self.relu(self.inst1(self.conv1(x)))
x = self.relu(self.inst2(self.conv2(x)))
x = self.relu(self.inst3(self.conv3(x)))
x = self.res1(x)
x = self.res2(x)
x = self.res3(x)
x = self.res4(x)
x = self.res5(x)
x = self.relu(self.upsample1(x))
x = self.relu(self.upsample2(x))
x = self.relu(self.upsample3(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, math as tl_math
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_4(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_5(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_6(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_convolution_relu_repeat_7(
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 = 256
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 % 128
tmp0 = tl.load(in_ptr0 + x0 % 128, None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x0 % 128, None, eviction_policy='evict_last')
tmp2 = tl.load(in_out_ptr0 + (r3 + 256 * 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], 256, 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 = 256.0
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 + 256 * x0), tmp4, None)
tl.debug_barrier()
tl.store(in_out_ptr1 + x0, tmp22, None)
tl.store(out_ptr3 + (r3 + 256 * x0), tmp28, None)
tl.store(out_ptr2 + x0, tmp12, None)
@triton.jit
def triton_poi_fused_reflection_pad2d_8(in_ptr0, 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 % 18
x1 = xindex // 18 % 18
x2 = xindex // 324
x3 = xindex
tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x2),
None, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, None)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_9(in_out_ptr0,
in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
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 % 128
tmp0 = tl.load(in_out_ptr0 + (r2 + 256 * 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], 256, 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 = 256.0
tmp17 = tmp15 / tmp16
tmp18 = 1e-05
tmp19 = tmp17 + tmp18
tmp20 = libdevice.rsqrt(tmp19)
tl.store(in_out_ptr0 + (r2 + 256 * 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_10(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0 % 128, xmask)
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused_reflection_pad2d_relu_11(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 % 18
x1 = xindex // 18 % 18
x2 = xindex // 324
x3 = xindex
tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * 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_per_fused__native_batch_norm_legit_add_convolution_repeat_12(
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 = 256
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 % 128
tmp0 = tl.load(in_ptr0 + x0 % 128, None, eviction_policy='evict_last')
tmp1 = tl.load(in_out_ptr0 + (r3 + 256 * x0), None)
tmp2 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp17 = tl.load(in_out_ptr1 + (r3 + 256 * x0), None)
tmp26 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
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], 256, 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))
tmp18 = tmp3 - tmp11
tmp19 = 256.0
tmp20 = tmp16 / tmp19
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tmp24 = tmp18 * tmp23
tmp25 = tmp24 * tmp0
tmp27 = tmp25 + tmp26
tmp28 = tmp17 + tmp27
tl.store(out_ptr0 + x0, tmp0, None)
tl.store(in_out_ptr0 + (r3 + 256 * x0), tmp3, None)
tl.store(in_out_ptr1 + (r3 + 256 * x0), tmp28, None)
tl.store(out_ptr3 + x0, tmp23, None)
tl.store(out_ptr1 + x0, tmp11, None)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_13(in_out_ptr0,
in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
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 % 128
tmp0 = tl.load(in_out_ptr0 + (r2 + 256 * 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], 256, 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 = 256.0
tmp17 = tmp15 / tmp16
tmp18 = 1e-05
tmp19 = tmp17 + tmp18
tmp20 = libdevice.rsqrt(tmp19)
tl.store(in_out_ptr0 + (r2 + 256 * 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_14(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 32
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_15(out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 32
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_16(in_ptr0, in_ptr1,
in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, 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 // 34 % 34
x0 = xindex % 34
x4 = xindex // 1156
x2 = xindex // 1156 % 128
x7 = xindex
tmp0 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
x1))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
x0))), None, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr3 + x4, None, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr4 + x4, None, eviction_policy='evict_last')
tmp20 = tl.load(in_ptr5 + x4, None, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr6 + x2, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 16, 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 + 16 * tmp4 + 256 * x4), None,
eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + (tmp8 + 16 * tmp4 + 256 * x4), None,
eviction_policy='evict_last')
tmp12 = tmp10 - tmp11
tmp14 = 256.0
tmp15 = tmp13 / tmp14
tmp16 = 1e-05
tmp17 = tmp15 + tmp16
tmp18 = libdevice.rsqrt(tmp17)
tmp19 = tmp12 * tmp18
tmp21 = tmp19 * tmp20
tmp23 = tmp21 + tmp22
tmp24 = tmp9 + tmp23
tl.store(out_ptr0 + x7, tmp24, None)
@triton.jit
def triton_poi_fused_arange_17(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_18(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_convolution_reflection_pad2d_relu_19(in_ptr0
, in_ptr1, in_ptr2, 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 + 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
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tl.store(out_ptr0 + x7, tmp14, xmask)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_20(in_ptr0, in_ptr1,
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
x4 = xindex // 5184
x2 = xindex // 5184 % 32
x5 = 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 * x4),
None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tl.store(out_ptr0 + x5, tmp5, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_21(in_ptr0,
in_ptr1, out_ptr0, out_ptr1, out_ptr2, 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_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = 0.0
tmp7 = tmp4 <= tmp6
tmp8 = tmp5 <= tmp6
tl.store(out_ptr0 + x3, tmp5, None)
tl.store(out_ptr1 + x3, tmp7, None)
tl.store(out_ptr2 + x3, tmp8, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_22(in_ptr0,
in_ptr1, 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)
x3 = xindex
x1 = xindex // 4096 % 32
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = 0.0
tmp7 = tmp5 <= tmp6
tmp8 = tmp4 <= tmp6
tl.store(out_ptr0 + x3, tmp7, None)
tl.store(out_ptr1 + x3, tmp8, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_23(in_ptr0,
in_ptr1, 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)
x3 = xindex
x1 = xindex // 1024 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = 0.0
tmp7 = tmp5 <= tmp6
tmp8 = tmp4 <= tmp6
tl.store(out_ptr0 + x3, tmp7, None)
tl.store(out_ptr1 + x3, tmp8, 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, primals_32,
primals_33, primals_34, primals_35, primals_36, primals_37,
primals_38, primals_39, primals_40, primals_41, primals_42,
primals_43, primals_44, primals_45, primals_46, primals_47,
primals_48, primals_49, primals_50, primals_51, primals_52,
primals_53, primals_54, primals_55, primals_56, primals_57,
primals_58, primals_59) = 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, (128, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_11, (128,), (1,))
assert_size_stride(primals_12, (128,), (1,))
assert_size_stride(primals_13, (128,), (1,))
assert_size_stride(primals_14, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_15, (128,), (1,))
assert_size_stride(primals_16, (128,), (1,))
assert_size_stride(primals_17, (128,), (1,))
assert_size_stride(primals_18, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_19, (128,), (1,))
assert_size_stride(primals_20, (128,), (1,))
assert_size_stride(primals_21, (128,), (1,))
assert_size_stride(primals_22, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_23, (128,), (1,))
assert_size_stride(primals_24, (128,), (1,))
assert_size_stride(primals_25, (128,), (1,))
assert_size_stride(primals_26, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_27, (128,), (1,))
assert_size_stride(primals_28, (128,), (1,))
assert_size_stride(primals_29, (128,), (1,))
assert_size_stride(primals_30, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_31, (128,), (1,))
assert_size_stride(primals_32, (128,), (1,))
assert_size_stride(primals_33, (128,), (1,))
assert_size_stride(primals_34, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_35, (128,), (1,))
assert_size_stride(primals_36, (128,), (1,))
assert_size_stride(primals_37, (128,), (1,))
assert_size_stride(primals_38, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_39, (128,), (1,))
assert_size_stride(primals_40, (128,), (1,))
assert_size_stride(primals_41, (128,), (1,))
assert_size_stride(primals_42, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_43, (128,), (1,))
assert_size_stride(primals_44, (128,), (1,))
assert_size_stride(primals_45, (128,), (1,))
assert_size_stride(primals_46, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_47, (128,), (1,))
assert_size_stride(primals_48, (128,), (1,))
assert_size_stride(primals_49, (128,), (1,))
assert_size_stride(primals_50, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_51, (128,), (1,))
assert_size_stride(primals_52, (128,), (1,))
assert_size_stride(primals_53, (128,), (1,))
assert_size_stride(primals_54, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_55, (64,), (1,))
assert_size_stride(primals_56, (32, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_57, (32,), (1,))
assert_size_stride(primals_58, (3, 32, 9, 9), (2592, 81, 9, 1))
assert_size_stride(primals_59, (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=1024, num_warps=4,
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))
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
triton_per_fused__native_batch_norm_legit_convolution_4[grid(256)](
buf11, buf17, primals_7, buf14, 256, 1024, num_warps=8,
num_stages=1)
del primals_7
buf12 = empty_strided_cuda((256,), (1,), torch.float32)
triton_poi_fused_repeat_5[grid(256)](primals_8, buf12, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_8
buf13 = empty_strided_cuda((256,), (1,), torch.float32)
triton_poi_fused_repeat_5[grid(256)](primals_9, buf13, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_9
buf18 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_relu_6[grid(295936)](buf11, buf14,
buf17, buf12, buf13, buf18, 295936, XBLOCK=512, num_warps=8,
num_stages=1)
buf19 = extern_kernels.convolution(buf18, primals_10, stride=(2, 2),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf19, (4, 128, 16, 16), (32768, 256, 16, 1))
buf21 = empty_strided_cuda((512,), (1,), torch.float32)
buf22 = empty_strided_cuda((512,), (1,), torch.float32)
buf20 = buf19
del buf19
buf23 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
float32)
buf24 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
buf26 = reinterpret_tensor(buf24, (1, 512, 1, 1), (512, 1, 1, 1), 0)
del buf24
buf27 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
torch.float32)
triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_7[
grid(512)](buf20, buf26, primals_12, primals_13, primals_11,
buf21, buf22, buf23, buf27, 512, 256, num_warps=2, num_stages=1)
del primals_11
del primals_12
del primals_13
buf28 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_8[grid(165888)](buf27, buf28,
165888, XBLOCK=512, num_warps=8, 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, 128, 16, 16), (32768, 256, 16, 1))
buf30 = buf29
del buf29
buf33 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
float32)
buf34 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
buf36 = reinterpret_tensor(buf34, (1, 512, 1, 1), (512, 1, 1, 1), 0)
del buf34
triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
buf30, buf36, primals_15, buf33, 512, 256, num_warps=2,
num_stages=1)
del primals_15
buf31 = empty_strided_cuda((512,), (1,), torch.float32)
triton_poi_fused_repeat_10[grid(512)](primals_16, buf31, 512,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_16
buf32 = empty_strided_cuda((512,), (1,), torch.float32)
triton_poi_fused_repeat_10[grid(512)](primals_17, buf32, 512,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_17
buf37 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf30,
buf33, buf36, buf31, buf32, buf37, 165888, 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, 128, 16, 16), (32768, 256, 16, 1))
buf40 = empty_strided_cuda((512,), (1,), torch.float32)
buf39 = buf38
del buf38
buf41 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
buf45 = buf27
del buf27
buf44 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12[
grid(512)](buf39, buf45, primals_20, primals_19, primals_21,
buf40, buf41, buf44, 512, 256, num_warps=2, num_stages=1)
del primals_19
del primals_20
del primals_21
buf46 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_8[grid(165888)](buf45, buf46,
165888, XBLOCK=512, num_warps=8, 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, 128, 16, 16), (32768, 256, 16, 1))
buf48 = buf47
del buf47
buf51 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
float32)
buf52 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
buf54 = reinterpret_tensor(buf52, (1, 512, 1, 1), (512, 1, 1, 1), 0)
del buf52
triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
buf48, buf54, primals_23, buf51, 512, 256, num_warps=2,
num_stages=1)
del primals_23
buf49 = empty_strided_cuda((512,), (1,), torch.float32)
triton_poi_fused_repeat_10[grid(512)](primals_24, buf49, 512,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_24
buf50 = empty_strided_cuda((512,), (1,), torch.float32)
triton_poi_fused_repeat_10[grid(512)](primals_25, buf50, 512,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_25
buf55 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf48,
buf51, buf54, buf49, buf50, buf55, 165888, XBLOCK=1024,
num_warps=4, num_stages=1)
buf56 = extern_kernels.convolution(buf55, primals_26, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf56, (4, 128, 16, 16), (32768, 256, 16, 1))
buf58 = empty_strided_cuda((512,), (1,), torch.float32)
buf57 = buf56
del buf56
buf59 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
buf63 = buf45
del buf45
buf62 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12[
grid(512)](buf57, buf63, primals_28, primals_27, primals_29,
buf58, buf59, buf62, 512, 256, num_warps=2, num_stages=1)
del primals_27
del primals_28
del primals_29
buf64 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_8[grid(165888)](buf63, buf64,
165888, XBLOCK=512, num_warps=8, num_stages=1)
buf65 = extern_kernels.convolution(buf64, primals_30, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf65, (4, 128, 16, 16), (32768, 256, 16, 1))
buf66 = buf65
del buf65
buf69 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
float32)
buf70 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
buf72 = reinterpret_tensor(buf70, (1, 512, 1, 1), (512, 1, 1, 1), 0)
del buf70
triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
buf66, buf72, primals_31, buf69, 512, 256, num_warps=2,
num_stages=1)
del primals_31
buf67 = empty_strided_cuda((512,), (1,), torch.float32)
triton_poi_fused_repeat_10[grid(512)](primals_32, buf67, 512,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_32
buf68 = empty_strided_cuda((512,), (1,), torch.float32)
triton_poi_fused_repeat_10[grid(512)](primals_33, buf68, 512,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_33
buf73 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf66,
buf69, buf72, buf67, buf68, buf73, 165888, XBLOCK=1024,
num_warps=4, num_stages=1)
buf74 = extern_kernels.convolution(buf73, primals_34, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf74, (4, 128, 16, 16), (32768, 256, 16, 1))
buf76 = empty_strided_cuda((512,), (1,), torch.float32)
buf75 = buf74
del buf74
buf77 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
buf81 = buf63
del buf63
buf80 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12[
grid(512)](buf75, buf81, primals_36, primals_35, primals_37,
buf76, buf77, buf80, 512, 256, num_warps=2, num_stages=1)
del primals_35
del primals_36
del primals_37
buf82 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_8[grid(165888)](buf81, buf82,
165888, XBLOCK=512, num_warps=8, num_stages=1)
buf83 = extern_kernels.convolution(buf82, primals_38, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf83, (4, 128, 16, 16), (32768, 256, 16, 1))
buf84 = buf83
del buf83
buf87 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
float32)
buf88 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
buf90 = reinterpret_tensor(buf88, (1, 512, 1, 1), (512, 1, 1, 1), 0)
del buf88
triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
buf84, buf90, primals_39, buf87, 512, 256, num_warps=2,
num_stages=1)
del primals_39
buf85 = empty_strided_cuda((512,), (1,), torch.float32)
triton_poi_fused_repeat_10[grid(512)](primals_40, buf85, 512,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_40
buf86 = empty_strided_cuda((512,), (1,), torch.float32)
triton_poi_fused_repeat_10[grid(512)](primals_41, buf86, 512,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_41
buf91 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf84,
buf87, buf90, buf85, buf86, buf91, 165888, XBLOCK=1024,
num_warps=4, num_stages=1)
buf92 = extern_kernels.convolution(buf91, primals_42, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf92, (4, 128, 16, 16), (32768, 256, 16, 1))
buf94 = empty_strided_cuda((512,), (1,), torch.float32)
buf93 = buf92
del buf92
buf95 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
buf99 = buf81
del buf81
buf98 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12[
grid(512)](buf93, buf99, primals_44, primals_43, primals_45,
buf94, buf95, buf98, 512, 256, num_warps=2, num_stages=1)
del primals_43
del primals_44
del primals_45
buf100 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_8[grid(165888)](buf99, buf100,
165888, XBLOCK=512, num_warps=8, num_stages=1)
buf101 = extern_kernels.convolution(buf100, primals_46, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf101, (4, 128, 16, 16), (32768, 256, 16, 1))
buf102 = buf101
del buf101
buf105 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
float32)
buf106 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
buf108 = reinterpret_tensor(buf106, (1, 512, 1, 1), (512, 1, 1, 1), 0)
del buf106
triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
buf102, buf108, primals_47, buf105, 512, 256, num_warps=2,
num_stages=1)
del primals_47
buf103 = empty_strided_cuda((512,), (1,), torch.float32)
triton_poi_fused_repeat_10[grid(512)](primals_48, buf103, 512,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_48
buf104 = empty_strided_cuda((512,), (1,), torch.float32)
triton_poi_fused_repeat_10[grid(512)](primals_49, buf104, 512,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_49
buf109 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf102,
buf105, buf108, buf103, buf104, buf109, 165888, XBLOCK=1024,
num_warps=4, num_stages=1)
buf110 = extern_kernels.convolution(buf109, primals_50, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf110, (4, 128, 16, 16), (32768, 256, 16, 1))
buf111 = buf110
del buf110
buf113 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
buf114 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
buf116 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
torch.float32)
triton_per_fused__native_batch_norm_legit_convolution_13[grid(512)](
buf111, primals_51, buf113, buf114, buf116, 512, 256, num_warps
=2, num_stages=1)
del primals_51
buf112 = empty_strided_cuda((512,), (1,), torch.float32)
triton_poi_fused_repeat_10[grid(512)](primals_52, buf112, 512,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_52
buf117 = empty_strided_cuda((32,), (1,), torch.int64)
triton_poi_fused_arange_14[grid(32)](buf117, 32, XBLOCK=32,
num_warps=1, num_stages=1)
buf118 = empty_strided_cuda((32,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_15[grid(32)](buf118, 32,
XBLOCK=32, num_warps=1, num_stages=1)
buf119 = empty_strided_cuda((4, 128, 34, 34), (147968, 1156, 34, 1),
torch.float32)
triton_poi_fused__unsafe_index_add_reflection_pad2d_16[grid(591872)](
buf118, buf99, buf111, buf113, buf114, buf112, primals_53,
buf119, 591872, XBLOCK=512, num_warps=8, num_stages=1)
del buf114
del buf99
del primals_53
buf120 = extern_kernels.convolution(buf119, primals_54, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf120, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf121 = empty_strided_cuda((64,), (1,), torch.int64)
triton_poi_fused_arange_17[grid(64)](buf121, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf122 = empty_strided_cuda((64,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_18[grid(64)](buf122, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf123 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1),
torch.float32)
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_19[
grid(1115136)](buf122, buf120, primals_55, buf123, 1115136,
XBLOCK=1024, num_warps=4, num_stages=1)
buf124 = extern_kernels.convolution(buf123, primals_56, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf124, (4, 32, 64, 64), (131072, 4096, 64, 1))
buf125 = empty_strided_cuda((4, 32, 72, 72), (165888, 5184, 72, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_20[grid(663552)](
buf124, primals_57, buf125, 663552, XBLOCK=512, num_warps=8,
num_stages=1)
buf126 = extern_kernels.convolution(buf125, primals_58, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf126, (4, 3, 64, 64), (12288, 4096, 64, 1))
buf127 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
torch.float32)
buf129 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
torch.bool)
buf128 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_21[grid(49152)](
buf126, primals_59, buf127, buf129, buf128, 49152, XBLOCK=256,
num_warps=4, num_stages=1)
del buf126
del primals_59
buf130 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
torch.bool)
buf131 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_22[grid(524288)](
buf124, primals_57, buf130, buf131, 524288, XBLOCK=1024,
num_warps=4, num_stages=1)
del buf124
del primals_57
buf132 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.bool)
buf133 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_23[grid(262144)](
buf120, primals_55, buf132, buf133, 262144, XBLOCK=1024,
num_warps=4, num_stages=1)
del buf120
del primals_55
return (buf127, primals_2, primals_6, primals_10, primals_14,
primals_18, primals_22, primals_26, primals_30, primals_34,
primals_38, primals_42, primals_46, primals_50, primals_54,
primals_56, primals_58, buf0, buf2, buf3, buf4, buf5, buf8, buf9,
buf11, buf12, buf13, buf14, buf17, buf18, buf20, buf21, buf22,
buf23, buf26, buf28, buf30, buf31, buf32, buf33, buf36, buf37,
buf39, buf40, reinterpret_tensor(buf44, (512,), (1,), 0), buf46,
buf48, buf49, buf50, buf51, buf54, buf55, buf57, buf58,
reinterpret_tensor(buf62, (512,), (1,), 0), buf64, buf66, buf67,
buf68, buf69, buf72, buf73, buf75, buf76, reinterpret_tensor(buf80,
(512,), (1,), 0), buf82, buf84, buf85, buf86, buf87, buf90, buf91,
buf93, buf94, reinterpret_tensor(buf98, (512,), (1,), 0), buf100,
buf102, buf103, buf104, buf105, buf108, buf109, buf111, buf112,
reinterpret_tensor(buf116, (512,), (1,), 0), buf117, buf118, buf119,
buf121, buf122, buf123, buf125, buf128, buf129, buf130, buf131,
buf132, buf133, reinterpret_tensor(buf113, (1, 512, 1, 1), (512, 1,
1, 1), 0), reinterpret_tensor(buf95, (1, 512, 1, 1), (512, 1, 1, 1),
0), reinterpret_tensor(buf77, (1, 512, 1, 1), (512, 1, 1, 1), 0),
reinterpret_tensor(buf59, (1, 512, 1, 1), (512, 1, 1, 1), 0),
reinterpret_tensor(buf41, (1, 512, 1, 1), (512, 1, 1, 1), 0))
class Convlayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1):
super().__init__()
padding = kernel_size // 2
self.refl = torch.nn.ReflectionPad2d(padding)
self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size,
stride)
def forward(self, x):
x = self.refl(x)
return self.conv(x)
class Residential(torch.nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv1 = Convlayer(in_channels=in_channels, out_channels=
in_channels, kernel_size=3)
self.inst1 = torch.nn.InstanceNorm2d(in_channels, affine=True)
self.conv2 = Convlayer(in_channels=in_channels, out_channels=
in_channels, kernel_size=3)
self.inst2 = torch.nn.InstanceNorm2d(in_channels, affine=True)
self.relu = torch.nn.ReLU()
def forward(self, x):
resident = x
x = self.relu(self.inst1(self.conv1(x)))
x = self.inst2(self.conv2(x))
return resident + x
class UpsampleConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride,
upsample=None):
super().__init__()
self.upsample = upsample
reflectpad = kernel_size // 2
self.reflectionpad = torch.nn.ReflectionPad2d(reflectpad)
self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size,
stride)
self.relu = torch.nn.ReLU()
def forward(self, x):
if self.upsample:
x = torch.nn.functional.interpolate(x, scale_factor=self.
upsample, mode='nearest')
x = self.reflectionpad(x)
return self.relu(self.conv(x))
class TransformerNew(torch.nn.Module):
"""
the min input size is [1, 3, 16, 16] as the kernel size and stride reduce the height and width
otherwise exception might caused as the input_size != output_size
"""
def __init__(self):
super().__init__()
self.conv1 = Convlayer(in_channels=3, out_channels=32, kernel_size=
9, stride=1)
self.inst1 = torch.nn.InstanceNorm2d(num_features=32, affine=True)
self.conv2 = Convlayer(in_channels=32, out_channels=64, kernel_size
=3, stride=2)
self.inst2 = torch.nn.InstanceNorm2d(num_features=64, affine=True)
self.conv3 = Convlayer(in_channels=64, out_channels=128,
kernel_size=3, stride=2)
self.inst3 = torch.nn.InstanceNorm2d(num_features=128, affine=True)
self.res1 = Residential(128)
self.res2 = Residential(128)
self.res3 = Residential(128)
self.res4 = Residential(128)
self.res5 = Residential(128)
self.upsample1 = UpsampleConvLayer(in_channels=128, out_channels=64,
kernel_size=3, stride=1, upsample=2)
self.upsample2 = UpsampleConvLayer(in_channels=64, out_channels=32,
kernel_size=3, stride=1, upsample=2)
self.upsample3 = UpsampleConvLayer(in_channels=32, out_channels=3,
kernel_size=9, stride=1)
self.relu = torch.nn.ReLU()
def forward(self, input_0):
primals_2 = self.conv1.conv.weight
primals_3 = self.conv1.conv.bias
primals_4 = self.inst1.weight
primals_5 = self.inst1.bias
primals_6 = self.conv2.conv.weight
primals_7 = self.conv2.conv.bias
primals_8 = self.inst2.weight
primals_9 = self.inst2.bias
primals_10 = self.conv3.conv.weight
primals_11 = self.conv3.conv.bias
primals_12 = self.inst3.weight
primals_13 = self.inst3.bias
primals_14 = self.res1.conv1.conv.weight
primals_15 = self.res1.conv1.conv.bias
primals_16 = self.res1.inst1.weight
primals_17 = self.res1.inst1.bias
primals_18 = self.res1.conv2.conv.weight
primals_19 = self.res1.conv2.conv.bias
primals_20 = self.res1.inst2.weight
primals_21 = self.res1.inst2.bias
primals_22 = self.res2.conv1.conv.weight
primals_23 = self.res2.conv1.conv.bias
primals_24 = self.res2.inst1.weight
primals_25 = self.res2.inst1.bias
primals_26 = self.res2.conv2.conv.weight
primals_27 = self.res2.conv2.conv.bias
primals_28 = self.res2.inst2.weight
primals_29 = self.res2.inst2.bias
primals_30 = self.res3.conv1.conv.weight
primals_31 = self.res3.conv1.conv.bias
primals_32 = self.res3.inst1.weight
primals_33 = self.res3.inst1.bias
primals_34 = self.res3.conv2.conv.weight
primals_35 = self.res3.conv2.conv.bias
primals_36 = self.res3.inst2.weight
primals_37 = self.res3.inst2.bias
primals_38 = self.res4.conv1.conv.weight
primals_39 = self.res4.conv1.conv.bias
primals_40 = self.res4.inst1.weight
primals_41 = self.res4.inst1.bias
primals_42 = self.res4.conv2.conv.weight
primals_43 = self.res4.conv2.conv.bias
primals_44 = self.res4.inst2.weight
primals_45 = self.res4.inst2.bias
primals_46 = self.res5.conv1.conv.weight
primals_47 = self.res5.conv1.conv.bias
primals_48 = self.res5.inst1.weight
primals_49 = self.res5.inst1.bias
primals_50 = self.res5.conv2.conv.weight
primals_51 = self.res5.conv2.conv.bias
primals_52 = self.res5.inst2.weight
primals_53 = self.res5.inst2.bias
primals_54 = self.upsample1.conv.weight
primals_55 = self.upsample1.conv.bias
primals_56 = self.upsample2.conv.weight
primals_57 = self.upsample2.conv.bias
primals_58 = self.upsample3.conv.weight
primals_59 = self.upsample3.conv.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, primals_32, primals_33, primals_34,
primals_35, primals_36, primals_37, primals_38, primals_39,
primals_40, primals_41, primals_42, primals_43, primals_44,
primals_45, primals_46, primals_47, primals_48, primals_49,
primals_50, primals_51, primals_52, primals_53, primals_54,
primals_55, primals_56, primals_57, primals_58, primals_59])
return output[0]
|
bruchano/ImageStyler
|
Transformer
| false
| 9,945
|
[
"MIT"
] | 0
|
7bde13bc954566088c477065adb5c4e4214c28bb
|
https://github.com/bruchano/ImageStyler/tree/7bde13bc954566088c477065adb5c4e4214c28bb
|
CSAM
|
import torch
import torch.nn as nn
class CSAM(nn.Module):
"""
Compact Spatial Attention Module
"""
def __init__(self, channels):
super(CSAM, self).__init__()
mid_channels = 4
self.relu1 = nn.ReLU()
self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, padding=0
)
self.conv2 = nn.Conv2d(mid_channels, 1, kernel_size=3, padding=1,
bias=False)
self.sigmoid = nn.Sigmoid()
nn.init.constant_(self.conv1.bias, 0)
def forward(self, x):
y = self.relu1(x)
y = self.conv1(y)
y = self.conv2(y)
y = self.sigmoid(y)
return x * y
def get_inputs():
return [torch.rand([4, 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
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_relu_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.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_convolution_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
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')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_mul_sigmoid_2(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
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.sigmoid(tmp1)
tmp3 = tmp0 * tmp2
tl.store(out_ptr0 + x3, 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, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (1, 4, 3, 3), (36, 9, 3, 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_relu_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
num_warps=4, num_stages=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, 4, 4, 4), (64, 16, 4, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_1[grid(256)](buf2, primals_3, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_3
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1))
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_mul_sigmoid_2[grid(256)](primals_1, buf3, buf4,
256, XBLOCK=128, num_warps=4, num_stages=1)
return buf4, primals_1, primals_2, primals_4, buf0, buf2, buf3
class CSAMNew(nn.Module):
"""
Compact Spatial Attention Module
"""
def __init__(self, channels):
super(CSAMNew, self).__init__()
mid_channels = 4
self.relu1 = nn.ReLU()
self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, padding=0
)
self.conv2 = nn.Conv2d(mid_channels, 1, kernel_size=3, padding=1,
bias=False)
self.sigmoid = nn.Sigmoid()
nn.init.constant_(self.conv1.bias, 0)
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = self.conv1.bias
primals_4 = self.conv2.weight
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
arkel23/mmgeneration
|
CSAM
| false
| 9,946
|
[
"Apache-2.0"
] | 0
|
41a30e2972f2037f6aac60ed761bed3fe47bfe4d
|
https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d
|
MiniBatchStddevLayer
|
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as autograd
class AllGatherLayer(autograd.Function):
"""All gather layer with backward propagation path.
Indeed, this module is to make ``dist.all_gather()`` in the backward graph.
Such kind of operation has been widely used in Moco and other contrastive
learning algorithms.
"""
@staticmethod
def forward(ctx, x):
"""Forward function."""
ctx.save_for_backward(x)
output = [torch.zeros_like(x) for _ in range(dist.get_world_size())]
dist.all_gather(output, x)
return tuple(output)
@staticmethod
def backward(ctx, *grad_outputs):
"""Backward function."""
x, = ctx.saved_tensors
grad_out = torch.zeros_like(x)
grad_out = grad_outputs[dist.get_rank()]
return grad_out
class MiniBatchStddevLayer(nn.Module):
"""Minibatch standard deviation.
Args:
group_size (int, optional): The size of groups in batch dimension.
Defaults to 4.
eps (float, optional): Epsilon value to avoid computation error.
Defaults to 1e-8.
gather_all_batch (bool, optional): Whether gather batch from all GPUs.
Defaults to False.
"""
def __init__(self, group_size=4, eps=1e-08, gather_all_batch=False):
super().__init__()
self.group_size = group_size
self.eps = eps
self.gather_all_batch = gather_all_batch
if self.gather_all_batch:
assert torch.distributed.is_initialized(
), 'Only in distributed training can the tensors be all gathered.'
def forward(self, x):
"""Forward function.
Args:
x (Tensor): Input tensor with shape (n, c, h, w).
Returns:
Tensor: Forward results.
"""
if self.gather_all_batch:
x = torch.cat(AllGatherLayer.apply(x), dim=0)
assert x.shape[0] <= self.group_size or x.shape[0
] % self.group_size == 0, f'Batch size be smaller than or equal to group size. Otherwise, batch size should be divisible by the group size.But got batch size {x.shape[0]}, group size {self.group_size}'
n, c, h, w = x.shape
group_size = min(n, self.group_size)
y = torch.reshape(x, (group_size, -1, c, h, w))
y = y - y.mean(dim=0, keepdim=True)
y = y.pow(2).mean(dim=0, keepdim=False)
y = torch.sqrt(y + self.eps)
y = y.mean(dim=(1, 2, 3), keepdim=True)
y = y.repeat(group_size, 1, h, w)
return torch.cat([x, y], dim=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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as autograd
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_mean_pow_repeat_sqrt_sub_0(in_ptr0, out_ptr1,
xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 64
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
r1 = rindex % 16
r2 = rindex // 16
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr0 + (64 + r0), None)
tmp3 = tl.load(in_ptr0 + (128 + r0), None)
tmp5 = tl.load(in_ptr0 + (192 + r0), None)
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-08
tmp22 = tmp20 + tmp21
tmp23 = libdevice.sqrt(tmp22)
tmp24 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
tmp26 = tl.sum(tmp24, 1)[:, None]
tmp27 = 64.0
tmp28 = tmp26 / tmp27
tl.store(out_ptr1 + tl.broadcast_to(r1 + 80 * r2, [XBLOCK, RBLOCK]),
tmp28, None)
@triton.jit
def triton_poi_fused_cat_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
x0 = xindex % 64
x1 = xindex // 64
tmp0 = tl.load(in_ptr0 + x2, xmask)
tl.store(out_ptr0 + (x0 + 80 * x1), tmp0, 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)
buf3 = empty_strided_cuda((4, 5, 4, 4), (80, 16, 4, 1), torch.float32)
buf2 = reinterpret_tensor(buf3, (4, 1, 4, 4), (80, 16, 4, 1), 64)
get_raw_stream(0)
triton_per_fused_add_mean_pow_repeat_sqrt_sub_0[grid(1)](arg0_1,
buf2, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
buf1 = reinterpret_tensor(buf3, (4, 4, 4, 4), (80, 16, 4, 1), 0)
triton_poi_fused_cat_1[grid(256)](arg0_1, buf1, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
return buf3,
class AllGatherLayer(autograd.Function):
"""All gather layer with backward propagation path.
Indeed, this module is to make ``dist.all_gather()`` in the backward graph.
Such kind of operation has been widely used in Moco and other contrastive
learning algorithms.
"""
@staticmethod
def forward(ctx, x):
"""Forward function."""
ctx.save_for_backward(x)
output = [torch.zeros_like(x) for _ in range(dist.get_world_size())]
dist.all_gather(output, x)
return tuple(output)
@staticmethod
def backward(ctx, *grad_outputs):
"""Backward function."""
x, = ctx.saved_tensors
grad_out = torch.zeros_like(x)
grad_out = grad_outputs[dist.get_rank()]
return grad_out
class MiniBatchStddevLayerNew(nn.Module):
"""Minibatch standard deviation.
Args:
group_size (int, optional): The size of groups in batch dimension.
Defaults to 4.
eps (float, optional): Epsilon value to avoid computation error.
Defaults to 1e-8.
gather_all_batch (bool, optional): Whether gather batch from all GPUs.
Defaults to False.
"""
def __init__(self, group_size=4, eps=1e-08, gather_all_batch=False):
super().__init__()
self.group_size = group_size
self.eps = eps
self.gather_all_batch = gather_all_batch
if self.gather_all_batch:
assert torch.distributed.is_initialized(
), 'Only in distributed training can the tensors be all gathered.'
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
arkel23/mmgeneration
|
MiniBatchStddevLayer
| false
| 9,947
|
[
"Apache-2.0"
] | 0
|
41a30e2972f2037f6aac60ed761bed3fe47bfe4d
|
https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d
|
AdaptiveInstanceNorm
|
import torch
import torch.nn as nn
from torch.nn.init import _calculate_correct_fan
def equalized_lr(module, name='weight', gain=2 ** 0.5, mode='fan_in',
lr_mul=1.0):
"""Equalized Learning Rate.
This trick is proposed in:
Progressive Growing of GANs for Improved Quality, Stability, and Variation
The general idea is to dynamically rescale the weight in training instead
of in initializing so that the variance of the responses in each layer is
guaranteed with some statistical properties.
Note that this function is always combined with a convolution module which
is initialized with :math:`\\mathcal{N}(0, 1)`.
Args:
module (nn.Module): Module to be wrapped.
name (str | optional): The name of weights. Defaults to 'weight'.
mode (str, optional): The mode of computing ``fan`` which is the
same as ``kaiming_init`` in pytorch. You can choose one from
['fan_in', 'fan_out']. Defaults to 'fan_in'.
Returns:
nn.Module: Module that is registered with equalized lr hook.
"""
EqualizedLR.apply(module, name, gain=gain, mode=mode, lr_mul=lr_mul)
return module
class EqualizedLR:
"""Equalized Learning Rate.
This trick is proposed in:
Progressive Growing of GANs for Improved Quality, Stability, and Variation
The general idea is to dynamically rescale the weight in training instead
of in initializing so that the variance of the responses in each layer is
guaranteed with some statistical properties.
Note that this function is always combined with a convolution module which
is initialized with :math:`\\mathcal{N}(0, 1)`.
Args:
name (str | optional): The name of weights. Defaults to 'weight'.
mode (str, optional): The mode of computing ``fan`` which is the
same as ``kaiming_init`` in pytorch. You can choose one from
['fan_in', 'fan_out']. Defaults to 'fan_in'.
"""
def __init__(self, name='weight', gain=2 ** 0.5, mode='fan_in', lr_mul=1.0
):
self.name = name
self.mode = mode
self.gain = gain
self.lr_mul = lr_mul
def compute_weight(self, module):
"""Compute weight with equalized learning rate.
Args:
module (nn.Module): A module that is wrapped with equalized lr.
Returns:
torch.Tensor: Updated weight.
"""
weight = getattr(module, self.name + '_orig')
if weight.ndim == 5:
fan = _calculate_correct_fan(weight[0], self.mode)
else:
assert weight.ndim <= 4
fan = _calculate_correct_fan(weight, self.mode)
weight = weight * torch.tensor(self.gain, device=weight.device
) * torch.sqrt(torch.tensor(1.0 / fan, device=weight.device)
) * self.lr_mul
return weight
def __call__(self, module, inputs):
"""Standard interface for forward pre hooks."""
setattr(module, self.name, self.compute_weight(module))
@staticmethod
def apply(module, name, gain=2 ** 0.5, mode='fan_in', lr_mul=1.0):
"""Apply function.
This function is to register an equalized learning rate hook in an
``nn.Module``.
Args:
module (nn.Module): Module to be wrapped.
name (str | optional): The name of weights. Defaults to 'weight'.
mode (str, optional): The mode of computing ``fan`` which is the
same as ``kaiming_init`` in pytorch. You can choose one from
['fan_in', 'fan_out']. Defaults to 'fan_in'.
Returns:
nn.Module: Module that is registered with equalized lr hook.
"""
for _, hook in module._forward_pre_hooks.items():
if isinstance(hook, EqualizedLR):
raise RuntimeError(
f'Cannot register two equalized_lr hooks on the same parameter {name} in {module} module.'
)
fn = EqualizedLR(name, gain=gain, mode=mode, lr_mul=lr_mul)
weight = module._parameters[name]
delattr(module, name)
module.register_parameter(name + '_orig', weight)
setattr(module, name, weight.data)
module.register_forward_pre_hook(fn)
return fn
class EqualizedLRLinearModule(nn.Linear):
"""Equalized LR LinearModule.
In this module, we adopt equalized lr in ``nn.Linear``. The equalized
learning rate is proposed in:
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Note that, the initialization of ``self.weight`` will be overwritten as
:math:`\\mathcal{N}(0, 1)`.
Args:
equalized_lr_cfg (dict | None, optional): Config for ``EqualizedLR``.
If ``None``, equalized learning rate is ignored. Defaults to
dict(mode='fan_in').
"""
def __init__(self, *args, equalized_lr_cfg=dict(mode='fan_in'), **kwargs):
super().__init__(*args, **kwargs)
self.with_equalized_lr = equalized_lr_cfg is not None
if self.with_equalized_lr:
self.lr_mul = equalized_lr_cfg.get('lr_mul', 1.0)
else:
self.lr_mul = 1.0
if self.with_equalized_lr:
equalized_lr(self, **equalized_lr_cfg)
self._init_linear_weights()
def _init_linear_weights(self):
"""Initialize linear weights as described in PGGAN."""
nn.init.normal_(self.weight, 0, 1.0 / self.lr_mul)
if self.bias is not None:
nn.init.constant_(self.bias, 0.0)
class AdaptiveInstanceNorm(nn.Module):
"""Adaptive Instance Normalization Module.
Ref: https://github.com/rosinality/style-based-gan-pytorch/blob/master/model.py # noqa
Args:
in_channel (int): The number of input's channel.
style_dim (int): Style latent dimension.
"""
def __init__(self, in_channel, style_dim):
super().__init__()
self.norm = nn.InstanceNorm2d(in_channel)
self.affine = EqualizedLRLinearModule(style_dim, in_channel * 2)
self.affine.bias.data[:in_channel] = 1
self.affine.bias.data[in_channel:] = 0
def forward(self, input, style):
"""Forward function.
Args:
input (Tensor): Input tensor with shape (n, c, h, w).
style (Tensor): Input style tensor with shape (n, c).
Returns:
Tensor: Forward results.
"""
style = self.affine(style).unsqueeze(2).unsqueeze(3)
gamma, beta = style.chunk(2, 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])]
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 as nn
from torch.nn.init import _calculate_correct_fan
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_lift_fresh_mul_sqrt_0(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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 1.4142135381698608
tmp2 = tmp0 * tmp1
tmp3 = 0.5
tmp4 = tmp2 * tmp3
tmp5 = 1.0
tmp6 = tmp4 * tmp5
tl.store(out_ptr0 + x0, tmp6, xmask)
@triton.jit
def triton_per_fused__native_batch_norm_legit_add_mul_1(in_out_ptr0,
in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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
x2 = xindex % 4
x3 = xindex // 4
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp22 = tl.load(in_ptr1 + (x2 + 8 * x3), xmask, eviction_policy=
'evict_last')
tmp23 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp28 = tl.load(in_ptr1 + (4 + x2 + 8 * x3), xmask, eviction_policy=
'evict_last')
tmp29 = tl.load(in_ptr2 + (4 + x2), xmask, eviction_policy='evict_last')
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tl.where(xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = 16.0
tmp18 = tmp16 / tmp17
tmp19 = 1e-05
tmp20 = tmp18 + tmp19
tmp21 = libdevice.rsqrt(tmp20)
tmp24 = tmp22 + tmp23
tmp25 = tmp0 - tmp10
tmp26 = tmp25 * tmp21
tmp27 = tmp24 * tmp26
tmp30 = tmp28 + tmp29
tmp31 = tmp27 + tmp30
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp21, xmask)
tl.store(out_ptr1 + (r1 + 16 * x0), tmp31, xmask)
tl.store(out_ptr0 + x0, tmp10, 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, 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((8, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_lift_fresh_mul_sqrt_0[grid(32)](primals_1, buf0,
32, XBLOCK=32, num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
extern_kernels.mm(primals_3, reinterpret_tensor(buf0, (4, 8), (1, 4
), 0), out=buf1)
buf2 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
)
buf5 = reinterpret_tensor(buf3, (1, 16, 1, 1), (16, 1, 1, 1), 0)
del buf3
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_per_fused__native_batch_norm_legit_add_mul_1[grid(16)](buf5,
primals_4, buf1, primals_2, buf2, buf6, 16, 16, XBLOCK=1,
num_warps=2, num_stages=1)
del buf1
del primals_2
return buf6, buf0, primals_3, primals_4, buf2, buf5
def equalized_lr(module, name='weight', gain=2 ** 0.5, mode='fan_in',
lr_mul=1.0):
"""Equalized Learning Rate.
This trick is proposed in:
Progressive Growing of GANs for Improved Quality, Stability, and Variation
The general idea is to dynamically rescale the weight in training instead
of in initializing so that the variance of the responses in each layer is
guaranteed with some statistical properties.
Note that this function is always combined with a convolution module which
is initialized with :math:`\\mathcal{N}(0, 1)`.
Args:
module (nn.Module): Module to be wrapped.
name (str | optional): The name of weights. Defaults to 'weight'.
mode (str, optional): The mode of computing ``fan`` which is the
same as ``kaiming_init`` in pytorch. You can choose one from
['fan_in', 'fan_out']. Defaults to 'fan_in'.
Returns:
nn.Module: Module that is registered with equalized lr hook.
"""
EqualizedLR.apply(module, name, gain=gain, mode=mode, lr_mul=lr_mul)
return module
class EqualizedLR:
"""Equalized Learning Rate.
This trick is proposed in:
Progressive Growing of GANs for Improved Quality, Stability, and Variation
The general idea is to dynamically rescale the weight in training instead
of in initializing so that the variance of the responses in each layer is
guaranteed with some statistical properties.
Note that this function is always combined with a convolution module which
is initialized with :math:`\\mathcal{N}(0, 1)`.
Args:
name (str | optional): The name of weights. Defaults to 'weight'.
mode (str, optional): The mode of computing ``fan`` which is the
same as ``kaiming_init`` in pytorch. You can choose one from
['fan_in', 'fan_out']. Defaults to 'fan_in'.
"""
def __init__(self, name='weight', gain=2 ** 0.5, mode='fan_in', lr_mul=1.0
):
self.name = name
self.mode = mode
self.gain = gain
self.lr_mul = lr_mul
def compute_weight(self, module):
"""Compute weight with equalized learning rate.
Args:
module (nn.Module): A module that is wrapped with equalized lr.
Returns:
torch.Tensor: Updated weight.
"""
weight = getattr(module, self.name + '_orig')
if weight.ndim == 5:
fan = _calculate_correct_fan(weight[0], self.mode)
else:
assert weight.ndim <= 4
fan = _calculate_correct_fan(weight, self.mode)
weight = weight * torch.tensor(self.gain, device=weight.device
) * torch.sqrt(torch.tensor(1.0 / fan, device=weight.device)
) * self.lr_mul
return weight
def __call__(self, module, inputs):
"""Standard interface for forward pre hooks."""
setattr(module, self.name, self.compute_weight(module))
@staticmethod
def apply(module, name, gain=2 ** 0.5, mode='fan_in', lr_mul=1.0):
"""Apply function.
This function is to register an equalized learning rate hook in an
``nn.Module``.
Args:
module (nn.Module): Module to be wrapped.
name (str | optional): The name of weights. Defaults to 'weight'.
mode (str, optional): The mode of computing ``fan`` which is the
same as ``kaiming_init`` in pytorch. You can choose one from
['fan_in', 'fan_out']. Defaults to 'fan_in'.
Returns:
nn.Module: Module that is registered with equalized lr hook.
"""
for _, hook in module._forward_pre_hooks.items():
if isinstance(hook, EqualizedLR):
raise RuntimeError(
f'Cannot register two equalized_lr hooks on the same parameter {name} in {module} module.'
)
fn = EqualizedLR(name, gain=gain, mode=mode, lr_mul=lr_mul)
weight = module._parameters[name]
delattr(module, name)
module.register_parameter(name + '_orig', weight)
setattr(module, name, weight.data)
module.register_forward_pre_hook(fn)
return fn
class EqualizedLRLinearModule(nn.Linear):
"""Equalized LR LinearModule.
In this module, we adopt equalized lr in ``nn.Linear``. The equalized
learning rate is proposed in:
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Note that, the initialization of ``self.weight`` will be overwritten as
:math:`\\mathcal{N}(0, 1)`.
Args:
equalized_lr_cfg (dict | None, optional): Config for ``EqualizedLR``.
If ``None``, equalized learning rate is ignored. Defaults to
dict(mode='fan_in').
"""
def __init__(self, *args, equalized_lr_cfg=dict(mode='fan_in'), **kwargs):
super().__init__(*args, **kwargs)
self.with_equalized_lr = equalized_lr_cfg is not None
if self.with_equalized_lr:
self.lr_mul = equalized_lr_cfg.get('lr_mul', 1.0)
else:
self.lr_mul = 1.0
if self.with_equalized_lr:
equalized_lr(self, **equalized_lr_cfg)
self._init_linear_weights()
def _init_linear_weights(self):
"""Initialize linear weights as described in PGGAN."""
nn.init.normal_(self.weight, 0, 1.0 / self.lr_mul)
if self.bias is not None:
nn.init.constant_(self.bias, 0.0)
class AdaptiveInstanceNormNew(nn.Module):
"""Adaptive Instance Normalization Module.
Ref: https://github.com/rosinality/style-based-gan-pytorch/blob/master/model.py # noqa
Args:
in_channel (int): The number of input's channel.
style_dim (int): Style latent dimension.
"""
def __init__(self, in_channel, style_dim):
super().__init__()
self.norm = nn.InstanceNorm2d(in_channel)
self.affine = EqualizedLRLinearModule(style_dim, in_channel * 2)
self.affine.bias.data[:in_channel] = 1
self.affine.bias.data[in_channel:] = 0
def forward(self, input_0, input_1):
primals_2 = self.affine.bias
primals_1 = self.affine.weight_orig
primals_4 = input_0
primals_3 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
arkel23/mmgeneration
|
AdaptiveInstanceNorm
| false
| 9,948
|
[
"Apache-2.0"
] | 0
|
41a30e2972f2037f6aac60ed761bed3fe47bfe4d
|
https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d
|
PDCBlock_converted
|
import torch
import torch.nn as nn
class PDCBlock_converted(nn.Module):
"""
CPDC, APDC can be converted to vanilla 3x3 convolution
RPDC can be converted to vanilla 5x5 convolution
"""
def __init__(self, pdc, inplane, ouplane, stride=1):
super(PDCBlock_converted, self).__init__()
self.stride = stride
if self.stride > 1:
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1,
padding=0)
if pdc == 'rd':
self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=5, padding
=2, groups=inplane, bias=False)
else:
self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=3, padding
=1, groups=inplane, bias=False)
self.relu2 = nn.ReLU()
self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0,
bias=False)
def forward(self, x):
if self.stride > 1:
x = self.pool(x)
y = self.conv1(x)
y = self.relu2(y)
y = self.conv2(y)
if self.stride > 1:
x = self.shortcut(x)
y = y + x
return y
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'pdc': 4, 'inplane': 4, 'ouplane': 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
@triton.jit
def triton_poi_fused_relu_0(in_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)
tl.store(in_out_ptr0 + x0, tmp2, 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
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask)
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x0, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 1, 3, 3), (9, 9, 3, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 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=4, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_relu_0[grid(256)](buf1, 256, XBLOCK=256, num_warps
=4, num_stages=1)
buf2 = extern_kernels.convolution(buf1, primals_3, 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, 4, 4), (64, 16, 4, 1))
buf3 = buf2
del buf2
triton_poi_fused_add_1[grid(256)](buf3, primals_2, 256, XBLOCK=256,
num_warps=4, num_stages=1)
return buf3, primals_1, primals_2, primals_3, buf1
class PDCBlock_convertedNew(nn.Module):
"""
CPDC, APDC can be converted to vanilla 3x3 convolution
RPDC can be converted to vanilla 5x5 convolution
"""
def __init__(self, pdc, inplane, ouplane, stride=1):
super(PDCBlock_convertedNew, self).__init__()
self.stride = stride
if self.stride > 1:
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1,
padding=0)
if pdc == 'rd':
self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=5, padding
=2, groups=inplane, bias=False)
else:
self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=3, padding
=1, groups=inplane, bias=False)
self.relu2 = nn.ReLU()
self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0,
bias=False)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_3 = self.conv2.weight
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
arkel23/mmgeneration
|
PDCBlock_converted
| false
| 9,949
|
[
"Apache-2.0"
] | 0
|
41a30e2972f2037f6aac60ed761bed3fe47bfe4d
|
https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d
|
Sine
|
import torch
import torch.nn as nn
class Sine(nn.Module):
def __init__(self, w0: 'float'=30.0):
super(Sine, self).__init__()
self.w0 = w0
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
return torch.sin(self.w0 * 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 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_mul_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 = 30.0
tmp2 = tmp0 * tmp1
tmp3 = tl_math.sin(tmp2)
tl.store(out_ptr0 + x0, tmp3, 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_mul_sin_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class SineNew(nn.Module):
def __init__(self, w0: 'float'=30.0):
super(SineNew, self).__init__()
self.w0 = w0
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
brandstetter-johannes/ocp
|
Sine
| false
| 9,950
|
[
"MIT",
"BSD-3-Clause"
] | 0
|
69cc90e6bed8aa09222cd77b926d7a34e96302ed
|
https://github.com/brandstetter-johannes/ocp/tree/69cc90e6bed8aa09222cd77b926d7a34e96302ed
|
wide_basic
|
import torch
import torch.nn as nn
def get_norm(n_filters, norm):
if norm is None:
return Identity()
elif norm == 'batch':
return nn.BatchNorm2d(n_filters, momentum=0.9)
elif norm == 'instance':
return nn.InstanceNorm2d(n_filters, affine=True)
elif norm == 'layer':
return nn.GroupNorm(1, n_filters)
elif norm == 'act':
return norms.ActNorm(n_filters, False)
class Identity(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
def forward(self, x):
return x
class wide_basic(nn.Module):
def __init__(self, in_planes, planes, dropout_rate, stride=1, norm=None,
leak=0.2):
super(wide_basic, self).__init__()
self.lrelu = nn.LeakyReLU(leak)
self.bn1 = get_norm(in_planes, norm)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1,
bias=True)
self.dropout = Identity() if dropout_rate == 0.0 else nn.Dropout(p=
dropout_rate)
self.bn2 = get_norm(planes, norm)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=True)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != planes:
self.shortcut = nn.Sequential(nn.Conv2d(in_planes, planes,
kernel_size=1, stride=stride, bias=True))
def forward(self, x):
out = self.dropout(self.conv1(self.lrelu(self.bn1(x))))
out = self.conv2(self.lrelu(self.bn2(out)))
out += self.shortcut(x)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_planes': 4, 'planes': 4, 'dropout_rate': 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
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_leaky_relu_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 = tmp0 > tmp1
tmp3 = 0.2
tmp4 = tmp0 * tmp3
tmp5 = tl.where(tmp2, tmp0, tmp4)
tl.store(out_ptr0 + x0, tmp5, xmask)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(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
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.2
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, xmask)
tl.store(out_ptr1 + x3, tmp7, xmask)
@triton.jit
def triton_poi_fused_add_convolution_2(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
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 + x3, xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(in_out_ptr0 + x3, tmp4, 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, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 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, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_leaky_relu_0[grid(256)](primals_1, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=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, 4, 4, 4), (64, 16, 4, 1))
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_convolution_leaky_relu_1[grid(256)](buf1,
primals_3, buf2, buf3, 256, XBLOCK=128, num_warps=4, num_stages=1)
del buf1
del primals_3
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
buf5 = buf4
del buf4
triton_poi_fused_add_convolution_2[grid(256)](buf5, primals_5,
primals_1, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
del primals_5
return buf5, primals_2, primals_4, buf0, buf2, buf3
def get_norm(n_filters, norm):
if norm is None:
return Identity()
elif norm == 'batch':
return nn.BatchNorm2d(n_filters, momentum=0.9)
elif norm == 'instance':
return nn.InstanceNorm2d(n_filters, affine=True)
elif norm == 'layer':
return nn.GroupNorm(1, n_filters)
elif norm == 'act':
return norms.ActNorm(n_filters, False)
class Identity(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
def forward(self, x):
return x
class wide_basicNew(nn.Module):
def __init__(self, in_planes, planes, dropout_rate, stride=1, norm=None,
leak=0.2):
super(wide_basicNew, self).__init__()
self.lrelu = nn.LeakyReLU(leak)
self.bn1 = get_norm(in_planes, norm)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1,
bias=True)
self.dropout = Identity() if dropout_rate == 0.0 else nn.Dropout(p=
dropout_rate)
self.bn2 = get_norm(planes, norm)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=True)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != planes:
self.shortcut = nn.Sequential(nn.Conv2d(in_planes, planes,
kernel_size=1, stride=stride, bias=True))
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
csadrian/JEM
|
wide_basic
| false
| 9,951
|
[
"Apache-2.0"
] | 0
|
72d9af20126cf1410506b2c149d740a41ef04e78
|
https://github.com/csadrian/JEM/tree/72d9af20126cf1410506b2c149d740a41ef04e78
|
BertPooler
|
from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertPooler(nn.Module):
def __init__(self, config):
super(BertPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(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.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_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 % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tl.store(out_ptr0 + x2, tmp0, xmask)
@triton.jit
def triton_poi_fused_add_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)
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, 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), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
del primals_2
buf2 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
del buf1
triton_poi_fused_add_tanh_1[grid(64)](buf2, primals_3, 64, XBLOCK=
64, num_warps=1, num_stages=1)
del primals_3
return buf2, reinterpret_tensor(buf0, (16, 4), (4, 1), 0), buf2
class BertPoolerNew(nn.Module):
def __init__(self, config):
super(BertPoolerNew, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, input_0):
primals_2 = self.dense.weight
primals_3 = self.dense.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Ago3/VLP
|
BertPooler
| false
| 9,952
|
[
"Apache-2.0"
] | 0
|
4dec0e04b8592f4a74fe66c253dbb92574e7e2ba
|
https://github.com/Ago3/VLP/tree/4dec0e04b8592f4a74fe66c253dbb92574e7e2ba
|
DuelingDeepQNetwork
|
import torch
import torch as T
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class DuelingDeepQNetwork(nn.Module):
def __init__(self, lr, input_dim, output_dim, fc1_dim, fc2_dim):
super(DuelingDeepQNetwork, self).__init__()
self.fc1 = nn.Linear(input_dim, fc1_dim)
self.fc2 = nn.Linear(fc1_dim, fc2_dim)
self.V = nn.Linear(fc2_dim, 1)
self.A = nn.Linear(fc2_dim, output_dim)
self.optimizer = optim.RMSprop(self.parameters(), lr=lr)
self.loss = nn.MSELoss()
self.device = T.device('cuda:0' if T.cuda.is_available() else 'cpu')
self
def forward(self, state):
flat1 = F.relu(self.fc1(state))
flat2 = F.relu(self.fc2(flat1))
v = self.V(flat2)
a = self.A(flat2)
return v, a
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'lr': 4, 'input_dim': 4, 'output_dim': 4, 'fc1_dim': 4,
'fc2_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
import torch as T
import torch.nn as nn
import torch.optim as optim
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, primals_6,
primals_7, primals_8, primals_9) = 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,))
assert_size_stride(primals_6, (1, 4), (4, 1))
assert_size_stride(primals_7, (1,), (1,))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (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
buf8 = 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, buf8, 256, XBLOCK=256, 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
buf7 = 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, buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_6, (4, 1), (1, 4), 0),
alpha=1, beta=1, out=buf5)
del primals_7
buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf6)
del primals_9
return reinterpret_tensor(buf5, (4, 4, 4, 1), (16, 4, 1, 1), 0
), reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(
buf3, (64, 4), (4, 1), 0), primals_8, primals_6, buf7, primals_4, buf8
class DuelingDeepQNetworkNew(nn.Module):
def __init__(self, lr, input_dim, output_dim, fc1_dim, fc2_dim):
super(DuelingDeepQNetworkNew, self).__init__()
self.fc1 = nn.Linear(input_dim, fc1_dim)
self.fc2 = nn.Linear(fc1_dim, fc2_dim)
self.V = nn.Linear(fc2_dim, 1)
self.A = nn.Linear(fc2_dim, output_dim)
self.optimizer = optim.RMSprop(self.parameters(), lr=lr)
self.loss = nn.MSELoss()
self.device = T.device('cuda:0' if T.cuda.is_available() else 'cpu')
self
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.V.weight
primals_7 = self.V.bias
primals_8 = self.A.weight
primals_9 = self.A.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], output[1]
|
MonteyMontey/deep-reinforcement-learning-sandbox
|
DuelingDeepQNetwork
| false
| 9,953
|
[
"MIT"
] | 0
|
0e93760a994b6af54f0a665f5bc4f9d5ffd45c0a
|
https://github.com/MonteyMontey/deep-reinforcement-learning-sandbox/tree/0e93760a994b6af54f0a665f5bc4f9d5ffd45c0a
|
BertIntermediate
|
from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
def gelu(x):
"""Gaussian Error Linear Unitという活性化関数です。
LeLUが0でカクっと不連続なので、そこを連続になるように滑らかにした形のLeLUです。
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class BertIntermediate(nn.Module):
"""BERTのTransformerBlockモジュールのFeedForwardです"""
def __init__(self, config):
super(BertIntermediate, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
self.intermediate_act_fn = gelu
def forward(self, hidden_states):
"""
hidden_states： BertAttentionの出力テンソル
"""
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(hidden_size=4, intermediate_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 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_add_div_erf_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 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = 0.7071067811865475
tmp4 = tmp0 * tmp3
tmp5 = libdevice.erf(tmp4)
tmp6 = 1.0
tmp7 = tmp5 + tmp6
tmp8 = tmp2 * tmp7
tl.store(out_ptr0 + x0, tmp8, 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.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)
get_raw_stream(0)
triton_poi_fused_add_div_erf_mul_0[grid(256)](buf0, buf1, 256,
XBLOCK=256, num_warps=4, num_stages=1)
return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0
def gelu(x):
"""Gaussian Error Linear Unitという活性化関数です。
LeLUが0でカクっと不連続なので、そこを連続になるように滑らかにした形のLeLUです。
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class BertIntermediateNew(nn.Module):
"""BERTのTransformerBlockモジュールのFeedForwardです"""
def __init__(self, config):
super(BertIntermediateNew, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
self.intermediate_act_fn = gelu
def forward(self, input_0):
primals_1 = self.dense.weight
primals_2 = self.dense.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Cyndi-Tokyotech/Fin_Text_Analysis_ML
|
BertIntermediate
| false
| 9,954
|
[
"MIT"
] | 0
|
7f9b6c1ea78f8e6f32c003b2de32809722df88d4
|
https://github.com/Cyndi-Tokyotech/Fin_Text_Analysis_ML/tree/7f9b6c1ea78f8e6f32c003b2de32809722df88d4
|
PyTorchMLP
|
import torch
import torch.nn as nn
class PyTorchMLP(nn.Module):
"""
A feed forward network to make single step predictions on 1D time series data.
"""
def __init__(self, inputsize, prefix):
super(PyTorchMLP, self).__init__()
self.fc1 = nn.Linear(in_features=inputsize, out_features=round(
inputsize / 2))
self.fc2 = nn.Linear(in_features=round(inputsize / 2), out_features=1)
self.act = nn.ReLU()
self.prefix = prefix
def forward(self, x):
y = torch.squeeze(x)
output = self.fc1(y)
output = self.act(output)
output = self.fc2(output)
return output
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'inputsize': 4, 'prefix': 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 = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 2
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, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (2, 4), (4, 1))
assert_size_stride(primals_3, (2,), (1,))
assert_size_stride(primals_4, (1, 2), (2, 1))
assert_size_stride(primals_5, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 2), (1, 4), 0), out=buf0)
del primals_2
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 2), (32, 8, 2, 1), 0)
del buf0
buf4 = empty_strided_cuda((4, 4, 4, 2), (32, 8, 2, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(128)](buf1,
primals_3, buf4, 128, XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 2), (
2, 1), 0), reinterpret_tensor(primals_4, (2, 1), (1, 2), 0),
alpha=1, beta=1, out=buf3)
del primals_5
return reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0
), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 2), (2, 1), 0), primals_4, buf4
class PyTorchMLPNew(nn.Module):
"""
A feed forward network to make single step predictions on 1D time series data.
"""
def __init__(self, inputsize, prefix):
super(PyTorchMLPNew, self).__init__()
self.fc1 = nn.Linear(in_features=inputsize, out_features=round(
inputsize / 2))
self.fc2 = nn.Linear(in_features=round(inputsize / 2), out_features=1)
self.act = nn.ReLU()
self.prefix = prefix
def forward(self, input_0):
primals_2 = self.fc1.weight
primals_3 = self.fc1.bias
primals_4 = self.fc2.weight
primals_5 = self.fc2.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
danvran/ASM
|
PyTorchMLP
| false
| 9,955
|
[
"MIT"
] | 0
|
e678fa507f847ec2ff947ec4ca123858ffe46d4d
|
https://github.com/danvran/ASM/tree/e678fa507f847ec2ff947ec4ca123858ffe46d4d
|
GaussianSmearing
|
import torch
import torch.nn as nn
class GaussianSmearing(nn.Module):
def __init__(self, in_features, start=0, end=1, num_freqs=50):
super(GaussianSmearing, self).__init__()
self.num_freqs = num_freqs
offset = torch.linspace(start, end, num_freqs)
self.coeff = -0.5 / (offset[1] - offset[0]).item() ** 2
self.offset = nn.Parameter(offset.view(-1, 1).repeat(1, in_features
).view(1, -1), requires_grad=False)
def forward(self, x):
x = x.repeat(1, self.num_freqs)
x = x - self.offset
return torch.exp(self.coeff * torch.pow(x, 2))
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_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
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_exp_mul_pow_repeat_sub_0(in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 800
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 200
x1 = xindex // 200
x2 = xindex
tmp0 = tl.load(in_ptr0 + (4 * x1 + x0 % 4), xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = -1200.5000491943226
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, (4, 4), (4, 1))
assert_size_stride(arg1_1, (1, 200), (200, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 200), (200, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_exp_mul_pow_repeat_sub_0[grid(800)](arg0_1, arg1_1,
buf0, 800, XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
return buf0,
class GaussianSmearingNew(nn.Module):
def __init__(self, in_features, start=0, end=1, num_freqs=50):
super(GaussianSmearingNew, self).__init__()
self.num_freqs = num_freqs
offset = torch.linspace(start, end, num_freqs)
self.coeff = -0.5 / (offset[1] - offset[0]).item() ** 2
self.offset = nn.Parameter(offset.view(-1, 1).repeat(1, in_features
).view(1, -1), requires_grad=False)
def forward(self, input_0):
arg1_1 = self.offset
arg0_1 = input_0
output = call([arg0_1, arg1_1])
return output[0]
|
brandstetter-johannes/ocp
|
GaussianSmearing
| false
| 9,956
|
[
"MIT",
"BSD-3-Clause"
] | 0
|
69cc90e6bed8aa09222cd77b926d7a34e96302ed
|
https://github.com/brandstetter-johannes/ocp/tree/69cc90e6bed8aa09222cd77b926d7a34e96302ed
|
GatedLinear
|
import torch
import torch.nn as nn
class GatedLinear(nn.Module):
def __init__(self, input_size, output_size):
super(GatedLinear, self).__init__()
self.linear = nn.Linear(input_size, output_size * 2)
self.glu = nn.GLU(dim=-1)
def forward(self, x, y=None, x_mask=None, y_mask=None, rel_embed=None):
return self.glu(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
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_glu_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
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
tmp1 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
tmp2 = tl.sigmoid(tmp1)
tmp3 = tmp0 * tmp2
tl.store(out_ptr0 + x2, tmp3, xmask)
def call(args):
primals_1, primals_2, primals_3 = 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))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 8), (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)
get_raw_stream(0)
triton_poi_fused_glu_0[grid(256)](buf0, buf1, 256, XBLOCK=256,
num_warps=4, num_stages=1)
return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf0, (4, 4, 4, 8), (128, 32, 8, 1), 0)
class GatedLinearNew(nn.Module):
def __init__(self, input_size, output_size):
super(GatedLinearNew, self).__init__()
self.linear = nn.Linear(input_size, output_size * 2)
self.glu = nn.GLU(dim=-1)
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]
|
cuiyuhao1996/mmnas
|
GatedLinear
| false
| 9,957
|
[
"Apache-2.0"
] | 0
|
d62e0b3ddc6d15e8f01d0d66367e05fc9691cd3b
|
https://github.com/cuiyuhao1996/mmnas/tree/d62e0b3ddc6d15e8f01d0d66367e05fc9691cd3b
|
LayerNorm
|
import torch
import torch.nn.init
import torch.optim.lr_scheduler
import torch.nn
import torch.autograd
class LayerNorm(torch.nn.Module):
"""
An implementation of `Layer Normalization
<https://www.semanticscholar.org/paper/Layer-Normalization-Ba-Kiros/97fb4e3d45bb098e27e0071448b6152217bd35a5>`_ .
Layer Normalization stabilises the training of deep neural networks by
normalising the outputs of neurons from a particular layer. It computes:
output = (gamma * (tensor - mean) / (std + eps)) + beta
Parameters
----------
dimension : ``int``, required.
The dimension of the layer output to normalize.
eps : ``float``, optional, (default = 1e-6)
An epsilon to prevent dividing by zero in the case
the layer has zero variance.
Returns
-------
The normalized layer output.
"""
def __init__(self, dimension: 'int', eps: 'float'=1e-06) ->None:
super().__init__()
self.gamma = torch.nn.Parameter(torch.ones(dimension))
self.beta = torch.nn.Parameter(torch.zeros(dimension))
self.eps = eps
def forward(self, tensor: 'torch.Tensor'):
mean = tensor.mean(-1, keepdim=True)
std = tensor.std(-1, unbiased=False, keepdim=True)
return self.gamma * (tensor - mean) / (std + self.eps) + self.beta
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'dimension': 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.triton_helpers import libdevice
import torch.nn.init
import torch.optim.lr_scheduler
import torch.nn
import torch.autograd
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_mean_mul_std_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 % 4
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp29 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp8 = tmp6 + tmp7
tmp9 = 4.0
tmp10 = tmp8 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp0 * tmp11
tmp13 = tmp2 - tmp10
tmp14 = tmp13 * tmp13
tmp15 = tmp3 - tmp10
tmp16 = tmp15 * tmp15
tmp17 = tmp14 + tmp16
tmp18 = tmp5 - tmp10
tmp19 = tmp18 * tmp18
tmp20 = tmp17 + tmp19
tmp21 = tmp7 - tmp10
tmp22 = tmp21 * tmp21
tmp23 = tmp20 + tmp22
tmp24 = tmp23 / tmp9
tmp25 = libdevice.sqrt(tmp24)
tmp26 = 1e-06
tmp27 = tmp25 + tmp26
tmp28 = tmp12 / tmp27
tmp30 = tmp28 + tmp29
tl.store(out_ptr0 + x2, tmp30, 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,), (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_mean_mul_std_sub_0[grid(256)](primals_2,
primals_1, primals_3, buf0, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del primals_2
del primals_3
return buf0, primals_1
class LayerNormNew(torch.nn.Module):
"""
An implementation of `Layer Normalization
<https://www.semanticscholar.org/paper/Layer-Normalization-Ba-Kiros/97fb4e3d45bb098e27e0071448b6152217bd35a5>`_ .
Layer Normalization stabilises the training of deep neural networks by
normalising the outputs of neurons from a particular layer. It computes:
output = (gamma * (tensor - mean) / (std + eps)) + beta
Parameters
----------
dimension : ``int``, required.
The dimension of the layer output to normalize.
eps : ``float``, optional, (default = 1e-6)
An epsilon to prevent dividing by zero in the case
the layer has zero variance.
Returns
-------
The normalized layer output.
"""
def __init__(self, dimension: 'int', eps: 'float'=1e-06) ->None:
super().__init__()
self.gamma = torch.nn.Parameter(torch.ones(dimension))
self.beta = torch.nn.Parameter(torch.zeros(dimension))
self.eps = eps
def forward(self, input_0):
primals_2 = self.gamma
primals_3 = self.beta
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
codedecde/BiMPM
|
LayerNorm
| false
| 9,958
|
[
"Apache-2.0"
] | 0
|
818602fcf7a018632707b8fbfe33200036795731
|
https://github.com/codedecde/BiMPM/tree/818602fcf7a018632707b8fbfe33200036795731
|
Linear
|
import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.cuda
import torch.distributed
def quantize_weights(W, numbits=8):
W = W.clamp(-2 ** (numbits - 1), 2 ** (numbits - 1))
W = W.mul(2 ** (numbits - 1)).round().div(2 ** (numbits - 1))
return W
class quant_weights(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return quantize_weights(x)
@staticmethod
def backward(ctx, g):
return g
class Linear(nn.modules.Linear):
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__(in_features, out_features)
self.in_features = in_features
self.out_features = out_features
self.W_LR_scale = np.float32(1.0 / np.sqrt(1.5 / (in_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)
self.reset_parameters()
def reset_parameters(self):
stdv = 1.0 / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward_t(self, input):
return F.linear(input, self.weight, self.bias)
def extra_repr(self):
return 'in_features={}, out_features={}, bias={}'.format(self.
in_features, self.out_features, self.bias is not None)
def forward(self, input):
Wr = self.weight.data
self.Wb = quant_weights.apply(self.weight.data)
self.input_b = quant_weights.apply(input)
self.weight.data = self.Wb
rvalue = self.forward_t(self.input_b)
self.weight.data = Wr
return rvalue
def return_W_scale(self):
return self.W_LR_scale
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.cuda
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_clamp_div_mul_round_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 = -128.0
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp3 = 128.0
tmp4 = triton_helpers.minimum(tmp2, tmp3)
tmp5 = tmp4 * tmp3
tmp6 = libdevice.nearbyint(tmp5)
tmp7 = 0.0078125
tmp8 = tmp6 * tmp7
tl.store(out_ptr0 + x0, tmp8, xmask)
@triton.jit
def triton_poi_fused_clamp_div_mul_round_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 = -128.0
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp3 = 128.0
tmp4 = triton_helpers.minimum(tmp2, tmp3)
tmp5 = tmp4 * tmp3
tmp6 = libdevice.nearbyint(tmp5)
tmp7 = 0.0078125
tmp8 = tmp6 * tmp7
tl.store(out_ptr0 + x0, tmp8, 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, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clamp_div_mul_round_0[grid(16)](primals_1, buf0,
16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clamp_div_mul_round_1[grid(256)](primals_2, buf1,
256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_3, reinterpret_tensor(buf1, (64, 4), (
4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), alpha=1,
beta=1, out=buf2)
del primals_3
return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
), buf1, buf0, reinterpret_tensor(buf1, (64, 4), (4, 1), 0)
def quantize_weights(W, numbits=8):
W = W.clamp(-2 ** (numbits - 1), 2 ** (numbits - 1))
W = W.mul(2 ** (numbits - 1)).round().div(2 ** (numbits - 1))
return W
class quant_weights(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return quantize_weights(x)
@staticmethod
def backward(ctx, g):
return g
class LinearNew(nn.modules.Linear):
def __init__(self, in_features, out_features, bias=True):
super(LinearNew, self).__init__(in_features, out_features)
self.in_features = in_features
self.out_features = out_features
self.W_LR_scale = np.float32(1.0 / np.sqrt(1.5 / (in_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)
self.reset_parameters()
def reset_parameters(self):
stdv = 1.0 / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward_t(self, input):
return F.linear(input, self.weight, self.bias)
def extra_repr(self):
return 'in_features={}, out_features={}, bias={}'.format(self.
in_features, self.out_features, self.bias is not None)
def return_W_scale(self):
return self.W_LR_scale
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]
|
csk7/CS550-NLP-McGill-
|
Linear
| false
| 9,959
|
[
"MIT"
] | 0
|
a6f295b88539015d8accdbd410357c42df7c4287
|
https://github.com/csk7/CS550-NLP-McGill-/tree/a6f295b88539015d8accdbd410357c42df7c4287
|
FCN32s
|
import torch
import numpy as np
from torch import nn
def get_upsampling_weight(in_channels, out_channels, kernel_size):
"""Make a 2D bilinear kernel suitable for upsampling"""
factor = (kernel_size + 1) // 2
if kernel_size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = np.ogrid[:kernel_size, :kernel_size]
filt = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) /
factor)
weight = np.zeros((in_channels, out_channels, kernel_size, kernel_size),
dtype=np.float64)
weight[range(in_channels), range(out_channels), :, :] = filt
return torch.from_numpy(weight).float()
class FCN32s(nn.Module):
def __init__(self, n_channels=5, n_class=1):
super(FCN32s, self).__init__()
self.conv1_1 = nn.Conv2d(5, 64, 3, padding=100)
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
self.relu1_2 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)
self.relu2_1 = nn.ReLU(inplace=True)
self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)
self.relu2_2 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)
self.relu3_1 = nn.ReLU(inplace=True)
self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1)
self.relu3_2 = nn.ReLU(inplace=True)
self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1)
self.relu3_3 = nn.ReLU(inplace=True)
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1)
self.relu4_1 = nn.ReLU(inplace=True)
self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1)
self.relu4_2 = nn.ReLU(inplace=True)
self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1)
self.relu4_3 = nn.ReLU(inplace=True)
self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1)
self.relu5_1 = nn.ReLU(inplace=True)
self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1)
self.relu5_2 = nn.ReLU(inplace=True)
self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1)
self.relu5_3 = nn.ReLU(inplace=True)
self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.fc6 = nn.Conv2d(512, 4096, 7)
self.relu6 = nn.ReLU(inplace=True)
self.drop6 = nn.Dropout2d()
self.fc7 = nn.Conv2d(4096, 4096, 1)
self.relu7 = nn.ReLU(inplace=True)
self.drop7 = nn.Dropout2d()
self.score_fr = nn.Conv2d(4096, n_class, 1)
self.upscore = nn.ConvTranspose2d(n_class, n_class, 64, stride=32,
bias=False)
self._initialize_weights()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
m.weight.data.zero_()
if m.bias is not None:
m.bias.data.zero_()
if isinstance(m, nn.ConvTranspose2d):
assert m.kernel_size[0] == m.kernel_size[1]
initial_weight = get_upsampling_weight(m.in_channels, m.
out_channels, m.kernel_size[0])
m.weight.data.copy_(initial_weight)
def forward(self, x):
h = x
h = self.relu1_1(self.conv1_1(h))
h = self.relu1_2(self.conv1_2(h))
h = self.pool1(h)
h = self.relu2_1(self.conv2_1(h))
h = self.relu2_2(self.conv2_2(h))
h = self.pool2(h)
h = self.relu3_1(self.conv3_1(h))
h = self.relu3_2(self.conv3_2(h))
h = self.relu3_3(self.conv3_3(h))
h = self.pool3(h)
h = self.relu4_1(self.conv4_1(h))
h = self.relu4_2(self.conv4_2(h))
h = self.relu4_3(self.conv4_3(h))
h = self.pool4(h)
h = self.relu5_1(self.conv5_1(h))
h = self.relu5_2(self.conv5_2(h))
h = self.relu5_3(self.conv5_3(h))
h = self.pool5(h)
h = self.relu6(self.fc6(h))
h = self.drop6(h)
h = self.relu7(self.fc7(h))
h = self.drop7(h)
h = self.score_fr(h)
h = self.upscore(h)
h = h[:, :, 19:19 + x.size()[2], 19:19 + x.size()[3]].contiguous()
return h
def copy_params_from_vgg16(self, vgg16):
features = [self.conv1_1, self.relu1_1, self.conv1_2, self.relu1_2,
self.pool1, self.conv2_1, self.relu2_1, self.conv2_2, self.
relu2_2, self.pool2, self.conv3_1, self.relu3_1, self.conv3_2,
self.relu3_2, self.conv3_3, self.relu3_3, self.pool3, self.
conv4_1, self.relu4_1, self.conv4_2, self.relu4_2, self.conv4_3,
self.relu4_3, self.pool4, self.conv5_1, self.relu5_1, self.
conv5_2, self.relu5_2, self.conv5_3, self.relu5_3, self.pool5]
for l1, l2 in zip(vgg16.features, features):
if isinstance(l1, nn.Conv2d) and isinstance(l2, nn.Conv2d):
assert l1.weight.size() == l2.weight.size()
assert l1.bias.size() == l2.bias.size()
l2.weight.data = l1.weight.data
l2.bias.data = l1.bias.data
for i, name in zip([0, 3], ['fc6', 'fc7']):
l1 = vgg16.classifier[i]
l2 = getattr(self, name)
l2.weight.data = l1.weight.data.view(l2.weight.size())
l2.bias.data = l1.bias.data.view(l2.bias.size())
def get_inputs():
return [torch.rand([4, 5, 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
import numpy as np
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 20
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]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
x2 = xindex
y3 = yindex
y0 = yindex % 5
y1 = yindex // 5
tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 5 * x2 + 20480 * y1), tmp0, ymask)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 320
xnumel = 9
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 % 5
y1 = yindex // 5
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 5 * x2 + 45 * 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) * 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_3(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_4(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_5(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_6(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_7(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_8(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 % 512
y1 = yindex // 512
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 512 * x2 + 4608 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_9(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 49
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 % 512
y1 = yindex // 512
tmp0 = tl.load(in_ptr0 + (x2 + 49 * y3), xmask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 512 * x2 + 25088 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_10(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 17572864
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
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_max_pool2d_with_indices_11(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 4393216
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 64
x1 = xindex // 64 % 131
x2 = xindex // 8384
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1 + 33536 * x2), xmask)
tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 33536 * x2), xmask)
tmp3 = tl.load(in_ptr0 + (16768 + x0 + 128 * x1 + 33536 * x2), xmask)
tmp5 = tl.load(in_ptr0 + (16832 + x0 + 128 * x1 + 33536 * x2), xmask)
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, xmask)
tl.store(out_ptr1 + x3, tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_12(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 8786432
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 128
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_max_pool2d_with_indices_13(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)
x2 = xindex // 8448 % 66
x1 = xindex // 128 % 66
x0 = xindex % 128
x3 = xindex // 557568
x6 = xindex
tmp0 = 2 * x2
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 131, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = 2 * x1
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = tmp5 & tmp9
tmp11 = tl.load(in_ptr0 + (x0 + 256 * x1 + 33536 * x2 + 2196608 * x3),
tmp10, other=float('-inf'))
tmp12 = 1 + 2 * x1
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp13 & tmp14
tmp16 = tmp5 & tmp15
tmp17 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 33536 * x2 + 2196608 *
x3), tmp16, other=float('-inf'))
tmp18 = triton_helpers.maximum(tmp17, tmp11)
tmp19 = 1 + 2 * x2
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp20 & tmp21
tmp23 = tmp22 & tmp9
tmp24 = tl.load(in_ptr0 + (16768 + x0 + 256 * x1 + 33536 * x2 + 2196608 *
x3), tmp23, other=float('-inf'))
tmp25 = triton_helpers.maximum(tmp24, tmp18)
tmp26 = tmp22 & tmp15
tmp27 = tl.load(in_ptr0 + (16896 + x0 + 256 * x1 + 33536 * x2 + 2196608 *
x3), tmp26, other=float('-inf'))
tmp28 = triton_helpers.maximum(tmp27, tmp25)
tmp29 = tmp17 > tmp11
tmp30 = tl.full([1], 1, tl.int8)
tmp31 = tl.full([1], 0, tl.int8)
tmp32 = tl.where(tmp29, tmp30, tmp31)
tmp33 = tmp24 > tmp18
tmp34 = tl.full([1], 2, tl.int8)
tmp35 = tl.where(tmp33, tmp34, tmp32)
tmp36 = tmp27 > tmp25
tmp37 = tl.full([1], 3, tl.int8)
tmp38 = tl.where(tmp36, tmp37, tmp35)
tl.store(out_ptr0 + x6, tmp28, None)
tl.store(out_ptr1 + x6, tmp38, None)
@triton.jit
def triton_poi_fused_convolution_relu_14(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_15(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 1115136
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 256
x1 = xindex // 256 % 33
x2 = xindex // 8448
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 512 * x1 + 33792 * x2), xmask)
tmp1 = tl.load(in_ptr0 + (256 + x0 + 512 * x1 + 33792 * x2), xmask)
tmp3 = tl.load(in_ptr0 + (16896 + x0 + 512 * x1 + 33792 * x2), xmask)
tmp5 = tl.load(in_ptr0 + (17152 + x0 + 512 * x1 + 33792 * x2), xmask)
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, xmask)
tl.store(out_ptr1 + x3, tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_16(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 % 512
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_17(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)
x2 = xindex // 8704 % 17
x1 = xindex // 512 % 17
x0 = xindex % 512
x3 = xindex // 147968
x6 = xindex
tmp0 = 2 * x2
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 33, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = 2 * x1
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = tmp5 & tmp9
tmp11 = tl.load(in_ptr0 + (x0 + 1024 * x1 + 33792 * x2 + 557568 * x3),
tmp10, other=float('-inf'))
tmp12 = 1 + 2 * x1
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp13 & tmp14
tmp16 = tmp5 & tmp15
tmp17 = tl.load(in_ptr0 + (512 + x0 + 1024 * x1 + 33792 * x2 + 557568 *
x3), tmp16, other=float('-inf'))
tmp18 = triton_helpers.maximum(tmp17, tmp11)
tmp19 = 1 + 2 * x2
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp20 & tmp21
tmp23 = tmp22 & tmp9
tmp24 = tl.load(in_ptr0 + (16896 + x0 + 1024 * x1 + 33792 * x2 + 557568 *
x3), tmp23, other=float('-inf'))
tmp25 = triton_helpers.maximum(tmp24, tmp18)
tmp26 = tmp22 & tmp15
tmp27 = tl.load(in_ptr0 + (17408 + x0 + 1024 * x1 + 33792 * x2 + 557568 *
x3), tmp26, other=float('-inf'))
tmp28 = triton_helpers.maximum(tmp27, tmp25)
tmp29 = tmp17 > tmp11
tmp30 = tl.full([1], 1, tl.int8)
tmp31 = tl.full([1], 0, tl.int8)
tmp32 = tl.where(tmp29, tmp30, tmp31)
tmp33 = tmp24 > tmp18
tmp34 = tl.full([1], 2, tl.int8)
tmp35 = tl.where(tmp33, tmp34, tmp32)
tmp36 = tmp27 > tmp25
tmp37 = tl.full([1], 3, tl.int8)
tmp38 = tl.where(tmp36, tmp37, tmp35)
tl.store(out_ptr0 + x6, tmp28, None)
tl.store(out_ptr1 + x6, tmp38, None)
@triton.jit
def triton_poi_fused_convolution_relu_18(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 % 512
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_19(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)
x2 = xindex // 4608 % 9
x1 = xindex // 512 % 9
x0 = xindex % 512
x3 = xindex // 41472
x6 = xindex
tmp0 = 2 * x2
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 17, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = 2 * x1
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = tmp5 & tmp9
tmp11 = tl.load(in_ptr0 + (x0 + 1024 * x1 + 17408 * x2 + 147968 * x3),
tmp10, other=float('-inf'))
tmp12 = 1 + 2 * x1
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp13 & tmp14
tmp16 = tmp5 & tmp15
tmp17 = tl.load(in_ptr0 + (512 + x0 + 1024 * x1 + 17408 * x2 + 147968 *
x3), tmp16, other=float('-inf'))
tmp18 = triton_helpers.maximum(tmp17, tmp11)
tmp19 = 1 + 2 * x2
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp20 & tmp21
tmp23 = tmp22 & tmp9
tmp24 = tl.load(in_ptr0 + (8704 + x0 + 1024 * x1 + 17408 * x2 + 147968 *
x3), tmp23, other=float('-inf'))
tmp25 = triton_helpers.maximum(tmp24, tmp18)
tmp26 = tmp22 & tmp15
tmp27 = tl.load(in_ptr0 + (9216 + x0 + 1024 * x1 + 17408 * x2 + 147968 *
x3), tmp26, other=float('-inf'))
tmp28 = triton_helpers.maximum(tmp27, tmp25)
tmp29 = tmp17 > tmp11
tmp30 = tl.full([1], 1, tl.int8)
tmp31 = tl.full([1], 0, tl.int8)
tmp32 = tl.where(tmp29, tmp30, tmp31)
tmp33 = tmp24 > tmp18
tmp34 = tl.full([1], 2, tl.int8)
tmp35 = tl.where(tmp33, tmp34, tmp32)
tmp36 = tmp27 > tmp25
tmp37 = tl.full([1], 3, tl.int8)
tmp38 = tl.where(tmp36, tmp37, tmp35)
tl.store(out_ptr0 + x6, tmp28, None)
tl.store(out_ptr1 + x6, tmp38, None)
@triton.jit
def triton_poi_fused_convolution_relu_20(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 % 4096
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_convolution_21(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 36
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
tl.store(in_out_ptr0 + x0, tmp3, xmask)
@triton.jit
def triton_poi_fused_clone_22(in_ptr0, 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 % 64
x1 = xindex // 64 % 64
x2 = xindex // 4096
x3 = xindex
tmp0 = tl.load(in_ptr0 + (2451 + x0 + 128 * x1 + 16384 * x2), None)
tl.store(out_ptr0 + x3, tmp0, 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, primals_32,
primals_33, primals_34) = args
args.clear()
assert_size_stride(primals_1, (4, 5, 64, 64), (20480, 4096, 64, 1))
assert_size_stride(primals_2, (64, 5, 3, 3), (45, 9, 3, 1))
assert_size_stride(primals_3, (64,), (1,))
assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_7, (128,), (1,))
assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_9, (128,), (1,))
assert_size_stride(primals_10, (256, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_11, (256,), (1,))
assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_13, (256,), (1,))
assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_15, (256,), (1,))
assert_size_stride(primals_16, (512, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_17, (512,), (1,))
assert_size_stride(primals_18, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_19, (512,), (1,))
assert_size_stride(primals_20, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_21, (512,), (1,))
assert_size_stride(primals_22, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_23, (512,), (1,))
assert_size_stride(primals_24, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_25, (512,), (1,))
assert_size_stride(primals_26, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_27, (512,), (1,))
assert_size_stride(primals_28, (4096, 512, 7, 7), (25088, 49, 7, 1))
assert_size_stride(primals_29, (4096,), (1,))
assert_size_stride(primals_30, (4096, 4096, 1, 1), (4096, 1, 1, 1))
assert_size_stride(primals_31, (4096,), (1,))
assert_size_stride(primals_32, (1, 4096, 1, 1), (4096, 1, 1, 1))
assert_size_stride(primals_33, (1,), (1,))
assert_size_stride(primals_34, (1, 1, 64, 64), (4096, 4096, 64, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 5, 64, 64), (20480, 1, 320, 5), torch
.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(20, 4096)](primals_1, buf0, 20, 4096,
XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((64, 5, 3, 3), (45, 1, 15, 5), torch.float32)
triton_poi_fused_1[grid(320, 9)](primals_2, buf1, 320, 9, XBLOCK=16,
YBLOCK=64, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
float32)
triton_poi_fused_2[grid(4096, 9)](primals_4, buf2, 4096, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch
.float32)
triton_poi_fused_3[grid(8192, 9)](primals_6, buf3, 8192, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_6
buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
torch.float32)
triton_poi_fused_4[grid(16384, 9)](primals_8, buf4, 16384, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_8
buf5 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128),
torch.float32)
triton_poi_fused_5[grid(32768, 9)](primals_10, buf5, 32768, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_10
buf6 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_6[grid(65536, 9)](primals_12, buf6, 65536, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_12
buf7 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_6[grid(65536, 9)](primals_14, buf7, 65536, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_14
buf8 = empty_strided_cuda((512, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_7[grid(131072, 9)](primals_16, buf8, 131072, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_16
buf9 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
torch.float32)
triton_poi_fused_8[grid(262144, 9)](primals_18, buf9, 262144, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_18
buf10 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
torch.float32)
triton_poi_fused_8[grid(262144, 9)](primals_20, buf10, 262144, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_20
buf11 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
torch.float32)
triton_poi_fused_8[grid(262144, 9)](primals_22, buf11, 262144, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_22
buf12 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
torch.float32)
triton_poi_fused_8[grid(262144, 9)](primals_24, buf12, 262144, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_24
buf13 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
torch.float32)
triton_poi_fused_8[grid(262144, 9)](primals_26, buf13, 262144, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_26
buf14 = empty_strided_cuda((4096, 512, 7, 7), (25088, 1, 3584, 512),
torch.float32)
triton_poi_fused_9[grid(2097152, 49)](primals_28, buf14, 2097152,
49, XBLOCK=32, YBLOCK=64, num_warps=8, num_stages=1)
del primals_28
buf15 = extern_kernels.convolution(buf0, buf1, stride=(1, 1),
padding=(100, 100), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf15, (4, 64, 262, 262), (4393216, 1, 16768, 64))
buf16 = buf15
del buf15
triton_poi_fused_convolution_relu_10[grid(17572864)](buf16,
primals_3, 17572864, XBLOCK=512, num_warps=8, num_stages=1)
del primals_3
buf17 = extern_kernels.convolution(buf16, buf2, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf17, (4, 64, 262, 262), (4393216, 1, 16768, 64))
buf18 = buf17
del buf17
triton_poi_fused_convolution_relu_10[grid(17572864)](buf18,
primals_5, 17572864, XBLOCK=512, num_warps=8, num_stages=1)
del primals_5
buf19 = empty_strided_cuda((4, 64, 131, 131), (1098304, 1, 8384, 64
), torch.float32)
buf20 = empty_strided_cuda((4, 64, 131, 131), (1098304, 1, 8384, 64
), torch.int8)
triton_poi_fused_max_pool2d_with_indices_11[grid(4393216)](buf18,
buf19, buf20, 4393216, XBLOCK=512, num_warps=8, num_stages=1)
buf21 = extern_kernels.convolution(buf19, buf3, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf21, (4, 128, 131, 131), (2196608, 1, 16768, 128))
buf22 = buf21
del buf21
triton_poi_fused_convolution_relu_12[grid(8786432)](buf22,
primals_7, 8786432, XBLOCK=512, num_warps=8, num_stages=1)
del primals_7
buf23 = extern_kernels.convolution(buf22, buf4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf23, (4, 128, 131, 131), (2196608, 1, 16768, 128))
buf24 = buf23
del buf23
triton_poi_fused_convolution_relu_12[grid(8786432)](buf24,
primals_9, 8786432, XBLOCK=512, num_warps=8, num_stages=1)
del primals_9
buf25 = empty_strided_cuda((4, 128, 66, 66), (557568, 1, 8448, 128),
torch.float32)
buf26 = empty_strided_cuda((4, 128, 66, 66), (557568, 1, 8448, 128),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_13[grid(2230272)](buf24,
buf25, buf26, 2230272, XBLOCK=512, num_warps=8, num_stages=1)
buf27 = extern_kernels.convolution(buf25, buf5, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf27, (4, 256, 66, 66), (1115136, 1, 16896, 256))
buf28 = buf27
del buf27
triton_poi_fused_convolution_relu_14[grid(4460544)](buf28,
primals_11, 4460544, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_11
buf29 = extern_kernels.convolution(buf28, buf6, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf29, (4, 256, 66, 66), (1115136, 1, 16896, 256))
buf30 = buf29
del buf29
triton_poi_fused_convolution_relu_14[grid(4460544)](buf30,
primals_13, 4460544, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_13
buf31 = extern_kernels.convolution(buf30, buf7, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf31, (4, 256, 66, 66), (1115136, 1, 16896, 256))
buf32 = buf31
del buf31
triton_poi_fused_convolution_relu_14[grid(4460544)](buf32,
primals_15, 4460544, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_15
buf33 = empty_strided_cuda((4, 256, 33, 33), (278784, 1, 8448, 256),
torch.float32)
buf34 = empty_strided_cuda((4, 256, 33, 33), (278784, 1, 8448, 256),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_15[grid(1115136)](buf32,
buf33, buf34, 1115136, XBLOCK=512, num_warps=8, num_stages=1)
buf35 = extern_kernels.convolution(buf33, buf8, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf35, (4, 512, 33, 33), (557568, 1, 16896, 512))
buf36 = buf35
del buf35
triton_poi_fused_convolution_relu_16[grid(2230272)](buf36,
primals_17, 2230272, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_17
buf37 = extern_kernels.convolution(buf36, buf9, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf37, (4, 512, 33, 33), (557568, 1, 16896, 512))
buf38 = buf37
del buf37
triton_poi_fused_convolution_relu_16[grid(2230272)](buf38,
primals_19, 2230272, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_19
buf39 = extern_kernels.convolution(buf38, buf10, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf39, (4, 512, 33, 33), (557568, 1, 16896, 512))
buf40 = buf39
del buf39
triton_poi_fused_convolution_relu_16[grid(2230272)](buf40,
primals_21, 2230272, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_21
buf41 = empty_strided_cuda((4, 512, 17, 17), (147968, 1, 8704, 512),
torch.float32)
buf42 = empty_strided_cuda((4, 512, 17, 17), (147968, 1, 8704, 512),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_17[grid(591872)](buf40,
buf41, buf42, 591872, XBLOCK=1024, num_warps=4, num_stages=1)
buf43 = extern_kernels.convolution(buf41, buf11, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf43, (4, 512, 17, 17), (147968, 1, 8704, 512))
buf44 = buf43
del buf43
triton_poi_fused_convolution_relu_18[grid(591872)](buf44,
primals_23, 591872, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_23
buf45 = extern_kernels.convolution(buf44, buf12, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf45, (4, 512, 17, 17), (147968, 1, 8704, 512))
buf46 = buf45
del buf45
triton_poi_fused_convolution_relu_18[grid(591872)](buf46,
primals_25, 591872, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_25
buf47 = extern_kernels.convolution(buf46, buf13, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf47, (4, 512, 17, 17), (147968, 1, 8704, 512))
buf48 = buf47
del buf47
triton_poi_fused_convolution_relu_18[grid(591872)](buf48,
primals_27, 591872, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_27
buf49 = empty_strided_cuda((4, 512, 9, 9), (41472, 1, 4608, 512),
torch.float32)
buf50 = empty_strided_cuda((4, 512, 9, 9), (41472, 1, 4608, 512),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_19[grid(165888)](buf48,
buf49, buf50, 165888, XBLOCK=512, num_warps=8, num_stages=1)
buf51 = extern_kernels.convolution(buf49, buf14, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf51, (4, 4096, 3, 3), (36864, 1, 12288, 4096))
buf52 = buf51
del buf51
triton_poi_fused_convolution_relu_20[grid(147456)](buf52,
primals_29, 147456, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_29
buf53 = extern_kernels.convolution(buf52, primals_30, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf53, (4, 4096, 3, 3), (36864, 1, 12288, 4096))
buf54 = buf53
del buf53
triton_poi_fused_convolution_relu_20[grid(147456)](buf54,
primals_31, 147456, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_31
buf55 = extern_kernels.convolution(buf54, primals_32, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf55, (4, 1, 3, 3), (9, 1, 3, 1))
buf56 = buf55
del buf55
triton_poi_fused_convolution_21[grid(36)](buf56, primals_33, 36,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_33
buf57 = extern_kernels.convolution(buf56, primals_34, stride=(32,
32), padding=(0, 0), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf57, (4, 1, 128, 128), (16384, 1, 128, 1))
buf58 = empty_strided_cuda((4, 1, 64, 64), (4096, 4096, 64, 1),
torch.float32)
triton_poi_fused_clone_22[grid(16384)](buf57, buf58, 16384, XBLOCK=
128, num_warps=4, num_stages=1)
del buf57
return (buf58, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8,
buf9, buf10, buf11, buf12, buf13, buf14, primals_30, primals_32,
primals_34, buf16, buf18, buf19, buf20, buf22, buf24, buf25, buf26,
buf28, buf30, buf32, buf33, buf34, buf36, buf38, buf40, buf41,
buf42, buf44, buf46, buf48, buf49, buf50, buf52, buf54, buf56)
def get_upsampling_weight(in_channels, out_channels, kernel_size):
"""Make a 2D bilinear kernel suitable for upsampling"""
factor = (kernel_size + 1) // 2
if kernel_size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = np.ogrid[:kernel_size, :kernel_size]
filt = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) /
factor)
weight = np.zeros((in_channels, out_channels, kernel_size, kernel_size),
dtype=np.float64)
weight[range(in_channels), range(out_channels), :, :] = filt
return torch.from_numpy(weight).float()
class FCN32sNew(nn.Module):
def __init__(self, n_channels=5, n_class=1):
super(FCN32sNew, self).__init__()
self.conv1_1 = nn.Conv2d(5, 64, 3, padding=100)
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
self.relu1_2 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)
self.relu2_1 = nn.ReLU(inplace=True)
self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)
self.relu2_2 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)
self.relu3_1 = nn.ReLU(inplace=True)
self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1)
self.relu3_2 = nn.ReLU(inplace=True)
self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1)
self.relu3_3 = nn.ReLU(inplace=True)
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1)
self.relu4_1 = nn.ReLU(inplace=True)
self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1)
self.relu4_2 = nn.ReLU(inplace=True)
self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1)
self.relu4_3 = nn.ReLU(inplace=True)
self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1)
self.relu5_1 = nn.ReLU(inplace=True)
self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1)
self.relu5_2 = nn.ReLU(inplace=True)
self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1)
self.relu5_3 = nn.ReLU(inplace=True)
self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.fc6 = nn.Conv2d(512, 4096, 7)
self.relu6 = nn.ReLU(inplace=True)
self.drop6 = nn.Dropout2d()
self.fc7 = nn.Conv2d(4096, 4096, 1)
self.relu7 = nn.ReLU(inplace=True)
self.drop7 = nn.Dropout2d()
self.score_fr = nn.Conv2d(4096, n_class, 1)
self.upscore = nn.ConvTranspose2d(n_class, n_class, 64, stride=32,
bias=False)
self._initialize_weights()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
m.weight.data.zero_()
if m.bias is not None:
m.bias.data.zero_()
if isinstance(m, nn.ConvTranspose2d):
assert m.kernel_size[0] == m.kernel_size[1]
initial_weight = get_upsampling_weight(m.in_channels, m.
out_channels, m.kernel_size[0])
m.weight.data.copy_(initial_weight)
def copy_params_from_vgg16(self, vgg16):
features = [self.conv1_1, self.relu1_1, self.conv1_2, self.relu1_2,
self.pool1, self.conv2_1, self.relu2_1, self.conv2_2, self.
relu2_2, self.pool2, self.conv3_1, self.relu3_1, self.conv3_2,
self.relu3_2, self.conv3_3, self.relu3_3, self.pool3, self.
conv4_1, self.relu4_1, self.conv4_2, self.relu4_2, self.conv4_3,
self.relu4_3, self.pool4, self.conv5_1, self.relu5_1, self.
conv5_2, self.relu5_2, self.conv5_3, self.relu5_3, self.pool5]
for l1, l2 in zip(vgg16.features, features):
if isinstance(l1, nn.Conv2d) and isinstance(l2, nn.Conv2d):
assert l1.weight.size() == l2.weight.size()
assert l1.bias.size() == l2.bias.size()
l2.weight.data = l1.weight.data
l2.bias.data = l1.bias.data
for i, name in zip([0, 3], ['fc6', 'fc7']):
l1 = vgg16.classifier[i]
l2 = getattr(self, name)
l2.weight.data = l1.weight.data.view(l2.weight.size())
l2.bias.data = l1.bias.data.view(l2.bias.size())
def forward(self, input_0):
primals_2 = self.conv1_1.weight
primals_3 = self.conv1_1.bias
primals_4 = self.conv1_2.weight
primals_5 = self.conv1_2.bias
primals_6 = self.conv2_1.weight
primals_7 = self.conv2_1.bias
primals_8 = self.conv2_2.weight
primals_9 = self.conv2_2.bias
primals_10 = self.conv3_1.weight
primals_11 = self.conv3_1.bias
primals_12 = self.conv3_2.weight
primals_13 = self.conv3_2.bias
primals_14 = self.conv3_3.weight
primals_15 = self.conv3_3.bias
primals_16 = self.conv4_1.weight
primals_17 = self.conv4_1.bias
primals_18 = self.conv4_2.weight
primals_19 = self.conv4_2.bias
primals_20 = self.conv4_3.weight
primals_21 = self.conv4_3.bias
primals_22 = self.conv5_1.weight
primals_23 = self.conv5_1.bias
primals_24 = self.conv5_2.weight
primals_25 = self.conv5_2.bias
primals_26 = self.conv5_3.weight
primals_27 = self.conv5_3.bias
primals_28 = self.fc6.weight
primals_29 = self.fc6.bias
primals_30 = self.fc7.weight
primals_31 = self.fc7.bias
primals_32 = self.score_fr.weight
primals_33 = self.score_fr.bias
primals_34 = self.upscore.weight
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, primals_32, primals_33, primals_34])
return output[0]
|
Yusoi/mmdetection
|
FCN32s
| false
| 9,960
|
[
"Apache-2.0"
] | 0
|
cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a
|
https://github.com/Yusoi/mmdetection/tree/cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a
|
ResBlock
|
import torch
import torch.nn as nn
class ResBlock(nn.Module):
def __init__(self, input_channels: 'int', output_channels: 'int',
batch_norm=False) ->None:
super().__init__()
self.conv1 = nn.Conv2d(input_channels, output_channels, kernel_size
=3, stride=1, padding=1)
self.bn1 = nn.Identity()
self.conv2 = nn.Conv2d(output_channels, output_channels,
kernel_size=3, stride=1, padding=1)
self.bn2 = nn.Identity()
self.conv_skip = nn.Conv2d(input_channels, output_channels,
kernel_size=3, stride=1, padding=1)
self.relu1 = nn.ReLU()
self.relu2 = nn.ReLU()
if batch_norm:
self.bn1 = nn.BatchNorm2d(output_channels)
self.bn2 = nn.BatchNorm2d(output_channels)
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
"""
Parameters
----------
x
of dimensions (B, C, H, W)
Returns
-------
torch.Tensor
of dimensions (B, C, H, W)
"""
y = self.conv1(x)
y = self.bn1(y)
y = self.relu1(y)
y = self.conv2(y)
y = self.bn2(y)
skip_y = self.conv_skip(x)
y = y + skip_y
y = self.relu2(y)
return y
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_channels': 4, 'output_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
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_relu_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
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')
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_add_convolution_relu_threshold_backward_1(in_out_ptr0,
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_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x3, xmask)
tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp7 = tl.full([1], 0, tl.int32)
tmp8 = triton_helpers.maximum(tmp7, tmp6)
tmp9 = 0.0
tmp10 = tmp8 <= tmp9
tl.store(in_out_ptr0 + x3, tmp8, xmask)
tl.store(out_ptr0 + x3, tmp10, 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, 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, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_7, (4,), (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
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(256)](buf1, primals_2, 256,
XBLOCK=256, 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, 4, 4, 4), (64, 16, 4, 1))
buf3 = extern_kernels.convolution(primals_3, primals_6, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = buf2
del buf2
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_add_convolution_relu_threshold_backward_1[grid(256)](
buf4, primals_5, buf3, primals_7, buf5, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf3
del primals_5
del primals_7
return buf4, primals_1, primals_3, primals_4, primals_6, buf1, buf5
class ResBlockNew(nn.Module):
def __init__(self, input_channels: 'int', output_channels: 'int',
batch_norm=False) ->None:
super().__init__()
self.conv1 = nn.Conv2d(input_channels, output_channels, kernel_size
=3, stride=1, padding=1)
self.bn1 = nn.Identity()
self.conv2 = nn.Conv2d(output_channels, output_channels,
kernel_size=3, stride=1, padding=1)
self.bn2 = nn.Identity()
self.conv_skip = nn.Conv2d(input_channels, output_channels,
kernel_size=3, stride=1, padding=1)
self.relu1 = nn.ReLU()
self.relu2 = nn.ReLU()
if batch_norm:
self.bn1 = nn.BatchNorm2d(output_channels)
self.bn2 = nn.BatchNorm2d(output_channels)
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.conv_skip.weight
primals_7 = self.conv_skip.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
cluePrints/fsdl-text-recognizer-2021-labs
|
ResBlock
| false
| 9,961
|
[
"MIT"
] | 0
|
d166dcbd00513b2f0031fbc991af3a852bc2d605
|
https://github.com/cluePrints/fsdl-text-recognizer-2021-labs/tree/d166dcbd00513b2f0031fbc991af3a852bc2d605
|
Critic
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 1024)
self.l2 = nn.Linear(1024, 512)
self.l3 = nn.Linear(512, 256)
self.l4 = nn.Linear(256, 1)
self.l5 = nn.Linear(state_dim + action_dim, 1024)
self.l6 = nn.Linear(1024, 512)
self.l7 = nn.Linear(512, 256)
self.l8 = nn.Linear(256, 1)
def forward(self, state, action):
sa = torch.cat([state, action], 1)
q1 = F.relu(self.l1(sa))
q1 = F.relu(self.l2(q1))
q1 = F.relu(self.l3(q1))
q1 = self.l4(q1)
q2 = F.relu(self.l5(sa))
q2 = F.relu(self.l6(q2))
q2 = F.relu(self.l7(q2))
q2 = self.l8(q2)
return q1, q2
def Q1(self, state, action):
sa = torch.cat([state, action], 1)
q1 = F.relu(self.l1(sa))
q1 = F.relu(self.l2(q1))
q1 = F.relu(self.l3(q1))
q1 = self.l4(q1)
return q1
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'state_dim': 4, 'action_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
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
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 = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
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
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x2, tmp10, xmask)
@triton.jit
def triton_poi_fused_relu_1(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 % 1024
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_relu_2(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 % 512
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_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 256
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, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17, primals_18
) = 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, (1024, 8), (8, 1))
assert_size_stride(primals_4, (1024,), (1,))
assert_size_stride(primals_5, (512, 1024), (1024, 1))
assert_size_stride(primals_6, (512,), (1,))
assert_size_stride(primals_7, (256, 512), (512, 1))
assert_size_stride(primals_8, (256,), (1,))
assert_size_stride(primals_9, (1, 256), (256, 1))
assert_size_stride(primals_10, (1,), (1,))
assert_size_stride(primals_11, (1024, 8), (8, 1))
assert_size_stride(primals_12, (1024,), (1,))
assert_size_stride(primals_13, (512, 1024), (1024, 1))
assert_size_stride(primals_14, (512,), (1,))
assert_size_stride(primals_15, (256, 512), (512, 1))
assert_size_stride(primals_16, (256,), (1,))
assert_size_stride(primals_17, (1, 256), (256, 1))
assert_size_stride(primals_18, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
XBLOCK=32, num_warps=1, num_stages=1)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 1024), (1,
8), 0), out=buf1)
del primals_3
buf2 = buf1
del buf1
triton_poi_fused_relu_1[grid(4096)](buf2, primals_4, 4096, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (1024, 512),
(1, 1024), 0), out=buf3)
buf4 = buf3
del buf3
triton_poi_fused_relu_2[grid(2048)](buf4, primals_6, 2048, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_6
buf5 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
extern_kernels.mm(buf4, reinterpret_tensor(primals_7, (512, 256), (
1, 512), 0), out=buf5)
buf6 = buf5
del buf5
triton_poi_fused_relu_3[grid(1024)](buf6, primals_8, 1024, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_8
buf8 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_10, buf6, reinterpret_tensor(primals_9,
(256, 1), (1, 256), 0), alpha=1, beta=1, out=buf8)
del primals_10
buf9 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
extern_kernels.mm(buf0, reinterpret_tensor(primals_11, (8, 1024), (
1, 8), 0), out=buf9)
del primals_11
buf10 = buf9
del buf9
triton_poi_fused_relu_1[grid(4096)](buf10, primals_12, 4096, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_12
buf11 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
extern_kernels.mm(buf10, reinterpret_tensor(primals_13, (1024, 512),
(1, 1024), 0), out=buf11)
buf12 = buf11
del buf11
triton_poi_fused_relu_2[grid(2048)](buf12, primals_14, 2048, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_14
buf13 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
extern_kernels.mm(buf12, reinterpret_tensor(primals_15, (512, 256),
(1, 512), 0), out=buf13)
buf14 = buf13
del buf13
triton_poi_fused_relu_3[grid(1024)](buf14, primals_16, 1024, XBLOCK
=256, num_warps=4, num_stages=1)
del primals_16
buf16 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_18, buf14, reinterpret_tensor(
primals_17, (256, 1), (1, 256), 0), alpha=1, beta=1, out=buf16)
del primals_18
return (buf8, buf16, buf0, buf2, buf4, buf6, buf10, buf12, buf14,
primals_17, primals_15, primals_13, primals_9, primals_7, primals_5)
class CriticNew(nn.Module):
def __init__(self, state_dim, action_dim):
super(CriticNew, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 1024)
self.l2 = nn.Linear(1024, 512)
self.l3 = nn.Linear(512, 256)
self.l4 = nn.Linear(256, 1)
self.l5 = nn.Linear(state_dim + action_dim, 1024)
self.l6 = nn.Linear(1024, 512)
self.l7 = nn.Linear(512, 256)
self.l8 = nn.Linear(256, 1)
def Q1(self, state, action):
sa = torch.cat([state, action], 1)
q1 = F.relu(self.l1(sa))
q1 = F.relu(self.l2(q1))
q1 = F.relu(self.l3(q1))
q1 = self.l4(q1)
return q1
def forward(self, input_0, input_1):
primals_3 = self.l1.weight
primals_4 = self.l1.bias
primals_5 = self.l2.weight
primals_6 = self.l2.bias
primals_7 = self.l3.weight
primals_8 = self.l3.bias
primals_9 = self.l4.weight
primals_10 = self.l4.bias
primals_11 = self.l5.weight
primals_12 = self.l5.bias
primals_13 = self.l6.weight
primals_14 = self.l6.bias
primals_15 = self.l7.weight
primals_16 = self.l7.bias
primals_17 = self.l8.weight
primals_18 = self.l8.bias
primals_1 = input_0
primals_2 = input_1
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])
return output[0], output[1]
|
ctoto93/TD3
|
Critic
| false
| 9,962
|
[
"MIT"
] | 0
|
88482b9f1fb4441d74426ece60d5da13414aeb77
|
https://github.com/ctoto93/TD3/tree/88482b9f1fb4441d74426ece60d5da13414aeb77
|
Swish
|
import torch
import torch.nn as nn
class Swish(nn.Module):
def __init__(self, beta=1):
super(Swish, self).__init__()
self.beta = beta
def forward(self, x):
return x * torch.sigmoid(self.beta * x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_mul_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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tmp3 = tl.sigmoid(tmp2)
tmp4 = tmp0 * tmp3
tl.store(out_ptr0 + x0, tmp4, 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_mul_sigmoid_0[grid(256)](arg0_1, buf0, 256, XBLOCK
=256, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class SwishNew(nn.Module):
def __init__(self, beta=1):
super(SwishNew, self).__init__()
self.beta = beta
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
brandstetter-johannes/ocp
|
Swish
| false
| 9,963
|
[
"MIT",
"BSD-3-Clause"
] | 0
|
69cc90e6bed8aa09222cd77b926d7a34e96302ed
|
https://github.com/brandstetter-johannes/ocp/tree/69cc90e6bed8aa09222cd77b926d7a34e96302ed
|
Module_CharbonnierLoss
|
import torch
import torch.nn as nn
class Module_CharbonnierLoss(nn.Module):
def __init__(self, epsilon=0.001):
super(Module_CharbonnierLoss, self).__init__()
self.epsilon = epsilon
def forward(self, output, gt):
return torch.mean(torch.sqrt((output - gt) ** 2 + self.epsilon ** 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._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
@triton.jit
def triton_per_fused_add_mean_pow_sqrt_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 = tmp2 * tmp2
tmp4 = 1e-06
tmp5 = tmp3 + tmp4
tmp6 = libdevice.sqrt(tmp5)
tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
tmp10 = 256.0
tmp11 = tmp9 / tmp10
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp11, 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_mean_pow_sqrt_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 Module_CharbonnierLossNew(nn.Module):
def __init__(self, epsilon=0.001):
super(Module_CharbonnierLossNew, self).__init__()
self.epsilon = epsilon
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
danielism97/FLAVR
|
Module_CharbonnierLoss
| false
| 9,964
|
[
"Apache-2.0"
] | 0
|
17f62c681bb2a5799e3bc23cf60936ac4d2b9407
|
https://github.com/danielism97/FLAVR/tree/17f62c681bb2a5799e3bc23cf60936ac4d2b9407
|
Actor
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 1024)
self.l2 = nn.Linear(1024, 512)
self.l3 = nn.Linear(512, 256)
self.l4 = nn.Linear(256, action_dim)
self.max_action = max_action
def forward(self, state):
a = F.relu(self.l1(state))
a = F.relu(self.l2(a))
a = F.relu(self.l3(a))
return self.max_action * torch.tanh(self.l4(a))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'state_dim': 4, 'action_dim': 4, 'max_action': 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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_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 % 1024
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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(out_ptr0 + x2, tmp6, None)
@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
out_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 % 512
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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(out_ptr0 + x2, tmp6, None)
@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
out_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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(out_ptr0 + x2, tmp6, None)
@triton.jit
def triton_poi_fused_mul_tanh_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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = libdevice.tanh(tmp0)
tmp2 = 4.0
tmp3 = tmp1 * tmp2
tl.store(out_ptr0 + x0, tmp3, 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, (1024, 4), (4, 1))
assert_size_stride(primals_2, (1024,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (512, 1024), (1024, 1))
assert_size_stride(primals_5, (512,), (1,))
assert_size_stride(primals_6, (256, 512), (512, 1))
assert_size_stride(primals_7, (256,), (1,))
assert_size_stride(primals_8, (4, 256), (256, 1))
assert_size_stride(primals_9, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 1024), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 1024), (16384, 4096, 1024,
1), 0)
del buf0
buf10 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(65536)](buf1,
primals_2, buf10, 65536, XBLOCK=512, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 512), (512, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 1024), (1024, 1), 0
), reinterpret_tensor(primals_4, (1024, 512), (1, 1024), 0),
out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 512), (8192, 2048, 512, 1), 0
)
del buf2
buf9 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
torch.bool)
triton_poi_fused_relu_threshold_backward_1[grid(32768)](buf3,
primals_5, buf9, 32768, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf3, (64, 512), (512, 1), 0),
reinterpret_tensor(primals_6, (512, 256), (1, 512), 0), out=buf4)
buf5 = reinterpret_tensor(buf4, (4, 4, 4, 256), (4096, 1024, 256, 1), 0
)
del buf4
buf8 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
torch.bool)
triton_poi_fused_relu_threshold_backward_2[grid(16384)](buf5,
primals_7, buf8, 16384, XBLOCK=256, num_warps=4, num_stages=1)
del primals_7
buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (64, 256),
(256, 1), 0), reinterpret_tensor(primals_8, (256, 4), (1, 256),
0), alpha=1, beta=1, out=buf6)
del primals_9
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_mul_tanh_3[grid(256)](buf6, buf7, 256, XBLOCK=128,
num_warps=4, num_stages=1)
return buf7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 1024), (1024, 1), 0
), reinterpret_tensor(buf3, (64, 512), (512, 1), 0
), reinterpret_tensor(buf5, (64, 256), (256, 1), 0
), buf6, primals_8, buf8, primals_6, buf9, primals_4, buf10
class ActorNew(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(ActorNew, self).__init__()
self.l1 = nn.Linear(state_dim, 1024)
self.l2 = nn.Linear(1024, 512)
self.l3 = nn.Linear(512, 256)
self.l4 = nn.Linear(256, action_dim)
self.max_action = max_action
def forward(self, input_0):
primals_1 = self.l1.weight
primals_2 = self.l1.bias
primals_4 = self.l2.weight
primals_5 = self.l2.bias
primals_6 = self.l3.weight
primals_7 = self.l3.bias
primals_8 = self.l4.weight
primals_9 = self.l4.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]
|
ctoto93/TD3
|
Actor
| false
| 9,965
|
[
"MIT"
] | 0
|
88482b9f1fb4441d74426ece60d5da13414aeb77
|
https://github.com/ctoto93/TD3/tree/88482b9f1fb4441d74426ece60d5da13414aeb77
|
SeqRNN
|
import torch
import torch.nn as nn
class SeqRNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size, n_layers):
super(SeqRNN, self).__init__()
self.hidden_size = hidden_size
self.i2h = nn.Linear(in_features=input_size + hidden_size,
out_features=hidden_size)
self.i2o = nn.Linear(in_features=input_size + hidden_size,
out_features=output_size)
self.softmax = nn.Softmax(dim=1)
def forward(self, input, hidden):
combined = torch.cat((input, hidden), dim=1)
hidden = self.i2h(combined)
out = self.i2o(combined)
out = self.softmax(out)
return out, hidden
def init_hidden(self, batch):
return torch.zeros(batch, self.hidden_size, device=device)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'hidden_size': 4, 'output_size': 4,
'n_layers': 1}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
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
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x2, tmp10, 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)
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, 8), (8, 1))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (4, 8), (8, 1))
assert_size_stride(primals_6, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
XBLOCK=32, num_warps=1, num_stages=1)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_4, buf0, reinterpret_tensor(primals_3,
(8, 4), (1, 8), 0), alpha=1, beta=1, out=buf1)
del primals_3
del primals_4
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_6, buf0, reinterpret_tensor(primals_5,
(8, 4), (1, 8), 0), alpha=1, beta=1, out=buf2)
del primals_5
del primals_6
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 = buf2
del buf2
triton_poi_fused__softmax_2[grid(16)](buf3, buf4, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf3
return buf4, buf1, buf0, buf4
class SeqRNNNew(nn.Module):
def __init__(self, input_size, hidden_size, output_size, n_layers):
super(SeqRNNNew, self).__init__()
self.hidden_size = hidden_size
self.i2h = nn.Linear(in_features=input_size + hidden_size,
out_features=hidden_size)
self.i2o = nn.Linear(in_features=input_size + hidden_size,
out_features=output_size)
self.softmax = nn.Softmax(dim=1)
def init_hidden(self, batch):
return torch.zeros(batch, self.hidden_size, device=device)
def forward(self, input_0, input_1):
primals_3 = self.i2h.weight
primals_4 = self.i2h.bias
primals_5 = self.i2o.weight
primals_6 = self.i2o.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0], output[1]
|
dblakely/FastSK
|
SeqRNN
| false
| 9,966
|
[
"Apache-2.0"
] | 0
|
bd0d4cef89c3d7d661f4c6abc094423ab6d1c7e1
|
https://github.com/dblakely/FastSK/tree/bd0d4cef89c3d7d661f4c6abc094423ab6d1c7e1
|
ToeplitzBlock
|
import torch
import torch.nn as nn
def expand_toeplitz(diag, lower_diags, upper_diags):
pattern = torch.cat([upper_diags, diag, lower_diags], 0)
d = lower_diags.size(0)
columns = []
for i in range(d + 1):
columns.append(pattern[d - i:d - i + d + 1])
return torch.stack(columns, 0)
class ToeplitzBlock(nn.Module):
def __init__(self, dim):
super().__init__()
self.diag = nn.Parameter(torch.Tensor([0]))
self.lower_diags = nn.Parameter(torch.Tensor(dim - 1).zero_())
self.upper_diags = nn.Parameter(torch.Tensor(dim - 1).zero_())
def diagonals(self):
return [self.diag + 1, self.lower_diags, self.upper_diags]
def forward(self, x):
return torch.matmul(expand_toeplitz(*self.diagonals()), x)
def get_inputs():
return [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
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, in_ptr1, in_ptr2, 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
tmp15 = tl.load(in_ptr1 + 0)
tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = 3 + x0
tmp7 = tl.full([1], 3, tl.int64)
tmp8 = tmp5 < tmp7
tmp9 = tmp8 & tmp4
tmp10 = tl.load(in_ptr0 + (3 + x0), tmp9 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp11 = tmp5 >= tmp7
tmp12 = tmp5 < tmp3
tmp13 = tmp11 & tmp12
tmp14 = tmp13 & tmp4
tmp17 = 1.0
tmp18 = tmp16 + tmp17
tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
tmp20 = tl.where(tmp14, tmp18, tmp19)
tmp21 = tmp5 >= tmp3
tl.full([1], 7, tl.int64)
tmp24 = tmp21 & tmp4
tmp25 = tl.load(in_ptr2 + (-1 + x0), tmp24 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp26 = tl.where(tmp13, tmp20, tmp25)
tmp27 = tl.where(tmp8, tmp10, tmp26)
tmp28 = tl.full(tmp27.shape, 0.0, tmp27.dtype)
tmp29 = tl.where(tmp4, tmp27, tmp28)
tmp30 = tmp0 >= tmp3
tmp31 = tl.full([1], 8, tl.int64)
tmp32 = tmp0 < tmp31
tmp33 = tmp30 & tmp32
tmp34 = 2 + (-4 + x0)
tmp36 = tmp34 < tmp7
tmp37 = tmp36 & tmp33
tmp38 = tl.load(in_ptr0 + (2 + (-4 + x0)), tmp37 & xmask,
eviction_policy='evict_last', other=0.0)
tmp39 = tmp34 >= tmp7
tmp40 = tmp34 < tmp3
tmp41 = tmp39 & tmp40
tmp42 = tmp41 & tmp33
tmp43 = tl.where(tmp42, tmp18, tmp19)
tmp44 = tmp34 >= tmp3
tmp46 = tmp44 & tmp33
tmp47 = tl.load(in_ptr2 + (-2 + (-4 + x0)), tmp46 & xmask,
eviction_policy='evict_last', other=0.0)
tmp48 = tl.where(tmp41, tmp43, tmp47)
tmp49 = tl.where(tmp36, tmp38, tmp48)
tmp50 = tl.full(tmp49.shape, 0.0, tmp49.dtype)
tmp51 = tl.where(tmp33, tmp49, tmp50)
tmp52 = tmp0 >= tmp31
tmp53 = tl.full([1], 12, tl.int64)
tmp54 = tmp0 < tmp53
tmp55 = tmp52 & tmp54
tmp56 = 1 + (-8 + x0)
tmp58 = tmp56 < tmp7
tmp59 = tmp58 & tmp55
tmp60 = tl.load(in_ptr0 + (1 + (-8 + x0)), tmp59 & xmask,
eviction_policy='evict_last', other=0.0)
tmp61 = tmp56 >= tmp7
tmp62 = tmp56 < tmp3
tmp63 = tmp61 & tmp62
tmp64 = tmp63 & tmp55
tmp65 = tl.where(tmp64, tmp18, tmp19)
tmp66 = tmp56 >= tmp3
tmp68 = tmp66 & tmp55
tmp69 = tl.load(in_ptr2 + (-3 + (-8 + x0)), tmp68 & xmask,
eviction_policy='evict_last', other=0.0)
tmp70 = tl.where(tmp63, tmp65, tmp69)
tmp71 = tl.where(tmp58, tmp60, tmp70)
tmp72 = tl.full(tmp71.shape, 0.0, tmp71.dtype)
tmp73 = tl.where(tmp55, tmp71, tmp72)
tmp74 = tmp0 >= tmp53
tl.full([1], 16, tl.int64)
tmp77 = -12 + x0
tmp79 = tmp77 < tmp7
tmp80 = tmp79 & tmp74
tmp81 = tl.load(in_ptr0 + (-12 + x0), tmp80 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp82 = tmp77 >= tmp7
tmp83 = tmp77 < tmp3
tmp84 = tmp82 & tmp83
tmp85 = tmp84 & tmp74
tmp86 = tl.where(tmp85, tmp18, tmp19)
tmp87 = tmp77 >= tmp3
tmp89 = tmp87 & tmp74
tmp90 = tl.load(in_ptr2 + (-4 + (-12 + x0)), tmp89 & xmask,
eviction_policy='evict_last', other=0.0)
tmp91 = tl.where(tmp84, tmp86, tmp90)
tmp92 = tl.where(tmp79, tmp81, tmp91)
tmp93 = tl.full(tmp92.shape, 0.0, tmp92.dtype)
tmp94 = tl.where(tmp74, tmp92, tmp93)
tmp95 = tl.where(tmp55, tmp73, tmp94)
tmp96 = tl.where(tmp33, tmp51, tmp95)
tmp97 = tl.where(tmp4, tmp29, tmp96)
tl.store(out_ptr0 + x0, tmp97, xmask)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 64
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 = args
args.clear()
assert_size_stride(primals_1, (1,), (1,))
assert_size_stride(primals_2, (3,), (1,))
assert_size_stride(primals_3, (3,), (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((16,), (1,), torch.float32)
get_raw_stream(0)
triton_poi_fused_stack_0[grid(16)](primals_3, primals_1, primals_2,
buf0, 16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_1
del primals_2
del primals_3
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_1[grid(64, 4)](primals_4, buf1, 64, 4,
XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
del primals_4
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
reinterpret_tensor(buf0, (4, 4), (1, 4), 0), out=buf2)
del buf0
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_1[grid(64, 4)](buf2, buf3, 64, 4, XBLOCK=4,
YBLOCK=32, num_warps=4, num_stages=1)
del buf2
return buf3, reinterpret_tensor(buf1, (64, 4), (4, 1), 0)
def expand_toeplitz(diag, lower_diags, upper_diags):
pattern = torch.cat([upper_diags, diag, lower_diags], 0)
d = lower_diags.size(0)
columns = []
for i in range(d + 1):
columns.append(pattern[d - i:d - i + d + 1])
return torch.stack(columns, 0)
class ToeplitzBlockNew(nn.Module):
def __init__(self, dim):
super().__init__()
self.diag = nn.Parameter(torch.Tensor([0]))
self.lower_diags = nn.Parameter(torch.Tensor(dim - 1).zero_())
self.upper_diags = nn.Parameter(torch.Tensor(dim - 1).zero_())
def diagonals(self):
return [self.diag + 1, self.lower_diags, self.upper_diags]
def forward(self, input_0):
primals_1 = self.diag
primals_2 = self.lower_diags
primals_3 = self.upper_diags
primals_4 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
daemon/toepl.it.z
|
ToeplitzBlock
| false
| 9,967
|
[
"MIT"
] | 0
|
b16754b11f03f33bbfa05cf8544ef0dca3574ed4
|
https://github.com/daemon/toepl.it.z/tree/b16754b11f03f33bbfa05cf8544ef0dca3574ed4
|
GlobalAttention
|
import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.cuda
import torch.distributed
def quantize_weights(W, numbits=8):
W = W.clamp(-2 ** (numbits - 1), 2 ** (numbits - 1))
W = W.mul(2 ** (numbits - 1)).round().div(2 ** (numbits - 1))
return W
def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments
), 'Not all arguments have the same value: ' + str(args)
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_len).type_as(lengths).repeat(batch_size, 1).lt(
lengths.unsqueeze(1))
class quant_weights(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return quantize_weights(x)
@staticmethod
def backward(ctx, g):
return g
class Linear(nn.modules.Linear):
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__(in_features, out_features)
self.in_features = in_features
self.out_features = out_features
self.W_LR_scale = np.float32(1.0 / np.sqrt(1.5 / (in_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)
self.reset_parameters()
def reset_parameters(self):
stdv = 1.0 / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward_t(self, input):
return F.linear(input, self.weight, self.bias)
def extra_repr(self):
return 'in_features={}, out_features={}, bias={}'.format(self.
in_features, self.out_features, self.bias is not None)
def forward(self, input):
Wr = self.weight.data
self.Wb = quant_weights.apply(self.weight.data)
self.input_b = quant_weights.apply(input)
self.weight.data = self.Wb
rvalue = self.forward_t(self.input_b)
self.weight.data = Wr
return rvalue
def return_W_scale(self):
return self.W_LR_scale
class GlobalAttention(nn.Module):
"""
Global attention takes a matrix and a query vector. It
then computes a parameterized convex combination of the matrix
based on the input query.
Constructs a unit mapping a query `q` of size `dim`
and a source matrix `H` of size `n x dim`, to an output
of size `dim`.
.. mermaid::
graph BT
A[Query]
subgraph RNN
C[H 1]
D[H 2]
E[H N]
end
F[Attn]
G[Output]
A --> F
C --> F
D --> F
E --> F
C -.-> G
D -.-> G
E -.-> G
F --> G
All models compute the output as
:math:`c = sum_{j=1}^{SeqLength} a_j H_j` where
:math:`a_j` is the softmax of a score function.
Then then apply a projection layer to [q, c].
However they
differ on how they compute the attention score.
* Luong Attention (dot, general):
* dot: :math:`score(H_j,q) = H_j^T q`
* general: :math:`score(H_j, q) = H_j^T W_a q`
* Bahdanau Attention (mlp):
* :math:`score(H_j, q) = v_a^T tanh(W_a q + U_a h_j)`
Args:
dim (int): dimensionality of query and key
coverage (bool): use coverage term
attn_type (str): type of attention to use, options [dot,general,mlp]
"""
def __init__(self, dim, coverage=False, attn_type='dot', attn_func=
'softmax'):
super(GlobalAttention, self).__init__()
self.dim = dim
assert attn_type in ['dot', 'general', 'mlp'
], 'Please select a valid attention type.'
self.attn_type = attn_type
assert attn_func in ['softmax', 'sparsemax'
], 'Please select a valid attention function.'
self.attn_func = attn_func
if self.attn_type == 'general':
self.linear_in = Linear(dim, dim, bias=False)
elif self.attn_type == 'mlp':
self.linear_context = Linear(dim, dim, bias=False)
self.linear_query = Linear(dim, dim, bias=True)
self.v = Linear(dim, 1, bias=False)
out_bias = self.attn_type == 'mlp'
self.linear_out = Linear(dim * 2, dim, bias=out_bias)
if coverage:
self.linear_cover = Linear(1, dim, bias=False)
def score(self, h_t, h_s):
"""
Args:
h_t (`FloatTensor`): sequence of queries `[batch x tgt_len x dim]`
h_s (`FloatTensor`): sequence of sources `[batch x src_len x dim]`
Returns:
:obj:`FloatTensor`:
raw attention scores (unnormalized) for each src index
`[batch x tgt_len x src_len]`
"""
src_batch, src_len, src_dim = h_s.size()
tgt_batch, tgt_len, tgt_dim = h_t.size()
aeq(src_batch, tgt_batch)
aeq(src_dim, tgt_dim)
aeq(self.dim, src_dim)
if self.attn_type in ['general', 'dot']:
if self.attn_type == 'general':
h_t_ = h_t.view(tgt_batch * tgt_len, tgt_dim)
h_t_ = self.linear_in(h_t_)
h_t = h_t_.view(tgt_batch, tgt_len, tgt_dim)
h_s_ = h_s.transpose(1, 2)
return torch.bmm(h_t, h_s_)
else:
dim = self.dim
wq = self.linear_query(h_t.view(-1, dim))
wq = wq.view(tgt_batch, tgt_len, 1, dim)
wq = wq.expand(tgt_batch, tgt_len, src_len, dim)
uh = self.linear_context(h_s.contiguous().view(-1, dim))
uh = uh.view(src_batch, 1, src_len, dim)
uh = uh.expand(src_batch, tgt_len, src_len, dim)
wquh = torch.tanh(wq + uh)
return self.v(wquh.view(-1, dim)).view(tgt_batch, tgt_len, src_len)
def forward(self, source, memory_bank, memory_lengths=None, coverage=None):
"""
Args:
source (`FloatTensor`): query vectors `[batch x tgt_len x dim]`
memory_bank (`FloatTensor`): source vectors `[batch x src_len x dim]`
memory_lengths (`LongTensor`): the source context lengths `[batch]`
coverage (`FloatTensor`): None (not supported yet)
Returns:
(`FloatTensor`, `FloatTensor`):
* Computed vector `[tgt_len x batch x dim]`
* Attention distribtutions for each query
`[tgt_len x batch x src_len]`
"""
if source.dim() == 2:
one_step = True
source = source.unsqueeze(1)
else:
one_step = False
batch, source_l, dim = memory_bank.size()
batch_, target_l, dim_ = source.size()
aeq(batch, batch_)
aeq(dim, dim_)
aeq(self.dim, dim)
if coverage is not None:
batch_, source_l_ = coverage.size()
aeq(batch, batch_)
aeq(source_l, source_l_)
if coverage is not None:
cover = coverage.view(-1).unsqueeze(1)
memory_bank += self.linear_cover(cover).view_as(memory_bank)
memory_bank = torch.tanh(memory_bank)
align = self.score(source, memory_bank)
if memory_lengths is not None:
mask = sequence_mask(memory_lengths, max_len=align.size(-1))
mask = mask.unsqueeze(1)
align.masked_fill_(1 - mask, -float('inf'))
if self.attn_func == 'softmax':
align_vectors = F.softmax(align.view(batch * target_l, source_l
), -1)
else:
align_vectors = sparsemax(align.view(batch * target_l, source_l
), -1)
align_vectors = align_vectors.view(batch, target_l, source_l)
c = torch.bmm(align_vectors, memory_bank)
concat_c = torch.cat([c, source], 2).view(batch * target_l, dim * 2)
attn_h = self.linear_out(concat_c).view(batch, target_l, dim)
if self.attn_type in ['general', 'dot']:
attn_h = torch.tanh(attn_h)
if one_step:
attn_h = attn_h.squeeze(1)
align_vectors = align_vectors.squeeze(1)
batch_, dim_ = attn_h.size()
aeq(batch, batch_)
aeq(dim, dim_)
batch_, source_l_ = align_vectors.size()
aeq(batch, batch_)
aeq(source_l, source_l_)
else:
attn_h = attn_h.transpose(0, 1).contiguous()
align_vectors = align_vectors.transpose(0, 1).contiguous()
target_l_, batch_, dim_ = attn_h.size()
aeq(target_l, target_l_)
aeq(batch, batch_)
aeq(dim, dim_)
target_l_, batch_, source_l_ = align_vectors.size()
aeq(target_l, target_l_)
aeq(batch, batch_)
aeq(source_l, source_l_)
return attn_h, align_vectors
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.cuda
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__softmax_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
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_1(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_clamp_div_mul_round_2(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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = -128.0
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp3 = 128.0
tmp4 = triton_helpers.minimum(tmp2, tmp3)
tmp5 = tmp4 * tmp3
tmp6 = libdevice.nearbyint(tmp5)
tmp7 = 0.0078125
tmp8 = tmp6 * tmp7
tl.store(out_ptr0 + x0, tmp8, xmask)
@triton.jit
def triton_poi_fused_clamp_div_mul_round_3(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
x1 = xindex // 8
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
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tmp11 = -128.0
tmp12 = triton_helpers.maximum(tmp10, tmp11)
tmp13 = 128.0
tmp14 = triton_helpers.minimum(tmp12, tmp13)
tmp15 = tmp14 * tmp13
tmp16 = libdevice.nearbyint(tmp15)
tmp17 = 0.0078125
tmp18 = tmp16 * tmp17
tl.store(out_ptr0 + x2, tmp18, xmask)
@triton.jit
def triton_poi_fused_clone_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
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
tmp1 = libdevice.tanh(tmp0)
tl.store(out_ptr0 + x3, tmp1, xmask)
@triton.jit
def triton_poi_fused_clone_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
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)
def call(args):
primals_1, primals_2, primals_3 = 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, 8), (8, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(primals_1, reinterpret_tensor(primals_2, (4, 4,
4), (16, 1, 4), 0), out=buf0)
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__softmax_0[grid(64)](buf0, buf1, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf2 = reinterpret_tensor(buf0, (16, 4), (4, 1), 0)
del buf0
triton_poi_fused__softmax_1[grid(64)](buf1, buf2, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
del buf1
extern_kernels.bmm(reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1),
0), primals_2, out=buf3)
del primals_2
buf4 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
triton_poi_fused_clamp_div_mul_round_2[grid(32)](primals_3, buf4,
32, XBLOCK=32, num_warps=1, num_stages=1)
del primals_3
buf5 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
triton_poi_fused_clamp_div_mul_round_3[grid(128)](buf3, primals_1,
buf5, 128, XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
buf6 = reinterpret_tensor(buf3, (16, 4), (4, 1), 0)
del buf3
extern_kernels.mm(buf5, reinterpret_tensor(buf4, (8, 4), (1, 8), 0),
out=buf6)
buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_clone_4[grid(64)](buf6, buf7, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_clone_5[grid(64)](buf2, buf8, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del buf2
return buf7, buf8, buf5, buf4, buf5, buf6
def quantize_weights(W, numbits=8):
W = W.clamp(-2 ** (numbits - 1), 2 ** (numbits - 1))
W = W.mul(2 ** (numbits - 1)).round().div(2 ** (numbits - 1))
return W
def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments
), 'Not all arguments have the same value: ' + str(args)
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return torch.arange(0, max_len).type_as(lengths).repeat(batch_size, 1).lt(
lengths.unsqueeze(1))
class quant_weights(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return quantize_weights(x)
@staticmethod
def backward(ctx, g):
return g
class Linear(nn.modules.Linear):
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__(in_features, out_features)
self.in_features = in_features
self.out_features = out_features
self.W_LR_scale = np.float32(1.0 / np.sqrt(1.5 / (in_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)
self.reset_parameters()
def reset_parameters(self):
stdv = 1.0 / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward_t(self, input):
return F.linear(input, self.weight, self.bias)
def extra_repr(self):
return 'in_features={}, out_features={}, bias={}'.format(self.
in_features, self.out_features, self.bias is not None)
def forward(self, input):
Wr = self.weight.data
self.Wb = quant_weights.apply(self.weight.data)
self.input_b = quant_weights.apply(input)
self.weight.data = self.Wb
rvalue = self.forward_t(self.input_b)
self.weight.data = Wr
return rvalue
def return_W_scale(self):
return self.W_LR_scale
class GlobalAttentionNew(nn.Module):
"""
Global attention takes a matrix and a query vector. It
then computes a parameterized convex combination of the matrix
based on the input query.
Constructs a unit mapping a query `q` of size `dim`
and a source matrix `H` of size `n x dim`, to an output
of size `dim`.
.. mermaid::
graph BT
A[Query]
subgraph RNN
C[H 1]
D[H 2]
E[H N]
end
F[Attn]
G[Output]
A --> F
C --> F
D --> F
E --> F
C -.-> G
D -.-> G
E -.-> G
F --> G
All models compute the output as
:math:`c = sum_{j=1}^{SeqLength} a_j H_j` where
:math:`a_j` is the softmax of a score function.
Then then apply a projection layer to [q, c].
However they
differ on how they compute the attention score.
* Luong Attention (dot, general):
* dot: :math:`score(H_j,q) = H_j^T q`
* general: :math:`score(H_j, q) = H_j^T W_a q`
* Bahdanau Attention (mlp):
* :math:`score(H_j, q) = v_a^T tanh(W_a q + U_a h_j)`
Args:
dim (int): dimensionality of query and key
coverage (bool): use coverage term
attn_type (str): type of attention to use, options [dot,general,mlp]
"""
def __init__(self, dim, coverage=False, attn_type='dot', attn_func=
'softmax'):
super(GlobalAttentionNew, self).__init__()
self.dim = dim
assert attn_type in ['dot', 'general', 'mlp'
], 'Please select a valid attention type.'
self.attn_type = attn_type
assert attn_func in ['softmax', 'sparsemax'
], 'Please select a valid attention function.'
self.attn_func = attn_func
if self.attn_type == 'general':
self.linear_in = Linear(dim, dim, bias=False)
elif self.attn_type == 'mlp':
self.linear_context = Linear(dim, dim, bias=False)
self.linear_query = Linear(dim, dim, bias=True)
self.v = Linear(dim, 1, bias=False)
out_bias = self.attn_type == 'mlp'
self.linear_out = Linear(dim * 2, dim, bias=out_bias)
if coverage:
self.linear_cover = Linear(1, dim, bias=False)
def score(self, h_t, h_s):
"""
Args:
h_t (`FloatTensor`): sequence of queries `[batch x tgt_len x dim]`
h_s (`FloatTensor`): sequence of sources `[batch x src_len x dim]`
Returns:
:obj:`FloatTensor`:
raw attention scores (unnormalized) for each src index
`[batch x tgt_len x src_len]`
"""
src_batch, src_len, src_dim = h_s.size()
tgt_batch, tgt_len, tgt_dim = h_t.size()
aeq(src_batch, tgt_batch)
aeq(src_dim, tgt_dim)
aeq(self.dim, src_dim)
if self.attn_type in ['general', 'dot']:
if self.attn_type == 'general':
h_t_ = h_t.view(tgt_batch * tgt_len, tgt_dim)
h_t_ = self.linear_in(h_t_)
h_t = h_t_.view(tgt_batch, tgt_len, tgt_dim)
h_s_ = h_s.transpose(1, 2)
return torch.bmm(h_t, h_s_)
else:
dim = self.dim
wq = self.linear_query(h_t.view(-1, dim))
wq = wq.view(tgt_batch, tgt_len, 1, dim)
wq = wq.expand(tgt_batch, tgt_len, src_len, dim)
uh = self.linear_context(h_s.contiguous().view(-1, dim))
uh = uh.view(src_batch, 1, src_len, dim)
uh = uh.expand(src_batch, tgt_len, src_len, dim)
wquh = torch.tanh(wq + uh)
return self.v(wquh.view(-1, dim)).view(tgt_batch, tgt_len, src_len)
def forward(self, input_0, input_1):
primals_3 = self.linear_out.weight
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3])
return output[0], output[1]
|
csk7/CS550-NLP-McGill-
|
GlobalAttention
| false
| 9,968
|
[
"MIT"
] | 0
|
a6f295b88539015d8accdbd410357c42df7c4287
|
https://github.com/csk7/CS550-NLP-McGill-/tree/a6f295b88539015d8accdbd410357c42df7c4287
|
BBoxTransform
|
import torch
from torch import nn
class BBoxTransform(nn.Module):
def forward(self, anchors, regression):
"""
decode_box_outputs adapted from https://github.com/google/automl/blob/master/efficientdet/anchors.py
Args:
anchors: [batchsize, boxes, (y1, x1, y2, x2)]
regression: [batchsize, boxes, (dy, dx, dh, dw)]
Returns:
"""
y_centers_a = (anchors[..., 0] + anchors[..., 2]) / 2
x_centers_a = (anchors[..., 1] + anchors[..., 3]) / 2
ha = anchors[..., 2] - anchors[..., 0]
wa = anchors[..., 3] - anchors[..., 1]
w = regression[..., 3].exp() * wa
h = regression[..., 2].exp() * ha
y_centers = regression[..., 0] * ha + y_centers_a
x_centers = regression[..., 1] * wa + x_centers_a
ymin = y_centers - h / 2.0
xmin = x_centers - w / 2.0
ymax = y_centers + h / 2.0
xmax = x_centers + w / 2.0
return torch.stack([xmin, ymin, xmax, ymax], dim=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.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_stack_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 % 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 + (1 + 4 * x0 + 16 * x1), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp6 = tl.load(in_ptr1 + (3 + 4 * x0 + 16 * x1), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp7 = tl.load(in_ptr1 + (1 + 4 * x0 + 16 * x1), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp8 = tmp6 - tmp7
tmp9 = tmp5 * tmp8
tmp10 = tmp7 + tmp6
tmp11 = 0.5
tmp12 = tmp10 * tmp11
tmp13 = tmp9 + tmp12
tmp14 = tl.load(in_ptr0 + (3 + 4 * x0 + 16 * x1), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp15 = tl_math.exp(tmp14)
tmp16 = tmp15 * tmp8
tmp17 = tmp16 * tmp11
tmp18 = tmp13 - tmp17
tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
tmp20 = tl.where(tmp4, tmp18, tmp19)
tmp21 = tmp0 >= tmp3
tmp22 = tl.full([1], 8, tl.int64)
tmp23 = tmp0 < tmp22
tmp24 = tmp21 & tmp23
tmp25 = tl.load(in_ptr0 + (4 * (-4 + x0) + 16 * x1), tmp24 & xmask,
eviction_policy='evict_last', other=0.0)
tmp26 = tl.load(in_ptr1 + (2 + 4 * (-4 + x0) + 16 * x1), tmp24 & xmask,
eviction_policy='evict_last', other=0.0)
tmp27 = tl.load(in_ptr1 + (4 * (-4 + x0) + 16 * x1), tmp24 & xmask,
eviction_policy='evict_last', other=0.0)
tmp28 = tmp26 - tmp27
tmp29 = tmp25 * tmp28
tmp30 = tmp27 + tmp26
tmp31 = tmp30 * tmp11
tmp32 = tmp29 + tmp31
tmp33 = tl.load(in_ptr0 + (2 + 4 * (-4 + x0) + 16 * x1), tmp24 & xmask,
eviction_policy='evict_last', other=0.0)
tmp34 = tl_math.exp(tmp33)
tmp35 = tmp34 * tmp28
tmp36 = tmp35 * tmp11
tmp37 = tmp32 - tmp36
tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
tmp39 = tl.where(tmp24, tmp37, tmp38)
tmp40 = tmp0 >= tmp22
tmp41 = tl.full([1], 12, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tmp40 & tmp42
tmp44 = tl.load(in_ptr0 + (1 + 4 * (-8 + x0) + 16 * x1), tmp43 & xmask,
eviction_policy='evict_last', other=0.0)
tmp45 = tl.load(in_ptr1 + (3 + 4 * (-8 + x0) + 16 * x1), tmp43 & xmask,
eviction_policy='evict_last', other=0.0)
tmp46 = tl.load(in_ptr1 + (1 + 4 * (-8 + x0) + 16 * x1), tmp43 & xmask,
eviction_policy='evict_last', other=0.0)
tmp47 = tmp45 - tmp46
tmp48 = tmp44 * tmp47
tmp49 = tmp46 + tmp45
tmp50 = tmp49 * tmp11
tmp51 = tmp48 + tmp50
tmp52 = tl.load(in_ptr0 + (3 + 4 * (-8 + x0) + 16 * x1), tmp43 & xmask,
eviction_policy='evict_last', other=0.0)
tmp53 = tl_math.exp(tmp52)
tmp54 = tmp53 * tmp47
tmp55 = tmp54 * tmp11
tmp56 = tmp51 + tmp55
tmp57 = tl.full(tmp56.shape, 0.0, tmp56.dtype)
tmp58 = tl.where(tmp43, tmp56, tmp57)
tmp59 = tmp0 >= tmp41
tl.full([1], 16, tl.int64)
tmp62 = tl.load(in_ptr0 + (4 * (-12 + x0) + 16 * x1), tmp59 & xmask,
eviction_policy='evict_last', other=0.0)
tmp63 = tl.load(in_ptr1 + (2 + 4 * (-12 + x0) + 16 * x1), tmp59 & xmask,
eviction_policy='evict_last', other=0.0)
tmp64 = tl.load(in_ptr1 + (4 * (-12 + x0) + 16 * x1), tmp59 & xmask,
eviction_policy='evict_last', other=0.0)
tmp65 = tmp63 - tmp64
tmp66 = tmp62 * tmp65
tmp67 = tmp64 + tmp63
tmp68 = tmp67 * tmp11
tmp69 = tmp66 + tmp68
tmp70 = tl.load(in_ptr0 + (2 + 4 * (-12 + x0) + 16 * x1), tmp59 & xmask,
eviction_policy='evict_last', other=0.0)
tmp71 = tl_math.exp(tmp70)
tmp72 = tmp71 * tmp65
tmp73 = tmp72 * tmp11
tmp74 = tmp69 + tmp73
tmp75 = tl.full(tmp74.shape, 0.0, tmp74.dtype)
tmp76 = tl.where(tmp59, tmp74, tmp75)
tmp77 = tl.where(tmp43, tmp58, tmp76)
tmp78 = tl.where(tmp24, tmp39, tmp77)
tmp79 = tl.where(tmp4, tmp20, tmp78)
tl.store(out_ptr0 + x2, tmp79, 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, 16), (64, 16, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_stack_0[grid(256)](arg1_1, arg0_1, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
return reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0),
class BBoxTransformNew(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]
|
cosmos1982/pytorch_efficientdet_openvino_demo
|
BBoxTransform
| false
| 9,969
|
[
"Apache-2.0"
] | 0
|
f626af448a827c0df655eb2af52ae3dbd10f2478
|
https://github.com/cosmos1982/pytorch_efficientdet_openvino_demo/tree/f626af448a827c0df655eb2af52ae3dbd10f2478
|
DuelingQNetwork
|
from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class DuelingQNetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, config_dict):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
action_size (int): Dimension of each action
config_dict (dict): Config (fc1_units and fc2_units)
"""
super(DuelingQNetwork, self).__init__()
self.seed = torch.manual_seed(config_dict['seed'])
self.fc1 = nn.Linear(state_size, config_dict['fc1_units'])
self.fc2 = nn.Linear(config_dict['fc1_units'], config_dict['fc2_units']
)
self.advantages = nn.Linear(config_dict['fc2_units'], action_size)
self.values = nn.Linear(config_dict['fc2_units'], 1)
def forward(self, state):
"""Build a network that maps state -> action values."""
x = F.relu(self.fc1(state))
x = F.relu(self.fc2(x))
v = self.values(x)
a = self.advantages(x)
return v + a - a.mean()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'state_size': 4, 'action_size': 4, 'config_dict':
_mock_config(seed=4, fc1_units=4, fc2_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
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 = 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_per_fused_add_mean_sub_1(in_ptr0, in_ptr1, in_ptr2, out_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
r2 = rindex // 4
tmp0 = tl.load(in_ptr0 + r0, None)
tmp4 = tl.load(in_ptr1 + r2, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr2 + 0)
tmp6 = tl.broadcast_to(tmp5, [RBLOCK])
tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
tmp7 = tmp4 + tmp6
tmp8 = tmp7 + tmp0
tmp9 = 256.0
tmp10 = tmp3 / tmp9
tmp11 = tmp8 - tmp10
tl.store(out_ptr1 + tl.broadcast_to(r0, [RBLOCK]), tmp11, None)
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, (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,))
assert_size_stride(primals_6, (1, 4), (4, 1))
assert_size_stride(primals_7, (1,), (1,))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (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
buf9 = 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, buf9, 256, XBLOCK=256, 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
buf8 = 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, buf8, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 1), (1, 4), 0), out=buf4)
buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf5)
del primals_9
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_per_fused_add_mean_sub_1[grid(1)](buf5, buf4, primals_7,
buf7, 1, 256, num_warps=2, num_stages=1)
del buf4
del buf5
del primals_7
return buf7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(
buf3, (64, 4), (4, 1), 0), primals_8, primals_6, buf8, primals_4, buf9
class DuelingQNetworkNew(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, config_dict):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
action_size (int): Dimension of each action
config_dict (dict): Config (fc1_units and fc2_units)
"""
super(DuelingQNetworkNew, self).__init__()
self.seed = torch.manual_seed(config_dict['seed'])
self.fc1 = nn.Linear(state_size, config_dict['fc1_units'])
self.fc2 = nn.Linear(config_dict['fc1_units'], config_dict['fc2_units']
)
self.advantages = nn.Linear(config_dict['fc2_units'], action_size)
self.values = nn.Linear(config_dict['fc2_units'], 1)
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_8 = self.advantages.weight
primals_9 = self.advantages.bias
primals_6 = self.values.weight
primals_7 = self.values.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]
|
czarrar/udacity_rl
|
DuelingQNetwork
| false
| 9,970
|
[
"MIT"
] | 0
|
d5e9a878b24e6234ab4ac9f612be103bb7f933c4
|
https://github.com/czarrar/udacity_rl/tree/d5e9a878b24e6234ab4ac9f612be103bb7f933c4
|
GlyphNet
|
import torch
from torch import nn
from torch.nn import functional as f
class GlyphNet(nn.Module):
def __init__(self, dimension):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 32, 3, padding=1)
self.fc = nn.Linear(32, dimension)
nn.init.kaiming_uniform_(self.conv1.weight)
nn.init.kaiming_uniform_(self.conv2.weight)
nn.init.xavier_uniform_(self.fc.weight)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = f.adaptive_avg_pool2d(x, output_size=1).squeeze()
x = self.fc(x)
return x
def get_inputs():
return [torch.rand([4, 3, 64, 64])]
def get_init_inputs():
return [[], {'dimension': 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 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):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 4096 % 32
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)
@triton.jit
def triton_red_fused_convolution_mean_1(in_out_ptr0, in_ptr0, in_ptr1,
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_ptr1 + x0, xmask, eviction_policy='evict_last')
_tmp4 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = rindex
tmp0 = tl.load(in_ptr0 + (r2 + 4096 * x3), rmask & xmask,
eviction_policy='evict_first', other=0.0)
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = _tmp4 + tmp3
_tmp4 = tl.where(rmask & xmask, tmp5, _tmp4)
tmp4 = tl.sum(_tmp4, 1)[:, None]
tmp6 = 4096.0
tmp7 = tmp4 / tmp6
tl.debug_barrier()
tl.store(in_out_ptr0 + x3, tmp7, 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, (32, 3, 3, 3), (27, 9, 3, 1))
assert_size_stride(primals_2, (32,), (1,))
assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
assert_size_stride(primals_5, (32,), (1,))
assert_size_stride(primals_6, (4, 32), (32, 1))
assert_size_stride(primals_7, (4,), (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, 32, 64, 64), (131072, 4096, 64, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(524288)](buf1, primals_2,
524288, XBLOCK=1024, 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, 32, 64, 64), (131072, 4096, 64, 1))
buf3 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
float32)
buf4 = buf3
del buf3
triton_red_fused_convolution_mean_1[grid(128)](buf4, buf2,
primals_5, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
num_stages=1)
del buf2
del primals_5
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf4, (4, 32), (
32, 1), 0), reinterpret_tensor(primals_6, (32, 4), (1, 32), 0),
alpha=1, beta=1, out=buf5)
del primals_7
return buf5, primals_1, primals_3, primals_4, buf1, reinterpret_tensor(buf4
, (4, 32), (32, 1), 0), primals_6
class GlyphNetNew(nn.Module):
def __init__(self, dimension):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 32, 3, padding=1)
self.fc = nn.Linear(32, dimension)
nn.init.kaiming_uniform_(self.conv1.weight)
nn.init.kaiming_uniform_(self.conv2.weight)
nn.init.xavier_uniform_(self.fc.weight)
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.fc.weight
primals_7 = self.fc.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
cmsflash/ocean-text
|
GlyphNet
| false
| 9,971
|
[
"MIT"
] | 0
|
d2f98077cb5e6949aec87f88a369ba4c2e99d178
|
https://github.com/cmsflash/ocean-text/tree/d2f98077cb5e6949aec87f88a369ba4c2e99d178
|
NanoNet
|
import torch
from torch import nn
from torch.nn import functional as f
class NanoNet(nn.Module):
def __init__(self, dimension):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 32, 3, padding=1)
self.conv3 = nn.Conv2d(32, 32, 3, padding=1)
self.fc = nn.Linear(32, dimension)
nn.init.kaiming_uniform_(self.conv1.weight)
nn.init.kaiming_uniform_(self.conv2.weight)
nn.init.kaiming_uniform_(self.conv3.weight)
nn.init.xavier_uniform_(self.fc.weight)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = f.adaptive_avg_pool2d(x, output_size=1).squeeze()
x = self.fc(x)
return x
def get_inputs():
return [torch.rand([4, 1, 64, 64])]
def get_init_inputs():
return [[], {'dimension': 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 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):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 4096 % 32
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)
@triton.jit
def triton_red_fused_convolution_mean_1(in_out_ptr0, in_ptr0, in_ptr1,
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_ptr1 + x0, xmask, eviction_policy='evict_last')
_tmp4 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = rindex
tmp0 = tl.load(in_ptr0 + (r2 + 4096 * x3), rmask & xmask,
eviction_policy='evict_first', other=0.0)
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = _tmp4 + tmp3
_tmp4 = tl.where(rmask & xmask, tmp5, _tmp4)
tmp4 = tl.sum(_tmp4, 1)[:, None]
tmp6 = 4096.0
tmp7 = tmp4 / tmp6
tl.debug_barrier()
tl.store(in_out_ptr0 + x3, tmp7, 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, (32, 1, 3, 3), (9, 9, 3, 1))
assert_size_stride(primals_2, (32,), (1,))
assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
assert_size_stride(primals_5, (32,), (1,))
assert_size_stride(primals_6, (32, 32, 3, 3), (288, 9, 3, 1))
assert_size_stride(primals_7, (32,), (1,))
assert_size_stride(primals_8, (4, 32), (32, 1))
assert_size_stride(primals_9, (4,), (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, 32, 64, 64), (131072, 4096, 64, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(524288)](buf1, primals_2,
524288, XBLOCK=1024, 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, 32, 64, 64), (131072, 4096, 64, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_0[grid(524288)](buf3, primals_5,
524288, XBLOCK=1024, 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, 32, 64, 64), (131072, 4096, 64, 1))
buf5 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
float32)
buf6 = buf5
del buf5
triton_red_fused_convolution_mean_1[grid(128)](buf6, buf4,
primals_7, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
num_stages=1)
del buf4
del primals_7
buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_9, reinterpret_tensor(buf6, (4, 32), (
32, 1), 0), reinterpret_tensor(primals_8, (32, 4), (1, 32), 0),
alpha=1, beta=1, out=buf7)
del primals_9
return (buf7, primals_1, primals_3, primals_4, primals_6, buf1, buf3,
reinterpret_tensor(buf6, (4, 32), (32, 1), 0), primals_8)
class NanoNetNew(nn.Module):
def __init__(self, dimension):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 32, 3, padding=1)
self.conv3 = nn.Conv2d(32, 32, 3, padding=1)
self.fc = nn.Linear(32, dimension)
nn.init.kaiming_uniform_(self.conv1.weight)
nn.init.kaiming_uniform_(self.conv2.weight)
nn.init.kaiming_uniform_(self.conv3.weight)
nn.init.xavier_uniform_(self.fc.weight)
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.fc.weight
primals_9 = self.fc.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]
|
cmsflash/ocean-text
|
NanoNet
| false
| 9,972
|
[
"MIT"
] | 0
|
d2f98077cb5e6949aec87f88a369ba4c2e99d178
|
https://github.com/cmsflash/ocean-text/tree/d2f98077cb5e6949aec87f88a369ba4c2e99d178
|
VGGSiameseNet
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class VGGSiameseNet(nn.Module):
def __init__(self):
super(VGGSiameseNet, self).__init__()
self.conv11 = nn.Conv2d(1, 64, 3)
self.conv12 = nn.Conv2d(64, 64, 3)
self.conv21 = nn.Conv2d(64, 128, 3)
self.conv22 = nn.Conv2d(128, 128, 3)
self.conv31 = nn.Conv2d(128, 256, 3)
self.conv32 = nn.Conv2d(256, 256, 3)
self.conv33 = nn.Conv2d(256, 256, 3)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(256 * 8 * 8, 4096)
self.fc2 = nn.Linear(4096, 4096)
self.fcOut = nn.Linear(4096, 1)
self.sigmoid = nn.Sigmoid()
def convs(self, x):
x = F.relu(self.conv11(x))
x = F.relu(self.conv12(x))
x = F.max_pool2d(x, (2, 2))
x = F.relu(self.conv21(x))
x = F.relu(self.conv22(x))
x = F.max_pool2d(x, (2, 2))
x = F.relu(self.conv31(x))
x = F.relu(self.conv32(x))
x = F.relu(self.conv33(x))
x = F.max_pool2d(x, (2, 2))
return x
def forward(self, x1, x2):
x1 = self.convs(x1)
x1 = x1.view(-1, 256 * 8 * 8)
x1 = self.fc1(x1)
x1 = self.sigmoid(self.fc2(x1))
x2 = self.convs(x2)
x2 = x2.view(-1, 256 * 8 * 8)
x2 = self.fc1(x2)
x2 = self.sigmoid(self.fc2(x2))
x = torch.abs(x1 - x2)
x = self.fcOut(x)
return x
def get_inputs():
return [torch.rand([4, 1, 72, 72]), torch.rand([4, 1, 72, 72])]
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.nn.functional as F
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 % 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_2(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_3(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_4(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_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 256
xnumel = 4900
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 + 4900 * y3), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr2 + (x2 + 4900 * y3), xmask & ymask,
eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1, 1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = tmp5 + tmp1
tmp7 = triton_helpers.maximum(tmp3, tmp6)
tl.store(out_ptr0 + (y0 + 64 * x2 + 313600 * y1), tmp4, xmask & ymask)
tl.store(out_ptr1 + (y0 + 64 * x2 + 313600 * y1), tmp7, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_out_ptr1, 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 % 64
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp5 = tl.load(in_out_ptr1 + x2, None)
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = tmp5 + tmp1
tmp7 = triton_helpers.maximum(tmp3, tmp6)
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(in_out_ptr1 + x2, tmp7, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_7(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 295936
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 64
x1 = xindex // 64 % 34
x2 = xindex // 2176
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1 + 8704 * x2), xmask)
tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 8704 * x2), xmask)
tmp3 = tl.load(in_ptr0 + (4352 + x0 + 128 * x1 + 8704 * x2), xmask)
tmp5 = tl.load(in_ptr0 + (4416 + x0 + 128 * x1 + 8704 * x2), xmask)
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, xmask)
tl.store(out_ptr1 + x3, tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_out_ptr1, 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')
tmp5 = tl.load(in_out_ptr1 + x2, None)
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = tmp5 + tmp1
tmp7 = triton_helpers.maximum(tmp3, tmp6)
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(in_out_ptr1 + x2, tmp7, None)
@triton.jit
def triton_poi_fused_convolution_relu_9(in_out_ptr0, in_out_ptr1, 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')
tmp5 = tl.load(in_out_ptr1 + x2, None)
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = tmp5 + tmp1
tmp7 = triton_helpers.maximum(tmp3, tmp6)
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(in_out_ptr1 + x2, tmp7, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_10(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 115200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 128
x1 = xindex // 128 % 15
x2 = xindex // 1920
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1 + 7680 * x2), xmask)
tmp1 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 7680 * x2), xmask)
tmp3 = tl.load(in_ptr0 + (3840 + x0 + 256 * x1 + 7680 * x2), xmask)
tmp5 = tl.load(in_ptr0 + (3968 + x0 + 256 * x1 + 7680 * x2), xmask)
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, xmask)
tl.store(out_ptr1 + x3, tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_11(in_out_ptr0, in_out_ptr1, in_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 173056
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 256
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_out_ptr1 + x2, xmask)
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = tmp5 + tmp1
tmp7 = triton_helpers.maximum(tmp3, tmp6)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(in_out_ptr1 + x2, tmp7, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_12(in_out_ptr0, in_out_ptr1, in_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 123904
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 256
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_out_ptr1 + x2, xmask)
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = tmp5 + tmp1
tmp7 = triton_helpers.maximum(tmp3, tmp6)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(in_out_ptr1 + x2, tmp7, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_13(in_out_ptr0, in_out_ptr1, in_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 82944
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 256
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_out_ptr1 + x2, xmask)
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = tmp5 + tmp1
tmp7 = triton_helpers.maximum(tmp3, tmp6)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(in_out_ptr1 + x2, tmp7, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_14(in_ptr0, out_ptr0, out_ptr1,
ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 64
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
x3 = xindex
y0 = yindex % 4
y1 = yindex // 4 % 4
y2 = yindex // 16
y4 = yindex
y5 = yindex % 16
tmp0 = tl.load(in_ptr0 + (x3 + 512 * y0 + 4608 * y1 + 20736 * y2),
xmask & ymask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (256 + x3 + 512 * y0 + 4608 * y1 + 20736 * y2),
xmask & ymask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2304 + x3 + 512 * y0 + 4608 * y1 + 20736 * y2
), xmask & ymask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (2560 + x3 + 512 * y0 + 4608 * y1 + 20736 *
y2), 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 + (x3 + 256 * y4), tmp15, xmask & ymask)
tl.store(out_ptr1 + (y5 + 16 * x3 + 4096 * y2), tmp16, xmask & ymask)
@triton.jit
def triton_poi_fused_abs_sigmoid_sub_15(in_ptr0, in_ptr1, 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
tmp0 = tl.load(in_ptr0 + x0, None)
tmp2 = tl.load(in_ptr1 + x0, None)
tmp1 = tl.sigmoid(tmp0)
tmp3 = tl.sigmoid(tmp2)
tmp4 = tmp1 - tmp3
tmp5 = tl_math.abs(tmp4)
tl.store(out_ptr0 + x0, tmp5, 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) = 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, 72, 72), (5184, 5184, 72, 1))
assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_7, (128,), (1,))
assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_9, (128,), (1,))
assert_size_stride(primals_10, (256, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_11, (256,), (1,))
assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_13, (256,), (1,))
assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_15, (256,), (1,))
assert_size_stride(primals_16, (4096, 16384), (16384, 1))
assert_size_stride(primals_17, (4096,), (1,))
assert_size_stride(primals_18, (4096, 4096), (4096, 1))
assert_size_stride(primals_19, (4096,), (1,))
assert_size_stride(primals_20, (4, 1, 72, 72), (5184, 5184, 72, 1))
assert_size_stride(primals_21, (1, 4096), (4096, 1))
assert_size_stride(primals_22, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
float32)
get_raw_stream(0)
triton_poi_fused_0[grid(4096, 9)](primals_4, buf0, 4096, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_4
buf1 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch
.float32)
triton_poi_fused_1[grid(8192, 9)](primals_6, buf1, 8192, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_6
buf2 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
torch.float32)
triton_poi_fused_2[grid(16384, 9)](primals_8, buf2, 16384, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_8
buf3 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128),
torch.float32)
triton_poi_fused_3[grid(32768, 9)](primals_10, buf3, 32768, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_10
buf4 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_4[grid(65536, 9)](primals_12, buf4, 65536, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_12
buf5 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_4[grid(65536, 9)](primals_14, buf5, 65536, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_14
buf6 = 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(buf6, (4, 64, 70, 70), (313600, 4900, 70, 1))
buf28 = extern_kernels.convolution(primals_20, primals_1, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf28, (4, 64, 70, 70), (313600, 4900, 70, 1))
buf7 = empty_strided_cuda((4, 64, 70, 70), (313600, 1, 4480, 64),
torch.float32)
buf29 = empty_strided_cuda((4, 64, 70, 70), (313600, 1, 4480, 64),
torch.float32)
triton_poi_fused_convolution_relu_5[grid(256, 4900)](buf6,
primals_2, buf28, buf7, buf29, 256, 4900, XBLOCK=16, YBLOCK=256,
num_warps=8, num_stages=1)
del buf28
del buf6
del primals_2
buf8 = extern_kernels.convolution(buf7, buf0, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 64, 68, 68), (295936, 1, 4352, 64))
buf30 = extern_kernels.convolution(buf29, buf0, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf30, (4, 64, 68, 68), (295936, 1, 4352, 64))
buf9 = buf8
del buf8
buf31 = buf30
del buf30
triton_poi_fused_convolution_relu_6[grid(1183744)](buf9, buf31,
primals_5, 1183744, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_5
buf10 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64),
torch.float32)
buf11 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_7[grid(295936)](buf9,
buf10, buf11, 295936, XBLOCK=512, num_warps=8, num_stages=1)
buf12 = extern_kernels.convolution(buf10, buf1, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf12, (4, 128, 32, 32), (131072, 1, 4096, 128))
buf32 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64),
torch.float32)
buf33 = empty_strided_cuda((4, 64, 34, 34), (73984, 1, 2176, 64),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_7[grid(295936)](buf31,
buf32, buf33, 295936, XBLOCK=512, num_warps=8, num_stages=1)
buf34 = extern_kernels.convolution(buf32, buf1, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf34, (4, 128, 32, 32), (131072, 1, 4096, 128))
buf13 = buf12
del buf12
buf35 = buf34
del buf34
triton_poi_fused_convolution_relu_8[grid(524288)](buf13, buf35,
primals_7, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_7
buf14 = extern_kernels.convolution(buf13, buf2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf14, (4, 128, 30, 30), (115200, 1, 3840, 128))
buf36 = extern_kernels.convolution(buf35, buf2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf36, (4, 128, 30, 30), (115200, 1, 3840, 128))
buf15 = buf14
del buf14
buf37 = buf36
del buf36
triton_poi_fused_convolution_relu_9[grid(460800)](buf15, buf37,
primals_9, 460800, XBLOCK=512, num_warps=8, num_stages=1)
del primals_9
buf16 = empty_strided_cuda((4, 128, 15, 15), (28800, 1, 1920, 128),
torch.float32)
buf17 = empty_strided_cuda((4, 128, 15, 15), (28800, 1, 1920, 128),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_10[grid(115200)](buf15,
buf16, buf17, 115200, XBLOCK=512, num_warps=8, num_stages=1)
buf18 = extern_kernels.convolution(buf16, buf3, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf18, (4, 256, 13, 13), (43264, 1, 3328, 256))
buf38 = empty_strided_cuda((4, 128, 15, 15), (28800, 1, 1920, 128),
torch.float32)
buf39 = empty_strided_cuda((4, 128, 15, 15), (28800, 1, 1920, 128),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_10[grid(115200)](buf37,
buf38, buf39, 115200, XBLOCK=512, num_warps=8, num_stages=1)
buf40 = extern_kernels.convolution(buf38, buf3, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf40, (4, 256, 13, 13), (43264, 1, 3328, 256))
buf19 = buf18
del buf18
buf41 = buf40
del buf40
triton_poi_fused_convolution_relu_11[grid(173056)](buf19, buf41,
primals_11, 173056, XBLOCK=512, num_warps=8, num_stages=1)
del primals_11
buf20 = extern_kernels.convolution(buf19, buf4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf20, (4, 256, 11, 11), (30976, 1, 2816, 256))
buf42 = extern_kernels.convolution(buf41, buf4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf42, (4, 256, 11, 11), (30976, 1, 2816, 256))
buf21 = buf20
del buf20
buf43 = buf42
del buf42
triton_poi_fused_convolution_relu_12[grid(123904)](buf21, buf43,
primals_13, 123904, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_13
buf22 = extern_kernels.convolution(buf21, buf5, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf22, (4, 256, 9, 9), (20736, 1, 2304, 256))
buf44 = extern_kernels.convolution(buf43, buf5, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf44, (4, 256, 9, 9), (20736, 1, 2304, 256))
buf23 = buf22
del buf22
buf45 = buf44
del buf44
triton_poi_fused_convolution_relu_13[grid(82944)](buf23, buf45,
primals_15, 82944, XBLOCK=512, num_warps=8, num_stages=1)
del primals_15
buf24 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256),
torch.int8)
buf25 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
float32)
triton_poi_fused_max_pool2d_with_indices_14[grid(64, 256)](buf23,
buf24, buf25, 64, 256, XBLOCK=4, YBLOCK=64, num_warps=4,
num_stages=1)
buf26 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
extern_kernels.addmm(primals_17, reinterpret_tensor(buf25, (1,
16384), (0, 1), 0), reinterpret_tensor(primals_16, (16384, 4096
), (1, 16384), 0), alpha=1, beta=1, out=buf26)
buf27 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
extern_kernels.addmm(primals_19, buf26, reinterpret_tensor(
primals_18, (4096, 4096), (1, 4096), 0), alpha=1, beta=1, out=buf27
)
buf46 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256),
torch.int8)
buf47 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
float32)
triton_poi_fused_max_pool2d_with_indices_14[grid(64, 256)](buf45,
buf46, buf47, 64, 256, XBLOCK=4, YBLOCK=64, num_warps=4,
num_stages=1)
buf48 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
extern_kernels.addmm(primals_17, reinterpret_tensor(buf47, (1,
16384), (0, 1), 0), reinterpret_tensor(primals_16, (16384, 4096
), (1, 16384), 0), alpha=1, beta=1, out=buf48)
del primals_17
buf49 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
extern_kernels.addmm(primals_19, buf48, reinterpret_tensor(
primals_18, (4096, 4096), (1, 4096), 0), alpha=1, beta=1, out=buf49
)
del primals_19
buf50 = empty_strided_cuda((1, 4096), (4096, 1), torch.float32)
triton_poi_fused_abs_sigmoid_sub_15[grid(4096)](buf27, buf49, buf50,
4096, XBLOCK=256, num_warps=4, num_stages=1)
buf52 = empty_strided_cuda((1, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_22, buf50, reinterpret_tensor(
primals_21, (4096, 1), (1, 4096), 0), alpha=1, beta=1, out=buf52)
del primals_22
return (buf52, primals_1, primals_3, buf0, buf1, buf2, buf3, buf4, buf5,
primals_20, buf7, buf9, buf10, buf11, buf13, buf15, buf16, buf17,
buf19, buf21, buf23, buf24, reinterpret_tensor(buf25, (1, 16384), (
16384, 1), 0), buf26, buf27, buf29, buf31, buf32, buf33, buf35,
buf37, buf38, buf39, buf41, buf43, buf45, buf46, reinterpret_tensor
(buf47, (1, 16384), (16384, 1), 0), buf48, buf49, buf50, primals_21,
primals_18, primals_16)
class VGGSiameseNetNew(nn.Module):
def __init__(self):
super(VGGSiameseNetNew, self).__init__()
self.conv11 = nn.Conv2d(1, 64, 3)
self.conv12 = nn.Conv2d(64, 64, 3)
self.conv21 = nn.Conv2d(64, 128, 3)
self.conv22 = nn.Conv2d(128, 128, 3)
self.conv31 = nn.Conv2d(128, 256, 3)
self.conv32 = nn.Conv2d(256, 256, 3)
self.conv33 = nn.Conv2d(256, 256, 3)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(256 * 8 * 8, 4096)
self.fc2 = nn.Linear(4096, 4096)
self.fcOut = nn.Linear(4096, 1)
self.sigmoid = nn.Sigmoid()
def convs(self, x):
x = F.relu(self.conv11(x))
x = F.relu(self.conv12(x))
x = F.max_pool2d(x, (2, 2))
x = F.relu(self.conv21(x))
x = F.relu(self.conv22(x))
x = F.max_pool2d(x, (2, 2))
x = F.relu(self.conv31(x))
x = F.relu(self.conv32(x))
x = F.relu(self.conv33(x))
x = F.max_pool2d(x, (2, 2))
return x
def forward(self, input_0, input_1):
primals_1 = self.conv11.weight
primals_2 = self.conv11.bias
primals_4 = self.conv12.weight
primals_5 = self.conv12.bias
primals_6 = self.conv21.weight
primals_7 = self.conv21.bias
primals_8 = self.conv22.weight
primals_9 = self.conv22.bias
primals_10 = self.conv31.weight
primals_11 = self.conv31.bias
primals_12 = self.conv32.weight
primals_13 = self.conv32.bias
primals_14 = self.conv33.weight
primals_15 = self.conv33.bias
primals_16 = self.fc1.weight
primals_17 = self.fc1.bias
primals_18 = self.fc2.weight
primals_19 = self.fc2.bias
primals_21 = self.fcOut.weight
primals_22 = self.fcOut.bias
primals_3 = input_0
primals_20 = input_1
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])
return output[0]
|
christnp/comse6998-project
|
VGGSiameseNet
| false
| 9,973
|
[
"MIT"
] | 0
|
7deffaceb945ae0bd4851ff9478a7efe6e486d39
|
https://github.com/christnp/comse6998-project/tree/7deffaceb945ae0bd4851ff9478a7efe6e486d39
|
IrisClassifier
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class IrisClassifier(nn.Module):
def __init__(self):
super(IrisClassifier, self).__init__()
self.fc1 = nn.Linear(4, 100)
self.fc2 = nn.Linear(100, 100)
self.fc3 = nn.Linear(100, 3)
self.softmax = nn.Softmax(dim=1)
def forward(self, X):
X = F.relu(self.fc1(X))
X = self.fc2(X)
X = self.fc3(X)
X = self.softmax(X)
return X
def get_inputs():
return [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
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_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 6400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x4 = xindex
x0 = xindex % 100
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__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 12
x2 = xindex // 48
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 48 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (12 + x0 + 48 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (24 + x0 + 48 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (36 + x0 + 48 * 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_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 12
x2 = xindex // 48
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 48 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (12 + x0 + 48 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (24 + x0 + 48 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (36 + x0 + 48 * 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):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (100, 4), (4, 1))
assert_size_stride(primals_2, (100,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (100, 100), (100, 1))
assert_size_stride(primals_5, (100,), (1,))
assert_size_stride(primals_6, (3, 100), (100, 1))
assert_size_stride(primals_7, (3,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 100), (100, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 100), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 100), (1600, 400, 100, 1), 0)
del buf0
buf6 = empty_strided_cuda((4, 4, 4, 100), (1664, 400, 100, 1),
torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(6400)](buf1,
primals_2, buf6, 6400, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 100), (100, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 100),
(100, 1), 0), reinterpret_tensor(primals_4, (100, 100), (1, 100
), 0), alpha=1, beta=1, out=buf2)
del primals_5
buf3 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
extern_kernels.addmm(primals_7, buf2, reinterpret_tensor(primals_6,
(100, 3), (1, 100), 0), alpha=1, beta=1, out=buf3)
del primals_7
buf4 = empty_strided_cuda((4, 4, 4, 3), (48, 12, 3, 1), torch.float32)
triton_poi_fused__softmax_1[grid(192)](buf3, buf4, 192, XBLOCK=128,
num_warps=4, num_stages=1)
buf5 = reinterpret_tensor(buf3, (4, 4, 4, 3), (48, 12, 3, 1), 0)
del buf3
triton_poi_fused__softmax_2[grid(192)](buf4, buf5, 192, XBLOCK=256,
num_warps=4, num_stages=1)
del buf4
return buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 100), (100, 1), 0
), buf2, buf5, primals_6, primals_4, buf6
class IrisClassifierNew(nn.Module):
def __init__(self):
super(IrisClassifierNew, self).__init__()
self.fc1 = nn.Linear(4, 100)
self.fc2 = nn.Linear(100, 100)
self.fc3 = nn.Linear(100, 3)
self.softmax = nn.Softmax(dim=1)
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]
|
dbinoy/pytorch-iris-sagemaker
|
IrisClassifier
| false
| 9,974
|
[
"MIT-0"
] | 0
|
afc5bd95f6dd0431338708bc179029fa08724a2f
|
https://github.com/dbinoy/pytorch-iris-sagemaker/tree/afc5bd95f6dd0431338708bc179029fa08724a2f
|
Gated_Recurrent_Unit
|
import torch
from torchvision.transforms import functional as F
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class Gated_Recurrent_Unit(nn.Module):
def __init__(self, fea_size, dropout):
super(Gated_Recurrent_Unit, self).__init__()
self.wih = nn.Linear(fea_size, fea_size, bias=True)
self.whh = nn.Linear(fea_size, fea_size, bias=True)
self.dropout = dropout
def forward(self, input, hidden):
output = self.wih(F.relu(input)) + self.whh(F.relu(hidden))
if self.dropout:
output = F.dropout(output, training=self.training)
return output
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'fea_size': 4, '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 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_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.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_add_1(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
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)
tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tl.store(in_out_ptr0 + x2, tmp6, 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, 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, 4), (64, 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((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_relu_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
del primals_2
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_relu_0[grid(256)](primals_4, buf2, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf3)
del primals_5
buf4 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf1
triton_poi_fused_add_1[grid(256)](buf4, primals_3, buf3, primals_6,
256, XBLOCK=128, num_warps=4, num_stages=1)
del buf3
del primals_3
del primals_6
return buf4, reinterpret_tensor(buf0, (64, 4), (4, 1), 0
), reinterpret_tensor(buf2, (64, 4), (4, 1), 0)
class Gated_Recurrent_UnitNew(nn.Module):
def __init__(self, fea_size, dropout):
super(Gated_Recurrent_UnitNew, self).__init__()
self.wih = nn.Linear(fea_size, fea_size, bias=True)
self.whh = nn.Linear(fea_size, fea_size, bias=True)
self.dropout = dropout
def forward(self, input_0, input_1):
primals_2 = self.wih.weight
primals_3 = self.wih.bias
primals_5 = self.whh.weight
primals_6 = self.whh.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]
|
champon1020/scene_graph_benchmark
|
Gated_Recurrent_Unit
| false
| 9,975
|
[
"MIT"
] | 0
|
970a7499f8fa2854810bd650f6c991bcad5748db
|
https://github.com/champon1020/scene_graph_benchmark/tree/970a7499f8fa2854810bd650f6c991bcad5748db
|
SimpleNet
|
import torch
import torch.nn as nn
class SimpleNet(nn.Module):
def __init__(self, width, input_size, output_size, pool='max'):
super(SimpleNet, self).__init__()
self.pool = nn.MaxPool2d(width, stride=width
) if pool == 'max' else nn.AvgPool2d(width, stride=width)
self.fc1 = nn.Linear(input_size, output_size)
def forward(self, x):
out = x.permute(0, 3, 1, 2)
out = self.pool(out)
out = torch.flatten(out, start_dim=1)
out = self.fc1(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'width': 4, '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
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_pool2d_with_indices_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 + 64 * x1), xmask)
tmp1 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
tmp5 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
tmp7 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp9 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
tmp11 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
tmp13 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
tmp15 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp17 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
tmp19 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
tmp21 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
tmp23 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp25 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
tmp27 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
tmp29 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp8 = triton_helpers.maximum(tmp7, tmp6)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tmp12 = triton_helpers.maximum(tmp11, tmp10)
tmp14 = triton_helpers.maximum(tmp13, tmp12)
tmp16 = triton_helpers.maximum(tmp15, tmp14)
tmp18 = triton_helpers.maximum(tmp17, tmp16)
tmp20 = triton_helpers.maximum(tmp19, tmp18)
tmp22 = triton_helpers.maximum(tmp21, tmp20)
tmp24 = triton_helpers.maximum(tmp23, tmp22)
tmp26 = triton_helpers.maximum(tmp25, tmp24)
tmp28 = triton_helpers.maximum(tmp27, tmp26)
tmp30 = triton_helpers.maximum(tmp29, tmp28)
tl.store(out_ptr0 + x2, tmp30, 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, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_max_pool2d_with_indices_0[grid(16)](primals_1,
buf0, 16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (4, 4), (4,
1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha
=1, beta=1, out=buf1)
del primals_2
del primals_3
return buf1, reinterpret_tensor(buf0, (4, 4), (4, 1), 0)
class SimpleNetNew(nn.Module):
def __init__(self, width, input_size, output_size, pool='max'):
super(SimpleNetNew, self).__init__()
self.pool = nn.MaxPool2d(width, stride=width
) if pool == 'max' else nn.AvgPool2d(width, stride=width)
self.fc1 = nn.Linear(input_size, output_size)
def forward(self, input_0):
primals_2 = self.fc1.weight
primals_3 = self.fc1.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
daniel-zeng/SegSort
|
SimpleNet
| false
| 9,976
|
[
"MIT"
] | 0
|
7a50e6253df23a7719f962b34acff2626c916354
|
https://github.com/daniel-zeng/SegSort/tree/7a50e6253df23a7719f962b34acff2626c916354
|
Message_Passing_Unit_v2
|
import torch
from torchvision.transforms import functional as F
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class Message_Passing_Unit_v2(nn.Module):
def __init__(self, fea_size, filter_size=128):
super(Message_Passing_Unit_v2, self).__init__()
self.w = nn.Linear(fea_size, filter_size, bias=True)
self.fea_size = fea_size
self.filter_size = filter_size
def forward(self, unary_term, pair_term):
if unary_term.size()[0] == 1 and pair_term.size()[0] > 1:
unary_term = unary_term.expand(pair_term.size()[0], unary_term.
size()[1])
if unary_term.size()[0] > 1 and pair_term.size()[0] == 1:
pair_term = pair_term.expand(unary_term.size()[0], pair_term.
size()[1])
gate = self.w(F.relu(unary_term)) * self.w(F.relu(pair_term))
gate = torch.sigmoid(gate.sum(1))
output = pair_term * gate.expand(gate.size()[0], pair_term.size()[1])
return output
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'fea_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.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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_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 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_per_fused_mul_sigmoid_sigmoid_backward_sum_1(in_ptr0, in_ptr1,
out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 4
RBLOCK: tl.constexpr = 128
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 + 128 * x0), xmask, other=0.0)
tmp1 = tl.load(in_ptr1 + (r1 + 128 * x0), xmask, other=0.0)
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = tl.where(xmask, tmp3, 0)
tmp6 = tl.sum(tmp5, 1)[:, None]
tmp7 = tl.sigmoid(tmp6)
tmp8 = 1.0
tmp9 = tmp8 - tmp7
tmp10 = tmp7 * tmp9
tl.store(out_ptr1 + x0, tmp10, xmask)
tl.store(out_ptr0 + x0, tmp6, xmask)
@triton.jit
def triton_poi_fused_mul_2(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
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tl.sigmoid(tmp1)
tmp3 = tmp0 * tmp2
tl.store(out_ptr0 + x2, tmp3, 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, (128, 4), (4, 1))
assert_size_stride(primals_4, (128,), (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_relu_0[grid(16)](primals_1, buf0, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
extern_kernels.addmm(primals_4, buf0, reinterpret_tensor(primals_3,
(4, 128), (1, 4), 0), alpha=1, beta=1, out=buf1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_relu_0[grid(16)](primals_2, buf2, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf3 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
extern_kernels.addmm(primals_4, buf2, reinterpret_tensor(primals_3,
(4, 128), (1, 4), 0), alpha=1, beta=1, out=buf3)
del primals_3
del primals_4
buf4 = empty_strided_cuda((4,), (1,), torch.float32)
buf6 = empty_strided_cuda((4,), (1,), torch.float32)
triton_per_fused_mul_sigmoid_sigmoid_backward_sum_1[grid(4)](buf1,
buf3, buf4, buf6, 4, 128, XBLOCK=1, num_warps=2, num_stages=1)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_mul_2[grid(16)](primals_2, buf4, buf5, 16, XBLOCK=
16, num_warps=1, num_stages=1)
del buf4
return buf5, primals_2, buf0, buf1, buf2, buf3, buf6
class Message_Passing_Unit_v2New(nn.Module):
def __init__(self, fea_size, filter_size=128):
super(Message_Passing_Unit_v2New, self).__init__()
self.w = nn.Linear(fea_size, filter_size, bias=True)
self.fea_size = fea_size
self.filter_size = filter_size
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]
|
champon1020/scene_graph_benchmark
|
Message_Passing_Unit_v2
| false
| 9,977
|
[
"MIT"
] | 0
|
970a7499f8fa2854810bd650f6c991bcad5748db
|
https://github.com/champon1020/scene_graph_benchmark/tree/970a7499f8fa2854810bd650f6c991bcad5748db
|
QNetwork
|
import torch
import torch.nn.functional as F
import torch.nn as nn
class QNetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=37,
fc2_units=64):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
action_size (int): Dimension of each action
seed (int): Random seed
fc1_units (int): Number of nodes in first hidden layer
fc2_units (int): Number of nodes in second hidden layer
"""
super(QNetwork, self).__init__()
self.seed = torch.manual_seed(seed)
self.fc1 = nn.Linear(state_size, fc1_units)
self.fc2 = nn.Linear(fc1_units, fc2_units)
self.fc3 = nn.Linear(fc2_units, action_size)
def forward(self, state):
"""Build a network that maps state -> action values."""
x = F.relu(self.fc1(state))
x = F.relu(self.fc2(x))
return self.fc3(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'state_size': 4, 'action_size': 4, 'seed': 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 = 2368
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 37
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_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
out_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 % 64
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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, None)
tl.store(out_ptr0 + x2, tmp6, None)
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, (37, 4), (4, 1))
assert_size_stride(primals_2, (37,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (64, 37), (37, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (4, 64), (64, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 37), (37, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 37), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 37), (592, 148, 37, 1), 0)
del buf0
buf6 = empty_strided_cuda((4, 4, 4, 37), (592, 148, 37, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(2368)](buf1,
primals_2, buf6, 2368, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 37), (37, 1), 0),
reinterpret_tensor(primals_4, (37, 64), (1, 37), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
del buf2
buf5 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool
)
triton_poi_fused_relu_threshold_backward_1[grid(4096)](buf3,
primals_5, buf5, 4096, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 64),
(64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0),
alpha=1, beta=1, out=buf4)
del primals_7
return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 37), (37, 1), 0), reinterpret_tensor(
buf3, (64, 64), (64, 1), 0), primals_6, buf5, primals_4, buf6
class QNetworkNew(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=37,
fc2_units=64):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
action_size (int): Dimension of each action
seed (int): Random seed
fc1_units (int): Number of nodes in first hidden layer
fc2_units (int): Number of nodes in second hidden layer
"""
super(QNetworkNew, self).__init__()
self.seed = torch.manual_seed(seed)
self.fc1 = nn.Linear(state_size, fc1_units)
self.fc2 = nn.Linear(fc1_units, fc2_units)
self.fc3 = nn.Linear(fc2_units, action_size)
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]
|
deeplearningrobotics/p1nav
|
QNetwork
| false
| 9,978
|
[
"Apache-2.0"
] | 0
|
433ff8d8b5fec6c8bb3c346e5b8dfff2865f4a55
|
https://github.com/deeplearningrobotics/p1nav/tree/433ff8d8b5fec6c8bb3c346e5b8dfff2865f4a55
|
ScModel
|
import torch
import torch as t
import torch.nn as nn
from torch.nn.parameter import Parameter
class ScModel(nn.Module):
""" Model for singel cell data """
def __init__(self, n_genes: 'int', n_celltypes: 'int', device: 't.device'
) ->None:
super().__init__()
self.K = n_celltypes
self.G = n_genes
self.theta = Parameter(t.Tensor(self.G, self.K))
self.R = t.Tensor(self.G, self.K)
self.o = Parameter(t.Tensor(self.G, 1))
nn.init.normal_(self.o, mean=0.0, std=1.0)
nn.init.normal_(self.theta, mean=0.0, std=1.0)
self.nb = t.distributions.NegativeBinomial
self.softpl = nn.functional.softplus
self.logsig = nn.functional.logsigmoid
def _llnb(self, x: 't.Tensor', meta: 't.LongTensor', sf: 't.Tensor'
) ->t.Tensor:
"""Log Likelihood for NB-model
Returns the log likelihood for rates and logodds
taken as a function of the observed counts.
Assumes that single cell data is negative
binomial distributed.
Returns
-------
The log likelihood
"""
log_unnormalized_prob = sf * self.R[:, meta] * self.logsig(-self.o
) + x * self.logsig(self.o)
log_normalization = -t.lgamma(sf * self.R[:, meta] + x) + t.lgamma(
1.0 + x) + t.lgamma(sf * self.R[:, meta])
ll = t.sum(log_unnormalized_prob - log_normalization)
return ll
def forward(self, x: 't.Tensor', meta: 't.LongTensor', sf: 't.Tensor',
**kwargs) ->t.Tensor:
"""Forward pass during optimization"""
self.R = self.softpl(self.theta)
self.loss = -self._llnb(x.transpose(1, 0), meta, sf)
return self.loss
def __str__(self):
return 'sc_model'
def get_inputs():
return [torch.ones([4, 4], dtype=torch.int64), torch.ones([4], dtype=
torch.int64), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'n_genes': 4, 'n_celltypes': 4, 'device': 0}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch as t
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
@triton.jit
def triton_poi_fused_softplus_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 = 20.0
tmp2 = tmp0 > tmp1
tmp3 = tl_math.exp(tmp0)
tmp4 = libdevice.log1p(tmp3)
tmp5 = tl.where(tmp2, tmp0, tmp4)
tl.store(out_ptr0 + x0, tmp5, xmask)
@triton.jit
def triton_red_fused_add_index_lgamma_log_sigmoid_forward_mul_neg_sub_sum_1(
in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, xnumel,
rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
rnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rbase = tl.arange(0, RBLOCK)[None, :]
_tmp40 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r3 = rindex
r0 = rindex % 4
r1 = rindex // 4 % 4
tmp0 = tl.load(in_ptr0 + r3, rmask, eviction_policy='evict_first',
other=0.0)
tmp1 = tl.load(in_ptr1 + r0, rmask, eviction_policy='evict_last',
other=0.0)
tmp9 = tl.load(in_ptr3 + r1, rmask, eviction_policy='evict_last',
other=0.0)
tmp19 = tl.load(in_ptr4 + (r1 + 4 * r0), rmask, eviction_policy=
'evict_last', other=0.0)
tmp2 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp3 = tmp1 + tmp2
tmp4 = tmp1 < 0
tmp5 = tl.where(tmp4, tmp3, tmp1)
tl.device_assert((0 <= tmp5) & (tmp5 < 4) | ~rmask,
'index out of bounds: 0 <= tmp5 < 4')
tmp7 = tl.load(in_ptr2 + (tmp5 + 4 * r1), rmask, eviction_policy=
'evict_last', other=0.0)
tmp8 = tmp0 * tmp7
tmp10 = -tmp9
tmp11 = 0.0
tmp12 = triton_helpers.minimum(tmp11, tmp10)
tmp13 = tl_math.abs(tmp10)
tmp14 = -tmp13
tmp15 = tl_math.exp(tmp14)
tmp16 = libdevice.log1p(tmp15)
tmp17 = tmp12 - tmp16
tmp18 = tmp8 * tmp17
tmp20 = tmp19.to(tl.float32)
tmp21 = triton_helpers.minimum(tmp11, tmp9)
tmp22 = tl_math.abs(tmp9)
tmp23 = -tmp22
tmp24 = tl_math.exp(tmp23)
tmp25 = libdevice.log1p(tmp24)
tmp26 = tmp21 - tmp25
tmp27 = tmp20 * tmp26
tmp28 = tmp18 + tmp27
tmp29 = tmp8 + tmp20
tmp30 = libdevice.lgamma(tmp29)
tmp31 = -tmp30
tmp32 = 1.0
tmp33 = tmp20 + tmp32
tmp34 = libdevice.lgamma(tmp33)
tmp35 = tmp31 + tmp34
tmp36 = libdevice.lgamma(tmp8)
tmp37 = tmp35 + tmp36
tmp38 = tmp28 - tmp37
tmp39 = tl.broadcast_to(tmp38, [XBLOCK, RBLOCK])
tmp41 = _tmp40 + tmp39
_tmp40 = tl.where(rmask, tmp41, _tmp40)
tmp40 = tl.sum(_tmp40, 1)[:, None]
tmp42 = -tmp40
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp42, None)
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,), (1,))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (4, 1), (1, 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_softplus_0[grid(16)](primals_1, buf0, 16, XBLOCK=
16, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((), (), torch.float32)
buf3 = buf2
del buf2
triton_red_fused_add_index_lgamma_log_sigmoid_forward_mul_neg_sub_sum_1[
grid(1)](buf3, primals_4, primals_3, buf0, primals_5, primals_2,
1, 256, XBLOCK=1, RBLOCK=256, num_warps=8, num_stages=1)
return (buf3, buf0, primals_1, primals_2, primals_3, primals_4,
primals_5, buf0)
class ScModelNew(nn.Module):
""" Model for singel cell data """
def __init__(self, n_genes: 'int', n_celltypes: 'int', device: 't.device'
) ->None:
super().__init__()
self.K = n_celltypes
self.G = n_genes
self.theta = Parameter(t.Tensor(self.G, self.K))
self.R = t.Tensor(self.G, self.K)
self.o = Parameter(t.Tensor(self.G, 1))
nn.init.normal_(self.o, mean=0.0, std=1.0)
nn.init.normal_(self.theta, mean=0.0, std=1.0)
self.nb = t.distributions.NegativeBinomial
self.softpl = nn.functional.softplus
self.logsig = nn.functional.logsigmoid
def _llnb(self, x: 't.Tensor', meta: 't.LongTensor', sf: 't.Tensor'
) ->t.Tensor:
"""Log Likelihood for NB-model
Returns the log likelihood for rates and logodds
taken as a function of the observed counts.
Assumes that single cell data is negative
binomial distributed.
Returns
-------
The log likelihood
"""
log_unnormalized_prob = sf * self.R[:, meta] * self.logsig(-self.o
) + x * self.logsig(self.o)
log_normalization = -t.lgamma(sf * self.R[:, meta] + x) + t.lgamma(
1.0 + x) + t.lgamma(sf * self.R[:, meta])
ll = t.sum(log_unnormalized_prob - log_normalization)
return ll
def __str__(self):
return 'sc_model'
def forward(self, input_0, input_1, input_2):
primals_1 = self.theta
primals_5 = self.o
primals_2 = input_0
primals_3 = input_1
primals_4 = input_2
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
denizcangi/stereoscope
|
ScModel
| false
| 9,979
|
[
"MIT"
] | 0
|
cfe70e5d1e174dedd2d1a0c4a86ae0131e8e4218
|
https://github.com/denizcangi/stereoscope/tree/cfe70e5d1e174dedd2d1a0c4a86ae0131e8e4218
|
BertSelfAttention
|
from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidden size (%d) is not a multiple of the number of attention heads (%d)'
% (config.hidden_size, config.num_attention_heads))
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.
num_attention_heads)
self.all_head_size = (self.num_attention_heads * self.
attention_head_size)
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.
attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, hidden_states, attention_mask, history_states=None):
if history_states is None:
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_states)
else:
x_states = torch.cat((history_states, hidden_states), dim=1)
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(x_states)
mixed_value_layer = self.value(x_states)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1,
-2))
attention_scores = attention_scores / math.sqrt(self.
attention_head_size)
attention_scores = attention_scores + attention_mask
attention_probs = nn.Softmax(dim=-1)(attention_scores)
self.attention_probs = attention_probs
attention_probs = self.dropout(attention_probs)
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.
all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
return context_layer
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(hidden_size=4, num_attention_heads=
4, attention_probs_dropout_prob=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 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 + 4 * x2 + 16 * 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__softmax_add_div_1(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
x2 = xindex
x0 = xindex % 16
tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp15 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp17 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp5 * tmp1
tmp8 = tmp6 + tmp7
tmp9 = triton_helpers.maximum(tmp4, tmp8)
tmp11 = tmp10 * tmp1
tmp13 = tmp11 + tmp12
tmp14 = triton_helpers.maximum(tmp9, tmp13)
tmp16 = tmp15 * tmp1
tmp18 = tmp16 + tmp17
tmp19 = triton_helpers.maximum(tmp14, tmp18)
tmp20 = tmp4 - tmp19
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp8 - tmp19
tmp23 = tl_math.exp(tmp22)
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp19
tmp26 = tl_math.exp(tmp25)
tmp27 = tmp24 + tmp26
tmp28 = tmp18 - tmp19
tmp29 = tl_math.exp(tmp28)
tmp30 = tmp27 + tmp29
tl.store(out_ptr0 + x2, tmp19, xmask)
tl.store(out_ptr1 + x2, tmp30, xmask)
@triton.jit
def triton_poi_fused__softmax_add_div_2(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
x3 = xindex
x4 = xindex % 64
x5 = xindex // 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp3 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 - tmp5
tmp7 = tl_math.exp(tmp6)
tmp9 = tmp7 / tmp8
tl.store(in_out_ptr0 + x3, tmp9, xmask)
@triton.jit
def triton_poi_fused_clone_3(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,
primals_7, primals_8) = 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), (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,))
assert_size_stride(primals_8, (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_3, (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_3, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
del primals_4
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2)
del primals_6
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, primals_2, buf3, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_2
buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf0
triton_poi_fused_clone_0[grid(16, 4)](buf1, primals_5, buf4, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_5
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 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 64), 0)
del buf1
buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
triton_poi_fused__softmax_add_div_1[grid(64)](buf5, primals_8, buf6,
buf7, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf8 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused__softmax_add_div_2[grid(256)](buf8, primals_8,
buf6, buf7, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_8
buf9 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf7
triton_poi_fused_clone_0[grid(16, 4)](buf2, primals_7, buf9, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_7
buf10 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
del buf2
extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf9, (16, 4, 1), (4, 1, 0), 0), out=buf10)
buf11 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf6
triton_poi_fused_clone_3[grid(16, 4)](buf10, buf11, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
del buf10
return reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0
), buf8, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
), buf8, reinterpret_tensor(buf9, (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 BertSelfAttentionNew(nn.Module):
def __init__(self, config):
super(BertSelfAttentionNew, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidden size (%d) is not a multiple of the number of attention heads (%d)'
% (config.hidden_size, config.num_attention_heads))
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.
num_attention_heads)
self.all_head_size = (self.num_attention_heads * self.
attention_head_size)
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.
attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, input_0, input_1):
primals_1 = self.query.weight
primals_2 = self.query.bias
primals_4 = self.key.weight
primals_5 = self.key.bias
primals_6 = self.value.weight
primals_7 = self.value.bias
primals_3 = input_0
primals_8 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
Ago3/VLP
|
BertSelfAttention
| false
| 9,980
|
[
"Apache-2.0"
] | 0
|
4dec0e04b8592f4a74fe66c253dbb92574e7e2ba
|
https://github.com/Ago3/VLP/tree/4dec0e04b8592f4a74fe66c253dbb92574e7e2ba
|
TianzigeCNN
|
import torch
from torch import nn
from torch.nn import functional as f
class TianzigeCNN(nn.Module):
def __init__(self, dimension):
super().__init__()
self.conv1 = nn.Conv2d(3, 1024, 5)
self.relu = nn.ReLU(inplace=True)
self.max_pool = nn.MaxPool2d(4)
self.conv2 = nn.Conv2d(1024, 256, 1, groups=8)
self.conv3 = nn.Conv2d(256, dimension, 2, groups=16)
nn.init.kaiming_uniform_(self.conv1.weight)
nn.init.kaiming_uniform_(self.conv2.weight)
nn.init.kaiming_uniform_(self.conv3.weight)
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.max_pool(x)
x = self.conv2(x)
x = self.conv3(x)
x = f.adaptive_avg_pool2d(x, output_size=1).squeeze()
return x
def get_inputs():
return [torch.rand([4, 3, 64, 64])]
def get_init_inputs():
return [[], {'dimension': 32}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_ptr0, in_ptr1, 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
x1 = xindex // 3600 % 1024
x0 = xindex % 3600
x4 = xindex // 3600
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x0 + 3616 * x4), tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(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 % 15
x1 = xindex // 15 % 15
x2 = xindex // 225
x3 = xindex
tmp0 = tl.load(in_ptr0 + (4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (60 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (61 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (62 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp13 = tl.load(in_ptr0 + (63 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp15 = tl.load(in_ptr0 + (120 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp17 = tl.load(in_ptr0 + (121 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp19 = tl.load(in_ptr0 + (122 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp21 = tl.load(in_ptr0 + (123 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp23 = tl.load(in_ptr0 + (180 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp25 = tl.load(in_ptr0 + (181 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp27 = tl.load(in_ptr0 + (182 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp29 = tl.load(in_ptr0 + (183 + 4 * x0 + 240 * x1 + 3616 * x2), None,
eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp8 = triton_helpers.maximum(tmp7, tmp6)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tmp12 = triton_helpers.maximum(tmp11, tmp10)
tmp14 = triton_helpers.maximum(tmp13, tmp12)
tmp16 = triton_helpers.maximum(tmp15, tmp14)
tmp18 = triton_helpers.maximum(tmp17, tmp16)
tmp20 = triton_helpers.maximum(tmp19, tmp18)
tmp22 = triton_helpers.maximum(tmp21, tmp20)
tmp24 = triton_helpers.maximum(tmp23, tmp22)
tmp26 = triton_helpers.maximum(tmp25, tmp24)
tmp28 = triton_helpers.maximum(tmp27, tmp26)
tmp30 = triton_helpers.maximum(tmp29, tmp28)
tmp31 = tmp1 > tmp0
tmp32 = tl.full([1], 1, tl.int8)
tmp33 = tl.full([1], 0, tl.int8)
tmp34 = tl.where(tmp31, tmp32, tmp33)
tmp35 = tmp3 > tmp2
tmp36 = tl.full([1], 2, tl.int8)
tmp37 = tl.where(tmp35, tmp36, tmp34)
tmp38 = tmp5 > tmp4
tmp39 = tl.full([1], 3, tl.int8)
tmp40 = tl.where(tmp38, tmp39, tmp37)
tmp41 = tmp7 > tmp6
tmp42 = tl.full([1], 4, tl.int8)
tmp43 = tl.where(tmp41, tmp42, tmp40)
tmp44 = tmp9 > tmp8
tmp45 = tl.full([1], 5, tl.int8)
tmp46 = tl.where(tmp44, tmp45, tmp43)
tmp47 = tmp11 > tmp10
tmp48 = tl.full([1], 6, tl.int8)
tmp49 = tl.where(tmp47, tmp48, tmp46)
tmp50 = tmp13 > tmp12
tmp51 = tl.full([1], 7, tl.int8)
tmp52 = tl.where(tmp50, tmp51, tmp49)
tmp53 = tmp15 > tmp14
tmp54 = tl.full([1], 8, tl.int8)
tmp55 = tl.where(tmp53, tmp54, tmp52)
tmp56 = tmp17 > tmp16
tmp57 = tl.full([1], 9, tl.int8)
tmp58 = tl.where(tmp56, tmp57, tmp55)
tmp59 = tmp19 > tmp18
tmp60 = tl.full([1], 10, tl.int8)
tmp61 = tl.where(tmp59, tmp60, tmp58)
tmp62 = tmp21 > tmp20
tmp63 = tl.full([1], 11, tl.int8)
tmp64 = tl.where(tmp62, tmp63, tmp61)
tmp65 = tmp23 > tmp22
tmp66 = tl.full([1], 12, tl.int8)
tmp67 = tl.where(tmp65, tmp66, tmp64)
tmp68 = tmp25 > tmp24
tmp69 = tl.full([1], 13, tl.int8)
tmp70 = tl.where(tmp68, tmp69, tmp67)
tmp71 = tmp27 > tmp26
tmp72 = tl.full([1], 14, tl.int8)
tmp73 = tl.where(tmp71, tmp72, tmp70)
tmp74 = tmp29 > tmp28
tmp75 = tl.full([1], 15, tl.int8)
tmp76 = tl.where(tmp74, tmp75, tmp73)
tl.store(out_ptr0 + x3, tmp30, None)
tl.store(out_ptr1 + x3, tmp76, None)
@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 230400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 225 % 256
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_per_fused_convolution_mean_3(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 128
rnumel = 196
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
rmask = rindex < rnumel
r2 = rindex
x3 = xindex
x0 = xindex % 32
tmp0 = tl.load(in_ptr0 + (r2 + 196 * x3), rmask & xmask, other=0.0)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = tl.where(rmask & xmask, tmp3, 0)
tmp6 = tl.sum(tmp5, 1)[:, None]
tmp7 = 196.0
tmp8 = tmp6 / tmp7
tl.debug_barrier()
tl.store(in_out_ptr0 + x3, 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, (1024, 3, 5, 5), (75, 25, 5, 1))
assert_size_stride(primals_2, (1024,), (1,))
assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_4, (256, 128, 1, 1), (128, 1, 1, 1))
assert_size_stride(primals_5, (256,), (1,))
assert_size_stride(primals_6, (32, 16, 2, 2), (64, 4, 2, 1))
assert_size_stride(primals_7, (32,), (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, 1024, 60, 60), (3686400, 3600, 60, 1))
buf1 = empty_strided_cuda((4, 1024, 60, 60), (3702784, 3616, 60, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(14745600)](buf0, primals_2,
buf1, 14745600, XBLOCK=1024, num_warps=4, num_stages=1)
del buf0
del primals_2
buf2 = empty_strided_cuda((4, 1024, 15, 15), (230400, 225, 15, 1),
torch.float32)
buf3 = empty_strided_cuda((4, 1024, 15, 15), (230400, 225, 15, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_1[grid(921600)](buf1, buf2,
buf3, 921600, XBLOCK=512, num_warps=8, 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=8, bias=None)
assert_size_stride(buf4, (4, 256, 15, 15), (57600, 225, 15, 1))
buf5 = buf4
del buf4
triton_poi_fused_convolution_2[grid(230400)](buf5, primals_5,
230400, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_5
buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=16, bias=None)
assert_size_stride(buf6, (4, 32, 14, 14), (6272, 196, 14, 1))
buf7 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
float32)
buf8 = buf7
del buf7
triton_per_fused_convolution_mean_3[grid(128)](buf8, buf6,
primals_7, 128, 196, XBLOCK=1, num_warps=2, num_stages=1)
del buf6
del primals_7
return reinterpret_tensor(buf8, (4, 32), (32, 1), 0
), primals_1, primals_3, primals_4, primals_6, buf1, buf2, buf3, buf5
class TianzigeCNNNew(nn.Module):
def __init__(self, dimension):
super().__init__()
self.conv1 = nn.Conv2d(3, 1024, 5)
self.relu = nn.ReLU(inplace=True)
self.max_pool = nn.MaxPool2d(4)
self.conv2 = nn.Conv2d(1024, 256, 1, groups=8)
self.conv3 = nn.Conv2d(256, dimension, 2, groups=16)
nn.init.kaiming_uniform_(self.conv1.weight)
nn.init.kaiming_uniform_(self.conv2.weight)
nn.init.kaiming_uniform_(self.conv3.weight)
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_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
cmsflash/ocean-text
|
TianzigeCNN
| false
| 9,981
|
[
"MIT"
] | 0
|
d2f98077cb5e6949aec87f88a369ba4c2e99d178
|
https://github.com/cmsflash/ocean-text/tree/d2f98077cb5e6949aec87f88a369ba4c2e99d178
|
MaskedCrossEntropyCriterion
|
import torch
import torch.nn as nn
from torch.nn.modules.loss import _WeightedLoss
class MaskedCrossEntropyCriterion(_WeightedLoss):
def __init__(self, ignore_index=[-100], reduce=None):
super(MaskedCrossEntropyCriterion, self).__init__()
self.padding_idx = ignore_index
self.reduce = reduce
def forward(self, outputs, targets):
lprobs = nn.functional.log_softmax(outputs, dim=-1)
lprobs = lprobs.view(-1, lprobs.size(-1))
for idx in self.padding_idx:
targets[targets == idx] = 0
nll_loss = -lprobs.gather(dim=-1, index=targets.unsqueeze(1))
if self.reduce:
nll_loss = nll_loss.sum()
return nll_loss.squeeze()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.ones([4], dtype=torch.int64)]
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.nn.modules.loss import _WeightedLoss
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 = 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
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_gather_index_put_lift_fresh_neg_1(in_ptr0, in_ptr1,
out_ptr0, out_ptr1, 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 + x0, xmask)
tmp11 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp16 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp19 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp1 = tl.full([1], -100, tl.int64)
tmp2 = tmp0 == tmp1
tmp3 = tl.full([1], 0, tl.int64)
tmp4 = tl.where(tmp2, tmp3, tmp0)
tmp5 = tl.full([XBLOCK], 4, tl.int32)
tmp6 = tmp4 + tmp5
tmp7 = tmp4 < 0
tmp8 = tl.where(tmp7, tmp6, tmp4)
tl.device_assert((0 <= tmp8) & (tmp8 < 4) | ~xmask,
'index out of bounds: 0 <= tmp8 < 4')
tmp10 = tl.load(in_ptr1 + (tmp8 + 4 * x0), xmask, eviction_policy=
'evict_last')
tmp12 = tl_math.exp(tmp11)
tmp14 = tl_math.exp(tmp13)
tmp15 = tmp12 + tmp14
tmp17 = tl_math.exp(tmp16)
tmp18 = tmp15 + tmp17
tmp20 = tl_math.exp(tmp19)
tmp21 = tmp18 + tmp20
tmp22 = tl_math.log(tmp21)
tmp23 = tmp10 - tmp22
tmp24 = -tmp23
tl.store(out_ptr0 + x0, tmp4, xmask)
tl.store(out_ptr1 + x0, tmp24, 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,), (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_0[grid(256)](arg0_1, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
buf3 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
triton_poi_fused_gather_index_put_lift_fresh_neg_1[grid(4)](arg1_1,
buf0, arg1_1, buf3, 4, XBLOCK=4, num_warps=1, num_stages=1)
del arg1_1
del buf0
return reinterpret_tensor(buf3, (4,), (1,), 0),
class MaskedCrossEntropyCriterionNew(_WeightedLoss):
def __init__(self, ignore_index=[-100], reduce=None):
super(MaskedCrossEntropyCriterionNew, self).__init__()
self.padding_idx = ignore_index
self.reduce = reduce
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
dataJSA/batch7_tomorrow_datascience
|
MaskedCrossEntropyCriterion
| false
| 9,982
|
[
"MIT"
] | 0
|
e2dc6bc59c456fa927e0a1f6d12024ba410f520c
|
https://github.com/dataJSA/batch7_tomorrow_datascience/tree/e2dc6bc59c456fa927e0a1f6d12024ba410f520c
|
InstanceLayerNorm2d
|
import torch
import torch.nn as nn
class InstanceLayerNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.9,
using_moving_average=True, using_bn=False):
super(InstanceLayerNorm2d, self).__init__()
self.eps = eps
self.momentum = momentum
self.using_moving_average = using_moving_average
self.using_bn = using_bn
self.num_features = num_features
if self.using_bn:
self.rho = nn.Parameter(torch.Tensor(1, num_features, 3))
self.rho[:, :, 0].data.fill_(1)
self.rho[:, :, 1].data.fill_(3)
self.rho[:, :, 2].data.fill_(3)
self.register_buffer('running_mean', torch.zeros(1,
num_features, 1, 1))
self.register_buffer('running_var', torch.zeros(1, num_features,
1, 1))
self.running_mean.zero_()
self.running_var.zero_()
else:
self.rho = nn.Parameter(torch.Tensor(1, num_features, 2))
self.rho[:, :, 0].data.fill_(1)
self.rho[:, :, 1].data.fill_(3.2)
self.gamma = nn.Parameter(torch.Tensor(1, num_features, 1, 1))
self.beta = nn.Parameter(torch.Tensor(1, num_features, 1, 1))
self.gamma.data.fill_(1.0)
self.beta.data.fill_(0.0)
def forward(self, input):
in_mean, in_var = torch.mean(input, dim=[2, 3], keepdim=True
), torch.var(input, dim=[2, 3], keepdim=True)
out_in = (input - in_mean) / torch.sqrt(in_var + self.eps)
ln_mean, ln_var = torch.mean(input, dim=[1, 2, 3], keepdim=True
), torch.var(input, dim=[1, 2, 3], keepdim=True)
out_ln = (input - ln_mean) / torch.sqrt(ln_var + self.eps)
softmax = nn.Softmax(2)
rho = softmax(self.rho)
if self.using_bn:
if self.training:
bn_mean, bn_var = torch.mean(input, dim=[0, 2, 3], keepdim=True
), torch.var(input, dim=[0, 2, 3], keepdim=True)
if self.using_moving_average:
self.running_mean.mul_(self.momentum)
self.running_mean.add_((1 - self.momentum) * bn_mean.data)
self.running_var.mul_(self.momentum)
self.running_var.add_((1 - self.momentum) * bn_var.data)
else:
self.running_mean.add_(bn_mean.data)
self.running_var.add_(bn_mean.data ** 2 + bn_var.data)
else:
bn_mean = torch.autograd.Variable(self.running_mean)
bn_var = torch.autograd.Variable(self.running_var)
out_bn = (input - bn_mean) / torch.sqrt(bn_var + self.eps)
rho_0 = rho[:, :, 0]
rho_1 = rho[:, :, 1]
rho_2 = rho[:, :, 2]
rho_0 = rho_0.view(1, self.num_features, 1, 1)
rho_1 = rho_1.view(1, self.num_features, 1, 1)
rho_2 = rho_2.view(1, self.num_features, 1, 1)
rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
rho_2 = rho_2.expand(input.shape[0], -1, -1, -1)
out = rho_0 * out_in + rho_1 * out_ln + rho_2 * out_bn
else:
rho_0 = rho[:, :, 0]
rho_1 = rho[:, :, 1]
rho_0 = rho_0.view(1, self.num_features, 1, 1)
rho_1 = rho_1.view(1, self.num_features, 1, 1)
rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
out = rho_0 * out_in + rho_1 * out_ln
out = out * self.gamma.expand(input.shape[0], -1, -1, -1
) + self.beta.expand(input.shape[0], -1, -1, -1)
return out
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
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_per_fused_add_mean_sqrt_var_0(in_out_ptr0, in_out_ptr1, in_ptr0,
xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 4
RBLOCK: tl.constexpr = 64
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 + 64 * x0), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp8 = tl.where(xmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp10 = tl.full([XBLOCK, 1], 64, tl.int32)
tmp11 = tmp10.to(tl.float32)
tmp12 = tmp9 / tmp11
tmp13 = tmp1 - tmp12
tmp14 = tmp13 * tmp13
tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
tmp17 = tl.where(xmask, tmp15, 0)
tmp18 = tl.sum(tmp17, 1)[:, None]
tmp19 = 64.0
tmp20 = tmp4 / tmp19
tmp21 = 63.0
tmp22 = tmp18 / tmp21
tmp23 = 1e-05
tmp24 = tmp22 + tmp23
tmp25 = libdevice.sqrt(tmp24)
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp20, xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + x0, tmp25, xmask)
@triton.jit
def triton_per_fused_add_div_mean_mul_sqrt_sub_var_1(in_out_ptr0,
in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5,
out_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
x2 = xindex % 4
x3 = xindex // 4
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp26 = tl.load(in_ptr1 + 2 * x2, xmask, eviction_policy='evict_last')
tmp27 = tl.load(in_ptr1 + (1 + 2 * x2), xmask, eviction_policy='evict_last'
)
tmp39 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
tmp41 = tl.load(in_ptr3 + x3, xmask, eviction_policy='evict_last')
tmp45 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
tmp47 = tl.load(in_ptr5 + x2, xmask, eviction_policy='evict_last')
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp8 = tl.where(xmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp11 = tmp10.to(tl.float32)
tmp12 = tmp9 / tmp11
tmp13 = tmp1 - tmp12
tmp14 = tmp13 * tmp13
tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
tmp17 = tl.where(xmask, tmp15, 0)
tmp18 = tl.sum(tmp17, 1)[:, None]
tmp19 = 16.0
tmp20 = tmp4 / tmp19
tmp21 = 15.0
tmp22 = tmp18 / tmp21
tmp23 = 1e-05
tmp24 = tmp22 + tmp23
tmp25 = libdevice.sqrt(tmp24)
tmp28 = triton_helpers.maximum(tmp26, tmp27)
tmp29 = tmp26 - tmp28
tmp30 = tl_math.exp(tmp29)
tmp31 = tmp27 - tmp28
tmp32 = tl_math.exp(tmp31)
tmp33 = tmp30 + tmp32
tmp34 = tmp30 / tmp33
tmp35 = tmp0 - tmp20
tmp36 = tmp35 / tmp25
tmp37 = tmp34 * tmp36
tmp38 = tmp32 / tmp33
tmp40 = tmp0 - tmp39
tmp42 = tmp40 / tmp41
tmp43 = tmp38 * tmp42
tmp44 = tmp37 + tmp43
tmp46 = tmp44 * tmp45
tmp48 = tmp46 + tmp47
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp20, xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + x0, tmp25, xmask)
tl.store(out_ptr0 + (r1 + 16 * x0), tmp48, 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, (1, 4, 2), (8, 2, 1))
assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_4, (1, 4, 1, 1), (4, 1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf6 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
buf9 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
buf7 = reinterpret_tensor(buf6, (4, 1, 1, 1), (1, 1, 1, 1), 0)
del buf6
buf11 = reinterpret_tensor(buf9, (4, 1, 1, 1), (1, 1, 1, 1), 0)
del buf9
get_raw_stream(0)
triton_per_fused_add_mean_sqrt_var_0[grid(4)](buf7, buf11,
primals_1, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
del buf0
buf5 = reinterpret_tensor(buf3, (4, 4, 1, 1), (4, 1, 1, 1), 0)
del buf3
buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_per_fused_add_div_mean_mul_sqrt_sub_var_1[grid(16)](buf1,
buf5, primals_1, primals_2, buf7, buf11, primals_3, primals_4,
buf12, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
del primals_4
return buf12, primals_1, primals_2, primals_3, buf1, buf5, buf7, buf11
class InstanceLayerNorm2dNew(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.9,
using_moving_average=True, using_bn=False):
super(InstanceLayerNorm2dNew, self).__init__()
self.eps = eps
self.momentum = momentum
self.using_moving_average = using_moving_average
self.using_bn = using_bn
self.num_features = num_features
if self.using_bn:
self.rho = nn.Parameter(torch.Tensor(1, num_features, 3))
self.rho[:, :, 0].data.fill_(1)
self.rho[:, :, 1].data.fill_(3)
self.rho[:, :, 2].data.fill_(3)
self.register_buffer('running_mean', torch.zeros(1,
num_features, 1, 1))
self.register_buffer('running_var', torch.zeros(1, num_features,
1, 1))
self.running_mean.zero_()
self.running_var.zero_()
else:
self.rho = nn.Parameter(torch.Tensor(1, num_features, 2))
self.rho[:, :, 0].data.fill_(1)
self.rho[:, :, 1].data.fill_(3.2)
self.gamma = nn.Parameter(torch.Tensor(1, num_features, 1, 1))
self.beta = nn.Parameter(torch.Tensor(1, num_features, 1, 1))
self.gamma.data.fill_(1.0)
self.beta.data.fill_(0.0)
def forward(self, input_0):
primals_2 = self.rho
primals_3 = self.gamma
primals_4 = self.beta
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
belphegor2211/khoa_luan
|
InstanceLayerNorm2d
| false
| 9,983
|
[
"MIT"
] | 0
|
c9c163ebf3aff3005639ce7e4020e510295d1c75
|
https://github.com/belphegor2211/khoa_luan/tree/c9c163ebf3aff3005639ce7e4020e510295d1c75
|
Message_Passing_Unit_v1
|
import torch
from torchvision.transforms import functional as F
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class Message_Passing_Unit_v1(nn.Module):
def __init__(self, fea_size, filter_size=128):
super(Message_Passing_Unit_v1, self).__init__()
self.w = nn.Linear(fea_size * 2, filter_size, bias=True)
self.fea_size = fea_size
self.filter_size = filter_size
def forward(self, unary_term, pair_term):
if unary_term.size()[0] == 1 and pair_term.size()[0] > 1:
unary_term = unary_term.expand(pair_term.size()[0], unary_term.
size()[1])
if unary_term.size()[0] > 1 and pair_term.size()[0] == 1:
pair_term = pair_term.expand(unary_term.size()[0], pair_term.
size()[1])
gate = torch.cat([unary_term, pair_term], 1)
gate = F.relu(gate)
gate = torch.sigmoid(self.w(gate)).mean(1)
output = pair_term * gate.view(-1, 1).expand(gate.size()[0],
pair_term.size()[1])
return output
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'fea_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.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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_relu_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
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
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tmp11 = tl.full([1], 0, tl.int32)
tmp12 = triton_helpers.maximum(tmp11, tmp10)
tl.store(out_ptr0 + x2, tmp12, xmask)
@triton.jit
def triton_per_fused_mean_sigmoid_1(in_ptr0, out_ptr0, xnumel, rnumel,
XBLOCK: tl.constexpr):
xnumel = 4
RBLOCK: tl.constexpr = 128
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 + 128 * x0), xmask, other=0.0)
tmp1 = tl.sigmoid(tmp0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = tl.where(xmask, tmp2, 0)
tmp5 = tl.sum(tmp4, 1)[:, None]
tl.store(out_ptr0 + x0, tmp5, xmask)
@triton.jit
def triton_poi_fused_mul_2(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
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = 128.0
tmp3 = tmp1 / tmp2
tmp4 = tmp0 * tmp3
tl.store(out_ptr0 + x2, tmp4, 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, (128, 8), (8, 1))
assert_size_stride(primals_4, (128,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_relu_0[grid(32)](primals_1, primals_2, buf0,
32, XBLOCK=32, num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
extern_kernels.addmm(primals_4, buf0, reinterpret_tensor(primals_3,
(8, 128), (1, 8), 0), alpha=1, beta=1, out=buf1)
del primals_3
del primals_4
buf2 = empty_strided_cuda((4,), (1,), torch.float32)
triton_per_fused_mean_sigmoid_1[grid(4)](buf1, buf2, 4, 128, XBLOCK
=1, num_warps=2, num_stages=1)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_mul_2[grid(16)](primals_2, buf2, buf3, 16, XBLOCK=
16, num_warps=1, num_stages=1)
del buf2
return buf3, primals_2, buf0, buf1
class Message_Passing_Unit_v1New(nn.Module):
def __init__(self, fea_size, filter_size=128):
super(Message_Passing_Unit_v1New, self).__init__()
self.w = nn.Linear(fea_size * 2, filter_size, bias=True)
self.fea_size = fea_size
self.filter_size = filter_size
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]
|
champon1020/scene_graph_benchmark
|
Message_Passing_Unit_v1
| false
| 9,984
|
[
"MIT"
] | 0
|
970a7499f8fa2854810bd650f6c991bcad5748db
|
https://github.com/champon1020/scene_graph_benchmark/tree/970a7499f8fa2854810bd650f6c991bcad5748db
|
CosMargin
|
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class CosMargin(nn.Module):
def __init__(self, in_size, out_size, s=None, m=0.0):
super(CosMargin, self).__init__()
self.in_size = in_size
self.out_size = out_size
self.W = nn.Parameter(torch.randn(out_size, in_size), requires_grad
=True)
self.W.data.uniform_(-1, 1).renorm_(2, 1, 1e-05).mul_(100000.0)
self.s = nn.Parameter(torch.randn(1), requires_grad=True
) if s is None else s
self.m = m
def forward(self, x, label=None):
cosine = F.linear(F.normalize(x), F.normalize(self.W))
if label is not None and math.fabs(self.m) > 1e-06:
one_hot = torch.zeros_like(cosine)
one_hot.scatter_(1, label.view(-1, 1), 1.0)
output = (cosine - one_hot * self.m) * self.s
else:
output = cosine * self.s
return output
def __repr__(self):
return (self.__class__.__name__ +
'(in_size={}, out_size={}, s={}, m={})'.format(self.in_size,
self.out_size, 'learn' if isinstance(self.s, nn.Parameter) else
self.s, 'learn' if isinstance(self.m, nn.Parameter) else self.m))
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._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_div_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-12
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + x3, tmp15, xmask)
@triton.jit
def triton_poi_fused_div_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-12
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + x2, tmp15, xmask)
@triton.jit
def triton_poi_fused_mul_2(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 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 * tmp2
tl.store(out_ptr0 + x0, tmp3, 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, 1))
assert_size_stride(primals_3, (1,), (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_div_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_div_1[grid(16)](primals_2, 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(buf0, (64, 4), (4, 1), 0),
reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2)
del buf1
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_mul_2[grid(256)](buf2, primals_3, buf3, 256,
XBLOCK=256, num_warps=4, num_stages=1)
return buf3, primals_2, primals_3, reinterpret_tensor(buf0, (64, 4), (4,
1), 0), buf2
class CosMarginNew(nn.Module):
def __init__(self, in_size, out_size, s=None, m=0.0):
super(CosMarginNew, self).__init__()
self.in_size = in_size
self.out_size = out_size
self.W = nn.Parameter(torch.randn(out_size, in_size), requires_grad
=True)
self.W.data.uniform_(-1, 1).renorm_(2, 1, 1e-05).mul_(100000.0)
self.s = nn.Parameter(torch.randn(1), requires_grad=True
) if s is None else s
self.m = m
def __repr__(self):
return (self.__class__.__name__ +
'(in_size={}, out_size={}, s={}, m={})'.format(self.in_size,
self.out_size, 'learn' if isinstance(self.s, nn.Parameter) else
self.s, 'learn' if isinstance(self.m, nn.Parameter) else self.m))
def forward(self, input_0):
primals_2 = self.W
primals_3 = self.s
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
belphegor2211/khoa_luan
|
CosMargin
| false
| 9,985
|
[
"MIT"
] | 0
|
c9c163ebf3aff3005639ce7e4020e510295d1c75
|
https://github.com/belphegor2211/khoa_luan/tree/c9c163ebf3aff3005639ce7e4020e510295d1c75
|
MultiheadAttention
|
import torch
import torch.nn as nn
from torch.nn import Parameter
import torch.nn.functional as F
def fill_with_neg_inf(t):
"""FP16-compatible function that fills a tensor with -inf."""
return t.float().fill_(float('-inf')).type_as(t)
def _get_full_incremental_state_key(module_instance, key):
module_name = module_instance.__class__.__name__
if not hasattr(module_instance, '_fairseq_instance_id'):
INCREMENTAL_STATE_INSTANCE_ID[module_name] += 1
module_instance._fairseq_instance_id = INCREMENTAL_STATE_INSTANCE_ID[
module_name]
return '{}.{}.{}'.format(module_name, module_instance.
_fairseq_instance_id, key)
def get_incremental_state(module, incremental_state, key):
"""Helper for getting incremental state for an nn.Module."""
full_key = _get_full_incremental_state_key(module, key)
if incremental_state is None or full_key not in incremental_state:
return None
return incremental_state[full_key]
def set_incremental_state(module, incremental_state, key, value):
"""Helper for setting incremental state for an nn.Module."""
if incremental_state is not None:
full_key = _get_full_incremental_state_key(module, key)
incremental_state[full_key] = value
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, 'embed_dim must be divisible by num_heads'
self.scaling = self.head_dim ** -0.5
self._mask = None
self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim))
if bias:
self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim))
else:
self.register_parameter('in_proj_bias', None)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.reset_parameters()
def reset_parameters(self):
nn.init.xavier_uniform_(self.in_proj_weight)
nn.init.xavier_uniform_(self.out_proj.weight)
if self.in_proj_bias is not None:
nn.init.constant_(self.in_proj_bias, 0.0)
nn.init.constant_(self.out_proj.bias, 0.0)
def forward(self, query, key, value, mask_future_timesteps=False,
key_padding_mask=None, incremental_state=None, need_weights=True,
static_kv=False):
"""Input shape: Time x Batch x Channel
Self-attention can be implemented by passing in the same arguments for
query, key and value. Future timesteps can be masked with the
`mask_future_timesteps` argument. Padding elements can be excluded from
the key by passing a binary ByteTensor (`key_padding_mask`) with shape:
batch x src_len, where padding elements are indicated by 1s.
"""
qkv_same = query.data_ptr() == key.data_ptr() == value.data_ptr()
kv_same = key.data_ptr() == value.data_ptr()
tgt_len, bsz, embed_dim = query.size()
assert embed_dim == self.embed_dim
assert list(query.size()) == [tgt_len, bsz, embed_dim]
assert key.size() == value.size()
if incremental_state is not None:
saved_state = self._get_input_buffer(incremental_state)
if 'prev_key' in saved_state:
if static_kv:
assert kv_same and not qkv_same
key = value = None
else:
saved_state = None
if qkv_same:
q, k, v = self.in_proj_qkv(query)
elif kv_same:
q = self.in_proj_q(query)
if key is None:
assert value is None
k = v = q.new(0)
else:
k, v = self.in_proj_kv(key)
else:
q = self.in_proj_q(query)
k = self.in_proj_k(key)
v = self.in_proj_v(value)
q *= self.scaling
if saved_state is not None:
if 'prev_key' in saved_state:
k = torch.cat((saved_state['prev_key'], k), dim=0)
if 'prev_value' in saved_state:
v = torch.cat((saved_state['prev_value'], v), dim=0)
saved_state['prev_key'] = k
saved_state['prev_value'] = v
self._set_input_buffer(incremental_state, saved_state)
src_len = k.size(0)
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == src_len
q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim
).transpose(0, 1)
k = k.contiguous().view(src_len, bsz * self.num_heads, self.head_dim
).transpose(0, 1)
v = v.contiguous().view(src_len, bsz * self.num_heads, self.head_dim
).transpose(0, 1)
attn_weights = torch.bmm(q, k.transpose(1, 2))
assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len,
src_len]
if mask_future_timesteps and incremental_state is None:
assert query.size() == key.size(
), 'mask_future_timesteps only applies to self-attention'
attn_weights += self.buffered_mask(attn_weights).unsqueeze(0)
if key_padding_mask is not None:
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len,
src_len)
attn_weights = attn_weights.float().masked_fill(key_padding_mask
.unsqueeze(1).unsqueeze(2), float('-inf')).type_as(attn_weights
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len,
src_len)
attn_weights = F.softmax(attn_weights.float(), dim=-1).type_as(
attn_weights)
attn_weights = F.dropout(attn_weights, p=self.dropout, training=
self.training)
attn = torch.bmm(attn_weights, v)
assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.
head_dim]
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn = self.out_proj(attn)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.sum(dim=1) / self.num_heads
return attn, attn_weights
def in_proj_qkv(self, query):
return self._in_proj(query).chunk(3, dim=-1)
def in_proj_kv(self, key):
return self._in_proj(key, start=self.embed_dim).chunk(2, dim=-1)
def in_proj_q(self, query):
return self._in_proj(query, end=self.embed_dim)
def in_proj_k(self, key):
return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim)
def in_proj_v(self, value):
return self._in_proj(value, start=2 * self.embed_dim)
def _in_proj(self, input, start=None, end=None):
weight = self.in_proj_weight
bias = self.in_proj_bias
if end is not None:
weight = weight[:end, :]
if bias is not None:
bias = bias[:end]
if start is not None:
weight = weight[start:, :]
if bias is not None:
bias = bias[start:]
return F.linear(input, weight, bias)
def buffered_mask(self, tensor):
dim = tensor.size(-1)
if self._mask is None:
self._mask = torch.triu(fill_with_neg_inf(tensor.new(dim, dim)), 1)
if self._mask.size(0) < dim:
self._mask = torch.triu(fill_with_neg_inf(self._mask.resize_(
dim, dim)), 1)
return self._mask[:dim, :dim]
def reorder_incremental_state(self, incremental_state, new_order):
"""Reorder buffered internal state (for incremental generation)."""
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
for k in input_buffer.keys():
input_buffer[k] = input_buffer[k].index_select(1, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return get_incremental_state(self, incremental_state, 'attn_state'
) or {}
def _set_input_buffer(self, incremental_state, buffer):
set_incremental_state(self, incremental_state, 'attn_state', buffer)
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4])
]
def get_init_inputs():
return [[], {'embed_dim': 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
from torch.nn import Parameter
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_mul_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 = 1.0
tmp4 = tmp2 * tmp3
tl.store(in_out_ptr0 + x2, tmp4, 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 = 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 = 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)
@triton.jit
def triton_poi_fused_clone_3(in_ptr0, 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
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (x1 + 16 * y0), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_div_sum_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
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 = 0.25
tmp8 = tmp6 * 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, (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), (16, 4, 1))
assert_size_stride(primals_4, (12, 4), (4, 1))
assert_size_stride(primals_5, (12,), (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((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf0)
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(reinterpret_tensor(primals_5, (4,), (1,), 4),
reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 16), alpha=1,
beta=1, out=buf1)
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(reinterpret_tensor(primals_5, (4,), (1,), 8),
reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 32), alpha=1,
beta=1, out=buf2)
del primals_4
buf3 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_mul_0[grid(64)](buf3, primals_5, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (1, 16, 0),
0), reinterpret_tensor(buf1, (16, 1, 4), (1, 1, 16), 0), out=buf4)
buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__softmax_1[grid(256)](buf4, buf5, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf6 = buf4
del buf4
triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del buf5
buf7 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
extern_kernels.bmm(buf6, reinterpret_tensor(buf2, (16, 4, 1), (1,
16, 1), 0), out=buf7)
buf8 = empty_strided_cuda((4, 16, 1), (16, 1, 1), torch.float32)
triton_poi_fused_clone_3[grid(4, 16)](buf7, buf8, 4, 16, XBLOCK=16,
YBLOCK=4, num_warps=1, num_stages=1)
buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0)
del buf7
extern_kernels.addmm(primals_7, reinterpret_tensor(buf8, (16, 4), (
4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf9)
del primals_7
buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_div_sum_4[grid(64)](buf6, buf10, 64, XBLOCK=64,
num_warps=1, num_stages=1)
return reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0
), buf10, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
), buf6, reinterpret_tensor(buf8, (16, 4), (4, 1), 0
), primals_6, reinterpret_tensor(buf2, (16, 1, 4), (1, 1, 16), 0
), reinterpret_tensor(buf3, (16, 1, 4), (1, 1, 16), 0
), reinterpret_tensor(buf1, (16, 4, 1), (1, 16, 1), 0)
def fill_with_neg_inf(t):
"""FP16-compatible function that fills a tensor with -inf."""
return t.float().fill_(float('-inf')).type_as(t)
def _get_full_incremental_state_key(module_instance, key):
module_name = module_instance.__class__.__name__
if not hasattr(module_instance, '_fairseq_instance_id'):
INCREMENTAL_STATE_INSTANCE_ID[module_name] += 1
module_instance._fairseq_instance_id = INCREMENTAL_STATE_INSTANCE_ID[
module_name]
return '{}.{}.{}'.format(module_name, module_instance.
_fairseq_instance_id, key)
def get_incremental_state(module, incremental_state, key):
"""Helper for getting incremental state for an nn.Module."""
full_key = _get_full_incremental_state_key(module, key)
if incremental_state is None or full_key not in incremental_state:
return None
return incremental_state[full_key]
def set_incremental_state(module, incremental_state, key, value):
"""Helper for setting incremental state for an nn.Module."""
if incremental_state is not None:
full_key = _get_full_incremental_state_key(module, key)
incremental_state[full_key] = value
class MultiheadAttentionNew(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, 'embed_dim must be divisible by num_heads'
self.scaling = self.head_dim ** -0.5
self._mask = None
self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim))
if bias:
self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim))
else:
self.register_parameter('in_proj_bias', None)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.reset_parameters()
def reset_parameters(self):
nn.init.xavier_uniform_(self.in_proj_weight)
nn.init.xavier_uniform_(self.out_proj.weight)
if self.in_proj_bias is not None:
nn.init.constant_(self.in_proj_bias, 0.0)
nn.init.constant_(self.out_proj.bias, 0.0)
def in_proj_qkv(self, query):
return self._in_proj(query).chunk(3, dim=-1)
def in_proj_kv(self, key):
return self._in_proj(key, start=self.embed_dim).chunk(2, dim=-1)
def in_proj_q(self, query):
return self._in_proj(query, end=self.embed_dim)
def in_proj_k(self, key):
return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim)
def in_proj_v(self, value):
return self._in_proj(value, start=2 * self.embed_dim)
def _in_proj(self, input, start=None, end=None):
weight = self.in_proj_weight
bias = self.in_proj_bias
if end is not None:
weight = weight[:end, :]
if bias is not None:
bias = bias[:end]
if start is not None:
weight = weight[start:, :]
if bias is not None:
bias = bias[start:]
return F.linear(input, weight, bias)
def buffered_mask(self, tensor):
dim = tensor.size(-1)
if self._mask is None:
self._mask = torch.triu(fill_with_neg_inf(tensor.new(dim, dim)), 1)
if self._mask.size(0) < dim:
self._mask = torch.triu(fill_with_neg_inf(self._mask.resize_(
dim, dim)), 1)
return self._mask[:dim, :dim]
def reorder_incremental_state(self, incremental_state, new_order):
"""Reorder buffered internal state (for incremental generation)."""
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
for k in input_buffer.keys():
input_buffer[k] = input_buffer[k].index_select(1, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return get_incremental_state(self, incremental_state, 'attn_state'
) or {}
def _set_input_buffer(self, incremental_state, buffer):
set_incremental_state(self, incremental_state, 'attn_state', buffer)
def forward(self, input_0, input_1, input_2):
primals_4 = self.in_proj_weight
primals_5 = self.in_proj_bias
primals_6 = self.out_proj.weight
primals_7 = self.out_proj.bias
primals_1 = input_0
primals_2 = input_1
primals_3 = input_2
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0], output[1]
|
dataJSA/batch7_tomorrow_datascience
|
MultiheadAttention
| false
| 9,986
|
[
"MIT"
] | 0
|
e2dc6bc59c456fa927e0a1f6d12024ba410f520c
|
https://github.com/dataJSA/batch7_tomorrow_datascience/tree/e2dc6bc59c456fa927e0a1f6d12024ba410f520c
|
Model
|
import torch
import torch.nn as nn
import torch._C
import torch.serialization
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(2, 2, 1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 2, 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
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_convolution_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 // 4096 % 2
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 = args
args.clear()
assert_size_stride(primals_1, (2, 2, 1, 1), (2, 1, 1, 1))
assert_size_stride(primals_2, (2,), (1,))
assert_size_stride(primals_3, (4, 2, 64, 64), (8192, 4096, 64, 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, 2, 64, 64), (8192, 4096, 64, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(32768)](buf1, primals_2, 32768,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
return buf1, primals_1, primals_3
class ModelNew(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(2, 2, 1)
def forward(self, input_0):
primals_1 = self.conv.weight
primals_2 = self.conv.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
devolfnn/mmsegmentation
|
Model
| false
| 9,987
|
[
"Apache-2.0"
] | 0
|
c0dccc1725b80b643419cc008cb93e8dcb4209c8
|
https://github.com/devolfnn/mmsegmentation/tree/c0dccc1725b80b643419cc008cb93e8dcb4209c8
|
DiceBCELoss
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class DiceBCELoss(nn.Module):
def __init__(self):
super(DiceBCELoss, self).__init__()
def forward(self, predicted, target):
batch = predicted.size()[0]
batch_loss = 0
smooth = 1
for index in range(batch):
pre = predicted[index]
tar = target[index]
intersection = torch.mul(pre, tar).sum()
coefficient = (2 * intersection + smooth) / (pre.sum() + tar.
sum() + smooth)
batch_loss += coefficient
batch_loss = batch_loss / batch
BCE = F.binary_cross_entropy(predicted, target)
Dice_BCE = BCE + (1 - batch_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
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_binary_cross_entropy_0(in_ptr0, in_ptr1, out_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)
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))
tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp15, None)
@triton.jit
def triton_per_fused_add_binary_cross_entropy_div_mul_rsub_sum_1(in_out_ptr1,
in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 64
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 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp12 = tl.load(in_ptr0 + (64 + r0), None)
tmp13 = tl.load(in_ptr1 + (64 + r0), None)
tmp24 = tl.load(in_ptr0 + (128 + r0), None)
tmp25 = tl.load(in_ptr1 + (128 + r0), None)
tmp36 = tl.load(in_ptr0 + (192 + r0), None)
tmp37 = tl.load(in_ptr1 + (192 + r0), None)
tmp75 = tl.load(in_out_ptr1 + 0)
tmp76 = tl.broadcast_to(tmp75, [XBLOCK, 1])
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = tl.sum(tmp3, 1)[:, None]
tmp6 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp8 = tl.sum(tmp6, 1)[:, None]
tmp9 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp11 = tl.sum(tmp9, 1)[:, None]
tmp14 = tmp12 * tmp13
tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
tmp17 = tl.sum(tmp15, 1)[:, None]
tmp18 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp20 = tl.sum(tmp18, 1)[:, None]
tmp21 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
tmp23 = tl.sum(tmp21, 1)[:, None]
tmp26 = tmp24 * tmp25
tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
tmp29 = tl.sum(tmp27, 1)[:, None]
tmp30 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
tmp32 = tl.sum(tmp30, 1)[:, None]
tmp33 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
tmp35 = tl.sum(tmp33, 1)[:, None]
tmp38 = tmp36 * tmp37
tmp39 = tl.broadcast_to(tmp38, [XBLOCK, RBLOCK])
tmp41 = tl.sum(tmp39, 1)[:, None]
tmp42 = tl.broadcast_to(tmp36, [XBLOCK, RBLOCK])
tmp44 = tl.sum(tmp42, 1)[:, None]
tmp45 = tl.broadcast_to(tmp37, [XBLOCK, RBLOCK])
tmp47 = tl.sum(tmp45, 1)[:, None]
tmp48 = 2.0
tmp49 = tmp5 * tmp48
tmp50 = 1.0
tmp51 = tmp49 + tmp50
tmp52 = tmp8 + tmp11
tmp53 = tmp52 + tmp50
tmp54 = tmp51 / tmp53
tmp55 = 0.0
tmp56 = tmp54 + tmp55
tmp57 = tmp17 * tmp48
tmp58 = tmp57 + tmp50
tmp59 = tmp20 + tmp23
tmp60 = tmp59 + tmp50
tmp61 = tmp58 / tmp60
tmp62 = tmp56 + tmp61
tmp63 = tmp29 * tmp48
tmp64 = tmp63 + tmp50
tmp65 = tmp32 + tmp35
tmp66 = tmp65 + tmp50
tmp67 = tmp64 / tmp66
tmp68 = tmp62 + tmp67
tmp69 = tmp41 * tmp48
tmp70 = tmp69 + tmp50
tmp71 = tmp44 + tmp47
tmp72 = tmp71 + tmp50
tmp73 = tmp70 / tmp72
tmp74 = tmp68 + tmp73
tmp77 = 256.0
tmp78 = tmp76 / tmp77
tmp79 = 0.25
tmp80 = tmp74 * tmp79
tmp81 = tmp50 - tmp80
tmp82 = tmp78 + tmp81
tl.debug_barrier()
tl.store(in_out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp82, 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)
get_raw_stream(0)
triton_per_fused_binary_cross_entropy_0[grid(1)](arg1_1, arg0_1,
buf0, 1, 256, num_warps=2, num_stages=1)
buf14 = buf0
del buf0
triton_per_fused_add_binary_cross_entropy_div_mul_rsub_sum_1[grid(1)](
buf14, arg0_1, arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf14,
class DiceBCELossNew(nn.Module):
def __init__(self):
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]
|
daoducanhc/Tumor_Segmentation
|
DiceBCELoss
| false
| 9,988
|
[
"MIT"
] | 0
|
485a70492f7efb65a0f88f61a0eeffd6f0c92cc9
|
https://github.com/daoducanhc/Tumor_Segmentation/tree/485a70492f7efb65a0f88f61a0eeffd6f0c92cc9
|
_ASPP
|
import torch
import torch.nn as nn
class _ASPP(nn.Module):
"""
Atrous spatial pyramid pooling (ASPP)
"""
def __init__(self, in_ch, out_ch, rates):
super(_ASPP, self).__init__()
self.aspp_num = len(rates)
for i, rate in enumerate(rates):
self.add_module('c{}'.format(i), nn.Conv2d(in_ch, out_ch, 3, 1,
padding=rate, dilation=rate, bias=True))
self.add_module('bcm', nn.Conv2d(in_ch, out_ch, 1))
for m in self.children():
nn.init.normal_(m.weight, mean=0, std=0.01)
nn.init.constant_(m.bias, 0)
def forward(self, x):
aspp = [stage(x) for stage in self.children()]
bcm = aspp[-1]
return sum(aspp[:self.aspp_num]), bcm
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_ch': 4, 'out_ch': 4, 'rates': [4, 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
@triton.jit
def triton_poi_fused_convolution_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
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')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_add_convolution_1(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
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')
tmp5 = tl.load(in_ptr1 + x3, xmask)
tmp6 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 + tmp3
tmp7 = tmp5 + tmp6
tmp8 = tmp4 + tmp7
tl.store(in_out_ptr0 + x3, 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, (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, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(4, 4), dilation=(4, 4), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = extern_kernels.convolution(primals_3, primals_4, stride=(1,
1), padding=(4, 4), dilation=(4, 4), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = extern_kernels.convolution(primals_3, primals_6, 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, 4, 4), (64, 16, 4, 1))
buf3 = buf2
del buf2
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(256)](buf3, primals_7, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
buf4 = buf0
del buf0
triton_poi_fused_add_convolution_1[grid(256)](buf4, primals_2, buf1,
primals_5, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf1
del primals_2
del primals_5
return buf4, buf3, primals_1, primals_3, primals_4, primals_6
class _ASPPNew(nn.Module):
"""
Atrous spatial pyramid pooling (ASPP)
"""
def __init__(self, in_ch, out_ch, rates):
super(_ASPPNew, self).__init__()
self.aspp_num = len(rates)
for i, rate in enumerate(rates):
self.add_module('c{}'.format(i), nn.Conv2d(in_ch, out_ch, 3, 1,
padding=rate, dilation=rate, bias=True))
self.add_module('bcm', nn.Conv2d(in_ch, out_ch, 1))
for m in self.children():
nn.init.normal_(m.weight, mean=0, std=0.01)
nn.init.constant_(m.bias, 0)
def forward(self, input_0):
primals_1 = self.c0.weight
primals_2 = self.c0.bias
primals_4 = self.c1.weight
primals_5 = self.c1.bias
primals_6 = self.bcm.weight
primals_7 = self.bcm.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0], output[1]
|
developfeng/BCM
|
_ASPP
| false
| 9,989
|
[
"BSD-3-Clause-Attribution"
] | 0
|
8eb5ac950a2d67d10fc707519bb66cd9ea4f14f2
|
https://github.com/developfeng/BCM/tree/8eb5ac950a2d67d10fc707519bb66cd9ea4f14f2
|
AR
|
import torch
import torch.nn as nn
class AR(nn.Module):
def __init__(self, window):
super(AR, self).__init__()
self.linear = nn.Linear(window, 1)
def forward(self, x):
x = torch.transpose(x, 1, 2)
x = self.linear(x)
x = torch.transpose(x, 1, 2)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'window': 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_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_add_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
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
tl.store(in_out_ptr0 + x0, tmp3, 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, (1, 4), (4, 1))
assert_size_stride(primals_3, (1,), (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_clone_0[grid(256)](primals_1, buf0, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 1), (1, 4), 0), out=buf1)
del primals_2
buf2 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf1
triton_poi_fused_add_1[grid(64)](buf2, primals_3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_3
return reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 1, 4, 1), 0
), reinterpret_tensor(buf0, (64, 4), (4, 1), 0)
class ARNew(nn.Module):
def __init__(self, window):
super(ARNew, self).__init__()
self.linear = nn.Linear(window, 1)
def forward(self, input_0):
primals_2 = self.linear.weight
primals_3 = self.linear.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
chenghaoliu89/TSForecasting_FT
|
AR
| false
| 9,990
|
[
"MIT"
] | 0
|
e29227e67f754919672eab9002a1b37b13ed28a0
|
https://github.com/chenghaoliu89/TSForecasting_FT/tree/e29227e67f754919672eab9002a1b37b13ed28a0
|
MODEL
|
from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class MODEL(nn.Module):
def __init__(self, args):
super(MODEL, self).__init__()
self.fc = nn.Linear(args.in_dim, 1)
self.sigmoid = nn.Sigmoid()
nn.init.constant_(self.fc.weight, 0)
nn.init.constant_(self.fc.bias, 0)
def forward(self, x):
return self.sigmoid(self.fc(x)).flatten()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'args': _mock_config(in_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
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_sigmoid_sigmoid_backward_0(in_out_ptr0, 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
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)
tmp5 = 1.0
tmp6 = tmp5 - tmp4
tmp7 = tmp4 * tmp6
tl.store(in_out_ptr0 + x0, tmp4, xmask)
tl.store(out_ptr0 + x0, tmp7, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (1, 4), (4, 1))
assert_size_stride(primals_2, (1,), (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, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf0
buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_sigmoid_sigmoid_backward_0[grid(64)](buf1,
primals_2, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
del primals_2
return reinterpret_tensor(buf1, (64,), (1,), 0), reinterpret_tensor(
primals_3, (64, 4), (4, 1), 0), buf2
class MODELNew(nn.Module):
def __init__(self, args):
super(MODELNew, self).__init__()
self.fc = nn.Linear(args.in_dim, 1)
self.sigmoid = nn.Sigmoid()
nn.init.constant_(self.fc.weight, 0)
nn.init.constant_(self.fc.bias, 0)
def forward(self, input_0):
primals_1 = self.fc.weight
primals_2 = self.fc.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
cuis15/xorder
|
MODEL
| false
| 9,991
|
[
"MIT"
] | 0
|
6dde5a18552ffa07f29100038464a38c49495527
|
https://github.com/cuis15/xorder/tree/6dde5a18552ffa07f29100038464a38c49495527
|
AmdimNCELoss
|
import torch
import torch.nn as nn
def tanh_clip(x, clip_val=10.0):
"""
soft clip values to the range [-clip_val, +clip_val]
"""
if clip_val is not None:
x_clip = clip_val * torch.tanh(1.0 / clip_val * x)
else:
x_clip = x
return x_clip
class AmdimNCELoss(nn.Module):
"""
Compute the NCE scores for predicting r_src->r_trg.
"""
def __init__(self, tclip):
super().__init__()
self.tclip = tclip
def forward(self, anchor_representations, positive_representations,
mask_mat):
"""
Args:
anchor_representations: (batch_size, emb_dim)
positive_representations: (emb_dim, n_batch * w* h) (ie: nb_feat_vectors x embedding_dim)
mask_mat: (n_batch_gpu, n_batch)
Output:
raw_scores: (n_batch_gpu, n_locs)
nce_scores: (n_batch_gpu, n_locs)
lgt_reg : scalar
"""
r_src = anchor_representations
r_trg = positive_representations
batch_size, emb_dim = r_src.size()
nb_feat_vectors = r_trg.size(1) // batch_size
mask_pos = mask_mat.unsqueeze(dim=2).expand(-1, -1, nb_feat_vectors
).float()
mask_neg = 1.0 - mask_pos
raw_scores = torch.mm(r_src, r_trg).float()
raw_scores = raw_scores.reshape(batch_size, batch_size, nb_feat_vectors
)
raw_scores = raw_scores / emb_dim ** 0.5
lgt_reg = 0.05 * (raw_scores ** 2.0).mean()
raw_scores = tanh_clip(raw_scores, clip_val=self.tclip)
"""
pos_scores includes scores for all the positive samples
neg_scores includes scores for all the negative samples, with
scores for positive samples set to the min score (-self.tclip here)
"""
pos_scores = (mask_pos * raw_scores).sum(dim=1)
neg_scores = mask_neg * raw_scores - self.tclip * mask_pos
neg_scores = neg_scores.reshape(batch_size, -1)
mask_neg = mask_neg.reshape(batch_size, -1)
neg_maxes = torch.max(neg_scores, dim=1, keepdim=True)[0]
neg_sumexp = (mask_neg * torch.exp(neg_scores - neg_maxes)).sum(dim
=1, keepdim=True)
all_logsumexp = torch.log(torch.exp(pos_scores - neg_maxes) +
neg_sumexp)
pos_shiftexp = pos_scores - neg_maxes
nce_scores = pos_shiftexp - all_logsumexp
nce_scores = -nce_scores.mean()
return nce_scores, lgt_reg
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'tclip': 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
@triton.jit
def triton_per_fused_add_div_exp_log_max_mean_mul_neg_sub_sum_tanh_0(
in_out_ptr0, in_ptr0, in_ptr1, 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 + 4 * r0, None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
tmp27 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
tmp36 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
tmp38 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
tmp1 = 1.0
tmp2 = tmp1 - tmp0
tmp4 = 0.5
tmp5 = tmp3 * tmp4
tmp6 = 0.25
tmp7 = tmp5 * tmp6
tmp8 = libdevice.tanh(tmp7)
tmp9 = 4.0
tmp10 = tmp8 * tmp9
tmp11 = tmp2 * tmp10
tmp12 = tmp0 * tmp9
tmp13 = tmp11 - tmp12
tmp15 = tmp1 - tmp14
tmp17 = tmp16 * tmp4
tmp18 = tmp17 * tmp6
tmp19 = libdevice.tanh(tmp18)
tmp20 = tmp19 * tmp9
tmp21 = tmp15 * tmp20
tmp22 = tmp14 * tmp9
tmp23 = tmp21 - tmp22
tmp24 = triton_helpers.maximum(tmp13, tmp23)
tmp26 = tmp1 - tmp25
tmp28 = tmp27 * tmp4
tmp29 = tmp28 * tmp6
tmp30 = libdevice.tanh(tmp29)
tmp31 = tmp30 * tmp9
tmp32 = tmp26 * tmp31
tmp33 = tmp25 * tmp9
tmp34 = tmp32 - tmp33
tmp35 = triton_helpers.maximum(tmp24, tmp34)
tmp37 = tmp1 - tmp36
tmp39 = tmp38 * tmp4
tmp40 = tmp39 * tmp6
tmp41 = libdevice.tanh(tmp40)
tmp42 = tmp41 * tmp9
tmp43 = tmp37 * tmp42
tmp44 = tmp36 * tmp9
tmp45 = tmp43 - tmp44
tmp46 = triton_helpers.maximum(tmp35, tmp45)
tmp47 = tmp13 - tmp46
tmp48 = tl_math.exp(tmp47)
tmp49 = tmp2 * tmp48
tmp50 = tmp23 - tmp46
tmp51 = tl_math.exp(tmp50)
tmp52 = tmp15 * tmp51
tmp53 = tmp49 + tmp52
tmp54 = tmp34 - tmp46
tmp55 = tl_math.exp(tmp54)
tmp56 = tmp26 * tmp55
tmp57 = tmp53 + tmp56
tmp58 = tmp45 - tmp46
tmp59 = tl_math.exp(tmp58)
tmp60 = tmp37 * tmp59
tmp61 = tmp57 + tmp60
tmp62 = tmp0 * tmp10
tmp63 = tmp14 * tmp20
tmp64 = tmp62 + tmp63
tmp65 = tmp25 * tmp31
tmp66 = tmp64 + tmp65
tmp67 = tmp36 * tmp42
tmp68 = tmp66 + tmp67
tmp69 = tmp68 - tmp46
tmp70 = tl_math.exp(tmp69)
tmp71 = tmp70 + tmp61
tmp72 = tl_math.log(tmp71)
tmp73 = tmp69 - tmp72
tmp74 = tl.broadcast_to(tmp73, [XBLOCK, RBLOCK])
tmp76 = tl.sum(tmp74, 1)[:, None]
tmp77 = tmp76 / tmp9
tmp78 = -tmp77
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp78, None)
@triton.jit
def triton_per_fused_div_mean_mul_pow_1(in_out_ptr0, in_ptr0, 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
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = tmp2 * tmp2
tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp6 = tl.sum(tmp4, 1)[:, None]
tmp7 = 16.0
tmp8 = tmp6 / tmp7
tmp9 = 0.05
tmp10 = tmp8 * tmp9
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp10, None)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4), (4, 1))
assert_size_stride(arg1_1, (4, 4), (4, 1))
assert_size_stride(arg2_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(arg0_1, arg1_1, out=buf0)
del arg0_1
del arg1_1
buf4 = empty_strided_cuda((), (), torch.float32)
buf6 = buf4
del buf4
get_raw_stream(0)
triton_per_fused_add_div_exp_log_max_mean_mul_neg_sub_sum_tanh_0[grid
(1)](buf6, arg2_1, buf0, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
del arg2_1
buf5 = empty_strided_cuda((), (), torch.float32)
buf7 = buf5
del buf5
triton_per_fused_div_mean_mul_pow_1[grid(1)](buf7, buf0, 1, 16,
XBLOCK=1, num_warps=2, num_stages=1)
del buf0
return buf6, buf7
def tanh_clip(x, clip_val=10.0):
"""
soft clip values to the range [-clip_val, +clip_val]
"""
if clip_val is not None:
x_clip = clip_val * torch.tanh(1.0 / clip_val * x)
else:
x_clip = x
return x_clip
class AmdimNCELossNew(nn.Module):
"""
Compute the NCE scores for predicting r_src->r_trg.
"""
def __init__(self, tclip):
super().__init__()
self.tclip = tclip
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]
|
bartolkaruza/pytorch-lightning-bolts
|
AmdimNCELoss
| false
| 9,992
|
[
"Apache-2.0"
] | 0
|
2e903c333c37ea83394c7da2ce826de1b82fb356
|
https://github.com/bartolkaruza/pytorch-lightning-bolts/tree/2e903c333c37ea83394c7da2ce826de1b82fb356
|
Net5
|
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Net5(nn.Module):
def __init__(self, n_in, n_out, dropout_p=0.0):
super(Net5, self).__init__()
self.insize = n_in
self.outsize = n_out
self.drop = dropout_p
if self.drop != 0.0:
self.dropout_layer = nn.Dropout(p=self.drop)
self.conv1 = nn.Conv2d(1, 4, 6, padding=3)
self.pool = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(4, 2, 3, padding=3)
_N, C_out, H_out, W_out = self.conv_2d_output_shape(1, 1, self.
insize, self.insize, self.conv1)
_N, C_out, H_out, W_out = self.conv_2d_output_shape(1, 4, int(H_out /
2), int(W_out / 2), self.conv2)
self.linear_one_input = C_out * int(H_out / 2) * int(W_out / 2)
self.linear_one_output = int(self.linear_one_input / 2)
self.l1 = nn.Linear(self.linear_one_input, self.linear_one_output)
self.l2 = nn.Linear(self.linear_one_output, 30)
self.l3 = nn.Linear(30, self.outsize)
def conv_2d_output_shape(self, N, C, H, W, conv):
C_out = conv.out_channels
H_out = H + 2 * conv.padding[0] - conv.dilation[0] * (conv.
kernel_size[0] - 1) - 1
H_out = H_out / conv.stride[0] + 1
H_out = int(np.floor(H_out))
W_out = W + 2 * conv.padding[1] - conv.dilation[1] * (conv.
kernel_size[1] - 1) - 1
W_out = W_out / conv.stride[1] + 1
W_out = int(np.floor(W_out))
return N, C_out, H_out, W_out
def forward(self, x, train=True):
if self.drop != 0.0 and train:
x = self.dropout_layer(x)
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, self.linear_one_input)
x = F.relu(self.l1(x))
x = F.relu(self.l2(x))
x = self.l3(x)
return x
def get_inputs():
return [torch.rand([4, 1, 64, 64])]
def get_init_inputs():
return [[], {'n_in': 4, 'n_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 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_relu_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 67600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 4225 % 4
x0 = xindex % 4225
x4 = xindex // 4225
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)
tl.store(out_ptr0 + (x0 + 4256 * x4), tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(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 % 32
x1 = xindex // 32 % 32
x2 = xindex // 1024
x3 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 130 * x1 + 4256 * x2), None,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 130 * x1 + 4256 * x2), None,
eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (65 + 2 * x0 + 130 * x1 + 4256 * x2), None,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (66 + 2 * x0 + 130 * x1 + 4256 * x2), None,
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 + x3, tmp6, None)
tl.store(out_ptr1 + x3, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 10368
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 1296 % 2
x0 = xindex % 1296
x4 = xindex // 1296
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)
tl.store(out_ptr0 + (x0 + 1312 * x4), tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 2592
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 + (2 * x0 + 72 * x1 + 1312 * x2), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 72 * x1 + 1312 * x2), xmask,
eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (36 + 2 * x0 + 72 * x1 + 1312 * x2), xmask,
eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (37 + 2 * x0 + 72 * x1 + 1312 * x2), 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 + x3, tmp15, xmask)
tl.store(out_ptr1 + x3, tmp16, xmask)
@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 1296
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 9
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 = 4320
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 30
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, (4, 1, 6, 6), (36, 36, 6, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
assert_size_stride(primals_4, (2, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (2,), (1,))
assert_size_stride(primals_6, (9, 18), (18, 1))
assert_size_stride(primals_7, (9,), (1,))
assert_size_stride(primals_8, (30, 9), (9, 1))
assert_size_stride(primals_9, (30,), (1,))
assert_size_stride(primals_10, (4, 30), (30, 1))
assert_size_stride(primals_11, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(3, 3), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 65, 65), (16900, 4225, 65, 1))
buf1 = empty_strided_cuda((4, 4, 65, 65), (17024, 4256, 65, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(67600)](buf0, primals_2,
buf1, 67600, XBLOCK=1024, num_warps=4, num_stages=1)
del buf0
del primals_2
buf2 = empty_strided_cuda((4, 4, 32, 32), (4096, 1024, 32, 1),
torch.float32)
buf3 = empty_strided_cuda((4, 4, 32, 32), (4096, 1024, 32, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_1[grid(16384)](buf1, buf2,
buf3, 16384, XBLOCK=128, num_warps=4, num_stages=1)
buf4 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(3, 3), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 2, 36, 36), (2592, 1296, 36, 1))
buf5 = empty_strided_cuda((4, 2, 36, 36), (2624, 1312, 36, 1),
torch.float32)
triton_poi_fused_convolution_relu_2[grid(10368)](buf4, primals_5,
buf5, 10368, XBLOCK=256, num_warps=4, num_stages=1)
del buf4
del primals_5
buf6 = empty_strided_cuda((4, 2, 18, 18), (648, 324, 18, 1), torch.int8
)
buf7 = empty_strided_cuda((4, 2, 18, 18), (648, 324, 18, 1), torch.
float32)
triton_poi_fused_max_pool2d_with_indices_3[grid(2592)](buf5, buf6,
buf7, 2592, XBLOCK=256, num_warps=4, num_stages=1)
buf8 = empty_strided_cuda((144, 9), (9, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf7, (144, 18), (18, 1), 0),
reinterpret_tensor(primals_6, (18, 9), (1, 18), 0), out=buf8)
buf9 = buf8
del buf8
triton_poi_fused_relu_4[grid(1296)](buf9, primals_7, 1296, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_7
buf10 = empty_strided_cuda((144, 30), (30, 1), torch.float32)
extern_kernels.mm(buf9, reinterpret_tensor(primals_8, (9, 30), (1,
9), 0), out=buf10)
buf11 = buf10
del buf10
triton_poi_fused_relu_5[grid(4320)](buf11, primals_9, 4320, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_9
buf12 = empty_strided_cuda((144, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_11, buf11, reinterpret_tensor(
primals_10, (30, 4), (1, 30), 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, (144, 18), (18, 1), 0), buf9, buf11,
primals_10, primals_8, primals_6)
class Net5New(nn.Module):
def __init__(self, n_in, n_out, dropout_p=0.0):
super(Net5New, self).__init__()
self.insize = n_in
self.outsize = n_out
self.drop = dropout_p
if self.drop != 0.0:
self.dropout_layer = nn.Dropout(p=self.drop)
self.conv1 = nn.Conv2d(1, 4, 6, padding=3)
self.pool = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(4, 2, 3, padding=3)
_N, C_out, H_out, W_out = self.conv_2d_output_shape(1, 1, self.
insize, self.insize, self.conv1)
_N, C_out, H_out, W_out = self.conv_2d_output_shape(1, 4, int(H_out /
2), int(W_out / 2), self.conv2)
self.linear_one_input = C_out * int(H_out / 2) * int(W_out / 2)
self.linear_one_output = int(self.linear_one_input / 2)
self.l1 = nn.Linear(self.linear_one_input, self.linear_one_output)
self.l2 = nn.Linear(self.linear_one_output, 30)
self.l3 = nn.Linear(30, self.outsize)
def conv_2d_output_shape(self, N, C, H, W, conv):
C_out = conv.out_channels
H_out = H + 2 * conv.padding[0] - conv.dilation[0] * (conv.
kernel_size[0] - 1) - 1
H_out = H_out / conv.stride[0] + 1
H_out = int(np.floor(H_out))
W_out = W + 2 * conv.padding[1] - conv.dilation[1] * (conv.
kernel_size[1] - 1) - 1
W_out = W_out / conv.stride[1] + 1
W_out = int(np.floor(W_out))
return N, C_out, H_out, W_out
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.l1.weight
primals_7 = self.l1.bias
primals_8 = self.l2.weight
primals_9 = self.l2.bias
primals_10 = self.l3.weight
primals_11 = self.l3.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]
|
derangedhk417/ML-Lessons
|
Net5
| false
| 9,993
|
[
"MIT"
] | 0
|
3433e3fa6324791b74771fcfd8a6c5361ba69c53
|
https://github.com/derangedhk417/ML-Lessons/tree/3433e3fa6324791b74771fcfd8a6c5361ba69c53
|
Conv2dBlock
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class AdaptiveInstanceLayerNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.9,
using_moving_average=True, using_bn=False):
super(AdaptiveInstanceLayerNorm2d, self).__init__()
self.eps = eps
self.momentum = momentum
self.using_moving_average = using_moving_average
self.using_bn = using_bn
self.num_features = num_features
if self.using_bn:
self.rho = nn.Parameter(torch.Tensor(1, num_features, 3))
self.rho[:, :, 0].data.fill_(3)
self.rho[:, :, 1].data.fill_(1)
self.rho[:, :, 2].data.fill_(1)
self.register_buffer('running_mean', torch.zeros(1,
num_features, 1, 1))
self.register_buffer('running_var', torch.zeros(1, num_features,
1, 1))
self.running_mean.zero_()
self.running_var.zero_()
else:
self.rho = nn.Parameter(torch.Tensor(1, num_features, 2))
self.rho[:, :, 0].data.fill_(3.2)
self.rho[:, :, 1].data.fill_(1)
self.weight = None
self.bias = None
def forward(self, input):
assert self.weight is not None and self.bias is not None, 'Please assign AdaILN weight first'
in_mean, in_var = torch.mean(input, dim=[2, 3], keepdim=True
), torch.var(input, dim=[2, 3], keepdim=True)
out_in = (input - in_mean) / torch.sqrt(in_var + self.eps)
ln_mean, ln_var = torch.mean(input, dim=[1, 2, 3], keepdim=True
), torch.var(input, dim=[1, 2, 3], keepdim=True)
out_ln = (input - ln_mean) / torch.sqrt(ln_var + self.eps)
softmax = nn.Softmax(2)
rho = softmax(self.rho)
if self.using_bn:
if self.training:
bn_mean, bn_var = torch.mean(input, dim=[0, 2, 3], keepdim=True
), torch.var(input, dim=[0, 2, 3], keepdim=True)
if self.using_moving_average:
self.running_mean.mul_(self.momentum)
self.running_mean.add_((1 - self.momentum) * bn_mean.data)
self.running_var.mul_(self.momentum)
self.running_var.add_((1 - self.momentum) * bn_var.data)
else:
self.running_mean.add_(bn_mean.data)
self.running_var.add_(bn_mean.data ** 2 + bn_var.data)
else:
bn_mean = torch.autograd.Variable(self.running_mean)
bn_var = torch.autograd.Variable(self.running_var)
out_bn = (input - bn_mean) / torch.sqrt(bn_var + self.eps)
rho_0 = rho[:, :, 0]
rho_1 = rho[:, :, 1]
rho_2 = rho[:, :, 2]
rho_0 = rho_0.view(1, self.num_features, 1, 1)
rho_1 = rho_1.view(1, self.num_features, 1, 1)
rho_2 = rho_2.view(1, self.num_features, 1, 1)
rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
rho_2 = rho_2.expand(input.shape[0], -1, -1, -1)
out = rho_0 * out_in + rho_1 * out_ln + rho_2 * out_bn
else:
rho_0 = rho[:, :, 0]
rho_1 = rho[:, :, 1]
rho_0 = rho_0.view(1, self.num_features, 1, 1)
rho_1 = rho_1.view(1, self.num_features, 1, 1)
rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
out = rho_0 * out_in + rho_1 * out_ln
out = out * self.weight.unsqueeze(2).unsqueeze(3
) + self.bias.unsqueeze(2).unsqueeze(3)
return out
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.weight = None
self.bias = None
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
def forward(self, x):
assert self.weight is not None and self.bias is not None, 'Please assign AdaIN weight first'
b, c = x.size(0), x.size(1)
running_mean = self.running_mean.repeat(b)
running_var = self.running_var.repeat(b)
x_reshaped = x.contiguous().view(1, b * c, *x.size()[2:])
out = F.batch_norm(x_reshaped, running_mean, running_var, self.
weight, self.bias, True, self.momentum, self.eps)
return out.view(b, c, *x.size()[2:])
def __repr__(self):
return self.__class__.__name__ + '(' + str(self.num_features) + ')'
class Identity(nn.Module):
def forward(self, x):
return x
class InstanceLayerNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.9,
using_moving_average=True, using_bn=False):
super(InstanceLayerNorm2d, self).__init__()
self.eps = eps
self.momentum = momentum
self.using_moving_average = using_moving_average
self.using_bn = using_bn
self.num_features = num_features
if self.using_bn:
self.rho = nn.Parameter(torch.Tensor(1, num_features, 3))
self.rho[:, :, 0].data.fill_(1)
self.rho[:, :, 1].data.fill_(3)
self.rho[:, :, 2].data.fill_(3)
self.register_buffer('running_mean', torch.zeros(1,
num_features, 1, 1))
self.register_buffer('running_var', torch.zeros(1, num_features,
1, 1))
self.running_mean.zero_()
self.running_var.zero_()
else:
self.rho = nn.Parameter(torch.Tensor(1, num_features, 2))
self.rho[:, :, 0].data.fill_(1)
self.rho[:, :, 1].data.fill_(3.2)
self.gamma = nn.Parameter(torch.Tensor(1, num_features, 1, 1))
self.beta = nn.Parameter(torch.Tensor(1, num_features, 1, 1))
self.gamma.data.fill_(1.0)
self.beta.data.fill_(0.0)
def forward(self, input):
in_mean, in_var = torch.mean(input, dim=[2, 3], keepdim=True
), torch.var(input, dim=[2, 3], keepdim=True)
out_in = (input - in_mean) / torch.sqrt(in_var + self.eps)
ln_mean, ln_var = torch.mean(input, dim=[1, 2, 3], keepdim=True
), torch.var(input, dim=[1, 2, 3], keepdim=True)
out_ln = (input - ln_mean) / torch.sqrt(ln_var + self.eps)
softmax = nn.Softmax(2)
rho = softmax(self.rho)
if self.using_bn:
if self.training:
bn_mean, bn_var = torch.mean(input, dim=[0, 2, 3], keepdim=True
), torch.var(input, dim=[0, 2, 3], keepdim=True)
if self.using_moving_average:
self.running_mean.mul_(self.momentum)
self.running_mean.add_((1 - self.momentum) * bn_mean.data)
self.running_var.mul_(self.momentum)
self.running_var.add_((1 - self.momentum) * bn_var.data)
else:
self.running_mean.add_(bn_mean.data)
self.running_var.add_(bn_mean.data ** 2 + bn_var.data)
else:
bn_mean = torch.autograd.Variable(self.running_mean)
bn_var = torch.autograd.Variable(self.running_var)
out_bn = (input - bn_mean) / torch.sqrt(bn_var + self.eps)
rho_0 = rho[:, :, 0]
rho_1 = rho[:, :, 1]
rho_2 = rho[:, :, 2]
rho_0 = rho_0.view(1, self.num_features, 1, 1)
rho_1 = rho_1.view(1, self.num_features, 1, 1)
rho_2 = rho_2.view(1, self.num_features, 1, 1)
rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
rho_2 = rho_2.expand(input.shape[0], -1, -1, -1)
out = rho_0 * out_in + rho_1 * out_ln + rho_2 * out_bn
else:
rho_0 = rho[:, :, 0]
rho_1 = rho[:, :, 1]
rho_0 = rho_0.view(1, self.num_features, 1, 1)
rho_1 = rho_1.view(1, self.num_features, 1, 1)
rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
out = rho_0 * out_in + rho_1 * out_ln
out = out * self.gamma.expand(input.shape[0], -1, -1, -1
) + self.beta.expand(input.shape[0], -1, -1, -1)
return out
class Conv2dBlock(nn.Module):
def __init__(self, in_dim, out_dim, ks, st, padding=0, norm='none',
activation='relu', pad_type='zero', use_bias=True, activation_first
=False, groups=1, sn=False):
super(Conv2dBlock, self).__init__()
self.use_bias = use_bias
self.activation_first = activation_first
if padding == 0:
self.pad = Identity()
elif pad_type == 'reflect':
self.pad = nn.ReflectionPad2d(padding)
elif pad_type == 'replicate':
self.pad = nn.ReplicationPad2d(padding)
elif pad_type == 'zero':
self.pad = nn.ZeroPad2d(padding)
else:
assert 0, 'Unsupported padding type: {}'.format(pad_type)
norm_dim = out_dim
if norm == 'bn':
self.norm = nn.BatchNorm2d(norm_dim)
elif norm == 'gn':
self.norm = nn.GroupNorm(4, norm_dim, 0.8)
elif norm == 'in':
self.norm = nn.InstanceNorm2d(norm_dim)
elif norm == 'adain':
self.norm = AdaptiveInstanceNorm2d(norm_dim)
elif norm == 'iln':
self.norm = InstanceLayerNorm2d(norm_dim)
elif norm == 'adailn':
self.norm = AdaptiveInstanceLayerNorm2d(norm_dim)
elif norm == 'none':
self.norm = None
else:
assert 0, 'Unsupported normalization: {}'.format(norm)
if activation == 'relu':
self.activation = nn.ReLU(inplace=False)
elif activation == 'lrelu':
self.activation = nn.LeakyReLU(0.2, inplace=False)
elif activation == 'tanh':
self.activation = nn.Tanh()
elif activation == 'none':
self.activation = None
else:
assert 0, 'Unsupported activation: {}'.format(activation)
if sn:
self.conv = nn.utils.spectral_norm(nn.Conv2d(in_dim, out_dim,
ks, st, bias=self.use_bias, groups=groups))
else:
self.conv = nn.Conv2d(in_dim, out_dim, ks, st, bias=self.
use_bias, groups=groups)
def forward(self, x):
if self.activation_first:
if self.activation:
x = self.activation(x)
x = self.conv(self.pad(x))
if self.norm:
x = self.norm(x)
else:
x = self.conv(self.pad(x))
if self.norm:
x = self.norm(x)
if self.activation:
x = self.activation(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_dim': 4, 'out_dim': 4, 'ks': 4, 'st': 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.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_convolution_relu_threshold_backward_0(in_out_ptr0,
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
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, 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))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4,
4), 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
buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_convolution_relu_threshold_backward_0[grid(16)](buf1,
primals_2, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_2
return buf1, primals_1, primals_3, buf2
class AdaptiveInstanceLayerNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.9,
using_moving_average=True, using_bn=False):
super(AdaptiveInstanceLayerNorm2d, self).__init__()
self.eps = eps
self.momentum = momentum
self.using_moving_average = using_moving_average
self.using_bn = using_bn
self.num_features = num_features
if self.using_bn:
self.rho = nn.Parameter(torch.Tensor(1, num_features, 3))
self.rho[:, :, 0].data.fill_(3)
self.rho[:, :, 1].data.fill_(1)
self.rho[:, :, 2].data.fill_(1)
self.register_buffer('running_mean', torch.zeros(1,
num_features, 1, 1))
self.register_buffer('running_var', torch.zeros(1, num_features,
1, 1))
self.running_mean.zero_()
self.running_var.zero_()
else:
self.rho = nn.Parameter(torch.Tensor(1, num_features, 2))
self.rho[:, :, 0].data.fill_(3.2)
self.rho[:, :, 1].data.fill_(1)
self.weight = None
self.bias = None
def forward(self, input):
assert self.weight is not None and self.bias is not None, 'Please assign AdaILN weight first'
in_mean, in_var = torch.mean(input, dim=[2, 3], keepdim=True
), torch.var(input, dim=[2, 3], keepdim=True)
out_in = (input - in_mean) / torch.sqrt(in_var + self.eps)
ln_mean, ln_var = torch.mean(input, dim=[1, 2, 3], keepdim=True
), torch.var(input, dim=[1, 2, 3], keepdim=True)
out_ln = (input - ln_mean) / torch.sqrt(ln_var + self.eps)
softmax = nn.Softmax(2)
rho = softmax(self.rho)
if self.using_bn:
if self.training:
bn_mean, bn_var = torch.mean(input, dim=[0, 2, 3], keepdim=True
), torch.var(input, dim=[0, 2, 3], keepdim=True)
if self.using_moving_average:
self.running_mean.mul_(self.momentum)
self.running_mean.add_((1 - self.momentum) * bn_mean.data)
self.running_var.mul_(self.momentum)
self.running_var.add_((1 - self.momentum) * bn_var.data)
else:
self.running_mean.add_(bn_mean.data)
self.running_var.add_(bn_mean.data ** 2 + bn_var.data)
else:
bn_mean = torch.autograd.Variable(self.running_mean)
bn_var = torch.autograd.Variable(self.running_var)
out_bn = (input - bn_mean) / torch.sqrt(bn_var + self.eps)
rho_0 = rho[:, :, 0]
rho_1 = rho[:, :, 1]
rho_2 = rho[:, :, 2]
rho_0 = rho_0.view(1, self.num_features, 1, 1)
rho_1 = rho_1.view(1, self.num_features, 1, 1)
rho_2 = rho_2.view(1, self.num_features, 1, 1)
rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
rho_2 = rho_2.expand(input.shape[0], -1, -1, -1)
out = rho_0 * out_in + rho_1 * out_ln + rho_2 * out_bn
else:
rho_0 = rho[:, :, 0]
rho_1 = rho[:, :, 1]
rho_0 = rho_0.view(1, self.num_features, 1, 1)
rho_1 = rho_1.view(1, self.num_features, 1, 1)
rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
out = rho_0 * out_in + rho_1 * out_ln
out = out * self.weight.unsqueeze(2).unsqueeze(3
) + self.bias.unsqueeze(2).unsqueeze(3)
return out
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.weight = None
self.bias = None
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
def forward(self, x):
assert self.weight is not None and self.bias is not None, 'Please assign AdaIN weight first'
b, c = x.size(0), x.size(1)
running_mean = self.running_mean.repeat(b)
running_var = self.running_var.repeat(b)
x_reshaped = x.contiguous().view(1, b * c, *x.size()[2:])
out = F.batch_norm(x_reshaped, running_mean, running_var, self.
weight, self.bias, True, self.momentum, self.eps)
return out.view(b, c, *x.size()[2:])
def __repr__(self):
return self.__class__.__name__ + '(' + str(self.num_features) + ')'
class Identity(nn.Module):
def forward(self, x):
return x
class InstanceLayerNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.9,
using_moving_average=True, using_bn=False):
super(InstanceLayerNorm2d, self).__init__()
self.eps = eps
self.momentum = momentum
self.using_moving_average = using_moving_average
self.using_bn = using_bn
self.num_features = num_features
if self.using_bn:
self.rho = nn.Parameter(torch.Tensor(1, num_features, 3))
self.rho[:, :, 0].data.fill_(1)
self.rho[:, :, 1].data.fill_(3)
self.rho[:, :, 2].data.fill_(3)
self.register_buffer('running_mean', torch.zeros(1,
num_features, 1, 1))
self.register_buffer('running_var', torch.zeros(1, num_features,
1, 1))
self.running_mean.zero_()
self.running_var.zero_()
else:
self.rho = nn.Parameter(torch.Tensor(1, num_features, 2))
self.rho[:, :, 0].data.fill_(1)
self.rho[:, :, 1].data.fill_(3.2)
self.gamma = nn.Parameter(torch.Tensor(1, num_features, 1, 1))
self.beta = nn.Parameter(torch.Tensor(1, num_features, 1, 1))
self.gamma.data.fill_(1.0)
self.beta.data.fill_(0.0)
def forward(self, input):
in_mean, in_var = torch.mean(input, dim=[2, 3], keepdim=True
), torch.var(input, dim=[2, 3], keepdim=True)
out_in = (input - in_mean) / torch.sqrt(in_var + self.eps)
ln_mean, ln_var = torch.mean(input, dim=[1, 2, 3], keepdim=True
), torch.var(input, dim=[1, 2, 3], keepdim=True)
out_ln = (input - ln_mean) / torch.sqrt(ln_var + self.eps)
softmax = nn.Softmax(2)
rho = softmax(self.rho)
if self.using_bn:
if self.training:
bn_mean, bn_var = torch.mean(input, dim=[0, 2, 3], keepdim=True
), torch.var(input, dim=[0, 2, 3], keepdim=True)
if self.using_moving_average:
self.running_mean.mul_(self.momentum)
self.running_mean.add_((1 - self.momentum) * bn_mean.data)
self.running_var.mul_(self.momentum)
self.running_var.add_((1 - self.momentum) * bn_var.data)
else:
self.running_mean.add_(bn_mean.data)
self.running_var.add_(bn_mean.data ** 2 + bn_var.data)
else:
bn_mean = torch.autograd.Variable(self.running_mean)
bn_var = torch.autograd.Variable(self.running_var)
out_bn = (input - bn_mean) / torch.sqrt(bn_var + self.eps)
rho_0 = rho[:, :, 0]
rho_1 = rho[:, :, 1]
rho_2 = rho[:, :, 2]
rho_0 = rho_0.view(1, self.num_features, 1, 1)
rho_1 = rho_1.view(1, self.num_features, 1, 1)
rho_2 = rho_2.view(1, self.num_features, 1, 1)
rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
rho_2 = rho_2.expand(input.shape[0], -1, -1, -1)
out = rho_0 * out_in + rho_1 * out_ln + rho_2 * out_bn
else:
rho_0 = rho[:, :, 0]
rho_1 = rho[:, :, 1]
rho_0 = rho_0.view(1, self.num_features, 1, 1)
rho_1 = rho_1.view(1, self.num_features, 1, 1)
rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
out = rho_0 * out_in + rho_1 * out_ln
out = out * self.gamma.expand(input.shape[0], -1, -1, -1
) + self.beta.expand(input.shape[0], -1, -1, -1)
return out
class Conv2dBlockNew(nn.Module):
def __init__(self, in_dim, out_dim, ks, st, padding=0, norm='none',
activation='relu', pad_type='zero', use_bias=True, activation_first
=False, groups=1, sn=False):
super(Conv2dBlockNew, self).__init__()
self.use_bias = use_bias
self.activation_first = activation_first
if padding == 0:
self.pad = Identity()
elif pad_type == 'reflect':
self.pad = nn.ReflectionPad2d(padding)
elif pad_type == 'replicate':
self.pad = nn.ReplicationPad2d(padding)
elif pad_type == 'zero':
self.pad = nn.ZeroPad2d(padding)
else:
assert 0, 'Unsupported padding type: {}'.format(pad_type)
norm_dim = out_dim
if norm == 'bn':
self.norm = nn.BatchNorm2d(norm_dim)
elif norm == 'gn':
self.norm = nn.GroupNorm(4, norm_dim, 0.8)
elif norm == 'in':
self.norm = nn.InstanceNorm2d(norm_dim)
elif norm == 'adain':
self.norm = AdaptiveInstanceNorm2d(norm_dim)
elif norm == 'iln':
self.norm = InstanceLayerNorm2d(norm_dim)
elif norm == 'adailn':
self.norm = AdaptiveInstanceLayerNorm2d(norm_dim)
elif norm == 'none':
self.norm = None
else:
assert 0, 'Unsupported normalization: {}'.format(norm)
if activation == 'relu':
self.activation = nn.ReLU(inplace=False)
elif activation == 'lrelu':
self.activation = nn.LeakyReLU(0.2, inplace=False)
elif activation == 'tanh':
self.activation = nn.Tanh()
elif activation == 'none':
self.activation = None
else:
assert 0, 'Unsupported activation: {}'.format(activation)
if sn:
self.conv = nn.utils.spectral_norm(nn.Conv2d(in_dim, out_dim,
ks, st, bias=self.use_bias, groups=groups))
else:
self.conv = nn.Conv2d(in_dim, out_dim, ks, st, bias=self.
use_bias, groups=groups)
def forward(self, input_0):
primals_1 = self.conv.weight
primals_2 = self.conv.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
belphegor2211/khoa_luan
|
Conv2dBlock
| false
| 9,994
|
[
"MIT"
] | 0
|
c9c163ebf3aff3005639ce7e4020e510295d1c75
|
https://github.com/belphegor2211/khoa_luan/tree/c9c163ebf3aff3005639ce7e4020e510295d1c75
|
PositionwiseFeedForward
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionwiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Conv1d(d_in, d_hid, 1)
self.w_2 = nn.Conv1d(d_hid, d_in, 1)
self.layer_norm = nn.LayerNorm(d_in)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
residual = x
output = x.transpose(1, 2)
output = self.w_2(F.relu(self.w_1(output)))
output = output.transpose(1, 2)
output = self.dropout(output)
output = self.layer_norm(output + residual)
return output
def get_inputs():
return [torch.rand([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
@triton.jit
def triton_poi_fused_convolution_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')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_relu_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
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
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_convolution_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
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_add_native_layer_norm_3(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, 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_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
tmp4 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
tmp8 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 + tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 + tmp12
tmp14 = tmp10 + tmp13
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp2 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp5 - tmp16
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp9 - tmp16
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp16
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = tmp27 / tmp15
tl.store(out_ptr0 + x2, tmp16, xmask)
tl.store(out_ptr1 + x2, tmp28, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_4(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, 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 = tl.load(in_ptr1 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr2 + y3, ymask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + y3, ymask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 - tmp3
tmp6 = 1e-05
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + (x2 + 4 * y3), tmp13, xmask & ymask)
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), (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, (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), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_relu_1[grid(64)](buf2, primals_3, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_3
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4), (16, 4, 1))
buf4 = buf3
del buf3
triton_poi_fused_convolution_2[grid(64)](buf4, primals_5, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_5
buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
triton_poi_fused_add_native_layer_norm_3[grid(16)](buf4, primals_1,
buf5, buf6, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf7 = buf0
del buf0
triton_poi_fused_add_native_layer_norm_4[grid(16, 4)](buf4,
primals_1, buf5, buf6, primals_6, primals_7, buf7, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
del buf5
del buf6
del primals_7
return buf7, primals_1, primals_2, primals_4, primals_6, buf2, buf4
class PositionwiseFeedForwardNew(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Conv1d(d_in, d_hid, 1)
self.w_2 = nn.Conv1d(d_hid, d_in, 1)
self.layer_norm = nn.LayerNorm(d_in)
self.dropout = nn.Dropout(dropout)
def forward(self, input_0):
primals_2 = self.w_1.weight
primals_3 = self.w_1.bias
primals_4 = self.w_2.weight
primals_5 = self.w_2.bias
primals_6 = 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]
|
chenghaoliu89/TSForecasting_FT
|
PositionwiseFeedForward
| false
| 9,995
|
[
"MIT"
] | 0
|
e29227e67f754919672eab9002a1b37b13ed28a0
|
https://github.com/chenghaoliu89/TSForecasting_FT/tree/e29227e67f754919672eab9002a1b37b13ed28a0
|
CNNCifar
|
from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class CNNCifar(nn.Module):
def __init__(self, args):
super(CNNCifar, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, args.num_classes)
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 [[], {'args': _mock_config(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_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 = 480
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 120
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, (120, 400), (400, 1))
assert_size_stride(primals_7, (120,), (1,))
assert_size_stride(primals_8, (84, 120), (120, 1))
assert_size_stride(primals_9, (84,), (1,))
assert_size_stride(primals_10, (4, 84), (84, 1))
assert_size_stride(primals_11, (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, 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=256, 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=256, 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=128, num_warps=4, num_stages=1)
buf8 = empty_strided_cuda((4, 120), (120, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf7, (4, 400), (400, 1), 0),
reinterpret_tensor(primals_6, (400, 120), (1, 400), 0), out=buf8)
buf9 = buf8
del buf8
triton_poi_fused_relu_4[grid(480)](buf9, primals_7, 480, 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, (120, 84), (1,
120), 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, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_11, buf11, reinterpret_tensor(
primals_10, (84, 4), (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 CNNCifarNew(nn.Module):
def __init__(self, args):
super(CNNCifarNew, 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, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, args.num_classes)
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]
|
C3atUofU/Hierarchical-SGD
|
CNNCifar
| false
| 9,996
|
[
"MIT"
] | 0
|
ecc0f25065f78e70ed8deff7dfc9809331e19f21
|
https://github.com/C3atUofU/Hierarchical-SGD/tree/ecc0f25065f78e70ed8deff7dfc9809331e19f21
|
FakeRKHSConvNet
|
import math
import torch
import numpy as np
import torch.nn as nn
class MaybeBatchNorm2d(nn.Module):
def __init__(self, n_ftr, affine, use_bn):
super(MaybeBatchNorm2d, self).__init__()
self.bn = nn.BatchNorm2d(n_ftr, affine=affine)
self.use_bn = use_bn
def forward(self, x):
if self.use_bn:
x = self.bn(x)
return x
class FakeRKHSConvNet(nn.Module):
def __init__(self, n_input, n_output, use_bn=False):
super(FakeRKHSConvNet, self).__init__()
self.conv1 = nn.Conv2d(n_input, n_output, kernel_size=1, stride=1,
padding=0, bias=False)
self.bn1 = MaybeBatchNorm2d(n_output, True, use_bn)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(n_output, n_output, kernel_size=1, stride=1,
padding=0, bias=False)
self.bn_out = MaybeBatchNorm2d(n_output, True, True)
self.shortcut = nn.Conv2d(n_input, n_output, kernel_size=1, stride=
1, padding=0, bias=True)
if n_output >= n_input:
eye_mask = np.zeros((n_output, n_input, 1, 1), dtype=np.bool)
for i in range(n_input):
eye_mask[i, i, 0, 0] = 1
self.shortcut.weight.data.uniform_(-0.01, 0.01)
self.shortcut.weight.data.masked_fill_(torch.tensor(eye_mask), 1.0)
def init_weights(self, init_scale=1.0):
nn.init.kaiming_uniform_(self.conv1.weight, a=math.sqrt(5))
self.conv1.weight.data.mul_(init_scale)
nn.init.constant_(self.conv2.weight, 0.0)
def forward(self, x):
h_res = self.conv2(self.relu1(self.bn1(self.conv1(x))))
h = self.bn_out(h_res + self.shortcut(x))
return h
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'n_input': 4, 'n_output': 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 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_relu_0(in_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)
tl.store(in_out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_add_convolution_native_batch_norm_backward_1(
in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, 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
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x3, xmask)
tmp2 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr5 + x1, xmask, eviction_policy='evict_last')
tmp18 = tl.load(in_ptr6 + x1, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tmp6 = tmp4 - tmp5
tmp8 = 1e-05
tmp9 = tmp7 + tmp8
tmp10 = libdevice.sqrt(tmp9)
tmp11 = tl.full([1], 1, tl.int32)
tmp12 = tmp11 / tmp10
tmp13 = 1.0
tmp14 = tmp12 * tmp13
tmp15 = tmp6 * tmp14
tmp17 = tmp15 * tmp16
tmp19 = tmp17 + tmp18
tl.store(out_ptr0 + x3, tmp19, xmask)
tl.store(out_ptr1 + x3, tmp6, 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, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_2, 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, 4, 4), (64, 16, 4, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_relu_0[grid(256)](buf1, 256, XBLOCK=256, num_warps
=4, num_stages=1)
buf2 = extern_kernels.convolution(buf1, primals_3, 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, 4, 4), (64, 16, 4, 1))
buf3 = extern_kernels.convolution(primals_2, 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, 4, 4, 4), (64, 16, 4, 1))
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__native_batch_norm_legit_no_training_add_convolution_native_batch_norm_backward_1[
grid(256)](buf2, buf3, primals_5, primals_6, primals_7,
primals_8, primals_9, buf4, buf5, 256, XBLOCK=256, num_warps=4,
num_stages=1)
del buf2
del buf3
del primals_5
del primals_6
del primals_9
return (buf4, primals_1, primals_2, primals_3, primals_4, primals_7,
primals_8, buf1, buf5)
class MaybeBatchNorm2d(nn.Module):
def __init__(self, n_ftr, affine, use_bn):
super(MaybeBatchNorm2d, self).__init__()
self.bn = nn.BatchNorm2d(n_ftr, affine=affine)
self.use_bn = use_bn
def forward(self, x):
if self.use_bn:
x = self.bn(x)
return x
class FakeRKHSConvNetNew(nn.Module):
def __init__(self, n_input, n_output, use_bn=False):
super(FakeRKHSConvNetNew, self).__init__()
self.conv1 = nn.Conv2d(n_input, n_output, kernel_size=1, stride=1,
padding=0, bias=False)
self.bn1 = MaybeBatchNorm2d(n_output, True, use_bn)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(n_output, n_output, kernel_size=1, stride=1,
padding=0, bias=False)
self.bn_out = MaybeBatchNorm2d(n_output, True, True)
self.shortcut = nn.Conv2d(n_input, n_output, kernel_size=1, stride=
1, padding=0, bias=True)
if n_output >= n_input:
eye_mask = np.zeros((n_output, n_input, 1, 1), dtype=np.bool)
for i in range(n_input):
eye_mask[i, i, 0, 0] = 1
self.shortcut.weight.data.uniform_(-0.01, 0.01)
self.shortcut.weight.data.masked_fill_(torch.tensor(eye_mask), 1.0)
def init_weights(self, init_scale=1.0):
nn.init.kaiming_uniform_(self.conv1.weight, a=math.sqrt(5))
self.conv1.weight.data.mul_(init_scale)
nn.init.constant_(self.conv2.weight, 0.0)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_5 = self.bn1.bn.weight
primals_6 = self.bn1.bn.bias
primals_3 = self.conv2.weight
primals_7 = self.bn_out.bn.weight
primals_8 = self.bn_out.bn.bias
primals_4 = self.shortcut.weight
primals_9 = self.shortcut.bias
primals_2 = 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]
|
bartolkaruza/pytorch-lightning-bolts
|
FakeRKHSConvNet
| false
| 9,997
|
[
"Apache-2.0"
] | 0
|
2e903c333c37ea83394c7da2ce826de1b82fb356
|
https://github.com/bartolkaruza/pytorch-lightning-bolts/tree/2e903c333c37ea83394c7da2ce826de1b82fb356
|
UNet
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class down(nn.Module):
def __init__(self, inChannels, outChannels, filterSize):
super(down, self).__init__()
self.conv1 = nn.Conv2d(inChannels, outChannels, filterSize, stride=
1, padding=int((filterSize - 1) / 2))
self.conv2 = nn.Conv2d(outChannels, outChannels, filterSize, stride
=1, padding=int((filterSize - 1) / 2))
def forward(self, x):
x = F.avg_pool2d(x, 2)
x = F.leaky_relu(self.conv1(x), negative_slope=0.1)
x = F.leaky_relu(self.conv2(x), negative_slope=0.1)
return x
class up(nn.Module):
def __init__(self, inChannels, outChannels):
super(up, self).__init__()
self.conv1 = nn.Conv2d(inChannels, outChannels, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(2 * outChannels, outChannels, 3, stride=1,
padding=1)
def forward(self, x, skpCn):
x = F.interpolate(x, scale_factor=2, mode='bilinear')
x = F.leaky_relu(self.conv1(x), negative_slope=0.1)
x = F.leaky_relu(self.conv2(torch.cat((x, skpCn), 1)),
negative_slope=0.1)
return x
class UNet(nn.Module):
def __init__(self, inChannels, outChannels):
super(UNet, self).__init__()
self.conv1 = nn.Conv2d(inChannels, 32, 7, stride=1, padding=3)
self.conv2 = nn.Conv2d(32, 32, 7, stride=1, padding=3)
self.down1 = down(32, 64, 5)
self.down2 = down(64, 128, 3)
self.down3 = down(128, 256, 3)
self.down4 = down(256, 512, 3)
self.down5 = down(512, 512, 3)
self.up1 = up(512, 512)
self.up2 = up(512, 256)
self.up3 = up(256, 128)
self.up4 = up(128, 64)
self.up5 = up(64, 32)
self.conv3 = nn.Conv2d(32, outChannels, 3, stride=1, padding=1)
def forward(self, x):
x = F.leaky_relu(self.conv1(x), negative_slope=0.1)
s1 = F.leaky_relu(self.conv2(x), negative_slope=0.1)
s2 = self.down1(s1)
s3 = self.down2(s2)
s4 = self.down3(s3)
s5 = self.down4(s4)
x = self.down5(s5)
x = self.up1(x, s5)
x = self.up2(x, s4)
x = self.up3(x, s3)
x = self.up4(x, s2)
x = self.up5(x, s1)
x = F.leaky_relu(self.conv3(x), negative_slope=0.1)
return x
def get_inputs():
return [torch.rand([4, 4, 64, 64])]
def get_init_inputs():
return [[], {'inChannels': 4, 'outChannels': 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.functional as F
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_leaky_relu_0(in_ptr0, in_ptr1, 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)
x3 = xindex
x1 = xindex // 4096 % 32
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.1
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, None)
tl.store(out_ptr1 + x3, tmp7, None)
@triton.jit
def triton_poi_fused_avg_pool2d_1(in_ptr0, 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 % 32
x1 = xindex // 32
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 128 * x1), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 128 * x1), None, eviction_policy
='evict_last')
tmp3 = tl.load(in_ptr0 + (64 + 2 * x0 + 128 * x1), None,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (65 + 2 * x0 + 128 * x1), None,
eviction_policy='evict_last')
tmp2 = tmp1 + tmp0
tmp4 = tmp3 + tmp2
tmp6 = tmp5 + tmp4
tmp7 = 0.25
tmp8 = tmp6 * tmp7
tl.store(out_ptr0 + x2, tmp8, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_2(in_ptr0, in_ptr1, 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)
x3 = xindex
x1 = xindex // 1024 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.1
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, None)
tl.store(out_ptr1 + x3, tmp7, None)
@triton.jit
def triton_poi_fused_avg_pool2d_3(in_ptr0, 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 % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 64 * x1), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 64 * x1), None, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (32 + 2 * x0 + 64 * x1), None, eviction_policy
='evict_last')
tmp5 = tl.load(in_ptr0 + (33 + 2 * x0 + 64 * x1), None, eviction_policy
='evict_last')
tmp2 = tmp1 + tmp0
tmp4 = tmp3 + tmp2
tmp6 = tmp5 + tmp4
tmp7 = 0.25
tmp8 = tmp6 * tmp7
tl.store(out_ptr0 + x2, tmp8, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_4(in_ptr0, in_ptr1, 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)
x3 = xindex
x1 = xindex // 256 % 128
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.1
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, None)
tl.store(out_ptr1 + x3, tmp7, None)
@triton.jit
def triton_poi_fused_avg_pool2d_5(in_ptr0, 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 % 8
x1 = xindex // 8
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 32 * x1), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 32 * x1), None, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (16 + 2 * x0 + 32 * x1), None, eviction_policy
='evict_last')
tmp5 = tl.load(in_ptr0 + (17 + 2 * x0 + 32 * x1), None, eviction_policy
='evict_last')
tmp2 = tmp1 + tmp0
tmp4 = tmp3 + tmp2
tmp6 = tmp5 + tmp4
tmp7 = 0.25
tmp8 = tmp6 * tmp7
tl.store(out_ptr0 + x2, tmp8, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_6(in_ptr0, in_ptr1, 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)
x3 = xindex
x1 = xindex // 64 % 256
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.1
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, None)
tl.store(out_ptr1 + x3, tmp7, None)
@triton.jit
def triton_poi_fused_avg_pool2d_7(in_ptr0, 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 % 4
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 16 * x1), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 16 * x1), None, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (8 + 2 * x0 + 16 * x1), None, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr0 + (9 + 2 * x0 + 16 * x1), None, eviction_policy=
'evict_last')
tmp2 = tmp1 + tmp0
tmp4 = tmp3 + tmp2
tmp6 = tmp5 + tmp4
tmp7 = 0.25
tmp8 = tmp6 * tmp7
tl.store(out_ptr0 + x2, tmp8, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_8(in_ptr0, in_ptr1, 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)
x3 = xindex
x1 = xindex // 16 % 512
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.1
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, None)
tl.store(out_ptr1 + x3, tmp7, None)
@triton.jit
def triton_poi_fused_avg_pool2d_9(in_ptr0, 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 % 2
x1 = xindex // 2
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), None, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), None, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), None, eviction_policy=
'evict_last')
tmp2 = tmp1 + tmp0
tmp4 = tmp3 + tmp2
tmp6 = tmp5 + tmp4
tmp7 = 0.25
tmp8 = tmp6 * tmp7
tl.store(out_ptr0 + x2, tmp8, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_10(in_ptr0, in_ptr1, 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)
x3 = xindex
x1 = xindex // 4 % 512
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.1
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, None)
tl.store(out_ptr1 + x3, tmp7, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_11(in_ptr0, in_ptr1, 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
x1 = xindex // 4 % 512
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tl.store(out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused__to_copy_12(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 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 + tmp2
tmp4 = tmp3 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tl.store(out_ptr0 + x0, tmp8, xmask)
@triton.jit
def triton_poi_fused_add_clamp_13(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 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 + tmp2
tmp4 = tmp3 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.full([1], 1, tl.int64)
tmp10 = tmp8 + tmp9
tmp11 = triton_helpers.minimum(tmp10, tmp9)
tl.store(out_ptr0 + x0, tmp11, xmask)
@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_14(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 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 + tmp2
tmp4 = tmp3 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 - tmp9
tmp11 = triton_helpers.maximum(tmp10, tmp6)
tmp12 = 1.0
tmp13 = triton_helpers.minimum(tmp11, tmp12)
tl.store(out_ptr0 + x0, tmp13, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_15(
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):
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
x6 = xindex // 16
x2 = xindex // 16 % 512
x4 = xindex
tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
tmp35 = tl.load(in_ptr7 + x0, None, eviction_policy='evict_last')
tmp47 = tl.load(in_ptr8 + x1, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 2, 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_ptr2 + (tmp8 + 2 * tmp4 + 4 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp10 = tl.load(in_ptr3 + (tmp8 + 2 * tmp4 + 4 * x6), None,
eviction_policy='evict_last')
tmp12 = tmp10 + tmp11
tmp13 = 0.1
tmp14 = tmp12 * tmp13
tmp15 = tl.where(tmp9, tmp12, tmp14)
tmp17 = tmp16 + tmp1
tmp18 = tmp16 < 0
tmp19 = tl.where(tmp18, tmp17, tmp16)
tmp20 = tl.load(in_ptr2 + (tmp8 + 2 * tmp19 + 4 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp21 = tl.load(in_ptr3 + (tmp8 + 2 * tmp19 + 4 * x6), None,
eviction_policy='evict_last')
tmp22 = tmp21 + tmp11
tmp23 = tmp22 * tmp13
tmp24 = tl.where(tmp20, tmp22, tmp23)
tmp26 = tmp25 + tmp1
tmp27 = tmp25 < 0
tmp28 = tl.where(tmp27, tmp26, tmp25)
tmp29 = tl.load(in_ptr2 + (tmp28 + 2 * tmp19 + 4 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp30 = tl.load(in_ptr3 + (tmp28 + 2 * tmp19 + 4 * x6), None,
eviction_policy='evict_last')
tmp31 = tmp30 + tmp11
tmp32 = tmp31 * tmp13
tmp33 = tl.where(tmp29, tmp31, tmp32)
tmp34 = tmp33 - tmp24
tmp36 = tmp34 * tmp35
tmp37 = tmp24 + tmp36
tmp38 = tl.load(in_ptr2 + (tmp28 + 2 * tmp4 + 4 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp39 = tl.load(in_ptr3 + (tmp28 + 2 * tmp4 + 4 * x6), None,
eviction_policy='evict_last')
tmp40 = tmp39 + tmp11
tmp41 = tmp40 * tmp13
tmp42 = tl.where(tmp38, tmp40, tmp41)
tmp43 = tmp42 - tmp15
tmp44 = tmp43 * tmp35
tmp45 = tmp15 + tmp44
tmp46 = tmp45 - tmp37
tmp48 = tmp46 * tmp47
tmp49 = tmp37 + tmp48
tl.store(in_out_ptr1 + x4, tmp49, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_16(in_ptr0, in_ptr1, 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
x1 = xindex // 16 % 512
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tl.store(out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_cat_17(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 16 % 1024
x0 = xindex % 16
x2 = xindex // 16384
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 512, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 8192 * x2), tmp4, other=0.0).to(tl
.int1)
tmp6 = tl.load(in_ptr1 + (x0 + 16 * x1 + 8192 * x2), tmp4, other=0.0)
tmp7 = tl.load(in_ptr2 + x1, tmp4, eviction_policy='evict_last', other=0.0)
tmp8 = tmp6 + tmp7
tmp9 = 0.1
tmp10 = tmp8 * tmp9
tmp11 = tl.where(tmp5, tmp8, tmp10)
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp4, tmp11, tmp12)
tmp14 = tmp0 >= tmp3
tl.full([1], 1024, tl.int64)
tmp17 = tl.load(in_ptr3 + (x0 + 16 * (-512 + x1) + 8192 * x2), tmp14,
other=0.0)
tmp18 = tl.where(tmp4, tmp13, tmp17)
tl.store(out_ptr0 + x3, tmp18, None)
@triton.jit
def triton_poi_fused__to_copy_18(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 8
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 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tl.store(out_ptr0 + x0, tmp8, xmask)
@triton.jit
def triton_poi_fused_add_clamp_19(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 8
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 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.full([1], 1, tl.int64)
tmp10 = tmp8 + tmp9
tmp11 = tl.full([1], 3, tl.int64)
tmp12 = triton_helpers.minimum(tmp10, tmp11)
tl.store(out_ptr0 + x0, tmp12, xmask)
@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_20(out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 8
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 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 - tmp9
tmp11 = triton_helpers.maximum(tmp10, tmp6)
tmp12 = 1.0
tmp13 = triton_helpers.minimum(tmp11, tmp12)
tl.store(out_ptr0 + x0, tmp13, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_21(
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):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 8 % 8
x0 = xindex % 8
x6 = xindex // 64
x2 = xindex // 64 % 512
x4 = xindex
tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
tmp35 = tl.load(in_ptr7 + x0, None, eviction_policy='evict_last')
tmp47 = tl.load(in_ptr8 + x1, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 4, 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_ptr2 + (tmp8 + 4 * tmp4 + 16 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp10 = tl.load(in_ptr3 + (tmp8 + 4 * tmp4 + 16 * x6), None,
eviction_policy='evict_last')
tmp12 = tmp10 + tmp11
tmp13 = 0.1
tmp14 = tmp12 * tmp13
tmp15 = tl.where(tmp9, tmp12, tmp14)
tmp17 = tmp16 + tmp1
tmp18 = tmp16 < 0
tmp19 = tl.where(tmp18, tmp17, tmp16)
tmp20 = tl.load(in_ptr2 + (tmp8 + 4 * tmp19 + 16 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp21 = tl.load(in_ptr3 + (tmp8 + 4 * tmp19 + 16 * x6), None,
eviction_policy='evict_last')
tmp22 = tmp21 + tmp11
tmp23 = tmp22 * tmp13
tmp24 = tl.where(tmp20, tmp22, tmp23)
tmp26 = tmp25 + tmp1
tmp27 = tmp25 < 0
tmp28 = tl.where(tmp27, tmp26, tmp25)
tmp29 = tl.load(in_ptr2 + (tmp28 + 4 * tmp19 + 16 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp30 = tl.load(in_ptr3 + (tmp28 + 4 * tmp19 + 16 * x6), None,
eviction_policy='evict_last')
tmp31 = tmp30 + tmp11
tmp32 = tmp31 * tmp13
tmp33 = tl.where(tmp29, tmp31, tmp32)
tmp34 = tmp33 - tmp24
tmp36 = tmp34 * tmp35
tmp37 = tmp24 + tmp36
tmp38 = tl.load(in_ptr2 + (tmp28 + 4 * tmp4 + 16 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp39 = tl.load(in_ptr3 + (tmp28 + 4 * tmp4 + 16 * x6), None,
eviction_policy='evict_last')
tmp40 = tmp39 + tmp11
tmp41 = tmp40 * tmp13
tmp42 = tl.where(tmp38, tmp40, tmp41)
tmp43 = tmp42 - tmp15
tmp44 = tmp43 * tmp35
tmp45 = tmp15 + tmp44
tmp46 = tmp45 - tmp37
tmp48 = tmp46 * tmp47
tmp49 = tmp37 + tmp48
tl.store(in_out_ptr1 + x4, tmp49, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_22(in_ptr0, in_ptr1, 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
x1 = xindex // 64 % 256
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tl.store(out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_cat_23(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 64 % 512
x0 = xindex % 64
x2 = xindex // 32768
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 256, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 64 * x1 + 16384 * x2), tmp4, other=0.0).to(
tl.int1)
tmp6 = tl.load(in_ptr1 + (x0 + 64 * x1 + 16384 * x2), tmp4, other=0.0)
tmp7 = tl.load(in_ptr2 + x1, tmp4, eviction_policy='evict_last', other=0.0)
tmp8 = tmp6 + tmp7
tmp9 = 0.1
tmp10 = tmp8 * tmp9
tmp11 = tl.where(tmp5, tmp8, tmp10)
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp4, tmp11, tmp12)
tmp14 = tmp0 >= tmp3
tl.full([1], 512, tl.int64)
tmp17 = tl.load(in_ptr3 + (x0 + 64 * (-256 + x1) + 16384 * x2), tmp14,
other=0.0)
tmp18 = tl.where(tmp4, tmp13, tmp17)
tl.store(out_ptr0 + x3, tmp18, None)
@triton.jit
def triton_poi_fused__to_copy_24(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 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 + tmp2
tmp4 = tmp3 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tl.store(out_ptr0 + x0, tmp8, xmask)
@triton.jit
def triton_poi_fused_add_clamp_25(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 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 + tmp2
tmp4 = tmp3 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.full([1], 1, tl.int64)
tmp10 = tmp8 + tmp9
tmp11 = tl.full([1], 7, tl.int64)
tmp12 = triton_helpers.minimum(tmp10, tmp11)
tl.store(out_ptr0 + x0, tmp12, xmask)
@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_26(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 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 + tmp2
tmp4 = tmp3 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 - tmp9
tmp11 = triton_helpers.maximum(tmp10, tmp6)
tmp12 = 1.0
tmp13 = triton_helpers.minimum(tmp11, tmp12)
tl.store(out_ptr0 + x0, tmp13, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_27(
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):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 16 % 16
x0 = xindex % 16
x6 = xindex // 256
x2 = xindex // 256 % 256
x4 = xindex
tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
tmp35 = tl.load(in_ptr7 + x0, None, eviction_policy='evict_last')
tmp47 = tl.load(in_ptr8 + x1, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 8, 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_ptr2 + (tmp8 + 8 * tmp4 + 64 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp10 = tl.load(in_ptr3 + (tmp8 + 8 * tmp4 + 64 * x6), None,
eviction_policy='evict_last')
tmp12 = tmp10 + tmp11
tmp13 = 0.1
tmp14 = tmp12 * tmp13
tmp15 = tl.where(tmp9, tmp12, tmp14)
tmp17 = tmp16 + tmp1
tmp18 = tmp16 < 0
tmp19 = tl.where(tmp18, tmp17, tmp16)
tmp20 = tl.load(in_ptr2 + (tmp8 + 8 * tmp19 + 64 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp21 = tl.load(in_ptr3 + (tmp8 + 8 * tmp19 + 64 * x6), None,
eviction_policy='evict_last')
tmp22 = tmp21 + tmp11
tmp23 = tmp22 * tmp13
tmp24 = tl.where(tmp20, tmp22, tmp23)
tmp26 = tmp25 + tmp1
tmp27 = tmp25 < 0
tmp28 = tl.where(tmp27, tmp26, tmp25)
tmp29 = tl.load(in_ptr2 + (tmp28 + 8 * tmp19 + 64 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp30 = tl.load(in_ptr3 + (tmp28 + 8 * tmp19 + 64 * x6), None,
eviction_policy='evict_last')
tmp31 = tmp30 + tmp11
tmp32 = tmp31 * tmp13
tmp33 = tl.where(tmp29, tmp31, tmp32)
tmp34 = tmp33 - tmp24
tmp36 = tmp34 * tmp35
tmp37 = tmp24 + tmp36
tmp38 = tl.load(in_ptr2 + (tmp28 + 8 * tmp4 + 64 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp39 = tl.load(in_ptr3 + (tmp28 + 8 * tmp4 + 64 * x6), None,
eviction_policy='evict_last')
tmp40 = tmp39 + tmp11
tmp41 = tmp40 * tmp13
tmp42 = tl.where(tmp38, tmp40, tmp41)
tmp43 = tmp42 - tmp15
tmp44 = tmp43 * tmp35
tmp45 = tmp15 + tmp44
tmp46 = tmp45 - tmp37
tmp48 = tmp46 * tmp47
tmp49 = tmp37 + tmp48
tl.store(in_out_ptr1 + x4, tmp49, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_28(in_ptr0, in_ptr1, 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
x1 = xindex // 256 % 128
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tl.store(out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_cat_29(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 256 % 256
x0 = xindex % 256
x2 = xindex // 65536
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 128, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 256 * x1 + 32768 * x2), tmp4, other=0.0).to(
tl.int1)
tmp6 = tl.load(in_ptr1 + (x0 + 256 * x1 + 32768 * x2), tmp4, other=0.0)
tmp7 = tl.load(in_ptr2 + x1, tmp4, eviction_policy='evict_last', other=0.0)
tmp8 = tmp6 + tmp7
tmp9 = 0.1
tmp10 = tmp8 * tmp9
tmp11 = tl.where(tmp5, tmp8, tmp10)
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp4, tmp11, tmp12)
tmp14 = tmp0 >= tmp3
tl.full([1], 256, tl.int64)
tmp17 = tl.load(in_ptr3 + (x0 + 256 * (-128 + x1) + 32768 * x2), tmp14,
other=0.0)
tmp18 = tl.where(tmp4, tmp13, tmp17)
tl.store(out_ptr0 + x3, tmp18, None)
@triton.jit
def triton_poi_fused__to_copy_30(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 32
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 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tl.store(out_ptr0 + x0, tmp8, xmask)
@triton.jit
def triton_poi_fused_add_clamp_31(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 32
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 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.full([1], 1, tl.int64)
tmp10 = tmp8 + tmp9
tmp11 = tl.full([1], 15, tl.int64)
tmp12 = triton_helpers.minimum(tmp10, tmp11)
tl.store(out_ptr0 + x0, tmp12, xmask)
@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_32(out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 32
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 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 - tmp9
tmp11 = triton_helpers.maximum(tmp10, tmp6)
tmp12 = 1.0
tmp13 = triton_helpers.minimum(tmp11, tmp12)
tl.store(out_ptr0 + x0, tmp13, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_33(
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):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 32 % 32
x0 = xindex % 32
x6 = xindex // 1024
x2 = xindex // 1024 % 128
x4 = xindex
tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
tmp35 = tl.load(in_ptr7 + x0, None, eviction_policy='evict_last')
tmp47 = tl.load(in_ptr8 + x1, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 16, 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_ptr2 + (tmp8 + 16 * tmp4 + 256 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp10 = tl.load(in_ptr3 + (tmp8 + 16 * tmp4 + 256 * x6), None,
eviction_policy='evict_last')
tmp12 = tmp10 + tmp11
tmp13 = 0.1
tmp14 = tmp12 * tmp13
tmp15 = tl.where(tmp9, tmp12, tmp14)
tmp17 = tmp16 + tmp1
tmp18 = tmp16 < 0
tmp19 = tl.where(tmp18, tmp17, tmp16)
tmp20 = tl.load(in_ptr2 + (tmp8 + 16 * tmp19 + 256 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp21 = tl.load(in_ptr3 + (tmp8 + 16 * tmp19 + 256 * x6), None,
eviction_policy='evict_last')
tmp22 = tmp21 + tmp11
tmp23 = tmp22 * tmp13
tmp24 = tl.where(tmp20, tmp22, tmp23)
tmp26 = tmp25 + tmp1
tmp27 = tmp25 < 0
tmp28 = tl.where(tmp27, tmp26, tmp25)
tmp29 = tl.load(in_ptr2 + (tmp28 + 16 * tmp19 + 256 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp30 = tl.load(in_ptr3 + (tmp28 + 16 * tmp19 + 256 * x6), None,
eviction_policy='evict_last')
tmp31 = tmp30 + tmp11
tmp32 = tmp31 * tmp13
tmp33 = tl.where(tmp29, tmp31, tmp32)
tmp34 = tmp33 - tmp24
tmp36 = tmp34 * tmp35
tmp37 = tmp24 + tmp36
tmp38 = tl.load(in_ptr2 + (tmp28 + 16 * tmp4 + 256 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp39 = tl.load(in_ptr3 + (tmp28 + 16 * tmp4 + 256 * x6), None,
eviction_policy='evict_last')
tmp40 = tmp39 + tmp11
tmp41 = tmp40 * tmp13
tmp42 = tl.where(tmp38, tmp40, tmp41)
tmp43 = tmp42 - tmp15
tmp44 = tmp43 * tmp35
tmp45 = tmp15 + tmp44
tmp46 = tmp45 - tmp37
tmp48 = tmp46 * tmp47
tmp49 = tmp37 + tmp48
tl.store(in_out_ptr1 + x4, tmp49, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_34(in_ptr0, in_ptr1, 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
x1 = xindex // 1024 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tl.store(out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_cat_35(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 1024 % 128
x0 = xindex % 1024
x2 = xindex // 131072
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 64, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 1024 * x1 + 65536 * x2), tmp4, other=0.0
).to(tl.int1)
tmp6 = tl.load(in_ptr1 + (x0 + 1024 * x1 + 65536 * x2), tmp4, other=0.0)
tmp7 = tl.load(in_ptr2 + x1, tmp4, eviction_policy='evict_last', other=0.0)
tmp8 = tmp6 + tmp7
tmp9 = 0.1
tmp10 = tmp8 * tmp9
tmp11 = tl.where(tmp5, tmp8, tmp10)
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp4, tmp11, tmp12)
tmp14 = tmp0 >= tmp3
tl.full([1], 128, tl.int64)
tmp17 = tl.load(in_ptr3 + (x0 + 1024 * (-64 + x1) + 65536 * x2), tmp14,
other=0.0)
tmp18 = tl.where(tmp4, tmp13, tmp17)
tl.store(out_ptr0 + x3, tmp18, None)
@triton.jit
def triton_poi_fused__to_copy_36(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 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tl.store(out_ptr0 + x0, tmp8, xmask)
@triton.jit
def triton_poi_fused_add_clamp_37(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 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.full([1], 1, tl.int64)
tmp10 = tmp8 + tmp9
tmp11 = tl.full([1], 31, tl.int64)
tmp12 = triton_helpers.minimum(tmp10, tmp11)
tl.store(out_ptr0 + x0, tmp12, xmask)
@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_38(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 * tmp2
tmp5 = tmp4 - tmp2
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp7.to(tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 - tmp9
tmp11 = triton_helpers.maximum(tmp10, tmp6)
tmp12 = 1.0
tmp13 = triton_helpers.minimum(tmp11, tmp12)
tl.store(out_ptr0 + x0, tmp13, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_39(
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):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 64 % 64
x0 = xindex % 64
x6 = xindex // 4096
x2 = xindex // 4096 % 64
x4 = xindex
tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
tmp35 = tl.load(in_ptr7 + x0, None, eviction_policy='evict_last')
tmp47 = tl.load(in_ptr8 + x1, None, 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_ptr2 + (tmp8 + 32 * tmp4 + 1024 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp10 = tl.load(in_ptr3 + (tmp8 + 32 * tmp4 + 1024 * x6), None,
eviction_policy='evict_last')
tmp12 = tmp10 + tmp11
tmp13 = 0.1
tmp14 = tmp12 * tmp13
tmp15 = tl.where(tmp9, tmp12, tmp14)
tmp17 = tmp16 + tmp1
tmp18 = tmp16 < 0
tmp19 = tl.where(tmp18, tmp17, tmp16)
tmp20 = tl.load(in_ptr2 + (tmp8 + 32 * tmp19 + 1024 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp21 = tl.load(in_ptr3 + (tmp8 + 32 * tmp19 + 1024 * x6), None,
eviction_policy='evict_last')
tmp22 = tmp21 + tmp11
tmp23 = tmp22 * tmp13
tmp24 = tl.where(tmp20, tmp22, tmp23)
tmp26 = tmp25 + tmp1
tmp27 = tmp25 < 0
tmp28 = tl.where(tmp27, tmp26, tmp25)
tmp29 = tl.load(in_ptr2 + (tmp28 + 32 * tmp19 + 1024 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp30 = tl.load(in_ptr3 + (tmp28 + 32 * tmp19 + 1024 * x6), None,
eviction_policy='evict_last')
tmp31 = tmp30 + tmp11
tmp32 = tmp31 * tmp13
tmp33 = tl.where(tmp29, tmp31, tmp32)
tmp34 = tmp33 - tmp24
tmp36 = tmp34 * tmp35
tmp37 = tmp24 + tmp36
tmp38 = tl.load(in_ptr2 + (tmp28 + 32 * tmp4 + 1024 * x6), None,
eviction_policy='evict_last').to(tl.int1)
tmp39 = tl.load(in_ptr3 + (tmp28 + 32 * tmp4 + 1024 * x6), None,
eviction_policy='evict_last')
tmp40 = tmp39 + tmp11
tmp41 = tmp40 * tmp13
tmp42 = tl.where(tmp38, tmp40, tmp41)
tmp43 = tmp42 - tmp15
tmp44 = tmp43 * tmp35
tmp45 = tmp15 + tmp44
tmp46 = tmp45 - tmp37
tmp48 = tmp46 * tmp47
tmp49 = tmp37 + tmp48
tl.store(in_out_ptr1 + x4, tmp49, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_40(in_ptr0, in_ptr1, 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
x1 = xindex // 4096 % 32
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tl.store(out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_cat_41(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 4096 % 64
x0 = xindex % 4096
x2 = xindex // 262144
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 32, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 131072 * x2), tmp4, other=0.0
).to(tl.int1)
tmp6 = tl.load(in_ptr1 + (x0 + 4096 * x1 + 131072 * x2), tmp4, other=0.0)
tmp7 = tl.load(in_ptr2 + x1, tmp4, eviction_policy='evict_last', other=0.0)
tmp8 = tmp6 + tmp7
tmp9 = 0.1
tmp10 = tmp8 * tmp9
tmp11 = tl.where(tmp5, tmp8, tmp10)
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp4, tmp11, tmp12)
tmp14 = tmp0 >= tmp3
tl.full([1], 64, tl.int64)
tmp17 = tl.load(in_ptr3 + (x0 + 4096 * (-32 + x1) + 131072 * x2), tmp14,
other=0.0)
tmp18 = tl.where(tmp4, tmp13, tmp17)
tl.store(out_ptr0 + x3, tmp18, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_42(in_ptr0, in_ptr1, 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)
x3 = xindex
x1 = xindex // 4096 % 4
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.1
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, None)
tl.store(out_ptr1 + x3, tmp7, 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, primals_32,
primals_33, primals_34, primals_35, primals_36, primals_37,
primals_38, primals_39, primals_40, primals_41, primals_42,
primals_43, primals_44, primals_45, primals_46, primals_47) = args
args.clear()
assert_size_stride(primals_1, (32, 4, 7, 7), (196, 49, 7, 1))
assert_size_stride(primals_2, (32,), (1,))
assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 1))
assert_size_stride(primals_4, (32, 32, 7, 7), (1568, 49, 7, 1))
assert_size_stride(primals_5, (32,), (1,))
assert_size_stride(primals_6, (64, 32, 5, 5), (800, 25, 5, 1))
assert_size_stride(primals_7, (64,), (1,))
assert_size_stride(primals_8, (64, 64, 5, 5), (1600, 25, 5, 1))
assert_size_stride(primals_9, (64,), (1,))
assert_size_stride(primals_10, (128, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_11, (128,), (1,))
assert_size_stride(primals_12, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_13, (128,), (1,))
assert_size_stride(primals_14, (256, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_15, (256,), (1,))
assert_size_stride(primals_16, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_17, (256,), (1,))
assert_size_stride(primals_18, (512, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_19, (512,), (1,))
assert_size_stride(primals_20, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_21, (512,), (1,))
assert_size_stride(primals_22, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_23, (512,), (1,))
assert_size_stride(primals_24, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_25, (512,), (1,))
assert_size_stride(primals_26, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_27, (512,), (1,))
assert_size_stride(primals_28, (512, 1024, 3, 3), (9216, 9, 3, 1))
assert_size_stride(primals_29, (512,), (1,))
assert_size_stride(primals_30, (256, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_31, (256,), (1,))
assert_size_stride(primals_32, (256, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_33, (256,), (1,))
assert_size_stride(primals_34, (128, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_35, (128,), (1,))
assert_size_stride(primals_36, (128, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_37, (128,), (1,))
assert_size_stride(primals_38, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_39, (64,), (1,))
assert_size_stride(primals_40, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_41, (64,), (1,))
assert_size_stride(primals_42, (32, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_43, (32,), (1,))
assert_size_stride(primals_44, (32, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_45, (32,), (1,))
assert_size_stride(primals_46, (4, 32, 3, 3), (288, 9, 3, 1))
assert_size_stride(primals_47, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(3, 3), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 32, 64, 64), (131072, 4096, 64, 1))
buf1 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
torch.bool)
buf2 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf0,
primals_2, buf1, buf2, 524288, XBLOCK=1024, num_warps=4,
num_stages=1)
del primals_2
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(3, 3), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 32, 64, 64), (131072, 4096, 64, 1))
buf4 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
torch.bool)
buf5 = buf0
del buf0
triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf3,
primals_5, buf4, buf5, 524288, XBLOCK=1024, num_warps=4,
num_stages=1)
del primals_5
buf6 = empty_strided_cuda((4, 32, 32, 32), (32768, 1024, 32, 1),
torch.float32)
triton_poi_fused_avg_pool2d_1[grid(131072)](buf5, buf6, 131072,
XBLOCK=512, num_warps=8, num_stages=1)
buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1),
padding=(2, 2), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf8 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.bool)
buf9 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.float32)
triton_poi_fused_convolution_leaky_relu_2[grid(262144)](buf7,
primals_7, buf8, buf9, 262144, XBLOCK=512, num_warps=8,
num_stages=1)
del primals_7
buf10 = extern_kernels.convolution(buf9, primals_8, stride=(1, 1),
padding=(2, 2), 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))
buf11 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.bool)
buf12 = buf7
del buf7
triton_poi_fused_convolution_leaky_relu_2[grid(262144)](buf10,
primals_9, buf11, buf12, 262144, XBLOCK=512, num_warps=8,
num_stages=1)
del primals_9
buf13 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
torch.float32)
triton_poi_fused_avg_pool2d_3[grid(65536)](buf12, buf13, 65536,
XBLOCK=512, num_warps=4, num_stages=1)
buf14 = extern_kernels.convolution(buf13, primals_10, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf14, (4, 128, 16, 16), (32768, 256, 16, 1))
buf15 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
torch.bool)
buf16 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
torch.float32)
triton_poi_fused_convolution_leaky_relu_4[grid(131072)](buf14,
primals_11, buf15, buf16, 131072, XBLOCK=512, num_warps=8,
num_stages=1)
del primals_11
buf17 = extern_kernels.convolution(buf16, primals_12, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf17, (4, 128, 16, 16), (32768, 256, 16, 1))
buf18 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
torch.bool)
buf19 = buf14
del buf14
triton_poi_fused_convolution_leaky_relu_4[grid(131072)](buf17,
primals_13, buf18, buf19, 131072, XBLOCK=512, num_warps=8,
num_stages=1)
del primals_13
buf20 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.
float32)
triton_poi_fused_avg_pool2d_5[grid(32768)](buf19, buf20, 32768,
XBLOCK=128, num_warps=4, num_stages=1)
buf21 = extern_kernels.convolution(buf20, primals_14, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf21, (4, 256, 8, 8), (16384, 64, 8, 1))
buf22 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.bool)
buf23 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.float32)
triton_poi_fused_convolution_leaky_relu_6[grid(65536)](buf21,
primals_15, buf22, buf23, 65536, XBLOCK=256, num_warps=4,
num_stages=1)
del primals_15
buf24 = extern_kernels.convolution(buf23, primals_16, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf24, (4, 256, 8, 8), (16384, 64, 8, 1))
buf25 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.bool)
buf26 = buf21
del buf21
triton_poi_fused_convolution_leaky_relu_6[grid(65536)](buf24,
primals_17, buf25, buf26, 65536, XBLOCK=256, num_warps=4,
num_stages=1)
del primals_17
buf27 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
float32)
triton_poi_fused_avg_pool2d_7[grid(16384)](buf26, buf27, 16384,
XBLOCK=128, num_warps=4, num_stages=1)
buf28 = extern_kernels.convolution(buf27, primals_18, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf28, (4, 512, 4, 4), (8192, 16, 4, 1))
buf29 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool
)
buf30 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.
float32)
triton_poi_fused_convolution_leaky_relu_8[grid(32768)](buf28,
primals_19, buf29, buf30, 32768, XBLOCK=128, num_warps=4,
num_stages=1)
del primals_19
buf31 = extern_kernels.convolution(buf30, primals_20, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf31, (4, 512, 4, 4), (8192, 16, 4, 1))
buf32 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool
)
buf33 = buf28
del buf28
triton_poi_fused_convolution_leaky_relu_8[grid(32768)](buf31,
primals_21, buf32, buf33, 32768, XBLOCK=128, num_warps=4,
num_stages=1)
del primals_21
buf34 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.
float32)
triton_poi_fused_avg_pool2d_9[grid(8192)](buf33, buf34, 8192,
XBLOCK=128, num_warps=4, num_stages=1)
buf35 = extern_kernels.convolution(buf34, primals_22, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf35, (4, 512, 2, 2), (2048, 4, 2, 1))
buf36 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.bool)
buf37 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.
float32)
triton_poi_fused_convolution_leaky_relu_10[grid(8192)](buf35,
primals_23, buf36, buf37, 8192, XBLOCK=256, num_warps=4,
num_stages=1)
del buf35
del primals_23
buf38 = extern_kernels.convolution(buf37, primals_24, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf38, (4, 512, 2, 2), (2048, 4, 2, 1))
buf39 = empty_strided_cuda((4, 512, 2, 2), (2048, 4, 2, 1), torch.bool)
triton_poi_fused_convolution_leaky_relu_11[grid(8192)](buf38,
primals_25, buf39, 8192, XBLOCK=256, num_warps=4, num_stages=1)
buf40 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
triton_poi_fused__to_copy_12[grid(4)](buf40, 4, XBLOCK=4, num_warps
=1, num_stages=1)
buf41 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
triton_poi_fused_add_clamp_13[grid(4)](buf41, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf42 = empty_strided_cuda((4,), (1,), torch.int64)
triton_poi_fused__to_copy_12[grid(4)](buf42, 4, XBLOCK=4, num_warps
=1, num_stages=1)
buf43 = empty_strided_cuda((4,), (1,), torch.int64)
triton_poi_fused_add_clamp_13[grid(4)](buf43, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf46 = empty_strided_cuda((4,), (1,), torch.float32)
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_14[grid(4)](buf46,
4, XBLOCK=4, num_warps=1, num_stages=1)
buf48 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_14[grid(4)](buf48,
4, XBLOCK=4, num_warps=1, num_stages=1)
buf45 = buf31
del buf31
buf49 = buf45
del buf45
buf50 = buf49
del buf49
triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_15[
grid(32768)](buf50, buf41, buf42, buf39, buf38, primals_25,
buf40, buf43, buf46, buf48, 32768, XBLOCK=128, num_warps=4,
num_stages=1)
del buf38
del primals_25
buf51 = extern_kernels.convolution(buf50, primals_26, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf51, (4, 512, 4, 4), (8192, 16, 4, 1))
buf52 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool
)
triton_poi_fused_convolution_leaky_relu_16[grid(32768)](buf51,
primals_27, buf52, 32768, XBLOCK=256, num_warps=4, num_stages=1)
buf53 = reinterpret_tensor(buf24, (4, 1024, 4, 4), (16384, 16, 4, 1), 0
)
del buf24
triton_poi_fused_cat_17[grid(65536)](buf52, buf51, primals_27,
buf33, buf53, 65536, XBLOCK=512, num_warps=4, num_stages=1)
del buf51
del primals_27
buf54 = extern_kernels.convolution(buf53, primals_28, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf54, (4, 512, 4, 4), (8192, 16, 4, 1))
buf55 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool
)
triton_poi_fused_convolution_leaky_relu_16[grid(32768)](buf54,
primals_29, buf55, 32768, XBLOCK=256, num_warps=4, num_stages=1)
buf56 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
triton_poi_fused__to_copy_18[grid(8)](buf56, 8, XBLOCK=8, num_warps
=1, num_stages=1)
buf57 = empty_strided_cuda((8, 1), (1, 1), torch.int64)
triton_poi_fused_add_clamp_19[grid(8)](buf57, 8, XBLOCK=8,
num_warps=1, num_stages=1)
buf58 = empty_strided_cuda((8,), (1,), torch.int64)
triton_poi_fused__to_copy_18[grid(8)](buf58, 8, XBLOCK=8, num_warps
=1, num_stages=1)
buf59 = empty_strided_cuda((8,), (1,), torch.int64)
triton_poi_fused_add_clamp_19[grid(8)](buf59, 8, XBLOCK=8,
num_warps=1, num_stages=1)
buf62 = empty_strided_cuda((8,), (1,), torch.float32)
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_20[grid(8)](buf62,
8, XBLOCK=8, num_warps=1, num_stages=1)
buf64 = empty_strided_cuda((8, 1), (1, 1), torch.float32)
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_20[grid(8)](buf64,
8, XBLOCK=8, num_warps=1, num_stages=1)
buf61 = reinterpret_tensor(buf17, (4, 512, 8, 8), (32768, 64, 8, 1), 0)
del buf17
buf65 = buf61
del buf61
buf66 = buf65
del buf65
triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_21[
grid(131072)](buf66, buf57, buf58, buf55, buf54, primals_29,
buf56, buf59, buf62, buf64, 131072, XBLOCK=512, num_warps=8,
num_stages=1)
del buf54
del primals_29
buf67 = extern_kernels.convolution(buf66, primals_30, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf67, (4, 256, 8, 8), (16384, 64, 8, 1))
buf68 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_leaky_relu_22[grid(65536)](buf67,
primals_31, buf68, 65536, XBLOCK=512, num_warps=4, num_stages=1)
buf69 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch
.float32)
triton_poi_fused_cat_23[grid(131072)](buf68, buf67, primals_31,
buf26, buf69, 131072, XBLOCK=512, num_warps=8, num_stages=1)
del buf67
del primals_31
buf70 = extern_kernels.convolution(buf69, primals_32, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf70, (4, 256, 8, 8), (16384, 64, 8, 1))
buf71 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_leaky_relu_22[grid(65536)](buf70,
primals_33, buf71, 65536, XBLOCK=512, num_warps=4, num_stages=1)
buf72 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
triton_poi_fused__to_copy_24[grid(16)](buf72, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf73 = empty_strided_cuda((16, 1), (1, 1), torch.int64)
triton_poi_fused_add_clamp_25[grid(16)](buf73, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf74 = empty_strided_cuda((16,), (1,), torch.int64)
triton_poi_fused__to_copy_24[grid(16)](buf74, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf75 = empty_strided_cuda((16,), (1,), torch.int64)
triton_poi_fused_add_clamp_25[grid(16)](buf75, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf78 = empty_strided_cuda((16,), (1,), torch.float32)
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_26[grid(16)](buf78,
16, XBLOCK=16, num_warps=1, num_stages=1)
buf80 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_26[grid(16)](buf80,
16, XBLOCK=16, num_warps=1, num_stages=1)
buf77 = reinterpret_tensor(buf10, (4, 256, 16, 16), (65536, 256, 16,
1), 0)
del buf10
buf81 = buf77
del buf77
buf82 = buf81
del buf81
triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_27[
grid(262144)](buf82, buf73, buf74, buf71, buf70, primals_33,
buf72, buf75, buf78, buf80, 262144, XBLOCK=512, num_warps=8,
num_stages=1)
del primals_33
buf83 = extern_kernels.convolution(buf82, primals_34, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf83, (4, 128, 16, 16), (32768, 256, 16, 1))
buf84 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
torch.bool)
triton_poi_fused_convolution_leaky_relu_28[grid(131072)](buf83,
primals_35, buf84, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
buf85 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1),
torch.float32)
triton_poi_fused_cat_29[grid(262144)](buf84, buf83, primals_35,
buf19, buf85, 262144, XBLOCK=512, num_warps=8, num_stages=1)
del buf83
del primals_35
buf86 = extern_kernels.convolution(buf85, primals_36, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf86, (4, 128, 16, 16), (32768, 256, 16, 1))
buf87 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
torch.bool)
triton_poi_fused_convolution_leaky_relu_28[grid(131072)](buf86,
primals_37, buf87, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
buf88 = empty_strided_cuda((32, 1), (1, 1), torch.int64)
triton_poi_fused__to_copy_30[grid(32)](buf88, 32, XBLOCK=32,
num_warps=1, num_stages=1)
buf89 = empty_strided_cuda((32, 1), (1, 1), torch.int64)
triton_poi_fused_add_clamp_31[grid(32)](buf89, 32, XBLOCK=32,
num_warps=1, num_stages=1)
buf90 = empty_strided_cuda((32,), (1,), torch.int64)
triton_poi_fused__to_copy_30[grid(32)](buf90, 32, XBLOCK=32,
num_warps=1, num_stages=1)
buf91 = empty_strided_cuda((32,), (1,), torch.int64)
triton_poi_fused_add_clamp_31[grid(32)](buf91, 32, XBLOCK=32,
num_warps=1, num_stages=1)
buf94 = empty_strided_cuda((32,), (1,), torch.float32)
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_32[grid(32)](buf94,
32, XBLOCK=32, num_warps=1, num_stages=1)
buf96 = empty_strided_cuda((32, 1), (1, 1), torch.float32)
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_32[grid(32)](buf96,
32, XBLOCK=32, num_warps=1, num_stages=1)
buf93 = reinterpret_tensor(buf3, (4, 128, 32, 32), (131072, 1024,
32, 1), 0)
del buf3
buf97 = buf93
del buf93
buf98 = buf97
del buf97
triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_33[
grid(524288)](buf98, buf89, buf90, buf87, buf86, primals_37,
buf88, buf91, buf94, buf96, 524288, XBLOCK=512, num_warps=8,
num_stages=1)
del buf86
del primals_37
buf99 = extern_kernels.convolution(buf98, primals_38, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf99, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf100 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.bool)
triton_poi_fused_convolution_leaky_relu_34[grid(262144)](buf99,
primals_39, buf100, 262144, XBLOCK=1024, num_warps=4, num_stages=1)
buf101 = empty_strided_cuda((4, 128, 32, 32), (131072, 1024, 32, 1),
torch.float32)
triton_poi_fused_cat_35[grid(524288)](buf100, buf99, primals_39,
buf12, buf101, 524288, XBLOCK=512, num_warps=8, num_stages=1)
del buf99
del primals_39
buf102 = extern_kernels.convolution(buf101, primals_40, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf102, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf103 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.bool)
triton_poi_fused_convolution_leaky_relu_34[grid(262144)](buf102,
primals_41, buf103, 262144, XBLOCK=1024, num_warps=4, num_stages=1)
buf104 = empty_strided_cuda((64, 1), (1, 1), torch.int64)
triton_poi_fused__to_copy_36[grid(64)](buf104, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf105 = empty_strided_cuda((64, 1), (1, 1), torch.int64)
triton_poi_fused_add_clamp_37[grid(64)](buf105, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf106 = empty_strided_cuda((64,), (1,), torch.int64)
triton_poi_fused__to_copy_36[grid(64)](buf106, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf107 = empty_strided_cuda((64,), (1,), torch.int64)
triton_poi_fused_add_clamp_37[grid(64)](buf107, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf110 = empty_strided_cuda((64,), (1,), torch.float32)
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_38[grid(64)](buf110,
64, XBLOCK=64, num_warps=1, num_stages=1)
buf112 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_38[grid(64)](buf112,
64, XBLOCK=64, num_warps=1, num_stages=1)
buf109 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.float32)
buf113 = buf109
del buf109
buf114 = buf113
del buf113
triton_poi_fused__unsafe_index_add_convolution_leaky_relu_mul_sub_39[
grid(1048576)](buf114, buf105, buf106, buf103, buf102,
primals_41, buf104, buf107, buf110, buf112, 1048576, XBLOCK=
1024, num_warps=4, num_stages=1)
del buf102
del primals_41
buf115 = extern_kernels.convolution(buf114, primals_42, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf115, (4, 32, 64, 64), (131072, 4096, 64, 1))
buf116 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_leaky_relu_40[grid(524288)](buf115,
primals_43, buf116, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
buf117 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.float32)
triton_poi_fused_cat_41[grid(1048576)](buf116, buf115, primals_43,
buf5, buf117, 1048576, XBLOCK=512, num_warps=8, num_stages=1)
del primals_43
buf118 = extern_kernels.convolution(buf117, primals_44, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf118, (4, 32, 64, 64), (131072, 4096, 64, 1))
buf119 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
torch.bool)
buf120 = buf115
del buf115
triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf118,
primals_45, buf119, buf120, 524288, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf118
del primals_45
buf121 = extern_kernels.convolution(buf120, primals_46, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf121, (4, 4, 64, 64), (16384, 4096, 64, 1))
buf122 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1),
torch.bool)
buf123 = reinterpret_tensor(buf70, (4, 4, 64, 64), (16384, 4096, 64,
1), 0)
del buf70
triton_poi_fused_convolution_leaky_relu_42[grid(65536)](buf121,
primals_47, buf122, buf123, 65536, XBLOCK=512, num_warps=4,
num_stages=1)
del buf121
del primals_47
return (buf123, primals_1, primals_3, primals_4, primals_6, primals_8,
primals_10, primals_12, primals_14, primals_16, primals_18,
primals_20, primals_22, primals_24, primals_26, primals_28,
primals_30, primals_32, primals_34, primals_36, primals_38,
primals_40, primals_42, primals_44, primals_46, buf1, buf2, buf4,
buf5, buf6, buf8, buf9, buf11, buf12, buf13, buf15, buf16, buf18,
buf19, buf20, buf22, buf23, buf25, buf26, buf27, buf29, buf30,
buf32, buf33, buf34, buf36, buf37, buf39, buf40, buf41, buf42,
buf43, buf46, buf48, buf50, buf52, buf53, buf55, buf56, buf57,
buf58, buf59, buf62, buf64, buf66, buf68, buf69, buf71, buf72,
buf73, buf74, buf75, buf78, buf80, buf82, buf84, buf85, buf87,
buf88, buf89, buf90, buf91, buf94, buf96, buf98, buf100, buf101,
buf103, buf104, buf105, buf106, buf107, buf110, buf112, buf114,
buf116, buf117, buf119, buf120, buf122)
class down(nn.Module):
def __init__(self, inChannels, outChannels, filterSize):
super(down, self).__init__()
self.conv1 = nn.Conv2d(inChannels, outChannels, filterSize, stride=
1, padding=int((filterSize - 1) / 2))
self.conv2 = nn.Conv2d(outChannels, outChannels, filterSize, stride
=1, padding=int((filterSize - 1) / 2))
def forward(self, x):
x = F.avg_pool2d(x, 2)
x = F.leaky_relu(self.conv1(x), negative_slope=0.1)
x = F.leaky_relu(self.conv2(x), negative_slope=0.1)
return x
class up(nn.Module):
def __init__(self, inChannels, outChannels):
super(up, self).__init__()
self.conv1 = nn.Conv2d(inChannels, outChannels, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(2 * outChannels, outChannels, 3, stride=1,
padding=1)
def forward(self, x, skpCn):
x = F.interpolate(x, scale_factor=2, mode='bilinear')
x = F.leaky_relu(self.conv1(x), negative_slope=0.1)
x = F.leaky_relu(self.conv2(torch.cat((x, skpCn), 1)),
negative_slope=0.1)
return x
class UNetNew(nn.Module):
def __init__(self, inChannels, outChannels):
super(UNetNew, self).__init__()
self.conv1 = nn.Conv2d(inChannels, 32, 7, stride=1, padding=3)
self.conv2 = nn.Conv2d(32, 32, 7, stride=1, padding=3)
self.down1 = down(32, 64, 5)
self.down2 = down(64, 128, 3)
self.down3 = down(128, 256, 3)
self.down4 = down(256, 512, 3)
self.down5 = down(512, 512, 3)
self.up1 = up(512, 512)
self.up2 = up(512, 256)
self.up3 = up(256, 128)
self.up4 = up(128, 64)
self.up5 = up(64, 32)
self.conv3 = nn.Conv2d(32, outChannels, 3, stride=1, 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.down1.conv1.weight
primals_7 = self.down1.conv1.bias
primals_8 = self.down1.conv2.weight
primals_9 = self.down1.conv2.bias
primals_10 = self.down2.conv1.weight
primals_11 = self.down2.conv1.bias
primals_12 = self.down2.conv2.weight
primals_13 = self.down2.conv2.bias
primals_14 = self.down3.conv1.weight
primals_15 = self.down3.conv1.bias
primals_16 = self.down3.conv2.weight
primals_17 = self.down3.conv2.bias
primals_18 = self.down4.conv1.weight
primals_19 = self.down4.conv1.bias
primals_20 = self.down4.conv2.weight
primals_21 = self.down4.conv2.bias
primals_22 = self.down5.conv1.weight
primals_23 = self.down5.conv1.bias
primals_24 = self.down5.conv2.weight
primals_25 = self.down5.conv2.bias
primals_26 = self.up1.conv1.weight
primals_27 = self.up1.conv1.bias
primals_28 = self.up1.conv2.weight
primals_29 = self.up1.conv2.bias
primals_30 = self.up2.conv1.weight
primals_31 = self.up2.conv1.bias
primals_32 = self.up2.conv2.weight
primals_33 = self.up2.conv2.bias
primals_34 = self.up3.conv1.weight
primals_35 = self.up3.conv1.bias
primals_36 = self.up3.conv2.weight
primals_37 = self.up3.conv2.bias
primals_38 = self.up4.conv1.weight
primals_39 = self.up4.conv1.bias
primals_40 = self.up4.conv2.weight
primals_41 = self.up4.conv2.bias
primals_42 = self.up5.conv1.weight
primals_43 = self.up5.conv1.bias
primals_44 = self.up5.conv2.weight
primals_45 = self.up5.conv2.bias
primals_46 = self.conv3.weight
primals_47 = self.conv3.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, 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, primals_32, primals_33, primals_34,
primals_35, primals_36, primals_37, primals_38, primals_39,
primals_40, primals_41, primals_42, primals_43, primals_44,
primals_45, primals_46, primals_47])
return output[0]
|
brainma/ASRNet
|
UNet
| false
| 9,998
|
[
"MIT"
] | 0
|
b88edbcfbcee2cc77f7f4b2a8d139ced303a4f14
|
https://github.com/brainma/ASRNet/tree/b88edbcfbcee2cc77f7f4b2a8d139ced303a4f14
|
SchedulerTestNet
|
import torch
from torch.nn import functional as F
class SchedulerTestNet(torch.nn.Module):
"""
adapted from: https://github.com/pytorch/pytorch/blob/master/test/test_optim.py
"""
def __init__(self):
super(SchedulerTestNet, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 1, 1)
self.conv2 = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv2(F.relu(self.conv1(x)))
def get_inputs():
return [torch.rand([4, 1, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@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)
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, None)
tmp1 = tl.load(in_ptr0 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tmp4 = tl.full([1], 0, tl.int32)
tmp5 = triton_helpers.maximum(tmp4, tmp3)
tl.store(in_out_ptr0 + x0, tmp5, None)
@triton.jit
def triton_poi_fused_convolution_1(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)
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, None)
tmp1 = tl.load(in_ptr0 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tl.store(in_out_ptr0 + x0, tmp3, None)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (1, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_2, (1,), (1,))
assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
assert_size_stride(primals_4, (1, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_5, (1,), (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, 1, 64, 64), (4096, 4096, 64, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(16384)](buf1, primals_2,
16384, XBLOCK=256, num_warps=4, num_stages=1)
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, 1, 64, 64), (4096, 4096, 64, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_1[grid(16384)](buf3, primals_5, 16384,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
return buf3, primals_1, primals_3, primals_4, buf1
class SchedulerTestNetNew(torch.nn.Module):
"""
adapted from: https://github.com/pytorch/pytorch/blob/master/test/test_optim.py
"""
def __init__(self):
super(SchedulerTestNetNew, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 1, 1)
self.conv2 = torch.nn.Conv2d(1, 1, 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]
|
bartolkaruza/pytorch-lightning-bolts
|
SchedulerTestNet
| false
| 9,999
|
[
"Apache-2.0"
] | 0
|
2e903c333c37ea83394c7da2ce826de1b82fb356
|
https://github.com/bartolkaruza/pytorch-lightning-bolts/tree/2e903c333c37ea83394c7da2ce826de1b82fb356
|
BasicBlock
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1, norm='instancenorm'):
super(BasicBlock, self).__init__()
self.norm = norm
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=
stride, padding=1, bias=False)
self.bn1 = nn.GroupNorm(planes, planes, affine=True
) if self.norm == 'instancenorm' else nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,
padding=1, bias=False)
self.bn2 = nn.GroupNorm(planes, planes, affine=True
) if self.norm == 'instancenorm' else nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(nn.Conv2d(in_planes, self.
expansion * planes, kernel_size=1, stride=stride, bias=
False), nn.GroupNorm(self.expansion * planes, self.
expansion * planes, affine=True) if self.norm ==
'instancenorm' else nn.BatchNorm2d(self.expansion * planes))
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_planes': 4, 'planes': 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_per_fused_native_group_norm_relu_0(in_ptr0, in_ptr1, in_ptr2,
out_ptr0, out_ptr2, out_ptr3, 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
x2 = xindex % 4
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp24 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp26 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tl.where(xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = tmp0 - tmp10
tmp18 = 16.0
tmp19 = tmp16 / tmp18
tmp20 = 1e-05
tmp21 = tmp19 + tmp20
tmp22 = libdevice.rsqrt(tmp21)
tmp23 = tmp17 * tmp22
tmp25 = tmp23 * tmp24
tmp27 = tmp25 + tmp26
tmp28 = tl.full([1, 1], 0, tl.int32)
tmp29 = triton_helpers.maximum(tmp28, tmp27)
tl.store(out_ptr2 + (r1 + 16 * x0), tmp29, xmask)
tl.store(out_ptr3 + x0, tmp22, xmask)
tl.store(out_ptr0 + x0, tmp10, xmask)
@triton.jit
def triton_per_fused_add_native_group_norm_relu_threshold_backward_1(in_ptr0,
in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr2, out_ptr3, out_ptr4,
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
x2 = xindex % 4
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp24 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp26 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp28 = tl.load(in_ptr3 + (r1 + 16 * x0), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tl.where(xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = tmp0 - tmp10
tmp18 = 16.0
tmp19 = tmp16 / tmp18
tmp20 = 1e-05
tmp21 = tmp19 + tmp20
tmp22 = libdevice.rsqrt(tmp21)
tmp23 = tmp17 * tmp22
tmp25 = tmp23 * tmp24
tmp27 = tmp25 + tmp26
tmp29 = tmp27 + tmp28
tmp30 = tl.full([1, 1], 0, tl.int32)
tmp31 = triton_helpers.maximum(tmp30, tmp29)
tmp32 = 0.0
tmp33 = tmp31 <= tmp32
tl.store(out_ptr2 + (r1 + 16 * x0), tmp31, xmask)
tl.store(out_ptr3 + (r1 + 16 * x0), tmp33, xmask)
tl.store(out_ptr4 + x0, tmp22, xmask)
tl.store(out_ptr0 + x0, tmp10, 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, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (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 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
get_raw_stream(0)
triton_per_fused_native_group_norm_relu_0[grid(16)](buf0, primals_3,
primals_4, buf1, buf5, buf4, 16, 16, XBLOCK=1, num_warps=2,
num_stages=1)
del primals_4
buf6 = extern_kernels.convolution(buf5, primals_5, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf10 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
triton_per_fused_add_native_group_norm_relu_threshold_backward_1[grid
(16)](buf6, primals_6, primals_7, primals_2, buf7, buf11, buf12,
buf10, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
del primals_7
return (buf11, primals_1, primals_2, primals_3, primals_5, primals_6,
buf0, reinterpret_tensor(buf1, (4, 4), (4, 1), 0),
reinterpret_tensor(buf4, (4, 4), (4, 1), 0), buf5, buf6,
reinterpret_tensor(buf7, (4, 4), (4, 1), 0), reinterpret_tensor(
buf10, (4, 4), (4, 1), 0), buf12)
class BasicBlockNew(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1, norm='instancenorm'):
super(BasicBlockNew, self).__init__()
self.norm = norm
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=
stride, padding=1, bias=False)
self.bn1 = nn.GroupNorm(planes, planes, affine=True
) if self.norm == 'instancenorm' else nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,
padding=1, bias=False)
self.bn2 = nn.GroupNorm(planes, planes, affine=True
) if self.norm == 'instancenorm' else nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(nn.Conv2d(in_planes, self.
expansion * planes, kernel_size=1, stride=stride, bias=
False), nn.GroupNorm(self.expansion * planes, self.
expansion * planes, affine=True) if self.norm ==
'instancenorm' else nn.BatchNorm2d(self.expansion * planes))
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_3 = self.bn1.weight
primals_4 = self.bn1.bias
primals_5 = self.conv2.weight
primals_6 = self.bn2.weight
primals_7 = self.bn2.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
cuijiaxing/DatasetCondensation
|
BasicBlock
| false
| 10,000
|
[
"MIT"
] | 0
|
aec1f7bf08d10d0f9e5d2fd5c2e4193d9687fefd
|
https://github.com/cuijiaxing/DatasetCondensation/tree/aec1f7bf08d10d0f9e5d2fd5c2e4193d9687fefd
|
ParsingRelationLoss
|
import torch
import torch.nn.modules
import torch.nn as nn
class ParsingRelationLoss(nn.Module):
def __init__(self):
super(ParsingRelationLoss, self).__init__()
def forward(self, logits):
_n, _c, h, _w = logits.shape
loss_all = []
for i in range(0, h - 1):
loss_all.append(logits[:, :, i, :] - logits[:, :, i + 1, :])
loss = torch.cat(loss_all)
return torch.nn.functional.smooth_l1_loss(loss, torch.zeros_like(loss))
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.modules
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_smooth_l1_loss_0(in_out_ptr0, in_ptr0, xnumel, rnumel,
XBLOCK: tl.constexpr):
rnumel = 192
RBLOCK: tl.constexpr = 256
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, :]
rmask = rindex < rnumel
r2 = rindex // 16
r0 = rindex % 4
r1 = rindex // 4 % 4
tmp0 = r2
tl.full([1, 1], 0, tl.int64)
tmp3 = tl.full([1, 1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + tl.broadcast_to(r0 + 16 * r1 + 64 * r2, [
XBLOCK, RBLOCK]), rmask & tmp4, other=0.0)
tmp6 = tl.load(in_ptr0 + tl.broadcast_to(4 + r0 + 16 * r1 + 64 * r2, [
XBLOCK, RBLOCK]), rmask & tmp4, other=0.0)
tmp7 = tmp5 - tmp6
tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
tmp9 = tl.where(tmp4, tmp7, tmp8)
tmp10 = tmp0 >= tmp3
tmp11 = tl.full([1, 1], 8, tl.int64)
tmp12 = tmp0 < tmp11
tmp13 = tmp10 & tmp12
tmp14 = tl.load(in_ptr0 + tl.broadcast_to(4 + r0 + 16 * r1 + 64 * (-4 +
r2), [XBLOCK, RBLOCK]), rmask & tmp13, other=0.0)
tmp15 = tl.load(in_ptr0 + tl.broadcast_to(8 + r0 + 16 * r1 + 64 * (-4 +
r2), [XBLOCK, RBLOCK]), rmask & tmp13, other=0.0)
tmp16 = tmp14 - tmp15
tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
tmp18 = tl.where(tmp13, tmp16, tmp17)
tmp19 = tmp0 >= tmp11
tl.full([1, 1], 12, tl.int64)
tmp22 = tl.load(in_ptr0 + tl.broadcast_to(8 + r0 + 16 * r1 + 64 * (-8 +
r2), [XBLOCK, RBLOCK]), rmask & tmp19, other=0.0)
tmp23 = tl.load(in_ptr0 + tl.broadcast_to(12 + r0 + 16 * r1 + 64 * (-8 +
r2), [XBLOCK, RBLOCK]), rmask & tmp19, other=0.0)
tmp24 = tmp22 - tmp23
tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
tmp26 = tl.where(tmp19, tmp24, tmp25)
tmp27 = tl.where(tmp13, tmp18, tmp26)
tmp28 = tl.where(tmp4, tmp9, tmp27)
tmp29 = tl_math.abs(tmp28)
tmp30 = 1.0
tmp31 = tmp29 < tmp30
tmp32 = tmp29 * tmp29
tmp33 = 0.5
tmp34 = tmp32 * tmp33
tmp35 = tmp34 * tmp30
tmp36 = tmp29 - tmp33
tmp37 = tl.where(tmp31, tmp35, tmp36)
tmp38 = tl.broadcast_to(tmp37, [XBLOCK, RBLOCK])
tmp40 = tl.where(rmask, tmp38, 0)
tmp41 = tl.sum(tmp40, 1)[:, None]
tmp42 = 192.0
tmp43 = tmp41 / tmp42
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp43, 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)
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = buf1
del buf1
get_raw_stream(0)
triton_per_fused_smooth_l1_loss_0[grid(1)](buf2, arg0_1, 1, 192,
XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
return buf2,
class ParsingRelationLossNew(nn.Module):
def __init__(self):
super(ParsingRelationLossNew, self).__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
daveMcelf/Ultra-Fast-Lane-Detection
|
ParsingRelationLoss
| false
| 10,001
|
[
"MIT"
] | 0
|
357f1f0f4538a125e9a9c1509e5f72ce2321f078
|
https://github.com/daveMcelf/Ultra-Fast-Lane-Detection/tree/357f1f0f4538a125e9a9c1509e5f72ce2321f078
|
Bottleneck_nobn
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck_nobn(nn.Module):
def __init__(self, in_planes, growth_rate):
super(Bottleneck_nobn, self).__init__()
self.conv1 = nn.Conv2d(in_planes, 4 * growth_rate, kernel_size=1,
bias=False)
self.conv2 = nn.Conv2d(4 * growth_rate, growth_rate, kernel_size=3,
padding=1, bias=False)
def forward(self, x):
out = self.conv1(F.relu(x))
out = self.conv2(F.relu(out))
out = torch.cat([out, x], 1)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_planes': 4, 'growth_rate': 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
@triton.jit
def triton_poi_fused_relu_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.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_relu_1(in_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_out_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(in_out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_cat_2(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):
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, (16, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (4, 16, 3, 3), (144, 9, 3, 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_relu_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
num_warps=4, num_stages=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, 16, 4, 4), (256, 16, 4, 1))
buf2 = buf1
del buf1
triton_poi_fused_relu_1[grid(1024)](buf2, 1024, XBLOCK=128,
num_warps=4, num_stages=1)
buf3 = extern_kernels.convolution(buf2, primals_3, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
triton_poi_fused_cat_2[grid(512)](buf3, primals_1, buf4, 512,
XBLOCK=256, num_warps=4, num_stages=1)
del buf3
del primals_1
return buf4, primals_2, primals_3, buf0, buf2
class Bottleneck_nobnNew(nn.Module):
def __init__(self, in_planes, growth_rate):
super(Bottleneck_nobnNew, self).__init__()
self.conv1 = nn.Conv2d(in_planes, 4 * growth_rate, kernel_size=1,
bias=False)
self.conv2 = nn.Conv2d(4 * growth_rate, growth_rate, kernel_size=3,
padding=1, bias=False)
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = self.conv2.weight
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
daroczyb/tangent_sensitivity
|
Bottleneck_nobn
| false
| 10,002
|
[
"MIT"
] | 0
|
925258ab381ca5ab95620c411f72836a90baeb7f
|
https://github.com/daroczyb/tangent_sensitivity/tree/925258ab381ca5ab95620c411f72836a90baeb7f
|
MLP1x
|
import torch
import torch.nn as nn
class MLP1x(nn.Module):
def __init__(self, dim, hidd, num_classes=10):
super(MLP1x, self).__init__()
self.fc1 = nn.Linear(dim, hidd)
self.fc2 = nn.Linear(hidd, num_classes)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'dim': 4, 'hidd': 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 = 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_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 + x4, tmp6, xmask)
@triton.jit
def triton_poi_fused_view_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 % 4
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * (x1 % 4 // 4) + 64 * ((4 *
(x1 // 4 % 4) + x1 % 4) // 16)), xmask)
tl.store(out_ptr0 + x2, tmp0, 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, (10, 4), (4, 1))
assert_size_stride(primals_5, (10,), (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
buf4 = 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, buf4, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
triton_poi_fused_view_1[grid(256)](buf1, buf2, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf1
buf3 = empty_strided_cuda((64, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_5, buf2, reinterpret_tensor(primals_4,
(4, 10), (1, 4), 0), alpha=1, beta=1, out=buf3)
del primals_5
return reinterpret_tensor(buf3, (4, 4, 4, 10), (160, 40, 10, 1), 0
), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf2, primals_4, buf4
class MLP1xNew(nn.Module):
def __init__(self, dim, hidd, num_classes=10):
super(MLP1xNew, self).__init__()
self.fc1 = nn.Linear(dim, hidd)
self.fc2 = nn.Linear(hidd, num_classes)
self.relu = nn.ReLU(inplace=True)
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]
|
daroczyb/tangent_sensitivity
|
MLP1x
| false
| 10,003
|
[
"MIT"
] | 0
|
925258ab381ca5ab95620c411f72836a90baeb7f
|
https://github.com/daroczyb/tangent_sensitivity/tree/925258ab381ca5ab95620c411f72836a90baeb7f
|
Discriminator
|
import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
class Discriminator(nn.Module):
def __init__(self, img_shape, hidden_dim=1024):
super().__init__()
in_dim = int(np.prod(img_shape))
self.fc1 = nn.Linear(in_dim, hidden_dim)
self.fc2 = nn.Linear(self.fc1.out_features, self.fc1.out_features // 2)
self.fc3 = nn.Linear(self.fc2.out_features, self.fc2.out_features // 2)
self.fc4 = nn.Linear(self.fc3.out_features, 1)
def forward(self, img):
x = img.view(img.size(0), -1)
x = F.leaky_relu(self.fc1(x), 0.2)
x = F.dropout(x, 0.3)
x = F.leaky_relu(self.fc2(x), 0.2)
x = F.dropout(x, 0.3)
x = F.leaky_relu(self.fc3(x), 0.2)
x = F.dropout(x, 0.3)
return torch.sigmoid(self.fc4(x))
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'img_shape': 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 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_leaky_relu_0(in_ptr0, in_ptr1, 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)
x2 = xindex
x0 = xindex % 1024
tmp0 = tl.load(in_ptr0 + x2, None)
tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.2
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x2, tmp4, None)
tl.store(out_ptr1 + x2, tmp7, None)
@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, in_ptr1, 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)
x2 = xindex
x0 = xindex % 512
tmp0 = tl.load(in_ptr0 + x2, None)
tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.2
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x2, tmp4, None)
tl.store(out_ptr1 + x2, tmp7, None)
@triton.jit
def triton_poi_fused_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 256
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.2
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr1 + x2, tmp7, xmask)
@triton.jit
def triton_poi_fused_sigmoid_3(in_out_ptr0, in_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_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, (4, 4), (4, 1))
assert_size_stride(primals_2, (1024, 4), (4, 1))
assert_size_stride(primals_3, (1024,), (1,))
assert_size_stride(primals_4, (512, 1024), (1024, 1))
assert_size_stride(primals_5, (512,), (1,))
assert_size_stride(primals_6, (256, 512), (512, 1))
assert_size_stride(primals_7, (256,), (1,))
assert_size_stride(primals_8, (1, 256), (256, 1))
assert_size_stride(primals_9, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 1024
), (1, 4), 0), out=buf0)
del primals_2
buf1 = empty_strided_cuda((4, 1024), (1024, 1), torch.bool)
buf2 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_leaky_relu_0[grid(4096)](buf0, primals_3, buf1,
buf2, 4096, XBLOCK=256, num_warps=4, num_stages=1)
del buf0
del primals_3
buf3 = torch.ops.aten.native_dropout.default(buf2, 0.3, True)
del buf2
buf4 = buf3[0]
buf5 = buf3[1]
del buf3
buf6 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
extern_kernels.mm(buf4, reinterpret_tensor(primals_4, (1024, 512),
(1, 1024), 0), out=buf6)
buf7 = empty_strided_cuda((4, 512), (512, 1), torch.bool)
buf8 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
triton_poi_fused_leaky_relu_1[grid(2048)](buf6, primals_5, buf7,
buf8, 2048, XBLOCK=128, num_warps=4, num_stages=1)
del buf6
del primals_5
buf9 = torch.ops.aten.native_dropout.default(buf8, 0.3, True)
del buf8
buf10 = buf9[0]
buf11 = buf9[1]
del buf9
buf12 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
extern_kernels.mm(buf10, reinterpret_tensor(primals_6, (512, 256),
(1, 512), 0), out=buf12)
buf13 = empty_strided_cuda((4, 256), (256, 1), torch.bool)
buf14 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
triton_poi_fused_leaky_relu_2[grid(1024)](buf12, primals_7, buf13,
buf14, 1024, XBLOCK=128, num_warps=4, num_stages=1)
del buf12
del primals_7
buf15 = torch.ops.aten.native_dropout.default(buf14, 0.3, True)
del buf14
buf16 = buf15[0]
buf17 = buf15[1]
del buf15
buf18 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.mm(buf16, reinterpret_tensor(primals_8, (256, 1), (1,
256), 0), out=buf18)
buf19 = buf18
del buf18
triton_poi_fused_sigmoid_3[grid(4)](buf19, primals_9, 4, XBLOCK=4,
num_warps=1, num_stages=1)
del primals_9
return (buf19, primals_1, buf1, buf4, buf5, buf7, buf10, buf11, buf13,
buf16, buf17, buf19, primals_8, primals_6, primals_4)
class DiscriminatorNew(nn.Module):
def __init__(self, img_shape, hidden_dim=1024):
super().__init__()
in_dim = int(np.prod(img_shape))
self.fc1 = nn.Linear(in_dim, hidden_dim)
self.fc2 = nn.Linear(self.fc1.out_features, self.fc1.out_features // 2)
self.fc3 = nn.Linear(self.fc2.out_features, self.fc2.out_features // 2)
self.fc4 = nn.Linear(self.fc3.out_features, 1)
def forward(self, input_0):
primals_2 = self.fc1.weight
primals_3 = self.fc1.bias
primals_4 = self.fc2.weight
primals_5 = self.fc2.bias
primals_6 = self.fc3.weight
primals_7 = self.fc3.bias
primals_8 = self.fc4.weight
primals_9 = self.fc4.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])
return output[0]
|
bartolkaruza/pytorch-lightning-bolts
|
Discriminator
| false
| 10,004
|
[
"Apache-2.0"
] | 0
|
2e903c333c37ea83394c7da2ce826de1b82fb356
|
https://github.com/bartolkaruza/pytorch-lightning-bolts/tree/2e903c333c37ea83394c7da2ce826de1b82fb356
|
NCHWLayerNorm
|
import torch
from torch import nn
class NCHWLayerNorm(nn.LayerNorm):
"""Applies LayerNorm to the channel dimension of NCHW tensors."""
def forward(self, x):
x = x.permute(0, 2, 3, 1)
x = super().forward(x)
return x.permute(0, 3, 1, 2)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'normalized_shape': 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.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_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 % 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
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 + x2, tmp8, xmask)
tl.store(out_ptr1 + x2, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
constexpr):
ynumel = 64
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 % 16
y1 = yindex // 16
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y3, ymask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + y3, ymask, 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
tl.store(out_ptr0 + (x2 + 4 * y3), tmp8, xmask & ymask)
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,), (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, 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(64, 4)](primals_1, buf0,
buf1, primals_2, primals_3, buf2, 64, 4, XBLOCK=4, YBLOCK=64,
num_warps=4, num_stages=1)
del buf0
del buf1
del primals_2
del primals_3
return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 1, 16, 4), 0), primals_1
class NCHWLayerNormNew(nn.LayerNorm):
"""Applies LayerNorm to the channel dimension of NCHW tensors."""
def forward(self, input_0):
primals_2 = self.weight
primals_3 = self.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
cobypenso/pytorch-generative
|
NCHWLayerNorm
| false
| 10,005
|
[
"MIT"
] | 0
|
72d1a3d8045179bd3a83ee3783aa070e74a1e400
|
https://github.com/cobypenso/pytorch-generative/tree/72d1a3d8045179bd3a83ee3783aa070e74a1e400
|
Transition_nobn
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Transition_nobn(nn.Module):
def __init__(self, in_planes, out_planes):
super(Transition_nobn, self).__init__()
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False)
def forward(self, x):
out = self.conv(F.relu(x))
out = F.avg_pool2d(out, 2)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_planes': 4, 'out_planes': 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
@triton.jit
def triton_poi_fused_relu_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.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_avg_pool2d_1(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 % 2
x1 = xindex // 2
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), xmask, eviction_policy=
'evict_last')
tmp2 = tmp1 + tmp0
tmp4 = tmp3 + tmp2
tmp6 = tmp5 + tmp4
tmp7 = 0.25
tmp8 = tmp6 * tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 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_relu_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
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, 4, 4, 4), (64, 16, 4, 1))
buf2 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
triton_poi_fused_avg_pool2d_1[grid(64)](buf1, buf2, 64, XBLOCK=64,
num_warps=1, num_stages=1)
return buf2, primals_2, buf0, buf1
class Transition_nobnNew(nn.Module):
def __init__(self, in_planes, out_planes):
super(Transition_nobnNew, self).__init__()
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False)
def forward(self, input_0):
primals_2 = self.conv.weight
primals_1 = input_0
output = call([primals_1, primals_2])
return output[0]
|
daroczyb/tangent_sensitivity
|
Transition_nobn
| false
| 10,006
|
[
"MIT"
] | 0
|
925258ab381ca5ab95620c411f72836a90baeb7f
|
https://github.com/daroczyb/tangent_sensitivity/tree/925258ab381ca5ab95620c411f72836a90baeb7f
|
Illumination_Alone
|
from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
def get_conv2d_layer(in_c, out_c, k, s, p=0, dilation=1, groups=1):
return nn.Conv2d(in_channels=in_c, out_channels=out_c, kernel_size=k,
stride=s, padding=p, dilation=dilation, groups=groups)
class Illumination_Alone(nn.Module):
def __init__(self, opts):
super().__init__()
self.opts = opts
self.conv1 = get_conv2d_layer(in_c=1, out_c=32, k=5, s=1, p=2)
self.conv2 = get_conv2d_layer(in_c=32, out_c=32, k=5, s=1, p=2)
self.conv3 = get_conv2d_layer(in_c=32, out_c=32, k=5, s=1, p=2)
self.conv4 = get_conv2d_layer(in_c=32, out_c=32, k=5, s=1, p=2)
self.conv5 = get_conv2d_layer(in_c=32, out_c=1, k=1, s=1, p=0)
self.leaky_relu_1 = nn.LeakyReLU(0.2, inplace=True)
self.leaky_relu_2 = nn.LeakyReLU(0.2, inplace=True)
self.leaky_relu_3 = nn.LeakyReLU(0.2, inplace=True)
self.leaky_relu_4 = nn.LeakyReLU(0.2, inplace=True)
self.relu = nn.ReLU()
def forward(self, l):
x = l
x1 = self.leaky_relu_1(self.conv1(x))
x2 = self.leaky_relu_2(self.conv2(x1))
x3 = self.leaky_relu_3(self.conv3(x2))
x4 = self.leaky_relu_4(self.conv4(x3))
x5 = self.relu(self.conv5(x4))
return x5
def get_inputs():
return [torch.rand([4, 1, 64, 64])]
def get_init_inputs():
return [[], {'opts': _mock_config()}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime 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_convolution_leaky_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 // 4096 % 32
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.2
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(in_out_ptr0 + x3, tmp7, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_1(in_out_ptr0,
in_ptr0, 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
tmp0 = tl.load(in_out_ptr0 + x0, None)
tmp1 = tl.load(in_ptr0 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tmp4 = tl.full([1], 0, tl.int32)
tmp5 = triton_helpers.maximum(tmp4, tmp3)
tmp6 = 0.0
tmp7 = tmp5 <= tmp6
tl.store(in_out_ptr0 + x0, tmp5, None)
tl.store(out_ptr0 + x0, tmp7, 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, (4, 1, 64, 64), (4096, 4096, 64, 1))
assert_size_stride(primals_2, (32, 1, 5, 5), (25, 25, 5, 1))
assert_size_stride(primals_3, (32,), (1,))
assert_size_stride(primals_4, (32, 32, 5, 5), (800, 25, 5, 1))
assert_size_stride(primals_5, (32,), (1,))
assert_size_stride(primals_6, (32, 32, 5, 5), (800, 25, 5, 1))
assert_size_stride(primals_7, (32,), (1,))
assert_size_stride(primals_8, (32, 32, 5, 5), (800, 25, 5, 1))
assert_size_stride(primals_9, (32,), (1,))
assert_size_stride(primals_10, (1, 32, 1, 1), (32, 1, 1, 1))
assert_size_stride(primals_11, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,
1), padding=(2, 2), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 32, 64, 64), (131072, 4096, 64, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf1,
primals_3, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_3
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
padding=(2, 2), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 32, 64, 64), (131072, 4096, 64, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf3,
primals_5, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_5
buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
padding=(2, 2), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 32, 64, 64), (131072, 4096, 64, 1))
buf5 = buf4
del buf4
triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf5,
primals_7, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_7
buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
padding=(2, 2), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 32, 64, 64), (131072, 4096, 64, 1))
buf7 = buf6
del buf6
triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf7,
primals_9, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_9
buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 1, 64, 64), (4096, 4096, 64, 1))
buf9 = buf8
del buf8
buf10 = empty_strided_cuda((4, 1, 64, 64), (4096, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_1[grid(16384)](
buf9, primals_11, buf10, 16384, XBLOCK=256, num_warps=4,
num_stages=1)
del primals_11
return (buf9, primals_1, primals_2, primals_4, primals_6, primals_8,
primals_10, buf1, buf3, buf5, buf7, buf10)
def get_conv2d_layer(in_c, out_c, k, s, p=0, dilation=1, groups=1):
return nn.Conv2d(in_channels=in_c, out_channels=out_c, kernel_size=k,
stride=s, padding=p, dilation=dilation, groups=groups)
class Illumination_AloneNew(nn.Module):
def __init__(self, opts):
super().__init__()
self.opts = opts
self.conv1 = get_conv2d_layer(in_c=1, out_c=32, k=5, s=1, p=2)
self.conv2 = get_conv2d_layer(in_c=32, out_c=32, k=5, s=1, p=2)
self.conv3 = get_conv2d_layer(in_c=32, out_c=32, k=5, s=1, p=2)
self.conv4 = get_conv2d_layer(in_c=32, out_c=32, k=5, s=1, p=2)
self.conv5 = get_conv2d_layer(in_c=32, out_c=1, k=1, s=1, p=0)
self.leaky_relu_1 = nn.LeakyReLU(0.2, inplace=True)
self.leaky_relu_2 = nn.LeakyReLU(0.2, inplace=True)
self.leaky_relu_3 = nn.LeakyReLU(0.2, inplace=True)
self.leaky_relu_4 = nn.LeakyReLU(0.2, inplace=True)
self.relu = nn.ReLU()
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = 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.conv5.weight
primals_11 = self.conv5.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]
|
AndersonYong/URetinex-Net-Retinex-based-Deep-Unfolding-Network-for-Low-light-Image-Enhancem
|
Illumination_Alone
| false
| 10,007
|
[
"MIT"
] | 0
|
9d837b8df9c761defb1eca390b3a60aa4a6fbb1a
|
https://github.com/AndersonYong/URetinex-Net-Retinex-based-Deep-Unfolding-Network-for-Low-light-Image-Enhancem/tree/9d837b8df9c761defb1eca390b3a60aa4a6fbb1a
|
GatedActivation
|
import torch
from torch import nn
class GatedActivation(nn.Module):
"""Activation function which computes actiation_fn(f) * sigmoid(g).
The f and g correspond to the top 1/2 and bottom 1/2 of the input channels.
"""
def __init__(self, activation_fn=torch.tanh):
"""Initializes a new GatedActivation instance.
Args:
activation_fn: Activation to use for the top 1/2 input channels.
"""
super().__init__()
self._activation_fn = activation_fn
def forward(self, x):
_, c, _, _ = x.shape
assert c % 2 == 0, 'x must have an even number of channels.'
x, gate = x[:, :c // 2, :, :], x[:, c // 2:, :, :]
return self._activation_fn(x) * torch.sigmoid(gate)
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
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_sigmoid_tanh_0(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 % 32
x1 = xindex // 32
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tmp2 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp1 = libdevice.tanh(tmp0)
tmp3 = tl.sigmoid(tmp2)
tmp4 = tmp1 * tmp3
tl.store(out_ptr0 + x2, tmp4, 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, 2, 4, 4), (32, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_sigmoid_tanh_0[grid(128)](arg0_1, buf0, 128,
XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class GatedActivationNew(nn.Module):
"""Activation function which computes actiation_fn(f) * sigmoid(g).
The f and g correspond to the top 1/2 and bottom 1/2 of the input channels.
"""
def __init__(self, activation_fn=torch.tanh):
"""Initializes a new GatedActivation instance.
Args:
activation_fn: Activation to use for the top 1/2 input channels.
"""
super().__init__()
self._activation_fn = activation_fn
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
cobypenso/pytorch-generative
|
GatedActivation
| false
| 10,008
|
[
"MIT"
] | 0
|
72d1a3d8045179bd3a83ee3783aa070e74a1e400
|
https://github.com/cobypenso/pytorch-generative/tree/72d1a3d8045179bd3a83ee3783aa070e74a1e400
|
SigmoidCrossEntropyLoss
|
import torch
from torch import nn
import torch.nn.functional as F
class SigmoidCrossEntropyLoss(nn.Module):
def __init__(self):
"""
:param num_negs: number of negative instances in bpr loss.
"""
super(SigmoidCrossEntropyLoss, self).__init__()
def forward(self, y_pred, y_true):
"""
:param y_true: Labels
:param y_pred: Predicted result
"""
logits = y_pred.flatten()
labels = y_true.flatten()
loss = F.binary_cross_entropy_with_logits(logits, labels, reduction
='sum')
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 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_with_logits_0(in_ptr0, in_ptr1,
out_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)
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 = tl.broadcast_to(tmp12, [RBLOCK])
tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp15, 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)
get_raw_stream(0)
triton_per_fused_binary_cross_entropy_with_logits_0[grid(1)](arg1_1,
arg0_1, buf0, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf0,
class SigmoidCrossEntropyLossNew(nn.Module):
def __init__(self):
"""
:param num_negs: number of negative instances in bpr loss.
"""
super(SigmoidCrossEntropyLossNew, 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]
|
byzhang/OpenMatch
|
SigmoidCrossEntropyLoss
| false
| 10,009
|
[
"Apache-2.0"
] | 0
|
28b2d49a5eec2e1dc3934767c747ff0ca6c93d96
|
https://github.com/byzhang/OpenMatch/tree/28b2d49a5eec2e1dc3934767c747ff0ca6c93d96
|
MaskedAveragePooling
|
import torch
from torch import nn
class MaskedAveragePooling(nn.Module):
def __init__(self):
super(MaskedAveragePooling, self).__init__()
def forward(self, embedding_matrix):
sum_pooling_matrix = torch.sum(embedding_matrix, dim=1)
non_padding_length = (embedding_matrix.sum(dim=-1) != 0).sum(dim=1,
keepdim=True)
embedding_vec = sum_pooling_matrix / (non_padding_length.float() +
1e-12)
return embedding_vec
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 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_ne_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 + (4 * x0 + 64 * x1), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0 + 64 * x1), xmask, eviction_policy
='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0 + 64 * x1), xmask, eviction_policy
='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0 + 64 * x1), xmask, eviction_policy
='evict_last')
tmp10 = tl.load(in_ptr0 + (16 + 4 * x0 + 64 * x1), xmask,
eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (17 + 4 * x0 + 64 * x1), xmask,
eviction_policy='evict_last')
tmp13 = tl.load(in_ptr0 + (18 + 4 * x0 + 64 * x1), xmask,
eviction_policy='evict_last')
tmp15 = tl.load(in_ptr0 + (19 + 4 * x0 + 64 * x1), xmask,
eviction_policy='evict_last')
tmp20 = tl.load(in_ptr0 + (32 + 4 * x0 + 64 * x1), xmask,
eviction_policy='evict_last')
tmp21 = tl.load(in_ptr0 + (33 + 4 * x0 + 64 * x1), xmask,
eviction_policy='evict_last')
tmp23 = tl.load(in_ptr0 + (34 + 4 * x0 + 64 * x1), xmask,
eviction_policy='evict_last')
tmp25 = tl.load(in_ptr0 + (35 + 4 * x0 + 64 * x1), xmask,
eviction_policy='evict_last')
tmp30 = tl.load(in_ptr0 + (48 + 4 * x0 + 64 * x1), xmask,
eviction_policy='evict_last')
tmp31 = tl.load(in_ptr0 + (49 + 4 * x0 + 64 * x1), xmask,
eviction_policy='evict_last')
tmp33 = tl.load(in_ptr0 + (50 + 4 * x0 + 64 * x1), xmask,
eviction_policy='evict_last')
tmp35 = tl.load(in_ptr0 + (51 + 4 * x0 + 64 * x1), xmask,
eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 0.0
tmp8 = tmp6 != tmp7
tmp9 = tmp8.to(tl.int64)
tmp12 = tmp10 + tmp11
tmp14 = tmp12 + tmp13
tmp16 = tmp14 + tmp15
tmp17 = tmp16 != tmp7
tmp18 = tmp17.to(tl.int64)
tmp19 = tmp9 + tmp18
tmp22 = tmp20 + tmp21
tmp24 = tmp22 + tmp23
tmp26 = tmp24 + tmp25
tmp27 = tmp26 != tmp7
tmp28 = tmp27.to(tl.int64)
tmp29 = tmp19 + tmp28
tmp32 = tmp30 + tmp31
tmp34 = tmp32 + tmp33
tmp36 = tmp34 + tmp35
tmp37 = tmp36 != tmp7
tmp38 = tmp37.to(tl.int64)
tmp39 = tmp29 + tmp38
tl.store(out_ptr0 + x2, tmp39, xmask)
@triton.jit
def triton_poi_fused__to_copy_add_div_sum_1(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 // 16
x3 = xindex % 16
x0 = xindex % 4
x4 = xindex
tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
tmp1 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
tmp3 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
tmp5 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
tmp7 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp8 = tmp7.to(tl.float32)
tmp9 = 1e-12
tmp10 = tmp8 + tmp9
tmp11 = tmp6 / tmp10
tl.store(out_ptr0 + x4, 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, 1, 4), (4, 16, 1), torch.int64)
get_raw_stream(0)
triton_poi_fused_ne_sum_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__to_copy_add_div_sum_1[grid(64)](arg0_1, buf0,
buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
del arg0_1
del buf0
return buf1,
class MaskedAveragePoolingNew(nn.Module):
def __init__(self):
super(MaskedAveragePoolingNew, self).__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
byzhang/OpenMatch
|
MaskedAveragePooling
| false
| 10,010
|
[
"Apache-2.0"
] | 0
|
28b2d49a5eec2e1dc3934767c747ff0ca6c93d96
|
https://github.com/byzhang/OpenMatch/tree/28b2d49a5eec2e1dc3934767c747ff0ca6c93d96
|
FullyConnectedHead
|
import torch
from typing import Any
from typing import Dict
from typing import Optional
import torch.nn as nn
import torch.nn.modules as nn
import torch.optim
from torch import nn
def is_pos_int(number):
"""
Returns True if a number is a positive integer.
"""
return type(number) == int and number >= 0
class ClassyHead(nn.Module):
"""
Base class for heads that can be attached to :class:`ClassyModel`.
A head is a regular :class:`torch.nn.Module` that can be attached to a
pretrained model. This enables a form of transfer learning: utilizing a
model trained for one dataset to extract features that can be used for
other problems. A head must be attached to a :class:`models.ClassyBlock`
within a :class:`models.ClassyModel`.
"""
def __init__(self, unique_id: 'Optional[str]'=None, num_classes:
'Optional[int]'=None):
"""
Constructs a ClassyHead.
Args:
unique_id: A unique identifier for the head. Multiple instances of
the same head might be attached to a model, and unique_id is used
to refer to them.
num_classes: Number of classes for the head.
"""
super().__init__()
self.unique_id = unique_id or self.__class__.__name__
self.num_classes = num_classes
@classmethod
def from_config(cls, config: 'Dict[str, Any]') ->'ClassyHead':
"""Instantiates a ClassyHead from a configuration.
Args:
config: A configuration for the ClassyHead.
Returns:
A ClassyHead instance.
"""
raise NotImplementedError
def forward(self, x):
"""
Performs inference on the head.
This is a regular PyTorch method, refer to :class:`torch.nn.Module` for
more details
"""
raise NotImplementedError
class FullyConnectedHead(ClassyHead):
"""This head defines a 2d average pooling layer
(:class:`torch.nn.AdaptiveAvgPool2d`) followed by a fully connected
layer (:class:`torch.nn.Linear`).
"""
def __init__(self, unique_id: 'str', num_classes: 'int', in_plane:
'int', zero_init_bias: 'bool'=False):
"""Constructor for FullyConnectedHead
Args:
unique_id: A unique identifier for the head. Multiple instances of
the same head might be attached to a model, and unique_id is used
to refer to them.
num_classes: Number of classes for the head. If None, then the fully
connected layer is not applied.
in_plane: Input size for the fully connected layer.
"""
super().__init__(unique_id, num_classes)
assert num_classes is None or is_pos_int(num_classes)
assert is_pos_int(in_plane)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = None if num_classes is None else nn.Linear(in_plane,
num_classes)
if zero_init_bias:
self.fc.bias.data.zero_()
@classmethod
def from_config(cls, config: 'Dict[str, Any]') ->'FullyConnectedHead':
"""Instantiates a FullyConnectedHead from a configuration.
Args:
config: A configuration for a FullyConnectedHead.
See :func:`__init__` for parameters expected in the config.
Returns:
A FullyConnectedHead instance.
"""
num_classes = config.get('num_classes', None)
in_plane = config['in_plane']
return cls(config['unique_id'], num_classes, in_plane,
zero_init_bias=config.get('zero_init_bias', False))
def forward(self, x):
out = self.avgpool(x)
out = out.flatten(start_dim=1)
if self.fc is not None:
out = self.fc(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'unique_id': 4, 'num_classes': 4, 'in_plane': 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 typing import Any
from typing import Dict
from typing import Optional
import torch.nn as nn
import torch.nn.modules as nn
import torch.optim
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_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):
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, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=8,
num_warps=2, num_stages=1)
del primals_1
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_3, reinterpret_tensor(buf1, (4, 4), (4,
1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha
=1, beta=1, out=buf2)
del primals_2
del primals_3
return buf2, reinterpret_tensor(buf1, (4, 4), (4, 1), 0)
def is_pos_int(number):
"""
Returns True if a number is a positive integer.
"""
return type(number) == int and number >= 0
class ClassyHead(nn.Module):
"""
Base class for heads that can be attached to :class:`ClassyModel`.
A head is a regular :class:`torch.nn.Module` that can be attached to a
pretrained model. This enables a form of transfer learning: utilizing a
model trained for one dataset to extract features that can be used for
other problems. A head must be attached to a :class:`models.ClassyBlock`
within a :class:`models.ClassyModel`.
"""
def __init__(self, unique_id: 'Optional[str]'=None, num_classes:
'Optional[int]'=None):
"""
Constructs a ClassyHead.
Args:
unique_id: A unique identifier for the head. Multiple instances of
the same head might be attached to a model, and unique_id is used
to refer to them.
num_classes: Number of classes for the head.
"""
super().__init__()
self.unique_id = unique_id or self.__class__.__name__
self.num_classes = num_classes
@classmethod
def from_config(cls, config: 'Dict[str, Any]') ->'ClassyHead':
"""Instantiates a ClassyHead from a configuration.
Args:
config: A configuration for the ClassyHead.
Returns:
A ClassyHead instance.
"""
raise NotImplementedError
def forward(self, x):
"""
Performs inference on the head.
This is a regular PyTorch method, refer to :class:`torch.nn.Module` for
more details
"""
raise NotImplementedError
class FullyConnectedHeadNew(ClassyHead):
"""This head defines a 2d average pooling layer
(:class:`torch.nn.AdaptiveAvgPool2d`) followed by a fully connected
layer (:class:`torch.nn.Linear`).
"""
def __init__(self, unique_id: 'str', num_classes: 'int', in_plane:
'int', zero_init_bias: 'bool'=False):
"""Constructor for FullyConnectedHead
Args:
unique_id: A unique identifier for the head. Multiple instances of
the same head might be attached to a model, and unique_id is used
to refer to them.
num_classes: Number of classes for the head. If None, then the fully
connected layer is not applied.
in_plane: Input size for the fully connected layer.
"""
super().__init__(unique_id, num_classes)
assert num_classes is None or is_pos_int(num_classes)
assert is_pos_int(in_plane)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = None if num_classes is None else nn.Linear(in_plane,
num_classes)
if zero_init_bias:
self.fc.bias.data.zero_()
@classmethod
def from_config(cls, config: 'Dict[str, Any]') ->'FullyConnectedHead':
"""Instantiates a FullyConnectedHead from a configuration.
Args:
config: A configuration for a FullyConnectedHead.
See :func:`__init__` for parameters expected in the config.
Returns:
A FullyConnectedHead instance.
"""
num_classes = config.get('num_classes', None)
in_plane = config['in_plane']
return cls(config['unique_id'], num_classes, in_plane,
zero_init_bias=config.get('zero_init_bias', False))
def forward(self, input_0):
primals_2 = self.fc.weight
primals_3 = self.fc.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
dendisuhubdy/ClassyVision
|
FullyConnectedHead
| false
| 10,011
|
[
"MIT"
] | 0
|
c7f8de4615181b5a14dd5ec44fa72bebb790e886
|
https://github.com/dendisuhubdy/ClassyVision/tree/c7f8de4615181b5a14dd5ec44fa72bebb790e886
|
MaskedSumPooling
|
import torch
from torch import nn
class MaskedSumPooling(nn.Module):
def __init__(self):
super(MaskedSumPooling, self).__init__()
def forward(self, embedding_matrix):
return torch.sum(embedding_matrix, dim=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
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_sum_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
tl.store(out_ptr0 + x2, 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), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_sum_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del arg0_1
return buf0,
class MaskedSumPoolingNew(nn.Module):
def __init__(self):
super(MaskedSumPoolingNew, self).__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
byzhang/OpenMatch
|
MaskedSumPooling
| false
| 10,012
|
[
"Apache-2.0"
] | 0
|
28b2d49a5eec2e1dc3934767c747ff0ca6c93d96
|
https://github.com/byzhang/OpenMatch/tree/28b2d49a5eec2e1dc3934767c747ff0ca6c93d96
|
Self_Attn
|
import torch
import torch.nn as nn
class Self_Attn(nn.Module):
""" Self attention Layer"""
def __init__(self, in_dim):
super(Self_Attn, self).__init__()
self.chanel_in = in_dim
self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim,
kernel_size=1, bias=False)
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
"""
inputs :
x : input feature maps( B X C X W X H)
returns :
out : self attention value + input feature
attention: B X N X N (N is Width*Height)
"""
m_batchsize, C, width, height = x.size()
proj_query = x.reshape(m_batchsize, C, -1).permute(0, 2, 1)
proj_key = x.reshape(m_batchsize, C, -1)
energy = torch.bmm(proj_key, proj_query)
attention = self.softmax(energy)
proj_value = self.value_conv(x).view(m_batchsize, -1, width * height)
out = torch.bmm(attention, proj_value)
out = out.view(m_batchsize, C, width, height)
out = self.gamma * out + x
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_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 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 = 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_1(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_add_mul_2(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 + 0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
tmp2 = tl.load(in_ptr1 + x0, xmask)
tmp4 = tl.load(in_ptr2 + x0, xmask)
tmp3 = tmp1 * tmp2
tmp5 = tmp3 + tmp4
tl.store(out_ptr0 + x0, tmp5, 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, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(primals_1, (4, 4, 16), (64,
16, 1), 0), reinterpret_tensor(primals_1, (4, 16, 4), (64, 1,
16), 0), out=buf0)
buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__softmax_0[grid(64)](buf0, buf1, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf2 = buf0
del buf0
triton_poi_fused__softmax_1[grid(64)](buf1, buf2, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del buf1
buf3 = extern_kernels.convolution(primals_1, primals_2, 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, 4, 4), (64, 16, 4, 1))
buf4 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
extern_kernels.bmm(buf2, reinterpret_tensor(buf3, (4, 4, 16), (64,
16, 1), 0), out=buf4)
buf5 = buf3
del buf3
triton_poi_fused_add_mul_2[grid(256)](primals_3, buf4, primals_1,
buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
return buf5, primals_1, primals_2, primals_3, buf4, reinterpret_tensor(buf2
, (4, 4, 4), (16, 1, 4), 0)
class Self_AttnNew(nn.Module):
""" Self attention Layer"""
def __init__(self, in_dim):
super(Self_AttnNew, self).__init__()
self.chanel_in = in_dim
self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim,
kernel_size=1, bias=False)
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=-1)
def forward(self, input_0):
primals_3 = self.gamma
primals_2 = self.value_conv.weight
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
douya1997/pytorch-cifar
|
Self_Attn
| false
| 10,013
|
[
"MIT"
] | 0
|
d5c73f6c1eddf3a2e74cb2dbd0eab6cc6dc4d14b
|
https://github.com/douya1997/pytorch-cifar/tree/d5c73f6c1eddf3a2e74cb2dbd0eab6cc6dc4d14b
|
GradLoss
|
import torch
import torch.nn as nn
import torch.utils.data
import torch.optim
class GradLoss(nn.Module):
def __init__(model):
super(GradLoss, model).__init__()
def forward(model, grad_fake, grad_real):
return torch.sum(torch.mean(torch.abs(grad_real - grad_fake)))
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
import torch.utils.data
import torch.optim
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_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_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_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 GradLossNew(nn.Module):
def __init__(model):
super(GradLossNew, model).__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]
|
domo23/DeepSFM
|
GradLoss
| false
| 10,014
|
[
"BSD-3-Clause"
] | 0
|
9456c1505e63b467417496545f17363ca17d02e4
|
https://github.com/domo23/DeepSFM/tree/9456c1505e63b467417496545f17363ca17d02e4
|
GCN_encoder
|
import torch
import torch.nn as nn
import torch.nn.init as init
class GraphConv(nn.Module):
def __init__(self, input_dim, output_dim):
super(GraphConv, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.weight = nn.Parameter(torch.FloatTensor(input_dim, output_dim))
def forward(self, x, adj):
y = torch.matmul(adj, x)
y = torch.matmul(y, self.weight)
return y
class GCN_encoder(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(GCN_encoder, self).__init__()
self.conv1 = GraphConv(input_dim=input_dim, output_dim=hidden_dim)
self.conv2 = GraphConv(input_dim=hidden_dim, output_dim=output_dim)
self.relu = nn.ReLU()
for m in self.modules():
if isinstance(m, GraphConv):
m.weight.data = init.xavier_uniform(m.weight.data, gain=nn.
init.calculate_gain('relu'))
elif isinstance(m, nn.BatchNorm1d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def forward(self, x, adj):
x = self.conv1(x, adj)
x = self.relu(x)
x = self.conv2(x, adj)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_dim': 4, 'hidden_dim': 4, 'output_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
import torch.nn as nn
import torch.nn.init as init
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, 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, 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((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(primals_2, (16, 4, 4), (16, 4,
1), 0), reinterpret_tensor(primals_1, (16, 4, 4), (16, 4, 1), 0
), out=buf0)
del primals_1
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
primals_3, out=buf1)
del primals_3
buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf1
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)](buf2, buf5,
256, XBLOCK=128, num_warps=4, num_stages=1)
buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(primals_2, (16, 4, 4), (16, 4,
1), 0), reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0),
out=buf3)
buf4 = reinterpret_tensor(buf2, (64, 4), (4, 1), 0)
del buf2
extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
primals_4, out=buf4)
return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(buf3, (4, 64), (1, 4), 0), reinterpret_tensor(
primals_4, (4, 4), (1, 4), 0), reinterpret_tensor(primals_2, (16, 4,
4), (16, 1, 4), 0), buf5, reinterpret_tensor(buf0, (4, 64), (1, 4), 0)
class GraphConv(nn.Module):
def __init__(self, input_dim, output_dim):
super(GraphConv, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.weight = nn.Parameter(torch.FloatTensor(input_dim, output_dim))
def forward(self, x, adj):
y = torch.matmul(adj, x)
y = torch.matmul(y, self.weight)
return y
class GCN_encoderNew(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(GCN_encoderNew, self).__init__()
self.conv1 = GraphConv(input_dim=input_dim, output_dim=hidden_dim)
self.conv2 = GraphConv(input_dim=hidden_dim, output_dim=output_dim)
self.relu = nn.ReLU()
for m in self.modules():
if isinstance(m, GraphConv):
m.weight.data = init.xavier_uniform(m.weight.data, gain=nn.
init.calculate_gain('relu'))
elif isinstance(m, nn.BatchNorm1d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def forward(self, input_0, input_1):
primals_3 = self.conv1.weight
primals_4 = self.conv2.weight
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
bwalker1/graph-generation
|
GCN_encoder
| false
| 10,015
|
[
"MIT"
] | 0
|
e068769cb021760eb2549ced382b1a217609db86
|
https://github.com/bwalker1/graph-generation/tree/e068769cb021760eb2549ced382b1a217609db86
|
PatchEmbed
|
import torch
from torch import nn
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
num_patches_h = img_size[0] // patch_size
num_patches_w = img_size[1] // patch_size
num_patches = num_patches_h * num_patches_w
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.num_patches_h = num_patches_h
self.num_patches_w = num_patches_w
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size,
stride=patch_size)
def forward(self, x):
_B, _C, _H, _W = x.shape
x = self.proj(x).flatten(2).transpose(1, 2)
return x
def get_inputs():
return [torch.rand([4, 3, 64, 64])]
def get_init_inputs():
return [[], {'img_size': [4, 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 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):
ynumel = 12
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]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
x2 = xindex
y3 = yindex
y0 = yindex % 3
y1 = yindex // 3
tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp0, ymask)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 2304
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
y3 = yindex
y0 = yindex % 3
y1 = yindex // 3
tmp0 = tl.load(in_ptr0 + (x2 + 256 * y3), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (y0 + 3 * x2 + 768 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_2(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 % 768
y1 = yindex // 768
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 768 * x2 + 12288 * 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 = args
args.clear()
assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_2, (768, 3, 16, 16), (768, 256, 16, 1))
assert_size_stride(primals_3, (768,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(12, 4096)](primals_1, buf0, 12, 4096,
XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((768, 3, 16, 16), (768, 1, 48, 3), torch.
float32)
triton_poi_fused_1[grid(2304, 256)](primals_2, buf1, 2304, 256,
XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
del primals_2
buf2 = extern_kernels.convolution(buf0, buf1, stride=(16, 16),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 768, 4, 4), (12288, 1, 3072, 768))
buf3 = empty_strided_cuda((4, 768, 4, 4), (12288, 16, 4, 1), torch.
float32)
triton_poi_fused_convolution_2[grid(3072, 16)](buf2, primals_3,
buf3, 3072, 16, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del buf2
del primals_3
return reinterpret_tensor(buf3, (4, 16, 768), (12288, 1, 16), 0
), buf0, buf1
class PatchEmbedNew(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
num_patches_h = img_size[0] // patch_size
num_patches_w = img_size[1] // patch_size
num_patches = num_patches_h * num_patches_w
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.num_patches_h = num_patches_h
self.num_patches_w = num_patches_w
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size,
stride=patch_size)
def forward(self, input_0):
primals_2 = self.proj.weight
primals_3 = self.proj.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
daniel347x/dino
|
PatchEmbed
| false
| 10,016
|
[
"Apache-2.0"
] | 0
|
bb96d041de246ad0dc9672471911467fe635b018
|
https://github.com/daniel347x/dino/tree/bb96d041de246ad0dc9672471911467fe635b018
|
PSNR
|
import torch
import torch as th
class PSNR(th.nn.Module):
def __init__(self):
super(PSNR, self).__init__()
self.mse = th.nn.MSELoss()
def forward(self, out, ref):
mse = self.mse(out, ref)
return -10 * th.log10(mse)
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 as th
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_log10_mse_loss_mul_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 = 256.0
tmp8 = tmp6 / tmp7
tmp9 = libdevice.log10(tmp8)
tmp10 = -10.0
tmp11 = tmp9 * tmp10
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp11, 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_log10_mse_loss_mul_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 PSNRNew(th.nn.Module):
def __init__(self):
super(PSNRNew, self).__init__()
self.mse = th.nn.MSELoss()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
endrol/demosaicnet
|
PSNR
| false
| 10,017
|
[
"MIT"
] | 0
|
4b3726a08dcbbb5b70240687f211b39ebd15ad54
|
https://github.com/endrol/demosaicnet/tree/4b3726a08dcbbb5b70240687f211b39ebd15ad54
|
PredictionHead
|
import torch
import torch.nn as nn
import torch.onnx
class PredictionHead(nn.Module):
def __init__(self, in_channels, num_classes, num_anchors):
super(PredictionHead, self).__init__()
self.classification = nn.Conv2d(in_channels, num_classes *
num_anchors, kernel_size=1)
self.regression = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1
)
self.num_classes = num_classes
self.num_anchors = num_anchors
def forward(self, x):
bs = x.shape[0]
class_logits = self.classification(x)
box_regression = self.regression(x)
class_logits = class_logits.permute(0, 2, 3, 1).reshape(bs, -1,
self.num_classes)
box_regression = box_regression.permute(0, 2, 3, 1).reshape(bs, -1, 4)
return class_logits, box_regression
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'num_classes': 4, 'num_anchors': 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
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__unsafe_view_clone_0(in_out_ptr0, in_ptr0, in_ptr1,
ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 64
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
y0 = yindex % 16
y1 = yindex // 16
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 256 * 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 + 16 * y3), tmp2, xmask & ymask)
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, (16, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (16,), (1,))
assert_size_stride(primals_4, (16, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_5, (16,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 16, 4, 4), (256, 16, 4, 1))
buf1 = extern_kernels.convolution(primals_1, primals_4, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 16, 4, 4), (256, 16, 4, 1))
buf2 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
float32)
buf3 = reinterpret_tensor(buf2, (4, 64, 4), (256, 4, 1), 0)
del buf2
get_raw_stream(0)
triton_poi_fused__unsafe_view_clone_0[grid(64, 16)](buf3, buf0,
primals_3, 64, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
del primals_3
buf4 = reinterpret_tensor(buf0, (4, 4, 4, 16), (256, 64, 16, 1), 0)
del buf0
buf5 = reinterpret_tensor(buf4, (4, 64, 4), (256, 4, 1), 0)
del buf4
triton_poi_fused__unsafe_view_clone_0[grid(64, 16)](buf5, buf1,
primals_5, 64, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
del buf1
del primals_5
return buf3, buf5, primals_1, primals_2, primals_4
class PredictionHeadNew(nn.Module):
def __init__(self, in_channels, num_classes, num_anchors):
super(PredictionHeadNew, self).__init__()
self.classification = nn.Conv2d(in_channels, num_classes *
num_anchors, kernel_size=1)
self.regression = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1
)
self.num_classes = num_classes
self.num_anchors = num_anchors
def forward(self, input_0):
primals_2 = self.classification.weight
primals_3 = self.classification.bias
primals_4 = self.regression.weight
primals_5 = self.regression.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0], output[1]
|
danshirron/inference
|
PredictionHead
| false
| 10,018
|
[
"Apache-2.0"
] | 0
|
31ae9b30ca5b1081a2d35f73ffcde10ae1fdaf41
|
https://github.com/danshirron/inference/tree/31ae9b30ca5b1081a2d35f73ffcde10ae1fdaf41
|
RobertaClassificationHead_R
|
from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.utils.checkpoint
class RobertaClassificationHead_R(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(0.1)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features[:, 0, :]
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(hidden_size=4, num_labels=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.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_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 % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tl.store(out_ptr0 + x2, tmp0, 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)
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((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
del primals_2
buf2 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
del buf1
triton_poi_fused_tanh_1[grid(64)](buf2, primals_3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_3
buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (16, 4), (
4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf3)
del primals_5
return reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(buf0, (16, 4), (4, 1), 0), buf2, primals_4
class RobertaClassificationHead_RNew(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(0.1)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
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]
|
Delecis/bert-classification
|
RobertaClassificationHead_R
| false
| 10,019
|
[
"Apache-2.0"
] | 0
|
00e0d295ecf22a1bd364f2d63244469692ff23a3
|
https://github.com/Delecis/bert-classification/tree/00e0d295ecf22a1bd364f2d63244469692ff23a3
|
ContrastiveLoss
|
import torch
from torch import nn
from torch.nn import CosineSimilarity
class ContrastiveLoss(nn.Module):
"""
Contrastive loss
Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 otherwise
"""
def __init__(self, margin=0.5):
super(ContrastiveLoss, self).__init__()
self.margin = margin
self.distance = CosineSimilarity()
self.eps = 1e-06
self.mse = torch.nn.MSELoss()
def forward(self, output1, output2, target, size_average=True):
distances = self.distance(output1, output2)
losses = (1 - target.float()) * nn.functional.relu(self.margin -
distances).pow(2) + target.float() * (1 - distances).pow(2) / 4
return losses.mean() if size_average else losses.sum(), distances
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
from torch import nn
from torch.nn import CosineSimilarity
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_clamp_min_div_linalg_vector_norm_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
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')
tmp16 = tl.load(in_ptr1 + x3, xmask)
tmp17 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp19 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp22 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp25 = tl.load(in_ptr1 + (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-08
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tmp18 = tmp17 * tmp17
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = libdevice.sqrt(tmp27)
tmp29 = triton_helpers.maximum(tmp28, tmp13)
tmp30 = tmp16 / tmp29
tmp31 = tmp15 * tmp30
tl.store(out_ptr0 + x3, tmp31, xmask)
@triton.jit
def triton_poi_fused_sum_1(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
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_per_fused_add_div_mean_mul_pow_relu_rsub_2(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 = 0.5
tmp5 = tmp4 - tmp3
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp8 = tmp7 * tmp7
tmp9 = tmp2 * tmp8
tmp10 = tmp1 - tmp3
tmp11 = tmp10 * tmp10
tmp12 = tmp0 * tmp11
tmp13 = 0.25
tmp14 = tmp12 * tmp13
tmp15 = tmp9 + tmp14
tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
tmp19 = 256.0
tmp20 = tmp18 / tmp19
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp20, 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, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0[grid(256)](
arg1_1, arg0_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_sum_1[grid(64)](buf0, buf1, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del buf0
buf2 = empty_strided_cuda((), (), torch.float32)
buf3 = buf2
del buf2
triton_per_fused_add_div_mean_mul_pow_relu_rsub_2[grid(1)](buf3,
arg2_1, buf1, 1, 256, num_warps=2, num_stages=1)
del arg2_1
return buf3, buf1
class ContrastiveLossNew(nn.Module):
"""
Contrastive loss
Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 otherwise
"""
def __init__(self, margin=0.5):
super(ContrastiveLossNew, self).__init__()
self.margin = margin
self.distance = CosineSimilarity()
self.eps = 1e-06
self.mse = torch.nn.MSELoss()
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]
|
elloworl/FRMiner2.0
|
ContrastiveLoss
| false
| 10,020
|
[
"MIT"
] | 0
|
f596530d18512a1b1b8b8d56772f006f9f53f429
|
https://github.com/elloworl/FRMiner2.0/tree/f596530d18512a1b1b8b8d56772f006f9f53f429
|
PositionwiseFeedForward
|
import torch
import torch.nn as nn
class LayerNorm(nn.Module):
"""
Layer Normalization class
"""
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(features))
self.bias = nn.Parameter(torch.zeros(features))
self.eps = eps
def forward(self, x):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return self.weight * (x - mean) / (std + self.eps) + self.bias
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.intermediate = nn.Linear(d_model, d_ff)
self.output = nn.Linear(d_ff, d_model)
self.layer_norm = LayerNorm(d_model)
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.intermediate(self.layer_norm(x)))
)
output = self.dropout_2(self.output(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
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_mean_mul_std_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 % 4
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp30 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp8 = tmp6 + tmp7
tmp9 = 4.0
tmp10 = tmp8 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp0 * tmp11
tmp13 = tmp2 - tmp10
tmp14 = tmp13 * tmp13
tmp15 = tmp3 - tmp10
tmp16 = tmp15 * tmp15
tmp17 = tmp14 + tmp16
tmp18 = tmp5 - tmp10
tmp19 = tmp18 * tmp18
tmp20 = tmp17 + tmp19
tmp21 = tmp7 - tmp10
tmp22 = tmp21 * tmp21
tmp23 = tmp20 + tmp22
tmp24 = 3.0
tmp25 = tmp23 / tmp24
tmp26 = libdevice.sqrt(tmp25)
tmp27 = 1e-06
tmp28 = tmp26 + tmp27
tmp29 = tmp12 / tmp28
tmp31 = tmp29 + tmp30
tl.store(out_ptr0 + x2, tmp31, xmask)
@triton.jit
def triton_poi_fused_relu_threshold_backward_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
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_2(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, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_div_mean_mul_std_sub_0[grid(256)](primals_2,
primals_1, primals_3, buf0, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del primals_2
del primals_3
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf1
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_1[grid(256)](buf2,
primals_5, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf3)
buf4 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf3
triton_poi_fused_add_2[grid(256)](buf4, primals_7, primals_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
return buf4, primals_1, reinterpret_tensor(buf0, (64, 4), (4, 1), 0
), reinterpret_tensor(buf2, (64, 4), (4, 1), 0
), primals_6, buf5, primals_4
class LayerNorm(nn.Module):
"""
Layer Normalization class
"""
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(features))
self.bias = nn.Parameter(torch.zeros(features))
self.eps = eps
def forward(self, x):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return self.weight * (x - mean) / (std + self.eps) + self.bias
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.intermediate = nn.Linear(d_model, d_ff)
self.output = nn.Linear(d_ff, d_model)
self.layer_norm = LayerNorm(d_model)
self.dropout_1 = nn.Dropout(dropout)
self.relu = nn.ReLU()
self.dropout_2 = nn.Dropout(dropout)
def forward(self, input_0):
primals_4 = self.intermediate.weight
primals_2 = self.intermediate.bias
primals_6 = self.output.weight
primals_3 = self.output.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]
|
czhao39/NeuralCodeSum
|
PositionwiseFeedForward
| false
| 10,021
|
[
"MIT"
] | 0
|
d06f8165a8af993239ec6d796bac1d378aa8be91
|
https://github.com/czhao39/NeuralCodeSum/tree/d06f8165a8af993239ec6d796bac1d378aa8be91
|
Net
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 3)
self.conv2 = nn.Conv2d(6, 16, 3)
self.fc1 = nn.Linear(16 * 28 * 28, 512)
self.fc2 = nn.Linear(512, 64)
self.fc3 = nn.Linear(64, 2)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = x.view(-1, 16 * 28 * 28)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
x = F.relu(x)
x = self.fc3(x)
return x
def get_inputs():
return [torch.rand([4, 1, 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
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 = 21600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 900 % 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_convolution_relu_threshold_backward_1(in_out_ptr0,
in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 50176
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 784 % 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)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x3, tmp4, xmask)
tl.store(out_ptr0 + x3, tmp6, xmask)
@triton.jit
def triton_poi_fused_relu_2(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 % 512
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_relu_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 % 64
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, 1, 3, 3), (9, 9, 3, 1))
assert_size_stride(primals_2, (6,), (1,))
assert_size_stride(primals_3, (4, 1, 32, 32), (1024, 1024, 32, 1))
assert_size_stride(primals_4, (16, 6, 3, 3), (54, 9, 3, 1))
assert_size_stride(primals_5, (16,), (1,))
assert_size_stride(primals_6, (512, 12544), (12544, 1))
assert_size_stride(primals_7, (512,), (1,))
assert_size_stride(primals_8, (64, 512), (512, 1))
assert_size_stride(primals_9, (64,), (1,))
assert_size_stride(primals_10, (2, 64), (64, 1))
assert_size_stride(primals_11, (2,), (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, 30, 30), (5400, 900, 30, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(21600)](buf1, primals_2,
21600, XBLOCK=256, num_warps=4, num_stages=1)
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, 16, 28, 28), (12544, 784, 28, 1))
buf3 = buf2
del buf2
buf9 = empty_strided_cuda((4, 16, 28, 28), (12544, 784, 28, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_1[grid(50176)](
buf3, primals_5, buf9, 50176, XBLOCK=512, num_warps=4, num_stages=1
)
del primals_5
buf4 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf3, (4, 12544), (12544, 1),
0), reinterpret_tensor(primals_6, (12544, 512), (1, 12544), 0),
out=buf4)
buf5 = buf4
del buf4
triton_poi_fused_relu_2[grid(2048)](buf5, primals_7, 2048, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_7
buf6 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
extern_kernels.mm(buf5, reinterpret_tensor(primals_8, (512, 64), (1,
512), 0), out=buf6)
buf7 = buf6
del buf6
triton_poi_fused_relu_3[grid(256)](buf7, primals_9, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_9
buf8 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
extern_kernels.addmm(primals_11, buf7, reinterpret_tensor(
primals_10, (64, 2), (1, 64), 0), alpha=1, beta=1, out=buf8)
del primals_11
return buf8, primals_1, primals_3, primals_4, buf1, reinterpret_tensor(buf3
, (4, 12544), (12544, 1), 0
), buf5, buf7, primals_10, primals_8, primals_6, buf9
class NetNew(nn.Module):
def __init__(self):
super(NetNew, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 3)
self.conv2 = nn.Conv2d(6, 16, 3)
self.fc1 = nn.Linear(16 * 28 * 28, 512)
self.fc2 = nn.Linear(512, 64)
self.fc3 = nn.Linear(64, 2)
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]
|
dollarkillerx/PyTorchStudy
|
Net
| false
| 10,022
|
[
"MIT"
] | 0
|
c17b2973c89e3a2f088513f29bd5eb6f47957585
|
https://github.com/dollarkillerx/PyTorchStudy/tree/c17b2973c89e3a2f088513f29bd5eb6f47957585
|
CoFusion
|
import torch
import torch.nn.functional as F
import torch.nn as nn
class CoFusion(nn.Module):
def __init__(self, in_ch, out_ch):
super(CoFusion, self).__init__()
self.conv1 = nn.Conv2d(in_ch, 64, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, out_ch, kernel_size=3, stride=1, padding=1)
self.relu = nn.ReLU()
self.norm_layer1 = nn.GroupNorm(4, 64)
self.norm_layer2 = nn.GroupNorm(4, 64)
def forward(self, x):
attn = self.relu(self.norm_layer1(self.conv1(x)))
attn = self.relu(self.norm_layer2(self.conv2(attn)))
attn = F.softmax(self.conv3(attn), dim=1)
return (x * attn).sum(1).unsqueeze(1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_ch': 4, 'out_ch': 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_per_fused_convolution_native_group_norm_0(in_out_ptr0, in_ptr0,
out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
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)
r5 = rindex
x4 = xindex
r3 = rindex // 16
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (r5 + 256 * x4), None)
tmp1 = tl.load(in_ptr0 + (r3 + 16 * 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], 256, 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 = 256.0
tmp17 = tmp15 / tmp16
tmp18 = 1e-05
tmp19 = tmp17 + tmp18
tmp20 = libdevice.rsqrt(tmp19)
tl.store(in_out_ptr0 + (r5 + 256 * x4), tmp2, None)
tl.store(out_ptr2 + x4, tmp20, None)
tl.store(out_ptr0 + x4, tmp10, None)
tl.store(out_ptr1 + x4, tmp15, None)
@triton.jit
def triton_poi_fused_native_group_norm_relu_1(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)
x3 = xindex
x4 = xindex // 16
x1 = xindex // 16 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x4 // 16, None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x4 // 16, None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = 256.0
tmp5 = tmp3 / tmp4
tmp6 = 1e-05
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp2 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tmp14 = tl.full([1], 0, tl.int32)
tmp15 = triton_helpers.maximum(tmp14, tmp13)
tl.store(out_ptr0 + x3, tmp15, None)
@triton.jit
def triton_poi_fused_convolution_2(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
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')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, 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
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_mul_sum_4(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)
tmp2 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
tmp4 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
tmp6 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
tmp10 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp14 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp18 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp1 / tmp7
tmp9 = tmp0 * tmp8
tmp11 = tmp2 / tmp7
tmp12 = tmp10 * tmp11
tmp13 = tmp9 + tmp12
tmp15 = tmp4 / tmp7
tmp16 = tmp14 * tmp15
tmp17 = tmp13 + tmp16
tmp19 = tmp6 / tmp7
tmp20 = tmp18 * tmp19
tmp21 = tmp17 + tmp20
tl.store(out_ptr0 + x2, tmp21, 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, (64, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (64,), (1,))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (64, 64, 3, 3), (576, 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, (4, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_11, (4,), (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, 64, 4, 4), (1024, 16, 4, 1))
buf1 = buf0
del buf0
buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf5 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
get_raw_stream(0)
triton_per_fused_convolution_native_group_norm_0[grid(16)](buf1,
primals_2, buf2, buf3, buf5, 16, 256, num_warps=2, num_stages=1)
del primals_2
buf6 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.
float32)
triton_poi_fused_native_group_norm_relu_1[grid(4096)](buf1, buf2,
buf3, primals_4, primals_5, buf6, 4096, XBLOCK=256, num_warps=4,
num_stages=1)
del primals_5
buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 64, 4, 4), (1024, 16, 4, 1))
buf8 = buf7
del buf7
buf9 = buf3
del buf3
buf10 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf12 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
triton_per_fused_convolution_native_group_norm_0[grid(16)](buf8,
primals_7, buf9, buf10, buf12, 16, 256, num_warps=2, num_stages=1)
del primals_7
buf13 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.
float32)
triton_poi_fused_native_group_norm_relu_1[grid(4096)](buf8, buf9,
buf10, primals_8, primals_9, buf13, 4096, XBLOCK=256, num_warps
=4, num_stages=1)
del buf10
del primals_9
buf14 = extern_kernels.convolution(buf13, primals_10, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf14, (4, 4, 4, 4), (64, 16, 4, 1))
buf15 = buf14
del buf14
triton_poi_fused_convolution_2[grid(256)](buf15, primals_11, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_11
buf16 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_3[grid(256)](buf15, buf16, 256, XBLOCK=
128, num_warps=4, num_stages=1)
buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__softmax_mul_sum_4[grid(64)](primals_3, buf16,
buf17, 64, XBLOCK=64, num_warps=1, num_stages=1)
del buf16
return (reinterpret_tensor(buf17, (4, 1, 4, 4), (16, 16, 4, 1), 0),
primals_1, primals_3, primals_4, primals_6, primals_8, primals_10,
buf1, reinterpret_tensor(buf2, (4, 4), (4, 1), 0),
reinterpret_tensor(buf5, (4, 4), (4, 1), 0), buf6, buf8,
reinterpret_tensor(buf9, (4, 4), (4, 1), 0), reinterpret_tensor(
buf12, (4, 4), (4, 1), 0), buf13, buf15)
class CoFusionNew(nn.Module):
def __init__(self, in_ch, out_ch):
super(CoFusionNew, self).__init__()
self.conv1 = nn.Conv2d(in_ch, 64, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, out_ch, kernel_size=3, stride=1, padding=1)
self.relu = nn.ReLU()
self.norm_layer1 = nn.GroupNorm(4, 64)
self.norm_layer2 = nn.GroupNorm(4, 64)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_6 = self.conv2.weight
primals_4 = self.conv2.bias
primals_10 = self.conv3.weight
primals_11 = self.conv3.bias
primals_5 = self.norm_layer1.weight
primals_7 = self.norm_layer1.bias
primals_8 = self.norm_layer2.weight
primals_9 = self.norm_layer2.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]
|
cordob/DexiNed
|
CoFusion
| false
| 10,023
|
[
"MIT"
] | 0
|
9e084652f8051155c98277c02eecefa927bfe04c
|
https://github.com/cordob/DexiNed/tree/9e084652f8051155c98277c02eecefa927bfe04c
|
Gaussian
|
import torch
import torch.utils.tensorboard
import torch.utils.data
class Gaussian(torch.nn.Module):
"""Gaussian activation"""
def forward(self, x):
return torch.exp(-x * 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 math as tl_math
import torch.utils.tensorboard
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_exp_mul_neg_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
tmp2 = tmp1 * tmp0
tmp3 = tl_math.exp(tmp2)
tl.store(out_ptr0 + x0, tmp3, 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_exp_mul_neg_0[grid(256)](arg0_1, buf0, 256, XBLOCK
=128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class GaussianNew(torch.nn.Module):
"""Gaussian activation"""
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
chc273/torchani
|
Gaussian
| false
| 10,024
|
[
"MIT"
] | 0
|
bbcd7bedc254796f0c2f839c4868ac211ad9078d
|
https://github.com/chc273/torchani/tree/bbcd7bedc254796f0c2f839c4868ac211ad9078d
|
GatedTransition
|
import torch
from torch import nn
class GatedTransition(nn.Module):
"""
Parameterizes the gaussian latent transition probability p(z_t | z_{t-1})
"""
def __init__(self, z_dim, transition_dim):
super().__init__()
self.lin_gate_z_to_hidden = nn.Linear(z_dim, transition_dim)
self.lin_gate_hidden_to_hidden = nn.Linear(transition_dim,
transition_dim)
self.lin_gate_hidden_to_z = nn.Linear(transition_dim, z_dim)
self.lin_proposed_mean_z_to_hidden = nn.Linear(z_dim, transition_dim)
self.lin_proposed_mean_hidden_to_hidden = nn.Linear(transition_dim,
transition_dim)
self.lin_proposed_mean_hidden_to_z = nn.Linear(transition_dim, z_dim)
self.lin_sig = nn.Linear(z_dim, z_dim)
self.lin_z_to_loc = nn.Linear(z_dim, z_dim)
self.lin_z_to_loc.weight.data = torch.eye(z_dim)
self.lin_z_to_loc.bias.data = torch.zeros(z_dim)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
self.softplus = nn.Softplus()
def forward(self, z_t_1):
"""
Given the latent z_{t-1} corresponding to the time step t-1
we return the mean and scale vectors that parameterize the
(diagonal) gaussian distribution p(z_t | z_{t-1})
"""
_gate = self.relu(self.lin_gate_z_to_hidden(z_t_1))
_gate = self.relu(self.lin_gate_hidden_to_hidden(_gate))
gate = self.sigmoid(self.lin_gate_hidden_to_z(_gate))
_proposed_mean = self.relu(self.lin_proposed_mean_z_to_hidden(z_t_1))
_proposed_mean = self.relu(self.lin_proposed_mean_hidden_to_hidden(
_proposed_mean))
proposed_mean = self.lin_proposed_mean_hidden_to_z(_proposed_mean)
loc = (1 - gate) * self.lin_z_to_loc(z_t_1) + gate * proposed_mean
scale = self.softplus(self.lin_sig(self.relu(proposed_mean)))
return loc, scale
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'z_dim': 4, 'transition_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
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_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)
@triton.jit
def triton_poi_fused_add_mul_relu_rsub_sigmoid_threshold_backward_1(in_ptr0,
in_ptr1, in_ptr2, 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)
tmp4 = tl.load(in_ptr1 + x0, xmask)
tmp6 = tl.load(in_ptr2 + x0, xmask)
tmp1 = tl.sigmoid(tmp0)
tmp2 = 1.0
tmp3 = tmp2 - tmp1
tmp5 = tmp3 * tmp4
tmp7 = tmp1 * tmp6
tmp8 = tmp5 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp6)
tmp11 = 0.0
tmp12 = tmp10 <= tmp11
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp10, xmask)
tl.store(out_ptr2 + x0, tmp12, xmask)
@triton.jit
def triton_poi_fused_softplus_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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tmp3 = 20.0
tmp4 = tmp2 > tmp3
tmp5 = tl_math.exp(tmp2)
tmp6 = libdevice.log1p(tmp5)
tmp7 = tmp6 * tmp1
tmp8 = tl.where(tmp4, tmp0, tmp7)
tl.store(out_ptr0 + x0, tmp8, 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, primals_14, primals_15, primals_16, primals_17) = 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,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (4,), (1,))
assert_size_stride(primals_10, (4, 4), (4, 1))
assert_size_stride(primals_11, (4,), (1,))
assert_size_stride(primals_12, (4, 4), (4, 1))
assert_size_stride(primals_13, (4,), (1,))
assert_size_stride(primals_14, (4, 4), (4, 1))
assert_size_stride(primals_15, (4,), (1,))
assert_size_stride(primals_16, (4, 4), (4, 1))
assert_size_stride(primals_17, (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
buf19 = 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, buf19, 256, XBLOCK=256, 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
buf18 = 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, buf18, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf4)
del primals_7
buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf5)
del primals_8
buf6 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf6,
primals_9, buf17, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_9
buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf6, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf7)
buf8 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf7
buf16 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf8,
primals_11, buf16, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_11
buf9 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_13, reinterpret_tensor(buf8, (64, 4),
(4, 1), 0), reinterpret_tensor(primals_12, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf9)
del primals_13
buf10 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_15, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_14, (4, 4), (1, 4),
0), alpha=1, beta=1, out=buf10)
del primals_14
del primals_15
buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_add_mul_relu_rsub_sigmoid_threshold_backward_1[grid
(256)](buf4, buf10, buf9, buf11, buf12, buf15, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf13 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_17, reinterpret_tensor(buf12, (64, 4),
(4, 1), 0), reinterpret_tensor(primals_16, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf13)
del primals_17
buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_softplus_2[grid(256)](buf13, buf14, 256, XBLOCK=
256, num_warps=4, num_stages=1)
return (buf11, buf14, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(
buf3, (64, 4), (4, 1), 0), buf4, reinterpret_tensor(buf6, (64, 4),
(4, 1), 0), reinterpret_tensor(buf8, (64, 4), (4, 1), 0), buf9,
buf10, reinterpret_tensor(buf12, (64, 4), (4, 1), 0), buf13,
primals_16, buf15, primals_12, buf16, primals_10, buf17, primals_6,
buf18, primals_4, buf19)
class GatedTransitionNew(nn.Module):
"""
Parameterizes the gaussian latent transition probability p(z_t | z_{t-1})
"""
def __init__(self, z_dim, transition_dim):
super().__init__()
self.lin_gate_z_to_hidden = nn.Linear(z_dim, transition_dim)
self.lin_gate_hidden_to_hidden = nn.Linear(transition_dim,
transition_dim)
self.lin_gate_hidden_to_z = nn.Linear(transition_dim, z_dim)
self.lin_proposed_mean_z_to_hidden = nn.Linear(z_dim, transition_dim)
self.lin_proposed_mean_hidden_to_hidden = nn.Linear(transition_dim,
transition_dim)
self.lin_proposed_mean_hidden_to_z = nn.Linear(transition_dim, z_dim)
self.lin_sig = nn.Linear(z_dim, z_dim)
self.lin_z_to_loc = nn.Linear(z_dim, z_dim)
self.lin_z_to_loc.weight.data = torch.eye(z_dim)
self.lin_z_to_loc.bias.data = torch.zeros(z_dim)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
self.softplus = nn.Softplus()
def forward(self, input_0):
primals_1 = self.lin_gate_z_to_hidden.weight
primals_2 = self.lin_gate_z_to_hidden.bias
primals_4 = self.lin_gate_hidden_to_hidden.weight
primals_5 = self.lin_gate_hidden_to_hidden.bias
primals_6 = self.lin_gate_hidden_to_z.weight
primals_7 = self.lin_gate_hidden_to_z.bias
primals_8 = self.lin_proposed_mean_z_to_hidden.weight
primals_9 = self.lin_proposed_mean_z_to_hidden.bias
primals_10 = self.lin_proposed_mean_hidden_to_hidden.weight
primals_11 = self.lin_proposed_mean_hidden_to_hidden.bias
primals_12 = self.lin_proposed_mean_hidden_to_z.weight
primals_13 = self.lin_proposed_mean_hidden_to_z.bias
primals_14 = self.lin_sig.weight
primals_15 = self.lin_sig.bias
primals_16 = self.lin_z_to_loc.weight
primals_17 = self.lin_z_to_loc.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, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17])
return output[0], output[1]
|
devonjkohler/sysbioDMM
|
GatedTransition
| false
| 10,025
|
[
"MIT"
] | 0
|
3967a084a492f5b7abd1f3274f1dc5ee9ef868ff
|
https://github.com/devonjkohler/sysbioDMM/tree/3967a084a492f5b7abd1f3274f1dc5ee9ef868ff
|
Combiner
|
import torch
from torch import nn
class Combiner(nn.Module):
"""
Parameterizes q(z_t | z_{t-1}, x_{t:T}), which is the basic building block
of the guide (i.e. the variational distribution). The dependence on x_{t:T} is
through the hidden state of the RNN (see the pytorch module `rnn` below).
The guide is used to approximate the posterior p(z_{1:T}| x_{1:T}). In training
we use the data `D` to estimate p(z^1_{1:T}, z^2_{1:T}, .., z^N_{1:T}|D).
"""
def __init__(self, z_dim, rnn_dim):
super().__init__()
self.lin_z_to_hidden = nn.Linear(z_dim, rnn_dim)
self.lin_hidden_to_loc = nn.Linear(rnn_dim, z_dim)
self.lin_hidden_to_scale = nn.Linear(rnn_dim, z_dim)
self.tanh = nn.Tanh()
self.softplus = nn.Softplus()
def forward(self, z_t_1, h_rnn):
"""
Given the latent z at at a particular time step t-1 as well as the hidden
state of the RNN h(x_{t:T}) we return the mean and scale vectors that
parameterize the (diagonal) gaussian distribution q(z_t | z_{t-1}, x_{t:T})
"""
h_combined = 0.5 * (self.tanh(self.lin_z_to_hidden(z_t_1)) + h_rnn)
loc = self.lin_hidden_to_loc(h_combined)
scale = self.softplus(self.lin_hidden_to_scale(h_combined))
return loc, scale
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'z_dim': 4, 'rnn_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, 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_add_mul_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)
tmp2 = tl.load(in_ptr1 + x0, xmask)
tmp1 = libdevice.tanh(tmp0)
tmp3 = tmp1 + tmp2
tmp4 = 0.5
tmp5 = tmp3 * tmp4
tl.store(out_ptr0 + x0, tmp5, xmask)
@triton.jit
def triton_poi_fused_softplus_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 = 1.0
tmp2 = tmp0 * tmp1
tmp3 = 20.0
tmp4 = tmp2 > tmp3
tmp5 = tl_math.exp(tmp2)
tmp6 = libdevice.log1p(tmp5)
tmp7 = tmp6 * tmp1
tmp8 = tl.where(tmp4, tmp0, tmp7)
tl.store(out_ptr0 + x0, tmp8, 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, 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))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (4, 4), (4, 1))
assert_size_stride(primals_8, (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)
get_raw_stream(0)
triton_poi_fused_add_mul_tanh_0[grid(256)](buf0, primals_4, buf1,
256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_4
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_6, reinterpret_tensor(buf1, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf2)
del primals_6
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_8, reinterpret_tensor(buf1, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf3)
del primals_8
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_softplus_1[grid(256)](buf3, buf4, 256, XBLOCK=256,
num_warps=4, num_stages=1)
return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
), buf4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf0, reinterpret_tensor(buf1, (64, 4), (4, 1), 0
), buf3, primals_7, primals_5
class CombinerNew(nn.Module):
"""
Parameterizes q(z_t | z_{t-1}, x_{t:T}), which is the basic building block
of the guide (i.e. the variational distribution). The dependence on x_{t:T} is
through the hidden state of the RNN (see the pytorch module `rnn` below).
The guide is used to approximate the posterior p(z_{1:T}| x_{1:T}). In training
we use the data `D` to estimate p(z^1_{1:T}, z^2_{1:T}, .., z^N_{1:T}|D).
"""
def __init__(self, z_dim, rnn_dim):
super().__init__()
self.lin_z_to_hidden = nn.Linear(z_dim, rnn_dim)
self.lin_hidden_to_loc = nn.Linear(rnn_dim, z_dim)
self.lin_hidden_to_scale = nn.Linear(rnn_dim, z_dim)
self.tanh = nn.Tanh()
self.softplus = nn.Softplus()
def forward(self, input_0, input_1):
primals_1 = self.lin_z_to_hidden.weight
primals_2 = self.lin_z_to_hidden.bias
primals_5 = self.lin_hidden_to_loc.weight
primals_6 = self.lin_hidden_to_loc.bias
primals_7 = self.lin_hidden_to_scale.weight
primals_8 = self.lin_hidden_to_scale.bias
primals_3 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0], output[1]
|
devonjkohler/sysbioDMM
|
Combiner
| false
| 10,026
|
[
"MIT"
] | 0
|
3967a084a492f5b7abd1f3274f1dc5ee9ef868ff
|
https://github.com/devonjkohler/sysbioDMM/tree/3967a084a492f5b7abd1f3274f1dc5ee9ef868ff
|
BertMultiPairPooler
|
from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertMultiPairPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size * 2, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
hidden_states_first_cls = hidden_states[:, 0].unsqueeze(1).repeat([
1, hidden_states.shape[1], 1])
pooled_outputs = self.dense(torch.cat([hidden_states_first_cls,
hidden_states], 2))
pooled_outputs = self.activation(pooled_outputs)
return pooled_outputs
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(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.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_cat_0(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 % 8
x2 = xindex // 32
x3 = xindex // 8
x4 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (16 * x2 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr0 + (4 * x3 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x4, 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)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 8), (8, 1))
assert_size_stride(primals_3, (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_1, buf0, 128, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 8), (8, 1), 0),
reinterpret_tensor(primals_2, (8, 4), (1, 8), 0), out=buf1)
del primals_2
buf2 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
del buf1
triton_poi_fused_tanh_1[grid(64)](buf2, primals_3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_3
return buf2, reinterpret_tensor(buf0, (16, 8), (8, 1), 0), buf2
class BertMultiPairPoolerNew(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size * 2, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, input_0):
primals_2 = self.dense.weight
primals_3 = self.dense.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
doduo-anonymous/doduo-submission
|
BertMultiPairPooler
| false
| 10,027
|
[
"Apache-2.0"
] | 0
|
34d397c14174d64e6a3026d51cc25560a4f1e29f
|
https://github.com/doduo-anonymous/doduo-submission/tree/34d397c14174d64e6a3026d51cc25560a4f1e29f
|
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