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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stringlengths 1
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class | uuid
int64 0
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listlengths 1
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CriterionCWD
|
import torch
import torch.nn as nn
import torch._utils
import torch.optim
class ChannelNorm(nn.Module):
def __init__(self):
super(ChannelNorm, self).__init__()
def forward(self, featmap):
n, c, _h, _w = featmap.shape
featmap = featmap.reshape((n, c, -1))
featmap = featmap.softmax(dim=-1)
return featmap
class CriterionCWD(nn.Module):
def __init__(self, norm_type='channel', divergence='kl', temperature=4.0):
super(CriterionCWD, self).__init__()
if norm_type == 'channel':
self.normalize = ChannelNorm()
elif norm_type == 'spatial':
self.normalize = nn.Softmax(dim=1)
elif norm_type == 'channel_mean':
self.normalize = lambda x: x.view(x.size(0), x.size(1), -1).mean(-1
)
else:
self.normalize = None
self.norm_type = norm_type
self.temperature = 1.0
if divergence == 'mse':
self.criterion = nn.MSELoss(reduction='sum')
elif divergence == 'kl':
self.criterion = nn.KLDivLoss(reduction='sum')
self.temperature = temperature
self.divergence = divergence
def forward(self, preds_S, preds_T):
n, c, h, w = preds_S.shape
if self.normalize is not None:
norm_s = self.normalize(preds_S / self.temperature)
norm_t = self.normalize(preds_T.detach() / self.temperature)
else:
norm_s = preds_S
norm_t = preds_T.detach()
if self.divergence == 'kl':
norm_s = norm_s.log()
loss = self.criterion(norm_s, norm_t)
if self.norm_type == 'channel' or self.norm_type == 'channel_mean':
loss /= n * c
else:
loss /= n * h * w
return loss * self.temperature ** 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, math as tl_math
import torch.nn as nn
import torch._utils
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__softmax_0(in_ptr0, 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
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp1 = 0.25
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = tl.where(xmask, tmp3, float('-inf'))
tmp6 = triton_helpers.max2(tmp5, 1)[:, None]
tmp7 = tmp2 - tmp6
tmp8 = tl_math.exp(tmp7)
tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
tmp11 = tl.where(xmask, tmp9, 0)
tmp12 = tl.sum(tmp11, 1)[:, None]
tl.store(out_ptr0 + x0, tmp6, xmask)
tl.store(out_ptr1 + x0, tmp12, xmask)
@triton.jit
def triton_per_fused__softmax_div_log_mul_sub_sum_xlogy_1(in_out_ptr0,
in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, 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
r1 = rindex // 16
tmp0 = tl.load(in_ptr0 + r2, None)
tmp3 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr3 + r2, None)
tmp18 = tl.load(in_ptr4 + r1, None, eviction_policy='evict_last')
tmp21 = tl.load(in_ptr5 + r1, None, eviction_policy='evict_last')
tmp1 = 0.25
tmp2 = tmp0 * tmp1
tmp4 = tmp2 - tmp3
tmp5 = tl_math.exp(tmp4)
tmp7 = tmp5 / tmp6
tmp8 = libdevice.isnan(tmp7).to(tl.int1)
tmp9 = 0.0
tmp10 = tmp7 == tmp9
tmp11 = tl_math.log(tmp7)
tmp12 = tmp7 * tmp11
tmp13 = tl.where(tmp10, tmp9, tmp12)
tmp14 = float('nan')
tmp15 = tl.where(tmp8, tmp14, tmp13)
tmp17 = tmp16 * tmp1
tmp19 = tmp17 - tmp18
tmp20 = tl_math.exp(tmp19)
tmp22 = tmp20 / tmp21
tmp23 = tl_math.log(tmp22)
tmp24 = tmp7 * tmp23
tmp25 = tmp15 - tmp24
tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
tmp29 = 0.0625
tmp30 = tmp28 * tmp29
tmp31 = 16.0
tmp32 = tmp30 * tmp31
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp32, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
get_raw_stream(0)
triton_per_fused__softmax_0[grid(16)](arg1_1, buf0, buf1, 16, 16,
XBLOCK=1, num_warps=2, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf3 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
triton_per_fused__softmax_0[grid(16)](arg0_1, buf2, buf3, 16, 16,
XBLOCK=1, num_warps=2, num_stages=1)
buf4 = empty_strided_cuda((), (), torch.float32)
buf5 = buf4
del buf4
triton_per_fused__softmax_div_log_mul_sub_sum_xlogy_1[grid(1)](buf5,
arg1_1, buf0, buf1, arg0_1, buf2, buf3, 1, 256, num_warps=2,
num_stages=1)
del arg0_1
del arg1_1
del buf0
del buf1
del buf2
del buf3
return buf5,
class ChannelNorm(nn.Module):
def __init__(self):
super(ChannelNorm, self).__init__()
def forward(self, featmap):
n, c, _h, _w = featmap.shape
featmap = featmap.reshape((n, c, -1))
featmap = featmap.softmax(dim=-1)
return featmap
class CriterionCWDNew(nn.Module):
def __init__(self, norm_type='channel', divergence='kl', temperature=4.0):
super(CriterionCWDNew, self).__init__()
if norm_type == 'channel':
self.normalize = ChannelNorm()
elif norm_type == 'spatial':
self.normalize = nn.Softmax(dim=1)
elif norm_type == 'channel_mean':
self.normalize = lambda x: x.view(x.size(0), x.size(1), -1).mean(-1
)
else:
self.normalize = None
self.norm_type = norm_type
self.temperature = 1.0
if divergence == 'mse':
self.criterion = nn.MSELoss(reduction='sum')
elif divergence == 'kl':
self.criterion = nn.KLDivLoss(reduction='sum')
self.temperature = temperature
self.divergence = divergence
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
yubin1219/Semantic-Seg
|
CriterionCWD
| false
| 4,642
|
[
"BSD-2-Clause"
] | 0
|
c40bd43d3d7e44bc995b8d041736580dec084251
|
https://github.com/yubin1219/Semantic-Seg/tree/c40bd43d3d7e44bc995b8d041736580dec084251
|
SelfAttentionGated
|
import torch
import torch.utils.data
import torch.nn.functional as F
def masked_softmax(x, m=None, dim=-1):
"""
Softmax with mask
:param x:
:param m:
:param dim:
:return:
"""
if m is not None:
m = m.float()
x = x * m
e_x = torch.exp(x - torch.max(x, dim=dim, keepdim=True)[0])
if m is not None:
e_x = e_x * m
softmax = e_x / (torch.sum(e_x, dim=dim, keepdim=True) + 1e-06)
return softmax
class SelfAttentionGated(torch.nn.Module):
"""
Self-Attention Gated layer, it`s not weighted sum in the last, but just weighted
math: \\softmax(W* anh(W*x)) * x
Args:
input_size: The number of expected features in the input x
Inputs: input, mask
- **input** (seq_len, batch, input_size): tensor containing the features
of the input sequence.
- **mask** (batch, seq_len): tensor show whether a padding index for each element in the batch.
Outputs: output
- **output** (seq_len, batch, input_size): gated output tensor
"""
def __init__(self, input_size):
super(SelfAttentionGated, self).__init__()
self.linear_g = torch.nn.Linear(input_size, input_size)
self.linear_t = torch.nn.Linear(input_size, 1)
def forward(self, x, x_mask):
g_tanh = F.tanh(self.linear_g(x))
gt = self.linear_t.forward(g_tanh).squeeze(2).transpose(0, 1)
gt_prop = masked_softmax(gt, x_mask, dim=1)
gt_prop = gt_prop.transpose(0, 1).unsqueeze(2)
x_gt = x * gt_prop
return x_gt
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
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_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, xmask)
@triton.jit
def triton_poi_fused_exp_max_mul_sub_sum_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
x3 = xindex // 4
x2 = xindex // 16
x4 = xindex % 16
x5 = xindex
tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x4 + 64 * x2), xmask)
tmp3 = tl.load(in_ptr0 + (16 + x3), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (16 + x4 + 64 * x2), xmask)
tmp7 = tl.load(in_ptr0 + (32 + x3), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (32 + x4 + 64 * x2), xmask)
tmp11 = tl.load(in_ptr0 + (48 + x3), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr1 + (48 + x4 + 64 * x2), xmask)
tmp2 = tmp0 * tmp1
tmp5 = tmp3 * tmp4
tmp6 = triton_helpers.maximum(tmp2, tmp5)
tmp9 = tmp7 * tmp8
tmp10 = triton_helpers.maximum(tmp6, tmp9)
tmp13 = tmp11 * tmp12
tmp14 = triton_helpers.maximum(tmp10, tmp13)
tmp15 = tmp2 - tmp14
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp16 * tmp1
tmp18 = tmp5 - tmp14
tmp19 = tl_math.exp(tmp18)
tmp20 = tmp19 * tmp4
tmp21 = tmp17 + tmp20
tmp22 = tmp9 - tmp14
tmp23 = tl_math.exp(tmp22)
tmp24 = tmp23 * tmp8
tmp25 = tmp21 + tmp24
tmp26 = tmp13 - tmp14
tmp27 = tl_math.exp(tmp26)
tmp28 = tmp27 * tmp12
tmp29 = tmp25 + tmp28
tl.store(out_ptr0 + x5, tmp14, xmask)
tl.store(out_ptr1 + x5, tmp29, xmask)
@triton.jit
def triton_poi_fused_mul_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x5 = xindex % 256
x1 = xindex // 4 % 4
x6 = xindex // 64
x3 = xindex // 64 % 4
x4 = xindex // 256
x7 = xindex % 16
x8 = xindex
tmp0 = tl.load(in_ptr0 + x5, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x1 + 4 * x6), xmask, eviction_policy='evict_last'
)
tmp2 = tl.load(in_ptr2 + (x7 + 16 * x4 + 64 * x3), xmask,
eviction_policy='evict_last')
tmp4 = tl.load(in_ptr3 + (x7 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp8 = tl.load(in_ptr4 + (x7 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp3 = tmp1 * tmp2
tmp5 = tmp3 - tmp4
tmp6 = tl_math.exp(tmp5)
tmp7 = tmp6 * tmp2
tmp9 = 1e-06
tmp10 = tmp8 + tmp9
tmp11 = tmp7 / tmp10
tmp12 = tmp0 * tmp11
tl.store(out_ptr0 + x8, tmp12, 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,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (1, 4), (4, 1))
assert_size_stride(primals_5, (1,), (1,))
assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_tanh_0[grid(256)](buf1, primals_2, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del primals_2
buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0),
alpha=1, beta=1, out=buf3)
del primals_5
buf4 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
buf5 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
triton_poi_fused_exp_max_mul_sub_sum_1[grid(64)](buf3, primals_6,
buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf6 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
torch.float32)
triton_poi_fused_mul_2[grid(1024)](primals_3, buf3, primals_6, buf4,
buf5, buf6, 1024, XBLOCK=128, num_warps=4, num_stages=1)
del buf4
del buf5
return buf6, primals_3, primals_6, buf1, buf3, primals_4
def masked_softmax(x, m=None, dim=-1):
"""
Softmax with mask
:param x:
:param m:
:param dim:
:return:
"""
if m is not None:
m = m.float()
x = x * m
e_x = torch.exp(x - torch.max(x, dim=dim, keepdim=True)[0])
if m is not None:
e_x = e_x * m
softmax = e_x / (torch.sum(e_x, dim=dim, keepdim=True) + 1e-06)
return softmax
class SelfAttentionGatedNew(torch.nn.Module):
"""
Self-Attention Gated layer, it`s not weighted sum in the last, but just weighted
math: \\softmax(W* anh(W*x)) * x
Args:
input_size: The number of expected features in the input x
Inputs: input, mask
- **input** (seq_len, batch, input_size): tensor containing the features
of the input sequence.
- **mask** (batch, seq_len): tensor show whether a padding index for each element in the batch.
Outputs: output
- **output** (seq_len, batch, input_size): gated output tensor
"""
def __init__(self, input_size):
super(SelfAttentionGatedNew, self).__init__()
self.linear_g = torch.nn.Linear(input_size, input_size)
self.linear_t = torch.nn.Linear(input_size, 1)
def forward(self, input_0, input_1):
primals_1 = self.linear_g.weight
primals_2 = self.linear_g.bias
primals_4 = self.linear_t.weight
primals_5 = self.linear_t.bias
primals_3 = input_0
primals_6 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
xdong73S/Match_LSTM_v2.0
|
SelfAttentionGated
| false
| 4,643
|
[
"MIT"
] | 0
|
dfb8cfbc2a5dafc6655eecf151a7dbcf808cd729
|
https://github.com/xdong73S/Match_LSTM_v2.0/tree/dfb8cfbc2a5dafc6655eecf151a7dbcf808cd729
|
Normalize
|
import torch
import torch.nn as nn
class Normalize(nn.Module):
def __init__(self, features, epsilon=1e-06):
super(Normalize, self).__init__()
self.gain = nn.Parameter(torch.ones(features))
self.bias = nn.Parameter(torch.zeros(features))
self.epsilon = epsilon
def forward(self, x, dim=-1):
mu = x.mean(dim, keepdim=True)
sigma = torch.sqrt(x.var(dim, keepdim=True) + self.epsilon)
gain = self.gain
bias = self.bias
if dim != -1:
shape = [1] * len(mu.size())
shape[dim] = self.gain.size()[0]
gain = gain.view(shape)
bias = bias.view(shape)
return gain * (x - mu) / (sigma + self.epsilon) + bias
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'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 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_mean_mul_sqrt_sub_var_0(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 % 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')
tmp31 = 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 = 1e-06
tmp27 = tmp25 + tmp26
tmp28 = libdevice.sqrt(tmp27)
tmp29 = tmp28 + tmp26
tmp30 = tmp12 / tmp29
tmp32 = tmp30 + tmp31
tl.store(in_out_ptr0 + x2, tmp32, 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)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_add_div_mean_mul_sqrt_sub_var_0[grid(256)](buf1,
primals_2, primals_1, primals_3, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del primals_2
del primals_3
return buf1, primals_1
class NormalizeNew(nn.Module):
def __init__(self, features, epsilon=1e-06):
super(NormalizeNew, self).__init__()
self.gain = nn.Parameter(torch.ones(features))
self.bias = nn.Parameter(torch.zeros(features))
self.epsilon = epsilon
def forward(self, input_0):
primals_2 = self.gain
primals_3 = self.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
yuri20198/neurips19-graph-protein-design
|
Normalize
| false
| 4,644
|
[
"MIT"
] | 0
|
068e8cdfcbba629f996e99d3765cc2f3233f71a3
|
https://github.com/yuri20198/neurips19-graph-protein-design/tree/068e8cdfcbba629f996e99d3765cc2f3233f71a3
|
PixelWiseBias
|
import torch
import torch.nn as nn
class PixelWiseBias(nn.Module):
"""Some Information about PixelWiseBias"""
def __init__(self, channels):
super(PixelWiseBias, self).__init__()
self.channels = channels
self.bias = nn.Parameter(torch.zeros(channels))
def forward(self, x):
x = x + self.bias[None, :, None, None]
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'channels': 4}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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_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
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
tl.store(out_ptr0 + x3, tmp2, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4,), (1,))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_0[grid(256)](primals_2, primals_1, buf0, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
del primals_2
return buf0,
class PixelWiseBiasNew(nn.Module):
"""Some Information about PixelWiseBias"""
def __init__(self, channels):
super(PixelWiseBiasNew, self).__init__()
self.channels = channels
self.bias = nn.Parameter(torch.zeros(channels))
def forward(self, input_0):
primals_1 = self.bias
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
uthree/gan-image-generator2
|
PixelWiseBias
| false
| 4,645
|
[
"MIT"
] | 0
|
63a9f458f1f78fe13311157a219a5637a59afee4
|
https://github.com/uthree/gan-image-generator2/tree/63a9f458f1f78fe13311157a219a5637a59afee4
|
CausalConv1d
|
import torch
from torch import nn
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super(CausalConv1d, self).__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channels, kernel_size,
padding=self.padding, dilation=dilation)
def forward(self, minibatch):
return self.causal_conv(minibatch)[:, :, :-self.padding]
def get_inputs():
return [torch.rand([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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 96
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 6 % 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, 2), (8, 2, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,),
padding=(2,), dilation=(2,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 6), (24, 6, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(96)](buf1, primals_2, 96,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
return reinterpret_tensor(buf1, (4, 4, 4), (24, 6, 1), 0
), primals_1, primals_3
class CausalConv1dNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super(CausalConv1dNew, self).__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channels, kernel_size,
padding=self.padding, dilation=dilation)
def forward(self, input_0):
primals_1 = self.causal_conv.weight
primals_2 = self.causal_conv.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
yuwl798180/FewRel
|
CausalConv1d
| false
| 4,646
|
[
"MIT"
] | 0
|
8126e440b5d5d178e221cfb4a97a69cabd771fa4
|
https://github.com/yuwl798180/FewRel/tree/8126e440b5d5d178e221cfb4a97a69cabd771fa4
|
DenseBlock
|
import torch
from torch import nn
from torch.nn import functional as F
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super(CausalConv1d, self).__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channels, kernel_size,
padding=self.padding, dilation=dilation)
def forward(self, minibatch):
return self.causal_conv(minibatch)[:, :, :-self.padding]
class DenseBlock(nn.Module):
def __init__(self, in_channels, filters, dilation=2):
super(DenseBlock, self).__init__()
self.causal_conv1 = CausalConv1d(in_channels, filters, dilation=
dilation)
self.causal_conv2 = CausalConv1d(in_channels, filters, dilation=
dilation)
def forward(self, minibatch):
tanh = F.tanh(self.causal_conv1(minibatch))
sig = F.sigmoid(self.causal_conv2(minibatch))
out = torch.cat([minibatch, tanh * sig], dim=1)
return out
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'filters': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 96
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 6 % 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_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 8
x0 = xindex % 4
x2 = xindex // 32
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 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (x0 + 6 * (-4 + x1) + 24 * x2), tmp6 & xmask,
other=0.0)
tmp10 = libdevice.tanh(tmp9)
tmp11 = tl.load(in_ptr2 + (x0 + 6 * (-4 + x1) + 24 * x2), tmp6 & xmask,
other=0.0)
tmp12 = tl.sigmoid(tmp11)
tmp13 = tmp10 * tmp12
tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
tmp15 = tl.where(tmp6, tmp13, tmp14)
tmp16 = tl.where(tmp4, tmp5, tmp15)
tl.store(out_ptr0 + x3, tmp16, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 2), (8, 2, 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, 2), (8, 2, 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,),
padding=(2,), dilation=(2,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 6), (24, 6, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(96)](buf1, primals_2, 96,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = extern_kernels.convolution(primals_3, primals_4, stride=(1,),
padding=(2,), dilation=(2,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf2, (4, 4, 6), (24, 6, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_0[grid(96)](buf3, primals_5, 96,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
triton_poi_fused_cat_1[grid(128)](primals_3, buf1, buf3, buf4, 128,
XBLOCK=128, num_warps=4, num_stages=1)
return buf4, primals_1, primals_3, primals_4, buf1, buf3
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super(CausalConv1d, self).__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channels, kernel_size,
padding=self.padding, dilation=dilation)
def forward(self, minibatch):
return self.causal_conv(minibatch)[:, :, :-self.padding]
class DenseBlockNew(nn.Module):
def __init__(self, in_channels, filters, dilation=2):
super(DenseBlockNew, self).__init__()
self.causal_conv1 = CausalConv1d(in_channels, filters, dilation=
dilation)
self.causal_conv2 = CausalConv1d(in_channels, filters, dilation=
dilation)
def forward(self, input_0):
primals_1 = self.causal_conv1.causal_conv.weight
primals_2 = self.causal_conv1.causal_conv.bias
primals_4 = self.causal_conv2.causal_conv.weight
primals_5 = self.causal_conv2.causal_conv.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
yuwl798180/FewRel
|
DenseBlock
| false
| 4,647
|
[
"MIT"
] | 0
|
8126e440b5d5d178e221cfb4a97a69cabd771fa4
|
https://github.com/yuwl798180/FewRel/tree/8126e440b5d5d178e221cfb4a97a69cabd771fa4
|
UnStackDelta
|
import torch
import torch.nn as nn
class UnStackDelta(nn.Module):
"""Reverse of StackDelta"""
def __init__(self):
super().__init__()
def forward(self, x: 'torch.Tensor'):
assert x.dim() == 4
if x.requires_grad:
out = x.transpose(1, 2).contiguous()
else:
out = x.transpose_(1, 2).contiguous()
out = out.view(out.size(0), out.size(1), out.size(2) * out.size(3))
return 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
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)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 4, 16, 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)](arg0_1, buf0, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
return reinterpret_tensor(buf0, (4, 4, 16), (64, 16, 1), 0),
class UnStackDeltaNew(nn.Module):
"""Reverse of StackDelta"""
def __init__(self):
super().__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
wenjie-p/CAT
|
UnStackDelta
| false
| 4,648
|
[
"Apache-2.0"
] | 0
|
0e6904658dd3d14afe51faf1d0141ae95fef44e8
|
https://github.com/wenjie-p/CAT/tree/0e6904658dd3d14afe51faf1d0141ae95fef44e8
|
ToRGB
|
import torch
import torch.nn as nn
class ToRGB(nn.Module):
"""Some Information about ToRGB"""
def __init__(self, input_channels):
super(ToRGB, self).__init__()
self.conv = nn.Conv2d(input_channels, 3, kernel_size=1, stride=1,
padding=0)
self.tanh = nn.Tanh()
def forward(self, x):
x = self.conv(x)
x = self.tanh(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_convolution_tanh_0(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 3
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tl.store(in_out_ptr0 + x3, tmp3, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (3, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_2, (3,), (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=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 3, 4, 4), (48, 16, 4, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_tanh_0[grid(192)](buf1, primals_2, 192,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
return buf1, primals_1, primals_3, buf1
class ToRGBNew(nn.Module):
"""Some Information about ToRGB"""
def __init__(self, input_channels):
super(ToRGBNew, self).__init__()
self.conv = nn.Conv2d(input_channels, 3, kernel_size=1, stride=1,
padding=0)
self.tanh = nn.Tanh()
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]
|
uthree/gan-image-generator2
|
ToRGB
| false
| 4,649
|
[
"MIT"
] | 0
|
63a9f458f1f78fe13311157a219a5637a59afee4
|
https://github.com/uthree/gan-image-generator2/tree/63a9f458f1f78fe13311157a219a5637a59afee4
|
MinibatchStdDev
|
import torch
import torch.nn as nn
class MinibatchStdDev(nn.Module):
"""
Minibatch standard deviation layer for the discriminator
"""
def __init__(self):
"""
derived class constructor
"""
super().__init__()
def forward(self, x, alpha=1e-08):
"""
forward pass of the layer
:param x: input activation volume
:param alpha: small number for numerical stability
:return: y => x appended with standard deviation constant map
"""
batch_size, _, height, width = x.shape
y = x - x.mean(dim=0, keepdim=True)
y = torch.sqrt(y.pow(2.0).mean(dim=0, keepdim=False) + alpha)
y = y.mean().view(1, 1, 1, 1)
y = y.repeat(batch_size, 1, height, width)
y = torch.cat([x, y], 1)
return y
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
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 MinibatchStdDevNew(nn.Module):
"""
Minibatch standard deviation layer for the discriminator
"""
def __init__(self):
"""
derived class constructor
"""
super().__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
zd-daniel/GANs-ZOO
|
MinibatchStdDev
| false
| 4,650
|
[
"MIT"
] | 0
|
fe72391e1db46616f97d1dec62441a299aa9c636
|
https://github.com/zd-daniel/GANs-ZOO/tree/fe72391e1db46616f97d1dec62441a299aa9c636
|
EncoderLayer
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class ScaledDotProductAttention(nn.Module):
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
def forward(self, q, k, v, mask=None):
attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
if mask is not None:
attn = attn.masked_fill(mask == 1, -float('inf'))
attn = F.softmax(attn, dim=-1)
output = torch.matmul(attn, v)
return output, attn
class MultiHeadAttention(nn.Module):
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
super().__init__()
self.n_head = n_head
self.d_k = d_k
self.d_v = d_v
self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)
def forward(self, q, k, v, mask=None):
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
residual = q
q = self.layer_norm(q)
q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
if mask is not None:
mask = mask.unsqueeze(1)
q, attn = self.attention(q, k, v, mask=mask)
q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
q = self.dropout(self.fc(q))
out = q + residual
return out, attn
class EncoderLayer(nn.Module):
""" Compose with two layers """
def __init__(self, d_model, n_head, d_k, d_v, dropout=0.1):
super(EncoderLayer, self).__init__()
self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v,
dropout=dropout)
def forward(self, enc_input, slf_attn_mask=None):
enc_output, enc_slf_attn = self.slf_attn(enc_input, enc_input,
enc_input, mask=slf_attn_mask)
return enc_output, enc_slf_attn
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'d_model': 4, 'n_head': 4, 'd_k': 4, 'd_v': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
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_native_layer_norm_0(in_ptr0, 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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-06
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_clone_div_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 % 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)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x4, tmp2, xmask)
@triton.jit
def triton_poi_fused_clone_3(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 % 16
y1 = yindex // 16
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused__softmax_4(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_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
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_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
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_7(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 + x0, xmask)
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x0, tmp2, 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,), (1,))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (16, 4), (4, 1))
assert_size_stride(primals_5, (16, 4), (4, 1))
assert_size_stride(primals_6, (16, 4), (4, 1))
assert_size_stride(primals_7, (4, 16), (16, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(16)](primals_1, buf0,
buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(64)](primals_1, buf0,
buf1, primals_2, primals_3, buf2, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del buf0
del buf1
del primals_2
del primals_3
buf3 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf3)
buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_5, (4, 16), (1, 4), 0), out=buf4)
del primals_5
buf5 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 16), (1, 4), 0), out=buf5)
del primals_6
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_div_2[grid(256)](buf3, buf6, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf7 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf3
triton_poi_fused_clone_3[grid(64, 4)](buf4, buf7, 64, 4, XBLOCK=4,
YBLOCK=32, num_warps=4, num_stages=1)
buf8 = reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0)
del buf4
extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), out=buf8)
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_4[grid(256)](buf8, buf9, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf8
triton_poi_fused__softmax_5[grid(256)](buf9, buf10, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf11 = buf9
del buf9
triton_poi_fused_clone_6[grid(256)](buf5, buf11, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf12 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0)
del buf5
extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1), 0), out=buf12
)
buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_6[grid(256)](buf12, buf13, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf12
buf14 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf13, (16, 16), (16, 1), 0),
reinterpret_tensor(primals_7, (16, 4), (1, 16), 0), out=buf14)
buf15 = reinterpret_tensor(buf14, (4, 4, 4), (16, 4, 1), 0)
del buf14
triton_poi_fused_add_7[grid(64)](buf15, primals_1, 64, XBLOCK=64,
num_warps=1, num_stages=1)
return buf15, buf10, primals_1, reinterpret_tensor(buf2, (16, 4), (4, 1), 0
), buf10, reinterpret_tensor(buf13, (16, 16), (16, 1), 0
), primals_7, reinterpret_tensor(buf11, (16, 4, 4), (16, 1, 4), 0
), reinterpret_tensor(buf6, (16, 4, 4), (16, 1, 4), 0
), reinterpret_tensor(buf7, (16, 4, 4), (16, 1, 4), 0), primals_4
class ScaledDotProductAttention(nn.Module):
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
def forward(self, q, k, v, mask=None):
attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
if mask is not None:
attn = attn.masked_fill(mask == 1, -float('inf'))
attn = F.softmax(attn, dim=-1)
output = torch.matmul(attn, v)
return output, attn
class MultiHeadAttention(nn.Module):
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
super().__init__()
self.n_head = n_head
self.d_k = d_k
self.d_v = d_v
self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)
def forward(self, q, k, v, mask=None):
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
residual = q
q = self.layer_norm(q)
q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
if mask is not None:
mask = mask.unsqueeze(1)
q, attn = self.attention(q, k, v, mask=mask)
q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
q = self.dropout(self.fc(q))
out = q + residual
return out, attn
class EncoderLayerNew(nn.Module):
""" Compose with two layers """
def __init__(self, d_model, n_head, d_k, d_v, dropout=0.1):
super(EncoderLayerNew, self).__init__()
self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v,
dropout=dropout)
def forward(self, input_0):
primals_4 = self.slf_attn.w_qs.weight
primals_5 = self.slf_attn.w_ks.weight
primals_6 = self.slf_attn.w_vs.weight
primals_7 = self.slf_attn.fc.weight
primals_2 = self.slf_attn.layer_norm.weight
primals_3 = self.slf_attn.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], output[1]
|
yuanweining/DTI
|
EncoderLayer
| false
| 4,651
|
[
"Apache-2.0"
] | 0
|
11eacb46a221da04d0e9b01d41c89c7ce51ea302
|
https://github.com/yuanweining/DTI/tree/11eacb46a221da04d0e9b01d41c89c7ce51ea302
|
FFModule
|
import torch
import torch.nn as nn
class FFModule(nn.Module):
"""Feed-forward module
default output dimension = idim
x0 -> LayerNorm -> FC -> Swish -> Dropout -> FC -> Dropout -> x1
x0 + res_factor * x1 -> output
"""
def __init__(self, idim: 'int', res_factor: 'float'=0.5, dropout:
'float'=0.0) ->None:
super().__init__()
assert res_factor > 0.0 and dropout >= 0.0
self._res_factor = res_factor
self.ln = nn.LayerNorm([idim])
self.fc0 = nn.Linear(idim, idim * 4)
self.swish = nn.SiLU()
self.dropout0 = nn.Dropout(dropout)
self.fc1 = nn.Linear(idim * 4, idim)
self.dropout1 = nn.Dropout(dropout)
def forward(self, x: 'torch.Tensor'):
output = self.ln(x)
output = self.fc0(output)
output = self.swish(output)
output = self.dropout0(output)
output = self.fc1(output)
output = self.dropout1(output)
output = x + self._res_factor * output
return output
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'idim': 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_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_silu_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.sigmoid(tmp0)
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_add_mul_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_out_ptr0 + x2, xmask)
tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = 0.5
tmp5 = tmp3 * tmp4
tmp6 = tmp0 + tmp5
tl.store(in_out_ptr0 + x2, 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,), (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, (16, 4), (4, 1))
assert_size_stride(primals_5, (16,), (1,))
assert_size_stride(primals_6, (4, 16), (16, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(64)](primals_3, buf0,
buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(256)](primals_3, buf0,
buf1, primals_1, primals_2, buf2, 256, XBLOCK=256, num_warps=4,
num_stages=1)
del buf0
del buf1
del primals_1
del primals_2
buf3 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 0),
alpha=1, beta=1, out=buf3)
del primals_5
buf4 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
float32)
triton_poi_fused_silu_2[grid(1024)](buf3, buf4, 1024, XBLOCK=256,
num_warps=4, num_stages=1)
buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf4, (64, 16), (16, 1), 0),
reinterpret_tensor(primals_6, (16, 4), (1, 16), 0), out=buf5)
buf6 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused_add_mul_3[grid(256)](buf6, primals_3, primals_7,
256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_7
return buf6, primals_3, reinterpret_tensor(buf2, (64, 4), (4, 1), 0
), buf3, reinterpret_tensor(buf4, (64, 16), (16, 1), 0
), primals_6, primals_4
class FFModuleNew(nn.Module):
"""Feed-forward module
default output dimension = idim
x0 -> LayerNorm -> FC -> Swish -> Dropout -> FC -> Dropout -> x1
x0 + res_factor * x1 -> output
"""
def __init__(self, idim: 'int', res_factor: 'float'=0.5, dropout:
'float'=0.0) ->None:
super().__init__()
assert res_factor > 0.0 and dropout >= 0.0
self._res_factor = res_factor
self.ln = nn.LayerNorm([idim])
self.fc0 = nn.Linear(idim, idim * 4)
self.swish = nn.SiLU()
self.dropout0 = nn.Dropout(dropout)
self.fc1 = nn.Linear(idim * 4, idim)
self.dropout1 = nn.Dropout(dropout)
def forward(self, input_0):
primals_1 = self.ln.weight
primals_2 = self.ln.bias
primals_4 = self.fc0.weight
primals_5 = self.fc0.bias
primals_6 = self.fc1.weight
primals_7 = self.fc1.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
wenjie-p/CAT
|
FFModule
| false
| 4,652
|
[
"Apache-2.0"
] | 0
|
0e6904658dd3d14afe51faf1d0141ae95fef44e8
|
https://github.com/wenjie-p/CAT/tree/0e6904658dd3d14afe51faf1d0141ae95fef44e8
|
MultiHeadAttention
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class ScaledDotProductAttention(nn.Module):
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
def forward(self, q, k, v, mask=None):
attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
if mask is not None:
attn = attn.masked_fill(mask == 1, -float('inf'))
attn = F.softmax(attn, dim=-1)
output = torch.matmul(attn, v)
return output, attn
class MultiHeadAttention(nn.Module):
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
super().__init__()
self.n_head = n_head
self.d_k = d_k
self.d_v = d_v
self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)
def forward(self, q, k, v, mask=None):
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
residual = q
q = self.layer_norm(q)
q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
if mask is not None:
mask = mask.unsqueeze(1)
q, attn = self.attention(q, k, v, mask=mask)
q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
q = self.dropout(self.fc(q))
out = q + residual
return out, attn
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4])
]
def get_init_inputs():
return [[], {'n_head': 4, 'd_model': 4, 'd_k': 4, 'd_v': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
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_native_layer_norm_0(in_ptr0, 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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-06
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_clone_div_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 % 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)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x4, tmp2, xmask)
@triton.jit
def triton_poi_fused_clone_3(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 % 16
y1 = yindex // 16
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused__softmax_4(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_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
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_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
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_7(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 + x0, xmask)
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x0, tmp2, 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), (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, (4,), (1,))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (16, 4), (4, 1))
assert_size_stride(primals_7, (16, 4), (4, 1))
assert_size_stride(primals_8, (16, 4), (4, 1))
assert_size_stride(primals_9, (4, 16), (16, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(16)](primals_1, buf0,
buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(64)](primals_1, buf0,
buf1, primals_4, primals_5, buf2, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del buf0
del buf1
del primals_4
del primals_5
buf3 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 16), (1, 4), 0), out=buf3)
buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_7, (4, 16), (1, 4), 0), out=buf4)
del primals_7
buf5 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_8, (4, 16), (1, 4), 0), out=buf5)
del primals_8
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_div_2[grid(256)](buf3, buf6, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf7 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf3
triton_poi_fused_clone_3[grid(64, 4)](buf4, buf7, 64, 4, XBLOCK=4,
YBLOCK=32, num_warps=4, num_stages=1)
buf8 = reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0)
del buf4
extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), out=buf8)
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_4[grid(256)](buf8, buf9, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf8
triton_poi_fused__softmax_5[grid(256)](buf9, buf10, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf11 = buf9
del buf9
triton_poi_fused_clone_6[grid(256)](buf5, buf11, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf12 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0)
del buf5
extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1), 0), out=buf12
)
buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_6[grid(256)](buf12, buf13, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf12
buf14 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf13, (16, 16), (16, 1), 0),
reinterpret_tensor(primals_9, (16, 4), (1, 16), 0), out=buf14)
buf15 = reinterpret_tensor(buf14, (4, 4, 4), (16, 4, 1), 0)
del buf14
triton_poi_fused_add_7[grid(64)](buf15, primals_1, 64, XBLOCK=64,
num_warps=1, num_stages=1)
return buf15, buf10, primals_1, reinterpret_tensor(buf2, (16, 4), (4, 1), 0
), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
), buf10, reinterpret_tensor(buf13, (16, 16), (16, 1), 0
), primals_9, reinterpret_tensor(buf11, (16, 4, 4), (16, 1, 4), 0
), reinterpret_tensor(buf6, (16, 4, 4), (16, 1, 4), 0
), reinterpret_tensor(buf7, (16, 4, 4), (16, 1, 4), 0), primals_6
class ScaledDotProductAttention(nn.Module):
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
def forward(self, q, k, v, mask=None):
attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
if mask is not None:
attn = attn.masked_fill(mask == 1, -float('inf'))
attn = F.softmax(attn, dim=-1)
output = torch.matmul(attn, v)
return output, attn
class MultiHeadAttentionNew(nn.Module):
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
super().__init__()
self.n_head = n_head
self.d_k = d_k
self.d_v = d_v
self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)
def forward(self, input_0, input_1, input_2):
primals_6 = self.w_qs.weight
primals_7 = self.w_ks.weight
primals_8 = self.w_vs.weight
primals_9 = self.fc.weight
primals_4 = self.layer_norm.weight
primals_5 = self.layer_norm.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, primals_8, primals_9])
return output[0], output[1]
|
yuanweining/DTI
|
MultiHeadAttention
| false
| 4,653
|
[
"Apache-2.0"
] | 0
|
11eacb46a221da04d0e9b01d41c89c7ce51ea302
|
https://github.com/yuanweining/DTI/tree/11eacb46a221da04d0e9b01d41c89c7ce51ea302
|
Lookahead
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Lookahead(nn.Module):
def __init__(self, n_features, context):
super(Lookahead, self).__init__()
assert context > 0
self.context = context
self.n_features = n_features
self.pad = 0, self.context - 1
self.conv = nn.Conv1d(self.n_features, self.n_features, kernel_size
=self.context, stride=1, groups=self.n_features, padding=0,
bias=None)
def forward(self, x):
x = x.transpose(1, 2)
x = F.pad(x, pad=self.pad, value=0)
x = self.conv(x)
x = x.transpose(1, 2).contiguous()
return x
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'n_features': 4, 'context': 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_constant_pad_nd_0(in_ptr0, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 7
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 = x2
tmp1 = tl.full([1, 1], 4, tl.int64)
tmp2 = tmp0 < tmp1
tmp3 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), tmp2 & xmask & ymask,
eviction_policy='evict_last', other=0.0)
tl.store(out_ptr0 + (x2 + 7 * y3), tmp3, xmask & ymask)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 1, 4), (4, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 7), (28, 7, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_constant_pad_nd_0[grid(16, 7)](primals_1, buf0, 16,
7, XBLOCK=8, YBLOCK=16, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=4, bias=None)
assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_clone_1[grid(16, 4)](buf1, buf2, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
del buf1
return buf2, primals_2, buf0
class LookaheadNew(nn.Module):
def __init__(self, n_features, context):
super(LookaheadNew, self).__init__()
assert context > 0
self.context = context
self.n_features = n_features
self.pad = 0, self.context - 1
self.conv = nn.Conv1d(self.n_features, self.n_features, kernel_size
=self.context, stride=1, groups=self.n_features, padding=0,
bias=None)
def forward(self, input_0):
primals_2 = self.conv.weight
primals_1 = input_0
output = call([primals_1, primals_2])
return output[0]
|
wenjie-p/CAT
|
Lookahead
| false
| 4,654
|
[
"Apache-2.0"
] | 0
|
0e6904658dd3d14afe51faf1d0141ae95fef44e8
|
https://github.com/wenjie-p/CAT/tree/0e6904658dd3d14afe51faf1d0141ae95fef44e8
|
PositionGenerator
|
import torch
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, hidden_size, variance_epsilon=1e-12):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(hidden_size))
self.beta = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = variance_epsilon
def forward(self, x):
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
return self.gamma * x + self.beta
class PositionGenerator(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, d_model):
super(PositionGenerator, self).__init__()
self.norm = LayerNorm(d_model)
self.proj = nn.Linear(d_model, 3)
def forward(self, x, mask):
mask = mask.unsqueeze(-1).float()
out_masked = self.norm(x) * mask
projected = self.proj(out_masked)
return projected
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'d_model': 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_mean_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
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 = 4.0
tmp9 = tmp7 / tmp8
tmp10 = tmp0 - tmp9
tl.store(out_ptr0 + x2, tmp10, xmask)
@triton.jit
def triton_poi_fused_add_div_mean_mul_pow_sqrt_1(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')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp20 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp2 * tmp2
tmp5 = tmp4 * tmp4
tmp6 = tmp3 + tmp5
tmp8 = tmp7 * tmp7
tmp9 = tmp6 + tmp8
tmp11 = tmp10 * tmp10
tmp12 = tmp9 + tmp11
tmp13 = 4.0
tmp14 = tmp12 / tmp13
tmp15 = 1e-12
tmp16 = tmp14 + tmp15
tmp17 = libdevice.sqrt(tmp16)
tmp18 = tmp1 / tmp17
tmp19 = tmp0 * tmp18
tmp21 = tmp19 + tmp20
tl.store(out_ptr0 + x2, tmp21, xmask)
@triton.jit
def triton_poi_fused_add_div_mean_mul_pow_sqrt_2(in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex % 256
x4 = xindex // 4
x5 = xindex
tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x5, tmp2, 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, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (3, 4), (4, 1))
assert_size_stride(primals_6, (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_mean_sub_0[grid(256)](primals_2, buf0, 256, XBLOCK
=128, num_warps=4, num_stages=1)
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_div_mean_mul_pow_sqrt_1[grid(256)](primals_3,
buf0, primals_4, buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
del buf0
del primals_3
del primals_4
buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
torch.float32)
triton_poi_fused_add_div_mean_mul_pow_sqrt_2[grid(1024)](buf1,
primals_1, buf2, 1024, XBLOCK=256, num_warps=4, num_stages=1)
del buf1
buf3 = empty_strided_cuda((256, 3), (3, 1), torch.float32)
extern_kernels.addmm(primals_6, reinterpret_tensor(buf2, (256, 4),
(4, 1), 0), reinterpret_tensor(primals_5, (4, 3), (1, 4), 0),
alpha=1, beta=1, out=buf3)
del primals_6
return reinterpret_tensor(buf3, (4, 4, 4, 4, 3), (192, 48, 12, 3, 1), 0
), primals_1, primals_2, reinterpret_tensor(buf2, (256, 4), (4, 1), 0
), primals_5
class LayerNorm(nn.Module):
def __init__(self, hidden_size, variance_epsilon=1e-12):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(hidden_size))
self.beta = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = variance_epsilon
def forward(self, x):
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
return self.gamma * x + self.beta
class PositionGeneratorNew(nn.Module):
"""Define standard linear + softmax generation step."""
def __init__(self, d_model):
super(PositionGeneratorNew, self).__init__()
self.norm = LayerNorm(d_model)
self.proj = nn.Linear(d_model, 3)
def forward(self, input_0, input_1):
primals_3 = self.norm.gamma
primals_4 = self.norm.beta
primals_5 = self.proj.weight
primals_6 = self.proj.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]
|
zhandand/MolRep
|
PositionGenerator
| false
| 4,655
|
[
"MIT"
] | 0
|
d81de22000f1245e1d9280af0cb329e745ce4bde
|
https://github.com/zhandand/MolRep/tree/d81de22000f1245e1d9280af0cb329e745ce4bde
|
EnergyEstimateWidthRescale
|
import torch
from torch import nn as nn
from torch.nn.parameter import Parameter
class EnergyEstimateWidthRescale(nn.Module):
def __init__(self, scales):
super(EnergyEstimateWidthRescale, self).__init__()
self.scales = Parameter(torch.tensor(scales, dtype=torch.float32),
requires_grad=False)
def forward(self, x):
assert x.dim() != 1
x = x / self.scales
return torch.cat([(x[:, 0].detach() * x[:, 1]).unsqueeze(1), x[:, 1
:-2] * x[:, 2:-1], (x[:, -2] * x[:, -1].detach()).unsqueeze(1)],
dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'scales': 1.0}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import 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_cat_0(in_ptr0, in_ptr1, 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 // 16 % 3
x0 = xindex % 16
x2 = xindex // 48
x3 = xindex
tmp6 = tl.load(in_ptr1 + 0)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 64 * x2), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp8 = tmp5 / tmp7
tmp9 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tmp9 / tmp7
tmp11 = tmp8 * tmp10
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp4, tmp11, tmp12)
tmp14 = tmp0 >= tmp3
tmp15 = tl.full([1], 2, tl.int64)
tmp16 = tmp0 < tmp15
tmp17 = tmp14 & tmp16
tmp18 = tl.load(in_ptr0 + (16 + x0 + 16 * (-1 + x1) + 64 * x2), tmp17 &
xmask, other=0.0)
tmp19 = tmp18 / tmp7
tmp20 = tl.load(in_ptr0 + (32 + x0 + 16 * (-1 + x1) + 64 * x2), tmp17 &
xmask, other=0.0)
tmp21 = tmp20 / tmp7
tmp22 = tmp19 * tmp21
tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
tmp24 = tl.where(tmp17, tmp22, tmp23)
tmp25 = tmp0 >= tmp15
tl.full([1], 3, tl.int64)
tmp28 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), tmp25 & xmask,
eviction_policy='evict_last', other=0.0)
tmp29 = tmp28 / tmp7
tmp30 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), tmp25 & xmask,
eviction_policy='evict_last', other=0.0)
tmp31 = tmp30 / tmp7
tmp32 = tmp29 * tmp31
tmp33 = tl.full(tmp32.shape, 0.0, tmp32.dtype)
tmp34 = tl.where(tmp25, tmp32, tmp33)
tmp35 = tl.where(tmp17, tmp24, tmp34)
tmp36 = tl.where(tmp4, tmp13, tmp35)
tl.store(out_ptr0 + x3, tmp36, 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, (), ())
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(192)](arg0_1, arg1_1, buf0, 192, XBLOCK
=256, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
return buf0,
class EnergyEstimateWidthRescaleNew(nn.Module):
def __init__(self, scales):
super(EnergyEstimateWidthRescaleNew, self).__init__()
self.scales = Parameter(torch.tensor(scales, dtype=torch.float32),
requires_grad=False)
def forward(self, input_0):
arg1_1 = self.scales
arg0_1 = input_0
output = call([arg0_1, arg1_1])
return output[0]
|
zhanhuijing/ECC_PYCHARM
|
EnergyEstimateWidthRescale
| false
| 4,656
|
[
"MIT"
] | 0
|
c5e8fb747d70a2548e9866356f8dacc8df26a077
|
https://github.com/zhanhuijing/ECC_PYCHARM/tree/c5e8fb747d70a2548e9866356f8dacc8df26a077
|
Actor
|
import torch
from torch import nn
import torch.nn.functional as F
class Actor(nn.Module):
"""Actor model
Parameters:
args (object): Parameter class
"""
def __init__(self, state_dim, action_dim, wwid):
super(Actor, self).__init__()
self.wwid = torch.Tensor([wwid])
l1 = 400
l2 = 300
self.f1 = nn.Linear(state_dim, l1)
self.ln1 = nn.LayerNorm(l1)
self.f2 = nn.Linear(l1, l2)
self.ln2 = nn.LayerNorm(l2)
self.w_out = nn.Linear(l2, action_dim)
def forward(self, input):
"""Method to forward propagate through the actor's graph
Parameters:
input (tensor): states
Returns:
action (tensor): actions
"""
out = F.elu(self.f1(input))
out = self.ln1(out)
out = F.elu(self.f2(out))
out = self.ln2(out)
return torch.sigmoid(self.w_out(out))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'state_dim': 4, 'action_dim': 4, 'wwid': 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
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_elu_native_layer_norm_0(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
rnumel = 400
RBLOCK: tl.constexpr = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 400 * x0), rmask, other=0.0)
tmp31 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
other=0.0)
tmp33 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
other=0.0)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
tl.where(rmask, tmp8, 0)
tmp11 = tl.broadcast_to(tmp8, [RBLOCK])
tmp13 = tl.where(rmask, tmp11, 0)
tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
tmp15 = tl.full([1], 400, tl.int32)
tmp16 = tmp15.to(tl.float32)
tmp17 = tmp14 / tmp16
tmp18 = tmp8 - tmp17
tmp19 = tmp18 * tmp18
tmp20 = tl.broadcast_to(tmp19, [RBLOCK])
tmp22 = tl.where(rmask, tmp20, 0)
tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
tmp24 = 400.0
tmp25 = tmp23 / tmp24
tmp26 = 1e-05
tmp27 = tmp25 + tmp26
tmp28 = libdevice.rsqrt(tmp27)
tmp29 = tmp7 - tmp17
tmp30 = tmp29 * tmp28
tmp32 = tmp30 * tmp31
tmp34 = tmp32 + tmp33
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp28, None)
tl.store(out_ptr1 + (r1 + 400 * x0), tmp34, rmask)
tl.store(out_ptr0 + x0, tmp17, None)
@triton.jit
def triton_per_fused_elu_native_layer_norm_1(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
rnumel = 300
RBLOCK: tl.constexpr = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
x2 = xindex % 4
x3 = xindex // 4
tmp0 = tl.load(in_ptr0 + (r1 + 300 * x0), rmask, other=0.0)
tmp31 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
other=0.0)
tmp33 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
other=0.0)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
tl.where(rmask, tmp8, 0)
tmp11 = tl.broadcast_to(tmp8, [RBLOCK])
tmp13 = tl.where(rmask, tmp11, 0)
tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
tmp15 = tl.full([1], 300, tl.int32)
tmp16 = tmp15.to(tl.float32)
tmp17 = tmp14 / tmp16
tmp18 = tmp8 - tmp17
tmp19 = tmp18 * tmp18
tmp20 = tl.broadcast_to(tmp19, [RBLOCK])
tmp22 = tl.where(rmask, tmp20, 0)
tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
tmp24 = 300.0
tmp25 = tmp23 / tmp24
tmp26 = 1e-05
tmp27 = tmp25 + tmp26
tmp28 = libdevice.rsqrt(tmp27)
tmp29 = tmp7 - tmp17
tmp30 = tmp29 * tmp28
tmp32 = tmp30 * tmp31
tmp34 = tmp32 + tmp33
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp28, None)
tl.store(out_ptr1 + (r1 + 300 * x2 + 1216 * x3), tmp34, rmask)
tl.store(out_ptr0 + x0, tmp17, None)
@triton.jit
def triton_poi_fused_elu_native_layer_norm_view_2(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 19200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 300
x1 = xindex // 300
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 300 * (x1 % 4) + 1216 * (x1 // 4)), xmask)
tl.store(out_ptr0 + x2, tmp0, xmask)
@triton.jit
def triton_poi_fused_sigmoid_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.sigmoid(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (400, 4), (4, 1))
assert_size_stride(primals_2, (400,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (400,), (1,))
assert_size_stride(primals_5, (400,), (1,))
assert_size_stride(primals_6, (300, 400), (400, 1))
assert_size_stride(primals_7, (300,), (1,))
assert_size_stride(primals_8, (300,), (1,))
assert_size_stride(primals_9, (300,), (1,))
assert_size_stride(primals_10, (4, 300), (300, 1))
assert_size_stride(primals_11, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 400), (400, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 400), (1, 4),
0), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf4 = reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf2
buf5 = empty_strided_cuda((4, 4, 4, 400), (6400, 1600, 400, 1),
torch.float32)
get_raw_stream(0)
triton_per_fused_elu_native_layer_norm_0[grid(64)](buf4, buf0,
primals_4, primals_5, buf1, buf5, 64, 400, num_warps=4,
num_stages=1)
del primals_5
buf6 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf5, (64, 400),
(400, 1), 0), reinterpret_tensor(primals_6, (400, 300), (1, 400
), 0), alpha=1, beta=1, out=buf6)
del primals_7
buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf10 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf8
buf11 = empty_strided_cuda((4, 4, 4, 300), (4864, 1216, 300, 1),
torch.float32)
triton_per_fused_elu_native_layer_norm_1[grid(64)](buf10, buf6,
primals_8, primals_9, buf7, buf11, 64, 300, num_warps=4,
num_stages=1)
del primals_9
buf12 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
triton_poi_fused_elu_native_layer_norm_view_2[grid(19200)](buf11,
buf12, 19200, XBLOCK=256, num_warps=4, num_stages=1)
del buf11
buf13 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(buf12, reinterpret_tensor(primals_10, (300, 4), (
1, 300), 0), out=buf13)
buf14 = reinterpret_tensor(buf13, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf13
triton_poi_fused_sigmoid_3[grid(256)](buf14, primals_11, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_11
return buf14, primals_4, primals_8, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), buf0, buf1, buf4, reinterpret_tensor(buf5, (64, 400
), (400, 1), 0), buf6, buf7, buf10, buf12, buf14, primals_10, primals_6
class ActorNew(nn.Module):
"""Actor model
Parameters:
args (object): Parameter class
"""
def __init__(self, state_dim, action_dim, wwid):
super(ActorNew, self).__init__()
self.wwid = torch.Tensor([wwid])
l1 = 400
l2 = 300
self.f1 = nn.Linear(state_dim, l1)
self.ln1 = nn.LayerNorm(l1)
self.f2 = nn.Linear(l1, l2)
self.ln2 = nn.LayerNorm(l2)
self.w_out = nn.Linear(l2, action_dim)
def forward(self, input_0):
primals_1 = self.f1.weight
primals_2 = self.f1.bias
primals_4 = self.ln1.weight
primals_5 = self.ln1.bias
primals_6 = self.f2.weight
primals_7 = self.f2.bias
primals_8 = self.ln2.weight
primals_9 = self.ln2.bias
primals_10 = self.w_out.weight
primals_11 = self.w_out.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]
|
zhan0903/cerl
|
Actor
| false
| 4,657
|
[
"Apache-2.0"
] | 0
|
6fb8aca9cb78b72947237edf2b9ed8362bd43829
|
https://github.com/zhan0903/cerl/tree/6fb8aca9cb78b72947237edf2b9ed8362bd43829
|
Encoder
|
import torch
from torch import nn
class Encoder(nn.Module):
def __init__(self, embedding_dim, nhead, dropout, k=4):
super(Encoder, self).__init__()
self.transformer = nn.TransformerEncoderLayer(embedding_dim, nhead,
dim_feedforward=k * embedding_dim, dropout=dropout, activation=
'gelu')
def forward(self, x):
x = x.transpose(0, 1)
h = self.transformer(x)
out = h.mean(dim=0)
return out
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'embedding_dim': 4, 'nhead': 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
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_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
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_mul_1(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 + (4 + 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__safe_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__safe_softmax_3(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
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp18 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp25 = tl.load(in_ptr1 + x2, xmask)
tmp26 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
tmp27 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp29 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp31 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp1 = float('-inf')
tmp2 = tmp0 == tmp1
tmp3 = tmp2 == 0
tmp4 = tmp3.to(tl.int64)
tmp5 = tmp4 != 0
tmp7 = tmp6 == tmp1
tmp8 = tmp7 == 0
tmp9 = tmp8.to(tl.int64)
tmp10 = tmp9 != 0
tmp11 = tmp5 | tmp10
tmp13 = tmp12 == tmp1
tmp14 = tmp13 == 0
tmp15 = tmp14.to(tl.int64)
tmp16 = tmp15 != 0
tmp17 = tmp11 | tmp16
tmp19 = tmp18 == tmp1
tmp20 = tmp19 == 0
tmp21 = tmp20.to(tl.int64)
tmp22 = tmp21 != 0
tmp23 = tmp17 | tmp22
tmp24 = tmp23 == 0
tmp28 = tmp26 + tmp27
tmp30 = tmp28 + tmp29
tmp32 = tmp30 + tmp31
tmp33 = tmp25 / tmp32
tmp34 = 0.0
tmp35 = tl.where(tmp24, tmp34, tmp33)
tl.store(out_ptr0 + x2, tmp35, xmask)
@triton.jit
def triton_poi_fused_add_4(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 + (8 + x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x2, tmp2, xmask)
@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 4
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
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_6(in_out_ptr0, in_ptr0, in_ptr1,
ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 4
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
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
tmp1 = tl.load(in_out_ptr0 + (x1 + 4 * y0), xmask & ymask)
tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + (x1 + 4 * y0), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_native_layer_norm_7(in_ptr0, 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 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, 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
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_gelu_9(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_ptr0 + x0, xmask)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = 0.7071067811865476
tmp4 = tmp0 * tmp3
tmp5 = libdevice.erf(tmp4)
tmp6 = 1.0
tmp7 = tmp5 + tmp6
tmp8 = tmp2 * tmp7
tl.store(out_ptr0 + x0, tmp8, xmask)
@triton.jit
def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, in_ptr1, 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_out_ptr0 + x2, xmask)
tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_mean_native_layer_norm_11(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp4 = tl.load(in_ptr2 + 0)
tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
tmp7 = tl.load(in_ptr3 + x0, xmask)
tmp9 = tl.load(in_ptr4 + x0, xmask)
tmp11 = tl.load(in_ptr0 + (4 + x0), xmask)
tmp12 = tl.load(in_ptr1 + 1)
tmp13 = tl.broadcast_to(tmp12, [XBLOCK])
tmp15 = tl.load(in_ptr2 + 1)
tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
tmp21 = tl.load(in_ptr0 + (8 + x0), xmask)
tmp22 = tl.load(in_ptr1 + 2)
tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
tmp25 = tl.load(in_ptr2 + 2)
tmp26 = tl.broadcast_to(tmp25, [XBLOCK])
tmp31 = tl.load(in_ptr0 + (12 + x0), xmask)
tmp32 = tl.load(in_ptr1 + 3)
tmp33 = tl.broadcast_to(tmp32, [XBLOCK])
tmp35 = tl.load(in_ptr2 + 3)
tmp36 = tl.broadcast_to(tmp35, [XBLOCK])
tmp3 = tmp0 - tmp2
tmp6 = tmp3 * tmp5
tmp8 = tmp6 * tmp7
tmp10 = tmp8 + tmp9
tmp14 = tmp11 - tmp13
tmp17 = tmp14 * tmp16
tmp18 = tmp17 * tmp7
tmp19 = tmp18 + tmp9
tmp20 = tmp10 + tmp19
tmp24 = tmp21 - tmp23
tmp27 = tmp24 * tmp26
tmp28 = tmp27 * tmp7
tmp29 = tmp28 + tmp9
tmp30 = tmp20 + tmp29
tmp34 = tmp31 - tmp33
tmp37 = tmp34 * tmp36
tmp38 = tmp37 * tmp7
tmp39 = tmp38 + tmp9
tmp40 = tmp30 + tmp39
tmp41 = 4.0
tmp42 = tmp40 / tmp41
tl.store(out_ptr0 + x0, tmp42, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (12, 4), (4, 1))
assert_size_stride(primals_3, (12,), (1,))
assert_size_stride(primals_4, (4, 4), (4, 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, (16, 4), (4, 1))
assert_size_stride(primals_9, (16,), (1,))
assert_size_stride(primals_10, (4, 16), (16, 1))
assert_size_stride(primals_11, (4,), (1,))
assert_size_stride(primals_12, (4,), (1,))
assert_size_stride(primals_13, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (1, 4), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (1, 4), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 16), out=buf1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (1, 4), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 32), out=buf2)
del primals_2
buf3 = reinterpret_tensor(buf0, (1, 4, 4, 1), (16, 1, 4, 16), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_mul_0[grid(16)](buf3, primals_3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf4 = reinterpret_tensor(buf1, (1, 4, 1, 4), (16, 1, 16, 4), 0)
del buf1
triton_poi_fused_mul_1[grid(16)](buf4, primals_3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf3, (4, 4, 1), (1, 4, 0), 0
), reinterpret_tensor(buf4, (4, 1, 4), (1, 0, 4), 0), out=buf5)
buf6 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__safe_softmax_2[grid(64)](buf5, buf6, 64, XBLOCK=
64, num_warps=1, num_stages=1)
buf7 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__safe_softmax_3[grid(64)](buf5, buf6, buf7, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf8 = reinterpret_tensor(buf2, (4, 1, 4), (4, 4, 1), 0)
del buf2
triton_poi_fused_add_4[grid(16)](buf8, primals_3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_3
buf9 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf7, (4, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf8, (4, 4, 1), (1, 4, 0), 0), out=buf9)
buf10 = empty_strided_cuda((4, 1, 4, 1), (4, 1, 1, 4), torch.float32)
triton_poi_fused_clone_5[grid(4, 4)](buf9, buf10, 4, 4, XBLOCK=4,
YBLOCK=4, num_warps=1, num_stages=1)
buf11 = reinterpret_tensor(buf9, (4, 4), (4, 1), 0)
del buf9
extern_kernels.mm(reinterpret_tensor(buf10, (4, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf11)
buf12 = buf11
del buf11
triton_poi_fused_add_native_layer_norm_6[grid(4, 4)](buf12,
primals_1, primals_5, 4, 4, XBLOCK=4, YBLOCK=4, num_warps=1,
num_stages=1)
del primals_5
buf13 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf14 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
triton_poi_fused_native_layer_norm_7[grid(4)](buf12, buf13, buf14,
4, XBLOCK=4, num_warps=1, num_stages=1)
buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_native_layer_norm_8[grid(16)](buf12, buf13, buf14,
primals_6, primals_7, buf15, 16, XBLOCK=16, num_warps=1,
num_stages=1)
del primals_7
buf16 = reinterpret_tensor(buf6, (4, 16), (16, 1), 0)
del buf6
extern_kernels.addmm(primals_9, buf15, reinterpret_tensor(primals_8,
(4, 16), (1, 4), 0), alpha=1, beta=1, out=buf16)
del primals_9
buf17 = reinterpret_tensor(buf5, (4, 16), (16, 1), 0)
del buf5
triton_poi_fused_gelu_9[grid(64)](buf16, buf17, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf17, reinterpret_tensor(primals_10, (16, 4), (1,
16), 0), out=buf18)
buf19 = buf18
del buf18
triton_poi_fused_add_10[grid(16)](buf19, buf15, primals_11, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del primals_11
buf20 = buf14
del buf14
buf21 = buf13
del buf13
triton_poi_fused_native_layer_norm_7[grid(4)](buf19, buf20, buf21,
4, XBLOCK=4, num_warps=1, num_stages=1)
buf22 = empty_strided_cuda((4,), (1,), torch.float32)
triton_poi_fused_mean_native_layer_norm_11[grid(4)](buf19, buf20,
buf21, primals_12, primals_13, buf22, 4, XBLOCK=4, num_warps=1,
num_stages=1)
del buf20
del buf21
del primals_13
return (buf22, primals_6, primals_12, reinterpret_tensor(primals_1, (4,
4), (1, 4), 0), buf7, reinterpret_tensor(buf10, (4, 4), (4, 1), 0),
buf12, buf15, buf16, buf17, buf19, primals_10, primals_8, primals_4,
reinterpret_tensor(buf8, (4, 1, 4), (1, 1, 4), 0),
reinterpret_tensor(buf3, (4, 1, 4), (1, 4, 4), 0),
reinterpret_tensor(buf4, (4, 4, 1), (1, 4, 16), 0))
class EncoderNew(nn.Module):
def __init__(self, embedding_dim, nhead, dropout, k=4):
super(EncoderNew, self).__init__()
self.transformer = nn.TransformerEncoderLayer(embedding_dim, nhead,
dim_feedforward=k * embedding_dim, dropout=dropout, activation=
'gelu')
def forward(self, input_0):
primals_2 = self.transformer.self_attn.in_proj_weight
primals_3 = self.transformer.self_attn.in_proj_bias
primals_1 = self.transformer.self_attn.out_proj.weight
primals_5 = self.transformer.self_attn.out_proj.bias
primals_8 = self.transformer.linear1.weight
primals_9 = self.transformer.linear1.bias
primals_10 = self.transformer.linear2.weight
primals_6 = self.transformer.linear2.bias
primals_7 = self.transformer.norm1.weight
primals_11 = self.transformer.norm1.bias
primals_12 = self.transformer.norm2.weight
primals_13 = self.transformer.norm2.bias
primals_4 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13])
return output[0]
|
yukiar/distil_wic
|
Encoder
| false
| 4,658
|
[
"MIT"
] | 0
|
1f9c5c7252105dd9f4f264f8533753f0cd08ca5b
|
https://github.com/yukiar/distil_wic/tree/1f9c5c7252105dd9f4f264f8533753f0cd08ca5b
|
GCN
|
from torch.nn import Module
import math
import torch
from torchvision.transforms import functional as F
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, 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(xariv=True)
def reset_parameters(self, xariv=False):
if xariv:
torch.nn.init.xavier_normal_(self.weight, gain=1.0)
if self.bias is not None:
torch.nn.init.constant_(self.bias, 0)
else:
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, adj):
support = torch.mm(input, self.weight)
output = torch.mm(adj, 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 GCN(nn.Module):
def __init__(self, nfeat, nhid, dropout):
super(GCN, self).__init__()
self.gc1 = GraphConvolution(nfeat, nhid, bias=True)
self.gc2 = GraphConvolution(nhid, nhid, bias=True)
self.dropout = dropout
def forward(self, input, adj):
x1 = F.relu(self.gc1(input, adj))
x1 = F.dropout(x1, self.dropout, training=self.training)
x2 = F.relu(self.gc2(x1, adj))
x2 = F.dropout(x2, self.dropout, training=self.training)
return x2 + input
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'nfeat': 4, 'nhid': 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
from torch.nn import Module
import math
import torch.utils.data
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
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_add_relu_threshold_backward_1(in_ptr0, in_ptr1,
in_ptr2, 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
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr2 + x2, xmask)
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = tmp4 + tmp5
tmp7 = 0.0
tmp8 = tmp4 <= tmp7
tl.store(out_ptr0 + x2, tmp6, xmask)
tl.store(out_ptr1 + 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, 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,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_2, primals_1, out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf0, out=buf1)
buf2 = buf1
del buf1
get_raw_stream(0)
triton_poi_fused_add_relu_0[grid(16)](buf2, primals_4, 16, XBLOCK=
16, num_warps=1, num_stages=1)
del primals_4
buf3 = buf0
del buf0
extern_kernels.mm(buf2, primals_5, out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf3, out=buf4)
buf5 = buf3
del buf3
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_add_relu_threshold_backward_1[grid(16)](buf4,
primals_6, primals_2, buf5, buf6, 16, XBLOCK=16, num_warps=1,
num_stages=1)
del buf4
del primals_6
return buf5, buf2, buf6, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0
), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0
), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0)
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, 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(xariv=True)
def reset_parameters(self, xariv=False):
if xariv:
torch.nn.init.xavier_normal_(self.weight, gain=1.0)
if self.bias is not None:
torch.nn.init.constant_(self.bias, 0)
else:
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, adj):
support = torch.mm(input, self.weight)
output = torch.mm(adj, 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 GCNNew(nn.Module):
def __init__(self, nfeat, nhid, dropout):
super(GCNNew, self).__init__()
self.gc1 = GraphConvolution(nfeat, nhid, bias=True)
self.gc2 = GraphConvolution(nhid, nhid, bias=True)
self.dropout = dropout
def forward(self, input_0, input_1):
primals_1 = self.gc1.weight
primals_4 = self.gc1.bias
primals_2 = self.gc2.weight
primals_6 = self.gc2.bias
primals_3 = input_0
primals_5 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
zhanwenchen/Scene-Graph-Benchmark.pytorch
|
GCN
| false
| 4,659
|
[
"MIT"
] | 0
|
c86475bcbdaefcc1656a2890194355c2b32aa694
|
https://github.com/zhanwenchen/Scene-Graph-Benchmark.pytorch/tree/c86475bcbdaefcc1656a2890194355c2b32aa694
|
ApplySingleAttention
|
import torch
import torch.utils.data
import torch.nn as nn
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
self.drop_value = drop
self.drop = nn.Dropout(drop)
self.activate = activate.lower() if activate is not None else None
if activate == 'relu':
self.ac_fn = nn.ReLU()
elif activate == 'sigmoid':
self.ac_fn = nn.Sigmoid()
elif activate == 'tanh':
self.ac_fn = nn.Tanh()
def forward(self, x):
if self.drop_value > 0:
x = self.drop(x)
x = self.lin(x)
if self.activate is not None:
x = self.ac_fn(x)
return x
class ApplySingleAttention(nn.Module):
def __init__(self, v_features, q_features, mid_features, drop=0.0):
super(ApplySingleAttention, self).__init__()
self.lin_v = FCNet(v_features, mid_features, activate='relu', drop=drop
)
self.lin_q = FCNet(q_features, mid_features, activate='relu', drop=drop
)
self.lin_atten = FCNet(mid_features, mid_features, drop=drop)
def forward(self, v, q, atten):
"""
v = batch, num_obj, dim
q = batch, que_len, dim
atten: batch x v_num x q_num
"""
v_ = self.lin_v(v).transpose(1, 2).unsqueeze(2)
q_ = self.lin_q(q).transpose(1, 2).unsqueeze(3)
v_ = torch.matmul(v_, atten.unsqueeze(1))
h_ = torch.matmul(v_, q_)
h_ = h_.squeeze(3).squeeze(2)
atten_h = self.lin_atten(h_.unsqueeze(1))
return atten_h
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4])
]
def get_init_inputs():
return [[], {'v_features': 4, 'q_features': 4, 'mid_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 torch.utils.data
import torch.nn as nn
from torch.nn.utils import weight_norm
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_div_mul_norm_0(in_out_ptr0, in_ptr0, in_ptr1, out_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)
tmp6 = tl.load(in_ptr1 + 0)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = tl.sum(tmp2, 1)[:, None]
tmp5 = libdevice.sqrt(tmp4)
tmp8 = tmp7 / tmp5
tmp9 = tmp0 * tmp8
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp5, None)
tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp9, None)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (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
tmp3 = tl.full([1, 1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_clone_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 % 16
x2 = xindex // 64
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
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, primals_10, primals_11, primals_12
) = args
args.clear()
assert_size_stride(primals_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), (16, 4, 1))
assert_size_stride(primals_5, (), ())
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))
assert_size_stride(primals_9, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_10, (), ())
assert_size_stride(primals_11, (4, 4), (4, 1))
assert_size_stride(primals_12, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_per_fused_div_mul_norm_0[grid(1)](buf1, primals_2, primals_1,
buf2, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
reinterpret_tensor(buf2, (4, 4), (1, 4), 0), out=buf3)
buf4 = empty_strided_cuda((), (), torch.float32)
buf5 = buf4
del buf4
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_per_fused_div_mul_norm_0[grid(1)](buf5, primals_6, primals_5,
buf6, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_8, (16, 4), (4, 1), 0),
reinterpret_tensor(buf6, (4, 4), (1, 4), 0), out=buf7)
buf8 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
triton_poi_fused_clone_1[grid(16, 4)](buf3, primals_3, buf8, 16, 4,
XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_2[grid(256)](primals_9, buf9, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_9
buf10 = empty_strided_cuda((16, 1, 4), (4, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf8, (16, 1, 4), (4, 0, 1),
0), reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1), 0), out=buf10)
buf11 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf8
triton_poi_fused_clone_1[grid(16, 4)](buf7, primals_7, buf11, 16, 4,
XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
buf12 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
extern_kernels.bmm(buf10, reinterpret_tensor(buf11, (16, 4, 1), (4,
1, 0), 0), out=buf12)
buf13 = empty_strided_cuda((), (), torch.float32)
buf14 = buf13
del buf13
buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_per_fused_div_mul_norm_0[grid(1)](buf14, primals_11,
primals_10, buf15, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_12, reinterpret_tensor(buf12, (4, 4),
(4, 1), 0), reinterpret_tensor(buf15, (4, 4), (1, 4), 0), alpha
=1, beta=1, out=buf16)
del primals_12
buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_3[grid(64)](buf7,
primals_7, buf17, 64, XBLOCK=64, num_warps=1, num_stages=1)
del buf7
del primals_7
buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_3[grid(64)](buf3,
primals_3, buf18, 64, XBLOCK=64, num_warps=1, num_stages=1)
del buf3
del primals_3
return (reinterpret_tensor(buf16, (4, 1, 4), (4, 4, 1), 0), buf2, buf6,
buf15, primals_1, primals_2, primals_5, primals_6, primals_10,
primals_11, buf1, reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
buf5, reinterpret_tensor(primals_8, (16, 4), (4, 1), 0), buf14,
reinterpret_tensor(buf12, (4, 4), (4, 1), 0), buf15,
reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 4), 0),
reinterpret_tensor(buf11, (16, 1, 4), (4, 1, 1), 0),
reinterpret_tensor(buf9, (16, 4, 4), (16, 1, 4), 0), buf17, buf18)
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
self.drop_value = drop
self.drop = nn.Dropout(drop)
self.activate = activate.lower() if activate is not None else None
if activate == 'relu':
self.ac_fn = nn.ReLU()
elif activate == 'sigmoid':
self.ac_fn = nn.Sigmoid()
elif activate == 'tanh':
self.ac_fn = nn.Tanh()
def forward(self, x):
if self.drop_value > 0:
x = self.drop(x)
x = self.lin(x)
if self.activate is not None:
x = self.ac_fn(x)
return x
class ApplySingleAttentionNew(nn.Module):
def __init__(self, v_features, q_features, mid_features, drop=0.0):
super(ApplySingleAttentionNew, self).__init__()
self.lin_v = FCNet(v_features, mid_features, activate='relu', drop=drop
)
self.lin_q = FCNet(q_features, mid_features, activate='relu', drop=drop
)
self.lin_atten = FCNet(mid_features, mid_features, drop=drop)
def forward(self, input_0, input_1, input_2):
primals_3 = self.lin_v.lin.bias
primals_1 = self.lin_v.lin.weight_g
primals_2 = self.lin_v.lin.weight_v
primals_7 = self.lin_q.lin.bias
primals_5 = self.lin_q.lin.weight_g
primals_6 = self.lin_q.lin.weight_v
primals_12 = self.lin_atten.lin.bias
primals_10 = self.lin_atten.lin.weight_g
primals_11 = self.lin_atten.lin.weight_v
primals_4 = input_0
primals_8 = input_1
primals_9 = input_2
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])
return output[0]
|
zhanwenchen/Scene-Graph-Benchmark.pytorch
|
ApplySingleAttention
| false
| 4,660
|
[
"MIT"
] | 0
|
c86475bcbdaefcc1656a2890194355c2b32aa694
|
https://github.com/zhanwenchen/Scene-Graph-Benchmark.pytorch/tree/c86475bcbdaefcc1656a2890194355c2b32aa694
|
Fcn8s
|
import torch
import numpy as np
import torch.nn as nn
def _upsampling_weights(in_channels, out_channels, kernel_size):
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 Fcn8s(nn.Module):
def __init__(self, n_class=21):
"""
Create the FCN-8s network the the given number of classes.
Args:
n_class: The number of semantic classes.
"""
super(Fcn8s, self).__init__()
self.conv1_1 = nn.Conv2d(3, 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.score_pool3 = nn.Conv2d(256, n_class, 1)
self.score_pool4 = nn.Conv2d(512, n_class, 1)
self.upscore2 = nn.ConvTranspose2d(n_class, n_class, 4, stride=2,
bias=True)
self.upscore8 = nn.ConvTranspose2d(n_class, n_class, 16, stride=8,
bias=False)
self.upscore_pool4 = nn.ConvTranspose2d(n_class, n_class, 4, stride
=2, 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 = _upsampling_weights(m.in_channels, m.
out_channels, m.kernel_size[0])
m.weight.data.copy_(initial_weight)
def forward(self, image):
h = self.relu1_1(self.conv1_1(image))
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)
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)
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.upscore2(h)
upscore2 = h
h = self.score_pool4(pool4)
h = h[:, :, 5:5 + upscore2.size()[2], 5:5 + upscore2.size()[3]]
score_pool4c = h
h = upscore2 + score_pool4c
h = self.upscore_pool4(h)
upscore_pool4 = h
h = self.score_pool3(pool3)
h = h[:, :, 9:9 + upscore_pool4.size()[2], 9:9 + upscore_pool4.size
()[3]]
score_pool3c = h
h = upscore_pool4 + score_pool3c
h = self.upscore8(h)
h = h[:, :, 31:31 + image.size()[2], 31:31 + image.size()[3]
].contiguous()
return h
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
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 192
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 % 3
y1 = yindex // 3
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 3 * x2 + 27 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_1(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_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_10(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 441
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 21
y1 = yindex // 21
tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask, eviction_policy
='evict_last')
tl.store(out_ptr0 + (y0 + 21 * x2 + 336 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_11(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 441
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 % 21
y1 = yindex // 21
tmp0 = tl.load(in_ptr0 + (x2 + 256 * y3), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (y0 + 21 * x2 + 5376 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_relu_12(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_13(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_14(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_15(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_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 % 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_17(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_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 // 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_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 % 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_21(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_22(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_23(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 756
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 21
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_convolution_24(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 5376
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x4 = xindex
x0 = xindex % 21
x2 = xindex // 168 % 8
x3 = xindex // 1344
x5 = xindex % 168
tmp0 = tl.load(in_out_ptr0 + x4, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (1890 + x5 + 357 * x2 + 6069 * x3), 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 + x4, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_25(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
tl.constexpr):
xnumel = 27216
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x4 = xindex
x2 = xindex // 378 % 18
x3 = xindex // 6804
x5 = xindex % 378
x0 = xindex % 21
tmp0 = tl.load(in_out_ptr0 + x4, xmask)
tmp1 = tl.load(in_ptr0 + (6426 + x5 + 693 * x2 + 22869 * x3), xmask)
tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + x4, tmp4, xmask)
@triton.jit
def triton_poi_fused_clone_26(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl
.constexpr, XBLOCK: tl.constexpr):
ynumel = 84
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 % 64
x3 = xindex // 64
y0 = yindex % 21
y1 = yindex // 21
x5 = xindex
y4 = yindex
tmp0 = tl.load(in_ptr0 + (99603 + y0 + 21 * x2 + 3192 * x3 + 485184 *
y1), ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x5 + 4096 * y4), tmp0, ymask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, 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) = args
args.clear()
assert_size_stride(primals_1, (64, 3, 3, 3), (27, 9, 3, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 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, (21, 4096, 1, 1), (4096, 1, 1, 1))
assert_size_stride(primals_33, (21,), (1,))
assert_size_stride(primals_34, (21, 21, 4, 4), (336, 16, 4, 1))
assert_size_stride(primals_35, (21,), (1,))
assert_size_stride(primals_36, (21, 512, 1, 1), (512, 1, 1, 1))
assert_size_stride(primals_37, (21,), (1,))
assert_size_stride(primals_38, (21, 21, 4, 4), (336, 16, 4, 1))
assert_size_stride(primals_39, (21, 256, 1, 1), (256, 1, 1, 1))
assert_size_stride(primals_40, (21,), (1,))
assert_size_stride(primals_41, (21, 21, 16, 16), (5376, 256, 16, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 3, 3, 3), (27, 1, 9, 3), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(192, 9)](primals_1, buf0, 192, 9, XBLOCK=16,
YBLOCK=64, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
.float32)
triton_poi_fused_1[grid(12, 4096)](primals_3, buf1, 12, 4096,
XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
del primals_3
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 = empty_strided_cuda((21, 21, 4, 4), (336, 1, 84, 21), torch.
float32)
triton_poi_fused_10[grid(441, 16)](primals_34, buf15, 441, 16,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_34
buf16 = empty_strided_cuda((21, 21, 4, 4), (336, 1, 84, 21), torch.
float32)
triton_poi_fused_10[grid(441, 16)](primals_38, buf16, 441, 16,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_38
buf17 = empty_strided_cuda((21, 21, 16, 16), (5376, 1, 336, 21),
torch.float32)
triton_poi_fused_11[grid(441, 256)](primals_41, buf17, 441, 256,
XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
del primals_41
buf18 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
padding=(100, 100), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf18, (4, 64, 262, 262), (4393216, 1, 16768, 64))
buf19 = buf18
del buf18
triton_poi_fused_convolution_relu_12[grid(17572864)](buf19,
primals_2, 17572864, XBLOCK=512, num_warps=8, num_stages=1)
del primals_2
buf20 = extern_kernels.convolution(buf19, buf2, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf20, (4, 64, 262, 262), (4393216, 1, 16768, 64))
buf21 = buf20
del buf20
triton_poi_fused_convolution_relu_12[grid(17572864)](buf21,
primals_5, 17572864, XBLOCK=512, num_warps=8, num_stages=1)
del primals_5
buf22 = empty_strided_cuda((4, 64, 131, 131), (1098304, 1, 8384, 64
), torch.float32)
buf23 = empty_strided_cuda((4, 64, 131, 131), (1098304, 1, 8384, 64
), torch.int8)
triton_poi_fused_max_pool2d_with_indices_13[grid(4393216)](buf21,
buf22, buf23, 4393216, XBLOCK=512, num_warps=8, num_stages=1)
buf24 = extern_kernels.convolution(buf22, buf3, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf24, (4, 128, 131, 131), (2196608, 1, 16768, 128))
buf25 = buf24
del buf24
triton_poi_fused_convolution_relu_14[grid(8786432)](buf25,
primals_7, 8786432, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_7
buf26 = extern_kernels.convolution(buf25, buf4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf26, (4, 128, 131, 131), (2196608, 1, 16768, 128))
buf27 = buf26
del buf26
triton_poi_fused_convolution_relu_14[grid(8786432)](buf27,
primals_9, 8786432, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_9
buf28 = empty_strided_cuda((4, 128, 66, 66), (557568, 1, 8448, 128),
torch.float32)
buf29 = empty_strided_cuda((4, 128, 66, 66), (557568, 1, 8448, 128),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_15[grid(2230272)](buf27,
buf28, buf29, 2230272, XBLOCK=512, num_warps=8, num_stages=1)
buf30 = extern_kernels.convolution(buf28, buf5, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf30, (4, 256, 66, 66), (1115136, 1, 16896, 256))
buf31 = buf30
del buf30
triton_poi_fused_convolution_relu_16[grid(4460544)](buf31,
primals_11, 4460544, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_11
buf32 = extern_kernels.convolution(buf31, buf6, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf32, (4, 256, 66, 66), (1115136, 1, 16896, 256))
buf33 = buf32
del buf32
triton_poi_fused_convolution_relu_16[grid(4460544)](buf33,
primals_13, 4460544, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_13
buf34 = extern_kernels.convolution(buf33, buf7, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf34, (4, 256, 66, 66), (1115136, 1, 16896, 256))
buf35 = buf34
del buf34
triton_poi_fused_convolution_relu_16[grid(4460544)](buf35,
primals_15, 4460544, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_15
buf36 = empty_strided_cuda((4, 256, 33, 33), (278784, 1, 8448, 256),
torch.float32)
buf37 = empty_strided_cuda((4, 256, 33, 33), (278784, 1, 8448, 256),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_17[grid(1115136)](buf35,
buf36, buf37, 1115136, XBLOCK=512, num_warps=8, num_stages=1)
buf38 = extern_kernels.convolution(buf36, buf8, 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, 33, 33), (557568, 1, 16896, 512))
buf39 = buf38
del buf38
triton_poi_fused_convolution_relu_18[grid(2230272)](buf39,
primals_17, 2230272, XBLOCK=512, num_warps=8, num_stages=1)
del primals_17
buf40 = extern_kernels.convolution(buf39, buf9, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf40, (4, 512, 33, 33), (557568, 1, 16896, 512))
buf41 = buf40
del buf40
triton_poi_fused_convolution_relu_18[grid(2230272)](buf41,
primals_19, 2230272, XBLOCK=512, num_warps=8, num_stages=1)
del primals_19
buf42 = extern_kernels.convolution(buf41, buf10, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf42, (4, 512, 33, 33), (557568, 1, 16896, 512))
buf43 = buf42
del buf42
triton_poi_fused_convolution_relu_18[grid(2230272)](buf43,
primals_21, 2230272, XBLOCK=512, num_warps=8, num_stages=1)
del primals_21
buf44 = empty_strided_cuda((4, 512, 17, 17), (147968, 1, 8704, 512),
torch.float32)
buf45 = empty_strided_cuda((4, 512, 17, 17), (147968, 1, 8704, 512),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_19[grid(591872)](buf43,
buf44, buf45, 591872, XBLOCK=1024, num_warps=4, num_stages=1)
buf46 = extern_kernels.convolution(buf44, buf11, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf46, (4, 512, 17, 17), (147968, 1, 8704, 512))
buf47 = buf46
del buf46
triton_poi_fused_convolution_relu_20[grid(591872)](buf47,
primals_23, 591872, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_23
buf48 = extern_kernels.convolution(buf47, buf12, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf48, (4, 512, 17, 17), (147968, 1, 8704, 512))
buf49 = buf48
del buf48
triton_poi_fused_convolution_relu_20[grid(591872)](buf49,
primals_25, 591872, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_25
buf50 = extern_kernels.convolution(buf49, buf13, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf50, (4, 512, 17, 17), (147968, 1, 8704, 512))
buf51 = buf50
del buf50
triton_poi_fused_convolution_relu_20[grid(591872)](buf51,
primals_27, 591872, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_27
buf52 = empty_strided_cuda((4, 512, 9, 9), (41472, 1, 4608, 512),
torch.float32)
buf53 = empty_strided_cuda((4, 512, 9, 9), (41472, 1, 4608, 512),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_21[grid(165888)](buf51,
buf52, buf53, 165888, XBLOCK=512, num_warps=8, num_stages=1)
buf54 = extern_kernels.convolution(buf52, buf14, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf54, (4, 4096, 3, 3), (36864, 1, 12288, 4096))
buf55 = buf54
del buf54
triton_poi_fused_convolution_relu_22[grid(147456)](buf55,
primals_29, 147456, XBLOCK=512, num_warps=8, num_stages=1)
del primals_29
buf56 = extern_kernels.convolution(buf55, primals_30, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf56, (4, 4096, 3, 3), (36864, 1, 12288, 4096))
buf57 = buf56
del buf56
triton_poi_fused_convolution_relu_22[grid(147456)](buf57,
primals_31, 147456, XBLOCK=512, num_warps=8, num_stages=1)
del primals_31
buf58 = extern_kernels.convolution(buf57, primals_32, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf58, (4, 21, 3, 3), (189, 1, 63, 21))
buf59 = buf58
del buf58
triton_poi_fused_convolution_23[grid(756)](buf59, primals_33, 756,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_33
buf60 = extern_kernels.convolution(buf59, buf15, stride=(2, 2),
padding=(0, 0), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf60, (4, 21, 8, 8), (1344, 1, 168, 21))
buf61 = extern_kernels.convolution(buf44, primals_36, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf61, (4, 21, 17, 17), (6069, 1, 357, 21))
buf62 = buf60
del buf60
triton_poi_fused_add_convolution_24[grid(5376)](buf62, primals_35,
buf61, primals_37, 5376, XBLOCK=256, num_warps=4, num_stages=1)
del buf61
del primals_35
del primals_37
buf63 = extern_kernels.convolution(buf62, buf16, stride=(2, 2),
padding=(0, 0), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf63, (4, 21, 18, 18), (6804, 1, 378, 21))
buf64 = extern_kernels.convolution(buf36, primals_39, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf64, (4, 21, 33, 33), (22869, 1, 693, 21))
buf65 = buf63
del buf63
triton_poi_fused_add_25[grid(27216)](buf65, buf64, primals_40,
27216, XBLOCK=256, num_warps=4, num_stages=1)
del buf64
del primals_40
buf66 = extern_kernels.convolution(buf65, buf17, stride=(8, 8),
padding=(0, 0), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf66, (4, 21, 152, 152), (485184, 1, 3192, 21))
buf67 = empty_strided_cuda((4, 21, 64, 64), (86016, 4096, 64, 1),
torch.float32)
triton_poi_fused_clone_26[grid(84, 4096)](buf66, buf67, 84, 4096,
XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
del buf66
return (buf67, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8,
buf9, buf10, buf11, buf12, buf13, buf14, primals_30, primals_32,
buf15, primals_36, buf16, primals_39, buf17, buf19, buf21, buf22,
buf23, buf25, buf27, buf28, buf29, buf31, buf33, buf35, buf36,
buf37, buf39, buf41, buf43, buf44, buf45, buf47, buf49, buf51,
buf52, buf53, buf55, buf57, buf59, buf62, buf65)
def _upsampling_weights(in_channels, out_channels, kernel_size):
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 Fcn8sNew(nn.Module):
def __init__(self, n_class=21):
"""
Create the FCN-8s network the the given number of classes.
Args:
n_class: The number of semantic classes.
"""
super(Fcn8sNew, self).__init__()
self.conv1_1 = nn.Conv2d(3, 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.score_pool3 = nn.Conv2d(256, n_class, 1)
self.score_pool4 = nn.Conv2d(512, n_class, 1)
self.upscore2 = nn.ConvTranspose2d(n_class, n_class, 4, stride=2,
bias=True)
self.upscore8 = nn.ConvTranspose2d(n_class, n_class, 16, stride=8,
bias=False)
self.upscore_pool4 = nn.ConvTranspose2d(n_class, n_class, 4, stride
=2, 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 = _upsampling_weights(m.in_channels, m.
out_channels, m.kernel_size[0])
m.weight.data.copy_(initial_weight)
def forward(self, input_0):
primals_1 = self.conv1_1.weight
primals_2 = 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_39 = self.score_pool3.weight
primals_35 = self.score_pool3.bias
primals_36 = self.score_pool4.weight
primals_37 = self.score_pool4.bias
primals_34 = self.upscore2.weight
primals_40 = self.upscore2.bias
primals_41 = self.upscore8.weight
primals_38 = self.upscore_pool4.weight
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])
return output[0]
|
jgibson2/crfasrnn_pytorch
|
Fcn8s
| false
| 4,661
|
[
"MIT"
] | 0
|
04c8477343bc1a186b3712f876b497f00e43ae72
|
https://github.com/jgibson2/crfasrnn_pytorch/tree/04c8477343bc1a186b3712f876b497f00e43ae72
|
BiaffineAttention
|
import torch
import torch.nn as nn
from torch.nn import Module as Layer
class BiaffineAttention(Layer):
"""Implements a biaffine attention operator for binary relation classification."""
def __init__(self, in_features, out_features):
super(BiaffineAttention, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.bilinear = nn.Bilinear(in_features, in_features, out_features,
bias=False)
self.linear = nn.Linear(2 * in_features, out_features)
def forward(self, x_1, x_2):
return self.bilinear(x_1, x_2) + self.linear(torch.cat((x_1, x_2),
dim=-1))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import Module as Layer
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 = 512
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_add_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
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x2, xmask)
tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + x2, 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), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 8), (8, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = torch.ops.aten._trilinear.default(reinterpret_tensor(
primals_3, (64, 4), (4, 1), 0), primals_1, reinterpret_tensor(
primals_2, (64, 4), (4, 1), 0), [1, 3], [0], [1, 2], [2, 3])
del primals_1
buf1 = buf0
del buf0
buf2 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(512)](primals_3, primals_2, buf2, 512,
XBLOCK=256, num_warps=4, num_stages=1)
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf2, (64, 8), (8, 1), 0),
reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), out=buf3)
del primals_4
buf4 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf1
triton_poi_fused_add_1[grid(256)](buf4, buf3, primals_5, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del buf3
del primals_5
return buf4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
), reinterpret_tensor(buf2, (64, 8), (8, 1), 0)
class BiaffineAttentionNew(Layer):
"""Implements a biaffine attention operator for binary relation classification."""
def __init__(self, in_features, out_features):
super(BiaffineAttentionNew, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.bilinear = nn.Bilinear(in_features, in_features, out_features,
bias=False)
self.linear = nn.Linear(2 * in_features, out_features)
def forward(self, input_0, input_1):
primals_1 = self.bilinear.weight
primals_4 = self.linear.weight
primals_5 = self.linear.bias
primals_2 = input_0
primals_3 = input_1
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
verages/PaddleOCR2Pytorch
|
BiaffineAttention
| false
| 4,662
|
[
"Apache-2.0"
] | 0
|
201f0d5d6007f49620c49af7d222c3b220eb3e70
|
https://github.com/verages/PaddleOCR2Pytorch/tree/201f0d5d6007f49620c49af7d222c3b220eb3e70
|
StateAttention
|
import torch
import torch.nn as nn
class StateAttention(nn.Module):
def __init__(self):
super(StateAttention, self).__init__()
self.sm = nn.Softmax(dim=1)
def forward(self, a_t, r_t, input_embedding, padded_mask):
new_a_t = torch.zeros_like(a_t)
for i in range(a_t.shape[1]):
if i == 0:
new_a_t[:, i] = a_t[:, 0] * r_t[:, 0]
else:
window = a_t[:, i - 1:i + 1]
window_sum = window[:, 0] * r_t[:, 1] + window[:, 1] * r_t[:, 0
]
new_a_t[:, i - 1] += (1 - padded_mask[:, i]) * window_sum
new_a_t[:, i] += padded_mask[:, i] * window_sum
new_a_t = new_a_t.unsqueeze(dim=1)
output = torch.matmul(new_a_t, input_embedding).squeeze(dim=1)
return output, new_a_t.squeeze(dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_mul_rsub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x1 = xindex // 16
x2 = xindex
tmp2 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tmp3 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
tmp7 = tl.load(in_ptr2 + (16 + x0 + 64 * x1), xmask)
tmp10 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
tmp12 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp28 = tl.load(in_ptr2 + (32 + x0 + 64 * x1), xmask)
tmp31 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp0 = tl.full([1], 0, tl.int32)
tmp1 = tmp0 == tmp0
tmp4 = tmp2 * tmp3
tmp5 = 0.0
tmp6 = tl.where(tmp1, tmp4, tmp5)
tmp8 = 1.0
tmp9 = tmp8 - tmp7
tmp11 = tmp2 * tmp10
tmp13 = tmp12 * tmp3
tmp14 = tmp11 + tmp13
tmp15 = tmp9 * tmp14
tmp16 = tmp6 + tmp15
tmp17 = tl.full([1], 1, tl.int32)
tmp18 = tmp17 == tmp0
tmp19 = tl.where(tmp1, tmp16, tmp6)
tmp20 = tl.where(tmp18, tmp4, tmp5)
tmp21 = tl.where(tmp18, tmp16, tmp20)
tmp22 = tl.where(tmp18, tmp19, tmp21)
tmp23 = tmp7 * tmp14
tmp24 = tmp22 + tmp23
tmp25 = tmp17 == tmp17
tmp26 = tl.where(tmp25, tmp24, tmp22)
tmp27 = tl.where(tmp25, tmp26, tmp26)
tmp29 = tmp8 - tmp28
tmp30 = tmp12 * tmp10
tmp32 = tmp31 * tmp3
tmp33 = tmp30 + tmp32
tmp34 = tmp29 * tmp33
tmp35 = tmp27 + tmp34
tl.store(out_ptr0 + x2, tmp16, xmask)
tl.store(out_ptr1 + x2, tmp24, xmask)
tl.store(out_ptr2 + x2, tmp35, xmask)
@triton.jit
def triton_poi_fused_add_copy_mul_rsub_zeros_like_1(in_ptr0, in_ptr1,
in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16 % 4
x0 = xindex % 16
x2 = xindex // 64
x3 = xindex
tmp3 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr2 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp10 = tl.load(in_ptr3 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp11 = tl.load(in_ptr4 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp0 = x1
tmp1 = tl.full([1], 1, tl.int32)
tmp2 = tmp0 == tmp1
tmp4 = tmp1 == tmp1
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = tmp1 == tmp6
tmp8 = tmp6 == tmp6
tmp12 = tmp10 * tmp11
tmp13 = 0.0
tmp14 = tl.where(tmp8, tmp12, tmp13)
tmp15 = tl.where(tmp8, tmp9, tmp14)
tmp16 = tl.where(tmp7, tmp12, tmp13)
tmp17 = tl.where(tmp7, tmp9, tmp16)
tmp18 = tl.where(tmp7, tmp15, tmp17)
tmp19 = tl.where(tmp4, tmp5, tmp18)
tmp20 = tmp0 == tmp6
tmp21 = tl.where(tmp20, tmp12, tmp13)
tmp22 = tl.where(tmp20, tmp9, tmp21)
tmp23 = tl.where(tmp20, tmp15, tmp22)
tmp24 = tl.where(tmp2, tmp5, tmp23)
tmp25 = tl.where(tmp2, tmp19, tmp24)
tmp26 = tl.where(tmp2, tmp3, tmp25)
tl.store(out_ptr0 + x3, tmp26, xmask)
@triton.jit
def triton_poi_fused_add_mul_rsub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
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
tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp6 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
tmp7 = tl.load(in_ptr2 + (16 + x0 + 64 * x1), xmask)
tmp8 = tl.load(in_ptr3 + (16 + x0 + 64 * x1), xmask)
tmp10 = tl.load(in_ptr2 + (32 + x0 + 64 * x1), xmask)
tmp11 = tl.load(in_ptr3 + (x0 + 64 * x1), xmask)
tmp19 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
tmp23 = tl.load(in_ptr2 + (48 + x0 + 64 * x1), xmask)
tmp0 = tl.full([1], 2, tl.int32)
tmp1 = tl.full([1], 1, tl.int32)
tmp2 = tmp0 == tmp1
tmp5 = tl.where(tmp2, tmp3, tmp4)
tmp9 = tmp7 * tmp8
tmp12 = tmp10 * tmp11
tmp13 = tmp9 + tmp12
tmp14 = tmp6 * tmp13
tmp15 = tmp5 + tmp14
tmp16 = tmp0 == tmp0
tmp17 = tl.where(tmp16, tmp15, tmp5)
tmp18 = tl.where(tmp16, tmp17, tmp17)
tmp20 = 1.0
tmp21 = tmp20 - tmp19
tmp22 = tmp10 * tmp8
tmp24 = tmp23 * tmp11
tmp25 = tmp22 + tmp24
tmp26 = tmp21 * tmp25
tmp27 = tmp18 + tmp26
tl.store(out_ptr0 + x2, tmp15, xmask)
tl.store(out_ptr1 + x2, tmp27, xmask)
@triton.jit
def triton_poi_fused_add_mul_rsub_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
x1 = xindex // 16 % 4
x0 = xindex % 16
x2 = xindex // 64
x3 = xindex
tmp3 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp8 = tl.load(in_ptr2 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr2 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp13 = tl.load(in_ptr2 + x3, xmask)
tmp0 = x1
tmp1 = tl.full([1], 2, tl.int32)
tmp2 = tmp0 == tmp1
tmp4 = tmp1 == tmp1
tmp6 = tl.full([1], 1, tl.int32)
tmp7 = tmp1 == tmp6
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tl.where(tmp4, tmp5, tmp10)
tmp12 = tmp0 == tmp6
tmp14 = tl.where(tmp12, tmp8, tmp13)
tmp15 = tl.where(tmp2, tmp5, tmp14)
tmp16 = tl.where(tmp2, tmp11, tmp15)
tmp17 = tl.where(tmp2, tmp3, tmp16)
tl.store(out_ptr0 + x3, tmp17, xmask)
@triton.jit
def triton_poi_fused_add_mul_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp4 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp6 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
tmp7 = tl.load(in_ptr2 + (32 + x0 + 64 * x1), xmask)
tmp8 = tl.load(in_ptr3 + (16 + x0 + 64 * x1), xmask)
tmp10 = tl.load(in_ptr2 + (48 + x0 + 64 * x1), xmask)
tmp11 = tl.load(in_ptr3 + (x0 + 64 * x1), xmask)
tmp0 = tl.full([1], 3, tl.int32)
tmp1 = tl.full([1], 2, tl.int32)
tmp2 = tmp0 == tmp1
tmp5 = tl.where(tmp2, tmp3, tmp4)
tmp9 = tmp7 * tmp8
tmp12 = tmp10 * tmp11
tmp13 = tmp9 + tmp12
tmp14 = tmp6 * tmp13
tmp15 = tmp5 + tmp14
tl.store(out_ptr0 + x2, tmp15, xmask)
@triton.jit
def triton_poi_fused_clone_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16 % 4
x0 = xindex % 16
x3 = xindex // 256
x5 = xindex % 64
x6 = xindex
tmp4 = tl.load(in_ptr0 + (x0 + 16 * x3), xmask, eviction_policy=
'evict_last')
tmp7 = tl.load(in_ptr1 + (32 + x0 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp8 = tl.load(in_ptr1 + (48 + x0 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp12 = tl.load(in_ptr1 + (x5 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp0 = x1
tmp1 = tl.full([1], 3, tl.int32)
tmp2 = tmp0 == tmp1
tmp3 = tmp1 == tmp1
tmp5 = tl.full([1], 2, tl.int32)
tmp6 = tmp1 == tmp5
tmp9 = tl.where(tmp6, tmp7, tmp8)
tmp10 = tl.where(tmp3, tmp4, tmp9)
tmp11 = tmp0 == tmp5
tmp13 = tl.where(tmp11, tmp7, tmp12)
tmp14 = tl.where(tmp2, tmp4, tmp13)
tmp15 = tl.where(tmp2, tmp10, tmp14)
tl.store(out_ptr0 + x6, tmp15, xmask)
@triton.jit
def triton_poi_fused_clone_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 256
x2 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x2, tmp0, xmask)
@triton.jit
def triton_poi_fused_add_mul_7(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16 % 4
x0 = xindex % 16
x2 = xindex // 64
x3 = xindex
tmp4 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp7 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp8 = tl.load(in_ptr1 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp12 = tl.load(in_ptr1 + x3, xmask)
tmp0 = x1
tmp1 = tl.full([1], 3, tl.int32)
tmp2 = tmp0 == tmp1
tmp3 = tmp1 == tmp1
tmp5 = tl.full([1], 2, tl.int32)
tmp6 = tmp1 == tmp5
tmp9 = tl.where(tmp6, tmp7, tmp8)
tmp10 = tl.where(tmp3, tmp4, tmp9)
tmp11 = tmp0 == tmp5
tmp13 = tl.where(tmp11, tmp7, tmp12)
tmp14 = tl.where(tmp2, tmp4, tmp13)
tmp15 = tl.where(tmp2, tmp10, tmp14)
tl.store(out_ptr0 + x3, tmp15, xmask)
def call(args):
arg0_1, arg1_1, arg2_1, arg3_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))
assert_size_stride(arg3_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)
buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_mul_rsub_0[grid(64)](arg0_1, arg1_1, arg2_1,
buf0, buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_copy_mul_rsub_zeros_like_1[grid(256)](buf2,
buf1, buf0, arg0_1, arg1_1, buf3, 256, XBLOCK=256, num_warps=4,
num_stages=1)
del buf0
buf4 = buf2
del buf2
buf5 = buf1
del buf1
triton_poi_fused_add_mul_rsub_2[grid(64)](buf3, arg2_1, arg0_1,
arg1_1, buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_mul_rsub_3[grid(256)](buf5, buf4, buf3, buf6,
256, XBLOCK=256, num_warps=4, num_stages=1)
del buf4
buf7 = buf5
del buf5
triton_poi_fused_add_mul_4[grid(64)](buf6, arg2_1, arg0_1, arg1_1,
buf7, 64, XBLOCK=64, num_warps=1, num_stages=1)
del arg0_1
del arg1_1
del arg2_1
buf8 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
torch.float32)
triton_poi_fused_clone_5[grid(1024)](buf7, buf6, buf8, 1024, XBLOCK
=256, num_warps=4, num_stages=1)
buf9 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
torch.float32)
triton_poi_fused_clone_6[grid(1024)](arg3_1, buf9, 1024, XBLOCK=256,
num_warps=4, num_stages=1)
del arg3_1
buf10 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf8, (64, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf9, (64, 4, 4), (16, 4, 1), 0), out=buf10)
del buf8
del buf9
buf11 = buf3
del buf3
triton_poi_fused_add_mul_7[grid(256)](buf7, buf6, buf11, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del buf6
del buf7
return reinterpret_tensor(buf10, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0
), buf11
class StateAttentionNew(nn.Module):
def __init__(self):
super(StateAttentionNew, self).__init__()
self.sm = nn.Softmax(dim=1)
def forward(self, input_0, input_1, input_2, input_3):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
arg3_1 = input_3
output = call([arg0_1, arg1_1, arg2_1, arg3_1])
return output[0], output[1]
|
zhangyuejoslin/selfmonitoring-agent
|
StateAttention
| false
| 4,663
|
[
"MIT"
] | 0
|
9401ceb492f6c4576d62404b62e815d184136b24
|
https://github.com/zhangyuejoslin/selfmonitoring-agent/tree/9401ceb492f6c4576d62404b62e815d184136b24
|
C1
|
import torch
import torch.nn as nn
from collections import OrderedDict
class C1(nn.Module):
def __init__(self):
super(C1, self).__init__()
self.c1 = nn.Sequential(OrderedDict([('c1', nn.Conv2d(1, 6,
kernel_size=(5, 5))), ('relu1', nn.ReLU()), ('s1', nn.MaxPool2d
(kernel_size=(2, 2), stride=2))]))
def forward(self, img):
output = self.c1(img)
return output
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
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_convolution_relu_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 86400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 3600 % 6
x0 = xindex % 3600
x4 = xindex // 3600
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x0 + 3616 * x4), tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 21600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 30
x1 = xindex // 30 % 30
x2 = xindex // 900
x5 = xindex
x4 = xindex // 5400
x6 = xindex % 5400
tmp0 = tl.load(in_ptr0 + (2 * x0 + 120 * x1 + 3616 * x2), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 120 * x1 + 3616 * x2), xmask,
eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (60 + 2 * x0 + 120 * x1 + 3616 * x2), xmask,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (61 + 2 * x0 + 120 * x1 + 3616 * x2), 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 + x5, tmp6, xmask)
tl.store(out_ptr1 + (x6 + 5504 * x4), tmp16, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (6, 1, 5, 5), (25, 25, 5, 1))
assert_size_stride(primals_2, (6,), (1,))
assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 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, 6, 60, 60), (21600, 3600, 60, 1))
buf1 = empty_strided_cuda((4, 6, 60, 60), (21696, 3616, 60, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(86400)](buf0, primals_2,
buf1, 86400, XBLOCK=1024, num_warps=4, num_stages=1)
del buf0
del primals_2
buf2 = empty_strided_cuda((4, 6, 30, 30), (5400, 900, 30, 1), torch
.float32)
buf3 = empty_strided_cuda((4, 6, 30, 30), (5504, 900, 30, 1), torch
.int8)
triton_poi_fused_max_pool2d_with_indices_1[grid(21600)](buf1, buf2,
buf3, 21600, XBLOCK=256, num_warps=4, num_stages=1)
return buf2, primals_1, primals_3, buf1, buf3
class C1New(nn.Module):
def __init__(self):
super(C1New, self).__init__()
self.c1 = nn.Sequential(OrderedDict([('c1', nn.Conv2d(1, 6,
kernel_size=(5, 5))), ('relu1', nn.ReLU()), ('s1', nn.MaxPool2d
(kernel_size=(2, 2), stride=2))]))
def forward(self, input_0):
primals_1 = self.c1.c1.weight
primals_2 = self.c1.c1.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
zjgbz/img_cls
|
C1
| false
| 4,664
|
[
"MIT"
] | 0
|
513d5ae423d95e008a82a6ffe443db49f8ed9ac2
|
https://github.com/zjgbz/img_cls/tree/513d5ae423d95e008a82a6ffe443db49f8ed9ac2
|
SEModule
|
import torch
import torch.nn as nn
import torch.nn.functional as F
def hard_sigmoid(x, slope=0.1666667, offset=0.5):
return torch.clamp(slope * x + offset, 0.0, 1.0)
class SEModule(nn.Module):
def __init__(self, in_channels, reduction=4, name=''):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=
in_channels // reduction, kernel_size=1, stride=1, padding=0,
bias=True)
self.conv2 = nn.Conv2d(in_channels=in_channels // reduction,
out_channels=in_channels, kernel_size=1, stride=1, padding=0,
bias=True)
def forward(self, inputs):
outputs = self.avg_pool(inputs)
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
outputs = hard_sigmoid(outputs, slope=0.2, offset=0.5)
return inputs * outputs
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
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_add_clamp_convolution_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
x3 = xindex
x4 = xindex // 16
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = 0.2
tmp5 = tmp3 * tmp4
tmp6 = 0.5
tmp7 = tmp5 + tmp6
tmp8 = 0.0
tmp9 = triton_helpers.maximum(tmp7, tmp8)
tmp10 = 1.0
tmp11 = triton_helpers.minimum(tmp9, tmp10)
tmp12 = tmp0 * tmp11
tl.store(out_ptr0 + x3, tmp12, xmask)
@triton.jit
def triton_poi_fused_add_convolution_ge_le_logical_and_mul_3(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
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 = 0.2
tmp4 = tmp2 * tmp3
tmp5 = 0.5
tmp6 = tmp4 + tmp5
tmp7 = 0.0
tmp8 = tmp6 >= tmp7
tmp9 = 1.0
tmp10 = tmp6 <= tmp9
tmp11 = tmp8 & tmp10
tl.store(out_ptr0 + x2, tmp11, 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, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (1,), (1,))
assert_size_stride(primals_4, (4, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_5, (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 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
del buf0
get_raw_stream(0)
triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=1,
num_warps=2, num_stages=1)
buf2 = extern_kernels.convolution(buf1, primals_2, 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, 1, 1), (1, 1, 1, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_relu_1[grid(4)](buf3, primals_3, 4,
XBLOCK=4, num_warps=1, num_stages=1)
del primals_3
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, 4, 1, 1), (4, 1, 1, 1))
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_clamp_convolution_mul_2[grid(256)](primals_1,
buf4, primals_5, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
buf6 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
triton_poi_fused_add_convolution_ge_le_logical_and_mul_3[grid(16)](buf4
, primals_5, buf6, 16, XBLOCK=16, num_warps=1, num_stages=1)
del buf4
del primals_5
return buf5, primals_1, primals_2, primals_4, buf1, buf3, buf6
def hard_sigmoid(x, slope=0.1666667, offset=0.5):
return torch.clamp(slope * x + offset, 0.0, 1.0)
class SEModuleNew(nn.Module):
def __init__(self, in_channels, reduction=4, name=''):
super(SEModuleNew, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=
in_channels // reduction, kernel_size=1, stride=1, padding=0,
bias=True)
self.conv2 = nn.Conv2d(in_channels=in_channels // reduction,
out_channels=in_channels, kernel_size=1, stride=1, padding=0,
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]
|
verages/PaddleOCR2Pytorch
|
SEModule
| false
| 4,665
|
[
"Apache-2.0"
] | 0
|
201f0d5d6007f49620c49af7d222c3b220eb3e70
|
https://github.com/verages/PaddleOCR2Pytorch/tree/201f0d5d6007f49620c49af7d222c3b220eb3e70
|
BiAttention
|
import torch
from torchvision.transforms import functional as F
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
self.drop_value = drop
self.drop = nn.Dropout(drop)
self.activate = activate.lower() if activate is not None else None
if activate == 'relu':
self.ac_fn = nn.ReLU()
elif activate == 'sigmoid':
self.ac_fn = nn.Sigmoid()
elif activate == 'tanh':
self.ac_fn = nn.Tanh()
def forward(self, x):
if self.drop_value > 0:
x = self.drop(x)
x = self.lin(x)
if self.activate is not None:
x = self.ac_fn(x)
return x
class BiAttention(nn.Module):
def __init__(self, v_features, q_features, mid_features, glimpses, drop=0.0
):
super(BiAttention, self).__init__()
self.hidden_aug = 3
self.glimpses = glimpses
self.lin_v = FCNet(v_features, int(mid_features * self.hidden_aug),
activate='relu', drop=drop / 2.5)
self.lin_q = FCNet(q_features, int(mid_features * self.hidden_aug),
activate='relu', drop=drop / 2.5)
self.h_weight = nn.Parameter(torch.Tensor(1, glimpses, 1, int(
mid_features * self.hidden_aug)).normal_())
self.h_bias = nn.Parameter(torch.Tensor(1, glimpses, 1, 1).normal_())
self.drop = nn.Dropout(drop)
def forward(self, v, q):
"""
v = batch, num_obj, dim
q = batch, que_len, dim
"""
v_num = v.size(1)
q_num = q.size(1)
batch_num = v.size(0)
v_ = self.lin_v(v).unsqueeze(1)
q_ = self.lin_q(q).unsqueeze(1)
v_ = self.drop(v_)
h_ = v_ * self.h_weight
logits = torch.matmul(h_, q_.transpose(2, 3))
logits = logits + self.h_bias
atten = F.softmax(logits.view(batch_num, self.glimpses, v_num *
q_num), 2)
return atten.view(batch_num, self.glimpses, v_num, q_num)
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'v_features': 4, 'q_features': 4, 'mid_features': 4,
'glimpses': 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.utils.data
import torch.nn as nn
from torch.nn.utils import weight_norm
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_div_mul_norm_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0,
xnumel, rnumel, XBLOCK: tl.constexpr):
rnumel = 48
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, :]
rmask = rindex < rnumel
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, rmask, other=0.0)
tmp7 = tl.load(in_ptr1 + 0)
tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = tl.where(rmask, tmp2, 0)
tmp5 = tl.sum(tmp4, 1)[:, None]
tmp6 = libdevice.sqrt(tmp5)
tmp9 = tmp8 / tmp6
tmp10 = tmp0 * tmp9
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp6, None)
tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp10, rmask)
@triton.jit
def triton_poi_fused_mul_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 768
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex // 192
x4 = xindex % 48
x0 = xindex % 12
x2 = xindex // 48 % 4
x5 = xindex
tmp0 = tl.load(in_ptr0 + (x4 + 48 * x3), xmask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr1 + (x0 + 12 * x2), xmask, eviction_policy=
'evict_last')
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + x5, tmp4, xmask)
@triton.jit
def triton_poi_fused_clone_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 768
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 12
x3 = xindex // 192
x4 = xindex
tmp0 = tl.load(in_ptr0 + (x1 + 12 * x0 + 48 * x3), xmask,
eviction_policy='evict_last')
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 + x4, tmp4, xmask)
@triton.jit
def triton_per_fused__softmax_3(in_ptr0, in_ptr1, out_ptr2, 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_ptr0 + (r2 + 16 * x3), 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(xmask, tmp3, float('-inf'))
tmp6 = triton_helpers.max2(tmp5, 1)[:, None]
tmp7 = tmp2 - tmp6
tmp8 = tl_math.exp(tmp7)
tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
tmp11 = tl.where(xmask, tmp9, 0)
tmp12 = tl.sum(tmp11, 1)[:, None]
tmp13 = tmp8 / tmp12
tl.store(out_ptr2 + (r2 + 16 * x3), tmp13, xmask)
@triton.jit
def triton_poi_fused_relu_threshold_backward_4(in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 12
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x2, tmp6, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10) = 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, (), ())
assert_size_stride(primals_4, (12, 4), (4, 1))
assert_size_stride(primals_5, (12,), (1,))
assert_size_stride(primals_6, (), ())
assert_size_stride(primals_7, (12, 4), (4, 1))
assert_size_stride(primals_8, (12,), (1,))
assert_size_stride(primals_9, (1, 4, 1, 12), (48, 12, 12, 1))
assert_size_stride(primals_10, (1, 4, 1, 1), (4, 1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
buf2 = empty_strided_cuda((12, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_per_fused_div_mul_norm_0[grid(1)](buf1, primals_4, primals_3,
buf2, 1, 48, XBLOCK=1, num_warps=2, num_stages=1)
buf3 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16,
4), (4, 1), 0), reinterpret_tensor(buf2, (4, 12), (1, 4), 0),
alpha=1, beta=1, out=buf3)
del primals_5
buf4 = empty_strided_cuda((), (), torch.float32)
buf5 = buf4
del buf4
buf6 = empty_strided_cuda((12, 4), (4, 1), torch.float32)
triton_per_fused_div_mul_norm_0[grid(1)](buf5, primals_7, primals_6,
buf6, 1, 48, XBLOCK=1, num_warps=2, num_stages=1)
buf7 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
reinterpret_tensor(buf6, (4, 12), (1, 4), 0), out=buf7)
buf8 = empty_strided_cuda((4, 4, 4, 12), (192, 48, 12, 1), torch.
float32)
triton_poi_fused_mul_1[grid(768)](buf3, primals_9, buf8, 768,
XBLOCK=256, num_warps=4, num_stages=1)
buf9 = empty_strided_cuda((4, 4, 12, 4), (192, 48, 4, 1), torch.float32
)
triton_poi_fused_clone_2[grid(768)](buf7, primals_8, buf9, 768,
XBLOCK=128, num_warps=4, num_stages=1)
buf10 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 12), (48, 12, 1
), 0), reinterpret_tensor(buf9, (16, 12, 4), (48, 4, 1), 0),
out=buf10)
buf13 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
triton_per_fused__softmax_3[grid(16)](buf10, primals_10, buf13, 16,
16, XBLOCK=8, num_warps=2, num_stages=1)
del buf10
del primals_10
buf14 = empty_strided_cuda((4, 4, 12), (48, 12, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_4[grid(192)](buf7,
primals_8, buf14, 192, XBLOCK=256, num_warps=4, num_stages=1)
del buf7
del primals_8
return (reinterpret_tensor(buf13, (4, 4, 4, 4), (64, 16, 4, 1), 0),
buf2, buf6, primals_3, primals_4, primals_6, primals_7, primals_9,
buf1, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf3, buf5,
reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), buf13,
reinterpret_tensor(buf8, (16, 12, 4), (48, 1, 12), 0),
reinterpret_tensor(buf9, (16, 4, 12), (48, 1, 4), 0), buf14)
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
self.drop_value = drop
self.drop = nn.Dropout(drop)
self.activate = activate.lower() if activate is not None else None
if activate == 'relu':
self.ac_fn = nn.ReLU()
elif activate == 'sigmoid':
self.ac_fn = nn.Sigmoid()
elif activate == 'tanh':
self.ac_fn = nn.Tanh()
def forward(self, x):
if self.drop_value > 0:
x = self.drop(x)
x = self.lin(x)
if self.activate is not None:
x = self.ac_fn(x)
return x
class BiAttentionNew(nn.Module):
def __init__(self, v_features, q_features, mid_features, glimpses, drop=0.0
):
super(BiAttentionNew, self).__init__()
self.hidden_aug = 3
self.glimpses = glimpses
self.lin_v = FCNet(v_features, int(mid_features * self.hidden_aug),
activate='relu', drop=drop / 2.5)
self.lin_q = FCNet(q_features, int(mid_features * self.hidden_aug),
activate='relu', drop=drop / 2.5)
self.h_weight = nn.Parameter(torch.Tensor(1, glimpses, 1, int(
mid_features * self.hidden_aug)).normal_())
self.h_bias = nn.Parameter(torch.Tensor(1, glimpses, 1, 1).normal_())
self.drop = nn.Dropout(drop)
def forward(self, input_0, input_1):
primals_9 = self.h_weight
primals_10 = self.h_bias
primals_5 = self.lin_v.lin.bias
primals_3 = self.lin_v.lin.weight_g
primals_4 = self.lin_v.lin.weight_v
primals_8 = self.lin_q.lin.bias
primals_6 = self.lin_q.lin.weight_g
primals_7 = self.lin_q.lin.weight_v
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])
return output[0]
|
zhanwenchen/Scene-Graph-Benchmark.pytorch
|
BiAttention
| false
| 4,666
|
[
"MIT"
] | 0
|
c86475bcbdaefcc1656a2890194355c2b32aa694
|
https://github.com/zhanwenchen/Scene-Graph-Benchmark.pytorch/tree/c86475bcbdaefcc1656a2890194355c2b32aa694
|
RSELayer
|
import torch
import torch.nn as nn
import torch.nn.functional as F
def hard_sigmoid(x, slope=0.1666667, offset=0.5):
return torch.clamp(slope * x + offset, 0.0, 1.0)
class SEModule(nn.Module):
def __init__(self, in_channels, reduction=4, name=''):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=
in_channels // reduction, kernel_size=1, stride=1, padding=0,
bias=True)
self.conv2 = nn.Conv2d(in_channels=in_channels // reduction,
out_channels=in_channels, kernel_size=1, stride=1, padding=0,
bias=True)
def forward(self, inputs):
outputs = self.avg_pool(inputs)
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
outputs = hard_sigmoid(outputs, slope=0.2, offset=0.5)
return inputs * outputs
class RSELayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, shortcut=True):
super(RSELayer, self).__init__()
self.out_channels = out_channels
self.in_conv = nn.Conv2d(in_channels=in_channels, out_channels=self
.out_channels, kernel_size=kernel_size, padding=int(kernel_size //
2), bias=False)
self.se_block = SEModule(self.out_channels)
self.shortcut = shortcut
def forward(self, ins):
x = self.in_conv(ins)
if self.shortcut:
out = x + self.se_block(x)
else:
out = self.se_block(x)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime 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_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
tl.constexpr):
xnumel = 16
rnumel = 25
RBLOCK: tl.constexpr = 32
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 + 25 * x0), rmask & xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(rmask & xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 25.0
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_clamp_mul_3(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 25
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = 0.2
tmp3 = tmp1 * tmp2
tmp4 = 0.5
tmp5 = tmp3 + tmp4
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = 1.0
tmp9 = triton_helpers.minimum(tmp7, tmp8)
tmp10 = tmp0 * tmp9
tmp11 = tmp0 + tmp10
tl.store(out_ptr0 + x2, tmp11, 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, 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=(2, 2), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 5, 5), (100, 25, 5, 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, buf0, 16, 25, XBLOCK=1,
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, 5, 5), (100, 25, 5, 1), torch.float32)
triton_poi_fused_add_clamp_mul_3[grid(400)](buf0, buf6, buf7, 400,
XBLOCK=256, num_warps=4, num_stages=1)
return (buf7, primals_1, primals_2, primals_3, primals_5, buf0, buf2,
buf4, buf6)
def hard_sigmoid(x, slope=0.1666667, offset=0.5):
return torch.clamp(slope * x + offset, 0.0, 1.0)
class SEModule(nn.Module):
def __init__(self, in_channels, reduction=4, name=''):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=
in_channels // reduction, kernel_size=1, stride=1, padding=0,
bias=True)
self.conv2 = nn.Conv2d(in_channels=in_channels // reduction,
out_channels=in_channels, kernel_size=1, stride=1, padding=0,
bias=True)
def forward(self, inputs):
outputs = self.avg_pool(inputs)
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
outputs = hard_sigmoid(outputs, slope=0.2, offset=0.5)
return inputs * outputs
class RSELayerNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, shortcut=True):
super(RSELayerNew, self).__init__()
self.out_channels = out_channels
self.in_conv = nn.Conv2d(in_channels=in_channels, out_channels=self
.out_channels, kernel_size=kernel_size, padding=int(kernel_size //
2), bias=False)
self.se_block = SEModule(self.out_channels)
self.shortcut = shortcut
def forward(self, input_0):
primals_1 = self.in_conv.weight
primals_3 = self.se_block.conv1.weight
primals_4 = self.se_block.conv1.bias
primals_5 = self.se_block.conv2.weight
primals_6 = self.se_block.conv2.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
verages/PaddleOCR2Pytorch
|
RSELayer
| false
| 4,667
|
[
"Apache-2.0"
] | 0
|
201f0d5d6007f49620c49af7d222c3b220eb3e70
|
https://github.com/verages/PaddleOCR2Pytorch/tree/201f0d5d6007f49620c49af7d222c3b220eb3e70
|
F_fully_connected
|
import torch
import torch.nn as nn
import torch.optim
class F_fully_connected(nn.Module):
"""Fully connected tranformation, not reversible, but used below."""
def __init__(self, size_in, size, internal_size=None, dropout=0.0):
super().__init__()
if not internal_size:
internal_size = 2 * size
self.d1 = nn.Dropout(p=dropout)
self.d2 = nn.Dropout(p=dropout)
self.d2b = nn.Dropout(p=dropout)
self.fc1 = nn.Linear(size_in, internal_size)
self.fc2 = nn.Linear(internal_size, internal_size)
self.fc2b = nn.Linear(internal_size, internal_size)
self.fc3 = nn.Linear(internal_size, size)
self.nl1 = nn.ReLU()
self.nl2 = nn.ReLU()
self.nl2b = nn.ReLU()
def forward(self, x):
out = self.nl1(self.d1(self.fc1(x)))
out = self.nl2(self.d2(self.fc2(out)))
out = self.nl2b(self.d2b(self.fc2b(out)))
out = self.fc3(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'size_in': 4, 'size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.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 = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 8
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
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, (8, 4), (4, 1))
assert_size_stride(primals_2, (8,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (8, 8), (8, 1))
assert_size_stride(primals_5, (8,), (1,))
assert_size_stride(primals_6, (8, 8), (8, 1))
assert_size_stride(primals_7, (8,), (1,))
assert_size_stride(primals_8, (4, 8), (8, 1))
assert_size_stride(primals_9, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 8), (128, 32, 8, 1), 0)
del buf0
buf9 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(512)](buf1,
primals_2, buf9, 512, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 8), (8, 1), 0),
reinterpret_tensor(primals_4, (8, 8), (1, 8), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 8), (128, 32, 8, 1), 0)
del buf2
buf8 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(512)](buf3,
primals_5, buf8, 512, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf3, (64, 8), (8, 1), 0),
reinterpret_tensor(primals_6, (8, 8), (1, 8), 0), out=buf4)
buf5 = reinterpret_tensor(buf4, (4, 4, 4, 8), (128, 32, 8, 1), 0)
del buf4
buf7 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(512)](buf5,
primals_7, buf7, 512, 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, 8), (
8, 1), 0), reinterpret_tensor(primals_8, (8, 4), (1, 8), 0),
alpha=1, beta=1, out=buf6)
del primals_9
return 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, 8), (8, 1), 0), reinterpret_tensor(
buf3, (64, 8), (8, 1), 0), reinterpret_tensor(buf5, (64, 8), (8, 1), 0
), primals_8, buf7, primals_6, buf8, primals_4, buf9
class F_fully_connectedNew(nn.Module):
"""Fully connected tranformation, not reversible, but used below."""
def __init__(self, size_in, size, internal_size=None, dropout=0.0):
super().__init__()
if not internal_size:
internal_size = 2 * size
self.d1 = nn.Dropout(p=dropout)
self.d2 = nn.Dropout(p=dropout)
self.d2b = nn.Dropout(p=dropout)
self.fc1 = nn.Linear(size_in, internal_size)
self.fc2 = nn.Linear(internal_size, internal_size)
self.fc2b = nn.Linear(internal_size, internal_size)
self.fc3 = nn.Linear(internal_size, size)
self.nl1 = nn.ReLU()
self.nl2 = nn.ReLU()
self.nl2b = nn.ReLU()
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.fc2b.weight
primals_7 = self.fc2b.bias
primals_8 = self.fc3.weight
primals_9 = 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])
return output[0]
|
zimmerrol/FrEIA
|
F_fully_connected
| false
| 4,668
|
[
"MIT"
] | 0
|
73d01ab8c90e0deb5e242d66405bd168db06dc19
|
https://github.com/zimmerrol/FrEIA/tree/73d01ab8c90e0deb5e242d66405bd168db06dc19
|
C2
|
import torch
import torch.nn as nn
from collections import OrderedDict
class C2(nn.Module):
def __init__(self):
super(C2, self).__init__()
self.c2 = nn.Sequential(OrderedDict([('c2', nn.Conv2d(6, 16,
kernel_size=(5, 5))), ('relu2', nn.ReLU()), ('s2', nn.MaxPool2d
(kernel_size=(2, 2), stride=2))]))
def forward(self, img):
output = self.c2(img)
return output
def get_inputs():
return [torch.rand([4, 6, 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 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_convolution_relu_0(in_ptr0, in_ptr1, out_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 // 3600 % 16
x0 = xindex % 3600
x4 = xindex // 3600
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x0 + 3616 * x4), tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 57600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 30
x1 = xindex // 30 % 30
x2 = xindex // 900
x5 = xindex
x4 = xindex // 14400
x6 = xindex % 14400
tmp0 = tl.load(in_ptr0 + (2 * x0 + 120 * x1 + 3616 * x2), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 120 * x1 + 3616 * x2), xmask,
eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (60 + 2 * x0 + 120 * x1 + 3616 * x2), xmask,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (61 + 2 * x0 + 120 * x1 + 3616 * x2), 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 + x5, tmp6, xmask)
tl.store(out_ptr1 + (x6 + 14464 * x4), tmp16, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (16, 6, 5, 5), (150, 25, 5, 1))
assert_size_stride(primals_2, (16,), (1,))
assert_size_stride(primals_3, (4, 6, 64, 64), (24576, 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, 16, 60, 60), (57600, 3600, 60, 1))
buf1 = empty_strided_cuda((4, 16, 60, 60), (57856, 3616, 60, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(230400)](buf0, primals_2,
buf1, 230400, XBLOCK=1024, num_warps=4, num_stages=1)
del buf0
del primals_2
buf2 = empty_strided_cuda((4, 16, 30, 30), (14400, 900, 30, 1),
torch.float32)
buf3 = empty_strided_cuda((4, 16, 30, 30), (14464, 900, 30, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_1[grid(57600)](buf1, buf2,
buf3, 57600, XBLOCK=256, num_warps=4, num_stages=1)
return buf2, primals_1, primals_3, buf1, buf3
class C2New(nn.Module):
def __init__(self):
super(C2New, self).__init__()
self.c2 = nn.Sequential(OrderedDict([('c2', nn.Conv2d(6, 16,
kernel_size=(5, 5))), ('relu2', nn.ReLU()), ('s2', nn.MaxPool2d
(kernel_size=(2, 2), stride=2))]))
def forward(self, input_0):
primals_1 = self.c2.c2.weight
primals_2 = self.c2.c2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
zjgbz/img_cls
|
C2
| false
| 4,669
|
[
"MIT"
] | 0
|
513d5ae423d95e008a82a6ffe443db49f8ed9ac2
|
https://github.com/zjgbz/img_cls/tree/513d5ae423d95e008a82a6ffe443db49f8ed9ac2
|
SubSample
|
import torch
import torch.nn as nn
class SubSample(nn.Module):
def __init__(self, in_channels, out_channels, types='Pool', stride=[2,
1], sub_norm='nn.LayerNorm', act=None):
super().__init__()
self.types = types
if types == 'Pool':
self.avgpool = nn.AvgPool2d(kernel_size=[3, 5], stride=stride,
padding=[1, 2])
self.maxpool = nn.MaxPool2d(kernel_size=[3, 5], stride=stride,
padding=[1, 2])
self.proj = nn.Linear(in_channels, out_channels)
else:
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1)
self.norm = eval(sub_norm)(out_channels)
if act is not None:
self.act = act()
else:
self.act = None
def forward(self, x):
if self.types == 'Pool':
x1 = self.avgpool(x)
x2 = self.maxpool(x)
x = (x1 + x2) * 0.5
out = self.proj(x.flatten(2).permute(0, 2, 1))
else:
x = self.conv(x)
out = x.flatten(2).permute(0, 2, 1)
out = self.norm(out)
if self.act is not None:
out = self.act(out)
return out
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
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_avg_pool2d_clone_max_pool2d_with_indices_0(in_ptr0,
out_ptr2, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 8
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 // 4
x1 = xindex % 4
y0 = yindex
x5 = xindex
y3 = yindex % 4
y4 = yindex // 4
tmp0 = -1 + 2 * x2
tmp1 = tl.full([1, 1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1, 1], 4, 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 + (-6 + x1 + 8 * x2 + 16 * y0), tmp10 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp12 = -1 + x1
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp13 & tmp14
tmp16 = tmp5 & tmp15
tmp17 = tl.load(in_ptr0 + (-5 + x1 + 8 * x2 + 16 * y0), tmp16 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp18 = tmp17 + tmp11
tmp19 = x1
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp20 & tmp21
tmp23 = tmp5 & tmp22
tmp24 = tl.load(in_ptr0 + (-4 + x1 + 8 * x2 + 16 * y0), tmp23 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp25 = tmp24 + tmp18
tmp26 = 1 + x1
tmp27 = tmp26 >= tmp1
tmp28 = tmp26 < tmp3
tmp29 = tmp27 & tmp28
tmp30 = tmp5 & tmp29
tmp31 = tl.load(in_ptr0 + (-3 + x1 + 8 * x2 + 16 * y0), tmp30 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp32 = tmp31 + tmp25
tmp33 = 2 + x1
tmp34 = tmp33 >= tmp1
tmp35 = tmp33 < tmp3
tmp36 = tmp34 & tmp35
tmp37 = tmp5 & tmp36
tmp38 = tl.load(in_ptr0 + (-2 + x1 + 8 * x2 + 16 * y0), tmp37 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp39 = tmp38 + tmp32
tmp40 = 2 * x2
tmp41 = tmp40 >= tmp1
tmp42 = tmp40 < tmp3
tmp43 = tmp41 & tmp42
tmp44 = tmp43 & tmp9
tmp45 = tl.load(in_ptr0 + (-2 + x1 + 8 * x2 + 16 * y0), tmp44 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp46 = tmp45 + tmp39
tmp47 = tmp43 & tmp15
tmp48 = tl.load(in_ptr0 + (-1 + x1 + 8 * x2 + 16 * y0), tmp47 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp49 = tmp48 + tmp46
tmp50 = tmp43 & tmp22
tmp51 = tl.load(in_ptr0 + (x1 + 8 * x2 + 16 * y0), tmp50 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp52 = tmp51 + tmp49
tmp53 = tmp43 & tmp29
tmp54 = tl.load(in_ptr0 + (1 + x1 + 8 * x2 + 16 * y0), tmp53 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp55 = tmp54 + tmp52
tmp56 = tmp43 & tmp36
tmp57 = tl.load(in_ptr0 + (2 + x1 + 8 * x2 + 16 * y0), tmp56 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp58 = tmp57 + tmp55
tmp59 = 1 + 2 * x2
tmp60 = tmp59 >= tmp1
tmp61 = tmp59 < tmp3
tmp62 = tmp60 & tmp61
tmp63 = tmp62 & tmp9
tmp64 = tl.load(in_ptr0 + (2 + x1 + 8 * x2 + 16 * y0), tmp63 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp65 = tmp64 + tmp58
tmp66 = tmp62 & tmp15
tmp67 = tl.load(in_ptr0 + (3 + x1 + 8 * x2 + 16 * y0), tmp66 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp68 = tmp67 + tmp65
tmp69 = tmp62 & tmp22
tmp70 = tl.load(in_ptr0 + (4 + x1 + 8 * x2 + 16 * y0), tmp69 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp71 = tmp70 + tmp68
tmp72 = tmp62 & tmp29
tmp73 = tl.load(in_ptr0 + (5 + x1 + 8 * x2 + 16 * y0), tmp72 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp74 = tmp73 + tmp71
tmp75 = tmp62 & tmp36
tmp76 = tl.load(in_ptr0 + (6 + x1 + 8 * x2 + 16 * y0), tmp75 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tmp77 = tmp76 + tmp74
tmp78 = 2 + -1 * x1 + -4 * x2 + 2 * (5 * (5 <= 2 + 2 * x2) + (2 + 2 *
x2) * (2 + 2 * x2 < 5)) + (5 * (5 <= 2 + 2 * x2) + (2 + 2 * x2) * (
2 + 2 * x2 < 5)) * (6 * (6 <= 3 + x1) + (3 + x1) * (3 + x1 < 6)
) + -1 * x1 * (5 * (5 <= 2 + 2 * x2) + (2 + 2 * x2) * (2 + 2 * x2 < 5)
) + -2 * x2 * (6 * (6 <= 3 + x1) + (3 + x1) * (3 + x1 < 6)
) + 2 * x1 * x2 + (6 * (6 <= 3 + x1) + (3 + x1) * (3 + x1 < 6))
tmp79 = tmp77 / tmp78
tmp80 = tl.load(in_ptr0 + (-6 + x1 + 8 * x2 + 16 * y0), tmp10 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp81 = tl.load(in_ptr0 + (-5 + x1 + 8 * x2 + 16 * y0), tmp16 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp82 = triton_helpers.maximum(tmp81, tmp80)
tmp83 = tl.load(in_ptr0 + (-4 + x1 + 8 * x2 + 16 * y0), tmp23 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp84 = triton_helpers.maximum(tmp83, tmp82)
tmp85 = tl.load(in_ptr0 + (-3 + x1 + 8 * x2 + 16 * y0), tmp30 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp86 = triton_helpers.maximum(tmp85, tmp84)
tmp87 = tl.load(in_ptr0 + (-2 + x1 + 8 * x2 + 16 * y0), tmp37 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp88 = triton_helpers.maximum(tmp87, tmp86)
tmp89 = tl.load(in_ptr0 + (-2 + x1 + 8 * x2 + 16 * y0), tmp44 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp90 = triton_helpers.maximum(tmp89, tmp88)
tmp91 = tl.load(in_ptr0 + (-1 + x1 + 8 * x2 + 16 * y0), tmp47 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp92 = triton_helpers.maximum(tmp91, tmp90)
tmp93 = tl.load(in_ptr0 + (x1 + 8 * x2 + 16 * y0), tmp50 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp94 = triton_helpers.maximum(tmp93, tmp92)
tmp95 = tl.load(in_ptr0 + (1 + x1 + 8 * x2 + 16 * y0), tmp53 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp96 = triton_helpers.maximum(tmp95, tmp94)
tmp97 = tl.load(in_ptr0 + (2 + x1 + 8 * x2 + 16 * y0), tmp56 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp98 = triton_helpers.maximum(tmp97, tmp96)
tmp99 = tl.load(in_ptr0 + (2 + x1 + 8 * x2 + 16 * y0), tmp63 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp100 = triton_helpers.maximum(tmp99, tmp98)
tmp101 = tl.load(in_ptr0 + (3 + x1 + 8 * x2 + 16 * y0), tmp66 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp102 = triton_helpers.maximum(tmp101, tmp100)
tmp103 = tl.load(in_ptr0 + (4 + x1 + 8 * x2 + 16 * y0), tmp69 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp104 = triton_helpers.maximum(tmp103, tmp102)
tmp105 = tl.load(in_ptr0 + (5 + x1 + 8 * x2 + 16 * y0), tmp72 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp106 = triton_helpers.maximum(tmp105, tmp104)
tmp107 = tl.load(in_ptr0 + (6 + x1 + 8 * x2 + 16 * y0), tmp75 & xmask &
ymask, eviction_policy='evict_last', other=float('-inf'))
tmp108 = triton_helpers.maximum(tmp107, tmp106)
tmp109 = tmp79 + tmp108
tmp110 = 0.5
tmp111 = tmp109 * tmp110
tl.store(out_ptr2 + (y3 + 4 * x5 + 32 * y4), tmp111, xmask & ymask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_1(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, 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 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp4 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + 1)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp9 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr1 + 2)
tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
tmp14 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp15 = tl.load(in_ptr1 + 3)
tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
tmp3 = tmp0 + tmp2
tmp7 = tmp4 + tmp6
tmp8 = tmp3 + tmp7
tmp12 = tmp9 + tmp11
tmp13 = tmp8 + tmp12
tmp17 = tmp14 + tmp16
tmp18 = tmp13 + tmp17
tmp19 = 4.0
tmp20 = tmp18 / tmp19
tmp21 = tmp3 - tmp20
tmp22 = tmp21 * tmp21
tmp23 = tmp7 - tmp20
tmp24 = tmp23 * tmp23
tmp25 = tmp22 + tmp24
tmp26 = tmp12 - tmp20
tmp27 = tmp26 * tmp26
tmp28 = tmp25 + tmp27
tmp29 = tmp17 - tmp20
tmp30 = tmp29 * tmp29
tmp31 = tmp28 + tmp30
tmp32 = tmp31 / tmp19
tl.store(out_ptr0 + x0, tmp20, xmask)
tl.store(out_ptr1 + x0, tmp32, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_2(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, 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 % 4
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + x0, 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, tmp13, 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,), (1,))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf2 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_avg_pool2d_clone_max_pool2d_with_indices_0[grid(16, 8)
](primals_1, buf2, 16, 8, XBLOCK=8, YBLOCK=16, num_warps=4,
num_stages=1)
del primals_1
buf3 = empty_strided_cuda((32, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf2, (32, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf3)
del primals_2
buf4 = empty_strided_cuda((4, 8, 1), (8, 1, 32), torch.float32)
buf5 = empty_strided_cuda((4, 8, 1), (8, 1, 32), torch.float32)
triton_poi_fused_add_native_layer_norm_1[grid(32)](buf3, primals_3,
buf4, buf5, 32, XBLOCK=32, num_warps=1, num_stages=1)
buf6 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
triton_poi_fused_add_native_layer_norm_2[grid(128)](buf3, primals_3,
buf4, buf5, primals_4, primals_5, buf6, 128, XBLOCK=128,
num_warps=4, num_stages=1)
del buf4
del buf5
del primals_5
return buf6, primals_3, primals_4, reinterpret_tensor(buf2, (32, 4), (4,
1), 0), buf3
class SubSampleNew(nn.Module):
def __init__(self, in_channels, out_channels, types='Pool', stride=[2,
1], sub_norm='nn.LayerNorm', act=None):
super().__init__()
self.types = types
if types == 'Pool':
self.avgpool = nn.AvgPool2d(kernel_size=[3, 5], stride=stride,
padding=[1, 2])
self.maxpool = nn.MaxPool2d(kernel_size=[3, 5], stride=stride,
padding=[1, 2])
self.proj = nn.Linear(in_channels, out_channels)
else:
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1)
self.norm = eval(sub_norm)(out_channels)
if act is not None:
self.act = act()
else:
self.act = None
def forward(self, input_0):
primals_2 = self.proj.weight
primals_3 = self.proj.bias
primals_4 = self.norm.weight
primals_5 = self.norm.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
verages/PaddleOCR2Pytorch
|
SubSample
| false
| 4,670
|
[
"Apache-2.0"
] | 0
|
201f0d5d6007f49620c49af7d222c3b220eb3e70
|
https://github.com/verages/PaddleOCR2Pytorch/tree/201f0d5d6007f49620c49af7d222c3b220eb3e70
|
Critic
|
import torch
from torch import nn
import torch.nn.functional as F
class Critic(nn.Module):
"""Critic model
Parameters:
args (object): Parameter class
"""
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
l1 = 400
l2 = 300
self.q1f1 = nn.Linear(state_dim + action_dim, l1)
self.q1ln1 = nn.LayerNorm(l1)
self.q1f2 = nn.Linear(l1, l2)
self.q1ln2 = nn.LayerNorm(l2)
self.q1out = nn.Linear(l2, 1)
self.q2f1 = nn.Linear(state_dim + action_dim, l1)
self.q2ln1 = nn.LayerNorm(l1)
self.q2f2 = nn.Linear(l1, l2)
self.q2ln2 = nn.LayerNorm(l2)
self.q2out = nn.Linear(l2, 1)
self.vf1 = nn.Linear(state_dim, l1)
self.vln1 = nn.LayerNorm(l1)
self.vf2 = nn.Linear(l1, l2)
self.vln2 = nn.LayerNorm(l2)
self.vout = nn.Linear(l2, 1)
def forward(self, obs, action):
"""Method to forward propagate through the critic's graph
Parameters:
input (tensor): states
input (tensor): actions
Returns:
Q1 (tensor): Qval 1
Q2 (tensor): Qval 2
V (tensor): Value
"""
state = torch.cat([obs, action], 1)
q1 = F.elu(self.q1f1(state))
q1 = self.q1ln1(q1)
q1 = F.elu(self.q1f2(q1))
q1 = self.q1ln2(q1)
q1 = self.q1out(q1)
q2 = F.elu(self.q2f1(state))
q2 = self.q2ln1(q2)
q2 = F.elu(self.q2f2(q2))
q2 = self.q2ln2(q2)
q2 = self.q2out(q2)
v = F.elu(self.vf1(obs))
v = self.vln1(v)
v = F.elu(self.vf2(v))
v = self.vln2(v)
v = self.vout(v)
return q1, q2, v
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
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_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_per_fused_elu_native_layer_norm_1(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
rnumel = 400
RBLOCK: tl.constexpr = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 400 * x0), rmask, other=0.0)
tmp31 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
other=0.0)
tmp33 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
other=0.0)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
tl.where(rmask, tmp8, 0)
tmp11 = tl.broadcast_to(tmp8, [RBLOCK])
tmp13 = tl.where(rmask, tmp11, 0)
tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
tmp15 = tl.full([1], 400, tl.int32)
tmp16 = tmp15.to(tl.float32)
tmp17 = tmp14 / tmp16
tmp18 = tmp8 - tmp17
tmp19 = tmp18 * tmp18
tmp20 = tl.broadcast_to(tmp19, [RBLOCK])
tmp22 = tl.where(rmask, tmp20, 0)
tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
tmp24 = 400.0
tmp25 = tmp23 / tmp24
tmp26 = 1e-05
tmp27 = tmp25 + tmp26
tmp28 = libdevice.rsqrt(tmp27)
tmp29 = tmp7 - tmp17
tmp30 = tmp29 * tmp28
tmp32 = tmp30 * tmp31
tmp34 = tmp32 + tmp33
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp28, None)
tl.store(out_ptr1 + (r1 + 400 * x0), tmp34, rmask)
tl.store(out_ptr0 + x0, tmp17, None)
@triton.jit
def triton_per_fused_elu_native_layer_norm_2(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
rnumel = 300
RBLOCK: tl.constexpr = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 300 * x0), rmask, other=0.0)
tmp31 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
other=0.0)
tmp33 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
other=0.0)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
tl.where(rmask, tmp8, 0)
tmp11 = tl.broadcast_to(tmp8, [RBLOCK])
tmp13 = tl.where(rmask, tmp11, 0)
tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
tmp15 = tl.full([1], 300, tl.int32)
tmp16 = tmp15.to(tl.float32)
tmp17 = tmp14 / tmp16
tmp18 = tmp8 - tmp17
tmp19 = tmp18 * tmp18
tmp20 = tl.broadcast_to(tmp19, [RBLOCK])
tmp22 = tl.where(rmask, tmp20, 0)
tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
tmp24 = 300.0
tmp25 = tmp23 / tmp24
tmp26 = 1e-05
tmp27 = tmp25 + tmp26
tmp28 = libdevice.rsqrt(tmp27)
tmp29 = tmp7 - tmp17
tmp30 = tmp29 * tmp28
tmp32 = tmp30 * tmp31
tmp34 = tmp32 + tmp33
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp28, None)
tl.store(out_ptr1 + (r1 + 300 * x0), tmp34, rmask)
tl.store(out_ptr0 + x0, tmp17, 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) = 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, (400, 8), (8, 1))
assert_size_stride(primals_4, (400,), (1,))
assert_size_stride(primals_5, (400,), (1,))
assert_size_stride(primals_6, (400,), (1,))
assert_size_stride(primals_7, (300, 400), (400, 1))
assert_size_stride(primals_8, (300,), (1,))
assert_size_stride(primals_9, (300,), (1,))
assert_size_stride(primals_10, (300,), (1,))
assert_size_stride(primals_11, (1, 300), (300, 1))
assert_size_stride(primals_12, (1,), (1,))
assert_size_stride(primals_13, (400, 8), (8, 1))
assert_size_stride(primals_14, (400,), (1,))
assert_size_stride(primals_15, (400,), (1,))
assert_size_stride(primals_16, (400,), (1,))
assert_size_stride(primals_17, (300, 400), (400, 1))
assert_size_stride(primals_18, (300,), (1,))
assert_size_stride(primals_19, (300,), (1,))
assert_size_stride(primals_20, (300,), (1,))
assert_size_stride(primals_21, (1, 300), (300, 1))
assert_size_stride(primals_22, (1,), (1,))
assert_size_stride(primals_23, (400, 4), (4, 1))
assert_size_stride(primals_24, (400,), (1,))
assert_size_stride(primals_25, (400,), (1,))
assert_size_stride(primals_26, (400,), (1,))
assert_size_stride(primals_27, (300, 400), (400, 1))
assert_size_stride(primals_28, (300,), (1,))
assert_size_stride(primals_29, (300,), (1,))
assert_size_stride(primals_30, (300,), (1,))
assert_size_stride(primals_31, (1, 300), (300, 1))
assert_size_stride(primals_32, (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_2
buf1 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
extern_kernels.addmm(primals_4, buf0, reinterpret_tensor(primals_3,
(8, 400), (1, 8), 0), alpha=1, beta=1, out=buf1)
del primals_3
del primals_4
buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
buf3 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf5 = reinterpret_tensor(buf3, (4, 1), (1, 1), 0)
del buf3
buf6 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
triton_per_fused_elu_native_layer_norm_1[grid(4)](buf5, buf1,
primals_5, primals_6, buf2, buf6, 4, 400, num_warps=4, num_stages=1
)
del primals_6
buf7 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
extern_kernels.addmm(primals_8, buf6, reinterpret_tensor(primals_7,
(400, 300), (1, 400), 0), alpha=1, beta=1, out=buf7)
del primals_8
buf8 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
buf9 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf11 = reinterpret_tensor(buf9, (4, 1), (1, 1), 0)
del buf9
buf12 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
triton_per_fused_elu_native_layer_norm_2[grid(4)](buf11, buf7,
primals_9, primals_10, buf8, buf12, 4, 300, num_warps=4,
num_stages=1)
del primals_10
buf14 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_12, buf12, reinterpret_tensor(
primals_11, (300, 1), (1, 300), 0), alpha=1, beta=1, out=buf14)
del primals_12
buf15 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
extern_kernels.addmm(primals_14, buf0, reinterpret_tensor(
primals_13, (8, 400), (1, 8), 0), alpha=1, beta=1, out=buf15)
del primals_13
del primals_14
buf16 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
buf17 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf19 = reinterpret_tensor(buf17, (4, 1), (1, 1), 0)
del buf17
buf20 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
triton_per_fused_elu_native_layer_norm_1[grid(4)](buf19, buf15,
primals_15, primals_16, buf16, buf20, 4, 400, num_warps=4,
num_stages=1)
del primals_16
buf21 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
extern_kernels.addmm(primals_18, buf20, reinterpret_tensor(
primals_17, (400, 300), (1, 400), 0), alpha=1, beta=1, out=buf21)
del primals_18
buf22 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
buf23 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf25 = reinterpret_tensor(buf23, (4, 1), (1, 1), 0)
del buf23
buf26 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
triton_per_fused_elu_native_layer_norm_2[grid(4)](buf25, buf21,
primals_19, primals_20, buf22, buf26, 4, 300, num_warps=4,
num_stages=1)
del primals_20
buf28 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_22, buf26, reinterpret_tensor(
primals_21, (300, 1), (1, 300), 0), alpha=1, beta=1, out=buf28)
del primals_22
buf29 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
extern_kernels.addmm(primals_24, primals_1, reinterpret_tensor(
primals_23, (4, 400), (1, 4), 0), alpha=1, beta=1, out=buf29)
del primals_23
del primals_24
buf30 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
buf31 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf33 = reinterpret_tensor(buf31, (4, 1), (1, 1), 0)
del buf31
buf34 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
triton_per_fused_elu_native_layer_norm_1[grid(4)](buf33, buf29,
primals_25, primals_26, buf30, buf34, 4, 400, num_warps=4,
num_stages=1)
del primals_26
buf35 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
extern_kernels.addmm(primals_28, buf34, reinterpret_tensor(
primals_27, (400, 300), (1, 400), 0), alpha=1, beta=1, out=buf35)
del primals_28
buf36 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
buf37 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf39 = reinterpret_tensor(buf37, (4, 1), (1, 1), 0)
del buf37
buf40 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
triton_per_fused_elu_native_layer_norm_2[grid(4)](buf39, buf35,
primals_29, primals_30, buf36, buf40, 4, 300, num_warps=4,
num_stages=1)
del primals_30
buf42 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_32, buf40, reinterpret_tensor(
primals_31, (300, 1), (1, 300), 0), alpha=1, beta=1, out=buf42)
del primals_32
return (buf14, buf28, buf42, primals_1, primals_5, primals_9,
primals_15, primals_19, primals_25, primals_29, buf0, buf1, buf2,
buf5, buf6, buf7, buf8, buf11, buf12, buf15, buf16, buf19, buf20,
buf21, buf22, buf25, buf26, buf29, buf30, buf33, buf34, buf35,
buf36, buf39, buf40, primals_31, primals_27, primals_21, primals_17,
primals_11, primals_7)
class CriticNew(nn.Module):
"""Critic model
Parameters:
args (object): Parameter class
"""
def __init__(self, state_dim, action_dim):
super(CriticNew, self).__init__()
l1 = 400
l2 = 300
self.q1f1 = nn.Linear(state_dim + action_dim, l1)
self.q1ln1 = nn.LayerNorm(l1)
self.q1f2 = nn.Linear(l1, l2)
self.q1ln2 = nn.LayerNorm(l2)
self.q1out = nn.Linear(l2, 1)
self.q2f1 = nn.Linear(state_dim + action_dim, l1)
self.q2ln1 = nn.LayerNorm(l1)
self.q2f2 = nn.Linear(l1, l2)
self.q2ln2 = nn.LayerNorm(l2)
self.q2out = nn.Linear(l2, 1)
self.vf1 = nn.Linear(state_dim, l1)
self.vln1 = nn.LayerNorm(l1)
self.vf2 = nn.Linear(l1, l2)
self.vln2 = nn.LayerNorm(l2)
self.vout = nn.Linear(l2, 1)
def forward(self, input_0, input_1):
primals_3 = self.q1f1.weight
primals_4 = self.q1f1.bias
primals_5 = self.q1ln1.weight
primals_6 = self.q1ln1.bias
primals_7 = self.q1f2.weight
primals_8 = self.q1f2.bias
primals_9 = self.q1ln2.weight
primals_10 = self.q1ln2.bias
primals_11 = self.q1out.weight
primals_12 = self.q1out.bias
primals_13 = self.q2f1.weight
primals_14 = self.q2f1.bias
primals_15 = self.q2ln1.weight
primals_16 = self.q2ln1.bias
primals_17 = self.q2f2.weight
primals_18 = self.q2f2.bias
primals_19 = self.q2ln2.weight
primals_20 = self.q2ln2.bias
primals_21 = self.q2out.weight
primals_22 = self.q2out.bias
primals_23 = self.vf1.weight
primals_24 = self.vf1.bias
primals_25 = self.vln1.weight
primals_26 = self.vln1.bias
primals_27 = self.vf2.weight
primals_28 = self.vf2.bias
primals_29 = self.vln2.weight
primals_30 = self.vln2.bias
primals_31 = self.vout.weight
primals_32 = self.vout.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, 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])
return output[0], output[1], output[2]
|
zhan0903/cerl
|
Critic
| false
| 4,671
|
[
"Apache-2.0"
] | 0
|
6fb8aca9cb78b72947237edf2b9ed8362bd43829
|
https://github.com/zhan0903/cerl/tree/6fb8aca9cb78b72947237edf2b9ed8362bd43829
|
F_conv
|
import torch
import warnings
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class F_conv(nn.Module):
"""ResNet transformation, not itself reversible, just used below"""
def __init__(self, in_channels, channels, channels_hidden=None, stride=
None, kernel_size=3, leaky_slope=0.1, batch_norm=False):
super().__init__()
if stride:
warnings.warn(
"Stride doesn't do anything, the argument should be removed",
DeprecationWarning)
if not channels_hidden:
channels_hidden = channels
pad = kernel_size // 2
self.leaky_slope = leaky_slope
self.conv1 = nn.Conv2d(in_channels, channels_hidden, kernel_size=
kernel_size, padding=pad, bias=not batch_norm)
self.conv2 = nn.Conv2d(channels_hidden, channels_hidden,
kernel_size=kernel_size, padding=pad, bias=not batch_norm)
self.conv3 = nn.Conv2d(channels_hidden, channels, kernel_size=
kernel_size, padding=pad, bias=not batch_norm)
if batch_norm:
self.bn1 = nn.BatchNorm2d(channels_hidden)
self.bn1.weight.data.fill_(1)
self.bn2 = nn.BatchNorm2d(channels_hidden)
self.bn2.weight.data.fill_(1)
self.bn3 = nn.BatchNorm2d(channels)
self.bn3.weight.data.fill_(1)
self.batch_norm = batch_norm
def forward(self, x):
out = self.conv1(x)
if self.batch_norm:
out = self.bn1(out)
out = F.leaky_relu(out, self.leaky_slope)
out = self.conv2(out)
if self.batch_norm:
out = self.bn2(out)
out = F.leaky_relu(out, self.leaky_slope)
out = self.conv3(out)
if self.batch_norm:
out = self.bn3(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, '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 warnings
import torch.nn as nn
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_poi_fused_convolution_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
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 = 0.1
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_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)
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 = 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_convolution_leaky_relu_0[grid(256)](buf0,
primals_2, buf1, buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
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, 4, 4, 4), (64, 16, 4, 1))
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf5 = buf0
del buf0
triton_poi_fused_convolution_leaky_relu_0[grid(256)](buf3,
primals_5, buf4, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf3
del primals_5
buf6 = extern_kernels.convolution(buf5, primals_6, 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 = buf6
del buf6
triton_poi_fused_convolution_1[grid(256)](buf7, primals_7, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
return (buf7, primals_1, primals_3, primals_4, primals_6, buf1, buf2,
buf4, buf5)
class F_convNew(nn.Module):
"""ResNet transformation, not itself reversible, just used below"""
def __init__(self, in_channels, channels, channels_hidden=None, stride=
None, kernel_size=3, leaky_slope=0.1, batch_norm=False):
super().__init__()
if stride:
warnings.warn(
"Stride doesn't do anything, the argument should be removed",
DeprecationWarning)
if not channels_hidden:
channels_hidden = channels
pad = kernel_size // 2
self.leaky_slope = leaky_slope
self.conv1 = nn.Conv2d(in_channels, channels_hidden, kernel_size=
kernel_size, padding=pad, bias=not batch_norm)
self.conv2 = nn.Conv2d(channels_hidden, channels_hidden,
kernel_size=kernel_size, padding=pad, bias=not batch_norm)
self.conv3 = nn.Conv2d(channels_hidden, channels, kernel_size=
kernel_size, padding=pad, bias=not batch_norm)
if batch_norm:
self.bn1 = nn.BatchNorm2d(channels_hidden)
self.bn1.weight.data.fill_(1)
self.bn2 = nn.BatchNorm2d(channels_hidden)
self.bn2.weight.data.fill_(1)
self.bn3 = nn.BatchNorm2d(channels)
self.bn3.weight.data.fill_(1)
self.batch_norm = batch_norm
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]
|
zimmerrol/FrEIA
|
F_conv
| false
| 4,672
|
[
"MIT"
] | 0
|
73d01ab8c90e0deb5e242d66405bd168db06dc19
|
https://github.com/zimmerrol/FrEIA/tree/73d01ab8c90e0deb5e242d66405bd168db06dc19
|
LR_PAD
|
import torch
import torch.nn as nn
def lr_pad(x, padding=1):
""" Pad left/right-most to each other instead of zero padding """
return torch.cat([x[..., -padding:], x, x[..., :padding]], dim=3)
class LR_PAD(nn.Module):
""" Pad left/right-most to each other instead of zero padding """
def __init__(self, padding=1):
super(LR_PAD, self).__init__()
self.padding = padding
def forward(self, x):
return lr_pad(x, self.padding)
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 = 384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (3 + 4 * x1), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 5, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr0 + (4 * x1 + (-1 + x0)), tmp9 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = tmp0 >= tmp7
tl.full([1], 6, tl.int64)
tmp14 = tl.load(in_ptr0 + 4 * x1, tmp11 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp15 = tl.where(tmp9, tmp10, tmp14)
tmp16 = tl.where(tmp4, tmp5, tmp15)
tl.store(out_ptr0 + x2, tmp16, 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, 6), (96, 24, 6, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(384)](arg0_1, buf0, 384, XBLOCK=128,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
def lr_pad(x, padding=1):
""" Pad left/right-most to each other instead of zero padding """
return torch.cat([x[..., -padding:], x, x[..., :padding]], dim=3)
class LR_PADNew(nn.Module):
""" Pad left/right-most to each other instead of zero padding """
def __init__(self, padding=1):
super(LR_PADNew, self).__init__()
self.padding = padding
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
zokin/HorizonNet
|
LR_PAD
| false
| 4,673
|
[
"MIT"
] | 0
|
a93a76ec7fdc76a5ba023adaed869e34f7f3cea4
|
https://github.com/zokin/HorizonNet/tree/a93a76ec7fdc76a5ba023adaed869e34f7f3cea4
|
MLPLayer
|
import torch
from torch import nn
class MLPLayer(nn.Module):
def __init__(self, input_size, output_size, non_linearity=torch.sigmoid):
super().__init__()
self.lin1 = nn.Linear(input_size, input_size // 2)
self.lin2 = nn.Linear(input_size // 2, output_size)
self.non_lin = non_linearity
def forward(self, x):
out = self.non_lin(self.lin1(x))
return self.non_lin(self.lin2(out))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'output_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch 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_sigmoid_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 % 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.sigmoid(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, xmask)
@triton.jit
def triton_poi_fused_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.sigmoid(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (2, 4), (4, 1))
assert_size_stride(primals_2, (2,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 2), (2, 1))
assert_size_stride(primals_5, (4,), (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_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 2), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 2), (32, 8, 2, 1), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_sigmoid_0[grid(128)](buf1, primals_2, 128, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 2), (2, 1), 0),
reinterpret_tensor(primals_4, (2, 4), (1, 2), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf2
triton_poi_fused_sigmoid_1[grid(256)](buf3, primals_5, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_5
return buf3, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf1, buf3, primals_4
class MLPLayerNew(nn.Module):
def __init__(self, input_size, output_size, non_linearity=torch.sigmoid):
super().__init__()
self.lin1 = nn.Linear(input_size, input_size // 2)
self.lin2 = nn.Linear(input_size // 2, output_size)
self.non_lin = non_linearity
def forward(self, input_0):
primals_1 = self.lin1.weight
primals_2 = self.lin1.bias
primals_4 = self.lin2.weight
primals_5 = self.lin2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
zoranmedic/LCR-design
|
MLPLayer
| false
| 4,674
|
[
"MIT"
] | 0
|
b722e4e9d00e8aaae36dd51ddc8131477ee805fd
|
https://github.com/zoranmedic/LCR-design/tree/b722e4e9d00e8aaae36dd51ddc8131477ee805fd
|
MultiheadAttention
|
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn.init import xavier_uniform_
class MultiheadAttention(nn.Module):
"""Allows the model to jointly attend to information
from different representation subspaces.
See reference: Attention Is All You Need
.. math::
ext{MultiHead}(Q, K, V) = ext{Concat}(head_1,\\dots,head_h)W^O
ext{where} head_i = ext{Attention}(QW_i^Q, KW_i^K, VW_i^V)
Args:
embed_dim: total dimension of the model
num_heads: parallel attention layers, or heads
"""
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
add_bias_kv=False, add_zero_attn=False):
super(MultiheadAttention, self).__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.out_proj = Linear(embed_dim, embed_dim, bias=bias)
self._reset_parameters()
self.conv1 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
self.conv2 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
self.conv3 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
def _reset_parameters(self):
xavier_uniform_(self.out_proj.weight)
def forward(self, query, key, value, key_padding_mask=None,
incremental_state=None, attn_mask=None):
"""
Inputs of forward function
query: [target length, batch size, embed dim]
key: [sequence length, batch size, embed dim]
value: [sequence length, batch size, embed dim]
key_padding_mask: if True, mask padding based on batch size
incremental_state: if provided, previous time steps are cashed
need_weights: output attn_output_weights
static_kv: key and value are static
Outputs of forward function
attn_output: [target length, batch size, embed dim]
attn_output_weights: [batch size, target length, sequence length]
"""
q_shape = query.shape
src_shape = key.shape
q = self._in_proj_q(query)
k = self._in_proj_k(key)
v = self._in_proj_v(value)
q *= self.scaling
q = torch.reshape(q, (q_shape[0], q_shape[1], self.num_heads, self.
head_dim))
q = q.permute(1, 2, 0, 3)
k = torch.reshape(k, (src_shape[0], q_shape[1], self.num_heads,
self.head_dim))
k = k.permute(1, 2, 0, 3)
v = torch.reshape(v, (src_shape[0], q_shape[1], self.num_heads,
self.head_dim))
v = v.permute(1, 2, 0, 3)
if key_padding_mask is not None:
assert key_padding_mask.shape[0] == q_shape[1]
assert key_padding_mask.shape[1] == src_shape[0]
attn_output_weights = torch.matmul(q, k.permute(0, 1, 3, 2))
if attn_mask is not None:
attn_mask = torch.unsqueeze(torch.unsqueeze(attn_mask, 0), 0)
attn_output_weights += attn_mask
if key_padding_mask is not None:
attn_output_weights = torch.reshape(attn_output_weights, [
q_shape[1], self.num_heads, q_shape[0], src_shape[0]])
key = torch.unsqueeze(torch.unsqueeze(key_padding_mask, 1), 2)
key = key.type(torch.float32)
y = torch.full(size=key.shape, fill_value=float('-Inf'), dtype=
torch.float32)
y = torch.where(key == 0.0, key, y)
attn_output_weights += y
attn_output_weights = F.softmax(attn_output_weights.type(torch.
float32), dim=-1, dtype=torch.float32 if attn_output_weights.
dtype == torch.float16 else attn_output_weights.dtype)
attn_output_weights = F.dropout(attn_output_weights, p=self.dropout,
training=self.training)
attn_output = torch.matmul(attn_output_weights, v)
attn_output = torch.reshape(attn_output.permute(2, 0, 1, 3), [
q_shape[0], q_shape[1], self.embed_dim])
attn_output = self.out_proj(attn_output)
return attn_output
def _in_proj_q(self, query):
query = query.permute(1, 2, 0)
query = torch.unsqueeze(query, dim=2)
res = self.conv1(query)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def _in_proj_k(self, key):
key = key.permute(1, 2, 0)
key = torch.unsqueeze(key, dim=2)
res = self.conv2(key)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def _in_proj_v(self, value):
value = value.permute(1, 2, 0)
value = torch.unsqueeze(value, dim=2)
res = self.conv3(value)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
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 Linear
from torch.nn.init import xavier_uniform_
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_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
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x1), xmask & ymask, eviction_policy
='evict_last')
tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_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
tl.store(in_out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_mul_2(in_ptr0, in_ptr1, 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
x3 = xindex
y0 = yindex
x1 = xindex % 4
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x3), xmask & ymask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + (x3 + 16 * y0), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 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_5(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_add_6(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
tl.store(in_out_ptr0 + x2, tmp2, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = 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, 1, 1), (4, 1, 1, 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, 4, 4), (16, 4, 1))
assert_size_stride(primals_8, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_9, (4,), (1,))
assert_size_stride(primals_10, (4, 4), (4, 1))
assert_size_stride(primals_11, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 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_3, 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, 1, 4), (16, 4, 4, 1))
buf2 = buf0
del buf0
triton_poi_fused_convolution_0[grid(16, 4)](primals_2, buf2, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
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, 1, 4), (16, 4, 4, 1))
buf4 = buf2
del buf2
triton_poi_fused_convolution_0[grid(16, 4)](primals_7, buf4, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
buf5 = extern_kernels.convolution(buf4, primals_8, 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, 4), (16, 4, 4, 1))
buf6 = buf3
del buf3
triton_poi_fused_convolution_1[grid(64)](buf6, primals_6, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_6
buf7 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0)
del buf4
triton_poi_fused_mul_2[grid(4, 16)](buf1, primals_4, buf7, 4, 16,
XBLOCK=16, YBLOCK=4, num_warps=1, num_stages=1)
del primals_4
buf8 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 1), (1, 16, 0),
0), reinterpret_tensor(buf6, (16, 1, 4), (4, 0, 1), 0), out=buf8)
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_3[grid(256)](buf8, buf9, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf8
triton_poi_fused__softmax_4[grid(256)](buf9, buf10, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del buf9
buf11 = buf5
del buf5
triton_poi_fused_convolution_1[grid(64)](buf11, primals_9, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_9
buf12 = reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 1), 0)
del buf1
extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf11, (16, 4, 1), (4, 1, 0), 0), out=buf12)
buf13 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_clone_5[grid(4, 16)](buf12, buf13, 4, 16, XBLOCK=
16, YBLOCK=4, num_warps=1, num_stages=1)
buf14 = reinterpret_tensor(buf12, (16, 4), (4, 1), 0)
del buf12
extern_kernels.mm(reinterpret_tensor(buf13, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf14)
buf15 = reinterpret_tensor(buf14, (4, 4, 4), (16, 4, 1), 0)
del buf14
triton_poi_fused_add_6[grid(64)](buf15, primals_11, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_11
buf16 = empty_strided_cuda((16, 1, 4), (4, 4, 1), torch.float32)
triton_poi_fused_convolution_0[grid(16, 4)](buf7, buf16, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
del buf7
return buf15, primals_3, primals_5, primals_8, reinterpret_tensor(primals_1
, (4, 4, 1, 4), (4, 1, 64, 16), 0), reinterpret_tensor(primals_2, (
4, 4, 1, 4), (4, 1, 64, 16), 0), reinterpret_tensor(primals_7, (4,
4, 1, 4), (4, 1, 64, 16), 0), buf10, reinterpret_tensor(buf13, (16,
4), (4, 1), 0), primals_10, reinterpret_tensor(buf11, (16, 1, 4), (
4, 4, 1), 0), buf16, reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 4), 0)
class MultiheadAttentionNew(nn.Module):
"""Allows the model to jointly attend to information
from different representation subspaces.
See reference: Attention Is All You Need
.. math::
ext{MultiHead}(Q, K, V) = ext{Concat}(head_1,\\dots,head_h)W^O
ext{where} head_i = ext{Attention}(QW_i^Q, KW_i^K, VW_i^V)
Args:
embed_dim: total dimension of the model
num_heads: parallel attention layers, or heads
"""
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
add_bias_kv=False, add_zero_attn=False):
super(MultiheadAttentionNew, self).__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.out_proj = Linear(embed_dim, embed_dim, bias=bias)
self._reset_parameters()
self.conv1 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
self.conv2 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
self.conv3 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
def _reset_parameters(self):
xavier_uniform_(self.out_proj.weight)
def _in_proj_q(self, query):
query = query.permute(1, 2, 0)
query = torch.unsqueeze(query, dim=2)
res = self.conv1(query)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def _in_proj_k(self, key):
key = key.permute(1, 2, 0)
key = torch.unsqueeze(key, dim=2)
res = self.conv2(key)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def _in_proj_v(self, value):
value = value.permute(1, 2, 0)
value = torch.unsqueeze(value, dim=2)
res = self.conv3(value)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def forward(self, input_0, input_1, input_2):
primals_10 = self.out_proj.weight
primals_4 = self.out_proj.bias
primals_3 = self.conv1.weight
primals_6 = self.conv1.bias
primals_5 = self.conv2.weight
primals_9 = self.conv2.bias
primals_8 = self.conv3.weight
primals_11 = self.conv3.bias
primals_1 = input_0
primals_2 = input_1
primals_7 = input_2
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]
|
verages/PaddleOCR2Pytorch
|
MultiheadAttention
| false
| 4,675
|
[
"Apache-2.0"
] | 0
|
201f0d5d6007f49620c49af7d222c3b220eb3e70
|
https://github.com/verages/PaddleOCR2Pytorch/tree/201f0d5d6007f49620c49af7d222c3b220eb3e70
|
ReadUnit
|
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_
def linear(in_dim, out_dim, bias=True):
lin = nn.Linear(in_dim, out_dim, bias=bias)
xavier_uniform_(lin.weight)
if bias:
lin.bias.data.zero_()
return lin
class ReadUnit(nn.Module):
def __init__(self, dim):
super().__init__()
self.mem = linear(dim, dim)
self.concat = linear(dim * 2, dim)
self.attn = linear(dim, 1)
def forward(self, memories, k, c):
"""
:param memories:
:param k: knowledge
:param c: control
:return: r_i
"""
m_prev = memories[-1]
I = self.mem(m_prev).unsqueeze(2) * k
I = self.concat(torch.cat([I, k], 1).permute(0, 2, 1))
attn = I * c[-1].unsqueeze(1)
attn = self.attn(attn).squeeze(2)
attn = F.softmax(attn, 1).unsqueeze(1)
read = (attn * k).sum(2)
return read
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([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 math as tl_math
from torch import nn
from torch.nn.init import xavier_uniform_
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, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x2 = xindex // 32
x1 = xindex // 8 % 4
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 + (4 * x2 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last',
other=0.0)
tmp7 = tmp5 + tmp6
tmp8 = tl.load(in_ptr2 + (x1 + 4 * x0 + 16 * x2), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp9 = tmp7 * tmp8
tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
tmp11 = tl.where(tmp4, tmp9, tmp10)
tmp12 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp15 = tl.load(in_ptr2 + (x1 + 4 * (-4 + x0) + 16 * x2), tmp12 & xmask,
eviction_policy='evict_last', other=0.0)
tmp16 = tl.where(tmp4, tmp11, tmp15)
tl.store(out_ptr0 + x4, tmp16, xmask)
@triton.jit
def triton_poi_fused_add_mul_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 4
x1 = xindex // 4 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (12 + x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 * tmp3
tl.store(in_out_ptr0 + x3, tmp4, 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 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_mul_sum_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
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
tl.store(out_ptr0 + x2, tmp14, 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), (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), (16, 4, 1))
assert_size_stride(primals_5, (4, 8), (8, 1))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (4, 4), (4, 1))
assert_size_stride(primals_8, (1, 4), (4, 1))
assert_size_stride(primals_9, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (4, 1), 48),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
del primals_2
buf1 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(128)](buf0, primals_3, primals_4,
buf1, 128, XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (16, 8), (8, 1), 0),
reinterpret_tensor(primals_5, (8, 4), (1, 8), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0)
del buf2
triton_poi_fused_add_mul_1[grid(64)](buf3, primals_6, primals_7, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_6
buf5 = reinterpret_tensor(buf0, (16, 1), (1, 1), 0)
del buf0
extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (16, 4), (
4, 1), 0), reinterpret_tensor(primals_8, (4, 1), (1, 4), 0),
alpha=1, beta=1, out=buf5)
del primals_9
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused__softmax_2[grid(16)](buf5, buf6, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused__softmax_3[grid(16)](buf6, buf7, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf8 = buf6
del buf6
triton_poi_fused_mul_sum_4[grid(16)](buf7, primals_4, buf8, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del buf7
return buf8, primals_4, reinterpret_tensor(primals_1, (4, 4), (4, 1), 48
), reinterpret_tensor(buf1, (16, 8), (8, 1), 0), reinterpret_tensor(
primals_7, (4, 1), (1, 1), 12), reinterpret_tensor(buf3, (16, 4), (
4, 1), 0), buf5, primals_8, primals_5
def linear(in_dim, out_dim, bias=True):
lin = nn.Linear(in_dim, out_dim, bias=bias)
xavier_uniform_(lin.weight)
if bias:
lin.bias.data.zero_()
return lin
class ReadUnitNew(nn.Module):
def __init__(self, dim):
super().__init__()
self.mem = linear(dim, dim)
self.concat = linear(dim * 2, dim)
self.attn = linear(dim, 1)
def forward(self, input_0, input_1, input_2):
primals_2 = self.mem.weight
primals_3 = self.mem.bias
primals_5 = self.concat.weight
primals_6 = self.concat.bias
primals_8 = self.attn.weight
primals_9 = self.attn.bias
primals_1 = input_0
primals_4 = input_1
primals_7 = input_2
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9])
return output[0]
|
zorache/mac-network-pytorch-gqa
|
ReadUnit
| false
| 4,676
|
[
"MIT"
] | 0
|
5de0a906410af0596f7b5dc159ce7db82bd37418
|
https://github.com/zorache/mac-network-pytorch-gqa/tree/5de0a906410af0596f7b5dc159ce7db82bd37418
|
CriterionKD
|
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch._utils
import torch.optim
class CriterionKD(nn.Module):
"""
knowledge distillation loss
"""
def __init__(self, upsample=False, temperature=4):
super(CriterionKD, self).__init__()
self.upsample = upsample
self.temperature = temperature
self.criterion_kd = torch.nn.KLDivLoss()
def forward(self, pred, soft):
soft.detach()
h, w = soft.size(2), soft.size(3)
if self.upsample:
scale_pred = F.interpolate(input=pred, size=(h * 2, w * 2),
mode='bilinear', align_corners=True)
scale_soft = F.interpolate(input=soft, size=(h * 2, w * 2),
mode='bilinear', align_corners=True)
else:
scale_pred = pred
scale_soft = soft
loss = self.criterion_kd(F.log_softmax(scale_pred / self.
temperature, dim=1), F.softmax(scale_soft / self.temperature,
dim=1))
return loss * self.temperature * self.temperature
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch._utils
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_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
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 = 0.25
tmp16 = tmp14 * tmp15
tmp17 = tl_math.exp(tmp16)
tl.store(out_ptr0 + x3, tmp17, xmask)
@triton.jit
def triton_poi_fused_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)
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 = 0.25
tmp16 = tmp14 * tmp15
tl.store(out_ptr0 + x3, tmp16, xmask)
@triton.jit
def triton_per_fused__log_softmax__softmax_mean_mul_sub_xlogy_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)
r3 = rindex
r0 = rindex % 16
r2 = rindex // 64
tmp0 = tl.load(in_ptr0 + r3, None)
tmp1 = tl.load(in_ptr0 + (r0 + 64 * r2), None, eviction_policy='evict_last'
)
tmp2 = tl.load(in_ptr0 + (16 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp17 = tl.load(in_ptr1 + r3, None)
tmp18 = tl.load(in_ptr1 + (r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp20 = tl.load(in_ptr1 + (16 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp23 = tl.load(in_ptr1 + (32 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp26 = tl.load(in_ptr1 + (48 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tmp9 = libdevice.isnan(tmp8).to(tl.int1)
tmp10 = 0.0
tmp11 = tmp8 == tmp10
tmp12 = tl_math.log(tmp8)
tmp13 = tmp8 * tmp12
tmp14 = tl.where(tmp11, tmp10, tmp13)
tmp15 = float('nan')
tmp16 = tl.where(tmp9, tmp15, tmp14)
tmp19 = tl_math.exp(tmp18)
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp27 = tl_math.exp(tmp26)
tmp28 = tmp25 + tmp27
tmp29 = tl_math.log(tmp28)
tmp30 = tmp17 - tmp29
tmp31 = tmp8 * tmp30
tmp32 = tmp16 - tmp31
tmp33 = tl.broadcast_to(tmp32, [RBLOCK])
tmp35 = triton_helpers.promote_to_tensor(tl.sum(tmp33, 0))
tmp36 = 256.0
tmp37 = tmp35 / tmp36
tmp38 = 4.0
tmp39 = tmp37 * tmp38
tmp40 = tmp39 * tmp38
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp40, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__softmax_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del arg0_1
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_1[grid(256)](arg1_1, buf2, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del arg1_1
buf3 = empty_strided_cuda((), (), torch.float32)
buf4 = buf3
del buf3
triton_per_fused__log_softmax__softmax_mean_mul_sub_xlogy_2[grid(1)](
buf4, buf0, buf2, 1, 256, num_warps=2, num_stages=1)
del buf0
del buf2
return buf4,
class CriterionKDNew(nn.Module):
"""
knowledge distillation loss
"""
def __init__(self, upsample=False, temperature=4):
super(CriterionKDNew, self).__init__()
self.upsample = upsample
self.temperature = temperature
self.criterion_kd = torch.nn.KLDivLoss()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
yubin1219/Semantic-Seg
|
CriterionKD
| false
| 4,677
|
[
"BSD-2-Clause"
] | 0
|
c40bd43d3d7e44bc995b8d041736580dec084251
|
https://github.com/yubin1219/Semantic-Seg/tree/c40bd43d3d7e44bc995b8d041736580dec084251
|
SiaLoss
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
class SiaLoss(nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=2.0):
super(SiaLoss, self).__init__()
self.margin = margin
def forward(self, output1, output2, label):
euclidean_distance = F.pairwise_distance(output1, output2)
loss = torch.mean(label * torch.pow(euclidean_distance, 2) + (1 -
label) * torch.pow(torch.clamp(self.margin - euclidean_distance,
min=0.0), 2))
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_norm_sub_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp13 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp18 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp19 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 - tmp1
tmp3 = 1e-06
tmp4 = tmp2 + tmp3
tmp5 = tmp4 * tmp4
tmp8 = tmp6 - tmp7
tmp9 = tmp8 + tmp3
tmp10 = tmp9 * tmp9
tmp11 = tmp5 + tmp10
tmp14 = tmp12 - tmp13
tmp15 = tmp14 + tmp3
tmp16 = tmp15 * tmp15
tmp17 = tmp11 + tmp16
tmp20 = tmp18 - tmp19
tmp21 = tmp20 + tmp3
tmp22 = tmp21 * tmp21
tmp23 = tmp17 + tmp22
tmp24 = libdevice.sqrt(tmp23)
tl.store(out_ptr0 + x0, tmp24, xmask)
@triton.jit
def triton_per_fused_add_clamp_mean_mul_pow_rsub_1(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r2 = rindex
r0 = rindex % 64
tmp0 = tl.load(in_ptr0 + r2, None)
tmp1 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp3 = tmp0 * tmp2
tmp4 = 1.0
tmp5 = tmp4 - tmp0
tmp6 = 2.0
tmp7 = tmp6 - tmp1
tmp8 = 0.0
tmp9 = triton_helpers.maximum(tmp7, tmp8)
tmp10 = tmp9 * tmp9
tmp11 = tmp5 * tmp10
tmp12 = tmp3 + tmp11
tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
tmp16 = 256.0
tmp17 = tmp15 / tmp16
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp17, None)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_norm_sub_0[grid(64)](arg1_1, arg0_1, buf0, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = buf1
del buf1
triton_per_fused_add_clamp_mean_mul_pow_rsub_1[grid(1)](buf2,
arg2_1, buf0, 1, 256, num_warps=2, num_stages=1)
del arg2_1
del buf0
return buf2,
class SiaLossNew(nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=2.0):
super(SiaLossNew, self).__init__()
self.margin = margin
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]
|
zwzhang121/OpenUnReID
|
SiaLoss
| false
| 4,678
|
[
"Apache-2.0"
] | 0
|
4f399efca3d560c608fb4c9c2ed43f522b17596a
|
https://github.com/zwzhang121/OpenUnReID/tree/4f399efca3d560c608fb4c9c2ed43f522b17596a
|
F_fully_convolutional
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class F_fully_convolutional(nn.Module):
def __init__(self, in_channels, out_channels, internal_size=256,
kernel_size=3, leaky_slope=0.02):
super().__init__()
pad = kernel_size // 2
self.leaky_slope = leaky_slope
self.conv1 = nn.Conv2d(in_channels, internal_size, kernel_size=
kernel_size, padding=pad)
self.conv2 = nn.Conv2d(in_channels + internal_size, internal_size,
kernel_size=kernel_size, padding=pad)
self.conv3 = nn.Conv2d(in_channels + 2 * internal_size,
out_channels, kernel_size=1, padding=0)
def forward(self, x):
x1 = F.leaky_relu(self.conv1(x), self.leaky_slope)
x2 = F.leaky_relu(self.conv2(torch.cat([x, x1], 1)), self.leaky_slope)
return self.conv3(torch.cat([x, x1, x2], 1))
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
import torch.nn as nn
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_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 4
y1 = yindex // 4
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 4 * x2 + 36 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 4
y1 = yindex // 4
tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 260
y1 = yindex // 260
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 260 * x2 + 2340 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_3(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)
x2 = xindex
x0 = xindex % 256
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
tl.store(out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_cat_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 16640
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 260
x1 = xindex // 260
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], 260, tl.int64)
tmp9 = tl.load(in_ptr1 + (256 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0).to(tl.int1)
tmp10 = tl.load(in_ptr2 + (256 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = tl.load(in_ptr3 + (-4 + x0), tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp12 = tmp10 + tmp11
tmp13 = 0.02
tmp14 = tmp12 * tmp13
tmp15 = tl.where(tmp9, tmp12, tmp14)
tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp17 = tl.where(tmp6, tmp15, tmp16)
tmp18 = tl.where(tmp4, tmp5, tmp17)
tl.store(out_ptr0 + x2, tmp18, xmask)
@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 33024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 516
x1 = xindex // 516
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 260, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr1 + (256 * x1 + (-4 + x0)), tmp9 & xmask,
eviction_policy='evict_last', other=0.0).to(tl.int1)
tmp11 = tl.load(in_ptr2 + (256 * x1 + (-4 + x0)), tmp9 & xmask,
eviction_policy='evict_last', other=0.0)
tmp12 = tl.load(in_ptr3 + (-4 + x0), tmp9 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp13 = tmp11 + tmp12
tmp14 = 0.02
tmp15 = tmp13 * tmp14
tmp16 = tl.where(tmp10, tmp13, tmp15)
tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
tmp18 = tl.where(tmp9, tmp16, tmp17)
tmp19 = tmp0 >= tmp7
tl.full([1], 516, tl.int64)
tmp22 = tl.load(in_ptr4 + (256 * x1 + (-260 + x0)), tmp19 & xmask,
eviction_policy='evict_last', other=0.0).to(tl.int1)
tmp23 = tl.load(in_ptr5 + (256 * x1 + (-260 + x0)), tmp19 & xmask,
eviction_policy='evict_last', other=0.0)
tmp24 = tl.load(in_ptr6 + (-260 + x0), tmp19 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp25 = tmp23 + tmp24
tmp26 = tmp25 * tmp14
tmp27 = tl.where(tmp22, tmp25, tmp26)
tmp28 = tl.full(tmp27.shape, 0.0, tmp27.dtype)
tmp29 = tl.where(tmp19, tmp27, tmp28)
tmp30 = tl.where(tmp9, tmp18, tmp29)
tmp31 = tl.where(tmp4, tmp5, tmp30)
tl.store(out_ptr0 + x2, tmp31, xmask)
@triton.jit
def triton_poi_fused_convolution_6(in_ptr0, in_ptr1, out_ptr0, ynumel,
xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, 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, (256, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (256,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (256, 260, 3, 3), (2340, 9, 3, 1))
assert_size_stride(primals_5, (256,), (1,))
assert_size_stride(primals_6, (4, 516, 1, 1), (516, 1, 1, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((256, 4, 3, 3), (36, 1, 12, 4), torch.float32
)
get_raw_stream(0)
triton_poi_fused_0[grid(1024, 9)](primals_1, buf0, 1024, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
triton_poi_fused_1[grid(16, 16)](primals_3, buf1, 16, 16, XBLOCK=16,
YBLOCK=16, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((256, 260, 3, 3), (2340, 1, 780, 260),
torch.float32)
triton_poi_fused_2[grid(66560, 9)](primals_4, buf2, 66560, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_4
buf3 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf4 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256),
torch.bool)
triton_poi_fused_convolution_leaky_relu_3[grid(16384)](buf3,
primals_2, buf4, 16384, XBLOCK=128, num_warps=4, num_stages=1)
buf5 = empty_strided_cuda((4, 260, 4, 4), (4160, 1, 1040, 260),
torch.float32)
triton_poi_fused_cat_4[grid(16640)](buf1, buf4, buf3, primals_2,
buf5, 16640, XBLOCK=256, num_warps=4, num_stages=1)
buf6 = extern_kernels.convolution(buf5, buf2, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf7 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256),
torch.bool)
triton_poi_fused_convolution_leaky_relu_3[grid(16384)](buf6,
primals_5, buf7, 16384, XBLOCK=128, num_warps=4, num_stages=1)
buf8 = empty_strided_cuda((4, 516, 4, 4), (8256, 1, 2064, 516),
torch.float32)
triton_poi_fused_cat_5[grid(33024)](buf1, buf4, buf3, primals_2,
buf7, buf6, primals_5, buf8, 33024, XBLOCK=512, num_warps=4,
num_stages=1)
del buf3
del buf6
del primals_2
del primals_5
buf9 = extern_kernels.convolution(buf8, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 4, 4, 4), (64, 1, 16, 4))
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_convolution_6[grid(16, 16)](buf9, primals_7, buf10,
16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
del buf9
del primals_7
return buf10, buf0, buf1, buf2, primals_6, buf4, buf5, buf7, buf8
class F_fully_convolutionalNew(nn.Module):
def __init__(self, in_channels, out_channels, internal_size=256,
kernel_size=3, leaky_slope=0.02):
super().__init__()
pad = kernel_size // 2
self.leaky_slope = leaky_slope
self.conv1 = nn.Conv2d(in_channels, internal_size, kernel_size=
kernel_size, padding=pad)
self.conv2 = nn.Conv2d(in_channels + internal_size, internal_size,
kernel_size=kernel_size, padding=pad)
self.conv3 = nn.Conv2d(in_channels + 2 * internal_size,
out_channels, kernel_size=1, padding=0)
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]
|
zimmerrol/FrEIA
|
F_fully_convolutional
| false
| 4,679
|
[
"MIT"
] | 0
|
73d01ab8c90e0deb5e242d66405bd168db06dc19
|
https://github.com/zimmerrol/FrEIA/tree/73d01ab8c90e0deb5e242d66405bd168db06dc19
|
C3
|
import torch
import torch.nn as nn
from collections import OrderedDict
class C3(nn.Module):
def __init__(self):
super(C3, self).__init__()
self.c3 = nn.Sequential(OrderedDict([('c3', nn.Conv2d(16, 120,
kernel_size=(5, 5))), ('relu3', nn.ReLU())]))
def forward(self, img):
output = self.c3(img)
return output
def get_inputs():
return [torch.rand([4, 16, 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 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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 1920
xnumel = 25
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 % 16
y1 = yindex // 16
tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask & ymask, eviction_policy
='evict_last')
tl.store(out_ptr0 + (y0 + 16 * x2 + 400 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 64
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 % 16
y1 = yindex // 16
tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 16 * x2 + 65536 * y1), tmp0, ymask)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_ptr0, in_ptr1,
out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
constexpr):
ynumel = 480
xnumel = 3600
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 % 120
y1 = yindex // 120
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 120 * x2 + 432000 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1, 1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x2 + 3600 * y3), tmp4, xmask & ymask)
tl.store(out_ptr1 + (y0 + 120 * x2 + 432000 * y1), tmp6, xmask & ymask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (120, 16, 5, 5), (400, 25, 5, 1))
assert_size_stride(primals_2, (120,), (1,))
assert_size_stride(primals_3, (4, 16, 64, 64), (65536, 4096, 64, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((120, 16, 5, 5), (400, 1, 80, 16), torch.
float32)
get_raw_stream(0)
triton_poi_fused_0[grid(1920, 25)](primals_1, buf0, 1920, 25,
XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 16, 64, 64), (65536, 1, 1024, 16),
torch.float32)
triton_poi_fused_1[grid(64, 4096)](primals_3, buf1, 64, 4096,
XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
del primals_3
buf2 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 120, 60, 60), (432000, 1, 7200, 120))
buf3 = empty_strided_cuda((4, 120, 60, 60), (432000, 3600, 60, 1),
torch.float32)
buf4 = empty_strided_cuda((4, 120, 60, 60), (432000, 1, 7200, 120),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_2[grid(480, 3600)
](buf2, primals_2, buf3, buf4, 480, 3600, XBLOCK=32, YBLOCK=32,
num_warps=4, num_stages=1)
del buf2
del primals_2
return buf3, buf0, buf1, buf4
class C3New(nn.Module):
def __init__(self):
super(C3New, self).__init__()
self.c3 = nn.Sequential(OrderedDict([('c3', nn.Conv2d(16, 120,
kernel_size=(5, 5))), ('relu3', nn.ReLU())]))
def forward(self, input_0):
primals_1 = self.c3.c3.weight
primals_2 = self.c3.c3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
zjgbz/img_cls
|
C3
| false
| 4,680
|
[
"MIT"
] | 0
|
513d5ae423d95e008a82a6ffe443db49f8ed9ac2
|
https://github.com/zjgbz/img_cls/tree/513d5ae423d95e008a82a6ffe443db49f8ed9ac2
|
AngleSimpleLinear
|
import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.onnx
import torch.nn
class AngleSimpleLinear(nn.Module):
"""Computes cos of angles between input vectors and weights vectors"""
def __init__(self, in_features, out_features):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.Tensor(in_features, out_features))
self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-05).mul_(100000.0)
def forward(self, x):
cos_theta = F.normalize(x, dim=1).mm(F.normalize(self.weight, dim=0))
return cos_theta.clamp(-1.0 + 1e-07, 1.0 - 1e-07),
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
from torchvision import models as models
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.onnx
import torch.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 = 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_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
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (12 + x0), 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_clamp_ge_le_logical_and_2(in_ptr0, 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
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = -0.9999999
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp3 = 0.9999999
tmp4 = triton_helpers.minimum(tmp2, tmp3)
tmp5 = tmp0 >= tmp1
tmp6 = tmp0 <= tmp3
tmp7 = tmp5 & tmp6
tl.store(out_ptr0 + x0, tmp4, xmask)
tl.store(out_ptr1 + x0, tmp7, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_div_0[grid(16)](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)
triton_poi_fused_div_1[grid(16)](primals_2, buf1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf0, buf1, out=buf2)
buf3 = buf1
del buf1
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_clamp_ge_le_logical_and_2[grid(16)](buf2, buf3,
buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
del buf2
return buf3, primals_2, buf4, reinterpret_tensor(buf0, (4, 4), (1, 4), 0)
class AngleSimpleLinearNew(nn.Module):
"""Computes cos of angles between input vectors and weights vectors"""
def __init__(self, in_features, out_features):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.Tensor(in_features, out_features))
self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-05).mul_(100000.0)
def forward(self, input_0):
primals_1 = self.weight
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
ygnn123/training_extensions
|
AngleSimpleLinear
| false
| 4,681
|
[
"Apache-2.0"
] | 0
|
c3aeba9359b0d4e0ef9c054de777d3ec081a9892
|
https://github.com/ygnn123/training_extensions/tree/c3aeba9359b0d4e0ef9c054de777d3ec081a9892
|
TKipfGCN
|
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn import Parameter
class BaseModel(nn.Module):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
pass
@classmethod
def build_model_from_args(cls, args):
"""Build a new model instance."""
raise NotImplementedError(
'Models must implement the build_model_from_args method')
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, 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.normal_(-stdv, stdv)
if self.bias is not None:
self.bias.data.normal_(-stdv, stdv)
def forward(self, input, adj):
support = torch.mm(input, self.weight)
output = torch.spmm(adj, 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 TKipfGCN(BaseModel):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument('--num-features', type=int)
parser.add_argument('--num-classes', type=int)
parser.add_argument('--hidden-size', type=int, default=64)
parser.add_argument('--dropout', type=float, default=0.5)
@classmethod
def build_model_from_args(cls, args):
return cls(args.num_features, args.hidden_size, args.num_classes,
args.dropout)
def __init__(self, nfeat, nhid, nclass, dropout):
super(TKipfGCN, self).__init__()
self.gc1 = GraphConvolution(nfeat, nhid)
self.gc2 = GraphConvolution(nhid, nclass)
self.dropout = dropout
def forward(self, x, adj):
x = F.relu(self.gc1(x, adj))
x = F.dropout(x, self.dropout, training=self.training)
x = self.gc2(x, adj)
return F.log_softmax(x, dim=-1)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'nfeat': 4, 'nhid': 4, 'nclass': 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
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import torch.utils.data
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn 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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
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__log_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
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused__log_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')
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 = 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 + x2, tmp13, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4,), (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, 1), torch.float32)
extern_kernels.mm(primals_2, primals_1, out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf0, out=buf1)
buf2 = buf1
del buf1
get_raw_stream(0)
triton_poi_fused_add_relu_0[grid(16)](buf2, primals_4, 16, XBLOCK=
16, num_warps=1, num_stages=1)
del primals_4
buf3 = buf0
del buf0
extern_kernels.mm(buf2, primals_5, out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_6, primals_3, buf3, alpha=1, beta=1,
out=buf4)
del primals_6
buf5 = buf3
del buf3
triton_poi_fused__log_softmax_1[grid(16)](buf4, buf5, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf6 = buf4
del buf4
triton_poi_fused__log_softmax_2[grid(16)](buf5, buf6, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf5
return buf6, buf2, buf6, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0
), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0
), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0)
class BaseModel(nn.Module):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
pass
@classmethod
def build_model_from_args(cls, args):
"""Build a new model instance."""
raise NotImplementedError(
'Models must implement the build_model_from_args method')
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, 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.normal_(-stdv, stdv)
if self.bias is not None:
self.bias.data.normal_(-stdv, stdv)
def forward(self, input, adj):
support = torch.mm(input, self.weight)
output = torch.spmm(adj, 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 TKipfGCNNew(BaseModel):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument('--num-features', type=int)
parser.add_argument('--num-classes', type=int)
parser.add_argument('--hidden-size', type=int, default=64)
parser.add_argument('--dropout', type=float, default=0.5)
@classmethod
def build_model_from_args(cls, args):
return cls(args.num_features, args.hidden_size, args.num_classes,
args.dropout)
def __init__(self, nfeat, nhid, nclass, dropout):
super(TKipfGCNNew, self).__init__()
self.gc1 = GraphConvolution(nfeat, nhid)
self.gc2 = GraphConvolution(nhid, nclass)
self.dropout = dropout
def forward(self, input_0, input_1):
primals_1 = self.gc1.weight
primals_4 = self.gc1.bias
primals_2 = self.gc2.weight
primals_6 = self.gc2.bias
primals_3 = input_0
primals_5 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
zxhhh97/cogdl
|
TKipfGCN
| false
| 4,682
|
[
"MIT"
] | 0
|
de21c78d9bbbf0c6cafbc72ff241cda35693ec37
|
https://github.com/zxhhh97/cogdl/tree/de21c78d9bbbf0c6cafbc72ff241cda35693ec37
|
FCDiscriminator_Local
|
import torch
import torch.nn as nn
class FCDiscriminator_Local(nn.Module):
def __init__(self, num_classes, ndf=64):
super(FCDiscriminator_Local, self).__init__()
self.conv1 = nn.Conv2d(num_classes + 2048, ndf, kernel_size=4,
stride=2, padding=1)
self.conv2 = nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1
)
self.conv3 = nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2,
padding=1)
self.classifier = nn.Conv2d(ndf * 4, 1, kernel_size=4, stride=2,
padding=1)
self.leaky_relu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.up_sample = nn.Upsample(scale_factor=32, mode='bilinear')
def forward(self, x):
x = self.conv1(x)
x = self.leaky_relu(x)
x = self.conv2(x)
x = self.leaky_relu(x)
x = self.conv3(x)
x = self.leaky_relu(x)
x = self.classifier(x)
x = self.up_sample(x)
return x
def get_inputs():
return [torch.rand([4, 2052, 64, 64])]
def get_init_inputs():
return [[], {'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_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 // 1024 % 64
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_leaky_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)
x3 = xindex
x1 = xindex // 256 % 128
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_leaky_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)
x3 = xindex
x1 = xindex // 64 % 256
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__to_copy_3(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 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 + tmp2
tmp4 = 0.03125
tmp5 = tmp3 * tmp4
tmp6 = tmp5 - tmp2
tmp7 = 0.0
tmp8 = triton_helpers.maximum(tmp6, tmp7)
tmp9 = tmp8.to(tl.int32)
tl.store(out_ptr0 + x0, tmp9, xmask)
@triton.jit
def triton_poi_fused_add_clamp_4(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 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 + tmp2
tmp4 = 0.03125
tmp5 = tmp3 * tmp4
tmp6 = tmp5 - tmp2
tmp7 = 0.0
tmp8 = triton_helpers.maximum(tmp6, tmp7)
tmp9 = tmp8.to(tl.int32)
tmp10 = tl.full([1], 1, tl.int64)
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 3, tl.int64)
tmp13 = triton_helpers.minimum(tmp11, tmp12)
tl.store(out_ptr0 + x0, tmp13, xmask)
@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_5(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 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 + tmp2
tmp4 = 0.03125
tmp5 = tmp3 * tmp4
tmp6 = tmp5 - tmp2
tmp7 = 0.0
tmp8 = triton_helpers.maximum(tmp6, tmp7)
tmp9 = tmp8.to(tl.int32)
tmp10 = tmp9.to(tl.float32)
tmp11 = tmp8 - tmp10
tmp12 = triton_helpers.maximum(tmp11, tmp7)
tmp13 = 1.0
tmp14 = triton_helpers.minimum(tmp12, tmp13)
tl.store(out_ptr0 + x0, tmp14, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_6(in_out_ptr0,
in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 128 % 128
x0 = xindex % 128
x2 = xindex // 16384
x3 = xindex
tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr3 + 0)
tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
tmp13 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
tmp20 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
tmp23 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
tmp35 = tl.load(in_ptr7 + 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 * x2), None,
eviction_policy='evict_last')
tmp12 = tmp9 + tmp11
tmp14 = tmp13 + tmp1
tmp15 = tmp13 < 0
tmp16 = tl.where(tmp15, tmp14, tmp13)
tmp17 = tl.load(in_ptr2 + (tmp16 + 4 * tmp4 + 16 * x2), None,
eviction_policy='evict_last')
tmp18 = tmp17 + tmp11
tmp19 = tmp18 - tmp12
tmp21 = tmp19 * tmp20
tmp22 = tmp12 + tmp21
tmp24 = tmp23 + tmp1
tmp25 = tmp23 < 0
tmp26 = tl.where(tmp25, tmp24, tmp23)
tmp27 = tl.load(in_ptr2 + (tmp8 + 4 * tmp26 + 16 * x2), None,
eviction_policy='evict_last')
tmp28 = tmp27 + tmp11
tmp29 = tl.load(in_ptr2 + (tmp16 + 4 * tmp26 + 16 * x2), None,
eviction_policy='evict_last')
tmp30 = tmp29 + tmp11
tmp31 = tmp30 - tmp28
tmp32 = tmp31 * tmp20
tmp33 = tmp28 + tmp32
tmp34 = tmp33 - tmp22
tmp36 = tmp34 * tmp35
tmp37 = tmp22 + tmp36
tl.store(in_out_ptr0 + x3, tmp37, 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, (64, 2052, 4, 4), (32832, 16, 4, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 2052, 64, 64), (8404992, 4096, 64, 1))
assert_size_stride(primals_4, (128, 64, 4, 4), (1024, 16, 4, 1))
assert_size_stride(primals_5, (128,), (1,))
assert_size_stride(primals_6, (256, 128, 4, 4), (2048, 16, 4, 1))
assert_size_stride(primals_7, (256,), (1,))
assert_size_stride(primals_8, (1, 256, 4, 4), (4096, 16, 4, 1))
assert_size_stride(primals_9, (1,), (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, 64, 32, 32), (65536, 1024, 32, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0[grid(262144)](buf1,
primals_2, 262144, XBLOCK=512, num_warps=8, num_stages=1)
del primals_2
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 128, 16, 16), (32768, 256, 16, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_leaky_relu_1[grid(131072)](buf3,
primals_5, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_5
buf4 = extern_kernels.convolution(buf3, primals_6, stride=(2, 2),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 256, 8, 8), (16384, 64, 8, 1))
buf5 = buf4
del buf4
triton_poi_fused_convolution_leaky_relu_2[grid(65536)](buf5,
primals_7, 65536, XBLOCK=512, num_warps=4, num_stages=1)
del primals_7
buf6 = extern_kernels.convolution(buf5, primals_8, stride=(2, 2),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 1, 4, 4), (16, 16, 4, 1))
buf7 = empty_strided_cuda((128, 1), (1, 1), torch.int64)
triton_poi_fused__to_copy_3[grid(128)](buf7, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf8 = empty_strided_cuda((128, 1), (1, 1), torch.int64)
triton_poi_fused_add_clamp_4[grid(128)](buf8, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf9 = empty_strided_cuda((128,), (1,), torch.int64)
triton_poi_fused__to_copy_3[grid(128)](buf9, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf10 = empty_strided_cuda((128,), (1,), torch.int64)
triton_poi_fused_add_clamp_4[grid(128)](buf10, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf11 = empty_strided_cuda((128,), (1,), torch.float32)
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_5[grid(128)](buf11,
128, XBLOCK=128, num_warps=4, num_stages=1)
buf13 = empty_strided_cuda((128, 1), (1, 1), torch.float32)
triton_poi_fused__to_copy_add_arange_clamp_mul_sub_5[grid(128)](buf13,
128, XBLOCK=128, num_warps=4, num_stages=1)
buf14 = empty_strided_cuda((4, 1, 128, 128), (16384, 65536, 128, 1),
torch.float32)
buf15 = reinterpret_tensor(buf14, (4, 1, 128, 128), (16384, 16384,
128, 1), 0)
del buf14
triton_poi_fused__unsafe_index_add_convolution_mul_sub_6[grid(65536)](
buf15, buf7, buf9, buf6, primals_9, buf10, buf11, buf8, buf13,
65536, XBLOCK=256, num_warps=4, num_stages=1)
del buf6
del primals_9
return (buf15, primals_1, primals_3, primals_4, primals_6, primals_8,
buf1, buf3, buf5, buf7, buf8, buf9, buf10, buf11, buf13)
class FCDiscriminator_LocalNew(nn.Module):
def __init__(self, num_classes, ndf=64):
super(FCDiscriminator_LocalNew, self).__init__()
self.conv1 = nn.Conv2d(num_classes + 2048, ndf, kernel_size=4,
stride=2, padding=1)
self.conv2 = nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1
)
self.conv3 = nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2,
padding=1)
self.classifier = nn.Conv2d(ndf * 4, 1, kernel_size=4, stride=2,
padding=1)
self.leaky_relu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.up_sample = nn.Upsample(scale_factor=32, mode='bilinear')
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.classifier.weight
primals_9 = self.classifier.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]
|
shiyutang/CLAN
|
FCDiscriminator_Local
| false
| 4,683
|
[
"MIT"
] | 0
|
920bd7cb592ba79ee5058f8cd662d20eda50457e
|
https://github.com/shiyutang/CLAN/tree/920bd7cb592ba79ee5058f8cd662d20eda50457e
|
VAE
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class VAE(nn.Module):
def __init__(self, z_dim):
super().__init__()
self.z_dim = z_dim
self.fc1 = nn.Linear(784, 500)
self.fc21 = nn.Linear(500, self.z_dim)
self.fc22 = nn.Linear(500, self.z_dim)
self.fc3 = nn.Linear(self.z_dim, 500)
self.fc4 = nn.Linear(500, 784)
def encode(self, x):
h1 = F.relu(self.fc1(x))
mu = self.fc21(h1)
logvar = self.fc22(h1)
return mu, logvar
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.rand_like(std)
return mu + eps * std
def decode(self, z):
h3 = F.relu(self.fc3(z))
return torch.sigmoid(self.fc4(h3))
def forward(self, x):
x = x.view(-1, 784)
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
return self.decode(z), mu, logvar
def get_inputs():
return [torch.rand([4, 784])]
def get_init_inputs():
return [[], {'z_dim': 4}]
|
import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 2000
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 500
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_add_exp_mul_1(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
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask)
tmp2 = tl.load(in_ptr2 + x0, xmask)
tmp3 = 0.5
tmp4 = tmp2 * tmp3
tmp5 = tl_math.exp(tmp4)
tmp6 = tmp1 * tmp5
tmp7 = tmp0 + tmp6
tl.store(out_ptr0 + x0, tmp7, xmask)
@triton.jit
def triton_poi_fused_sigmoid_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 3136
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 784
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.sigmoid(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (4, 784), (784, 1))
assert_size_stride(primals_2, (500, 784), (784, 1))
assert_size_stride(primals_3, (500,), (1,))
assert_size_stride(primals_4, (4, 500), (500, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 500), (500, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (500, 4), (4, 1))
assert_size_stride(primals_9, (500,), (1,))
assert_size_stride(primals_10, (784, 500), (500, 1))
assert_size_stride(primals_11, (784,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 500), (500, 1), torch.float32)
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (784,
500), (1, 784), 0), out=buf0)
del primals_2
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_relu_0[grid(2000)](buf1, primals_3, 2000, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
(500, 4), (1, 500), 0), alpha=1, beta=1, out=buf2)
del primals_5
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, buf1, reinterpret_tensor(primals_6,
(500, 4), (1, 500), 0), alpha=1, beta=1, out=buf3)
del primals_7
buf4 = torch.ops.aten.rand.default([4, 4], dtype=torch.float32,
device=device(type='cuda', index=0), pin_memory=False)
buf5 = buf4
del buf4
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_add_exp_mul_1[grid(16)](buf2, buf5, buf3, buf6, 16,
XBLOCK=16, num_warps=1, num_stages=1)
buf7 = empty_strided_cuda((4, 500), (500, 1), torch.float32)
extern_kernels.mm(buf6, reinterpret_tensor(primals_8, (4, 500), (1,
4), 0), out=buf7)
buf8 = buf7
del buf7
triton_poi_fused_relu_0[grid(2000)](buf8, primals_9, 2000, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_9
buf9 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
extern_kernels.mm(buf8, reinterpret_tensor(primals_10, (500, 784),
(1, 500), 0), out=buf9)
buf10 = buf9
del buf9
triton_poi_fused_sigmoid_2[grid(3136)](buf10, primals_11, 3136,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_11
return (buf10, buf2, buf3, primals_1, buf1, buf3, buf5, buf6, buf8,
buf10, primals_10, primals_8, primals_6, primals_4)
class VAENew(nn.Module):
def __init__(self, z_dim):
super().__init__()
self.z_dim = z_dim
self.fc1 = nn.Linear(784, 500)
self.fc21 = nn.Linear(500, self.z_dim)
self.fc22 = nn.Linear(500, self.z_dim)
self.fc3 = nn.Linear(self.z_dim, 500)
self.fc4 = nn.Linear(500, 784)
def encode(self, x):
h1 = F.relu(self.fc1(x))
mu = self.fc21(h1)
logvar = self.fc22(h1)
return mu, logvar
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.rand_like(std)
return mu + eps * std
def decode(self, z):
h3 = F.relu(self.fc3(z))
return torch.sigmoid(self.fc4(h3))
def forward(self, input_0):
primals_2 = self.fc1.weight
primals_3 = self.fc1.bias
primals_4 = self.fc21.weight
primals_5 = self.fc21.bias
primals_6 = self.fc22.weight
primals_7 = self.fc22.bias
primals_8 = self.fc3.weight
primals_9 = self.fc3.bias
primals_10 = self.fc4.weight
primals_11 = 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,
primals_10, primals_11])
return output[0], output[1], output[2]
|
zyzisyz/torch-practice
|
VAE
| false
| 4,684
|
[
"Apache-2.0"
] | 0
|
92f2b7f1a01bbabd1a2cf2a4dd9099a0eeb9cf00
|
https://github.com/zyzisyz/torch-practice/tree/92f2b7f1a01bbabd1a2cf2a4dd9099a0eeb9cf00
|
Greedy
|
import torch
import torch.nn as nn
from matplotlib.font_manager import *
class Greedy(nn.Module):
def __init__(self):
super().__init__()
def forward(self, log_p):
return torch.argmax(log_p, dim=1).long()
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
from matplotlib.font_manager 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_poi_fused_argmax_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)
tmp17 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp32 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp2 = tmp0 > tmp1
tmp3 = tmp0 == tmp1
tmp4 = tmp0 != tmp0
tmp5 = tmp1 != tmp1
tmp6 = tmp4 > tmp5
tmp7 = tmp2 | tmp6
tmp8 = tmp4 & tmp5
tmp9 = tmp3 | tmp8
tmp10 = tl.full([1], 0, tl.int64)
tmp11 = tl.full([1], 1, tl.int64)
tmp12 = tmp10 < tmp11
tmp13 = tmp9 & tmp12
tmp14 = tmp7 | tmp13
tmp15 = tl.where(tmp14, tmp0, tmp1)
tmp16 = tl.where(tmp14, tmp10, tmp11)
tmp18 = tmp15 > tmp17
tmp19 = tmp15 == tmp17
tmp20 = tmp15 != tmp15
tmp21 = tmp17 != tmp17
tmp22 = tmp20 > tmp21
tmp23 = tmp18 | tmp22
tmp24 = tmp20 & tmp21
tmp25 = tmp19 | tmp24
tmp26 = tl.full([1], 2, tl.int64)
tmp27 = tmp16 < tmp26
tmp28 = tmp25 & tmp27
tmp29 = tmp23 | tmp28
tmp30 = tl.where(tmp29, tmp15, tmp17)
tmp31 = tl.where(tmp29, tmp16, tmp26)
tmp33 = tmp30 > tmp32
tmp34 = tmp30 == tmp32
tmp35 = tmp30 != tmp30
tmp36 = tmp32 != tmp32
tmp37 = tmp35 > tmp36
tmp38 = tmp33 | tmp37
tmp39 = tmp35 & tmp36
tmp40 = tmp34 | tmp39
tmp41 = tl.full([1], 3, tl.int64)
tmp42 = tmp31 < tmp41
tmp43 = tmp40 & tmp42
tmp44 = tmp38 | tmp43
tl.where(tmp44, tmp30, tmp32)
tmp46 = tl.where(tmp44, tmp31, tmp41)
tl.store(out_ptr0 + x2, tmp46, 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.int64)
get_raw_stream(0)
triton_poi_fused_argmax_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del arg0_1
return buf0,
class GreedyNew(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
zifeiyu0531/TSP_DRL_PtrNet
|
Greedy
| false
| 4,685
|
[
"MIT"
] | 0
|
c62fab73347556173d301c1561edf927e6fbe1d7
|
https://github.com/zifeiyu0531/TSP_DRL_PtrNet/tree/c62fab73347556173d301c1561edf927e6fbe1d7
|
Categorical
|
import torch
import torch.nn as nn
from matplotlib.font_manager import *
class Categorical(nn.Module):
def __init__(self):
super().__init__()
def forward(self, log_p):
return torch.multinomial(log_p.exp(), 1).long().squeeze(1)
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from matplotlib.font_manager import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_exp_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_math.exp(tmp0)
tl.store(out_ptr0 + x0, tmp1, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_exp_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del arg0_1
buf1 = torch.ops.aten.multinomial.default(buf0, 1)
del buf0
buf2 = buf1
del buf1
return reinterpret_tensor(buf2, (4,), (1,), 0),
class CategoricalNew(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
zifeiyu0531/TSP_DRL_PtrNet
|
Categorical
| false
| 4,686
|
[
"MIT"
] | 0
|
c62fab73347556173d301c1561edf927e6fbe1d7
|
https://github.com/zifeiyu0531/TSP_DRL_PtrNet/tree/c62fab73347556173d301c1561edf927e6fbe1d7
|
ScaledDotProductAttention
|
import math
import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class ScaledDotProductAttention(nn.Module):
def __init__(self, dropout_ratio=0):
super().__init__()
self.dropout = nn.Dropout(dropout_ratio)
def forward(self, query, keys, values, mask=None):
attn = torch.matmul(query, keys.transpose(-2, -1))
attn /= math.sqrt(query.shape[-1])
if mask is None:
mask = attn.new_ones(attn.shape)
if mask.dim() < attn.dim():
mask = mask.unsqueeze(-2)
mask = self.dropout(mask)
attn = attn.masked_fill(mask == 0, -1000.0)
attn = F.softmax(attn, dim=-1)
output = torch.matmul(attn, values)
return output, attn
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.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_eq_masked_fill_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)
tmp6 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp17 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = tl.full([1], False, tl.int1)
tmp4 = -1000.0
tmp5 = tl.where(tmp3, tmp4, tmp2)
tmp7 = tmp6 * tmp1
tmp8 = tl.where(tmp3, tmp4, tmp7)
tmp10 = tmp9 * tmp1
tmp11 = tl.where(tmp3, tmp4, tmp10)
tmp12 = triton_helpers.maximum(tmp8, tmp11)
tmp14 = tmp13 * tmp1
tmp15 = tl.where(tmp3, tmp4, tmp14)
tmp16 = triton_helpers.maximum(tmp12, tmp15)
tmp18 = tmp17 * tmp1
tmp19 = tl.where(tmp3, tmp4, tmp18)
tmp20 = triton_helpers.maximum(tmp16, tmp19)
tmp21 = tmp5 - tmp20
tmp22 = tl_math.exp(tmp21)
tl.store(out_ptr0 + x2, tmp22, xmask)
@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
), 0), reinterpret_tensor(arg0_1, (16, 4, 4), (16, 1, 4), 0),
out=buf0)
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__softmax_eq_masked_fill_0[grid(256)](buf0, buf1,
256, XBLOCK=256, num_warps=4, num_stages=1)
buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
triton_poi_fused__softmax_1[grid(256)](buf1, buf2, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf3 = reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0)
del buf1
extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(arg2_1, (16, 4, 4), (16, 4, 1), 0), out=buf3
)
del arg2_1
return reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf2
class ScaledDotProductAttentionNew(nn.Module):
def __init__(self, dropout_ratio=0):
super().__init__()
self.dropout = nn.Dropout(dropout_ratio)
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]
|
ygnn123/training_extensions
|
ScaledDotProductAttention
| false
| 4,687
|
[
"Apache-2.0"
] | 0
|
c3aeba9359b0d4e0ef9c054de777d3ec081a9892
|
https://github.com/ygnn123/training_extensions/tree/c3aeba9359b0d4e0ef9c054de777d3ec081a9892
|
SageConv
|
from torch.nn import Module
import torch
import torch.nn as nn
from torch.nn.modules.module import Module
class SageConv(Module):
"""
Simple Graphsage layer
"""
def __init__(self, in_features, out_features, bias=False):
super(SageConv, self).__init__()
self.proj = nn.Linear(in_features * 2, out_features, bias=bias)
self.reset_parameters()
def reset_parameters(self):
nn.init.normal_(self.proj.weight)
if self.proj.bias is not None:
nn.init.constant_(self.proj.bias, 0.0)
def forward(self, features, adj):
"""
Args:
adj: can be sparse or dense matrix.
"""
if not isinstance(adj, torch.sparse.FloatTensor):
if len(adj.shape) == 3:
neigh_feature = torch.bmm(adj, features) / (adj.sum(dim=1).
reshape((adj.shape[0], adj.shape[1], -1)) + 1)
else:
neigh_feature = torch.mm(adj, features) / (adj.sum(dim=1).
reshape(adj.shape[0], -1) + 1)
else:
neigh_feature = torch.spmm(adj, features) / (adj.to_dense().sum
(dim=1).reshape(adj.shape[0], -1) + 1)
data = torch.cat([features, neigh_feature], dim=-1)
combined = self.proj(data)
return combined
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import torch.nn as nn
from torch.nn.modules.module import Module
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, in_ptr2, 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.load(in_ptr2 + 4 * x1, tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp11 = tl.load(in_ptr2 + (1 + 4 * x1), tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp12 = tmp10 + tmp11
tmp13 = tl.load(in_ptr2 + (2 + 4 * x1), tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp14 = tmp12 + tmp13
tmp15 = tl.load(in_ptr2 + (3 + 4 * x1), tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp16 = tmp14 + tmp15
tmp17 = 1.0
tmp18 = tmp16 + tmp17
tmp19 = tmp9 / tmp18
tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
tmp21 = tl.where(tmp6, tmp19, tmp20)
tmp22 = tl.where(tmp4, tmp5, tmp21)
tl.store(out_ptr0 + x2, tmp22, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 8), (8, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, primals_2, out=buf0)
buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(32)](primals_2, buf0, primals_1, buf1,
32, XBLOCK=32, num_warps=1, num_stages=1)
del primals_1
del primals_2
buf2 = buf0
del buf0
extern_kernels.mm(buf1, reinterpret_tensor(primals_3, (8, 4), (1, 8
), 0), out=buf2)
del primals_3
return buf2, buf1
class SageConvNew(Module):
"""
Simple Graphsage layer
"""
def __init__(self, in_features, out_features, bias=False):
super(SageConvNew, self).__init__()
self.proj = nn.Linear(in_features * 2, out_features, bias=bias)
self.reset_parameters()
def reset_parameters(self):
nn.init.normal_(self.proj.weight)
if self.proj.bias is not None:
nn.init.constant_(self.proj.bias, 0.0)
def forward(self, input_0, input_1):
primals_3 = self.proj.weight
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3])
return output[0]
|
yutaoming/Rare-Category-Detection
|
SageConv
| false
| 4,688
|
[
"MIT"
] | 0
|
76cf023dff44eef3ecc17f0ebf2b11a08cd63a73
|
https://github.com/yutaoming/Rare-Category-Detection/tree/76cf023dff44eef3ecc17f0ebf2b11a08cd63a73
|
LogitKLDivLoss
|
import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class LogitKLDivLoss(nn.Module):
"""Kullback–Leibler divergence loss. Inputs predicted and ground truth logits.
Args:
T (float): Softmax temperature.
"""
def __init__(self, T=1):
super().__init__()
self.T = T
def forward(self, p_logits, q_logits, **kwargs):
log_p = F.log_softmax(p_logits / self.T, dim=1)
q = F.softmax(q_logits / self.T, dim=1)
return F.kl_div(log_p, q, reduction='batchmean') * self.T ** 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, math as tl_math
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
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 = tmp14 * tmp1
tmp16 = tl_math.exp(tmp15)
tl.store(out_ptr0 + x3, tmp16, xmask)
@triton.jit
def triton_poi_fused_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)
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 = tmp14 * tmp1
tl.store(out_ptr0 + x3, tmp15, xmask)
@triton.jit
def triton_per_fused__log_softmax__softmax_div_mul_sub_sum_xlogy_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)
r3 = rindex
r0 = rindex % 16
r2 = rindex // 64
tmp0 = tl.load(in_ptr0 + r3, None)
tmp1 = tl.load(in_ptr0 + (r0 + 64 * r2), None, eviction_policy='evict_last'
)
tmp2 = tl.load(in_ptr0 + (16 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp17 = tl.load(in_ptr1 + r3, None)
tmp18 = tl.load(in_ptr1 + (r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp20 = tl.load(in_ptr1 + (16 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp23 = tl.load(in_ptr1 + (32 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp26 = tl.load(in_ptr1 + (48 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tmp9 = libdevice.isnan(tmp8).to(tl.int1)
tmp10 = 0.0
tmp11 = tmp8 == tmp10
tmp12 = tl_math.log(tmp8)
tmp13 = tmp8 * tmp12
tmp14 = tl.where(tmp11, tmp10, tmp13)
tmp15 = float('nan')
tmp16 = tl.where(tmp9, tmp15, tmp14)
tmp19 = tl_math.exp(tmp18)
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp27 = tl_math.exp(tmp26)
tmp28 = tmp25 + tmp27
tmp29 = tl_math.log(tmp28)
tmp30 = tmp17 - tmp29
tmp31 = tmp8 * tmp30
tmp32 = tmp16 - tmp31
tmp33 = tl.broadcast_to(tmp32, [RBLOCK])
tmp35 = triton_helpers.promote_to_tensor(tl.sum(tmp33, 0))
tmp36 = 0.25
tmp37 = tmp35 * tmp36
tmp38 = 1.0
tmp39 = tmp37 * tmp38
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp39, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__softmax_0[grid(256)](arg1_1, buf0, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del arg1_1
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_1[grid(256)](arg0_1, buf2, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
buf3 = empty_strided_cuda((), (), torch.float32)
buf4 = buf3
del buf3
triton_per_fused__log_softmax__softmax_div_mul_sub_sum_xlogy_2[grid(1)
](buf4, buf0, buf2, 1, 256, num_warps=2, num_stages=1)
del buf0
del buf2
return buf4,
class LogitKLDivLossNew(nn.Module):
"""Kullback–Leibler divergence loss. Inputs predicted and ground truth logits.
Args:
T (float): Softmax temperature.
"""
def __init__(self, T=1):
super().__init__()
self.T = T
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
ygnn123/training_extensions
|
LogitKLDivLoss
| false
| 4,689
|
[
"Apache-2.0"
] | 0
|
c3aeba9359b0d4e0ef9c054de777d3ec081a9892
|
https://github.com/ygnn123/training_extensions/tree/c3aeba9359b0d4e0ef9c054de777d3ec081a9892
|
TransformerEncoderLayer
|
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn.init import xavier_uniform_
from torch.nn import Dropout
from torch.nn import LayerNorm
class MultiheadAttention(nn.Module):
"""Allows the model to jointly attend to information
from different representation subspaces.
See reference: Attention Is All You Need
.. math::
ext{MultiHead}(Q, K, V) = ext{Concat}(head_1,\\dots,head_h)W^O
ext{where} head_i = ext{Attention}(QW_i^Q, KW_i^K, VW_i^V)
Args:
embed_dim: total dimension of the model
num_heads: parallel attention layers, or heads
"""
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
add_bias_kv=False, add_zero_attn=False):
super(MultiheadAttention, self).__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.out_proj = Linear(embed_dim, embed_dim, bias=bias)
self._reset_parameters()
self.conv1 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
self.conv2 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
self.conv3 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
def _reset_parameters(self):
xavier_uniform_(self.out_proj.weight)
def forward(self, query, key, value, key_padding_mask=None,
incremental_state=None, attn_mask=None):
"""
Inputs of forward function
query: [target length, batch size, embed dim]
key: [sequence length, batch size, embed dim]
value: [sequence length, batch size, embed dim]
key_padding_mask: if True, mask padding based on batch size
incremental_state: if provided, previous time steps are cashed
need_weights: output attn_output_weights
static_kv: key and value are static
Outputs of forward function
attn_output: [target length, batch size, embed dim]
attn_output_weights: [batch size, target length, sequence length]
"""
q_shape = query.shape
src_shape = key.shape
q = self._in_proj_q(query)
k = self._in_proj_k(key)
v = self._in_proj_v(value)
q *= self.scaling
q = torch.reshape(q, (q_shape[0], q_shape[1], self.num_heads, self.
head_dim))
q = q.permute(1, 2, 0, 3)
k = torch.reshape(k, (src_shape[0], q_shape[1], self.num_heads,
self.head_dim))
k = k.permute(1, 2, 0, 3)
v = torch.reshape(v, (src_shape[0], q_shape[1], self.num_heads,
self.head_dim))
v = v.permute(1, 2, 0, 3)
if key_padding_mask is not None:
assert key_padding_mask.shape[0] == q_shape[1]
assert key_padding_mask.shape[1] == src_shape[0]
attn_output_weights = torch.matmul(q, k.permute(0, 1, 3, 2))
if attn_mask is not None:
attn_mask = torch.unsqueeze(torch.unsqueeze(attn_mask, 0), 0)
attn_output_weights += attn_mask
if key_padding_mask is not None:
attn_output_weights = torch.reshape(attn_output_weights, [
q_shape[1], self.num_heads, q_shape[0], src_shape[0]])
key = torch.unsqueeze(torch.unsqueeze(key_padding_mask, 1), 2)
key = key.type(torch.float32)
y = torch.full(size=key.shape, fill_value=float('-Inf'), dtype=
torch.float32)
y = torch.where(key == 0.0, key, y)
attn_output_weights += y
attn_output_weights = F.softmax(attn_output_weights.type(torch.
float32), dim=-1, dtype=torch.float32 if attn_output_weights.
dtype == torch.float16 else attn_output_weights.dtype)
attn_output_weights = F.dropout(attn_output_weights, p=self.dropout,
training=self.training)
attn_output = torch.matmul(attn_output_weights, v)
attn_output = torch.reshape(attn_output.permute(2, 0, 1, 3), [
q_shape[0], q_shape[1], self.embed_dim])
attn_output = self.out_proj(attn_output)
return attn_output
def _in_proj_q(self, query):
query = query.permute(1, 2, 0)
query = torch.unsqueeze(query, dim=2)
res = self.conv1(query)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def _in_proj_k(self, key):
key = key.permute(1, 2, 0)
key = torch.unsqueeze(key, dim=2)
res = self.conv2(key)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def _in_proj_v(self, value):
value = value.permute(1, 2, 0)
value = torch.unsqueeze(value, dim=2)
res = self.conv3(value)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
class TransformerEncoderLayer(nn.Module):
"""TransformerEncoderLayer is made up of self-attn and feedforward network.
This standard encoder layer is based on the paper "Attention Is All You Need".
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
in a different way during application.
Args:
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multiheadattention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
"""
def __init__(self, d_model, nhead, dim_feedforward=2048,
attention_dropout_rate=0.0, residual_dropout_rate=0.1):
super(TransformerEncoderLayer, self).__init__()
self.self_attn = MultiheadAttention(d_model, nhead, dropout=
attention_dropout_rate)
self.conv1 = nn.Conv2d(in_channels=d_model, out_channels=
dim_feedforward, kernel_size=(1, 1))
self.conv2 = nn.Conv2d(in_channels=dim_feedforward, out_channels=
d_model, kernel_size=(1, 1))
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.dropout1 = Dropout(residual_dropout_rate)
self.dropout2 = Dropout(residual_dropout_rate)
def forward(self, src, src_mask=None, src_key_padding_mask=None):
"""Pass the input through the endocder layer.
Args:
src: the sequnce to the encoder layer (required).
src_mask: the mask for the src sequence (optional).
src_key_padding_mask: the mask for the src keys per batch (optional).
"""
src2 = self.self_attn(src, src, src, attn_mask=src_mask,
key_padding_mask=src_key_padding_mask)
src = src + self.dropout1(src2)
src = self.norm1(src)
src = src.permute(1, 2, 0)
src = torch.unsqueeze(src, 2)
src2 = self.conv2(F.relu(self.conv1(src)))
src2 = torch.squeeze(src2, 2)
src2 = src2.permute(2, 0, 1)
src = torch.squeeze(src, 2)
src = src.permute(2, 0, 1)
src = src + self.dropout2(src2)
src = self.norm2(src)
return src
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'d_model': 4, 'nhead': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn.init import xavier_uniform_
from torch.nn import Dropout
from torch.nn import LayerNorm
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_ptr0, out_ptr0, out_ptr1, out_ptr2,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 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)
tl.store(out_ptr1 + x3, tmp0, xmask)
tl.store(out_ptr2 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, in_ptr1, out_ptr0, ynumel,
xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (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_mul_2(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 % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK:
tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x5 = xindex
y4 = yindex
x3 = xindex // 4
y0 = yindex % 4
y1 = yindex // 4
tmp0 = tl.load(in_ptr0 + (x5 + 16 * y4), xmask & ymask)
tmp1 = tl.load(in_ptr0 + (4 * x3 + 16 * y4), xmask & ymask,
eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x3 + 16 * y4), xmask & ymask,
eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x3 + 16 * y4), xmask & ymask,
eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x3 + 16 * y4), xmask & ymask,
eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + (y0 + 4 * x5 + 64 * y1), tmp8, xmask & ymask)
@triton.jit
def triton_poi_fused_bmm_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 % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (4 * x1 + 64 * (x0 // 4) + x0 % 4), xmask)
tl.store(out_ptr0 + x2, tmp0, xmask)
@triton.jit
def triton_poi_fused_clone_6(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_add_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2,
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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + 0)
tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr2 + 1)
tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp14 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp15 = tl.load(in_ptr2 + 2)
tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp21 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp22 = tl.load(in_ptr2 + 3)
tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
tmp4 = tmp1 + tmp3
tmp5 = tmp0 + tmp4
tmp10 = tmp7 + tmp9
tmp11 = tmp6 + tmp10
tmp12 = tmp5 + tmp11
tmp17 = tmp14 + tmp16
tmp18 = tmp13 + tmp17
tmp19 = tmp12 + tmp18
tmp24 = tmp21 + tmp23
tmp25 = tmp20 + tmp24
tmp26 = tmp19 + tmp25
tmp27 = 4.0
tmp28 = tmp26 / tmp27
tmp29 = tmp5 - tmp28
tmp30 = tmp29 * tmp29
tmp31 = tmp11 - tmp28
tmp32 = tmp31 * tmp31
tmp33 = tmp30 + tmp32
tmp34 = tmp18 - tmp28
tmp35 = tmp34 * tmp34
tmp36 = tmp33 + tmp35
tmp37 = tmp25 - tmp28
tmp38 = tmp37 * tmp37
tmp39 = tmp36 + tmp38
tmp40 = tmp39 / tmp27
tl.store(out_ptr0 + x0, tmp28, xmask)
tl.store(out_ptr1 + x0, tmp40, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_8(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tl.load(in_ptr2 + x0, 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')
tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr6 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tmp6 = tmp4 - tmp5
tmp8 = 1e-05
tmp9 = tmp7 + tmp8
tmp10 = libdevice.rsqrt(tmp9)
tmp11 = tmp6 * tmp10
tmp13 = tmp11 * tmp12
tmp15 = tmp13 + tmp14
tl.store(out_ptr0 + x2, tmp15, xmask)
@triton.jit
def triton_poi_fused_unsqueeze_9(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_10(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 2048
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_11(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
tl.store(in_out_ptr0 + x2, tmp2, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_12(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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), 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 + x0, tmp16, xmask)
tl.store(out_ptr1 + x0, tmp28, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_13(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x4 = xindex // 4
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x3, xmask)
tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + x0, 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 + (x0 + 4 * x2 + 16 * x1), tmp13, xmask)
@triton.jit
def triton_poi_fused_transpose_14(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
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x1), xmask & ymask, eviction_policy
='evict_last')
tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (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, 1, 1), (4, 1, 1, 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,))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (4,), (1,))
assert_size_stride(primals_10, (4,), (1,))
assert_size_stride(primals_11, (4,), (1,))
assert_size_stride(primals_12, (2048, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_13, (2048,), (1,))
assert_size_stride(primals_14, (4, 2048, 1, 1), (2048, 1, 1, 1))
assert_size_stride(primals_15, (4,), (1,))
assert_size_stride(primals_16, (4,), (1,))
assert_size_stride(primals_17, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
buf2 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
buf4 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(64)](primals_1, buf0, buf2,
buf4, 64, XBLOCK=64, num_warps=1, 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, 1, 4), (16, 1, 16, 4))
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 1, 4), (16, 1, 16, 4))
buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 4, 1, 4), (16, 1, 16, 4))
buf6 = reinterpret_tensor(buf4, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf4
triton_poi_fused_convolution_1[grid(16, 4)](buf3, primals_5, buf6,
16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_5
buf7 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0)
del buf3
triton_poi_fused_mul_2[grid(64)](buf1, primals_3, buf7, 64, XBLOCK=
64, num_warps=1, num_stages=1)
del primals_3
buf8 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 1), (1, 16, 0),
0), reinterpret_tensor(buf6, (16, 1, 4), (4, 0, 1), 0), out=buf8)
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_3[grid(256)](buf8, buf9, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 1, 16, 4), 0)
del buf8
triton_poi_fused__softmax_4[grid(16, 16)](buf9, buf10, 16, 16,
XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
buf11 = reinterpret_tensor(buf1, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf1
triton_poi_fused_convolution_1[grid(16, 4)](buf5, primals_7, buf11,
16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_7
buf12 = reinterpret_tensor(buf9, (16, 4, 4), (1, 64, 16), 0)
del buf9
triton_poi_fused_bmm_5[grid(256)](buf10, buf12, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf13 = reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 1), 0)
del buf5
extern_kernels.bmm(buf12, reinterpret_tensor(buf11, (16, 4, 1), (4,
1, 0), 0), out=buf13)
del buf12
buf14 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0)
del buf2
triton_poi_fused_clone_6[grid(4, 16)](buf13, buf14, 4, 16, XBLOCK=
16, YBLOCK=4, num_warps=1, num_stages=1)
buf15 = reinterpret_tensor(buf13, (16, 4), (4, 1), 0)
del buf13
extern_kernels.mm(reinterpret_tensor(buf14, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf15)
buf16 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf17 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
triton_poi_fused_add_native_layer_norm_7[grid(16)](primals_1, buf15,
primals_9, buf16, buf17, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf18 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
del buf0
triton_poi_fused_add_native_layer_norm_8[grid(64)](primals_1, buf15,
primals_9, buf16, buf17, primals_10, primals_11, buf18, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_11
buf19 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
triton_poi_fused_unsqueeze_9[grid(64)](buf18, buf19, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf20 = extern_kernels.convolution(buf19, primals_12, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf20, (4, 2048, 1, 4), (8192, 1, 8192, 2048))
buf21 = buf20
del buf20
triton_poi_fused_convolution_relu_10[grid(32768)](buf21, primals_13,
32768, XBLOCK=256, num_warps=4, num_stages=1)
del primals_13
buf22 = extern_kernels.convolution(buf21, primals_14, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf22, (4, 4, 1, 4), (16, 1, 16, 4))
buf23 = buf22
del buf22
triton_poi_fused_convolution_11[grid(64)](buf23, primals_15, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_15
buf24 = reinterpret_tensor(buf17, (4, 4, 1), (1, 4, 16), 0)
del buf17
buf25 = reinterpret_tensor(buf16, (4, 4, 1), (1, 4, 16), 0)
del buf16
triton_poi_fused_add_native_layer_norm_12[grid(16)](buf19, buf23,
buf24, buf25, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf26 = buf18
del buf18
triton_poi_fused_add_native_layer_norm_13[grid(64)](buf19, buf23,
buf24, buf25, primals_16, primals_17, buf26, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del buf24
del buf25
del primals_17
buf27 = empty_strided_cuda((16, 1, 4), (4, 4, 1), torch.float32)
triton_poi_fused_transpose_14[grid(16, 4)](buf7, buf27, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
del buf7
return (buf26, primals_1, primals_2, primals_4, primals_6, primals_9,
primals_10, primals_12, primals_14, primals_16, buf10,
reinterpret_tensor(buf14, (16, 4), (4, 1), 0), buf15, buf19, buf21,
buf23, primals_8, reinterpret_tensor(buf11, (16, 1, 4), (4, 4, 1),
0), buf27, reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 4), 0))
class MultiheadAttention(nn.Module):
"""Allows the model to jointly attend to information
from different representation subspaces.
See reference: Attention Is All You Need
.. math::
ext{MultiHead}(Q, K, V) = ext{Concat}(head_1,\\dots,head_h)W^O
ext{where} head_i = ext{Attention}(QW_i^Q, KW_i^K, VW_i^V)
Args:
embed_dim: total dimension of the model
num_heads: parallel attention layers, or heads
"""
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
add_bias_kv=False, add_zero_attn=False):
super(MultiheadAttention, self).__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.out_proj = Linear(embed_dim, embed_dim, bias=bias)
self._reset_parameters()
self.conv1 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
self.conv2 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
self.conv3 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
def _reset_parameters(self):
xavier_uniform_(self.out_proj.weight)
def forward(self, query, key, value, key_padding_mask=None,
incremental_state=None, attn_mask=None):
"""
Inputs of forward function
query: [target length, batch size, embed dim]
key: [sequence length, batch size, embed dim]
value: [sequence length, batch size, embed dim]
key_padding_mask: if True, mask padding based on batch size
incremental_state: if provided, previous time steps are cashed
need_weights: output attn_output_weights
static_kv: key and value are static
Outputs of forward function
attn_output: [target length, batch size, embed dim]
attn_output_weights: [batch size, target length, sequence length]
"""
q_shape = query.shape
src_shape = key.shape
q = self._in_proj_q(query)
k = self._in_proj_k(key)
v = self._in_proj_v(value)
q *= self.scaling
q = torch.reshape(q, (q_shape[0], q_shape[1], self.num_heads, self.
head_dim))
q = q.permute(1, 2, 0, 3)
k = torch.reshape(k, (src_shape[0], q_shape[1], self.num_heads,
self.head_dim))
k = k.permute(1, 2, 0, 3)
v = torch.reshape(v, (src_shape[0], q_shape[1], self.num_heads,
self.head_dim))
v = v.permute(1, 2, 0, 3)
if key_padding_mask is not None:
assert key_padding_mask.shape[0] == q_shape[1]
assert key_padding_mask.shape[1] == src_shape[0]
attn_output_weights = torch.matmul(q, k.permute(0, 1, 3, 2))
if attn_mask is not None:
attn_mask = torch.unsqueeze(torch.unsqueeze(attn_mask, 0), 0)
attn_output_weights += attn_mask
if key_padding_mask is not None:
attn_output_weights = torch.reshape(attn_output_weights, [
q_shape[1], self.num_heads, q_shape[0], src_shape[0]])
key = torch.unsqueeze(torch.unsqueeze(key_padding_mask, 1), 2)
key = key.type(torch.float32)
y = torch.full(size=key.shape, fill_value=float('-Inf'), dtype=
torch.float32)
y = torch.where(key == 0.0, key, y)
attn_output_weights += y
attn_output_weights = F.softmax(attn_output_weights.type(torch.
float32), dim=-1, dtype=torch.float32 if attn_output_weights.
dtype == torch.float16 else attn_output_weights.dtype)
attn_output_weights = F.dropout(attn_output_weights, p=self.dropout,
training=self.training)
attn_output = torch.matmul(attn_output_weights, v)
attn_output = torch.reshape(attn_output.permute(2, 0, 1, 3), [
q_shape[0], q_shape[1], self.embed_dim])
attn_output = self.out_proj(attn_output)
return attn_output
def _in_proj_q(self, query):
query = query.permute(1, 2, 0)
query = torch.unsqueeze(query, dim=2)
res = self.conv1(query)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def _in_proj_k(self, key):
key = key.permute(1, 2, 0)
key = torch.unsqueeze(key, dim=2)
res = self.conv2(key)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def _in_proj_v(self, value):
value = value.permute(1, 2, 0)
value = torch.unsqueeze(value, dim=2)
res = self.conv3(value)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
class TransformerEncoderLayerNew(nn.Module):
"""TransformerEncoderLayer is made up of self-attn and feedforward network.
This standard encoder layer is based on the paper "Attention Is All You Need".
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
in a different way during application.
Args:
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multiheadattention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
"""
def __init__(self, d_model, nhead, dim_feedforward=2048,
attention_dropout_rate=0.0, residual_dropout_rate=0.1):
super(TransformerEncoderLayerNew, self).__init__()
self.self_attn = MultiheadAttention(d_model, nhead, dropout=
attention_dropout_rate)
self.conv1 = nn.Conv2d(in_channels=d_model, out_channels=
dim_feedforward, kernel_size=(1, 1))
self.conv2 = nn.Conv2d(in_channels=dim_feedforward, out_channels=
d_model, kernel_size=(1, 1))
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.dropout1 = Dropout(residual_dropout_rate)
self.dropout2 = Dropout(residual_dropout_rate)
def forward(self, input_0):
primals_8 = self.self_attn.out_proj.weight
primals_3 = self.self_attn.out_proj.bias
primals_2 = self.self_attn.conv1.weight
primals_5 = self.self_attn.conv1.bias
primals_4 = self.self_attn.conv2.weight
primals_7 = self.self_attn.conv2.bias
primals_6 = self.self_attn.conv3.weight
primals_9 = self.self_attn.conv3.bias
primals_12 = self.conv1.weight
primals_13 = self.conv1.bias
primals_14 = self.conv2.weight
primals_10 = self.conv2.bias
primals_11 = self.norm1.weight
primals_15 = self.norm1.bias
primals_16 = self.norm2.weight
primals_17 = self.norm2.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])
return output[0]
|
verages/PaddleOCR2Pytorch
|
TransformerEncoderLayer
| false
| 4,690
|
[
"Apache-2.0"
] | 0
|
201f0d5d6007f49620c49af7d222c3b220eb3e70
|
https://github.com/verages/PaddleOCR2Pytorch/tree/201f0d5d6007f49620c49af7d222c3b220eb3e70
|
GaussianKernel
|
import torch
import torch.nn as nn
class GaussianKernel(nn.Module):
"""
Gaussian kernel module.
:param mu: Float, mean of the kernel.
:param sigma: Float, sigma of the kernel.
Examples:
>>> import torch
>>> kernel = GaussianKernel()
>>> x = torch.randn(4, 5, 10)
>>> x.shape
torch.Size([4, 5, 10])
>>> kernel(x).shape
torch.Size([4, 5, 10])
"""
def __init__(self, mu: 'float'=1.0, sigma: 'float'=1.0):
"""Gaussian kernel constructor."""
super().__init__()
self.mu = mu
self.sigma = sigma
def forward(self, x):
"""Forward."""
return torch.exp(-0.5 * (x - self.mu) ** 2 / self.sigma ** 2)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.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_div_exp_mul_pow_sub_0(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 1.0
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = -0.5
tmp5 = tmp3 * tmp4
tmp6 = tmp5 * tmp1
tmp7 = tl_math.exp(tmp6)
tl.store(out_ptr0 + x0, tmp7, 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_div_exp_mul_pow_sub_0[grid(256)](arg0_1, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class GaussianKernelNew(nn.Module):
"""
Gaussian kernel module.
:param mu: Float, mean of the kernel.
:param sigma: Float, sigma of the kernel.
Examples:
>>> import torch
>>> kernel = GaussianKernel()
>>> x = torch.randn(4, 5, 10)
>>> x.shape
torch.Size([4, 5, 10])
>>> kernel(x).shape
torch.Size([4, 5, 10])
"""
def __init__(self, mu: 'float'=1.0, sigma: 'float'=1.0):
"""Gaussian kernel constructor."""
super().__init__()
self.mu = mu
self.sigma = sigma
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
zfjsail/MatchZoo-py
|
GaussianKernel
| false
| 4,691
|
[
"Apache-2.0"
] | 0
|
c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2
|
https://github.com/zfjsail/MatchZoo-py/tree/c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2
|
Pointwise
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Pointwise(nn.Module):
def __init__(self, Cin=4, K=1, Cout=10):
super(Pointwise, self).__init__()
self.conv1 = nn.Conv2d(Cin, Cout, kernel_size=K, bias=False,
padding=0, stride=1)
def forward(self, x):
return F.relu(self.conv1(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
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_threshold_backward_0(in_out_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 640
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(in_out_ptr0 + x0, tmp2, xmask)
tl.store(out_ptr0 + x0, tmp4, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (10, 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, 10, 4, 4), (160, 16, 4, 1))
buf1 = buf0
del buf0
buf2 = empty_strided_cuda((4, 10, 4, 4), (160, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(640)](buf1, buf2,
640, XBLOCK=256, num_warps=4, num_stages=1)
return buf1, primals_1, primals_2, buf2
class PointwiseNew(nn.Module):
def __init__(self, Cin=4, K=1, Cout=10):
super(PointwiseNew, self).__init__()
self.conv1 = nn.Conv2d(Cin, Cout, kernel_size=K, bias=False,
padding=0, stride=1)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
sfu-arch/TensorBricks
|
Pointwise
| false
| 4,692
|
[
"MIT"
] | 0
|
c46c60d0939b7deb65f103bf34961d47419ce571
|
https://github.com/sfu-arch/TensorBricks/tree/c46c60d0939b7deb65f103bf34961d47419ce571
|
GCN
|
from torch.nn import Module
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.randn(in_features, out_features))
if bias:
self.bias = Parameter(torch.zeros(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, adj):
support = torch.mm(input.float(), self.weight.float())
output = torch.spmm(adj, support)
if self.bias is not None:
return output + self.bias
else:
return output
class GCN(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(GCN, self).__init__()
self.gc1 = GraphConvolution(nfeat, nhid)
self.gc2 = GraphConvolution(nhid, nclass)
self.dropout = dropout
def forward(self, x, adj):
x = F.relu(self.gc1(x, adj))
x = F.dropout(x, self.dropout, training=self.training)
x = self.gc2(x, adj)
return F.log_softmax(x, dim=1)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'nfeat': 4, 'nhid': 4, 'nclass': 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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
import math
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
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__log_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
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused__log_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')
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 = 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 + x2, tmp13, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4,), (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, 1), torch.float32)
extern_kernels.mm(primals_1, primals_2, out=buf0)
del primals_2
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf0, out=buf1)
buf2 = buf1
del buf1
get_raw_stream(0)
triton_poi_fused_add_relu_0[grid(16)](buf2, primals_4, 16, XBLOCK=
16, num_warps=1, num_stages=1)
del primals_4
buf3 = buf0
del buf0
extern_kernels.mm(buf2, primals_5, out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_6, primals_3, buf3, alpha=1, beta=1,
out=buf4)
del primals_6
buf5 = buf3
del buf3
triton_poi_fused__log_softmax_1[grid(16)](buf4, buf5, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf6 = buf4
del buf4
triton_poi_fused__log_softmax_2[grid(16)](buf5, buf6, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf5
return buf6, buf2, buf6, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0
), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0
), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0)
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.randn(in_features, out_features))
if bias:
self.bias = Parameter(torch.zeros(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, adj):
support = torch.mm(input.float(), self.weight.float())
output = torch.spmm(adj, support)
if self.bias is not None:
return output + self.bias
else:
return output
class GCNNew(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(GCNNew, self).__init__()
self.gc1 = GraphConvolution(nfeat, nhid)
self.gc2 = GraphConvolution(nhid, nclass)
self.dropout = dropout
def forward(self, input_0, input_1):
primals_1 = self.gc1.weight
primals_4 = self.gc1.bias
primals_2 = self.gc2.weight
primals_6 = self.gc2.bias
primals_3 = input_0
primals_5 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
yutaoming/Rare-Category-Detection
|
GCN
| false
| 4,693
|
[
"MIT"
] | 0
|
76cf023dff44eef3ecc17f0ebf2b11a08cd63a73
|
https://github.com/yutaoming/Rare-Category-Detection/tree/76cf023dff44eef3ecc17f0ebf2b11a08cd63a73
|
Sage
|
from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.module import Module
class SageConv(Module):
"""
Simple Graphsage layer
"""
def __init__(self, in_features, out_features, bias=False):
super(SageConv, self).__init__()
self.proj = nn.Linear(in_features * 2, out_features, bias=bias)
self.reset_parameters()
def reset_parameters(self):
nn.init.normal_(self.proj.weight)
if self.proj.bias is not None:
nn.init.constant_(self.proj.bias, 0.0)
def forward(self, features, adj):
"""
Args:
adj: can be sparse or dense matrix.
"""
if not isinstance(adj, torch.sparse.FloatTensor):
if len(adj.shape) == 3:
neigh_feature = torch.bmm(adj, features) / (adj.sum(dim=1).
reshape((adj.shape[0], adj.shape[1], -1)) + 1)
else:
neigh_feature = torch.mm(adj, features) / (adj.sum(dim=1).
reshape(adj.shape[0], -1) + 1)
else:
neigh_feature = torch.spmm(adj, features) / (adj.to_dense().sum
(dim=1).reshape(adj.shape[0], -1) + 1)
data = torch.cat([features, neigh_feature], dim=-1)
combined = self.proj(data)
return combined
class Sage(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(Sage, self).__init__()
self.sage1 = SageConv(nfeat, nhid)
self.mlp = nn.Linear(nhid, nclass)
self.dropout = dropout
self.reset_parameters()
def reset_parameters(self):
nn.init.normal_(self.mlp.weight, std=0.05)
def forward(self, x, adj):
x = F.relu(self.sage1(x, adj))
x = F.dropout(x, self.dropout, training=self.training)
x = self.mlp(x)
return x
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'nfeat': 4, 'nhid': 4, 'nclass': 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
from torch.nn import Module
import torch.nn as nn
from torch.nn.modules.module import Module
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, in_ptr2, 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.load(in_ptr2 + 4 * x1, tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp11 = tl.load(in_ptr2 + (1 + 4 * x1), tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp12 = tmp10 + tmp11
tmp13 = tl.load(in_ptr2 + (2 + 4 * x1), tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp14 = tmp12 + tmp13
tmp15 = tl.load(in_ptr2 + (3 + 4 * x1), tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp16 = tmp14 + tmp15
tmp17 = 1.0
tmp18 = tmp16 + tmp17
tmp19 = tmp9 / tmp18
tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
tmp21 = tl.where(tmp6, tmp19, tmp20)
tmp22 = tl.where(tmp4, tmp5, tmp21)
tl.store(out_ptr0 + x2, tmp22, xmask)
@triton.jit
def triton_poi_fused_relu_1(in_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_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)
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, 8), (8, 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, 1), torch.float32)
extern_kernels.mm(primals_1, primals_2, out=buf0)
buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(32)](primals_2, buf0, primals_1, buf1,
32, XBLOCK=32, num_warps=1, num_stages=1)
del primals_1
del primals_2
buf2 = buf0
del buf0
extern_kernels.mm(buf1, reinterpret_tensor(primals_3, (8, 4), (1, 8
), 0), out=buf2)
del primals_3
buf3 = buf2
del buf2
triton_poi_fused_relu_1[grid(16)](buf3, 16, XBLOCK=16, num_warps=1,
num_stages=1)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, buf3, reinterpret_tensor(primals_4,
(4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4)
del primals_5
return buf4, buf1, buf3, primals_4
class SageConv(Module):
"""
Simple Graphsage layer
"""
def __init__(self, in_features, out_features, bias=False):
super(SageConv, self).__init__()
self.proj = nn.Linear(in_features * 2, out_features, bias=bias)
self.reset_parameters()
def reset_parameters(self):
nn.init.normal_(self.proj.weight)
if self.proj.bias is not None:
nn.init.constant_(self.proj.bias, 0.0)
def forward(self, features, adj):
"""
Args:
adj: can be sparse or dense matrix.
"""
if not isinstance(adj, torch.sparse.FloatTensor):
if len(adj.shape) == 3:
neigh_feature = torch.bmm(adj, features) / (adj.sum(dim=1).
reshape((adj.shape[0], adj.shape[1], -1)) + 1)
else:
neigh_feature = torch.mm(adj, features) / (adj.sum(dim=1).
reshape(adj.shape[0], -1) + 1)
else:
neigh_feature = torch.spmm(adj, features) / (adj.to_dense().sum
(dim=1).reshape(adj.shape[0], -1) + 1)
data = torch.cat([features, neigh_feature], dim=-1)
combined = self.proj(data)
return combined
class SageNew(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(SageNew, self).__init__()
self.sage1 = SageConv(nfeat, nhid)
self.mlp = nn.Linear(nhid, nclass)
self.dropout = dropout
self.reset_parameters()
def reset_parameters(self):
nn.init.normal_(self.mlp.weight, std=0.05)
def forward(self, input_0, input_1):
primals_3 = self.sage1.proj.weight
primals_1 = self.mlp.weight
primals_5 = self.mlp.bias
primals_2 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
yutaoming/Rare-Category-Detection
|
Sage
| false
| 4,694
|
[
"MIT"
] | 0
|
76cf023dff44eef3ecc17f0ebf2b11a08cd63a73
|
https://github.com/yutaoming/Rare-Category-Detection/tree/76cf023dff44eef3ecc17f0ebf2b11a08cd63a73
|
RankCrossEntropyLoss
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class RankCrossEntropyLoss(nn.Module):
"""Creates a criterion that measures rank cross entropy loss."""
__constants__ = ['num_neg']
def __init__(self, num_neg: 'int'=1):
"""
:class:`RankCrossEntropyLoss` constructor.
:param num_neg: Number of negative instances in hinge loss.
"""
super().__init__()
self.num_neg = num_neg
def forward(self, y_pred: 'torch.Tensor', y_true: 'torch.Tensor'):
"""
Calculate rank cross entropy loss.
:param y_pred: Predicted result.
:param y_true: Label.
:return: Rank cross loss.
"""
logits = y_pred[::self.num_neg + 1, :]
labels = y_true[::self.num_neg + 1, :]
for neg_idx in range(self.num_neg):
neg_logits = y_pred[neg_idx + 1::self.num_neg + 1, :]
neg_labels = y_true[neg_idx + 1::self.num_neg + 1, :]
logits = torch.cat((logits, neg_logits), dim=-1)
labels = torch.cat((labels, neg_labels), dim=-1)
return -torch.mean(torch.sum(labels * torch.log(F.softmax(logits,
dim=-1) + torch.finfo(float).eps), dim=-1))
@property
def num_neg(self):
"""`num_neg` getter."""
return self._num_neg
@num_neg.setter
def num_neg(self, value):
"""`num_neg` setter."""
self._num_neg = value
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused__softmax_add_cat_log_mul_sum_0(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 32
RBLOCK: tl.constexpr = 8
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
x0 = xindex % 16
x1 = xindex // 16
x3 = xindex
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 + (4 * x0 + 128 * x1 + r2), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1, 1], 8, tl.int64)
tmp9 = tl.load(in_ptr0 + (64 + 4 * x0 + 128 * x1 + (-4 + r2)), tmp6 &
xmask, eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
tmp13 = tl.where(xmask, tmp11, float('-inf'))
tmp14 = triton_helpers.max2(tmp13, 1)[:, None]
tmp15 = tmp10 - tmp14
tmp16 = tl_math.exp(tmp15)
tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
tmp19 = tl.where(xmask, tmp17, 0)
tmp20 = tl.sum(tmp19, 1)[:, None]
tmp21 = tl.load(in_ptr1 + (4 * x0 + 128 * x1 + r2), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp22 = tl.load(in_ptr1 + (64 + 4 * x0 + 128 * x1 + (-4 + r2)), tmp6 &
xmask, eviction_policy='evict_last', other=0.0)
tmp23 = tl.where(tmp4, tmp21, tmp22)
tmp24 = tmp16 / tmp20
tmp25 = 2.220446049250313e-16
tmp26 = tmp24 + tmp25
tmp27 = tl_math.log(tmp26)
tmp28 = tmp23 * tmp27
tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
tmp31 = tl.where(xmask, tmp29, 0)
tmp32 = tl.sum(tmp31, 1)[:, None]
tl.store(in_out_ptr0 + x3, tmp32, xmask)
@triton.jit
def triton_per_fused_mean_neg_1(in_out_ptr0, in_ptr0, xnumel, rnumel,
XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 32
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.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.sum(tmp1, 1)[:, None]
tmp4 = 32.0
tmp5 = tmp3 / tmp4
tmp6 = -tmp5
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp6, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf1 = empty_strided_cuda((2, 4, 4, 1), (16, 4, 1, 32), torch.float32)
buf2 = reinterpret_tensor(buf1, (2, 4, 4), (16, 4, 1), 0)
del buf1
get_raw_stream(0)
triton_per_fused__softmax_add_cat_log_mul_sum_0[grid(32)](buf2,
arg0_1, arg1_1, 32, 8, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
buf3 = empty_strided_cuda((), (), torch.float32)
buf4 = buf3
del buf3
triton_per_fused_mean_neg_1[grid(1)](buf4, buf2, 1, 32, XBLOCK=1,
num_warps=2, num_stages=1)
del buf2
return buf4,
class RankCrossEntropyLossNew(nn.Module):
"""Creates a criterion that measures rank cross entropy loss."""
__constants__ = ['num_neg']
def __init__(self, num_neg: 'int'=1):
"""
:class:`RankCrossEntropyLoss` constructor.
:param num_neg: Number of negative instances in hinge loss.
"""
super().__init__()
self.num_neg = num_neg
@property
def num_neg(self):
"""`num_neg` getter."""
return self._num_neg
@num_neg.setter
def num_neg(self, value):
"""`num_neg` setter."""
self._num_neg = value
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
zfjsail/MatchZoo-py
|
RankCrossEntropyLoss
| false
| 4,695
|
[
"Apache-2.0"
] | 0
|
c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2
|
https://github.com/zfjsail/MatchZoo-py/tree/c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2
|
TransformerDecoderLayer
|
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn.init import xavier_uniform_
from torch.nn import Dropout
from torch.nn import LayerNorm
class MultiheadAttention(nn.Module):
"""Allows the model to jointly attend to information
from different representation subspaces.
See reference: Attention Is All You Need
.. math::
ext{MultiHead}(Q, K, V) = ext{Concat}(head_1,\\dots,head_h)W^O
ext{where} head_i = ext{Attention}(QW_i^Q, KW_i^K, VW_i^V)
Args:
embed_dim: total dimension of the model
num_heads: parallel attention layers, or heads
"""
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
add_bias_kv=False, add_zero_attn=False):
super(MultiheadAttention, self).__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.out_proj = Linear(embed_dim, embed_dim, bias=bias)
self._reset_parameters()
self.conv1 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
self.conv2 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
self.conv3 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
def _reset_parameters(self):
xavier_uniform_(self.out_proj.weight)
def forward(self, query, key, value, key_padding_mask=None,
incremental_state=None, attn_mask=None):
"""
Inputs of forward function
query: [target length, batch size, embed dim]
key: [sequence length, batch size, embed dim]
value: [sequence length, batch size, embed dim]
key_padding_mask: if True, mask padding based on batch size
incremental_state: if provided, previous time steps are cashed
need_weights: output attn_output_weights
static_kv: key and value are static
Outputs of forward function
attn_output: [target length, batch size, embed dim]
attn_output_weights: [batch size, target length, sequence length]
"""
q_shape = query.shape
src_shape = key.shape
q = self._in_proj_q(query)
k = self._in_proj_k(key)
v = self._in_proj_v(value)
q *= self.scaling
q = torch.reshape(q, (q_shape[0], q_shape[1], self.num_heads, self.
head_dim))
q = q.permute(1, 2, 0, 3)
k = torch.reshape(k, (src_shape[0], q_shape[1], self.num_heads,
self.head_dim))
k = k.permute(1, 2, 0, 3)
v = torch.reshape(v, (src_shape[0], q_shape[1], self.num_heads,
self.head_dim))
v = v.permute(1, 2, 0, 3)
if key_padding_mask is not None:
assert key_padding_mask.shape[0] == q_shape[1]
assert key_padding_mask.shape[1] == src_shape[0]
attn_output_weights = torch.matmul(q, k.permute(0, 1, 3, 2))
if attn_mask is not None:
attn_mask = torch.unsqueeze(torch.unsqueeze(attn_mask, 0), 0)
attn_output_weights += attn_mask
if key_padding_mask is not None:
attn_output_weights = torch.reshape(attn_output_weights, [
q_shape[1], self.num_heads, q_shape[0], src_shape[0]])
key = torch.unsqueeze(torch.unsqueeze(key_padding_mask, 1), 2)
key = key.type(torch.float32)
y = torch.full(size=key.shape, fill_value=float('-Inf'), dtype=
torch.float32)
y = torch.where(key == 0.0, key, y)
attn_output_weights += y
attn_output_weights = F.softmax(attn_output_weights.type(torch.
float32), dim=-1, dtype=torch.float32 if attn_output_weights.
dtype == torch.float16 else attn_output_weights.dtype)
attn_output_weights = F.dropout(attn_output_weights, p=self.dropout,
training=self.training)
attn_output = torch.matmul(attn_output_weights, v)
attn_output = torch.reshape(attn_output.permute(2, 0, 1, 3), [
q_shape[0], q_shape[1], self.embed_dim])
attn_output = self.out_proj(attn_output)
return attn_output
def _in_proj_q(self, query):
query = query.permute(1, 2, 0)
query = torch.unsqueeze(query, dim=2)
res = self.conv1(query)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def _in_proj_k(self, key):
key = key.permute(1, 2, 0)
key = torch.unsqueeze(key, dim=2)
res = self.conv2(key)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def _in_proj_v(self, value):
value = value.permute(1, 2, 0)
value = torch.unsqueeze(value, dim=2)
res = self.conv3(value)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
class TransformerDecoderLayer(nn.Module):
"""TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.
This standard decoder layer is based on the paper "Attention Is All You Need".
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
in a different way during application.
Args:
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multiheadattention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
"""
def __init__(self, d_model, nhead, dim_feedforward=2048,
attention_dropout_rate=0.0, residual_dropout_rate=0.1):
super(TransformerDecoderLayer, self).__init__()
self.self_attn = MultiheadAttention(d_model, nhead, dropout=
attention_dropout_rate)
self.multihead_attn = MultiheadAttention(d_model, nhead, dropout=
attention_dropout_rate)
self.conv1 = nn.Conv2d(in_channels=d_model, out_channels=
dim_feedforward, kernel_size=(1, 1))
self.conv2 = nn.Conv2d(in_channels=dim_feedforward, out_channels=
d_model, kernel_size=(1, 1))
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.norm3 = LayerNorm(d_model)
self.dropout1 = Dropout(residual_dropout_rate)
self.dropout2 = Dropout(residual_dropout_rate)
self.dropout3 = Dropout(residual_dropout_rate)
def forward(self, tgt, memory, tgt_mask=None, memory_mask=None,
tgt_key_padding_mask=None, memory_key_padding_mask=None):
"""Pass the inputs (and mask) through the decoder layer.
Args:
tgt: the sequence to the decoder layer (required).
memory: the sequnce from the last layer of the encoder (required).
tgt_mask: the mask for the tgt sequence (optional).
memory_mask: the mask for the memory sequence (optional).
tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
memory_key_padding_mask: the mask for the memory keys per batch (optional).
"""
tgt2 = self.self_attn(tgt, tgt, tgt, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask)
tgt = tgt + self.dropout1(tgt2)
tgt = self.norm1(tgt)
tgt2 = self.multihead_attn(tgt, memory, memory, attn_mask=
memory_mask, key_padding_mask=memory_key_padding_mask)
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
tgt = tgt.permute(1, 2, 0)
tgt = torch.unsqueeze(tgt, 2)
tgt2 = self.conv2(F.relu(self.conv1(tgt)))
tgt2 = torch.squeeze(tgt2, 2)
tgt2 = tgt2.permute(2, 0, 1)
tgt = torch.squeeze(tgt, 2)
tgt = tgt.permute(2, 0, 1)
tgt = tgt + self.dropout3(tgt2)
tgt = self.norm3(tgt)
return tgt
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'d_model': 4, 'nhead': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn.init import xavier_uniform_
from torch.nn import Dropout
from torch.nn import LayerNorm
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_ptr0, out_ptr0, out_ptr1, out_ptr2,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 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)
tl.store(out_ptr1 + x3, tmp0, xmask)
tl.store(out_ptr2 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, in_ptr1, out_ptr0, ynumel,
xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (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_mul_2(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 % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK:
tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x5 = xindex
y4 = yindex
x3 = xindex // 4
y0 = yindex % 4
y1 = yindex // 4
tmp0 = tl.load(in_ptr0 + (x5 + 16 * y4), xmask & ymask)
tmp1 = tl.load(in_ptr0 + (4 * x3 + 16 * y4), xmask & ymask,
eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x3 + 16 * y4), xmask & ymask,
eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x3 + 16 * y4), xmask & ymask,
eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x3 + 16 * y4), xmask & ymask,
eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + (y0 + 4 * x5 + 64 * y1), tmp8, xmask & ymask)
@triton.jit
def triton_poi_fused_bmm_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 % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (4 * x1 + 64 * (x0 // 4) + x0 % 4), xmask)
tl.store(out_ptr0 + x2, tmp0, xmask)
@triton.jit
def triton_poi_fused_clone_6(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_add_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2,
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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + 0)
tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr2 + 1)
tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp14 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp15 = tl.load(in_ptr2 + 2)
tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp21 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp22 = tl.load(in_ptr2 + 3)
tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
tmp4 = tmp1 + tmp3
tmp5 = tmp0 + tmp4
tmp10 = tmp7 + tmp9
tmp11 = tmp6 + tmp10
tmp12 = tmp5 + tmp11
tmp17 = tmp14 + tmp16
tmp18 = tmp13 + tmp17
tmp19 = tmp12 + tmp18
tmp24 = tmp21 + tmp23
tmp25 = tmp20 + tmp24
tmp26 = tmp19 + tmp25
tmp27 = 4.0
tmp28 = tmp26 / tmp27
tmp29 = tmp5 - tmp28
tmp30 = tmp29 * tmp29
tmp31 = tmp11 - tmp28
tmp32 = tmp31 * tmp31
tmp33 = tmp30 + tmp32
tmp34 = tmp18 - tmp28
tmp35 = tmp34 * tmp34
tmp36 = tmp33 + tmp35
tmp37 = tmp25 - tmp28
tmp38 = tmp37 * tmp37
tmp39 = tmp36 + tmp38
tmp40 = tmp39 / tmp27
tl.store(out_ptr0 + x0, tmp28, xmask)
tl.store(out_ptr1 + x0, tmp40, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_8(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tl.load(in_ptr2 + x0, 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')
tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr6 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tmp6 = tmp4 - tmp5
tmp8 = 1e-05
tmp9 = tmp7 + tmp8
tmp10 = libdevice.rsqrt(tmp9)
tmp11 = tmp6 * tmp10
tmp13 = tmp11 * tmp12
tmp15 = tmp13 + tmp14
tl.store(out_ptr0 + x2, tmp15, xmask)
@triton.jit
def triton_poi_fused_unsqueeze_9(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x2, xmask)
tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_11(in_ptr0, 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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_12(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, 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
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_13(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 2048
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_14(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
tl.store(in_out_ptr0 + x2, tmp2, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_15(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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), 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 + x0, tmp16, xmask)
tl.store(out_ptr1 + x0, tmp28, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_16(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x4 = xindex // 4
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x3, xmask)
tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + x0, 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 + (x0 + 4 * x2 + 16 * x1), tmp13, xmask)
@triton.jit
def triton_poi_fused_transpose_17(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
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x1), xmask & ymask, eviction_policy
='evict_last')
tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, 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
) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (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, 1, 1), (4, 1, 1, 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,))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (4,), (1,))
assert_size_stride(primals_10, (4,), (1,))
assert_size_stride(primals_11, (4,), (1,))
assert_size_stride(primals_12, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_13, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_14, (4,), (1,))
assert_size_stride(primals_15, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_16, (4,), (1,))
assert_size_stride(primals_17, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_18, (4,), (1,))
assert_size_stride(primals_19, (4, 4), (4, 1))
assert_size_stride(primals_20, (4,), (1,))
assert_size_stride(primals_21, (4,), (1,))
assert_size_stride(primals_22, (4,), (1,))
assert_size_stride(primals_23, (2048, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_24, (2048,), (1,))
assert_size_stride(primals_25, (4, 2048, 1, 1), (2048, 1, 1, 1))
assert_size_stride(primals_26, (4,), (1,))
assert_size_stride(primals_27, (4,), (1,))
assert_size_stride(primals_28, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
buf2 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
buf4 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(64)](primals_1, buf0, buf2,
buf4, 64, XBLOCK=64, num_warps=1, 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, 1, 4), (16, 1, 16, 4))
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 1, 4), (16, 1, 16, 4))
buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 4, 1, 4), (16, 1, 16, 4))
buf6 = reinterpret_tensor(buf4, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf4
triton_poi_fused_convolution_1[grid(16, 4)](buf3, primals_5, buf6,
16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_5
buf7 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0)
del buf3
triton_poi_fused_mul_2[grid(64)](buf1, primals_3, buf7, 64, XBLOCK=
64, num_warps=1, num_stages=1)
del primals_3
buf8 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 1), (1, 16, 0),
0), reinterpret_tensor(buf6, (16, 1, 4), (4, 0, 1), 0), out=buf8)
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_3[grid(256)](buf8, buf9, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 1, 16, 4), 0)
del buf8
triton_poi_fused__softmax_4[grid(16, 16)](buf9, buf10, 16, 16,
XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
buf11 = reinterpret_tensor(buf1, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf1
triton_poi_fused_convolution_1[grid(16, 4)](buf5, primals_7, buf11,
16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_7
buf12 = reinterpret_tensor(buf9, (16, 4, 4), (1, 64, 16), 0)
del buf9
triton_poi_fused_bmm_5[grid(256)](buf10, buf12, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf13 = reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 1), 0)
del buf5
extern_kernels.bmm(buf12, reinterpret_tensor(buf11, (16, 4, 1), (4,
1, 0), 0), out=buf13)
buf14 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0)
del buf2
triton_poi_fused_clone_6[grid(4, 16)](buf13, buf14, 4, 16, XBLOCK=
16, YBLOCK=4, num_warps=1, num_stages=1)
buf15 = reinterpret_tensor(buf13, (16, 4), (4, 1), 0)
del buf13
extern_kernels.mm(reinterpret_tensor(buf14, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf15)
buf16 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf17 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
triton_poi_fused_add_native_layer_norm_7[grid(16)](primals_1, buf15,
primals_9, buf16, buf17, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf18 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
del buf0
triton_poi_fused_add_native_layer_norm_8[grid(64)](primals_1, buf15,
primals_9, buf16, buf17, primals_10, primals_11, buf18, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_11
buf19 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
triton_poi_fused_unsqueeze_9[grid(64)](buf18, buf19, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf20 = extern_kernels.convolution(buf19, primals_13, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf20, (4, 4, 1, 4), (16, 1, 16, 4))
buf21 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
triton_poi_fused_unsqueeze_9[grid(64)](primals_12, buf21, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_12
buf22 = extern_kernels.convolution(buf21, primals_15, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf22, (4, 4, 1, 4), (16, 1, 16, 4))
buf23 = extern_kernels.convolution(buf21, primals_17, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf23, (4, 4, 1, 4), (16, 1, 16, 4))
buf24 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
triton_poi_fused_convolution_1[grid(16, 4)](buf22, primals_16,
buf24, 16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_16
buf25 = reinterpret_tensor(buf22, (4, 4, 4), (16, 4, 1), 0)
del buf22
triton_poi_fused_mul_2[grid(64)](buf20, primals_14, buf25, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_14
buf26 = reinterpret_tensor(buf12, (16, 4, 4), (16, 4, 1), 0)
del buf12
extern_kernels.bmm(reinterpret_tensor(buf25, (16, 4, 1), (1, 16, 0),
0), reinterpret_tensor(buf24, (16, 1, 4), (4, 0, 1), 0), out=buf26)
buf27 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_3[grid(256)](buf26, buf27, 256, XBLOCK=
128, num_warps=4, num_stages=1)
buf28 = reinterpret_tensor(buf26, (4, 4, 4, 4), (64, 1, 16, 4), 0)
del buf26
triton_poi_fused__softmax_4[grid(16, 16)](buf27, buf28, 16, 16,
XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
buf29 = reinterpret_tensor(buf20, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf20
triton_poi_fused_convolution_1[grid(16, 4)](buf23, primals_18,
buf29, 16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_18
buf30 = reinterpret_tensor(buf27, (16, 4, 4), (1, 64, 16), 0)
del buf27
triton_poi_fused_bmm_5[grid(256)](buf28, buf30, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf31 = reinterpret_tensor(buf23, (16, 4, 1), (4, 1, 1), 0)
del buf23
extern_kernels.bmm(buf30, reinterpret_tensor(buf29, (16, 4, 1), (4,
1, 0), 0), out=buf31)
del buf30
buf32 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_clone_6[grid(4, 16)](buf31, buf32, 4, 16, XBLOCK=
16, YBLOCK=4, num_warps=1, num_stages=1)
buf33 = reinterpret_tensor(buf31, (16, 4), (4, 1), 0)
del buf31
extern_kernels.mm(reinterpret_tensor(buf32, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_19, (4, 4), (1, 4), 0), out=buf33)
buf34 = buf18
del buf18
triton_poi_fused_add_10[grid(64)](buf34, buf33, primals_20, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_20
buf35 = buf17
del buf17
buf36 = buf16
del buf16
triton_poi_fused_native_layer_norm_11[grid(16)](buf34, buf35, buf36,
16, XBLOCK=16, num_warps=1, num_stages=1)
buf37 = reinterpret_tensor(buf33, (4, 4, 4), (16, 4, 1), 0)
del buf33
triton_poi_fused_native_layer_norm_12[grid(64)](buf34, buf35, buf36,
primals_21, primals_22, buf37, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del primals_22
buf38 = empty_strided_cuda((4, 4, 1, 4), (16, 1, 16, 4), torch.float32)
triton_poi_fused_unsqueeze_9[grid(64)](buf37, buf38, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf39 = extern_kernels.convolution(buf38, primals_23, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf39, (4, 2048, 1, 4), (8192, 1, 8192, 2048))
buf40 = buf39
del buf39
triton_poi_fused_convolution_relu_13[grid(32768)](buf40, primals_24,
32768, XBLOCK=256, num_warps=4, num_stages=1)
del primals_24
buf41 = extern_kernels.convolution(buf40, primals_25, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf41, (4, 4, 1, 4), (16, 1, 16, 4))
buf42 = buf41
del buf41
triton_poi_fused_convolution_14[grid(64)](buf42, primals_26, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_26
buf43 = reinterpret_tensor(buf36, (4, 4, 1), (1, 4, 16), 0)
del buf36
buf44 = reinterpret_tensor(buf35, (4, 4, 1), (1, 4, 16), 0)
del buf35
triton_poi_fused_add_native_layer_norm_15[grid(16)](buf38, buf42,
buf43, buf44, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf45 = buf37
del buf37
triton_poi_fused_add_native_layer_norm_16[grid(64)](buf38, buf42,
buf43, buf44, primals_27, primals_28, buf45, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del buf43
del buf44
del primals_28
buf46 = empty_strided_cuda((16, 1, 4), (4, 4, 1), torch.float32)
triton_poi_fused_transpose_17[grid(16, 4)](buf25, buf46, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
buf47 = reinterpret_tensor(buf25, (16, 1, 4), (4, 4, 1), 0)
del buf25
triton_poi_fused_transpose_17[grid(16, 4)](buf7, buf47, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
del buf7
return (buf45, primals_1, primals_2, primals_4, primals_6, primals_9,
primals_10, primals_13, primals_15, primals_17, primals_21,
primals_23, primals_25, primals_27, buf10, reinterpret_tensor(buf14,
(16, 4), (4, 1), 0), buf15, buf19, buf21, buf28, reinterpret_tensor
(buf32, (16, 4), (4, 1), 0), buf34, buf38, buf40, buf42, primals_19,
reinterpret_tensor(buf29, (16, 1, 4), (4, 4, 1), 0), buf46,
reinterpret_tensor(buf24, (16, 4, 1), (4, 1, 4), 0), primals_8,
reinterpret_tensor(buf11, (16, 1, 4), (4, 4, 1), 0), buf47,
reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 4), 0))
class MultiheadAttention(nn.Module):
"""Allows the model to jointly attend to information
from different representation subspaces.
See reference: Attention Is All You Need
.. math::
ext{MultiHead}(Q, K, V) = ext{Concat}(head_1,\\dots,head_h)W^O
ext{where} head_i = ext{Attention}(QW_i^Q, KW_i^K, VW_i^V)
Args:
embed_dim: total dimension of the model
num_heads: parallel attention layers, or heads
"""
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
add_bias_kv=False, add_zero_attn=False):
super(MultiheadAttention, self).__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.out_proj = Linear(embed_dim, embed_dim, bias=bias)
self._reset_parameters()
self.conv1 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
self.conv2 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
self.conv3 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
embed_dim, kernel_size=(1, 1))
def _reset_parameters(self):
xavier_uniform_(self.out_proj.weight)
def forward(self, query, key, value, key_padding_mask=None,
incremental_state=None, attn_mask=None):
"""
Inputs of forward function
query: [target length, batch size, embed dim]
key: [sequence length, batch size, embed dim]
value: [sequence length, batch size, embed dim]
key_padding_mask: if True, mask padding based on batch size
incremental_state: if provided, previous time steps are cashed
need_weights: output attn_output_weights
static_kv: key and value are static
Outputs of forward function
attn_output: [target length, batch size, embed dim]
attn_output_weights: [batch size, target length, sequence length]
"""
q_shape = query.shape
src_shape = key.shape
q = self._in_proj_q(query)
k = self._in_proj_k(key)
v = self._in_proj_v(value)
q *= self.scaling
q = torch.reshape(q, (q_shape[0], q_shape[1], self.num_heads, self.
head_dim))
q = q.permute(1, 2, 0, 3)
k = torch.reshape(k, (src_shape[0], q_shape[1], self.num_heads,
self.head_dim))
k = k.permute(1, 2, 0, 3)
v = torch.reshape(v, (src_shape[0], q_shape[1], self.num_heads,
self.head_dim))
v = v.permute(1, 2, 0, 3)
if key_padding_mask is not None:
assert key_padding_mask.shape[0] == q_shape[1]
assert key_padding_mask.shape[1] == src_shape[0]
attn_output_weights = torch.matmul(q, k.permute(0, 1, 3, 2))
if attn_mask is not None:
attn_mask = torch.unsqueeze(torch.unsqueeze(attn_mask, 0), 0)
attn_output_weights += attn_mask
if key_padding_mask is not None:
attn_output_weights = torch.reshape(attn_output_weights, [
q_shape[1], self.num_heads, q_shape[0], src_shape[0]])
key = torch.unsqueeze(torch.unsqueeze(key_padding_mask, 1), 2)
key = key.type(torch.float32)
y = torch.full(size=key.shape, fill_value=float('-Inf'), dtype=
torch.float32)
y = torch.where(key == 0.0, key, y)
attn_output_weights += y
attn_output_weights = F.softmax(attn_output_weights.type(torch.
float32), dim=-1, dtype=torch.float32 if attn_output_weights.
dtype == torch.float16 else attn_output_weights.dtype)
attn_output_weights = F.dropout(attn_output_weights, p=self.dropout,
training=self.training)
attn_output = torch.matmul(attn_output_weights, v)
attn_output = torch.reshape(attn_output.permute(2, 0, 1, 3), [
q_shape[0], q_shape[1], self.embed_dim])
attn_output = self.out_proj(attn_output)
return attn_output
def _in_proj_q(self, query):
query = query.permute(1, 2, 0)
query = torch.unsqueeze(query, dim=2)
res = self.conv1(query)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def _in_proj_k(self, key):
key = key.permute(1, 2, 0)
key = torch.unsqueeze(key, dim=2)
res = self.conv2(key)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
def _in_proj_v(self, value):
value = value.permute(1, 2, 0)
value = torch.unsqueeze(value, dim=2)
res = self.conv3(value)
res = torch.squeeze(res, dim=2)
res = res.permute(2, 0, 1)
return res
class TransformerDecoderLayerNew(nn.Module):
"""TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.
This standard decoder layer is based on the paper "Attention Is All You Need".
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
in a different way during application.
Args:
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multiheadattention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
"""
def __init__(self, d_model, nhead, dim_feedforward=2048,
attention_dropout_rate=0.0, residual_dropout_rate=0.1):
super(TransformerDecoderLayerNew, self).__init__()
self.self_attn = MultiheadAttention(d_model, nhead, dropout=
attention_dropout_rate)
self.multihead_attn = MultiheadAttention(d_model, nhead, dropout=
attention_dropout_rate)
self.conv1 = nn.Conv2d(in_channels=d_model, out_channels=
dim_feedforward, kernel_size=(1, 1))
self.conv2 = nn.Conv2d(in_channels=dim_feedforward, out_channels=
d_model, kernel_size=(1, 1))
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.norm3 = LayerNorm(d_model)
self.dropout1 = Dropout(residual_dropout_rate)
self.dropout2 = Dropout(residual_dropout_rate)
self.dropout3 = Dropout(residual_dropout_rate)
def forward(self, input_0, input_1):
primals_8 = self.self_attn.out_proj.weight
primals_3 = self.self_attn.out_proj.bias
primals_2 = self.self_attn.conv1.weight
primals_5 = self.self_attn.conv1.bias
primals_4 = self.self_attn.conv2.weight
primals_7 = self.self_attn.conv2.bias
primals_6 = self.self_attn.conv3.weight
primals_9 = self.self_attn.conv3.bias
primals_19 = self.multihead_attn.out_proj.weight
primals_10 = self.multihead_attn.out_proj.bias
primals_13 = self.multihead_attn.conv1.weight
primals_11 = self.multihead_attn.conv1.bias
primals_15 = self.multihead_attn.conv2.weight
primals_14 = self.multihead_attn.conv2.bias
primals_17 = self.multihead_attn.conv3.weight
primals_16 = self.multihead_attn.conv3.bias
primals_23 = self.conv1.weight
primals_24 = self.conv1.bias
primals_25 = self.conv2.weight
primals_18 = self.conv2.bias
primals_20 = self.norm1.weight
primals_21 = self.norm1.bias
primals_22 = self.norm2.weight
primals_26 = self.norm2.bias
primals_27 = self.norm3.weight
primals_28 = self.norm3.bias
primals_1 = input_0
primals_12 = 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, primals_23, primals_24,
primals_25, primals_26, primals_27, primals_28])
return output[0]
|
verages/PaddleOCR2Pytorch
|
TransformerDecoderLayer
| false
| 4,696
|
[
"Apache-2.0"
] | 0
|
201f0d5d6007f49620c49af7d222c3b220eb3e70
|
https://github.com/verages/PaddleOCR2Pytorch/tree/201f0d5d6007f49620c49af7d222c3b220eb3e70
|
ReLU
|
import torch
import torch.nn as nn
from abc import abstractmethod
import torch.utils.data
import torch.nn
class EfficientBlockBase(nn.Module):
"""
PyTorchVideo/accelerator provides a set of efficient blocks
that have optimal efficiency for each target hardware device.
Each efficient block has two forms:
- original form: this form is for training. When efficient block is instantiated,
it is in this original form.
- deployable form: this form is for deployment. Once the network is ready for
deploy, it can be converted into deployable form for efficient execution
on target hardware. One block is transformed into deployable form by calling
convert() method. By conversion to deployable form,
various optimization (operator fuse, kernel optimization, etc.) are applied.
EfficientBlockBase is the base class for efficient blocks.
All efficient blocks should inherit this base class
and implement following methods:
- forward(): same as required by nn.Module
- convert(): called to convert block into deployable form
"""
@abstractmethod
def convert(self):
pass
@abstractmethod
def forward(self):
pass
class ReLU(EfficientBlockBase):
"""
ReLU activation function for EfficientBlockBase.
"""
def __init__(self):
super().__init__()
self.act = nn.ReLU(inplace=True)
def forward(self, x):
return self.act(x)
def convert(self, *args, **kwarg):
pass
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from abc import abstractmethod
import torch.utils.data
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_relu_0(in_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(out_ptr1 + x0, tmp2, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
get_raw_stream(0)
triton_poi_fused_relu_0[grid(256)](arg0_1, arg0_1, 256, XBLOCK=256,
num_warps=4, num_stages=1)
return arg0_1,
class EfficientBlockBase(nn.Module):
"""
PyTorchVideo/accelerator provides a set of efficient blocks
that have optimal efficiency for each target hardware device.
Each efficient block has two forms:
- original form: this form is for training. When efficient block is instantiated,
it is in this original form.
- deployable form: this form is for deployment. Once the network is ready for
deploy, it can be converted into deployable form for efficient execution
on target hardware. One block is transformed into deployable form by calling
convert() method. By conversion to deployable form,
various optimization (operator fuse, kernel optimization, etc.) are applied.
EfficientBlockBase is the base class for efficient blocks.
All efficient blocks should inherit this base class
and implement following methods:
- forward(): same as required by nn.Module
- convert(): called to convert block into deployable form
"""
@abstractmethod
def convert(self):
pass
@abstractmethod
def forward(self):
pass
class ReLUNew(EfficientBlockBase):
"""
ReLU activation function for EfficientBlockBase.
"""
def __init__(self):
super().__init__()
self.act = nn.ReLU(inplace=True)
def convert(self, *args, **kwarg):
pass
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
zijian-hu/pytorchvideo
|
ReLU
| false
| 4,697
|
[
"Apache-2.0"
] | 0
|
51589b100437af2285c56ce2ccc7ccecb7f9b18b
|
https://github.com/zijian-hu/pytorchvideo/tree/51589b100437af2285c56ce2ccc7ccecb7f9b18b
|
Depthwise
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Depthwise(nn.Module):
def __init__(self, Cin=10, K=3, depth_multiplier=1):
super(Depthwise, self).__init__()
self.conv1 = nn.Conv2d(Cin, depth_multiplier * Cin, kernel_size=K,
groups=Cin, bias=False, padding=0, stride=1)
def forward(self, x):
return F.relu(self.conv1(x))
def get_inputs():
return [torch.rand([4, 10, 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 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_threshold_backward_0(in_out_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 153760
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
x1 = xindex % 3844
x2 = xindex // 3844
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 + (x1 + 3968 * x2), tmp4, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (10, 1, 3, 3), (9, 9, 3, 1))
assert_size_stride(primals_2, (4, 10, 64, 64), (40960, 4096, 64, 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=10, bias=None)
assert_size_stride(buf0, (4, 10, 62, 62), (38440, 3844, 62, 1))
buf1 = buf0
del buf0
buf2 = empty_strided_cuda((4, 10, 62, 62), (39680, 3968, 62, 1),
torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(153760)](buf1, buf2,
153760, XBLOCK=512, num_warps=8, num_stages=1)
return buf1, primals_1, primals_2, buf2
class DepthwiseNew(nn.Module):
def __init__(self, Cin=10, K=3, depth_multiplier=1):
super(DepthwiseNew, self).__init__()
self.conv1 = nn.Conv2d(Cin, depth_multiplier * Cin, kernel_size=K,
groups=Cin, bias=False, padding=0, stride=1)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
sfu-arch/TensorBricks
|
Depthwise
| false
| 4,698
|
[
"MIT"
] | 0
|
c46c60d0939b7deb65f103bf34961d47419ce571
|
https://github.com/sfu-arch/TensorBricks/tree/c46c60d0939b7deb65f103bf34961d47419ce571
|
LearnMaskedDefault
|
import torch
import torch.nn as nn
import torch.utils.data
import torch.nn
class LearnMaskedDefault(nn.Module):
"""
Learns default values to fill invalid entries within input tensors. The
invalid entries are represented by a mask which is passed into forward alongside
the input tensor. Note the default value is only used if all entries in the batch row are
invalid rather than just a portion of invalid entries within each batch row.
"""
def __init__(self, feature_dim: 'int', init_method: 'str'='gaussian',
freeze: 'bool'=False):
"""
Args:
feature_dim (int): the size of the default value parameter, this must match the
input tensor size.
init_method (str): the initial default value parameter. Options:
'guassian'
'zeros'
freeze (bool): If True, the learned default parameter weights are frozen.
"""
super().__init__()
if init_method == 'zeros':
self._learned_defaults = nn.Parameter(torch.zeros(feature_dim),
requires_grad=not freeze)
elif init_method == 'gaussian':
self._learned_defaults = nn.Parameter(torch.Tensor(feature_dim),
requires_grad=not freeze)
nn.init.normal_(self._learned_defaults)
else:
raise NotImplementedError(
f"{init_method} not available. Options are: 'zeros' or 'gaussian'"
)
def forward(self, x: 'torch.Tensor', mask: 'torch.Tensor') ->torch.Tensor:
"""
Args:
x (torch.Tensor): tensor of shape (batch_size, feature_dim).
mask (torch.Tensor): bool tensor of shape (batch_size, seq_len) If all elements
in the batch dimension are False the learned default parameter is used for
that batch element.
Returns:
Tensor with shape (batch_size, feature_dim)
"""
mask = mask.view(mask.shape[0], -1).any(dim=-1)
for i in range(1, x.dim()):
mask = mask.unsqueeze(i)
x = x * mask.float() + self._learned_defaults * (1 - mask.float())
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'feature_dim': 4}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torch.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__to_copy_add_any_mul_rsub_0(in_ptr0, in_ptr1, in_ptr2,
out_ptr0, out_ptr1, 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
r2 = rindex % 4
tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
tmp6 = tl.load(in_ptr1 + (r1 + 64 * x0), xmask, other=0.0)
tmp9 = tl.load(in_ptr2 + r2, None, eviction_policy='evict_last')
tmp1 = tmp0 != 0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = tl.where(xmask, tmp2, 0)
tmp5 = triton_helpers.any(tmp4, 1)[:, None]
tmp7 = tmp5.to(tl.float32)
tmp8 = tmp6 * tmp7
tmp10 = 1.0
tmp11 = tmp10 - tmp7
tmp12 = tmp9 * tmp11
tmp13 = tmp8 + tmp12
tl.store(out_ptr1 + (r1 + 64 * x0), tmp13, xmask)
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, 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,), (1,), torch.bool)
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_per_fused__to_copy_add_any_mul_rsub_0[grid(4)](primals_1,
primals_2, primals_3, buf0, buf1, 4, 64, XBLOCK=1, num_warps=2,
num_stages=1)
del primals_1
del primals_2
del primals_3
return buf1, reinterpret_tensor(buf0, (4, 1, 1, 1), (1, 1, 1, 1), 0)
class LearnMaskedDefaultNew(nn.Module):
"""
Learns default values to fill invalid entries within input tensors. The
invalid entries are represented by a mask which is passed into forward alongside
the input tensor. Note the default value is only used if all entries in the batch row are
invalid rather than just a portion of invalid entries within each batch row.
"""
def __init__(self, feature_dim: 'int', init_method: 'str'='gaussian',
freeze: 'bool'=False):
"""
Args:
feature_dim (int): the size of the default value parameter, this must match the
input tensor size.
init_method (str): the initial default value parameter. Options:
'guassian'
'zeros'
freeze (bool): If True, the learned default parameter weights are frozen.
"""
super().__init__()
if init_method == 'zeros':
self._learned_defaults = nn.Parameter(torch.zeros(feature_dim),
requires_grad=not freeze)
elif init_method == 'gaussian':
self._learned_defaults = nn.Parameter(torch.Tensor(feature_dim),
requires_grad=not freeze)
nn.init.normal_(self._learned_defaults)
else:
raise NotImplementedError(
f"{init_method} not available. Options are: 'zeros' or 'gaussian'"
)
def forward(self, input_0, input_1):
primals_3 = self._learned_defaults
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3])
return output[0]
|
zijian-hu/pytorchvideo
|
LearnMaskedDefault
| false
| 4,699
|
[
"Apache-2.0"
] | 0
|
51589b100437af2285c56ce2ccc7ccecb7f9b18b
|
https://github.com/zijian-hu/pytorchvideo/tree/51589b100437af2285c56ce2ccc7ccecb7f9b18b
|
MatchingTensor
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class MatchingTensor(nn.Module):
"""
Module that captures the basic interactions between two tensors.
:param matching_dims: Word dimension of two interaction texts.
:param channels: Number of word interaction tensor channels.
:param normalize: Whether to L2-normalize samples along the
dot product axis before taking the dot product.
If set to True, then the output of the dot product
is the cosine proximity between the two samples.
:param init_diag: Whether to initialize the diagonal elements
of the matrix.
Examples:
>>> import matchzoo as mz
>>> matching_dim = 5
>>> matching_tensor = mz.modules.MatchingTensor(
... matching_dim,
... channels=4,
... normalize=True,
... init_diag=True
... )
"""
def __init__(self, matching_dim: 'int', channels: 'int'=4, normalize:
'bool'=True, init_diag: 'bool'=True):
""":class:`MatchingTensor` constructor."""
super().__init__()
self._matching_dim = matching_dim
self._channels = channels
self._normalize = normalize
self._init_diag = init_diag
self.interaction_matrix = torch.empty(self._channels, self.
_matching_dim, self._matching_dim)
if self._init_diag:
self.interaction_matrix = self.interaction_matrix.uniform_(-
0.05, 0.05)
for channel_index in range(self._channels):
self.interaction_matrix[channel_index].fill_diagonal_(0.1)
self.interaction_matrix = nn.Parameter(self.interaction_matrix)
else:
self.interaction_matrix = nn.Parameter(self.interaction_matrix.
uniform_())
def forward(self, x, y):
"""
The computation logic of MatchingTensor.
:param inputs: two input tensors.
"""
if self._normalize:
x = F.normalize(x, p=2, dim=-1)
y = F.normalize(y, p=2, dim=-1)
output = torch.einsum('bld,cde,bre->bclr', x, self.
interaction_matrix, y)
return output
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'matching_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
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 = 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')
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_clone_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 % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex % 4
x3 = xindex // 4
y0 = yindex % 4
y1 = yindex // 4
x5 = xindex
y4 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x3 + 16 * x2 + 64 * y1), xmask & ymask)
tl.store(out_ptr0 + (x5 + 16 * y4), tmp0, xmask & ymask)
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, 4, 4), (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_div_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 4, 1, 4, 1, 1), (16, 4, 4, 1, 1, 1),
torch.float32)
triton_poi_fused_clone_1[grid(64)](primals_3, buf1, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf0, (1, 16, 4), (0, 4, 1),
0), reinterpret_tensor(buf1, (1, 4, 16), (0, 16, 1), 0), out=buf2)
buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
del buf1
triton_poi_fused_div_0[grid(64)](primals_2, buf3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_2
buf4 = empty_strided_cuda((4, 4, 4, 4, 1, 1), (64, 16, 4, 1, 1, 1),
torch.float32)
triton_poi_fused_clone_2[grid(16, 16)](buf2, buf4, 16, 16, XBLOCK=
16, YBLOCK=16, num_warps=4, num_stages=1)
buf5 = reinterpret_tensor(buf2, (4, 4, 16), (64, 16, 1), 0)
del buf2
extern_kernels.bmm(buf3, reinterpret_tensor(buf4, (4, 4, 16), (64,
16, 1), 0), out=buf5)
del buf4
return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 4, 1, 16), 0
), reinterpret_tensor(buf3, (4, 4, 4), (16, 1, 4), 0
), reinterpret_tensor(buf0, (1, 4, 16), (64, 1, 4), 0)
class MatchingTensorNew(nn.Module):
"""
Module that captures the basic interactions between two tensors.
:param matching_dims: Word dimension of two interaction texts.
:param channels: Number of word interaction tensor channels.
:param normalize: Whether to L2-normalize samples along the
dot product axis before taking the dot product.
If set to True, then the output of the dot product
is the cosine proximity between the two samples.
:param init_diag: Whether to initialize the diagonal elements
of the matrix.
Examples:
>>> import matchzoo as mz
>>> matching_dim = 5
>>> matching_tensor = mz.modules.MatchingTensor(
... matching_dim,
... channels=4,
... normalize=True,
... init_diag=True
... )
"""
def __init__(self, matching_dim: 'int', channels: 'int'=4, normalize:
'bool'=True, init_diag: 'bool'=True):
""":class:`MatchingTensor` constructor."""
super().__init__()
self._matching_dim = matching_dim
self._channels = channels
self._normalize = normalize
self._init_diag = init_diag
self.interaction_matrix = torch.empty(self._channels, self.
_matching_dim, self._matching_dim)
if self._init_diag:
self.interaction_matrix = self.interaction_matrix.uniform_(-
0.05, 0.05)
for channel_index in range(self._channels):
self.interaction_matrix[channel_index].fill_diagonal_(0.1)
self.interaction_matrix = nn.Parameter(self.interaction_matrix)
else:
self.interaction_matrix = nn.Parameter(self.interaction_matrix.
uniform_())
def forward(self, input_0, input_1):
primals_1 = self.interaction_matrix
primals_2 = input_0
primals_3 = input_1
output = call([primals_1, primals_2, primals_3])
return output[0]
|
zfjsail/MatchZoo-py
|
MatchingTensor
| false
| 4,700
|
[
"Apache-2.0"
] | 0
|
c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2
|
https://github.com/zfjsail/MatchZoo-py/tree/c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2
|
AdaptiveAvgPool3dOutSize1
|
import torch
from typing import Tuple
import torch.nn as nn
from abc import abstractmethod
import torch.utils.data
import torch.nn
class EfficientBlockBase(nn.Module):
"""
PyTorchVideo/accelerator provides a set of efficient blocks
that have optimal efficiency for each target hardware device.
Each efficient block has two forms:
- original form: this form is for training. When efficient block is instantiated,
it is in this original form.
- deployable form: this form is for deployment. Once the network is ready for
deploy, it can be converted into deployable form for efficient execution
on target hardware. One block is transformed into deployable form by calling
convert() method. By conversion to deployable form,
various optimization (operator fuse, kernel optimization, etc.) are applied.
EfficientBlockBase is the base class for efficient blocks.
All efficient blocks should inherit this base class
and implement following methods:
- forward(): same as required by nn.Module
- convert(): called to convert block into deployable form
"""
@abstractmethod
def convert(self):
pass
@abstractmethod
def forward(self):
pass
class AdaptiveAvgPool3dOutSize1(EfficientBlockBase):
"""
Implements AdaptiveAvgPool3d with output (T, H, W) = (1, 1, 1). This operator has
better efficiency than AdaptiveAvgPool for mobile CPU.
"""
def __init__(self):
super().__init__()
self.pool = nn.AdaptiveAvgPool3d(1)
self.convert_flag = False
def convert(self, input_blob_size: 'Tuple', **kwargs):
"""
Converts AdaptiveAvgPool into AvgPool with constant kernel size for better
efficiency.
Args:
input_blob_size (tuple): blob size at the input of
AdaptiveAvgPool3dOutSize1 instance during forward.
kwargs (any): any keyword argument (unused).
"""
assert self.convert_flag is False, 'AdaptiveAvgPool3dOutSize1: already converted, cannot be converted again'
kernel_size = input_blob_size[2:]
self.pool = nn.AvgPool3d(kernel_size)
self.convert_flag = True
def forward(self, x):
return self.pool(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 typing import Tuple
import torch.nn as nn
from abc import abstractmethod
import torch.utils.data
import torch.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 = 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]
tmp5 = 64.0
tmp6 = tmp4 / tmp5
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp6, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
buf1 = reinterpret_tensor(buf0, (4, 1, 1, 1), (1, 1, 1, 1), 0)
del buf0
get_raw_stream(0)
triton_per_fused_mean_0[grid(4)](buf1, arg0_1, 4, 64, XBLOCK=1,
num_warps=2, num_stages=1)
del arg0_1
return buf1,
class EfficientBlockBase(nn.Module):
"""
PyTorchVideo/accelerator provides a set of efficient blocks
that have optimal efficiency for each target hardware device.
Each efficient block has two forms:
- original form: this form is for training. When efficient block is instantiated,
it is in this original form.
- deployable form: this form is for deployment. Once the network is ready for
deploy, it can be converted into deployable form for efficient execution
on target hardware. One block is transformed into deployable form by calling
convert() method. By conversion to deployable form,
various optimization (operator fuse, kernel optimization, etc.) are applied.
EfficientBlockBase is the base class for efficient blocks.
All efficient blocks should inherit this base class
and implement following methods:
- forward(): same as required by nn.Module
- convert(): called to convert block into deployable form
"""
@abstractmethod
def convert(self):
pass
@abstractmethod
def forward(self):
pass
class AdaptiveAvgPool3dOutSize1New(EfficientBlockBase):
"""
Implements AdaptiveAvgPool3d with output (T, H, W) = (1, 1, 1). This operator has
better efficiency than AdaptiveAvgPool for mobile CPU.
"""
def __init__(self):
super().__init__()
self.pool = nn.AdaptiveAvgPool3d(1)
self.convert_flag = False
def convert(self, input_blob_size: 'Tuple', **kwargs):
"""
Converts AdaptiveAvgPool into AvgPool with constant kernel size for better
efficiency.
Args:
input_blob_size (tuple): blob size at the input of
AdaptiveAvgPool3dOutSize1 instance during forward.
kwargs (any): any keyword argument (unused).
"""
assert self.convert_flag is False, 'AdaptiveAvgPool3dOutSize1: already converted, cannot be converted again'
kernel_size = input_blob_size[2:]
self.pool = nn.AvgPool3d(kernel_size)
self.convert_flag = True
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
zijian-hu/pytorchvideo
|
AdaptiveAvgPool3dOutSize1
| false
| 4,701
|
[
"Apache-2.0"
] | 0
|
51589b100437af2285c56ce2ccc7ccecb7f9b18b
|
https://github.com/zijian-hu/pytorchvideo/tree/51589b100437af2285c56ce2ccc7ccecb7f9b18b
|
Cat
|
import torch
import torch.nn as nn
class Cat(nn.Module):
def __init__(self):
super(Cat, self).__init__()
def forward(self, x):
addition = torch.split(x, 2, dim=1)[0]
None
x = torch.cat([x, addition], dim=1)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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 = 384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16 % 6
x0 = xindex % 16
x2 = xindex // 96
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], 6, tl.int64)
tmp9 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x3, tmp10, xmask)
def call(args):
arg0_1, = 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, 6, 4, 4), (96, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(384)](arg0_1, buf0, 384, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class CatNew(nn.Module):
def __init__(self):
super(CatNew, self).__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
Cat
| false
| 4,702
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
MaskedTemporalPooling
|
import torch
from typing import Optional
import torch.utils.data
import torch.nn
class MaskedTemporalPooling(torch.nn.Module):
"""
Applies temporal pooling operations on masked inputs. For each pooling operation
all masked values are ignored.
"""
def __init__(self, method: 'str'):
"""
method (str): the method of pooling to use. Options:
'max': reduces temporal dimension to each valid max value.
'avg': averages valid values in the temporal dimension.
'sum': sums valid values in the temporal dimension.
Note if all batch row elements are invalid, the temporal dimension is
pooled to 0 values.
"""
super().__init__()
assert method in ('max', 'avg', 'sum')
self._method = method
def forward(self, x: 'torch.Tensor', mask: 'Optional[torch.Tensor]'=None
) ->torch.Tensor:
"""
Args:
x (torch.Tensor): tensor with shape (batch_size, seq_len, feature_dim)
mask (torch.Tensor): bool tensor with shape (batch_size, seq_len).
Sequence elements that are False are invalid.
Returns:
Tensor with shape (batch_size, feature_dim)
"""
assert x.dim(
) == 3, 'Requires x shape (batch_size x seq_len x feature_dim)'
b, t = x.shape[0], x.shape[1]
if mask is None:
mask = torch.ones((b, t), dtype=torch.bool)
if self._method == 'max':
x[~mask, :] = float('-inf')
invalid_first_dim = ~mask.view(b, -1).any(dim=-1)
x[invalid_first_dim, :] = 0
x = torch.max(x, dim=1)[0]
elif self._method == 'avg':
x = x * mask.unsqueeze(-1).float()
mask = mask.view(b, t, -1).any(dim=-1)
valid_lengths = mask.float().sum(dim=-1).int()
x = x.sum(dim=1)
x = x.div(valid_lengths.clamp(min=1).unsqueeze(-1).expand(x.
size()).float())
elif self._method == 'sum':
x = x * mask.unsqueeze(-1).float()
x = x.sum(dim=1)
else:
raise NotImplementedError(
f"{self._method} not available options are: 'max', 'avg', 'sum'"
)
return x
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'method': 'max'}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn
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_index_put_lift_fresh_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
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.full([1], False, tl.int1)
tmp2 = float('-inf')
tmp3 = tl.where(tmp1, tmp2, tmp0)
tl.store(out_ptr0 + x0, tmp3, xmask)
@triton.jit
def triton_poi_fused_index_put_lift_fresh_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
tmp5 = tl.load(in_ptr0 + x0, xmask)
tmp0 = tl.full([1], True, tl.int1)
tmp1 = tmp0 | tmp0
tmp2 = tmp1 | tmp0
tmp3 = tmp2 | tmp0
tmp4 = tmp3 == 0
tmp6 = 0.0
tmp7 = tl.where(tmp4, tmp6, tmp5)
tl.store(out_ptr0 + x0, tmp7, xmask)
@triton.jit
def triton_poi_fused_max_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp6 = triton_helpers.maximum(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), (16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
get_raw_stream(0)
triton_poi_fused_index_put_lift_fresh_0[grid(64)](arg0_1, arg0_1,
64, XBLOCK=64, num_warps=1, num_stages=1)
triton_poi_fused_index_put_lift_fresh_1[grid(64)](arg0_1, arg0_1,
64, XBLOCK=64, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_max_2[grid(16)](arg0_1, buf2, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del arg0_1
return buf2,
class MaskedTemporalPoolingNew(torch.nn.Module):
"""
Applies temporal pooling operations on masked inputs. For each pooling operation
all masked values are ignored.
"""
def __init__(self, method: 'str'):
"""
method (str): the method of pooling to use. Options:
'max': reduces temporal dimension to each valid max value.
'avg': averages valid values in the temporal dimension.
'sum': sums valid values in the temporal dimension.
Note if all batch row elements are invalid, the temporal dimension is
pooled to 0 values.
"""
super().__init__()
assert method in ('max', 'avg', 'sum')
self._method = method
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
zijian-hu/pytorchvideo
|
MaskedTemporalPooling
| false
| 4,703
|
[
"Apache-2.0"
] | 0
|
51589b100437af2285c56ce2ccc7ccecb7f9b18b
|
https://github.com/zijian-hu/pytorchvideo/tree/51589b100437af2285c56ce2ccc7ccecb7f9b18b
|
Add
|
import torch
import torch.nn as nn
class Add(nn.Module):
def __init__(self):
super(Add, self).__init__()
def forward(self, x):
x = torch.add(x, 20)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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_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 = 20.0
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class AddNew(nn.Module):
def __init__(self):
super(AddNew, self).__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
Add
| false
| 4,704
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
SemanticComposite
|
import torch
import torch.nn as nn
class SemanticComposite(nn.Module):
"""
SemanticComposite module.
Apply a self-attention layer and a semantic composite fuse gate to compute the
encoding result of one tensor.
:param in_features: Feature size of input.
:param dropout_rate: The dropout rate.
Examples:
>>> import torch
>>> module = SemanticComposite(in_features=10)
>>> x = torch.randn(4, 5, 10)
>>> x.shape
torch.Size([4, 5, 10])
>>> module(x).shape
torch.Size([4, 5, 10])
"""
def __init__(self, in_features, dropout_rate: 'float'=0.0):
"""Init."""
super().__init__()
self.att_linear = nn.Linear(3 * in_features, 1, False)
self.z_gate = nn.Linear(2 * in_features, in_features, True)
self.r_gate = nn.Linear(2 * in_features, in_features, True)
self.f_gate = nn.Linear(2 * in_features, in_features, True)
self.dropout = nn.Dropout(p=dropout_rate)
def forward(self, x):
"""Forward."""
seq_length = x.shape[1]
x_1 = x.unsqueeze(dim=2).repeat(1, 1, seq_length, 1)
x_2 = x.unsqueeze(dim=1).repeat(1, seq_length, 1, 1)
x_concat = torch.cat([x_1, x_2, x_1 * x_2], dim=-1)
x_concat = self.dropout(x_concat)
attn_matrix = self.att_linear(x_concat).squeeze(dim=-1)
attn_weight = torch.softmax(attn_matrix, dim=2)
attn = torch.bmm(attn_weight, x)
x_attn_concat = self.dropout(torch.cat([x, attn], dim=-1))
x_attn_concat = torch.cat([x, attn], dim=-1)
z = torch.tanh(self.z_gate(x_attn_concat))
r = torch.sigmoid(self.r_gate(x_attn_concat))
f = torch.sigmoid(self.f_gate(x_attn_concat))
encoding = r * x + f * z
return encoding
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'in_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 768
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 12
x4 = xindex // 48
x1 = xindex // 12 % 4
x3 = xindex // 192
x5 = 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 * x4 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr0 + (4 * x1 + 16 * x3 + (-4 + x0)), tmp9 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = tmp0 >= tmp7
tl.full([1], 12, tl.int64)
tmp14 = tl.load(in_ptr0 + (4 * x4 + (-8 + x0)), tmp11 & xmask,
eviction_policy='evict_last', other=0.0)
tmp15 = tl.load(in_ptr0 + (4 * x1 + 16 * x3 + (-8 + x0)), tmp11 & xmask,
eviction_policy='evict_last', other=0.0)
tmp16 = tmp14 * tmp15
tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
tmp18 = tl.where(tmp11, tmp16, tmp17)
tmp19 = tl.where(tmp9, tmp10, tmp18)
tmp20 = tl.where(tmp4, tmp5, tmp19)
tl.store(out_ptr0 + x5, tmp20, 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 = 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 = 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_cat_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)
tl.store(out_ptr0 + x2, tmp10, xmask)
@triton.jit
def triton_poi_fused_add_mul_sigmoid_tanh_4(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp2 = tl.load(in_ptr1 + x0, xmask)
tmp4 = tl.load(in_ptr2 + x0, xmask)
tmp6 = tl.load(in_ptr3 + x0, xmask)
tmp1 = tl.sigmoid(tmp0)
tmp3 = tmp1 * tmp2
tmp5 = tl.sigmoid(tmp4)
tmp7 = libdevice.tanh(tmp6)
tmp8 = tmp5 * tmp7
tmp9 = tmp3 + tmp8
tl.store(out_ptr0 + x0, tmp9, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (1, 12), (12, 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,))
assert_size_stride(primals_7, (4, 8), (8, 1))
assert_size_stride(primals_8, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 12), (192, 48, 12, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(768)](primals_1, buf0, 768, XBLOCK=128,
num_warps=4, num_stages=1)
buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (64, 12), (12, 1), 0),
reinterpret_tensor(primals_2, (12, 1), (1, 12), 0), out=buf1)
del primals_2
buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
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
triton_poi_fused__softmax_2[grid(64)](buf2, buf3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf4 = buf2
del buf2
extern_kernels.bmm(buf3, primals_1, out=buf4)
buf5 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
triton_poi_fused_cat_3[grid(128)](primals_1, buf4, buf5, 128,
XBLOCK=128, num_warps=4, num_stages=1)
buf6 = reinterpret_tensor(buf4, (16, 4), (4, 1), 0)
del buf4
extern_kernels.addmm(primals_4, reinterpret_tensor(buf5, (16, 8), (
8, 1), 0), reinterpret_tensor(primals_3, (8, 4), (1, 8), 0),
alpha=1, beta=1, out=buf6)
del primals_4
buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_6, reinterpret_tensor(buf5, (16, 8), (
8, 1), 0), reinterpret_tensor(primals_5, (8, 4), (1, 8), 0),
alpha=1, beta=1, out=buf7)
del primals_6
buf8 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_8, reinterpret_tensor(buf5, (16, 8), (
8, 1), 0), reinterpret_tensor(primals_7, (8, 4), (1, 8), 0),
alpha=1, beta=1, out=buf8)
del primals_8
buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_add_mul_sigmoid_tanh_4[grid(64)](buf7, primals_1,
buf8, buf6, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1)
return buf9, primals_1, reinterpret_tensor(buf0, (64, 12), (12, 1), 0
), buf3, reinterpret_tensor(buf5, (16, 8), (8, 1), 0
), buf6, buf7, buf8, primals_7, primals_5, primals_3
class SemanticCompositeNew(nn.Module):
"""
SemanticComposite module.
Apply a self-attention layer and a semantic composite fuse gate to compute the
encoding result of one tensor.
:param in_features: Feature size of input.
:param dropout_rate: The dropout rate.
Examples:
>>> import torch
>>> module = SemanticComposite(in_features=10)
>>> x = torch.randn(4, 5, 10)
>>> x.shape
torch.Size([4, 5, 10])
>>> module(x).shape
torch.Size([4, 5, 10])
"""
def __init__(self, in_features, dropout_rate: 'float'=0.0):
"""Init."""
super().__init__()
self.att_linear = nn.Linear(3 * in_features, 1, False)
self.z_gate = nn.Linear(2 * in_features, in_features, True)
self.r_gate = nn.Linear(2 * in_features, in_features, True)
self.f_gate = nn.Linear(2 * in_features, in_features, True)
self.dropout = nn.Dropout(p=dropout_rate)
def forward(self, input_0):
primals_2 = self.att_linear.weight
primals_3 = self.z_gate.weight
primals_4 = self.z_gate.bias
primals_5 = self.r_gate.weight
primals_6 = self.r_gate.bias
primals_7 = self.f_gate.weight
primals_8 = self.f_gate.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
zfjsail/MatchZoo-py
|
SemanticComposite
| false
| 4,705
|
[
"Apache-2.0"
] | 0
|
c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2
|
https://github.com/zfjsail/MatchZoo-py/tree/c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2
|
Pow
|
import torch
import torch.nn as nn
class Pow(nn.Module):
def __init__(self):
super(Pow, self).__init__()
def forward(self, x):
x = torch.pow(x, 2)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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_pow_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tmp0 * tmp0
tl.store(out_ptr0 + x0, tmp1, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_pow_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class PowNew(nn.Module):
def __init__(self):
super(PowNew, self).__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
Pow
| false
| 4,706
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
Div
|
import torch
import torch.nn as nn
class Div(nn.Module):
def __init__(self):
super(Div, self).__init__()
def forward(self, x):
x = torch.div(x, 0.5)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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_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
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 2.0
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
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)](arg0_1, buf0, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class DivNew(nn.Module):
def __init__(self):
super(DivNew, self).__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
Div
| false
| 4,707
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
MatchModule
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class MatchModule(nn.Module):
"""
Computing the match representation for Match LSTM.
:param hidden_size: Size of hidden vectors.
:param dropout_rate: Dropout rate of the projection layer. Defaults to 0.
Examples:
>>> import torch
>>> attention = MatchModule(hidden_size=10)
>>> v1 = torch.randn(4, 5, 10)
>>> v1.shape
torch.Size([4, 5, 10])
>>> v2 = torch.randn(4, 5, 10)
>>> v2_mask = torch.ones(4, 5).to(dtype=torch.uint8)
>>> attention(v1, v2, v2_mask).shape
torch.Size([4, 5, 20])
"""
def __init__(self, hidden_size, dropout_rate=0):
"""Init."""
super().__init__()
self.v2_proj = nn.Linear(hidden_size, hidden_size)
self.proj = nn.Linear(hidden_size * 4, hidden_size * 2)
self.dropout = nn.Dropout(p=dropout_rate)
def forward(self, v1, v2, v2_mask):
"""Computing attention vectors and projection vectors."""
proj_v2 = self.v2_proj(v2)
similarity_matrix = v1.bmm(proj_v2.transpose(2, 1).contiguous())
v1_v2_attn = F.softmax(similarity_matrix.masked_fill(v2_mask.
unsqueeze(1).bool(), -1e-07), dim=2)
v2_wsum = v1_v2_attn.bmm(v2)
fusion = torch.cat([v1, v2_wsum, v1 - v2_wsum, v1 * v2_wsum], dim=2)
match = self.dropout(F.relu(self.proj(fusion)))
return match
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'hidden_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn 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_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
tl.store(in_out_ptr0 + x2, tmp2, xmask)
@triton.jit
def triton_poi_fused__to_copy_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tmp0 != 0
tl.store(out_ptr0 + x0, tmp1, xmask)
@triton.jit
def triton_poi_fused__softmax_masked_fill_2(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
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp5 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp9 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp13 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp2 = -1.0000000116860974e-07
tmp3 = tl.where(tmp0, tmp2, tmp1)
tmp6 = tl.where(tmp4, tmp2, tmp5)
tmp7 = triton_helpers.maximum(tmp3, tmp6)
tmp10 = tl.where(tmp8, tmp2, tmp9)
tmp11 = triton_helpers.maximum(tmp7, tmp10)
tmp14 = tl.where(tmp12, tmp2, tmp13)
tmp15 = triton_helpers.maximum(tmp11, tmp14)
tmp16 = tmp3 - tmp15
tmp17 = tl_math.exp(tmp16)
tmp18 = tmp6 - tmp15
tmp19 = tl_math.exp(tmp18)
tmp20 = tmp17 + tmp19
tmp21 = tmp10 - tmp15
tmp22 = tl_math.exp(tmp21)
tmp23 = tmp20 + tmp22
tmp24 = tmp14 - tmp15
tmp25 = tl_math.exp(tmp24)
tmp26 = tmp23 + tmp25
tl.store(out_ptr0 + x2, tmp15, xmask)
tl.store(out_ptr1 + x2, tmp26, xmask)
@triton.jit
def triton_poi_fused__softmax_masked_fill_3(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex // 16
x3 = xindex
x4 = xindex // 4
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp1 = tl.load(in_out_ptr0 + x3, xmask)
tmp4 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
tmp2 = -1.0000000116860974e-07
tmp3 = tl.where(tmp0, tmp2, tmp1)
tmp5 = tmp3 - tmp4
tmp6 = tl_math.exp(tmp5)
tmp8 = tmp6 / tmp7
tl.store(in_out_ptr0 + x3, tmp8, xmask)
@triton.jit
def triton_poi_fused_cat_4(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 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = tmp0 >= tmp7
tmp12 = tl.full([1], 12, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tmp11 & tmp13
tmp15 = tl.load(in_ptr0 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
eviction_policy='evict_last', other=0.0)
tmp16 = tl.load(in_ptr1 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
eviction_policy='evict_last', other=0.0)
tmp17 = tmp15 - tmp16
tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
tmp19 = tl.where(tmp14, tmp17, tmp18)
tmp20 = tmp0 >= tmp12
tl.full([1], 16, tl.int64)
tmp23 = tl.load(in_ptr0 + (4 * x1 + (-12 + x0)), tmp20 & xmask,
eviction_policy='evict_last', other=0.0)
tmp24 = tl.load(in_ptr1 + (4 * x1 + (-12 + x0)), tmp20 & xmask,
eviction_policy='evict_last', other=0.0)
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp20, tmp25, tmp26)
tmp28 = tl.where(tmp14, tmp19, tmp27)
tmp29 = tl.where(tmp9, tmp10, tmp28)
tmp30 = tl.where(tmp4, tmp5, tmp29)
tl.store(out_ptr0 + x2, tmp30, xmask)
@triton.jit
def triton_poi_fused_relu_threshold_backward_5(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 % 8
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
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, 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), (16, 4, 1))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (8, 16), (16, 1))
assert_size_stride(primals_7, (8,), (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 = reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_clone_0[grid(64)](buf1, primals_2, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(primals_4, buf1, out=buf2)
buf3 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.bool)
triton_poi_fused__to_copy_1[grid(16)](primals_5, buf3, 16, XBLOCK=
16, num_warps=1, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
triton_poi_fused__softmax_masked_fill_2[grid(16)](buf3, buf2, buf4,
buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf6 = buf2
del buf2
triton_poi_fused__softmax_masked_fill_3[grid(64)](buf6, buf3, buf4,
buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
del buf4
del buf5
buf7 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
del buf1
extern_kernels.bmm(buf6, primals_3, out=buf7)
buf8 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
triton_poi_fused_cat_4[grid(256)](primals_4, buf7, buf8, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del buf7
buf9 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf8, (16, 16), (16, 1), 0),
reinterpret_tensor(primals_6, (16, 8), (1, 16), 0), out=buf9)
buf10 = reinterpret_tensor(buf9, (4, 4, 8), (32, 8, 1), 0)
del buf9
buf11 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_5[grid(128)](buf10,
primals_7, buf11, 128, XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
return buf10, primals_3, primals_4, buf3, buf6, reinterpret_tensor(buf8,
(16, 16), (16, 1), 0), buf11, primals_6
class MatchModuleNew(nn.Module):
"""
Computing the match representation for Match LSTM.
:param hidden_size: Size of hidden vectors.
:param dropout_rate: Dropout rate of the projection layer. Defaults to 0.
Examples:
>>> import torch
>>> attention = MatchModule(hidden_size=10)
>>> v1 = torch.randn(4, 5, 10)
>>> v1.shape
torch.Size([4, 5, 10])
>>> v2 = torch.randn(4, 5, 10)
>>> v2_mask = torch.ones(4, 5).to(dtype=torch.uint8)
>>> attention(v1, v2, v2_mask).shape
torch.Size([4, 5, 20])
"""
def __init__(self, hidden_size, dropout_rate=0):
"""Init."""
super().__init__()
self.v2_proj = nn.Linear(hidden_size, hidden_size)
self.proj = nn.Linear(hidden_size * 4, hidden_size * 2)
self.dropout = nn.Dropout(p=dropout_rate)
def forward(self, input_0, input_1, input_2):
primals_1 = self.v2_proj.weight
primals_2 = self.v2_proj.bias
primals_6 = self.proj.weight
primals_7 = self.proj.bias
primals_3 = input_0
primals_4 = input_1
primals_5 = input_2
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
zfjsail/MatchZoo-py
|
MatchModule
| false
| 4,708
|
[
"Apache-2.0"
] | 0
|
c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2
|
https://github.com/zfjsail/MatchZoo-py/tree/c93e52e7db7e257b46bb8bf8df8ce1ab1944e2f2
|
Net
|
import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=3)
self.conv2 = nn.Conv2d(10, 20, kernel_size=3)
self.conv3 = nn.Conv2d(20, 50, kernel_size=3)
self.conv4 = nn.Conv2d(50, 2, kernel_size=1, bias=False, padding=0,
stride=1)
self.max_pool2d = nn.MaxPool2d((4, 4))
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = self.conv3(x)
x = self.conv4(x)
x = self.max_pool2d(x)
x = self.softmax(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 math as tl_math
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.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_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 153760
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 3844 % 10
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_max_pool2d_with_indices_relu_1(in_ptr0, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 38440
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 31
x3 = xindex // 31
x2 = xindex // 9610
x4 = xindex % 9610
x5 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 124 * x3), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 124 * x3), xmask,
eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (62 + 2 * x0 + 124 * x3), xmask,
eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (63 + 2 * x0 + 124 * x3), 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)
tmp17 = tl.full([1], 0, tl.int32)
tmp18 = triton_helpers.maximum(tmp17, tmp16)
tl.store(out_ptr0 + (x4 + 9728 * x2), tmp15, xmask)
tl.store(out_ptr1 + x5, tmp18, xmask)
@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 67280
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 841 % 20
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_max_pool2d_with_indices_relu_3(in_ptr0, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 15680
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 14
x1 = xindex // 14 % 14
x4 = xindex // 196
x3 = xindex // 3920
x5 = xindex % 3920
x6 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 58 * x1 + 841 * x4), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 58 * x1 + 841 * x4), xmask,
eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (29 + 2 * x0 + 58 * x1 + 841 * x4), xmask,
eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (30 + 2 * x0 + 58 * x1 + 841 * x4), 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)
tmp17 = tl.full([1], 0, tl.int32)
tmp18 = triton_helpers.maximum(tmp17, tmp16)
tl.store(out_ptr0 + (x5 + 3968 * x3), tmp15, xmask)
tl.store(out_ptr1 + x6, tmp18, xmask)
@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 28800
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 144 % 50
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_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 72
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 3
x1 = xindex // 3
x2 = xindex
tmp0 = tl.load(in_ptr0 + (4 * x0 + 48 * x1), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0 + 48 * x1), xmask, eviction_policy
='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0 + 48 * x1), xmask, eviction_policy
='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0 + 48 * x1), xmask, eviction_policy
='evict_last')
tmp7 = tl.load(in_ptr0 + (12 + 4 * x0 + 48 * x1), xmask,
eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (13 + 4 * x0 + 48 * x1), xmask,
eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (14 + 4 * x0 + 48 * x1), xmask,
eviction_policy='evict_last')
tmp13 = tl.load(in_ptr0 + (15 + 4 * x0 + 48 * x1), xmask,
eviction_policy='evict_last')
tmp15 = tl.load(in_ptr0 + (24 + 4 * x0 + 48 * x1), xmask,
eviction_policy='evict_last')
tmp17 = tl.load(in_ptr0 + (25 + 4 * x0 + 48 * x1), xmask,
eviction_policy='evict_last')
tmp19 = tl.load(in_ptr0 + (26 + 4 * x0 + 48 * x1), xmask,
eviction_policy='evict_last')
tmp21 = tl.load(in_ptr0 + (27 + 4 * x0 + 48 * x1), xmask,
eviction_policy='evict_last')
tmp23 = tl.load(in_ptr0 + (36 + 4 * x0 + 48 * x1), xmask,
eviction_policy='evict_last')
tmp25 = tl.load(in_ptr0 + (37 + 4 * x0 + 48 * x1), xmask,
eviction_policy='evict_last')
tmp27 = tl.load(in_ptr0 + (38 + 4 * x0 + 48 * x1), xmask,
eviction_policy='evict_last')
tmp29 = tl.load(in_ptr0 + (39 + 4 * x0 + 48 * x1), xmask,
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 + x2, tmp30, xmask)
tl.store(out_ptr1 + x2, tmp76, xmask)
@triton.jit
def triton_poi_fused__softmax_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 72
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 9
x2 = xindex // 18
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 18 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (9 + x0 + 18 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp4 = tmp0 - tmp3
tmp5 = tl_math.exp(tmp4)
tmp6 = tmp1 - tmp3
tmp7 = tl_math.exp(tmp6)
tmp8 = tmp2 - tmp3
tmp9 = tl_math.exp(tmp8)
tmp10 = tmp7 + tmp9
tmp11 = tmp5 / tmp10
tl.store(out_ptr0 + x3, tmp11, xmask)
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, (10, 3, 3, 3), (27, 9, 3, 1))
assert_size_stride(primals_2, (10,), (1,))
assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_4, (20, 10, 3, 3), (90, 9, 3, 1))
assert_size_stride(primals_5, (20,), (1,))
assert_size_stride(primals_6, (50, 20, 3, 3), (180, 9, 3, 1))
assert_size_stride(primals_7, (50,), (1,))
assert_size_stride(primals_8, (2, 50, 1, 1), (50, 1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 10, 62, 62), (38440, 3844, 62, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(153760)](buf1, primals_2,
153760, XBLOCK=512, num_warps=8, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((4, 10, 31, 31), (9728, 961, 31, 1),
torch.int8)
buf3 = empty_strided_cuda((4, 10, 31, 31), (9610, 961, 31, 1),
torch.float32)
triton_poi_fused_max_pool2d_with_indices_relu_1[grid(38440)](buf1,
buf2, buf3, 38440, XBLOCK=512, 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, 20, 29, 29), (16820, 841, 29, 1))
buf5 = buf4
del buf4
triton_poi_fused_convolution_2[grid(67280)](buf5, primals_5, 67280,
XBLOCK=512, num_warps=8, num_stages=1)
del primals_5
buf6 = empty_strided_cuda((4, 20, 14, 14), (3968, 196, 14, 1),
torch.int8)
buf7 = empty_strided_cuda((4, 20, 14, 14), (3920, 196, 14, 1),
torch.float32)
triton_poi_fused_max_pool2d_with_indices_relu_3[grid(15680)](buf5,
buf6, buf7, 15680, XBLOCK=256, num_warps=4, num_stages=1)
buf8 = extern_kernels.convolution(buf7, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 50, 12, 12), (7200, 144, 12, 1))
buf9 = buf8
del buf8
triton_poi_fused_convolution_4[grid(28800)](buf9, primals_7, 28800,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_7
buf10 = extern_kernels.convolution(buf9, primals_8, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 2, 12, 12), (288, 144, 12, 1))
buf11 = empty_strided_cuda((4, 2, 3, 3), (18, 9, 3, 1), torch.float32)
buf12 = empty_strided_cuda((4, 2, 3, 3), (18, 9, 3, 1), torch.int8)
triton_poi_fused_max_pool2d_with_indices_5[grid(72)](buf10, buf11,
buf12, 72, XBLOCK=128, num_warps=4, num_stages=1)
buf13 = empty_strided_cuda((4, 2, 3, 3), (18, 9, 3, 1), torch.float32)
triton_poi_fused__softmax_6[grid(72)](buf11, buf13, 72, XBLOCK=128,
num_warps=4, num_stages=1)
del buf11
return (buf13, primals_1, primals_3, primals_4, primals_6, primals_8,
buf1, buf2, buf3, buf5, buf6, buf7, buf9, buf10, buf12, buf13)
class NetNew(nn.Module):
def __init__(self):
super(NetNew, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=3)
self.conv2 = nn.Conv2d(10, 20, kernel_size=3)
self.conv3 = nn.Conv2d(20, 50, kernel_size=3)
self.conv4 = nn.Conv2d(50, 2, kernel_size=1, bias=False, padding=0,
stride=1)
self.max_pool2d = nn.MaxPool2d((4, 4))
self.softmax = nn.Softmax(dim=1)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.conv3.weight
primals_7 = self.conv3.bias
primals_8 = self.conv4.weight
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
ygnn123/training_extensions
|
Net
| false
| 4,709
|
[
"Apache-2.0"
] | 0
|
c3aeba9359b0d4e0ef9c054de777d3ec081a9892
|
https://github.com/ygnn123/training_extensions/tree/c3aeba9359b0d4e0ef9c054de777d3ec081a9892
|
Hardtanh
|
import torch
import torch.nn as nn
class Hardtanh(nn.Module):
def __init__(self):
super(Hardtanh, self).__init__()
self.layer = nn.Hardtanh(-2, 2)
def forward(self, x):
x = self.layer(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime 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_hardtanh_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 = -2.0
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp3 = 2.0
tmp4 = triton_helpers.minimum(tmp2, 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_hardtanh_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class HardtanhNew(nn.Module):
def __init__(self):
super(HardtanhNew, self).__init__()
self.layer = nn.Hardtanh(-2, 2)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
Hardtanh
| false
| 4,710
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
AdaptiveMaxPool2d
|
import torch
import torch.nn as nn
class AdaptiveMaxPool2d(nn.Module):
def __init__(self):
super(AdaptiveMaxPool2d, self).__init__()
self.layer = nn.AdaptiveMaxPool2d((5, 7))
def forward(self, x):
x = self.layer(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime 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_adaptive_max_pool2d_0(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 560
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 7 % 5
x0 = xindex % 7
x2 = xindex // 35
x4 = xindex
tmp0 = 4 * x1 // 5
tmp1 = (8 + 4 * x1) // 5
tmp2 = tmp0 < tmp1
tmp3 = 4 * x0 // 7
tmp4 = (10 + 4 * x0) // 7
tmp5 = tmp3 < tmp4
tmp6 = tmp2 & tmp5
tmp7 = tl.load(in_ptr0 + (4 * (4 * x1 // 5) + 16 * x2 + 4 * x0 // 7),
tmp6 & xmask, eviction_policy='evict_last', other=float('-inf'))
tmp8 = 1 + 4 * x0 // 7
tmp9 = tmp8 < tmp4
tmp10 = tmp2 & tmp9
tmp11 = tl.load(in_ptr0 + (1 + 4 * (4 * x1 // 5) + 16 * x2 + 4 * x0 //
7), tmp10 & xmask, eviction_policy='evict_last', other=float('-inf'))
tmp12 = triton_helpers.maximum(tmp11, tmp7)
tmp13 = 1 + 4 * x1 // 5
tmp14 = tmp13 < tmp1
tmp15 = tmp14 & tmp5
tmp16 = tl.load(in_ptr0 + (4 + 4 * (4 * x1 // 5) + 16 * x2 + 4 * x0 //
7), tmp15 & xmask, eviction_policy='evict_last', other=float('-inf'))
tmp17 = triton_helpers.maximum(tmp16, tmp12)
tmp18 = tmp14 & tmp9
tmp19 = tl.load(in_ptr0 + (5 + 4 * (4 * x1 // 5) + 16 * x2 + 4 * x0 //
7), tmp18 & xmask, eviction_policy='evict_last', other=float('-inf'))
tmp20 = triton_helpers.maximum(tmp19, tmp17)
tl.store(out_ptr0 + x4, tmp20, 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, 5, 7), (140, 35, 7, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_adaptive_max_pool2d_0[grid(560)](arg0_1, buf0, 560,
XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class AdaptiveMaxPool2dNew(nn.Module):
def __init__(self):
super(AdaptiveMaxPool2dNew, self).__init__()
self.layer = nn.AdaptiveMaxPool2d((5, 7))
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
AdaptiveMaxPool2d
| false
| 4,711
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
CustomClassificationHead
|
from _paritybench_helpers import _mock_config
import torch
from torch import nn
class CustomClassificationHead(nn.Module):
def __init__(self, config, input_dim, n_labels):
super().__init__()
self.config = config
self.fc1 = nn.Linear(input_dim, 4096)
self.fc2 = nn.Linear(4096, 2048)
self.fc3 = nn.Linear(2048, 1024)
self.fc4 = nn.Linear(1024, n_labels)
self.dropout = nn.Dropout(p=0.3)
self.prelu1 = nn.PReLU()
self.prelu2 = nn.PReLU()
self.prelu3 = nn.PReLU()
nn.init.kaiming_normal_(self.fc1.weight)
nn.init.kaiming_normal_(self.fc2.weight)
nn.init.kaiming_normal_(self.fc3.weight)
nn.init.kaiming_normal_(self.fc4.weight)
def forward(self, x):
if len(x.size()) == 3:
x = x[:, 0, :]
x = self.prelu1(self.fc1(x))
x = self.prelu2(self.fc2(self.dropout(x)))
x = self.prelu3(self.fc3(self.dropout(x)))
return self.fc4(self.dropout(x))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(), 'input_dim': 4, 'n_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 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__prelu_kernel_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)
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, None)
tmp3 = tl.load(in_ptr1 + 0)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp5 = tmp4 * tmp0
tmp6 = tl.where(tmp2, tmp0, tmp5)
tl.store(out_ptr0 + x0, tmp6, None)
@triton.jit
def triton_poi_fused__prelu_kernel_1(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)
tmp3 = tl.load(in_ptr1 + 0)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp5 = tmp4 * tmp0
tmp6 = tl.where(tmp2, tmp0, tmp5)
tl.store(out_ptr0 + x0, tmp6, None)
@triton.jit
def triton_poi_fused__prelu_kernel_2(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)
tmp3 = tl.load(in_ptr1 + 0)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp5 = tmp4 * tmp0
tmp6 = tl.where(tmp2, tmp0, tmp5)
tl.store(out_ptr0 + x0, tmp6, 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
) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4096, 4), (4, 1))
assert_size_stride(primals_3, (4096,), (1,))
assert_size_stride(primals_4, (1,), (1,))
assert_size_stride(primals_5, (2048, 4096), (4096, 1))
assert_size_stride(primals_6, (2048,), (1,))
assert_size_stride(primals_7, (1,), (1,))
assert_size_stride(primals_8, (1024, 2048), (2048, 1))
assert_size_stride(primals_9, (1024,), (1,))
assert_size_stride(primals_10, (1,), (1,))
assert_size_stride(primals_11, (4, 1024), (1024, 1))
assert_size_stride(primals_12, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4096), (4096, 1), torch.float32)
extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64,
4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4096), (1, 4),
0), alpha=1, beta=1, out=buf0)
del primals_2
del primals_3
buf1 = empty_strided_cuda((4, 4, 4, 4096), (65536, 16384, 4096, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused__prelu_kernel_0[grid(262144)](buf0, primals_4,
buf1, 262144, XBLOCK=1024, num_warps=4, num_stages=1)
buf2 = empty_strided_cuda((64, 2048), (2048, 1), torch.float32)
extern_kernels.addmm(primals_6, reinterpret_tensor(buf1, (64, 4096),
(4096, 1), 0), reinterpret_tensor(primals_5, (4096, 2048), (1,
4096), 0), alpha=1, beta=1, out=buf2)
del primals_6
buf3 = empty_strided_cuda((4, 4, 4, 2048), (32768, 8192, 2048, 1),
torch.float32)
triton_poi_fused__prelu_kernel_1[grid(131072)](buf2, primals_7,
buf3, 131072, XBLOCK=512, num_warps=8, num_stages=1)
buf4 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (64, 2048),
(2048, 1), 0), reinterpret_tensor(primals_8, (2048, 1024), (1,
2048), 0), alpha=1, beta=1, out=buf4)
del primals_9
buf5 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
torch.float32)
triton_poi_fused__prelu_kernel_2[grid(65536)](buf4, primals_10,
buf5, 65536, XBLOCK=512, num_warps=4, num_stages=1)
buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_12, reinterpret_tensor(buf5, (64, 1024
), (1024, 1), 0), reinterpret_tensor(primals_11, (1024, 4), (1,
1024), 0), alpha=1, beta=1, out=buf6)
del primals_12
return reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0
), primals_4, primals_7, primals_10, reinterpret_tensor(primals_1,
(64, 4), (4, 1), 0), buf0, reinterpret_tensor(buf1, (64, 4096), (
4096, 1), 0), buf2, reinterpret_tensor(buf3, (64, 2048), (2048, 1), 0
), buf4, reinterpret_tensor(buf5, (64, 1024), (1024, 1), 0
), primals_11, primals_8, primals_5
class CustomClassificationHeadNew(nn.Module):
def __init__(self, config, input_dim, n_labels):
super().__init__()
self.config = config
self.fc1 = nn.Linear(input_dim, 4096)
self.fc2 = nn.Linear(4096, 2048)
self.fc3 = nn.Linear(2048, 1024)
self.fc4 = nn.Linear(1024, n_labels)
self.dropout = nn.Dropout(p=0.3)
self.prelu1 = nn.PReLU()
self.prelu2 = nn.PReLU()
self.prelu3 = nn.PReLU()
nn.init.kaiming_normal_(self.fc1.weight)
nn.init.kaiming_normal_(self.fc2.weight)
nn.init.kaiming_normal_(self.fc3.weight)
nn.init.kaiming_normal_(self.fc4.weight)
def forward(self, input_0):
primals_2 = self.fc1.weight
primals_3 = self.fc1.bias
primals_5 = self.fc2.weight
primals_6 = self.fc2.bias
primals_8 = self.fc3.weight
primals_9 = self.fc3.bias
primals_11 = self.fc4.weight
primals_12 = self.fc4.bias
primals_4 = self.prelu1.weight
primals_7 = self.prelu2.weight
primals_10 = self.prelu3.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])
return output[0]
|
y-kamiya/emotion-classification
|
CustomClassificationHead
| false
| 4,712
|
[
"MIT"
] | 0
|
8d5b6ab4aafd60607260dc87e5360c04bf149e18
|
https://github.com/y-kamiya/emotion-classification/tree/8d5b6ab4aafd60607260dc87e5360c04bf149e18
|
TransposeMultiheadAttention
|
import torch
import torch.nn as nn
from typing import Optional
import torch.utils.data
import torch.nn
class TransposeMultiheadAttention(nn.Module):
"""
Wrapper for nn.MultiheadAttention which first transposes the input tensor
from (batch_size, seq_len, feature_dim) to (seq_length, batch_size, feature_dim),
then applies the attention and transposes the attention outputs back to the input
shape.
"""
def __init__(self, feature_dim: 'int', num_heads: 'int'=1):
"""
Args:
feature_dim (int): attention embedding dimension
num_heads (int): number of attention heads
"""
super().__init__()
self._attention = nn.MultiheadAttention(embed_dim=feature_dim,
num_heads=num_heads)
self._attention_weights = None
@property
def attention_weights(self) ->Optional[torch.Tensor]:
"""
Contains attention weights from last forward call.
"""
return self._attention_weights
def forward(self, x: 'torch.Tensor', mask: 'Optional[torch.Tensor]'=None
) ->torch.Tensor:
"""
Args:
x (torch.Tensor): tensor of shape (batch_size, seq_len, feature_dim)
mask (torch.Tensor): bool tensor with shape (batch_size, seq_len).
Sequence elements that are False are invalid.
Returns:
Tensor with shape (batch_size, seq_len, feature_dim)
"""
assert x.dim(
) == 3, 'Requires x shape (batch_size x seq_len x feature_dim)'
if mask is not None:
mask[:, 0] = True
mask = ~mask
x = x.transpose(0, 1)
attn_output, self._attention_weights = self._attention(x, x, x,
key_padding_mask=mask)
attn_output = attn_output.transpose(0, 1)
return attn_output
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'feature_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
from typing import Optional
import torch.utils.data
import torch.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 % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 16
x2 = xindex // 64
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 12 * x1), xmask)
tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_mul_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = 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_mean_4(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
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
tmp9 = 1.0
tmp10 = tmp8 / tmp9
tl.store(out_ptr0 + x2, tmp8, xmask)
tl.store(out_ptr1 + x2, tmp10, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (12,), (1,))
assert_size_stride(primals_3, (12, 4), (4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_3, (4, 12), (1, 4), 0), out=buf1)
del primals_3
buf2 = empty_strided_cuda((3, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_1[grid(192)](buf1, primals_2, buf2, 192,
XBLOCK=256, num_warps=4, num_stages=1)
del buf1
del primals_2
buf3 = empty_strided_cuda((4, 4, 4), (4, 16, 1), torch.float32)
triton_poi_fused_mul_2[grid(64)](buf2, buf3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(buf3, reinterpret_tensor(buf2, (4, 4, 4), (4, 1,
16), 64), out=buf4)
buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__softmax_3[grid(64)](buf4, buf5, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf6 = buf4
del buf4
buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__softmax_mean_4[grid(64)](buf5, buf6, buf10, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf7 = buf5
del buf5
extern_kernels.bmm(buf6, reinterpret_tensor(buf2, (4, 4, 4), (4, 16,
1), 128), out=buf7)
buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_clone_0[grid(64)](buf7, buf8, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0)
del buf7
extern_kernels.addmm(primals_5, reinterpret_tensor(buf8, (16, 4), (
4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf9)
del primals_5
return reinterpret_tensor(buf9, (4, 4, 4), (4, 16, 1), 0
), buf10, reinterpret_tensor(buf0, (16, 4), (4, 1), 0
), buf6, reinterpret_tensor(buf8, (16, 4), (4, 1), 0
), primals_4, reinterpret_tensor(buf2, (4, 4, 4), (4, 1, 16), 128
), reinterpret_tensor(buf3, (4, 4, 4), (4, 1, 16), 0
), reinterpret_tensor(buf2, (4, 4, 4), (4, 16, 1), 64)
class TransposeMultiheadAttentionNew(nn.Module):
"""
Wrapper for nn.MultiheadAttention which first transposes the input tensor
from (batch_size, seq_len, feature_dim) to (seq_length, batch_size, feature_dim),
then applies the attention and transposes the attention outputs back to the input
shape.
"""
def __init__(self, feature_dim: 'int', num_heads: 'int'=1):
"""
Args:
feature_dim (int): attention embedding dimension
num_heads (int): number of attention heads
"""
super().__init__()
self._attention = nn.MultiheadAttention(embed_dim=feature_dim,
num_heads=num_heads)
self._attention_weights = None
@property
def attention_weights(self) ->Optional[torch.Tensor]:
"""
Contains attention weights from last forward call.
"""
return self._attention_weights
def forward(self, input_0):
primals_3 = self._attention.in_proj_weight
primals_2 = self._attention.in_proj_bias
primals_4 = self._attention.out_proj.weight
primals_5 = self._attention.out_proj.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
zijian-hu/pytorchvideo
|
TransposeMultiheadAttention
| false
| 4,713
|
[
"Apache-2.0"
] | 0
|
51589b100437af2285c56ce2ccc7ccecb7f9b18b
|
https://github.com/zijian-hu/pytorchvideo/tree/51589b100437af2285c56ce2ccc7ccecb7f9b18b
|
Interpolate
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Interpolate(nn.Module):
def __init__(self):
super(Interpolate, self).__init__()
def forward(self, x):
x = F.interpolate(x, scale_factor=8, mode='nearest', align_corners=None
)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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__unsafe_index_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)
x1 = xindex // 32 % 32
x0 = xindex % 32
x2 = xindex // 1024
x4 = xindex
tmp0 = x1
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.125
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tmp5 = x0
tmp6 = tmp5.to(tl.float32)
tmp7 = tmp6 * tmp2
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * tmp4 + 16 * x2), None,
eviction_policy='evict_last')
tl.store(out_ptr0 + x4, tmp9, None)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 32, 32), (4096, 1024, 32, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused__unsafe_index_0[grid(16384)](arg0_1, buf0, 16384,
XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class InterpolateNew(nn.Module):
def __init__(self):
super(InterpolateNew, self).__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
Interpolate
| false
| 4,714
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
PReLU
|
import torch
import torch.nn as nn
class PReLU(nn.Module):
def __init__(self):
super(PReLU, self).__init__()
self.layer = nn.PReLU()
def forward(self, x):
x = self.layer(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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_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)
tmp3 = tl.load(in_ptr1 + 0)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp5 = tmp4 * tmp0
tmp6 = tl.where(tmp2, tmp0, tmp5)
tl.store(out_ptr0 + x0, tmp6, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_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 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__prelu_kernel_0[grid(256)](primals_2, primals_1,
buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_1
return buf0, primals_2
class PReLUNew(nn.Module):
def __init__(self):
super(PReLUNew, self).__init__()
self.layer = nn.PReLU()
def forward(self, input_0):
primals_1 = self.layer.weight
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
yifanpu001/PytorchToCaffe
|
PReLU
| false
| 4,715
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
leakyrelu
|
import torch
import torch.nn as nn
class leakyrelu(nn.Module):
def __init__(self, layer=10, channels=32):
super(leakyrelu, self).__init__()
layers = []
for i in range(layer):
layers.append(nn.LeakyReLU(inplace=True))
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_leaky_relu_0(in_ptr0, out_ptr3, 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 = 0.01
tmp4 = tmp0 * tmp3
tmp5 = tl.where(tmp2, tmp0, tmp4)
tmp6 = tmp5 > tmp1
tmp7 = tmp5 * tmp3
tmp8 = tl.where(tmp6, tmp5, tmp7)
tmp9 = tmp8 > tmp1
tmp10 = tmp8 * tmp3
tmp11 = tl.where(tmp9, tmp8, tmp10)
tmp12 = tmp11 > tmp1
tmp13 = tmp11 * tmp3
tmp14 = tl.where(tmp12, tmp11, tmp13)
tmp15 = tmp14 > tmp1
tmp16 = tmp14 * tmp3
tmp17 = tl.where(tmp15, tmp14, tmp16)
tmp18 = tmp17 > tmp1
tmp19 = tmp17 * tmp3
tmp20 = tl.where(tmp18, tmp17, tmp19)
tmp21 = tmp20 > tmp1
tmp22 = tmp20 * tmp3
tmp23 = tl.where(tmp21, tmp20, tmp22)
tmp24 = tmp23 > tmp1
tmp25 = tmp23 * tmp3
tmp26 = tl.where(tmp24, tmp23, tmp25)
tmp27 = tmp26 > tmp1
tmp28 = tmp26 * tmp3
tmp29 = tl.where(tmp27, tmp26, tmp28)
tmp30 = tmp29 > tmp1
tmp31 = tmp29 * tmp3
tmp32 = tl.where(tmp30, tmp29, tmp31)
tl.store(out_ptr3 + x0, tmp32, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
get_raw_stream(0)
triton_poi_fused_leaky_relu_0[grid(256)](arg0_1, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
return arg0_1,
class leakyreluNew(nn.Module):
def __init__(self, layer=10, channels=32):
super(leakyreluNew, self).__init__()
layers = []
for i in range(layer):
layers.append(nn.LeakyReLU(inplace=True))
self.layers = nn.Sequential(*layers)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
leakyrelu
| false
| 4,716
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
MaxPool2d
|
import torch
import torch.nn as nn
class MaxPool2d(nn.Module):
def __init__(self):
super(MaxPool2d, self).__init__()
self.layer = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x = self.layer(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime 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_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
tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
)
tmp3 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
)
tmp5 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy='evict_last'
)
tmp7 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy='evict_last'
)
tmp9 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy='evict_last'
)
tmp11 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp13 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp15 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, 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)
tl.store(out_ptr0 + x0, tmp16, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_max_pool2d_with_indices_0[grid(16)](arg0_1, buf0,
16, XBLOCK=16, num_warps=1, num_stages=1)
del arg0_1
return buf0,
class MaxPool2dNew(nn.Module):
def __init__(self):
super(MaxPool2dNew, self).__init__()
self.layer = nn.MaxPool2d(3, stride=2)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
MaxPool2d
| false
| 4,717
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
PetarVGAT
|
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class BaseModel(nn.Module):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
pass
@classmethod
def build_model_from_args(cls, args):
"""Build a new model instance."""
raise NotImplementedError(
'Models must implement the build_model_from_args method')
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, self).__init__()
self.dropout = dropout
self.in_features = in_features
self.out_features = out_features
self.alpha = alpha
self.concat = concat
self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
nn.init.xavier_uniform_(self.W.data, gain=1.414)
self.a = nn.Parameter(torch.zeros(size=(2 * out_features, 1)))
nn.init.xavier_uniform_(self.a.data, gain=1.414)
self.leakyrelu = nn.LeakyReLU(self.alpha)
def forward(self, input, adj):
h = torch.mm(input, self.W)
N = h.size()[0]
a_input = torch.cat([h.repeat(1, N).view(N * N, -1), h.repeat(N, 1)
], dim=1).view(N, -1, 2 * self.out_features)
e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(2))
zero_vec = -9000000000000000.0 * torch.ones_like(e)
attention = torch.where(adj > 0, e, zero_vec)
attention = F.softmax(attention, dim=1)
attention = F.dropout(attention, self.dropout, training=self.training)
h_prime = torch.matmul(attention, h)
if self.concat:
return F.elu(h_prime)
else:
return h_prime
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
class PetarVGAT(BaseModel):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument('--num-features', type=int)
parser.add_argument('--num-classes', type=int)
parser.add_argument('--hidden-size', type=int, default=8)
parser.add_argument('--dropout', type=float, default=0.6)
parser.add_argument('--alpha', type=float, default=0.2)
parser.add_argument('--nheads', type=int, default=8)
@classmethod
def build_model_from_args(cls, args):
return cls(args.num_features, args.hidden_size, args.num_classes,
args.dropout, args.alpha, args.nheads)
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
"""Dense version of GAT."""
super(PetarVGAT, self).__init__()
self.dropout = dropout
self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout,
alpha=alpha, concat=True) for _ in range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=
dropout, alpha=alpha, concat=False)
def forward(self, x, adj):
x = F.dropout(x, self.dropout, training=self.training)
x = torch.cat([att(x, adj) for att in self.attentions], dim=1)
x = F.dropout(x, self.dropout, training=self.training)
x = F.elu(self.out_att(x, adj))
return F.log_softmax(x, dim=1)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'nfeat': 4, 'nhid': 4, 'nclass': 4, 'dropout': 0.5,
'alpha': 4, 'nheads': 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.utils.data
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, 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 * ((4 * x1 + x0) // 16 % 4) + (4 * x1 + x0) %
16 % 4), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr0 + (4 * (x1 % 4) + (-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_leaky_relu_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_2(in_ptr0, in_ptr1,
in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, out_ptr0,
out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, out_ptr6, out_ptr7,
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 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp2 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp9 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp10 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp15 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp16 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp17 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp22 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp23 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp24 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp40 = tl.load(in_ptr3 + 4 * x0, xmask, eviction_policy='evict_last').to(
tl.int1)
tmp41 = tl.load(in_ptr4 + 4 * x0, xmask, eviction_policy='evict_last')
tmp45 = tl.load(in_ptr3 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp46 = tl.load(in_ptr4 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp51 = tl.load(in_ptr3 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp52 = tl.load(in_ptr4 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp57 = tl.load(in_ptr3 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp58 = tl.load(in_ptr4 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp74 = tl.load(in_ptr5 + 4 * x0, xmask, eviction_policy='evict_last').to(
tl.int1)
tmp75 = tl.load(in_ptr6 + 4 * x0, xmask, eviction_policy='evict_last')
tmp79 = tl.load(in_ptr5 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp80 = tl.load(in_ptr6 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp85 = tl.load(in_ptr5 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp86 = tl.load(in_ptr6 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp91 = tl.load(in_ptr5 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp92 = tl.load(in_ptr6 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp108 = tl.load(in_ptr7 + 4 * x0, xmask, eviction_policy='evict_last').to(
tl.int1)
tmp109 = tl.load(in_ptr8 + 4 * x0, xmask, eviction_policy='evict_last')
tmp113 = tl.load(in_ptr7 + (1 + 4 * x0), xmask, eviction_policy=
'evict_last').to(tl.int1)
tmp114 = tl.load(in_ptr8 + (1 + 4 * x0), xmask, eviction_policy=
'evict_last')
tmp119 = tl.load(in_ptr7 + (2 + 4 * x0), xmask, eviction_policy=
'evict_last').to(tl.int1)
tmp120 = tl.load(in_ptr8 + (2 + 4 * x0), xmask, eviction_policy=
'evict_last')
tmp125 = tl.load(in_ptr7 + (3 + 4 * x0), xmask, eviction_policy=
'evict_last').to(tl.int1)
tmp126 = tl.load(in_ptr8 + (3 + 4 * x0), xmask, eviction_policy=
'evict_last')
tmp3 = 4.0
tmp4 = tmp2 * tmp3
tmp5 = tl.where(tmp1, tmp2, tmp4)
tmp6 = -8999999815811072.0
tmp7 = tl.where(tmp0, tmp5, tmp6)
tmp11 = tmp10 * tmp3
tmp12 = tl.where(tmp9, tmp10, tmp11)
tmp13 = tl.where(tmp8, tmp12, tmp6)
tmp14 = triton_helpers.maximum(tmp7, tmp13)
tmp18 = tmp17 * tmp3
tmp19 = tl.where(tmp16, tmp17, tmp18)
tmp20 = tl.where(tmp15, tmp19, tmp6)
tmp21 = triton_helpers.maximum(tmp14, tmp20)
tmp25 = tmp24 * tmp3
tmp26 = tl.where(tmp23, tmp24, tmp25)
tmp27 = tl.where(tmp22, tmp26, tmp6)
tmp28 = triton_helpers.maximum(tmp21, tmp27)
tmp29 = tmp7 - tmp28
tmp30 = tl_math.exp(tmp29)
tmp31 = tmp13 - tmp28
tmp32 = tl_math.exp(tmp31)
tmp33 = tmp30 + tmp32
tmp34 = tmp20 - tmp28
tmp35 = tl_math.exp(tmp34)
tmp36 = tmp33 + tmp35
tmp37 = tmp27 - tmp28
tmp38 = tl_math.exp(tmp37)
tmp39 = tmp36 + tmp38
tmp42 = tmp41 * tmp3
tmp43 = tl.where(tmp40, tmp41, tmp42)
tmp44 = tl.where(tmp0, tmp43, tmp6)
tmp47 = tmp46 * tmp3
tmp48 = tl.where(tmp45, tmp46, tmp47)
tmp49 = tl.where(tmp8, tmp48, tmp6)
tmp50 = triton_helpers.maximum(tmp44, tmp49)
tmp53 = tmp52 * tmp3
tmp54 = tl.where(tmp51, tmp52, tmp53)
tmp55 = tl.where(tmp15, tmp54, tmp6)
tmp56 = triton_helpers.maximum(tmp50, tmp55)
tmp59 = tmp58 * tmp3
tmp60 = tl.where(tmp57, tmp58, tmp59)
tmp61 = tl.where(tmp22, tmp60, tmp6)
tmp62 = triton_helpers.maximum(tmp56, tmp61)
tmp63 = tmp44 - tmp62
tmp64 = tl_math.exp(tmp63)
tmp65 = tmp49 - tmp62
tmp66 = tl_math.exp(tmp65)
tmp67 = tmp64 + tmp66
tmp68 = tmp55 - tmp62
tmp69 = tl_math.exp(tmp68)
tmp70 = tmp67 + tmp69
tmp71 = tmp61 - tmp62
tmp72 = tl_math.exp(tmp71)
tmp73 = tmp70 + tmp72
tmp76 = tmp75 * tmp3
tmp77 = tl.where(tmp74, tmp75, tmp76)
tmp78 = tl.where(tmp0, tmp77, tmp6)
tmp81 = tmp80 * tmp3
tmp82 = tl.where(tmp79, tmp80, tmp81)
tmp83 = tl.where(tmp8, tmp82, tmp6)
tmp84 = triton_helpers.maximum(tmp78, tmp83)
tmp87 = tmp86 * tmp3
tmp88 = tl.where(tmp85, tmp86, tmp87)
tmp89 = tl.where(tmp15, tmp88, tmp6)
tmp90 = triton_helpers.maximum(tmp84, tmp89)
tmp93 = tmp92 * tmp3
tmp94 = tl.where(tmp91, tmp92, tmp93)
tmp95 = tl.where(tmp22, tmp94, tmp6)
tmp96 = triton_helpers.maximum(tmp90, tmp95)
tmp97 = tmp78 - tmp96
tmp98 = tl_math.exp(tmp97)
tmp99 = tmp83 - tmp96
tmp100 = tl_math.exp(tmp99)
tmp101 = tmp98 + tmp100
tmp102 = tmp89 - tmp96
tmp103 = tl_math.exp(tmp102)
tmp104 = tmp101 + tmp103
tmp105 = tmp95 - tmp96
tmp106 = tl_math.exp(tmp105)
tmp107 = tmp104 + tmp106
tmp110 = tmp109 * tmp3
tmp111 = tl.where(tmp108, tmp109, tmp110)
tmp112 = tl.where(tmp0, tmp111, tmp6)
tmp115 = tmp114 * tmp3
tmp116 = tl.where(tmp113, tmp114, tmp115)
tmp117 = tl.where(tmp8, tmp116, tmp6)
tmp118 = triton_helpers.maximum(tmp112, tmp117)
tmp121 = tmp120 * tmp3
tmp122 = tl.where(tmp119, tmp120, tmp121)
tmp123 = tl.where(tmp15, tmp122, tmp6)
tmp124 = triton_helpers.maximum(tmp118, tmp123)
tmp127 = tmp126 * tmp3
tmp128 = tl.where(tmp125, tmp126, tmp127)
tmp129 = tl.where(tmp22, tmp128, tmp6)
tmp130 = triton_helpers.maximum(tmp124, tmp129)
tmp131 = tmp112 - tmp130
tmp132 = tl_math.exp(tmp131)
tmp133 = tmp117 - tmp130
tmp134 = tl_math.exp(tmp133)
tmp135 = tmp132 + tmp134
tmp136 = tmp123 - tmp130
tmp137 = tl_math.exp(tmp136)
tmp138 = tmp135 + tmp137
tmp139 = tmp129 - tmp130
tmp140 = tl_math.exp(tmp139)
tmp141 = tmp138 + tmp140
tl.store(out_ptr0 + x0, tmp28, xmask)
tl.store(out_ptr1 + x0, tmp39, xmask)
tl.store(out_ptr2 + x0, tmp62, xmask)
tl.store(out_ptr3 + x0, tmp73, xmask)
tl.store(out_ptr4 + x0, tmp96, xmask)
tl.store(out_ptr5 + x0, tmp107, xmask)
tl.store(out_ptr6 + x0, tmp130, xmask)
tl.store(out_ptr7 + x0, tmp141, xmask)
@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_3(in_out_ptr0,
in_out_ptr1, in_out_ptr2, in_out_ptr3, in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, in_ptr10,
in_ptr11, in_ptr12, 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).to(tl.int1)
tmp1 = tl.load(in_ptr1 + x2, xmask).to(tl.int1)
tmp2 = tl.load(in_out_ptr0 + x2, xmask)
tmp8 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr4 + x2, xmask).to(tl.int1)
tmp14 = tl.load(in_out_ptr1 + x2, xmask)
tmp18 = tl.load(in_ptr5 + x1, xmask, eviction_policy='evict_last')
tmp21 = tl.load(in_ptr6 + x1, xmask, eviction_policy='evict_last')
tmp23 = tl.load(in_ptr7 + x2, xmask).to(tl.int1)
tmp24 = tl.load(in_out_ptr2 + x2, xmask)
tmp28 = tl.load(in_ptr8 + x1, xmask, eviction_policy='evict_last')
tmp31 = tl.load(in_ptr9 + x1, xmask, eviction_policy='evict_last')
tmp33 = tl.load(in_ptr10 + x2, xmask).to(tl.int1)
tmp34 = tl.load(in_out_ptr3 + x2, xmask)
tmp38 = tl.load(in_ptr11 + x1, xmask, eviction_policy='evict_last')
tmp41 = tl.load(in_ptr12 + x1, xmask, eviction_policy='evict_last')
tmp3 = 4.0
tmp4 = tmp2 * tmp3
tmp5 = tl.where(tmp1, tmp2, tmp4)
tmp6 = -8999999815811072.0
tmp7 = tl.where(tmp0, tmp5, tmp6)
tmp9 = tmp7 - tmp8
tmp10 = tl_math.exp(tmp9)
tmp12 = tmp10 / tmp11
tmp15 = tmp14 * tmp3
tmp16 = tl.where(tmp13, tmp14, tmp15)
tmp17 = tl.where(tmp0, tmp16, tmp6)
tmp19 = tmp17 - tmp18
tmp20 = tl_math.exp(tmp19)
tmp22 = tmp20 / tmp21
tmp25 = tmp24 * tmp3
tmp26 = tl.where(tmp23, tmp24, tmp25)
tmp27 = tl.where(tmp0, tmp26, tmp6)
tmp29 = tmp27 - tmp28
tmp30 = tl_math.exp(tmp29)
tmp32 = tmp30 / tmp31
tmp35 = tmp34 * tmp3
tmp36 = tl.where(tmp33, tmp34, tmp35)
tmp37 = tl.where(tmp0, tmp36, tmp6)
tmp39 = tmp37 - tmp38
tmp40 = tl_math.exp(tmp39)
tmp42 = tmp40 / tmp41
tl.store(in_out_ptr0 + x2, tmp12, xmask)
tl.store(in_out_ptr1 + x2, tmp22, xmask)
tl.store(in_out_ptr2 + x2, tmp32, xmask)
tl.store(in_out_ptr3 + x2, tmp42, xmask)
@triton.jit
def triton_poi_fused_cat_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 = 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 = 0.0
tmp7 = tmp5 > tmp6
tmp8 = 1.0
tmp9 = tmp5 * tmp8
tmp10 = libdevice.expm1(tmp9)
tmp11 = tmp10 * tmp8
tmp12 = tl.where(tmp7, tmp9, tmp11)
tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
tmp14 = tl.where(tmp4, tmp12, tmp13)
tmp15 = tmp0 >= tmp3
tmp16 = tl.full([1], 8, tl.int64)
tmp17 = tmp0 < tmp16
tmp18 = tmp15 & tmp17
tmp19 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp18 & xmask,
eviction_policy='evict_last', other=0.0)
tmp20 = tmp19 > tmp6
tmp21 = tmp19 * tmp8
tmp22 = libdevice.expm1(tmp21)
tmp23 = tmp22 * tmp8
tmp24 = tl.where(tmp20, tmp21, tmp23)
tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
tmp26 = tl.where(tmp18, tmp24, tmp25)
tmp27 = tmp0 >= tmp16
tmp28 = tl.full([1], 12, tl.int64)
tmp29 = tmp0 < tmp28
tmp30 = tmp27 & tmp29
tmp31 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp30 & xmask,
eviction_policy='evict_last', other=0.0)
tmp32 = tmp31 > tmp6
tmp33 = tmp31 * tmp8
tmp34 = libdevice.expm1(tmp33)
tmp35 = tmp34 * tmp8
tmp36 = tl.where(tmp32, tmp33, tmp35)
tmp37 = tl.full(tmp36.shape, 0.0, tmp36.dtype)
tmp38 = tl.where(tmp30, tmp36, tmp37)
tmp39 = tmp0 >= tmp28
tl.full([1], 16, tl.int64)
tmp42 = tl.load(in_ptr3 + (4 * x1 + (-12 + x0)), tmp39 & xmask,
eviction_policy='evict_last', other=0.0)
tmp43 = tmp42 > tmp6
tmp44 = tmp42 * tmp8
tmp45 = libdevice.expm1(tmp44)
tmp46 = tmp45 * tmp8
tmp47 = tl.where(tmp43, tmp44, tmp46)
tmp48 = tl.full(tmp47.shape, 0.0, tmp47.dtype)
tmp49 = tl.where(tmp39, tmp47, tmp48)
tmp50 = tl.where(tmp30, tmp38, tmp49)
tmp51 = tl.where(tmp18, tmp26, tmp50)
tmp52 = tl.where(tmp4, tmp14, tmp51)
tl.store(out_ptr0 + x2, tmp52, xmask)
@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_5(in_ptr0, in_ptr1,
in_ptr2, 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 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp2 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp9 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp10 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp15 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp16 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp17 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp22 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp23 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp24 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp3 = 4.0
tmp4 = tmp2 * tmp3
tmp5 = tl.where(tmp1, tmp2, tmp4)
tmp6 = -8999999815811072.0
tmp7 = tl.where(tmp0, tmp5, tmp6)
tmp11 = tmp10 * tmp3
tmp12 = tl.where(tmp9, tmp10, tmp11)
tmp13 = tl.where(tmp8, tmp12, tmp6)
tmp14 = triton_helpers.maximum(tmp7, tmp13)
tmp18 = tmp17 * tmp3
tmp19 = tl.where(tmp16, tmp17, tmp18)
tmp20 = tl.where(tmp15, tmp19, tmp6)
tmp21 = triton_helpers.maximum(tmp14, tmp20)
tmp25 = tmp24 * tmp3
tmp26 = tl.where(tmp23, tmp24, tmp25)
tmp27 = tl.where(tmp22, tmp26, tmp6)
tmp28 = triton_helpers.maximum(tmp21, tmp27)
tmp29 = tmp7 - tmp28
tmp30 = tl_math.exp(tmp29)
tmp31 = tmp13 - tmp28
tmp32 = tl_math.exp(tmp31)
tmp33 = tmp30 + tmp32
tmp34 = tmp20 - tmp28
tmp35 = tl_math.exp(tmp34)
tmp36 = tmp33 + tmp35
tmp37 = tmp27 - tmp28
tmp38 = tl_math.exp(tmp37)
tmp39 = tmp36 + tmp38
tl.store(out_ptr0 + x0, tmp28, xmask)
tl.store(out_ptr1 + x0, tmp39, xmask)
@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_6(in_out_ptr0, in_ptr0,
in_ptr1, in_ptr2, in_ptr3, 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).to(tl.int1)
tmp1 = tl.load(in_ptr1 + x2, xmask).to(tl.int1)
tmp2 = tl.load(in_out_ptr0 + x2, xmask)
tmp8 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp3 = 4.0
tmp4 = tmp2 * tmp3
tmp5 = tl.where(tmp1, tmp2, tmp4)
tmp6 = -8999999815811072.0
tmp7 = tl.where(tmp0, tmp5, tmp6)
tmp9 = tmp7 - tmp8
tmp10 = tl_math.exp(tmp9)
tmp12 = tmp10 / tmp11
tl.store(in_out_ptr0 + x2, tmp12, xmask)
@triton.jit
def triton_poi_fused__log_softmax_elu_7(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)
tmp8 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp21 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp28 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tmp9 = tmp8 > tmp1
tmp10 = tmp8 * tmp3
tmp11 = libdevice.expm1(tmp10)
tmp12 = tmp11 * tmp3
tmp13 = tl.where(tmp9, tmp10, tmp12)
tmp15 = tmp14 > tmp1
tmp16 = tmp14 * tmp3
tmp17 = libdevice.expm1(tmp16)
tmp18 = tmp17 * tmp3
tmp19 = tl.where(tmp15, tmp16, tmp18)
tmp20 = triton_helpers.maximum(tmp13, tmp19)
tmp22 = tmp21 > tmp1
tmp23 = tmp21 * tmp3
tmp24 = libdevice.expm1(tmp23)
tmp25 = tmp24 * tmp3
tmp26 = tl.where(tmp22, tmp23, tmp25)
tmp27 = triton_helpers.maximum(tmp20, tmp26)
tmp29 = tmp28 > tmp1
tmp30 = tmp28 * tmp3
tmp31 = libdevice.expm1(tmp30)
tmp32 = tmp31 * tmp3
tmp33 = tl.where(tmp29, tmp30, tmp32)
tmp34 = triton_helpers.maximum(tmp27, tmp33)
tmp35 = tmp7 - tmp34
tl.store(out_ptr0 + x2, tmp35, xmask)
@triton.jit
def triton_poi_fused__log_softmax_8(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 = 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 + x2, tmp13, 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
) = 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, (8, 1), (1, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (8, 1), (1, 1))
assert_size_stride(primals_7, (4, 4), (4, 1))
assert_size_stride(primals_8, (8, 1), (1, 1))
assert_size_stride(primals_9, (4, 4), (4, 1))
assert_size_stride(primals_10, (8, 1), (1, 1))
assert_size_stride(primals_11, (16, 4), (4, 1))
assert_size_stride(primals_12, (8, 1), (1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, primals_2, out=buf0)
del primals_2
buf1 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(128)](buf0, buf1, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf2 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(buf1, primals_3, out=buf2)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(16)](buf2, buf3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(16)](primals_4, buf4, 16, XBLOCK
=16, num_warps=1, num_stages=1)
del primals_4
buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, primals_5, out=buf9)
del primals_5
buf10 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
triton_poi_fused_cat_0[grid(128)](buf9, buf10, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf11 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(buf10, primals_6, out=buf11)
buf12 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(16)](buf11, buf12, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf17 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, primals_7, out=buf17)
del primals_7
buf18 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
triton_poi_fused_cat_0[grid(128)](buf17, buf18, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf19 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(buf18, primals_8, out=buf19)
buf20 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(16)](buf19, buf20, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf25 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, primals_9, out=buf25)
del primals_9
buf26 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
triton_poi_fused_cat_0[grid(128)](buf25, buf26, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf27 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(buf26, primals_10, out=buf27)
buf28 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(16)](buf27, buf28, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf6 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf13 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf14 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf21 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf22 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf29 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf30 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
triton_poi_fused__softmax_leaky_relu_mul_where_2[grid(4)](buf4,
buf3, buf2, buf12, buf11, buf20, buf19, buf28, buf27, buf5,
buf6, buf13, buf14, buf21, buf22, buf29, buf30, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf7 = reinterpret_tensor(buf2, (4, 4), (4, 1), 0)
del buf2
buf15 = reinterpret_tensor(buf11, (4, 4), (4, 1), 0)
del buf11
buf23 = reinterpret_tensor(buf19, (4, 4), (4, 1), 0)
del buf19
buf31 = reinterpret_tensor(buf27, (4, 4), (4, 1), 0)
del buf27
triton_poi_fused__softmax_leaky_relu_mul_where_3[grid(16)](buf7,
buf15, buf23, buf31, buf4, buf3, buf5, buf6, buf12, buf13,
buf14, buf20, buf21, buf22, buf28, buf29, buf30, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf13
del buf14
del buf21
del buf22
del buf29
del buf30
buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf7, buf0, out=buf8)
buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf15, buf9, out=buf16)
buf24 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf23, buf17, out=buf24)
buf32 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf31, buf25, out=buf32)
buf33 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
triton_poi_fused_cat_4[grid(64)](buf8, buf16, buf24, buf32, buf33,
64, XBLOCK=64, num_warps=1, num_stages=1)
buf34 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf33, primals_11, out=buf34)
buf35 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
triton_poi_fused_cat_0[grid(128)](buf34, buf35, 128, XBLOCK=128,
num_warps=4, num_stages=1)
buf36 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
extern_kernels.mm(buf35, primals_12, out=buf36)
buf37 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(16)](buf36, buf37, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf38 = buf6
del buf6
buf39 = buf5
del buf5
triton_poi_fused__softmax_leaky_relu_mul_where_5[grid(4)](buf4,
buf37, buf36, buf38, buf39, 4, XBLOCK=4, num_warps=1, num_stages=1)
buf40 = reinterpret_tensor(buf36, (4, 4), (4, 1), 0)
del buf36
triton_poi_fused__softmax_leaky_relu_mul_where_6[grid(16)](buf40,
buf4, buf37, buf38, buf39, 16, XBLOCK=16, num_warps=1, num_stages=1
)
del buf38
del buf39
buf41 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf40, buf34, out=buf41)
buf42 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused__log_softmax_elu_7[grid(16)](buf41, buf42, 16,
XBLOCK=16, num_warps=1, num_stages=1)
buf43 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused__log_softmax_8[grid(16)](buf42, buf43, 16, XBLOCK=
16, num_warps=1, num_stages=1)
del buf42
return (buf43, buf3, buf4, buf7, buf8, buf12, buf15, buf16, buf20,
buf23, buf24, buf28, buf31, buf32, buf37, buf40, buf41, buf43,
reinterpret_tensor(buf34, (4, 4), (1, 4), 0), reinterpret_tensor(
buf35, (8, 16), (1, 8), 0), reinterpret_tensor(primals_12, (1, 8),
(1, 1), 0), reinterpret_tensor(buf33, (16, 4), (1, 16), 0),
reinterpret_tensor(primals_11, (4, 16), (1, 4), 0),
reinterpret_tensor(buf25, (4, 4), (1, 4), 0), reinterpret_tensor(
buf26, (8, 16), (1, 8), 0), reinterpret_tensor(primals_10, (1, 8),
(1, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0),
reinterpret_tensor(buf17, (4, 4), (1, 4), 0), reinterpret_tensor(
buf18, (8, 16), (1, 8), 0), reinterpret_tensor(primals_8, (1, 8), (
1, 1), 0), reinterpret_tensor(buf9, (4, 4), (1, 4), 0),
reinterpret_tensor(buf10, (8, 16), (1, 8), 0), reinterpret_tensor(
primals_6, (1, 8), (1, 1), 0), reinterpret_tensor(buf0, (4, 4), (1,
4), 0), reinterpret_tensor(buf1, (8, 16), (1, 8), 0),
reinterpret_tensor(primals_3, (1, 8), (1, 1), 0))
class BaseModel(nn.Module):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
pass
@classmethod
def build_model_from_args(cls, args):
"""Build a new model instance."""
raise NotImplementedError(
'Models must implement the build_model_from_args method')
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, self).__init__()
self.dropout = dropout
self.in_features = in_features
self.out_features = out_features
self.alpha = alpha
self.concat = concat
self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
nn.init.xavier_uniform_(self.W.data, gain=1.414)
self.a = nn.Parameter(torch.zeros(size=(2 * out_features, 1)))
nn.init.xavier_uniform_(self.a.data, gain=1.414)
self.leakyrelu = nn.LeakyReLU(self.alpha)
def forward(self, input, adj):
h = torch.mm(input, self.W)
N = h.size()[0]
a_input = torch.cat([h.repeat(1, N).view(N * N, -1), h.repeat(N, 1)
], dim=1).view(N, -1, 2 * self.out_features)
e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(2))
zero_vec = -9000000000000000.0 * torch.ones_like(e)
attention = torch.where(adj > 0, e, zero_vec)
attention = F.softmax(attention, dim=1)
attention = F.dropout(attention, self.dropout, training=self.training)
h_prime = torch.matmul(attention, h)
if self.concat:
return F.elu(h_prime)
else:
return h_prime
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
class PetarVGATNew(BaseModel):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument('--num-features', type=int)
parser.add_argument('--num-classes', type=int)
parser.add_argument('--hidden-size', type=int, default=8)
parser.add_argument('--dropout', type=float, default=0.6)
parser.add_argument('--alpha', type=float, default=0.2)
parser.add_argument('--nheads', type=int, default=8)
@classmethod
def build_model_from_args(cls, args):
return cls(args.num_features, args.hidden_size, args.num_classes,
args.dropout, args.alpha, args.nheads)
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
"""Dense version of GAT."""
super(PetarVGATNew, self).__init__()
self.dropout = dropout
self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout,
alpha=alpha, concat=True) for _ in range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=
dropout, alpha=alpha, concat=False)
def forward(self, input_0, input_1):
primals_1 = self.attention_0.W
primals_3 = self.attention_0.a
primals_2 = self.attention_1.W
primals_6 = self.attention_1.a
primals_4 = self.attention_2.W
primals_8 = self.attention_2.a
primals_5 = self.attention_3.W
primals_10 = self.attention_3.a
primals_11 = self.out_att.W
primals_12 = self.out_att.a
primals_7 = input_0
primals_9 = 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])
return output[0]
|
zxhhh97/cogdl
|
PetarVGAT
| false
| 4,718
|
[
"MIT"
] | 0
|
de21c78d9bbbf0c6cafbc72ff241cda35693ec37
|
https://github.com/zxhhh97/cogdl/tree/de21c78d9bbbf0c6cafbc72ff241cda35693ec37
|
ConvTranspose2d
|
import torch
import torch.nn as nn
class ConvTranspose2d(nn.Module):
def __init__(self):
super(ConvTranspose2d, self).__init__()
self.convtranspose2d = nn.ConvTranspose2d(16, 33, 3, stride=2)
def forward(self, x):
x = self.convtranspose2d(x)
return x
def get_inputs():
return [torch.rand([4, 16, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 10692
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 81 % 33
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, (16, 33, 3, 3), (297, 9, 3, 1))
assert_size_stride(primals_2, (33,), (1,))
assert_size_stride(primals_3, (4, 16, 4, 4), (256, 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=(0, 0), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 33, 9, 9), (2673, 81, 9, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(10692)](buf1, primals_2, 10692,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
return buf1, primals_1, primals_3
class ConvTranspose2dNew(nn.Module):
def __init__(self):
super(ConvTranspose2dNew, self).__init__()
self.convtranspose2d = nn.ConvTranspose2d(16, 33, 3, stride=2)
def forward(self, input_0):
primals_1 = self.convtranspose2d.weight
primals_2 = self.convtranspose2d.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
yifanpu001/PytorchToCaffe
|
ConvTranspose2d
| false
| 4,719
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
_Transition
|
from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class _Transition(nn.Module):
def __init__(self, in_channels, args):
super(_Transition, self).__init__()
self.pool = nn.Conv2d(in_channels, in_channels, kernel_size=2,
stride=2, groups=in_channels)
def forward(self, x):
x = self.pool(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'args': _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
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, 1, 2, 2), (4, 4, 2, 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=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=4, 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 _TransitionNew(nn.Module):
def __init__(self, in_channels, args):
super(_TransitionNew, self).__init__()
self.pool = nn.Conv2d(in_channels, in_channels, kernel_size=2,
stride=2, groups=in_channels)
def forward(self, input_0):
primals_1 = self.pool.weight
primals_2 = self.pool.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
yifanpu001/PytorchToCaffe
|
_Transition
| false
| 4,720
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
Mul
|
import torch
import torch.nn as nn
class Mul(nn.Module):
def __init__(self):
super(Mul, self).__init__()
def forward(self, x):
x = torch.mul(x, 20)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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_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 = 20.0
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class MulNew(nn.Module):
def __init__(self):
super(MulNew, self).__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
Mul
| false
| 4,721
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
relu
|
import torch
import torch.nn as nn
class relu(nn.Module):
def __init__(self, layer=10, channels=32):
super(relu, self).__init__()
layers = []
for i in range(layer):
layers.append(nn.ReLU(inplace=True))
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_relu_0(in_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp4 = triton_helpers.maximum(tmp1, tmp3)
tmp5 = triton_helpers.maximum(tmp1, tmp4)
tmp6 = triton_helpers.maximum(tmp1, tmp5)
tmp7 = triton_helpers.maximum(tmp1, tmp6)
tmp8 = triton_helpers.maximum(tmp1, tmp7)
tmp9 = triton_helpers.maximum(tmp1, tmp8)
tmp10 = triton_helpers.maximum(tmp1, tmp9)
tmp11 = triton_helpers.maximum(tmp1, tmp10)
tl.store(out_ptr1 + x0, tmp11, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
get_raw_stream(0)
triton_poi_fused_relu_0[grid(256)](arg0_1, arg0_1, 256, XBLOCK=256,
num_warps=4, num_stages=1)
return arg0_1,
class reluNew(nn.Module):
def __init__(self, layer=10, channels=32):
super(reluNew, self).__init__()
layers = []
for i in range(layer):
layers.append(nn.ReLU(inplace=True))
self.layers = nn.Sequential(*layers)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
relu
| false
| 4,722
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
Sub
|
import torch
import torch.nn as nn
class Sub(nn.Module):
def __init__(self):
super(Sub, self).__init__()
def forward(self, x):
x = torch.sub(x, 20)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 20.0
tmp2 = tmp0 - tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_sub_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class SubNew(nn.Module):
def __init__(self):
super(SubNew, self).__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
Sub
| false
| 4,723
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
maxpool
|
import torch
import torch.nn as nn
class maxpool(nn.Module):
def __init__(self, layer=10, channels=32):
super(maxpool, self).__init__()
layers = []
for i in range(layer):
layers.append(nn.MaxPool2d(3, 1, 1))
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_0(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 4
x0 = xindex % 4
x4 = xindex
tmp0 = -1 + x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = -1 + x0
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = tmp5 & tmp9
tmp11 = tl.load(in_ptr0 + (-5 + x4), tmp10 & xmask, other=float('-inf'))
tmp12 = x0
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp13 & tmp14
tmp16 = tmp5 & tmp15
tmp17 = tl.load(in_ptr0 + (-4 + x4), tmp16 & xmask, other=float('-inf'))
tmp18 = triton_helpers.maximum(tmp17, tmp11)
tmp19 = 1 + x0
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp20 & tmp21
tmp23 = tmp5 & tmp22
tmp24 = tl.load(in_ptr0 + (-3 + x4), tmp23 & xmask, other=float('-inf'))
tmp25 = triton_helpers.maximum(tmp24, tmp18)
tmp26 = x1
tmp27 = tmp26 >= tmp1
tmp28 = tmp26 < tmp3
tmp29 = tmp27 & tmp28
tmp30 = tmp29 & tmp9
tmp31 = tl.load(in_ptr0 + (-1 + x4), tmp30 & xmask, other=float('-inf'))
tmp32 = triton_helpers.maximum(tmp31, tmp25)
tmp33 = tmp29 & tmp15
tmp34 = tl.load(in_ptr0 + x4, tmp33 & xmask, other=float('-inf'))
tmp35 = triton_helpers.maximum(tmp34, tmp32)
tmp36 = tmp29 & tmp22
tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=float('-inf'))
tmp38 = triton_helpers.maximum(tmp37, tmp35)
tmp39 = 1 + x1
tmp40 = tmp39 >= tmp1
tmp41 = tmp39 < tmp3
tmp42 = tmp40 & tmp41
tmp43 = tmp42 & tmp9
tmp44 = tl.load(in_ptr0 + (3 + x4), tmp43 & xmask, other=float('-inf'))
tmp45 = triton_helpers.maximum(tmp44, tmp38)
tmp46 = tmp42 & tmp15
tmp47 = tl.load(in_ptr0 + (4 + x4), tmp46 & xmask, other=float('-inf'))
tmp48 = triton_helpers.maximum(tmp47, tmp45)
tmp49 = tmp42 & tmp22
tmp50 = tl.load(in_ptr0 + (5 + x4), tmp49 & xmask, other=float('-inf'))
tmp51 = triton_helpers.maximum(tmp50, tmp48)
tl.store(out_ptr0 + x4, tmp51, 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_max_pool2d_with_indices_0[grid(256)](arg0_1, buf0,
256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_max_pool2d_with_indices_0[grid(256)](buf0, buf1,
256, XBLOCK=128, num_warps=4, num_stages=1)
buf2 = buf0
del buf0
triton_poi_fused_max_pool2d_with_indices_0[grid(256)](buf1, buf2,
256, XBLOCK=128, num_warps=4, num_stages=1)
buf3 = buf1
del buf1
triton_poi_fused_max_pool2d_with_indices_0[grid(256)](buf2, buf3,
256, XBLOCK=128, num_warps=4, num_stages=1)
buf4 = buf2
del buf2
triton_poi_fused_max_pool2d_with_indices_0[grid(256)](buf3, buf4,
256, XBLOCK=128, num_warps=4, num_stages=1)
buf5 = buf3
del buf3
triton_poi_fused_max_pool2d_with_indices_0[grid(256)](buf4, buf5,
256, XBLOCK=128, num_warps=4, num_stages=1)
buf6 = buf4
del buf4
triton_poi_fused_max_pool2d_with_indices_0[grid(256)](buf5, buf6,
256, XBLOCK=128, num_warps=4, num_stages=1)
buf7 = buf5
del buf5
triton_poi_fused_max_pool2d_with_indices_0[grid(256)](buf6, buf7,
256, XBLOCK=128, num_warps=4, num_stages=1)
buf8 = buf6
del buf6
triton_poi_fused_max_pool2d_with_indices_0[grid(256)](buf7, buf8,
256, XBLOCK=128, num_warps=4, num_stages=1)
buf9 = buf7
del buf7
triton_poi_fused_max_pool2d_with_indices_0[grid(256)](buf8, buf9,
256, XBLOCK=128, num_warps=4, num_stages=1)
del buf8
return buf9,
class maxpoolNew(nn.Module):
def __init__(self, layer=10, channels=32):
super(maxpoolNew, self).__init__()
layers = []
for i in range(layer):
layers.append(nn.MaxPool2d(3, 1, 1))
self.layers = nn.Sequential(*layers)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
maxpool
| false
| 4,724
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
PositionWiseFeedForward
|
from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function by Hugging Face"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class PositionWiseFeedForward(nn.Module):
""" FeedForward Neural Networks for each position """
def __init__(self, cfg):
super().__init__()
self.fc1 = nn.Linear(cfg.dim, cfg.dim_ff)
self.fc2 = nn.Linear(cfg.dim_ff, cfg.dim)
def forward(self, x):
return self.fc2(gelu(self.fc1(x)))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'cfg': _mock_config(dim=4, dim_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.triton_helpers import libdevice
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_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, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((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)
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf2)
del primals_5
return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf0, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), primals_4
def gelu(x):
"""Implementation of the gelu activation function by Hugging Face"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
class PositionWiseFeedForwardNew(nn.Module):
""" FeedForward Neural Networks for each position """
def __init__(self, cfg):
super().__init__()
self.fc1 = nn.Linear(cfg.dim, cfg.dim_ff)
self.fc2 = nn.Linear(cfg.dim_ff, cfg.dim)
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]
|
akakakakakaa/pytorchic-bert
|
PositionWiseFeedForward
| false
| 4,725
|
[
"Apache-2.0"
] | 0
|
055d72adce9a41c322d23145840f31a94d9ffec4
|
https://github.com/akakakakakaa/pytorchic-bert/tree/055d72adce9a41c322d23145840f31a94d9ffec4
|
Conv2d
|
import torch
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self):
super(Conv2d, self).__init__()
self.conv2d = nn.Conv2d(16, 33, kernel_size=1, padding=1, stride=2)
def forward(self, x):
x = self.conv2d(x)
return x
def get_inputs():
return [torch.rand([4, 16, 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
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 = 143748
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 1089 % 33
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, (33, 16, 1, 1), (16, 1, 1, 1))
assert_size_stride(primals_2, (33,), (1,))
assert_size_stride(primals_3, (4, 16, 64, 64), (65536, 4096, 64, 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, 33, 33, 33), (35937, 1089, 33, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(143748)](buf1, primals_2,
143748, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_2
return buf1, primals_1, primals_3
class Conv2dNew(nn.Module):
def __init__(self):
super(Conv2dNew, self).__init__()
self.conv2d = nn.Conv2d(16, 33, kernel_size=1, padding=1, stride=2)
def forward(self, input_0):
primals_1 = self.conv2d.weight
primals_2 = self.conv2d.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
yifanpu001/PytorchToCaffe
|
Conv2d
| false
| 4,726
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
softmax
|
import torch
import torch.nn as nn
class softmax(nn.Module):
def __init__(self, layer=10, channels=32):
super(softmax, self).__init__()
layers = []
for i in range(layer):
layers.append(nn.Softmax(dim=1))
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x3, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = 0.0
tmp9 = tmp7 >= tmp8
tmp10 = 1.0
tmp11 = -1.0
tmp12 = tl.where(tmp9, tmp10, tmp11)
tmp13 = tmp0 * tmp12
tl.store(out_ptr0 + x3, tmp13, xmask)
@triton.jit
def triton_poi_fused__softmax_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_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')
tmp9 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp10 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp12 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp14 = tl.load(in_ptr1 + (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
tmp11 = tmp9 + tmp10
tmp13 = tmp11 + tmp12
tmp15 = tmp13 + tmp14
tmp16 = 0.0
tmp17 = tmp15 >= tmp16
tmp18 = 1.0
tmp19 = -1.0
tmp20 = tl.where(tmp17, tmp18, tmp19)
tmp21 = tmp20 * tmp15
tmp22 = tmp8 / tmp21
tl.store(out_ptr0 + x3, tmp22, 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)
tmp2 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp7 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp10 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp1 = tl_math.exp(tmp0)
tmp3 = tl_math.exp(tmp2)
tmp5 = tl_math.exp(tmp4)
tmp6 = tmp3 + tmp5
tmp8 = tl_math.exp(tmp7)
tmp9 = tmp6 + tmp8
tmp11 = tl_math.exp(tmp10)
tmp12 = tmp9 + tmp11
tmp13 = 0.0
tmp14 = tmp12 >= tmp13
tmp15 = 1.0
tmp16 = -1.0
tmp17 = tl.where(tmp14, tmp15, tmp16)
tmp18 = tmp1 * tmp17
tl.store(out_ptr0 + x3, tmp18, xmask)
@triton.jit
def triton_poi_fused__softmax_4(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')
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')
tmp9 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp11 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp14 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp17 = tl.load(in_ptr1 + (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
tmp10 = tl_math.exp(tmp9)
tmp12 = tl_math.exp(tmp11)
tmp13 = tmp10 + tmp12
tmp15 = tl_math.exp(tmp14)
tmp16 = tmp13 + tmp15
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp20 = 0.0
tmp21 = tmp19 >= tmp20
tmp22 = 1.0
tmp23 = -1.0
tmp24 = tl.where(tmp21, tmp22, tmp23)
tmp25 = tmp24 * tmp19
tmp26 = tmp8 / tmp25
tl.store(out_ptr0 + x3, tmp26, xmask)
@triton.jit
def triton_poi_fused__softmax_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
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp2 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp7 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp10 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp1 = tl_math.exp(tmp0)
tmp3 = tl_math.exp(tmp2)
tmp5 = tl_math.exp(tmp4)
tmp6 = tmp3 + tmp5
tmp8 = tl_math.exp(tmp7)
tmp9 = tmp6 + tmp8
tmp11 = tl_math.exp(tmp10)
tmp12 = tmp9 + tmp11
tmp13 = tmp1 / 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__softmax_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del arg0_1
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_1[grid(256)](buf0, buf1, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_2[grid(256)](buf1, buf0, buf2, 256,
XBLOCK=256, num_warps=4, num_stages=1)
buf3 = buf1
del buf1
triton_poi_fused__softmax_3[grid(256)](buf2, buf3, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf4 = buf0
del buf0
triton_poi_fused__softmax_4[grid(256)](buf3, buf2, buf4, 256,
XBLOCK=256, num_warps=4, num_stages=1)
buf5 = buf3
del buf3
triton_poi_fused__softmax_3[grid(256)](buf4, buf5, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf6 = buf2
del buf2
triton_poi_fused__softmax_4[grid(256)](buf5, buf4, buf6, 256,
XBLOCK=256, num_warps=4, num_stages=1)
buf7 = buf5
del buf5
triton_poi_fused__softmax_3[grid(256)](buf6, buf7, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf8 = buf4
del buf4
triton_poi_fused__softmax_4[grid(256)](buf7, buf6, buf8, 256,
XBLOCK=256, num_warps=4, num_stages=1)
buf9 = buf7
del buf7
triton_poi_fused__softmax_3[grid(256)](buf8, buf9, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf10 = buf6
del buf6
triton_poi_fused__softmax_4[grid(256)](buf9, buf8, buf10, 256,
XBLOCK=256, num_warps=4, num_stages=1)
buf11 = buf9
del buf9
triton_poi_fused__softmax_3[grid(256)](buf10, buf11, 256, XBLOCK=
256, num_warps=4, num_stages=1)
buf12 = buf8
del buf8
triton_poi_fused__softmax_4[grid(256)](buf11, buf10, buf12, 256,
XBLOCK=256, num_warps=4, num_stages=1)
buf13 = buf11
del buf11
triton_poi_fused__softmax_3[grid(256)](buf12, buf13, 256, XBLOCK=
256, num_warps=4, num_stages=1)
buf14 = buf10
del buf10
triton_poi_fused__softmax_4[grid(256)](buf13, buf12, buf14, 256,
XBLOCK=256, num_warps=4, num_stages=1)
buf15 = buf13
del buf13
triton_poi_fused__softmax_3[grid(256)](buf14, buf15, 256, XBLOCK=
256, num_warps=4, num_stages=1)
buf16 = buf12
del buf12
triton_poi_fused__softmax_4[grid(256)](buf15, buf14, buf16, 256,
XBLOCK=256, num_warps=4, num_stages=1)
buf17 = buf15
del buf15
triton_poi_fused__softmax_3[grid(256)](buf16, buf17, 256, XBLOCK=
256, num_warps=4, num_stages=1)
buf18 = buf14
del buf14
triton_poi_fused__softmax_4[grid(256)](buf17, buf16, buf18, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del buf16
buf19 = buf17
del buf17
triton_poi_fused__softmax_5[grid(256)](buf18, buf19, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del buf18
return buf19,
class softmaxNew(nn.Module):
def __init__(self, layer=10, channels=32):
super(softmaxNew, self).__init__()
layers = []
for i in range(layer):
layers.append(nn.Softmax(dim=1))
self.layers = nn.Sequential(*layers)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
yifanpu001/PytorchToCaffe
|
softmax
| false
| 4,727
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
Attention
|
from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
from torch.nn import Dropout
from torch.nn import Softmax
from torch.nn import Linear
class Attention(nn.Module):
def __init__(self, config):
super(Attention, self).__init__()
self.num_attention_heads = config['num_heads']
self.attention_head_size = int(config['hidden_size'] / self.
num_attention_heads)
self.all_head_size = (self.num_attention_heads * self.
attention_head_size)
self.query = Linear(config['hidden_size'], self.all_head_size)
self.key = Linear(config['hidden_size'], self.all_head_size)
self.value = Linear(config['hidden_size'], self.all_head_size)
self.out = Linear(self.all_head_size, config['hidden_size'])
self.attn_dropout = Dropout(config['attention_dropout_rate'])
self.proj_dropout = Dropout(config['attention_dropout_rate'])
self.softmax = Softmax(dim=-1)
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):
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_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_probs = self.softmax(attention_scores)
attention_probs = self.attn_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)
attention_output = self.out(context_layer)
attention_output = self.proj_dropout(attention_output)
return attention_output
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(num_heads=4, hidden_size=4,
attention_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
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 Dropout
from torch.nn import Softmax
from torch.nn import Linear
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, 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
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_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_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
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp18 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp25 = tl.load(in_ptr1 + x2, xmask)
tmp26 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
tmp27 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp29 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp31 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp1 = float('-inf')
tmp2 = tmp0 == tmp1
tmp3 = tmp2 == 0
tmp4 = tmp3.to(tl.int64)
tmp5 = tmp4 != 0
tmp7 = tmp6 == tmp1
tmp8 = tmp7 == 0
tmp9 = tmp8.to(tl.int64)
tmp10 = tmp9 != 0
tmp11 = tmp5 | tmp10
tmp13 = tmp12 == tmp1
tmp14 = tmp13 == 0
tmp15 = tmp14.to(tl.int64)
tmp16 = tmp15 != 0
tmp17 = tmp11 | tmp16
tmp19 = tmp18 == tmp1
tmp20 = tmp19 == 0
tmp21 = tmp20.to(tl.int64)
tmp22 = tmp21 != 0
tmp23 = tmp17 | tmp22
tmp24 = tmp23 == 0
tmp28 = tmp26 + tmp27
tmp30 = tmp28 + tmp29
tmp32 = tmp30 + tmp31
tmp33 = tmp25 / tmp32
tmp34 = 0.0
tmp35 = tl.where(tmp24, tmp34, tmp33)
tl.store(out_ptr0 + x2, tmp35, xmask)
@triton.jit
def triton_poi_fused_3(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_clone_4(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, 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), (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,))
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_0[grid(16, 4)](buf0, primals_2, buf3, 16, 4,
XBLOCK=2, YBLOCK=16, 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_0[grid(16, 4)](buf1, primals_5, buf4, 16, 4,
XBLOCK=2, YBLOCK=16, 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 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_1[grid(256)](buf5, buf6, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_2[grid(256)](buf5, buf6, buf7, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del buf5
del buf6
buf8 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf1
triton_poi_fused_3[grid(16, 4)](buf2, primals_7, buf8, 16, 4,
XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
del primals_7
buf9 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
del buf2
extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9)
buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
triton_poi_fused_clone_4[grid(16, 4)](buf9, buf10, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf11 = reinterpret_tensor(buf9, (16, 4), (4, 1), 0)
del buf9
extern_kernels.addmm(primals_9, reinterpret_tensor(buf10, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf11)
del primals_9
return reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
), buf7, reinterpret_tensor(buf8, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0
), reinterpret_tensor(buf10, (16, 4), (4, 1), 0), primals_8
class AttentionNew(nn.Module):
def __init__(self, config):
super(AttentionNew, self).__init__()
self.num_attention_heads = config['num_heads']
self.attention_head_size = int(config['hidden_size'] / self.
num_attention_heads)
self.all_head_size = (self.num_attention_heads * self.
attention_head_size)
self.query = Linear(config['hidden_size'], self.all_head_size)
self.key = Linear(config['hidden_size'], self.all_head_size)
self.value = Linear(config['hidden_size'], self.all_head_size)
self.out = Linear(self.all_head_size, config['hidden_size'])
self.attn_dropout = Dropout(config['attention_dropout_rate'])
self.proj_dropout = Dropout(config['attention_dropout_rate'])
self.softmax = Softmax(dim=-1)
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):
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_8 = self.out.weight
primals_9 = self.out.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]
|
LJOVO/TranSalNet
|
Attention
| false
| 4,728
|
[
"MIT"
] | 0
|
a2aba83e3b8f54c47b712511bf4f515f236326ed
|
https://github.com/LJOVO/TranSalNet/tree/a2aba83e3b8f54c47b712511bf4f515f236326ed
|
LengthPredictor
|
import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class LengthPredictionLoss(nn.Module):
def __init__(self, max_delta=50):
super().__init__()
self.max_delta = max_delta
def forward(self, logits, src_mask, tgt_mask):
src_lens, tgt_lens = src_mask.sum(1), tgt_mask.sum(1)
delta = (tgt_lens - src_lens + self.max_delta).clamp(0, self.
max_delta * 2 - 1).long()
loss = F.cross_entropy(logits, delta, reduction='mean')
return {'length_prediction_loss': loss}
class LengthPredictor(nn.Module):
def __init__(self, hidden_size, max_delta=50):
super().__init__()
self.hidden_size = hidden_size
self.max_delta = max_delta
self._init_modules()
self._init_loss()
def forward(self, src, src_mask, tgt_len=None):
src_mean = self._compute_mean_emb(src, src_mask)
logits, delta = self._predict_delta(src_mean)
return logits, delta
def _predict_delta(self, src):
logits = self.length_predictor(src)
delta = logits.argmax(-1) - float(self.max_delta)
return logits, delta
def _compute_mean_emb(self, src, src_mask):
mean_emb = (src * src_mask[:, :, None]).sum(1) / src_mask.sum(1)[:,
None]
return mean_emb
def _init_modules(self):
self.length_predictor = nn.Linear(self.hidden_size, self.max_delta * 2)
def _init_loss(self):
self.loss = LengthPredictionLoss(self.max_delta)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'hidden_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.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_mul_sum_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 % 64
x0 = xindex % 16
x2 = xindex // 64
x4 = xindex
tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (64 + x3), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp7 = tl.load(in_ptr0 + (128 + x3), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp11 = tl.load(in_ptr0 + (192 + x3), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr1 + (48 + x0 + 64 * 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 = tmp1 + tmp4
tmp16 = tmp15 + tmp8
tmp17 = tmp16 + tmp12
tmp18 = tmp14 / tmp17
tl.store(out_ptr0 + x4, tmp18, xmask)
@triton.jit
def triton_per_fused_argmax_sub_1(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK:
tl.constexpr):
xnumel = 64
rnumel = 100
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, :]
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 100 * x0), rmask & xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(rmask & xmask, tmp1, float('-inf'))
tmp4 = tl.broadcast_to(rindex, tmp3.shape)
_, tmp2_tmp = triton_helpers.max_with_index(tmp3, tmp4, 1)
tmp2 = tmp2_tmp[:, None]
tmp5 = tmp2.to(tl.float32)
tmp6 = 50.0
tmp7 = tmp5 - tmp6
tl.store(out_ptr1 + x0, tmp7, 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, (100, 4), (4, 1))
assert_size_stride(primals_4, (100,), (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_mul_sum_0[grid(256)](primals_2, primals_1,
buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_1
del primals_2
buf1 = empty_strided_cuda((64, 100), (100, 1), torch.float32)
extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_3, (4, 100), (1, 4), 0),
alpha=1, beta=1, out=buf1)
del primals_3
del primals_4
buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_per_fused_argmax_sub_1[grid(64)](buf1, buf3, 64, 100, XBLOCK
=8, num_warps=8, num_stages=1)
return reinterpret_tensor(buf1, (4, 4, 4, 100), (1600, 400, 100, 1), 0
), buf3, reinterpret_tensor(buf0, (64, 4), (4, 1), 0)
class LengthPredictionLoss(nn.Module):
def __init__(self, max_delta=50):
super().__init__()
self.max_delta = max_delta
def forward(self, logits, src_mask, tgt_mask):
src_lens, tgt_lens = src_mask.sum(1), tgt_mask.sum(1)
delta = (tgt_lens - src_lens + self.max_delta).clamp(0, self.
max_delta * 2 - 1).long()
loss = F.cross_entropy(logits, delta, reduction='mean')
return {'length_prediction_loss': loss}
class LengthPredictorNew(nn.Module):
def __init__(self, hidden_size, max_delta=50):
super().__init__()
self.hidden_size = hidden_size
self.max_delta = max_delta
self._init_modules()
self._init_loss()
def _predict_delta(self, src):
logits = self.length_predictor(src)
delta = logits.argmax(-1) - float(self.max_delta)
return logits, delta
def _compute_mean_emb(self, src, src_mask):
mean_emb = (src * src_mask[:, :, None]).sum(1) / src_mask.sum(1)[:,
None]
return mean_emb
def _init_modules(self):
self.length_predictor = nn.Linear(self.hidden_size, self.max_delta * 2)
def _init_loss(self):
self.loss = LengthPredictionLoss(self.max_delta)
def forward(self, input_0, input_1):
primals_3 = self.length_predictor.weight
primals_4 = self.length_predictor.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0], output[1]
|
ygnn123/training_extensions
|
LengthPredictor
| false
| 4,729
|
[
"Apache-2.0"
] | 0
|
c3aeba9359b0d4e0ef9c054de777d3ec081a9892
|
https://github.com/ygnn123/training_extensions/tree/c3aeba9359b0d4e0ef9c054de777d3ec081a9892
|
toy_yolov3
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class toy_yolov3(nn.Module):
def __init__(self):
super(toy_yolov3, self).__init__()
self.conv1 = nn.Conv2d(3, 128, kernel_size=3, stride=2, padding=1)
self.conv2_1 = nn.Conv2d(128, 128, kernel_size=1, stride=1, padding=0)
self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1)
self.conv4 = nn.Conv2d(256, 255, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.Conv2d(255, 255, kernel_size=3, stride=2, padding=1)
self.conv6 = nn.Conv2d(255, 255, kernel_size=3, stride=2, padding=1)
def forward(self, x):
x = self.conv1(x)
identity = x
out = self.conv2_1(x)
out = self.conv2_2(out)
out += identity
out = self.conv3(out)
out = F.interpolate(out, scale_factor=2)
out = torch.cat((out, identity), dim=1)
out1 = self.conv4(out)
out2 = self.conv5(out1)
out3 = self.conv6(out2)
return out3
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
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, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 384
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 % 3
y1 = yindex // 3
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 3 * x2 + 27 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_1(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_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):
ynumel = 65280
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 % 256
y1 = yindex // 256
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 65025
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 % 255
y1 = yindex // 255
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 255 * x2 + 2295 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 128
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x2, tmp2, None)
@triton.jit
def triton_poi_fused_add_convolution_6(in_out_ptr0, in_ptr0, in_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 % 128
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x2, None)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_7(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_cat_8(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)
x0 = xindex % 256
x2 = xindex // 8192 % 32
x1 = xindex // 256 % 32
x3 = xindex // 262144
x5 = xindex // 256
x6 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 128, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + x2, tmp4, eviction_policy='evict_last', other=0.0)
tmp6 = tl.full([XBLOCK], 16, tl.int32)
tmp7 = tmp5 + tmp6
tmp8 = tmp5 < 0
tmp9 = tl.where(tmp8, tmp7, tmp5)
tmp10 = tl.load(in_ptr0 + x1, tmp4, eviction_policy='evict_last', other=0.0
)
tmp11 = tmp10 + tmp6
tmp12 = tmp10 < 0
tmp13 = tl.where(tmp12, tmp11, tmp10)
tmp14 = tl.load(in_ptr1 + (128 * tmp13 + 2048 * tmp9 + 32768 * x3 + x0),
tmp4, eviction_policy='evict_last', other=0.0)
tmp15 = tl.load(in_ptr2 + x0, tmp4, eviction_policy='evict_last', other=0.0
)
tmp16 = tmp14 + tmp15
tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
tmp18 = tl.where(tmp4, tmp16, tmp17)
tmp19 = tmp0 >= tmp3
tl.full([1], 256, tl.int64)
tmp22 = tl.load(in_ptr3 + (128 * x5 + (-128 + x0)), tmp19,
eviction_policy='evict_last', other=0.0)
tmp23 = tl.where(tmp4, tmp18, tmp22)
tl.store(out_ptr0 + x6, tmp23, None)
@triton.jit
def triton_poi_fused_convolution_9(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 % 255
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x2, tmp2, None)
@triton.jit
def triton_poi_fused_convolution_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 261120
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 255
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_convolution_11(in_ptr0, in_ptr1, out_ptr0, ynumel,
xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 1020
xnumel = 64
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 % 255
y1 = yindex // 255
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 255 * x2 + 16320 * 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 + 64 * y3), tmp2, xmask & ymask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15) = args
args.clear()
assert_size_stride(primals_1, (128, 3, 3, 3), (27, 9, 3, 1))
assert_size_stride(primals_2, (128,), (1,))
assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_4, (128, 128, 1, 1), (128, 1, 1, 1))
assert_size_stride(primals_5, (128,), (1,))
assert_size_stride(primals_6, (128, 128, 3, 3), (1152, 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, (255, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_11, (255,), (1,))
assert_size_stride(primals_12, (255, 255, 3, 3), (2295, 9, 3, 1))
assert_size_stride(primals_13, (255,), (1,))
assert_size_stride(primals_14, (255, 255, 3, 3), (2295, 9, 3, 1))
assert_size_stride(primals_15, (255,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((128, 3, 3, 3), (27, 1, 9, 3), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(384, 9)](primals_1, buf0, 384, 9, XBLOCK=16,
YBLOCK=64, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
.float32)
triton_poi_fused_1[grid(12, 4096)](primals_3, buf1, 12, 4096,
XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
torch.float32)
triton_poi_fused_2[grid(16384, 9)](primals_6, buf2, 16384, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_6
buf3 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
torch.float32)
triton_poi_fused_2[grid(16384, 9)](primals_8, buf3, 16384, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_8
buf4 = empty_strided_cuda((255, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_3[grid(65280, 9)](primals_10, buf4, 65280, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_10
buf5 = empty_strided_cuda((255, 255, 3, 3), (2295, 1, 765, 255),
torch.float32)
triton_poi_fused_4[grid(65025, 9)](primals_12, buf5, 65025, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_12
buf6 = empty_strided_cuda((255, 255, 3, 3), (2295, 1, 765, 255),
torch.float32)
triton_poi_fused_4[grid(65025, 9)](primals_14, buf6, 65025, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_14
buf7 = extern_kernels.convolution(buf1, buf0, stride=(2, 2),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 128, 32, 32), (131072, 1, 4096, 128))
buf8 = buf7
del buf7
triton_poi_fused_convolution_5[grid(524288)](buf8, primals_2,
524288, XBLOCK=512, num_warps=8, num_stages=1)
del primals_2
buf9 = extern_kernels.convolution(buf8, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 128, 32, 32), (131072, 1, 4096, 128))
buf10 = buf9
del buf9
triton_poi_fused_convolution_5[grid(524288)](buf10, primals_5,
524288, XBLOCK=512, num_warps=8, num_stages=1)
del primals_5
buf11 = extern_kernels.convolution(buf10, buf2, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf11, (4, 128, 32, 32), (131072, 1, 4096, 128))
buf12 = buf11
del buf11
triton_poi_fused_add_convolution_6[grid(524288)](buf12, primals_7,
buf8, 524288, XBLOCK=512, num_warps=8, num_stages=1)
del primals_7
buf13 = extern_kernels.convolution(buf12, buf3, stride=(2, 2),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf13, (4, 128, 16, 16), (32768, 1, 2048, 128))
buf14 = empty_strided_cuda((32,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_7[grid(32)](buf14, 32,
XBLOCK=32, num_warps=1, num_stages=1)
buf15 = empty_strided_cuda((4, 256, 32, 32), (262144, 1, 8192, 256),
torch.float32)
triton_poi_fused_cat_8[grid(1048576)](buf14, buf13, primals_9, buf8,
buf15, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
del buf13
del primals_9
buf16 = extern_kernels.convolution(buf15, buf4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf16, (4, 255, 32, 32), (261120, 1, 8160, 255))
buf17 = buf16
del buf16
triton_poi_fused_convolution_9[grid(1044480)](buf17, primals_11,
1044480, XBLOCK=512, num_warps=8, num_stages=1)
del primals_11
buf18 = extern_kernels.convolution(buf17, buf5, stride=(2, 2),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf18, (4, 255, 16, 16), (65280, 1, 4080, 255))
buf19 = buf18
del buf18
triton_poi_fused_convolution_10[grid(261120)](buf19, primals_13,
261120, XBLOCK=512, num_warps=8, num_stages=1)
del primals_13
buf20 = extern_kernels.convolution(buf19, buf6, stride=(2, 2),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf20, (4, 255, 8, 8), (16320, 1, 2040, 255))
buf21 = empty_strided_cuda((4, 255, 8, 8), (16320, 64, 8, 1), torch
.float32)
triton_poi_fused_convolution_11[grid(1020, 64)](buf20, primals_15,
buf21, 1020, 64, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
del buf20
del primals_15
return (buf21, buf0, buf1, primals_4, buf2, buf3, buf4, buf5, buf6,
buf8, buf10, buf12, buf14, buf15, buf17, buf19)
class toy_yolov3New(nn.Module):
def __init__(self):
super(toy_yolov3New, self).__init__()
self.conv1 = nn.Conv2d(3, 128, kernel_size=3, stride=2, padding=1)
self.conv2_1 = nn.Conv2d(128, 128, kernel_size=1, stride=1, padding=0)
self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1)
self.conv4 = nn.Conv2d(256, 255, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.Conv2d(255, 255, kernel_size=3, stride=2, padding=1)
self.conv6 = nn.Conv2d(255, 255, kernel_size=3, stride=2, padding=1)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2_1.weight
primals_5 = self.conv2_1.bias
primals_6 = self.conv2_2.weight
primals_7 = self.conv2_2.bias
primals_8 = self.conv3.weight
primals_9 = self.conv3.bias
primals_10 = self.conv4.weight
primals_11 = self.conv4.bias
primals_12 = self.conv5.weight
primals_13 = self.conv5.bias
primals_14 = self.conv6.weight
primals_15 = self.conv6.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])
return output[0]
|
yifanpu001/PytorchToCaffe
|
toy_yolov3
| false
| 4,730
|
[
"MIT"
] | 0
|
37c1ebfc3547e93b1c174721036d03c831c60e48
|
https://github.com/yifanpu001/PytorchToCaffe/tree/37c1ebfc3547e93b1c174721036d03c831c60e48
|
RobertaClassificationHead
|
from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size * 2, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, 2)
def forward(self, features, **kwargs):
x = features[:, 0, :]
x = x.reshape(-1, x.size(-1) * 2)
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, hidden_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.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_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 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, 8), (8, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (2, 4), (4, 1))
assert_size_stride(primals_5, (2,), (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((8, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (8, 8), (8, 1), 0),
reinterpret_tensor(primals_2, (8, 4), (1, 8), 0), out=buf1)
del primals_2
buf2 = buf1
del buf1
triton_poi_fused_tanh_1[grid(32)](buf2, primals_3, 32, XBLOCK=32,
num_warps=1, num_stages=1)
del primals_3
buf3 = empty_strided_cuda((8, 2), (2, 1), torch.float32)
extern_kernels.addmm(primals_5, buf2, reinterpret_tensor(primals_4,
(4, 2), (1, 4), 0), alpha=1, beta=1, out=buf3)
del primals_5
return buf3, reinterpret_tensor(buf0, (8, 8), (8, 1), 0), buf2, primals_4
class RobertaClassificationHeadNew(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size * 2, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, 2)
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]
|
Masum06/CodeXGLUE
|
RobertaClassificationHead
| false
| 4,731
|
[
"CC0-1.0",
"MIT"
] | 0
|
bf1ab8c8878f978bd4ef3cb5e030e52f03e92854
|
https://github.com/Masum06/CodeXGLUE/tree/bf1ab8c8878f978bd4ef3cb5e030e52f03e92854
|
RobustLogisticRegression
|
import torch
import numpy as np
from torch import nn
from torch.utils.data import DataLoader
from torchvision import transforms
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from torch.utils.data import Dataset
def compute_auc(labels, scores, pos_label=1):
fpr, tpr, _thresholds = metrics.roc_curve(labels, scores, pos_label=
pos_label)
return metrics.auc(fpr, tpr)
class Subset(Dataset):
def __init__(self, data, labels, normalize=False):
self.ims = data
self.labels = labels
self.normalize = normalize
if normalize:
self.T = transforms.Normalize(0.5, 0.5)
else:
self.T = lambda x: x
def __getitem__(self, idx):
ret = {'ims': self.T(self.ims[idx]), 'labels': self.labels[idx]}
return ret
def __len__(self):
return self.labels.shape[0]
class RobustLogisticRegression(nn.Module):
def __init__(self, alpha=[1.0, 1.0, 1.0, 1.0], beta=1.0, alpha0=[1.0,
0.0, 0.0, 0.0]):
super(RobustLogisticRegression, self).__init__()
self.alpha = nn.Parameter(torch.Tensor(alpha))
if not len(alpha0) == 4:
raise ValueError('alpha0 must be a vector og length 4')
self.alpha0 = torch.Tensor(alpha0)
self.beta = beta
self.scaler = StandardScaler()
def get_alpha(self):
return self.alpha
def forward(self, x):
t = torch.Tensor(x)
t = t * self.get_alpha()
t = t.sum(1)
return t
def predict_prob(self, x):
return 1 / (1 + torch.exp(-self(x)))
def predict(self, x):
return torch.round(self.predict_prob(x))
def prior_loss(self):
return self.beta * torch.sqrt(torch.square(self.alpha0 - self.alpha
).sum())
def binary_acc(self, x, y):
y_true = np.array(y)
y_pred = self.predict(x).detach().numpy()
nhits = (y_pred == y_true).sum()
return nhits / y_true.shape[0]
def auc(self, x, y):
scores = self.predict_prob(x)
return compute_auc(y, scores.detach().numpy())
def scaler_fit(self, x):
self.scaler.fit(x)
def scaler_transform(self, x):
return self.scaler.transform(x)
def save_weights(self, f):
np.save(f, self.alpha_params.detach().numpy())
def fit(self, x, y, tst_x=None, tst_y=None, nepochs=2, batch_size=256,
lr=0.01, opt_type='lbfgs', workers=1, balanced=True, verb=True,
scale=False, early_stopping=False, patience=10):
if scale:
self.scaler_fit(x)
x = self.scaler_transform(x)
tst_x = tst_x if tst_x is None else self.scaler_transform(tst_x)
if balanced:
n1 = int(sum(y))
n0 = len(y) - n1
if n0 < n1:
p = int(np.floor(n1 / n0))
X = np.concatenate((x[y == 0].repeat(p, 0), x[y == 1]), 0)
Y = np.concatenate((y[y == 0].repeat(p, 0), y[y == 1]), 0)
else:
p = int(np.floor(n0 / n1))
X = np.concatenate((x[y == 1].repeat(p, 0), x[y == 0]), 0)
Y = np.concatenate((y[y == 1].repeat(p, 0), y[y == 0]), 0)
else:
X = x
Y = y
loader = DataLoader(Subset(torch.tensor(X).float(), torch.Tensor(Y)
), batch_size=batch_size, shuffle=True, num_workers=workers)
criterion = nn.BCEWithLogitsLoss()
if opt_type == 'lbfgs':
opt = torch.optim.LBFGS(self.parameters(), lr=lr)
elif opt_type == 'adam':
opt = torch.optim.Adam(self.parameters(), lr=lr)
if nepochs == 2:
nepochs = 200
else:
raise ValueError('opt_type must be lbfgs or adam')
best_auc = self.auc(x, y)
pat = 0
if verb:
None
for epoch in range(nepochs):
if verb:
criterion(self(torch.Tensor(x)), torch.Tensor(y)).detach(
).numpy().round(3)
self.prior_loss().detach().numpy().round(3)
self.auc(x, y).round(3)
self.binary_acc(x, y).round(3)
np.NAN if tst_x is None else self.auc(tst_x, tst_y).round(3)
_alpha = self.get_alpha().detach().numpy()
None
if early_stopping:
cur_auc = self.auc(x, y)
if cur_auc < best_auc:
if pat < patience:
pat += 1
else:
if verb:
None
return
else:
best_auc = cur_auc
pat = 0
for batch in loader:
_x, _y = batch['ims'], batch['labels']
def loss_closure():
opt.zero_grad()
pred = self(_x)
loss = criterion(pred, _y) + self.prior_loss()
loss.backward()
return loss
if opt_type == 'lbfgs':
opt.step(loss_closure)
elif opt_type == 'adam':
loss_closure()
opt.step()
else:
raise ValueError('opt_type must be lbfgs or adam')
if (abs(self.alpha.detach().numpy()) > 1000000.0).any():
raise RuntimeError('convergence failed')
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 numpy as np
from torch import nn
from torch.utils.data import DataLoader
from torchvision import transforms
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from torch.utils.data import Dataset
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_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex // 16
x3 = xindex % 16
x0 = xindex % 4
x4 = xindex
tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
tmp6 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
tmp9 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
tmp2 = tmp0 * tmp1
tmp4 = tmp3 * tmp1
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp1
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp1
tmp11 = tmp8 + tmp10
tl.store(out_ptr0 + x4, tmp11, 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,), (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_mul_sum_0[grid(64)](primals_1, primals_2, buf0, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_2
return buf0, primals_1
def compute_auc(labels, scores, pos_label=1):
fpr, tpr, _thresholds = metrics.roc_curve(labels, scores, pos_label=
pos_label)
return metrics.auc(fpr, tpr)
class Subset(Dataset):
def __init__(self, data, labels, normalize=False):
self.ims = data
self.labels = labels
self.normalize = normalize
if normalize:
self.T = transforms.Normalize(0.5, 0.5)
else:
self.T = lambda x: x
def __getitem__(self, idx):
ret = {'ims': self.T(self.ims[idx]), 'labels': self.labels[idx]}
return ret
def __len__(self):
return self.labels.shape[0]
class RobustLogisticRegressionNew(nn.Module):
def __init__(self, alpha=[1.0, 1.0, 1.0, 1.0], beta=1.0, alpha0=[1.0,
0.0, 0.0, 0.0]):
super(RobustLogisticRegressionNew, self).__init__()
self.alpha = nn.Parameter(torch.Tensor(alpha))
if not len(alpha0) == 4:
raise ValueError('alpha0 must be a vector og length 4')
self.alpha0 = torch.Tensor(alpha0)
self.beta = beta
self.scaler = StandardScaler()
def get_alpha(self):
return self.alpha
def predict_prob(self, x):
return 1 / (1 + torch.exp(-self(x)))
def predict(self, x):
return torch.round(self.predict_prob(x))
def prior_loss(self):
return self.beta * torch.sqrt(torch.square(self.alpha0 - self.alpha
).sum())
def binary_acc(self, x, y):
y_true = np.array(y)
y_pred = self.predict(x).detach().numpy()
nhits = (y_pred == y_true).sum()
return nhits / y_true.shape[0]
def auc(self, x, y):
scores = self.predict_prob(x)
return compute_auc(y, scores.detach().numpy())
def scaler_fit(self, x):
self.scaler.fit(x)
def scaler_transform(self, x):
return self.scaler.transform(x)
def save_weights(self, f):
np.save(f, self.alpha_params.detach().numpy())
def fit(self, x, y, tst_x=None, tst_y=None, nepochs=2, batch_size=256,
lr=0.01, opt_type='lbfgs', workers=1, balanced=True, verb=True,
scale=False, early_stopping=False, patience=10):
if scale:
self.scaler_fit(x)
x = self.scaler_transform(x)
tst_x = tst_x if tst_x is None else self.scaler_transform(tst_x)
if balanced:
n1 = int(sum(y))
n0 = len(y) - n1
if n0 < n1:
p = int(np.floor(n1 / n0))
X = np.concatenate((x[y == 0].repeat(p, 0), x[y == 1]), 0)
Y = np.concatenate((y[y == 0].repeat(p, 0), y[y == 1]), 0)
else:
p = int(np.floor(n0 / n1))
X = np.concatenate((x[y == 1].repeat(p, 0), x[y == 0]), 0)
Y = np.concatenate((y[y == 1].repeat(p, 0), y[y == 0]), 0)
else:
X = x
Y = y
loader = DataLoader(Subset(torch.tensor(X).float(), torch.Tensor(Y)
), batch_size=batch_size, shuffle=True, num_workers=workers)
criterion = nn.BCEWithLogitsLoss()
if opt_type == 'lbfgs':
opt = torch.optim.LBFGS(self.parameters(), lr=lr)
elif opt_type == 'adam':
opt = torch.optim.Adam(self.parameters(), lr=lr)
if nepochs == 2:
nepochs = 200
else:
raise ValueError('opt_type must be lbfgs or adam')
best_auc = self.auc(x, y)
pat = 0
if verb:
None
for epoch in range(nepochs):
if verb:
criterion(self(torch.Tensor(x)), torch.Tensor(y)).detach(
).numpy().round(3)
self.prior_loss().detach().numpy().round(3)
self.auc(x, y).round(3)
self.binary_acc(x, y).round(3)
np.NAN if tst_x is None else self.auc(tst_x, tst_y).round(3)
_alpha = self.get_alpha().detach().numpy()
None
if early_stopping:
cur_auc = self.auc(x, y)
if cur_auc < best_auc:
if pat < patience:
pat += 1
else:
if verb:
None
return
else:
best_auc = cur_auc
pat = 0
for batch in loader:
_x, _y = batch['ims'], batch['labels']
def loss_closure():
opt.zero_grad()
pred = self(_x)
loss = criterion(pred, _y) + self.prior_loss()
loss.backward()
return loss
if opt_type == 'lbfgs':
opt.step(loss_closure)
elif opt_type == 'adam':
loss_closure()
opt.step()
else:
raise ValueError('opt_type must be lbfgs or adam')
if (abs(self.alpha.detach().numpy()) > 1000000.0).any():
raise RuntimeError('convergence failed')
def forward(self, input_0):
primals_2 = self.alpha
primals_1 = input_0
output = call([primals_1, primals_2])
return output[0]
|
vitskvara/shape-guided-anomaly-detection
|
RobustLogisticRegression
| false
| 4,732
|
[
"MIT"
] | 0
|
6685b2e0b97968a6d0f478d2920486da107b277f
|
https://github.com/vitskvara/shape-guided-anomaly-detection/tree/6685b2e0b97968a6d0f478d2920486da107b277f
|
RobertaClassificationHead
|
from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.utils.checkpoint
class RobertaClassificationHead(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(config.hidden_dropout_prob)
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, hidden_dropout_prob=
0.5, 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
import torch.nn as nn
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_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 RobertaClassificationHeadNew(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(config.hidden_dropout_prob)
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]
|
Hzfinfdu/Black-Box-Tuning
|
RobertaClassificationHead
| false
| 4,733
|
[
"MIT"
] | 0
|
64eb5505875dc1b242c6f0a2a2f07e4000c24cb4
|
https://github.com/Hzfinfdu/Black-Box-Tuning/tree/64eb5505875dc1b242c6f0a2a2f07e4000c24cb4
|
BertAttention
|
from _paritybench_helpers import _mock_config
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn
import torch as torch
import torch.sparse
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)
self.softmax = nn.Softmax(dim=-1)
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 transpose_key_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, 3, 1)
def forward(self, hidden_states, attention_mask):
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_key_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
attention_scores = torch.matmul(query_layer, key_layer)
attention_scores = attention_scores / math.sqrt(self.
attention_head_size)
attention_scores = attention_scores + attention_mask
attention_probs = self.softmax(attention_scores)
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
class BertSelfOutput(nn.Module):
def __init__(self, config):
super(BertSelfOutput, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dense.bert_output_layer = True
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class BertAttention(nn.Module):
def __init__(self, config):
super(BertAttention, self).__init__()
self.self = BertSelfAttention(config)
self.output = BertSelfOutput(config)
def forward(self, input_tensor, attention_mask):
self_output = self.self(input_tensor, attention_mask)
attention_output = self.output(self_output, input_tensor)
return attention_output
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, hidden_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 math
import torch.utils.data
import torch.nn as nn
import torch.nn
import torch as torch
import torch.sparse
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, primals_9, primals_10) = 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))
assert_size_stride(primals_9, (4, 4), (4, 1))
assert_size_stride(primals_10, (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=2, YBLOCK=16, 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=2, YBLOCK=16, 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=2, YBLOCK=16, 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)
buf12 = reinterpret_tensor(buf10, (16, 4), (4, 1), 0)
del buf10
extern_kernels.addmm(primals_10, reinterpret_tensor(buf11, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf12)
del primals_10
return reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
), buf8, reinterpret_tensor(buf11, (16, 4), (4, 1), 0
), primals_9, 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 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)
self.softmax = nn.Softmax(dim=-1)
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 transpose_key_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, 3, 1)
def forward(self, hidden_states, attention_mask):
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_key_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
attention_scores = torch.matmul(query_layer, key_layer)
attention_scores = attention_scores / math.sqrt(self.
attention_head_size)
attention_scores = attention_scores + attention_mask
attention_probs = self.softmax(attention_scores)
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
class BertSelfOutput(nn.Module):
def __init__(self, config):
super(BertSelfOutput, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dense.bert_output_layer = True
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class BertAttentionNew(nn.Module):
def __init__(self, config):
super(BertAttentionNew, self).__init__()
self.self = BertSelfAttention(config)
self.output = BertSelfOutput(config)
def forward(self, input_0, input_1):
primals_1 = self.self.query.weight
primals_2 = self.self.query.bias
primals_4 = self.self.key.weight
primals_5 = self.self.key.bias
primals_6 = self.self.value.weight
primals_7 = self.self.value.bias
primals_9 = self.output.dense.weight
primals_10 = self.output.dense.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, primals_9, primals_10])
return output[0]
|
Sengxian/cogdl
|
BertAttention
| false
| 4,734
|
[
"MIT"
] | 0
|
b0a855feef6a883bcc0f7df421fc6092ec18abde
|
https://github.com/Sengxian/cogdl/tree/b0a855feef6a883bcc0f7df421fc6092ec18abde
|
InnerProductLayer
|
import torch
import torch.nn as nn
from sklearn.metrics import *
class InnerProductLayer(nn.Module):
"""InnerProduct Layer used in PNN that compute the element-wise
product or inner product between feature vectors.
Input shape
- a list of 3D tensor with shape: ``(batch_size,1,embedding_size)``.
Output shape
- 3D tensor with shape: ``(batch_size, N*(N-1)/2 ,1)`` if use reduce_sum. or 3D tensor with shape:
``(batch_size, N*(N-1)/2, embedding_size )`` if not use reduce_sum.
Arguments
- **reduce_sum**: bool. Whether return inner product or element-wise product
References
- [Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//
Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.]
(https://arxiv.org/pdf/1611.00144.pdf)"""
def __init__(self, reduce_sum=True, device='cpu'):
super(InnerProductLayer, self).__init__()
self.reduce_sum = reduce_sum
self
def forward(self, inputs):
embed_list = inputs
row = []
col = []
num_inputs = len(embed_list)
for i in range(num_inputs - 1):
for j in range(i + 1, num_inputs):
row.append(i)
col.append(j)
p = torch.cat([embed_list[idx] for idx in row], dim=1)
q = torch.cat([embed_list[idx] for idx in col], dim=1)
inner_product = p * q
if self.reduce_sum:
inner_product = torch.sum(inner_product, dim=2, keepdim=True)
return inner_product
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
from sklearn.metrics 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_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 24
x0 = xindex % 4
x2 = xindex // 96
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 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr0 + (x0 + 4 * (-4 + x1) + 16 * x2), tmp9 & xmask,
other=0.0)
tmp11 = tmp0 >= tmp7
tmp12 = tl.full([1], 12, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tmp11 & tmp13
tmp15 = tl.load(in_ptr0 + (x0 + 4 * (-8 + x1) + 16 * x2), tmp14 & xmask,
other=0.0)
tmp16 = tmp0 >= tmp12
tmp17 = tl.full([1], 16, tl.int64)
tmp18 = tmp0 < tmp17
tmp19 = tmp16 & tmp18
tmp20 = tl.load(in_ptr0 + (64 + x0 + 4 * (-12 + x1) + 16 * x2), tmp19 &
xmask, other=0.0)
tmp21 = tmp0 >= tmp17
tmp22 = tl.full([1], 20, tl.int64)
tmp23 = tmp0 < tmp22
tmp24 = tmp21 & tmp23
tmp25 = tl.load(in_ptr0 + (64 + x0 + 4 * (-16 + x1) + 16 * x2), tmp24 &
xmask, other=0.0)
tmp26 = tmp0 >= tmp22
tl.full([1], 24, tl.int64)
tmp29 = tl.load(in_ptr0 + (128 + x0 + 4 * (-20 + x1) + 16 * x2), tmp26 &
xmask, other=0.0)
tmp30 = tl.where(tmp24, tmp25, tmp29)
tmp31 = tl.where(tmp19, tmp20, tmp30)
tmp32 = tl.where(tmp14, tmp15, tmp31)
tmp33 = tl.where(tmp9, tmp10, tmp32)
tmp34 = tl.where(tmp4, tmp5, tmp33)
tl.store(out_ptr0 + x3, tmp34, xmask)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 24
x0 = xindex % 4
x2 = xindex // 96
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 + (64 + x0 + 4 * x1 + 16 * x2), tmp4 & xmask,
other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr0 + (128 + x0 + 4 * (-4 + x1) + 16 * x2), tmp9 &
xmask, other=0.0)
tmp11 = tmp0 >= tmp7
tmp12 = tl.full([1], 12, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tmp11 & tmp13
tmp15 = tl.load(in_ptr0 + (192 + x0 + 4 * (-8 + x1) + 16 * x2), tmp14 &
xmask, other=0.0)
tmp16 = tmp0 >= tmp12
tmp17 = tl.full([1], 16, tl.int64)
tmp18 = tmp0 < tmp17
tmp19 = tmp16 & tmp18
tmp20 = tl.load(in_ptr0 + (128 + x0 + 4 * (-12 + x1) + 16 * x2), tmp19 &
xmask, other=0.0)
tmp21 = tmp0 >= tmp17
tmp22 = tl.full([1], 20, tl.int64)
tmp23 = tmp0 < tmp22
tmp24 = tmp21 & tmp23
tmp25 = tl.load(in_ptr0 + (192 + x0 + 4 * (-16 + x1) + 16 * x2), tmp24 &
xmask, other=0.0)
tmp26 = tmp0 >= tmp22
tl.full([1], 24, tl.int64)
tmp29 = tl.load(in_ptr0 + (192 + x0 + 4 * (-20 + x1) + 16 * x2), tmp26 &
xmask, other=0.0)
tmp30 = tl.where(tmp24, tmp25, tmp29)
tmp31 = tl.where(tmp19, tmp20, tmp30)
tmp32 = tl.where(tmp14, tmp15, tmp31)
tmp33 = tl.where(tmp9, tmp10, tmp32)
tmp34 = tl.where(tmp4, tmp5, tmp33)
tl.store(out_ptr0 + x3, tmp34, xmask)
@triton.jit
def triton_poi_fused_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 96
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), 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
tl.store(out_ptr0 + x0, tmp14, 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, 24, 4), (96, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(384)](arg0_1, buf0, 384, XBLOCK=256,
num_warps=4, num_stages=1)
buf1 = empty_strided_cuda((4, 24, 4), (96, 4, 1), torch.float32)
triton_poi_fused_cat_1[grid(384)](arg0_1, buf1, 384, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
buf2 = empty_strided_cuda((4, 24, 1), (24, 1, 1), torch.float32)
triton_poi_fused_mul_sum_2[grid(96)](buf0, buf1, buf2, 96, XBLOCK=
128, num_warps=4, num_stages=1)
del buf0
del buf1
return buf2,
class InnerProductLayerNew(nn.Module):
"""InnerProduct Layer used in PNN that compute the element-wise
product or inner product between feature vectors.
Input shape
- a list of 3D tensor with shape: ``(batch_size,1,embedding_size)``.
Output shape
- 3D tensor with shape: ``(batch_size, N*(N-1)/2 ,1)`` if use reduce_sum. or 3D tensor with shape:
``(batch_size, N*(N-1)/2, embedding_size )`` if not use reduce_sum.
Arguments
- **reduce_sum**: bool. Whether return inner product or element-wise product
References
- [Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//
Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.]
(https://arxiv.org/pdf/1611.00144.pdf)"""
def __init__(self, reduce_sum=True, device='cpu'):
super(InnerProductLayerNew, self).__init__()
self.reduce_sum = reduce_sum
self
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
zzz123xyz/DeepCTR-Torch
|
InnerProductLayer
| false
| 4,735
|
[
"Apache-2.0"
] | 0
|
d6b880cc6b3761dbef90920a28182ef6737dd665
|
https://github.com/zzz123xyz/DeepCTR-Torch/tree/d6b880cc6b3761dbef90920a28182ef6737dd665
|
BertLayer
|
from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
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 transform(self, x, linear_layer):
bs, seq_len = x.shape[:2]
proj = linear_layer(x)
proj = proj.view(bs, seq_len, self.num_attention_heads, self.
attention_head_size)
proj = proj.transpose(1, 2)
return proj
def attention(self, key, query, value, attention_mask):
S = torch.matmul(query, key.transpose(-1, -2)
) / self.attention_head_size ** (1 / 2)
if attention_mask is not None:
S += attention_mask
S_p = F.softmax(S, dim=-1)
S_p = self.dropout(S_p)
context_layer = torch.matmul(S_p, value)
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 forward(self, hidden_states, attention_mask):
"""
hidden_states: [bs, seq_len, hidden_state]
attention_mask: [bs, 1, 1, seq_len]
output: [bs, seq_len, hidden_state]
"""
key_layer = self.transform(hidden_states, self.key)
value_layer = self.transform(hidden_states, self.value)
query_layer = self.transform(hidden_states, self.query)
attn_value = self.attention(key_layer, query_layer, value_layer,
attention_mask)
return attn_value
class BertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.self_attention = BertSelfAttention(config)
self.attention_dense = nn.Linear(config.hidden_size, config.hidden_size
)
self.attention_layer_norm = nn.LayerNorm(config.hidden_size, eps=
config.layer_norm_eps)
self.attention_dropout = nn.Dropout(config.hidden_dropout_prob)
self.interm_dense = nn.Linear(config.hidden_size, config.
intermediate_size)
self.interm_af = F.gelu
self.out_dense = nn.Linear(config.intermediate_size, config.hidden_size
)
self.out_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.
layer_norm_eps)
self.out_dropout = nn.Dropout(config.hidden_dropout_prob)
def add_norm(self, input, output, dense_layer, dropout, ln_layer):
"""
input: the input
output: the input that requires the sublayer to transform
dense_layer, dropput: the sublayer
ln_layer: layer norm that takes input+sublayer(output) #SAM NOTE Really?
"""
output = dropout(dense_layer(output))
output = ln_layer(input + output)
return output
def forward(self, hidden_states, attention_mask):
"""
hidden_states: either from the embedding layer (first bert layer) or from the previous bert layer
as shown in the left of Figure 1 of https://arxiv.org/pdf/1706.03762.pdf
each block consists of
1. a multi-head attention layer (BertSelfAttention)
2. a add-norm that takes the output of BertSelfAttention and the input of BertSelfAttention
3. a feed forward layer
4. a add-norm that takes the output of feed forward layer and the input of feed forward layer
"""
attention = self.self_attention(hidden_states, attention_mask)
x = self.add_norm(hidden_states, attention, self.attention_dense,
self.attention_dropout, self.attention_layer_norm)
feed_forward = self.interm_dense(x)
feed_forward = self.interm_af(feed_forward)
out = self.add_norm(x, feed_forward, self.out_dense, self.
out_dropout, self.out_layer_norm)
return out
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'config': _mock_config(num_attention_heads=4, hidden_size=
4, attention_probs_dropout_prob=0.5, layer_norm_eps=1,
hidden_dropout_prob=0.5, 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_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
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2,
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')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = triton_helpers.maximum(tmp2, tmp5)
tmp9 = tmp7 + tmp8
tmp10 = triton_helpers.maximum(tmp6, tmp9)
tmp13 = tmp11 + tmp12
tmp14 = triton_helpers.maximum(tmp10, tmp13)
tmp15 = tmp2 - tmp14
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp5 - tmp14
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp20 = tmp9 - tmp14
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tmp13 - tmp14
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp26 = float('-inf')
tmp27 = tmp2 == tmp26
tmp28 = tmp27 == 0
tmp29 = tmp28.to(tl.int64)
tmp30 = tmp29 != 0
tmp31 = tmp5 == tmp26
tmp32 = tmp31 == 0
tmp33 = tmp32.to(tl.int64)
tmp34 = tmp33 != 0
tmp35 = tmp30 | tmp34
tmp36 = tmp9 == tmp26
tmp37 = tmp36 == 0
tmp38 = tmp37.to(tl.int64)
tmp39 = tmp38 != 0
tmp40 = tmp35 | tmp39
tmp41 = tmp13 == tmp26
tmp42 = tmp41 == 0
tmp43 = tmp42.to(tl.int64)
tmp44 = tmp43 != 0
tmp45 = tmp40 | tmp44
tl.store(out_ptr0 + x2, tmp14, xmask)
tl.store(out_ptr1 + x2, tmp25, xmask)
tl.store(out_ptr2 + x2, tmp45, xmask)
@triton.jit
def triton_poi_fused_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex // 4
x4 = xindex
x5 = xindex % 64
tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp2 = tl.load(in_out_ptr0 + x4, xmask)
tmp3 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr3 + x3, xmask, eviction_policy='evict_last')
tmp1 = tmp0 == 0
tmp4 = tmp2 + tmp3
tmp6 = tmp4 - tmp5
tmp7 = tl_math.exp(tmp6)
tmp9 = tmp7 / tmp8
tmp10 = 0.0
tmp11 = tl.where(tmp1, tmp10, tmp9)
tl.store(in_out_ptr0 + x4, tmp11, xmask)
@triton.jit
def triton_poi_fused_3(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_clone_4(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_add_native_layer_norm_5(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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), 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 + x0, tmp16, xmask)
tl.store(out_ptr1 + x0, tmp28, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_6(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, 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
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 - tmp3
tmp6 = 1.0
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + x2, tmp13, xmask)
@triton.jit
def triton_poi_fused_gelu_7(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_ptr0 + x0, xmask)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = 0.7071067811865476
tmp4 = tmp0 * tmp3
tmp5 = libdevice.erf(tmp4)
tmp6 = 1.0
tmp7 = tmp5 + tmp6
tmp8 = tmp2 * tmp7
tl.store(out_ptr0 + x0, tmp8, xmask)
@triton.jit
def triton_poi_fused_add_8(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_out_ptr0 + x2, xmask)
tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_9(in_ptr0, 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
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1.0
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_10(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, 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
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
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), (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,))
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))
assert_size_stride(primals_9, (4, 4), (4, 1))
assert_size_stride(primals_10, (4,), (1,))
assert_size_stride(primals_11, (4,), (1,))
assert_size_stride(primals_12, (4,), (1,))
assert_size_stride(primals_13, (4, 4), (4, 1))
assert_size_stride(primals_14, (4,), (1,))
assert_size_stride(primals_15, (4, 4), (4, 1))
assert_size_stride(primals_16, (4,), (1,))
assert_size_stride(primals_17, (4,), (1,))
assert_size_stride(primals_18, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
del primals_2
buf1 = 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=buf1)
del primals_4
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (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_0[grid(16, 4)](buf2, primals_7, buf3, 16, 4,
XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
del primals_7
buf4 = reinterpret_tensor(buf2, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf2
triton_poi_fused_0[grid(16, 4)](buf0, primals_3, buf4, 16, 4,
XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
del primals_3
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(buf0, (4, 4, 4, 1), (16, 4, 1, 64), 0)
del buf0
buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.bool)
triton_poi_fused_1[grid(64)](buf5, primals_8, buf6, buf7, buf8, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf9 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
triton_poi_fused_2[grid(256)](buf9, buf8, primals_8, buf6, buf7,
256, XBLOCK=128, num_warps=4, num_stages=1)
del buf8
del primals_8
buf10 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf7
triton_poi_fused_3[grid(16, 4)](buf1, primals_5, buf10, 16, 4,
XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
del primals_5
buf11 = reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 1), 0)
del buf1
extern_kernels.bmm(reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 0), 0), out=buf11)
buf12 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf6
triton_poi_fused_clone_4[grid(16, 4)](buf11, buf12, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf13 = reinterpret_tensor(buf11, (16, 4), (4, 1), 0)
del buf11
extern_kernels.addmm(primals_10, reinterpret_tensor(buf12, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf13)
del primals_10
buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf15 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
triton_poi_fused_add_native_layer_norm_5[grid(16)](primals_1, buf13,
buf14, buf15, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_add_native_layer_norm_6[grid(64)](primals_1, buf13,
buf14, buf15, primals_11, primals_12, buf16, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_12
buf17 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_14, reinterpret_tensor(buf16, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_13, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf17)
del primals_14
buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_gelu_7[grid(64)](buf17, buf18, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf19 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf18, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf19)
buf20 = reinterpret_tensor(buf19, (4, 4, 4), (16, 4, 1), 0)
del buf19
triton_poi_fused_add_8[grid(64)](buf20, buf16, primals_16, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_16
buf21 = buf15
del buf15
buf22 = buf14
del buf14
triton_poi_fused_native_layer_norm_9[grid(16)](buf20, buf21, buf22,
16, XBLOCK=16, num_warps=1, num_stages=1)
buf23 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_10[grid(64)](buf20, buf21, buf22,
primals_17, primals_18, buf23, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del buf21
del buf22
del primals_18
return buf23, primals_1, primals_11, primals_17, buf9, reinterpret_tensor(
buf10, (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
), reinterpret_tensor(buf12, (16, 4), (4, 1), 0
), buf13, reinterpret_tensor(buf16, (16, 4), (4, 1), 0
), buf17, reinterpret_tensor(buf18, (16, 4), (4, 1), 0
), buf20, primals_15, primals_13, primals_9
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
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 transform(self, x, linear_layer):
bs, seq_len = x.shape[:2]
proj = linear_layer(x)
proj = proj.view(bs, seq_len, self.num_attention_heads, self.
attention_head_size)
proj = proj.transpose(1, 2)
return proj
def attention(self, key, query, value, attention_mask):
S = torch.matmul(query, key.transpose(-1, -2)
) / self.attention_head_size ** (1 / 2)
if attention_mask is not None:
S += attention_mask
S_p = F.softmax(S, dim=-1)
S_p = self.dropout(S_p)
context_layer = torch.matmul(S_p, value)
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 forward(self, hidden_states, attention_mask):
"""
hidden_states: [bs, seq_len, hidden_state]
attention_mask: [bs, 1, 1, seq_len]
output: [bs, seq_len, hidden_state]
"""
key_layer = self.transform(hidden_states, self.key)
value_layer = self.transform(hidden_states, self.value)
query_layer = self.transform(hidden_states, self.query)
attn_value = self.attention(key_layer, query_layer, value_layer,
attention_mask)
return attn_value
class BertLayerNew(nn.Module):
def __init__(self, config):
super().__init__()
self.self_attention = BertSelfAttention(config)
self.attention_dense = nn.Linear(config.hidden_size, config.hidden_size
)
self.attention_layer_norm = nn.LayerNorm(config.hidden_size, eps=
config.layer_norm_eps)
self.attention_dropout = nn.Dropout(config.hidden_dropout_prob)
self.interm_dense = nn.Linear(config.hidden_size, config.
intermediate_size)
self.interm_af = F.gelu
self.out_dense = nn.Linear(config.intermediate_size, config.hidden_size
)
self.out_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.
layer_norm_eps)
self.out_dropout = nn.Dropout(config.hidden_dropout_prob)
def add_norm(self, input, output, dense_layer, dropout, ln_layer):
"""
input: the input
output: the input that requires the sublayer to transform
dense_layer, dropput: the sublayer
ln_layer: layer norm that takes input+sublayer(output) #SAM NOTE Really?
"""
output = dropout(dense_layer(output))
output = ln_layer(input + output)
return output
def forward(self, input_0, input_1):
primals_2 = self.self_attention.query.weight
primals_3 = self.self_attention.query.bias
primals_4 = self.self_attention.key.weight
primals_5 = self.self_attention.key.bias
primals_6 = self.self_attention.value.weight
primals_7 = self.self_attention.value.bias
primals_9 = self.attention_dense.weight
primals_10 = self.attention_dense.bias
primals_11 = self.attention_layer_norm.weight
primals_12 = self.attention_layer_norm.bias
primals_13 = self.interm_dense.weight
primals_14 = self.interm_dense.bias
primals_15 = self.out_dense.weight
primals_16 = self.out_dense.bias
primals_17 = self.out_layer_norm.weight
primals_18 = self.out_layer_norm.bias
primals_1 = input_0
primals_8 = 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]
|
SamarthMM/cs769-assignments
|
BertLayer
| false
| 4,736
|
[
"MIT"
] | 0
|
bac2ad57c50043608276df8e0f21181ef62696c7
|
https://github.com/SamarthMM/cs769-assignments/tree/bac2ad57c50043608276df8e0f21181ef62696c7
|
Gate
|
import torch
import torch.nn as nn
from scipy.stats import entropy as entropy
from scipy.spatial.distance import cosine as cosine
class Gate(nn.Module):
def __init__(self, hidden_size):
super(Gate, self).__init__()
self.transform = nn.Linear(hidden_size * 2, hidden_size)
nn.init.kaiming_normal_(self.transform.weight)
def forward(self, query, key):
r = self.transform(torch.cat((query.expand(key.size(0), -1), key), -1))
gate = torch.sigmoid(r)
return gate
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'hidden_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from scipy.stats import entropy as entropy
from scipy.spatial.distance import cosine as cosine
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_sigmoid_1(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
tmp3 = tl.sigmoid(tmp2)
tl.store(in_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, (4, 8), (8, 1))
assert_size_stride(primals_4, (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.mm(buf0, reinterpret_tensor(primals_3, (8, 4), (1, 8
), 0), out=buf1)
del primals_3
buf2 = buf1
del buf1
triton_poi_fused_sigmoid_1[grid(16)](buf2, primals_4, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_4
return buf2, buf0, buf2
class GateNew(nn.Module):
def __init__(self, hidden_size):
super(GateNew, self).__init__()
self.transform = nn.Linear(hidden_size * 2, hidden_size)
nn.init.kaiming_normal_(self.transform.weight)
def forward(self, input_0, input_1):
primals_3 = self.transform.weight
primals_4 = self.transform.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
yanda-wang/AMHSC
|
Gate
| false
| 4,737
|
[
"MIT"
] | 0
|
9b0a48d1f0992ca3272e7089835a946c49d5f50d
|
https://github.com/yanda-wang/AMHSC/tree/9b0a48d1f0992ca3272e7089835a946c49d5f50d
|
BertSelfAttention
|
from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint
class BertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if (config.hidden_size % config.num_attention_heads != 0 and not
hasattr(config, 'embedding_size')):
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)
self.position_embedding_type = getattr(config,
'position_embedding_type', 'absolute')
if (self.position_embedding_type == 'relative_key' or self.
position_embedding_type == 'relative_key_query'):
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.
max_position_embeddings - 1, self.attention_head_size)
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=None, head_mask=None,
encoder_hidden_states=None, encoder_attention_mask=None,
output_attentions=False):
mixed_query_layer = self.query(hidden_states)
if encoder_hidden_states is not None:
mixed_key_layer = self.key(encoder_hidden_states)
mixed_value_layer = self.value(encoder_hidden_states)
attention_mask = encoder_attention_mask
else:
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_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))
if (self.position_embedding_type == 'relative_key' or self.
position_embedding_type == 'relative_key_query'):
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long,
device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(seq_length, dtype=torch.long,
device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.
max_position_embeddings - 1)
positional_embedding = positional_embedding
if self.position_embedding_type == 'relative_key':
relative_position_scores = torch.einsum('bhld,lrd->bhlr',
query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == 'relative_key_query':
relative_position_scores_query = torch.einsum('bhld,lrd->bhlr',
query_layer, positional_embedding)
relative_position_scores_key = torch.einsum('bhrd,lrd->bhlr',
key_layer, positional_embedding)
attention_scores = (attention_scores +
relative_position_scores_query +
relative_position_scores_key)
attention_scores = attention_scores / math.sqrt(self.
attention_head_size)
if attention_mask is not None:
attention_scores = attention_scores + attention_mask
attention_probs = nn.Softmax(dim=-1)(attention_scores)
attention_probs = self.dropout(attention_probs)
if head_mask is not None:
attention_probs = attention_probs * head_mask
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)
outputs = (context_layer, attention_probs) if output_attentions else (
context_layer,)
return outputs
def get_inputs():
return [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, position_embedding_type=4)}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.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_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
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_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_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
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp18 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp25 = tl.load(in_ptr1 + x2, xmask)
tmp26 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
tmp27 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp29 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp31 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp1 = float('-inf')
tmp2 = tmp0 == tmp1
tmp3 = tmp2 == 0
tmp4 = tmp3.to(tl.int64)
tmp5 = tmp4 != 0
tmp7 = tmp6 == tmp1
tmp8 = tmp7 == 0
tmp9 = tmp8.to(tl.int64)
tmp10 = tmp9 != 0
tmp11 = tmp5 | tmp10
tmp13 = tmp12 == tmp1
tmp14 = tmp13 == 0
tmp15 = tmp14.to(tl.int64)
tmp16 = tmp15 != 0
tmp17 = tmp11 | tmp16
tmp19 = tmp18 == tmp1
tmp20 = tmp19 == 0
tmp21 = tmp20.to(tl.int64)
tmp22 = tmp21 != 0
tmp23 = tmp17 | tmp22
tmp24 = tmp23 == 0
tmp28 = tmp26 + tmp27
tmp30 = tmp28 + tmp29
tmp32 = tmp30 + tmp31
tmp33 = tmp25 / tmp32
tmp34 = 0.0
tmp35 = tl.where(tmp24, tmp34, tmp33)
tl.store(out_ptr0 + x2, tmp35, xmask)
@triton.jit
def triton_poi_fused_3(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_clone_4(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) = 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,))
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_0[grid(16, 4)](buf0, primals_2, buf3, 16, 4,
XBLOCK=2, YBLOCK=16, 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_0[grid(16, 4)](buf1, primals_5, buf4, 16, 4,
XBLOCK=2, YBLOCK=16, 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 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_1[grid(256)](buf5, buf6, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_2[grid(256)](buf5, buf6, buf7, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del buf5
del buf6
buf8 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf1
triton_poi_fused_3[grid(16, 4)](buf2, primals_7, buf8, 16, 4,
XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
del primals_7
buf9 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
del buf2
extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9)
buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
triton_poi_fused_clone_4[grid(16, 4)](buf9, buf10, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
del buf9
return reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
), buf7, reinterpret_tensor(buf8, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0)
class BertSelfAttentionNew(nn.Module):
def __init__(self, config):
super().__init__()
if (config.hidden_size % config.num_attention_heads != 0 and not
hasattr(config, 'embedding_size')):
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)
self.position_embedding_type = getattr(config,
'position_embedding_type', 'absolute')
if (self.position_embedding_type == 'relative_key' or self.
position_embedding_type == 'relative_key_query'):
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.
max_position_embeddings - 1, self.attention_head_size)
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):
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
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
Hzfinfdu/Black-Box-Tuning
|
BertSelfAttention
| false
| 4,738
|
[
"MIT"
] | 0
|
64eb5505875dc1b242c6f0a2a2f07e4000c24cb4
|
https://github.com/Hzfinfdu/Black-Box-Tuning/tree/64eb5505875dc1b242c6f0a2a2f07e4000c24cb4
|
Classifier3
|
import torch
import torch.nn
import torch.utils.data
import torch.nn.functional as F
import torch.nn.parallel
class Classifier3(torch.nn.Module):
def __init__(self):
super(Classifier3, self).__init__()
self.conv1 = torch.nn.Conv2d(in_channels=3, out_channels=64,
kernel_size=3, stride=1, padding=1)
self.conv2 = torch.nn.Conv2d(in_channels=64, out_channels=128,
kernel_size=3, stride=1, padding=1)
self.conv3 = torch.nn.Conv2d(in_channels=128, out_channels=256,
kernel_size=3, stride=1, padding=1)
self.pool = torch.nn.MaxPool2d(2, 2)
self.fc1 = torch.nn.Linear(256 * 8 * 8, 1024)
self.fc2 = torch.nn.Linear(1024, 512)
self.fc3 = torch.nn.Linear(512, 3)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.pool(F.relu(self.conv3(x)))
x = x.view(-1, 256 * 8 * 8)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
return self.fc3(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
import torch.nn
import torch.utils.data
import torch.nn.parallel
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 = 192
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 % 3
y1 = yindex // 3
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 3 * x2 + 27 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_1(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_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 % 128
y1 = yindex // 128
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 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)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 64
x1 = xindex // 64 % 32
x2 = xindex // 2048
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1 + 8192 * x2), None)
tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 8192 * x2), None)
tmp3 = tl.load(in_ptr0 + (4096 + x0 + 128 * x1 + 8192 * x2), None)
tmp5 = tl.load(in_ptr0 + (4160 + x0 + 128 * x1 + 8192 * x2), None)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x3, tmp6, None)
tl.store(out_ptr1 + x3, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 128
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_7(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 128
x1 = xindex // 128 % 16
x2 = xindex // 2048
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1 + 8192 * x2), None)
tmp1 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 8192 * x2), None)
tmp3 = tl.load(in_ptr0 + (4096 + x0 + 256 * x1 + 8192 * x2), None)
tmp5 = tl.load(in_ptr0 + (4224 + x0 + 256 * x1 + 8192 * x2), None)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x3, tmp6, None)
tl.store(out_ptr1 + x3, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 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_9(in_ptr0, out_ptr0, out_ptr1,
ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 256
xnumel = 256
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 8
y1 = yindex // 8
y5 = yindex
y4 = yindex // 64
y6 = yindex % 64
tmp0 = tl.load(in_ptr0 + (x2 + 512 * y0 + 8192 * y1), xmask & ymask)
tmp1 = tl.load(in_ptr0 + (256 + x2 + 512 * y0 + 8192 * y1), xmask & ymask)
tmp7 = tl.load(in_ptr0 + (4096 + x2 + 512 * y0 + 8192 * y1), xmask & ymask)
tmp12 = tl.load(in_ptr0 + (4352 + x2 + 512 * y0 + 8192 * y1), xmask & ymask
)
tmp2 = tmp1 > tmp0
tmp3 = tl.full([1, 1], 1, tl.int8)
tmp4 = tl.full([1, 1], 0, tl.int8)
tmp5 = tl.where(tmp2, tmp3, tmp4)
tmp6 = triton_helpers.maximum(tmp1, tmp0)
tmp8 = tmp7 > tmp6
tmp9 = tl.full([1, 1], 2, tl.int8)
tmp10 = tl.where(tmp8, tmp9, tmp5)
tmp11 = triton_helpers.maximum(tmp7, tmp6)
tmp13 = tmp12 > tmp11
tmp14 = tl.full([1, 1], 3, tl.int8)
tmp15 = tl.where(tmp13, tmp14, tmp10)
tmp16 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x2 + 256 * y5), tmp15, xmask & ymask)
tl.store(out_ptr1 + (y6 + 64 * x2 + 16384 * y4), tmp16, xmask & ymask)
@triton.jit
def triton_poi_fused_relu_10(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_11(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)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13) = args
args.clear()
assert_size_stride(primals_1, (64, 3, 3, 3), (27, 9, 3, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_4, (128, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (128,), (1,))
assert_size_stride(primals_6, (256, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_7, (256,), (1,))
assert_size_stride(primals_8, (1024, 16384), (16384, 1))
assert_size_stride(primals_9, (1024,), (1,))
assert_size_stride(primals_10, (512, 1024), (1024, 1))
assert_size_stride(primals_11, (512,), (1,))
assert_size_stride(primals_12, (3, 512), (512, 1))
assert_size_stride(primals_13, (3,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 3, 3, 3), (27, 1, 9, 3), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(192, 9)](primals_1, buf0, 192, 9, XBLOCK=16,
YBLOCK=64, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
.float32)
triton_poi_fused_1[grid(12, 4096)](primals_3, buf1, 12, 4096,
XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch
.float32)
triton_poi_fused_2[grid(8192, 9)](primals_4, buf2, 8192, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128),
torch.float32)
triton_poi_fused_3[grid(32768, 9)](primals_6, buf3, 32768, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_6
buf4 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 64, 64, 64), (262144, 1, 4096, 64))
buf5 = buf4
del buf4
triton_poi_fused_convolution_relu_4[grid(1048576)](buf5, primals_2,
1048576, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_2
buf6 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64),
torch.float32)
buf7 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_5[grid(262144)](buf5, buf6,
buf7, 262144, XBLOCK=1024, num_warps=4, num_stages=1)
buf8 = extern_kernels.convolution(buf6, buf2, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 128, 32, 32), (131072, 1, 4096, 128))
buf9 = buf8
del buf8
triton_poi_fused_convolution_relu_6[grid(524288)](buf9, primals_5,
524288, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_5
buf10 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128),
torch.float32)
buf11 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_7[grid(131072)](buf9,
buf10, buf11, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
buf12 = extern_kernels.convolution(buf10, buf3, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf12, (4, 256, 16, 16), (65536, 1, 4096, 256))
buf13 = buf12
del buf12
triton_poi_fused_convolution_relu_8[grid(262144)](buf13, primals_7,
262144, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_7
buf14 = empty_strided_cuda((4, 256, 8, 8), (16384, 1, 2048, 256),
torch.int8)
buf15 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.float32)
triton_poi_fused_max_pool2d_with_indices_9[grid(256, 256)](buf13,
buf14, buf15, 256, 256, XBLOCK=32, YBLOCK=32, num_warps=4,
num_stages=1)
buf16 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf15, (4, 16384), (16384, 1),
0), reinterpret_tensor(primals_8, (16384, 1024), (1, 16384), 0),
out=buf16)
buf17 = buf16
del buf16
triton_poi_fused_relu_10[grid(4096)](buf17, primals_9, 4096, XBLOCK
=256, num_warps=4, num_stages=1)
del primals_9
buf18 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
extern_kernels.mm(buf17, reinterpret_tensor(primals_10, (1024, 512),
(1, 1024), 0), out=buf18)
buf19 = buf18
del buf18
triton_poi_fused_relu_11[grid(2048)](buf19, primals_11, 2048,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_11
buf20 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
extern_kernels.addmm(primals_13, buf19, reinterpret_tensor(
primals_12, (512, 3), (1, 512), 0), alpha=1, beta=1, out=buf20)
del primals_13
return (buf20, buf0, buf1, buf2, buf3, buf5, buf6, buf7, buf9, buf10,
buf11, buf13, buf14, reinterpret_tensor(buf15, (4, 16384), (16384,
1), 0), buf17, buf19, primals_12, primals_10, primals_8)
class Classifier3New(torch.nn.Module):
def __init__(self):
super(Classifier3New, self).__init__()
self.conv1 = torch.nn.Conv2d(in_channels=3, out_channels=64,
kernel_size=3, stride=1, padding=1)
self.conv2 = torch.nn.Conv2d(in_channels=64, out_channels=128,
kernel_size=3, stride=1, padding=1)
self.conv3 = torch.nn.Conv2d(in_channels=128, out_channels=256,
kernel_size=3, stride=1, padding=1)
self.pool = torch.nn.MaxPool2d(2, 2)
self.fc1 = torch.nn.Linear(256 * 8 * 8, 1024)
self.fc2 = torch.nn.Linear(1024, 512)
self.fc3 = torch.nn.Linear(512, 3)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.conv3.weight
primals_7 = self.conv3.bias
primals_8 = self.fc1.weight
primals_9 = self.fc1.bias
primals_10 = self.fc2.weight
primals_11 = self.fc2.bias
primals_12 = self.fc3.weight
primals_13 = 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, primals_12, primals_13])
return output[0]
|
yuping1624/1082NCTU-Deep-Learning
|
Classifier3
| false
| 4,739
|
[
"MIT"
] | 0
|
dc83e1c8709e9610a996f02091fe626f07b3c10f
|
https://github.com/yuping1624/1082NCTU-Deep-Learning/tree/dc83e1c8709e9610a996f02091fe626f07b3c10f
|
Net
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._utils
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5, stride=(2, 2))
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5, stride=(2, 2))
self.fc1 = nn.Linear(16 * 13 * 13, 2048)
self.fc2 = nn.Linear(2048, 512)
self.fc3 = nn.Linear(512, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 13 * 13)
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, 432, 432])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch._utils
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 = 1099104
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 45796 % 6
x0 = xindex % 45796
x4 = xindex // 45796
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 + 45824 * x4), tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 274776
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 107
x1 = xindex // 107 % 107
x2 = xindex // 11449
x3 = xindex % 11449
tmp0 = tl.load(in_ptr0 + (2 * x0 + 428 * x1 + 45824 * x2), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 428 * x1 + 45824 * x2), xmask,
eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (214 + 2 * x0 + 428 * x1 + 45824 * x2), xmask,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (215 + 2 * x0 + 428 * x1 + 45824 * x2), 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 + (x3 + 11456 * x2), tmp6, xmask)
tl.store(out_ptr1 + (x3 + 11520 * x2), tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 173056
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 2704 % 16
x0 = xindex % 2704
x4 = xindex // 2704
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 + 2720 * 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 = 43264
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 26
x1 = xindex // 26 % 26
x4 = xindex // 676
x3 = xindex // 10816
x5 = xindex % 10816
x6 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 104 * x1 + 2720 * x4), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 104 * x1 + 2720 * x4), xmask,
eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (52 + 2 * x0 + 104 * x1 + 2720 * x4), xmask,
eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (53 + 2 * x0 + 104 * x1 + 2720 * x4), 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 + (x5 + 10880 * x3), tmp15, xmask)
tl.store(out_ptr1 + x6, tmp16, xmask)
@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 2048
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_relu_5(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)
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, 432, 432), (559872, 186624, 432, 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, (2048, 2704), (2704, 1))
assert_size_stride(primals_7, (2048,), (1,))
assert_size_stride(primals_8, (512, 2048), (2048, 1))
assert_size_stride(primals_9, (512,), (1,))
assert_size_stride(primals_10, (10, 512), (512, 1))
assert_size_stride(primals_11, (10,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2,
2), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 6, 214, 214), (274776, 45796, 214, 1))
buf1 = empty_strided_cuda((4, 6, 214, 214), (274944, 45824, 214, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(1099104)](buf0, primals_2,
buf1, 1099104, XBLOCK=1024, num_warps=4, num_stages=1)
del buf0
del primals_2
buf2 = empty_strided_cuda((4, 6, 107, 107), (68736, 11456, 107, 1),
torch.float32)
buf3 = empty_strided_cuda((4, 6, 107, 107), (69120, 11520, 107, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_1[grid(274776)](buf1, buf2,
buf3, 274776, XBLOCK=512, num_warps=8, num_stages=1)
buf4 = extern_kernels.convolution(buf2, primals_4, stride=(2, 2),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 16, 52, 52), (43264, 2704, 52, 1))
buf5 = empty_strided_cuda((4, 16, 52, 52), (43520, 2720, 52, 1),
torch.float32)
triton_poi_fused_convolution_relu_2[grid(173056)](buf4, primals_5,
buf5, 173056, XBLOCK=512, num_warps=8, num_stages=1)
del buf4
del primals_5
buf6 = empty_strided_cuda((4, 16, 26, 26), (10880, 676, 26, 1),
torch.int8)
buf7 = empty_strided_cuda((4, 16, 26, 26), (10816, 676, 26, 1),
torch.float32)
triton_poi_fused_max_pool2d_with_indices_3[grid(43264)](buf5, buf6,
buf7, 43264, XBLOCK=256, num_warps=4, num_stages=1)
buf8 = empty_strided_cuda((16, 2048), (2048, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf7, (16, 2704), (2704, 1), 0
), reinterpret_tensor(primals_6, (2704, 2048), (1, 2704), 0),
out=buf8)
buf9 = buf8
del buf8
triton_poi_fused_relu_4[grid(32768)](buf9, primals_7, 32768, XBLOCK
=256, num_warps=4, num_stages=1)
del primals_7
buf10 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
extern_kernels.mm(buf9, reinterpret_tensor(primals_8, (2048, 512),
(1, 2048), 0), out=buf10)
buf11 = buf10
del buf10
triton_poi_fused_relu_5[grid(8192)](buf11, primals_9, 8192, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_9
buf12 = empty_strided_cuda((16, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_11, buf11, reinterpret_tensor(
primals_10, (512, 10), (1, 512), 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, (16, 2704), (2704, 1), 0), buf9,
buf11, primals_10, primals_8, primals_6)
class NetNew(nn.Module):
def __init__(self):
super(NetNew, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5, stride=(2, 2))
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5, stride=(2, 2))
self.fc1 = nn.Linear(16 * 13 * 13, 2048)
self.fc2 = nn.Linear(2048, 512)
self.fc3 = nn.Linear(512, 10)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.fc1.weight
primals_7 = self.fc1.bias
primals_8 = self.fc2.weight
primals_9 = self.fc2.bias
primals_10 = self.fc3.weight
primals_11 = self.fc3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0]
|
zyouc518/crow
|
Net
| false
| 4,740
|
[
"Apache-2.0"
] | 0
|
e3fe92e329649fb82b3fef6c0ab5b732f1918900
|
https://github.com/zyouc518/crow/tree/e3fe92e329649fb82b3fef6c0ab5b732f1918900
|
CrossEntropyLoss
|
import torch
import torch.utils.cpp_extension
class CrossEntropyLoss(torch.nn.Module):
def __init__(self):
super(CrossEntropyLoss, self).__init__()
self.ce_loss = torch.nn.CrossEntropyLoss()
def forward(self, cls_output, label, **_):
return self.ce_loss(cls_output, label).mean()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + x3, tmp8, xmask)
@triton.jit
def triton_per_fused__log_softmax_div_mean_mul_neg_sum_1(in_out_ptr0,
in_ptr0, in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r3 = rindex
r0 = rindex % 16
r2 = rindex // 64
tmp0 = tl.load(in_ptr0 + r3, None)
tmp1 = tl.load(in_ptr0 + (r0 + 64 * r2), None, eviction_policy='evict_last'
)
tmp3 = tl.load(in_ptr0 + (16 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (32 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr0 + (48 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp14 = tl.load(in_ptr1 + r3, None)
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
tmp15 = tmp13 * tmp14
tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
tmp19 = -tmp18
tmp20 = 0.015625
tmp21 = tmp19 * tmp20
tmp22 = 1.0
tmp23 = tmp21 / tmp22
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp23, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf0, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del arg1_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = buf1
del buf1
triton_per_fused__log_softmax_div_mean_mul_neg_sum_1[grid(1)](buf2,
buf0, arg0_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del buf0
return buf2,
class CrossEntropyLossNew(torch.nn.Module):
def __init__(self):
super(CrossEntropyLossNew, self).__init__()
self.ce_loss = torch.nn.CrossEntropyLoss()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
yingnengd/MyGAN
|
CrossEntropyLoss
| false
| 4,741
|
[
"MIT"
] | 0
|
6e4abbe165c8f3b1e1b69d5d01177712761a3a1c
|
https://github.com/yingnengd/MyGAN/tree/6e4abbe165c8f3b1e1b69d5d01177712761a3a1c
|
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