# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/sr/csrzlteph4svc746shxzwrfzfygp3ngujwxcrnvcusqhc43dtftf.py
# Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   layer_norm => add, clone, rsqrt, var_mean
# Graph fragment:
#   %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone, [2]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_poi_fused_native_layer_norm_0 = async_compile.triton('triton_poi_fused_native_layer_norm_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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 % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
    tl.store(out_ptr1 + (x2), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ep/cepzni4orknhaka6ezxewg2ehebbjiupi52qelfw47wqs4ekerxq.py
# Topologically Sorted Source Nodes: [layer_norm, x_1, x_2], Original ATen: [aten.native_layer_norm, aten.leaky_relu, aten.convolution]
# Source node to ATen node mapping:
#   layer_norm => add, add_1, clone, mul, mul_1, rsqrt, sub, var_mean
#   x_1 => gt, mul_2, where
#   x_2 => convolution
# Graph fragment:
#   %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone, [2]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_3), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%permute_1, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute_1, 0.01), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %permute_1, %mul_2), kwargs = {})
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_4, %primals_5, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_leaky_relu_native_layer_norm_1 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_native_layer_norm_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_native_layer_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr1, out_ptr2, 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
    y3 = yindex
    y1 = (yindex // 4)
    y0 = yindex % 4
    tmp0 = tl.load(in_ptr0 + (x2 + (4*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + (4*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + (4*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (y0), ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 0.0
    tmp10 = tmp8 > tmp9
    tmp11 = 0.01
    tmp12 = tmp8 * tmp11
    tmp13 = tl.where(tmp10, tmp8, tmp12)
    tl.store(out_ptr1 + (y0 + (4*x2) + (16*y1)), tmp13, xmask & ymask)
    tl.store(out_ptr2 + (x2 + (4*y3)), tmp13, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/lf/clf7hs52i4bd5d3e73uio27ntyjfqmszkbsw6dta3r6rzgeftva3.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_2 => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_4, %primals_5, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2f/c2fmbms5yu7pndqccuviyfk3uh5ctm4s4m5duyocimrn6uqrpnx3.py
# Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv1d_1 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%where_1, %primals_8, %primals_9, [1], [1], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_3 = async_compile.triton('triton_poi_fused_convolution_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 5) % 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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6m/c6mzkhocn3islr7c5jwkrgvxbykm5jywftruw7si2p7sv5dljcno.py
# Topologically Sorted Source Nodes: [layer_norm_2], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   layer_norm_2 => add_4, clone_2, rsqrt_2, var_mean_2
# Graph fragment:
#   %clone_2 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute_4,), kwargs = {memory_format: torch.contiguous_format})
#   %var_mean_2 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone_2, [2]), kwargs = {correction: 0, keepdim: True})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_4, 1e-05), kwargs = {})
#   %rsqrt_2 : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_4,), kwargs = {})
triton_poi_fused_native_layer_norm_4 = async_compile.triton('triton_poi_fused_native_layer_norm_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_layer_norm_4(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 % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (20*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (5 + x0 + (20*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (10 + x0 + (20*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (15 + x0 + (20*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
    tl.store(out_ptr1 + (x2), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/nu/cnuopqsoegbagggwrkx7mrklpmg45rhcdsbpd5bwecwtnbzabt6t.py
# Topologically Sorted Source Nodes: [layer_norm_2, x_7, conv1d_2, conv1d_3], Original ATen: [aten.native_layer_norm, aten.leaky_relu, aten.convolution]
# Source node to ATen node mapping:
#   conv1d_2 => convolution_2
#   conv1d_3 => convolution_3
#   layer_norm_2 => add_4, add_5, clone_2, mul_6, mul_7, rsqrt_2, sub_2, var_mean_2
#   x_7 => gt_2, mul_8, where_2
# Graph fragment:
#   %clone_2 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute_4,), kwargs = {memory_format: torch.contiguous_format})
#   %var_mean_2 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone_2, [2]), kwargs = {correction: 0, keepdim: True})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_4, 1e-05), kwargs = {})
#   %rsqrt_2 : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_4,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone_2, %getitem_5), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %rsqrt_2), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_6, %primals_10), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_7, %primals_11), kwargs = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%permute_5, 0), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute_5, 0.01), kwargs = {})
#   %where_2 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %permute_5, %mul_8), kwargs = {})
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%where_2, %primals_12, %primals_13, [1], [0], [1], False, [0], 1), kwargs = {})
#   %convolution_3 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%where_2, %primals_14, %primals_15, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_leaky_relu_native_layer_norm_5 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_native_layer_norm_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_native_layer_norm_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_native_layer_norm_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr1, out_ptr2, out_ptr3, 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
    y3 = yindex
    y1 = (yindex // 4)
    y0 = yindex % 4
    tmp0 = tl.load(in_ptr0 + (x2 + (5*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + (4*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + (4*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (y0), ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 0.0
    tmp10 = tmp8 > tmp9
    tmp11 = 0.01
    tmp12 = tmp8 * tmp11
    tmp13 = tl.where(tmp10, tmp8, tmp12)
    tl.store(out_ptr1 + (y0 + (4*x2) + (16*y1)), tmp13, xmask & ymask)
    tl.store(out_ptr2 + (x2 + (4*y3)), tmp13, xmask & ymask)
    tl.store(out_ptr3 + (x2 + (4*y3)), tmp13, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5x/c5xlvcqikyssfno5q53dmxs4ui2xjosb4nsvzxrcridsmdua2jff.py
# Topologically Sorted Source Nodes: [conv1d_2, conv1d_3, sigmoid, x_8, x_9], Original ATen: [aten.convolution, aten.sigmoid, aten.mul, aten.add]
# Source node to ATen node mapping:
#   conv1d_2 => convolution_2
#   conv1d_3 => convolution_3
#   sigmoid => sigmoid
#   x_8 => mul_9
#   x_9 => add_6
# Graph fragment:
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%where_2, %primals_12, %primals_13, [1], [0], [1], False, [0], 1), kwargs = {})
#   %convolution_3 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%where_2, %primals_14, %primals_15, [1], [0], [1], False, [0], 1), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution_3,), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_2, %sigmoid), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_9, %primals_1), kwargs = {})
triton_poi_fused_add_convolution_mul_sigmoid_6 = async_compile.triton('triton_poi_fused_add_convolution_mul_sigmoid_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_mul_sigmoid_6', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_mul_sigmoid_6(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    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')
    tmp3 = tl.load(in_out_ptr1 + (x3), xmask)
    tmp4 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tl.sigmoid(tmp5)
    tmp7 = tmp2 * tmp6
    tmp9 = tmp7 + tmp8
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(in_out_ptr1 + (x3), tmp5, xmask)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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, (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, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4, 2), (8, 2, 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, 1), (4, 1, 1))
    assert_size_stride(primals_13, (4, ), (1, ))
    assert_size_stride(primals_14, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_15, (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)
        # Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0.run(primals_1, buf0, buf1, 16, grid=grid(16), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [layer_norm, x_1, x_2], Original ATen: [aten.native_layer_norm, aten.leaky_relu, aten.convolution]
        triton_poi_fused_convolution_leaky_relu_native_layer_norm_1.run(primals_1, buf0, buf1, primals_2, primals_3, buf3, buf4, 16, 4, grid=grid(16, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4), (16, 4, 1))
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf6, primals_5, 64, grid=grid(64), stream=stream0)
        del primals_5
        buf7 = buf1; del buf1  # reuse
        buf8 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [layer_norm_1], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_0.run(buf6, buf7, buf8, 16, grid=grid(16), stream=stream0)
        buf10 = reinterpret_tensor(buf4, (4, 4, 4), (16, 1, 4), 0); del buf4  # reuse
        buf11 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [layer_norm_1, x_4, conv1d_1], Original ATen: [aten.native_layer_norm, aten.leaky_relu, aten.convolution]
        triton_poi_fused_convolution_leaky_relu_native_layer_norm_1.run(buf6, buf7, buf8, primals_6, primals_7, buf10, buf11, 16, 4, grid=grid(16, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_8, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 5), (20, 5, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_3.run(buf13, primals_9, 80, grid=grid(80), stream=stream0)
        del primals_9
        buf14 = buf8; del buf8  # reuse
        buf15 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [layer_norm_2], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_4.run(buf13, buf14, buf15, 16, grid=grid(16), stream=stream0)
        buf17 = reinterpret_tensor(buf11, (4, 4, 4), (16, 1, 4), 0); del buf11  # reuse
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [layer_norm_2, x_7, conv1d_2, conv1d_3], Original ATen: [aten.native_layer_norm, aten.leaky_relu, aten.convolution]
        triton_poi_fused_convolution_leaky_relu_native_layer_norm_5.run(buf13, buf14, buf15, primals_10, primals_11, buf17, buf18, buf21, 16, 4, grid=grid(16, 4), stream=stream0)
        del buf14
        del buf15
        # Topologically Sorted Source Nodes: [conv1d_2], Original ATen: [aten.convolution]
        buf19 = extern_kernels.convolution(buf18, primals_12, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf19, (4, 4, 4), (16, 4, 1))
        del buf18
        # Topologically Sorted Source Nodes: [conv1d_3], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf21, primals_14, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf22, (4, 4, 4), (16, 4, 1))
        buf20 = buf19; del buf19  # reuse
        buf23 = buf22; del buf22  # reuse
        buf24 = buf21; del buf21  # reuse
        # Topologically Sorted Source Nodes: [conv1d_2, conv1d_3, sigmoid, x_8, x_9], Original ATen: [aten.convolution, aten.sigmoid, aten.mul, aten.add]
        triton_poi_fused_add_convolution_mul_sigmoid_6.run(buf20, buf23, primals_13, primals_15, primals_1, buf24, 64, grid=grid(64), stream=stream0)
        del primals_13
        del primals_15
    return (buf24, primals_1, primals_2, primals_3, primals_4, primals_6, primals_7, primals_8, primals_10, primals_11, primals_12, primals_14, buf3, buf6, buf10, buf13, buf17, buf20, buf23, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4, 2), (8, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: 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 print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
import torch.nn.functional as F


class DilConv1dWithGLU(nn.Module):

    def __init__(self, num_channels, dilation, lenght=100, kernel_size=2,
        activation=F.leaky_relu, residual_connection=True, dropout=0.2):
        super(DilConv1dWithGLU, self).__init__()
        self.dilation = dilation
        self.start_ln = nn.LayerNorm(num_channels)
        self.start_conv1x1 = nn.Conv1d(num_channels, num_channels,
            kernel_size=1)
        self.dilconv_ln = nn.LayerNorm(num_channels)
        self.dilated_conv = nn.Conv1d(num_channels, num_channels, dilation=
            dilation, kernel_size=kernel_size, padding=dilation)
        self.gate_ln = nn.LayerNorm(num_channels)
        self.end_conv1x1 = nn.Conv1d(num_channels, num_channels, kernel_size=1)
        self.gated_conv1x1 = nn.Conv1d(num_channels, num_channels,
            kernel_size=1)
        self.activation = activation
        self.buffer = None
        self.residual_connection = residual_connection

    def clear_buffer(self):
        self.buffer = None

    def forward(self, x_inp, sampling=False):
        x = self.start_ln(x_inp.transpose(1, 2)).transpose(1, 2)
        x = self.activation(x)
        x = self.start_conv1x1(x)
        x = self.dilconv_ln(x.transpose(1, 2)).transpose(1, 2)
        x = self.activation(x)
        if sampling:
            if self.buffer is None:
                self.buffer = x
            else:
                pre_buffer = torch.cat([self.buffer, x], dim=2)
                self.buffer = pre_buffer[:, :, -(self.dilation + 1):]
            if self.buffer.shape[2] == self.dilation + 1:
                x = self.buffer
            else:
                x = torch.cat([torch.zeros(self.buffer.shape[0], self.
                    buffer.shape[1], self.dilation + 1 - self.buffer.shape[
                    2], device=x_inp.device), self.buffer], dim=2)
            x = self.dilated_conv(x)[:, :, self.dilation:]
            x = x[:, :, :x_inp.shape[-1]]
        else:
            x = self.dilated_conv(x)[:, :, :x_inp.shape[-1]]
        x = self.gate_ln(x.transpose(1, 2)).transpose(1, 2)
        x = self.activation(x)
        x = self.end_conv1x1(x) * torch.sigmoid(self.gated_conv1x1(x))
        if self.residual_connection:
            x = x + x_inp
        return x


def get_inputs():
    return [torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'num_channels': 4, 'dilation': 1}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr1 + x2, tmp23, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_native_layer_norm_1(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr1, out_ptr2, 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
    y3 = yindex
    y1 = yindex // 4
    y0 = yindex % 4
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr3 + y0, ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 0.0
    tmp10 = tmp8 > tmp9
    tmp11 = 0.01
    tmp12 = tmp8 * tmp11
    tmp13 = tl.where(tmp10, tmp8, tmp12)
    tl.store(out_ptr1 + (y0 + 4 * x2 + 16 * y1), tmp13, xmask & ymask)
    tl.store(out_ptr2 + (x2 + 4 * y3), tmp13, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 5 % 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_native_layer_norm_4(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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 20 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (5 + x0 + 20 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (10 + x0 + 20 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (15 + x0 + 20 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr1 + x2, tmp23, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_native_layer_norm_5(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr1, out_ptr2, out_ptr3,
    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
    y3 = yindex
    y1 = yindex // 4
    y0 = yindex % 4
    tmp0 = tl.load(in_ptr0 + (x2 + 5 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr3 + y0, ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 0.0
    tmp10 = tmp8 > tmp9
    tmp11 = 0.01
    tmp12 = tmp8 * tmp11
    tmp13 = tl.where(tmp10, tmp8, tmp12)
    tl.store(out_ptr1 + (y0 + 4 * x2 + 16 * y1), tmp13, xmask & ymask)
    tl.store(out_ptr2 + (x2 + 4 * y3), tmp13, xmask & ymask)
    tl.store(out_ptr3 + (x2 + 4 * y3), tmp13, xmask & ymask)


@triton.jit
def triton_poi_fused_add_convolution_mul_sigmoid_6(in_out_ptr0, in_out_ptr1,
    in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    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')
    tmp3 = tl.load(in_out_ptr1 + x3, xmask)
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tl.sigmoid(tmp5)
    tmp7 = tmp2 * tmp6
    tmp9 = tmp7 + tmp8
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(in_out_ptr1 + x3, tmp5, xmask)
    tl.store(out_ptr0 + x3, tmp9, 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) = 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, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 2), (8, 2, 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, 1), (4, 1, 1))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_15, (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_poi_fused_native_layer_norm_0[grid(16)](primals_1, buf0,
            buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_convolution_leaky_relu_native_layer_norm_1[grid(16, 4)
            ](primals_1, buf0, buf1, primals_2, primals_3, buf3, buf4, 16, 
            4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4), (16, 4, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_2[grid(64)](buf6, primals_5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        buf7 = buf1
        del buf1
        buf8 = buf0
        del buf0
        triton_poi_fused_native_layer_norm_0[grid(16)](buf6, buf7, buf8, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf10 = reinterpret_tensor(buf4, (4, 4, 4), (16, 1, 4), 0)
        del buf4
        buf11 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_convolution_leaky_relu_native_layer_norm_1[grid(16, 4)
            ](buf6, buf7, buf8, primals_6, primals_7, buf10, buf11, 16, 4,
            XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        buf12 = extern_kernels.convolution(buf11, primals_8, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 5), (20, 5, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_3[grid(80)](buf13, primals_9, 80,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf14 = buf8
        del buf8
        buf15 = buf7
        del buf7
        triton_poi_fused_native_layer_norm_4[grid(16)](buf13, buf14, buf15,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf17 = reinterpret_tensor(buf11, (4, 4, 4), (16, 1, 4), 0)
        del buf11
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_convolution_leaky_relu_native_layer_norm_5[grid(16, 4)
            ](buf13, buf14, buf15, primals_10, primals_11, buf17, buf18,
            buf21, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        del buf14
        del buf15
        buf19 = extern_kernels.convolution(buf18, primals_12, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf19, (4, 4, 4), (16, 4, 1))
        del buf18
        buf22 = extern_kernels.convolution(buf21, primals_14, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf22, (4, 4, 4), (16, 4, 1))
        buf20 = buf19
        del buf19
        buf23 = buf22
        del buf22
        buf24 = buf21
        del buf21
        triton_poi_fused_add_convolution_mul_sigmoid_6[grid(64)](buf20,
            buf23, primals_13, primals_15, primals_1, buf24, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_13
        del primals_15
    return (buf24, primals_1, primals_2, primals_3, primals_4, primals_6,
        primals_7, primals_8, primals_10, primals_11, primals_12,
        primals_14, buf3, buf6, buf10, buf13, buf17, buf20, buf23)


class DilConv1dWithGLUNew(nn.Module):

    def __init__(self, num_channels, dilation, lenght=100, kernel_size=2,
        activation=F.leaky_relu, residual_connection=True, dropout=0.2):
        super(DilConv1dWithGLUNew, self).__init__()
        self.dilation = dilation
        self.start_ln = nn.LayerNorm(num_channels)
        self.start_conv1x1 = nn.Conv1d(num_channels, num_channels,
            kernel_size=1)
        self.dilconv_ln = nn.LayerNorm(num_channels)
        self.dilated_conv = nn.Conv1d(num_channels, num_channels, dilation=
            dilation, kernel_size=kernel_size, padding=dilation)
        self.gate_ln = nn.LayerNorm(num_channels)
        self.end_conv1x1 = nn.Conv1d(num_channels, num_channels, kernel_size=1)
        self.gated_conv1x1 = nn.Conv1d(num_channels, num_channels,
            kernel_size=1)
        self.activation = activation
        self.buffer = None
        self.residual_connection = residual_connection

    def clear_buffer(self):
        self.buffer = None

    def forward(self, input_0):
        primals_2 = self.start_ln.weight
        primals_3 = self.start_ln.bias
        primals_4 = self.start_conv1x1.weight
        primals_5 = self.start_conv1x1.bias
        primals_6 = self.dilconv_ln.weight
        primals_7 = self.dilconv_ln.bias
        primals_8 = self.dilated_conv.weight
        primals_9 = self.dilated_conv.bias
        primals_10 = self.gate_ln.weight
        primals_11 = self.gate_ln.bias
        primals_12 = self.end_conv1x1.weight
        primals_13 = self.end_conv1x1.bias
        primals_14 = self.gated_conv1x1.weight
        primals_15 = self.gated_conv1x1.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])
        return output[0]
