# 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/x6/cx65c7anrs2c3riaymjsb6mn6ohxklfcjshh3ecknq2f4xdr66qf.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   x => expm1, gt, mul, mul_1, mul_2, where
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 1.0507009873554805), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 1.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_1,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.7580993408473766), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {})
triton_poi_fused_elu_0 = async_compile.triton('triton_poi_fused_elu_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=[131072], 
    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_elu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_elu_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0507009873554805
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp0 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = 1.7580993408473766
    tmp9 = tmp7 * tmp8
    tmp10 = tl.where(tmp2, tmp4, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/gy/cgyyx66jhuwsuzcb24qaqbctecvvjauypfp6q3be6sjrt3suenf6.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   x_1 => expm1_1, gt_1, mul_3, mul_4, mul_5, where_1
# Graph fragment:
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_3, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 1.0507009873554805), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 1.0), kwargs = {})
#   %expm1_1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_4,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_1, 1.7580993408473766), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %mul_3, %mul_5), kwargs = {})
triton_poi_fused_elu_1 = async_compile.triton('triton_poi_fused_elu_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=[65536], 
    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_elu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_elu_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0507009873554805
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp0 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = 1.7580993408473766
    tmp9 = tmp7 * tmp8
    tmp10 = tl.where(tmp2, tmp4, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/wx/cwxar4rk2x5ajbatqjvts2od46iervhwx3dnv55krm6kmnormfto.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   x_2 => expm1_2, gt_2, mul_6, mul_7, mul_8, where_2
# Graph fragment:
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_5, 0), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, 1.0507009873554805), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, 1.0), kwargs = {})
#   %expm1_2 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_7,), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_2, 1.7580993408473766), kwargs = {})
#   %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %mul_6, %mul_8), kwargs = {})
triton_poi_fused_elu_2 = async_compile.triton('triton_poi_fused_elu_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=[65536], 
    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_elu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_elu_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 40960
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0507009873554805
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp0 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = 1.7580993408473766
    tmp9 = tmp7 * tmp8
    tmp10 = tl.where(tmp2, tmp4, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/dz/cdzkjcozhsc75uorevx7znrpgkzbspdwobijrpdw2vkua3exf2ii.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   x_3 => expm1_3, gt_3, mul_10, mul_11, mul_9, where_3
# Graph fragment:
#   %gt_3 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_7, 0), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_7, 1.0507009873554805), kwargs = {})
#   %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_7, 1.0), kwargs = {})
#   %expm1_3 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_10,), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_3, 1.7580993408473766), kwargs = {})
#   %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %mul_9, %mul_11), kwargs = {})
triton_poi_fused_elu_3 = async_compile.triton('triton_poi_fused_elu_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=[32768], 
    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_elu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_elu_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0507009873554805
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp0 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = 1.7580993408473766
    tmp9 = tmp7 * tmp8
    tmp10 = tl.where(tmp2, tmp4, tmp9)
    tl.store(out_ptr0 + (x0), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/cv/ccv4dzyrbocodwtc6uvuqfu6mqpxl43fxdr6kzafz6svjmgwoix3.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   x_4 => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_9,), kwargs = {})
triton_poi_fused_sigmoid_4 = async_compile.triton('triton_poi_fused_sigmoid_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=[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_sigmoid_4', '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_sigmoid_4(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)
''', 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 = args
    args.clear()
    assert_size_stride(primals_1, (2048, 4), (4, 1))
    assert_size_stride(primals_2, (2048, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1024, 2048), (2048, 1))
    assert_size_stride(primals_5, (1024, ), (1, ))
    assert_size_stride(primals_6, (640, 1024), (1024, 1))
    assert_size_stride(primals_7, (640, ), (1, ))
    assert_size_stride(primals_8, (512, 640), (640, 1))
    assert_size_stride(primals_9, (512, ), (1, ))
    assert_size_stride(primals_10, (2, 512), (512, 1))
    assert_size_stride(primals_11, (2, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 2048), (2048, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 2048), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 2048), (32768, 8192, 2048, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.elu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_elu_0.run(buf0, buf1, 131072, grid=grid(131072), stream=stream0)
        buf2 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 2048), (2048, 1), 0), reinterpret_tensor(primals_4, (2048, 1024), (1, 2048), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.elu]
        triton_poi_fused_elu_1.run(buf2, buf3, 65536, grid=grid(65536), stream=stream0)
        buf4 = empty_strided_cuda((64, 640), (640, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 1024), (1024, 1), 0), reinterpret_tensor(primals_6, (1024, 640), (1, 1024), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((4, 4, 4, 640), (10240, 2560, 640, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.elu]
        triton_poi_fused_elu_2.run(buf4, buf5, 40960, grid=grid(40960), stream=stream0)
        buf6 = empty_strided_cuda((64, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (64, 640), (640, 1), 0), reinterpret_tensor(primals_8, (640, 512), (1, 640), 0), alpha=1, beta=1, out=buf6)
        del primals_9
        buf7 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.elu]
        triton_poi_fused_elu_3.run(buf6, buf7, 32768, grid=grid(32768), stream=stream0)
        buf8 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 512), (512, 1), 0), reinterpret_tensor(primals_10, (512, 2), (1, 512), 0), out=buf8)
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 2), (32, 8, 2, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_4.run(buf9, primals_11, 128, grid=grid(128), stream=stream0)
        del primals_11
    return (buf9, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, reinterpret_tensor(buf1, (64, 2048), (2048, 1), 0), buf2, reinterpret_tensor(buf3, (64, 1024), (1024, 1), 0), buf4, reinterpret_tensor(buf5, (64, 640), (640, 1), 0), buf6, reinterpret_tensor(buf7, (64, 512), (512, 1), 0), buf9, primals_10, primals_8, primals_6, primals_4, )


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((2048, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((2048, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1024, 2048), (2048, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((640, 1024), (1024, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((640, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((512, 640), (640, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((2, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((2, ), (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])
    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 FiveNet(nn.Module):

    def __init__(self, n_features, e1=1024, e2=2048, e3=1024, e4=640, e5=
        512, p=0.4):
        super(FiveNet, self).__init__()
        self.a1 = nn.Linear(n_features, e2)
        self.a2 = nn.Linear(e2, e3)
        self.a3 = nn.Linear(e3, e4)
        self.a4 = nn.Linear(e4, e5)
        self.a5 = nn.Linear(e5, 2)
        self.dropout = nn.Dropout(p)

    def forward(self, x):
        x = F.selu(self.dropout(self.a1(x)))
        x = F.selu(self.dropout(self.a2(x)))
        x = F.selu(self.dropout(self.a3(x)))
        x = F.selu(self.dropout(self.a4(x)))
        x = torch.sigmoid(self.a5(x))
        return x


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


def get_init_inputs():
    return [[], {'n_features': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_elu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    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)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0507009873554805
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp0 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = 1.7580993408473766
    tmp9 = tmp7 * tmp8
    tmp10 = tl.where(tmp2, tmp4, tmp9)
    tl.store(out_ptr0 + x0, tmp10, None)


@triton.jit
def triton_poi_fused_elu_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0507009873554805
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp0 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = 1.7580993408473766
    tmp9 = tmp7 * tmp8
    tmp10 = tl.where(tmp2, tmp4, tmp9)
    tl.store(out_ptr0 + x0, tmp10, None)


@triton.jit
def triton_poi_fused_elu_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0507009873554805
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp0 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = 1.7580993408473766
    tmp9 = tmp7 * tmp8
    tmp10 = tl.where(tmp2, tmp4, tmp9)
    tl.store(out_ptr0 + x0, tmp10, None)


@triton.jit
def triton_poi_fused_elu_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0507009873554805
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp0 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = 1.7580993408473766
    tmp9 = tmp7 * tmp8
    tmp10 = tl.where(tmp2, tmp4, tmp9)
    tl.store(out_ptr0 + x0, tmp10, None)


@triton.jit
def triton_poi_fused_sigmoid_4(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)


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, (2048, 4), (4, 1))
    assert_size_stride(primals_2, (2048,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1024, 2048), (2048, 1))
    assert_size_stride(primals_5, (1024,), (1,))
    assert_size_stride(primals_6, (640, 1024), (1024, 1))
    assert_size_stride(primals_7, (640,), (1,))
    assert_size_stride(primals_8, (512, 640), (640, 1))
    assert_size_stride(primals_9, (512,), (1,))
    assert_size_stride(primals_10, (2, 512), (512, 1))
    assert_size_stride(primals_11, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 2048), (2048, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 2048), (1, 4),
            0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 2048), (32768, 8192, 2048, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_elu_0[grid(131072)](buf0, buf1, 131072, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf2 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 2048),
            (2048, 1), 0), reinterpret_tensor(primals_4, (2048, 1024), (1, 
            2048), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
            torch.float32)
        triton_poi_fused_elu_1[grid(65536)](buf2, buf3, 65536, XBLOCK=512,
            num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((64, 640), (640, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 1024),
            (1024, 1), 0), reinterpret_tensor(primals_6, (1024, 640), (1, 
            1024), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((4, 4, 4, 640), (10240, 2560, 640, 1),
            torch.float32)
        triton_poi_fused_elu_2[grid(40960)](buf4, buf5, 40960, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((64, 512), (512, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (64, 640),
            (640, 1), 0), reinterpret_tensor(primals_8, (640, 512), (1, 640
            ), 0), alpha=1, beta=1, out=buf6)
        del primals_9
        buf7 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.float32)
        triton_poi_fused_elu_3[grid(32768)](buf6, buf7, 32768, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 512), (512, 1), 0),
            reinterpret_tensor(primals_10, (512, 2), (1, 512), 0), out=buf8)
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 2), (32, 8, 2, 1), 0)
        del buf8
        triton_poi_fused_sigmoid_4[grid(128)](buf9, primals_11, 128, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_11
    return buf9, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(buf1, (64, 2048), (2048, 1), 0
        ), buf2, reinterpret_tensor(buf3, (64, 1024), (1024, 1), 0
        ), buf4, reinterpret_tensor(buf5, (64, 640), (640, 1), 0
        ), buf6, reinterpret_tensor(buf7, (64, 512), (512, 1), 0
        ), buf9, primals_10, primals_8, primals_6, primals_4


class FiveNetNew(nn.Module):

    def __init__(self, n_features, e1=1024, e2=2048, e3=1024, e4=640, e5=
        512, p=0.4):
        super(FiveNetNew, self).__init__()
        self.a1 = nn.Linear(n_features, e2)
        self.a2 = nn.Linear(e2, e3)
        self.a3 = nn.Linear(e3, e4)
        self.a4 = nn.Linear(e4, e5)
        self.a5 = nn.Linear(e5, 2)
        self.dropout = nn.Dropout(p)

    def forward(self, input_0):
        primals_1 = self.a1.weight
        primals_2 = self.a1.bias
        primals_4 = self.a2.weight
        primals_5 = self.a2.bias
        primals_6 = self.a3.weight
        primals_7 = self.a3.bias
        primals_8 = self.a4.weight
        primals_9 = self.a4.bias
        primals_10 = self.a5.weight
        primals_11 = self.a5.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]
