# 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/u7/cu7pejezhevt5zxeq5ul5qcdbafeenotlv2ryxcmgslsyxnyvsrm.py
# Topologically Sorted Source Nodes: [mean, std, add], Original ATen: [aten.mean, aten.std, aten.add]
# Source node to ATen node mapping:
#   add => add
#   mean => mean
#   std => sqrt, var
# Graph fragment:
#   %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [1], True), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view, [1]), kwargs = {correction: 1.0, keepdim: True})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-05), kwargs = {})
triton_poi_fused_add_mean_std_0 = async_compile.triton('triton_poi_fused_add_mean_std_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mean_std_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_add_mean_std_0(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 = 3.0
    tmp21 = tmp19 / tmp20
    tmp22 = libdevice.sqrt(tmp21)
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp24, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/i2/ci2y2v6wspi2i4izpra64qxcysplufszfhk3kb2ghcsthcfzud4a.py
# Topologically Sorted Source Nodes: [sub, truediv], Original ATen: [aten.sub, aten.div]
# Source node to ATen node mapping:
#   sub => sub
#   truediv => div
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %mean), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %add), kwargs = {})
triton_poi_fused_div_sub_1 = async_compile.triton('triton_poi_fused_div_sub_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], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_div_sub_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
    x2 = xindex
    x1 = (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')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 / tmp3
    tl.store(out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/br/cbrgejd3salrdu5bb3wf7dn6ggnaub42y43rj4ugcj37clbevjg3.py
# Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   output_1 => sigmoid
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_3), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_sigmoid_2 = async_compile.triton('triton_poi_fused_sigmoid_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=[8192], 
    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_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_sigmoid_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = 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.sigmoid(tmp2)
    tl.store(in_out_ptr0 + (x2), tmp3, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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, (2048, 4), (4, 1))
    assert_size_stride(primals_3, (2048, ), (1, ))
    assert_size_stride(primals_4, (4, 2048), (2048, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean, std, add], Original ATen: [aten.mean, aten.std, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mean_std_0.run(primals_1, buf0, buf1, 4, grid=grid(4), stream=stream0)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, truediv], Original ATen: [aten.sub, aten.div]
        triton_poi_fused_div_sub_1.run(primals_1, buf0, buf1, buf2, 16, grid=grid(16), stream=stream0)
        del primals_1
        buf3 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf2, reinterpret_tensor(primals_2, (4, 2048), (1, 4), 0), out=buf3)
        del primals_2
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_2.run(buf4, primals_3, 8192, grid=grid(8192), stream=stream0)
        del primals_3
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf4, reinterpret_tensor(primals_4, (2048, 4), (1, 2048), 0), alpha=1, beta=1, out=buf5)
        del primals_5
    return (buf5, buf1, buf0, buf2, buf4, 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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((2048, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((2048, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 2048), (2048, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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.utils.data


class DNNnet(torch.nn.Module):

    def __init__(self, n_layer, n_in_channel, n_out_channel):
        super(DNNnet, self).__init__()
        self.n_layer = n_layer
        self.fc_layers = torch.nn.ModuleList()
        self.act_func = torch.nn.Sigmoid()
        start_layer = torch.nn.Linear(n_in_channel, 2048)
        self.start = start_layer
        for i in range(self.n_layer - 1):
            fc_layer = torch.nn.Linear(2048, 2048)
            self.fc_layers.append(fc_layer)
        end_layer = torch.nn.Linear(2048, n_out_channel)
        self.end = end_layer

    def scale(self, x):
        """
        x = [batchsize , n_mel_channels x frames]
        """
        self.mm = torch.mean(x, dim=1, keepdim=True)
        self.std = torch.std(x, dim=1, keepdim=True) + 1e-05
        return (x - self.mm) / self.std

    def forward(self, forward_input):
        """
        forward_input = mel spectrongram of 11 input frames: [batchsize , n_mel_channels , frames]
        """
        forward_input = forward_input.contiguous().view(forward_input.size(
            0), -1)
        output = self.start(self.scale(forward_input))
        output = self.act_func(output)
        for i in range(self.n_layer - 1):
            output = self.fc_layers[i](output)
            output = self.act_func(output)
        output = self.end(output)
        return output


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


def get_init_inputs():
    return [[], {'n_layer': 1, 'n_in_channel': 4, 'n_out_channel': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
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_add_mean_std_0(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 = 3.0
    tmp21 = tmp19 / tmp20
    tmp22 = libdevice.sqrt(tmp21)
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp24, xmask)


@triton.jit
def triton_poi_fused_div_sub_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
    x2 = xindex
    x1 = 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')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 / tmp3
    tl.store(out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_sigmoid_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 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.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (2048, 4), (4, 1))
    assert_size_stride(primals_3, (2048,), (1,))
    assert_size_stride(primals_4, (4, 2048), (2048, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mean_std_0[grid(4)](primals_1, buf0, buf1, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_div_sub_1[grid(16)](primals_1, buf0, buf1, buf2, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
        buf3 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_2, (4, 2048), (1,
            4), 0), out=buf3)
        del primals_2
        buf4 = buf3
        del buf3
        triton_poi_fused_sigmoid_2[grid(8192)](buf4, primals_3, 8192,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf4, reinterpret_tensor(primals_4,
            (2048, 4), (1, 2048), 0), alpha=1, beta=1, out=buf5)
        del primals_5
    return buf5, buf1, buf0, buf2, buf4, primals_4


class DNNnetNew(torch.nn.Module):

    def __init__(self, n_layer, n_in_channel, n_out_channel):
        super(DNNnetNew, self).__init__()
        self.n_layer = n_layer
        self.fc_layers = torch.nn.ModuleList()
        self.act_func = torch.nn.Sigmoid()
        start_layer = torch.nn.Linear(n_in_channel, 2048)
        self.start = start_layer
        for i in range(self.n_layer - 1):
            fc_layer = torch.nn.Linear(2048, 2048)
            self.fc_layers.append(fc_layer)
        end_layer = torch.nn.Linear(2048, n_out_channel)
        self.end = end_layer

    def scale(self, x):
        """
        x = [batchsize , n_mel_channels x frames]
        """
        self.mm = torch.mean(x, dim=1, keepdim=True)
        self.std = torch.std(x, dim=1, keepdim=True) + 1e-05
        return (x - self.mm) / self.std

    def forward(self, input_0):
        primals_2 = self.start.weight
        primals_3 = self.start.bias
        primals_4 = self.end.weight
        primals_5 = self.end.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
