# 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/vw/cvwzmrf7u7j4rr5x5bjsxorzvl7rkn2io72j5uk3mjnapf4ukldf.py
# Topologically Sorted Source Nodes: [_weight_norm], Original ATen: [aten._weight_norm_interface]
# Source node to ATen node mapping:
#   _weight_norm => div, mul, pow_1, pow_2, sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_2, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1, 2], True), kwargs = {})
#   %pow_2 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, %pow_2), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %div), kwargs = {})
triton_per_fused__weight_norm_interface_0 = async_compile.triton('triton_per_fused__weight_norm_interface_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__weight_norm_interface_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused__weight_norm_interface_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp8 = tmp7 / tmp6
    tmp9 = tmp0 * tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp6, xmask)
    tl.store(out_ptr0 + (r1 + (16*x0)), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3n/c3n65i33xazak7nucmsk5rrmzwarmfgz7rlkjfrdavbshsqnxmb7.py
# Topologically Sorted Source Nodes: [input_1, input_2, conv_avg], Original ATen: [aten.convolution, aten.relu, aten.mean, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv_avg => mean
#   input_1 => convolution
#   input_2 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_4, %mul, %primals_3, [1], [4], [1], False, [0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%permute, [1]), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_per_fused_convolution_mean_relu_threshold_backward_1 = async_compile.triton('triton_per_fused_convolution_mean_relu_threshold_backward_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.persistent_reduction(
    size_hints=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32', 5: '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_per_fused_convolution_mean_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_convolution_mean_relu_threshold_backward_1(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 9
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + (9*x3)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tmp7 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp9 = tl.where(rmask & xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = 9.0
    tmp12 = tmp10 / tmp11
    tl.store(in_out_ptr0 + (r2 + (9*x3)), tmp4, rmask & xmask)
    tl.store(out_ptr0 + (r2 + (9*x3)), tmp6, rmask & xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp12, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 1), (1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 1), (1, 1, 1), 0); del buf0  # reuse
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [_weight_norm], Original ATen: [aten._weight_norm_interface]
        stream0 = get_raw_stream(0)
        triton_per_fused__weight_norm_interface_0.run(buf1, primals_2, primals_1, buf2, 4, 16, grid=grid(4), stream=stream0)
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(primals_4, buf2, stride=(1,), padding=(4,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 9), (36, 9, 1))
        buf4 = buf3; del buf3  # reuse
        buf7 = empty_strided_cuda((4, 4, 9), (36, 9, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [input_1, input_2, conv_avg], Original ATen: [aten.convolution, aten.relu, aten.mean, aten.threshold_backward]
        triton_per_fused_convolution_mean_relu_threshold_backward_1.run(buf4, buf6, primals_3, buf7, 16, 9, grid=grid(16), stream=stream0)
        del primals_3
    return (reinterpret_tensor(buf4, (4, 9, 4), (36, 1, 9), 0), buf6, buf2, primals_1, primals_2, primals_4, buf1, buf2, buf7, )


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, 1, 1), (1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 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, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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
from torch.nn.utils import weight_norm


class ConvLayer(nn.Module):

    def __init__(self, input_units, output_units, filter_size,
        padding_sizes, dropout=0.2):
        super(ConvLayer, self).__init__()
        self.conv = weight_norm(nn.Conv1d(in_channels=input_units,
            out_channels=output_units, kernel_size=filter_size[0], stride=1,
            padding=padding_sizes[0]))
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(dropout)
        self.net = nn.Sequential(self.conv, self.relu, self.dropout)
        self.init_weights()

    def init_weights(self):
        self.conv.weight.data.normal_(0, 0.01)

    def forward(self, input_x):
        """
        Convolution Layer.
        Args:
            input_x: [batch_size, embedding_size, sequence_length]
        Returns:
            conv_out: [batch_size, sequence_length, num_filters]
            conv_avg: [batch_size, num_filters]
        """
        conv_out = self.net(input_x)
        conv_out = conv_out.permute(0, 2, 1)
        conv_avg = torch.mean(conv_out, dim=1)
        return conv_out, conv_avg


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


def get_init_inputs():
    return [[], {'input_units': 4, 'output_units': 4, 'filter_size': [4, 4],
        'padding_sizes': [4, 4]}]

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


@triton.jit
def triton_per_fused__weight_norm_interface_0(in_out_ptr0, in_ptr0, in_ptr1,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp8 = tmp7 / tmp6
    tmp9 = tmp0 * tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp9, xmask)


@triton.jit
def triton_per_fused_convolution_mean_relu_threshold_backward_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    rnumel = 9
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + 9 * x3), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tmp7 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp9 = tl.where(rmask & xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = 9.0
    tmp12 = tmp10 / tmp11
    tl.store(in_out_ptr0 + (r2 + 9 * x3), tmp4, rmask & xmask)
    tl.store(out_ptr0 + (r2 + 9 * x3), tmp6, rmask & xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 1), (1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 1), (1, 1, 1), 0)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__weight_norm_interface_0[grid(4)](buf1, primals_2,
            primals_1, buf2, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = extern_kernels.convolution(primals_4, buf2, stride=(1,),
            padding=(4,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 9), (36, 9, 1))
        buf4 = buf3
        del buf3
        buf7 = empty_strided_cuda((4, 4, 9), (36, 9, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = buf5
        del buf5
        triton_per_fused_convolution_mean_relu_threshold_backward_1[grid(16)](
            buf4, buf6, primals_3, buf7, 16, 9, XBLOCK=1, num_warps=2,
            num_stages=1)
        del primals_3
    return reinterpret_tensor(buf4, (4, 9, 4), (36, 1, 9), 0
        ), buf6, buf2, primals_1, primals_2, primals_4, buf1, buf2, buf7


class ConvLayerNew(nn.Module):

    def __init__(self, input_units, output_units, filter_size,
        padding_sizes, dropout=0.2):
        super(ConvLayerNew, self).__init__()
        self.conv = weight_norm(nn.Conv1d(in_channels=input_units,
            out_channels=output_units, kernel_size=filter_size[0], stride=1,
            padding=padding_sizes[0]))
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(dropout)
        self.net = nn.Sequential(self.conv, self.relu, self.dropout)
        self.init_weights()

    def init_weights(self):
        self.conv.weight.data.normal_(0, 0.01)

    def forward(self, input_0):
        primals_3 = self.conv.bias
        primals_1 = self.conv.weight_g
        primals_2 = self.conv.weight_v
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]
