# 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_7/inductor_cache/yq/cyqt2poirgixclumgalkuyoro55ty2atzhkprmly4k5cvfobc6ld.py
# Topologically Sorted Source Nodes: [Hout], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   Hout => convolution
# Graph fragment:
#   %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1], [1], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[32], 
    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_0', '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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/gv/cgv4dyzkhkil4qdq7yp2xijbkzkmgk6fwk4wvx34mqp2qudzg6x2.py
# Topologically Sorted Source Nodes: [sigmoid, mul, sub, mul_1, add], Original ATen: [aten.sigmoid, aten.mul, aten.rsub, aten.add]
# Source node to ATen node mapping:
#   add => add
#   mul => mul
#   mul_1 => mul_1
#   sigmoid => sigmoid
#   sub => sub
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%slice_2,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %slice_5), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %primals_3), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
triton_poi_fused_add_mul_rsub_sigmoid_1 = async_compile.triton('triton_poi_fused_add_mul_rsub_sigmoid_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=[32], 
    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_add_mul_rsub_sigmoid_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_add_mul_rsub_sigmoid_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 2) % 4
    x2 = (xindex // 8)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + (8*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x1 + (8*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (x3), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (8, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 2), (8, 2, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [Hout], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 1), (8, 1, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [Hout], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 32, grid=grid(32), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 2), (8, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid, mul, sub, mul_1, add], Original ATen: [aten.sigmoid, aten.mul, aten.rsub, aten.add]
        triton_poi_fused_add_mul_rsub_sigmoid_1.run(buf1, primals_3, buf2, 32, grid=grid(32), stream=stream0)
    return (buf2, primals_1, primals_3, buf1, )


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((8, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 2), (8, 2, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
from torch import nn
import torch.nn.functional as F


class NonCausalConv1d(nn.Module):
    """Non causal Conv1d with appropriate padding to ensure sequence length stays the same.

    Note Convolutions always have stride of 1 following layout in paper.
    
    """

    def __init__(self, in_channels, out_channels, kernel_size, dilation):
        super().__init__()
        padding = (kernel_size - 1) * dilation // 2
        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, 1,
            padding, dilation)

    def forward(self, x):
        """
        Inputs:
            x(batch_size x input_dim x seq_len)

        """
        return self.conv(x)


class CausalConv1d(nn.Module):
    """Causal conv1d with appropriate padding to ensure sequence length stays the same.

    Note Convolutions always have stride of 1 following layout in paper.
    
    """

    def __init__(self, in_channels, out_channels, kernel_size, dilation):
        super().__init__()
        self.padding = (kernel_size - 1) * dilation
        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, 1,
            self.padding, dilation)

    def forward(self, x):
        """
        Inputs:
            x(batch_size x input_dim x seq_len)
        
        """
        x = self.conv(x)
        if self.padding > 0:
            return x[:, :, :-self.padding].contiguous()
        else:
            return x


class Highway(nn.Module):
    """Highway network with conv1d
    
    """

    def __init__(self, hidden, kernel_size, dilation, causal=False):
        self.d = hidden
        super().__init__()
        if causal:
            self.conv = CausalConv1d(hidden, 2 * hidden, kernel_size, dilation)
        else:
            self.conv = NonCausalConv1d(hidden, 2 * hidden, kernel_size,
                dilation)

    def forward(self, x):
        """
        Inputs:
            x(batch_size x input_dim x seq_len)
        
        """
        Hout = self.conv(x)
        H1 = Hout[:, :self.d, :]
        H2 = Hout[:, self.d:, :]
        return F.sigmoid(H1) * H2 + (1 - F.sigmoid(H1)) * x


def get_inputs():
    return [torch.rand([4, 4, 2])]


def get_init_inputs():
    return [[], {'hidden': 4, 'kernel_size': 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_1(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 2 % 4
    x2 = xindex // 8
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + 8 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tl.load(in_ptr0 + (4 + x1 + 8 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr1 + x3, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 2), (8, 2, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 1), (8, 1, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(32)](buf1, primals_2, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 2), (8, 2, 1), torch.float32)
        triton_poi_fused_add_mul_rsub_sigmoid_1[grid(32)](buf1, primals_3,
            buf2, 32, XBLOCK=32, num_warps=1, num_stages=1)
    return buf2, primals_1, primals_3, buf1


class NonCausalConv1d(nn.Module):
    """Non causal Conv1d with appropriate padding to ensure sequence length stays the same.

    Note Convolutions always have stride of 1 following layout in paper.
    
    """

    def __init__(self, in_channels, out_channels, kernel_size, dilation):
        super().__init__()
        padding = (kernel_size - 1) * dilation // 2
        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, 1,
            padding, dilation)

    def forward(self, x):
        """
        Inputs:
            x(batch_size x input_dim x seq_len)

        """
        return self.conv(x)


class CausalConv1d(nn.Module):
    """Causal conv1d with appropriate padding to ensure sequence length stays the same.

    Note Convolutions always have stride of 1 following layout in paper.
    
    """

    def __init__(self, in_channels, out_channels, kernel_size, dilation):
        super().__init__()
        self.padding = (kernel_size - 1) * dilation
        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, 1,
            self.padding, dilation)

    def forward(self, x):
        """
        Inputs:
            x(batch_size x input_dim x seq_len)
        
        """
        x = self.conv(x)
        if self.padding > 0:
            return x[:, :, :-self.padding].contiguous()
        else:
            return x


class HighwayNew(nn.Module):
    """Highway network with conv1d
    
    """

    def __init__(self, hidden, kernel_size, dilation, causal=False):
        self.d = hidden
        super().__init__()
        if causal:
            self.conv = CausalConv1d(hidden, 2 * hidden, kernel_size, dilation)
        else:
            self.conv = NonCausalConv1d(hidden, 2 * hidden, kernel_size,
                dilation)

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