# 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/5v/c5vq5yss2urzrxszwmorkaz2buvlv2swif7scty2kf5yaq7t5qwa.py
# Topologically Sorted Source Nodes: [conv1d, conv1d_1, conv_x_3], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv1d => convolution
#   conv1d_1 => convolution_1
#   conv_x_3 => convolution_2
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1], [0], [1], False, [0], 1), kwargs = {})
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_4, %primals_5, [1], [0], [1], False, [0], 1), kwargs = {})
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_6, %primals_7, [1], [0], [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=[16, 4], tile_hint=TileHint.DEFAULT,
    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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_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_convolution_0(in_ptr0, out_ptr0, 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
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (x2 + (4*y3)), tmp0, xmask & ymask)
    tl.store(out_ptr2 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ee/ceesrflbpcl4um5oexaht2gyxnwasw45c5ezcb4sa7u4w6pmfwq4.py
# Topologically Sorted Source Nodes: [conv1d, sig_re, conv1d_1, GLU_result, conv_x_3, temporal_result], Original ATen: [aten.convolution, aten.sigmoid, aten.mul, aten.add]
# Source node to ATen node mapping:
#   GLU_result => mul
#   conv1d => convolution
#   conv1d_1 => convolution_1
#   conv_x_3 => convolution_2
#   sig_re => sigmoid
#   temporal_result => add
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1], [0], [1], False, [0], 1), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution,), kwargs = {})
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_4, %primals_5, [1], [0], [1], False, [0], 1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, %sigmoid), kwargs = {})
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_6, %primals_7, [1], [0], [1], False, [0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %convolution_2), kwargs = {})
triton_poi_fused_add_convolution_mul_sigmoid_1 = async_compile.triton('triton_poi_fused_add_convolution_mul_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=[16], 
    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_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1', 'in_out_ptr2'], 'no_x_dim': False, 'num_load': 6, '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_1(in_out_ptr0, in_out_ptr1, in_out_ptr2, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp4 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_out_ptr2 + (x2), xmask)
    tmp9 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tl.sigmoid(tmp2)
    tmp7 = tmp5 * tmp6
    tmp10 = tmp8 + tmp9
    tmp11 = tmp7 + tmp10
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(in_out_ptr1 + (x2), tmp5, xmask)
    tl.store(in_out_ptr2 + (x2), tmp11, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 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))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv1d, conv1d_1, conv_x_3], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(primals_1, buf0, buf3, buf6, 16, 4, grid=grid(16, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1), (4, 1, 1))
        del buf0
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 1), (4, 1, 1))
        del buf3
        # Topologically Sorted Source Nodes: [conv_x_3], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 1), (4, 1, 1))
        del buf6
        buf2 = buf1; del buf1  # reuse
        buf5 = buf4; del buf4  # reuse
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [conv1d, sig_re, conv1d_1, GLU_result, conv_x_3, temporal_result], Original ATen: [aten.convolution, aten.sigmoid, aten.mul, aten.add]
        triton_poi_fused_add_convolution_mul_sigmoid_1.run(buf2, buf5, buf8, primals_3, primals_5, primals_7, 16, grid=grid(16), stream=stream0)
        del primals_3
        del primals_5
        del primals_7
    return (reinterpret_tensor(buf8, (4, 1, 4), (4, 1, 1), 0), primals_2, primals_4, primals_6, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), buf2, buf5, )


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, 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)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = 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])
    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


class Temporal_Gated_conv(nn.Module):
    """
    时序卷积模块，通过一位卷积提取时序关系
    """

    def __init__(self, in_channels, out_channels, kernel_size, padding=0,
        stride=1):
        super(Temporal_Gated_conv, self).__init__()
        self.conv_1 = nn.Conv1d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, padding=padding, stride=
            stride)
        self.conv_2 = nn.Conv1d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, padding=padding, stride=
            stride)
        self.conv_3 = nn.Conv1d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, padding=padding, stride=
            stride)

    def forward(self, X):
        """

        :param X: input size of X is [batch_size,num_node,in_channel]
        :return: output size is [batch_size,num_node,out_channel]
        """
        X = X.permute(0, 2, 1)
        sig_re = torch.sigmoid(self.conv_2(X))
        GLU_result = self.conv_1(X).mul(sig_re)
        conv_x_3 = self.conv_3(X)
        temporal_result = GLU_result + conv_x_3
        temporal_result = temporal_result.permute(0, 2, 1)
        return temporal_result


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 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
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_convolution_0(in_ptr0, out_ptr0, 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
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (x2 + 4 * y3), tmp0, xmask & ymask)
    tl.store(out_ptr2 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_convolution_mul_sigmoid_1(in_out_ptr0, in_out_ptr1,
    in_out_ptr2, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_out_ptr2 + x2, xmask)
    tmp9 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tl.sigmoid(tmp2)
    tmp7 = tmp5 * tmp6
    tmp10 = tmp8 + tmp9
    tmp11 = tmp7 + tmp10
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(in_out_ptr1 + x2, tmp5, xmask)
    tl.store(in_out_ptr2 + x2, tmp11, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 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))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, buf3,
            buf6, 16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1), (4, 1, 1))
        del buf0
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 1), (4, 1, 1))
        del buf3
        buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 1), (4, 1, 1))
        del buf6
        buf2 = buf1
        del buf1
        buf5 = buf4
        del buf4
        buf8 = buf7
        del buf7
        triton_poi_fused_add_convolution_mul_sigmoid_1[grid(16)](buf2, buf5,
            buf8, primals_3, primals_5, primals_7, 16, XBLOCK=16, num_warps
            =1, num_stages=1)
        del primals_3
        del primals_5
        del primals_7
    return reinterpret_tensor(buf8, (4, 1, 4), (4, 1, 1), 0
        ), primals_2, primals_4, primals_6, reinterpret_tensor(primals_1, (
        4, 4, 4), (16, 1, 4), 0), buf2, buf5


class Temporal_Gated_convNew(nn.Module):
    """
    时序卷积模块，通过一位卷积提取时序关系
    """

    def __init__(self, in_channels, out_channels, kernel_size, padding=0,
        stride=1):
        super(Temporal_Gated_convNew, self).__init__()
        self.conv_1 = nn.Conv1d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, padding=padding, stride=
            stride)
        self.conv_2 = nn.Conv1d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, padding=padding, stride=
            stride)
        self.conv_3 = nn.Conv1d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, padding=padding, stride=
            stride)

    def forward(self, input_0):
        primals_1 = self.conv_1.weight
        primals_3 = self.conv_1.bias
        primals_2 = self.conv_2.weight
        primals_5 = self.conv_2.bias
        primals_4 = self.conv_3.weight
        primals_7 = self.conv_3.bias
        primals_6 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
