# 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_6/inductor_cache/sm/csml7pk7dnkzryrz4bflxjet7qglas4fh2sq5uocotq763iheze5.py
# Topologically Sorted Source Nodes: [__2, mul, pow_1, mul_1, add_1, mul_2, tanh, add_2, __3], Original ATen: [aten.add, aten.mul, aten.pow, aten.tanh]
# Source node to ATen node mapping:
#   __2 => add
#   __3 => mul_3
#   add_1 => add_1
#   add_2 => add_2
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   pow_1 => pow_1
#   tanh => tanh
# Graph fragment:
#   %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_2, %primals_3), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.5), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add, 3), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 0.044715), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.7978845608028654), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%mul_2,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%tanh, 1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %add_2), kwargs = {})
triton_poi_fused_add_mul_pow_tanh_0 = async_compile.triton('triton_poi_fused_add_mul_pow_tanh_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=[1024], 
    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_pow_tanh_0', 'mutated_arg_names': [], '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_add_mul_pow_tanh_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp5 = tmp2 * tmp2
    tmp6 = tmp5 * tmp2
    tmp7 = 0.044715
    tmp8 = tmp6 * tmp7
    tmp9 = tmp2 + tmp8
    tmp10 = 0.7978845608028654
    tmp11 = tmp9 * tmp10
    tmp12 = libdevice.tanh(tmp11)
    tmp13 = 1.0
    tmp14 = tmp12 + tmp13
    tmp15 = tmp4 * tmp14
    tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/dt/cdt6y6j3wuazgkrbujrfautah7p2yc3ub56w5z2hajp5pvsyo6vy.py
# Topologically Sorted Source Nodes: [__5], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   __5 => add_3
# Graph fragment:
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_4, %primals_5), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_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=[1024], 
    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_add_1', '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_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 576
    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')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, 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, 36, 2048), (73728, 2048, 1))
    assert_size_stride(primals_2, (2048, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (144, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((144, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [__1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (144, 2048), (2048, 1), 0), primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 36, 4), (144, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [__2, mul, pow_1, mul_1, add_1, mul_2, tanh, add_2, __3], Original ATen: [aten.add, aten.mul, aten.pow, aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_pow_tanh_0.run(buf0, primals_3, buf1, 576, grid=grid(576), stream=stream0)
        buf2 = empty_strided_cuda((144, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [__4], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf1, (144, 4), (4, 1), 0), primals_4, out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 36, 4), (144, 4, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [__5], Original ATen: [aten.add]
        triton_poi_fused_add_1.run(buf3, primals_5, 576, grid=grid(576), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (4, 144), (144, 1), 0), primals_6, alpha=1, beta=1, out=buf4)
        del primals_7
    return (buf4, primals_3, buf0, reinterpret_tensor(buf3, (144, 4), (1, 144), 0), reinterpret_tensor(primals_6, (4, 144), (1, 4), 0), reinterpret_tensor(buf1, (4, 144), (1, 4), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), reinterpret_tensor(primals_1, (2048, 144), (1, 2048), 0), )


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, 36, 2048), (73728, 2048, 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((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((144, 4), (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 math
import torch
import numpy as np
import torch.nn as nn


def gelu(x):
    return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
        torch.pow(x, 3))))


class 全连接层(nn.Module):

    def __init__(self, 输入_接口, 输出_接口):
        super().__init__()
        np.random.seed(1)
        self.weight = nn.Parameter(torch.FloatTensor(np.random.uniform(-1 /
            np.sqrt(输入_接口), 1 / np.sqrt(输入_接口), (输入_接口, 输出_接口))))
        self.bias = nn.Parameter(torch.FloatTensor(np.random.uniform(-1 /
            np.sqrt(输入_接口), 1 / np.sqrt(输入_接口), 输出_接口)))

    def forward(self, x):
        输出 = torch.matmul(x, self.weight)
        输出 = 输出 + self.bias
        return 输出


class 判断状态(nn.Module):

    def __init__(self, 种类数, 隐藏层尺寸, 输入层尺寸=2048, 输入尺寸A=36):
        super().__init__()
        self.隐藏层尺寸 = 隐藏层尺寸
        self.输入层尺寸 = 输入层尺寸
        self.输入尺寸A = 输入尺寸A
        self.输入层 = 全连接层(输入层尺寸, 隐藏层尺寸)
        self.隐藏层 = 全连接层(隐藏层尺寸, 隐藏层尺寸)
        self.输出层 = 全连接层(隐藏层尺寸 * 输入尺寸A, 种类数)

    def forward(self, 图向量):
        图向量 = 图向量.reshape((图向量.shape[0], self.输入尺寸A, self.输入层尺寸))
        中间量 = gelu(self.输入层(图向量))
        中间量 = self.隐藏层(中间量)
        中间量 = 中间量.reshape((中间量.shape[0], self.隐藏层尺寸 * self.输入尺寸A))
        结果 = self.输出层(中间量)
        return 结果


def get_inputs():
    return [torch.rand([4, 36, 2048])]


def get_init_inputs():
    return [[], {'种类数': 4, '隐藏层尺寸': 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 math
import numpy as np
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_add_mul_pow_tanh_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp5 = tmp2 * tmp2
    tmp6 = tmp5 * tmp2
    tmp7 = 0.044715
    tmp8 = tmp6 * tmp7
    tmp9 = tmp2 + tmp8
    tmp10 = 0.7978845608028654
    tmp11 = tmp9 * tmp10
    tmp12 = libdevice.tanh(tmp11)
    tmp13 = 1.0
    tmp14 = tmp12 + tmp13
    tmp15 = tmp4 * tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 576
    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')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, 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, 36, 2048), (73728, 2048, 1))
    assert_size_stride(primals_2, (2048, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (144, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((144, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (144, 2048), (2048,
            1), 0), primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 36, 4), (144, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_pow_tanh_0[grid(576)](buf0, primals_3,
            buf1, 576, XBLOCK=256, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((144, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (144, 4), (4, 1), 0),
            primals_4, out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 36, 4), (144, 4, 1), 0)
        del buf2
        triton_poi_fused_add_1[grid(576)](buf3, primals_5, 576, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (4, 144),
            (144, 1), 0), primals_6, alpha=1, beta=1, out=buf4)
        del primals_7
    return buf4, primals_3, buf0, reinterpret_tensor(buf3, (144, 4), (1, 
        144), 0), reinterpret_tensor(primals_6, (4, 144), (1, 4), 0
        ), reinterpret_tensor(buf1, (4, 144), (1, 4), 0), reinterpret_tensor(
        primals_4, (4, 4), (1, 4), 0), reinterpret_tensor(primals_1, (2048,
        144), (1, 2048), 0)


def gelu(x):
    return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
        torch.pow(x, 3))))


class 全连接层(nn.Module):

    def __init__(self, 输入_接口, 输出_接口):
        super().__init__()
        np.random.seed(1)
        self.weight = nn.Parameter(torch.FloatTensor(np.random.uniform(-1 /
            np.sqrt(输入_接口), 1 / np.sqrt(输入_接口), (输入_接口, 输出_接口))))
        self.bias = nn.Parameter(torch.FloatTensor(np.random.uniform(-1 /
            np.sqrt(输入_接口), 1 / np.sqrt(输入_接口), 输出_接口)))

    def forward(self, x):
        输出 = torch.matmul(x, self.weight)
        输出 = 输出 + self.bias
        return 输出


class 判断状态New(nn.Module):

    def __init__(self, 种类数, 隐藏层尺寸, 输入层尺寸=2048, 输入尺寸A=36):
        super().__init__()
        self.隐藏层尺寸 = 隐藏层尺寸
        self.输入层尺寸 = 输入层尺寸
        self.输入尺寸A = 输入尺寸A
        self.输入层 = 全连接层(输入层尺寸, 隐藏层尺寸)
        self.隐藏层 = 全连接层(隐藏层尺寸, 隐藏层尺寸)
        self.输出层 = 全连接层(隐藏层尺寸 * 输入尺寸A, 种类数)

    def forward(self, input_0):
        primals_2 = self.输入层.weight
        primals_3 = self.输入层.bias
        primals_4 = self.隐藏层.weight
        primals_5 = self.隐藏层.bias
        primals_6 = self.输出层.weight
        primals_7 = self.输出层.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
