# 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/uw/cuwtfh3l4p2mtcmqs5637ldszmfl43k4argfvs4s6quwjtijuwha.py
# Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
# Source node to ATen node mapping:
#   max_1 => getitem, max_1
# Graph fragment:
#   %max_1 : [num_users=2] = call_function[target=torch.ops.aten.max.dim](args = (%view_2, 2), kwargs = {})
#   %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%max_1, 0), kwargs = {})
triton_poi_fused_max_0 = async_compile.triton('triton_poi_fused_max_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i64', 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_max_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_max_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = tmp0 > tmp1
    tmp8 = tmp0 == tmp1
    tmp9 = tmp0 != tmp0
    tmp10 = tmp1 != tmp1
    tmp11 = tmp9 > tmp10
    tmp12 = tmp7 | tmp11
    tmp13 = tmp9 & tmp10
    tmp14 = tmp8 | tmp13
    tmp15 = tl.full([1], 0, tl.int64)
    tmp16 = tl.full([1], 1, tl.int64)
    tmp17 = tmp15 < tmp16
    tmp18 = tmp14 & tmp17
    tmp19 = tmp12 | tmp18
    tmp20 = tl.where(tmp19, tmp0, tmp1)
    tmp21 = tl.where(tmp19, tmp15, tmp16)
    tmp22 = tmp20 > tmp3
    tmp23 = tmp20 == tmp3
    tmp24 = tmp20 != tmp20
    tmp25 = tmp3 != tmp3
    tmp26 = tmp24 > tmp25
    tmp27 = tmp22 | tmp26
    tmp28 = tmp24 & tmp25
    tmp29 = tmp23 | tmp28
    tmp30 = tl.full([1], 2, tl.int64)
    tmp31 = tmp21 < tmp30
    tmp32 = tmp29 & tmp31
    tmp33 = tmp27 | tmp32
    tmp34 = tl.where(tmp33, tmp20, tmp3)
    tmp35 = tl.where(tmp33, tmp21, tmp30)
    tmp36 = tmp34 > tmp5
    tmp37 = tmp34 == tmp5
    tmp38 = tmp34 != tmp34
    tmp39 = tmp5 != tmp5
    tmp40 = tmp38 > tmp39
    tmp41 = tmp36 | tmp40
    tmp42 = tmp38 & tmp39
    tmp43 = tmp37 | tmp42
    tmp44 = tl.full([1], 3, tl.int64)
    tmp45 = tmp35 < tmp44
    tmp46 = tmp43 & tmp45
    tmp47 = tmp41 | tmp46
    tmp48 = tl.where(tmp47, tmp34, tmp5)
    tmp49 = tl.where(tmp47, tmp35, tmp44)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
    tl.store(out_ptr1 + (x0), tmp49, 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, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.int64)
        # Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
        stream0 = get_raw_stream(0)
        triton_poi_fused_max_0.run(buf0, buf1, buf2, 256, grid=grid(256), stream=stream0)
        del buf0
    return (buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf2, (64, 4, 1), (4, 1, 1), 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((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 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
import torch.nn as nn
import torch.utils.data


class maxout(nn.Module):

    def __init__(self, in_feature, out_feature, pool_size):
        super(maxout, self).__init__()
        self.in_feature = in_feature
        self.out_feature = out_feature
        self.pool_size = pool_size
        self.linear = nn.Linear(in_feature, out_feature * pool_size)

    def forward(self, x):
        output = self.linear(x)
        output = output.view(-1, self.out_feature, self.pool_size)
        output = output.max(2)[0]
        return output


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


def get_init_inputs():
    return [[], {'in_feature': 4, 'out_feature': 4, 'pool_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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
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_max_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    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 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = tmp0 > tmp1
    tmp8 = tmp0 == tmp1
    tmp9 = tmp0 != tmp0
    tmp10 = tmp1 != tmp1
    tmp11 = tmp9 > tmp10
    tmp12 = tmp7 | tmp11
    tmp13 = tmp9 & tmp10
    tmp14 = tmp8 | tmp13
    tmp15 = tl.full([1], 0, tl.int64)
    tmp16 = tl.full([1], 1, tl.int64)
    tmp17 = tmp15 < tmp16
    tmp18 = tmp14 & tmp17
    tmp19 = tmp12 | tmp18
    tmp20 = tl.where(tmp19, tmp0, tmp1)
    tmp21 = tl.where(tmp19, tmp15, tmp16)
    tmp22 = tmp20 > tmp3
    tmp23 = tmp20 == tmp3
    tmp24 = tmp20 != tmp20
    tmp25 = tmp3 != tmp3
    tmp26 = tmp24 > tmp25
    tmp27 = tmp22 | tmp26
    tmp28 = tmp24 & tmp25
    tmp29 = tmp23 | tmp28
    tmp30 = tl.full([1], 2, tl.int64)
    tmp31 = tmp21 < tmp30
    tmp32 = tmp29 & tmp31
    tmp33 = tmp27 | tmp32
    tmp34 = tl.where(tmp33, tmp20, tmp3)
    tmp35 = tl.where(tmp33, tmp21, tmp30)
    tmp36 = tmp34 > tmp5
    tmp37 = tmp34 == tmp5
    tmp38 = tmp34 != tmp34
    tmp39 = tmp5 != tmp5
    tmp40 = tmp38 > tmp39
    tmp41 = tmp36 | tmp40
    tmp42 = tmp38 & tmp39
    tmp43 = tmp37 | tmp42
    tmp44 = tl.full([1], 3, tl.int64)
    tmp45 = tmp35 < tmp44
    tmp46 = tmp43 & tmp45
    tmp47 = tmp41 | tmp46
    tl.where(tmp47, tmp34, tmp5)
    tmp49 = tl.where(tmp47, tmp35, tmp44)
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp49, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 16), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.int64)
        get_raw_stream(0)
        triton_poi_fused_max_0[grid(256)](buf0, buf1, buf2, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf0
    return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf2, (64, 4, 1), (4, 1, 1), 0)


class maxoutNew(nn.Module):

    def __init__(self, in_feature, out_feature, pool_size):
        super(maxoutNew, self).__init__()
        self.in_feature = in_feature
        self.out_feature = out_feature
        self.pool_size = pool_size
        self.linear = nn.Linear(in_feature, out_feature * pool_size)

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