# 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/bg/cbgal66sbgu7gu4ebjptrwn33g5sqliygtzdyiujvlctnkexjgpo.py
# Topologically Sorted Source Nodes: [x_1, max_1], Original ATen: [aten.relu, aten.max]
# Source node to ATen node mapping:
#   max_1 => getitem, max_1
#   x_1 => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %max_1 : [num_users=2] = call_function[target=torch.ops.aten.max.dim](args = (%relu, 1), kwargs = {})
#   %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%max_1, 0), kwargs = {})
triton_poi_fused_max_relu_0 = async_compile.triton('triton_poi_fused_max_relu_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i64', 4: '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_max_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 16)
    x3 = xindex % 16
    x0 = xindex % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (64*x2)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (16 + x3 + (64*x2)), xmask)
    tmp9 = tl.load(in_ptr0 + (32 + x3 + (64*x2)), xmask)
    tmp13 = tl.load(in_ptr0 + (48 + x3 + (64*x2)), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp8 = triton_helpers.maximum(tmp4, tmp7)
    tmp10 = tmp9 + tmp1
    tmp11 = triton_helpers.maximum(tmp3, tmp10)
    tmp12 = triton_helpers.maximum(tmp8, tmp11)
    tmp14 = tmp13 + tmp1
    tmp15 = triton_helpers.maximum(tmp3, tmp14)
    tmp16 = triton_helpers.maximum(tmp12, tmp15)
    tmp17 = tmp4 > tmp7
    tmp18 = tmp4 == tmp7
    tmp19 = tmp4 != tmp4
    tmp20 = tmp7 != tmp7
    tmp21 = tmp19 > tmp20
    tmp22 = tmp17 | tmp21
    tmp23 = tmp19 & tmp20
    tmp24 = tmp18 | tmp23
    tmp25 = tl.full([1], 0, tl.int64)
    tmp26 = tl.full([1], 1, tl.int64)
    tmp27 = tmp25 < tmp26
    tmp28 = tmp24 & tmp27
    tmp29 = tmp22 | tmp28
    tmp30 = tl.where(tmp29, tmp4, tmp7)
    tmp31 = tl.where(tmp29, tmp25, tmp26)
    tmp32 = tmp30 > tmp11
    tmp33 = tmp30 == tmp11
    tmp34 = tmp30 != tmp30
    tmp35 = tmp11 != tmp11
    tmp36 = tmp34 > tmp35
    tmp37 = tmp32 | tmp36
    tmp38 = tmp34 & tmp35
    tmp39 = tmp33 | tmp38
    tmp40 = tl.full([1], 2, tl.int64)
    tmp41 = tmp31 < tmp40
    tmp42 = tmp39 & tmp41
    tmp43 = tmp37 | tmp42
    tmp44 = tl.where(tmp43, tmp30, tmp11)
    tmp45 = tl.where(tmp43, tmp31, tmp40)
    tmp46 = tmp44 > tmp15
    tmp47 = tmp44 == tmp15
    tmp48 = tmp44 != tmp44
    tmp49 = tmp15 != tmp15
    tmp50 = tmp48 > tmp49
    tmp51 = tmp46 | tmp50
    tmp52 = tmp48 & tmp49
    tmp53 = tmp47 | tmp52
    tmp54 = tl.full([1], 3, tl.int64)
    tmp55 = tmp45 < tmp54
    tmp56 = tmp53 & tmp55
    tmp57 = tmp51 | tmp56
    tmp58 = tl.where(tmp57, tmp44, tmp15)
    tmp59 = tl.where(tmp57, tmp45, tmp54)
    tl.store(out_ptr0 + (x4), tmp16, xmask)
    tl.store(out_ptr1 + (x4), tmp59, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/jv/cjvlaukj5mbmnchp5x7j2yykuosqng4mjdnj6xzckvcleskkjp35.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_1 => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_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.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_1', '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_relu_threshold_backward_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2), tmp6, 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, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (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, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        # Topologically Sorted Source Nodes: [x_1, max_1], Original ATen: [aten.relu, aten.max]
        stream0 = get_raw_stream(0)
        triton_poi_fused_max_relu_0.run(buf0, primals_2, buf1, buf2, 64, grid=grid(64), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_1.run(buf0, primals_2, buf3, 256, grid=grid(256), stream=stream0)
        del buf0
        del primals_2
    return (buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf2, (4, 1, 4, 4), (16, 16, 4, 1), 0), buf3, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (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 as th
import torch.nn.functional as F
import torch.nn as nn


class Sentence_Maxpool(nn.Module):

    def __init__(self, word_dimension, output_dim, relu=True):
        super(Sentence_Maxpool, self).__init__()
        self.fc = nn.Linear(word_dimension, output_dim)
        self.out_dim = output_dim
        self.relu = relu

    def forward(self, x):
        x = self.fc(x)
        if self.relu:
            x = F.relu(x)
        return th.max(x, dim=1)[0]


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


def get_init_inputs():
    return [[], {'word_dimension': 4, 'output_dim': 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
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_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x0 = xindex % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
    tmp9 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
    tmp13 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp8 = triton_helpers.maximum(tmp4, tmp7)
    tmp10 = tmp9 + tmp1
    tmp11 = triton_helpers.maximum(tmp3, tmp10)
    tmp12 = triton_helpers.maximum(tmp8, tmp11)
    tmp14 = tmp13 + tmp1
    tmp15 = triton_helpers.maximum(tmp3, tmp14)
    tmp16 = triton_helpers.maximum(tmp12, tmp15)
    tmp17 = tmp4 > tmp7
    tmp18 = tmp4 == tmp7
    tmp19 = tmp4 != tmp4
    tmp20 = tmp7 != tmp7
    tmp21 = tmp19 > tmp20
    tmp22 = tmp17 | tmp21
    tmp23 = tmp19 & tmp20
    tmp24 = tmp18 | tmp23
    tmp25 = tl.full([1], 0, tl.int64)
    tmp26 = tl.full([1], 1, tl.int64)
    tmp27 = tmp25 < tmp26
    tmp28 = tmp24 & tmp27
    tmp29 = tmp22 | tmp28
    tmp30 = tl.where(tmp29, tmp4, tmp7)
    tmp31 = tl.where(tmp29, tmp25, tmp26)
    tmp32 = tmp30 > tmp11
    tmp33 = tmp30 == tmp11
    tmp34 = tmp30 != tmp30
    tmp35 = tmp11 != tmp11
    tmp36 = tmp34 > tmp35
    tmp37 = tmp32 | tmp36
    tmp38 = tmp34 & tmp35
    tmp39 = tmp33 | tmp38
    tmp40 = tl.full([1], 2, tl.int64)
    tmp41 = tmp31 < tmp40
    tmp42 = tmp39 & tmp41
    tmp43 = tmp37 | tmp42
    tmp44 = tl.where(tmp43, tmp30, tmp11)
    tmp45 = tl.where(tmp43, tmp31, tmp40)
    tmp46 = tmp44 > tmp15
    tmp47 = tmp44 == tmp15
    tmp48 = tmp44 != tmp44
    tmp49 = tmp15 != tmp15
    tmp50 = tmp48 > tmp49
    tmp51 = tmp46 | tmp50
    tmp52 = tmp48 & tmp49
    tmp53 = tmp47 | tmp52
    tmp54 = tl.full([1], 3, tl.int64)
    tmp55 = tmp45 < tmp54
    tmp56 = tmp53 & tmp55
    tmp57 = tmp51 | tmp56
    tl.where(tmp57, tmp44, tmp15)
    tmp59 = tl.where(tmp57, tmp45, tmp54)
    tl.store(out_ptr0 + x4, tmp16, xmask)
    tl.store(out_ptr1 + x4, tmp59, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (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, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        get_raw_stream(0)
        triton_poi_fused_max_relu_0[grid(64)](buf0, primals_2, buf1, buf2, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(256)](buf0,
            primals_2, buf3, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
    return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf2, (4, 1, 4, 4), (16, 16, 4, 1), 0), buf3


class Sentence_MaxpoolNew(nn.Module):

    def __init__(self, word_dimension, output_dim, relu=True):
        super(Sentence_MaxpoolNew, self).__init__()
        self.fc = nn.Linear(word_dimension, output_dim)
        self.out_dim = output_dim
        self.relu = relu

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