# 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_8/inductor_cache/ps/cpsdiousc6yiecsvi65sn5soumm3zhxn42wltdaifs7qz4nyhb4r.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 = (%convolution,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[16384], 
    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_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_relu_threshold_backward_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 12000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 3000
    x1 = (xindex // 3000)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(out_ptr0 + (x0 + (3008*x1)), tmp2, xmask)
    tl.store(out_ptr1 + (x0 + (3072*x1)), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/xn/cxne5c6zn7w6kxaga7mqpdvaetuk3ckrwvl6awyuutq56onj2vfh.py
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.relu, aten.view]
# Source node to ATen node mapping:
#   x_1 => relu
#   x_2 => view
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
#   %view : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%relu, [-1, 100]), kwargs = {})
triton_poi_fused_relu_view_1 = async_compile.triton('triton_poi_fused_relu_view_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=[16384], 
    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_relu_view_1', '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_relu_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 12000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 100
    x1 = (xindex // 100)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((25*(((x0 + (100*x1)) // 25) % 120)) + (3008*((x0 + (100*x1)) // 3000)) + (x0 % 25)), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 3, 8), (24, 24, 8, 1))
    assert_size_stride(primals_2, (4, 1, 32, 32), (1024, 1024, 32, 1))
    assert_size_stride(primals_3, (1, 100), (100, 1))
    assert_size_stride(primals_4, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 30, 25), (3000, 750, 25, 1))
        buf1 = empty_strided_cuda((4, 4, 30, 25), (3008, 750, 25, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 30, 25), (3072, 750, 25, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf0, buf1, buf5, 12000, grid=grid(12000), stream=stream0)
        buf2 = reinterpret_tensor(buf0, (120, 100), (100, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.relu, aten.view]
        triton_poi_fused_relu_view_1.run(buf1, buf2, 12000, grid=grid(12000), stream=stream0)
        del buf1
        buf4 = empty_strided_cuda((120, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_4, buf2, reinterpret_tensor(primals_3, (100, 1), (1, 100), 0), alpha=1, beta=1, out=buf4)
        del primals_4
    return (buf4, primals_1, primals_2, buf2, primals_3, 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, 1, 3, 8), (24, 24, 8, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 1, 32, 32), (1024, 1024, 32, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 100), (100, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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.nn.functional as F


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 4, (3, 8), bias=False, stride=1)
        self.fc1 = nn.Linear(25 * 4, 1)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = x.view(-1, 25 * 4)
        x = self.fc1(x)
        return x


def get_inputs():
    return [torch.rand([4, 1, 32, 32])]


def get_init_inputs():
    return [[], {}]

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_relu_threshold_backward_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 12000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 3000
    x1 = xindex // 3000
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(out_ptr0 + (x0 + 3008 * x1), tmp2, xmask)
    tl.store(out_ptr1 + (x0 + 3072 * x1), tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 12000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 100
    x1 = xindex // 100
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (25 * ((x0 + 100 * x1) // 25 % 120) + 3008 * (
        (x0 + 100 * x1) // 3000) + x0 % 25), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 3, 8), (24, 24, 8, 1))
    assert_size_stride(primals_2, (4, 1, 32, 32), (1024, 1024, 32, 1))
    assert_size_stride(primals_3, (1, 100), (100, 1))
    assert_size_stride(primals_4, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 30, 25), (3000, 750, 25, 1))
        buf1 = empty_strided_cuda((4, 4, 30, 25), (3008, 750, 25, 1), torch
            .float32)
        buf5 = empty_strided_cuda((4, 4, 30, 25), (3072, 750, 25, 1), torch
            .bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(12000)](buf0, buf1,
            buf5, 12000, XBLOCK=256, num_warps=4, num_stages=1)
        buf2 = reinterpret_tensor(buf0, (120, 100), (100, 1), 0)
        del buf0
        triton_poi_fused_relu_view_1[grid(12000)](buf1, buf2, 12000, XBLOCK
            =128, num_warps=4, num_stages=1)
        del buf1
        buf4 = empty_strided_cuda((120, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_4, buf2, reinterpret_tensor(primals_3,
            (100, 1), (1, 100), 0), alpha=1, beta=1, out=buf4)
        del primals_4
    return buf4, primals_1, primals_2, buf2, primals_3, buf5


class NetNew(nn.Module):

    def __init__(self):
        super(NetNew, self).__init__()
        self.conv1 = nn.Conv2d(1, 4, (3, 8), bias=False, stride=1)
        self.fc1 = nn.Linear(25 * 4, 1)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_3 = self.fc1.weight
        primals_4 = self.fc1.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
