# 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/nn/cnnz7j7bvq4khlqkkeiwlx23s64rkadxb6sq4zk77hz6ujcjlnrw.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_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, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 1024
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 2
    y1 = (yindex // 2)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (2*x2) + (18*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/p7/cp7ejdryfxgctra2x2hn5zril5d6o2omn2dstqiqvvizs2zmhe4e.py
# Topologically Sorted Source Nodes: [pad], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   pad => constant_pad_nd
# Graph fragment:
#   %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [0, 2, 2, 0], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_1 = async_compile.triton('triton_poi_fused_constant_pad_nd_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=[8, 64], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_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_constant_pad_nd_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 8
    xnumel = 36
    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
    x3 = (xindex // 6)
    x2 = xindex % 6
    y4 = yindex
    x5 = xindex
    y0 = yindex % 2
    y1 = (yindex // 2)
    tmp0 = (-2) + x3
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = x2
    tmp4 = tl.full([1, 1], 4, tl.int64)
    tmp5 = tmp3 < tmp4
    tmp6 = tmp2 & tmp5
    tmp7 = tl.load(in_ptr0 + ((-8) + x2 + (4*x3) + (16*y4)), tmp6 & xmask & ymask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (y0 + (2*x5) + (72*y1)), tmp7, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/og/cogrelgx5xzmxfvrys7mrvsgxrpbnucpasz36qfgeyxjons5mqay.py
# Topologically Sorted Source Nodes: [x_conv_1, x_conv_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_conv_1 => convolution
#   x_conv_2 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %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_convolution_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_2', '''
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=[2048, 16], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_2', '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_convolution_relu_threshold_backward_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 2048
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 512
    y1 = (yindex // 512)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (512*x2) + (8192*y1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2 + (16*y3)), tmp4, xmask)
    tl.store(out_ptr1 + (y0 + (512*x2) + (8192*y1)), 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, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_2, (512, 2, 3, 3), (18, 9, 3, 1))
    assert_size_stride(primals_3, (512, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((512, 2, 3, 3), (18, 1, 6, 2), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_2, buf0, 1024, 9, grid=grid(1024, 9), stream=stream0)
        del primals_2
        buf1 = empty_strided_cuda((4, 2, 6, 6), (72, 1, 12, 2), torch.float32)
        # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_1.run(primals_1, buf1, 8, 36, grid=grid(8, 36), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [x_conv_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, buf0, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 512, 4, 4), (8192, 1, 2048, 512))
        buf3 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 512, 4, 4), (8192, 1, 2048, 512), torch.bool)
        # Topologically Sorted Source Nodes: [x_conv_1, x_conv_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_2.run(buf2, primals_3, buf3, buf4, 2048, 16, grid=grid(2048, 16), stream=stream0)
        del buf2
        del primals_3
    return (buf3, buf0, buf1, buf4, )


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, 2, 4, 4), (32, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((512, 2, 3, 3), (18, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((512, ), (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.nn.functional as F
import torch.utils
import torch.utils.data
import torch.optim


class BatchDHCN(nn.Module):
    """docstring for BatchDHCN"""

    def __init__(self, embed_size=512, output_size=512, num_channel=2,
        conv_size=3, batch_norm=True):
        super(BatchDHCN, self).__init__()
        self.batch_norm = batch_norm
        self.embed_size = embed_size
        self.output_size = output_size
        self.num_channel = num_channel
        self.padding = nn.ZeroPad2d((0, conv_size - 1, conv_size - 1, 0))
        self.conv_1 = nn.Conv2d(self.num_channel, self.output_size, (
            conv_size, conv_size))
        self.dropout = nn.Dropout(p=0.5)

    def forward(self, x):
        x_conv_1 = self.conv_1(self.padding(x))
        x_conv_1 = F.relu(x_conv_1)
        return x_conv_1


def get_inputs():
    return [torch.rand([4, 2, 4, 4])]


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
import torch.utils
import torch.utils.data
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 2
    y1 = yindex // 2
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 2 * x2 + 18 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_1(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 8
    xnumel = 36
    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
    x3 = xindex // 6
    x2 = xindex % 6
    y4 = yindex
    x5 = xindex
    y0 = yindex % 2
    y1 = yindex // 2
    tmp0 = -2 + x3
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = x2
    tmp4 = tl.full([1, 1], 4, tl.int64)
    tmp5 = tmp3 < tmp4
    tmp6 = tmp2 & tmp5
    tmp7 = tl.load(in_ptr0 + (-8 + x2 + 4 * x3 + 16 * y4), tmp6 & xmask &
        ymask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (y0 + 2 * x5 + 72 * y1), tmp7, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
    constexpr):
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 512
    y1 = yindex // 512
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 512 * x2 + 8192 * y1), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp4, xmask)
    tl.store(out_ptr1 + (y0 + 512 * x2 + 8192 * y1), tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_2, (512, 2, 3, 3), (18, 9, 3, 1))
    assert_size_stride(primals_3, (512,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((512, 2, 3, 3), (18, 1, 6, 2), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(1024, 9)](primals_2, buf0, 1024, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4, 2, 6, 6), (72, 1, 12, 2), torch.float32)
        triton_poi_fused_constant_pad_nd_1[grid(8, 36)](primals_1, buf1, 8,
            36, XBLOCK=32, YBLOCK=8, num_warps=4, num_stages=1)
        del primals_1
        buf2 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 512, 4, 4), (8192, 1, 2048, 512))
        buf3 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.
            float32)
        buf4 = empty_strided_cuda((4, 512, 4, 4), (8192, 1, 2048, 512),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_2[grid(2048, 16)](
            buf2, primals_3, buf3, buf4, 2048, 16, XBLOCK=16, YBLOCK=64,
            num_warps=4, num_stages=1)
        del buf2
        del primals_3
    return buf3, buf0, buf1, buf4


class BatchDHCNNew(nn.Module):
    """docstring for BatchDHCN"""

    def __init__(self, embed_size=512, output_size=512, num_channel=2,
        conv_size=3, batch_norm=True):
        super(BatchDHCNNew, self).__init__()
        self.batch_norm = batch_norm
        self.embed_size = embed_size
        self.output_size = output_size
        self.num_channel = num_channel
        self.padding = nn.ZeroPad2d((0, conv_size - 1, conv_size - 1, 0))
        self.conv_1 = nn.Conv2d(self.num_channel, self.output_size, (
            conv_size, conv_size))
        self.dropout = nn.Dropout(p=0.5)

    def forward(self, input_0):
        primals_2 = self.conv_1.weight
        primals_3 = self.conv_1.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
