# 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/75/c753nmnallr7352v6kci326ipcp72djogbwitjnsojsxpzcw7rbv.py
# Topologically Sorted Source Nodes: [pow_1, sum_1, add, demod], Original ATen: [aten.pow, aten.sum, aten.add, aten.rsqrt]
# Source node to ATen node mapping:
#   add => add
#   demod => rsqrt
#   pow_1 => pow_1
#   sum_1 => sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_2, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [2, 3, 4]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-08), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_per_fused_add_pow_rsqrt_sum_0 = async_compile.triton('triton_per_fused_add_pow_rsqrt_sum_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.persistent_reduction(
    size_hints=[4, 64],
    reduction_hint=ReductionHint.INNER,
    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_per_fused_add_pow_rsqrt_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_add_pow_rsqrt_sum_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 36
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (36*x0)), rmask & xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(rmask & xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = 1e-08
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qb/cqbmllyzpe2nhtr4xblvy4f2byhtbttctscjpfu4ltq23mz3g6al.py
# Topologically Sorted Source Nodes: [repeat], Original ATen: [aten.repeat]
# Source node to ATen node mapping:
#   repeat => repeat
# Graph fragment:
#   %repeat : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%rsqrt, [4, 1]), kwargs = {})
triton_poi_fused_repeat_1 = async_compile.triton('triton_poi_fused_repeat_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=[16], 
    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_repeat_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_repeat_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/o2/co2fztvn5bb5kaf22skkom7wt43kzeptwatzi2mwgb65eoysciue.py
# Topologically Sorted Source Nodes: [weight], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   weight => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %view), kwargs = {})
triton_poi_fused_mul_2 = async_compile.triton('triton_poi_fused_mul_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=[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_mul_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_mul_2(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
    x3 = xindex % 144
    x4 = (xindex // 36)
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x5), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 4, 3, 3), (144, 36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [pow_1, sum_1, add, demod], Original ATen: [aten.pow, aten.sum, aten.add, aten.rsqrt]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_pow_rsqrt_sum_0.run(buf1, primals_2, 4, 36, grid=grid(4), stream=stream0)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [repeat], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_1.run(buf1, buf2, 16, grid=grid(16), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4, 3, 3), (144, 36, 9, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [weight], Original ATen: [aten.mul]
        triton_poi_fused_mul_2.run(primals_2, buf2, buf3, 576, grid=grid(576), stream=stream0)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1, 16, 4, 4), (256, 16, 4, 1), 0), reinterpret_tensor(buf3, (16, 4, 3, 3), (36, 9, 3, 1), 0), stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf4, (1, 16, 2, 2), (64, 4, 2, 1))
    return (reinterpret_tensor(buf4, (4, 4, 2, 2), (16, 4, 2, 1), 0), primals_2, buf1, buf2, reinterpret_tensor(buf3, (16, 4, 3, 3), (36, 9, 3, 1), 0), reinterpret_tensor(primals_1, (1, 16, 4, 4), (256, 16, 4, 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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 4, 4, 3, 3), (144, 36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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.utils.data
import torch
from torchvision.transforms import functional as F
import torch.nn as nn
from torch.nn import functional as F


class DemodulatedConv2d(nn.Module):

    def __init__(self, in_channel, out_channel, kernel_size=3, stride=1,
        padding=0, bias=False, dilation=1):
        super().__init__()
        self.eps = 1e-08
        self.kernel_size = kernel_size
        self.in_channel = in_channel
        self.out_channel = out_channel
        self.weight = nn.Parameter(torch.randn(1, out_channel, in_channel,
            kernel_size, kernel_size))
        self.bias = None
        if bias:
            self.bias = nn.Parameter(torch.randn(out_channel))
        self.stride = stride
        self.padding = padding
        self.dilation = dilation

    def forward(self, input):
        batch, in_channel, height, width = input.shape
        demod = torch.rsqrt(self.weight.pow(2).sum([2, 3, 4]) + 1e-08)
        weight = self.weight * demod.repeat([batch, 1]).view(batch, self.
            out_channel, 1, 1, 1)
        weight = weight.view(batch * self.out_channel, in_channel, self.
            kernel_size, self.kernel_size)
        input = input.view(1, batch * in_channel, height, width)
        if self.bias is None:
            out = F.conv2d(input, weight, padding=self.padding, groups=
                batch, dilation=self.dilation, stride=self.stride)
        else:
            out = F.conv2d(input, weight, bias=self.bias, padding=self.
                padding, groups=batch, dilation=self.dilation, stride=self.
                stride)
        _, _, height, width = out.shape
        out = out.view(batch, self.out_channel, height, width)
        return out


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


def get_init_inputs():
    return [[], {'in_channel': 4, 'out_channel': 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 torch.utils.data
import torch
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_per_fused_add_pow_rsqrt_sum_0(in_out_ptr0, in_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    rnumel = 36
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 36 * x0), rmask & xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(rmask & xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = 1e-08
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_repeat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_mul_2(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
    x3 = xindex % 144
    x4 = xindex // 36
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x5, tmp2, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 4, 3, 3), (144, 36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_pow_rsqrt_sum_0[grid(4)](buf1, primals_2, 4, 
            36, XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_repeat_1[grid(16)](buf1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 3, 3), (144, 36, 9, 3, 1),
            torch.float32)
        triton_poi_fused_mul_2[grid(576)](primals_2, buf2, buf3, 576,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
            16, 4, 4), (256, 16, 4, 1), 0), reinterpret_tensor(buf3, (16, 4,
            3, 3), (36, 9, 3, 1), 0), stride=(1, 1), padding=(0, 0),
            dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=4, bias=None)
        assert_size_stride(buf4, (1, 16, 2, 2), (64, 4, 2, 1))
    return reinterpret_tensor(buf4, (4, 4, 2, 2), (16, 4, 2, 1), 0
        ), primals_2, buf1, buf2, reinterpret_tensor(buf3, (16, 4, 3, 3), (
        36, 9, 3, 1), 0), reinterpret_tensor(primals_1, (1, 16, 4, 4), (256,
        16, 4, 1), 0)


class DemodulatedConv2dNew(nn.Module):

    def __init__(self, in_channel, out_channel, kernel_size=3, stride=1,
        padding=0, bias=False, dilation=1):
        super().__init__()
        self.eps = 1e-08
        self.kernel_size = kernel_size
        self.in_channel = in_channel
        self.out_channel = out_channel
        self.weight = nn.Parameter(torch.randn(1, out_channel, in_channel,
            kernel_size, kernel_size))
        self.bias = None
        if bias:
            self.bias = nn.Parameter(torch.randn(out_channel))
        self.stride = stride
        self.padding = padding
        self.dilation = dilation

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