# 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/az/cazayodov2pejo62rnac6rludmval2wsivw6phwx6vee62yxa77i.py
# Topologically Sorted Source Nodes: [pow_1, sum_1], Original ATen: [aten.pow, aten.sum]
# Source node to ATen node mapping:
#   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, [1], True), kwargs = {})
triton_red_fused_pow_sum_0 = async_compile.triton('triton_red_fused_pow_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.reduction(
    size_hints=[256, 128],
    reduction_hint=ReductionHint.OUTER,
    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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_pow_sum_0', 'mutated_arg_names': [], '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_red_fused_pow_sum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 256
    rnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 16
    x1 = (xindex // 16)
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    x3 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_ptr0 + (x0 + (16*r2) + (2048*x1)), rmask & xmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask & xmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    tl.store(out_ptr0 + (x3), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/yb/cybccy6ik3u5wdvejbe7sswchqjx66t2tiskszpk5e3v4xddkzk3.py
# Topologically Sorted Source Nodes: [pow_1, sum_1, clamp, rsqrt], Original ATen: [aten.pow, aten.sum, aten.clamp, aten.rsqrt]
# Source node to ATen node mapping:
#   clamp => clamp_min
#   pow_1 => pow_1
#   rsqrt => rsqrt
#   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, [1], True), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sum_1, 1e-12), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%clamp_min,), kwargs = {})
triton_per_fused_clamp_pow_rsqrt_sum_1 = async_compile.triton('triton_per_fused_clamp_pow_rsqrt_sum_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.persistent_reduction(
    size_hints=[64, 4],
    reduction_hint=ReductionHint.OUTER_TINY,
    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_per_fused_clamp_pow_rsqrt_sum_1', '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_clamp_pow_rsqrt_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    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 = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 16
    x1 = (xindex // 16)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*r2) + (64*x1)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 1e-12
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = libdevice.rsqrt(tmp6)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/go/cgo6toghmmfwug7mqg2i6moqp5yn5zkdrlyxi73gxriq4acxdhks.py
# Topologically Sorted Source Nodes: [mul, mul_1], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %primals_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %rsqrt), 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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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_mul_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 16) % 512
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 8192)
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x3), None)
    tmp3 = tl.load(in_ptr2 + (x0 + (16*x2)), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x3), tmp4, None)
''', 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, (1, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_2, (4, 512, 4, 4), (8192, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 256, 4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, sum_1], Original ATen: [aten.pow, aten.sum]
        stream0 = get_raw_stream(0)
        triton_red_fused_pow_sum_0.run(primals_2, buf0, 256, 128, grid=grid(256), stream=stream0)
        buf1 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf1, (4, 1, 4, 4), (16, 16, 4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [pow_1, sum_1, clamp, rsqrt], Original ATen: [aten.pow, aten.sum, aten.clamp, aten.rsqrt]
        triton_per_fused_clamp_pow_rsqrt_sum_1.run(buf2, buf0, 64, 4, grid=grid(64), stream=stream0)
        del buf0
        buf3 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mul_1], Original ATen: [aten.mul]
        triton_poi_fused_mul_2.run(primals_1, primals_2, buf2, buf3, 32768, grid=grid(32768), stream=stream0)
        del primals_1
    return (buf3, primals_2, buf2, )


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((1, 512, 1, 1), (512, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 512, 4, 4), (8192, 16, 4, 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.nn as nn


class L2Norm(nn.Module):
    """
       Scale shall be learnable according to original paper
       scale: initial scale number
       chan_num: L2Norm channel number (norm over all channels)
    """

    def __init__(self, scale=20, chan_num=512):
        super(L2Norm, self).__init__()
        self.scale = nn.Parameter(torch.Tensor([scale] * chan_num).view(1,
            chan_num, 1, 1))

    def forward(self, data):
        return self.scale * data * data.pow(2).sum(dim=1, keepdim=True).clamp(
            min=1e-12).rsqrt()


def get_inputs():
    return [torch.rand([4, 512, 4, 4])]


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

import torch
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
from torch._inductor.runtime.triton_helpers import libdevice
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_red_fused_pow_sum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 256
    rnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 16
    x1 = xindex // 16
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    x3 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_ptr0 + (x0 + 16 * r2 + 2048 * x1), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask & xmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    tl.store(out_ptr0 + x3, tmp3, xmask)


@triton.jit
def triton_per_fused_clamp_pow_rsqrt_sum_1(in_out_ptr0, in_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 16
    x1 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * r2 + 64 * x1), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 1e-12
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = libdevice.rsqrt(tmp6)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_mul_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 16 % 512
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 8192
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x3, None)
    tmp3 = tl.load(in_ptr2 + (x0 + 16 * x2), None, eviction_policy='evict_last'
        )
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + x3, tmp4, None)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (1, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_2, (4, 512, 4, 4), (8192, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 256, 4, 1, 16),
            torch.float32)
        get_raw_stream(0)
        triton_red_fused_pow_sum_0[grid(256)](primals_2, buf0, 256, 128,
            XBLOCK=64, RBLOCK=8, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf1, (4, 1, 4, 4), (16, 16, 4, 1), 0)
        del buf1
        triton_per_fused_clamp_pow_rsqrt_sum_1[grid(64)](buf2, buf0, 64, 4,
            XBLOCK=64, num_warps=2, num_stages=1)
        del buf0
        buf3 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.
            float32)
        triton_poi_fused_mul_2[grid(32768)](primals_1, primals_2, buf2,
            buf3, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
    return buf3, primals_2, buf2


class L2NormNew(nn.Module):
    """
       Scale shall be learnable according to original paper
       scale: initial scale number
       chan_num: L2Norm channel number (norm over all channels)
    """

    def __init__(self, scale=20, chan_num=512):
        super(L2NormNew, self).__init__()
        self.scale = nn.Parameter(torch.Tensor([scale] * chan_num).view(1,
            chan_num, 1, 1))

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