# AOT ID: ['0_inference']
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_4/inductor_cache/wj/cwj2zg7w4p7el2iry546r777ce6iukvyptcynptlgzfjpz35x4it.py
# Topologically Sorted Source Nodes: [tx2], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   tx2 => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view), kwargs = {})
triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_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=[256], 
    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_mul_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_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = (xindex // 64)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((16*x1) + (64*(x0 // 16)) + (x0 % 16)), xmask)
    tmp1 = tmp0 * tmp0
    tl.store(out_ptr0 + (x2), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qt/cqtaa4uc3bevs2v4xpz4zfl7cmma7mfusmz774edckywsirvy2tv.py
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   y_1 => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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, 16], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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_clone_1', '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_clone_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 16
    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
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + (y0 + (4*x2) + (64*y1)), xmask & ymask)
    tmp2 = tl.load(in_ptr2 + (y0), ymask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = libdevice.sqrt(tmp3)
    tmp5 = tmp0 / tmp4
    tl.store(out_ptr0 + (x2 + (16*y3)), tmp5, xmask & ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    assert_size_stride(arg2_1, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [tx2], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tx2, mm], Original ATen: [aten.mul, aten.mm]
        extern_kernels.mm(buf0, arg1_1, out=buf1)
        del arg1_1
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(arg0_1, buf1, arg2_1, buf2, 16, 16, grid=grid(16, 16), stream=stream0)
        del arg0_1
        del arg2_1
        del buf1
    return (buf2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1])
    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)

from torch.autograd import Function
import torch
import torch.nn as nn
import torch.utils


class GdnFunction(Function):

    @staticmethod
    def forward(ctx, x, gamma, beta):
        ctx.save_for_backward(x, gamma, beta)
        n, c, h, w = list(x.size())
        tx = x.permute(0, 2, 3, 1).contiguous()
        tx = tx.view(-1, c)
        tx2 = tx * tx
        denominator = tx2.mm(gamma) + beta
        ty = tx / torch.sqrt(denominator)
        y = ty.view(n, h, w, c)
        y = y.permute(0, 3, 1, 2).contiguous()
        return y

    @staticmethod
    def backward(ctx, grad_output):
        x, gamma, beta = ctx.saved_variables
        n, c, h, w = list(grad_output.size())
        tx = x.permute(0, 2, 3, 1).contiguous()
        tx = tx.view(-1, c)
        tx2 = tx * tx
        denominator = tx2.mm(gamma) + beta
        tdzdy = grad_output.permute(0, 2, 3, 1).contiguous()
        tdzdy = tdzdy.view(-1, c)
        gy = tdzdy * torch.pow(denominator, -0.5) - (tdzdy * tx * torch.pow
            (denominator, -1.5)).mm(gamma.t()) * tx
        gy = gy.view(n, h, w, c)
        grad_input = gy.permute(0, 3, 1, 2).contiguous()
        tmp = -0.5 * torch.pow(denominator, -1.5) * tx * tdzdy
        grad_beta = torch.sum(tmp, 0)
        grad_gamma = tx2.t().mm(tmp)
        return grad_input, grad_gamma, grad_beta


class Gdn(nn.Module):

    def __init__(self, input_channel):
        super(Gdn, self).__init__()
        self.input_channel = input_channel
        self.gamma = nn.Parameter(torch.Tensor(input_channel, input_channel))
        self.beta = nn.Parameter(torch.Tensor(input_channel))

    def forward(self, input):
        return GdnFunction.apply(input, self.gamma, self.beta)

    def __str__(self):
        return (self.__class__.__name__ + 
            '(gamma_size=(%d, %d), beta_size=(%d))' % (self.gamma.size()[0],
            self.gamma.size()[1], self.beta.size()[0]))
    __repr__ = __str__


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


def get_init_inputs():
    return [[], {'input_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
from torch.autograd import Function
import torch.nn as nn
import torch.utils
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_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (16 * x1 + 64 * (x0 // 16) + x0 % 16), xmask)
    tmp1 = tmp0 * tmp0
    tl.store(out_ptr0 + x2, tmp1, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    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
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tmp2 = tl.load(in_ptr2 + y0, ymask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = libdevice.sqrt(tmp3)
    tmp5 = tmp0 / tmp4
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp5, xmask & ymask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    assert_size_stride(arg2_1, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, arg1_1, out=buf1)
        del arg1_1
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        triton_poi_fused_clone_1[grid(16, 16)](arg0_1, buf1, arg2_1, buf2, 
            16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del arg0_1
        del arg2_1
        del buf1
    return buf2,


class GdnFunction(Function):

    @staticmethod
    def forward(ctx, x, gamma, beta):
        ctx.save_for_backward(x, gamma, beta)
        n, c, h, w = list(x.size())
        tx = x.permute(0, 2, 3, 1).contiguous()
        tx = tx.view(-1, c)
        tx2 = tx * tx
        denominator = tx2.mm(gamma) + beta
        ty = tx / torch.sqrt(denominator)
        y = ty.view(n, h, w, c)
        y = y.permute(0, 3, 1, 2).contiguous()
        return y

    @staticmethod
    def backward(ctx, grad_output):
        x, gamma, beta = ctx.saved_variables
        n, c, h, w = list(grad_output.size())
        tx = x.permute(0, 2, 3, 1).contiguous()
        tx = tx.view(-1, c)
        tx2 = tx * tx
        denominator = tx2.mm(gamma) + beta
        tdzdy = grad_output.permute(0, 2, 3, 1).contiguous()
        tdzdy = tdzdy.view(-1, c)
        gy = tdzdy * torch.pow(denominator, -0.5) - (tdzdy * tx * torch.pow
            (denominator, -1.5)).mm(gamma.t()) * tx
        gy = gy.view(n, h, w, c)
        grad_input = gy.permute(0, 3, 1, 2).contiguous()
        tmp = -0.5 * torch.pow(denominator, -1.5) * tx * tdzdy
        grad_beta = torch.sum(tmp, 0)
        grad_gamma = tx2.t().mm(tmp)
        return grad_input, grad_gamma, grad_beta


class GdnNew(nn.Module):

    def __init__(self, input_channel):
        super(GdnNew, self).__init__()
        self.input_channel = input_channel
        self.gamma = nn.Parameter(torch.Tensor(input_channel, input_channel))
        self.beta = nn.Parameter(torch.Tensor(input_channel))

    def __str__(self):
        return (self.__class__.__name__ + 
            '(gamma_size=(%d, %d), beta_size=(%d))' % (self.gamma.size()[0],
            self.gamma.size()[1], self.beta.size()[0]))
    __repr__ = __str__

    def forward(self, input_0):
        arg1_1 = self.gamma
        arg2_1 = self.beta
        arg0_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
