# 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_6/inductor_cache/a2/ca2bypdozo4vzjrvjjoirhpfgvzfuin2hjpbbvp4ycptip36csey.py
# Topologically Sorted Source Nodes: [input1_mean, input1_1, norm, add, input1_l2], Original ATen: [aten.mean, aten.sub, aten.linalg_vector_norm, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   input1_1 => sub
#   input1_l2 => div
#   input1_mean => mean
#   norm => pow_1, sum_1
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [0], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %mean), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1], True), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand, 1e-06), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %add), kwargs = {})
triton_per_fused_add_div_linalg_vector_norm_mean_sub_0 = async_compile.triton('triton_per_fused_add_div_linalg_vector_norm_mean_sub_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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_linalg_vector_norm_mean_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_div_linalg_vector_norm_mean_sub_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    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 = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r1), None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (64 + r1), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (128 + r1), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (192 + r1), None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tmp11 = tmp10 * tmp10
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp16 = libdevice.sqrt(tmp15)
    tmp17 = 1e-06
    tmp18 = tmp16 + tmp17
    tmp19 = tmp10 / tmp18
    tl.store(out_ptr2 + (r1 + (64*x0)), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/e2/ce27barrmkjhojql257rnzuamyefwb737kzvhkz47ba5fumvkj2b.py
# Topologically Sorted Source Nodes: [pow_1, diff_loss], Original ATen: [aten.pow, aten.mean]
# Source node to ATen node mapping:
#   diff_loss => mean_2
#   pow_1 => pow_5
# Graph fragment:
#   %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mm, 2), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_5,), kwargs = {})
triton_red_fused_mean_pow_1 = async_compile.triton('triton_red_fused_mean_pow_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.reduction(
    size_hints=[1, 4096],
    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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_mean_pow_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_red_fused_mean_pow_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex
        tmp0 = tl.load(in_ptr0 + (r0), rmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    tmp5 = 4096.0
    tmp6 = tmp3 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp6, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_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, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf4 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input1_mean, input1_1, norm, add, input1_l2], Original ATen: [aten.mean, aten.sub, aten.linalg_vector_norm, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_linalg_vector_norm_mean_sub_0.run(arg0_1, buf4, 4, 64, grid=grid(4), stream=stream0)
        del arg0_1
        buf5 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input2_mean, input2_1, norm_1, add_1, input2_l2], Original ATen: [aten.mean, aten.sub, aten.linalg_vector_norm, aten.add, aten.div]
        triton_per_fused_add_div_linalg_vector_norm_mean_sub_0.run(arg1_1, buf5, 4, 64, grid=grid(4), stream=stream0)
        del arg1_1
        buf6 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add_1, input2_l2, mm], Original ATen: [aten.add, aten.div, aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf4, (64, 4), (1, 64), 0), buf5, out=buf6)
        del buf4
        del buf5
        buf7 = empty_strided_cuda((), (), torch.float32)
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [pow_1, diff_loss], Original ATen: [aten.pow, aten.mean]
        triton_red_fused_mean_pow_1.run(buf8, buf6, 1, 4096, grid=grid(1), stream=stream0)
        del buf6
    return (buf8, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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)

import torch
import torch.nn as nn


class DiffLoss(nn.Module):

    def __init__(self):
        super(DiffLoss, self).__init__()

    def forward(self, input1, input2):
        batch_size = input1.size(0)
        input1 = input1.view(batch_size, -1)
        input2 = input2.view(batch_size, -1)
        input1_mean = torch.mean(input1, dim=0, keepdims=True)
        input2_mean = torch.mean(input2, dim=0, keepdims=True)
        input1 = input1 - input1_mean
        input2 = input2 - input2_mean
        input1_l2_norm = torch.norm(input1, p=2, dim=1, keepdim=True).detach()
        input1_l2 = input1.div(input1_l2_norm.expand_as(input1) + 1e-06)
        input2_l2_norm = torch.norm(input2, p=2, dim=1, keepdim=True).detach()
        input2_l2 = input2.div(input2_l2_norm.expand_as(input2) + 1e-06)
        diff_loss = torch.mean(input1_l2.t().mm(input2_l2).pow(2))
        return diff_loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 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.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_per_fused_add_div_linalg_vector_norm_mean_sub_0(in_ptr0,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + r1, None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (64 + r1), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (128 + r1), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (192 + r1), None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tmp11 = tmp10 * tmp10
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp16 = libdevice.sqrt(tmp15)
    tmp17 = 1e-06
    tmp18 = tmp16 + tmp17
    tmp19 = tmp10 / tmp18
    tl.store(out_ptr2 + (r1 + 64 * x0), tmp19, xmask)


@triton.jit
def triton_red_fused_mean_pow_1(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex
        tmp0 = tl.load(in_ptr0 + r0, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    tmp5 = 4096.0
    tmp6 = tmp3 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp6, None)


def call(args):
    arg0_1, arg1_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, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf4 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_div_linalg_vector_norm_mean_sub_0[grid(4)](arg0_1,
            buf4, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        buf5 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        triton_per_fused_add_div_linalg_vector_norm_mean_sub_0[grid(4)](arg1_1,
            buf5, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        buf6 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (64, 4), (1, 64), 0),
            buf5, out=buf6)
        del buf4
        del buf5
        buf7 = empty_strided_cuda((), (), torch.float32)
        buf8 = buf7
        del buf7
        triton_red_fused_mean_pow_1[grid(1)](buf8, buf6, 1, 4096, XBLOCK=1,
            RBLOCK=2048, num_warps=16, num_stages=1)
        del buf6
    return buf8,


class DiffLossNew(nn.Module):

    def __init__(self):
        super(DiffLossNew, self).__init__()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
