# 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/ob/cobmo4t3ivrbtwszu6oeujnbbximkekwvemkpxfvuiklxspxoxhk.py
# Topologically Sorted Source Nodes: [abs_1, am, am_1, norm, add, am_2, abs_2, am_3, am_4, norm_2, add_1, am_5, loss], Original ATen: [aten.abs, aten.pow, aten.sum, aten.linalg_vector_norm, aten.add, aten.div, aten.mse_loss]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   abs_2 => abs_2
#   add => add
#   add_1 => add_1
#   am => pow_1
#   am_1 => sum_1
#   am_2 => div
#   am_3 => pow_4
#   am_4 => sum_3
#   am_5 => div_1
#   loss => sub
#   norm => pow_2, pow_3, sum_2
#   norm_2 => pow_5, pow_6, sum_4
# Graph fragment:
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_1, 4), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1], True), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_2, [2, 3], True), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_3, 1e-06), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %add), kwargs = {})
#   %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg1_1,), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_2, 4), kwargs = {})
#   %sum_3 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_4, [1], True), kwargs = {})
#   %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_3, 2), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_5, [2, 3], True), kwargs = {})
#   %pow_6 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_4, 0.5), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_6, 1e-06), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, %add_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %div_1), kwargs = {})
triton_per_fused_abs_add_div_linalg_vector_norm_mse_loss_pow_sum_0 = async_compile.triton('triton_per_fused_abs_add_div_linalg_vector_norm_mse_loss_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_div_linalg_vector_norm_mse_loss_pow_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 2, '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_abs_add_div_linalg_vector_norm_mse_loss_pow_sum_0(in_ptr0, in_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    tmp4 = tl.load(in_ptr0 + (16 + r1 + (64*x0)), xmask, other=0.0)
    tmp9 = tl.load(in_ptr0 + (32 + r1 + (64*x0)), xmask, other=0.0)
    tmp14 = tl.load(in_ptr0 + (48 + r1 + (64*x0)), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + (r1 + (64*x0)), xmask, other=0.0)
    tmp28 = tl.load(in_ptr1 + (16 + r1 + (64*x0)), xmask, other=0.0)
    tmp33 = tl.load(in_ptr1 + (32 + r1 + (64*x0)), xmask, other=0.0)
    tmp38 = tl.load(in_ptr1 + (48 + r1 + (64*x0)), xmask, other=0.0)
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tmp1 * tmp1
    tmp3 = tmp2 * tmp2
    tmp5 = tl_math.abs(tmp4)
    tmp6 = tmp5 * tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp10 = tl_math.abs(tmp9)
    tmp11 = tmp10 * tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp15 = tl_math.abs(tmp14)
    tmp16 = tmp15 * tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp22 = tl.where(xmask, tmp20, 0)
    tmp23 = tl.sum(tmp22, 1)[:, None]
    tmp25 = tl_math.abs(tmp24)
    tmp26 = tmp25 * tmp25
    tmp27 = tmp26 * tmp26
    tmp29 = tl_math.abs(tmp28)
    tmp30 = tmp29 * tmp29
    tmp31 = tmp30 * tmp30
    tmp32 = tmp27 + tmp31
    tmp34 = tl_math.abs(tmp33)
    tmp35 = tmp34 * tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = tmp32 + tmp36
    tmp39 = tl_math.abs(tmp38)
    tmp40 = tmp39 * tmp39
    tmp41 = tmp40 * tmp40
    tmp42 = tmp37 + tmp41
    tmp43 = tmp42 * tmp42
    tmp44 = tl.broadcast_to(tmp43, [XBLOCK, RBLOCK])
    tmp46 = tl.where(xmask, tmp44, 0)
    tmp47 = tl.sum(tmp46, 1)[:, None]
    tmp48 = libdevice.sqrt(tmp23)
    tmp49 = 1e-06
    tmp50 = tmp48 + tmp49
    tmp51 = tmp18 / tmp50
    tmp52 = libdevice.sqrt(tmp47)
    tmp53 = tmp52 + tmp49
    tmp54 = tmp42 / tmp53
    tmp55 = tmp51 - tmp54
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp55, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7g/c7gdlmgpqbkvrq2mwo2bg2axg63buflbkvyevuux75zepy4cwimo.py
# Topologically Sorted Source Nodes: [loss], Original ATen: [aten.mse_loss]
# Source node to ATen node mapping:
#   loss => mean, pow_7
# Graph fragment:
#   %pow_7 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_7,), kwargs = {})
triton_per_fused_mse_loss_1 = async_compile.triton('triton_per_fused_mse_loss_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=[1, 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mse_loss_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_mse_loss_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tmp5 = 64.0
    tmp6 = tmp4 / 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)
        buf2 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [abs_1, am, am_1, norm, add, am_2, abs_2, am_3, am_4, norm_2, add_1, am_5, loss], Original ATen: [aten.abs, aten.pow, aten.sum, aten.linalg_vector_norm, aten.add, aten.div, aten.mse_loss]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_add_div_linalg_vector_norm_mse_loss_pow_sum_0.run(arg0_1, arg1_1, buf2, 4, 16, grid=grid(4), stream=stream0)
        del arg0_1
        del arg1_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [loss], Original ATen: [aten.mse_loss]
        triton_per_fused_mse_loss_1.run(buf4, buf2, 1, 64, grid=grid(1), stream=stream0)
        del buf2
    return (buf4, )


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.functional as F
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed


class AT(nn.Module):
    """
	Paying More Attention to Attention: Improving the Performance of Convolutional
	Neural Netkworks wia Attention Transfer
	https://arxiv.org/pdf/1612.03928.pdf
	"""

    def __init__(self, p):
        super(AT, self).__init__()
        self.p = p

    def forward(self, fm_s, fm_t):
        loss = F.mse_loss(self.attention_map(fm_s), self.attention_map(fm_t))
        return loss

    def attention_map(self, fm, eps=1e-06):
        am = torch.pow(torch.abs(fm), self.p)
        am = torch.sum(am, dim=1, keepdim=True)
        norm = torch.norm(am, dim=(2, 3), keepdim=True)
        am = torch.div(am, norm + eps)
        return am


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


def get_init_inputs():
    return [[], {'p': 4}]

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.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_abs_add_div_linalg_vector_norm_mse_loss_pow_sum_0(in_ptr0,
    in_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 16
    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)
    tmp4 = tl.load(in_ptr0 + (16 + r1 + 64 * x0), xmask, other=0.0)
    tmp9 = tl.load(in_ptr0 + (32 + r1 + 64 * x0), xmask, other=0.0)
    tmp14 = tl.load(in_ptr0 + (48 + r1 + 64 * x0), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + (r1 + 64 * x0), xmask, other=0.0)
    tmp28 = tl.load(in_ptr1 + (16 + r1 + 64 * x0), xmask, other=0.0)
    tmp33 = tl.load(in_ptr1 + (32 + r1 + 64 * x0), xmask, other=0.0)
    tmp38 = tl.load(in_ptr1 + (48 + r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tmp1 * tmp1
    tmp3 = tmp2 * tmp2
    tmp5 = tl_math.abs(tmp4)
    tmp6 = tmp5 * tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp10 = tl_math.abs(tmp9)
    tmp11 = tmp10 * tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp15 = tl_math.abs(tmp14)
    tmp16 = tmp15 * tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp22 = tl.where(xmask, tmp20, 0)
    tmp23 = tl.sum(tmp22, 1)[:, None]
    tmp25 = tl_math.abs(tmp24)
    tmp26 = tmp25 * tmp25
    tmp27 = tmp26 * tmp26
    tmp29 = tl_math.abs(tmp28)
    tmp30 = tmp29 * tmp29
    tmp31 = tmp30 * tmp30
    tmp32 = tmp27 + tmp31
    tmp34 = tl_math.abs(tmp33)
    tmp35 = tmp34 * tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = tmp32 + tmp36
    tmp39 = tl_math.abs(tmp38)
    tmp40 = tmp39 * tmp39
    tmp41 = tmp40 * tmp40
    tmp42 = tmp37 + tmp41
    tmp43 = tmp42 * tmp42
    tmp44 = tl.broadcast_to(tmp43, [XBLOCK, RBLOCK])
    tmp46 = tl.where(xmask, tmp44, 0)
    tmp47 = tl.sum(tmp46, 1)[:, None]
    tmp48 = libdevice.sqrt(tmp23)
    tmp49 = 1e-06
    tmp50 = tmp48 + tmp49
    tmp51 = tmp18 / tmp50
    tmp52 = libdevice.sqrt(tmp47)
    tmp53 = tmp52 + tmp49
    tmp54 = tmp42 / tmp53
    tmp55 = tmp51 - tmp54
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp55, xmask)


@triton.jit
def triton_per_fused_mse_loss_1(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tmp5 = 64.0
    tmp6 = tmp4 / 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)
        buf2 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_abs_add_div_linalg_vector_norm_mse_loss_pow_sum_0[grid
            (4)](arg0_1, arg1_1, buf2, 4, 16, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_per_fused_mse_loss_1[grid(1)](buf4, buf2, 1, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf2
    return buf4,


class ATNew(nn.Module):
    """
	Paying More Attention to Attention: Improving the Performance of Convolutional
	Neural Netkworks wia Attention Transfer
	https://arxiv.org/pdf/1612.03928.pdf
	"""

    def __init__(self, p):
        super(ATNew, self).__init__()
        self.p = p

    def attention_map(self, fm, eps=1e-06):
        am = torch.pow(torch.abs(fm), self.p)
        am = torch.sum(am, dim=1, keepdim=True)
        norm = torch.norm(am, dim=(2, 3), keepdim=True)
        am = torch.div(am, norm + eps)
        return am

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