# 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/zk/czk5xfokmwnuegxn53eciq25366p2is3a6lxx47tlosf3q225vha.py
# Topologically Sorted Source Nodes: [feat], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   feat => div
# Graph fragment:
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %expand), kwargs = {})
triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_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=[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_div_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_div_0(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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/j3/cj3shsefw7hsddfqzy4aisf4myrksdkjbk5xdjzmmlvgsxwe4zt2.py
# Topologically Sorted Source Nodes: [pow_1, corr_mat_1, mul_1, corr_mat_2, pow_3, mul_2, corr_mat_3, pow_4, mul_3, corr_mat_4, pow_5, mul_4, corr_mat_5, pow_6, corr_mat_7, mul_6, corr_mat_8, pow_8, mul_7, corr_mat_9, pow_9, mul_8, corr_mat_10, pow_10, mul_9, corr_mat_11, loss], Original ATen: [aten.pow, aten.add, aten.mul, aten.mse_loss]
# Source node to ATen node mapping:
#   corr_mat_1 => mul
#   corr_mat_10 => add_8
#   corr_mat_11 => add_9
#   corr_mat_2 => add_1
#   corr_mat_3 => add_2
#   corr_mat_4 => add_3
#   corr_mat_5 => add_4
#   corr_mat_7 => mul_5
#   corr_mat_8 => add_6
#   corr_mat_9 => add_7
#   loss => mean, pow_15, sub
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_6 => mul_6
#   mul_7 => mul_7
#   mul_8 => mul_8
#   mul_9 => mul_9
#   pow_1 => pow_3
#   pow_10 => pow_14
#   pow_3 => pow_5
#   pow_4 => pow_6
#   pow_5 => pow_7
#   pow_6 => pow_10
#   pow_8 => pow_12
#   pow_9 => pow_13
# Graph fragment:
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mm, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_3, 0.00033546262790251185), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 0.002683701023220095), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mm, 2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_5, 0.01073480409288038), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mul_2), kwargs = {})
#   %pow_6 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mm, 3), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_6, 0.02862614424768101), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mul_3), kwargs = {})
#   %pow_7 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mm, 4), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_7, 0.05725228849536202), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %mul_4), kwargs = {})
#   %pow_10 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mm_1, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_10, 0.00033546262790251185), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_1, 0.002683701023220095), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %mul_6), kwargs = {})
#   %pow_12 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mm_1, 2), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_12, 0.01073480409288038), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_6, %mul_7), kwargs = {})
#   %pow_13 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mm_1, 3), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_13, 0.02862614424768101), kwargs = {})
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_7, %mul_8), kwargs = {})
#   %pow_14 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mm_1, 4), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_14, 0.05725228849536202), kwargs = {})
#   %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_8, %mul_9), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_4, %add_9), kwargs = {})
#   %pow_15 : [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_15,), kwargs = {})
triton_per_fused_add_mse_loss_mul_pow_1 = async_compile.triton('triton_per_fused_add_mse_loss_mul_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.persistent_reduction(
    size_hints=[1, 16],
    reduction_hint=ReductionHint.INNER,
    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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mse_loss_mul_pow_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 2, '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_mse_loss_mul_pow_1(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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_out_ptr0 + (r0), None)
    tmp17 = tl.load(in_ptr0 + (r0), None)
    tmp1 = 0.002683701023220095
    tmp2 = tmp0 * tmp1
    tmp3 = 0.00033546262790251185
    tmp4 = tmp3 + tmp2
    tmp5 = tmp0 * tmp0
    tmp6 = 0.01073480409288038
    tmp7 = tmp5 * tmp6
    tmp8 = tmp4 + tmp7
    tmp9 = tmp5 * tmp0
    tmp10 = 0.02862614424768101
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 + tmp11
    tmp13 = tmp5 * tmp5
    tmp14 = 0.05725228849536202
    tmp15 = tmp13 * tmp14
    tmp16 = tmp12 + tmp15
    tmp18 = tmp17 * tmp1
    tmp19 = tmp3 + tmp18
    tmp20 = tmp17 * tmp17
    tmp21 = tmp20 * tmp6
    tmp22 = tmp19 + tmp21
    tmp23 = tmp20 * tmp17
    tmp24 = tmp23 * tmp10
    tmp25 = tmp22 + tmp24
    tmp26 = tmp20 * tmp20
    tmp27 = tmp26 * tmp14
    tmp28 = tmp25 + tmp27
    tmp29 = tmp16 - tmp28
    tmp30 = tmp29 * tmp29
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
    tmp33 = tl.sum(tmp31, 1)[:, None]
    tmp34 = 16.0
    tmp35 = tmp33 / tmp34
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp35, 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, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [feat], Original ATen: [aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sim_mat], Original ATen: [aten.mm]
        extern_kernels.mm(buf0, reinterpret_tensor(buf0, (4, 4), (1, 4), 0), out=buf1)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [feat_1], Original ATen: [aten.div]
        triton_poi_fused_div_0.run(arg1_1, buf2, 16, grid=grid(16), stream=stream0)
        del arg1_1
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sim_mat_1], Original ATen: [aten.mm]
        extern_kernels.mm(buf2, reinterpret_tensor(buf2, (4, 4), (1, 4), 0), out=buf3)
        del buf2
        buf4 = buf1; del buf1  # reuse
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [pow_1, corr_mat_1, mul_1, corr_mat_2, pow_3, mul_2, corr_mat_3, pow_4, mul_3, corr_mat_4, pow_5, mul_4, corr_mat_5, pow_6, corr_mat_7, mul_6, corr_mat_8, pow_8, mul_7, corr_mat_9, pow_9, mul_8, corr_mat_10, pow_10, mul_9, corr_mat_11, loss], Original ATen: [aten.pow, aten.add, aten.mul, aten.mse_loss]
        triton_per_fused_add_mse_loss_mul_pow_1.run(buf4, buf6, buf3, 1, 16, grid=grid(1), stream=stream0)
        del buf3
        del buf4
    return (buf6, )


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, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (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 math
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 CC(nn.Module):
    """
	Correlation Congruence for Knowledge Distillation
	http://openaccess.thecvf.com/content_ICCV_2019/papers/
	Peng_Correlation_Congruence_for_Knowledge_Distillation_ICCV_2019_paper.pdf
	"""

    def __init__(self, gamma, P_order):
        super(CC, self).__init__()
        self.gamma = gamma
        self.P_order = P_order

    def forward(self, feat_s, feat_t):
        corr_mat_s = self.get_correlation_matrix(feat_s)
        corr_mat_t = self.get_correlation_matrix(feat_t)
        loss = F.mse_loss(corr_mat_s, corr_mat_t)
        return loss

    def get_correlation_matrix(self, feat):
        feat = F.normalize(feat, p=2, dim=-1)
        sim_mat = torch.matmul(feat, feat.t())
        corr_mat = torch.zeros_like(sim_mat)
        for p in range(self.P_order + 1):
            corr_mat += math.exp(-2 * self.gamma) * (2 * self.gamma
                ) ** p / math.factorial(p) * torch.pow(sim_mat, p)
        return corr_mat


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


def get_init_inputs():
    return [[], {'gamma': 4, 'P_order': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import math
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
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_div_0(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_per_fused_add_mse_loss_mul_pow_1(in_out_ptr0, in_out_ptr1,
    in_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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_out_ptr0 + r0, None)
    tmp17 = tl.load(in_ptr0 + r0, None)
    tmp1 = 0.002683701023220095
    tmp2 = tmp0 * tmp1
    tmp3 = 0.00033546262790251185
    tmp4 = tmp3 + tmp2
    tmp5 = tmp0 * tmp0
    tmp6 = 0.01073480409288038
    tmp7 = tmp5 * tmp6
    tmp8 = tmp4 + tmp7
    tmp9 = tmp5 * tmp0
    tmp10 = 0.02862614424768101
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 + tmp11
    tmp13 = tmp5 * tmp5
    tmp14 = 0.05725228849536202
    tmp15 = tmp13 * tmp14
    tmp16 = tmp12 + tmp15
    tmp18 = tmp17 * tmp1
    tmp19 = tmp3 + tmp18
    tmp20 = tmp17 * tmp17
    tmp21 = tmp20 * tmp6
    tmp22 = tmp19 + tmp21
    tmp23 = tmp20 * tmp17
    tmp24 = tmp23 * tmp10
    tmp25 = tmp22 + tmp24
    tmp26 = tmp20 * tmp20
    tmp27 = tmp26 * tmp14
    tmp28 = tmp25 + tmp27
    tmp29 = tmp16 - tmp28
    tmp30 = tmp29 * tmp29
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
    tmp33 = tl.sum(tmp31, 1)[:, None]
    tmp34 = 16.0
    tmp35 = tmp33 / tmp34
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp35, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(buf0, (4, 4), (1, 4), 0),
            out=buf1)
        buf2 = buf0
        del buf0
        triton_poi_fused_div_0[grid(16)](arg1_1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg1_1
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(buf2, (4, 4), (1, 4), 0),
            out=buf3)
        del buf2
        buf4 = buf1
        del buf1
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5
        del buf5
        triton_per_fused_add_mse_loss_mul_pow_1[grid(1)](buf4, buf6, buf3, 
            1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf3
        del buf4
    return buf6,


class CCNew(nn.Module):
    """
	Correlation Congruence for Knowledge Distillation
	http://openaccess.thecvf.com/content_ICCV_2019/papers/
	Peng_Correlation_Congruence_for_Knowledge_Distillation_ICCV_2019_paper.pdf
	"""

    def __init__(self, gamma, P_order):
        super(CCNew, self).__init__()
        self.gamma = gamma
        self.P_order = P_order

    def get_correlation_matrix(self, feat):
        feat = F.normalize(feat, p=2, dim=-1)
        sim_mat = torch.matmul(feat, feat.t())
        corr_mat = torch.zeros_like(sim_mat)
        for p in range(self.P_order + 1):
            corr_mat += math.exp(-2 * self.gamma) * (2 * self.gamma
                ) ** p / math.factorial(p) * torch.pow(sim_mat, p)
        return corr_mat

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