# 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_4/inductor_cache/6c/c6cl6a6owowm4yuwrcfyz23bjfjwnj6qu4ljfhsubauroazxyrna.py
# Topologically Sorted Source Nodes: [pow_1, features_square, pow_2, centers_square, add], Original ATen: [aten.pow, aten.sum, aten.add]
# Source node to ATen node mapping:
#   add => add
#   centers_square => sum_2
#   features_square => sum_1
#   pow_1 => pow_1
#   pow_2 => pow_2
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = 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 = (%primals_2, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_2, [0], True), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %sum_2), kwargs = {})
triton_poi_fused_add_pow_sum_0 = async_compile.triton('triton_poi_fused_add_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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_poi_fused_add_pow_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_add_pow_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 16)
    x3 = xindex % 16
    x0 = xindex % 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (64*x2)), xmask)
    tmp2 = tl.load(in_ptr0 + (16 + x3 + (64*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (32 + x3 + (64*x2)), xmask)
    tmp8 = tl.load(in_ptr0 + (48 + x3 + (64*x2)), xmask)
    tmp11 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 * tmp11
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 + tmp21
    tl.store(out_ptr0 + (x5), tmp22, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/sd/csdzt2kuxthndtyqbibk7xcwb4mmue3laapjju52lmfnclvgfljs.py
# Topologically Sorted Source Nodes: [pow_1, features_square, pow_2, centers_square, features_into_centers, add, dist, neg], Original ATen: [aten.pow, aten.sum, aten.mul, aten.add, aten.sub, aten.neg]
# Source node to ATen node mapping:
#   add => add
#   centers_square => sum_2
#   dist => sub
#   features_into_centers => mul
#   features_square => sum_1
#   neg => neg
#   pow_1 => pow_1
#   pow_2 => pow_2
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = 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 = (%primals_2, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_2, [0], True), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %sum_2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mul), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sub,), kwargs = {})
triton_poi_fused_add_mul_neg_pow_sub_sum_1 = async_compile.triton('triton_poi_fused_add_mul_neg_pow_sub_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.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_add_mul_neg_pow_sub_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_add_mul_neg_pow_sub_sum_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 - tmp3
    tmp5 = -tmp4
    tl.store(in_out_ptr0 + (x3), tmp5, xmask)
''', 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, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), primals_2, out=buf0)
        buf1 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, features_square, pow_2, centers_square, add], Original ATen: [aten.pow, aten.sum, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_pow_sum_0.run(primals_1, primals_2, buf1, 64, grid=grid(64), stream=stream0)
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [pow_1, features_square, pow_2, centers_square, features_into_centers, add, dist, neg], Original ATen: [aten.pow, aten.sum, aten.mul, aten.add, aten.sub, aten.neg]
        triton_poi_fused_add_mul_neg_pow_sub_sum_1.run(buf2, buf1, 256, grid=grid(256), stream=stream0)
        del buf1
    return (buf2, primals_2, reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), )


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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (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
from torch import nn


class dce_loss(nn.Module):

    def __init__(self, n_classes, feat_dim, init_weight=True):
        super(dce_loss, self).__init__()
        self.n_classes = n_classes
        self.feat_dim = feat_dim
        self.centers = nn.Parameter(torch.randn(self.feat_dim, self.
            n_classes), requires_grad=True)
        nn.init.kaiming_normal_(self.centers)

    def forward(self, x):
        features_square = torch.sum(torch.pow(x, 2), 1, keepdim=True)
        centers_square = torch.sum(torch.pow(self.centers, 2), 0, keepdim=True)
        features_into_centers = 2 * torch.matmul(x, self.centers)
        dist = features_square + centers_square - features_into_centers
        return self.centers, -dist


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


def get_init_inputs():
    return [[], {'n_classes': 4, 'feat_dim': 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 import 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_poi_fused_add_pow_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x0 = xindex % 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
    tmp2 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
    tmp8 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
    tmp11 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 * tmp11
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 + tmp21
    tl.store(out_ptr0 + x5, tmp22, xmask)


@triton.jit
def triton_poi_fused_add_mul_neg_pow_sub_sum_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 - tmp3
    tmp5 = -tmp4
    tl.store(in_out_ptr0 + x3, tmp5, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            primals_2, out=buf0)
        buf1 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_pow_sum_0[grid(64)](primals_1, primals_2, buf1,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        triton_poi_fused_add_mul_neg_pow_sub_sum_1[grid(256)](buf2, buf1, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
    return buf2, primals_2, reinterpret_tensor(primals_1, (4, 64), (1, 4), 0)


class dce_lossNew(nn.Module):

    def __init__(self, n_classes, feat_dim, init_weight=True):
        super(dce_lossNew, self).__init__()
        self.n_classes = n_classes
        self.feat_dim = feat_dim
        self.centers = nn.Parameter(torch.randn(self.feat_dim, self.
            n_classes), requires_grad=True)
        nn.init.kaiming_normal_(self.centers)

    def forward(self, input_0):
        primals_2 = self.centers
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0], output[1]
