# 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_7/inductor_cache/l2/cl24x5xdko564frvaahehy4ph2tgrmu2zj4aurhdvbsf6tmid3kr.py
# Topologically Sorted Source Nodes: [add, mul, inner_distance], Original ATen: [aten.add, aten.mul, aten.sub]
# Source node to ATen node mapping:
#   add => add
#   inner_distance => sub
#   mul => mul
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand, %expand_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%bmm, 2), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mul), kwargs = {})
triton_poi_fused_add_mul_sub_0 = async_compile.triton('triton_poi_fused_add_mul_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.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_mul_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 9, '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_sub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = (xindex // 4)
    x0 = xindex % 4
    x2 = (xindex // 16)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x3), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + ((4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (2 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (3 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_out_ptr0 + (x4), xmask)
    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
    tmp24 = 2.0
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 - tmp25
    tl.store(in_out_ptr0 + (x4), tmp26, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/dh/cdh5rwxv3pghd37myiz5hikjbatq46su7f4qqubwew5v2pi6z2du.py
# Topologically Sorted Source Nodes: [Attn, Attn_1, exp_Attn], Original ATen: [aten.neg, aten.sub, aten.exp]
# Source node to ATen node mapping:
#   Attn => neg
#   Attn_1 => sub_1
#   exp_Attn => exp
# Graph fragment:
#   %neg : [num_users=2] = call_function[target=torch.ops.aten.neg.default](args = (%sub,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%neg, %expand_2), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
triton_poi_fused_exp_neg_sub_1 = async_compile.triton('triton_poi_fused_exp_neg_sub_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=[64], 
    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_exp_neg_sub_1', '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_exp_neg_sub_1(in_ptr0, 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
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp2 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = -tmp0
    tmp3 = -tmp2
    tmp5 = -tmp4
    tmp6 = triton_helpers.maximum(tmp3, tmp5)
    tmp8 = -tmp7
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = -tmp10
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp13 = tmp1 - tmp12
    tmp14 = tl_math.exp(tmp13)
    tl.store(out_ptr0 + (x2), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/lb/clbirjf5g4bt2p7tkl34sepb5m7ugna5tesgymku7lkzzg3c3azk.py
# Topologically Sorted Source Nodes: [Attn_2], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   Attn_2 => div
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %expand_3), kwargs = {})
triton_poi_fused_div_2 = async_compile.triton('triton_poi_fused_div_2', '''
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: '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_2', '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_2(in_ptr0, 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
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', 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), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_target_mm], Original ATen: [aten.bmm]
        extern_kernels.bmm(arg1_1, reinterpret_tensor(arg0_1, (4, 4, 4), (16, 1, 4), 0), out=buf0)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [add, mul, inner_distance], Original ATen: [aten.add, aten.mul, aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_sub_0.run(buf1, arg1_1, arg0_1, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [Attn, Attn_1, exp_Attn], Original ATen: [aten.neg, aten.sub, aten.exp]
        triton_poi_fused_exp_neg_sub_1.run(buf1, buf2, 64, grid=grid(64), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [Attn_2], Original ATen: [aten.div]
        triton_poi_fused_div_2.run(buf2, buf3, 64, grid=grid(64), stream=stream0)
        del buf2
    return (buf3, buf1, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4), (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
import torch.utils.data


class L2_DistanceAttention(nn.Module):

    def __init__(self):
        super(L2_DistanceAttention, self).__init__()

    def forward(self, input_hidden_traces, target_hidden_traces):
        standard_size = input_hidden_traces.size(0), input_hidden_traces.size(1
            ), input_hidden_traces.size(1)
        target_hidden_traces_square = (target_hidden_traces ** 2).sum(2
            ).unsqueeze(2).expand(standard_size)
        input_hidden_traces_square = (input_hidden_traces ** 2).transpose(1, 2
            ).sum(1).unsqueeze(1).expand(standard_size)
        input_target_mm = torch.bmm(target_hidden_traces,
            input_hidden_traces.transpose(1, 2))
        inner_distance = (target_hidden_traces_square +
            input_hidden_traces_square - 2 * input_target_mm)
        Attn = -inner_distance
        Attn = Attn - Attn.max(2)[0].unsqueeze(2).expand(standard_size)
        exp_Attn = torch.exp(Attn)
        Attn = exp_Attn / exp_Attn.sum(2).unsqueeze(2).expand(standard_size)
        return Attn, inner_distance


def get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.data
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_mul_sub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x3, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (4 * x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (2 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (3 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp23 = tl.load(in_out_ptr0 + x4, xmask)
    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
    tmp24 = 2.0
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 - tmp25
    tl.store(in_out_ptr0 + x4, tmp26, xmask)


@triton.jit
def triton_poi_fused_exp_neg_sub_1(in_ptr0, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = -tmp0
    tmp3 = -tmp2
    tmp5 = -tmp4
    tmp6 = triton_helpers.maximum(tmp3, tmp5)
    tmp8 = -tmp7
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = -tmp10
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp13 = tmp1 - tmp12
    tmp14 = tl_math.exp(tmp13)
    tl.store(out_ptr0 + x2, tmp14, xmask)


@triton.jit
def triton_poi_fused_div_2(in_ptr0, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(arg1_1, reinterpret_tensor(arg0_1, (4, 4, 4), (
            16, 1, 4), 0), out=buf0)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_mul_sub_0[grid(64)](buf1, arg1_1, arg0_1, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_exp_neg_sub_1[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_div_2[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf2
    return buf3, buf1


class L2_DistanceAttentionNew(nn.Module):

    def __init__(self):
        super(L2_DistanceAttentionNew, 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], output[1]
