# 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_6/inductor_cache/oj/coji63cmjptmfiahnhfxrcymtijnwomdesxsksu5cd5o6hnjtmkc.py
# Topologically Sorted Source Nodes: [p, q, inner_product], Original ATen: [aten.cat, aten.mul]
# Source node to ATen node mapping:
#   inner_product => mul
#   p => cat
#   q => cat_1
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%select, %select, %select, %select_1, %select_1, %select_2], 1), kwargs = {})
#   %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%select_1, %select_2, %select_3, %select_2, %select_3, %select_3], 1), kwargs = {})
#   %mul : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%cat, %cat_1), kwargs = {})
triton_poi_fused_cat_mul_0 = async_compile.triton('triton_poi_fused_cat_mul_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=[512], 
    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_cat_mul_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 12, '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_cat_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 24
    x0 = xindex % 4
    x2 = (xindex // 96)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4*x1) + (16*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (x0 + (4*((-4) + x1)) + (16*x2)), tmp9 & xmask, other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (x0 + (4*((-8) + x1)) + (16*x2)), tmp14 & xmask, other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 16, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tl.load(in_ptr0 + (64 + x0 + (4*((-12) + x1)) + (16*x2)), tmp19 & xmask, other=0.0)
    tmp21 = tmp0 >= tmp17
    tmp22 = tl.full([1], 20, tl.int64)
    tmp23 = tmp0 < tmp22
    tmp24 = tmp21 & tmp23
    tmp25 = tl.load(in_ptr0 + (64 + x0 + (4*((-16) + x1)) + (16*x2)), tmp24 & xmask, other=0.0)
    tmp26 = tmp0 >= tmp22
    tmp27 = tl.full([1], 24, tl.int64)
    tmp28 = tmp0 < tmp27
    tmp29 = tl.load(in_ptr0 + (128 + x0 + (4*((-20) + x1)) + (16*x2)), tmp26 & xmask, other=0.0)
    tmp30 = tl.where(tmp24, tmp25, tmp29)
    tmp31 = tl.where(tmp19, tmp20, tmp30)
    tmp32 = tl.where(tmp14, tmp15, tmp31)
    tmp33 = tl.where(tmp9, tmp10, tmp32)
    tmp34 = tl.where(tmp4, tmp5, tmp33)
    tmp35 = tl.load(in_ptr0 + (64 + x0 + (4*x1) + (16*x2)), tmp4 & xmask, other=0.0)
    tmp36 = tl.load(in_ptr0 + (128 + x0 + (4*((-4) + x1)) + (16*x2)), tmp9 & xmask, other=0.0)
    tmp37 = tl.load(in_ptr0 + (192 + x0 + (4*((-8) + x1)) + (16*x2)), tmp14 & xmask, other=0.0)
    tmp38 = tl.load(in_ptr0 + (128 + x0 + (4*((-12) + x1)) + (16*x2)), tmp19 & xmask, other=0.0)
    tmp39 = tl.load(in_ptr0 + (192 + x0 + (4*((-16) + x1)) + (16*x2)), tmp24 & xmask, other=0.0)
    tmp40 = tl.load(in_ptr0 + (192 + x0 + (4*((-20) + x1)) + (16*x2)), tmp26 & xmask, other=0.0)
    tmp41 = tl.where(tmp24, tmp39, tmp40)
    tmp42 = tl.where(tmp19, tmp38, tmp41)
    tmp43 = tl.where(tmp14, tmp37, tmp42)
    tmp44 = tl.where(tmp9, tmp36, tmp43)
    tmp45 = tl.where(tmp4, tmp35, tmp44)
    tmp46 = tmp34 * tmp45
    tl.store(in_out_ptr0 + (x3), tmp46, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/j2/cj26ownu73m72kwjlseu3qfwtrz4f3ru464aa4zuhodtujlnjupm.py
# Topologically Sorted Source Nodes: [add, attention_temp], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   add => add
#   attention_temp => relu
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_2, %primals_3), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_add_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_add_relu_threshold_backward_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_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_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/p4/cp4mdcdve4y73ad5mzhckzksofhes3a2n2zye5hynnmbc62ct27d.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_5, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_5, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused__softmax_2 = async_compile.triton('triton_per_fused__softmax_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.persistent_reduction(
    size_hints=[4, 32],
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 24
    RBLOCK: tl.constexpr = 32
    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 = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (24*x0)), rmask & xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask & xmask, tmp1, float("-inf"))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(rmask & xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + (24*x0)), tmp11, rmask & xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ui/cui4pbynpryqmgmjhsdzeompa6sltxsmy5ggopxiaqdlvyafsjpl.py
# Topologically Sorted Source Nodes: [mul_1, attention_output], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   attention_output => sum_2
#   mul_1 => mul_1
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %mul), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [1]), kwargs = {})
triton_per_fused_mul_sum_3 = async_compile.triton('triton_per_fused_mul_sum_3', '''
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=[16, 32],
    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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mul_sum_3', 'mutated_arg_names': [], '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_mul_sum_3(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 24
    RBLOCK: tl.constexpr = 32
    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 = rindex < rnumel
    r2 = rindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + (24*x1)), rmask & xmask, eviction_policy='evict_last', other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + (4*r2) + (96*x1)), rmask & xmask, other=0.0)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(rmask & xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 1), (1, 1))
    assert_size_stride(primals_5, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 24, 4), (96, 4, 1), torch.float32)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [p, q, inner_product], Original ATen: [aten.cat, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_mul_0.run(buf2, primals_1, 384, grid=grid(384), stream=stream0)
        del primals_1
        buf3 = empty_strided_cuda((96, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tensordot], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf2, (96, 4), (4, 1), 0), primals_2, out=buf3)
        del primals_2
        buf4 = reinterpret_tensor(buf3, (4, 24, 4), (96, 4, 1), 0); del buf3  # reuse
        buf11 = empty_strided_cuda((4, 24, 4), (96, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [add, attention_temp], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
        triton_poi_fused_add_relu_threshold_backward_1.run(buf4, primals_3, buf11, 384, grid=grid(384), stream=stream0)
        del primals_3
        buf5 = empty_strided_cuda((96, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tensordot_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf4, (96, 4), (4, 1), 0), primals_4, out=buf5)
        buf8 = empty_strided_cuda((4, 24, 1), (24, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        triton_per_fused__softmax_2.run(buf5, buf8, 4, 24, grid=grid(4), stream=stream0)
        del buf5
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_1, attention_output], Original ATen: [aten.mul, aten.sum]
        triton_per_fused_mul_sum_3.run(buf8, buf2, buf9, 16, 24, grid=grid(16), stream=stream0)
        buf10 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [afm_out], Original ATen: [aten.mm]
        extern_kernels.mm(buf9, primals_5, out=buf10)
    return (buf10, buf8, buf2, buf8, reinterpret_tensor(buf9, (4, 4), (1, 4), 0), reinterpret_tensor(primals_5, (1, 4), (1, 1), 0), reinterpret_tensor(buf4, (4, 96), (1, 4), 0), reinterpret_tensor(primals_4, (1, 4), (1, 1), 0), buf11, )


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)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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 itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import *


class AFMLayer(nn.Module):
    """Attentonal Factorization Machine models pairwise (order-2) feature
    interactions without linear term and bias.
      Input shape
        - A list of 3D tensor with shape: ``(batch_size,1,embedding_size)``.
      Output shape
        - 2D tensor with shape: ``(batch_size, 1)``.
      Arguments
        - **in_features** : Positive integer, dimensionality of input features.
        - **attention_factor** : Positive integer, dimensionality of the
         attention network output space.
        - **l2_reg_w** : float between 0 and 1. L2 regularizer strength
         applied to attention network.
        - **dropout_rate** : float between in [0,1). Fraction of the attention net output units to dropout.
        - **seed** : A Python integer to use as random seed.
      References
        - [Attentional Factorization Machines : Learning the Weight of Feature
        Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf)
    """

    def __init__(self, in_features, attention_factor=4, l2_reg_w=0,
        dropout_rate=0, seed=1024, device='cpu'):
        super(AFMLayer, self).__init__()
        self.attention_factor = attention_factor
        self.l2_reg_w = l2_reg_w
        self.dropout_rate = dropout_rate
        self.seed = seed
        embedding_size = in_features
        self.attention_W = nn.Parameter(torch.Tensor(embedding_size, self.
            attention_factor))
        self.attention_b = nn.Parameter(torch.Tensor(self.attention_factor))
        self.projection_h = nn.Parameter(torch.Tensor(self.attention_factor, 1)
            )
        self.projection_p = nn.Parameter(torch.Tensor(embedding_size, 1))
        for tensor in [self.attention_W, self.projection_h, self.projection_p]:
            nn.init.xavier_normal_(tensor)
        for tensor in [self.attention_b]:
            nn.init.zeros_(tensor)
        self.dropout = nn.Dropout(dropout_rate)
        self

    def forward(self, inputs):
        embeds_vec_list = inputs
        row = []
        col = []
        for r, c in itertools.combinations(embeds_vec_list, 2):
            row.append(r)
            col.append(c)
        p = torch.cat(row, dim=1)
        q = torch.cat(col, dim=1)
        inner_product = p * q
        bi_interaction = inner_product
        attention_temp = F.relu(torch.tensordot(bi_interaction, self.
            attention_W, dims=([-1], [0])) + self.attention_b)
        self.normalized_att_score = F.softmax(torch.tensordot(
            attention_temp, self.projection_h, dims=([-1], [0])), dim=1)
        attention_output = torch.sum(self.normalized_att_score *
            bi_interaction, dim=1)
        attention_output = self.dropout(attention_output)
        afm_out = torch.tensordot(attention_output, self.projection_p, dims
            =([-1], [0]))
        return afm_out


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


def get_init_inputs():
    return [[], {'in_features': 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 math as tl_math
import torch.nn as nn
from sklearn.metrics import *
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_cat_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 24
    x0 = xindex % 4
    x2 = xindex // 96
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (x0 + 4 * (-4 + x1) + 16 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (x0 + 4 * (-8 + x1) + 16 * x2), tmp14 & xmask,
        other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 16, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tl.load(in_ptr0 + (64 + x0 + 4 * (-12 + x1) + 16 * x2), tmp19 &
        xmask, other=0.0)
    tmp21 = tmp0 >= tmp17
    tmp22 = tl.full([1], 20, tl.int64)
    tmp23 = tmp0 < tmp22
    tmp24 = tmp21 & tmp23
    tmp25 = tl.load(in_ptr0 + (64 + x0 + 4 * (-16 + x1) + 16 * x2), tmp24 &
        xmask, other=0.0)
    tmp26 = tmp0 >= tmp22
    tl.full([1], 24, tl.int64)
    tmp29 = tl.load(in_ptr0 + (128 + x0 + 4 * (-20 + x1) + 16 * x2), tmp26 &
        xmask, other=0.0)
    tmp30 = tl.where(tmp24, tmp25, tmp29)
    tmp31 = tl.where(tmp19, tmp20, tmp30)
    tmp32 = tl.where(tmp14, tmp15, tmp31)
    tmp33 = tl.where(tmp9, tmp10, tmp32)
    tmp34 = tl.where(tmp4, tmp5, tmp33)
    tmp35 = tl.load(in_ptr0 + (64 + x0 + 4 * x1 + 16 * x2), tmp4 & xmask,
        other=0.0)
    tmp36 = tl.load(in_ptr0 + (128 + x0 + 4 * (-4 + x1) + 16 * x2), tmp9 &
        xmask, other=0.0)
    tmp37 = tl.load(in_ptr0 + (192 + x0 + 4 * (-8 + x1) + 16 * x2), tmp14 &
        xmask, other=0.0)
    tmp38 = tl.load(in_ptr0 + (128 + x0 + 4 * (-12 + x1) + 16 * x2), tmp19 &
        xmask, other=0.0)
    tmp39 = tl.load(in_ptr0 + (192 + x0 + 4 * (-16 + x1) + 16 * x2), tmp24 &
        xmask, other=0.0)
    tmp40 = tl.load(in_ptr0 + (192 + x0 + 4 * (-20 + x1) + 16 * x2), tmp26 &
        xmask, other=0.0)
    tmp41 = tl.where(tmp24, tmp39, tmp40)
    tmp42 = tl.where(tmp19, tmp38, tmp41)
    tmp43 = tl.where(tmp14, tmp37, tmp42)
    tmp44 = tl.where(tmp9, tmp36, tmp43)
    tmp45 = tl.where(tmp4, tmp35, tmp44)
    tmp46 = tmp34 * tmp45
    tl.store(in_out_ptr0 + x3, tmp46, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_per_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    rnumel = 24
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 24 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask & xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(rmask & xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 24 * x0), tmp11, rmask & xmask)


@triton.jit
def triton_per_fused_mul_sum_3(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    rnumel = 24
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r2 = rindex
    x1 = xindex // 4
    x0 = xindex % 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + 24 * x1), rmask & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * r2 + 96 * x1), rmask & xmask, other=0.0)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(rmask & xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tl.store(out_ptr0 + x3, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 1), (1, 1))
    assert_size_stride(primals_5, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 24, 4), (96, 4, 1), torch.float32)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_cat_mul_0[grid(384)](buf2, primals_1, 384, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_1
        buf3 = empty_strided_cuda((96, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (96, 4), (4, 1), 0),
            primals_2, out=buf3)
        del primals_2
        buf4 = reinterpret_tensor(buf3, (4, 24, 4), (96, 4, 1), 0)
        del buf3
        buf11 = empty_strided_cuda((4, 24, 4), (96, 4, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_1[grid(384)](buf4,
            primals_3, buf11, 384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf5 = empty_strided_cuda((96, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (96, 4), (4, 1), 0),
            primals_4, out=buf5)
        buf8 = empty_strided_cuda((4, 24, 1), (24, 1, 1), torch.float32)
        triton_per_fused__softmax_2[grid(4)](buf5, buf8, 4, 24, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf5
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_per_fused_mul_sum_3[grid(16)](buf8, buf2, buf9, 16, 24,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf10 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf9, primals_5, out=buf10)
    return buf10, buf8, buf2, buf8, reinterpret_tensor(buf9, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_5, (1, 4), (1, 1), 0
        ), reinterpret_tensor(buf4, (4, 96), (1, 4), 0), reinterpret_tensor(
        primals_4, (1, 4), (1, 1), 0), buf11


class AFMLayerNew(nn.Module):
    """Attentonal Factorization Machine models pairwise (order-2) feature
    interactions without linear term and bias.
      Input shape
        - A list of 3D tensor with shape: ``(batch_size,1,embedding_size)``.
      Output shape
        - 2D tensor with shape: ``(batch_size, 1)``.
      Arguments
        - **in_features** : Positive integer, dimensionality of input features.
        - **attention_factor** : Positive integer, dimensionality of the
         attention network output space.
        - **l2_reg_w** : float between 0 and 1. L2 regularizer strength
         applied to attention network.
        - **dropout_rate** : float between in [0,1). Fraction of the attention net output units to dropout.
        - **seed** : A Python integer to use as random seed.
      References
        - [Attentional Factorization Machines : Learning the Weight of Feature
        Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf)
    """

    def __init__(self, in_features, attention_factor=4, l2_reg_w=0,
        dropout_rate=0, seed=1024, device='cpu'):
        super(AFMLayerNew, self).__init__()
        self.attention_factor = attention_factor
        self.l2_reg_w = l2_reg_w
        self.dropout_rate = dropout_rate
        self.seed = seed
        embedding_size = in_features
        self.attention_W = nn.Parameter(torch.Tensor(embedding_size, self.
            attention_factor))
        self.attention_b = nn.Parameter(torch.Tensor(self.attention_factor))
        self.projection_h = nn.Parameter(torch.Tensor(self.attention_factor, 1)
            )
        self.projection_p = nn.Parameter(torch.Tensor(embedding_size, 1))
        for tensor in [self.attention_W, self.projection_h, self.projection_p]:
            nn.init.xavier_normal_(tensor)
        for tensor in [self.attention_b]:
            nn.init.zeros_(tensor)
        self.dropout = nn.Dropout(dropout_rate)
        self

    def forward(self, input_0):
        primals_2 = self.attention_W
        primals_3 = self.attention_b
        primals_4 = self.projection_h
        primals_5 = self.projection_p
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
