# 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/52/c52kaji6eyiltsqxp7w6aivnhxv5zwp3fyswckudklooo5a6gore.py
# Topologically Sorted Source Nodes: [xl_w], Original ATen: [aten.clone, aten._unsafe_view]
# Source node to ATen node mapping:
#   xl_w => clone, view
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
#   %view : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%clone, [64, 4]), kwargs = {})
triton_poi_fused__unsafe_view_clone_0 = async_compile.triton('triton_poi_fused__unsafe_view_clone_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, 4], tile_hint=TileHint.SQUARE,
    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, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_view_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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__unsafe_view_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + ((16*x1) + (64*(y0 // 16)) + (y0 % 16)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x1 + (4*y0)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/c5/cc5niyk56fa3m2oc45eqgnf3eeposiu56eefgo6q2ozqlq3nwxnc.py
# Topologically Sorted Source Nodes: [dot_], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   dot_ => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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=[1024], 
    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_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    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')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/p5/cp5pohlvud22ry7jg7rk2cbrxg2zgd5m6ftrmu3zisebczw32ykt.py
# Topologically Sorted Source Nodes: [dot_], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   dot_ => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_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=[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_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_2(in_ptr0, out_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')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/rj/crjfvijs5tvs7lmrx3tnlbqpiqf6fftdmggpgew3khzaaiwhtbmp.py
# Topologically Sorted Source Nodes: [xl_w_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   xl_w_1 => clone_3
# Graph fragment:
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_2,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_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.pointwise(
    size_hints=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_clone_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x4 = (xindex // 256)
    x5 = (xindex // 16) % 16
    x2 = (xindex // 16) % 4
    x6 = (xindex // 4) % 16
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x5 + (16*x0) + (64*x4)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x6 + (16*x0) + (64*x4)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + (x7), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ho/cho3ytdwjrelha3yb4kblaihfa7pdkxvm7w76zy4lipwg7jvufdx.py
# Topologically Sorted Source Nodes: [add, x_l, add_2, x_l_1, x_l_2], Original ATen: [aten.add, aten.squeeze]
# Source node to ATen node mapping:
#   add => add
#   add_2 => add_2
#   x_l => add_1
#   x_l_1 => add_3
#   x_l_2 => squeeze
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_5, %select_1), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %unsqueeze), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_11, %select_3), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %add_1), kwargs = {})
#   %squeeze : [num_users=1] = call_function[target=torch.ops.aten.squeeze.dim](args = (%add_3, 2), kwargs = {})
triton_poi_fused_add_squeeze_4 = async_compile.triton('triton_poi_fused_add_squeeze_4', '''
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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_squeeze_4', '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_add_squeeze_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = (xindex // 4)
    x1 = (xindex // 4) % 4
    x3 = (xindex // 64)
    x6 = xindex % 16
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (x6 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 + tmp7
    tl.store(out_ptr0 + (x7), tmp8, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (2, 4, 1), (4, 1, 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: [xl_w], Original ATen: [aten.clone, aten._unsafe_view]
        stream0 = get_raw_stream(0)
        triton_poi_fused__unsafe_view_clone_0.run(primals_1, buf0, 64, 4, grid=grid(64, 4), stream=stream0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [xl_w], Original ATen: [aten.mm]
        extern_kernels.mm(buf0, reinterpret_tensor(primals_2, (4, 1), (1, 1), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dot_], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(primals_1, buf2, 1024, grid=grid(1024), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dot_], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf1, buf3, 256, grid=grid(256), stream=stream0)
        del buf1
        buf4 = empty_strided_cuda((64, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dot_], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf2, (64, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf3, (64, 4, 1), (4, 1, 0), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [xl_w_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf4, primals_3, primals_1, buf5, 1024, grid=grid(1024), stream=stream0)
        buf6 = reinterpret_tensor(buf3, (256, 1), (1, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [xl_w_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf5, (256, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 1), (1, 1), 4), out=buf6)
        buf7 = empty_strided_cuda((64, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dot__1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf2, (64, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf6, (64, 4, 1), (4, 1, 1), 0), out=buf7)
        del buf6
        buf8 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, x_l, add_2, x_l_1, x_l_2], Original ATen: [aten.add, aten.squeeze]
        triton_poi_fused_add_squeeze_4.run(buf7, primals_3, buf4, primals_1, buf8, 1024, grid=grid(1024), stream=stream0)
        del buf4
        del buf7
        del primals_1
        del primals_3
    return (buf8, reinterpret_tensor(buf2, (64, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf5, (4, 256), (1, 4), 0), reinterpret_tensor(primals_2, (1, 4), (1, 1), 4), reinterpret_tensor(buf0, (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((2, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((2, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 CrossNet(nn.Module):
    """The Cross Network part of Deep&Cross Network model,
    which leans both low and high degree cross feature.
      Input shape
        - 2D tensor with shape: ``(batch_size, units)``.
      Output shape
        - 2D tensor with shape: ``(batch_size, units)``.
      Arguments
        - **in_features** : Positive integer, dimensionality of input features.
        - **layer_num**: Positive integer, the cross layer number
        - **parameterization**: string, ``"vector"``  or ``"matrix"`` ,  way to parameterize the cross network.
        - **l2_reg**: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix
        - **seed**: A Python integer to use as random seed.
      References
        - [Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12.](https://arxiv.org/abs/1708.05123)
        - [Wang R, Shivanna R, Cheng D Z, et al. DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems[J]. 2020.](https://arxiv.org/abs/2008.13535)
    """

    def __init__(self, in_features, layer_num=2, parameterization='vector',
        seed=1024, device='cpu'):
        super(CrossNet, self).__init__()
        self.layer_num = layer_num
        self.parameterization = parameterization
        if self.parameterization == 'vector':
            self.kernels = nn.Parameter(torch.Tensor(self.layer_num,
                in_features, 1))
        elif self.parameterization == 'matrix':
            self.kernels = nn.Parameter(torch.Tensor(self.layer_num,
                in_features, in_features))
        else:
            raise ValueError("parameterization should be 'vector' or 'matrix'")
        self.bias = nn.Parameter(torch.Tensor(self.layer_num, in_features, 1))
        for i in range(self.kernels.shape[0]):
            nn.init.xavier_normal_(self.kernels[i])
        for i in range(self.bias.shape[0]):
            nn.init.zeros_(self.bias[i])
        self

    def forward(self, inputs):
        x_0 = inputs.unsqueeze(2)
        x_l = x_0
        for i in range(self.layer_num):
            if self.parameterization == 'vector':
                xl_w = torch.tensordot(x_l, self.kernels[i], dims=([1], [0]))
                dot_ = torch.matmul(x_0, xl_w)
                x_l = dot_ + self.bias[i] + x_l
            elif self.parameterization == 'matrix':
                xl_w = torch.matmul(self.kernels[i], x_l)
                dot_ = xl_w + self.bias[i]
                x_l = x_0 * dot_ + x_l
            else:
                raise ValueError(
                    "parameterization should be 'vector' or 'matrix'")
        x_l = torch.squeeze(x_l, dim=2)
        return x_l


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 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__unsafe_view_clone_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (16 * x1 + 64 * (y0 // 16) + y0 % 16), xmask &
        ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    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')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_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')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x4 = xindex // 256
    x5 = xindex // 16 % 16
    x2 = xindex // 16 % 4
    x6 = xindex // 4 % 16
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x5 + 16 * x0 + 64 * x4), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x6 + 16 * x0 + 64 * x4), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + x7, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_squeeze_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex // 4
    x1 = xindex // 4 % 4
    x3 = xindex // 64
    x6 = xindex % 16
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (x6 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 + tmp7
    tl.store(out_ptr0 + x7, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (2, 4, 1), (4, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_view_clone_0[grid(64, 4)](primals_1, buf0,
            64, 4, XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_2, (4, 1), (1, 1
            ), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_clone_1[grid(1024)](primals_1, buf2, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        triton_poi_fused_clone_2[grid(256)](buf1, buf3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf1
        buf4 = empty_strided_cuda((64, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (64, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf3, (64, 4, 1), (4, 1, 0), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_clone_3[grid(1024)](buf4, primals_3, primals_1,
            buf5, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf3, (256, 1), (1, 1), 0)
        del buf3
        extern_kernels.mm(reinterpret_tensor(buf5, (256, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 1), (1, 1), 4), out=buf6)
        buf7 = empty_strided_cuda((64, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (64, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf6, (64, 4, 1), (4, 1, 1), 0), out=buf7)
        del buf6
        buf8 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_add_squeeze_4[grid(1024)](buf7, primals_3, buf4,
            primals_1, buf8, 1024, XBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        del buf7
        del primals_1
        del primals_3
    return buf8, reinterpret_tensor(buf2, (64, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf5, (4, 256), (1, 4), 0), reinterpret_tensor(
        primals_2, (1, 4), (1, 1), 4), reinterpret_tensor(buf0, (4, 64), (1,
        4), 0)


class CrossNetNew(nn.Module):
    """The Cross Network part of Deep&Cross Network model,
    which leans both low and high degree cross feature.
      Input shape
        - 2D tensor with shape: ``(batch_size, units)``.
      Output shape
        - 2D tensor with shape: ``(batch_size, units)``.
      Arguments
        - **in_features** : Positive integer, dimensionality of input features.
        - **layer_num**: Positive integer, the cross layer number
        - **parameterization**: string, ``"vector"``  or ``"matrix"`` ,  way to parameterize the cross network.
        - **l2_reg**: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix
        - **seed**: A Python integer to use as random seed.
      References
        - [Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12.](https://arxiv.org/abs/1708.05123)
        - [Wang R, Shivanna R, Cheng D Z, et al. DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems[J]. 2020.](https://arxiv.org/abs/2008.13535)
    """

    def __init__(self, in_features, layer_num=2, parameterization='vector',
        seed=1024, device='cpu'):
        super(CrossNetNew, self).__init__()
        self.layer_num = layer_num
        self.parameterization = parameterization
        if self.parameterization == 'vector':
            self.kernels = nn.Parameter(torch.Tensor(self.layer_num,
                in_features, 1))
        elif self.parameterization == 'matrix':
            self.kernels = nn.Parameter(torch.Tensor(self.layer_num,
                in_features, in_features))
        else:
            raise ValueError("parameterization should be 'vector' or 'matrix'")
        self.bias = nn.Parameter(torch.Tensor(self.layer_num, in_features, 1))
        for i in range(self.kernels.shape[0]):
            nn.init.xavier_normal_(self.kernels[i])
        for i in range(self.bias.shape[0]):
            nn.init.zeros_(self.bias[i])
        self

    def forward(self, input_0):
        primals_2 = self.kernels
        primals_3 = self.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
