# 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_1/inductor_cache/ry/crymkmzyr52ux5i5fsugcweymgo3bqzkvrnx3ycb6v4j4irckdjn.py
# Topologically Sorted Source Nodes: [z], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   z => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%sum_1, %sum_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 9600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2400
    x1 = (xindex // 2400)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1200, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((2*x0) + (2400*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.full([1], 0, tl.int32)
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tmp8 = tl.load(in_ptr1 + ((2*x0) + (2400*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp10 = tmp7 * tmp9
    tmp11 = tl.load(in_ptr0 + (1 + (2*x0) + (2400*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = triton_helpers.maximum(tmp6, tmp11)
    tmp13 = tl.load(in_ptr1 + (1 + (2*x0) + (2400*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp14 = triton_helpers.maximum(tmp6, tmp13)
    tmp15 = tmp12 * tmp14
    tmp16 = tmp10 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tmp0 >= tmp3
    tmp20 = tl.full([1], 2400, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tl.load(in_ptr2 + ((2*((-1200) + x0)) + (2400*x1)), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp23 = triton_helpers.maximum(tmp6, tmp22)
    tmp24 = tl.load(in_ptr3 + ((2*((-1200) + x0)) + (2400*x1)), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = triton_helpers.maximum(tmp6, tmp24)
    tmp26 = tmp23 * tmp25
    tmp27 = tl.load(in_ptr0 + ((2*((-1200) + x0)) + (2400*x1)), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp28 = triton_helpers.maximum(tmp6, tmp27)
    tmp29 = tl.load(in_ptr1 + ((2*((-1200) + x0)) + (2400*x1)), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp30 = triton_helpers.maximum(tmp6, tmp29)
    tmp31 = tmp28 * tmp30
    tmp32 = tmp26 * tmp31
    tmp33 = tl.load(in_ptr2 + (1 + (2*((-1200) + x0)) + (2400*x1)), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp34 = triton_helpers.maximum(tmp6, tmp33)
    tmp35 = tl.load(in_ptr3 + (1 + (2*((-1200) + x0)) + (2400*x1)), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp36 = triton_helpers.maximum(tmp6, tmp35)
    tmp37 = tmp34 * tmp36
    tmp38 = tl.load(in_ptr0 + (1 + (2*((-1200) + x0)) + (2400*x1)), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp39 = triton_helpers.maximum(tmp6, tmp38)
    tmp40 = tl.load(in_ptr1 + (1 + (2*((-1200) + x0)) + (2400*x1)), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp41 = triton_helpers.maximum(tmp6, tmp40)
    tmp42 = tmp39 * tmp41
    tmp43 = tmp37 * tmp42
    tmp44 = tmp32 + tmp43
    tmp45 = tl.full(tmp44.shape, 0.0, tmp44.dtype)
    tmp46 = tl.where(tmp19, tmp44, tmp45)
    tmp47 = tl.where(tmp4, tmp18, tmp46)
    tl.store(out_ptr0 + (x2), tmp47, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/xo/cxoy25xst5lr7sipjfpjv7a72jjvrc7bpzabd73irv466cnlaugy.py
# Topologically Sorted Source Nodes: [z_6], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   z_6 => relu_4
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_11), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    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')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (2400, 4), (4, 1))
    assert_size_stride(primals_3, (2400, ), (1, ))
    assert_size_stride(primals_4, (2400, 4), (4, 1))
    assert_size_stride(primals_5, (2400, ), (1, ))
    assert_size_stride(primals_6, (2400, 4), (4, 1))
    assert_size_stride(primals_7, (2400, ), (1, ))
    assert_size_stride(primals_8, (2400, 4), (4, 1))
    assert_size_stride(primals_9, (2400, ), (1, ))
    assert_size_stride(primals_10, (4, 2400), (2400, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x0], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (4, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 2400), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (4, 4), (4, 1), 16), reinterpret_tensor(primals_4, (4, 2400), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x0_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_1, (4, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 2400), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_6
        del primals_7
        buf3 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x1_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(primals_1, (4, 4), (4, 1), 16), reinterpret_tensor(primals_8, (4, 2400), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_8
        del primals_9
        buf4 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [z], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(buf0, buf1, buf2, buf3, buf4, 9600, grid=grid(9600), stream=stream0)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf4, reinterpret_tensor(primals_10, (2400, 4), (1, 2400), 0), out=buf5)
        buf6 = buf5; del buf5  # reuse
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [z_6], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_1.run(buf6, primals_11, buf7, 16, grid=grid(16), stream=stream0)
        del primals_11
    return (buf6, reinterpret_tensor(primals_1, (4, 4), (4, 1), 0), buf0, reinterpret_tensor(primals_1, (4, 4), (4, 1), 16), buf1, buf2, buf3, buf4, buf7, primals_10, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((2400, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((2400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((2400, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((2400, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((2400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((2400, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((2400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 2400), (2400, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11])
    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
from torch.nn import functional as F


class MFH(nn.Module):

    def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
        activ_input='relu', activ_output='relu', normalize=False,
        dropout_input=0.0, dropout_pre_lin=0.0, dropout_output=0.0):
        super(MFH, self).__init__()
        self.input_dims = input_dims
        self.output_dim = output_dim
        self.mm_dim = mm_dim
        self.factor = factor
        self.activ_input = activ_input
        self.activ_output = activ_output
        self.normalize = normalize
        self.dropout_input = dropout_input
        self.dropout_pre_lin = dropout_pre_lin
        self.dropout_output = dropout_output
        self.linear0_0 = nn.Linear(input_dims[0], mm_dim * factor)
        self.linear1_0 = nn.Linear(input_dims[1], mm_dim * factor)
        self.linear0_1 = nn.Linear(input_dims[0], mm_dim * factor)
        self.linear1_1 = nn.Linear(input_dims[1], mm_dim * factor)
        self.linear_out = nn.Linear(mm_dim * 2, output_dim)
        self.n_params = sum(p.numel() for p in self.parameters() if p.
            requires_grad)

    def forward(self, x):
        x0 = self.linear0_0(x[0])
        x1 = self.linear1_0(x[1])
        if self.activ_input:
            x0 = getattr(F, self.activ_input)(x0)
            x1 = getattr(F, self.activ_input)(x1)
        if self.dropout_input > 0:
            x0 = F.dropout(x0, p=self.dropout_input, training=self.training)
            x1 = F.dropout(x1, p=self.dropout_input, training=self.training)
        z_0_skip = x0 * x1
        if self.dropout_pre_lin:
            z_0_skip = F.dropout(z_0_skip, p=self.dropout_pre_lin, training
                =self.training)
        z_0 = z_0_skip.view(z_0_skip.size(0), self.mm_dim, self.factor)
        z_0 = z_0.sum(2)
        if self.normalize:
            z_0 = torch.sqrt(F.relu(z_0)) - torch.sqrt(F.relu(-z_0))
            z_0 = F.normalize(z_0, p=2)
        x0 = self.linear0_1(x[0])
        x1 = self.linear1_1(x[1])
        if self.activ_input:
            x0 = getattr(F, self.activ_input)(x0)
            x1 = getattr(F, self.activ_input)(x1)
        if self.dropout_input > 0:
            x0 = F.dropout(x0, p=self.dropout_input, training=self.training)
            x1 = F.dropout(x1, p=self.dropout_input, training=self.training)
        z_1 = x0 * x1 * z_0_skip
        if self.dropout_pre_lin > 0:
            z_1 = F.dropout(z_1, p=self.dropout_pre_lin, training=self.training
                )
        z_1 = z_1.view(z_1.size(0), self.mm_dim, self.factor)
        z_1 = z_1.sum(2)
        if self.normalize:
            z_1 = torch.sqrt(F.relu(z_1)) - torch.sqrt(F.relu(-z_1))
            z_1 = F.normalize(z_1, p=2)
        cat_dim = z_0.dim() - 1
        z = torch.cat([z_0, z_1], cat_dim)
        z = self.linear_out(z)
        if self.activ_output:
            z = getattr(F, self.activ_output)(z)
        if self.dropout_output > 0:
            z = F.dropout(z, p=self.dropout_output, training=self.training)
        return z


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


def get_init_inputs():
    return [[], {'input_dims': [4, 4], 'output_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._inductor.runtime import triton_helpers
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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 9600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2400
    x1 = xindex // 2400
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1200, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (2 * x0 + 2400 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tl.full([1], 0, tl.int32)
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tmp8 = tl.load(in_ptr1 + (2 * x0 + 2400 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp10 = tmp7 * tmp9
    tmp11 = tl.load(in_ptr0 + (1 + 2 * x0 + 2400 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp12 = triton_helpers.maximum(tmp6, tmp11)
    tmp13 = tl.load(in_ptr1 + (1 + 2 * x0 + 2400 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp14 = triton_helpers.maximum(tmp6, tmp13)
    tmp15 = tmp12 * tmp14
    tmp16 = tmp10 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tmp0 >= tmp3
    tl.full([1], 2400, tl.int64)
    tmp22 = tl.load(in_ptr2 + (2 * (-1200 + x0) + 2400 * x1), tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp23 = triton_helpers.maximum(tmp6, tmp22)
    tmp24 = tl.load(in_ptr3 + (2 * (-1200 + x0) + 2400 * x1), tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = triton_helpers.maximum(tmp6, tmp24)
    tmp26 = tmp23 * tmp25
    tmp27 = tl.load(in_ptr0 + (2 * (-1200 + x0) + 2400 * x1), tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp28 = triton_helpers.maximum(tmp6, tmp27)
    tmp29 = tl.load(in_ptr1 + (2 * (-1200 + x0) + 2400 * x1), tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp30 = triton_helpers.maximum(tmp6, tmp29)
    tmp31 = tmp28 * tmp30
    tmp32 = tmp26 * tmp31
    tmp33 = tl.load(in_ptr2 + (1 + 2 * (-1200 + x0) + 2400 * x1), tmp19 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp34 = triton_helpers.maximum(tmp6, tmp33)
    tmp35 = tl.load(in_ptr3 + (1 + 2 * (-1200 + x0) + 2400 * x1), tmp19 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp36 = triton_helpers.maximum(tmp6, tmp35)
    tmp37 = tmp34 * tmp36
    tmp38 = tl.load(in_ptr0 + (1 + 2 * (-1200 + x0) + 2400 * x1), tmp19 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp39 = triton_helpers.maximum(tmp6, tmp38)
    tmp40 = tl.load(in_ptr1 + (1 + 2 * (-1200 + x0) + 2400 * x1), tmp19 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp41 = triton_helpers.maximum(tmp6, tmp40)
    tmp42 = tmp39 * tmp41
    tmp43 = tmp37 * tmp42
    tmp44 = tmp32 + tmp43
    tmp45 = tl.full(tmp44.shape, 0.0, tmp44.dtype)
    tmp46 = tl.where(tmp19, tmp44, tmp45)
    tmp47 = tl.where(tmp4, tmp18, tmp46)
    tl.store(out_ptr0 + x2, tmp47, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (2400, 4), (4, 1))
    assert_size_stride(primals_3, (2400,), (1,))
    assert_size_stride(primals_4, (2400, 4), (4, 1))
    assert_size_stride(primals_5, (2400,), (1,))
    assert_size_stride(primals_6, (2400, 4), (4, 1))
    assert_size_stride(primals_7, (2400,), (1,))
    assert_size_stride(primals_8, (2400, 4), (4, 1))
    assert_size_stride(primals_9, (2400,), (1,))
    assert_size_stride(primals_10, (4, 2400), (2400, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (4, 4
            ), (4, 1), 0), reinterpret_tensor(primals_2, (4, 2400), (1, 4),
            0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (4, 4
            ), (4, 1), 16), reinterpret_tensor(primals_4, (4, 2400), (1, 4),
            0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_1, (4, 4
            ), (4, 1), 0), reinterpret_tensor(primals_6, (4, 2400), (1, 4),
            0), alpha=1, beta=1, out=buf2)
        del primals_6
        del primals_7
        buf3 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(primals_1, (4, 4
            ), (4, 1), 16), reinterpret_tensor(primals_8, (4, 2400), (1, 4),
            0), alpha=1, beta=1, out=buf3)
        del primals_8
        del primals_9
        buf4 = empty_strided_cuda((4, 2400), (2400, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(9600)](buf0, buf1, buf2, buf3, buf4, 
            9600, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf4, reinterpret_tensor(primals_10, (2400, 4), (
            1, 2400), 0), out=buf5)
        buf6 = buf5
        del buf5
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(16)](buf6,
            primals_11, buf7, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_11
    return buf6, reinterpret_tensor(primals_1, (4, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(primals_1, (4, 4), (4, 1), 16
        ), buf1, buf2, buf3, buf4, buf7, primals_10


class MFHNew(nn.Module):

    def __init__(self, input_dims, output_dim, mm_dim=1200, factor=2,
        activ_input='relu', activ_output='relu', normalize=False,
        dropout_input=0.0, dropout_pre_lin=0.0, dropout_output=0.0):
        super(MFHNew, self).__init__()
        self.input_dims = input_dims
        self.output_dim = output_dim
        self.mm_dim = mm_dim
        self.factor = factor
        self.activ_input = activ_input
        self.activ_output = activ_output
        self.normalize = normalize
        self.dropout_input = dropout_input
        self.dropout_pre_lin = dropout_pre_lin
        self.dropout_output = dropout_output
        self.linear0_0 = nn.Linear(input_dims[0], mm_dim * factor)
        self.linear1_0 = nn.Linear(input_dims[1], mm_dim * factor)
        self.linear0_1 = nn.Linear(input_dims[0], mm_dim * factor)
        self.linear1_1 = nn.Linear(input_dims[1], mm_dim * factor)
        self.linear_out = nn.Linear(mm_dim * 2, output_dim)
        self.n_params = sum(p.numel() for p in self.parameters() if p.
            requires_grad)

    def forward(self, input_0):
        primals_2 = self.linear0_0.weight
        primals_3 = self.linear0_0.bias
        primals_4 = self.linear1_0.weight
        primals_5 = self.linear1_0.bias
        primals_6 = self.linear0_1.weight
        primals_7 = self.linear0_1.bias
        primals_8 = self.linear1_1.weight
        primals_9 = self.linear1_1.bias
        primals_10 = self.linear_out.weight
        primals_11 = self.linear_out.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
