# 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/cl/ccll32qwbxrxaynf6mdbrfktaf7uyk2kr7iwhm4uky4zvwbt2hly.py
# Topologically Sorted Source Nodes: [z], Original ATen: [aten.sum]
# Source node to ATen node mapping:
#   z => sum_1
# Graph fragment:
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_8, [1]), kwargs = {})
triton_per_fused_sum_0 = async_compile.triton('triton_per_fused_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[32768, 16],
    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=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_per_fused_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 25600
    rnumel = 15
    RBLOCK: tl.constexpr = 16
    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
    x0 = xindex % 1600
    x1 = (xindex // 1600)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (1600*r2) + (24000*x1)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + (1600*r2) + (24000*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, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1600, 4), (4, 1))
    assert_size_stride(primals_3, (1600, ), (1, ))
    assert_size_stride(primals_4, (1600, 4), (4, 1))
    assert_size_stride(primals_5, (1600, ), (1, ))
    assert_size_stride(primals_6, (24000, 1600), (1600, 1))
    assert_size_stride(primals_7, (24000, ), (1, ))
    assert_size_stride(primals_8, (24000, 1600), (1600, 1))
    assert_size_stride(primals_9, (24000, ), (1, ))
    assert_size_stride(primals_10, (4, 1600), (1600, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1600), (1600, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x0], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 1600), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((16, 1600), (1600, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 4), (4, 1), 64), reinterpret_tensor(primals_4, (4, 1600), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((16, 24000), (24000, 1), torch.float32)
        # Topologically Sorted Source Nodes: [m0], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, buf0, reinterpret_tensor(primals_6, (1600, 24000), (1, 1600), 0), alpha=1, beta=1, out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((16, 24000), (24000, 1), torch.float32)
        # Topologically Sorted Source Nodes: [m1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, buf1, reinterpret_tensor(primals_8, (1600, 24000), (1, 1600), 0), alpha=1, beta=1, out=buf3)
        del primals_9
        buf4 = empty_strided_cuda((16, 1600), (1600, 1), torch.float32)
        # Topologically Sorted Source Nodes: [z], Original ATen: [aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_sum_0.run(buf2, buf3, buf4, 25600, 15, grid=grid(25600), stream=stream0)
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [z_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, buf4, reinterpret_tensor(primals_10, (1600, 4), (1, 1600), 0), alpha=1, beta=1, out=buf5)
        del primals_11
    return (buf5, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (16, 4), (4, 1), 64), buf0, buf2, buf1, buf3, buf4, primals_10, primals_8, primals_6, )


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((1600, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1600, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1600, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1600, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((24000, 1600), (1600, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((24000, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((24000, 1600), (1600, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((24000, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 1600), (1600, 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 Mutan(nn.Module):

    def __init__(self, input_dims, output_dim, mm_dim=1600, rank=15, shared
        =False, normalize=False, dropout_input=0.0, dropout_pre_lin=0.0,
        dropout_output=0.0):
        super(Mutan, self).__init__()
        self.input_dims = input_dims
        self.shared = shared
        self.mm_dim = mm_dim
        self.rank = rank
        self.output_dim = output_dim
        self.dropout_input = dropout_input
        self.dropout_pre_lin = dropout_pre_lin
        self.dropout_output = dropout_output
        self.normalize = normalize
        self.linear0 = nn.Linear(input_dims[0], mm_dim)
        self.merge_linear0 = nn.Linear(mm_dim, mm_dim * rank)
        if self.shared:
            self.linear1 = self.linear0
            self.merge_linear1 = self.merge_linear0
        else:
            self.linear1 = nn.Linear(input_dims[1], mm_dim)
            self.merge_linear1 = nn.Linear(mm_dim, mm_dim * rank)
        self.linear_out = nn.Linear(mm_dim, output_dim)
        self.n_params = sum(p.numel() for p in self.parameters() if p.
            requires_grad)

    def forward(self, x):
        x0 = self.linear0(x[0])
        x1 = self.linear1(x[1])
        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)
        m0 = self.merge_linear0(x0)
        m1 = self.merge_linear1(x1)
        m = m0 * m1
        m = m.view(-1, self.rank, self.mm_dim)
        z = torch.sum(m, 1)
        if self.normalize:
            z = torch.sqrt(F.relu(z)) - torch.sqrt(F.relu(-z))
            z = F.normalize(z, p=2)
        if self.dropout_pre_lin > 0:
            z = F.dropout(z, p=self.dropout_pre_lin, training=self.training)
        z = self.linear_out(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, 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 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_per_fused_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 25600
    rnumel = 15
    RBLOCK: tl.constexpr = 16
    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
    x0 = xindex % 1600
    x1 = xindex // 1600
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 1600 * r2 + 24000 * x1), rmask & xmask,
        other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + 1600 * r2 + 24000 * 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, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1600, 4), (4, 1))
    assert_size_stride(primals_3, (1600,), (1,))
    assert_size_stride(primals_4, (1600, 4), (4, 1))
    assert_size_stride(primals_5, (1600,), (1,))
    assert_size_stride(primals_6, (24000, 1600), (1600, 1))
    assert_size_stride(primals_7, (24000,), (1,))
    assert_size_stride(primals_8, (24000, 1600), (1600, 1))
    assert_size_stride(primals_9, (24000,), (1,))
    assert_size_stride(primals_10, (4, 1600), (1600, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1600), (1600, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 1600), (1, 4),
            0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((16, 1600), (1600, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 64), reinterpret_tensor(primals_4, (4, 1600), (1, 4
            ), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((16, 24000), (24000, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf0, reinterpret_tensor(primals_6,
            (1600, 24000), (1, 1600), 0), alpha=1, beta=1, out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((16, 24000), (24000, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf1, reinterpret_tensor(primals_8,
            (1600, 24000), (1, 1600), 0), alpha=1, beta=1, out=buf3)
        del primals_9
        buf4 = empty_strided_cuda((16, 1600), (1600, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_sum_0[grid(25600)](buf2, buf3, buf4, 25600, 15,
            XBLOCK=32, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf4, reinterpret_tensor(
            primals_10, (1600, 4), (1, 1600), 0), alpha=1, beta=1, out=buf5)
        del primals_11
    return buf5, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 64
        ), buf0, buf2, buf1, buf3, buf4, primals_10, primals_8, primals_6


class MutanNew(nn.Module):

    def __init__(self, input_dims, output_dim, mm_dim=1600, rank=15, shared
        =False, normalize=False, dropout_input=0.0, dropout_pre_lin=0.0,
        dropout_output=0.0):
        super(MutanNew, self).__init__()
        self.input_dims = input_dims
        self.shared = shared
        self.mm_dim = mm_dim
        self.rank = rank
        self.output_dim = output_dim
        self.dropout_input = dropout_input
        self.dropout_pre_lin = dropout_pre_lin
        self.dropout_output = dropout_output
        self.normalize = normalize
        self.linear0 = nn.Linear(input_dims[0], mm_dim)
        self.merge_linear0 = nn.Linear(mm_dim, mm_dim * rank)
        if self.shared:
            self.linear1 = self.linear0
            self.merge_linear1 = self.merge_linear0
        else:
            self.linear1 = nn.Linear(input_dims[1], mm_dim)
            self.merge_linear1 = nn.Linear(mm_dim, mm_dim * rank)
        self.linear_out = nn.Linear(mm_dim, 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.weight
        primals_3 = self.linear0.bias
        primals_6 = self.merge_linear0.weight
        primals_7 = self.merge_linear0.bias
        primals_4 = self.linear1.weight
        primals_5 = self.linear1.bias
        primals_8 = self.merge_linear1.weight
        primals_9 = self.merge_linear1.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]
