# 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_8/inductor_cache/mu/cmu5ttts2hdvlceyfi5clqpgl6qqngyzjwbvur2iymh2lu5srqjt.py
# Topologically Sorted Source Nodes: [kernel_2], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   kernel_2 => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_3,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_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=[4096, 4], tile_hint=TileHint.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_poi_fused_clone_0', 'mutated_arg_names': [], '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_clone_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 4096
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/vb/cvbebkbwqtzfbsixehaz4axca6thbasspzufvjiuomusl5mbpdpw.py
# Topologically Sorted Source Nodes: [kernel_2], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   kernel_2 => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_9, %primals_9), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_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=[16384], 
    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_add_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_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, None)
''', 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (64, 4), (4, 1))
    assert_size_stride(primals_3, (64, ), (1, ))
    assert_size_stride(primals_4, (64, 4), (4, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 64), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 64), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((1024, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [kernel], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf1, (1024, 4, 1), (4, 1, 1), 0), reinterpret_tensor(buf0, (1024, 1, 4), (4, 4, 1), 0), out=buf2)
        buf3 = empty_strided_cuda((4096, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (4096, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((64, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [kernel_2], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(buf3, primals_7, buf4, 4096, 4, grid=grid(4096, 4), stream=stream0)
        del primals_7
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [kernel_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf4, (4096, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf5)
        buf6 = reinterpret_tensor(buf5, (64, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [kernel_2], Original ATen: [aten.add]
        triton_poi_fused_add_1.run(buf6, primals_9, 16384, grid=grid(16384), stream=stream0)
        del primals_9
    return (reinterpret_tensor(buf6, (64, 4, 4, 4, 4), (256, 1, 4, 64, 16), 0), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(buf2, (4096, 4), (4, 1), 0), reinterpret_tensor(buf4, (4096, 4), (4, 1), 0), primals_8, primals_6, reinterpret_tensor(buf1, (1024, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf0, (1024, 4, 1), (4, 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((64, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = 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])
    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)

from torch.nn import Module
import torch
from torch.nn import Linear


class SeparableBlock(Module):

    def __init__(self, input_size, kernel_channels_in, kernel_channels_out,
        kernel_size):
        super(SeparableBlock, self).__init__()
        self.input_size = input_size
        self.kernel_size = kernel_size
        self.kernel_channels_in = kernel_channels_in
        self.kernel_channels_out = kernel_channels_out
        self.make_kernel_in = Linear(input_size, kernel_size * kernel_size *
            kernel_channels_in)
        self.make_kernel_out = Linear(input_size, kernel_size * kernel_size *
            kernel_channels_out)
        self.kernel_linear_in = Linear(kernel_channels_in, kernel_channels_in)
        self.kernel_linear_out = Linear(kernel_channels_out,
            kernel_channels_out)

    def forward(self, features):
        features = features.view(-1, self.input_size)
        kernel_in = self.make_kernel_in(features).view(-1, self.kernel_size,
            self.kernel_size, 1, self.kernel_channels_in)
        kernel_out = self.make_kernel_out(features).view(-1, self.
            kernel_size, self.kernel_size, self.kernel_channels_out, 1)
        kernel = torch.matmul(kernel_out, kernel_in)
        kernel = self.kernel_linear_in(kernel).permute(0, 1, 2, 4, 3)
        kernel = self.kernel_linear_out(kernel)
        kernel = kernel.permute(0, 4, 3, 1, 2)
        return kernel


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


def get_init_inputs():
    return [[], {'input_size': 4, 'kernel_channels_in': 4,
        'kernel_channels_out': 4, 'kernel_size': 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.nn import Module
from torch.nn import Linear
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_clone_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask)


@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (64, 4), (4, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (64, 4), (4, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 64), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 64), (1, 4), 
            0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((1024, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (1024, 4, 1), (4, 1, 1),
            0), reinterpret_tensor(buf0, (1024, 1, 4), (4, 4, 1), 0), out=buf2)
        buf3 = empty_strided_cuda((4096, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (4096, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((64, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(4096, 4)](buf3, primals_7, buf4, 4096,
            4, XBLOCK=4, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_7
        buf5 = buf3
        del buf3
        extern_kernels.mm(reinterpret_tensor(buf4, (4096, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf5)
        buf6 = reinterpret_tensor(buf5, (64, 4, 4, 4, 4), (256, 64, 16, 4, 
            1), 0)
        del buf5
        triton_poi_fused_add_1[grid(16384)](buf6, primals_9, 16384, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
    return reinterpret_tensor(buf6, (64, 4, 4, 4, 4), (256, 1, 4, 64, 16), 0
        ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf2, (4096, 4), (4, 1), 0), reinterpret_tensor(
        buf4, (4096, 4), (4, 1), 0), primals_8, primals_6, reinterpret_tensor(
        buf1, (1024, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf0, (1024, 
        4, 1), (4, 1, 4), 0)


class SeparableBlockNew(Module):

    def __init__(self, input_size, kernel_channels_in, kernel_channels_out,
        kernel_size):
        super(SeparableBlockNew, self).__init__()
        self.input_size = input_size
        self.kernel_size = kernel_size
        self.kernel_channels_in = kernel_channels_in
        self.kernel_channels_out = kernel_channels_out
        self.make_kernel_in = Linear(input_size, kernel_size * kernel_size *
            kernel_channels_in)
        self.make_kernel_out = Linear(input_size, kernel_size * kernel_size *
            kernel_channels_out)
        self.kernel_linear_in = Linear(kernel_channels_in, kernel_channels_in)
        self.kernel_linear_out = Linear(kernel_channels_out,
            kernel_channels_out)

    def forward(self, input_0):
        primals_2 = self.make_kernel_in.weight
        primals_3 = self.make_kernel_in.bias
        primals_4 = self.make_kernel_out.weight
        primals_5 = self.make_kernel_out.bias
        primals_6 = self.kernel_linear_in.weight
        primals_7 = self.kernel_linear_in.bias
        primals_8 = self.kernel_linear_out.weight
        primals_9 = self.kernel_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])
        return output[0]
