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]


Measured latency_ms: 0.072465