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
import torch.nn as nn
from typing import Counter
from collections import Counter
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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 40
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 5 % 2
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_mul_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 40
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 10
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 3.0
    tmp4 = tmp2 * tmp3
    tmp5 = 1.0
    tmp6 = tmp4 + tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 2, 5), (10, 5, 1))
    assert_size_stride(primals_2, (2, 2, 1), (2, 1, 1))
    assert_size_stride(primals_3, (2,), (1,))
    assert_size_stride(primals_4, (10, 10), (10, 1))
    assert_size_stride(primals_5, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 2, 5), (10, 5, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(40)](buf1, primals_3, 40,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (4, 10), (10, 1), 0),
            reinterpret_tensor(primals_4, (10, 10), (1, 10), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_add_mul_1[grid(40)](buf3, primals_5, 40, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_5
    return buf3, primals_2, primals_1, reinterpret_tensor(buf1, (4, 10), (
        10, 1), 0), primals_4


class NestedNetInnerModuleNew(nn.Module):
    """
    A submodule for the nested net test module below.
    """

    def __init__(self, lin_op: 'str'='addmm') ->None:
        super().__init__()
        conv_input_size = 2, 5
        conv_in = 2
        conv_out = 2
        kernel_size = 1
        padding = 0
        fc_in = 10
        fc_out = 10
        self.conv = nn.Conv1d(in_channels=conv_in, out_channels=conv_out,
            kernel_size=kernel_size, padding=padding)
        self.fc = nn.Linear(in_features=fc_in, out_features=fc_out)
        fc_flops = fc_in * fc_out
        fc_flops = Counter({lin_op: fc_flops})
        spatial_pos = conv_input_size[1] + 2 * padding - 2 * (kernel_size // 2)
        conv_flops = spatial_pos * kernel_size * conv_in * conv_out
        conv_flops = Counter({'conv': conv_flops})
        model_flops = conv_flops + fc_flops
        self.flops = {'': model_flops, 'fc': fc_flops, 'conv': conv_flops}
        self.name_to_module = {'': self, 'fc': self.fc, 'conv': self.conv}

    def forward(self, input_0):
        primals_2 = self.conv.weight
        primals_3 = self.conv.bias
        primals_4 = self.fc.weight
        primals_5 = self.fc.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.044411