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._inductor.runtime.triton_helpers import math as tl_math
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_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.00392156862745098
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    RBLOCK: tl.constexpr = 2
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = triton_helpers.max2(tmp1, 1)[:, None]
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tmp9 = tmp5 / tmp8
    tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp9, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (1, 1, 28, 28), (784, 784, 28, 1))
    assert_size_stride(primals_2, (2, 784), (784, 1))
    assert_size_stride(primals_3, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 1, 28, 28), (784, 1, 28, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(784)](primals_1, buf0, 784, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((1, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (1, 784),
            (0, 1), 0), reinterpret_tensor(primals_2, (784, 2), (1, 784), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_2
        del primals_3
        buf4 = empty_strided_cuda((1, 2), (2, 1), torch.float32)
        triton_per_fused__softmax_1[grid(1)](buf1, buf4, 1, 2, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf1
    return buf4, reinterpret_tensor(buf0, (1, 784), (784, 1), 0), buf4


class BasicNNNew(nn.Module):

    def __init__(self):
        super(BasicNNNew, self).__init__()
        self.net = nn.Linear(28 * 28, 2)

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


Measured latency_ms: 0.035698