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
import torch.nn as 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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 2000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 500
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_per_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    rnumel = 10
    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
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 10 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask & xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(rmask & xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 10 * x0), tmp11, rmask & xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 784), (784, 1))
    assert_size_stride(primals_2, (500, 784), (784, 1))
    assert_size_stride(primals_3, (500,), (1,))
    assert_size_stride(primals_4, (256, 500), (500, 1))
    assert_size_stride(primals_5, (256,), (1,))
    assert_size_stride(primals_6, (10, 256), (256, 1))
    assert_size_stride(primals_7, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 500), (500, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (784, 
            500), (1, 784), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 500), (500, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 500), (500, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(2000)](buf0, primals_3, buf1,
            buf2, 2000, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_3
        buf3 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (500, 256), (
            1, 500), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 256), (256, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        triton_poi_fused_leaky_relu_1[grid(1024)](buf3, primals_5, buf4,
            buf5, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        del primals_5
        buf6 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf5, reinterpret_tensor(primals_6,
            (256, 10), (1, 256), 0), alpha=1, beta=1, out=buf6)
        del primals_7
        buf9 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        triton_per_fused__softmax_2[grid(4)](buf6, buf9, 4, 10, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf6
    return buf9, primals_1, buf1, buf2, buf4, buf5, buf9, primals_6, primals_4


class MLPNetNew(nn.Module):

    def __init__(self):
        super(MLPNetNew, self).__init__()
        self.fc1 = nn.Linear(28 * 28, 500)
        self.fc2 = nn.Linear(500, 256)
        self.fc3 = nn.Linear(256, 10)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.067396