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
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 300
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 100
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_per_fused__log_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 = tl_math.log(tmp10)
    tmp12 = tmp5 - tmp11
    tl.store(out_ptr2 + (r1 + 10 * x0), tmp12, 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, (300, 784), (784, 1))
    assert_size_stride(primals_3, (300,), (1,))
    assert_size_stride(primals_4, (100, 300), (300, 1))
    assert_size_stride(primals_5, (100,), (1,))
    assert_size_stride(primals_6, (10, 100), (100, 1))
    assert_size_stride(primals_7, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (784, 
            300), (1, 784), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(1200)](buf1, primals_3, 1200, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 100), (100, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (300, 100), (
            1, 300), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(400)](buf3, primals_5, 400, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf3, reinterpret_tensor(primals_6,
            (100, 10), (1, 100), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf7 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        triton_per_fused__log_softmax_2[grid(4)](buf4, buf7, 4, 10, XBLOCK=
            1, num_warps=2, num_stages=1)
        del buf4
    return buf7, primals_1, buf1, buf3, buf7, primals_6, primals_4


class LeNet_300_100New(nn.Module):
    """Simple NN with hidden layers [300, 100]

    Based on https://github.com/mi-lad/snip/blob/master/train.py
    by Milad Alizadeh.
    """

    def __init__(self, save_features=None, bench_model=False):
        super(LeNet_300_100New, self).__init__()
        self.fc1 = nn.Linear(28 * 28, 300, bias=True)
        self.fc2 = nn.Linear(300, 100, bias=True)
        self.fc3 = nn.Linear(100, 10, bias=True)
        self.mask = None

    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: 0.059634