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
import torch.nn as nn
import torch.nn.functional as F
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
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 4096
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)


@triton.jit
def triton_poi_fused_relu_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 % 2048
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)


@triton.jit
def triton_poi_fused_relu_2(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 % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)


@triton.jit
def triton_poi_fused_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    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_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    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_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4096, 4), (4, 1))
    assert_size_stride(primals_3, (4096,), (1,))
    assert_size_stride(primals_4, (2048, 4096), (4096, 1))
    assert_size_stride(primals_5, (2048,), (1,))
    assert_size_stride(primals_6, (512, 2048), (2048, 1))
    assert_size_stride(primals_7, (512,), (1,))
    assert_size_stride(primals_8, (128, 512), (512, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (64, 128), (128, 1))
    assert_size_stride(primals_11, (64,), (1,))
    assert_size_stride(primals_12, (32, 64), (64, 1))
    assert_size_stride(primals_13, (32,), (1,))
    assert_size_stride(primals_14, (4, 32), (32, 1))
    assert_size_stride(primals_15, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4096), (4096, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4096
            ), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(16384)](buf1, primals_3, 16384, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4096, 2048),
            (1, 4096), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(8192)](buf3, primals_5, 8192, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_6, (2048, 512),
            (1, 2048), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_relu_2[grid(2048)](buf5, primals_7, 2048, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_8, (512, 128), (
            1, 512), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_relu_3[grid(512)](buf7, primals_9, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(buf7, reinterpret_tensor(primals_10, (128, 64), (
            1, 128), 0), out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_relu_4[grid(256)](buf9, primals_11, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_11
        buf10 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf9, reinterpret_tensor(primals_12, (64, 32), (1,
            64), 0), out=buf10)
        buf11 = buf10
        del buf10
        triton_poi_fused_relu_5[grid(128)](buf11, primals_13, 128, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_13
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_15, buf11, reinterpret_tensor(
            primals_14, (32, 4), (1, 32), 0), alpha=1, beta=1, out=buf12)
        del primals_15
    return (buf12, primals_1, buf1, buf3, buf5, buf7, buf9, buf11,
        primals_14, primals_12, primals_10, primals_8, primals_6, primals_4)


class MLP_modelNew(nn.Module):
    """Feedfoward neural network with 6 hidden layer"""

    def __init__(self, in_size, out_size):
        super().__init__()
        self.linear1 = nn.Linear(in_size, 4096)
        self.linear2 = nn.Linear(4096, 2048)
        self.linear3 = nn.Linear(2048, 512)
        self.linear4 = nn.Linear(512, 128)
        self.linear5 = nn.Linear(128, 64)
        self.linear6 = nn.Linear(64, 32)
        self.linear7 = nn.Linear(32, out_size)

    def training_step(self, batch, criterion):
        images, labels = batch
        out = self(images)
        loss = criterion(out, labels)
        return loss

    def validation_step(self, batch):
        images, labels = batch
        out = self(images)
        loss = F.cross_entropy(out, labels)
        acc = self.accuracy(out, labels)
        return {'val_loss': loss, 'val_acc': acc}

    def validation_epoch_end(self, outputs):
        batch_losses = [x['val_loss'] for x in outputs]
        epoch_loss = torch.stack(batch_losses).mean()
        batch_accs = [x['val_acc'] for x in outputs]
        epoch_acc = torch.stack(batch_accs).mean()
        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}

    def epoch_end(self, epoch, result):
        None

    def accuracy(self, outputs, labels):
        _, preds = torch.max(outputs, dim=1)
        return torch.tensor(torch.sum(preds == labels).item() / len(preds))

    def forward(self, input_0):
        primals_2 = self.linear1.weight
        primals_3 = self.linear1.bias
        primals_4 = self.linear2.weight
        primals_5 = self.linear2.bias
        primals_6 = self.linear3.weight
        primals_7 = self.linear3.bias
        primals_8 = self.linear4.weight
        primals_9 = self.linear4.bias
        primals_10 = self.linear5.weight
        primals_11 = self.linear5.bias
        primals_12 = self.linear6.weight
        primals_13 = self.linear6.bias
        primals_14 = self.linear7.weight
        primals_15 = self.linear7.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,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15])
        return output[0]


Measured latency_ms: 0.123284