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
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_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 5120
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 80
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 640
    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 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, 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, (80, 4), (4, 1))
    assert_size_stride(primals_2, (80,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (10, 80), (80, 1))
    assert_size_stride(primals_5, (10,), (1,))
    assert_size_stride(primals_6, (4, 10), (10, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 80), (80, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 80), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 80), (1280, 320, 80, 1), 0)
        del buf0
        buf6 = empty_strided_cuda((4, 4, 4, 80), (1280, 320, 80, 1), torch.bool
            )
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(5120)](buf1,
            primals_2, buf6, 5120, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 10), (10, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 80), (80, 1), 0),
            reinterpret_tensor(primals_4, (80, 10), (1, 80), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 10), (160, 40, 10, 1), 0)
        del buf2
        buf5 = empty_strided_cuda((4, 4, 4, 10), (160, 40, 10, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(640)](buf3,
            primals_5, buf5, 640, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 10),
            (10, 1), 0), reinterpret_tensor(primals_6, (10, 4), (1, 10), 0),
            alpha=1, beta=1, out=buf4)
        del primals_7
    return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 80), (80, 1), 0), reinterpret_tensor(
        buf3, (64, 10), (10, 1), 0), primals_6, buf5, primals_4, buf6


class MLP_multiple_classNew(torch.nn.Module):

    def __init__(self, dim, n_labels, drop=0.3):
        super().__init__()
        self.fc_1 = torch.nn.Linear(dim, 80)
        self.fc_2 = torch.nn.Linear(80, 10)
        self.fc_3 = torch.nn.Linear(10, n_labels)
        self.act = torch.nn.ReLU()
        self.dropout = torch.nn.Dropout(p=drop, inplace=False)
        self.n_labels = n_labels

    def forward(self, input_0):
        primals_1 = self.fc_1.weight
        primals_2 = self.fc_1.bias
        primals_4 = self.fc_2.weight
        primals_5 = self.fc_2.bias
        primals_6 = self.fc_3.weight
        primals_7 = self.fc_3.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.026918