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 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_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 % 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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (2048, 16), (16, 1))
    assert_size_stride(primals_3, (2048,), (1,))
    assert_size_stride(primals_4, (2048, 2048), (2048, 1))
    assert_size_stride(primals_5, (2048,), (1,))
    assert_size_stride(primals_6, (2048, 2048), (2048, 1))
    assert_size_stride(primals_7, (2048,), (1,))
    assert_size_stride(primals_8, (4, 2048), (2048, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 16), (16, 1), 0
            ), reinterpret_tensor(primals_2, (16, 2048), (1, 16), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(8192)](buf1, primals_3, 8192, 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, (2048, 2048),
            (1, 2048), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_0[grid(8192)](buf3, primals_5, 8192, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_6, (2048, 2048),
            (1, 2048), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_relu_0[grid(8192)](buf5, primals_7, 8192, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf5, reinterpret_tensor(primals_8,
            (2048, 4), (1, 2048), 0), alpha=1, beta=1, out=buf6)
        del primals_9
    return buf6, reinterpret_tensor(primals_1, (4, 16), (16, 1), 0
        ), buf1, buf3, buf5, primals_8, primals_6, primals_4


class DNNNew(nn.Module):

    def __init__(self, n_concat, freq_bins, *, dropout=0.2):
        super().__init__()
        hidden_units = 2048
        self.dropout = dropout
        self.fc1 = nn.Linear(n_concat * freq_bins, hidden_units)
        self.fc2 = nn.Linear(hidden_units, hidden_units)
        self.fc3 = nn.Linear(hidden_units, hidden_units)
        self.fc4 = nn.Linear(hidden_units, freq_bins)
        self.init_weights()

    @staticmethod
    def init_layer(layer):
        """Initialize a Linear or Convolutional layer. 
        Ref: He, Kaiming, et al. "Delving deep into rectifiers: Surpassing 
        human-level performance on imagenet classification." Proceedings of the 
        IEEE international conference on computer vision. 2015.
        """
        if layer.weight.ndimension() == 4:
            _n_out, n_in, height, width = layer.weight.size()
            n = n_in * height * width
        elif layer.weight.ndimension() == 2:
            _n_out, n = layer.weight.size()
        std = math.sqrt(2.0 / n)
        scale = std * math.sqrt(3.0)
        layer.weight.data.uniform_(-scale, scale)
        if layer.bias is not None:
            layer.bias.data.fill_(0.0)

    @staticmethod
    def init_bn(bn):
        """Initialize a Batchnorm layer. """
        bn.bias.data.fill_(0.0)
        bn.weight.data.fill_(1.0)

    def init_weights(self):
        self.init_layer(self.fc1)
        self.init_layer(self.fc2)
        self.init_layer(self.fc3)
        self.init_layer(self.fc4)

    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_8 = self.fc4.weight
        primals_9 = self.fc4.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])
        return output[0]


Measured latency_ms: 0.07354