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
import numpy as np
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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_1(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
    x4 = xindex
    x0 = xindex % 4
    x2 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x4, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 1, 4), (4, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(256)](primals_1, buf0, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (16, 4, 4), (16, 4,
            1), 0), reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0),
            out=buf1)
        del buf0
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        triton_poi_fused_add_1[grid(256)](buf2, primals_3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_3
    return buf2, reinterpret_tensor(primals_2, (16, 4, 4), (16, 1, 4), 0)


class DenseParallelNew(nn.Module):

    def __init__(self, in_features: 'int', out_features: 'int', n_parallel:
        'int', bias: 'bool'=True, device=None, dtype=None) ->None:
        factory_kwargs = {'device': device, 'dtype': dtype}
        super(DenseParallelNew, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.n_parallel = n_parallel
        self.weight = nn.Parameter(torch.empty((n_parallel, in_features,
            out_features), **factory_kwargs))
        if bias:
            self.bias = nn.Parameter(torch.empty((n_parallel, 1,
                out_features), **factory_kwargs))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self) ->None:
        nn.init.kaiming_uniform_(self.weight, a=np.sqrt(5))
        if self.bias is not None:
            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
            bound = 1 / np.sqrt(fan_in) if fan_in > 0 else 0
            nn.init.uniform_(self.bias, -bound, bound)

    def extra_repr(self) ->str:
        return ('in_features={}, out_features={}, n_parallel={}, bias={}'.
            format(self.in_features, self.out_features, self.n_parallel, 
            self.bias is not None))

    def forward(self, input_0):
        primals_1 = self.weight
        primals_3 = self.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.034622