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 math
from torch import 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 = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    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_squeeze_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + x0, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(1024)](primals_1, buf0, 1024, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_clone_1[grid(1024)](primals_2, buf1, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (64, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf1, (64, 4, 4), (16, 4, 1), 0), out=buf2)
        del buf1
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0
            )
        del buf2
        buf4 = buf3
        del buf3
        triton_poi_fused_add_squeeze_2[grid(1024)](buf4, primals_3, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
    return buf4, reinterpret_tensor(buf0, (64, 4, 4), (16, 1, 4), 0)


class LocallyConnectedNew(nn.Module):
    """Local linear layer, i.e. Conv1dLocal() with filter size 1.

    Args:
        num_linear: num of local linear layers, i.e.
        in_features: m1
        out_features: m2
        bias: whether to include bias or not

    Shape:
        - Input: [n, d, m1]
        - Output: [n, d, m2]

    Attributes:
        weight: [d, m1, m2]
        bias: [d, m2]
    """

    def __init__(self, num_linear, input_features, output_features, bias=True):
        super(LocallyConnectedNew, self).__init__()
        self.num_linear = num_linear
        self.input_features = input_features
        self.output_features = output_features
        self.weight = nn.Parameter(torch.Tensor(num_linear, input_features,
            output_features))
        if bias:
            self.bias = nn.Parameter(torch.Tensor(num_linear, output_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    @torch.no_grad()
    def reset_parameters(self):
        k = 1.0 / self.input_features
        bound = math.sqrt(k)
        nn.init.uniform_(self.weight, -bound, bound)
        if self.bias is not None:
            nn.init.uniform_(self.bias, -bound, bound)

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

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


Measured latency_ms: 0.046351