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
import torch.nn as nn
from torch.nn.parameter import Parameter
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 = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 16
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2 + 16 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x4, tmp2, xmask)


@triton.jit
def triton_poi_fused_transpose_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, 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, 1, 4), (4, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (4, 16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf0, primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(64)](primals_2, buf1, buf2, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
        buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0)
        del buf1
        triton_poi_fused_transpose_2[grid(64)](buf0, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf0
    return reinterpret_tensor(buf2, (4, 16), (16, 1), 0), buf3


class BatchDenseNew(nn.Module):

    def __init__(self, batch, in_features, out_features, bias_init=None):
        super(BatchDenseNew, self).__init__()
        self.batch = batch
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.Tensor(batch, in_features, out_features))
        self.bias = Parameter(torch.Tensor(batch, 1, out_features))
        self.reset_parameters(bias_init)

    def reset_parameters(self, bias_init=None):
        stdv = math.sqrt(6.0 / (self.in_features + self.out_features))
        self.weight.data.uniform_(-stdv, stdv)
        if bias_init is not None:
            self.bias.data = torch.from_numpy(bias_init)
        else:
            self.bias.data.fill_(0)

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


Measured latency_ms: 0.047094