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
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
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_add_0(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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 64, 4), (256, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 64, 4), (256, 
            4, 1), 0), reinterpret_tensor(primals_1, (1, 4, 4), (16, 4, 1),
            0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(256)](buf1, primals_3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_3
    return buf1, reinterpret_tensor(primals_2, (1, 4, 64), (256, 1, 4), 0)


def get_einsum_string(ndims, einsum_symbols=None):
    if einsum_symbols is None:
        einsum_symbols = ['u', 'v', 'w', 'x', 'y', 'z']
    assert ndims <= len(einsum_symbols)
    einsum_prefix = ''
    for i in range(ndims):
        einsum_prefix += einsum_symbols[i]
    return einsum_prefix


def maybe_convert_to_list(x):
    if isinstance(x, (int, float)):
        return [x]
    elif isinstance(x, (list, tuple)):
        return list(x)


class DenseNew(nn.Module):
    """Dense layer."""

    def __init__(self, inp_shape, out_shape, bias=True, reverse_order=False):
        super(DenseNew, self).__init__()
        self.inp_shape = maybe_convert_to_list(inp_shape)
        self.out_shape = maybe_convert_to_list(out_shape)
        self.reverse_order = reverse_order
        if self.reverse_order:
            self.einsum_str = '...{0},{1}{0}->...{1}'.format(get_einsum_string
                (len(self.inp_shape), ['a', 'b', 'c', 'd']),
                get_einsum_string(len(self.out_shape), ['e', 'f', 'g', 'h']))
            weight_shape = self.out_shape + self.inp_shape
        else:
            self.einsum_str = '...{0},{0}{1}->...{1}'.format(get_einsum_string
                (len(self.inp_shape), ['a', 'b', 'c', 'd']),
                get_einsum_string(len(self.out_shape), ['e', 'f', 'g', 'h']))
            weight_shape = self.inp_shape + self.out_shape
        self.weight = nn.Parameter(torch.zeros(weight_shape))
        if bias:
            self.bias = nn.Parameter(torch.zeros(self.out_shape))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        fan_in = np.prod(self.inp_shape)
        fan_out = np.prod(self.out_shape)
        std = np.sqrt(1.0 / float(fan_in + fan_out))
        nn.init.normal_(self.weight, std=std)
        if self.bias is not None:
            nn.init.constant_(self.bias, 0.0)

    def extra_repr(self):
        return 'inp_shape={}, out_shape={}, bias={}'.format(self.inp_shape,
            self.out_shape, 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: 0.022174