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 warnings
from torch.nn.parameter import Parameter
from torch.nn import init
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_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(16)](primals_1, primals_2, buf0, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_4, (64, 
            4), (4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del buf0
        del primals_3
    return reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_1, primals_2, reinterpret_tensor(primals_4, (64, 4), (4,
        1), 0)


class L1LinearNew(torch.nn.Module):

    def __init__(self, l1: 'float', in_features: 'int', out_features: 'int',
        bias: 'bool'=True, init_zero=False, device=None, dtype=None) ->None:
        """Applies a linear transformation to the incoming data: :math:`y = xW^T + b`
           where the weights of :math:`W` are l1-penalized, i.e. with the term
               
           l1 * ||W||_1
           
           The weights :math:`W` are never actually optimized but rather they are split up into two factors
           ``self.weight_u`` and ``self.weight_v``. The lweights :math:`W` can be retrieved by ``self.get_weight()``.
           For further details see
            
             Equivalences Between Sparse Models and Neural Networks, Ryan Tibshirani, 2021.

        This module supports :ref:`TensorFloat32<tf32_on_ampere>`.
    
        Args:
            l1: float, regularization parameter
            in_features: size of each input sample
            out_features: size of each output sample
            bias: If set to ``False``, the layer will not learn an additive bias. The bias is not regularized.
                Default: ``True``
        
        Shape:
            - Input: :math:`(*, H_{in})` where :math:`*` means any number of
              dimensions including none and :math:`H_{in} = 	ext{in\\_features}`.
            - Output: :math:`(*, H_{out})` where all but the last dimension
              are the same shape as the input and :math:`H_{out} = 	ext{out\\_features}`.
    
        Attributes:
            weight_u: the learnable weights of the module of shape
                :math:`(	ext{out\\_features}, 	ext{in\\_features})`. The values are
                initialized to zero.
            weight_v: the learnable weights of the module of shape
                :math:`(	ext{out\\_features}, 	ext{in\\_features})`. The values are
                initialized from :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})`, where
                :math:`k = rac{1}{	ext{in\\_features}}`
            bias: the learnable bias of the module of shape :math:`(	ext{out\\_features})`.
                If :attr:`bias` is ``True``, the values are initialized from
                :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where
                :math:`k = rac{1}{	ext{in\\_features}}`
            init_zero: whether to initialize one of the weights at zero.
        
        Examples::
    
            TBD
        """
        factory_kwargs = {'device': device, 'dtype': dtype}
        super(L1LinearNew, self).__init__()
        assert l1 >= 0, 'l1 should be non-negative.'
        if l1 == 0:
            warnings.warn(
                'Choosing l1=0 means no regularization. You should use the standard Pytorch Linear module.'
                )
        self.l1 = l1
        self.in_features = in_features
        self.out_features = out_features
        self.weight_u = Parameter(torch.zeros((out_features, in_features),
            **factory_kwargs))
        self.weight_v = Parameter(torch.empty((out_features, in_features),
            **factory_kwargs))
        if bias:
            self.bias = Parameter(torch.empty(out_features, **factory_kwargs))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters(init_zero)
        return

    def reg(self):
        """
        compute l1 * ||W||_1 = (l1/2)* (||W_u||^2 + ||W_v||^2)
        """
        return self.l1 / 2 * (torch.linalg.norm(self.weight_u) ** 2 + torch
            .linalg.norm(self.weight_u) ** 2)

    def get_weight(self):
        return self.weight_u.mul(self.weight_v)

    def get_tol(self):
        """ Stopping criterion for the split, i.e. at the minimizer we have ``abs(self.weight_u) == abs(self.weight_v)`` """
        return torch.max(torch.abs(torch.abs(self.weight_u) - torch.abs(
            self.weight_v)))

    def reset_parameters(self, init_zero=False) ->None:
        if not init_zero:
            init.kaiming_uniform_(self.weight_u, a=math.sqrt(5))
        init.kaiming_uniform_(self.weight_v, a=math.sqrt(5))
        if self.bias is not None:
            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight_u)
            bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
            init.uniform_(self.bias, -bound, bound)

    def extra_repr(self) ->str:
        return 'l1={}, in_features={}, out_features={}, bias={}'.format(self
            .l1, self.in_features, self.out_features, self.bias is not None)

    def forward(self, input_0):
        primals_1 = self.weight_u
        primals_2 = self.weight_v
        primals_3 = self.bias
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.025268