# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_7/inductor_cache/ap/capazs53vf7hafcoa3lzpsnuw7vvpg5foinomftp5aheilxucgrj.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %primals_2), kwargs = {})
triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(primals_1, primals_2, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        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), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import math
import torch
import warnings
from torch import Tensor
from torch.nn.parameter import Parameter
from torch.nn import functional as F
from torch.nn import init


class L1Linear(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(L1Linear, 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 forward(self, input: 'Tensor') ->Tensor:
        return F.linear(input, self.weight_u.mul(self.weight_v), self.bias)

    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 get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'l1': 4, 'in_features': 4, 'out_features': 4}]

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]
