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
from torch._inductor.runtime.triton_helpers import math as tl_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_exp_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 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp0 * tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_exp_mul_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    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 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp0 * tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (), ())
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (), ())
    assert_size_stride(primals_5, (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_exp_mul_0[grid(16)](primals_1, primals_2, buf0, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_exp_mul_1[grid(4)](primals_3, primals_4, buf1, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(buf1, reinterpret_tensor(primals_5, (64, 4), (
            4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), alpha=1,
            beta=1, out=buf2)
        del buf0
        del buf1
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_1, primals_2, primals_3, primals_4, reinterpret_tensor(
        primals_5, (64, 4), (4, 1), 0)


class ScaledLinearNew(nn.Linear):
    """
    A modified version of nn.Linear where the parameters are scaled before
    use, via:
         weight = self.weight * self.weight_scale.exp()
         bias = self.bias * self.bias_scale.exp()

    Args:
        Accepts the standard args and kwargs that nn.Linear accepts
        e.g. in_features, out_features, bias=False.

        initial_scale: you can override this if you want to increase
           or decrease the initial magnitude of the module's output
           (affects the initialization of weight_scale and bias_scale).
           Another option, if you want to do something like this, is
           to re-initialize the parameters.
        initial_speed: this affects how fast the parameter will
           learn near the start of training; you can set it to a
           value less than one if you suspect that a module
           is contributing to instability near the start of training.
           Nnote: regardless of the use of this option, it's best to
           use schedulers like Noam that have a warm-up period.
           Alternatively you can set it to more than 1 if you want it to
           initially train faster.   Must be greater than 0.
    """

    def __init__(self, *args, initial_scale: float=1.0, initial_speed:
        float=1.0, **kwargs):
        super(ScaledLinearNew, self).__init__(*args, **kwargs)
        initial_scale = torch.tensor(initial_scale).log()
        self.weight_scale = nn.Parameter(initial_scale.clone().detach())
        if self.bias is not None:
            self.bias_scale = nn.Parameter(initial_scale.clone().detach())
        else:
            self.register_parameter('bias_scale', None)
        self._reset_parameters(initial_speed)

    def _reset_parameters(self, initial_speed: 'float'):
        std = 0.1 / initial_speed
        a = 3 ** 0.5 * std
        nn.init.uniform_(self.weight, -a, a)
        if self.bias is not None:
            nn.init.constant_(self.bias, 0.0)
        fan_in = self.weight.shape[1] * self.weight[0][0].numel()
        scale = fan_in ** -0.5
        with torch.no_grad():
            self.weight_scale += torch.tensor(scale / std).log()

    def get_weight(self):
        return self.weight * self.weight_scale.exp()

    def get_bias(self):
        return None if self.bias is None else self.bias * self.bias_scale.exp()

    def forward(self, input_0):
        primals_1 = self.weight
        primals_3 = self.bias
        primals_2 = self.weight_scale
        primals_4 = self.bias_scale
        primals_5 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.027898