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 torch.nn as 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_mul_0(in_ptr0, 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 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_1(in_ptr0, 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 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (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_2, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_mul_1[grid(4)](primals_1, buf1, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(buf1, reinterpret_tensor(primals_3, (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
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0)


class MyLinearNew(nn.Module):
    """Linear layer with equalized learning rate and custom learning rate multiplier."""

    def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
        False, lrmul=1, bias=True):
        super().__init__()
        he_std = gain * input_size ** -0.5
        if use_wscale:
            init_std = 1.0 / lrmul
            self.w_mul = he_std * lrmul
        else:
            init_std = he_std / lrmul
            self.w_mul = lrmul
        self.weight = torch.nn.Parameter(torch.randn(output_size,
            input_size) * init_std)
        if bias:
            self.bias = torch.nn.Parameter(torch.zeros(output_size))
            self.b_mul = lrmul
        else:
            self.bias = None

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


Measured latency_ms: 0.025013