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.nn import Module
import abc
from torch import Tensor
from torch.nn import Linear
from torch.nn import MSELoss
import torch.nn
from torch import rand
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 = 256
    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 = 2.5
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 3
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp5 = 3.1415
    tmp6 = tmp4 * tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (3, 4), (4, 1))
    assert_size_stride(primals_3, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 3), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 3), (48, 12, 3, 1), 0)
        del buf1
        triton_poi_fused_mul_1[grid(192)](buf2, primals_3, 192, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_3
    return buf2, reinterpret_tensor(buf0, (64, 4), (4, 1), 0)


class ConverterModule(Module, abc.ABC):
    """Interface class for test modules for converter."""

    @abc.abstractmethod
    def input_fn(self) ->Tensor:
        """Generate a fitting input for the module.

        Returns:
            an input
        """
        return

    def loss_fn(self) ->Module:
        """The loss function.

        Returns:
            loss function
        """
        return MSELoss()


class _MultiplyNew(ConverterModule):

    def __init__(self):
        super().__init__()
        self.batch_size = 2
        self.in_dim = 4
        out_dim = 3
        self.linear = Linear(self.in_dim, out_dim)

    def input_fn(self) ->Tensor:
        return rand(self.batch_size, self.in_dim)

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


Measured latency_ms: 0.034202