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 libdevice
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_add_mul_0(in_ptr0, in_ptr1, in_ptr2, 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 % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_gelu_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    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 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_mul_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x2, xmask)
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (1, 1, 4), (4, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, 1, 4), (4, 4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (4, 16), (16, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (1, 1, 4), (4, 4, 1))
    assert_size_stride(primals_9, (1, 1, 4), (4, 4, 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_add_mul_0[grid(256)](primals_1, primals_2,
            primals_3, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_3
        buf1 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf0, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
            float32)
        triton_poi_fused_gelu_1[grid(1024)](buf1, buf2, 1024, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf2, (64, 16),
            (16, 1), 0), reinterpret_tensor(primals_6, (16, 4), (1, 16), 0),
            alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_2[grid(256)](primals_8, buf3, primals_9,
            buf0, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
    return buf4, primals_2, primals_8, reinterpret_tensor(buf0, (64, 4), (4,
        1), 0), buf1, reinterpret_tensor(buf2, (64, 16), (16, 1), 0
        ), buf3, primals_6, primals_4


class AffineTransform(nn.Module):

    def __init__(self, num_features):
        super().__init__()
        self.alpha = nn.Parameter(torch.ones(1, 1, num_features))
        self.beta = nn.Parameter(torch.zeros(1, 1, num_features))

    def forward(self, x):
        return self.alpha * x + self.beta


class FeedForwardNew(nn.Module):

    def __init__(self, num_features, expansion_factor):
        super().__init__()
        num_hidden = num_features * expansion_factor
        self.aff1 = AffineTransform(num_features)
        self.fc1 = nn.Linear(num_features, num_hidden)
        self.fc2 = nn.Linear(num_hidden, num_features)
        self.aff2 = AffineTransform(num_features)

    def forward(self, input_0):
        primals_1 = self.aff1.alpha
        primals_3 = self.aff1.beta
        primals_4 = self.fc1.weight
        primals_5 = self.fc1.bias
        primals_6 = self.fc2.weight
        primals_7 = self.fc2.bias
        primals_8 = self.aff2.alpha
        primals_9 = self.aff2.beta
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.015912