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
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_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_silu_2(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 = tl.sigmoid(tmp0)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_mul_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = 0.5
    tmp5 = tmp3 * tmp4
    tmp6 = tmp0 + tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 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,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(64)](primals_3, buf0,
            buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(256)](primals_3, buf0,
            buf1, primals_1, primals_2, buf2, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf0
        del buf1
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
            float32)
        triton_poi_fused_silu_2[grid(1024)](buf3, buf4, 1024, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (64, 16), (16, 1), 0),
            reinterpret_tensor(primals_6, (16, 4), (1, 16), 0), out=buf5)
        buf6 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        triton_poi_fused_add_mul_3[grid(256)](buf6, primals_3, primals_7, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
    return buf6, primals_3, reinterpret_tensor(buf2, (64, 4), (4, 1), 0
        ), buf3, reinterpret_tensor(buf4, (64, 16), (16, 1), 0
        ), primals_6, primals_4


class FFModuleNew(nn.Module):
    """Feed-forward module

    default output dimension = idim
    x0 -> LayerNorm -> FC -> Swish -> Dropout -> FC -> Dropout -> x1
    x0 + res_factor * x1 -> output
    """

    def __init__(self, idim: 'int', res_factor: 'float'=0.5, dropout:
        'float'=0.0) ->None:
        super().__init__()
        assert res_factor > 0.0 and dropout >= 0.0
        self._res_factor = res_factor
        self.ln = nn.LayerNorm([idim])
        self.fc0 = nn.Linear(idim, idim * 4)
        self.swish = nn.SiLU()
        self.dropout0 = nn.Dropout(dropout)
        self.fc1 = nn.Linear(idim * 4, idim)
        self.dropout1 = nn.Dropout(dropout)

    def forward(self, input_0):
        primals_1 = self.ln.weight
        primals_2 = self.ln.bias
        primals_4 = self.fc0.weight
        primals_5 = self.fc0.bias
        primals_6 = self.fc1.weight
        primals_7 = self.fc1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.035748