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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import MultiheadAttention
from itertools import product as product
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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_6(in_out_ptr0, in_ptr0, in_ptr1,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
    tmp1 = tl.load(in_out_ptr0 + (x1 + 4 * y0), xmask & ymask)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + (x1 + 4 * y0), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused_native_layer_norm_7(in_ptr0, out_ptr0, out_ptr1,
    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 + 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_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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_relu_9(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    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 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (12, 4), (4, 1))
    assert_size_stride(primals_4, (12,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (16, 4), (4, 1))
    assert_size_stride(primals_10, (16,), (1,))
    assert_size_stride(primals_11, (4, 16), (16, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (4, 4), (1, 4), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (1, 4), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 16), out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (1, 4), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 32), out=buf2)
        del primals_3
        buf3 = reinterpret_tensor(buf2, (4, 1, 4), (4, 4, 1), 0)
        del buf2
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(16)](buf3, primals_4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf4 = reinterpret_tensor(buf0, (4, 4, 1), (1, 4, 16), 0)
        del buf0
        triton_poi_fused_mul_1[grid(16)](buf4, primals_4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf1, (4, 1, 4), (4, 4, 1), 0)
        del buf1
        triton_poi_fused_add_2[grid(16)](buf5, primals_4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_4
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf4, reinterpret_tensor(buf5, (4, 1, 4), (1, 0,
            4), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_3[grid(64)](buf6, buf7, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf8 = buf6
        del buf6
        triton_poi_fused__softmax_4[grid(64)](buf7, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf8, reinterpret_tensor(buf3, (4, 4, 1), (1, 4,
            0), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_clone_5[grid(4, 4)](buf9, buf10, 4, 4, XBLOCK=4,
            YBLOCK=4, num_warps=1, num_stages=1)
        buf11 = reinterpret_tensor(buf9, (4, 4), (4, 1), 0)
        del buf9
        extern_kernels.mm(reinterpret_tensor(buf10, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf11)
        buf12 = buf11
        del buf11
        triton_poi_fused_add_native_layer_norm_6[grid(4, 4)](buf12,
            primals_1, primals_6, 4, 4, XBLOCK=4, YBLOCK=4, num_warps=1,
            num_stages=1)
        del primals_6
        buf13 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf14 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused_native_layer_norm_7[grid(4)](buf12, buf13, buf14, 
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_8[grid(16)](buf12, buf13, buf14,
            primals_7, primals_8, buf15, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_8
        buf16 = reinterpret_tensor(buf7, (4, 16), (16, 1), 0)
        del buf7
        extern_kernels.mm(buf15, reinterpret_tensor(primals_9, (4, 16), (1,
            4), 0), out=buf16)
        buf17 = buf16
        del buf16
        triton_poi_fused_relu_9[grid(64)](buf17, primals_10, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_10
        buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf17, reinterpret_tensor(primals_11, (16, 4), (1,
            16), 0), out=buf18)
        buf19 = buf18
        del buf18
        triton_poi_fused_add_10[grid(16)](buf19, buf15, primals_12, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_12
        buf20 = buf14
        del buf14
        buf21 = buf13
        del buf13
        triton_poi_fused_native_layer_norm_7[grid(4)](buf19, buf20, buf21, 
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf22 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_8[grid(16)](buf19, buf20, buf21,
            primals_13, primals_14, buf22, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del buf20
        del buf21
        del primals_14
    return (reinterpret_tensor(buf22, (4, 4), (1, 4), 0), primals_7,
        primals_13, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0),
        reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), buf8,
        reinterpret_tensor(buf10, (4, 4), (4, 1), 0), buf12, buf15, buf17,
        buf19, primals_11, primals_9, primals_5, reinterpret_tensor(buf3, (
        4, 1, 4), (1, 1, 4), 0), reinterpret_tensor(buf4, (4, 1, 4), (1, 1,
        4), 0), reinterpret_tensor(buf5, (4, 4, 1), (1, 4, 1), 0))


class attentionLayerNew(nn.Module):

    def __init__(self, d_model, nhead, dropout=0.1):
        super(attentionLayerNew, self).__init__()
        self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout)
        self.linear1 = nn.Linear(d_model, d_model * 4)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(d_model * 4, d_model)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.activation = F.relu

    def forward(self, input_0, input_1):
        primals_3 = self.self_attn.in_proj_weight
        primals_4 = self.self_attn.in_proj_bias
        primals_1 = self.self_attn.out_proj.weight
        primals_6 = self.self_attn.out_proj.bias
        primals_9 = self.linear1.weight
        primals_10 = self.linear1.bias
        primals_11 = self.linear2.weight
        primals_7 = self.linear2.bias
        primals_8 = self.norm1.weight
        primals_12 = self.norm1.bias
        primals_13 = self.norm2.weight
        primals_14 = self.norm2.bias
        primals_2 = input_0
        primals_5 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14])
        return output[0]


Measured latency_ms: 0.031839