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 math
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 = 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-06
    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 = 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_div_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 + 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_clone_3(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_per_fused__softmax_4(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_12, (4, 4), (4, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(4)](primals_1, buf0, buf1,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(16)](primals_1, buf0,
            buf1, primals_2, primals_3, buf2, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del buf0
        del buf1
        del primals_2
        del primals_3
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4
            ), 0), out=buf3)
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_8, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4)
        del primals_6
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_11, (64, 4), (4, 1), 0
            ), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf5)
        del primals_9
        buf6 = reinterpret_tensor(buf3, (4, 4, 1, 1), (4, 1, 16, 16), 0)
        del buf3
        triton_poi_fused_div_2[grid(16)](buf6, primals_5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_5
        buf7 = empty_strided_cuda((4, 4, 1, 16), (64, 16, 16, 1), torch.float32
            )
        triton_poi_fused_clone_3[grid(16, 16)](buf4, primals_7, buf7, 16, 
            16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_7
        buf8 = reinterpret_tensor(buf4, (16, 1, 16), (16, 16, 1), 0)
        del buf4
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 1, 1), (1, 0, 0), 
            0), reinterpret_tensor(buf7, (16, 1, 16), (16, 0, 1), 0), out=buf8)
        buf11 = empty_strided_cuda((4, 4, 1, 16), (64, 16, 16, 1), torch.
            float32)
        triton_per_fused__softmax_4[grid(16)](buf8, buf11, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        buf12 = reinterpret_tensor(buf8, (4, 4, 16, 1), (64, 16, 1, 1), 0)
        del buf8
        triton_poi_fused_clone_3[grid(16, 16)](buf5, primals_10, buf12, 16,
            16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del buf5
        del primals_10
        buf13 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf11, (16, 1, 16), (16, 16, 
            1), 0), reinterpret_tensor(buf12, (16, 16, 1), (16, 1, 0), 0),
            out=buf13)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_13, reinterpret_tensor(buf13, (4, 4),
            (4, 1), 0), reinterpret_tensor(primals_12, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf14)
        del primals_13
    return buf14, buf11, primals_1, buf2, reinterpret_tensor(primals_8, (64,
        4), (4, 1), 0), reinterpret_tensor(primals_11, (64, 4), (4, 1), 0
        ), buf11, reinterpret_tensor(buf13, (4, 4), (4, 1), 0
        ), primals_12, reinterpret_tensor(buf12, (16, 1, 16), (16, 1, 1), 0
        ), reinterpret_tensor(buf6, (16, 1, 1), (1, 1, 4), 0
        ), reinterpret_tensor(buf7, (16, 16, 1), (16, 1, 16), 0), primals_4


class MultiHeadAttentionLayerNew(nn.Module):

    def __init__(self, hidden_dim, n_heads, dropout=0.1):
        super().__init__()
        assert hidden_dim % n_heads == 0
        self.hidden_dim = hidden_dim
        self.n_heads = n_heads
        self.head_dim = hidden_dim // n_heads
        self.fc_q = nn.Linear(hidden_dim, hidden_dim)
        self.fc_k = nn.Linear(hidden_dim, hidden_dim)
        self.fc_v = nn.Linear(hidden_dim, hidden_dim)
        self.fc_o = nn.Linear(hidden_dim, hidden_dim)
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(hidden_dim, eps=1e-06)
        self.scale = math.sqrt(self.head_dim)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.fc_q.weight
        primals_2 = self.fc_q.bias
        primals_4 = self.fc_k.weight
        primals_3 = self.fc_k.bias
        primals_6 = self.fc_v.weight
        primals_5 = self.fc_v.bias
        primals_9 = self.fc_o.weight
        primals_7 = self.fc_o.bias
        primals_10 = self.layer_norm.weight
        primals_13 = self.layer_norm.bias
        primals_12 = input_0
        primals_8 = input_1
        primals_11 = input_2
        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])
        return output[0], output[1]


Measured latency_ms: 0.033084