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_clone_0(in_ptr0, in_ptr1, 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)
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tmp2 - tmp2
    tmp4 = 0.5
    tmp5 = tmp3 * tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp6 / tmp6
    tl.store(in_out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (4 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (8 + x1), tmp14 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 4, tl.int64)
    tmp19 = tl.load(in_ptr0 + (12 + x1), tmp16 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x2, tmp22, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_relu_3(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')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = triton_helpers.maximum(tmp1, tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = triton_helpers.maximum(tmp1, tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tmp14 = tmp2 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tmp4 - tmp13
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp7 - tmp13
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 - tmp13
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp24 / tmp12
    tmp26 = 1e-05
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.rsqrt(tmp27)
    tl.store(out_ptr0 + x0, tmp13, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_relu_4(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)
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = tmp2 - tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + x2, tmp10, 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
        ) = 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, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (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(primals_2, reinterpret_tensor(primals_3, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_5, (4, 4),
            (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_7, (4, 4),
            (1, 4), 0), out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(4, 4)](buf1, primals_6, buf3, 4, 4,
            XBLOCK=4, YBLOCK=4, num_warps=1, num_stages=1)
        del primals_6
        buf4 = reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 16, 16), 0)
        del buf1
        triton_poi_fused_clone_0[grid(4, 4)](buf2, primals_8, buf4, 4, 4,
            XBLOCK=4, YBLOCK=4, num_warps=1, num_stages=1)
        del primals_8
        buf5 = reinterpret_tensor(buf2, (4, 4, 1, 1), (4, 1, 16, 16), 0)
        del buf2
        triton_poi_fused_clone_0[grid(4, 4)](buf0, primals_4, buf5, 4, 4,
            XBLOCK=4, YBLOCK=4, num_warps=1, num_stages=1)
        del primals_4
        buf6 = reinterpret_tensor(buf0, (16, 1, 1), (1, 1, 1), 0)
        del buf0
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 1, 1), (1, 0, 0), 
            0), reinterpret_tensor(buf5, (16, 1, 1), (1, 0, 0), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused__softmax_1[grid(16)](buf7, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf8 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(buf7, reinterpret_tensor(buf4, (16, 1, 1), (1, 0,
            0), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        triton_poi_fused_cat_2[grid(16)](buf8, buf9, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf10 = reinterpret_tensor(buf8, (4, 4), (4, 1), 0)
        del buf8
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf9, (4, 4), (
            4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf10)
        del primals_10
        buf11 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf12 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        triton_poi_fused_native_layer_norm_relu_3[grid(4)](buf10, buf11,
            buf12, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf13 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_relu_4[grid(16)](buf10, buf11,
            buf12, primals_11, primals_12, buf13, 16, XBLOCK=16, num_warps=
            1, num_stages=1)
        del buf11
        del buf12
        del primals_12
    return (buf13, buf7, primals_11, primals_2, primals_1, buf7,
        reinterpret_tensor(buf9, (4, 4), (4, 1), 0), buf10, primals_9,
        reinterpret_tensor(buf4, (16, 1, 1), (1, 1, 1), 0),
        reinterpret_tensor(buf3, (16, 1, 1), (1, 1, 1), 0),
        reinterpret_tensor(buf5, (16, 1, 1), (1, 1, 1), 0))


class AttentionNew(nn.Module):

    def __init__(self, num_heads, model_dim, k_dim=None, v_dim=None,
        out_dim=None, temperature=None, dropout=0, score_function=
        'scaled_dot_product'):
        super(AttentionNew, self).__init__()
        self.num_heads = num_heads
        self.model_dim = model_dim
        if k_dim is None:
            self.k_dim = model_dim // num_heads
        else:
            self.k_dim = k_dim
        if v_dim is None:
            self.v_dim = self.k_dim
        else:
            self.v_dim = v_dim
        if out_dim is None:
            self.out_dim = model_dim
        else:
            self.out_dim = out_dim
        self.w_k = nn.Linear(model_dim, num_heads * self.k_dim)
        self.w_q = nn.Linear(model_dim, num_heads * self.k_dim)
        self.w_v = nn.Linear(model_dim, num_heads * self.k_dim)
        self.dense = nn.Linear(num_heads * self.k_dim, self.out_dim)
        self.dropout = nn.Dropout(p=dropout)
        self.layer_norm = nn.LayerNorm(self.out_dim)
        self.score_function = score_function
        if temperature is None:
            self.temperature = math.sqrt(model_dim)
        else:
            self.temperature = temperature
        if score_function == 'mlp':
            self.weight = nn.Parameter(torch.Tensor(self.k_dim * 2))
        elif score_function == 'bi_linear':
            self.weight = nn.Parameter(torch.Tensor(self.k_dim, self.k_dim))
        else:
            self.register_parameter('weight', None)
        self._reset_parameters()

    def _reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.model_dim)
        if self.weight is not None:
            self.weight.data.uniform_(-stdv, stdv)

    def forward(self, input_0, input_1):
        primals_1 = self.w_k.weight
        primals_4 = self.w_k.bias
        primals_2 = self.w_q.weight
        primals_6 = self.w_q.bias
        primals_3 = self.w_v.weight
        primals_8 = self.w_v.bias
        primals_5 = self.dense.weight
        primals_10 = self.dense.bias
        primals_11 = self.layer_norm.weight
        primals_12 = self.layer_norm.bias
        primals_7 = input_0
        primals_9 = 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])
        return output[0], output[1]


Measured latency_ms: 0.167173