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.utils.data
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, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tmp14 * tmp1
    tmp16 = tl_math.exp(tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused__softmax_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
    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_native_layer_norm_3(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_4(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)


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, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](buf0, buf2, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf3 = reinterpret_tensor(buf0, (16, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf0
        triton_poi_fused_clone_0[grid(64, 4)](buf1, buf3, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (64, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf3, (64, 1, 4), (4, 0, 1), 0), out=buf4)
        buf5 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(1024)](buf4, buf5, 1024, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused__softmax_2[grid(1024)](buf5, buf6, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        del buf5
        buf7 = buf1
        del buf1
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf7)
        del primals_5
        buf8 = empty_strided_cuda((16, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_0[grid(64, 4)](buf7, buf8, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (64, 4, 1), (4, 1, 1), 0)
        del buf7
        extern_kernels.bmm(buf6, reinterpret_tensor(buf8, (64, 4, 1), (4, 1,
            0), 0), out=buf9)
        buf10 = empty_strided_cuda((16, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_0[grid(64, 4)](buf9, buf10, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf11 = reinterpret_tensor(buf9, (64, 4), (4, 1), 0)
        del buf9
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf10, (64, 4),
            (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf11)
        del primals_7
        buf12 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf13 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused_native_layer_norm_3[grid(64)](buf11, buf12, buf13,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_4[grid(256)](buf11, buf12, buf13,
            primals_8, primals_9, buf14, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf12
        del buf13
        del primals_9
    return buf14, reinterpret_tensor(buf6, (4, 4, 4, 4, 4), (256, 64, 16, 4,
        1), 0), primals_8, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_4, (64, 4), (4, 1), 0
        ), buf6, reinterpret_tensor(buf10, (64, 4), (4, 1), 0
        ), buf11, primals_6, reinterpret_tensor(buf8, (64, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf2, (64, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf3, (64, 4, 1), (4, 1, 1), 0)


class MultiHeadAttentionNew(nn.Module):

    def __init__(self, in_dim, out_dim, out_heads, relation_dim=0, residual
        =False, projection=True, layer_norm=True):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.out_heads = out_heads
        self.relation_dim = relation_dim
        assert self.out_dim % self.out_heads == 0
        self.query_layer = nn.Linear(self.in_dim + self.relation_dim, self.
            out_dim, bias=False)
        self.key_layer = nn.Linear(self.in_dim + self.relation_dim, self.
            out_dim, bias=False)
        self.value_layer = nn.Linear(self.in_dim, self.out_dim, bias=False)
        self.residual = residual
        self.projection = projection
        if self.projection:
            self.proj_layer = nn.Linear(self.out_dim, self.out_dim)
        self.layer_norm = layer_norm
        if self.layer_norm:
            self.ln = nn.LayerNorm(self.out_dim)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.uniform_(self.query_layer.weight, -0.1, 0.1)
        nn.init.uniform_(self.key_layer.weight, -0.1, 0.1)
        nn.init.uniform_(self.value_layer.weight, -0.1, 0.1)
        if self.projection:
            nn.init.uniform_(self.proj_layer.weight, -0.1, 0.1)

    def forward(self, input_0, input_1):
        primals_1 = self.query_layer.weight
        primals_3 = self.key_layer.weight
        primals_5 = self.value_layer.weight
        primals_6 = self.proj_layer.weight
        primals_7 = self.proj_layer.bias
        primals_8 = self.ln.weight
        primals_9 = self.ln.bias
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1]


Measured latency_ms: 0.052549