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 math as tl_math
from torch import 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_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 32
    x2 = xindex // 128
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * (x1 % 8) + 32 * x2 + 128 * (x1 // 8)
        ), xmask)
    tmp1 = 0.7071067811865475
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_div_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * (x2 % 8) + 32 * x1 + 128 * (x2 // 8)
        ), xmask)
    tmp1 = 0.7071067811865475
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 512
    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_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 512
    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_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 8
    x3 = xindex // 128
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 32 * x1 + 128 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 8
    x2 = xindex // 32 % 4
    x3 = xindex // 128
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 128 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_transpose_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 128 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (32, 4), (4, 1))
    assert_size_stride(primals_3, (32, 4), (4, 1))
    assert_size_stride(primals_4, (32, 4), (4, 1))
    assert_size_stride(primals_5, (4, 32), (32, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 32), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 32), (1, 4), 0), out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((32, 4, 4), (4, 128, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(512)](buf0, buf3, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf4 = reinterpret_tensor(buf0, (32, 4, 4), (16, 4, 1), 0)
        del buf0
        triton_poi_fused_div_1[grid(512)](buf1, buf4, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf5 = reinterpret_tensor(buf1, (32, 4, 4), (16, 4, 1), 0)
        del buf1
        extern_kernels.bmm(buf3, reinterpret_tensor(buf4, (32, 4, 4), (16, 
            1, 4), 0), out=buf5)
        buf6 = empty_strided_cuda((32, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(512)](buf5, buf6, 512, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf7 = buf5
        del buf5
        triton_poi_fused__softmax_3[grid(512)](buf6, buf7, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf6, (4, 8, 4, 4), (128, 16, 4, 1), 0)
        del buf6
        triton_poi_fused_clone_4[grid(512)](buf2, buf8, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf9 = reinterpret_tensor(buf2, (32, 4, 4), (16, 4, 1), 0)
        del buf2
        extern_kernels.bmm(buf7, reinterpret_tensor(buf8, (32, 4, 4), (16, 
            4, 1), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 8, 4), (128, 32, 4, 1), torch.float32
            )
        triton_poi_fused_clone_5[grid(512)](buf9, buf10, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf10, (16, 32),
            (32, 1), 0), reinterpret_tensor(primals_5, (32, 4), (1, 32), 0),
            alpha=1, beta=1, out=buf11)
        del primals_6
        buf12 = reinterpret_tensor(buf9, (32, 4, 4), (16, 1, 4), 0)
        del buf9
        triton_poi_fused_transpose_6[grid(512)](buf3, buf12, 512, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf3
    return reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf7, reinterpret_tensor(buf10, (16, 32), (32, 1), 0
        ), primals_5, reinterpret_tensor(buf8, (32, 4, 4), (16, 1, 4), 0
        ), buf12, buf4


class SelfAttentionNew(nn.Module):

    def __init__(self, k, heads=8):
        super().__init__()
        self.k, self.heads = k, heads
        self.toKeys = nn.Linear(k, k * heads, bias=False)
        self.toQueries = nn.Linear(k, k * heads, bias=False)
        self.toValues = nn.Linear(k, k * heads, bias=False)
        self.unifyHeads = nn.Linear(heads * k, k)

    def forward(self, input_0):
        primals_2 = self.toKeys.weight
        primals_3 = self.toQueries.weight
        primals_4 = self.toValues.weight
        primals_5 = self.unifyHeads.weight
        primals_6 = self.unifyHeads.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.132208