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
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_linalg_vector_norm_0(in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = tmp0 / tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex % 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x3, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0 + 64 * x2), xmask, eviction_policy
        ='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0 + 64 * x2), xmask, eviction_policy
        ='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0 + 64 * x2), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x4, tmp14, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (64 + x3), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (128 + x3), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (192 + x3), 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)
    tmp12 = triton_helpers.maximum(tmp10, tmp11)
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = tmp0 - tmp16
    tmp18 = tl_math.exp(tmp17)
    tl.store(out_ptr0 + x4, tmp9, xmask)
    tl.store(out_ptr1 + x4, tmp18, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16 % 4
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_sub_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_out_ptr0 + x0, xmask)
    tmp2 = tmp0 - tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_5(in_ptr0, 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
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (192 + x0), 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_sub_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tmp0 - tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_linalg_vector_norm_0[grid(256)](arg0_1, buf0, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_div_linalg_vector_norm_0[grid(256)](arg1_1, buf1, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sum_1[grid(256)](buf0, buf1, buf2, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf2, buf3, buf7, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = buf2
        del buf2
        triton_poi_fused_clone_3[grid(256)](buf3, buf4, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0)
        del buf3
        extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0), out=buf5)
        buf6 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        triton_poi_fused_sub_4[grid(256)](buf6, buf1, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf8 = buf4
        del buf4
        triton_poi_fused__softmax_5[grid(256)](buf7, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0)
        del buf7
        extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0), out=buf9)
        del buf1
        del buf8
        buf10 = buf0
        del buf0
        triton_poi_fused_sub_6[grid(256)](buf10, buf9, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf9
    return buf6, buf10


class AttentionNew(torch.nn.Module):

    def __init__(self):
        super(AttentionNew, self).__init__()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0], output[1]


Measured latency_ms: 0.115314