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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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_add_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    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_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 1)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + 2)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr1 + 3)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp7 = tmp4 + tmp6
    tmp8 = triton_helpers.maximum(tmp3, tmp7)
    tmp12 = tmp9 + tmp11
    tmp13 = triton_helpers.maximum(tmp8, tmp12)
    tmp17 = tmp14 + tmp16
    tmp18 = triton_helpers.maximum(tmp13, tmp17)
    tmp19 = tmp3 - tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp7 - tmp18
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tmp12 - tmp18
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp23 + tmp25
    tmp27 = tmp17 - tmp18
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp26 + tmp28
    tl.store(out_ptr0 + x0, tmp18, xmask)
    tl.store(out_ptr1 + x0, tmp29, xmask)


@triton.jit
def triton_poi_fused_mul_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + y0, ymask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr4 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 * tmp8
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp9, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(16, 4)](primals_1, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused__softmax_add_1[grid(16)](buf1, primals_3, buf2,
            buf3, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_mul_2[grid(16, 4)](primals_1, buf1, primals_3,
            buf2, buf3, buf4, 16, 4, XBLOCK=2, YBLOCK=16, num_warps=1,
            num_stages=1)
        del buf2
        del buf3
    return buf4, primals_1, primals_3, reinterpret_tensor(buf0, (16, 4), (4,
        1), 0), buf1


class Attention_layerNew(nn.Module):

    def __init__(self, sequence_length):
        super(Attention_layerNew, self).__init__()
        self.input_size = sequence_length
        self.output_size = sequence_length
        self.dense = nn.Linear(sequence_length, sequence_length)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, input_0):
        primals_2 = self.dense.weight
        primals_3 = self.dense.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.056577