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
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, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = xindex // 128 % 16
    x2 = xindex // 2048
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 128 * x2 + 512 * x1), None)
    tmp1 = tl.load(in_ptr1 + (x0 + 128 * x2), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused__softmax_1(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')
    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_2(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')
    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_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 8256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 516
    x3 = xindex // 516
    x2 = xindex // 2064
    x4 = xindex % 2064
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 512, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (128 * x3 + 2048 * (x0 // 128 % 4) + x0 % 128),
        tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 516, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x3 + (-512 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x4 + 2080 * x2), tmp10, xmask)


@triton.jit
def triton_poi_fused_cat_view_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 8256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (2080 * (x0 // 2064) + x0 % 2064), xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_tanh_tanh_backward_5(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = tmp3 * tmp3
    tmp5 = 1.0
    tmp6 = tmp5 - tmp4
    tl.store(in_out_ptr0 + x2, tmp3, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (512, 4), (4, 1))
    assert_size_stride(primals_4, (512,), (1,))
    assert_size_stride(primals_5, (512, 4), (4, 1))
    assert_size_stride(primals_6, (512,), (1,))
    assert_size_stride(primals_7, (4, 8), (8, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 512), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 512), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(8192)](buf0, primals_4, buf2, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
        buf3 = reinterpret_tensor(buf0, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf0
        triton_poi_fused_clone_0[grid(8192)](buf1, primals_6, buf3, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 128), (512, 128,
            1), 0), reinterpret_tensor(buf3, (16, 128, 4), (512, 1, 128), 0
            ), out=buf4)
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf4, buf5, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf5
        buf7 = reinterpret_tensor(buf1, (16, 4, 128), (512, 128, 1), 0)
        del buf1
        extern_kernels.bmm(buf6, reinterpret_tensor(buf3, (16, 4, 128), (
            512, 128, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 516), (2080, 516, 1), torch.float32)
        triton_poi_fused_cat_3[grid(8256)](buf7, primals_2, buf8, 8256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf7
        buf9 = empty_strided_cuda((1032, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_view_4[grid(8256)](buf8, buf9, 8256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del buf8
        buf10 = empty_strided_cuda((1032, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf9, reinterpret_tensor(primals_7, (8, 4), (1, 8
            ), 0), out=buf10)
        buf11 = buf10
        del buf10
        buf12 = empty_strided_cuda((1032, 4), (4, 1), torch.float32)
        triton_poi_fused_tanh_tanh_backward_5[grid(4128)](buf11, primals_8,
            buf12, 4128, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_8
    return reinterpret_tensor(buf11, (4, 258, 4), (1032, 4, 1), 0
        ), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf3, (16, 128, 4), (512, 1, 128), 0
        ), buf6, buf9, buf12, primals_7, reinterpret_tensor(buf2, (16, 128,
        4), (512, 1, 128), 0)


class MultiHeadAttentionNew(nn.Module):
    """
    Applies an multi-head attention mechanism on the output features from the decoder.

    Refer to 「State-of-the-art Speech Recognition With Sequence-to-Sequence Models」 Paper
    https://arxiv.org/abs/1712.01769


    Args:
        in_features (int): The number of expected features in the output
        n_head (int): number of heads. (default: 4)
        dim (int): dimension size of sub heads. (default: 128)

    Inputs: query, key
        - **query** (batch, output_len, dimensions): tensor containing the output features from the decoder.
        - **key** (batch, input_len, dimensions): tensor containing features of the encoded input sequence.

    Returns: output
        - **output** (batch, output_len, dimensions): tensor containing the attended output features from the decoder.

    Examples::
        >>> attention = MultiHeadAttention(in_features, n_head=4, dim=128)
        >>> output = attention(query, key)
    """

    def __init__(self, in_features, n_head=4, dim=128):
        super(MultiHeadAttentionNew, self).__init__()
        self.in_features = in_features
        self.linear_q = nn.Linear(in_features, dim * n_head)
        self.linear_k = nn.Linear(in_features, dim * n_head)
        self.n_head = n_head
        self.dim = dim
        self.out = nn.Linear(in_features << 1, in_features)

    def forward(self, input_0, input_1):
        primals_3 = self.linear_q.weight
        primals_4 = self.linear_q.bias
        primals_5 = self.linear_k.weight
        primals_6 = self.linear_k.bias
        primals_7 = self.out.weight
        primals_8 = self.out.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]


Measured latency_ms: 0.133341