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
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
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, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 64
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x1 + 64 * y0), tmp2, xmask & ymask)


@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask)


@triton.jit
def triton_poi_fused_clone_2(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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 64 * x1), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (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,), (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_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 16, 1), (64, 16, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(4, 64)](buf0, primals_3, buf2, 4, 64,
            XBLOCK=32, YBLOCK=4, num_warps=4, num_stages=1)
        del primals_3
        buf3 = reinterpret_tensor(buf0, (4, 4, 16, 1), (64, 16, 1, 1), 0)
        del buf0
        triton_poi_fused_clone_0[grid(4, 64)](buf1, primals_6, buf3, 4, 64,
            XBLOCK=32, YBLOCK=4, num_warps=4, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((16, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (16, 16, 1), (16, 1, 0),
            0), reinterpret_tensor(buf3, (16, 1, 16), (16, 0, 1), 0), out=buf4)
        buf7 = empty_strided_cuda((16, 16, 16), (256, 16, 1), torch.float32)
        triton_per_fused__softmax_1[grid(256)](buf4, buf7, 256, 16, XBLOCK=
            32, num_warps=4, num_stages=1)
        del buf4
        buf8 = reinterpret_tensor(buf1, (16, 16, 1), (16, 1, 1), 0)
        del buf1
        extern_kernels.bmm(buf7, reinterpret_tensor(buf3, (16, 16, 1), (16,
            1, 0), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 16, 4, 1), (64, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_2[grid(64, 4)](buf8, buf9, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        del buf8
    return reinterpret_tensor(buf9, (4, 16, 4), (64, 4, 1), 0
        ), reinterpret_tensor(primals_4, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf3, (16, 1, 16), (16, 1, 1), 0
        ), buf7, reinterpret_tensor(buf2, (16, 1, 16), (16, 1, 1), 0)


class ScaledDotProductAttention(nn.Module):
    """
    Scaled Dot-product Attention

    Args:
        dim (int): dimention of attention

    Inputs: query, value
        - **query** (batch_size, q_len, hidden_dim): tensor containing the output features from the decoder.
        - **value** (batch_size, v_len, hidden_dim): tensor containing features of the encoded input sequence.

    Returns: context, attn
        - **context**: tensor containing the context vector from attention mechanism.
        - **attn**: tensor containing the attention (alignment) from the encoder outputs.
    """

    def __init__(self, dim):
        super(ScaledDotProductAttention, self).__init__()
        self.dim = dim

    def forward(self, query, value):
        score = torch.bmm(query, value.transpose(1, 2)) / np.sqrt(self.dim)
        attn = F.softmax(score, dim=-1)
        context = torch.bmm(attn, value)
        return context, attn


class MultiHeadAttentionNew(nn.Module):
    """
    Applies a multi-headed scaled dot mechanism on the output features from the decoder.
    Multi-head attention proposed in "Attention Is All You Need" paper.

    Args:
        hidden_dim (int): The number of expected features in the output
        num_heads (int): The number of heads. (default: )

    Inputs: query, value, prev_attn
        - **query** (batch, q_len, hidden_dim): tensor containing the output features from the decoder.
        - **value** (batch, v_len, hidden_dim): tensor containing features of the encoded input sequence.
        - **prev_attn** (batch_size * num_heads, v_len): tensor containing previous timestep`s attention (alignment)

    Returns: context, attn
        - **context** (batch, output_len, dimensions): tensor containing the attended output features from the decoder.
        - **attn**: tensor containing the attention (alignment) from the encoder outputs.

    Reference:
        - **Attention Is All You Need**: https://arxiv.org/abs/1706.03762
        - **State-Of-The-Art Speech Recognition with Sequence-to-Sequence Models**: https://arxiv.org/abs/1712.01769
    """

    def __init__(self, hidden_dim, num_heads=4):
        super(MultiHeadAttentionNew, self).__init__()
        self.hidden_dim = hidden_dim
        self.num_heads = num_heads
        self.dim = int(hidden_dim / num_heads)
        self.scaled_dot = ScaledDotProductAttention(self.dim)
        self.query_projection = nn.Linear(hidden_dim, self.dim * num_heads)
        self.value_projection = nn.Linear(hidden_dim, self.dim * num_heads)

    def forward(self, input_0, input_1):
        primals_2 = self.query_projection.weight
        primals_3 = self.query_projection.bias
        primals_5 = self.value_projection.weight
        primals_6 = self.value_projection.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.038107