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.utils.data
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 = 16
    xnumel = 16
    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 + 64 * y1), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 16 * y3), 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
    x2 = xindex
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = 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, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (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_1, (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_6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_9, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((4, 4, 16, 1), (64, 16, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(16, 16)](buf0, primals_3, buf3, 16, 
            16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf4 = reinterpret_tensor(buf0, (4, 4, 1, 16), (64, 16, 16, 1), 0)
        del buf0
        triton_poi_fused_clone_0[grid(16, 16)](buf1, primals_5, buf4, 16, 
            16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((16, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 16, 1), (16, 1, 0),
            0), reinterpret_tensor(buf4, (16, 1, 16), (16, 0, 1), 0), out=buf5)
        buf8 = empty_strided_cuda((4, 4, 16, 16), (1024, 256, 16, 1), torch
            .float32)
        triton_per_fused__softmax_1[grid(256)](buf5, buf8, 256, 16, XBLOCK=
            8, num_warps=2, num_stages=1)
        del buf5
        buf9 = reinterpret_tensor(buf1, (4, 4, 16, 1), (64, 16, 1, 1), 0)
        del buf1
        triton_poi_fused_clone_0[grid(16, 16)](buf2, primals_8, buf9, 16, 
            16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_8
        buf10 = reinterpret_tensor(buf2, (16, 16, 1), (16, 1, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf8, (16, 16, 16), (256, 16,
            1), 0), reinterpret_tensor(buf9, (16, 16, 1), (16, 1, 0), 0),
            out=buf10)
        buf11 = empty_strided_cuda((4, 16, 4, 1), (64, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_2[grid(64, 4)](buf10, buf11, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf12 = reinterpret_tensor(buf10, (64, 4), (4, 1), 0)
        del buf10
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf11, (64, 4),
            (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf12)
        del primals_11
    return reinterpret_tensor(buf12, (4, 16, 4), (64, 4, 1), 0
        ), buf8, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_9, (64, 4), (4, 1), 0
        ), buf8, reinterpret_tensor(buf11, (64, 4), (4, 1), 0
        ), primals_10, reinterpret_tensor(buf9, (16, 1, 16), (16, 1, 1), 0
        ), reinterpret_tensor(buf3, (16, 1, 16), (16, 1, 1), 0
        ), reinterpret_tensor(buf4, (16, 16, 1), (16, 1, 16), 0)


class ScaledDotProductAttention(torch.nn.Module):
    """
    Scaled, softmax attention module for Transformer as defined by
    Attention(Q, K, V) on pg 4. Returns the final attention vectors as well as
    the attention matrices (pairwise scores). """

    def __init__(self):
        super(ScaledDotProductAttention, self).__init__()
        self.softmax = torch.nn.Softmax(dim=-1)

    def forward(self, Q, K, V, mask=None, dropout=None):
        scores = torch.matmul(Q, K.transpose(-2, -1))
        scores = scores / np.sqrt(K.shape[-1])
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -np.inf)
        scores = self.softmax(scores)
        if dropout is not None:
            scores = dropout(scores)
        return torch.matmul(scores, V), scores


class MultiHeadedAttentionNew(torch.nn.Module):
    """
    Multi-headed attention layer for the Transformer model. Wraps
    ScaledDotProductAttention. Assumes n_heads are applied by splitting up
    model in to n_heads, each of size dm / n_heads. Guided by
    http://nlp.seas.harvard.edu/2018/04/03/attention.html
    """

    def __init__(self, dm, n_heads, dropout=0.1):
        super(MultiHeadedAttentionNew, self).__init__()
        assert dm % n_heads == 0, 'The dimension of the model must be evenly divisible by the number of attn heads.'
        self.dm = dm
        self.dk = dm // n_heads
        self.n_heads = n_heads
        self.wq = torch.nn.Linear(self.dm, self.dm)
        self.wk = torch.nn.Linear(self.dm, self.dm)
        self.wv = torch.nn.Linear(self.dm, self.dm)
        self.wo = torch.nn.Linear(self.dm, self.dm)
        self.attn_scores = None
        self.attn = ScaledDotProductAttention()
        self.dropout = torch.nn.Dropout(dropout)

    def forward(self, input_0, input_1, input_2):
        primals_2 = self.wq.weight
        primals_3 = self.wq.bias
        primals_4 = self.wk.weight
        primals_5 = self.wk.bias
        primals_7 = self.wv.weight
        primals_8 = self.wv.bias
        primals_10 = self.wo.weight
        primals_11 = self.wo.bias
        primals_1 = input_0
        primals_6 = input_1
        primals_9 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]


Measured latency_ms: 0.041421