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 math
import torch.nn as nn
import torch.nn.functional as F
import torch.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__softmax_div_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr1 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr1 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr1 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp6 = tmp0 * tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = triton_helpers.maximum(tmp4, tmp7)
    tmp10 = tmp0 * tmp9
    tmp11 = tmp10 * tmp3
    tmp12 = triton_helpers.maximum(tmp8, tmp11)
    tmp14 = tmp0 * tmp13
    tmp15 = tmp14 * tmp3
    tmp16 = triton_helpers.maximum(tmp12, tmp15)
    tmp17 = tmp4 - tmp16
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp7 - tmp16
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp18 + tmp20
    tmp22 = tmp11 - tmp16
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp15 - tmp16
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tl.store(out_ptr0 + x3, tmp16, xmask)
    tl.store(out_ptr1 + x3, tmp27, xmask)


@triton.jit
def triton_poi_fused__softmax_div_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex // 4
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x3 = xindex // 64
    x2 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + 4 * x0 + 16 * x3), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp9, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, in_ptr1, 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')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_3(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_add_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 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, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (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((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_4
        del primals_5
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_2, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_6
        del primals_7
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf2)
        del primals_8
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_div_0[grid(64)](buf0, buf1, buf3, buf4, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_div_1[grid(256)](buf0, buf1, buf3, buf4,
            buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0)
        del buf4
        triton_poi_fused_clone_2[grid(16, 4)](buf2, primals_9, buf6, 16, 4,
            XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
        del primals_9
        buf7 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7)
        buf8 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0)
        del buf3
        triton_poi_fused_clone_3[grid(16, 4)](buf7, buf8, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0)
        del buf7
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0)
        del buf9
        triton_poi_fused_add_4[grid(64)](buf10, primals_11, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_11
    return buf10, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
        ), buf1, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), buf5, reinterpret_tensor(buf8, (16, 4), (4, 1), 0
        ), primals_10, reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0)


class MultiHeadAttentionNew(nn.Module):

    def __init__(self, embed_size, num_heads, dropout=0.2, batch_dim=0):
        super(MultiHeadAttentionNew, self).__init__()
        self.embed_size = embed_size
        self.num_heads = num_heads
        self.dropout = dropout
        self.batch_dim = batch_dim
        self.dropout_layer = nn.Dropout(dropout)
        self.head_size = self.embed_size // self.num_heads
        assert self.head_size * self.num_heads == self.embed_size, 'Heads cannot split Embedding size equally'
        self.Q = nn.Linear(self.embed_size, self.embed_size)
        self.K = nn.Linear(self.embed_size, self.embed_size)
        self.V = nn.Linear(self.embed_size, self.embed_size)
        self.linear = nn.Linear(self.embed_size, self.embed_size)

    def batch_0(self, q, k, v, mask=None):
        q_batch_size, q_seq_len, _q_embed_size = q.size()
        k_batch_size, k_seq_len, _k_embed_size = k.size()
        v_batch_size, v_seq_len, _v_embed_size = v.size()
        q = self.Q(q).reshape(q_batch_size, q_seq_len, self.num_heads, self
            .head_size)
        k = self.K(k).reshape(k_batch_size, k_seq_len, self.num_heads, self
            .head_size)
        v = self.V(v).reshape(v_batch_size, v_seq_len, self.num_heads, self
            .head_size)
        scores = self.attention(q, k, v, self.num_heads, mask)
        concatenated = scores.reshape(v_batch_size, -1, self.embed_size)
        out = self.linear(concatenated)
        return out

    def batch_1(self, q, k, v, mask=None):
        q_seq_len, q_batch_size, _q_embed_size = q.size()
        k_seq_len, k_batch_size, _k_embed_size = k.size()
        v_seq_len, v_batch_size, _v_embed_size = v.size()
        q = self.Q(q).reshape(q_batch_size, q_seq_len, self.num_heads, self
            .head_size)
        k = self.K(k).reshape(k_batch_size, k_seq_len, self.num_heads, self
            .head_size)
        v = self.V(v).reshape(v_batch_size, v_seq_len, self.num_heads, self
            .head_size)
        scores = self.attention(q, k, v, self.num_heads, mask)
        concatenated = scores.reshape(-1, v_batch_size, self.embed_size)
        out = self.linear(concatenated)
        return out

    def attention(self, q, k, v, d_k, mask=None):
        scores = torch.einsum('bqhe,bkhe->bhqk', [q, k]) / math.sqrt(d_k)
        scores = F.softmax(scores, dim=-1)
        scores = self.dropout_layer(scores)
        out = torch.einsum('bhql,blhd->bqhd', [scores, v])
        return out

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.Q.weight
        primals_5 = self.Q.bias
        primals_6 = self.K.weight
        primals_7 = self.K.bias
        primals_8 = self.V.weight
        primals_9 = self.V.bias
        primals_10 = self.linear.weight
        primals_11 = self.linear.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = 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: 0.041411