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 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__softmax_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 = 1.0
    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_clone_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 = 1.0
    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, 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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = 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, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf0)
        del primals_4
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1)
        del primals_5
        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_6, (4, 4), (1, 4), 0), out=buf2)
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf0, buf1, buf3, buf4, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        triton_poi_fused_clone_1[grid(256)](buf0, buf1, buf3, buf4, buf5, 
            256, XBLOCK=128, 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, buf6, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        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, 1), (16, 4, 1, 1), 0)
        del buf3
        triton_poi_fused_clone_2[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_7, (4, 4), (1, 4), 0), out=buf9)
    return reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0
        ), 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
        ), reinterpret_tensor(buf8, (16, 4), (4, 1), 0
        ), primals_7, reinterpret_tensor(buf5, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0)


class MultiHeadAttnNew(nn.Module):

    def __init__(self, query_dim, key_dim, value_dim, hidden_dim, num_head,
        dropatt=0.0, act_func='softmax', add_zero_attn=False, pre_lnorm=
        False, post_lnorm=False):
        super(MultiHeadAttnNew, self).__init__()
        assert hidden_dim % num_head == 0
        self.query_dim = query_dim
        self.key_dim = key_dim
        self.value_dim = value_dim
        self.hidden_dim = hidden_dim
        self.num_head = num_head
        self.dropatt = nn.Dropout(dropatt)
        head_dim = hidden_dim // num_head
        self.q_net = nn.Linear(query_dim, hidden_dim, bias=False)
        self.k_net = nn.Linear(key_dim, hidden_dim, bias=False)
        self.v_net = nn.Linear(value_dim, hidden_dim, bias=False)
        self.o_net = nn.Linear(hidden_dim, query_dim, bias=False)
        self.scale = 1 / head_dim ** 0.5
        self.act_func = act_func
        self.add_zero_attn = add_zero_attn
        self.pre_lnorm = pre_lnorm
        self.post_lnorm = post_lnorm
        if pre_lnorm:
            self.q_layer_norm = nn.LayerNorm(query_dim)
            self.k_layer_norm = nn.LayerNorm(key_dim)
            self.v_layer_norm = nn.LayerNorm(value_dim)
        if post_lnorm:
            self.o_layer_norm = nn.LayerNorm(query_dim)
        for net in [self.q_net, self.k_net, self.v_net, self.o_net]:
            nn.init.xavier_uniform_(net.weight, 1.0)
            if hasattr(net, 'bias') and net.bias is not None:
                nn.init.constant_(net.bias, 0.0)
        if self.pre_lnorm:
            for layer_norm in [self.q_layer_norm, self.k_layer_norm, self.
                v_layer_norm]:
                if hasattr(layer_norm, 'weight'):
                    nn.init.normal_(layer_norm.weight, 1.0, self.scale)
                if hasattr(layer_norm, 'bias') and layer_norm.bias is not None:
                    nn.init.constant_(layer_norm.bias, 0.0)
        if self.post_lnorm:
            if hasattr(self.o_layer_norm, 'weight'):
                nn.init.normal_(self.o_layer_norm.weight, 1.0, self.scale)
            if hasattr(self.o_layer_norm, 'bias'
                ) and self.o_layer_norm.bias is not None:
                nn.init.constant_(self.o_layer_norm.bias, 0.0)

    def get_output_dim(self):
        return self.query_dim

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.q_net.weight
        primals_5 = self.k_net.weight
        primals_6 = self.v_net.weight
        primals_7 = self.o_net.weight
        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])
        return output[0]


Measured latency_ms: 0.107377