# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/u5/cu56dhpcth43gy4shrd7mcexf4nfa6qetnnhwe4mno4v6ug76h6j.py
# Topologically Sorted Source Nodes: [att_out_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   att_out_1 => clone_3
# Graph fragment:
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_0(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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, (16, 4), (4, 1))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (16, ), (1, ))
    assert_size_stride(primals_8, (16, 4), (4, 1))
    assert_size_stride(primals_9, (16, ), (1, ))
    assert_size_stride(primals_10, (4, 16), (16, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_4
        del primals_5
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_6
        del primals_7
        buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_8
        del primals_9
        # Topologically Sorted Source Nodes: [], Original ATen: []
        buf3 = torch.ops.aten._scaled_dot_product_efficient_attention.default(reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 4, 16, 1), 0), reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 4, 16, 1), 0), reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 4, 16, 1), 0), None, True, scale=0.5)
        buf4 = buf3[0]
        buf5 = buf3[1]
        buf6 = buf3[2]
        buf7 = buf3[3]
        del buf3
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [att_out_1], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(buf4, buf8, 256, grid=grid(256), stream=stream0)
        buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [multi_head], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf8, (16, 16), (16, 1), 0), reinterpret_tensor(primals_10, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf9)
        del primals_11
    return (reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 4, 16, 1), 0), reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 4, 16, 1), 0), reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 4, 16, 1), 0), buf4, buf5, buf6, buf7, reinterpret_tensor(buf8, (16, 16), (16, 1), 0), primals_10, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import math
import torch
import torch.nn as nn


class SelfAttention(nn.Module):

    def __init__(self, dropout=0.1):
        super(SelfAttention, self).__init__()
        self.softmax = nn.Softmax(dim=-1)
        self._dropout = nn.Dropout(dropout)

    def forward(self, q, k, v, pad_mask=None):
        """
        :param q: [bz, len_q, Q]
        :param k: [bz, len_k, K]
        :param v: [bz, len_v, V]
        :param pad_mask: [bz, len_q, len_k]  填充部分的mask
        more: Q==K, len_k==len_v
        :return: [bz, len_q, V]
        """
        att_weights = torch.matmul(q, k.transpose(-1, -2)).div(math.sqrt(k.
            size(-1)))
        if pad_mask is not None:
            att_weights.masked_fill_(pad_mask, -1000000000.0)
        soft_att_weights = self.softmax(att_weights)
        if self.training:
            soft_att_weights = self._dropout(soft_att_weights)
        att_out = torch.matmul(soft_att_weights, v)
        return att_out


class MultiHeadAttention(nn.Module):

    def __init__(self, d_model, d_k, d_v, nb_heads, dropout=0.1):
        super(MultiHeadAttention, self).__init__()
        self._d_model = d_model
        self._d_k = d_k
        self._d_v = d_v
        self._nb_heads = nb_heads
        self._linear_qs = nn.Linear(in_features=d_model, out_features=d_k *
            nb_heads)
        self._linear_ks = nn.Linear(in_features=d_model, out_features=d_k *
            nb_heads)
        self._linear_vs = nn.Linear(in_features=d_model, out_features=d_v *
            nb_heads)
        self._linear_out = nn.Linear(in_features=d_v * nb_heads,
            out_features=d_model)
        self._self_attention = SelfAttention(dropout)
        self._dropout = nn.Dropout(dropout)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.normal_(self._linear_qs.weight, mean=0, std=math.sqrt(1 /
            self._d_model))
        nn.init.normal_(self._linear_ks.weight, mean=0, std=math.sqrt(1 /
            self._d_model))
        nn.init.normal_(self._linear_vs.weight, mean=0, std=math.sqrt(1 /
            self._d_model))
        nn.init.normal_(self._linear_out.weight, mean=0, std=math.sqrt(1 /
            self._d_model))

    def forward(self, q, k, v, att_mask=None):
        """
        :param q: [bz, len_q, d_model]
        :param k: [bz, len_k, d_model]
        :param v: [bz, len_v, d_model]
        :param att_mask: [bz, len_k]
        more: Q == K, len_k==len_v
        :return: [bz, len_q, d_model]
        """
        bz, len_q, _ = q.size()
        bz, len_k, _ = k.size()
        bz, len_v, _ = v.size()
        q_fc = self._linear_qs(q).reshape(bz, len_q, self._nb_heads, -1
            ).transpose(1, 2)
        k_fc = self._linear_ks(k).reshape(bz, len_k, self._nb_heads, -1
            ).transpose(1, 2)
        v_fc = self._linear_vs(v).reshape(bz, len_v, self._nb_heads, -1
            ).transpose(1, 2)
        if att_mask is not None:
            att_mask = att_mask[:, None, None, :]
        att_out = self._self_attention(q_fc, k_fc, v_fc, att_mask)
        att_out = att_out.transpose(1, 2).reshape(bz, len_q, -1)
        multi_head = self._linear_out(att_out)
        if self.training:
            multi_head = self._dropout(multi_head)
        return multi_head


def get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4])
        ]


def get_init_inputs():
    return [[], {'d_model': 4, 'd_k': 4, 'd_v': 4, 'nb_heads': 4}]

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
import 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, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tl.store(out_ptr0 + x0, tmp0, 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, (16, 4), (4, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (16,), (1,))
    assert_size_stride(primals_8, (16, 4), (4, 1))
    assert_size_stride(primals_9, (16,), (1,))
    assert_size_stride(primals_10, (4, 16), (16, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_4
        del primals_5
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_2, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 16), (1, 4), 
            0), alpha=1, beta=1, out=buf1)
        del primals_6
        del primals_7
        buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 16), (1, 4), 
            0), alpha=1, beta=1, out=buf2)
        del primals_8
        del primals_9
        buf3 = torch.ops.aten._scaled_dot_product_efficient_attention.default(
            reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 4, 16, 1), 0),
            reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 4, 16, 1), 0),
            reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 4, 16, 1), 0), None,
            True, scale=0.5)
        buf4 = buf3[0]
        buf5 = buf3[1]
        buf6 = buf3[2]
        buf7 = buf3[3]
        del buf3
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(256)](buf4, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf8, (16, 16),
            (16, 1), 0), reinterpret_tensor(primals_10, (16, 4), (1, 16), 0
            ), alpha=1, beta=1, out=buf9)
        del primals_11
    return reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 4, 16, 1), 0
        ), reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 4, 16, 1), 0
        ), reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 4, 16, 1), 0
        ), buf4, buf5, buf6, buf7, reinterpret_tensor(buf8, (16, 16), (16, 
        1), 0), primals_10


class SelfAttention(nn.Module):

    def __init__(self, dropout=0.1):
        super(SelfAttention, self).__init__()
        self.softmax = nn.Softmax(dim=-1)
        self._dropout = nn.Dropout(dropout)

    def forward(self, q, k, v, pad_mask=None):
        """
        :param q: [bz, len_q, Q]
        :param k: [bz, len_k, K]
        :param v: [bz, len_v, V]
        :param pad_mask: [bz, len_q, len_k]  填充部分的mask
        more: Q==K, len_k==len_v
        :return: [bz, len_q, V]
        """
        att_weights = torch.matmul(q, k.transpose(-1, -2)).div(math.sqrt(k.
            size(-1)))
        if pad_mask is not None:
            att_weights.masked_fill_(pad_mask, -1000000000.0)
        soft_att_weights = self.softmax(att_weights)
        if self.training:
            soft_att_weights = self._dropout(soft_att_weights)
        att_out = torch.matmul(soft_att_weights, v)
        return att_out


class MultiHeadAttentionNew(nn.Module):

    def __init__(self, d_model, d_k, d_v, nb_heads, dropout=0.1):
        super(MultiHeadAttentionNew, self).__init__()
        self._d_model = d_model
        self._d_k = d_k
        self._d_v = d_v
        self._nb_heads = nb_heads
        self._linear_qs = nn.Linear(in_features=d_model, out_features=d_k *
            nb_heads)
        self._linear_ks = nn.Linear(in_features=d_model, out_features=d_k *
            nb_heads)
        self._linear_vs = nn.Linear(in_features=d_model, out_features=d_v *
            nb_heads)
        self._linear_out = nn.Linear(in_features=d_v * nb_heads,
            out_features=d_model)
        self._self_attention = SelfAttention(dropout)
        self._dropout = nn.Dropout(dropout)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.normal_(self._linear_qs.weight, mean=0, std=math.sqrt(1 /
            self._d_model))
        nn.init.normal_(self._linear_ks.weight, mean=0, std=math.sqrt(1 /
            self._d_model))
        nn.init.normal_(self._linear_vs.weight, mean=0, std=math.sqrt(1 /
            self._d_model))
        nn.init.normal_(self._linear_out.weight, mean=0, std=math.sqrt(1 /
            self._d_model))

    def forward(self, input_0, input_1, input_2):
        primals_4 = self._linear_qs.weight
        primals_5 = self._linear_qs.bias
        primals_6 = self._linear_ks.weight
        primals_7 = self._linear_ks.bias
        primals_8 = self._linear_vs.weight
        primals_9 = self._linear_vs.bias
        primals_10 = self._linear_out.weight
        primals_11 = self._linear_out.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]
