# 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_6/inductor_cache/yj/cyjay5w6yqtzjhwrra5u36ugjzcr3epsjgaero2ugqpqx7xjycai.py
# Topologically Sorted Source Nodes: [attention_score_2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention_score_2 => exp, sum_1
# Graph fragment:
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_11, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 1.0), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (3 + (4*x1)), 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 = tmp17 * tmp3
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp7 - tmp16
    tmp21 = tmp20 * tmp3
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp19 + tmp22
    tmp24 = tmp11 - tmp16
    tmp25 = tmp24 * tmp3
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp23 + tmp26
    tmp28 = tmp15 - tmp16
    tmp29 = tmp28 * tmp3
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp27 + tmp30
    tl.store(out_ptr0 + (x2), tmp16, xmask)
    tl.store(out_ptr1 + (x2), tmp31, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/eq/ceqzjquocsdhf6v3jex4eilxuabityntyouuqz2e5hg6cpglxepm.py
# Topologically Sorted Source Nodes: [attention_score_2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention_score_2 => div_1, exp
# Graph fragment:
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_11, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 1.0), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', '''
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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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__softmax_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
    x3 = (xindex // 4)
    x0 = xindex % 4
    x2 = (xindex // 16)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x3), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + (x3), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(out_ptr0 + (x4), tmp10, 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, 1))
    assert_size_stride(primals_2, (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), (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), (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((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [prj_query], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [prj_key], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [prj_value], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_8, reinterpret_tensor(primals_9, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_7
        del primals_8
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [attention_score_2], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(buf0, buf1, buf3, buf4, 64, grid=grid(64), stream=stream0)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_score_2], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf0, buf1, buf3, buf4, buf5, 256, grid=grid(256), stream=stream0)
        buf6 = reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [result], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0), out=buf6)
        buf7 = reinterpret_tensor(buf3, (16, 4), (4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf7)
        del primals_11
    return (reinterpret_tensor(buf7, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), buf0, reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), buf1, reinterpret_tensor(primals_9, (16, 4), (4, 1), 0), buf5, reinterpret_tensor(buf6, (16, 4), (4, 1), 0), primals_10, reinterpret_tensor(buf2, (16, 1, 4), (4, 1, 1), 0), )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 4), (4, 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
import torch.nn.functional as F


class ScaledDotProductAttention(nn.Module):

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

    def forward(self, query, key, value, mask=None):
        _1, _2, query_sequence_length, _3 = query.size()
        batch_size, num_head, key_sequence_length, size_per_head = key.size()
        query = query.view(batch_size, num_head, query_sequence_length,
            size_per_head)
        key = key.view(batch_size, num_head, size_per_head, key_sequence_length
            )
        attention_score = torch.einsum('abcd, abde -> abce', query, key)
        attention_score = attention_score / math.sqrt(size_per_head)
        if mask is not None:
            attention_score = attention_score.masked_fill(mask == 0, -
                1000000000.0)
        attention_score = F.softmax(attention_score, dim=-1)
        result = attention_score @ value
        return result, attention_score


class MultiHeadAttention(nn.Module):

    def __init__(self, model_dim, key_dim, value_dim, num_head):
        super(MultiHeadAttention, self).__init__()
        self.model_dim = model_dim
        self.key_dim = key_dim
        self.value_dim = value_dim
        self.num_head = num_head
        self.Wq = nn.Linear(model_dim, key_dim)
        self.Wk = nn.Linear(model_dim, key_dim)
        self.Wv = nn.Linear(model_dim, value_dim)
        self.attention = ScaledDotProductAttention()
        self.Wo = nn.Linear(value_dim, model_dim)

    def forward(self, query, key, value, mask=None):
        prj_query = self.Wq(query)
        prj_key = self.Wk(key)
        prj_value = self.Wv(value)
        multihead_query = self.multihead_split(prj_query)
        multihead_key = self.multihead_split(prj_key)
        multihead_value = self.multihead_split(prj_value)
        attention_output, _attention_score = self.attention(multihead_query,
            multihead_key, multihead_value, mask=mask)
        output = self.multihead_concat(attention_output)
        output = self.Wo(output)
        return output

    def multihead_split(self, tensor):
        batch_size, sequence_length, hidden_size = tensor.size()
        size_per_head = hidden_size // self.num_head
        return tensor.view(batch_size, self.num_head, sequence_length,
            size_per_head)

    def multihead_concat(self, tensor):
        batch_size, num_head, sequence_length, size_per_head = tensor.size()
        hidden_size = num_head * size_per_head
        return tensor.view(batch_size, sequence_length, hidden_size)


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


def get_init_inputs():
    return [[], {'model_dim': 4, 'key_dim': 4, 'value_dim': 4, 'num_head': 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
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
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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (3 + 4 * x1), 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 = tmp17 * tmp3
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp7 - tmp16
    tmp21 = tmp20 * tmp3
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp19 + tmp22
    tmp24 = tmp11 - tmp16
    tmp25 = tmp24 * tmp3
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp23 + tmp26
    tmp28 = tmp15 - tmp16
    tmp29 = tmp28 * tmp3
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp27 + tmp30
    tl.store(out_ptr0 + x2, tmp16, xmask)
    tl.store(out_ptr1 + x2, tmp31, xmask)


@triton.jit
def triton_poi_fused__softmax_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
    x3 = xindex // 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(out_ptr0 + x4, tmp10, 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, 1))
    assert_size_stride(primals_2, (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), (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), (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((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(primals_9, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf2)
        del primals_7
        del primals_8
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), 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), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf0, buf1, buf3, buf4, buf5,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 1), 0)
        del buf4
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0), out=buf6)
        buf7 = reinterpret_tensor(buf3, (16, 4), (4, 1), 0)
        del buf3
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf6, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf7)
        del primals_11
    return reinterpret_tensor(buf7, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(primals_6, (16, 4), (4, 1), 0
        ), buf1, reinterpret_tensor(primals_9, (16, 4), (4, 1), 0
        ), buf5, reinterpret_tensor(buf6, (16, 4), (4, 1), 0
        ), primals_10, reinterpret_tensor(buf2, (16, 1, 4), (4, 1, 1), 0)


class ScaledDotProductAttention(nn.Module):

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

    def forward(self, query, key, value, mask=None):
        _1, _2, query_sequence_length, _3 = query.size()
        batch_size, num_head, key_sequence_length, size_per_head = key.size()
        query = query.view(batch_size, num_head, query_sequence_length,
            size_per_head)
        key = key.view(batch_size, num_head, size_per_head, key_sequence_length
            )
        attention_score = torch.einsum('abcd, abde -> abce', query, key)
        attention_score = attention_score / math.sqrt(size_per_head)
        if mask is not None:
            attention_score = attention_score.masked_fill(mask == 0, -
                1000000000.0)
        attention_score = F.softmax(attention_score, dim=-1)
        result = attention_score @ value
        return result, attention_score


class MultiHeadAttentionNew(nn.Module):

    def __init__(self, model_dim, key_dim, value_dim, num_head):
        super(MultiHeadAttentionNew, self).__init__()
        self.model_dim = model_dim
        self.key_dim = key_dim
        self.value_dim = value_dim
        self.num_head = num_head
        self.Wq = nn.Linear(model_dim, key_dim)
        self.Wk = nn.Linear(model_dim, key_dim)
        self.Wv = nn.Linear(model_dim, value_dim)
        self.attention = ScaledDotProductAttention()
        self.Wo = nn.Linear(value_dim, model_dim)

    def multihead_split(self, tensor):
        batch_size, sequence_length, hidden_size = tensor.size()
        size_per_head = hidden_size // self.num_head
        return tensor.view(batch_size, self.num_head, sequence_length,
            size_per_head)

    def multihead_concat(self, tensor):
        batch_size, num_head, sequence_length, size_per_head = tensor.size()
        hidden_size = num_head * size_per_head
        return tensor.view(batch_size, sequence_length, hidden_size)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.Wq.weight
        primals_2 = 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_3 = 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]
