# 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_7/inductor_cache/bt/cbtukfelygvwruykgqxvoito6trim7hi4gocn4mpohjjmw63qewu.py
# Topologically Sorted Source Nodes: [product_1, product_2, weights], Original ATen: [aten.mul, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   product_1 => mul
#   product_2 => add
#   weights => amax, exp, sub, sum_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_11, 0.5), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_7), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_add_mul_0 = async_compile.triton('triton_poi_fused__softmax_add_mul_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=[16], 
    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_add_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_add_mul_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp5 * tmp1
    tmp8 = tmp6 + tmp7
    tmp9 = triton_helpers.maximum(tmp4, tmp8)
    tmp11 = tmp10 * tmp1
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 * tmp1
    tmp18 = tmp16 + tmp17
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp20 = tmp4 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp8 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp18 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + (x2), tmp19, xmask)
    tl.store(out_ptr1 + (x2), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/zz/czzjxm5452x7hgytt4wj5g5gsmoluemkcheo5nyv5rfsehlnlt6g.py
# Topologically Sorted Source Nodes: [product_1, product_2, weights], Original ATen: [aten.mul, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   product_1 => mul
#   product_2 => add
#   weights => amax, div, exp, sub
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_11, 0.5), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_7), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_add_mul_1 = async_compile.triton('triton_poi_fused__softmax_add_mul_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=[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_add_mul_1', 'mutated_arg_names': ['in_out_ptr0'], '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_add_mul_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex % 16
    x5 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp3 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x5), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (x5), xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tl.store(in_out_ptr0 + (x3), tmp9, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 4), (16, 4, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 4), (16, 4, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 1, 4), (16, 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))
    assert_size_stride(primals_8, (4, 4), (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: [q], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [k], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (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)
        # Topologically Sorted Source Nodes: [v], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (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, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [product], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 1, 4, 1), (4, 16, 1, 16), torch.float32)
        buf5 = empty_strided_cuda((4, 1, 4, 1), (4, 16, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [product_1, product_2, weights], Original ATen: [aten.mul, aten.add, aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_add_mul_0.run(buf3, primals_7, buf4, buf5, 16, grid=grid(16), stream=stream0)
        buf6 = reinterpret_tensor(buf3, (4, 1, 4, 4), (16, 16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [product_1, product_2, weights], Original ATen: [aten.mul, aten.add, aten._softmax]
        triton_poi_fused__softmax_add_mul_1.run(buf6, primals_7, buf4, buf5, 64, grid=grid(64), stream=stream0)
        del buf4
        del buf5
        del primals_7
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf7, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf8)
    return (reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), buf6, reinterpret_tensor(buf7, (16, 4), (4, 1), 0), primals_8, reinterpret_tensor(buf2, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 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, 1, 4), (16, 4, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 1, 4), (16, 4, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 1, 4), (16, 4, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (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, 4), (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])
    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 torch
import torch.nn as nn
import torch.nn.functional as F


class MultiHeadAttention(nn.Module):
    """
    Multi-Head Attention
    """

    def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0.0):
        super(MultiHeadAttention, self).__init__()
        self.n_head = n_head
        self.d_key = d_key
        self.d_value = d_value
        self.d_model = d_model
        self.dropout_rate = dropout_rate
        self.q_fc = torch.nn.Linear(in_features=d_model, out_features=d_key *
            n_head, bias=False)
        self.k_fc = torch.nn.Linear(in_features=d_model, out_features=d_key *
            n_head, bias=False)
        self.v_fc = torch.nn.Linear(in_features=d_model, out_features=
            d_value * n_head, bias=False)
        self.proj_fc = torch.nn.Linear(in_features=d_value * n_head,
            out_features=d_model, bias=False)

    def _prepare_qkv(self, queries, keys, values, cache=None):
        if keys is None:
            keys, values = queries, queries
            static_kv = False
        else:
            static_kv = True
        q = self.q_fc(queries)
        q = torch.reshape(q, shape=[q.size(0), q.size(1), self.n_head, self
            .d_key])
        q = q.permute(0, 2, 1, 3)
        if cache is not None and static_kv and 'static_k' in cache:
            k = cache['static_k']
            v = cache['static_v']
        else:
            k = self.k_fc(keys)
            v = self.v_fc(values)
            k = torch.reshape(k, shape=[k.size(0), k.size(1), self.n_head,
                self.d_key])
            k = k.permute(0, 2, 1, 3)
            v = torch.reshape(v, shape=[v.size(0), v.size(1), self.n_head,
                self.d_value])
            v = v.permute(0, 2, 1, 3)
        if cache is not None:
            if static_kv and 'static_k' not in cache:
                cache['static_k'], cache['static_v'] = k, v
            elif not static_kv:
                cache_k, cache_v = cache['k'], cache['v']
                k = torch.cat([cache_k, k], dim=2)
                v = torch.cat([cache_v, v], dim=2)
                cache['k'], cache['v'] = k, v
        return q, k, v

    def forward(self, queries, keys, values, attn_bias, cache=None):
        keys = queries if keys is None else keys
        values = keys if values is None else values
        q, k, v = self._prepare_qkv(queries, keys, values, cache)
        product = torch.matmul(q, k.transpose(2, 3))
        product = product * self.d_model ** -0.5
        if attn_bias is not None:
            product += attn_bias
        weights = F.softmax(product, dim=-1)
        if self.dropout_rate:
            weights = F.dropout(weights, p=self.dropout_rate)
        out = torch.matmul(weights, v)
        out = out.permute(0, 2, 1, 3)
        out = torch.reshape(out, shape=[out.size(0), out.size(1), out.shape
            [2] * out.shape[3]])
        out = self.proj_fc(out)
        return out


def get_inputs():
    return [torch.rand([4, 4, 1, 4]), torch.rand([4, 4, 1, 4]), torch.rand(
        [4, 4, 1, 4]), torch.rand([4, 4])]


def get_init_inputs():
    return [[], {'d_key': 4, 'd_value': 4, 'd_model': 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 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_add_mul_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp17 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp5 * tmp1
    tmp8 = tmp6 + tmp7
    tmp9 = triton_helpers.maximum(tmp4, tmp8)
    tmp11 = tmp10 * tmp1
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 * tmp1
    tmp18 = tmp16 + tmp17
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp20 = tmp4 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp8 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp18 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + x2, tmp19, xmask)
    tl.store(out_ptr1 + x2, tmp30, xmask)


@triton.jit
def triton_poi_fused__softmax_add_mul_1(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex % 16
    x5 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp3 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tl.store(in_out_ptr0 + x3, tmp9, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 4), (16, 4, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 4), (16, 4, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 1, 4), (16, 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))
    assert_size_stride(primals_8, (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_4, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (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_2, (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, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 
            0), reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 1, 4, 1), (4, 16, 1, 16), torch.float32)
        buf5 = empty_strided_cuda((4, 1, 4, 1), (4, 16, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_add_mul_0[grid(16)](buf3, primals_7, buf4,
            buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf3, (4, 1, 4, 4), (16, 16, 4, 1), 0)
        del buf3
        triton_poi_fused__softmax_add_mul_1[grid(64)](buf6, primals_7, buf4,
            buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf4
        del buf5
        del primals_7
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 
            0), reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf8)
    return reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_4, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
        ), buf6, reinterpret_tensor(buf7, (16, 4), (4, 1), 0
        ), primals_8, reinterpret_tensor(buf2, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)


class MultiHeadAttentionNew(nn.Module):
    """
    Multi-Head Attention
    """

    def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0.0):
        super(MultiHeadAttentionNew, self).__init__()
        self.n_head = n_head
        self.d_key = d_key
        self.d_value = d_value
        self.d_model = d_model
        self.dropout_rate = dropout_rate
        self.q_fc = torch.nn.Linear(in_features=d_model, out_features=d_key *
            n_head, bias=False)
        self.k_fc = torch.nn.Linear(in_features=d_model, out_features=d_key *
            n_head, bias=False)
        self.v_fc = torch.nn.Linear(in_features=d_model, out_features=
            d_value * n_head, bias=False)
        self.proj_fc = torch.nn.Linear(in_features=d_value * n_head,
            out_features=d_model, bias=False)

    def _prepare_qkv(self, queries, keys, values, cache=None):
        if keys is None:
            keys, values = queries, queries
            static_kv = False
        else:
            static_kv = True
        q = self.q_fc(queries)
        q = torch.reshape(q, shape=[q.size(0), q.size(1), self.n_head, self
            .d_key])
        q = q.permute(0, 2, 1, 3)
        if cache is not None and static_kv and 'static_k' in cache:
            k = cache['static_k']
            v = cache['static_v']
        else:
            k = self.k_fc(keys)
            v = self.v_fc(values)
            k = torch.reshape(k, shape=[k.size(0), k.size(1), self.n_head,
                self.d_key])
            k = k.permute(0, 2, 1, 3)
            v = torch.reshape(v, shape=[v.size(0), v.size(1), self.n_head,
                self.d_value])
            v = v.permute(0, 2, 1, 3)
        if cache is not None:
            if static_kv and 'static_k' not in cache:
                cache['static_k'], cache['static_v'] = k, v
            elif not static_kv:
                cache_k, cache_v = cache['k'], cache['v']
                k = torch.cat([cache_k, k], dim=2)
                v = torch.cat([cache_v, v], dim=2)
                cache['k'], cache['v'] = k, v
        return q, k, v

    def forward(self, input_0, input_1, input_2, input_3):
        primals_3 = self.q_fc.weight
        primals_5 = self.k_fc.weight
        primals_6 = self.v_fc.weight
        primals_7 = self.proj_fc.weight
        primals_1 = input_0
        primals_2 = input_1
        primals_4 = input_2
        primals_8 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
