# 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/7o/c7o6kpxam7ua7xq5mlr7dszko5bkolv2zeooi3cve5lrg5vaqdfs.py
# Topologically Sorted Source Nodes: [kv, k], Original ATen: [aten.add, aten.clone]
# Source node to ATen node mapping:
#   k => clone
#   kv => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%permute_1, %primals_4), kwargs = {})
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%add,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_add_clone_0 = async_compile.triton('triton_poi_fused_add_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=[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_add_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_add_clone_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16)
    x3 = (xindex // 4)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x3), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + (x4), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/7b/c7bf34fgn2dhohe7ejneqlees25vyq6sbe4c5lfvoehzliak2nz6.py
# Topologically Sorted Source Nodes: [k], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   k => add_1
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_3, %primals_8), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_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: '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_add_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_add_1(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)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/la/clahfcfxag6dxo4xoucuhnybhz4wwfcawa36iswhy7nt6vkbs2gq.py
# Topologically Sorted Source Nodes: [flatten], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   flatten => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%view_8,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_2', '''
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, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_2(in_ptr0, in_ptr1, in_ptr2, 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
    y3 = yindex
    y0 = yindex % 4
    tmp0 = tl.load(in_ptr0 + (y3 + (16*x2)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + (4*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = 1.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 * tmp5
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp6, xmask & ymask)
''', 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, primals_12 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (16, 4), (4, 1))
    assert_size_stride(primals_3, (16, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (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, 1))
    assert_size_stride(primals_10, (4, ), (1, ))
    assert_size_stride(primals_11, (4, 16), (16, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 16), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [q], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, primals_1, reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_5
        del primals_6
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [kv, k], Original ATen: [aten.add, aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_clone_0.run(buf0, primals_3, primals_4, buf2, 64, grid=grid(64), stream=stream0)
        del primals_3
        del primals_4
        buf3 = reinterpret_tensor(buf0, (16, 4), (4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [k], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [k], Original ATen: [aten.add]
        triton_poi_fused_add_1.run(buf4, primals_8, 64, grid=grid(64), stream=stream0)
        del primals_8
        buf5 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [bmm], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 1), (1, 16, 0), 0), reinterpret_tensor(buf1, (16, 1, 1), (1, 1, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [flatten], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf6, primals_10, buf5, buf7, 16, 4, grid=grid(16, 4), stream=stream0)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf7, (4, 16), (16, 1), 0), reinterpret_tensor(primals_11, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf8)
        del primals_12
    return (buf8, primals_1, primals_10, reinterpret_tensor(buf2, (16, 4), (4, 1), 0), buf5, buf6, reinterpret_tensor(buf7, (4, 16), (16, 1), 0), primals_11, primals_9, reinterpret_tensor(buf4, (16, 1, 4), (1, 1, 16), 0), reinterpret_tensor(buf1, (16, 1, 1), (1, 1, 1), 0), primals_7, )


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((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 1), (4, 1, 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, ), (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, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = 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, primals_12])
    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.utils.data
from torch import nn


class MultiheadSimilarity(nn.Module):

    def __init__(self, d_model, num_head, seq_len):
        super().__init__()
        self.num_head = num_head
        self.seq_len = seq_len
        self.d_head = d_model // num_head
        self.q_in_proj = nn.Linear(d_model, seq_len * d_model, bias=True)
        self.q_proj = nn.Linear(d_model, d_model, bias=True)
        self.k_proj = nn.Linear(d_model, d_model, bias=True)
        self.v_proj = nn.Linear(d_model, d_model, bias=True)
        self.out_proj = nn.Linear(seq_len * d_model, d_model, bias=True)

    def forward(self, q, kv):
        bs, d_model = q.shape
        nbs = bs * self.num_head
        q_ = self.q_in_proj(q)
        q_ = q_.contiguous().view(bs, self.seq_len, d_model).transpose(0, 1)
        kv = q_ + kv
        q = self.q_proj(q)
        q = q.contiguous().view(nbs, self.d_head).unsqueeze(-1)
        k = self.k_proj(kv)
        k = k.contiguous().view(self.seq_len, nbs, self.d_head).transpose(0, 1)
        similarity = torch.bmm(k, q) * float(self.d_head) ** -0.5
        v = self.v_proj(kv)
        v = v.contiguous().view(self.seq_len, nbs, self.d_head).transpose(0, 1)
        v = (v * similarity).view(bs, self.num_head, self.seq_len, self.d_head)
        output = self.out_proj(v.flatten(1))
        return output


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


def get_init_inputs():
    return [[], {'d_model': 4, 'num_head': 4, 'seq_len': 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 torch.utils.data
from torch import 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_add_clone_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex // 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + x4, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_1(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)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, in_ptr1, in_ptr2, 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
    y3 = yindex
    y0 = yindex % 4
    tmp0 = tl.load(in_ptr0 + (y3 + 16 * x2), xmask & ymask, eviction_policy
        ='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = 1.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 * tmp5
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp6, xmask & ymask)


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, primals_12
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (16, 4), (4, 1))
    assert_size_stride(primals_3, (16,), (1,))
    assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (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, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 16), (16, 1))
    assert_size_stride(primals_12, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 16),
            (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, primals_1, reinterpret_tensor(
            primals_5, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_5
        del primals_6
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_clone_0[grid(64)](buf0, primals_3, primals_4,
            buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
        del primals_4
        buf3 = reinterpret_tensor(buf0, (16, 4), (4, 1), 0)
        del buf0
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0)
        del buf3
        triton_poi_fused_add_1[grid(64)](buf4, primals_8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_8
        buf5 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 1), (1, 16, 0),
            0), reinterpret_tensor(buf1, (16, 1, 1), (1, 1, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_2[grid(16, 4)](buf6, primals_10, buf5, buf7,
            16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf7, (4, 16),
            (16, 1), 0), reinterpret_tensor(primals_11, (16, 4), (1, 16), 0
            ), alpha=1, beta=1, out=buf8)
        del primals_12
    return buf8, primals_1, primals_10, reinterpret_tensor(buf2, (16, 4), (
        4, 1), 0), buf5, buf6, reinterpret_tensor(buf7, (4, 16), (16, 1), 0
        ), primals_11, primals_9, reinterpret_tensor(buf4, (16, 1, 4), (1, 
        1, 16), 0), reinterpret_tensor(buf1, (16, 1, 1), (1, 1, 1), 0
        ), primals_7


class MultiheadSimilarityNew(nn.Module):

    def __init__(self, d_model, num_head, seq_len):
        super().__init__()
        self.num_head = num_head
        self.seq_len = seq_len
        self.d_head = d_model // num_head
        self.q_in_proj = nn.Linear(d_model, seq_len * d_model, bias=True)
        self.q_proj = nn.Linear(d_model, d_model, bias=True)
        self.k_proj = nn.Linear(d_model, d_model, bias=True)
        self.v_proj = nn.Linear(d_model, d_model, bias=True)
        self.out_proj = nn.Linear(seq_len * d_model, d_model, bias=True)

    def forward(self, input_0, input_1):
        primals_2 = self.q_in_proj.weight
        primals_3 = self.q_in_proj.bias
        primals_1 = self.q_proj.weight
        primals_6 = self.q_proj.bias
        primals_5 = self.k_proj.weight
        primals_8 = self.k_proj.bias
        primals_7 = self.v_proj.weight
        primals_10 = self.v_proj.bias
        primals_11 = self.out_proj.weight
        primals_12 = self.out_proj.bias
        primals_9 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12])
        return output[0]
