# 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_1/inductor_cache/tp/ctpiguxmcpckre2mit64cbirohbry7ukusrmf4e7xnoeurunx2lg.py
# Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attn_prob => amax, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_11, [2], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_11, %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, [2], 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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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
    x3 = xindex
    x0 = xindex % 4
    x2 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (4 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (8 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (12 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp0 * tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = triton_helpers.maximum(tmp4, tmp7)
    tmp10 = tmp0 * tmp9
    tmp11 = tmp10 * tmp3
    tmp12 = triton_helpers.maximum(tmp8, tmp11)
    tmp14 = tmp0 * tmp13
    tmp15 = tmp14 * tmp3
    tmp16 = triton_helpers.maximum(tmp12, tmp15)
    tmp17 = tmp4 - tmp16
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp7 - tmp16
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp18 + tmp20
    tmp22 = tmp11 - tmp16
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp15 - tmp16
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tl.store(out_ptr0 + (x3), tmp16, xmask)
    tl.store(out_ptr1 + (x3), tmp27, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/mx/cmxglwcqshsz56cbqetuotr5dueji2qhvsgqhs3rspu5vyljlcxs.py
# Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   attn_vec => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = (xindex // 4)
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x3 = (xindex // 64)
    x2 = (xindex // 16) % 4
    tmp0 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + (4*x0) + (16*x3)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + (x4), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tl.store(out_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/3a/c3aaucsolrfdjphevn4qawbbrmr3jqka2dfrruxbjgx42g3fnwu6.py
# Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   attn_vec => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_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.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/oz/cozicgllm5gq5az3npyul2g2afvsz5ievjw5qizps6nqjsvarc22.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %view_19], 2), kwargs = {})
triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_3', '''
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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/6j/c6jx5qsbwmrvybg6qdxymggyqcxvc33mfvf2whbhr35uyx25r5or.py
# Topologically Sorted Source Nodes: [gate, mul, sub, mul_1, attn_out_1], Original ATen: [aten.sigmoid, aten.mul, aten.rsub, aten.add]
# Source node to ATen node mapping:
#   attn_out_1 => add
#   gate => sigmoid
#   mul => mul_2
#   mul_1 => mul_3
#   sub => sub_1
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_21,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %primals_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %view_19), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {})
triton_poi_fused_add_mul_rsub_sigmoid_4 = async_compile.triton('triton_poi_fused_add_mul_rsub_sigmoid_4', '''
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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_mul_rsub_sigmoid_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_mul_rsub_sigmoid_4(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
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp2 = tl.load(in_ptr1 + (x0), xmask)
    tmp6 = tl.load(in_ptr2 + (x0), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tl.store(out_ptr0 + (x0), tmp8, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, 8), (8, 1))
    assert_size_stride(primals_9, (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: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf0)
        del primals_4
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2)
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_prob], 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, 1), (64, 16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf0, buf1, buf3, buf4, buf5, 256, grid=grid(256), stream=stream0)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf2, buf6, 16, 4, grid=grid(16, 4), stream=stream0)
        buf7 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7)
        buf8 = reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf7, buf8, 16, 4, grid=grid(16, 4), stream=stream0)
        buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [attn_out], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(primals_1, buf9, buf10, 128, grid=grid(128), stream=stream0)
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf10, (16, 8), (8, 1), 0), reinterpret_tensor(primals_8, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf11)
        del primals_9
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [gate, mul, sub, mul_1, attn_out_1], Original ATen: [aten.sigmoid, aten.mul, aten.rsub, aten.add]
        triton_poi_fused_add_mul_rsub_sigmoid_4.run(buf11, primals_1, buf9, buf12, 64, grid=grid(64), stream=stream0)
    return (buf12, primals_1, buf0, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), buf1, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(buf8, (16, 4), (4, 1), 0), buf9, reinterpret_tensor(buf10, (16, 8), (8, 1), 0), buf11, primals_8, primals_7, reinterpret_tensor(buf5, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf6, (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), (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((4, 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, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = 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])
    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 GatedMultiHeadAttn(nn.Module):

    def __init__(self, query_dim, key_dim, value_dim, hidden_dim, num_head,
        dropatt=0.0, act_func='softmax', add_zero_attn=False, pre_lnorm=
        False, post_lnorm=False):
        super(GatedMultiHeadAttn, self).__init__()
        assert hidden_dim % num_head == 0
        self.query_dim = query_dim
        self.key_dim = key_dim
        self.value_dim = value_dim
        self.hidden_dim = hidden_dim
        self.num_head = num_head
        self.dropatt = nn.Dropout(dropatt)
        head_dim = hidden_dim // num_head
        self.q_net = nn.Linear(query_dim, hidden_dim, bias=False)
        self.k_net = nn.Linear(key_dim, hidden_dim, bias=False)
        self.v_net = nn.Linear(value_dim, hidden_dim, bias=False)
        self.o_net = nn.Linear(hidden_dim, query_dim, bias=False)
        self.g_net = nn.Linear(2 * query_dim, query_dim, bias=True)
        self.scale = 1 / head_dim ** 0.5
        self.act_func = act_func
        self.add_zero_attn = add_zero_attn
        self.pre_lnorm = pre_lnorm
        self.post_lnorm = post_lnorm
        if pre_lnorm:
            self.q_layer_norm = nn.LayerNorm(query_dim)
            self.k_layer_norm = nn.LayerNorm(key_dim)
            self.v_layer_norm = nn.LayerNorm(value_dim)
        if post_lnorm:
            self.o_layer_norm = nn.LayerNorm(query_dim)
        for net in [self.q_net, self.k_net, self.v_net, self.o_net]:
            nn.init.xavier_uniform_(net.weight, 1.0)
            if hasattr(net, 'bias') and net.bias is not None:
                nn.init.constant_(net.bias, 0.0)
        nn.init.normal_(self.g_net.weight, 0.0, self.scale)
        if hasattr(self.g_net, 'bias') and self.g_net.bias is not None:
            nn.init.constant_(self.g_net.bias, 1.0)
        if self.pre_lnorm:
            for layer_norm in [self.q_layer_norm, self.k_layer_norm, self.
                v_layer_norm]:
                if hasattr(layer_norm, 'weight'):
                    nn.init.normal_(layer_norm.weight, 1.0, self.scale)
                if hasattr(layer_norm, 'bias') and layer_norm.bias is not None:
                    nn.init.constant_(layer_norm.bias, 0.0)
        if self.post_lnorm:
            if hasattr(self.o_layer_norm, 'weight'):
                nn.init.normal_(self.o_layer_norm.weight, 1.0, self.scale)
            if hasattr(self.o_layer_norm, 'bias'
                ) and self.o_layer_norm.bias is not None:
                nn.init.constant_(self.o_layer_norm.bias, 0.0)

    def forward(self, query, key, value, attn_mask=None):
        bsz = query.size(0)
        if self.add_zero_attn:
            key = torch.cat([key, torch.zeros((bsz, 1) + key.size()[2:],
                dtype=key.dtype, device=key.device)], dim=1)
            value = torch.cat([value, torch.zeros((bsz, 1) + value.size()[2
                :], dtype=value.dtype, device=value.device)], dim=1)
            if attn_mask is not None:
                attn_mask = torch.cat([attn_mask, torch.ones((bsz, 1),
                    dtype=attn_mask.dtype, device=attn_mask.device)], dim=1)
        qlen, klen, vlen = query.size(1), key.size(1), value.size(1)
        if self.pre_lnorm:
            query = self.q_layer_norm(query)
            key = self.k_layer_norm(key)
            value = self.v_layer_norm(value)
        head_q = self.q_net(query).view(bsz, qlen, self.num_head, self.
            hidden_dim // self.num_head)
        head_k = self.k_net(key).view(bsz, klen, self.num_head, self.
            hidden_dim // self.num_head)
        head_v = self.v_net(value).view(bsz, vlen, self.num_head, self.
            hidden_dim // self.num_head)
        attn_score = torch.einsum('bind,bjnd->bijn', (head_q, head_k))
        attn_score.mul_(self.scale)
        if attn_mask is not None:
            if attn_mask.dim() == 2:
                attn_score.masked_fill_((attn_mask == 0).unsqueeze(1).
                    unsqueeze(-1), _INF)
            elif attn_mask.dim() == 3:
                attn_score.masked_fill_((attn_mask == 0).unsqueeze(-1), _INF)
        if self.act_func is None or self.act_func == 'None':
            attn_prob = attn_score
        elif self.act_func == 'softmax':
            attn_prob = F.softmax(attn_score, dim=2)
        elif self.act_func == 'sigmoid':
            attn_prob = F.sigmoid(attn_score)
        elif self.act_func == 'tanh':
            attn_prob = F.tanh(attn_score)
        elif self.act_func == 'relu':
            attn_prob = F.relu(attn_score)
        elif self.act_func == 'leaky_relu':
            attn_prob = F.leaky_relu(attn_score)
        elif self.act_func == 'maximum':
            max_score = torch.max(attn_score, dim=2, keepdim=True)[0]
            max_mask = attn_score == max_score
            cnt = torch.sum(max_mask, dim=2, keepdim=True)
            attn_prob = max_mask.float() / cnt.float()
        else:
            raise NotImplementedError
        attn_prob = self.dropatt(attn_prob)
        attn_vec = torch.einsum('bijn,bjnd->bind', (attn_prob, head_v))
        attn_vec = attn_vec.contiguous().view(bsz, qlen, self.hidden_dim)
        attn_out = self.o_net(attn_vec)
        gate = F.sigmoid(self.g_net(torch.cat([query, attn_out], dim=2)))
        attn_out = gate * query + (1 - gate) * attn_out
        if self.post_lnorm:
            attn_out = self.o_layer_norm(attn_out)
        return attn_out

    def get_output_dim(self):
        return self.query_dim


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


def get_init_inputs():
    return [[], {'query_dim': 4, 'key_dim': 4, 'value_dim': 4, 'hidden_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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr1 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr1 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr1 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp0 * tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = triton_helpers.maximum(tmp4, tmp7)
    tmp10 = tmp0 * tmp9
    tmp11 = tmp10 * tmp3
    tmp12 = triton_helpers.maximum(tmp8, tmp11)
    tmp14 = tmp0 * tmp13
    tmp15 = tmp14 * tmp3
    tmp16 = triton_helpers.maximum(tmp12, tmp15)
    tmp17 = tmp4 - tmp16
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp7 - tmp16
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp18 + tmp20
    tmp22 = tmp11 - tmp16
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp15 - tmp16
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tl.store(out_ptr0 + x3, tmp16, xmask)
    tl.store(out_ptr1 + x3, tmp27, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex // 4
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x3 = xindex // 64
    x2 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + 4 * x0 + 16 * x3), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp9, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_4(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp6 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, 8), (8, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf0)
        del primals_4
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2)
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf0, buf1, buf3, buf4, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        triton_poi_fused_clone_1[grid(256)](buf0, buf1, buf3, buf4, buf5, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0)
        del buf4
        triton_poi_fused_clone_2[grid(16, 4)](buf2, buf6, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf7 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7)
        buf8 = reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf3
        triton_poi_fused_clone_2[grid(16, 4)](buf7, buf8, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0)
        del buf7
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_3[grid(128)](primals_1, buf9, buf10, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf10, (16, 8),
            (8, 1), 0), reinterpret_tensor(primals_8, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf11)
        del primals_9
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_rsub_sigmoid_4[grid(64)](buf11, primals_1,
            buf9, buf12, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return buf12, primals_1, buf0, reinterpret_tensor(primals_2, (16, 4), (
        4, 1), 0), buf1, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf8, (16, 4), (4, 1), 0
        ), buf9, reinterpret_tensor(buf10, (16, 8), (8, 1), 0
        ), buf11, primals_8, primals_7, reinterpret_tensor(buf5, (16, 4, 4),
        (16, 1, 4), 0), reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0)


class GatedMultiHeadAttnNew(nn.Module):

    def __init__(self, query_dim, key_dim, value_dim, hidden_dim, num_head,
        dropatt=0.0, act_func='softmax', add_zero_attn=False, pre_lnorm=
        False, post_lnorm=False):
        super(GatedMultiHeadAttnNew, self).__init__()
        assert hidden_dim % num_head == 0
        self.query_dim = query_dim
        self.key_dim = key_dim
        self.value_dim = value_dim
        self.hidden_dim = hidden_dim
        self.num_head = num_head
        self.dropatt = nn.Dropout(dropatt)
        head_dim = hidden_dim // num_head
        self.q_net = nn.Linear(query_dim, hidden_dim, bias=False)
        self.k_net = nn.Linear(key_dim, hidden_dim, bias=False)
        self.v_net = nn.Linear(value_dim, hidden_dim, bias=False)
        self.o_net = nn.Linear(hidden_dim, query_dim, bias=False)
        self.g_net = nn.Linear(2 * query_dim, query_dim, bias=True)
        self.scale = 1 / head_dim ** 0.5
        self.act_func = act_func
        self.add_zero_attn = add_zero_attn
        self.pre_lnorm = pre_lnorm
        self.post_lnorm = post_lnorm
        if pre_lnorm:
            self.q_layer_norm = nn.LayerNorm(query_dim)
            self.k_layer_norm = nn.LayerNorm(key_dim)
            self.v_layer_norm = nn.LayerNorm(value_dim)
        if post_lnorm:
            self.o_layer_norm = nn.LayerNorm(query_dim)
        for net in [self.q_net, self.k_net, self.v_net, self.o_net]:
            nn.init.xavier_uniform_(net.weight, 1.0)
            if hasattr(net, 'bias') and net.bias is not None:
                nn.init.constant_(net.bias, 0.0)
        nn.init.normal_(self.g_net.weight, 0.0, self.scale)
        if hasattr(self.g_net, 'bias') and self.g_net.bias is not None:
            nn.init.constant_(self.g_net.bias, 1.0)
        if self.pre_lnorm:
            for layer_norm in [self.q_layer_norm, self.k_layer_norm, self.
                v_layer_norm]:
                if hasattr(layer_norm, 'weight'):
                    nn.init.normal_(layer_norm.weight, 1.0, self.scale)
                if hasattr(layer_norm, 'bias') and layer_norm.bias is not None:
                    nn.init.constant_(layer_norm.bias, 0.0)
        if self.post_lnorm:
            if hasattr(self.o_layer_norm, 'weight'):
                nn.init.normal_(self.o_layer_norm.weight, 1.0, self.scale)
            if hasattr(self.o_layer_norm, 'bias'
                ) and self.o_layer_norm.bias is not None:
                nn.init.constant_(self.o_layer_norm.bias, 0.0)

    def get_output_dim(self):
        return self.query_dim

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.q_net.weight
        primals_5 = self.k_net.weight
        primals_6 = self.v_net.weight
        primals_7 = self.o_net.weight
        primals_8 = self.g_net.weight
        primals_9 = self.g_net.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])
        return output[0]
