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

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


# kernel path: runs/run_shard_4/inductor_cache/u7/cu7vpvizjd4aqykmop7o4bb3wko4x255a6cbzzzpnk3hxneiqhcw.py
# Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   contiguous => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], '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_clone_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = (xindex // 128) % 16
    x2 = (xindex // 2048)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (128*x2) + (512*x1)), None)
    tmp1 = tl.load(in_ptr1 + (x0 + (128*x2)), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hz/chz2sqsqk26mwhf2dxhgh44jfpu2er5yqjftwkzfav5ctqtx5e7f.py
# Topologically Sorted Source Nodes: [attn_distribution], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attn_distribution => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%bmm, [2], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%bmm, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), 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: '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__softmax_1', '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_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3f/c3fx6bzkalkw7u7askqdnz4rzlcoyqiec4r434sjc5x3axxgkrmr.py
# Topologically Sorted Source Nodes: [attn_distribution], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attn_distribution => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', '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_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7q/c7q5bvn7twuxhcrmxt4henfecjupsuo5yrwvo2hhumobsmaauisq.py
# Topologically Sorted Source Nodes: [combined], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   combined => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view_9, %primals_2], 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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), 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': '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_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 516
    x3 = (xindex // 516)
    x2 = (xindex // 2064)
    x4 = xindex % 2064
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 512, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((128*x3) + (2048*((x0 // 128) % 4)) + (x0 % 128)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 516, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((4*x3) + ((-512) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x4 + (2080*x2)), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/wf/cwfbwzeyrr36zca5qiiebhnejmrgzdsjwmzyit7xy4nubbdp6hnq.py
# Topologically Sorted Source Nodes: [combined, view_6], Original ATen: [aten.cat, aten.view]
# Source node to ATen node mapping:
#   combined => cat
#   view_6 => view_10
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view_9, %primals_2], 2), kwargs = {})
#   %view_10 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%cat, [-1, 8]), kwargs = {})
triton_poi_fused_cat_view_4 = async_compile.triton('triton_poi_fused_cat_view_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=[16384], 
    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_cat_view_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_view_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + ((2080*(x0 // 2064)) + (x0 % 2064)), xmask)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ag/cagrpzpgi7zvecwkentweyxoalabxs5bqjnsvvbboefkenvqapba.py
# Topologically Sorted Source Nodes: [tanh], Original ATen: [aten.tanh, aten.tanh_backward]
# Source node to ATen node mapping:
#   tanh => tanh
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_8), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_tensor,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, %tanh), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %mul), kwargs = {})
triton_poi_fused_tanh_tanh_backward_5 = async_compile.triton('triton_poi_fused_tanh_tanh_backward_5', '''
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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_tanh_backward_5', '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_tanh_tanh_backward_5(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4128
    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
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = tmp3 * tmp3
    tmp5 = 1.0
    tmp6 = tmp5 - tmp4
    tl.store(in_out_ptr0 + (x2), tmp3, xmask)
    tl.store(out_ptr0 + (x2), tmp6, 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, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (512, 4), (4, 1))
    assert_size_stride(primals_4, (512, ), (1, ))
    assert_size_stride(primals_5, (512, 4), (4, 1))
    assert_size_stride(primals_6, (512, ), (1, ))
    assert_size_stride(primals_7, (4, 8), (8, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 512), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 512), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(buf0, primals_4, buf2, 8192, grid=grid(8192), stream=stream0)
        del primals_4
        buf3 = reinterpret_tensor(buf0, (4, 4, 4, 128), (2048, 512, 128, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [contiguous_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_0.run(buf1, primals_6, buf3, 8192, grid=grid(8192), stream=stream0)
        del primals_6
        buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_score], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 128), (512, 128, 1), 0), reinterpret_tensor(buf3, (16, 128, 4), (512, 1, 128), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_distribution], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf4, buf5, 256, grid=grid(256), stream=stream0)
        buf6 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [attn_distribution], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf5, buf6, 256, grid=grid(256), stream=stream0)
        del buf5
        buf7 = reinterpret_tensor(buf1, (16, 4, 128), (512, 128, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [bmm_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf6, reinterpret_tensor(buf3, (16, 4, 128), (512, 128, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 516), (2080, 516, 1), torch.float32)
        # Topologically Sorted Source Nodes: [combined], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(buf7, primals_2, buf8, 8256, grid=grid(8256), stream=stream0)
        del buf7
        buf9 = empty_strided_cuda((1032, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [combined, view_6], Original ATen: [aten.cat, aten.view]
        triton_poi_fused_cat_view_4.run(buf8, buf9, 8256, grid=grid(8256), stream=stream0)
        del buf8
        buf10 = empty_strided_cuda((1032, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf9, reinterpret_tensor(primals_7, (8, 4), (1, 8), 0), out=buf10)
        buf11 = buf10; del buf10  # reuse
        buf12 = empty_strided_cuda((1032, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh], Original ATen: [aten.tanh, aten.tanh_backward]
        triton_poi_fused_tanh_tanh_backward_5.run(buf11, primals_8, buf12, 4128, grid=grid(4128), stream=stream0)
        del primals_8
    return (reinterpret_tensor(buf11, (4, 258, 4), (1032, 4, 1), 0), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(buf3, (16, 128, 4), (512, 1, 128), 0), buf6, buf9, buf12, primals_7, reinterpret_tensor(buf2, (16, 128, 4), (512, 1, 128), 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((512, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((512, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = 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])
    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):
    """
    Applies an multi-head attention mechanism on the output features from the decoder.

    Refer to 「State-of-the-art Speech Recognition With Sequence-to-Sequence Models」 Paper
    https://arxiv.org/abs/1712.01769


    Args:
        in_features (int): The number of expected features in the output
        n_head (int): number of heads. (default: 4)
        dim (int): dimension size of sub heads. (default: 128)

    Inputs: query, key
        - **query** (batch, output_len, dimensions): tensor containing the output features from the decoder.
        - **key** (batch, input_len, dimensions): tensor containing features of the encoded input sequence.

    Returns: output
        - **output** (batch, output_len, dimensions): tensor containing the attended output features from the decoder.

    Examples::
        >>> attention = MultiHeadAttention(in_features, n_head=4, dim=128)
        >>> output = attention(query, key)
    """

    def __init__(self, in_features, n_head=4, dim=128):
        super(MultiHeadAttention, self).__init__()
        self.in_features = in_features
        self.linear_q = nn.Linear(in_features, dim * n_head)
        self.linear_k = nn.Linear(in_features, dim * n_head)
        self.n_head = n_head
        self.dim = dim
        self.out = nn.Linear(in_features << 1, in_features)

    def forward(self, query, key):
        batch_size = key.size(0)
        query_length = query.size(1)
        key_length = key.size(1)
        preserved = query
        query = self.linear_q(query).view(batch_size, query_length, self.
            n_head, self.dim).permute(2, 0, 1, 3)
        key = self.linear_k(key).view(batch_size, key_length, self.n_head,
            self.dim).permute(2, 0, 1, 3)
        query = query.contiguous().view(-1, query_length, self.dim)
        key = key.contiguous().view(-1, key_length, self.dim)
        attn_score = torch.bmm(query, key.transpose(1, 2))
        attn_distribution = F.softmax(attn_score, dim=2)
        context = torch.bmm(attn_distribution, key).view(self.n_head,
            batch_size, query_length, self.dim)
        context = context.permute(1, 2, 0, 3).contiguous().view(batch_size,
            query_length, -1)
        combined = torch.cat([context, preserved], dim=2)
        output = torch.tanh(self.out(combined.view(-1, 2 * self.in_features))
            ).view(batch_size, -1, self.in_features)
        return output


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


def get_init_inputs():
    return [[], {'in_features': 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 libdevice, 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_clone_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = xindex // 128 % 16
    x2 = xindex // 2048
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 128 * x2 + 512 * x1), None)
    tmp1 = tl.load(in_ptr1 + (x0 + 128 * x2), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 8256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 516
    x3 = xindex // 516
    x2 = xindex // 2064
    x4 = xindex % 2064
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 512, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (128 * x3 + 2048 * (x0 // 128 % 4) + x0 % 128),
        tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 516, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x3 + (-512 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x4 + 2080 * x2), tmp10, xmask)


@triton.jit
def triton_poi_fused_cat_view_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 8256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (2080 * (x0 // 2064) + x0 % 2064), xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_tanh_tanh_backward_5(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4128
    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
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = tmp3 * tmp3
    tmp5 = 1.0
    tmp6 = tmp5 - tmp4
    tl.store(in_out_ptr0 + x2, tmp3, xmask)
    tl.store(out_ptr0 + x2, tmp6, 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, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (512, 4), (4, 1))
    assert_size_stride(primals_4, (512,), (1,))
    assert_size_stride(primals_5, (512, 4), (4, 1))
    assert_size_stride(primals_6, (512,), (1,))
    assert_size_stride(primals_7, (4, 8), (8, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 512), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 512), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(8192)](buf0, primals_4, buf2, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
        buf3 = reinterpret_tensor(buf0, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf0
        triton_poi_fused_clone_0[grid(8192)](buf1, primals_6, buf3, 8192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 128), (512, 128,
            1), 0), reinterpret_tensor(buf3, (16, 128, 4), (512, 1, 128), 0
            ), out=buf4)
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf4, buf5, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf5
        buf7 = reinterpret_tensor(buf1, (16, 4, 128), (512, 128, 1), 0)
        del buf1
        extern_kernels.bmm(buf6, reinterpret_tensor(buf3, (16, 4, 128), (
            512, 128, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 516), (2080, 516, 1), torch.float32)
        triton_poi_fused_cat_3[grid(8256)](buf7, primals_2, buf8, 8256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf7
        buf9 = empty_strided_cuda((1032, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_view_4[grid(8256)](buf8, buf9, 8256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del buf8
        buf10 = empty_strided_cuda((1032, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf9, reinterpret_tensor(primals_7, (8, 4), (1, 8
            ), 0), out=buf10)
        buf11 = buf10
        del buf10
        buf12 = empty_strided_cuda((1032, 4), (4, 1), torch.float32)
        triton_poi_fused_tanh_tanh_backward_5[grid(4128)](buf11, primals_8,
            buf12, 4128, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_8
    return reinterpret_tensor(buf11, (4, 258, 4), (1032, 4, 1), 0
        ), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf3, (16, 128, 4), (512, 1, 128), 0
        ), buf6, buf9, buf12, primals_7, reinterpret_tensor(buf2, (16, 128,
        4), (512, 1, 128), 0)


class MultiHeadAttentionNew(nn.Module):
    """
    Applies an multi-head attention mechanism on the output features from the decoder.

    Refer to 「State-of-the-art Speech Recognition With Sequence-to-Sequence Models」 Paper
    https://arxiv.org/abs/1712.01769


    Args:
        in_features (int): The number of expected features in the output
        n_head (int): number of heads. (default: 4)
        dim (int): dimension size of sub heads. (default: 128)

    Inputs: query, key
        - **query** (batch, output_len, dimensions): tensor containing the output features from the decoder.
        - **key** (batch, input_len, dimensions): tensor containing features of the encoded input sequence.

    Returns: output
        - **output** (batch, output_len, dimensions): tensor containing the attended output features from the decoder.

    Examples::
        >>> attention = MultiHeadAttention(in_features, n_head=4, dim=128)
        >>> output = attention(query, key)
    """

    def __init__(self, in_features, n_head=4, dim=128):
        super(MultiHeadAttentionNew, self).__init__()
        self.in_features = in_features
        self.linear_q = nn.Linear(in_features, dim * n_head)
        self.linear_k = nn.Linear(in_features, dim * n_head)
        self.n_head = n_head
        self.dim = dim
        self.out = nn.Linear(in_features << 1, in_features)

    def forward(self, input_0, input_1):
        primals_3 = self.linear_q.weight
        primals_4 = self.linear_q.bias
        primals_5 = self.linear_k.weight
        primals_6 = self.linear_k.bias
        primals_7 = self.out.weight
        primals_8 = self.out.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
