# 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/ao/caoovxtqrx42gvkmjirowqmmbh6kppvfh5ebrzzv4kzkgwm2umii.py
# Topologically Sorted Source Nodes: [q], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   q => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_3,), 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=[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_clone_0', '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_clone_0(in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1)), xmask)
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ah/cahcbdgzcypclgrmenrcgftl53kemvcm53v6yoxzwdqjyblrincb.py
# Topologically Sorted Source Nodes: [score], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   score => clone_3
# Graph fragment:
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_17,), 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: '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_clone_1', '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_clone_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
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), xmask)
    tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mz/cmzlu2lip25blpsdqeby7ek5757op6xw3pdkxbdediou5szw32tx.py
# Topologically Sorted Source Nodes: [score], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   score => clone_4
# Graph fragment:
#   %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_18,), 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=[64, 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=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_clone_2', '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_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    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 % 16
    y1 = (yindex // 16)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (16*x2) + (64*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_7/inductor_cache/xk/cxkynbikahz2hisijkpmmcfv7nekqjhfkqpiktt76kla5eook3hc.py
# Topologically Sorted Source Nodes: [wrapped_sqrt, score_2], Original ATen: [aten.sqrt, aten._softmax]
# Source node to ATen node mapping:
#   score_2 => exp
#   wrapped_sqrt => full_default
# Graph fragment:
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([], 2.0), kwargs = {dtype: torch.float64, layout: torch.strided, device: cpu, pin_memory: False})
#   %scalar_tensor_default : [num_users=2] = call_function[target=torch.ops.aten.scalar_tensor.default](args = (1,), kwargs = {dtype: torch.float32, device: cuda:0, pin_memory: False})
#   %ge_scalar : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%full_default, 0), kwargs = {})
#   %neg_default : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%scalar_tensor_default,), kwargs = {})
#   %where_self : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%ge_scalar, %scalar_tensor_default, %neg_default), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_15, %where_self), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %mul_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_self, %full_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, %mul_tensor_1), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
triton_poi_fused__softmax_sqrt_3 = async_compile.triton('triton_poi_fused__softmax_sqrt_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=[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_sqrt_3', '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_sqrt_3(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)
    tmp8 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 2.0, tl.float64)
    tmp2 = tl.full([1], 0.0, tl.float64)
    tmp3 = tmp1 >= tmp2
    tmp4 = 1.0
    tmp5 = -1.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp0 * tmp6
    tmp9 = tmp8 * tmp6
    tmp11 = tmp10 * tmp6
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp14 = tmp13 * tmp6
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp17 = tmp16 * tmp6
    tmp18 = triton_helpers.maximum(tmp15, tmp17)
    tmp19 = tmp7 - tmp18
    tmp20 = tmp6.to(tl.float64)
    tmp21 = tmp20 * tmp1
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp19 / tmp22
    tmp24 = tl_math.exp(tmp23)
    tl.store(out_ptr0 + (x2), tmp24, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/zh/czh6tw7ngffcygnivwvcjex5edxy3ms4t27ymyn2hemxlpspxzq7.py
# Topologically Sorted Source Nodes: [score_2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   score_2 => div_1, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_4 = async_compile.triton('triton_poi_fused__softmax_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=[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_4', '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_4(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')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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), (16, 4, 1))
    assert_size_stride(primals_5, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1, 1), (16, 4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [q], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_1, buf0, 64, grid=grid(64), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [q], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4, 1), 0), reinterpret_tensor(buf0, (1, 4, 16), (0, 16, 1), 0), out=buf1)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [k], Original ATen: [aten.clone]
        triton_poi_fused_clone_0.run(primals_3, buf2, 64, grid=grid(64), stream=stream0)
        del primals_3
        buf3 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [k], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4, 1), 0), reinterpret_tensor(buf2, (1, 4, 16), (0, 16, 1), 0), out=buf3)
        buf4 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [v], Original ATen: [aten.clone]
        triton_poi_fused_clone_0.run(primals_4, buf4, 64, grid=grid(64), stream=stream0)
        del primals_4
        buf5 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4, 1), 0), reinterpret_tensor(buf4, (1, 4, 16), (0, 16, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [score], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf1, buf6, 256, grid=grid(256), stream=stream0)
        buf7 = reinterpret_tensor(buf1, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [score], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf3, buf7, 64, 4, grid=grid(64, 4), stream=stream0)
        buf8 = reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [score], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [wrapped_sqrt, score_2], Original ATen: [aten.sqrt, aten._softmax]
        triton_poi_fused__softmax_sqrt_3.run(buf8, buf9, 256, grid=grid(256), stream=stream0)
        buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [score_2], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf9, buf10, 256, grid=grid(256), stream=stream0)
        buf11 = reinterpret_tensor(buf9, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0); del buf9  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf5, buf11, 256, grid=grid(256), stream=stream0)
        buf12 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1), 0), out=buf12)
        buf13 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf12, buf13, 64, 4, grid=grid(64, 4), stream=stream0)
        del buf12
        buf14 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_0.run(primals_5, buf14, 64, grid=grid(64), stream=stream0)
        del primals_5
        buf15 = empty_strided_cuda((1, 16, 4), (64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf13, (1, 16, 16), (0, 16, 1), 0), reinterpret_tensor(buf14, (1, 16, 4), (0, 4, 1), 0), out=buf15)
    return (reinterpret_tensor(buf15, (4, 4, 4), (16, 4, 1), 0), buf10, reinterpret_tensor(buf13, (1, 16, 16), (256, 1, 16), 0), reinterpret_tensor(buf14, (1, 4, 16), (64, 1, 4), 0), reinterpret_tensor(buf11, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf6, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf7, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(primals_2, (1, 4, 16), (64, 1, 4), 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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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 numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch.distributed
import torch.distributions


def compute_attention(q, k, v, dropout=None, mask=None):
    """
    :param q: Query [B, NH, NQ, EL] or [NH, 1, EL] (in this case NQ=1)
    :param k: Key [B, NH, NK, EL]
    :param v: Value [B, NH, NK, EL]
    :param mask: [B, NQ, NK]
    :param dropout:
    :return:
    """
    if q.ndim + 1 == k.ndim:
        score = torch.einsum('nij,bnkj->bnik', q, k)
    elif q.ndim == k.ndim:
        score = torch.einsum('bnij,bnkj->bnik', q, k)
    score = score / np.sqrt(q.shape[-1])
    if mask is not None:
        mask = mask[:, None]
        score = score * mask + -100000000.0 * (1 - mask)
    score = F.softmax(score, dim=-1)
    if dropout is not None:
        score = dropout(score)
    return torch.einsum('bnij,bnjk->bnik', score, v)


class MultiHeadedAttentionBase(nn.Module):

    def __init__(self, embed_dim, num_heads, latent_dim, dropout=None):
        """
        :param embed_dim: The dimension of feature in each entity.
        :param num_heads: The number of attention heads.
        :param latent_dim:
        :param dropout:
        """
        super().__init__()
        self.w_k = nn.Parameter(torch.empty(num_heads, embed_dim, latent_dim))
        self.w_v = nn.Parameter(torch.empty(num_heads, embed_dim, latent_dim))
        self.w_o = nn.Parameter(torch.empty(num_heads, latent_dim, embed_dim))
        self.dropout = nn.Dropout(dropout) if dropout else nn.Identity()

    def _reset_parameters(self):
        nn.init.xavier_normal_(self.w_k)
        nn.init.xavier_normal_(self.w_v)
        nn.init.xavier_normal_(self.w_o)
        if hasattr(self, 'q'):
            nn.init.xavier_normal_(self.q)
        if hasattr(self, 'w_q'):
            nn.init.xavier_normal_(self.w_q)


class MultiHeadSelfAttention(MultiHeadedAttentionBase):

    def __init__(self, embed_dim, num_heads, latent_dim, dropout=None):
        super().__init__(embed_dim, num_heads, latent_dim, dropout)
        self.w_q = nn.Parameter(torch.empty(num_heads, embed_dim, latent_dim))
        self._reset_parameters()

    def forward(self, x, mask=None):
        """
        :param x: [B, N, E] [batch size, length, embed_dim] the input to the layer, a tensor of shape
        :param mask: [B, N, N] [batch size, length, length]
        :return: [B, N, E] [batch_size, length, embed_dim] Features after self attention
        """
        q = torch.einsum('blj,njd->bnld', x, self.w_q)
        k = torch.einsum('blj,njd->bnld', x, self.w_k)
        v = torch.einsum('blj,njd->bnld', x, self.w_v)
        out = compute_attention(q, k, v, self.dropout, mask)
        out = torch.einsum('bnlj,njk->blk', out, self.w_o)
        out = self.dropout(out)
        return out


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


def get_init_inputs():
    return [[], {'embed_dim': 4, 'num_heads': 4, 'latent_dim': 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 numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch.distributed
import torch.distributions
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, 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
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    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 % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_sqrt_3(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)
    tmp8 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.full([1], 2.0, tl.float64)
    tmp2 = tl.full([1], 0.0, tl.float64)
    tmp3 = tmp1 >= tmp2
    tmp4 = 1.0
    tmp5 = -1.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp0 * tmp6
    tmp9 = tmp8 * tmp6
    tmp11 = tmp10 * tmp6
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp14 = tmp13 * tmp6
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp17 = tmp16 * tmp6
    tmp18 = triton_helpers.maximum(tmp15, tmp17)
    tmp19 = tmp7 - tmp18
    tmp20 = tmp6.to(tl.float64)
    tmp21 = tmp20 * tmp1
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp19 / tmp22
    tmp24 = tl_math.exp(tmp23)
    tl.store(out_ptr0 + x2, tmp24, xmask)


@triton.jit
def triton_poi_fused__softmax_4(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)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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), (16, 4, 1))
    assert_size_stride(primals_5, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1, 1), (16, 4, 1, 1, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4,
            1), 0), reinterpret_tensor(buf0, (1, 4, 16), (0, 16, 1), 0),
            out=buf1)
        buf2 = buf0
        del buf0
        triton_poi_fused_clone_0[grid(64)](primals_3, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4,
            1), 0), reinterpret_tensor(buf2, (1, 4, 16), (0, 16, 1), 0),
            out=buf3)
        buf4 = buf2
        del buf2
        triton_poi_fused_clone_0[grid(64)](primals_4, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_4
        buf5 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4,
            1), 0), reinterpret_tensor(buf4, (1, 4, 16), (0, 16, 1), 0),
            out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        triton_poi_fused_clone_1[grid(256)](buf1, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf1, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0)
        del buf1
        triton_poi_fused_clone_2[grid(64, 4)](buf3, buf7, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0)
        del buf3
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_sqrt_3[grid(256)](buf8, buf9, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf8
        triton_poi_fused__softmax_4[grid(256)](buf9, buf10, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf11 = reinterpret_tensor(buf9, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0)
        del buf9
        triton_poi_fused_clone_1[grid(256)](buf5, buf11, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf12 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1), 0), out=buf12
            )
        buf13 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1),
            torch.float32)
        triton_poi_fused_clone_2[grid(64, 4)](buf12, buf13, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        del buf12
        buf14 = buf4
        del buf4
        triton_poi_fused_clone_0[grid(64)](primals_5, buf14, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_5
        buf15 = empty_strided_cuda((1, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf13, (1, 16, 16), (0, 16, 1
            ), 0), reinterpret_tensor(buf14, (1, 16, 4), (0, 4, 1), 0), out
            =buf15)
    return reinterpret_tensor(buf15, (4, 4, 4), (16, 4, 1), 0
        ), buf10, reinterpret_tensor(buf13, (1, 16, 16), (256, 1, 16), 0
        ), reinterpret_tensor(buf14, (1, 4, 16), (64, 1, 4), 0
        ), reinterpret_tensor(buf11, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf6, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf7, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(primals_2, (1, 4, 16), (64, 1, 4), 0)


def compute_attention(q, k, v, dropout=None, mask=None):
    """
    :param q: Query [B, NH, NQ, EL] or [NH, 1, EL] (in this case NQ=1)
    :param k: Key [B, NH, NK, EL]
    :param v: Value [B, NH, NK, EL]
    :param mask: [B, NQ, NK]
    :param dropout:
    :return:
    """
    if q.ndim + 1 == k.ndim:
        score = torch.einsum('nij,bnkj->bnik', q, k)
    elif q.ndim == k.ndim:
        score = torch.einsum('bnij,bnkj->bnik', q, k)
    score = score / np.sqrt(q.shape[-1])
    if mask is not None:
        mask = mask[:, None]
        score = score * mask + -100000000.0 * (1 - mask)
    score = F.softmax(score, dim=-1)
    if dropout is not None:
        score = dropout(score)
    return torch.einsum('bnij,bnjk->bnik', score, v)


class MultiHeadedAttentionBase(nn.Module):

    def __init__(self, embed_dim, num_heads, latent_dim, dropout=None):
        """
        :param embed_dim: The dimension of feature in each entity.
        :param num_heads: The number of attention heads.
        :param latent_dim:
        :param dropout:
        """
        super().__init__()
        self.w_k = nn.Parameter(torch.empty(num_heads, embed_dim, latent_dim))
        self.w_v = nn.Parameter(torch.empty(num_heads, embed_dim, latent_dim))
        self.w_o = nn.Parameter(torch.empty(num_heads, latent_dim, embed_dim))
        self.dropout = nn.Dropout(dropout) if dropout else nn.Identity()

    def _reset_parameters(self):
        nn.init.xavier_normal_(self.w_k)
        nn.init.xavier_normal_(self.w_v)
        nn.init.xavier_normal_(self.w_o)
        if hasattr(self, 'q'):
            nn.init.xavier_normal_(self.q)
        if hasattr(self, 'w_q'):
            nn.init.xavier_normal_(self.w_q)


class MultiHeadSelfAttentionNew(MultiHeadedAttentionBase):

    def __init__(self, embed_dim, num_heads, latent_dim, dropout=None):
        super().__init__(embed_dim, num_heads, latent_dim, dropout)
        self.w_q = nn.Parameter(torch.empty(num_heads, embed_dim, latent_dim))
        self._reset_parameters()

    def forward(self, input_0):
        primals_1 = self.w_k
        primals_2 = self.w_v
        primals_3 = self.w_o
        primals_4 = self.w_q
        primals_5 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
