# 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/xe/cxeuttfzx4xq2jmzwzvkech4crjirky5wjckb34lnep5o6sog3uw.py
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   matmul => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_2,), 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=[256], 
    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 = 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)
    tmp1 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x4), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/q7/cq7ilgx3ypqrdvru2o5zoj6pcjbcl6tijh4jkr6bayvxbghgaapd.py
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   matmul => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_3,), 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=[512], 
    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_1', '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_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8) % 16
    x2 = (xindex // 128)
    x3 = 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 + (x1 + (16*x0) + (64*x2)), 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 + (x1 + (16*((-4) + x0))), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = 2.0
    tmp11 = tmp10 * tmp9
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x3), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/kc/ckc7kgv2ad42cooz262kobndus7frgbxepqvr7mo7fb2e2cvxhka.py
# Topologically Sorted Source Nodes: [wrapped_sqrt, att_1], Original ATen: [aten.sqrt, aten._softmax]
# Source node to ATen node mapping:
#   att_1 => div_1, exp, sum_1
#   wrapped_sqrt => full_default
# Graph fragment:
#   %full_default : [num_users=4] = 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_11, %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 = {})
#   %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_per_fused__softmax_sqrt_2 = async_compile.triton('triton_per_fused__softmax_sqrt_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.persistent_reduction(
    size_hints=[64, 8],
    reduction_hint=ReductionHint.INNER,
    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_per_fused__softmax_sqrt_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused__softmax_sqrt_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 8
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (8*x0)), xmask, other=0.0)
    tmp1 = tl.full([1, 1], 2.0, tl.float64)
    tmp2 = tl.full([1, 1], 0.0, tl.float64)
    tmp3 = tmp1 >= tmp2
    tmp4 = 1.0
    tmp5 = -1.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp0 * tmp6
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, float("-inf"))
    tmp11 = triton_helpers.max2(tmp10, 1)[:, None]
    tmp12 = tmp7 - tmp11
    tmp13 = tmp6.to(tl.float64)
    tmp14 = tmp13 * tmp1
    tmp15 = tmp14.to(tl.float32)
    tmp16 = tmp12 / tmp15
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
    tmp20 = tl.where(xmask, tmp18, 0)
    tmp21 = tl.sum(tmp20, 1)[:, None]
    tmp22 = tmp17 / tmp21
    tl.store(out_ptr2 + (r1 + (8*x0)), tmp22, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/24/c2465xv6ssrlybnm7a4fp34al2molz27awwb3plzwdnqjdy72gz6.py
# Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   matmul_1 => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_5,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_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=[512], 
    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_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_clone_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 8
    x0 = xindex % 4
    x2 = (xindex // 32) % 4
    x3 = (xindex // 128)
    x4 = xindex
    tmp0 = x1
    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 + (x0 + (4*x2) + (16*x1) + (64*x3)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (4*x2) + (16*((-4) + x1))), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = 2.0
    tmp11 = tmp10 * tmp9
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x4), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6b/c6busvilz5nn36jjet3bmw7cqddirh4sgalamjr3fsrp3sbsacfi.py
# Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   contiguous => clone_3
# Graph fragment:
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_6,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_4 = async_compile.triton('triton_poi_fused_clone_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_clone_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_clone_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
    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')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = 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, (1, 4, 16), (64, 16, 1))
    assert_size_stride(primals_4, (1, 4, 16), (64, 16, 1))
    assert_size_stride(primals_5, (16, 4), (4, 1))
    assert_size_stride(primals_6, (16, ), (1, ))
    assert_size_stride(primals_7, (16, 4), (4, 1))
    assert_size_stride(primals_8, (16, ), (1, ))
    assert_size_stride(primals_9, (16, 4), (4, 1))
    assert_size_stride(primals_10, (16, ), (1, ))
    assert_size_stride(primals_11, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_12, (4, 16), (16, 1))
    assert_size_stride(primals_13, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 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, 16), (1, 4), 0), out=buf0)
        del primals_5
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_8, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_7
        del primals_8
        buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_10, reinterpret_tensor(primals_11, (16, 4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_10
        del primals_9
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(buf0, primals_6, buf3, 256, grid=grid(256), stream=stream0)
        del buf0
        del primals_6
        buf4 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf1, primals_3, buf4, 512, grid=grid(512), stream=stream0)
        del primals_3
        buf5 = empty_strided_cuda((16, 4, 8), (32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf4, (16, 4, 8), (32, 8, 1), 0), out=buf5)
        buf8 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [wrapped_sqrt, att_1], Original ATen: [aten.sqrt, aten._softmax]
        triton_per_fused__softmax_sqrt_2.run(buf5, buf8, 64, 8, grid=grid(64), stream=stream0)
        buf9 = reinterpret_tensor(buf5, (4, 4, 8, 4), (128, 32, 4, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf2, primals_4, buf9, 512, grid=grid(512), stream=stream0)
        del primals_4
        buf10 = reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 8), (32, 8, 1), 0), reinterpret_tensor(buf9, (16, 8, 4), (32, 4, 1), 0), out=buf10)
        buf11 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
        triton_poi_fused_clone_4.run(buf10, buf11, 256, grid=grid(256), stream=stream0)
        del buf10
        buf12 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_13, reinterpret_tensor(buf11, (16, 16), (16, 1), 0), reinterpret_tensor(primals_12, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf12)
        del primals_13
    return (reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_11, (16, 4), (4, 1), 0), buf8, reinterpret_tensor(buf11, (16, 16), (16, 1), 0), primals_12, reinterpret_tensor(buf9, (16, 4, 8), (32, 1, 4), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf4, (16, 8, 4), (32, 1, 8), 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((1, 4, 16), (64, 16, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 4, 16), (64, 16, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13])
    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 as nn
import torch.utils.data
import torch.nn


class ScaledDotProductAttentionMemory(nn.Module):
    """
    Scaled dot-product attention with memory
    """

    def __init__(self, d_model, d_k, d_v, h, m):
        """
        :param d_model: Output dimensionality of the model
        :param d_k: Dimensionality of queries and keys
        :param d_v: Dimensionality of values
        :param h: Number of heads
        :param m: Number of memory slots
        """
        super(ScaledDotProductAttentionMemory, self).__init__()
        self.fc_q = nn.Linear(d_model, h * d_k)
        self.fc_k = nn.Linear(d_model, h * d_k)
        self.fc_v = nn.Linear(d_model, h * d_v)
        self.fc_o = nn.Linear(h * d_v, d_model)
        self.m_k = nn.Parameter(torch.FloatTensor(1, m, h * d_k))
        self.m_v = nn.Parameter(torch.FloatTensor(1, m, h * d_v))
        self.d_model = d_model
        self.d_k = d_k
        self.d_v = d_v
        self.h = h
        self.m = m
        self.init_weights()

    def init_weights(self):
        nn.init.xavier_uniform_(self.fc_q.weight)
        nn.init.xavier_uniform_(self.fc_k.weight)
        nn.init.xavier_uniform_(self.fc_v.weight)
        nn.init.xavier_uniform_(self.fc_o.weight)
        nn.init.normal_(self.m_k, 0, 1 / self.d_k)
        nn.init.normal_(self.m_v, 0, 1 / self.m)
        nn.init.constant_(self.fc_q.bias, 0)
        nn.init.constant_(self.fc_k.bias, 0)
        nn.init.constant_(self.fc_v.bias, 0)
        nn.init.constant_(self.fc_o.bias, 0)

    def forward(self, queries, keys, values, attention_mask=None,
        attention_weights=None):
        """
        Computes
        :param queries: Queries (b_s, nq, d_model)
        :param keys: Keys (b_s, nk, d_model)
        :param values: Values (b_s, nk, d_model)
        :param attention_mask: Mask over attention values (b_s, h, nq, nk). True indicates masking.
        :param attention_weights: Multiplicative weights for attention values (b_s, h, nq, nk).
        :return:
        """
        b_s, nq = queries.shape[:2]
        nk = keys.shape[1]
        m_k = np.sqrt(self.d_k) * self.m_k.expand(b_s, self.m, self.h *
            self.d_k)
        m_v = np.sqrt(self.m) * self.m_v.expand(b_s, self.m, self.h * self.d_v)
        q = self.fc_q(queries).view(b_s, nq, self.h, self.d_k).permute(0, 2,
            1, 3)
        k = torch.cat([self.fc_k(keys), m_k], 1).view(b_s, nk + self.m,
            self.h, self.d_k).permute(0, 2, 3, 1)
        v = torch.cat([self.fc_v(values), m_v], 1).view(b_s, nk + self.m,
            self.h, self.d_v).permute(0, 2, 1, 3)
        att = torch.matmul(q, k) / np.sqrt(self.d_k)
        if attention_weights is not None:
            att = torch.cat([att[:, :, :, :nk] * attention_weights, att[:,
                :, :, nk:]], -1)
        if attention_mask is not None:
            att[:, :, :, :nk] = att[:, :, :, :nk].masked_fill(attention_mask,
                -np.inf)
        att = torch.softmax(att, -1)
        out = torch.matmul(att, v).permute(0, 2, 1, 3).contiguous().view(b_s,
            nq, self.h * self.d_v)
        out = self.fc_o(out)
        return out


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


def get_init_inputs():
    return [[], {'d_model': 4, 'd_k': 4, 'd_v': 4, 'h': 4, 'm': 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
import torch.utils.data
import torch.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):
    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)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x4, tmp2, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8 % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x1 + 16 * x0 + 64 * x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x1 + 16 * (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = 2.0
    tmp11 = tmp10 * tmp9
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x3, tmp14, xmask)


@triton.jit
def triton_per_fused__softmax_sqrt_2(in_ptr0, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 8 * x0), xmask, other=0.0)
    tmp1 = tl.full([1, 1], 2.0, tl.float64)
    tmp2 = tl.full([1, 1], 0.0, tl.float64)
    tmp3 = tmp1 >= tmp2
    tmp4 = 1.0
    tmp5 = -1.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp0 * tmp6
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, float('-inf'))
    tmp11 = triton_helpers.max2(tmp10, 1)[:, None]
    tmp12 = tmp7 - tmp11
    tmp13 = tmp6.to(tl.float64)
    tmp14 = tmp13 * tmp1
    tmp15 = tmp14.to(tl.float32)
    tmp16 = tmp12 / tmp15
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
    tmp20 = tl.where(xmask, tmp18, 0)
    tmp21 = tl.sum(tmp20, 1)[:, None]
    tmp22 = tmp17 / tmp21
    tl.store(out_ptr2 + (r1 + 8 * x0), tmp22, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 8
    x0 = xindex % 4
    x2 = xindex // 32 % 4
    x3 = xindex // 128
    x4 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp4 &
        xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4 * x2 + 16 * (-4 + x1)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = 2.0
    tmp11 = tmp10 * tmp9
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x4, tmp14, xmask)


@triton.jit
def triton_poi_fused_clone_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
    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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = 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, (1, 4, 16), (64, 16, 1))
    assert_size_stride(primals_4, (1, 4, 16), (64, 16, 1))
    assert_size_stride(primals_5, (16, 4), (4, 1))
    assert_size_stride(primals_6, (16,), (1,))
    assert_size_stride(primals_7, (16, 4), (4, 1))
    assert_size_stride(primals_8, (16,), (1,))
    assert_size_stride(primals_9, (16, 4), (4, 1))
    assert_size_stride(primals_10, (16,), (1,))
    assert_size_stride(primals_11, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_12, (4, 16), (16, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 16), (1, 4), 0), out=buf0)
        del primals_5
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(primals_2, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 16), (1, 4), 
            0), alpha=1, beta=1, out=buf1)
        del primals_7
        del primals_8
        buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_10, reinterpret_tensor(primals_11, (16,
            4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 16), (1, 4), 
            0), alpha=1, beta=1, out=buf2)
        del primals_10
        del primals_9
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(256)](buf0, primals_6, buf3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del primals_6
        buf4 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        triton_poi_fused_clone_1[grid(512)](buf1, primals_3, buf4, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf5 = empty_strided_cuda((16, 4, 8), (32, 8, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf4, (16, 4, 8), (32, 8, 1), 0), out=buf5)
        buf8 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        triton_per_fused__softmax_sqrt_2[grid(64)](buf5, buf8, 64, 8,
            XBLOCK=8, num_warps=2, num_stages=1)
        buf9 = reinterpret_tensor(buf5, (4, 4, 8, 4), (128, 32, 4, 1), 0)
        del buf5
        triton_poi_fused_clone_3[grid(512)](buf2, primals_4, buf9, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf10 = reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 8), (32, 8, 1),
            0), reinterpret_tensor(buf9, (16, 8, 4), (32, 4, 1), 0), out=buf10)
        buf11 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        triton_poi_fused_clone_4[grid(256)](buf10, buf11, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf10
        buf12 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_13, reinterpret_tensor(buf11, (16, 16),
            (16, 1), 0), reinterpret_tensor(primals_12, (16, 4), (1, 16), 0
            ), alpha=1, beta=1, out=buf12)
        del primals_13
    return reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_11, (16, 4), (4, 1), 0
        ), buf8, reinterpret_tensor(buf11, (16, 16), (16, 1), 0
        ), primals_12, reinterpret_tensor(buf9, (16, 4, 8), (32, 1, 4), 0
        ), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf4, (16, 8, 4), (32, 1, 8), 0)


class ScaledDotProductAttentionMemoryNew(nn.Module):
    """
    Scaled dot-product attention with memory
    """

    def __init__(self, d_model, d_k, d_v, h, m):
        """
        :param d_model: Output dimensionality of the model
        :param d_k: Dimensionality of queries and keys
        :param d_v: Dimensionality of values
        :param h: Number of heads
        :param m: Number of memory slots
        """
        super(ScaledDotProductAttentionMemoryNew, self).__init__()
        self.fc_q = nn.Linear(d_model, h * d_k)
        self.fc_k = nn.Linear(d_model, h * d_k)
        self.fc_v = nn.Linear(d_model, h * d_v)
        self.fc_o = nn.Linear(h * d_v, d_model)
        self.m_k = nn.Parameter(torch.FloatTensor(1, m, h * d_k))
        self.m_v = nn.Parameter(torch.FloatTensor(1, m, h * d_v))
        self.d_model = d_model
        self.d_k = d_k
        self.d_v = d_v
        self.h = h
        self.m = m
        self.init_weights()

    def init_weights(self):
        nn.init.xavier_uniform_(self.fc_q.weight)
        nn.init.xavier_uniform_(self.fc_k.weight)
        nn.init.xavier_uniform_(self.fc_v.weight)
        nn.init.xavier_uniform_(self.fc_o.weight)
        nn.init.normal_(self.m_k, 0, 1 / self.d_k)
        nn.init.normal_(self.m_v, 0, 1 / self.m)
        nn.init.constant_(self.fc_q.bias, 0)
        nn.init.constant_(self.fc_k.bias, 0)
        nn.init.constant_(self.fc_v.bias, 0)
        nn.init.constant_(self.fc_o.bias, 0)

    def forward(self, input_0, input_1, input_2):
        primals_3 = self.m_k
        primals_4 = self.m_v
        primals_5 = self.fc_q.weight
        primals_6 = self.fc_q.bias
        primals_7 = self.fc_k.weight
        primals_8 = self.fc_k.bias
        primals_9 = self.fc_v.weight
        primals_10 = self.fc_v.bias
        primals_12 = self.fc_o.weight
        primals_13 = self.fc_o.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_11 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]
