# 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/pk/cpk5n3igge6w4dypc2un5gad324fghftzwfdytoko35prykob5rm.py
# Topologically Sorted Source Nodes: [input_all], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   input_all => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_2, %primals_1], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[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_cat_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_cat_0(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 // 64) % 2
    x0 = xindex % 64
    x2 = (xindex // 128)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (64*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (64*x2)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/dw/cdwnta4op3j5o5gpvwxq4g5ehtysablbdwmrhp2xr424jkdvzuqq.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_1,), 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, 2], 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_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, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 256
    xnumel = 2
    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 % 64
    y1 = (yindex // 64)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (64*x2) + (128*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (2*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2b/c2bsssxh3o2n7ut56wpjw35pnbyaxf2zcjh6ya5mujjdgn2ezcvf.py
# Topologically Sorted Source Nodes: [wrapped_sqrt, p_attn], Original ATen: [aten.sqrt, aten._softmax]
# Source node to ATen node mapping:
#   p_attn => div_1, exp, sum_1
#   wrapped_sqrt => full_default
# Graph fragment:
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([], 4.0), 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_12, %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_poi_fused__softmax_sqrt_2 = async_compile.triton('triton_poi_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.pointwise(
    size_hints=[32], 
    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_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_sqrt_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 2)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp8 = tl.load(in_ptr0 + (2*x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + (2*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 4.0, 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)
    tmp13 = tmp7 - tmp12
    tmp14 = tmp6.to(tl.float64)
    tmp15 = tmp14 * tmp1
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp13 / tmp16
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp9 - tmp12
    tmp20 = tmp19 / tmp16
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp11 - tmp12
    tmp23 = tmp22 / tmp16
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp21 + tmp24
    tmp26 = tmp18 / tmp25
    tl.store(out_ptr0 + (x2), tmp26, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/f4/cf45yxwk7d3bjoqarfjnwtphtjpu6m6khjd2oobodhjyifufvyoy.py
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   output => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_3,), 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: '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_3', '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_3(in_ptr0, 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 % 16
    x1 = (xindex // 16) % 2
    x2 = (xindex // 32) % 4
    x3 = (xindex // 128)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x2) + (64*x1) + (128*x3)), xmask)
    tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/fd/cfd25pzqi5ycxpig4csiumm6vna2mmh5aqcgnk2vue3tfvwqzryj.py
# Topologically Sorted Source Nodes: [add, result], Original ATen: [aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   result => div_2
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_3, %squeeze), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, 2), kwargs = {})
triton_poi_fused_add_div_4 = async_compile.triton('triton_poi_fused_add_div_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_add_div_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + (x0), tmp4, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64), (64, 64, 1))
    assert_size_stride(primals_2, (4, 1, 64), (64, 64, 1))
    assert_size_stride(primals_3, (64, 64), (64, 1))
    assert_size_stride(primals_4, (64, 64), (64, 1))
    assert_size_stride(primals_5, (64, 64), (64, 1))
    assert_size_stride(primals_6, (64, 64), (64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2, 64), (128, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_all], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_2, primals_1, buf0, 512, grid=grid(512), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((8, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (8, 64), (64, 1), 0), reinterpret_tensor(primals_3, (64, 64), (1, 64), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((8, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (8, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (4, 64), (64, 1), 0), reinterpret_tensor(primals_5, (64, 64), (1, 64), 0), out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 16, 2), (128, 32, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf1, buf4, 256, 2, grid=grid(256, 2), stream=stream0)
        buf5 = empty_strided_cuda((16, 1, 2), (2, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 1, 16), (16, 16, 1), 0), reinterpret_tensor(buf4, (16, 16, 2), (32, 2, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 1, 2), (8, 2, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [wrapped_sqrt, p_attn], Original ATen: [aten.sqrt, aten._softmax]
        triton_poi_fused__softmax_sqrt_2.run(buf5, buf6, 32, grid=grid(32), stream=stream0)
        del buf5
        buf7 = reinterpret_tensor(buf1, (4, 4, 2, 16), (128, 32, 16, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf2, buf7, 512, grid=grid(512), stream=stream0)
        del buf2
        buf8 = empty_strided_cuda((16, 1, 16), (16, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 1, 2), (2, 2, 1), 0), reinterpret_tensor(buf7, (16, 2, 16), (32, 16, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf8, (4, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 64), (1, 64), 0), out=buf9)
        buf10 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [add, result], Original ATen: [aten.add, aten.div]
        triton_poi_fused_add_div_4.run(buf10, primals_2, 256, grid=grid(256), stream=stream0)
    return (buf10, buf6, reinterpret_tensor(buf0, (8, 64), (64, 1), 0), reinterpret_tensor(primals_2, (4, 64), (64, 1), 0), buf6, reinterpret_tensor(buf8, (4, 64), (64, 1), 0), primals_6, reinterpret_tensor(buf7, (16, 16, 2), (32, 1, 16), 0), reinterpret_tensor(buf3, (16, 16, 1), (16, 1, 64), 0), reinterpret_tensor(buf4, (16, 2, 16), (32, 1, 2), 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, 1, 64), (64, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 1, 64), (64, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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
from torch.nn import functional as F


def activation_factory(activation_type):
    if activation_type == 'RELU':
        return F.relu
    elif activation_type == 'TANH':
        return torch.tanh
    elif activation_type == 'ELU':
        return nn.ELU()
    else:
        raise ValueError('Unknown activation_type: {}'.format(activation_type))


def attention(query, key, value, mask=None, dropout=None):
    """
    Compute a Scaled Dot Product Attention.

    Parameters
    ----------
    query
        size: batch, head, 1 (ego-entity), features
    key
        size: batch, head, entities, features
    value
        size: batch, head, entities, features
    mask
        size: batch,  head, 1 (absence feature), 1 (ego-entity)
    dropout

    Returns
    -------
    The attention softmax(QK^T/sqrt(dk))V
    """
    d_k = query.size(-1)
    scores = torch.matmul(query, key.transpose(-2, -1)) / np.sqrt(d_k)
    if mask is not None:
        scores = scores.masked_fill(mask, -1000000000.0)
    p_attn = F.softmax(scores, dim=-1)
    if dropout is not None:
        p_attn = dropout(p_attn)
    output = torch.matmul(p_attn, value)
    return output, p_attn


class BaseModule(torch.nn.Module):
    """
    Base torch.nn.Module implementing basic features:
        - initialization factory
        - normalization parameters
    """

    def __init__(self, activation_type='RELU', reset_type='XAVIER'):
        super().__init__()
        self.activation = activation_factory(activation_type)
        self.reset_type = reset_type

    def _init_weights(self, m):
        if hasattr(m, 'weight'):
            if self.reset_type == 'XAVIER':
                torch.nn.init.xavier_uniform_(m.weight.data)
            elif self.reset_type == 'ZEROS':
                torch.nn.init.constant_(m.weight.data, 0.0)
            else:
                raise ValueError('Unknown reset type')
        if hasattr(m, 'bias') and m.bias is not None:
            torch.nn.init.constant_(m.bias.data, 0.0)

    def reset(self):
        self.apply(self._init_weights)


class EgoAttention(BaseModule):

    def __init__(self, feature_size=64, heads=4, dropout_factor=0):
        super().__init__()
        self.feature_size = feature_size
        self.heads = heads
        self.dropout_factor = dropout_factor
        self.features_per_head = int(self.feature_size / self.heads)
        self.value_all = nn.Linear(self.feature_size, self.feature_size,
            bias=False)
        self.key_all = nn.Linear(self.feature_size, self.feature_size, bias
            =False)
        self.query_ego = nn.Linear(self.feature_size, self.feature_size,
            bias=False)
        self.attention_combine = nn.Linear(self.feature_size, self.
            feature_size, bias=False)

    @classmethod
    def default_config(cls):
        return {}

    def forward(self, ego, others, mask=None):
        batch_size = others.shape[0]
        n_entities = others.shape[1] + 1
        input_all = torch.cat((ego.view(batch_size, 1, self.feature_size),
            others), dim=1)
        key_all = self.key_all(input_all).view(batch_size, n_entities, self
            .heads, self.features_per_head)
        value_all = self.value_all(input_all).view(batch_size, n_entities,
            self.heads, self.features_per_head)
        query_ego = self.query_ego(ego).view(batch_size, 1, self.heads,
            self.features_per_head)
        key_all = key_all.permute(0, 2, 1, 3)
        value_all = value_all.permute(0, 2, 1, 3)
        query_ego = query_ego.permute(0, 2, 1, 3)
        if mask is not None:
            mask = mask.view((batch_size, 1, 1, n_entities)).repeat((1,
                self.heads, 1, 1))
        value, attention_matrix = attention(query_ego, key_all, value_all,
            mask, nn.Dropout(self.dropout_factor))
        result = (self.attention_combine(value.reshape((batch_size, self.
            feature_size))) + ego.squeeze(1)) / 2
        return result, attention_matrix


def get_inputs():
    return [torch.rand([4, 1, 64]), torch.rand([4, 1, 64])]


def get_init_inputs():
    return [[], {}]

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 as nn
from torch.nn import functional as F
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_cat_0(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 // 64 % 2
    x0 = xindex % 64
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 64 * x2), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    xnumel = 2
    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 % 64
    y1 = yindex // 64
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 64 * x2 + 128 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 2 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_sqrt_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 2
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp8 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.full([1], 4.0, 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)
    tmp13 = tmp7 - tmp12
    tmp14 = tmp6.to(tl.float64)
    tmp15 = tmp14 * tmp1
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp13 / tmp16
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp9 - tmp12
    tmp20 = tmp19 / tmp16
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp11 - tmp12
    tmp23 = tmp22 / tmp16
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp21 + tmp24
    tmp26 = tmp18 / tmp25
    tl.store(out_ptr0 + x2, tmp26, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, 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 % 16
    x1 = xindex // 16 % 2
    x2 = xindex // 32 % 4
    x3 = xindex // 128
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2 + 64 * x1 + 128 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_div_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64), (64, 64, 1))
    assert_size_stride(primals_2, (4, 1, 64), (64, 64, 1))
    assert_size_stride(primals_3, (64, 64), (64, 1))
    assert_size_stride(primals_4, (64, 64), (64, 1))
    assert_size_stride(primals_5, (64, 64), (64, 1))
    assert_size_stride(primals_6, (64, 64), (64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2, 64), (128, 64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_2, primals_1, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((8, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (8, 64), (64, 1), 0),
            reinterpret_tensor(primals_3, (64, 64), (1, 64), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((8, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (8, 64), (64, 1), 0),
            reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (4, 64), (64, 1), 0
            ), reinterpret_tensor(primals_5, (64, 64), (1, 64), 0), out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 16, 2), (128, 32, 2, 1), torch.float32
            )
        triton_poi_fused_clone_1[grid(256, 2)](buf1, buf4, 256, 2, XBLOCK=2,
            YBLOCK=64, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((16, 1, 2), (2, 2, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 1, 16), (16, 16, 1
            ), 0), reinterpret_tensor(buf4, (16, 16, 2), (32, 2, 1), 0),
            out=buf5)
        buf6 = empty_strided_cuda((4, 4, 1, 2), (8, 2, 2, 1), torch.float32)
        triton_poi_fused__softmax_sqrt_2[grid(32)](buf5, buf6, 32, XBLOCK=
            32, num_warps=1, num_stages=1)
        del buf5
        buf7 = reinterpret_tensor(buf1, (4, 4, 2, 16), (128, 32, 16, 1), 0)
        del buf1
        triton_poi_fused_clone_3[grid(512)](buf2, buf7, 512, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf2
        buf8 = empty_strided_cuda((16, 1, 16), (16, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 1, 2), (2, 2, 1), 
            0), reinterpret_tensor(buf7, (16, 2, 16), (32, 16, 1), 0), out=buf8
            )
        buf9 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (4, 64), (64, 1), 0),
            reinterpret_tensor(primals_6, (64, 64), (1, 64), 0), out=buf9)
        buf10 = buf9
        del buf9
        triton_poi_fused_add_div_4[grid(256)](buf10, primals_2, 256, XBLOCK
            =128, num_warps=4, num_stages=1)
    return buf10, buf6, reinterpret_tensor(buf0, (8, 64), (64, 1), 0
        ), reinterpret_tensor(primals_2, (4, 64), (64, 1), 0
        ), buf6, reinterpret_tensor(buf8, (4, 64), (64, 1), 0
        ), primals_6, reinterpret_tensor(buf7, (16, 16, 2), (32, 1, 16), 0
        ), reinterpret_tensor(buf3, (16, 16, 1), (16, 1, 64), 0
        ), reinterpret_tensor(buf4, (16, 2, 16), (32, 1, 2), 0)


def activation_factory(activation_type):
    if activation_type == 'RELU':
        return F.relu
    elif activation_type == 'TANH':
        return torch.tanh
    elif activation_type == 'ELU':
        return nn.ELU()
    else:
        raise ValueError('Unknown activation_type: {}'.format(activation_type))


def attention(query, key, value, mask=None, dropout=None):
    """
    Compute a Scaled Dot Product Attention.

    Parameters
    ----------
    query
        size: batch, head, 1 (ego-entity), features
    key
        size: batch, head, entities, features
    value
        size: batch, head, entities, features
    mask
        size: batch,  head, 1 (absence feature), 1 (ego-entity)
    dropout

    Returns
    -------
    The attention softmax(QK^T/sqrt(dk))V
    """
    d_k = query.size(-1)
    scores = torch.matmul(query, key.transpose(-2, -1)) / np.sqrt(d_k)
    if mask is not None:
        scores = scores.masked_fill(mask, -1000000000.0)
    p_attn = F.softmax(scores, dim=-1)
    if dropout is not None:
        p_attn = dropout(p_attn)
    output = torch.matmul(p_attn, value)
    return output, p_attn


class BaseModule(torch.nn.Module):
    """
    Base torch.nn.Module implementing basic features:
        - initialization factory
        - normalization parameters
    """

    def __init__(self, activation_type='RELU', reset_type='XAVIER'):
        super().__init__()
        self.activation = activation_factory(activation_type)
        self.reset_type = reset_type

    def _init_weights(self, m):
        if hasattr(m, 'weight'):
            if self.reset_type == 'XAVIER':
                torch.nn.init.xavier_uniform_(m.weight.data)
            elif self.reset_type == 'ZEROS':
                torch.nn.init.constant_(m.weight.data, 0.0)
            else:
                raise ValueError('Unknown reset type')
        if hasattr(m, 'bias') and m.bias is not None:
            torch.nn.init.constant_(m.bias.data, 0.0)

    def reset(self):
        self.apply(self._init_weights)


class EgoAttentionNew(BaseModule):

    def __init__(self, feature_size=64, heads=4, dropout_factor=0):
        super().__init__()
        self.feature_size = feature_size
        self.heads = heads
        self.dropout_factor = dropout_factor
        self.features_per_head = int(self.feature_size / self.heads)
        self.value_all = nn.Linear(self.feature_size, self.feature_size,
            bias=False)
        self.key_all = nn.Linear(self.feature_size, self.feature_size, bias
            =False)
        self.query_ego = nn.Linear(self.feature_size, self.feature_size,
            bias=False)
        self.attention_combine = nn.Linear(self.feature_size, self.
            feature_size, bias=False)

    @classmethod
    def default_config(cls):
        return {}

    def forward(self, input_0, input_1):
        primals_3 = self.value_all.weight
        primals_4 = self.key_all.weight
        primals_5 = self.query_ego.weight
        primals_6 = self.attention_combine.weight
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]
