# 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_6/inductor_cache/cz/cczdmlbbmfz5zwvgavqgsp7p2chtvjm2zzbxhjk7w5jaagtfot3j.py
# Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_1 => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%tanh, %tanh_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
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = libdevice.tanh(tmp5)
    tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
    tmp8 = tl.where(tmp4, tmp6, tmp7)
    tmp9 = tmp0 >= tmp3
    tmp10 = tl.full([1], 8, tl.int64)
    tmp11 = tmp0 < tmp10
    tmp12 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp13 = libdevice.tanh(tmp12)
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp9, tmp13, tmp14)
    tmp16 = tl.where(tmp4, tmp8, tmp15)
    tl.store(out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/m6/cm6ozsdmt5vl54fxwk7cgktzswysgn2c37vsaybpucplzehkrnnz.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_1', '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_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 25600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 400
    x2 = xindex % 1600
    x3 = (xindex // 1600)
    tmp0 = tl.load(in_out_ptr0 + (x4), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x4), tmp4, xmask)
    tl.store(out_ptr0 + (x2 + (1664*x3)), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/64/c64g5uxk2a5hbzuhd6oikla2gb5eyfjjb6kbh7btzswha52gl5ex.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_1 => relu_1
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_2', '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_relu_threshold_backward_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 19200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 300
    x2 = (xindex // 1200)
    x3 = xindex % 1200
    tmp0 = tl.load(in_ptr0 + (x4), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3 + (1216*x2)), tmp4, xmask)
    tl.store(out_ptr1 + (x3 + (1280*x2)), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/4h/c4h6r6vefoeuinm5eqv2d6wqmfj2mnjacalp633y3m6bnseb2bnk.py
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.relu, aten.view]
# Source node to ATen node mapping:
#   x_1 => relu_1
#   x_2 => view_4
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {})
#   %view_4 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%relu_1, [64, 300]), kwargs = {})
triton_poi_fused_relu_view_3 = async_compile.triton('triton_poi_fused_relu_view_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=[32768], 
    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_relu_view_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_relu_view_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 19200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 300
    x1 = (xindex // 300)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (300*(x1 % 4)) + (1216*(x1 // 4))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/yl/cyl3pepuyms7had2pbaeyfxi5l2wn7v5miawhd67mloipgsmgxwf.py
# Topologically Sorted Source Nodes: [tanh_1, action], Original ATen: [aten.tanh, aten.mul]
# Source node to ATen node mapping:
#   action => mul
#   tanh_1 => tanh_2
# Graph fragment:
#   %tanh_2 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%view_5,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh_2, 4), kwargs = {})
triton_poi_fused_mul_tanh_4 = async_compile.triton('triton_poi_fused_mul_tanh_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_mul_tanh_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_mul_tanh_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
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tl.store(out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/tc/ctcyhc3j7gcndovmump6plsamm3m4336gv7k75wru64k3klwdqb5.py
# Topologically Sorted Source Nodes: [msg_down], Original ATen: [aten.linalg_vector_norm, aten.div]
# Source node to ATen node mapping:
#   msg_down => div, pow_1, pow_2, sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_11, 2.0), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1], True), kwargs = {})
#   %pow_2 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_11, %expand), kwargs = {})
triton_per_fused_div_linalg_vector_norm_5 = async_compile.triton('triton_per_fused_div_linalg_vector_norm_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.persistent_reduction(
    size_hints=[64, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_linalg_vector_norm_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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_div_linalg_vector_norm_5(in_out_ptr0, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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 + (16*x0)), xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = 1e-12
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp0 / tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp6, xmask)
    tl.store(out_ptr0 + (r1 + (16*x0)), tmp9, 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, primals_14 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (400, 8), (8, 1))
    assert_size_stride(primals_4, (400, ), (1, ))
    assert_size_stride(primals_5, (300, 400), (400, 1))
    assert_size_stride(primals_6, (300, ), (1, ))
    assert_size_stride(primals_7, (4, 300), (300, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (400, 8), (8, 1))
    assert_size_stride(primals_10, (400, ), (1, ))
    assert_size_stride(primals_11, (300, 400), (400, 1))
    assert_size_stride(primals_12, (300, ), (1, ))
    assert_size_stride(primals_13, (16, 300), (300, 1))
    assert_size_stride(primals_14, (16, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 512, grid=grid(512), stream=stream0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 400), (400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0), reinterpret_tensor(primals_3, (8, 400), (1, 8), 0), out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 400), (6400, 1600, 400, 1), 0); del buf1  # reuse
        buf20 = empty_strided_cuda((4, 4, 4, 400), (6656, 1664, 400, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_1.run(buf2, primals_4, buf20, 25600, grid=grid(25600), stream=stream0)
        del primals_4
        buf3 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 400), (400, 1), 0), reinterpret_tensor(primals_5, (400, 300), (1, 400), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 300), (4864, 1216, 300, 1), torch.float32)
        buf19 = empty_strided_cuda((4, 4, 4, 300), (5120, 1280, 300, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_2.run(buf3, primals_6, buf4, buf19, 19200, grid=grid(19200), stream=stream0)
        del primals_6
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.relu, aten.view]
        triton_poi_fused_relu_view_3.run(buf4, buf5, 19200, grid=grid(19200), stream=stream0)
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_8, buf5, reinterpret_tensor(primals_7, (300, 4), (1, 300), 0), alpha=1, beta=1, out=buf6)
        del primals_8
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh_1, action], Original ATen: [aten.tanh, aten.mul]
        triton_poi_fused_mul_tanh_4.run(buf6, buf7, 256, grid=grid(256), stream=stream0)
        buf8 = empty_strided_cuda((64, 400), (400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0), reinterpret_tensor(primals_9, (8, 400), (1, 8), 0), out=buf8)
        del primals_9
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 400), (6400, 1600, 400, 1), 0); del buf8  # reuse
        buf18 = empty_strided_cuda((4, 4, 4, 400), (6656, 1664, 400, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_1.run(buf9, primals_10, buf18, 25600, grid=grid(25600), stream=stream0)
        del primals_10
        buf10 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf9, (64, 400), (400, 1), 0), reinterpret_tensor(primals_11, (400, 300), (1, 400), 0), out=buf10)
        buf11 = buf4; del buf4  # reuse
        buf17 = empty_strided_cuda((4, 4, 4, 300), (5120, 1280, 300, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_2.run(buf10, primals_12, buf11, buf17, 19200, grid=grid(19200), stream=stream0)
        del primals_12
        buf12 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.relu, aten.view]
        triton_poi_fused_relu_view_3.run(buf11, buf12, 19200, grid=grid(19200), stream=stream0)
        del buf11
        buf13 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_14, buf12, reinterpret_tensor(primals_13, (300, 16), (1, 300), 0), alpha=1, beta=1, out=buf13)
        del primals_14
        buf14 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf15 = reinterpret_tensor(buf14, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf14  # reuse
        buf16 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [msg_down], Original ATen: [aten.linalg_vector_norm, aten.div]
        triton_per_fused_div_linalg_vector_norm_5.run(buf15, buf13, buf16, 64, 16, grid=grid(64), stream=stream0)
    return (buf7, buf16, reinterpret_tensor(buf0, (64, 8), (8, 1), 0), reinterpret_tensor(buf2, (64, 400), (400, 1), 0), buf5, buf6, reinterpret_tensor(buf9, (64, 400), (400, 1), 0), buf12, buf13, buf15, primals_13, buf17, primals_11, buf18, primals_7, buf19, primals_5, buf20, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((400, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((300, 400), (400, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((300, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 300), (300, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((400, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((300, 400), (400, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((300, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((16, 300), (300, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((16, ), (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, primals_14])
    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
from torch import nn
import torch.nn.functional as F


class MLPBase(nn.Module):

    def __init__(self, num_inputs, num_outputs):
        super(MLPBase, self).__init__()
        self.l1 = nn.Linear(num_inputs, 400)
        self.l2 = nn.Linear(400, 300)
        self.l3 = nn.Linear(300, num_outputs)

    def forward(self, inputs):
        x = F.relu(self.l1(inputs))
        x = F.relu(self.l2(x))
        x = self.l3(x)
        return x


class ActorDownAction(nn.Module):
    """a top-down module used in bothway message passing that passes messages to children and outputs action"""

    def __init__(self, self_input_dim, action_dim, msg_dim, max_action,
        max_children):
        super(ActorDownAction, self).__init__()
        self.max_action = max_action
        self.action_base = MLPBase(self_input_dim + msg_dim, action_dim)
        self.msg_base = MLPBase(self_input_dim + msg_dim, msg_dim *
            max_children)

    def forward(self, x, m):
        xm = torch.cat((x, m), dim=-1)
        xm = torch.tanh(xm)
        action = self.max_action * torch.tanh(self.action_base(xm))
        msg_down = self.msg_base(xm)
        msg_down = F.normalize(msg_down, dim=-1)
        return action, msg_down


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


def get_init_inputs():
    return [[], {'self_input_dim': 4, 'action_dim': 4, 'msg_dim': 4,
        'max_action': 4, 'max_children': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.nn.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
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = libdevice.tanh(tmp5)
    tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
    tmp8 = tl.where(tmp4, tmp6, tmp7)
    tmp9 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp12 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp13 = libdevice.tanh(tmp12)
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp9, tmp13, tmp14)
    tmp16 = tl.where(tmp4, tmp8, tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 25600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 400
    x2 = xindex % 1600
    x3 = xindex // 1600
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x4, tmp4, xmask)
    tl.store(out_ptr0 + (x2 + 1664 * x3), tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 19200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 300
    x2 = xindex // 1200
    x3 = xindex % 1200
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3 + 1216 * x2), tmp4, xmask)
    tl.store(out_ptr1 + (x3 + 1280 * x2), tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_view_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 19200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 300
    x1 = xindex // 300
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 300 * (x1 % 4) + 1216 * (x1 // 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_mul_tanh_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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_per_fused_div_linalg_vector_norm_5(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 16
    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 + 16 * x0), xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = 1e-12
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp0 / tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp9, 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, primals_14) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (400, 8), (8, 1))
    assert_size_stride(primals_4, (400,), (1,))
    assert_size_stride(primals_5, (300, 400), (400, 1))
    assert_size_stride(primals_6, (300,), (1,))
    assert_size_stride(primals_7, (4, 300), (300, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (400, 8), (8, 1))
    assert_size_stride(primals_10, (400,), (1,))
    assert_size_stride(primals_11, (300, 400), (400, 1))
    assert_size_stride(primals_12, (300,), (1,))
    assert_size_stride(primals_13, (16, 300), (300, 1))
    assert_size_stride(primals_14, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 400), (400, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_3, (8, 400), (1, 8), 0), out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 400), (6400, 1600, 400, 1), 0
            )
        del buf1
        buf20 = empty_strided_cuda((4, 4, 4, 400), (6656, 1664, 400, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(25600)](buf2,
            primals_4, buf20, 25600, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 400), (400, 1), 0),
            reinterpret_tensor(primals_5, (400, 300), (1, 400), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 300), (4864, 1216, 300, 1),
            torch.float32)
        buf19 = empty_strided_cuda((4, 4, 4, 300), (5120, 1280, 300, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(19200)](buf3,
            primals_6, buf4, buf19, 19200, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_6
        buf5 = buf3
        del buf3
        triton_poi_fused_relu_view_3[grid(19200)](buf4, buf5, 19200, XBLOCK
            =128, num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf5, reinterpret_tensor(primals_7,
            (300, 4), (1, 300), 0), alpha=1, beta=1, out=buf6)
        del primals_8
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_tanh_4[grid(256)](buf6, buf7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((64, 400), (400, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_9, (8, 400), (1, 8), 0), out=buf8)
        del primals_9
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 400), (6400, 1600, 400, 1), 0
            )
        del buf8
        buf18 = empty_strided_cuda((4, 4, 4, 400), (6656, 1664, 400, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(25600)](buf9,
            primals_10, buf18, 25600, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_10
        buf10 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf9, (64, 400), (400, 1), 0),
            reinterpret_tensor(primals_11, (400, 300), (1, 400), 0), out=buf10)
        buf11 = buf4
        del buf4
        buf17 = empty_strided_cuda((4, 4, 4, 300), (5120, 1280, 300, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(19200)](buf10,
            primals_12, buf11, buf17, 19200, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_12
        buf12 = buf10
        del buf10
        triton_poi_fused_relu_view_3[grid(19200)](buf11, buf12, 19200,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf11
        buf13 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_14, buf12, reinterpret_tensor(
            primals_13, (300, 16), (1, 300), 0), alpha=1, beta=1, out=buf13)
        del primals_14
        buf14 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf15 = reinterpret_tensor(buf14, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf14
        buf16 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
            float32)
        triton_per_fused_div_linalg_vector_norm_5[grid(64)](buf15, buf13,
            buf16, 64, 16, XBLOCK=32, num_warps=4, num_stages=1)
    return (buf7, buf16, reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
        reinterpret_tensor(buf2, (64, 400), (400, 1), 0), buf5, buf6,
        reinterpret_tensor(buf9, (64, 400), (400, 1), 0), buf12, buf13,
        buf15, primals_13, buf17, primals_11, buf18, primals_7, buf19,
        primals_5, buf20)


class MLPBase(nn.Module):

    def __init__(self, num_inputs, num_outputs):
        super(MLPBase, self).__init__()
        self.l1 = nn.Linear(num_inputs, 400)
        self.l2 = nn.Linear(400, 300)
        self.l3 = nn.Linear(300, num_outputs)

    def forward(self, inputs):
        x = F.relu(self.l1(inputs))
        x = F.relu(self.l2(x))
        x = self.l3(x)
        return x


class ActorDownActionNew(nn.Module):
    """a top-down module used in bothway message passing that passes messages to children and outputs action"""

    def __init__(self, self_input_dim, action_dim, msg_dim, max_action,
        max_children):
        super(ActorDownActionNew, self).__init__()
        self.max_action = max_action
        self.action_base = MLPBase(self_input_dim + msg_dim, action_dim)
        self.msg_base = MLPBase(self_input_dim + msg_dim, msg_dim *
            max_children)

    def forward(self, input_0, input_1):
        primals_3 = self.action_base.l1.weight
        primals_4 = self.action_base.l1.bias
        primals_5 = self.action_base.l2.weight
        primals_6 = self.action_base.l2.bias
        primals_7 = self.action_base.l3.weight
        primals_8 = self.action_base.l3.bias
        primals_9 = self.msg_base.l1.weight
        primals_10 = self.msg_base.l1.bias
        primals_11 = self.msg_base.l2.weight
        primals_12 = self.msg_base.l2.bias
        primals_13 = self.msg_base.l3.weight
        primals_14 = self.msg_base.l3.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14])
        return output[0], output[1]
