# 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/vx/cvxzmthv4i2niuhjkx7pdwegys74ubmwp36fuzpk743r7lkqg4tm.py
# Topologically Sorted Source Nodes: [_weight_norm], Original ATen: [aten.norm, aten.div, aten.mul]
# Source node to ATen node mapping:
#   _weight_norm => div, mul, pow_1, pow_2, sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_2, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, None), 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 = (%primals_1, %pow_2), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %div), kwargs = {})
triton_per_fused_div_mul_norm_0 = async_compile.triton('triton_per_fused_div_mul_norm_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.persistent_reduction(
    size_hints=[1, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_mul_norm_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_mul_norm_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp6 = tl.load(in_ptr1 + (0))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tmp5 = libdevice.sqrt(tmp4)
    tmp8 = tmp7 / tmp5
    tmp9 = tmp0 * tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp5, None)
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/vd/cvdmeqqfxsccexypcbaqz2voldkyplxpggqi62aifgun23zqncmo.py
# Topologically Sorted Source Nodes: [v__1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   v__1 => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), 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=[16, 4], tile_hint=TileHint.DEFAULT,
    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), 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, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp4, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/p5/cp5pohlvud22ry7jg7rk2cbrxg2zgd5m6ftrmu3zisebczw32ykt.py
# Topologically Sorted Source Nodes: [v__1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   v__1 => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/n7/cn7r4oqyybihm35ca45aju4wmhzx7y2kroklysz4mf5pfmkv6ecf.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_3 => relu_1
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_3 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[64], 
    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_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_relu_threshold_backward_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), 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 + (x2), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12 = args
    args.clear()
    assert_size_stride(primals_1, (), ())
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (), ())
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_9, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_10, (), ())
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [_weight_norm], Original ATen: [aten.norm, aten.div, aten.mul]
        stream0 = get_raw_stream(0)
        triton_per_fused_div_mul_norm_0.run(buf1, primals_2, primals_1, buf2, 1, 16, grid=grid(1), stream=stream0)
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(buf2, (4, 4), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4; del buf4  # reuse
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [_weight_norm_1], Original ATen: [aten.norm, aten.div, aten.mul]
        triton_per_fused_div_mul_norm_0.run(buf5, primals_6, primals_5, buf6, 1, 16, grid=grid(1), stream=stream0)
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_8, (16, 4), (4, 1), 0), reinterpret_tensor(buf6, (4, 4), (1, 4), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v__1], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf3, primals_3, buf8, 16, 4, grid=grid(16, 4), stream=stream0)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v__1], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(primals_9, buf9, 256, grid=grid(256), stream=stream0)
        del primals_9
        buf10 = empty_strided_cuda((16, 1, 4), (4, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v__1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf8, (16, 1, 4), (4, 0, 1), 0), reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1), 0), out=buf10)
        buf11 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [h_], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf7, primals_7, buf11, 16, 4, grid=grid(16, 4), stream=stream0)
        buf12 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf10, reinterpret_tensor(buf11, (16, 4, 1), (4, 1, 0), 0), out=buf12)
        buf13 = empty_strided_cuda((), (), torch.float32)
        buf14 = buf13; del buf13  # reuse
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [_weight_norm_2], Original ATen: [aten.norm, aten.div, aten.mul]
        triton_per_fused_div_mul_norm_0.run(buf14, primals_11, primals_10, buf15, 1, 16, grid=grid(1), stream=stream0)
        buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf12, (4, 4), (4, 1), 0), reinterpret_tensor(buf15, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf16)
        del primals_12
        buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_3.run(buf7, primals_7, buf17, 64, grid=grid(64), stream=stream0)
        del buf7
        del primals_7
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_3.run(buf3, primals_3, buf18, 64, grid=grid(64), stream=stream0)
        del buf3
        del primals_3
    return (reinterpret_tensor(buf16, (4, 1, 4), (4, 4, 1), 0), buf2, buf6, buf15, primals_1, primals_2, primals_5, primals_6, primals_10, primals_11, buf1, reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), buf5, reinterpret_tensor(primals_8, (16, 4), (4, 1), 0), buf14, reinterpret_tensor(buf12, (4, 4), (4, 1), 0), buf15, reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 4), 0), reinterpret_tensor(buf11, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf9, (16, 4, 4), (16, 1, 4), 0), buf17, buf18, )


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((), (), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((), (), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((), (), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = 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])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.utils.data
import torch.nn as nn
from torch.nn.utils import weight_norm


class FCNet(nn.Module):

    def __init__(self, in_size, out_size, activate=None, drop=0.0):
        super(FCNet, self).__init__()
        self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
        self.drop_value = drop
        self.drop = nn.Dropout(drop)
        self.activate = activate.lower() if activate is not None else None
        if activate == 'relu':
            self.ac_fn = nn.ReLU()
        elif activate == 'sigmoid':
            self.ac_fn = nn.Sigmoid()
        elif activate == 'tanh':
            self.ac_fn = nn.Tanh()

    def forward(self, x):
        if self.drop_value > 0:
            x = self.drop(x)
        x = self.lin(x)
        if self.activate is not None:
            x = self.ac_fn(x)
        return x


class ApplySingleAttention(nn.Module):

    def __init__(self, v_features, q_features, mid_features, drop=0.0):
        super(ApplySingleAttention, self).__init__()
        self.lin_v = FCNet(v_features, mid_features, activate='relu', drop=drop
            )
        self.lin_q = FCNet(q_features, mid_features, activate='relu', drop=drop
            )
        self.lin_atten = FCNet(mid_features, mid_features, drop=drop)

    def forward(self, v, q, atten):
        """
        v = batch, num_obj, dim
        q = batch, que_len, dim
        atten:  batch x v_num x q_num
        """
        v_ = self.lin_v(v).transpose(1, 2).unsqueeze(2)
        q_ = self.lin_q(q).transpose(1, 2).unsqueeze(3)
        v_ = torch.matmul(v_, atten.unsqueeze(1))
        h_ = torch.matmul(v_, q_)
        h_ = h_.squeeze(3).squeeze(2)
        atten_h = self.lin_atten(h_.unsqueeze(1))
        return atten_h


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


def get_init_inputs():
    return [[], {'v_features': 4, 'q_features': 4, 'mid_features': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
from torch.nn.utils import weight_norm
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_per_fused_div_mul_norm_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp6 = tl.load(in_ptr1 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tmp5 = libdevice.sqrt(tmp4)
    tmp8 = tmp7 / tmp5
    tmp9 = tmp0 * tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp5, None)
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp9, None)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, 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 + x2, tmp6, 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
        ) = args
    args.clear()
    assert_size_stride(primals_1, (), ())
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (), ())
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_9, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_10, (), ())
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_div_mul_norm_0[grid(1)](buf1, primals_2, primals_1,
            buf2, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf2, (4, 4), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_per_fused_div_mul_norm_0[grid(1)](buf5, primals_6, primals_5,
            buf6, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_8, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf6, (4, 4), (1, 4), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(16, 4)](buf3, primals_3, buf8, 16, 4,
            XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(256)](primals_9, buf9, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
        buf10 = empty_strided_cuda((16, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf8, (16, 1, 4), (4, 0, 1), 
            0), reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1), 0), out=buf10)
        buf11 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf8
        triton_poi_fused_clone_1[grid(16, 4)](buf7, primals_7, buf11, 16, 4,
            XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
        buf12 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(buf10, reinterpret_tensor(buf11, (16, 4, 1), (4,
            1, 0), 0), out=buf12)
        buf13 = empty_strided_cuda((), (), torch.float32)
        buf14 = buf13
        del buf13
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_per_fused_div_mul_norm_0[grid(1)](buf14, primals_11,
            primals_10, buf15, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf12, (4, 4),
            (4, 1), 0), reinterpret_tensor(buf15, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf16)
        del primals_12
        buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(64)](buf7,
            primals_7, buf17, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf7
        del primals_7
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(64)](buf3,
            primals_3, buf18, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf3
        del primals_3
    return (reinterpret_tensor(buf16, (4, 1, 4), (4, 4, 1), 0), buf2, buf6,
        buf15, primals_1, primals_2, primals_5, primals_6, primals_10,
        primals_11, buf1, reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
        buf5, reinterpret_tensor(primals_8, (16, 4), (4, 1), 0), buf14,
        reinterpret_tensor(buf12, (4, 4), (4, 1), 0), buf15,
        reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 4), 0),
        reinterpret_tensor(buf11, (16, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf9, (16, 4, 4), (16, 1, 4), 0), buf17, buf18)


class FCNet(nn.Module):

    def __init__(self, in_size, out_size, activate=None, drop=0.0):
        super(FCNet, self).__init__()
        self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
        self.drop_value = drop
        self.drop = nn.Dropout(drop)
        self.activate = activate.lower() if activate is not None else None
        if activate == 'relu':
            self.ac_fn = nn.ReLU()
        elif activate == 'sigmoid':
            self.ac_fn = nn.Sigmoid()
        elif activate == 'tanh':
            self.ac_fn = nn.Tanh()

    def forward(self, x):
        if self.drop_value > 0:
            x = self.drop(x)
        x = self.lin(x)
        if self.activate is not None:
            x = self.ac_fn(x)
        return x


class ApplySingleAttentionNew(nn.Module):

    def __init__(self, v_features, q_features, mid_features, drop=0.0):
        super(ApplySingleAttentionNew, self).__init__()
        self.lin_v = FCNet(v_features, mid_features, activate='relu', drop=drop
            )
        self.lin_q = FCNet(q_features, mid_features, activate='relu', drop=drop
            )
        self.lin_atten = FCNet(mid_features, mid_features, drop=drop)

    def forward(self, input_0, input_1, input_2):
        primals_3 = self.lin_v.lin.bias
        primals_1 = self.lin_v.lin.weight_g
        primals_2 = self.lin_v.lin.weight_v
        primals_7 = self.lin_q.lin.bias
        primals_5 = self.lin_q.lin.weight_g
        primals_6 = self.lin_q.lin.weight_v
        primals_12 = self.lin_atten.lin.bias
        primals_10 = self.lin_atten.lin.weight_g
        primals_11 = self.lin_atten.lin.weight_v
        primals_4 = input_0
        primals_8 = input_1
        primals_9 = 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])
        return output[0]
