# 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/qy/cqylihc3fogsj2yvocypgkbiuyzokt5bhrvhf7ocl2xzspsb4pd7.py
# Topologically Sorted Source Nodes: [cat, cat_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
#   cat_1 => cat_1
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2, %sub, %mul], -1), kwargs = {})
#   %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_5, %sub_1, %mul_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    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 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + ((4*x1) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + ((4*x1) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 - tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp14, tmp17, tmp18)
    tmp20 = tmp0 >= tmp12
    tmp21 = tl.full([1], 16, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tl.load(in_ptr0 + ((4*x1) + ((-12) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr1 + ((4*x1) + ((-12) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 * tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp20, tmp25, tmp26)
    tmp28 = tl.where(tmp14, tmp19, tmp27)
    tmp29 = tl.where(tmp9, tmp10, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tmp31 = tl.load(in_ptr2 + ((4*x1) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp32 = tl.load(in_ptr2 + ((4*x1) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp33 = tmp15 - tmp32
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp14, tmp33, tmp34)
    tmp36 = tl.load(in_ptr2 + ((4*x1) + ((-12) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp37 = tmp23 * tmp36
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp20, tmp37, tmp38)
    tmp40 = tl.where(tmp14, tmp35, tmp39)
    tmp41 = tl.where(tmp9, tmp31, tmp40)
    tmp42 = tl.where(tmp4, tmp5, tmp41)
    tl.store(out_ptr0 + (x2), tmp30, xmask)
    tl.store(out_ptr1 + (x2), tmp42, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/h6/ch6zblpwfmjn7hcldykcvpbnoddtxwkqhxrbwxb36uqcbtzxmtng.py
# Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_2 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%relu, %relu_1], -1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 = tl.load(in_ptr1 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 8, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tl.load(in_ptr2 + ((4*x1) + ((-4) + x0)), tmp12 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + ((-4) + x0), tmp12 & xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + (x2), tmp21, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wz/cwzdxfokndxiun3t7rpyof4msqxgkxkraoss6yojgn3bj2fclafj.py
# Topologically Sorted Source Nodes: [fuse_prob, mul_2, sub_2, mul_3, add], Original ATen: [aten.sigmoid, aten.mul, aten.rsub, aten.add]
# Source node to ATen node mapping:
#   add => add
#   fuse_prob => sigmoid
#   mul_2 => mul_2
#   mul_3 => mul_3
#   sub_2 => sub_2
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_5,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %primals_2), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %primals_5), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {})
triton_poi_fused_add_mul_rsub_sigmoid_2 = async_compile.triton('triton_poi_fused_add_mul_rsub_sigmoid_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: '*fp32', 3: '*fp32', 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_add_mul_rsub_sigmoid_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_add_mul_rsub_sigmoid_2(in_ptr0, in_ptr1, in_ptr2, 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)
    tmp2 = tl.load(in_ptr1 + (x0), xmask)
    tmp6 = tl.load(in_ptr2 + (x0), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/6y/c6yl4uv4syttx5p2rvialnpvw2b2afygk6u4wtiv3hxyrze6hevt.py
# Topologically Sorted Source Nodes: [out2], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out2 => 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=[256], 
    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 = 256
    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 = 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, (4, 16), (16, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_6, (4, 16), (16, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 8), (8, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat, cat_1], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, primals_5, buf0, buf2, 1024, grid=grid(1024), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 16), (16, 1), 0), reinterpret_tensor(primals_3, (16, 4), (1, 16), 0), out=buf1)
        del primals_3
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 16), (16, 1), 0), reinterpret_tensor(primals_6, (16, 4), (1, 16), 0), out=buf3)
        del primals_6
        buf4 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf1, primals_4, buf3, primals_7, buf4, 512, grid=grid(512), stream=stream0)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf4, (64, 8), (8, 1), 0), reinterpret_tensor(primals_8, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf5)
        del primals_9
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [fuse_prob, mul_2, sub_2, mul_3, add], Original ATen: [aten.sigmoid, aten.mul, aten.rsub, aten.add]
        triton_poi_fused_add_mul_rsub_sigmoid_2.run(buf5, primals_2, primals_5, buf6, 256, grid=grid(256), stream=stream0)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out2], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_3.run(buf3, primals_7, buf7, 256, grid=grid(256), stream=stream0)
        del buf3
        del primals_7
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_3.run(buf1, primals_4, buf8, 256, grid=grid(256), stream=stream0)
        del buf1
        del primals_4
    return (buf6, primals_2, primals_5, reinterpret_tensor(buf0, (64, 16), (16, 1), 0), reinterpret_tensor(buf2, (64, 16), (16, 1), 0), reinterpret_tensor(buf4, (64, 8), (8, 1), 0), buf5, primals_8, buf7, buf8, )


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((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = 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])
    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)

from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn


class FuseLayer(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.linear1 = nn.Linear(4 * config.hidden_size, config.hidden_size)
        self.linear2 = nn.Linear(4 * config.hidden_size, config.hidden_size)
        self.linear3 = nn.Linear(2 * config.hidden_size, config.hidden_size)
        self.activation = nn.ReLU()
        self.gate = nn.Sigmoid()

    def forward(self, orig, input1, input2):
        out1 = self.activation(self.linear1(torch.cat([orig, input1, orig -
            input1, orig * input1], dim=-1)))
        out2 = self.activation(self.linear2(torch.cat([orig, input2, orig -
            input2, orig * input2], dim=-1)))
        fuse_prob = self.gate(self.linear3(torch.cat([out1, out2], dim=-1)))
        return fuse_prob * input1 + (1 - fuse_prob) * input2


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


def get_init_inputs():
    return [[], {'config': _mock_config(hidden_size=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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    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 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 - tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp14, tmp17, tmp18)
    tmp20 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp23 = tl.load(in_ptr0 + (4 * x1 + (-12 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr1 + (4 * x1 + (-12 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 * tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp20, tmp25, tmp26)
    tmp28 = tl.where(tmp14, tmp19, tmp27)
    tmp29 = tl.where(tmp9, tmp10, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tmp31 = tl.load(in_ptr2 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp32 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp33 = tmp15 - tmp32
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp14, tmp33, tmp34)
    tmp36 = tl.load(in_ptr2 + (4 * x1 + (-12 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp37 = tmp23 * tmp36
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp20, tmp37, tmp38)
    tmp40 = tl.where(tmp14, tmp35, tmp39)
    tmp41 = tl.where(tmp9, tmp31, tmp40)
    tmp42 = tl.where(tmp4, tmp5, tmp41)
    tl.store(out_ptr0 + x2, tmp30, xmask)
    tl.store(out_ptr1 + x2, tmp42, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp15 = tl.load(in_ptr2 + (4 * x1 + (-4 + x0)), tmp12 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + (-4 + x0), tmp12 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + x2, tmp21, xmask)


@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_2(in_ptr0, in_ptr1, in_ptr2,
    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)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp6 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    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) = 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, (4, 16), (16, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_6, (4, 16), (16, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 8), (8, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
            float32)
        buf2 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(1024)](primals_1, primals_2, primals_5,
            buf0, buf2, 1024, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 16), (16, 1), 0),
            reinterpret_tensor(primals_3, (16, 4), (1, 16), 0), out=buf1)
        del primals_3
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 16), (16, 1), 0),
            reinterpret_tensor(primals_6, (16, 4), (1, 16), 0), out=buf3)
        del primals_6
        buf4 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(512)](buf1, primals_4, buf3, primals_7,
            buf4, 512, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf4, (64, 8), (
            8, 1), 0), reinterpret_tensor(primals_8, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf5)
        del primals_9
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_rsub_sigmoid_2[grid(256)](buf5, primals_2,
            primals_5, buf6, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(256)](buf3,
            primals_7, buf7, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        del primals_7
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(256)](buf1,
            primals_4, buf8, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_4
    return buf6, primals_2, primals_5, reinterpret_tensor(buf0, (64, 16), (
        16, 1), 0), reinterpret_tensor(buf2, (64, 16), (16, 1), 0
        ), reinterpret_tensor(buf4, (64, 8), (8, 1), 0
        ), buf5, primals_8, buf7, buf8


class FuseLayerNew(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.linear1 = nn.Linear(4 * config.hidden_size, config.hidden_size)
        self.linear2 = nn.Linear(4 * config.hidden_size, config.hidden_size)
        self.linear3 = nn.Linear(2 * config.hidden_size, config.hidden_size)
        self.activation = nn.ReLU()
        self.gate = nn.Sigmoid()

    def forward(self, input_0, input_1, input_2):
        primals_3 = self.linear1.weight
        primals_4 = self.linear1.bias
        primals_6 = self.linear2.weight
        primals_7 = self.linear2.bias
        primals_8 = self.linear3.weight
        primals_9 = self.linear3.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_5 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
