# 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_8/inductor_cache/vt/cvtlwsahn5oxfhga3dwjdswt3bhkvbh4zkaufpwgeopelblrwpnw.py
# Topologically Sorted Source Nodes: [pow_1, sum_1, add, pow_2], Original ATen: [aten.pow, aten.sum, aten.add]
# Source node to ATen node mapping:
#   add => add
#   pow_1 => pow_1
#   pow_2 => pow_2
#   sum_1 => sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [2, 3], True), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-05), kwargs = {})
#   %pow_2 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add, 0.5), kwargs = {})
triton_per_fused_add_pow_sum_0 = async_compile.triton('triton_per_fused_add_pow_sum_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=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_pow_sum_0', '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_add_pow_sum_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/bk/cbk3oabqcrqeiz2rnrvk4rxkdmk4ef46ye5mdaspijuw6pzegair.py
# Topologically Sorted Source Nodes: [embedding, pow_3, mean, add_1, pow_4], Original ATen: [aten.mul, aten.pow, aten.mean, aten.add]
# Source node to ATen node mapping:
#   add_1 => add_1
#   embedding => mul
#   mean => mean
#   pow_3 => pow_3
#   pow_4 => pow_4
# Graph fragment:
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, %primals_2), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mul, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_3, [1], True), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, 1e-05), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add_1, 0.5), kwargs = {})
triton_poi_fused_add_mean_mul_pow_1 = async_compile.triton('triton_poi_fused_add_mean_mul_pow_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mean_mul_pow_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_mean_mul_pow_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp5 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (1))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp11 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (2))
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK])
    tmp17 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr1 + (3))
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK])
    tmp3 = tmp0 * tmp2
    tmp4 = tmp3 * tmp3
    tmp8 = tmp5 * tmp7
    tmp9 = tmp8 * tmp8
    tmp10 = tmp4 + tmp9
    tmp14 = tmp11 * tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tmp10 + tmp15
    tmp20 = tmp17 * tmp19
    tmp21 = tmp20 * tmp20
    tmp22 = tmp16 + tmp21
    tmp23 = 4.0
    tmp24 = tmp22 / tmp23
    tmp25 = 1e-05
    tmp26 = tmp24 + tmp25
    tmp27 = libdevice.sqrt(tmp26)
    tl.store(out_ptr0 + (x0), tmp27, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ni/cnixk7pwsdlgsjxwsmpbrzopu3rzfg26mvtp5cemfxo35lnktysi.py
# Topologically Sorted Source Nodes: [embedding, pow_3, mean, add_1, pow_4, norm, mul_1, add_2, tanh, gate], Original ATen: [aten.mul, aten.pow, aten.mean, aten.add, aten.div, aten.tanh]
# Source node to ATen node mapping:
#   add_1 => add_1
#   add_2 => add_2
#   embedding => mul
#   gate => add_3
#   mean => mean
#   mul_1 => mul_1
#   norm => div
#   pow_3 => pow_3
#   pow_4 => pow_4
#   tanh => tanh
# Graph fragment:
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, %primals_2), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mul, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_3, [1], True), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, 1e-05), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add_1, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_3, %pow_4), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %div), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_4), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_2,), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%tanh, 1.0), kwargs = {})
triton_poi_fused_add_div_mean_mul_pow_tanh_2 = async_compile.triton('triton_poi_fused_add_div_mean_mul_pow_tanh_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_mul_pow_tanh_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_mean_mul_pow_tanh_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 / tmp4
    tmp6 = tmp2 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.tanh(tmp8)
    tmp10 = 1.0
    tmp11 = tmp9 + tmp10
    tl.store(out_ptr0 + (x2), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/vz/cvzd7uxhjcuuvhocpajasfl7npgg3w4tjtzribzneczbe5yv6xr6.py
# Topologically Sorted Source Nodes: [embedding, pow_3, mean, add_1, pow_4, norm, mul_1, add_2, tanh, gate, mul_2], Original ATen: [aten.mul, aten.pow, aten.mean, aten.add, aten.div, aten.tanh]
# Source node to ATen node mapping:
#   add_1 => add_1
#   add_2 => add_2
#   embedding => mul
#   gate => add_3
#   mean => mean
#   mul_1 => mul_1
#   mul_2 => mul_2
#   norm => div
#   pow_3 => pow_3
#   pow_4 => pow_4
#   tanh => tanh
# Graph fragment:
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, %primals_2), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mul, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_3, [1], True), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, 1e-05), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add_1, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_3, %pow_4), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %div), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_4), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_2,), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%tanh, 1.0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %add_3), kwargs = {})
triton_poi_fused_add_div_mean_mul_pow_tanh_3 = async_compile.triton('triton_poi_fused_add_div_mean_mul_pow_tanh_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: '*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_add_div_mean_mul_pow_tanh_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_add_div_mean_mul_pow_tanh_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
    x1 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [pow_1, sum_1, add, pow_2], Original ATen: [aten.pow, aten.sum, aten.add]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_pow_sum_0.run(buf1, primals_1, 16, 16, grid=grid(16), stream=stream0)
        buf2 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [embedding, pow_3, mean, add_1, pow_4], Original ATen: [aten.mul, aten.pow, aten.mean, aten.add]
        triton_poi_fused_add_mean_mul_pow_1.run(buf1, primals_2, buf2, 4, grid=grid(4), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [embedding, pow_3, mean, add_1, pow_4, norm, mul_1, add_2, tanh, gate], Original ATen: [aten.mul, aten.pow, aten.mean, aten.add, aten.div, aten.tanh]
        triton_poi_fused_add_div_mean_mul_pow_tanh_2.run(buf1, primals_2, primals_3, buf2, primals_4, buf3, 16, grid=grid(16), stream=stream0)
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [embedding, pow_3, mean, add_1, pow_4, norm, mul_1, add_2, tanh, gate, mul_2], Original ATen: [aten.mul, aten.pow, aten.mean, aten.add, aten.div, aten.tanh]
        triton_poi_fused_add_div_mean_mul_pow_tanh_3.run(primals_1, buf3, buf4, 256, grid=grid(256), stream=stream0)
        del buf3
    return (buf4, primals_1, primals_2, primals_3, primals_4, buf1, )


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((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 sys
import torch
import torch.nn as nn
import torch.utils.data.distributed


class GCT(nn.Module):

    def __init__(self, num_channels, epsilon=1e-05, mode='l2', after_relu=False
        ):
        super(GCT, self).__init__()
        self.alpha = nn.Parameter(torch.ones(1, num_channels, 1, 1))
        self.gamma = nn.Parameter(torch.zeros(1, num_channels, 1, 1))
        self.beta = nn.Parameter(torch.zeros(1, num_channels, 1, 1))
        self.epsilon = epsilon
        self.mode = mode
        self.after_relu = after_relu

    def forward(self, x):
        if self.mode == 'l2':
            embedding = (x.pow(2).sum((2, 3), keepdim=True) + self.epsilon
                ).pow(0.5) * self.alpha
            norm = self.gamma / (embedding.pow(2).mean(dim=1, keepdim=True) +
                self.epsilon).pow(0.5)
        elif self.mode == 'l1':
            if not self.after_relu:
                _x = torch.abs(x)
            else:
                _x = x
            embedding = _x.sum((2, 3), keepdim=True) * self.alpha
            norm = self.gamma / (torch.abs(embedding).mean(dim=1, keepdim=
                True) + self.epsilon)
        else:
            None
            sys.exit()
        gate = 1.0 + torch.tanh(embedding * norm + self.beta)
        return x * gate


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


def get_init_inputs():
    return [[], {'num_channels': 4}]

import torch
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.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data.distributed
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_add_pow_sum_0(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 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, :]
    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 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_mean_mul_pow_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + 1)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp11 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + 2)
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK])
    tmp17 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr1 + 3)
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK])
    tmp3 = tmp0 * tmp2
    tmp4 = tmp3 * tmp3
    tmp8 = tmp5 * tmp7
    tmp9 = tmp8 * tmp8
    tmp10 = tmp4 + tmp9
    tmp14 = tmp11 * tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tmp10 + tmp15
    tmp20 = tmp17 * tmp19
    tmp21 = tmp20 * tmp20
    tmp22 = tmp16 + tmp21
    tmp23 = 4.0
    tmp24 = tmp22 / tmp23
    tmp25 = 1e-05
    tmp26 = tmp24 + tmp25
    tmp27 = libdevice.sqrt(tmp26)
    tl.store(out_ptr0 + x0, tmp27, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_mul_pow_tanh_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 / tmp4
    tmp6 = tmp2 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.tanh(tmp8)
    tmp10 = 1.0
    tmp11 = tmp9 + tmp10
    tl.store(out_ptr0 + x2, tmp11, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_mul_pow_tanh_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
    x1 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_pow_sum_0[grid(16)](buf1, primals_1, 16, 16,
            XBLOCK=8, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        triton_poi_fused_add_mean_mul_pow_1[grid(4)](buf1, primals_2, buf2,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        triton_poi_fused_add_div_mean_mul_pow_tanh_2[grid(16)](buf1,
            primals_2, primals_3, buf2, primals_4, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_pow_tanh_3[grid(256)](primals_1,
            buf3, buf4, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf3
    return buf4, primals_1, primals_2, primals_3, primals_4, buf1


class GCTNew(nn.Module):

    def __init__(self, num_channels, epsilon=1e-05, mode='l2', after_relu=False
        ):
        super(GCTNew, self).__init__()
        self.alpha = nn.Parameter(torch.ones(1, num_channels, 1, 1))
        self.gamma = nn.Parameter(torch.zeros(1, num_channels, 1, 1))
        self.beta = nn.Parameter(torch.zeros(1, num_channels, 1, 1))
        self.epsilon = epsilon
        self.mode = mode
        self.after_relu = after_relu

    def forward(self, input_0):
        primals_2 = self.alpha
        primals_3 = self.gamma
        primals_4 = self.beta
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
