# 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_4/inductor_cache/gn/cgnivctsrskeah5qevtmk4jn4et3srqbxa65lkg64dcuby2p2f5a.py
# Topologically Sorted Source Nodes: [add, x], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   add => add
#   x => add_1, add_2, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %select), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add, [3]), kwargs = {correction: 0, keepdim: True})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %getitem_1), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_2), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_3), kwargs = {})
triton_red_fused_add_native_layer_norm_0 = async_compile.triton('triton_red_fused_add_native_layer_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.reduction(
    size_hints=[8192, 512],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_add_native_layer_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 2, '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_red_fused_add_native_layer_norm_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 8192
    rnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x3 = xindex
    x0 = xindex % 2048
    x1 = (xindex // 2048)
    tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_ptr0 + (r2 + (512*x3)), rmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tl.load(in_ptr1 + ((2*x1) + (8*r2) + (4096*x0)), rmask, eviction_policy='evict_last', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = triton_helpers.welford_reduce(
            tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0
        )
        tmp4_mean = tl.where(rmask, tmp4_mean_next, tmp4_mean)
        tmp4_m2 = tl.where(rmask, tmp4_m2_next, tmp4_m2)
        tmp4_weight = tl.where(rmask, tmp4_weight_next, tmp4_weight)
    tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(
        tmp4_mean, tmp4_m2, tmp4_weight, 1
    )
    tmp4 = tmp4_tmp[:, None]
    tmp5 = tmp5_tmp[:, None]
    tmp6 = tmp6_tmp[:, None]
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp7 = tl.load(in_ptr0 + (r2 + (512*x3)), rmask, eviction_policy='evict_first', other=0.0)
        tmp8 = tl.load(in_ptr1 + ((2*x1) + (8*r2) + (4096*x0)), rmask, eviction_policy='evict_last', other=0.0)
        tmp17 = tl.load(in_ptr2 + (r2), rmask, eviction_policy='evict_last', other=0.0)
        tmp19 = tl.load(in_ptr3 + (r2), rmask, eviction_policy='evict_last', other=0.0)
        tmp9 = tmp7 + tmp8
        tmp10 = tmp9 - tmp4
        tmp11 = 512.0
        tmp12 = tmp5 / tmp11
        tmp13 = 1e-05
        tmp14 = tmp12 + tmp13
        tmp15 = libdevice.rsqrt(tmp14)
        tmp16 = tmp10 * tmp15
        tmp18 = tmp16 * tmp17
        tmp20 = tmp18 + tmp19
        tl.store(out_ptr2 + (r2 + (512*x3)), tmp16, rmask)
        tl.store(out_ptr3 + (r2 + (512*x3)), tmp20, rmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2n/c2naj5ndsh4wnuxsq2jb2x2f6umriqkt7wtqe4uy55wluqbn6toa.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.gelu]
# Source node to ATen node mapping:
#   x_2 => add_3, erf, mul_2, mul_3, mul_4
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.5), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.7071067811865476), kwargs = {})
#   %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_3,), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %add_3), kwargs = {})
triton_poi_fused_gelu_1 = async_compile.triton('triton_poi_fused_gelu_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=[16777216], 
    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_gelu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_gelu_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16777216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + (x0), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/y6/cy6jvaxfr5c3jwkhvre7x2yx3jdb23q5kasza3emis2k2xq7l6td.py
# Topologically Sorted Source Nodes: [add_1, x_6], Original ATen: [aten.add, aten.native_layer_norm, aten.native_layer_norm_backward]
# Source node to ATen node mapping:
#   add_1 => add_4
#   x_6 => add_5, add_6, mul_5, mul_6, rsqrt_1, sub_1, var_mean_1
# Graph fragment:
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %view_3), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_4, [3]), kwargs = {correction: 0, keepdim: True})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_5,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_4, %getitem_3), kwargs = {})
#   %mul_5 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt_1), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_5, %primals_8), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_6, %primals_9), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%rsqrt_1, 512), kwargs = {})
triton_per_fused_add_native_layer_norm_native_layer_norm_backward_2 = async_compile.triton('triton_per_fused_add_native_layer_norm_native_layer_norm_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.persistent_reduction(
    size_hints=[8192, 512],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32', 9: '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, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_native_layer_norm_native_layer_norm_backward_2', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 5, 'num_reduction': 4, '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_native_layer_norm_native_layer_norm_backward_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr2, out_ptr3, out_ptr4, xnumel, rnumel):
    xnumel = 8192
    XBLOCK: tl.constexpr = 1
    rnumel = 512
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (512*x0)), None)
    tmp1 = tl.load(in_ptr1 + (r1 + (512*x0)), None)
    tmp2 = tl.load(in_ptr2 + (r1), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr3 + (r1), None, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr4 + (r1), None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = tl.full([1], 512, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp5 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = triton_helpers.promote_to_tensor(tl.sum(tmp15, 0))
    tmp18 = tmp4 - tmp12
    tmp19 = 512.0
    tmp20 = tmp17 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp18 * tmp23
    tmp26 = tmp24 * tmp25
    tmp28 = tmp26 + tmp27
    tmp29 = 0.001953125
    tmp30 = tmp23 * tmp29
    tl.store(out_ptr2 + (r1 + (512*x0)), tmp24, None)
    tl.store(out_ptr3 + (r1 + (512*x0)), tmp28, None)
    tl.store(out_ptr4 + (x0), tmp30, None)
''', 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, 512, 512), (1048576, 262144, 512, 1))
    assert_size_stride(primals_2, (512, ), (1, ))
    assert_size_stride(primals_3, (512, ), (1, ))
    assert_size_stride(primals_4, (2048, 512), (512, 1))
    assert_size_stride(primals_5, (2048, ), (1, ))
    assert_size_stride(primals_6, (512, 2048), (2048, 1))
    assert_size_stride(primals_7, (512, ), (1, ))
    assert_size_stride(primals_8, (512, ), (1, ))
    assert_size_stride(primals_9, (512, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [fft_fft], Original ATen: [aten._fft_r2c]
        buf0 = torch.ops.aten._fft_r2c.default(primals_1, [3], 0, False)
        buf1 = buf0
        del buf0
        # Topologically Sorted Source Nodes: [getattr_1], Original ATen: [aten.permute]
        buf2 = torch.ops.aten.permute.default(buf1, [3, 2, 1, 0])
        buf3 = buf2
        # Topologically Sorted Source Nodes: [fft_fft_1], Original ATen: [aten._fft_c2c]
        buf4 = torch.ops.aten._fft_c2c.default(buf3, [3], 0, True)
        del buf1
        del buf2
        del buf3
        buf5 = buf4
        del buf4
        # Topologically Sorted Source Nodes: [getattr_2], Original ATen: [aten.permute]
        buf6 = torch.ops.aten.permute.default(buf5, [3, 2, 1, 0])
        buf7 = buf6
        # Topologically Sorted Source Nodes: [x_fft], Original ATen: [aten.view_as_real]
        buf8 = torch.ops.aten.view_as_real.default(buf7)
        buf9 = buf8
        buf13 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, x], Original ATen: [aten.add, aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_red_fused_add_native_layer_norm_0.run(primals_1, buf9, primals_2, primals_3, buf13, buf14, 8192, 512, grid=grid(8192), stream=stream0)
        del buf5
        del buf6
        del buf7
        del buf8
        del buf9
        del primals_1
        del primals_2
        del primals_3
        buf15 = empty_strided_cuda((8192, 2048), (2048, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf14, (8192, 512), (512, 1), 0), reinterpret_tensor(primals_4, (512, 2048), (1, 512), 0), alpha=1, beta=1, out=buf15)
        del primals_5
        buf16 = empty_strided_cuda((4, 4, 512, 2048), (4194304, 1048576, 2048, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.gelu]
        triton_poi_fused_gelu_1.run(buf15, buf16, 16777216, grid=grid(16777216), stream=stream0)
        buf17 = empty_strided_cuda((8192, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf16, (8192, 2048), (2048, 1), 0), reinterpret_tensor(primals_6, (2048, 512), (1, 2048), 0), out=buf17)
        buf21 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512, 1), torch.float32)
        buf22 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512, 1), torch.float32)
        buf23 = empty_strided_cuda((4, 4, 512, 1), (2048, 512, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add_1, x_6], Original ATen: [aten.add, aten.native_layer_norm, aten.native_layer_norm_backward]
        triton_per_fused_add_native_layer_norm_native_layer_norm_backward_2.run(buf14, buf17, primals_7, primals_8, primals_9, buf21, buf22, buf23, 8192, 512, grid=grid(8192), stream=stream0)
        del buf17
        del primals_7
        del primals_9
    return (buf22, primals_8, buf13, reinterpret_tensor(buf14, (8192, 512), (512, 1), 0), buf15, reinterpret_tensor(buf16, (8192, 2048), (2048, 1), 0), buf21, buf23, primals_6, primals_4, )


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, 512, 512), (1048576, 262144, 512, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((2048, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2048, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((512, 2048), (2048, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((512, ), (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)

import torch
from torch import nn


class FeedForward(nn.Module):

    def __init__(self, dhidden, dropout_rate, **kwargs):
        super(FeedForward, self).__init__(**kwargs)
        self.dhidden = dhidden
        self.dropout_rate = dropout_rate
        self.dense_1 = nn.Linear(dhidden, 4 * dhidden)
        self.dense_2 = nn.Linear(4 * dhidden, dhidden)
        self.gelu = nn.GELU()
        self.dropout = nn.Dropout(dropout_rate)

    def get_config(self):
        config = super(FeedForward, self).get_config()
        config.update({'dhidden': self.dhidden, 'dropout_rate': self.
            dropout_rate})
        return config

    def forward(self, x):
        x = self.dense_1(x)
        x = self.gelu(x)
        x = self.dropout(x)
        x = self.dense_2(x)
        x = self.dropout(x)
        return x


class FNetEncoder(nn.Module):

    def __init__(self, dhidden=512, dropout_rate=0):
        super(FNetEncoder, self).__init__()
        self.feedforward = FeedForward(dhidden, dropout_rate)
        self.LayerNorm_1 = nn.LayerNorm(dhidden)
        self.LayerNorm_2 = nn.LayerNorm(dhidden)

    def get_config(self):
        config = super(FNetEncoder, self).get_config()
        config.update({'dhidden': self.dhidden, 'dropout_rate': self.
            dropout_rate})
        return config

    def forward(self, inputs):
        x_fft = torch.real(torch.fft.fft(torch.fft.fft(inputs).T).T)
        x = self.LayerNorm_1(inputs + x_fft)
        x_ff = self.feedforward(x)
        x = self.LayerNorm_2(x + x_ff)
        return x


def get_inputs():
    return [torch.rand([4, 4, 512, 512])]


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import 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_red_fused_add_native_layer_norm_0(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr,
    RBLOCK: tl.constexpr):
    rnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x3 = xindex
    x0 = xindex % 2048
    x1 = xindex // 2048
    tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_ptr0 + (r2 + 512 * x3), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = tl.load(in_ptr1 + (2 * x1 + 8 * r2 + 4096 * x0), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = (triton_helpers.
            welford_reduce(tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0)
            )
        tmp4_mean = tl.where(rmask, tmp4_mean_next, tmp4_mean)
        tmp4_m2 = tl.where(rmask, tmp4_m2_next, tmp4_m2)
        tmp4_weight = tl.where(rmask, tmp4_weight_next, tmp4_weight)
    tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(tmp4_mean,
        tmp4_m2, tmp4_weight, 1)
    tmp4 = tmp4_tmp[:, None]
    tmp5 = tmp5_tmp[:, None]
    tmp6_tmp[:, None]
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp7 = tl.load(in_ptr0 + (r2 + 512 * x3), rmask, eviction_policy=
            'evict_first', other=0.0)
        tmp8 = tl.load(in_ptr1 + (2 * x1 + 8 * r2 + 4096 * x0), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp17 = tl.load(in_ptr2 + r2, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp19 = tl.load(in_ptr3 + r2, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp9 = tmp7 + tmp8
        tmp10 = tmp9 - tmp4
        tmp11 = 512.0
        tmp12 = tmp5 / tmp11
        tmp13 = 1e-05
        tmp14 = tmp12 + tmp13
        tmp15 = libdevice.rsqrt(tmp14)
        tmp16 = tmp10 * tmp15
        tmp18 = tmp16 * tmp17
        tmp20 = tmp18 + tmp19
        tl.store(out_ptr2 + (r2 + 512 * x3), tmp16, rmask)
        tl.store(out_ptr3 + (r2 + 512 * x3), tmp20, rmask)


@triton.jit
def triton_poi_fused_gelu_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, None)


@triton.jit
def triton_per_fused_add_native_layer_norm_native_layer_norm_backward_2(in_ptr0
    , in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr2, out_ptr3, out_ptr4,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 512 * x0), None)
    tmp1 = tl.load(in_ptr1 + (r1 + 512 * x0), None)
    tmp2 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr3 + r1, None, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr4 + r1, None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = tl.full([1], 512, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp5 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = triton_helpers.promote_to_tensor(tl.sum(tmp15, 0))
    tmp18 = tmp4 - tmp12
    tmp19 = 512.0
    tmp20 = tmp17 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp18 * tmp23
    tmp26 = tmp24 * tmp25
    tmp28 = tmp26 + tmp27
    tmp29 = 0.001953125
    tmp30 = tmp23 * tmp29
    tl.store(out_ptr2 + (r1 + 512 * x0), tmp24, None)
    tl.store(out_ptr3 + (r1 + 512 * x0), tmp28, None)
    tl.store(out_ptr4 + x0, tmp30, None)


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, 512, 512), (1048576, 262144, 512, 1))
    assert_size_stride(primals_2, (512,), (1,))
    assert_size_stride(primals_3, (512,), (1,))
    assert_size_stride(primals_4, (2048, 512), (512, 1))
    assert_size_stride(primals_5, (2048,), (1,))
    assert_size_stride(primals_6, (512, 2048), (2048, 1))
    assert_size_stride(primals_7, (512,), (1,))
    assert_size_stride(primals_8, (512,), (1,))
    assert_size_stride(primals_9, (512,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten._fft_r2c.default(primals_1, [3], 0, False)
        buf1 = buf0
        del buf0
        buf2 = torch.ops.aten.permute.default(buf1, [3, 2, 1, 0])
        buf3 = buf2
        buf4 = torch.ops.aten._fft_c2c.default(buf3, [3], 0, True)
        del buf1
        del buf2
        del buf3
        buf5 = buf4
        del buf4
        buf6 = torch.ops.aten.permute.default(buf5, [3, 2, 1, 0])
        buf7 = buf6
        buf8 = torch.ops.aten.view_as_real.default(buf7)
        buf9 = buf8
        buf13 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512,
            1), torch.float32)
        buf14 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512,
            1), torch.float32)
        get_raw_stream(0)
        triton_red_fused_add_native_layer_norm_0[grid(8192)](primals_1,
            buf9, primals_2, primals_3, buf13, buf14, 8192, 512, XBLOCK=8,
            RBLOCK=512, num_warps=16, num_stages=1)
        del buf5
        del buf6
        del buf7
        del buf8
        del buf9
        del primals_1
        del primals_2
        del primals_3
        buf15 = empty_strided_cuda((8192, 2048), (2048, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf14, (8192, 
            512), (512, 1), 0), reinterpret_tensor(primals_4, (512, 2048),
            (1, 512), 0), alpha=1, beta=1, out=buf15)
        del primals_5
        buf16 = empty_strided_cuda((4, 4, 512, 2048), (4194304, 1048576, 
            2048, 1), torch.float32)
        triton_poi_fused_gelu_1[grid(16777216)](buf15, buf16, 16777216,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf17 = empty_strided_cuda((8192, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf16, (8192, 2048), (2048, 1),
            0), reinterpret_tensor(primals_6, (2048, 512), (1, 2048), 0),
            out=buf17)
        buf21 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512,
            1), torch.float32)
        buf22 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512,
            1), torch.float32)
        buf23 = empty_strided_cuda((4, 4, 512, 1), (2048, 512, 1, 1), torch
            .float32)
        triton_per_fused_add_native_layer_norm_native_layer_norm_backward_2[
            grid(8192)](buf14, buf17, primals_7, primals_8, primals_9,
            buf21, buf22, buf23, 8192, 512, num_warps=4, num_stages=1)
        del buf17
        del primals_7
        del primals_9
    return buf22, primals_8, buf13, reinterpret_tensor(buf14, (8192, 512),
        (512, 1), 0), buf15, reinterpret_tensor(buf16, (8192, 2048), (2048,
        1), 0), buf21, buf23, primals_6, primals_4


class FeedForward(nn.Module):

    def __init__(self, dhidden, dropout_rate, **kwargs):
        super(FeedForward, self).__init__(**kwargs)
        self.dhidden = dhidden
        self.dropout_rate = dropout_rate
        self.dense_1 = nn.Linear(dhidden, 4 * dhidden)
        self.dense_2 = nn.Linear(4 * dhidden, dhidden)
        self.gelu = nn.GELU()
        self.dropout = nn.Dropout(dropout_rate)

    def get_config(self):
        config = super(FeedForward, self).get_config()
        config.update({'dhidden': self.dhidden, 'dropout_rate': self.
            dropout_rate})
        return config

    def forward(self, x):
        x = self.dense_1(x)
        x = self.gelu(x)
        x = self.dropout(x)
        x = self.dense_2(x)
        x = self.dropout(x)
        return x


class FNetEncoderNew(nn.Module):

    def __init__(self, dhidden=512, dropout_rate=0):
        super(FNetEncoderNew, self).__init__()
        self.feedforward = FeedForward(dhidden, dropout_rate)
        self.LayerNorm_1 = nn.LayerNorm(dhidden)
        self.LayerNorm_2 = nn.LayerNorm(dhidden)

    def get_config(self):
        config = super(FNetEncoderNew, self).get_config()
        config.update({'dhidden': self.dhidden, 'dropout_rate': self.
            dropout_rate})
        return config

    def forward(self, input_0):
        primals_4 = self.feedforward.dense_1.weight
        primals_5 = self.feedforward.dense_1.bias
        primals_6 = self.feedforward.dense_2.weight
        primals_2 = self.feedforward.dense_2.bias
        primals_3 = self.LayerNorm_1.weight
        primals_7 = self.LayerNorm_1.bias
        primals_8 = self.LayerNorm_2.weight
        primals_9 = self.LayerNorm_2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
