# 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/vh/cvhtw4c7nwvnmcdnw6gcneuy3h7qtp7uh2lwza5ahdwkbcd5w32l.py
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.cat, aten.native_layer_norm]
# Source node to ATen node mapping:
#   out => cat
#   out_1 => add, add_1, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
#   %cat : [num_users=3] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_2, %expand], -1), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%cat, [3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%cat, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = 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_3), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_4), kwargs = {})
triton_per_fused_cat_native_layer_norm_0 = async_compile.triton('triton_per_fused_cat_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.persistent_reduction(
    size_hints=[64, 256],
    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_cat_native_layer_norm_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 4, '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_cat_native_layer_norm_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    xnumel = 64
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    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)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp31 = tl.load(in_ptr2 + (r2), None, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr3 + (r2), None, eviction_policy='evict_last')
    tmp0 = r2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((128*x3) + r2), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 256, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((128*x0) + ((-128) + r2)), tmp6, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = tl.broadcast_to(tmp11, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = tl.full([1], 256, tl.int32)
    tmp17 = tmp16.to(tl.float32)
    tmp18 = tmp15 / tmp17
    tmp19 = tmp11 - tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [RBLOCK])
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp21, 0))
    tmp24 = 256.0
    tmp25 = tmp23 / tmp24
    tmp26 = 1e-05
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.rsqrt(tmp27)
    tmp29 = tmp10 - tmp18
    tmp30 = tmp29 * tmp28
    tmp32 = tmp30 * tmp31
    tmp34 = tmp32 + tmp33
    tl.store(out_ptr0 + (r2 + (256*x3)), tmp10, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x3), tmp28, None)
    tl.store(out_ptr2 + (r2 + (256*x3)), tmp34, None)
    tl.store(out_ptr1 + (x3), tmp18, None)
''', 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, (512, 128), (128, 1))
    assert_size_stride(primals_2, (4, 4, 4, 128), (2048, 512, 128, 1))
    assert_size_stride(primals_3, (256, ), (1, ))
    assert_size_stride(primals_4, (256, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf2  # reuse
        buf5 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.cat, aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_per_fused_cat_native_layer_norm_0.run(buf4, primals_2, primals_1, primals_3, primals_4, buf0, buf1, buf5, 64, 256, grid=grid(64), stream=stream0)
        del primals_1
        del primals_2
        del primals_4
    return (buf5, primals_3, buf0, buf1, buf4, )


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((512, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 128), (2048, 512, 128, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((256, ), (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 torch
import torch.nn as nn


class ConcatPositionalEncoding(nn.Module):

    def __init__(self, d_model=256, max_len=512):
        super().__init__()
        self.timing_table = nn.Parameter(torch.FloatTensor(max_len, d_model //
            2))
        nn.init.normal_(self.timing_table)
        self.norm = nn.LayerNorm(d_model)

    def forward(self, x):
        timing = self.timing_table[None, :x.shape[1], :]
        x, timing = torch.broadcast_tensors(x, timing)
        out = torch.cat([x, timing], dim=-1)
        out = self.norm(out)
        return out


def get_inputs():
    return [torch.rand([4, 4, 4, 128])]


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

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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
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_per_fused_cat_native_layer_norm_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    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)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp31 = tl.load(in_ptr2 + r2, None, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr3 + r2, None, eviction_policy='evict_last')
    tmp0 = r2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 128, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (128 * x3 + r2), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 256, tl.int64)
    tmp9 = tl.load(in_ptr1 + (128 * x0 + (-128 + r2)), tmp6,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = tl.broadcast_to(tmp11, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = tl.full([1], 256, tl.int32)
    tmp17 = tmp16.to(tl.float32)
    tmp18 = tmp15 / tmp17
    tmp19 = tmp11 - tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [RBLOCK])
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp21, 0))
    tmp24 = 256.0
    tmp25 = tmp23 / tmp24
    tmp26 = 1e-05
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.rsqrt(tmp27)
    tmp29 = tmp10 - tmp18
    tmp30 = tmp29 * tmp28
    tmp32 = tmp30 * tmp31
    tmp34 = tmp32 + tmp33
    tl.store(out_ptr0 + (r2 + 256 * x3), tmp10, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x3, tmp28, None)
    tl.store(out_ptr2 + (r2 + 256 * x3), tmp34, None)
    tl.store(out_ptr1 + x3, tmp18, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (512, 128), (128, 1))
    assert_size_stride(primals_2, (4, 4, 4, 128), (2048, 512, 128, 1))
    assert_size_stride(primals_3, (256,), (1,))
    assert_size_stride(primals_4, (256,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf2
        buf5 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.float32)
        get_raw_stream(0)
        triton_per_fused_cat_native_layer_norm_0[grid(64)](buf4, primals_2,
            primals_1, primals_3, primals_4, buf0, buf1, buf5, 64, 256,
            num_warps=2, num_stages=1)
        del primals_1
        del primals_2
        del primals_4
    return buf5, primals_3, buf0, buf1, buf4


class ConcatPositionalEncodingNew(nn.Module):

    def __init__(self, d_model=256, max_len=512):
        super().__init__()
        self.timing_table = nn.Parameter(torch.FloatTensor(max_len, d_model //
            2))
        nn.init.normal_(self.timing_table)
        self.norm = nn.LayerNorm(d_model)

    def forward(self, input_0):
        primals_1 = self.timing_table
        primals_3 = self.norm.weight
        primals_4 = self.norm.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
