# 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/st/cstwzn5l7bhlj4n4vagkfouihy7mrpgfnmrscgux5h4odyej6347.py
# Topologically Sorted Source Nodes: [embs, add], Original ATen: [aten.cat, aten.add]
# Source node to ATen node mapping:
#   add => add
#   embs => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%expand, %expand_1], -1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %cat), kwargs = {})
triton_poi_fused_add_cat_0 = async_compile.triton('triton_poi_fused_add_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=[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_cat_0', '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_cat_0(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
    x4 = xindex
    x0 = xindex % 4
    x2 = (xindex // 16) % 4
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x4), xmask)
    tmp1 = x0
    tmp2 = tl.full([1], 0, tl.int64)
    tmp3 = tmp1 >= tmp2
    tmp4 = tl.full([1], 2, tl.int64)
    tmp5 = tmp1 < tmp4
    tmp6 = tl.load(in_ptr1 + ((2*x2) + x0), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp1 >= tmp4
    tmp8 = tl.full([1], 4, tl.int64)
    tmp9 = tmp1 < tmp8
    tmp10 = tl.load(in_ptr2 + ((2*x1) + ((-2) + x0)), tmp7 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tl.where(tmp5, tmp6, tmp10)
    tmp12 = tmp0 + tmp11
    tl.store(out_ptr0 + (x4), tmp12, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 2), (2, 1))
    assert_size_stride(primals_2, (4, 2), (2, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [embs, add], Original ATen: [aten.cat, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_cat_0.run(primals_3, primals_1, primals_2, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        del primals_2
        del primals_3
    return (buf0, )


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, 2), (2, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 2), (2, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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


def tensor_slice(x, begin, size):
    assert all([(b >= 0) for b in begin])
    size = [(l - b if s == -1 else s) for s, b, l in zip(size, begin, x.shape)]
    assert all([(s >= 0) for s in size])
    slices = [slice(b, b + s) for b, s in zip(begin, size)]
    return x[slices]


class AddBroadcastPosEmbed(nn.Module):

    def __init__(self, shape, embd_dim, dim=-1):
        super().__init__()
        assert dim in [-1, 1]
        self.shape = shape
        self.n_dim = n_dim = len(shape)
        self.embd_dim = embd_dim
        self.dim = dim
        assert embd_dim % n_dim == 0, f'{embd_dim} % {n_dim} != 0'
        self.emb = nn.ParameterDict({f'd_{i}': nn.Parameter(torch.randn(
            shape[i], embd_dim // n_dim) * 0.01 if dim == -1 else torch.
            randn(embd_dim // n_dim, shape[i]) * 0.01) for i in range(n_dim)})

    def forward(self, x, decode_step=None, decode_idx=None):
        embs = []
        for i in range(self.n_dim):
            e = self.emb[f'd_{i}']
            if self.dim == -1:
                e = e.view(1, *((1,) * i), self.shape[i], *((1,) * (self.
                    n_dim - i - 1)), -1)
                e = e.expand(1, *self.shape, -1)
            else:
                e = e.view(1, -1, *((1,) * i), self.shape[i], *((1,) * (
                    self.n_dim - i - 1)))
                e = e.expand(1, -1, *self.shape)
            embs.append(e)
        embs = torch.cat(embs, dim=self.dim)
        if decode_step is not None:
            embs = tensor_slice(embs, [0, *decode_idx, 0], [x.shape[0], *((
                1,) * self.n_dim), x.shape[-1]])
        return x + embs


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


def get_init_inputs():
    return [[], {'shape': [4, 4], 'embd_dim': 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
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


@triton.jit
def triton_poi_fused_add_cat_0(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
    x4 = xindex
    x0 = xindex % 4
    x2 = xindex // 16 % 4
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = x0
    tl.full([1], 0, tl.int64)
    tmp4 = tl.full([1], 2, tl.int64)
    tmp5 = tmp1 < tmp4
    tmp6 = tl.load(in_ptr1 + (2 * x2 + x0), tmp5 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tmp1 >= tmp4
    tl.full([1], 4, tl.int64)
    tmp10 = tl.load(in_ptr2 + (2 * x1 + (-2 + x0)), tmp7 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tl.where(tmp5, tmp6, tmp10)
    tmp12 = tmp0 + tmp11
    tl.store(out_ptr0 + x4, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 2), (2, 1))
    assert_size_stride(primals_2, (4, 2), (2, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_cat_0[grid(256)](primals_3, primals_1,
            primals_2, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        del primals_3
    return buf0,


def tensor_slice(x, begin, size):
    assert all([(b >= 0) for b in begin])
    size = [(l - b if s == -1 else s) for s, b, l in zip(size, begin, x.shape)]
    assert all([(s >= 0) for s in size])
    slices = [slice(b, b + s) for b, s in zip(begin, size)]
    return x[slices]


class AddBroadcastPosEmbedNew(nn.Module):

    def __init__(self, shape, embd_dim, dim=-1):
        super().__init__()
        assert dim in [-1, 1]
        self.shape = shape
        self.n_dim = n_dim = len(shape)
        self.embd_dim = embd_dim
        self.dim = dim
        assert embd_dim % n_dim == 0, f'{embd_dim} % {n_dim} != 0'
        self.emb = nn.ParameterDict({f'd_{i}': nn.Parameter(torch.randn(
            shape[i], embd_dim // n_dim) * 0.01 if dim == -1 else torch.
            randn(embd_dim // n_dim, shape[i]) * 0.01) for i in range(n_dim)})

    def forward(self, input_0):
        primals_1 = self.emb.d_0
        primals_2 = self.emb.d_1
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
