# AOT ID: ['0_inference']
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_6/inductor_cache/jt/cjtu7nbodp2kukwjkuxtms3s333c4f2joabfbg3dgzzmxmmffloy.py
# Topologically Sorted Source Nodes: [position_index, encoded_pos_features], Original ATen: [aten._to_copy, aten.embedding]
# Source node to ATen node mapping:
#   encoded_pos_features => embedding
#   position_index => convert_element_type
# Graph fragment:
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%arg0_1, torch.int64), kwargs = {})
#   %embedding : [num_users=1] = call_function[target=torch.ops.aten.embedding.default](args = (%arg1_1, %convert_element_type), kwargs = {})
triton_poi_fused__to_copy_embedding_0 = async_compile.triton('triton_poi_fused__to_copy_embedding_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: '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__to_copy_embedding_0', '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__to_copy_embedding_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tmp0.to(tl.int64)
    tmp2 = tl.full([XBLOCK], 5, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert(((0 <= tmp5) & (tmp5 < 5)) | ~(xmask), "index out of bounds: 0 <= tmp5 < 5")
    tmp7 = tl.load(in_ptr1 + (x0 + (4*tmp5)), xmask)
    tl.store(out_ptr0 + (x2), tmp7, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (5, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [position_index, encoded_pos_features], Original ATen: [aten._to_copy, aten.embedding]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_embedding_0.run(arg0_1, arg1_1, buf0, 1024, grid=grid(1024), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((5, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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 numpy as np
import torch.nn as nn


class PositionEncoder(nn.Module):
    """
    Encodes the information into vectors

    There are 2 pieces of information that goes into the encoded information:
    1. Word Embedding
    2. Position Embedding

    This set of codes would encode the position information

    """

    @staticmethod
    def pos_emb(pos, dim, d_model):
        return pos / np.power(10000, 2 * (dim // 2) / d_model)

    @staticmethod
    def cal_pos_emb(pos, d_emb_dim, d_model):
        return [PositionEncoder.pos_emb(pos, dim, d_model) for dim in range
            (d_emb_dim)]

    @staticmethod
    def get_position_embedding(max_index, d_model, d_emb_dim, requires_grad
        =False):
        position_embedding = np.array([PositionEncoder.cal_pos_emb(pos,
            d_emb_dim, d_model) for pos in range(max_index + 1)])
        position_embedding[:, 0::2] = np.sin(position_embedding[:, 0::2])
        position_embedding[:, 1::2] = np.cos(position_embedding[:, 1::2])
        position_embedding = torch.FloatTensor(position_embedding)
        position_embedding.requires_grad = requires_grad
        return position_embedding

    def __init__(self, config, max_index):
        super(PositionEncoder, self).__init__()
        self.config = config
        self.max_index = max_index
        self.d_model = self.config.d_model
        self.d_emb_dim = self.config.emb_dim
        self.requires_grad = self.config.train_pos_emb
        self.freeze = True if self.requires_grad is False else False
        self.pos_embd_weights = PositionEncoder.get_position_embedding(
            max_index=self.max_index, d_model=self.config.d_model,
            d_emb_dim=self.d_emb_dim, requires_grad=self.requires_grad)
        self.position_encoding = nn.Embedding.from_pretrained(self.
            pos_embd_weights, freeze=self.freeze)

    def forward(self, src_seq):
        """
        Ref:
        https://pytorch.org/docs/stable/nn.html

        Does encoding for the input:
        1. position encoding (The position encoding are based on the time stamp)

        <--------- POS Embedding --------->
        Input:
                src_seq :

        Output:
                encoded_pos_features :

        """
        position_index = src_seq.long()
        encoded_pos_features = self.position_encoding(position_index)
        return encoded_pos_features


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


def get_init_inputs():
    return [[], {'config': _mock_config(d_model=4, emb_dim=4, train_pos_emb
        =False), 'max_index': 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 numpy as np
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__to_copy_embedding_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tmp0.to(tl.int64)
    tmp2 = tl.full([XBLOCK], 5, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert((0 <= tmp5) & (tmp5 < 5) | ~xmask,
        'index out of bounds: 0 <= tmp5 < 5')
    tmp7 = tl.load(in_ptr1 + (x0 + 4 * tmp5), xmask)
    tl.store(out_ptr0 + x2, tmp7, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (5, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused__to_copy_embedding_0[grid(1024)](arg0_1, arg1_1,
            buf0, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class PositionEncoderNew(nn.Module):
    """
    Encodes the information into vectors

    There are 2 pieces of information that goes into the encoded information:
    1. Word Embedding
    2. Position Embedding

    This set of codes would encode the position information

    """

    @staticmethod
    def pos_emb(pos, dim, d_model):
        return pos / np.power(10000, 2 * (dim // 2) / d_model)

    @staticmethod
    def cal_pos_emb(pos, d_emb_dim, d_model):
        return [PositionEncoderNew.pos_emb(pos, dim, d_model) for dim in
            range(d_emb_dim)]

    @staticmethod
    def get_position_embedding(max_index, d_model, d_emb_dim, requires_grad
        =False):
        position_embedding = np.array([PositionEncoderNew.cal_pos_emb(pos,
            d_emb_dim, d_model) for pos in range(max_index + 1)])
        position_embedding[:, 0::2] = np.sin(position_embedding[:, 0::2])
        position_embedding[:, 1::2] = np.cos(position_embedding[:, 1::2])
        position_embedding = torch.FloatTensor(position_embedding)
        position_embedding.requires_grad = requires_grad
        return position_embedding

    def __init__(self, config, max_index):
        super(PositionEncoderNew, self).__init__()
        self.config = config
        self.max_index = max_index
        self.d_model = self.config.d_model
        self.d_emb_dim = self.config.emb_dim
        self.requires_grad = self.config.train_pos_emb
        self.freeze = True if self.requires_grad is False else False
        self.pos_embd_weights = PositionEncoderNew.get_position_embedding(
            max_index=self.max_index, d_model=self.config.d_model,
            d_emb_dim=self.d_emb_dim, requires_grad=self.requires_grad)
        self.position_encoding = nn.Embedding.from_pretrained(self.
            pos_embd_weights, freeze=self.freeze)

    def forward(self, input_0):
        arg1_1 = self.position_encoding.weight
        arg0_1 = input_0
        output = call([arg0_1, arg1_1])
        return output[0]
