static_ops.yaml

- op : all
  args : (Tensor x, IntArray axis={0}, bool keepdim=false, bool reduce_all=false, int in_dtype=-1, int out_dtype=-1)
  output : Tensor(out)
  infer_meta :
    func : ReduceInferMetaBase
  kernel :
    func : all

- op : all_gather
  args : (Tensor x, int ring_id = 0, int nranks=0)
  output : Tensor(out)
  infer_meta :
    func : AllGatherInferMeta
    param: [x, nranks]
  kernel :
    func : all_gather
    param: [x, nranks]

- op : all_reduce
  args : (Tensor x, int ring_id = 0, int reduce_type = 0)
  output : Tensor(out)
  infer_meta :
    func : AllReduceInferMeta
    param: [x]
  kernel :
    func : all_reduce
    param: [x, reduce_type]

- op : broadcast
  args : (Tensor x, int ring_id = 0, int root = 0)
  output : Tensor(out)
  infer_meta :
    func : DistBroadcastInferMeta
    param: [x]
  kernel :
    func : broadcast
    param: [x, root]

- op : embedding_with_eltwise_add_xpu
  args : (Tensor[] ids, Tensor[] tables, int64_t padding_idx)
  output: Tensor
  infer_meta :
    func: EmbeddingWithEltwiseAddXPUInferMeta
  kernel:
    func: embedding_with_eltwise_add_xpu
    data_type: tables

- op : equal
  args : (Tensor x, Tensor y, int axis = -1, bool force_cpu=false)
  output : Tensor(out)
  infer_meta :
    func : CompareRawInferMeta
    param : [x, y, axis]
  kernel :
    func : equal_raw
    param : [x, y, axis]
    backend : x
    force_backend : force_cpu

- op : fc_xpu
  args : (Tensor x, Tensor x_max, Tensor w, Tensor w_max, Tensor bias, int in_num_col_dims, bool transpose_x, float alpha, float beta, int act_type, float act_alpha)
  output : Tensor(out), Tensor(out_max)
  infer_meta :
    func : FcXPUInferMeta
  kernel :
    func : fc_xpu
    data_type : x
  optional : bias, x_max

- op : frobenius_norm
  args : (Tensor x, IntArray axis={0}, bool keepdim=false, bool reduce_all=false, int in_dtype=-1, int out_dtype=-1)
  output : Tensor(out)
  infer_meta :
    func : ReduceInferMetaBase
  kernel :
    func : frobenius_norm
    param : [x, axis, keepdim, reduce_all]
  backward : frobenius_norm_grad

- op : fused_multi_transformer_xpu
  args : (Tensor x, Tensor[] ln_scale, Tensor[] ln_bias, Tensor[] qkvw, Tensor[] qkvw_max, Tensor[] qkv_bias, Tensor[] out_linear_w, Tensor[] out_linear_wmax, Tensor[] out_linear_bias, Tensor[] ffn_ln_scale, Tensor[] ffn_ln_bias, Tensor[] ffn1_weight, Tensor[] ffn1_weight_max, Tensor[] ffn1_bias, Tensor[] ffn2_weight, Tensor[] ffn2_weight_max, Tensor[] ffn2_bias, Tensor[] cache_kv, Tensor[] pre_caches, Tensor rotary_pos_emb, Tensor time_step, Tensor seq_lengths, Tensor src_mask, bool pre_layer_norm, int rotary_emb_dims, float epsilon, float dropout_rate, bool is_test, str dropout_implementation, str act_method, bool trans_qkvw, int ring_id)
  output : Tensor(out), Tensor[](cache_kv_out){out_linear_w.size()}
  infer_meta :
    func : FusedMultiTransformerXpuInferMeta
  kernel :
    func : fused_multi_transformer_xpu
    data_type : x
  optional : cache_kv, pre_caches, rotary_pos_emb, time_step, seq_lengths, src_mask

- op : generate_sequence_xpu
  args : (Tensor x, DataType dtype)
  output : Tensor
  infer_meta :
    func : GenerateSequenceXPUInferMeta
  kernel :
    func : generate_sequence_xpu
    data_type : dtype

- op : greater_equal
  args : (Tensor x, Tensor y, int axis = -1, bool force_cpu=false)
  output : Tensor(out)
  infer_meta :
    func : CompareRawInferMeta
    param : [x, y, axis]
  kernel :
    func : greater_equal_raw
    param : [x, y, axis]
    backend : x
    force_backend : force_cpu

- op : greater_than
  args : (Tensor x, Tensor y, int axis = -1, bool force_cpu=false)
  output : Tensor(out)
  infer_meta :
    func : CompareRawInferMeta
    param : [x, y, axis]
  kernel :
    func : greater_than_raw
    param : [x, y, axis]
    backend : x
    force_backend : force_cpu

- op : less_equal
  args : (Tensor x, Tensor y, int axis = -1, bool force_cpu=false)
  output : Tensor(out)
  infer_meta :
    func : CompareRawInferMeta
    param : [x, y, axis]
  kernel :
    func : less_equal_raw
    param : [x, y, axis]
    backend : x
    force_backend : force_cpu

- op : less_than
  args : (Tensor x, Tensor y, int axis = -1, bool force_cpu=false)
  output : Tensor(out)
  infer_meta :
    func : CompareRawInferMeta
    param : [x, y, axis]
  kernel :
    func : less_than_raw
    param : [x, y, axis]
    backend : x
    force_backend : force_cpu

- op : multi_encoder_xpu
  args : (Tensor x, Tensor[] fc_weight, Tensor[] fc_weight_max, Tensor[] fc_bias, Tensor[] ln_scale, Tensor[] ln_bias, Tensor mask, int layer_num, bool norm_before, int hidden_dim, int head_num, int size_per_head, int ffn_hidden_dim_scale, int act_type, int relative_type, int slice_idx)
  output : Tensor(out), Tensor(x_fp16), Tensor(out_fp16)
  infer_meta :
    func : MultiEncoderXPUInferMeta
  kernel :
    func : multi_encoder_xpu
    data_type : x
  optional : mask, x_fp16, out_fp16

- op : not_equal
  args : (Tensor x, Tensor y, int axis = -1, bool force_cpu=false)
  output : Tensor(out)
  infer_meta :
    func : CompareRawInferMeta
    param : [x, y, axis]
  kernel :
    func : not_equal_raw
    param : [x, y, axis]
    backend : x
    force_backend : force_cpu

- op : reduce
  args : (Tensor x, int ring_id = 0, int root_id = 0, int reduce_type = 0)
  output : Tensor(out)
  infer_meta :
    func : DistReduceInferMeta
    param: [x]
  kernel :
    func : reduce
    param: [x, root_id, reduce_type]

- op : share_buffer
  args : (Tensor[] x, bool[] share_dims_and_dtype={})
  output : Tensor[](out){x.size()}, Tensor[](xout){x.size()}
  infer_meta :
    func : ShareBufferInferMeta
  kernel :
    func : share_buffer