add model file

config.json

@@ -0,0 +1,44 @@
+{
+    "architectures": [
+        "BailingMoeForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "auto_map": {
+        "AutoConfig": "configuration_bailing_moe.BailingMoeConfig",
+        "AutoModel": "modeling_bailing_moe.BailingMoeModel",
+        "AutoModelForCausalLM": "modeling_bailing_moe.BailingMoeForCausalLM"
+    },
+    "eos_token_id": 126081,
+    "pad_token_id": 126081,
+    "first_k_dense_replace": 0,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.006,
+    "intermediate_size": 1408,
+    "max_position_embeddings": 32768,
+    "model_type": "bailing_moe",
+    "moe_intermediate_size": 1408,
+    "num_experts": 64,
+    "num_shared_experts": 2,
+    "norm_topk_prob": true,
+    "num_attention_heads": 16,
+    "num_experts_per_tok": 6,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 4,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 600000,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.40.0",
+    "use_cache": true,
+    "use_bias": false,
+    "use_qkv_bias": false,
+    "vocab_size": 126464,
+    "output_router_logits": false,
+    "embedding_dropout": 0.0,
+    "norm_head": false,
+    "norm_softmax": false,
+    "output_dropout": 0.0
+}

configuration_bailing_moe.py

@@ -0,0 +1,78 @@
+""" Bailing MoE model configuration """
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class BailingMoeConfig(PretrainedConfig):
+    model_type = "bailing_moe"
+
+    def __init__(
+        self,
+        vocab_size=30592,
+        hidden_size=1024,
+        intermediate_size=None,
+        num_hidden_layers=24,
+        num_attention_heads=16,
+        num_key_value_heads=0,
+        hidden_act="silu",
+        use_qkv_bias=False,  # bailing only
+        use_bias=True,  # bailing only
+        rms_norm_eps=1e-05,
+        norm_head=False,  # bailing only
+        tie_word_embeddings=False,  # PretrainedConfig key, here change default value.
+        embedding_dropout=0.1,
+        attention_dropout=0.1,
+        output_dropout=0.1,
+        initializer_range=0.02,
+        max_position_embeddings=16384,
+        rope_theta=10000.0,
+        use_cache=True,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=28,
+        rope_scaling=None,
+        pad_token_id=126081,
+        num_experts=16,
+        num_shared_experts=0,
+        num_experts_per_tok=2,
+        norm_topk_prob=True,
+        moe_intermediate_size=None,
+        first_k_dense_replace=0,
+        head_dim=None,
+        output_router_logits=False,
+        **kwargs,
+    ):
+        self.num_hidden_layers = num_hidden_layers
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.use_qkv_bias = use_qkv_bias
+        self.use_bias = use_bias
+        self.norm_head = norm_head
+        self.rms_norm_eps = rms_norm_eps
+        self.embedding_dropout = embedding_dropout
+        self.attention_dropout = attention_dropout
+        self.output_dropout = output_dropout
+        self.initializer_range = initializer_range
+        self.max_position_embeddings = max_position_embeddings
+        self.rope_theta = rope_theta
+        self.use_cache = use_cache
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window
+        self.max_window_layers = max_window_layers
+        self.head_dim = head_dim or self.hidden_size // self.num_attention_heads
+        self.rope_scaling = rope_scaling
+
+        # MoE configs
+        self.num_experts = num_experts
+        self.num_shared_experts = num_shared_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.norm_topk_prob = norm_topk_prob
+        self.moe_intermediate_size = moe_intermediate_size
+        self.first_k_dense_replace = first_k_dense_replace
+        self.output_router_logits = output_router_logits
+
+        super().__init__(pad_token_id=pad_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs)

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "eos_token_id": 126081,
+  "pad_token_id": 126081,
+  "transformers_version": "4.40.0"
+}

model-00001-of-00004.safetensors

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+size 10000012352

model-00002-of-00004.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d5ccecf15d89900ba376bff70aac7266a90320205b549337883e0fbd0e7386a6
+size 9997403496

model-00003-of-00004.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b72c9cb181ce3ce4ba02afa6d78b8ef82f85c8b7da85b7cd472aa58517ef937c
+size 9995576736

model-00004-of-00004.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f5b3384dc88a60dbc5a20a7fe6617163374b1adbdd5a07c6669192e185a34abc
+size 3611653272

modeling_bailing_moe.py

@@ -0,0 +1,1443 @@
+# coding=utf-8
+# Copyright 2023 Antgroup and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch BailingMoE model."""
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import (
+    AttentionMaskConverter,
+    _prepare_4d_attention_mask,
+    _prepare_4d_causal_attention_mask,
+    _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from transformers.modeling_outputs import (
+    MoeModelOutputWithPast,
+    MoeCausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+from .configuration_bailing_moe import BailingMoeConfig
+
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+    if not is_torch_greater_or_equal_than_1_13:
+        import torch.fx
+
+    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "BailingMoeConfig"
+
+
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    warnings.warn(
+        "Calling `transformers.models.BailingMoe.modeling_BailingMoe._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
+    )
+    return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
+
+
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    warnings.warn(
+        "Calling `transformers.models.BailingMoe.modeling_BailingMoe._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.BailingMoe.modeling_BailingMoe.AttentionMaskConverter._make_causal_mask"
+    )
+    return AttentionMaskConverter._make_causal_mask(
+        input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
+    )
+
+
+class BailingMoeRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        BailingMoeRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+ALL_LAYERNORM_LAYERS.append(BailingMoeRMSNorm)
+
+
+class BailingMoeRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+        self.max_seq_len_cached = None
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.outer(t, self.inv_freq.to(t.device))
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->BailingMoe
+class BailingMoeLinearScalingRotaryEmbedding(BailingMoeRotaryEmbedding):
+    """BailingMoeRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->BailingMoe
+class BailingMoeDynamicNTKScalingRotaryEmbedding(BailingMoeRotaryEmbedding):
+    """BailingMoeRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class BailingMoeMLP(nn.Module):
+    def __init__(self, config: BailingMoeConfig, intermediate_size: int):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = intermediate_size
+
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class BailingMoeGate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.num_experts = config.num_experts
+
+        # topk selection algorithm
+        self.norm_topk_prob = config.norm_topk_prob
+        self.gating_dim = config.hidden_size
+        self.weight = nn.Parameter(torch.empty((self.num_experts, self.gating_dim)))
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        import torch.nn.init as init
+
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+    def forward(self, hidden_states):
+        bsz, seq_len, h = hidden_states.shape
+        # compute gating score
+        hidden_states = hidden_states.view(-1, h)
+        logits = F.linear(hidden_states, self.weight, None)
+        scores = logits.softmax(dim=-1, dtype=torch.float32)
+
+        # select top-k experts
+        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+        # norm gate to sum 1
+        if self.top_k > 1 and self.norm_topk_prob:
+            denominator = topk_weight.sum(dim=-1, keepdim=True)
+            topk_weight = topk_weight / denominator
+
+        return topk_idx, topk_weight, logits
+
+
+class BailingMoeSparseMoeBlock(nn.Module):
+    """
+    A mixed expert module containing shared experts.
+    """
+
+    def __init__(self, config: BailingMoeConfig):
+        super().__init__()
+        self.config = config
+        self.num_experts_per_tok = config.num_experts_per_tok
+        self.experts = self._setup_experts()
+        self.gate = BailingMoeGate(config)
+        if config.num_shared_experts is not None:
+            self.shared_experts = BailingMoeMLP(
+                config=config, intermediate_size=config.moe_intermediate_size * config.num_shared_experts
+            )
+
+    def _setup_experts(self):
+        return nn.ModuleList(
+            [
+                BailingMoeMLP(config=self.config, intermediate_size=self.config.moe_intermediate_size)
+                for _ in range(self.config.num_experts)
+            ]
+        )
+
+    def forward(self, hidden_states):
+        identity = hidden_states
+        bsz, seq_len, h = hidden_states.shape
+        topk_idx, topk_weight, router_logits = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        flat_topk_idx = topk_idx.view(-1)
+        if self.training:
+            hidden_states = hidden_states.repeat_interleave(self.num_experts_per_tok, dim=0)
+            y = torch.empty_like(hidden_states)
+            for i, expert in enumerate(self.experts):
+                y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
+            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+            y = y.to(hidden_states.dtype).view(bsz, seq_len, h)
+        else:
+            y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(bsz, seq_len, h)
+        if self.config.num_shared_experts is not None:
+            y = y + self.shared_experts(identity)
+        return y, (router_logits.view(bsz, seq_len, -1), topk_idx.view(bsz, seq_len, -1))
+
+    @torch.no_grad()
+    def moe_infer(self, x, topk_ids, topk_weight):
+        cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
+        cnts.scatter_(1, topk_ids, 1)
+        tokens_per_expert = cnts.sum(dim=0)
+        idxs = topk_ids.view(-1).argsort()
+        sorted_tokens = x[idxs // topk_ids.shape[1]]
+        sorted_tokens_shape = sorted_tokens.shape
+        tokens_per_expert = tokens_per_expert.cpu().numpy()
+        outputs = []
+        start_idx = 0
+        for i, num_tokens in enumerate(tokens_per_expert):
+            end_idx = start_idx + num_tokens
+            if num_tokens == 0:
+                continue
+            expert = self.experts[i]
+            tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
+            expert_out = expert(tokens_for_this_expert)
+            outputs.append(expert_out)
+            start_idx = end_idx
+
+        outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
+        new_x = torch.empty_like(outs)
+        new_x[idxs] = outs
+        final_out = (
+            new_x.view(*topk_ids.shape, -1)
+            .type(topk_weight.dtype)
+            .mul_(topk_weight.unsqueeze(dim=-1))
+            .sum(dim=1)
+            .type(new_x.dtype)
+        )
+        return final_out
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->BailingMoe
+class BailingMoeAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: BailingMoeConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.head_dim or self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+
+        self.query_key_value = nn.Linear(
+            self.hidden_size,
+            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
+            bias=config.use_qkv_bias,
+        )
+        self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.use_bias)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = BailingMoeRotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = BailingMoeLinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = BailingMoeDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv = self.query_key_value(hidden_states)
+        qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
+
+        query_states, key_states, value_states = qkv.split(
+            [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2
+        )
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states / math.sqrt(self.head_dim), key_states.transpose(2, 3))
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+
+        attn_output = self.dense(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->BailingMoe
+class BailingMoeFlashAttention2(BailingMoeAttention):
+    """
+    BailingMoe flash attention module. This module inherits from `BailingMoeAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # BailingMoeFlashAttention2 attention does not support output_attentions
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+
+        qkv = self.query_key_value(hidden_states)
+        qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
+
+        query_states, key_states, value_states = qkv.split(
+            [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2
+        )
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently cast in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slow down training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (BailingMoeRMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            # Handle the case where the model is quantized
+            if hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            elif torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(
+            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+        attn_output = self.dense(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+            query_length (`int`):
+                The length of the query sequence in terms of tokens. This represents the number of tokens in the
+                `query_states` tensor along the sequence dimension. It is used to determine the effective sequence
+                length for attention computations.
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in BailingMoeFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            attn_output = flash_attn_func(
+                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+            )
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->BailingMoe
+class BailingMoeSdpaAttention(BailingMoeAttention):
+    """
+    BailingMoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `BailingMoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from BailingMoeAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "BailingMoeModel is using BailingMoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv = self.query_key_value(hidden_states)
+        qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
+
+        query_states, key_states, value_states = qkv.split(
+            [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2
+        )
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+
+        attn_output = self.dense(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+BAILING_MOE_ATTENTION_CLASSES = {
+    "eager": BailingMoeAttention,
+    "flash_attention_2": BailingMoeFlashAttention2,
+    "sdpa": BailingMoeSdpaAttention,
+}
+
+
+class BailingMoeDecoderLayer(nn.Module):
+    def __init__(self, config: BailingMoeConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.attention = BAILING_MOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+        self.mlp = (
+            BailingMoeSparseMoeBlock(config)
+            if (config.num_experts is not None and layer_idx >= config.first_k_dense_replace)
+            else BailingMoeMLP(config=config, intermediate_size=config.intermediate_size)
+        )
+        self.input_layernorm = BailingMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = BailingMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        output_router_logits: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+                config.n_positions - 1]`.
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*):
+                cached past key and value projection states
+            output_attentions (`bool`, *optional*):
+                Whether to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_router_logits (`bool`, *optional*):
+                Whether or not to return the logits of all the routers. They are useful for computing the router loss,
+                and should not be returned during inference.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+        """
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.attention(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        if isinstance(hidden_states, tuple):
+            hidden_states, router_logits = hidden_states
+        else:
+            router_logits = None
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        if output_router_logits:
+            outputs += (router_logits,)
+
+        return outputs
+
+
+BAILINGMOE_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`BailingMoeConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare BailingMoe Model outputting raw hidden-states without any specific head on top.",
+    BAILINGMOE_START_DOCSTRING,
+)
+class BailingMoePreTrainedModel(PreTrainedModel):
+    config_class = BailingMoeConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["BailingMoeDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+BAILINGMOE_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare BailingMoe Model outputting raw hidden-states without any specific head on top.",
+    BAILINGMOE_START_DOCSTRING,
+)
+class BailingMoeModel(BailingMoePreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`BailingMoeDecoderLayer`]
+
+    Args:
+        config: BailingMoeConfig
+    """
+
+    def __init__(self, config: BailingMoeConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [BailingMoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._use_sdpa = config._attn_implementation == "sdpa"
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self.norm = BailingMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.word_embeddings = value
+
+    @add_start_docstrings_to_model_forward(BAILINGMOE_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_router_logits: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple, MoeModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`transformers."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        if self._use_flash_attention_2:
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self._use_sdpa and not output_attentions:
+            # output_attentions=True can not be supported when using SDPA, and we fall back on
+            # the manual implementation that requires a 4D causal mask in all cases.
+            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+            )
+        else:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        all_router_logits = () if output_router_logits else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    output_router_logits,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    output_router_logits=output_router_logits,
+                    use_cache=use_cache,
+                )
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+            if output_router_logits and layer_outputs[-1] is not None:
+                all_router_logits += (layer_outputs[-1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits]
+                if v is not None
+            )
+        return MoeModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            router_logits=all_router_logits,
+        )
+
+
+class BailingMoeForCausalLM(BailingMoePreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: BailingMoeConfig):
+        super().__init__(config)
+        self.model = BailingMoeModel(config)
+        self.vocab_size = config.vocab_size
+        self.norm_head = config.norm_head
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.word_embeddings = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(BAILINGMOE_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_router_logits: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer
+
+        >>> model = BailingMoeForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_router_logits=output_router_logits,
+            return_dict=return_dict,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+
+        if self.norm_head:
+            if self.training:
+                norm_weight = (
+                    self.lm_head.weight / (torch.norm(self.lm_head.weight, p=2, dim=0, keepdim=True) + 1e-7).detach()
+                )
+                logits = F.linear(hidden_states, norm_weight, None)
+            else:
+                self.lm_head.weight.data = (
+                    self.lm_head.weight.data.float()
+                    / (torch.norm(self.lm_head.weight.data.float(), p=2, dim=0, keepdim=True) + 1e-7)
+                ).to(hidden_states.dtype)
+                logits = F.linear(hidden_states, self.lm_head.weight.data, None)
+                self.norm_head = False
+        else:
+            logits = self.lm_head(hidden_states)
+
+        logits = logits.float()
+
+        loss = None
+        aux_loss = None
+
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            if output_router_logits:
+                output = (aux_loss,) + output
+            return (loss,) + output if loss is not None else output
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            aux_loss=aux_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, token_type_ids=None, **kwargs
+    ):
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = (
+                    past_key_values.get_max_length()
+                    if hasattr(past_key_values, "get_max_length")
+                    else past_key_values.get_max_cache_shape()
+                )
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past

special_tokens_map.json

@@ -0,0 +1,38 @@
+{
+  "additional_special_tokens": [
+    "<role>",
+    "</role>",
+    "<|arithmetic_start|>",
+    "<|arithmetic_end|>",
+    "<|number_start|>",
+    "<|number_end|>"
+  ],
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenization_bailing.py

@@ -0,0 +1,1068 @@
+#!/usr/bin/env python3
+# coding=utf-8
+# Copyright (c) Ant Group. All rights reserved.
+
+import itertools
+from typing import Any, Dict, List, Optional, Union
+
+import torch
+from transformers import PreTrainedTokenizerFast
+from transformers.tokenization_utils_base import AddedToken, BatchEncoding
+from transformers.utils import TensorType, logging
+
+logger = logging.get_logger(__name__)
+
+
+def is_system(msg):
+    return msg['role'].lower() == 'system'
+
+
+def is_user(msg):
+    return msg['role'].lower() in ['human', 'user']
+
+
+def is_assistant(msg):
+    return msg['role'].lower() == 'assistant'
+
+
+def _convert_to_conversation(query, system=None):
+    conversation = []
+    if system:
+        conversation.append({"role": "SYSTEM", "content": system})
+    if isinstance(query, str):
+        conversation.append({"role": "HUMAN", "content": query})
+    elif isinstance(query, List):
+        conversation.extend(query)
+    elif isinstance(query, Dict):
+        if "messages" in query:
+            conversation.extend(query["messages"])
+            if "system_message" in query and len(conversation) > 0 and not is_system(conversation[0]):
+                conversation.insert(0, {"role": "SYSTEM", "content": query["system_message"]})
+        else:
+            conversation.append(query)
+    return conversation
+
+
+class BailingTokenizer(PreTrainedTokenizerFast):
+    is_bailing_tokenizer = True
+    model_input_names = ["input_ids", "attention_mask"]
+    slow_tokenizer_class = None
+
+    # add gmask_token
+    SPECIAL_TOKENS_ATTRIBUTES = [
+        "bos_token",
+        "eos_token",
+        "unk_token",
+        "sep_token",
+        "pad_token",
+        "cls_token",
+        "mask_token",
+        "gmask_token",
+        "additional_special_tokens",
+    ]
+
+    def __init__(
+        self,
+        vocab_file=None,
+        merges_file=None,
+        tokenizer_file=None,
+        clean_up_tokenization_spaces=False,
+        bos_token="<|startoftext|>",
+        eos_token="<|endoftext|>",
+        cls_token="[CLS]",
+        pad_token="<|endoftext|>",
+        gmask_token="[gMASK]",
+        add_bos_token=False,
+        add_eos_token=False,
+        **kwargs,
+    ):
+        self.add_bos_token = add_bos_token
+
+        self._gmask_token = (
+            AddedToken(gmask_token, lstrip=False, rstrip=False, normalized=False)
+            if isinstance(gmask_token, str)
+            else gmask_token
+        )
+
+        self._sop_token = (
+            AddedToken(bos_token, lstrip=False, rstrip=False, normalized=False)
+            if isinstance(bos_token, str)
+            else bos_token
+        )
+
+        self._eop_token = (
+            AddedToken(eos_token, lstrip=False, rstrip=False, normalized=False)
+            if isinstance(eos_token, str)
+            else eos_token
+        )
+
+        super().__init__(
+            vocab_file=vocab_file,
+            merges_file=merges_file,
+            tokenizer_file=tokenizer_file,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            cls_token=cls_token,
+            pad_token=pad_token,
+            gmask_token=gmask_token,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            **kwargs,
+        )
+
+        self.check_special_tokens()
+
+    def check_special_tokens(self):
+        '''
+        eos_token, cls_token, mask_token
+        special tokens should init, check special token is not None
+        '''
+        for name, special_token in zip(
+            ['eos', 'bos', 'cls', 'gmask'],
+            [self.eos_token, self.bos_token, self.cls_token, self.gmask_token],
+        ):
+            assert special_token is not None, f'should init special token [{name}] in tokenizer_config.json'
+
+    @property
+    def gmask_token(self) -> Optional[str]:
+        if self._gmask_token is None:
+            if self.verbose:
+                logger.error("Using gmask_token, but it is not set yet.")
+            return None
+        return str(self._gmask_token)
+
+    @gmask_token.setter
+    def gmask_token(self, value):
+        if not isinstance(value, (str, AddedToken)) and value is not None:
+            raise ValueError("Cannot set a non-string value as the gmask token")
+        self._gmask_token = value
+
+    @property
+    def gmask_token_id(self) -> Optional[int]:
+        if self._gmask_token is None:
+            return None
+        return self.convert_tokens_to_ids(self.gmask_token)
+
+    @property
+    def sop_token(self) -> Optional[str]:
+        if self._sop_token is None:
+            if self.verbose:
+                logger.error("Using sop_token, but it is not set yet.")
+            return None
+        return str(self._sop_token)
+
+    @sop_token.setter
+    def sop_token(self, value):
+        if not isinstance(value, (str, AddedToken)) and value is not None:
+            raise ValueError("Cannot set a non-string value as the sop token")
+        self._sop_token = value
+
+    @property
+    def sop_token_id(self) -> Optional[int]:
+        if self._sop_token is None:
+            return None
+        return self.convert_tokens_to_ids(self.sop_token)
+
+    @property
+    def eop_token(self) -> Optional[str]:
+        if self._eop_token is None:
+            if self.verbose:
+                logger.error("Using eop_token, but it is not set yet.")
+            return None
+        return str(self._eop_token)
+
+    @eop_token.setter
+    def eop_token(self, value):
+        if not isinstance(value, (str, AddedToken)) and value is not None:
+            raise ValueError("Cannot set a non-string value as the eop token")
+        self._eop_token = value
+
+    @property
+    def eop_token_id(self) -> Optional[int]:
+        if self._eop_token is None:
+            return None
+        return self.convert_tokens_to_ids(self.eop_token)
+
+    @property
+    def vocab_size(self):
+        return len(self.get_vocab())
+
+    def _chat_from_json(self, chat, chat_format="antglm_chat", system=None):
+        msgs = chat if "messages" not in chat else chat["messages"]
+        _msgs = []
+        sys_msg = None
+        for msg in msgs:
+            if is_system(msg):
+                sys_msg = msg['content']
+            else:
+                _msgs.append(msg)
+        chat = {"messages": _msgs}
+        system = system or sys_msg
+        if system:
+            chat['system_message'] = system
+        from .chat_format import Chat
+
+        return Chat.from_json(chat, name=chat_format)
+
+    def apply_chat_template(
+        self,
+        conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]]],
+        tools: Optional[List[Dict]] = None,
+        documents: Optional[List[Dict[str, str]]] = None,
+        chat_template: Optional[str] = None,
+        add_generation_prompt: bool = False,
+        system: str = None,  # only used for legacy chatml
+        tokenize=False,
+        padding: bool = False,
+        truncation: bool = False,
+        max_length: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        return_dict: bool = False,
+        return_assistant_tokens_mask: bool = False,
+        tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ):
+        if hasattr(self, "chat_template") and self.chat_template:
+            if isinstance(conversation, Dict) and "messages" in conversation:
+                conversation = conversation["messages"]
+            # use transformers built-in method
+            return super().apply_chat_template(
+                conversation=conversation,
+                tools=tools,
+                documents=documents,
+                chat_template=chat_template,
+                add_generation_prompt=add_generation_prompt,
+                tokenize=tokenize,
+                padding=padding,
+                truncation=truncation,
+                return_tensors=return_tensors,
+                return_dict=return_dict,
+                return_assistant_tokens_mask=return_assistant_tokens_mask,
+                tokenizer_kwargs=tokenizer_kwargs,
+            )
+
+        # 非chat_template方式后续将不再支持。
+        logger.warning("Please set chat_template in tokenizer_config.json!")
+
+        chat_format = kwargs.get('chat_format', 'antglm_chat')
+
+        is_batched = False
+
+        if isinstance(conversation, List) and (
+            isinstance(conversation[0], (list, tuple)) or "messages" in conversation[0]
+        ):
+            conversations = conversation
+            is_batched = True
+
+        if not is_batched:
+            conversations = [conversation]
+
+        rendered = []
+        for chat in conversations:
+            rendered_chat = self._chat_from_json(chat, chat_format=chat_format, system=system).prompt_str
+            rendered.append(rendered_chat)
+
+        if not is_batched:
+            rendered = rendered[0]
+
+        if tokenize:
+            out = self(
+                rendered,
+                padding=padding,
+                truncation=truncation,
+                max_length=max_length,
+                add_special_tokens=False,
+                return_tensors=return_tensors,
+            )
+            if return_dict:
+                return out
+            else:
+                return out["input_ids"]
+        else:
+            return rendered
+
+    def _build_position_ids(
+        self,
+        mask_pos: int,
+        bos_pos: int,
+        max_output_length: int,
+        rotary_type: Optional[str] = "none",
+        **kwargs,
+    ) -> List[List[int]]:
+        window_size = kwargs.get("window_size", 1024) - 1
+        block_position_ids = [0] * bos_pos
+
+        # 获得mask所在的位置，用于后面output positionid的构造
+        if "1d" in rotary_type:
+            position_ids = list(range(bos_pos)) + list(range(mask_pos + 1, mask_pos + max_output_length + 2))
+            block_position_ids = block_position_ids + list(range(1, max_output_length + 2))
+        elif "2d" in rotary_type:
+            # 后面input_ids要加一个bos_id
+            position_ids = list(range(bos_pos))
+            position_ids = position_ids + [mask_pos] * (1 + max_output_length)
+            block_position_ids = block_position_ids + list(range(1, max_output_length + 2))
+        else:
+            # build position ids
+            position_ids = []
+            repeat_times = bos_pos // window_size
+            for _ in range(repeat_times):
+                position_ids += list(range(window_size))
+            position_ids += list(range(bos_pos - window_size * repeat_times))
+            # need consider additional bos_id after input_ids
+            mask_pos = position_ids[-1]
+            position_ids += [mask_pos] * (max_output_length + 1)
+
+            block_repeat_times = max_output_length // (window_size - 1)
+            additional_block_position_ids = []
+            for _ in range(block_repeat_times):
+                additional_block_position_ids += list(range(1, window_size))
+            additional_block_position_ids += list(
+                range(1, max_output_length + 2 - (window_size - 1) * block_repeat_times)
+            )
+            block_position_ids = block_position_ids + additional_block_position_ids
+
+        position_ids = [position_ids, block_position_ids]
+        return position_ids
+
+    def _build_inputs_for_generation(
+        self,
+        input_ids: List[int],
+        max_input_length=None,
+        left_truncate=True,
+        max_output_length=1024,
+        rotary_type="none",
+        unidirectional_attention: bool = True,
+        attention_dtype=None,
+        **kwargs,
+    ):
+        if max_input_length and len(input_ids) > max_input_length:
+            if left_truncate:
+                input_ids = input_ids[-max_input_length:]
+            else:
+                input_ids = input_ids[:max_input_length]
+
+        is_left_padding = input_ids[0] == self.eos_token_id
+        if not unidirectional_attention:
+            if input_ids[0] != self.cls_token_id:
+                input_ids = [self.cls_token_id] + input_ids
+
+            if self.gmask_token_id not in set(input_ids):
+                input_ids = input_ids + [self.gmask_token_id]
+
+            mask_pos = input_ids.index(self.gmask_token_id)
+            sep = len(input_ids)
+        else:
+            if self.add_bos_token:
+                input_ids = input_ids + [self.bos_token_id]
+                if self.eos_token_id in input_ids:
+                    mask_pos = input_ids.index(self.eos_token_id) - 1
+                else:
+                    mask_pos = len(input_ids) - 1
+                sep = len(input_ids) - 1
+            else:
+                sep = len(input_ids)
+                if self.eos_token_id in input_ids:
+                    if is_left_padding:
+                        ori_input_ids = input_ids
+                        input_ids = input_ids[::-1]
+                    mask_pos = input_ids.index(self.eos_token_id) - 1
+                    mask_pos = max(0, mask_pos)  # for empty sequence
+                    if is_left_padding:
+                        input_ids = ori_input_ids
+                        mask_pos = sep - 1 - mask_pos  # the first non-eos token
+
+                else:
+                    mask_pos = len(input_ids) - 1
+
+        position_ids = self._build_position_ids(mask_pos, sep, max_output_length, rotary_type, **kwargs)
+
+        if is_left_padding:
+            position_ids[0] = [max(0, i - mask_pos) for i in range(len(position_ids[0]))]
+
+        # 后面input_ids要加一个bos_id
+        total_length = sep + max_output_length
+        if self.add_bos_token:
+            total_length += 1
+
+        def build_mask_matrix(seq_length, sep, mask_pos, unidirectional_attention):
+            # 长序列使用bool类型节省显存
+            if unidirectional_attention:
+                attention_mask = torch.ones([seq_length, seq_length], dtype=attention_dtype)
+                attention_mask = torch.tril(attention_mask)
+                if is_left_padding:
+                    attention_mask[:, :mask_pos] = 0
+                else:
+                    attention_mask[:, mask_pos + 1 : sep] = 0
+            else:
+                attention_mask = torch.zeros([seq_length, seq_length], dtype=attention_dtype)
+                attention_mask[:, : mask_pos + 1] = 1
+                for i in range(sep, total_length):
+                    attention_mask[i, sep : i + 1] = 1
+            return attention_mask
+
+        if self.add_bos_token:
+            attention_mask = build_mask_matrix(total_length, sep + 1, mask_pos, unidirectional_attention)
+        else:
+            attention_mask = build_mask_matrix(total_length, sep, mask_pos, unidirectional_attention)
+        attention_mask = torch.unsqueeze(attention_mask, dim=0)
+        attention_mask = torch.unsqueeze(attention_mask, dim=1)
+        if attention_dtype is None:
+            attention_mask = attention_mask.long()
+        inputs = {
+            "input_ids": torch.Tensor([input_ids]).long(),
+            "position_ids": torch.Tensor([position_ids]).long(),
+            "attention_mask": attention_mask,
+        }
+        return BatchEncoding(inputs)
+
+    def build_inputs_for_generation(
+        self,
+        input_ids: Union[List[int], List[List[int]], torch.Tensor],
+        max_input_length=None,
+        left_truncate=True,
+        max_output_length=1024,
+        rotary_type="1d",
+        unidirectional_attention=True,
+        attention_dtype=None,
+        **kwargs,
+    ):
+        if isinstance(input_ids, torch.Tensor):
+            input_ids = input_ids.tolist()
+
+        if isinstance(input_ids[0], list):
+            input_ids_list = []
+            position_ids_list = []
+            attention_mask_list = []
+            for _input_ids in input_ids:
+                inputs = self._build_inputs_for_generation(
+                    _input_ids,
+                    max_input_length=max_input_length,
+                    left_truncate=left_truncate,
+                    max_output_length=max_output_length,
+                    rotary_type=rotary_type,
+                    unidirectional_attention=unidirectional_attention,
+                    attention_dtype=attention_dtype,
+                    **kwargs,
+                )
+                input_ids_list.append(inputs['input_ids'])
+                position_ids_list.append(inputs['position_ids'])
+                attention_mask_list.append(inputs["attention_mask"])
+
+            max_ids_length = max([input.size(1) for input in input_ids_list])
+
+            for i in range(len(input_ids)):
+                cur_ids_length = input_ids_list[i].size(1)
+                if cur_ids_length < max_ids_length:
+                    # pad input ids
+                    pad_input_ids = input_ids_list[i].new_zeros((1, max_ids_length - cur_ids_length))
+                    input_ids_list[i] = torch.cat([pad_input_ids, input_ids_list[i]], dim=-1)
+
+                    # pad postition ids with left pad
+                    # 0, 1, 2, 3, 4 ... -> 0, ..., 0, 1, 2, 3, 4, ...
+                    pad_position_ids = input_ids_list[i].new_zeros((1, 2, max_ids_length - cur_ids_length))
+                    position_ids_list[i] = torch.cat([pad_position_ids, position_ids_list[i]], dim=-1)
+
+                    # pad generation attention mask with left and bottom pad
+                    new_attention_mask = input_ids_list[i].new_zeros(
+                        1,
+                        1,
+                        max_ids_length + max_output_length,
+                        max_ids_length + max_output_length,
+                    )
+                    new_attention_mask[
+                        :,
+                        :,
+                        max_ids_length - cur_ids_length :,
+                        max_ids_length - cur_ids_length :,
+                    ] = attention_mask_list[i]
+                    attention_mask_list[i] = new_attention_mask.contiguous()
+
+            input_ids_list = torch.cat(input_ids_list, dim=0)
+            position_ids_list = torch.cat(position_ids_list, dim=0)
+            attention_mask_list = torch.cat(attention_mask_list, dim=0)
+
+            inputs = {
+                "input_ids": input_ids_list,
+                "position_ids": position_ids_list,
+                "attention_mask": attention_mask_list,
+            }
+
+            return BatchEncoding(inputs)
+        else:
+            return self._build_inputs_for_generation(
+                input_ids,
+                max_input_length=max_input_length,
+                left_truncate=left_truncate,
+                max_output_length=max_output_length,
+                rotary_type=rotary_type,
+                unidirectional_attention=unidirectional_attention,
+                **kwargs,
+            )
+
+    def _build_inputs_for_train(
+        self,
+        inputs: Union[str, List[str]],
+        outputs: Union[str, List[str]],
+        new_conversation_offset: List[int] = None,
+        max_length: int = 2048,
+        rotary_type: str = "1d",
+        left_truncate: bool = True,
+        unidirectional_attention: bool = True,
+        isolation_position_ids: bool = False,
+        padding: bool = True,
+        use_fa2: bool = True,
+        use_packed: bool = True,
+        use_baichuan_packed: bool = False,
+        skip_truncated_turn: bool = False,
+        return_attention_mask: bool = True,
+    ):
+        r"""
+        Build tensor input for model training. If inputs and outputs are list, will pack them.
+
+        Args:
+            inputs (str, List[str], List[Dict], List[List[Dict]]): the input prompts.
+            outputs (str, List[str]): the output responses.
+            max_length (int, Optional): the maximum length of the final input ids for training. Default: 2048
+            rotary_type (str, Optional): the rotary type of position embedding. Default: 1d
+            left_truncate (bool, Optional): whether truncate the inputs from left. Default: True
+            use_fa2 (bool, Optional): whether to build attention mask under flash attention 2.
+            new_conversation_offset (List[int], Optional): 第idx条样本是全新的对话，[0, 1]代表：inputs[0]和outputs[0]是一个对话，inputs[1]和outputs[1]是一个对话.
+        """
+        if use_packed and use_baichuan_packed and unidirectional_attention:
+            return self._build_baichuan_inputs_for_train(
+                inputs,
+                outputs,
+                new_conversation_offset,
+                max_length,
+                rotary_type,
+                left_truncate,
+                skip_truncated_turn,
+                use_fa2,
+                padding,
+            )
+        if isinstance(inputs, str):
+            inputs = [inputs]
+        if isinstance(outputs, str):
+            outputs = [outputs]
+
+        assert len(inputs) == len(outputs)
+
+        input_ids = [self(item)['input_ids'] for item in inputs]
+        output_ids = [self(item)['input_ids'] for item in outputs]
+
+        packed_input_ids = []
+        packed_output_ids = []
+        if new_conversation_offset is None:
+            new_conversation_offset = list(range(0, len(inputs)))
+        assert 0 in new_conversation_offset, f"没有0，请检查new_conversation_offset: {new_conversation_offset}"
+        current_len = 0
+
+        for idx, (input, output) in enumerate(zip(input_ids, output_ids)):
+            num_special_tokens = 0
+            if not unidirectional_attention:
+                if idx in new_conversation_offset:
+                    # cls and gmask
+                    num_special_tokens += 2
+                else:
+                    # only gmask
+                    num_special_tokens += 1
+            else:
+                # sop and eos
+                if self.add_bos_token:
+                    num_special_tokens += 2
+                else:
+                    num_special_tokens += 1
+
+            # truncate
+            if len(input) + len(output) + current_len > max_length - num_special_tokens:
+                if not use_packed or use_fa2 and unidirectional_attention:
+                    attention_mask = torch.tensor(0)
+                elif use_fa2:
+                    attention_mask = -1 * torch.ones([2, max_length])
+                else:
+                    attention_mask = torch.tril(torch.ones([max_length, max_length]))
+                # 返回一个空的样本，该样本不参与训练
+                default_return = {
+                    'input_ids': (torch.ones(max_length) * self.eos_token_id).long(),
+                    'position_ids': torch.zeros(2, max_length).long(),
+                    'attention_mask': (attention_mask.long()),
+                    'labels': (torch.ones(max_length) * -100).long(),
+                }
+                # 如果不截断，直接返回
+                if skip_truncated_turn:
+                    if current_len == 0:
+                        return default_return
+                    else:
+                        break
+                left_len = max_length - num_special_tokens - current_len
+                # 如果截断，只截断prompt
+                if left_len - len(output) > 0:
+                    if left_truncate:
+                        input = input[-(left_len - len(output)) :]
+                    else:
+                        input = input[: left_len - len(output)]
+                else:
+                    # response超过left_len，直接返回
+                    if current_len == 0:
+                        return default_return
+                    else:
+                        break
+            if unidirectional_attention:
+                packed_input_ids.append(list(input))
+            else:
+                if num_special_tokens == 4:
+                    packed_input_ids.append([self.cls_token_id] + list(input) + [self.gmask_token_id])
+                else:
+                    packed_input_ids.append(list(input) + [self.gmask_token_id])
+
+            packed_output_ids.append(list(output) + [self.eos_token_id])
+            current_len += len(input) + len(output) + num_special_tokens
+
+        assert current_len <= max_length
+
+        if use_packed:
+            # pack模式
+            def build_mask_matrix(seq_length, sep):
+                # https://github.com/pytorch/pytorch/issues/101932, fix triu/tril bf16 support
+                m = torch.ones((1, seq_length, seq_length))
+                mask = torch.arange(1, m.shape[-1] + 1).reshape(1, -1, 1).to(m.device)
+                ids = torch.arange(1, m.shape[-1] + 1).reshape(1, 1, -1).expand(1, m.shape[-1], -1).to(m.device)
+                m = (ids <= mask).type_as(m)
+
+                m[0, :, : int(sep)] = 1
+                m = m.squeeze(0)
+                return m
+
+            tokens = []
+            attention_mask_list = []
+            input_length_list = []
+            position_id_list = []
+            block_position_id_list = []
+            for input, output in zip(packed_input_ids, packed_output_ids):
+                if self.add_bos_token:
+                    data = input + [self.sop_token_id] + output
+                    mask_pos = len(input) - 1
+                else:
+                    data = input + output
+                    mask_pos = len(input) - 2
+                if return_attention_mask:
+                    if unidirectional_attention:
+                        attention_mask = build_mask_matrix(len(data), 0)
+                    else:
+                        attention_mask = build_mask_matrix(len(data), len(input))
+                    attention_mask = attention_mask.squeeze((0, 1))
+
+                    attention_mask_list.append(attention_mask)
+                input_length_list.append(len(input))
+                tokens += data
+
+                sop_pos = mask_pos + 1
+                position_ids, block_position_ids = self._build_position_ids(
+                    mask_pos=mask_pos, bos_pos=sop_pos, max_output_length=len(output), rotary_type=rotary_type
+                )
+
+                position_id_list.append(position_ids)
+                block_position_id_list.append(block_position_ids)
+
+            labels = []
+            for i in range(len(packed_input_ids)):
+                if self.add_bos_token:
+                    labels += [-100] * len(packed_input_ids[i]) + packed_output_ids[i] + [-100]
+                else:
+                    labels += [-100] * (len(packed_input_ids[i]) - 1) + packed_output_ids[i] + [-100]
+
+            total_len = 0
+            if use_fa2:
+                pack_attention_mask = -1 * torch.ones([2, current_len])
+            else:
+                pack_attention_mask = torch.tril(torch.ones([current_len, current_len]))
+
+            pack_position_ids = []
+            pack_block_position_ids = []
+            total_len = 0
+            max_index = 0
+            for i in range(len(position_id_list)):
+
+                if use_fa2:
+                    pack_attention_mask[0][i] = total_len
+                    pack_attention_mask[1][i] = total_len + input_length_list[i]
+                else:
+                    pack_attention_mask[
+                        total_len : total_len + attention_mask.shape[0],
+                        total_len : total_len + attention_mask.shape[0],
+                    ] = attention_mask
+                position_ids = [pid + max_index for pid in position_id_list[i]]
+                block_position_ids = block_position_id_list[i]
+                pack_position_ids.extend(position_ids)
+                pack_block_position_ids.extend(block_position_ids)
+                if not isolation_position_ids:
+                    max_index = pack_position_ids[-1] + 1
+                total_len += len(position_id_list[i])
+            position_ids = [pack_position_ids, pack_block_position_ids]
+        else:
+            # 单输入模式
+            # 真多轮下，一条样本可能会有好几轮对话，此时需要获取第一条样本的结束位置
+            if len(new_conversation_offset) > 1:
+                end_idx = new_conversation_offset[1]
+            else:
+                end_idx = 1
+            input, output = list(itertools.chain(*packed_input_ids[:end_idx])), list(
+                itertools.chain(*packed_output_ids[:end_idx])
+            )
+            if self.add_bos_token:
+                tokens = input + [self.sop_token_id] + output
+            else:
+                tokens = input + output
+
+            if self.add_bos_token:
+                labels = [-100] * len(input) + output + [-100]
+                position_ids = self._build_position_ids(
+                    mask_pos=len(input) - 1, bos_pos=len(input), max_output_length=len(output), rotary_type=rotary_type
+                )
+            else:
+                labels = [-100] * (len(input) - 1) + output + [-100]
+                position_ids = self._build_position_ids(
+                    mask_pos=len(input) - 2,
+                    bos_pos=len(input) - 1,
+                    max_output_length=len(output),
+                    rotary_type=rotary_type,
+                )
+            attention_mask = len(input)
+        assert current_len == len(tokens)
+
+        # 最大长度补全
+        if max_length > 0 and len(tokens) < max_length and padding:
+            pad_length = max_length - len(tokens)
+            tokens += [self.pad_token_id] * pad_length
+            labels.extend([-100] * pad_length)
+            position_ids[0] += [0] * pad_length
+            position_ids[1] += [0] * pad_length
+
+            if use_packed:
+                if use_fa2:
+                    new_attention_mask = -1 * torch.ones([2, max_length])
+                    new_attention_mask[:, :current_len] = pack_attention_mask
+                else:
+                    new_attention_mask = torch.tril(torch.ones([max_length, max_length]))
+                    new_attention_mask[:current_len, :current_len] = pack_attention_mask
+                pack_attention_mask = new_attention_mask.contiguous()
+
+        assert len(tokens) == len(labels)
+
+        if max_length > 0 and padding:
+            assert len(tokens) == max_length
+
+        if use_fa2 and unidirectional_attention:
+            # pack_attention_mask = torch.zeros([1], dtype=torch.long)
+            pack_attention_mask = torch.tensor(0)
+
+        if use_packed:
+            if not use_fa2:
+                attention_mask = pack_attention_mask.unsqueeze(0).long()
+            else:
+                attention_mask = pack_attention_mask
+        else:
+            attention_mask = torch.tensor(attention_mask).long()
+        return {
+            'input_ids': torch.tensor(tokens).long(),
+            'position_ids': torch.tensor(position_ids).long(),
+            'attention_mask': attention_mask,
+            'labels': torch.tensor(labels).long(),
+        }
+
+    def _build_baichuan_inputs_for_train(
+        self,
+        inputs: Union[str, List[str]],
+        outputs: Union[str, List[str]],
+        new_conversation_offset: List[int] = None,
+        max_length: int = 2048,
+        rotary_type: str = "1d",
+        left_truncate: bool = True,
+        skip_truncated_turn: bool = True,
+        use_fa2: bool = True,
+        padding: bool = True,
+    ):
+        '''
+        input:  <role> HUMAN </role> u1 <role>  ASSISTANT </role> a11 a12            <role> HUMAN </role> u2 <role> ASSISTANT </role> a21 a22           <|endoftext|> <role> HUMAN </role> u1 <role>  ASSISTANT </role> a11 a12            <role> HUMAN </role> u2 <role> ASSISTANT </role> a21 a22           <|endoftext|>
+        output: x      x     x       x  x       x         a11     a12 <|endoftext|>  x      x     x       x  x      x         a21     a22 <|endoftext|> x             x      x     x       x  x       x         a11     a12 <|endoftext|>  x      x     x       x  x      x         a21     a22 <|endoftext|> x
+        只适用真多轮+pack数据训练单向模型，需要打开use_true_multiturn
+        '''
+        if isinstance(inputs, str):
+            inputs = [inputs]
+        if isinstance(outputs, str):
+            outputs = [outputs]
+        assert len(inputs) == len(outputs)
+
+        input_ids = [self(item)['input_ids'] for item in inputs]
+        output_ids = [self(item)['input_ids'] for item in outputs]
+
+        packed_input_ids = []
+        packed_output_ids = []
+
+        if new_conversation_offset is None:
+            new_conversation_offset = list(range(0, len(inputs)))
+        assert 0 in new_conversation_offset, f"没有0，请检查new_conversation_offset: {new_conversation_offset}"
+        current_len = 0
+
+        for idx, (input, output) in enumerate(zip(input_ids, output_ids)):
+            num_special_tokens = 0
+            if idx != 0 and idx in new_conversation_offset:
+                # 在input_ids加入eos，只有第0条样本不加
+                num_special_tokens += 1
+
+            # truncate
+            if len(input) + len(output) + current_len > max_length - num_special_tokens:
+                if use_fa2:
+                    attention_mask = torch.tensor(0)
+                else:
+                    attention_mask = torch.tril(torch.ones([max_length, max_length]))
+                # 返回一个空的样本，该样本不参与训练
+                default_return = {
+                    'input_ids': (torch.ones(max_length) * self.eos_token_id).long(),
+                    'position_ids': torch.zeros(2, max_length).long(),
+                    'attention_mask': (attention_mask.long()),
+                    'labels': (torch.ones(max_length) * -100).long(),
+                }
+
+                # 如果不截断，直接返回
+                if skip_truncated_turn:
+                    if current_len == 0:
+                        return default_return
+                    else:
+                        break
+                left_len = max_length - num_special_tokens - current_len
+                # 如果截断，只截断prompt
+                if left_len - len(output) > 0:
+                    if left_truncate:
+                        input = input[-(left_len - len(output)) :]
+                    else:
+                        input = input[: left_len - len(output)]
+                else:
+                    # response超过left_len，直接返回
+                    if current_len == 0:
+                        return default_return
+                    else:
+                        break
+            # 这里拼的是input_ids
+            if num_special_tokens == 1:
+                packed_input_ids.append([self.eos_token_id] + list(input))
+            else:
+                packed_input_ids.append(list(input))
+            packed_output_ids.append(list(output))
+            current_len += len(input) + len(output) + num_special_tokens
+        assert current_len <= max_length
+
+        def build_mask_matrix(seq_length, sep):
+            # https://github.com/pytorch/pytorch/issues/101932, fix triu/tril bf16 support
+            m = torch.ones((1, seq_length, seq_length))
+            mask = torch.arange(1, m.shape[-1] + 1).reshape(1, -1, 1).to(m.device)
+            ids = torch.arange(1, m.shape[-1] + 1).reshape(1, 1, -1).expand(1, m.shape[-1], -1).to(m.device)
+            m = (ids <= mask).type_as(m)
+
+            m[0, :, : int(sep)] = 1
+            m = m.squeeze(0)
+            return m
+
+        tokens = []
+        attention_mask_list = []
+        position_id_list = []
+        block_position_id_list = []
+        token_lens = []
+        for input, output in zip(packed_input_ids, packed_output_ids):
+            data = input + output
+            if not use_fa2:
+                attention_mask = build_mask_matrix(len(data), 0)
+                attention_mask_list.append(attention_mask)
+            tokens += data
+            token_lens.append(len(data))
+
+            position_ids, block_position_ids = self._build_position_ids(
+                mask_pos=len(input) - 2, bos_pos=len(input) - 1, max_output_length=len(output), rotary_type=rotary_type
+            )
+
+            position_id_list.append(position_ids)
+            block_position_id_list.append(block_position_ids)
+
+        labels = []
+        for i in range(len(packed_input_ids)):
+            labels += [-100] * (len(packed_input_ids[i]) - 1) + packed_output_ids[i] + [self.eos_token_id]
+
+        total_len = 0
+        if use_fa2:
+            pack_attention_mask = torch.Tensor([[0], [1]])
+        else:
+            pack_attention_mask = torch.tril(torch.ones([max_length, max_length]))
+
+        pack_position_ids = []
+        pack_block_position_ids = []
+        total_len = 0
+        max_index = 0
+        for i in range(len(token_lens)):
+            if not use_fa2:
+                attention_mask = attention_mask_list[i]
+                pack_attention_mask[
+                    total_len : total_len + attention_mask.shape[0], total_len : total_len + attention_mask.shape[0]
+                ] = attention_mask
+            position_ids = [pid + max_index for pid in position_id_list[i]]
+            block_position_ids = block_position_id_list[i]
+            pack_position_ids.extend(position_ids)
+            pack_block_position_ids.extend(block_position_ids)
+            max_index = pack_position_ids[-1] + 1
+            total_len += token_lens[i]
+        position_ids = [pack_position_ids, pack_block_position_ids]
+
+        if max_length > 0 and len(tokens) < max_length and padding:
+            pad_length = max_length - len(tokens)
+            tokens += [self.pad_token_id] * pad_length
+            labels.extend([-100] * pad_length)
+            position_ids[0] += [0] * pad_length
+            position_ids[1] += [0] * pad_length
+
+        assert len(tokens) == len(labels)
+
+        if not use_fa2:
+            attention_mask = pack_attention_mask.unsqueeze(0).long()
+        else:
+            attention_mask = torch.tensor(0)
+        return {
+            'input_ids': torch.tensor(tokens).long(),
+            'position_ids': torch.tensor(position_ids).long(),
+            'attention_mask': attention_mask,
+            'labels': torch.tensor(labels).long(),
+        }
+
+    def build_inputs_for_train(
+        self,
+        data: Union[Dict, List[Dict]],
+        new_conversation_offset: List[int] = None,
+        chat_format="antglm_chat",
+        is_chat_format=True,  # 如果传入的是字符串，用于说明是否已经是
+        use_true_multiturn=False,
+        max_length: int = 2048,
+        rotary_type: str = "1d",
+        left_truncate: bool = True,
+        unidirectional_attention: bool = True,
+        isolation_position_ids: bool = False,
+        padding: bool = True,
+        use_fa2: bool = True,
+        use_packed: bool = True,
+        use_baichuan_packed: bool = False,
+        skip_truncated_turn: bool = False,
+        return_attention_mask: bool = True,
+    ):
+        r"""
+        Build tensor input for model training. If inputs and outputs are list, will pack them.
+
+        Args:
+            inputs (str, List[str], List[Dict], List[List[Dict]]): the input prompts.
+            outputs (str, List[str]): the output responses.
+            new_conversation_offset (List[int]): the offset index of the new conversation turn.
+            is_chat_format (bool): whether the input is already chatml format
+            max_length (int, Optional): the maximum length of the final input ids for training. Default: 2048
+            rotary_type (str, Optional): the rotary type of position embedding. Default: 1d
+            left_truncate (bool, Optional): whether truncate the inputs from left. Default: True
+            use_fa2 (bool, Optional): whether to build attention mask under flash attention 2.
+        """
+        if isinstance(data, List):
+            # chatml list
+            _inputs = []
+            _outputs = []
+            new_conversation_offset = []
+            for _input in data:
+                if use_true_multiturn:
+                    chat = self._chat_from_json(_input, chat_format=chat_format)
+                    chat_data = chat.prompt_pack
+                    new_conversation_offset.append(len(_inputs))
+                    _inputs.extend(chat_data['input'])
+                    _outputs.extend(chat_data['output'])
+                else:
+                    _conversation = _convert_to_conversation(_input)
+                    assert is_assistant(_conversation[-1])
+
+                    _inputs.append(
+                        self.apply_chat_template(_conversation[:-1], tokenize=False, add_generation_prompt=True)
+                    )
+                    _outputs.append(_conversation[-1]['content'])
+
+            return self._build_inputs_for_train(
+                inputs=_inputs,
+                outputs=_outputs,
+                new_conversation_offset=new_conversation_offset,
+                max_length=max_length,
+                rotary_type=rotary_type,
+                left_truncate=left_truncate,
+                unidirectional_attention=unidirectional_attention,
+                isolation_position_ids=isolation_position_ids,
+                padding=padding,
+                use_fa2=use_fa2,
+                use_packed=use_packed,
+                use_baichuan_packed=use_baichuan_packed,
+                skip_truncated_turn=skip_truncated_turn,
+                return_attention_mask=return_attention_mask,
+            )
+        elif isinstance(data, Dict):
+            if 'messages' in data:
+                # chatml format
+                if use_true_multiturn:
+                    chat = self._chat_from_json(data, chat_format=chat_format)
+                    chat_data = chat.prompt_pack
+                else:
+                    _conversation = _convert_to_conversation(data)
+                    assert is_assistant(_conversation[-1])
+
+                    chat_data = {
+                        "input": self.apply_chat_template(
+                            _conversation[:-1], tokenize=False, add_generation_prompt=True
+                        ),
+                        "output": _conversation[-1]['content'],
+                    }
+
+                return self._build_inputs_for_train(
+                    inputs=chat_data['input'],
+                    outputs=chat_data['output'],
+                    max_length=max_length,
+                    rotary_type=rotary_type,
+                    left_truncate=left_truncate,
+                    unidirectional_attention=unidirectional_attention,
+                    isolation_position_ids=isolation_position_ids,
+                    padding=padding,
+                    use_fa2=use_fa2,
+                    use_packed=use_packed,
+                    use_baichuan_packed=use_baichuan_packed,
+                    skip_truncated_turn=skip_truncated_turn,
+                    return_attention_mask=return_attention_mask,
+                )
+            else:
+                inputs = data['input']
+                outputs = data['output']
+
+                if isinstance(inputs, str):
+                    inputs = [inputs]
+                if isinstance(outputs, str):
+                    outputs = [outputs]
+
+                if not is_chat_format and chat_format:
+                    inputs = [
+                        self.apply_chat_template(
+                            [{"role": "HUMAN", "content": item}], tokenize=False, chat_format=chat_format
+                        )
+                        for item in inputs
+                    ]
+
+                return self._build_inputs_for_train(
+                    inputs=inputs,
+                    outputs=outputs,
+                    new_conversation_offset=new_conversation_offset,
+                    max_length=max_length,
+                    rotary_type=rotary_type,
+                    left_truncate=left_truncate,
+                    unidirectional_attention=unidirectional_attention,
+                    isolation_position_ids=isolation_position_ids,
+                    padding=padding,
+                    use_fa2=use_fa2,
+                    use_packed=use_packed,
+                    use_baichuan_packed=use_baichuan_packed,
+                    skip_truncated_turn=skip_truncated_turn,
+                    return_attention_mask=return_attention_mask,
+                )

tokenizer_config.json

@@ -0,0 +1,2155 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "126080": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126081": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126082": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126083": {
+      "content": "[gMASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126084": {
+      "content": "<|reserved_token_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126085": {
+      "content": "<|reserved_token_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126086": {
+      "content": "<|reserved_token_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126087": {
+      "content": "<|reserved_token_3|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126088": {
+      "content": "<|reserved_token_4|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126089": {
+      "content": "<|reserved_token_5|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126090": {
+      "content": "<|reserved_token_6|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126091": {
+      "content": "<|reserved_token_7|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126092": {
+      "content": "<|reserved_token_8|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126093": {
+      "content": "<|reserved_token_9|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126094": {
+      "content": "<|reserved_token_10|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126095": {
+      "content": "<|reserved_token_11|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126096": {
+      "content": "<|reserved_token_12|>",
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+    },
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+    },
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+    },
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+    },
+    "126340": {
+      "content": "<role>",
+      "lstrip": false,
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+      "single_word": false,
+      "special": true
+    },
+    "126341": {
+      "content": "</role>",
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+      "single_word": false,
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+    },
+    "126342": {
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+      "single_word": false,
+      "special": true
+    },
+    "126343": {
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+      "normalized": false,
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+      "single_word": false,
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+    },
+    "126344": {
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+    },
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+      "single_word": false,
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+    }
+  },
+  "additional_special_tokens": [
+    "<role>",
+    "</role>",
+    "<|arithmetic_start|>",
+    "<|arithmetic_end|>",
+    "<|number_start|>",
+    "<|number_end|>"
+  ],
+  "bos_token": "<|startoftext|>",
+  "chat_template": "{% for message in messages %}{% set role = message['role'] | lower %}{% if role == 'user' %}{% set role = 'HUMAN' %}{% endif %}{% set role = role | upper %}{{ '<role>' + role + '</role>' + message['content'] }}{% endfor %}{% if add_generation_prompt %}{{ '<role>ASSISTANT</role>' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "eos_token": "<|endoftext|>",
+  "fast_tokenizer": true,
+  "gmask_token": "[gMASK]",
+  "merges_file": null,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "trust_remote_code": true,
+  "vocab_file": null
+}