diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..52373fe24473b1aa44333d318f578ae6bf04b49b 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/config.json b/config.json new file mode 100644 index 0000000000000000000000000000000000000000..b182a864fa23faed6a1254ac2e43f22ac0e90004 --- /dev/null +++ b/config.json @@ -0,0 +1,68 @@ +{ + "architectures": [ + "BailingMoeV2_5ForCausalLM" + ], + "attention_dropout": 0.0, + "auto_map": { + "AutoConfig": "configuration_bailing_moe_v2_5.BailingMoeV2_5Config", + "AutoModel": "modeling_bailing_moe_v2_5.BailingMoeV2_5Model", + "AutoModelForCausalLM": "modeling_bailing_moe_v2_5.BailingMoeV2_5ForCausalLM" + }, + "embedding_dropout": 0.0, + "eos_token_id": 156895, + "first_k_dense_replace": 4, + "group_norm_size": 8, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 8192, + "initializer_range": 0.02, + "intermediate_size": 18432, + "kv_lora_rank": 512, + "layer_group_size": 8, + "linear_silu": false, + "max_position_embeddings": 131072, + "max_window_layers": 20, + "moe_intermediate_size": 2048, + "moe_router_enable_expert_bias": true, + "moe_shared_expert_intermediate_size": 2048, + "mtp_loss_scaling_factor": 0, + "n_group": 8, + "num_attention_heads": 64, + "num_experts": 256, + "num_experts_per_tok": 8, + "num_hidden_layers": 80, + "num_key_value_heads": 64, + "num_kv_heads_for_linear_attn": 64, + "num_nextn_predict_layers": 0, + "num_shared_experts": 1, + "output_dropout": 0.0, + "output_router_logits": false, + "pad_token_id": 156892, + "partial_rotary_factor": 0.5, + "q_lora_rank": 1536, + "qk_head_dim": 192, + "qk_nope_head_dim": 128, + "qk_rope_head_dim": 64, + "rms_norm_eps": 1e-06, + "rope_interleave": true, + "rope_scaling": null, + "rope_theta": 6000000, + "rotary_dim": 64, + "routed_scaling_factor": 2.5, + "router_dtype": "fp32", + "score_function": "sigmoid", + "scoring_func": "sigmoid", + "seq_aux": true, + "tie_word_embeddings": false, + "topk_group": 4, + "topk_method": "noaux_tc", + "transformers_version": "4.45.0", + "use_bias": false, + "use_cache": true, + "use_qk_norm": true, + "use_qkv_bias": false, + "v_head_dim": 128, + "vocab_size": 157184, + "model_type": "bailing_hybrid", + "torch_dtype": "bfloat16" +} \ No newline at end of file diff --git a/configuration_bailing_moe_v2_5.py b/configuration_bailing_moe_v2_5.py new file mode 100644 index 0000000000000000000000000000000000000000..513b1a59045a067871184697166dc0bff0786434 --- /dev/null +++ b/configuration_bailing_moe_v2_5.py @@ -0,0 +1,120 @@ +"""Bailing MoE V2.5 model configuration""" + +from transformers.configuration_utils import PretrainedConfig + + +class BailingMoeV2_5Config(PretrainedConfig): + + def __init__( + self, + vocab_size=157184, + hidden_size=2048, + intermediate_size=5120, + num_hidden_layers=20, + num_attention_heads=16, + num_key_value_heads=4, + hidden_act="silu", + use_qkv_bias=False, # bailing only + use_bias=False, # bailing only + rms_norm_eps=1e-06, + tie_word_embeddings=False, # PretrainedConfig key, here change default value. + embedding_dropout=0.0, + attention_dropout=0.0, + output_dropout=0.0, + initializer_range=0.02, + max_position_embeddings=32768, + rope_theta=600000.0, + use_cache=True, + max_window_layers=20, + rope_scaling=None, + pad_token_id=156892, + eos_token_id=156892, + num_experts=256, + num_shared_experts=1, + num_experts_per_tok=8, + n_group=8, + topk_group=4, + moe_intermediate_size=512, + first_k_dense_replace=1, + head_dim=128, + output_router_logits=False, + use_qk_norm=True, + num_nextn_predict_layers=0, + mtp_loss_scaling_factor=0, + moe_router_enable_expert_bias=True, + routed_scaling_factor=1.0, + layer_group_size=5, + group_norm_size=4, + linear_silu=False, + kv_lora_rank=512, + q_lora_rank=None, + qk_rope_head_dim=64, + v_head_dim=128, + qk_nope_head_dim=128, + rope_interleave=True, + partial_rotary_factor=0.5, + score_function="sigmoid", + scoring_func="sigmoid", + seq_aux=True, + topk_method="noaux_tc", + router_dtype="fp32", + **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.rms_norm_eps = rms_norm_eps + self.embedding_dropout = embedding_dropout + self.attention_dropout = attention_dropout + self.output_dropout = output_dropout + self.num_nextn_predict_layers = num_nextn_predict_layers + self.mtp_loss_scaling_factor = mtp_loss_scaling_factor + self.initializer_range = initializer_range + self.max_position_embeddings = max_position_embeddings + self.rope_theta = rope_theta + self.use_cache = use_cache + 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 + self.use_qk_norm = use_qk_norm + self.moe_router_enable_expert_bias = moe_router_enable_expert_bias + self.routed_scaling_factor = routed_scaling_factor + + # MoE configs + self.num_experts = num_experts + self.num_shared_experts = num_shared_experts + self.num_experts_per_tok = num_experts_per_tok + self.n_group = n_group + self.topk_group = topk_group + self.moe_intermediate_size = moe_intermediate_size + self.first_k_dense_replace = first_k_dense_replace + self.output_router_logits = output_router_logits + + # Linear configs + self.layer_group_size = layer_group_size + self.group_norm_size = group_norm_size + self.linear_silu = linear_silu + # mla + self.kv_lora_rank = kv_lora_rank + self.q_lora_rank = q_lora_rank + self.qk_rope_head_dim = qk_rope_head_dim + + self.score_function = score_function + self.scoring_func = scoring_func + self.seq_aux = seq_aux + self.topk_method = topk_method + self.v_head_dim = v_head_dim + self.qk_nope_head_dim = qk_nope_head_dim + self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim + self.rope_interleave = rope_interleave + self.router_dtype = router_dtype + self.partial_rotary_factor = partial_rotary_factor + super().__init__( + pad_token_id=pad_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs + ) diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000000000000000000000000000000000000..b4cd2462c7e9c82b95fda7ac494cbbfe3d28bfe9 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,8 @@ +{ + "bos_token_id": 156891, + "eos_token_id": [ + 156892, + 156895 + ], + "pad_token_id": 156892 +} \ No newline at end of file diff --git a/model-00021-of-00163.safetensors 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0000000000000000000000000000000000000000..f977d018d764dd6b832bac23ba54387e4aacd9f9 --- /dev/null +++ b/modeling_bailing_moe_v2_5.py @@ -0,0 +1,1603 @@ +# coding=utf-8 +# Copyright 2025 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, Callable + +import torch +import torch.nn.functional as F +from torch import nn + +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 +from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, 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, + logging, + replace_return_docstrings, +) +from transformers.utils.import_utils import is_torch_fx_available +from .configuration_bailing_moe_v2_5 import BailingMoeV2_5Config +from transformers.generation.utils import GenerationMixin +from dataclasses import dataclass +from transformers.utils import ModelOutput +from transformers import DynamicLayer +from transformers.processing_utils import Unpack +from transformers.utils import TransformersKwargs +from transformers.utils.deprecation import deprecate_kwarg +from transformers.modeling_flash_attention_utils import FlashAttentionKwargs + +from fla.ops.simple_gla.fused_recurrent import fused_recurrent_simple_gla +from fla.ops.simple_gla.chunk import chunk_simple_gla + + +# 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 = "BailingMoeV2_5Config" + + +def roll_tensor(tensor, shifts=-1, dims=-1, fill_value=0): + """Roll the tensor input along the given dimension(s). + Inserted elements are set to be 0.0. + """ + rolled_tensor = torch.roll(tensor, shifts=shifts, dims=dims) + rolled_tensor.select(dims, shifts).fill_(fill_value) + return rolled_tensor, rolled_tensor.sum() + + +@dataclass +class MoEV2_5CausalLMOutputWithPast(ModelOutput): + """ + Base class for causal language model (or autoregressive) outputs as well as Mixture of Expert's router hidden + states terms, to train a MoE model. + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + z_loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided): + z_loss for the sparse modules. + aux_loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided): + aux_loss for the sparse modules. + router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. + Router logits of the encoder model, useful to compute the auxiliary loss and the z_loss for the sparse + modules. + """ + + loss: Optional[torch.FloatTensor] = None + logits: Optional[torch.FloatTensor] = None + past_key_values: Optional[Cache] = None + hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None + attentions: Optional[tuple[torch.FloatTensor, ...]] = None + z_loss: Optional[torch.FloatTensor] = None + aux_loss: Optional[torch.FloatTensor] = None + router_logits: Optional[tuple[torch.FloatTensor]] = None + mtp_loss: Optional[torch.FloatTensor] = None + mtp_logits: Optional[tuple[torch.FloatTensor, ...]] = None + + +class MoeV2_5ModelOutputWithPast(MoeModelOutputWithPast): + + def __init__(self, mtp_hidden_states=None, **kwargs): + super().__init__(**kwargs) + self.mtp_hidden_states = mtp_hidden_states + + +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.BailingMoeV2_5.modeling_BailingMoeV2_5._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.BailingMoeV2_5.modeling_BailingMoeV2_5._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.BailingMoeV2_5.modeling_BailingMoeV2_5.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 BailingMoeV2_5RMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + BailingMoeV2_5RMSNorm 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) + + +class BailingMoeV2_5GroupRMSNorm(nn.Module): + def __init__(self, hidden_size, group_norm_size, eps=1e-6): + """ + BailingMoeV2_5RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.group_norm_size = group_norm_size + assert hidden_size % group_norm_size == 0, "hidden_size must be divisible by group_norm_size" + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + input_shape = hidden_states.size() + group_input_shape = input_shape[:-1] + (self.group_norm_size, input_shape[-1] // self.group_norm_size) + hidden_states = hidden_states.view(group_input_shape) + 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).view(input_shape) + + +ALL_LAYERNORM_LAYERS.append(BailingMoeV2_5RMSNorm) + + +class BailingMoeV2_5RotaryEmbedding(nn.Module): + def __init__(self, config: BailingMoeV2_5Config, device=None): + super().__init__() + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +# 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, 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. + 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.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + + # Keep half or full tensor for later concatenation + rotary_dim = cos.shape[-1] + q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:] + k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:] + + # Apply rotary embeddings on the first half or full tensor + q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin) + k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin) + + # Concatenate back to full shape + q_embed = torch.cat([q_embed, q_pass], dim=-1) + k_embed = torch.cat([k_embed, k_pass], dim=-1) + return q_embed, k_embed + + +class BailingMoeV2_5MLP(nn.Module): + def __init__(self, config: BailingMoeV2_5Config, 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 BailingMoeV2_5Gate(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 + + self.n_group = config.n_group + self.topk_group = config.topk_group + + # topk selection algorithm + self.gating_dim = config.hidden_size + self.weight = nn.Parameter(torch.empty((self.num_experts, self.gating_dim))) + self.routed_scaling_factor = config.routed_scaling_factor + + self.register_buffer("expert_bias", torch.zeros((self.num_experts))) + self.reset_parameters() + + def reset_parameters(self) -> None: + import torch.nn.init as init + + init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + + def group_limited_topk( + self, + scores: torch.Tensor, + ): + num_tokens, _ = scores.size() + # Organize the experts into groups + group_scores = scores.view(num_tokens, self.n_group, -1).topk(2, dim=-1)[0].sum(dim=-1) + group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1] + group_mask = torch.zeros_like(group_scores) + group_mask.scatter_(1, group_idx, 1) + + # Mask the experts based on selection groups + score_mask = ( + group_mask.unsqueeze(-1) + .expand(num_tokens, self.n_group, self.num_experts // self.n_group) + .reshape(num_tokens, -1) + ) + + masked_scores = scores.masked_fill(~score_mask.bool(), float('-inf')) + probs, top_indices = torch.topk(masked_scores, k=self.top_k, dim=-1) + + return probs, top_indices + + def forward(self, hidden_states): + # compute gating score + hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) + logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32)) + + scores = torch.sigmoid(logits.float()).type_as(logits) + + scores_for_routing = scores + self.expert_bias + _, topk_idx = self.group_limited_topk(scores_for_routing) + + scores = torch.gather(scores, dim=1, index=topk_idx).type_as(logits) + + topk_weight = scores / (scores.sum(dim=-1, keepdim=True) + 1e-20) if self.top_k > 1 else scores + topk_weight = topk_weight * self.routed_scaling_factor + + return topk_idx, topk_weight, logits + + +class BailingMoeV2_5SparseMoeBlock(nn.Module): + """ + A mixed expert module containing shared experts. + """ + + def __init__(self, config: BailingMoeV2_5Config): + super().__init__() + self.config = config + self.num_experts_per_tok = config.num_experts_per_tok + self._setup_experts() + self.gate = BailingMoeV2_5Gate(config) + if config.num_shared_experts is not None: + self.shared_experts = BailingMoeV2_5MLP( + config=config, intermediate_size=config.moe_intermediate_size * config.num_shared_experts + ) + + def _setup_experts(self): + self.experts = nn.ModuleList( + [ + BailingMoeV2_5MLP(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]] + 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.to(x.device)) + 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, head_first: bool = True) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). If head_first is True, the hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + if n_rep == 1: + return hidden_states + if head_first: + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + 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) + else: + batch, slen, num_key_value_heads, head_dim = hidden_states.shape + hidden_states = hidden_states[:, :, :, None, :].expand(batch, slen, num_key_value_heads, n_rep, head_dim) + return hidden_states.reshape(batch, slen, num_key_value_heads * n_rep, head_dim) + + +def repeat_kv2(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) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: Optional[torch.Tensor], + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv2(key, module.num_key_value_groups) + value_states = repeat_kv2(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights = attn_weights + causal_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def apply_rotary_pos_emb_interleave(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): + r""" + TODO let's just use the original freqcis computation to not have the view + transpose + reshape! This is not optimized! + 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 of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + + b, h, s, d = q.shape + q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) + + b, h, s, d = k.shape + k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) + + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class BailingMoeV2_5MLARotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: BailingMoeV2_5Config, device=None): + super().__init__() + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict): + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +def yarn_get_mscale(scale=1, mscale=1): + if scale <= 1: + return 1.0 + return 0.1 * mscale * math.log(scale) + 1.0 + + +class BailingMoeV2_5MultiLatentAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: BailingMoeV2_5Config, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.attention_dropout = config.attention_dropout + self.num_heads = config.num_attention_heads + self.rope_theta = config.rope_theta + self.q_lora_rank = config.q_lora_rank + self.qk_rope_head_dim = config.qk_rope_head_dim + self.kv_lora_rank = config.kv_lora_rank + self.v_head_dim = config.v_head_dim + self.qk_nope_head_dim = config.qk_nope_head_dim + self.qk_head_dim = config.qk_head_dim + + self.is_causal = True + if self.q_lora_rank is None: + self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False) + else: + self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.use_qkv_bias) + self.q_a_layernorm = BailingMoeV2_5RMSNorm(config.q_lora_rank) + self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False) + + self.kv_a_proj_with_mqa = nn.Linear( + config.hidden_size, + self.kv_lora_rank + self.qk_rope_head_dim, + bias=config.use_qkv_bias, + ) + self.kv_a_layernorm = BailingMoeV2_5RMSNorm(self.kv_lora_rank) + self.kv_b_proj = nn.Linear( + self.kv_lora_rank, + self.num_heads * (self.qk_nope_head_dim + self.v_head_dim), + bias=False, + ) + + self.dense = nn.Linear( + self.num_heads * self.v_head_dim, + config.hidden_size, + bias=config.use_qkv_bias, + ) + + self.scaling = self.qk_head_dim ** (-0.5) + if self.config.rope_scaling is not None: + mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0) + scaling_factor = self.config.rope_scaling["factor"] + if mscale_all_dim: + mscale = yarn_get_mscale(scaling_factor, mscale_all_dim) + self.scaling = self.scaling * mscale * mscale + + @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58") + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: Optional[torch.Tensor], + past_key_values: Optional[Cache] = None, + cache_position: Optional[torch.LongTensor] = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: + + batch_size, seq_length = hidden_states.shape[:-1] + query_shape = (batch_size, seq_length, -1, self.qk_head_dim) + key_shape = (batch_size, seq_length, -1, self.qk_nope_head_dim + self.v_head_dim) + + if self.q_lora_rank is None: + q_states = self.q_proj(hidden_states) + else: + q_states = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states))) + q_states = q_states.view(query_shape).transpose(1, 2) + q_pass, q_rot = torch.split(q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) + + compressed_kv = self.kv_a_proj_with_mqa(hidden_states) + k_pass, k_rot = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) + + k_pass = self.kv_b_proj(self.kv_a_layernorm(k_pass)).view(key_shape).transpose(1, 2) + k_pass, value_states = torch.split(k_pass, [self.qk_nope_head_dim, self.v_head_dim], dim=-1) + + k_rot = k_rot.view(batch_size, 1, seq_length, self.qk_rope_head_dim) + + cos, sin = position_embeddings # tptest + if self.config.rope_interleave: # support using interleaved weights for efficiency + q_rot, k_rot = apply_rotary_pos_emb_interleave(q_rot, k_rot, cos, sin) + else: + x = 1 / 0 + q_rot, k_rot = apply_rotary_pos_emb(q_rot, k_rot, cos, sin) + k_rot = k_rot.expand(*k_pass.shape[:-1], -1) + + query_states = torch.cat((q_pass, q_rot), dim=-1) + key_states = torch.cat((k_pass, k_rot), dim=-1) + + if past_key_values is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs) + + if self.config._attn_implementation == "flash_attention_2" and self.qk_head_dim != self.v_head_dim: + value_states = F.pad(value_states, [0, self.qk_head_dim - self.v_head_dim]) + + attention_interface: Callable = eager_attention_forward + + if self.config._attn_implementation != "eager": + attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + if self.config._attn_implementation == "flash_attention_2" and self.qk_head_dim != self.v_head_dim: + attn_output = attn_output[:, :, :, : self.v_head_dim] + + attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous() + attn_output = self.dense(attn_output) + return attn_output, attn_weights, past_key_values + + +class BailingMoeV2_5LinearAttention(nn.Module): + """ + BailingMoeAttention implements a linear attention mechanism based on Lightning Attention-2 + (https://arxiv.org/abs/2401.04658) with efficient computation using flash-linear-attention operators. + + The implementation leverages optimized kernels from the flash-linear-attention library + (https://github.com/fla-org/flash-linear-attention) for maximum performance. + """ + + def __init__(self, config: BailingMoeV2_5Config, 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.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_attention_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0 + self.rope_dim = int(self.head_dim * partial_rotary_factor) + + self.use_qk_norm = getattr(config, "use_qk_norm", False) + self.rms_norm_eps = getattr(config, "rms_norm_eps", 1e-5) + self.mode = 'chunk' + + 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, + ) + + if self.config.use_qk_norm: + self.query_layernorm = BailingMoeV2_5RMSNorm(self.head_dim, eps=config.rms_norm_eps) + self.key_layernorm = BailingMoeV2_5RMSNorm(self.head_dim, eps=config.rms_norm_eps) + + self.rotary_emb = BailingMoeV2_5RotaryEmbedding(config=config) + + self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.use_bias) + + self.g_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) + self.g_norm = BailingMoeV2_5GroupRMSNorm( + self.num_heads * self.head_dim, group_norm_size=config.group_norm_size, eps=self.rms_norm_eps + ) + slope = -BailingMoeV2_5LinearAttention.build_slope_tensor(self.num_heads) * ( + 1 - (self.layer_idx - 1) / (self.config.num_hidden_layers - 1) + 1e-5 + ) + self.register_buffer('slope', slope, persistent=False) + + self.lightning_attn_ops = {'chunk': chunk_simple_gla, 'fused_recurrent': fused_recurrent_simple_gla} + + @staticmethod + def build_slope_tensor(n_attention_heads: int): + """ + Build a tensor of slopes for Lightning Attention-2 as described in the paper: + "Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models" + (https://arxiv.org/abs/2401.04658) + + This function computes the slope values that control the decay rate of attention scores + based on the number of attention heads. The slopes are designed to have specific + mathematical properties that work optimally when the number of heads is a power of 2. + + For non-power-of-2 head counts, a workaround is implemented to maintain similar properties. + + Args: + n_attention_heads (int): Number of attention heads in the model + + Returns: + torch.Tensor: A tensor of shape [n_attention_heads] containing the computed slopes + + Note: + Code copied from: https://github.com/OpenNLPLab/lightning-attention/blob/d15c38529bbd5c2c82b44ddda3cac885825aa873/lightning_attn/utils/utils.py#L6 + """ + + def get_slopes(n): + def get_slopes_power_of_2(n): + start = 2 ** (-(2 ** -(math.log2(n) - 3))) + ratio = start + return [start * ratio**i for i in range(n)] + + if math.log2(n).is_integer(): + return get_slopes_power_of_2( + n + ) # In the paper, we only train models that have 2^a heads for some a. This function has + else: # some good properties that only occur when the input is a power of 2. To maintain that even + closest_power_of_2 = 2 ** math.floor( + math.log2(n) + ) # when the number of heads is not a power of 2, we use this workaround. + return ( + get_slopes_power_of_2(closest_power_of_2) + + get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2] + ) + + slopes = torch.tensor(get_slopes(n_attention_heads), dtype=torch.float) + return slopes + + 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, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if attention_mask is not None: + assert len(attention_mask.shape) == 2, ( + "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " + "for padding purposes (0 indicating padding). " + "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." + ) + + # launching the triton kernel for just one token will actually be slower + mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode + + # Currently output_attentions can only be False, returning attention weights is not supported + assert ( + not output_attentions + ), "output_attentions can only be False, returning attention weights is not supported" + + bsz, q_len, _ = hidden_states.size() + device = hidden_states.device + + 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 + ) + if self.config.use_qk_norm: + query_states = self.query_layernorm(query_states) + key_states = self.key_layernorm(key_states) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, unsqueeze_dim=2) + + if self.num_key_value_groups > 1: + # [bsz, q_len, n_kv_heads, head_dim] -> [bsz, q_len, n_heads, head_dim] + key_states = repeat_kv(key_states, self.num_key_value_groups, head_first=False) + value_states = repeat_kv(value_states, self.num_key_value_groups, head_first=False) + + recurrent_state = None + if past_key_value is not None and isinstance(past_key_value, Cache): + # ensure the cache list is long enough + while len(past_key_value.layers) <= self.layer_idx: + past_key_value.layers.append(DynamicLayer()) + + if past_key_value.layers[self.layer_idx].keys is not None: + recurrent_state = past_key_value.layers[self.layer_idx].keys + # ensure recurrent_state is on the same device as hidden_states + if recurrent_state.device != hidden_states.device: + recurrent_state = recurrent_state.to(device).contiguous() + + if recurrent_state is None: + # dealing with left-padding + if attention_mask is not None and use_cache: + value_states = value_states.mul_(attention_mask[:, -q_len:, None, None]) + + o, recurrent_state = self.lightning_attn_ops[mode]( + q=query_states, + k=key_states, + v=value_states, + g=self.slope[None, None, :].expand(bsz, q_len, self.num_heads), + initial_state=recurrent_state, + output_final_state=use_cache, + ) + + o = o.reshape(bsz, q_len, -1) + o = self.g_norm(o) + g_proj = self.g_proj(hidden_states) + o = o * torch.sigmoid_(g_proj) + o = self.dense(o) + + if use_cache and past_key_value is not None and isinstance(past_key_value, Cache): + target_device = None + for cache in past_key_value.layers: + if cache.keys is not None: + target_device = cache.keys.device + break + if target_device is None: + target_device = recurrent_state.device + + # move to target device + if recurrent_state.device != target_device: + recurrent_state = recurrent_state.to(target_device) + + past_key_value.layers[self.layer_idx].keys = recurrent_state + + return o, None, past_key_value + + +class BailingMoeV2_5MTPLayer(nn.Module): + def __init__(self, config: BailingMoeV2_5Config, layer_idx: int): + super().__init__() + self.layer_idx = layer_idx + self.input_layernorm = BailingMoeV2_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.enorm = BailingMoeV2_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False) + self.post_attention_layernorm = BailingMoeV2_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.attention = BailingMoeV2_5MultiLatentAttention(config=config, layer_idx=layer_idx) + self.mlp = BailingMoeV2_5SparseMoeBlock(config) + + self.hnorm = BailingMoeV2_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.final_layernorm = BailingMoeV2_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + input_embeds, + 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, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + input_embeds = self.enorm(input_embeds) + hidden_states = self.hnorm(hidden_states) + hidden_states = self.eh_proj(torch.cat([input_embeds, hidden_states], dim=-1)) + 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, + position_embeddings=position_embeddings, + 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.to(residual.device) + hidden_states = self.final_layernorm(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 + + +class BailingMoeV2_5DecoderLayer(nn.Module): + def __init__(self, config: BailingMoeV2_5Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.layer_idx = layer_idx + self.attention_layer_type = ( + "attention" + if (layer_idx + 1) % config.layer_group_size == 0 + or layer_idx >= config.num_hidden_layers // config.layer_group_size * config.layer_group_size + else "linear_attention" + ) + + if self.attention_layer_type == "attention": + self.attention = BailingMoeV2_5MultiLatentAttention(config=config, layer_idx=layer_idx) + else: + self.attention = BailingMoeV2_5LinearAttention(config=config, layer_idx=layer_idx) + + self.mlp = ( + BailingMoeV2_5SparseMoeBlock(config) + if (config.num_experts is not None and layer_idx >= config.first_k_dense_replace) + else BailingMoeV2_5MLP(config=config, intermediate_size=config.intermediate_size) + ) + self.input_layernorm = BailingMoeV2_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = BailingMoeV2_5RMSNorm(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, + cache_position: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = False, + output_router_logits: Optional[bool] = False, + use_cache: Optional[bool] = False, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC + position_embeddings_mla: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC + **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`). + """ + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + if self.attention_layer_type == "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_values=past_key_value, + use_cache=use_cache, + cache_position=cache_position, # + position_embeddings=position_embeddings_mla, # + **kwargs, + ) + else: + batch_size, seq_len = hidden_states.shape[0], hidden_states.shape[1] + device = hidden_states.device + + if attention_mask is None: + # if attention_mask is None, create a full mask + attention_mask = torch.ones((batch_size, seq_len), dtype=torch.int32, device=device) + elif attention_mask.dim() == 4 and attention_mask.shape[1] == 1: + attention_mask = attention_mask[:, 0, -1, :].to(torch.int32) + attention_mask = (attention_mask > -1e4).to(torch.int32) + elif attention_mask.dim() == 2: + attention_mask = attention_mask.to(torch.int32) + else: + raise ValueError(f"Unsupported mask dimension: {attention_mask.shape}") + + hidden_states, self_attn_weights, present_key_value = self.attention( + hidden_states=hidden_states, + attention_mask=attention_mask, + past_key_value=past_key_value, + position_ids=position_ids, + use_cache=use_cache, + output_attentions=output_attentions, + position_embeddings=position_embeddings, + ) + + 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.to(residual.device) + + 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 + + +BAILINGMOEV2_5_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 ([`BailingMoeV2_5Config`]): + 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 BailingMoeV2_5 Model outputting raw hidden-states without any specific head on top.", + BAILINGMOEV2_5_START_DOCSTRING, +) +class BailingMoeV2_5PreTrainedModel(PreTrainedModel): + config_class = BailingMoeV2_5Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["BailingMoeV2_5DecoderLayer"] + _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_() + + +BAILINGMOEV2_5_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 BailingMoeV2_5 Model outputting raw hidden-states without any specific head on top.", + BAILINGMOEV2_5_START_DOCSTRING, +) +class BailingMoeV2_5Model(BailingMoeV2_5PreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`BailingMoeV2_5DecoderLayer`] + Args: + config: BailingMoeV2_5Config + """ + + def __init__(self, config: BailingMoeV2_5Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + self.num_nextn_predict_layers = config.num_nextn_predict_layers + + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = [] + for layer_idx in range(config.num_hidden_layers + config.num_nextn_predict_layers): + layer_cls = BailingMoeV2_5DecoderLayer if layer_idx < config.num_hidden_layers else BailingMoeV2_5MTPLayer + self.layers.append(layer_cls(config, layer_idx)) + + self.layers = nn.ModuleList(self.layers) + + self._use_sdpa = config._attn_implementation == "sdpa" + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + self.norm = BailingMoeV2_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = BailingMoeV2_5RotaryEmbedding(config=config) + self.rotary_emb_mla = BailingMoeV2_5MLARotaryEmbedding(config=config) + 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(BAILINGMOEV2_5_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, + cache_position: 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, MoeV2_5ModelOutputWithPast]: + 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 + + if use_cache and past_key_values is None: + past_key_values = DynamicCache() + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + # For hybrid attention (MLA + Linear Attention), use the softmax attention layer's cache length + # to ensure consistent position tracking across different attention types + softmax_attention_layer_id = self.config.layer_group_size - 1 + if past_key_values is not None: + past_seen_tokens = past_key_values.get_seq_length(layer_idx=softmax_attention_layer_id) + else: + past_seen_tokens = 0 + + if cache_position is None: + cache_position = torch.arange( + past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device + ) + + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + 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_seen_tokens, + ) + else: + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, (batch_size, seq_length), inputs_embeds, past_seen_tokens + ) + + # embed positions + hidden_states = inputs_embeds + + # create position embeddings to be shared across the decoder layers + position_embeddings = self.rotary_emb(hidden_states, position_ids) + position_embeddings_mla = self.rotary_emb_mla(hidden_states, position_ids) + + # 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 + layers = self.layers[: -self.num_nextn_predict_layers] if self.num_nextn_predict_layers > 0 else self.layers + mtp_layers = self.layers[-self.num_nextn_predict_layers :] if self.num_nextn_predict_layers > 0 else None + + # tptest miss causal_mask = create_causal_mask( + + for decoder_layer in 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, + cache_position, + output_attentions, + output_router_logits, + use_cache, + position_embeddings, + position_embeddings_mla, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + cache_position=cache_position, + output_attentions=output_attentions, + output_router_logits=output_router_logits, + use_cache=use_cache, + position_embeddings=position_embeddings, + position_embeddings_mla=position_embeddings_mla, + ) + 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) + main_hidden_states = hidden_states + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (main_hidden_states,) + + mtp_hidden_states = None + + # MTP layers are only used during training, skip them during inference + if mtp_layers and self.training: + for decoder_layer in mtp_layers: + input_ids, _ = roll_tensor(input_ids, shifts=-1, dims=-1) + inputs_embeds = self.word_embeddings(input_ids) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + inputs_embeds, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + output_router_logits, + use_cache, + position_embeddings, + ) + else: + layer_outputs = decoder_layer( + inputs_embeds, + 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, + position_embeddings=position_embeddings, + ) + if mtp_hidden_states is None: + mtp_hidden_states = [] + hidden_states = layer_outputs[0] + mtp_hidden_states.append(hidden_states) + + if output_hidden_states: + all_hidden_states += (hidden_states,) + + 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],) + + next_cache = None + if use_cache: + next_cache = next_decoder_cache + if not return_dict: + return tuple( + v + for v in [main_hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits] + if v is not None + ) + return MoeV2_5ModelOutputWithPast( + last_hidden_state=main_hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + mtp_hidden_states=mtp_hidden_states, + attentions=all_self_attns, + router_logits=all_router_logits, + ) + + +class BailingMoeV2_5ForCausalLM(BailingMoeV2_5PreTrainedModel, GenerationMixin): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config: BailingMoeV2_5Config): + super().__init__(config) + self.model = BailingMoeV2_5Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.num_nextn_predict_layers = config.num_nextn_predict_layers + self.mtp_loss_scaling_factor = config.mtp_loss_scaling_factor + + # 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(BAILINGMOEV2_5_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=MoEV2_5CausalLMOutputWithPast, 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, MoEV2_5CausalLMOutputWithPast]: + 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 = BailingMoeV2_5ForCausalLM.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, + ) + + loss = None + all_mtp_loss = None + aux_loss = None + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + logits = logits.float() + + if labels is not None: + loss = self.loss_function(logits, labels, self.config.vocab_size, **kwargs) + + all_mtp_logits = None + if self.num_nextn_predict_layers > 0 and outputs.mtp_hidden_states is not None: + mtp_hidden_states = outputs.mtp_hidden_states + shift_labels_mtp = None + for i in range(self.num_nextn_predict_layers): + mtp_hidden_states = mtp_hidden_states[i] + mtp_logits = self.lm_head(mtp_hidden_states).float() + if all_mtp_logits is None: + all_mtp_logits = [] + all_mtp_logits.append(mtp_logits) + if labels is not None: + if shift_labels_mtp is None: + shift_labels_mtp = labels.clone() + shift_labels_mtp, _ = roll_tensor(shift_labels_mtp, shifts=-1, dims=-1, fill_value=-100) + mtp_logits_ = mtp_logits.view(-1, self.config.vocab_size) + mtp_loss = self.loss_function( + mtp_logits_, shift_labels_mtp.to(mtp_logits_.device).view(-1), self.config.vocab_size, **kwargs + ) + if loss is not None: + loss += self.mtp_loss_scaling_factor * mtp_loss + else: + loss = self.mtp_loss_scaling_factor * mtp_loss + + if all_mtp_loss is None: + all_mtp_loss = [] + all_mtp_loss.append(mtp_loss) + + 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 MoEV2_5CausalLMOutputWithPast( + loss=loss, + mtp_loss=all_mtp_loss, + aux_loss=aux_loss, + logits=logits, + mtp_logits=all_mtp_logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + router_logits=outputs.router_logits, + ) diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..19893d9fb19b5b509686d235568bc01324948cac --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,30 @@ +{ + "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": "<|role_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000000000000000000000000000000000000..e2e3b3ae9d33db880d47397059cc4f8f02010473 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1ce9d2d10f1d6da7b2439bc9655e51a00a8c5970f7dd015ae8407ca3962199f4 +size 12205770 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 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true + }, + "157151": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "157152": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "bos_token": "<|startoftext|>", + "chat_template": "{% set thinking_option = 'off' %}\n{{- 'SYSTEM' }}\n{%- if tools %}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n\\n\" }}\n {{- 'detailed thinking ' + thinking_option + '<|role_end|>' }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {%- if 'detailed thinking on' in messages[0].content or 'detailed thinking off' in messages[0].content %}\n {{- messages[0].content + '<|role_end|>' }}\n {%- else %}\n {{- messages[0].content + '\\n' }}\n {{- 'detailed thinking ' + thinking_option + '<|role_end|>' }}\n {%- endif %}\n {% else %}\n {{- 'detailed thinking ' + thinking_option + '<|role_end|>' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if message.role == \"user\" %}\n {{- 'HUMAN' + message.content + '<|role_end|>' }}\n {%- elif message.role == \"system\" and not loop.first %}\n {{- 'SYSTEM' + message.content + '<|role_end|>' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if reasoning_content %}\n {{- 'ASSISTANT' + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- 'ASSISTANT' + content }}\n {%- endif %}\n {%- else %}\n {{- 'ASSISTANT' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|role_end|>' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- 'OBSERVATION' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|role_end|>' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- 'ASSISTANT' }}\n{%- endif %}", + "clean_up_tokenization_spaces": false, + "cls_token": "[CLS]", + "eos_token": "<|role_end|>", + "fast_tokenizer": true, + "gmask_token": "[gMASK]", + "merges_file": null, + "model_max_length": 1000000000000000019884624838656, + "pad_token": "<|endoftext|>", + "tokenizer_class": "PreTrainedTokenizerFast", + "trust_remote_code": true +} \ No newline at end of file