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|
| from copy import copy
|
| from typing import Callable, Optional, Union
|
|
|
| import torch
|
| import torch.nn as nn
|
| import torch.nn.functional as F
|
|
|
| from transformers.activations import ACT2FN
|
| from transformers.cache_utils import Cache, DynamicCache
|
| from transformers.generation import GenerationMixin
|
| from transformers.integrations import use_kernel_forward_from_hub
|
| from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
|
| from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
|
| from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
|
| from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
|
| from transformers.processing_utils import Unpack
|
| from transformers.utils import TransformersKwargs, can_return_tuple, logging
|
|
|
| from .configuration_mimo_v2 import MiMoV2Config
|
|
|
|
|
| logger = logging.get_logger(__name__)
|
|
|
|
|
| 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)
|
|
|
|
|
| def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
| """Applies rotary position embedding to query and key tensors."""
|
| cos = cos.unsqueeze(unsqueeze_dim)
|
| sin = sin.unsqueeze(unsqueeze_dim)
|
| q_embed = (q * cos) + (rotate_half(q) * sin)
|
| k_embed = (k * cos) + (rotate_half(k) * sin)
|
| return q_embed, k_embed
|
|
|
|
|
| def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
| 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,
|
| sinks: Optional[torch.Tensor] = None,
|
| **kwargs,
|
| ):
|
| key_states = repeat_kv(key, module.num_key_value_groups)
|
| value_states = repeat_kv(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
|
|
|
| if sinks is not None:
|
| sinks = module.attention_sink_bias.reshape(1, -1, 1, 1).expand(query.shape[0], -1, query.shape[-2], -1)
|
| attn_weights = torch.cat([attn_weights, sinks], dim=-1)
|
|
|
| attn_weights = attn_weights - attn_weights.max(dim=-1, keepdim=True).values
|
| probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
|
|
|
| if sinks is not None:
|
| probs = probs[..., :-1]
|
|
|
| attn_weights = nn.functional.dropout(probs, 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
|
|
|
|
|
| @use_kernel_forward_from_hub("RMSNorm")
|
| class MiMoV2RMSNorm(nn.Module):
|
| def __init__(self, hidden_size, eps=1e-6):
|
| 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 MiMoV2MLP(nn.Module):
|
| def __init__(self, config, intermediate_size=None):
|
| super().__init__()
|
| self.config = config
|
| self.hidden_size = config.hidden_size
|
| self.intermediate_size = config.intermediate_size if intermediate_size is None else 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, hidden_states):
|
| return self.down_proj(self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
|
|
|
|
|
| class MiMoV2MoEGate(nn.Module):
|
| def __init__(self, config):
|
| super().__init__()
|
| self.config = config
|
| self.top_k = config.num_experts_per_tok
|
| self.n_routed_experts = config.n_routed_experts
|
| self.routed_scaling_factor = config.routed_scaling_factor if config.routed_scaling_factor is not None else 1.0
|
| self.scoring_func = config.scoring_func
|
| self.topk_method = config.topk_method
|
| self.n_group = config.n_group
|
| self.topk_group = config.topk_group
|
| self.norm_topk_prob = config.norm_topk_prob
|
| self.gating_dim = config.hidden_size
|
| self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
|
| if self.topk_method == "noaux_tc":
|
| self.e_score_correction_bias = nn.Parameter(torch.empty((self.n_routed_experts)))
|
|
|
| def forward(self, hidden_states):
|
| bsz, seq_len, h = hidden_states.shape
|
| hidden_states = hidden_states.view(-1, h)
|
| logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32), None)
|
| if self.scoring_func == "sigmoid":
|
| scores = logits.sigmoid()
|
| else:
|
| raise NotImplementedError(f"Unsupported scoring function for MoE gating: {self.scoring_func}")
|
|
|
| if self.topk_method == "noaux_tc":
|
| if self.training:
|
| raise ValueError("MiMoV2 noaux_tc routing is only implemented for inference.")
|
| scores_for_choice = scores.view(bsz * seq_len, -1) + self.e_score_correction_bias.unsqueeze(0)
|
| group_scores = scores_for_choice.view(bsz * seq_len, 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)
|
| score_mask = (
|
| group_mask.unsqueeze(-1)
|
| .expand(bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group)
|
| .reshape(bsz * seq_len, -1)
|
| )
|
| tmp_scores = scores_for_choice.masked_fill(~score_mask.bool(), float("-inf"))
|
| _, topk_idx = torch.topk(tmp_scores, k=self.top_k, dim=-1, sorted=False)
|
| topk_weight = scores.gather(1, topk_idx)
|
| else:
|
| raise NotImplementedError(f"Unsupported TopK function for MoE gating: {self.topk_method}")
|
|
|
| if self.top_k > 1 and self.norm_topk_prob:
|
| denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
|
| topk_weight = topk_weight / denominator
|
| topk_weight = topk_weight * self.routed_scaling_factor
|
| return topk_idx, topk_weight
|
|
|
|
|
| class MiMoV2MoE(nn.Module):
|
| def __init__(self, config):
|
| super().__init__()
|
| self.config = config
|
| self.experts = nn.ModuleList(
|
| [MiMoV2MLP(config, intermediate_size=config.moe_intermediate_size) for _ in range(config.n_routed_experts)]
|
| )
|
| self.gate = MiMoV2MoEGate(config)
|
|
|
| def moe(self, hidden_states: torch.Tensor, topk_indices: torch.Tensor, topk_weights: torch.Tensor):
|
| final_hidden_states = torch.zeros_like(hidden_states, dtype=topk_weights.dtype)
|
| expert_mask = torch.nn.functional.one_hot(topk_indices, num_classes=len(self.experts))
|
| expert_mask = expert_mask.permute(2, 0, 1)
|
|
|
| for expert_idx, expert in enumerate(self.experts):
|
| mask = expert_mask[expert_idx]
|
| token_indices, weight_indices = torch.where(mask)
|
| if token_indices.numel() > 0:
|
| expert_weights = topk_weights[token_indices, weight_indices]
|
| expert_input = hidden_states[token_indices]
|
| expert_output = expert(expert_input)
|
| final_hidden_states.index_add_(0, token_indices, expert_output * expert_weights.unsqueeze(-1))
|
|
|
| return final_hidden_states.type(hidden_states.dtype)
|
|
|
| def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
| orig_shape = hidden_states.shape
|
| topk_indices, topk_weights = self.gate(hidden_states)
|
| hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
|
| hidden_states = self.moe(hidden_states, topk_indices, topk_weights).view(*orig_shape)
|
| return hidden_states
|
|
|
|
|
| class MiMoV2Attention(nn.Module):
|
| """MiMoV2 attention.
|
|
|
| `projection_layout` only controls how checkpoint weights are named and
|
| stored: Flash uses separate q/k/v projections, while Pro uses fused qkv.
|
| The attention computation after projection is shared.
|
| """
|
|
|
| def __init__(self, config, is_swa: bool, layer_idx: int, projection_layout: str = "split"):
|
| super().__init__()
|
| if projection_layout not in {"split", "fused_qkv"}:
|
| raise ValueError(f"Unsupported MiMoV2 attention projection layout: {projection_layout}")
|
|
|
| self.config = config
|
| self.layer_idx = layer_idx
|
| self.is_swa = is_swa
|
| self.is_causal = True
|
| self.projection_layout = projection_layout
|
|
|
| default_head_dim = config.hidden_size // config.num_attention_heads
|
| default_v_head_dim = getattr(config, "v_head_dim", default_head_dim)
|
|
|
| if is_swa:
|
| self.head_dim = getattr(config, "swa_head_dim", getattr(config, "head_dim", default_head_dim))
|
| self.v_head_dim = getattr(config, "swa_v_head_dim", default_v_head_dim)
|
| self.num_attention_heads = getattr(config, "swa_num_attention_heads", config.num_attention_heads)
|
| self.num_key_value_heads = getattr(config, "swa_num_key_value_heads", config.num_key_value_heads)
|
| else:
|
| self.head_dim = getattr(config, "head_dim", default_head_dim)
|
| self.v_head_dim = getattr(config, "v_head_dim", self.head_dim)
|
| self.num_attention_heads = config.num_attention_heads
|
| self.num_key_value_heads = config.num_key_value_heads
|
|
|
| self.rope_dim = int(self.head_dim * getattr(config, "partial_rotary_factor", 1.0))
|
| if self.rope_dim % 2 != 0:
|
| raise ValueError(
|
| f"MiMoV2 rotary dimension must be even, got {self.rope_dim} from "
|
| f"head_dim={self.head_dim} and partial_rotary_factor={getattr(config, 'partial_rotary_factor', 1.0)}"
|
| )
|
| self.num_key_value_groups = self.num_attention_heads // self.num_key_value_heads
|
| self.attention_dropout = getattr(config, "attention_dropout", 0.0)
|
| self.scaling = self.head_dim**-0.5
|
| self.sliding_window = getattr(config, "sliding_window", None) if is_swa else None
|
| self.q_size = self.num_attention_heads * self.head_dim
|
| self.k_size = self.num_key_value_heads * self.head_dim
|
| self.v_size = self.num_key_value_heads * self.v_head_dim
|
| self.o_hidden_size = self.num_attention_heads * self.v_head_dim
|
| self.v_scale = getattr(config, "attention_value_scale", None)
|
| self.attention_sink_bias = (
|
| nn.Parameter(torch.empty(self.num_attention_heads), requires_grad=False)
|
| if (
|
| (getattr(config, "add_full_attention_sink_bias", False) and not is_swa)
|
| or (getattr(config, "add_swa_attention_sink_bias", False) and is_swa)
|
| )
|
| else None
|
| )
|
|
|
| attention_bias = getattr(config, "attention_bias", False)
|
| if self.projection_layout == "fused_qkv":
|
| self.qkv_proj = nn.Linear(
|
| config.hidden_size,
|
| self.q_size + self.k_size + self.v_size,
|
| bias=attention_bias,
|
| )
|
| else:
|
| self.q_proj = nn.Linear(config.hidden_size, self.q_size, bias=attention_bias)
|
| self.k_proj = nn.Linear(config.hidden_size, self.k_size, bias=attention_bias)
|
| self.v_proj = nn.Linear(config.hidden_size, self.v_size, bias=attention_bias)
|
| self.o_proj = nn.Linear(self.o_hidden_size, config.hidden_size, bias=False)
|
|
|
| def _forward_attention(
|
| self,
|
| query_states: torch.Tensor,
|
| key_states: torch.Tensor,
|
| value_states: torch.Tensor,
|
| input_shape: torch.Size,
|
| position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
| attention_mask: Optional[torch.Tensor],
|
| past_key_values: Optional[Cache] = None,
|
| cache_position: Optional[torch.LongTensor] = None,
|
| position_ids: Optional[torch.LongTensor] = None,
|
| ) -> tuple[torch.Tensor, torch.Tensor]:
|
| if self.v_scale is not None:
|
| value_states = value_states * self.v_scale
|
|
|
| cos, sin = position_embeddings
|
| query_rope, query_nope = query_states.split([self.rope_dim, self.head_dim - self.rope_dim], dim=-1)
|
| key_rope, key_nope = key_states.split([self.rope_dim, self.head_dim - self.rope_dim], dim=-1)
|
| query_rope, key_rope = apply_rotary_pos_emb(query_rope, key_rope, cos, sin)
|
| query_states = torch.cat([query_rope, query_nope], dim=-1)
|
| key_states = torch.cat([key_rope, key_nope], dim=-1)
|
|
|
| if past_key_values is not None:
|
| 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)
|
|
|
| attn_implementation = self.config._attn_implementation
|
| if attn_implementation is not None and attn_implementation.startswith("paged|"):
|
| raise ValueError(
|
| "MiMoV2 remote code does not support paged attention cache. "
|
| "Please use eager, sdpa, flex_attention, or flash_attention_2."
|
| )
|
|
|
| attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
|
| attn_implementation, eager_attention_forward
|
| )
|
| if self.attention_sink_bias is not None and attn_implementation == "sdpa":
|
| logger.warning_once(
|
| "MiMoV2 attention sink bias is not supported by SDPA; falling back to eager attention for correctness."
|
| )
|
| attention_interface = eager_attention_forward
|
|
|
| attention_kwargs = {
|
| "dropout": 0.0 if not self.training else self.attention_dropout,
|
| "scaling": self.scaling,
|
| "position_ids": position_ids,
|
| "is_causal": self.is_causal,
|
| }
|
| if attention_interface is eager_attention_forward:
|
| attention_kwargs["sinks"] = self.attention_sink_bias
|
| else:
|
| if self.attention_sink_bias is not None:
|
| attention_kwargs["s_aux"] = self.attention_sink_bias
|
| if self.sliding_window is not None:
|
| attention_kwargs["sliding_window"] = self.sliding_window
|
|
|
| attn_output, attn_weights = attention_interface(
|
| self,
|
| query_states,
|
| key_states,
|
| value_states,
|
| attention_mask,
|
| **attention_kwargs,
|
| )
|
| attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
| attn_output = self.o_proj(attn_output)
|
| return attn_output, attn_weights
|
|
|
| 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,
|
| position_ids: Optional[torch.LongTensor] = None,
|
| **kwargs: Unpack[TransformersKwargs],
|
| ) -> tuple[torch.Tensor, torch.Tensor]:
|
| input_shape = hidden_states.shape[:-1]
|
|
|
| if self.projection_layout == "fused_qkv":
|
| qkv_states = self.qkv_proj(hidden_states)
|
| query_states, key_states, value_states = qkv_states.split([self.q_size, self.k_size, self.v_size], dim=-1)
|
| else:
|
| query_states = self.q_proj(hidden_states)
|
| key_states = self.k_proj(hidden_states)
|
| value_states = self.v_proj(hidden_states)
|
|
|
| query_states = query_states.view(*input_shape, self.num_attention_heads, self.head_dim).transpose(1, 2)
|
| key_states = key_states.view(*input_shape, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| value_states = value_states.view(*input_shape, self.num_key_value_heads, self.v_head_dim).transpose(1, 2)
|
| return self._forward_attention(
|
| query_states,
|
| key_states,
|
| value_states,
|
| input_shape,
|
| position_embeddings,
|
| attention_mask,
|
| past_key_values=past_key_values,
|
| cache_position=cache_position,
|
| position_ids=position_ids,
|
| )
|
|
|
|
|
| class MiMoV2DecoderLayer(nn.Module):
|
| attention_projection_layout = "split"
|
|
|
| def __init__(self, config, layer_idx: int, attention_projection_layout: Optional[str] = None):
|
| super().__init__()
|
| attention_projection_layout = attention_projection_layout or self.attention_projection_layout
|
| is_swa_layer = config.hybrid_layer_pattern[layer_idx] == 1
|
| self.attention_type = "sliding_window_attention" if is_swa_layer else "full_attention"
|
| self.self_attn = MiMoV2Attention(
|
| config, is_swa_layer, layer_idx, projection_layout=attention_projection_layout
|
| )
|
| self.mlp = (
|
| MiMoV2MoE(config)
|
| if getattr(config, "n_routed_experts", None) is not None and config.moe_layer_freq[layer_idx]
|
| else MiMoV2MLP(config)
|
| )
|
| self.input_layernorm = MiMoV2RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
|
| self.post_attention_layernorm = MiMoV2RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
|
|
|
| def forward(
|
| self,
|
| hidden_states: torch.Tensor,
|
| attention_mask: Optional[torch.Tensor] = None,
|
| position_ids: Optional[torch.LongTensor] = None,
|
| past_key_values: Optional[Cache] = None,
|
| use_cache: Optional[bool] = False,
|
| cache_position: Optional[torch.LongTensor] = None,
|
| position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
|
| **kwargs: Unpack[TransformersKwargs],
|
| ) -> torch.Tensor:
|
| residual = hidden_states
|
| hidden_states = self.input_layernorm(hidden_states)
|
| hidden_states, _ = self.self_attn(
|
| hidden_states=hidden_states,
|
| attention_mask=attention_mask,
|
| position_ids=position_ids,
|
| past_key_values=past_key_values,
|
| use_cache=use_cache,
|
| cache_position=cache_position,
|
| position_embeddings=position_embeddings,
|
| **kwargs,
|
| )
|
| hidden_states = residual + hidden_states
|
|
|
| residual = hidden_states
|
| hidden_states = self.post_attention_layernorm(hidden_states)
|
| hidden_states = self.mlp(hidden_states)
|
| hidden_states = residual + hidden_states
|
| return hidden_states
|
|
|
|
|
| class MiMoV2RotaryEmbedding(nn.Module):
|
| inv_freq: torch.Tensor
|
|
|
| def __init__(self, config, is_swa: bool, device=None):
|
| super().__init__()
|
| 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", "default"))
|
| 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 = copy(config)
|
| self.config.rope_parameters = copy(getattr(config, "rope_parameters", None) or {})
|
| if is_swa:
|
| self.config.rope_theta = getattr(config, "swa_rope_theta", config.rope_theta)
|
| self.config.head_dim = getattr(config, "swa_head_dim", getattr(config, "head_dim", None))
|
| if self.config.rope_parameters:
|
| self.config.rope_parameters["rope_theta"] = self.config.rope_theta
|
| self.rope_init_fn = (
|
| self.compute_default_rope_parameters
|
| if self.rope_type == "default"
|
| else 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
|
|
|
| @staticmethod
|
| def compute_default_rope_parameters(config, device=None, seq_len=None, layer_type=None):
|
| config.standardize_rope_params()
|
| rope_parameters = config.rope_parameters[layer_type] if layer_type is not None else config.rope_parameters
|
| base = rope_parameters["rope_theta"]
|
| partial_rotary_factor = rope_parameters.get("partial_rotary_factor", 1.0)
|
| head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
|
| dim = int(head_dim * partial_rotary_factor)
|
| if dim % 2 != 0:
|
| raise ValueError(
|
| f"MiMoV2 rotary dimension must be even, got {dim} from "
|
| f"head_dim={head_dim} and partial_rotary_factor={partial_rotary_factor}"
|
| )
|
| inv_freq = 1.0 / (
|
| base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
|
| )
|
| return inv_freq, 1.0
|
|
|
| @torch.no_grad()
|
| @dynamic_rope_update
|
| 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):
|
| 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)
|
|
|
|
|
| class MiMoV2Model(PreTrainedModel):
|
| config_class = MiMoV2Config
|
| attention_projection_layout = "split"
|
|
|
| def __init__(self, config):
|
| super().__init__(config)
|
| self.attention_projection_layout = getattr(
|
| config, "attention_projection_layout", self.attention_projection_layout
|
| )
|
| self.vocab_size = config.vocab_size
|
| self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
|
| self.layers = nn.ModuleList(
|
| [
|
| MiMoV2DecoderLayer(
|
| config,
|
| layer_idx,
|
| attention_projection_layout=self.attention_projection_layout,
|
| )
|
| for layer_idx in range(config.num_hidden_layers)
|
| ]
|
| )
|
| self.norm = MiMoV2RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
|
| self.rotary_emb = MiMoV2RotaryEmbedding(config=config, is_swa=False)
|
| self.swa_rotary_emb = MiMoV2RotaryEmbedding(config=config, is_swa=True)
|
| self.has_sliding_layers = any(pattern == 1 for pattern in config.hybrid_layer_pattern)
|
| self.config.layer_types = [
|
| "sliding_attention" if config.hybrid_layer_pattern[i] == 1 else "full_attention"
|
| for i in range(config.num_hidden_layers)
|
| ]
|
| self.post_init()
|
|
|
| def get_input_embeddings(self):
|
| return self.embed_tokens
|
|
|
| def set_input_embeddings(self, value):
|
| self.embed_tokens = value
|
|
|
| def forward(
|
| self,
|
| input_ids: Optional[torch.LongTensor] = None,
|
| attention_mask: Optional[torch.Tensor] = None,
|
| position_ids: Optional[torch.LongTensor] = None,
|
| past_key_values: Optional[Cache] = None,
|
| inputs_embeds: Optional[torch.FloatTensor] = None,
|
| use_cache: Optional[bool] = None,
|
| cache_position: Optional[torch.LongTensor] = None,
|
| **kwargs: Unpack[TransformersKwargs],
|
| ) -> BaseModelOutputWithPast:
|
| if (input_ids is None) ^ (inputs_embeds is not None):
|
| raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
| use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
| if inputs_embeds is None:
|
| inputs_embeds = self.embed_tokens(input_ids)
|
|
|
| if use_cache and past_key_values is None:
|
| past_key_values = DynamicCache(config=self.config)
|
|
|
| if cache_position is None:
|
| past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
| 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 not isinstance(causal_mask_mapping := attention_mask, dict):
|
| mask_kwargs = {
|
| "config": self.config,
|
| "input_embeds": inputs_embeds,
|
| "attention_mask": attention_mask,
|
| "cache_position": cache_position,
|
| "past_key_values": past_key_values,
|
| "position_ids": position_ids,
|
| }
|
| causal_mask_mapping = {
|
| "full_attention": create_causal_mask(**mask_kwargs),
|
| }
|
| if self.has_sliding_layers:
|
| if getattr(self.config, "sliding_window", None) is None:
|
| raise ValueError("MiMoV2 config `sliding_window` must be set when hybrid_layer_pattern uses SWA.")
|
| causal_mask_mapping["sliding_window_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
|
|
|
| hidden_states = inputs_embeds
|
| position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
| swa_position_embeddings = self.swa_rotary_emb(hidden_states, position_ids)
|
|
|
| for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
| hidden_states = decoder_layer(
|
| hidden_states,
|
| attention_mask=causal_mask_mapping[decoder_layer.attention_type],
|
| position_embeddings=position_embeddings
|
| if decoder_layer.attention_type == "full_attention"
|
| else swa_position_embeddings,
|
| position_ids=position_ids,
|
| past_key_values=past_key_values,
|
| use_cache=use_cache,
|
| cache_position=cache_position,
|
| **kwargs,
|
| )
|
|
|
| hidden_states = self.norm(hidden_states)
|
| return BaseModelOutputWithPast(
|
| last_hidden_state=hidden_states,
|
| past_key_values=past_key_values if use_cache else None,
|
| )
|
|
|
|
|
| class MiMoV2ForCausalLM(PreTrainedModel, GenerationMixin):
|
| config_class = MiMoV2Config
|
| model_class = MiMoV2Model
|
| _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
|
| _tp_plan = {"lm_head": "colwise_rep"}
|
| _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
| _keys_to_ignore_on_load_unexpected = [
|
| r"model\.(swa_)?rotary_emb\.inv_freq",
|
| r"model\.layers\.\d+\.self_attn\.rotary_emb\.inv_freq",
|
| r"model\.layers\.\d+\.self_attn\.rotary_emb\.(cos_cached|sin_cached)",
|
| r"model\.mtp\..*",
|
| ]
|
|
|
| def __init__(self, config):
|
| super().__init__(config)
|
| self.model = self.model_class(config)
|
| self.vocab_size = config.vocab_size
|
| self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
| self.post_init()
|
|
|
| def get_input_embeddings(self):
|
| return self.model.embed_tokens
|
|
|
| def set_input_embeddings(self, value):
|
| self.model.embed_tokens = value
|
|
|
| def get_output_embeddings(self):
|
| return self.lm_head
|
|
|
| def set_output_embeddings(self, new_embeddings):
|
| self.lm_head = new_embeddings
|
|
|
| @can_return_tuple
|
| def forward(
|
| self,
|
| input_ids: Optional[torch.LongTensor] = None,
|
| attention_mask: Optional[torch.Tensor] = None,
|
| position_ids: Optional[torch.LongTensor] = None,
|
| past_key_values: Optional[Cache] = None,
|
| inputs_embeds: Optional[torch.FloatTensor] = None,
|
| labels: Optional[torch.LongTensor] = None,
|
| use_cache: Optional[bool] = None,
|
| cache_position: Optional[torch.LongTensor] = None,
|
| logits_to_keep: Union[int, torch.Tensor] = 0,
|
| **kwargs: Unpack[TransformersKwargs],
|
| ) -> CausalLMOutputWithPast:
|
| outputs: BaseModelOutputWithPast = 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,
|
| cache_position=cache_position,
|
| **kwargs,
|
| )
|
|
|
| hidden_states = outputs.last_hidden_state
|
| slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
| logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
| loss = None
|
| if labels is not None:
|
| loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
|
|
| return CausalLMOutputWithPast(
|
| loss=loss,
|
| logits=logits,
|
| past_key_values=outputs.past_key_values,
|
| hidden_states=outputs.hidden_states,
|
| attentions=outputs.attentions,
|
| )
|
|
|
|
|
| __all__ = [
|
| "MiMoV2Attention",
|
| "MiMoV2DecoderLayer",
|
| "MiMoV2ForCausalLM",
|
| "MiMoV2MLP",
|
| "MiMoV2MoE",
|
| "MiMoV2MoEGate",
|
| "MiMoV2Model",
|
| "MiMoV2RMSNorm",
|
| "MiMoV2RotaryEmbedding",
|
| ]
|
|
|
