import math from dataclasses import dataclass from functools import partial from typing import Any, Dict, Optional import mlx.core as mx import mlx.nn as nn import numpy as np from mlx_lm.models.switch_layers import SwitchGLU from ..base import ( LanguageModelOutput, create_attention_mask, scaled_dot_product_attention, ) from .config import ModelConfig, TextConfig def _compute_default_rope_parameters( config: Optional[TextConfig] = None, **rope_kwargs, ) -> tuple[mx.array, float]: if config is not None and len(rope_kwargs) > 0: raise ValueError( "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in " f"`_compute_default_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}" ) if len(rope_kwargs) > 0: base = rope_kwargs["base"] dim = rope_kwargs["dim"] elif config is not None: base = config.rope_theta partial_rotary_factor = ( config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0 ) head_dim = ( getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads ) dim = int(head_dim * partial_rotary_factor) attention_factor = 1.0 # Unused in this type of RoPE # Compute the inverse frequencies inv_freq = 1.0 / ( base ** (mx.arange(0, dim, 2, dtype=mx.int64).astype(mx.float32) / dim) ) return inv_freq, attention_factor class GLM4VRotaryEmbedding(nn.Module): def __init__(self, config: TextConfig): super().__init__() 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 = _compute_default_rope_parameters inv_freq, self.attention_scaling = self.rope_init_fn(self.config) self._inv_freq = mx.array(inv_freq, dtype=mx.float32) self._original_inv_freq = mx.array(inv_freq, dtype=mx.float32) def __call__(self, x, position_ids): inv_freq_expanded = self._inv_freq[None, None, :, None].astype(mx.float32) inv_freq_expanded = mx.broadcast_to( inv_freq_expanded, (3, position_ids.shape[1], self._inv_freq.shape[0], 1) ) position_ids_expanded = position_ids[:, :, None, :].astype(mx.float32) freqs = ( inv_freq_expanded.astype(mx.float32) @ position_ids_expanded.astype(mx.float32) ).transpose(0, 1, 3, 2) emb = mx.concatenate((freqs, freqs), axis=-1) cos = mx.cos(emb) * self.attention_scaling sin = mx.sin(emb) * self.attention_scaling return cos.astype(x.dtype), sin.astype(x.dtype) def rotate_half_llm(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return mx.concatenate([-x2, x1], axis=-1) def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section): """ Applies Rotary Position Embedding with Multimodal Sections to the query and key tensors. Args: q (mx.array): The query tensor. k (mx.array): The key tensor. cos (mx.array): The cosine part of the rotary embedding. sin (mx.array): The sine part of the rotary embedding. mrope_section (List[int]): Multimodal rope section for channel dimension of temporal, height and width. unsqueeze_dim (int, optional): Dimension to unsqueeze. Defaults to 1. Returns: tuple(mx.array): The rotated query and key tensors. """ mrope_section = np.cumsum(mrope_section * 2)[:-1].tolist() cos = mx.concatenate( [m[i % 3] for i, m in enumerate(mx.split(cos, mrope_section, axis=-1))], axis=-1 )[ :, None, :, : ] # unsqueeze dim 1 sin = mx.concatenate( [m[i % 3] for i, m in enumerate(mx.split(sin, mrope_section, axis=-1))], axis=-1 )[:, None, :, :] rotary_dim = cos.shape[-1] q_rot = q[..., :rotary_dim] q_pass = q[..., rotary_dim:] k_rot = k[..., :rotary_dim] k_pass = k[..., rotary_dim:] q_embed = (q_rot * cos) + (rotate_half_llm(q_rot) * sin) k_embed = (k_rot * cos) + (rotate_half_llm(k_rot) * sin) q_embed = mx.concatenate([q_embed, q_pass], axis=-1) k_embed = mx.concatenate([k_embed, k_pass], axis=-1) return q_embed, k_embed class Glm4vMoeAttention(nn.Module): def __init__(self, args: TextConfig): super().__init__() dim = args.hidden_size self.n_heads = n_heads = args.num_attention_heads self.n_kv_heads = n_kv_heads = args.num_key_value_heads head_dim = args.head_dim self.scale = head_dim**-0.5 self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=args.attention_bias) self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=args.attention_bias) self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=args.attention_bias) self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False) self.rope_scaling = args.rope_scaling def __call__( self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[Any] = None, position_embeddings: Optional[mx.array] = None, ) -> mx.array: B, L, D = x.shape queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x) queries = queries.reshape(B, L, self.n_heads, -1) keys = keys.reshape(B, L, self.n_kv_heads, -1) queries = queries.transpose(0, 2, 1, 3) keys = keys.transpose(0, 2, 1, 3) values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3) cos, sin = position_embeddings queries, keys = apply_multimodal_rotary_pos_emb( queries, keys, cos, sin, self.rope_scaling["mrope_section"] ) if cache is not None: keys, values = cache.update_and_fetch(keys, values) if mask is not None and isinstance(mask, mx.array): mask = mask[..., : keys.shape[-2]] output = scaled_dot_product_attention( queries, keys, values, cache=cache, scale=self.scale, mask=mask ) output = output.transpose(0, 2, 1, 3).reshape(B, L, -1) return self.o_proj(output) class Glm4vMoeMLP(nn.Module): def __init__( self, config: TextConfig, hidden_size: int = None, intermediate_size: int = None ): super().__init__() self.config = config self.hidden_size = config.hidden_size if hidden_size is None else 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) def __call__(self, x): down_proj = self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x)) return down_proj @mx.compile def group_expert_select( gates, e_score_correction_bias, top_k, n_group, topk_group, routed_scaling_factor, norm_topk_prob, ): scores = mx.sigmoid(gates.astype(mx.float32)) orig_scores = scores scores = scores + e_score_correction_bias if n_group > 1: k = top_k scores = mx.unflatten(scores, axis=-1, shape=(n_group, -1)) group_scores = mx.topk(scores, 2, axis=-1).sum(axis=-1, keepdims=True) k = n_group - topk_group group_idx = mx.argpartition(group_scores, kth=k - 1, axis=-2)[..., :k, :] scores = mx.put_along_axis( scores, mx.stop_gradient(group_idx), mx.array(0.0), axis=-2 ) scores = mx.flatten(scores, -2, -1) k = top_k inds = mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k] scores = mx.take_along_axis(orig_scores, inds, axis=-1) if top_k > 1 and norm_topk_prob: denominator = scores.sum(axis=-1, keepdims=True) scores = scores / denominator scores = scores * routed_scaling_factor return inds, scores class MoEGate(nn.Module): def __init__(self, config: TextConfig): super().__init__() self.config = config self.top_k = config.num_experts_per_tok self.norm_topk_prob = config.norm_topk_prob self.n_routed_experts = config.n_routed_experts self.routed_scaling_factor = config.routed_scaling_factor self.n_group = config.n_group self.topk_group = config.topk_group self.weight = mx.zeros((self.n_routed_experts, config.hidden_size)) self.e_score_correction_bias = mx.zeros((self.n_routed_experts,)) assert config.topk_method == "noaux_tc", "Unsupported topk method." def __call__(self, x): return group_expert_select( x @ self.weight.T, self.e_score_correction_bias, self.top_k, self.n_group, self.topk_group, self.routed_scaling_factor, self.norm_topk_prob, ) class MoE(nn.Module): def __init__(self, config: TextConfig): super().__init__() self.config = config self.num_experts_per_tok = config.num_experts_per_tok self.switch_mlp = SwitchGLU( config.hidden_size, config.moe_intermediate_size, config.n_routed_experts, ) self.gate = MoEGate(config) if config.n_shared_experts is not None: intermediate_size = config.moe_intermediate_size * config.n_shared_experts self.shared_experts = Glm4vMoeMLP( config=config, intermediate_size=intermediate_size ) def __call__(self, x): inds, scores = self.gate(x) y = self.switch_mlp(x, inds) y = (y * scores[..., None]).sum(axis=-2).astype(y.dtype) if self.config.n_shared_experts is not None: y = y + self.shared_experts(x) return y class Glm4vMoeDecoderLayer(nn.Module): def __init__(self, config: TextConfig, layer_idx: int): super().__init__() self.self_attn = Glm4vMoeAttention(config) self.mlp = ( MoE(config) if ( config.n_routed_experts is not None and layer_idx >= config.first_k_dense_replace ) else Glm4vMoeMLP(config) ) self.input_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = nn.RMSNorm( config.hidden_size, eps=config.rms_norm_eps ) def __call__( self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[Any] = None, position_embeddings: Optional[mx.array] = None, ) -> mx.array: r = self.self_attn(self.input_layernorm(x), mask, cache, position_embeddings) h = x + r r = self.mlp(self.post_attention_layernorm(h)) return h + r class GLM4VModel(nn.Module): def __init__(self, config: TextConfig): super().__init__() self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) self.layers = [ Glm4vMoeDecoderLayer(config, idx) for idx in range(config.num_hidden_layers) ] self.start_idx = 0 self.end_idx = len(self.layers) self.num_layers = self.end_idx self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = GLM4VRotaryEmbedding(config) def __call__( self, inputs: mx.array, inputs_embeds: Optional[mx.array] = None, cache: Optional[Any] = None, mask: Optional[mx.array] = None, position_ids: Optional[mx.array] = None, ) -> mx.array: if inputs_embeds is None: h = self.embed_tokens(inputs) else: h = inputs_embeds.astype(self.norm.weight.dtype) if position_ids is None: position_ids = mx.arange(cache[0].offset, cache[0].offset + h.shape[-2]) position_ids = mx.expand_dims(position_ids, axis=0) position_ids = mx.tile(position_ids, (3, 1, 1)) position_embeddings = self.rotary_emb(h, position_ids) if mask is None: mask = create_attention_mask(h, cache) if cache is None: cache = [None] * self.num_layers for i in range(self.num_layers): h = self.layers[self.start_idx + i](h, mask, cache[i], position_embeddings) return self.norm(h) class LanguageModel(nn.Module): def __init__(self, args: TextConfig, config: ModelConfig = None): super().__init__() self.args = args self.config = config self.model_type = args.model_type self.model = GLM4VModel(args) self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False) self._rope_deltas = None def get_rope_index( self, input_ids: mx.array, image_grid_thw: Optional[mx.array] = None, video_grid_thw: Optional[mx.array] = None, attention_mask: Optional[mx.array] = None, ): # Calculate RoPE index for image/video tokens batch_size, seq_length = input_ids.shape position_ids = mx.arange(seq_length, dtype=mx.int32) position_ids = mx.broadcast_to(position_ids[None, :], (batch_size, seq_length)) spatial_merge_size = self.config.vision_config.spatial_merge_size image_token_id = self.config.image_token_id video_token_id = self.config.video_token_id vision_start_token_id = self.config.vision_start_token_id mrope_position_deltas = [] if input_ids is not None and ( image_grid_thw is not None or video_grid_thw is not None ): total_input_ids = input_ids if attention_mask is None: attention_mask = mx.ones_like(input_ids) position_ids = mx.ones( (3, input_ids.shape[0], input_ids.shape[1]), dtype=input_ids.dtype ) image_index, video_index = 0, 0 for i, input_ids in enumerate(total_input_ids): input_ids = mx.where( attention_mask[i] == 1, input_ids, mx.zeros_like(input_ids) ) image_nums, video_nums = 0, 0 vision_start_indices = mx.sum( mx.where( input_ids == vision_start_token_id, mx.arange(input_ids.shape[0]), mx.zeros_like(input_ids), ) ) vision_tokens = input_ids[vision_start_indices + 1] image_nums = (vision_tokens == image_token_id).sum().item() video_nums = (vision_tokens == video_token_id).sum().item() input_tokens = input_ids.tolist() llm_pos_ids_list: list = [] st = 0 remain_images, remain_videos = image_nums, video_nums for _ in range(image_nums + video_nums): if image_token_id in input_tokens and remain_images > 0: ed_image = input_tokens.index(image_token_id, st) else: ed_image = len(input_tokens) + 1 if video_token_id in input_tokens and remain_videos > 0: ed_video = input_tokens.index(video_token_id, st) else: ed_video = len(input_tokens) + 1 if ed_image < ed_video: t, h, w = ( image_grid_thw[image_index][0], image_grid_thw[image_index][1], image_grid_thw[image_index][2], ) image_index += 1 remain_images -= 1 ed = ed_image else: t, h, w = ( video_grid_thw[video_index][0], video_grid_thw[video_index][1], video_grid_thw[video_index][2], ) video_index += 1 remain_videos -= 1 ed = ed_video llm_grid_t, llm_grid_h, llm_grid_w = ( t.item(), h.item() // spatial_merge_size, w.item() // spatial_merge_size, ) text_len = ed - st st_idx = ( llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 ) index = mx.arange(text_len).reshape(1, text_len) index = mx.broadcast_to(index, (3, text_len)) index = index + st_idx llm_pos_ids_list.append(index) t_index = mx.arange(llm_grid_t).reshape( llm_grid_t, 1 ) # Equivalent to .view(-1, 1) t_index = mx.broadcast_to( t_index, (llm_grid_t, llm_grid_h * llm_grid_w) ) # Equivalent to expand() t_index = t_index.flatten() # Flattens to 1D h_index = mx.arange(llm_grid_h).reshape( 1, llm_grid_h, 1 ) # Equivalent to .view(1, -1) h_index = mx.broadcast_to( h_index, (llm_grid_t, llm_grid_h, llm_grid_w) ) # Equivalent to expand() h_index = h_index.flatten() # Flattens to 1D w_index = mx.arange(llm_grid_w).reshape( 1, 1, llm_grid_w ) # Equivalent to .view(1, -1) w_index = mx.broadcast_to( w_index, (llm_grid_t, llm_grid_h, llm_grid_w) ) # Equivalent to expand() w_index = w_index.flatten() # Flattens to 1D llm_pos_ids_list.append( mx.stack([t_index, h_index, w_index]) + text_len + st_idx ) st = ed + llm_grid_t * llm_grid_h * llm_grid_w if st < len(input_tokens): st_idx = ( llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 ) text_len = len(input_tokens) - st t_index = mx.arange(text_len).reshape( 1, text_len ) # Equivalent to .view(-1, 1) t_index = mx.broadcast_to( t_index, (3, text_len) ) # Equivalent to expand(3, -1) llm_pos_ids_list.append(t_index + st_idx) llm_positions = mx.concatenate(llm_pos_ids_list, axis=1).reshape(3, -1) mask = mx.array(attention_mask[i] == 1) expanded_mask = mx.expand_dims(mask, axis=0) expanded_mask = mx.broadcast_to(expanded_mask, (3, 1, mask.shape[0])) expanded_positions = mx.expand_dims(llm_positions, axis=1) new_positions = mx.where( expanded_mask, expanded_positions, position_ids[:, i : i + 1, :] ) updated_position_ids = mx.concatenate( [ position_ids[:, :i, :], new_positions, position_ids[:, i + 1 :, :], ], axis=1, ) position_ids = updated_position_ids mrope_position_deltas.append( llm_positions.max() + 1 - len(total_input_ids[i]) ) mrope_position_deltas = mx.array(mrope_position_deltas)[0] return position_ids, mrope_position_deltas else: if attention_mask is not None: position_ids = mx.cumsum(attention_mask.astype(mx.int64), axis=-1) - 1 position_ids = mx.where( attention_mask == 0, mx.ones_like(position_ids), position_ids ) position_ids = mx.expand_dims(position_ids[0], axis=0) position_ids = mx.tile(position_ids, (3, 1, 1)) max_position_ids = position_ids.max(0, keepdims=False)[0].max( -1, keepdims=True )[0] mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1] else: position_ids = mx.arange(input_ids.shape[1]).reshape(1, -1) position_ids = mx.broadcast_to( position_ids, (3, input_ids.shape[0], input_ids.shape[1]) ) mrope_position_deltas = mx.zeros( [input_ids.shape[0], 1], dtype=input_ids.dtype, ) return position_ids, mrope_position_deltas def __call__( self, inputs: mx.array, inputs_embeds: Optional[mx.array] = None, mask: Optional[mx.array] = None, cache=None, **kwargs, ): position_ids = kwargs.pop("position_ids", None) pixel_values = kwargs.pop("pixel_values", None) image_grid_thw = kwargs.pop("image_grid_thw", None) video_grid_thw = kwargs.pop("video_grid_thw", None) # reset rope_deltas when processing a new image/video if pixel_values is not None: self._rope_deltas = None if position_ids is None and (mask is None or mask.ndim == 2): # Calculate RoPE index once per generation in the pre-fill stage only if ( (cache is not None and cache[0] is not None and cache[0].offset == 0) or self._rope_deltas is None or cache is None ): position_ids, rope_deltas = self.get_rope_index( inputs, image_grid_thw, video_grid_thw, mask ) self._rope_deltas = rope_deltas else: # Use the prev pre-calculated rope-deltas to get the correct position ids batch_size, seq_length = inputs.shape delta = cache[-1].offset + self._rope_deltas if cache is not None else 0 delta = delta[None, None, ...] position_ids = mx.arange(seq_length).reshape(1, seq_length) position_ids = mx.broadcast_to(position_ids, (batch_size, seq_length)) if cache is not None: delta = mx.repeat(delta, batch_size // delta.shape[0], axis=0) position_ids = mx.add(position_ids, delta).reshape(position_ids.shape) position_ids = mx.broadcast_to( position_ids, (3, batch_size, seq_length) ) out = self.model( inputs, cache=cache, inputs_embeds=inputs_embeds, position_ids=position_ids ) out = self.lm_head(out) return LanguageModelOutput(logits=out) def sanitize(self, weights): mpt_layer = self.args.num_hidden_layers # Stack experts for l in range(self.args.num_hidden_layers): prefix = f"language_model.model.layers.{l}" for n, m in [("w1", "gate_proj"), ("w2", "down_proj"), ("w3", "up_proj")]: for k in ["weight", "scales", "biases"]: if f"{prefix}.mlp.experts.0.{m}.{k}" in weights: to_join = [ weights.pop(f"{prefix}.mlp.experts.{e}.{m}.{k}") for e in range(self.args.n_routed_experts) ] weights[f"{prefix}.mlp.switch_mlp.{m}.{k}"] = mx.stack(to_join) # Remove multi-token prediction layer return { k: v for k, v in weights.items() if not k.startswith(f"language_model.model.layers.{mpt_layer}") } @property def layers(self): return self.model.layers @property def cast_predicate(self): def predicate(k): return "e_score_correction_bias" not in k return predicate @property def n_kv_heads(self): return self.args.num_key_value_heads