# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/minimax/modular_minimax.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_minimax.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. All rights reserved. # # # 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. from typing import Callable, Optional, Union 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.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_flash_attention_utils import FlashAttentionKwargs from transformers.modeling_layers import ( GenericForQuestionAnswering, GenericForSequenceClassification, GenericForTokenClassification, GradientCheckpointingLayer, ) from transformers.modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast 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, auto_docstring, can_return_tuple from transformers.utils.generic import OutputRecorder, check_model_inputs from configuration_minimax import MiniMaxConfig @use_kernel_forward_from_hub("RMSNorm") class MiniMaxRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ MiniMaxRMSNorm 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) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" class MiniMaxCache(DynamicCache): def __init__(self): super().__init__() self.linear_cache: list[torch.Tensor] = [] def set_linear_cache(self, layer_idx, linear_cache): # There may be skipped layers, fill them with empty lists for _ in range(len(self.linear_cache), layer_idx + 1): self.linear_cache.append([]) self.linear_cache[layer_idx] = linear_cache def get_linear_cache(self, layer_idx: int): if layer_idx < len(self): return self.linear_cache[layer_idx] return None def __len__(self): return max(super().__len__(), len(self.linear_cache)) def __getitem__(self, layer_idx: int): if layer_idx < len(self.linear_cache) and self.linear_cache[layer_idx] != []: return (self.linear_cache[layer_idx],) return super().__getitem__(layer_idx) def __iter__(self): for layer_idx in range(len(self)): yield self[layer_idx] def batch_repeat_interleave(self, repeats: int): for layer_idx in range(len(self)): if self.linear_cache[layer_idx] != []: self.linear_cache[layer_idx] = self.linear_cache[layer_idx].repeat_interleave(repeats, dim=0) else: self.layers[layer_idx].batch_repeat_interleave(repeats) def batch_select_indices(self, indices: torch.Tensor): for layer_idx in range(len(self)): if self.linear_cache[layer_idx] != []: self.linear_cache[layer_idx] = self.linear_cache[layer_idx][indices, ...] else: self.layers[layer_idx].batch_select_indices(indices) def crop(self, max_length: int): raise RuntimeError("MiniMaxCache doesnot support `crop` method") class MiniMaxLightningAttention(nn.Module): def __init__(self, config: MiniMaxConfig, layer_idx: int): super().__init__() self.layer_idx = layer_idx self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads self.num_attention_heads = config.num_attention_heads self.num_hidden_layers = config.num_hidden_layers self.block_size = config.block_size self.act_fn = ACT2FN[config.hidden_act] self.norm = MiniMaxRMSNorm(self.head_dim * self.num_attention_heads) self.qkv_proj = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim * 3, bias=False) self.out_proj = nn.Linear(self.num_attention_heads * self.head_dim, config.hidden_size, bias=False) self.output_gate = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim, bias=False) slope_rate = self.get_slope_rate() query_decay, key_decay, diagonal_decay = self.decay_factors(slope_rate) self.register_buffer("slope_rate", slope_rate) self.register_buffer("query_decay", query_decay) self.register_buffer("key_decay", key_decay) self.register_buffer("diagonal_decay", diagonal_decay) def get_slope_rate(self): base = 1 / (2 ** (8 / self.num_attention_heads)) exponent = torch.arange(self.num_attention_heads) + 1 factor = 1 - self.layer_idx / (self.num_hidden_layers - 1 + 1e-5) + 1e-5 rate = base**exponent rate = rate * factor rate = rate[:, None, None] return rate def decay_factors(self, slope_rate): block_size_range = torch.arange(self.block_size) + 1 query_decay = torch.exp(-slope_rate * block_size_range[:, None]) key_decay = torch.exp(-slope_rate * (self.block_size - block_size_range[:, None])) diagonal_decay = block_size_range[:, None] - block_size_range[None, :] diagonal_decay = diagonal_decay[None, None, :, :] diagonal_decay = slope_rate * diagonal_decay diagonal_decay = torch.where(diagonal_decay >= 0, -diagonal_decay, float("-inf")) diagonal_decay = torch.exp(diagonal_decay) return query_decay, key_decay, diagonal_decay 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_len, hidden_size = hidden_states.shape num_blocks = (seq_len + self.block_size - 1) // self.block_size qkv_states = self.act_fn(self.qkv_proj(hidden_states)) qkv_states = qkv_states.reshape(batch_size, seq_len, self.num_attention_heads, 3 * self.head_dim) query_states, key_states, value_states = torch.split(qkv_states, self.head_dim, dim=3) query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) # calculated (K.T @ V) and saved as cache attn_weights_inter = None if past_key_values is not None: attn_weights_inter = past_key_values.get_linear_cache(self.layer_idx) if attn_weights_inter is None: attn_weights_inter = torch.zeros(batch_size, self.num_attention_heads, self.head_dim, self.head_dim).to( value_states ) # apply attention_mask if attention_mask is not None: attention_mask = attention_mask.to(dtype=torch.bool) # Ensure it's a boolean tensor value_states = value_states.masked_fill(~attention_mask.unsqueeze(1).unsqueeze(-1), 0) attn_output = [] for i in range(num_blocks): start_idx = i * self.block_size end_idx = min(start_idx + self.block_size, seq_len) current_block_size = end_idx - start_idx current_query_states = query_states[:, :, start_idx:end_idx] current_key_states = key_states[:, :, start_idx:end_idx] current_value_states = value_states[:, :, start_idx:end_idx] current_query_decay = self.query_decay[:, :current_block_size] current_key_decay = self.key_decay[:, -current_block_size:] current_diagonal_decay = self.diagonal_decay[:, :, :current_block_size, :current_block_size] block_decay = torch.exp(-self.slope_rate * current_block_size) # intra: ( Q @ K.T ) @ V -> QK * V attn_weights_intra = torch.matmul(current_query_states, current_key_states.transpose(-1, -2)) attn_output_intra = torch.matmul(attn_weights_intra * current_diagonal_decay, current_value_states) # inter: Q @ ( K.T @ V ) -> Q * KV attn_output_inter = torch.matmul(current_query_states * current_query_decay, attn_weights_inter) # final attention output current_attn_output = attn_output_inter + attn_output_intra attn_output.append(current_attn_output) # calculate attn_weights_inter for next block or cache next_attn_weights_inter = torch.matmul( (current_key_states * current_key_decay).transpose(-1, -2), current_value_states ) attn_weights_inter = attn_weights_inter * block_decay + next_attn_weights_inter else: ratio = torch.exp(-self.slope_rate) attn_output = [] for i in range(seq_len): current_query_states = query_states[:, :, i : i + 1] current_key_states = key_states[:, :, i : i + 1] current_value_states = value_states[:, :, i : i + 1] current_attn_weights_inter = torch.matmul(current_key_states.transpose(-1, -2), current_value_states) attn_weights_inter = ratio * attn_weights_inter + current_attn_weights_inter current_attn_output = torch.matmul(current_query_states, attn_weights_inter) attn_output.append(current_attn_output) # concatenate attention outputs over all blocks attn_output = torch.cat(attn_output, dim=-2) # final output projection attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(batch_size, seq_len, self.num_attention_heads * self.head_dim) attn_output = self.norm(attn_output) attn_output = F.sigmoid(self.output_gate(hidden_states)) * attn_output attn_output = self.out_proj(attn_output) # update cache if past_key_values is not None: past_key_values.set_linear_cache(self.layer_idx, attn_weights_inter) return attn_output, attn_weights_inter 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 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`, *optional*): Deprecated and unused. 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) 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: """ 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_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 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 class MiniMaxAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: MiniMaxConfig, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.scaling = self.head_dim**-0.5 self.attention_dropout = config.attention_dropout self.is_causal = True self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False) self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False) self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False) self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) 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]]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) 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) 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, sliding_window=getattr(self.config, "sliding_window", None), # main diff with Llama **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class MiniMaxMLP(nn.Module): def __init__(self, config: MiniMaxConfig): super().__init__() self.ffn_dim = config.intermediate_size self.hidden_dim = config.hidden_size self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False) self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False) self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False) self.act_fn = ACT2FN[config.hidden_act] def forward(self, hidden_states): current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states) current_hidden_states = self.w2(current_hidden_states) return current_hidden_states class MiniMaxExperts(nn.ModuleList): """ ModuleList of experts. """ def __init__(self, config: MiniMaxConfig): super().__init__() self.top_k = config.num_experts_per_tok self.num_experts = config.num_local_experts for _ in range(self.num_experts): self.append(MiniMaxMLP(config)) def forward( self, hidden_states: torch.Tensor, top_k_index: torch.Tensor, top_k_weights: torch.Tensor ) -> torch.Tensor: """ Args: hidden_states: (batch_size * sequence_length, hidden_dim) selected_experts: (batch_size * sequence_length, top_k) routing_weights: (batch_size * sequence_length, top_k) Returns: (batch_size * sequence_length, hidden_dim) """ final_hidden_states = torch.zeros_like(hidden_states) expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts).permute(2, 1, 0) expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero() for expert_idx in expert_hit: idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0)) current_state = hidden_states[None, top_x].reshape(-1, hidden_states.shape[-1]) current_hidden_states = self[expert_idx](current_state) * top_k_weights[top_x, idx, None] final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype)) return final_hidden_states class MiniMaxSparseMoeBlock(nn.Module): def __init__(self, config): super().__init__() self.top_k = config.num_experts_per_tok self.jitter_noise = config.router_jitter_noise self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False) self.experts = MiniMaxExperts(config) def route_tokens_to_experts(self, router_logits): routing_weights = torch.nn.functional.softmax(router_logits.float(), dim=-1) top_k_weights, top_k_index = torch.topk(routing_weights, self.top_k, dim=-1) top_k_weights /= top_k_weights.sum(dim=-1, keepdim=True) return top_k_index, top_k_weights.to(router_logits.dtype) def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: batch_size, sequence_length, hidden_dim = hidden_states.shape if self.training and self.jitter_noise > 0: hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise) hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) router_logits = self.gate(hidden_states) top_k_index, top_k_weights = self.route_tokens_to_experts(router_logits) hidden_states = self.experts(hidden_states, top_k_index, top_k_weights.to(hidden_states.dtype)) hidden_states = hidden_states.reshape(batch_size, sequence_length, hidden_dim) return hidden_states class MiniMaxDecoderLayer(GradientCheckpointingLayer): def __init__(self, config: MiniMaxConfig, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.self_attn = MiniMaxAttention(config, layer_idx) self.block_sparse_moe = MiniMaxSparseMoeBlock(config) self.input_layernorm = MiniMaxRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = MiniMaxRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.layer_idx = layer_idx self.layer_type = config.layer_types[layer_idx] self.mlp_alpha_factor = config.mlp_alpha_factor self.mlp_beta_factor = config.mlp_beta_factor if self.layer_type == "linear_attention": self.self_attn = MiniMaxLightningAttention(config, layer_idx) self.attn_alpha_factor = config.linear_attn_alpha_factor self.attn_beta_factor = config.linear_attn_beta_factor else: self.self_attn = MiniMaxAttention(config, layer_idx) self.attn_alpha_factor = config.full_attn_alpha_factor self.attn_beta_factor = config.full_attn_beta_factor def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, 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, **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]: hidden_states = self.input_layernorm(hidden_states) residual = hidden_states hidden_states, _ = self.self_attn( hidden_states=hidden_states, position_embeddings=position_embeddings, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, cache_position=cache_position, **kwargs, ) hidden_states = residual * self.attn_alpha_factor + hidden_states * self.attn_beta_factor hidden_states = self.post_attention_layernorm(hidden_states) residual = hidden_states hidden_states = self.block_sparse_moe(hidden_states) hidden_states = residual * self.mlp_alpha_factor + hidden_states * self.mlp_beta_factor return hidden_states @auto_docstring class MiniMaxPreTrainedModel(PreTrainedModel): config: MiniMaxConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["MiniMaxDecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn = True _supports_sdpa = True _supports_flex_attn = True _can_compile_fullgraph = False _supports_attention_backend = True _can_record_outputs = { "router_logits": OutputRecorder(nn.Linear, layer_name="block_sparse_moe.gate", index=0), "hidden_states": MiniMaxDecoderLayer, "attentions": [MiniMaxAttention, MiniMaxLightningAttention], } class MiniMaxRotaryEmbedding(nn.Module): inv_freq: torch.Tensor # fix linting for `register_buffer` def __init__(self, config: MiniMaxConfig, 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) @auto_docstring class MiniMaxModel(MiniMaxPreTrainedModel): def __init__(self, config: MiniMaxConfig): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.layers = nn.ModuleList( [MiniMaxDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = MiniMaxRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = MiniMaxRotaryEmbedding(config=config) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() @check_model_inputs 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[MiniMaxCache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[TransformersKwargs], ) -> MoeModelOutputWithPast: if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if use_cache and past_key_values is None: past_key_values = MiniMaxCache() elif use_cache and not isinstance(past_key_values, MiniMaxCache): raise ValueError( f"MiniMax uses cache of its own and is not compatible with `past_key_values` of type {type(past_key_values)}." ) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) 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) mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask causal_mask = mask_function( 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, ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) for decoder_layer in self.layers: if decoder_layer.layer_type == "full_attention": input_attention_mask = causal_mask else: # lightning attention uses original attention_mask, and uses it only for the first step input_attention_mask = attention_mask hidden_states = decoder_layer( hidden_states, position_embeddings=position_embeddings, attention_mask=input_attention_mask, 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 MoeModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values, ) def load_balancing_loss_func( gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None], num_experts: Optional[int] = None, top_k=2, attention_mask: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, int]: r""" Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between experts is too unbalanced. Args: gate_logits: Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of shape [batch_size X sequence_length, num_experts]. num_experts: Number of experts top_k: The number of experts to route per-token, can be also interpreted as the `top-k` routing parameter. attention_mask (`torch.Tensor`, *optional*): The attention_mask used in forward function shape [batch_size X sequence_length] if not None. Returns: The auxiliary loss. """ if gate_logits is None or not isinstance(gate_logits, tuple): return 0 if isinstance(gate_logits, tuple): compute_device = gate_logits[0].device concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0) routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1) _, selected_experts = torch.topk(routing_weights, top_k, dim=-1) expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts) if attention_mask is None: # Compute the percentage of tokens routed to each experts tokens_per_expert = torch.mean(expert_mask.float(), dim=0) # Compute the average probability of routing to these experts router_prob_per_expert = torch.mean(routing_weights, dim=0) else: batch_size, sequence_length = attention_mask.shape num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length) # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask expert_attention_mask = ( attention_mask[None, :, :, None, None] .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts)) .reshape(-1, top_k, num_experts) .to(compute_device) ) # Compute the percentage of tokens routed to each experts tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum( expert_attention_mask, dim=0 ) # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert router_per_expert_attention_mask = ( attention_mask[None, :, :, None] .expand((num_hidden_layers, batch_size, sequence_length, num_experts)) .reshape(-1, num_experts) .to(compute_device) ) # Compute the average probability of routing to these experts router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum( router_per_expert_attention_mask, dim=0 ) overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0)) return overall_loss * num_experts @auto_docstring class MiniMaxForCausalLM(MiniMaxPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] _tp_plan = {"lm_head": "colwise_rep"} _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} def __init__(self, config): super().__init__(config) self.model = MiniMaxModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.router_aux_loss_coef = config.router_aux_loss_coef self.num_experts = config.num_local_experts self.num_experts_per_tok = config.num_experts_per_tok # Initialize weights and apply final processing self.post_init() @can_return_tuple @auto_docstring 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, output_router_logits: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs: Unpack[TransformersKwargs], ) -> MoeCausalLMOutputWithPast: r""" 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]`. Example: ```python >>> from transformers import AutoTokenizer, MiniMaxForCausalLM >>> model = MiniMaxForCausalLM.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf") >>> tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf") >>> 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_router_logits = ( output_router_logits if output_router_logits is not None else self.config.output_router_logits ) # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs: MoeModelOutputWithPast = 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_router_logits=output_router_logits, cache_position=cache_position, **kwargs, ) hidden_states = outputs.last_hidden_state # Only compute necessary logits, and do not upcast them to float if we are not computing the loss 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, labels, self.vocab_size, **kwargs) aux_loss = None if output_router_logits: aux_loss = load_balancing_loss_func( outputs.router_logits, self.num_experts, self.num_experts_per_tok, attention_mask, ) if labels is not None: loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device return MoeCausalLMOutputWithPast( loss=loss, aux_loss=aux_loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, router_logits=outputs.router_logits, ) class MiniMaxForSequenceClassification(GenericForSequenceClassification, MiniMaxPreTrainedModel): pass class MiniMaxForTokenClassification(GenericForTokenClassification, MiniMaxPreTrainedModel): pass class MiniMaxForQuestionAnswering(GenericForQuestionAnswering, MiniMaxPreTrainedModel): pass __all__ = [ "MiniMaxPreTrainedModel", "MiniMaxModel", "MiniMaxForCausalLM", "MiniMaxForSequenceClassification", "MiniMaxForTokenClassification", "MiniMaxForQuestionAnswering", ]