933 lines
41 KiB
Python
933 lines
41 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/minimax/modular_minimax.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_minimax.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Callable, Optional, Union
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import torch
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import torch.nn.functional as F
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from torch import nn
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from transformers.utils.generic import check_model_inputs
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache
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from ...generation import GenerationMixin
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from ...integrations import use_kernel_forward_from_hub
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from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_layers import (
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GenericForQuestionAnswering,
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GenericForSequenceClassification,
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GenericForTokenClassification,
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GradientCheckpointingLayer,
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)
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from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
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from ...utils.generic import OutputRecorder
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from .configuration_minimax import MiniMaxConfig
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@use_kernel_forward_from_hub("RMSNorm")
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class MiniMaxRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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MiniMaxRMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
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class MiniMaxCache(DynamicCache):
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def __init__(self):
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super().__init__()
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self.linear_cache: list[torch.Tensor] = []
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def set_linear_cache(self, layer_idx, linear_cache):
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# There may be skipped layers, fill them with empty lists
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for _ in range(len(self.linear_cache), layer_idx + 1):
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self.linear_cache.append([])
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self.linear_cache[layer_idx] = linear_cache
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def get_linear_cache(self, layer_idx: int):
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if layer_idx < len(self):
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return self.linear_cache[layer_idx]
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return None
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def __len__(self):
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return max(super().__len__(), len(self.linear_cache))
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def __getitem__(self, layer_idx: int):
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if layer_idx < len(self.linear_cache) and self.linear_cache[layer_idx] != []:
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return (self.linear_cache[layer_idx],)
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return super().__getitem__(layer_idx)
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def __iter__(self):
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for layer_idx in range(len(self)):
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yield self[layer_idx]
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def batch_repeat_interleave(self, repeats: int):
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for layer_idx in range(len(self)):
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if self.linear_cache[layer_idx] != []:
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self.linear_cache[layer_idx] = self.linear_cache[layer_idx].repeat_interleave(repeats, dim=0)
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else:
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self.layers[layer_idx].batch_repeat_interleave(repeats)
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def batch_select_indices(self, indices: torch.Tensor):
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for layer_idx in range(len(self)):
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if self.linear_cache[layer_idx] != []:
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self.linear_cache[layer_idx] = self.linear_cache[layer_idx][indices, ...]
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else:
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self.layers[layer_idx].batch_select_indices(indices)
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def crop(self, max_length: int):
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raise RuntimeError("MiniMaxCache doesnot support `crop` method")
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class MiniMaxLightningAttention(nn.Module):
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def __init__(self, config: MiniMaxConfig, layer_idx: int):
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super().__init__()
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self.layer_idx = layer_idx
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self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
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self.num_attention_heads = config.num_attention_heads
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self.num_hidden_layers = config.num_hidden_layers
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self.block_size = config.block_size
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self.act_fn = ACT2FN[config.hidden_act]
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self.norm = MiniMaxRMSNorm(self.head_dim * self.num_attention_heads)
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self.qkv_proj = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim * 3, bias=False)
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self.out_proj = nn.Linear(self.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
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self.output_gate = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim, bias=False)
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slope_rate = self.get_slope_rate()
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query_decay, key_decay, diagonal_decay = self.decay_factors(slope_rate)
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self.register_buffer("slope_rate", slope_rate)
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self.register_buffer("query_decay", query_decay)
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self.register_buffer("key_decay", key_decay)
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self.register_buffer("diagonal_decay", diagonal_decay)
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def get_slope_rate(self):
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base = 1 / (2 ** (8 / self.num_attention_heads))
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exponent = torch.arange(self.num_attention_heads) + 1
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factor = 1 - self.layer_idx / (self.num_hidden_layers - 1 + 1e-5) + 1e-5
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rate = base**exponent
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rate = rate * factor
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rate = rate[:, None, None]
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return rate
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def decay_factors(self, slope_rate):
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block_size_range = torch.arange(self.block_size) + 1
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query_decay = torch.exp(-slope_rate * block_size_range[:, None])
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key_decay = torch.exp(-slope_rate * (self.block_size - block_size_range[:, None]))
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diagonal_decay = block_size_range[:, None] - block_size_range[None, :]
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diagonal_decay = diagonal_decay[None, None, :, :]
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diagonal_decay = slope_rate * diagonal_decay
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diagonal_decay = torch.where(diagonal_decay >= 0, -diagonal_decay, float("-inf"))
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diagonal_decay = torch.exp(diagonal_decay)
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return query_decay, key_decay, diagonal_decay
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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batch_size, seq_len, hidden_size = hidden_states.shape
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num_blocks = (seq_len + self.block_size - 1) // self.block_size
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qkv_states = self.act_fn(self.qkv_proj(hidden_states))
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qkv_states = qkv_states.reshape(batch_size, seq_len, self.num_attention_heads, 3 * self.head_dim)
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query_states, key_states, value_states = torch.split(qkv_states, self.head_dim, dim=3)
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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# calculated (K.T @ V) and saved as cache
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attn_weights_inter = None
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if past_key_value is not None:
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attn_weights_inter = past_key_value.get_linear_cache(self.layer_idx)
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if attn_weights_inter is None:
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attn_weights_inter = torch.zeros(batch_size, self.num_attention_heads, self.head_dim, self.head_dim).to(
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value_states
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)
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# apply attention_mask
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if attention_mask is not None:
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attention_mask = attention_mask.to(dtype=torch.bool) # Ensure it's a boolean tensor
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value_states = value_states.masked_fill(~attention_mask.unsqueeze(1).unsqueeze(-1), 0)
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attn_output = []
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for i in range(num_blocks):
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start_idx = i * self.block_size
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end_idx = min(start_idx + self.block_size, seq_len)
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current_block_size = end_idx - start_idx
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current_query_states = query_states[:, :, start_idx:end_idx]
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current_key_states = key_states[:, :, start_idx:end_idx]
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current_value_states = value_states[:, :, start_idx:end_idx]
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current_query_decay = self.query_decay[:, :current_block_size]
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current_key_decay = self.key_decay[:, -current_block_size:]
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current_diagonal_decay = self.diagonal_decay[:, :, :current_block_size, :current_block_size]
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block_decay = torch.exp(-self.slope_rate * current_block_size)
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# intra: ( Q @ K.T ) @ V -> QK * V
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attn_weights_intra = torch.matmul(current_query_states, current_key_states.transpose(-1, -2))
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attn_output_intra = torch.matmul(attn_weights_intra * current_diagonal_decay, current_value_states)
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# inter: Q @ ( K.T @ V ) -> Q * KV
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attn_output_inter = torch.matmul(current_query_states * current_query_decay, attn_weights_inter)
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# final attention output
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current_attn_output = attn_output_inter + attn_output_intra
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attn_output.append(current_attn_output)
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# cacluate attn_weights_inter for next block or cache
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next_attn_weights_inter = torch.matmul(
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(current_key_states * current_key_decay).transpose(-1, -2), current_value_states
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)
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attn_weights_inter = attn_weights_inter * block_decay + next_attn_weights_inter
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else:
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ratio = torch.exp(-self.slope_rate)
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attn_output = []
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for i in range(seq_len):
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current_query_states = query_states[:, :, i : i + 1]
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current_key_states = key_states[:, :, i : i + 1]
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current_value_states = value_states[:, :, i : i + 1]
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current_attn_weights_inter = torch.matmul(current_key_states.transpose(-1, -2), current_value_states)
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attn_weights_inter = ratio * attn_weights_inter + current_attn_weights_inter
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current_attn_output = torch.matmul(current_query_states, attn_weights_inter)
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attn_output.append(current_attn_output)
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# concatenate attention outputs over all blocks
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attn_output = torch.cat(attn_output, dim=-2)
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# final output projection
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attn_output = attn_output.transpose(1, 2)
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attn_output = attn_output.reshape(batch_size, seq_len, self.num_attention_heads * self.head_dim)
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attn_output = self.norm(attn_output)
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attn_output = F.sigmoid(self.output_gate(hidden_states)) * attn_output
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attn_output = self.out_proj(attn_output)
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# update cache
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if past_key_value is not None:
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past_key_value.set_linear_cache(self.layer_idx, attn_weights_inter)
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return attn_output, attn_weights_inter
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`, *optional*):
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Deprecated and unused.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class MiniMaxAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: MiniMaxConfig, layer_idx: int):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.scaling = self.head_dim**-0.5
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self.attention_dropout = config.attention_dropout
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self.is_causal = True
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self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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sliding_window=getattr(self.config, "sliding_window", None), # main diff with Llama
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**kwargs,
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|
)
|
|
|
|
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
|
attn_output = self.o_proj(attn_output)
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class MiniMaxBlockSparseTop2MLP(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 MiniMaxSparseMoeBlock(nn.Module):
|
|
"""
|
|
This implementation is
|
|
strictly equivalent to standard MoE with full capacity (no
|
|
dropped tokens). It's faster since it formulates MoE operations
|
|
in terms of block-sparse operations to accommodate imbalanced
|
|
assignments of tokens to experts, whereas standard MoE either
|
|
(1) drop tokens at the cost of reduced performance or (2) set
|
|
capacity factor to number of experts and thus waste computation
|
|
and memory on padding.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.hidden_dim = config.hidden_size
|
|
self.ffn_dim = config.intermediate_size
|
|
self.num_experts = config.num_local_experts
|
|
self.top_k = config.num_experts_per_tok
|
|
|
|
# gating
|
|
self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
|
|
|
|
self.experts = nn.ModuleList([MiniMaxBlockSparseTop2MLP(config) for _ in range(self.num_experts)])
|
|
|
|
# Jitter parameters
|
|
self.jitter_noise = config.router_jitter_noise
|
|
|
|
def forward(self, hidden_states: 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_dim)
|
|
# router_logits: (batch * sequence_length, n_experts)
|
|
router_logits = self.gate(hidden_states)
|
|
|
|
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
|
|
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
|
|
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
|
# we cast back to the input dtype
|
|
routing_weights = routing_weights.to(hidden_states.dtype)
|
|
|
|
final_hidden_states = torch.zeros(
|
|
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
|
|
)
|
|
|
|
# One hot encode the selected experts to create an expert mask
|
|
# this will be used to easily index which expert is going to be sollicitated
|
|
expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
|
|
|
|
expert_hitted = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
|
|
for expert_idx in expert_hitted:
|
|
expert_layer = self.experts[expert_idx]
|
|
idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
|
|
# Index the correct hidden states and compute the expert hidden state for
|
|
# the current expert. We need to make sure to multiply the output hidden
|
|
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
|
|
current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
|
|
current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
|
|
|
|
# However `index_add_` only support torch tensors for indexing so we'll use
|
|
# the `top_x` tensor here.
|
|
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
|
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
|
|
return final_hidden_states, router_logits
|
|
|
|
|
|
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_value: Optional[tuple[torch.Tensor]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_router_logits: Optional[bool] = False,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[FlashAttentionKwargs],
|
|
) -> 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)`
|
|
position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`):
|
|
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
|
|
with `head_dim` being the embedding dimension of each attention head.
|
|
attention_mask (`torch.Tensor`, *optional*): attention mask of size
|
|
`(batch, sequence_length)` where padding elements are indicated by 0.
|
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
|
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_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`).
|
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
kwargs (`dict`, *optional*):
|
|
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
|
|
into the model
|
|
"""
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
residual = hidden_states
|
|
|
|
# Self Attention
|
|
hidden_states, _ = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
position_embeddings=position_embeddings,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
hidden_states = residual * self.attn_alpha_factor + hidden_states * self.attn_beta_factor
|
|
|
|
# Fully Connected
|
|
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(MiniMaxSparseMoeBlock, index=1),
|
|
"hidden_states": MiniMaxDecoderLayer,
|
|
"attentions": [MiniMaxAttention, MiniMaxLightningAttention],
|
|
}
|
|
|
|
|
|
class MiniMaxRotaryEmbedding(nn.Module):
|
|
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
|
|
@auto_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[MiniMaxCache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: 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_value=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()
|
|
|
|
def set_decoder(self, decoder):
|
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self.model = decoder
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|
|
|
def get_decoder(self):
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return self.model
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|
|
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@can_return_tuple
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|
@auto_docstring
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def forward(
|
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self,
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input_ids: Optional[torch.LongTensor] = None,
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|
attention_mask: Optional[torch.Tensor] = None,
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|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
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|
inputs_embeds: Optional[torch.FloatTensor] = None,
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|
labels: Optional[torch.LongTensor] = None,
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|
use_cache: Optional[bool] = None,
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|
output_router_logits: Optional[bool] = None,
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|
cache_position: Optional[torch.LongTensor] = None,
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|
logits_to_keep: Union[int, torch.Tensor] = 0,
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|
**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
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|
>>> 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",
|
|
]
|