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# Copyright 2024 Rhymes AI. All rights reserved.
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
import logging
import os
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import nn
from transformers import LlamaConfig
from transformers.models.llama.modeling_llama import (
ACT2FN,
LLAMA_ATTENTION_CLASSES,
LlamaDecoderLayer,
LlamaForCausalLM,
LlamaMLP,
LlamaModel,
LlamaRMSNorm,
LlamaRotaryEmbedding,
)
logger = logging.getLogger(__name__)
class AriaMoELMConfig(LlamaConfig):
"""
Configuration class for AriaMoE language model.
This class extends the LlamaConfig to include additional parameters specific to the Mixture of Experts (MoE) architecture.
"""
model_type = "aria_moe_lm"
def __init__(
self,
moe_intermediate_size: int = 4096,
moe_num_experts: int = 8,
moe_topk: int = 2,
moe_z_loss_coeff: float = 1e-5,
moe_aux_loss_coeff: float = 1e-3,
moe_num_shared_experts: int = 2,
**kwargs,
):
"""
Initialize the AriaMoELMConfig.
Args:
moe_intermediate_size (int): The intermediate size for MoE layers. Default is 4096.
moe_num_experts (int): The number of experts in the MoE layer. Default is 8.
moe_topk (int): The number of top experts to route to for each token. Default is 2.
moe_z_loss_coeff (float): The coefficient for the auxiliary z-loss. Default is 1e-5.
moe_aux_loss_coeff (float): The coefficient for the auxiliary load balancing loss. Default is 1e-3.
moe_num_shared_experts (int): The number of shared experts. Default is 2.
**kwargs: Additional keyword arguments to be passed to the parent LlamaConfig.
"""
super().__init__(**kwargs)
self.moe_intermediate_size = moe_intermediate_size
self.moe_num_experts = moe_num_experts
self.moe_topk = moe_topk
self.moe_z_loss_coeff = moe_z_loss_coeff
self.moe_aux_loss_coeff = moe_aux_loss_coeff
self.moe_num_shared_experts = moe_num_shared_experts
# copied from https://github.com/NVIDIA/Megatron-LM/blob/54f1f78529cbc2b9cddad313e7f9d96ac0420a27/megatron/core/transformer/moe/moe_utils.py#L101-L142
class MoEAuxLossAutoScaler(torch.autograd.Function):
"""An AutoScaler that compute and scales the grad for auxiliary loss."""
main_loss_backward_scale: torch.Tensor = torch.tensor(1.0)
@staticmethod
def forward(ctx, output: torch.Tensor, aux_loss: torch.Tensor):
"""Preserve the aux_loss by storing it in the context to avoid garbage collection.
Args:
output (torch.Tensor): The output tensor.
aux_loss (torch.Tensor): The auxiliary loss tensor.
Returns:
torch.Tensor: The output tensor.
"""
ctx.save_for_backward(aux_loss)
return output
@staticmethod
def backward(ctx, grad_output: torch.Tensor):
"""Compute and scale the gradient for auxiliary loss..
Args:
grad_output (torch.Tensor): The gradient of the output.
Returns:
Tuple[torch.Tensor, torch.Tensor]: The gradient of the output, scaled auxiliary loss gradient.
"""
(aux_loss,) = ctx.saved_tensors
aux_loss_backward_scale = MoEAuxLossAutoScaler.main_loss_backward_scale
scaled_aux_loss_grad = torch.ones_like(aux_loss) * aux_loss_backward_scale
return grad_output, scaled_aux_loss_grad
@staticmethod
def set_loss_scale(scale: torch.Tensor):
"""set the scale of the aux loss.
Args:
scale (torch.Tensor): The scale value to set. Please ensure that the scale passed in matches the scale of the main_loss.
"""
MoEAuxLossAutoScaler.main_loss_backward_scale = scale
def z_loss_func(logits, z_loss_coeff):
"""Encourages the router's logits to remain small to enhance stability.
Please refer to the ST-MoE paper (https://arxiv.org/pdf/2202.08906.pdf) for details.
Args:
logits (torch.Tensor): The logits of the router.
Returns:
torch.Tensor: The logits after applying the z-loss.
"""
z_loss = torch.mean(torch.square(torch.logsumexp(logits, dim=-1))) * z_loss_coeff
return z_loss
def switch_load_balancing_loss_func(
probs: torch.Tensor,
tokens_per_expert: torch.Tensor,
topk: int,
moe_aux_loss_coeff: float,
):
"""Calculate the auxiliary loss for better load balacing.
Please refer to the Switch Transformer paper (https://arxiv.org/abs/2101.03961) for details.
Args:
probs (torch.Tensor): The softmax probs output by the router for each token. [num_tokens, num_experts]
tokens_per_expert (torch.Tensor): The number of assigned tokens for each expert. [num_experts]
Returns:
torch.Tensor: The auxiliary loss for load balancing.
"""
num_tokens = probs.shape[0] * topk
num_experts = probs.shape[1]
probs_mean_per_expert = probs.mean(dim=0)
aux_loss = torch.sum(probs_mean_per_expert * tokens_per_expert) * (
num_experts / num_tokens * moe_aux_loss_coeff
)
return aux_loss
# adapted from https://github.com/NVIDIA/Megatron-LM/blob/54f1f78529cbc2b9cddad313e7f9d96ac0420a27/megatron/core/transformer/moe/router.py#L96-L304
class TopKRouter(nn.Module):
"""
Top-K Router for Mixture of Experts (MoE) models.
This router determines which experts should process each token based on the top-k scoring experts.
It also applies auxiliary losses to encourage load balancing among experts.
Args:
config (AriaMoELMConfig): Configuration object containing MoE-related parameters.
"""
def __init__(self, config: AriaMoELMConfig):
super().__init__()
self.config = config
self.weight = nn.Parameter(
torch.empty((self.config.moe_num_experts, self.config.hidden_size))
)
# FIXME: initialize the weight
def gating(self, input: torch.Tensor) -> torch.Tensor:
"""
Compute the gating logits for each token-expert pair.
Args:
input (torch.Tensor): Input tensor of shape [batch_size * seq_len, hidden_size].
Returns:
torch.Tensor: Logits tensor of shape [batch_size * seq_len, num_experts].
"""
logits = torch.nn.functional.linear(input, self.weight)
return logits
def apply_z_loss(self, logits: torch.Tensor) -> torch.Tensor:
"""
Apply z-loss to encourage router logits to remain small for enhanced stability.
Args:
logits (torch.Tensor): Router logits.
Returns:
torch.Tensor: Logits with z-loss applied.
"""
z_loss = z_loss_func(logits, self.config.moe_z_loss_coeff)
logits = MoEAuxLossAutoScaler.apply(logits, z_loss)
return logits
def apply_aux_loss(
self,
logits: torch.Tensor,
tokens_per_expert: torch.Tensor,
activation: torch.Tensor,
) -> torch.Tensor:
"""
Apply auxiliary loss for load balancing among experts.
Args:
logits (torch.Tensor): Router logits.
tokens_per_expert (torch.Tensor): Number of tokens assigned to each expert.
activation (torch.Tensor): Activation values.
Returns:
torch.Tensor: Activation with auxiliary loss applied.
"""
probs = torch.softmax(logits, dim=-1, dtype=torch.float32)
aux_loss = switch_load_balancing_loss_func(
probs,
tokens_per_expert,
self.config.moe_topk,
self.config.moe_aux_loss_coeff,
)
return MoEAuxLossAutoScaler.apply(activation, aux_loss)
def routing(
self, logits: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Perform the routing operation to determine expert assignments.
Args:
logits (torch.Tensor): Router logits.
Returns:
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- scores: Softmax probabilities for top-k experts.
- top_indices: Indices of top-k experts for each token.
- tokens_per_expert: Number of tokens assigned to each expert.
"""
logits = self.apply_z_loss(logits)
top_logits, top_indices = torch.topk(logits, k=self.config.moe_topk, dim=1)
scores = torch.softmax(top_logits, dim=-1, dtype=torch.float32).type_as(logits)
tokens_per_expert = torch.histc(
top_indices.flatten(),
bins=self.config.moe_num_experts,
min=0,
max=self.config.moe_num_experts - 1,
)
scores = self.apply_aux_loss(logits, tokens_per_expert, scores)
return scores, top_indices, tokens_per_expert
def forward(
self, input: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Forward pass of the TopKRouter.
Args:
input (torch.Tensor): Input tensor of shape [batch_size * seq_len, hidden_size].
Returns:
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- scores: Softmax probabilities for top-k experts.
- top_indices: Indices of top-k experts for each token.
- tokens_per_expert: Number of tokens assigned to each expert.
"""
logits = self.gating(input)
logits = logits.view(-1, self.config.moe_num_experts)
scores, top_indices, tokens_per_expert = self.routing(logits)
return scores, top_indices, tokens_per_expert
# adapted from https://github.com/NVIDIA/Megatron-LM/blob/54f1f78529cbc2b9cddad313e7f9d96ac0420a27/megatron/core/transformer/moe/token_dispatcher.py#L291-L587
class TokenDispatcher:
"""
Handles the dispatching and gathering of tokens to and from experts.
This class is responsible for permuting tokens based on expert assignments and
unpermuting them after expert processing.
Args:
config (AriaMoELMConfig): Configuration object containing MoE-related parameters.
"""
def __init__(self, config: AriaMoELMConfig):
self.config = config
self.hidden_states_shape = None
self.reversed_input_permutation_mapping = None
def token_permutation(
self, hidden_states: torch.Tensor, indices: torch.Tensor
) -> torch.Tensor:
"""
Permute tokens based on expert assignments.
Args:
hidden_states (torch.Tensor): Input hidden states.
indices (torch.Tensor): Expert assignment indices.
Returns:
torch.Tensor: Permuted tokens.
"""
self.hidden_states_shape = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_states.size(-1))
flatten_indices = indices.flatten()
sorted_indices = torch.argsort(flatten_indices, stable=True)
permuted_tokens = hidden_states.index_select(
0, sorted_indices // self.config.moe_topk
)
self.reversed_input_permutation_mapping = sorted_indices
return permuted_tokens
def token_unpermutation(
self, permuted_tokens: torch.Tensor, scores: torch.Tensor
) -> torch.Tensor:
"""
Unpermute tokens and combine expert outputs.
Args:
permuted_tokens (torch.Tensor): Tokens after expert processing.
scores (torch.Tensor): Expert assignment scores.
Returns:
torch.Tensor: Unpermuted and combined output.
"""
num_unpermuted_tokens = scores.numel()
unpermuted_tokens = torch.zeros(
(num_unpermuted_tokens, permuted_tokens.size(1)),
dtype=permuted_tokens.dtype,
device=permuted_tokens.device,
)
unpermuted_tokens.index_copy_(
0, self.reversed_input_permutation_mapping, permuted_tokens
)
unpermuted_tokens = unpermuted_tokens.reshape(
-1, self.config.moe_topk, permuted_tokens.size(1)
)
unpermuted_tokens = unpermuted_tokens * scores.unsqueeze(-1)
unpermuted_tokens = unpermuted_tokens.sum(dim=1).type_as(permuted_tokens)
output = unpermuted_tokens.view(self.hidden_states_shape)
return output
class SharedExpertMLP(LlamaMLP):
"""
Shared Expert MLP for shared experts.
Unlike routed experts, shared experts process all tokens without routing.
This class reconfigures the intermediate size in comparison to the LlamaMLP.
Args:
config (AriaMoELMConfig): Configuration object for the AriaMoE language model.
"""
def __init__(self, config: AriaMoELMConfig):
nn.Module.__init__(self)
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = (
config.moe_intermediate_size * config.moe_num_shared_experts
)
self.gate_proj = nn.Linear(
self.hidden_size, self.intermediate_size, bias=config.mlp_bias
)
self.up_proj = nn.Linear(
self.hidden_size, self.intermediate_size, bias=config.mlp_bias
)
self.down_proj = nn.Linear(
self.intermediate_size, self.hidden_size, bias=config.mlp_bias
)
self.act_fn = ACT2FN[config.hidden_act]
def sequential_gemm(input, weight, tokens_per_expert):
"""
Compute the matrix multiplication (GEMM) for each expert sequentially. This approach is computationally inefficient, especially when dealing with a large number of experts.
Args:
input (torch.Tensor): Input tensor of shape (num_tokens, in_features).
weight (torch.Tensor): Weight tensor of shape (num_experts, in_features, out_features).
tokens_per_expert (torch.Tensor): Number of tokens assigned to each expert.
Returns:
torch.Tensor: Output tensor of shape (num_tokens, out_features).
"""
num_tokens = input.shape[0]
out_features = weight.shape[-1]
output = torch.zeros(
num_tokens, out_features, dtype=input.dtype, device=input.device
)
cumsum_num_tokens = torch.cumsum(tokens_per_expert, dim=0)
# Insert zero at the begining for offset index's convenience
zero_tensor = torch.zeros(1, dtype=torch.long, device=cumsum_num_tokens.device)
cumsum_num_tokens = torch.cat((zero_tensor, cumsum_num_tokens))
for expert_num in range(weight.shape[0]):
start = cumsum_num_tokens[expert_num]
end = cumsum_num_tokens[expert_num + 1]
tokens = input[start:end]
out = torch.matmul(tokens, weight[expert_num])
output[start:end] = out
return output
class ExpertMLP(LlamaMLP):
"""
Expert MLP for the Mixture of Experts (MoE) layer.
This class represents an individual expert in the MoE architecture. It's a modified
version of LlamaMLP with a configurable intermediate size specific to MoE.
Args:
config (AriaMoELMConfig): Configuration object for the AriaMoE language model.
"""
def __init__(self, config: AriaMoELMConfig):
nn.Module.__init__(self)
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.moe_intermediate_size
self.gate_proj = nn.Linear(
self.hidden_size, self.intermediate_size, bias=config.mlp_bias
)
self.up_proj = nn.Linear(
self.hidden_size, self.intermediate_size, bias=config.mlp_bias
)
self.down_proj = nn.Linear(
self.intermediate_size, self.hidden_size, bias=config.mlp_bias
)
self.act_fn = ACT2FN[config.hidden_act]
class SequentialMLP(nn.Module):
"""
Sequential MLP for handling multiple experts in the Mixture of Experts (MoE) layer.
This class manages a collection of ExpertMLPs and processes tokens through them sequentially.
Args:
config (AriaMoELMConfig): Configuration object for the AriaMoE language model.
"""
def __init__(self, config: AriaMoELMConfig):
super().__init__()
self.config = config
self.experts = nn.ModuleList(
[ExpertMLP(config) for _ in range(config.moe_num_experts)]
)
def forward(self, permuted_tokens: torch.Tensor, tokens_per_expert: torch.Tensor) -> torch.Tensor:
"""
Forward pass of the SequentialMLP.
This method processes the permuted tokens through each expert sequentially,
based on the number of tokens assigned to each expert.
Args:
permuted_tokens (torch.Tensor): Permuted input tokens.
tokens_per_expert (torch.Tensor): Number of tokens assigned to each expert.
Returns:
torch.Tensor: Processed output from all experts.
"""
output = torch.zeros_like(permuted_tokens)
cumsum_num_tokens = torch.cumsum(tokens_per_expert, dim=0)
# Insert zero at the beginning for offset index's convenience
zero_tensor = torch.zeros(1, dtype=torch.long, device=cumsum_num_tokens.device)
cumsum_num_tokens = torch.cat((zero_tensor, cumsum_num_tokens))
for expert_num, expert in enumerate(self.experts):
start = cumsum_num_tokens[expert_num]
end = cumsum_num_tokens[expert_num + 1]
tokens = permuted_tokens[start:end]
out = expert(tokens)
output[start:end] = out
return output
class MoELayer(nn.Module):
"""
Mixture of Experts (MoE) Layer for the AriaMoE model.
This layer implements the MoE mechanism, which routes input tokens to different experts
based on a routing algorithm, processes them through the experts, and then combines
the outputs.
Args:
config (AriaMoELMConfig): Configuration object for the MoE layer.
"""
def __init__(self, config: AriaMoELMConfig):
super().__init__()
self.router = TopKRouter(config)
self.token_dispatcher = TokenDispatcher(config)
self.experts = SequentialMLP(config)
self.shared_experts = SharedExpertMLP(config)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
"""
Forward pass of the MoE Layer.
Args:
hidden_states (torch.Tensor): Input tensor of shape (batch_size, sequence_length, hidden_size).
Returns:
torch.Tensor: Output tensor after passing through the MoE layer.
Process:
1. Route tokens to experts using the router.
2. Permute tokens based on routing decisions.
3. Process tokens through experts.
4. Unpermute and combine expert outputs.
5. Add shared expert output to the final result.
"""
scores, indices, tokens_per_expert = self.router(hidden_states)
permuted_tokens = self.token_dispatcher.token_permutation(
hidden_states, indices
)
expert_output = self.experts(permuted_tokens, tokens_per_expert)
output = self.token_dispatcher.token_unpermutation(expert_output, scores)
shared_expert_output = self.shared_experts(hidden_states)
output += shared_expert_output
return output
class MoEDecoderLayer(LlamaDecoderLayer):
"""
Custom Decoder Layer for the AriaMoE model which modifies the standard `LlamaDecoderLayer` by
replacing the traditional MLP with a Mixture of Experts (MoE) Layer.
Args:
config (LlamaConfig): Configuration object for the layer.
layer_idx (int): Index of the current layer in the model.
"""
def __init__(self, config: LlamaConfig, layer_idx: int):
nn.Module.__init__(self)
self.hidden_size = config.hidden_size
self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](
config=config, layer_idx=layer_idx
)
self.mlp = MoELayer(config)
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = LlamaRMSNorm(
config.hidden_size, eps=config.rms_norm_eps
)
class AriaMoELMModel(LlamaModel):
"""
Custom LlamaModel for the AriaMoE model which modifies the standard LlamaModel by
replacing the `LlamaDecoderLayer` with `MoEDecoderLayer`.
This model implements a Mixture of Experts (MoE) approach, where each layer contains
multiple expert networks that specialize in different aspects of the input.
Args:
config (LlamaConfig): Configuration object for the model.
"""
def __init__(self, config: LlamaConfig):
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(
[
MoEDecoderLayer(config, layer_idx)
for layer_idx in range(config.num_hidden_layers)
]
)
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = LlamaRotaryEmbedding(config=config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
class AriaMoELMForCausalLM(LlamaForCausalLM):
"""
AriaMoE model for causal language modeling tasks.
This class extends LlamaForCausalLM to incorporate the Mixture of Experts (MoE) approach,
allowing for more efficient and scalable language modeling.
Args:
config (AriaMoELMConfig): Configuration object for the model.
"""
_tied_weights_keys = ["lm_head.weight"]
config_class = AriaMoELMConfig
_no_split_modules = ["MoEDecoderLayer"]
def __init__(self, config):
super().__init__(config)
self.model = AriaMoELMModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def set_z_loss_coeff(self, z_loss_coeff: float):
"""
Set the coefficient for the z-loss in the MoE routing.
Args:
z_loss_coeff (float): The coefficient for the z-loss.
"""
self.config.moe_z_loss_coeff = z_loss_coeff
def set_aux_loss_coeff(self, aux_loss_coeff: float):
"""
Set the coefficient for the auxiliary loss in the MoE routing.
Args:
aux_loss_coeff (float): The coefficient for the auxiliary loss.
"""
self.config.moe_aux_loss_coeff = aux_loss_coeff
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