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---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-edu
- HuggingFaceH4/MATH-500
- openai/gsm8k
language:
- en
pipeline_tag: text-generation
tags:
- mesh
- moe
- mesh-labs
- alpha
- preview
- research
- experiment
- routing
- innovative
- innovation
- mesh-moe
- custom_code
---
# Mesh-v0.1-2x2 (Stage 003)
## Introducing mesh
This is our first ever model! Allow us to explain how the `mesh` architecture works in detail.
- Neural Mesh extends the concept of Mixture of Experts by allowing bidirectional expert communication.
- The experts are shared in a bidimensional grid (2x2, 4x4, etc.) layout, that allows for them to communicate with their neighbors using the "Neighbor Exchange" method.
- Just like MoE models, Mesh models have dynamic routing, and through the `routing_k` parameter you can define the amount of active parameters. For this model (2x2):
- top-1 routing: 173M active parameters
- top-2 routing: 242M active parameters (default)
- dense routing: 302M active parameters
## Here's how the mesh architecture works:
## How to load the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig, PretrainedConfig, PreTrainedModel
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation import GenerationMixin
import os
class MeshConfig(PretrainedConfig):
model_type = "mesh"
def __init__(
self,
vocab_size=32000,
hidden_size=768,
intermediate_size=2048,
num_hidden_layers=12,
num_attention_heads=12,
num_key_value_heads=12,
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
mesh_grid_size=(2, 2),
expert_intermediate_size=256,
routing_k=2,
neighbor_exchange_enabled=True,
cross_expert_attention_enabled=True,
expert_scale_factor="sqrt_k",
load_in_8bit=False,
load_in_4bit=False,
**kwargs
):
super().__init__(
vocab_size=vocab_size,
hidden_size=hidden_size,
intermediate_size=intermediate_size,
num_hidden_layers=num_hidden_layers,
num_attention_heads=num_attention_heads,
num_key_value_heads=num_key_value_heads,
max_position_embeddings=max_position_embeddings,
initializer_range=initializer_range,
rms_norm_eps=rms_norm_eps,
use_cache=use_cache,
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.mesh_grid_size = mesh_grid_size
self.expert_intermediate_size = kwargs.pop("expert_intermediate_size", intermediate_size // (mesh_grid_size[0] * mesh_grid_size[1]))
self.routing_k = routing_k
self.neighbor_exchange_enabled = neighbor_exchange_enabled
self.cross_expert_attention_enabled = cross_expert_attention_enabled
self.expert_scale_factor = expert_scale_factor
self.load_in_8bit = load_in_8bit
self.load_in_4bit = load_in_4bit
class MeshExpert(nn.Module):
def __init__(self, config: MeshConfig):
super().__init__()
self.fc1 = nn.Linear(config.hidden_size, config.expert_intermediate_size)
self.gelu = nn.GELU()
self.fc2 = nn.Linear(config.expert_intermediate_size, config.hidden_size)
def forward(self, x):
return self.fc2(self.gelu(self.fc1(x)))
class MeshRouter(nn.Module):
def __init__(self, config: MeshConfig):
super().__init__()
self.gate = nn.Linear(config.hidden_size, config.mesh_grid_size[0] * config.mesh_grid_size[1])
self.softmax = nn.Softmax(dim=-1)
self.routing_k = config.routing_k
def forward(self, x):
gate_scores = self.gate(x)
gate_weights = self.softmax(gate_scores)
topk_weights, topk_indices = torch.topk(gate_weights, self.routing_k, dim=-1)
return topk_weights, topk_indices
class NeighborExchange(nn.Module):
def __init__(self, config: MeshConfig):
super().__init__()
self.config = config
self.num_experts_x = config.mesh_grid_size[0]
self.num_experts_y = config.mesh_grid_size[1]
self.num_experts = self.num_experts_x * self.num_experts_y
self.exchange_projection = nn.Linear(config.hidden_size, config.hidden_size)
def forward(self, expert_outputs, expert_indices=None):
if not self.config.neighbor_exchange_enabled:
return expert_outputs
batch_size, seq_length, num_experts, hidden_size = expert_outputs.shape
reshaped_outputs = expert_outputs.view(batch_size, seq_length, self.num_experts_x, self.num_experts_y, hidden_size)
aggregated_neighbor_info = torch.zeros_like(reshaped_outputs)
for i in range(self.num_experts_x):
for j in range(self.num_experts_y):
current_expert_output = reshaped_outputs[:, :, i, j, :]
neighbor_info = torch.zeros_like(current_expert_output)
neighbors = []
if i > 0: neighbors.append(reshaped_outputs[:, :, i-1, j, :])
if i < self.num_experts_x - 1: neighbors.append(reshaped_outputs[:, :, i+1, j, :])
if j > 0: neighbors.append(reshaped_outputs[:, :, i, j-1, :])
if j < self.num_experts_y - 1: neighbors.append(reshaped_outputs[:, :, i, j+1, :])
if neighbors:
neighbor_stack = torch.stack(neighbors, dim=-2)
aggregated_info = torch.mean(neighbor_stack, dim=-2)
neighbor_info = aggregated_info
transformed_neighbor_info = self.exchange_projection(neighbor_info)
aggregated_neighbor_info[:, :, i, j, :] = transformed_neighbor_info
aggregated_neighbor_info = aggregated_neighbor_info.view(batch_size, seq_length, num_experts, hidden_size)
exchanged_expert_outputs = expert_outputs + aggregated_neighbor_info
return exchanged_expert_outputs
class CrossExpertAttention(nn.Module):
def __init__(self, config: MeshConfig):
super().__init__()
self.config = config
self.cross_attention = nn.MultiheadAttention(
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
batch_first=True
)
def forward(self, expert_outputs):
if not self.config.cross_expert_attention_enabled:
return expert_outputs
batch_seq_size = expert_outputs.shape[0] * expert_outputs.shape[1]
reshaped_outputs = expert_outputs.view(batch_seq_size, self.config.mesh_grid_size[0] * self.config.mesh_grid_size[1], self.config.hidden_size)
cross_attn_output, _ = self.cross_attention(reshaped_outputs, reshaped_outputs, reshaped_outputs)
cross_attn_output = cross_attn_output.view(
expert_outputs.shape[0], expert_outputs.shape[1], self.config.mesh_grid_size[0] * self.config.mesh_grid_size[1], self.config.hidden_size
)
return cross_attn_output
class MeshLayer(nn.Module):
def __init__(self, config: MeshConfig):
super().__init__()
self.config = config
self.router = MeshRouter(config)
self.experts = nn.ModuleList([MeshExpert(config) for _ in range(config.mesh_grid_size[0] * config.mesh_grid_size[1])])
self.neighbor_exchange = NeighborExchange(config)
self.cross_expert_attention = CrossExpertAttention(config)
def forward(self, hidden_states):
topk_weights, topk_indices = self.router(hidden_states)
expanded_hidden_states = hidden_states.unsqueeze(2).expand(-1, -1, self.config.mesh_grid_size[0] * self.config.mesh_grid_size[1], -1)
if self.config.expert_scale_factor == "sqrt_k":
scaling_factor = math.sqrt(self.config.routing_k)
scaled_expert_inputs = expanded_hidden_states * scaling_factor
elif self.config.expert_scale_factor == "1_over_k":
scaling_factor = 1.0 / self.config.routing_k
scaled_expert_inputs = expanded_hidden_states * scaling_factor
else:
scaled_expert_inputs = expanded_hidden_states
expert_outputs_list = [expert(scaled_expert_inputs[:, :, i, :]) for i, expert in enumerate(self.experts)]
expert_outputs = torch.stack(expert_outputs_list, dim=2)
exchanged_expert_outputs = self.neighbor_exchange(expert_outputs, topk_indices)
cross_attned_expert_outputs = self.cross_expert_attention(exchanged_expert_outputs)
gathered_outputs = torch.gather(
cross_attned_expert_outputs,
dim=2,
index=topk_indices.unsqueeze(-1).expand(-1, -1, -1, self.config.hidden_size)
)
combined_output = (gathered_outputs * topk_weights.unsqueeze(-1)).sum(dim=2)
return combined_output, topk_indices
class MeshModel(PreTrainedModel, GenerationMixin):
config_class = MeshConfig
def __init__(self, config: MeshConfig):
super().__init__(config)
self.config = config
self.embedding = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([MeshLayer(config) for _ in range(config.num_hidden_layers)])
self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.post_init()
self._supports_gradient_checkpointing = True
self.gradient_checkpointing = False
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
return_dict=None,
output_attentions=None,
output_hidden_states=None,
past_key_values=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
inputs_embeds = self.embedding(input_ids)
elif inputs_embeds is not None:
pass
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
hidden_states = inputs_embeds
if self.gradient_checkpointing and self.training:
import torch.utils.checkpoint
for i, layer in enumerate(self.layers):
if hasattr(layer, 'forward') and callable(layer.forward):
if self.gradient_checkpointing and self.training:
checkpoint_output = torch.utils.checkpoint.checkpoint(
layer, hidden_states, use_reentrant=False
)
if isinstance(checkpoint_output, tuple):
hidden_states = checkpoint_output[0]
else:
hidden_states = checkpoint_output
else:
layer_output = layer(hidden_states)
hidden_states = layer_output[0]
else:
print(f"Warning: Layer {i} does not have a callable forward method. Skipping layer processing.")
hidden_states = self.norm(hidden_states)
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
if return_dict:
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
)
else:
return (loss, logits)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
if past_key_values is not None:
input_ids = input_ids[:, -1].unsqueeze(-1)
if inputs_embeds is not None:
inputs_embeds = inputs_embeds[:, -1, :].unsqueeze(1)
if inputs_embeds is not None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
if "attention_mask" in kwargs:
model_inputs["attention_mask"] = kwargs["attention_mask"]
return model_inputs
def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
self.gradient_checkpointing = True
self.config.gradient_checkpointing = True
print("Gradient checkpointing enabled on MeshModel.")
def gradient_checkpointing_disable(self):
self.gradient_checkpointing = False
self.config.gradient_checkpointing = False
print("Gradient checkpointing disabled on MeshModel.")
def _set_gradient_checkpointing(self, enable=True):
if enable:
self.gradient_checkpointing_enable()
else:
self.gradient_checkpointing_disable()
from transformers import AutoConfig
AutoConfig.register("mesh", MeshConfig)
AutoModelForCausalLM.register(MeshConfig, MeshModel)
HF_MERGED_REPO_STAGE003 = "mesh-labs/v0.1-2x2-stage003"
loaded_model_stage003 = None
loaded_tokenizer_stage003 = None
try:
print(f"Attempting to load Stage 003 merged model from HF: {HF_MERGED_REPO_STAGE003}...")
device_map = "auto"
loaded_model_stage003 = AutoModelForCausalLM.from_pretrained(
HF_MERGED_REPO_STAGE003,
trust_remote_code=True,
device_map=device_map,
torch_dtype=torch.float32
)
if torch.cuda.is_available():
loaded_model_stage003.to('cuda')
print("Stage 003 merged model moved to GPU.")
else:
print("Stage 003 merged model loaded on CPU.")
loaded_tokenizer_stage003 = AutoTokenizer.from_pretrained(
HF_MERGED_REPO_STAGE003,
trust_remote_code=True,
use_fast=False
)
print("Stage 003 merged model and tokenizer loaded successfully from Hugging Face Hub.")
except Exception as e:
print(f"Error loading Stage 003 merged model or tokenizer from Hugging Face Hub: {e}")
loaded_model_stage003 = None
loaded_tokenizer_stage003 = None
if loaded_model_stage003 is not None and loaded_tokenizer_stage003 is not None:
print("\n--- Starting Chat Interface ---")
print("Type your message and press Enter. Type 'quit' to exit.")
loaded_model_stage003.eval()
while True:
try:
user_input = input("You: ")
if user_input.lower() == 'quit':
break
prompt = f"Question: {user_input}\nAnswer:"
inputs = loaded_tokenizer_stage003(prompt, return_tensors="pt")
if torch.cuda.is_available():
inputs = {k: v.to('cuda') for k, v in inputs.items()}
with torch.no_grad():
outputs = loaded_model_stage003.generate(
**inputs,
max_new_tokens=128,
num_beams=1,
do_sample=False,
)
generated_sequence = loaded_tokenizer_stage003.decode(outputs[0], skip_special_tokens=True)
answer_prefix = "Answer:"
answer_start_index = generated_sequence.find(answer_prefix)
if answer_start_index != -1:
generated_answer = generated_sequence[answer_start_index + len(answer_prefix):].strip()
else:
print("Warning: 'Answer:' prefix not found in generated text. Showing full generated sequence.")
generated_answer = generated_sequence.strip()
print("Model:", generated_answer)
except Exception as e:
print(f"An error occurred: {e}")
print("Please try again or type 'quit' to exit.")
else:
print("\nModel or tokenizer not loaded. Cannot start chat interface.")
```
## Disclaimer
This small language model is just a proof-of-concept, paving the way to the final release, which is likely to happen in Q4 2025, and include more models and better support from external libraries such as Transformers and Llama.cpp. |