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from transformers import PretrainedConfig, PreTrainedModel, AutoModelForCausalLM
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from transformers.modeling_outputs import CausalLMOutputWithPast
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 specific configurations
mesh_grid_size=(2, 2), # 2x2 grid
expert_intermediate_size=256, # Example size for expert intermediate layer
routing_k=2, # Top-k routing
neighbor_exchange_enabled=True,
cross_expert_attention_enabled=True,
**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
# Calculate expert_intermediate_size based on the shared and expert parameter split
# Total parameters = Shared (Embedding, Norm, LM Head) + Experts + Overhead
# This calculation is complex and depends on the specific layer mapping.
# For now, let's use a placeholder or calculate it based on the target parameter count.
# Target A242M (top-2): 100M shared + 135M (2 experts) + 7M overhead = 242M
# Let's assume the 135M for 2 experts is primarily in the intermediate size.
# We need to determine how Gemma's intermediate size maps to the expert intermediate size.
# For now, I will keep a placeholder or a simple ratio.
self.expert_intermediate_size = intermediate_size // (mesh_grid_size[0] * mesh_grid_size[1]) # Example: divide intermediate size by number of experts
self.routing_k = routing_k
self.neighbor_exchange_enabled = neighbor_exchange_enabled
self.cross_expert_attention_enabled = cross_expert_attention_enabled
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