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# -*- coding: utf-8 -*-
"""# shared_subspace_encoder.py"""
from typing import Optional
import torch
from torch import nn
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_utils import PreTrainedModel
from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask_for_sdpa
from .mla import MultiheadLatentAttention, RotaryEmbedding
from .feedforward import SubspaceFeedForward
from .shared_space_config import SharedSpaceDecoderConfig
"""`RMSNorm`
From:
https://huggingface.co/deepseek-ai/DeepSeek-R1/blob/main/modeling_deepseek.py
TODO - May not need?
"""
class DeepseekV3RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
DeepseekV3RMSNorm 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 create_norm_layer(hidden_size: int, config: SharedSpaceDecoderConfig) -> nn.Module:
"""
Create a normalization layer based on the config norm_type.
Args:
hidden_size: The dimension to normalize over
config: Configuration containing norm_type and epsilon values
Returns:
Either a LayerNorm or RMSNorm layer
"""
if config.norm_type == "layernorm":
return nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
elif config.norm_type == "rmsnorm":
return DeepseekV3RMSNorm(hidden_size, eps=config.rms_norm_eps)
else:
# This should be caught by config validation, but being defensive
raise ValueError(f"Unknown norm_type: {config.norm_type}")
"""#### *PreTrainedModel"""
class SharedSpaceDecoderPreTrainedModel(PreTrainedModel):
"""
The **PreTrainedModel object:
- Is instantiated when TODO
- Initializes:
- TODO
- Provides access to TODO
- Executes TODO
"""
config_class = SharedSpaceDecoderConfig
base_model_prefix = "model"
def _init_weights(self, module: nn.Module) -> None:
"""Weight initialization hook used by :class:`PreTrainedModel`.
``PreTrainedModel.post_init`` will recursively apply this function to
every submodule right after construction. HuggingFace models override
it so that creating a model from scratch yields the same initialization
as ``from_pretrained`` when no checkpoint is supplied.
This decoder-specific initialization strategy includes:
- Proper handling of configurable normalization layers (LayerNorm or RMSNorm)
- Special initialization for language modeling heads
- Considerations for causal attention and autoregressive modeling
- Support for both dense and decomposed vocabulary embeddings
"""
if isinstance(module, nn.Linear):
# Standard linear layer initialization
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
# Initialize embeddings with normal distribution
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, DeepseekV3RMSNorm):
# RMSNorm initialization: weight to 1.0, no bias term
module.weight.data.fill_(1.0)
elif isinstance(module, nn.LayerNorm):
# LayerNorm initialization: bias to 0, weight to 1.0
module.bias.data.zero_()
module.weight.data.fill_(1.0)
"""# ▂▂▂▂▂▂▂▂▂▂▂▂
# Classes
"""
"""#### `*Layer`"""
class SharedSpaceDecoderLayer(nn.Module):
"""
The **Layer object:
- Is instantiated by :class:`SharedSpaceDecoderModel` for each
Transformer block in the decoder.
- Initializes:
- ``self_attn`` – multi-head latent attention implementing either
dense or latent projections depending on the configuration.
- ``ffn`` – a :class:`SubspaceFeedForward` block.
- RMSNorm layers for pre-attention and pre-FFN normalization.
- Provides access to the attention and feed-forward submodules via the
attributes ``self_attn`` and ``ffn``.
- Executes a single decoder block in :meth:`forward`.
"""
def __init__(self, config: SharedSpaceDecoderConfig, layer_idx: int) -> None:
super().__init__()
# Norm applied prior to attention.
self.attn_input_norm = create_norm_layer(config.hidden_size, config)
# Attention block
self.self_attn = MultiheadLatentAttention(config, layer_idx)
# Norm applied prior to FFN
self.ffn_input_norm = create_norm_layer(config.hidden_size, config)
# Feed-forward network used after attention
self.ffn = SubspaceFeedForward(config, layer_idx)
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor], # RoPE embeddings
attention_mask: Optional[torch.Tensor],
) -> torch.Tensor:
# ========================
# Self Attention
# ========================
residual_strm = hidden_states
# Normalize the hidden states to create the input to attention.
attn_input = self.attn_input_norm(hidden_states)
# Evaluate
attn_output = self.self_attn(
attn_input,
position_embeddings,
attention_mask,
)
# Add the attention output (the residual) back to the non-normalized
# hidden_states.
hidden_states = residual_strm + attn_output
# ===========================
# Feed-Forward Network
# ===========================
residual_strm = hidden_states
# Normalize the updated hidden states prior to the FFN
ffn_input = self.ffn_input_norm(hidden_states)
# Evaluate
ffn_output = self.ffn(ffn_input)
# Add the output the un-normalized hidden states.
hidden_states = residual_strm + ffn_output
return hidden_states
"""#### *Model"""
class SharedSpaceDecoderModel(SharedSpaceDecoderPreTrainedModel):
"""
The **Model object:
- Initializes:
- The vocabulary embeddings (and optional decomposition)
- Position embeddings (calculated in RotaryEmbedding)
- All of the **Layer objects.
- Provides interface to vocab embeddings.
- Executes the whole decoder model in `forward` with causal attention.
This is the base decoder without the language modeling head.
Use SubspaceDecoderForCausalLM for language modeling tasks.
"""
def __init__(self, config: SharedSpaceDecoderConfig) -> None:
super().__init__(config)
# ============================
# Vocabulary Embeddings
# ============================
# Decomposing the vocabulary (if enabled) defines a shared projection
# which constrains the model to store semantic information (and
# whatever other static token knowledge) into a limited set of
# feature directions.
# If we're decomposing the token embeddings,
# TODO - Rename to vocab_subspace.
if config.vocab_subspace:
# Create the embedding table. Vocabulary embeddings are learned
# in a lower dimensional latent space.
self.vocab_embed = nn.Embedding(
config.vocab_size, # Number of tokens
config.vocab_rank # Subspace dimension
)
# Create a
# Selected token latents will be projected up to model size.
# vocab_proj has shape [vocab_rank x model_size]
self.vocab_proj = nn.Linear(
config.vocab_rank, # Size of latents
config.hidden_size, # Model size
bias=False
)
# Otherwise, for a dense vocabulary,
else:
# Create the dense embedding table in model space.
self.vocab_embed = nn.Embedding(
config.vocab_size, # Number of tokens
config.hidden_size # Model size
)
self.vocab_proj = None
# =====================
# RoPE Embeddings
# =====================
# Pre-computes the table of RoPE embeddings, leaving them in
# GPU memory.
self.rope = RotaryEmbedding(config)
# ===================
# Create Layers
# ===================
layers = []
# For each layer,
for i in range(config.num_hidden_layers):
# Create a **Layer, providing the config and indicating its number.
layers.append(
SharedSpaceDecoderLayer(
config,
layer_idx = i
)
)
# Wrap in torch ModuleList
self.layers = nn.ModuleList(layers)
# Whatever huggingface does behind the scenes...
self.post_init()
# Agents: Do not define boilerplate helpers, e.g., get/set_input_embeddings
def embed(self, input_ids: torch.LongTensor) -> torch.Tensor:
"""
Return token embeddings for input ids.
This will perform the up projection to model space if the vocabulary is
decomposed.
input_ids have shape [batch_size, seq_len]
"""
# If the vocabulary is decomposed,
if self.vocab_proj is not None:
# Retrieve the latents
# input_ids: [batch_size, seq_len]
# x: [batch_size, seq_len, latent_dim]
x = self.vocab_embed(input_ids)
# Project the latents back to model space and return.
return(self.vocab_proj(x))
# If the vocabulary is dense,
else:
# Just return the embeddings.
return self.vocab_embed(input_ids)
def forward(
self,
input_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.Tensor:
"""
Run the full decoder stack with causal attention.
Inputs:
input_ids [batch_size, seq_len]
attention_mask [batch_size, seq_len] - 1 for real tokens, 0 for padding
Returns:
Final decoder layer output [batch_size, seq_len, model_size]
"""
# Retrieve the token embeddings for this sequence.
# These are model_size, regardless of whether the vocab is decompd.
hidden_states = self.embed(input_ids)
# Retrieve the rotary position embeddings for all of the positions in
# our current input sequence.
seq_len = hidden_states.size(1)
# Retrieves just the ones necessary for the sequence length of the
# input. These are vectors, two per token. Their length is the
# number of head dimensions we're applying RoPE to.
# Input
# cos: [max_seq_len, rope_dims]
# sin: [max_seq_len, rope_dims]
# Outputs:
# R_cos [seq_len, rope_dims]
# R_sin [seq_len, rope_dims]
R_cos = self.rope.cos[:seq_len]
R_sin = self.rope.sin[:seq_len]
# ===============================
# Attention Mask Conversion
# ===============================
"""
use_sdpa_attention_masks = (
self.attn_implementation == "sdpa"
and self.position_embedding_type == "absolute"
and head_mask is None
and not output_attentions
)
"""
# Expand the attention mask
#if use_sdpa_attention_masks and attention_mask.dim() == 2:
if True:
# Expand the attention mask for SDPA.
# [bsz, seq_len] -> [bsz, 1, seq_len, seq_len]
extended_attention_mask = _prepare_4d_attention_mask_for_sdpa(
attention_mask,
hidden_states.dtype,
tgt_len = seq_len
)
attention_mask = extended_attention_mask
# Run the model!
# For each decoder layer,
for layer_i, layer in enumerate(self.layers):
# Evaluate the layer
hidden_states = layer(
hidden_states, # Token embeddings
(R_cos, R_sin), # Rope embeddings, passed as a tuple.
attention_mask, # Attn mask
)
# Return the final output of the decoder stack.
return hidden_states
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