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import torch
import torch.nn as nn
import torch.nn.functional as F
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L69
class RMSNorm(nn.Module):
"""
Root Mean Square Layer Normalization (RMSNorm).
Normalizes the input across the last dimension using RMS normalization,
which scales the input without subtracting the mean. Commonly used as a
lighter alternative to LayerNorm in transformer models.
Args:
cfg: A configuration object containing:
- lm_hidden_dim (int): The dimensionality of the model hidden states.
- lm_rms_eps (float): A small constant to avoid division by zero.
"""
def __init__(self, cfg):
super().__init__()
self.weight = nn.Parameter(torch.ones(cfg.lm_hidden_dim))
self.eps = cfg.lm_rms_eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass for RMSNorm.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, sequence_length, lm_hidden_dim).
Returns:
torch.Tensor: Normalized tensor of the same shape as input.
"""
# Compute inverse of RMS: square the tensor element-wise, mean is computed across lm_hidden_dim.
irms = torch.rsqrt(torch.mean(x ** 2, dim=-1, keepdim=True) + self.eps) # inverse of RMS
x = x * irms * self.weight
return x
# Multiple derivates of Rotary Embeddings by now, this is a basic one with linear scaling to context length
# e.g. https://github.com/huggingface/smollm/blob/main/vision/m4/models/vllama3/modeling_vllama3.py#L190
class RotaryEmbedding(nn.Module):
"""
Compute Rotary Embedding to introduce positional dependency to input sequence without additional training parameters and
relative distance of token position ids through angle rotation.
Args:
cfg: Configuration object containing:
- lm_hidden_dim (int): Hidden dimension size.
- lm_n_heads (int): Number of attention heads.
- lm_re_base (float): Base for rotary embedding frequencies.
- lm_max_position_embeddings (int): Max sequence length supported for rotary embedding.
- lm_attn_scaling (float): Attention scaling factor.
"""
def __init__(self, cfg):
super().__init__()
assert cfg.lm_hidden_dim % cfg.lm_n_heads == 0, "Hidden dimension must be divisible by number of heads"
self.dim = cfg.lm_hidden_dim // cfg.lm_n_heads # dim of each head
self.base = cfg.lm_re_base
self.max_seq_len = cfg.lm_max_position_embeddings
# Standard RoPE implementation - create frequencies for each dimension
# freq_i = 1 / (base^(2i/dim)) where i is the dimension index
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float() / self.dim))
self.register_buffer("inv_freq", inv_freq)
self.original_max_seq_len = cfg.lm_max_position_embeddings
self.attention_scaling = cfg.lm_attn_scaling
@torch.no_grad()
def forward(self, position_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
"""
Compute rotary positional embeddings (cosine and sine components).
Args:
position_ids (torch.Tensor): Tensor of shape (batch_size, seq_len) containing position indices.
Returns:
Tuple[torch.Tensor, torch.Tensor]: Tuple of two tensors (cos, sin), each of shape
(batch_size, seq_len, dim), representing rotary embeddings.
"""
batch_size, seq_len = position_ids.shape
# Dynamic scaling for longer sequences
# Divide the angle frequency to fit more rotation into the embedding space.
max_seq = position_ids.max() + 1
if max_seq > self.original_max_seq_len:
scale = max_seq / self.original_max_seq_len
inv_freq = self.inv_freq / scale
else:
inv_freq = self.inv_freq
# Compute theta = position * frequency
# Flatten position_ids for batch processing
flat_position_ids = position_ids.reshape(-1).float()
# Element-wise outer product: [seq_len] x [dim/2] => [seq_len, dim/2]
freqs = flat_position_ids.unsqueeze(-1) * inv_freq.unsqueeze(0)
# Reshape to include batch dimension
freqs = freqs.reshape(batch_size, seq_len, -1)
# Now create interleaved pattern
emb = torch.cat([freqs, freqs], dim=-1)
# Compute cos and sin
cos = torch.cos(emb) * self.attention_scaling
sin = torch.sin(emb) * self.attention_scaling
return cos, sin
def rotate_half(x: torch.Tensor) -> torch.Tensor:
"""
Rotates the input by dividing the hidden dimension to two, then swapping and negating dimensions.
"""
x1, x2 = x.chunk(2, dim=-1)
return torch.cat((-x2, x1), dim=-1)
# Apply rotary position embeddings to queries and keys.
def apply_rotary_pos_embd(q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, unsqueeze_dim:int=1)-> tuple[torch.Tensor, torch.Tensor]:
"""
Applies rotary positional embeddings to query and key tensors in attention mechanisms.
Rotary positional embeddings inject position-dependent rotations into query and key vectors,
enabling transformers to encode positional information effectively without explicit positional encoding.
Args:
q (torch.Tensor): Query tensor with shape [batch_size, num_heads, seq_len, head_dim].
k (torch.Tensor): Key tensor with shape [batch_size, num_heads, seq_len, head_dim].
cos (torch.Tensor): Precomputed cosine positional embeddings with shape [batch_size, seq_len, head_dim].
sin (torch.Tensor): Precomputed sine positional embeddings with shape [batch_size, seq_len, head_dim].
unsqueeze_dim (int, optional): Dimension index to unsqueeze `cos` and `sin` to enable broadcasting.
Defaults to 1 (typically the heads dimension).
Returns:
tuple[torch.Tensor, torch.Tensor]: The rotated query and key tensors (`q_embed`, `k_embed`),
each with the same shape as the input tensors.
How it works:
- `cos` and `sin` tensors are unsqueezed at `unsqueeze_dim` to broadcast across attention heads.
- Rotary embeddings apply a complex number rotation in the embedding space using:
rotated = (original * cos) + (rotate_half(original) * sin)
- `rotate_half` performs a specific half-dimension rotation on the input tensor.
- This operation encodes relative position information in q and k without adding explicit positional vectors.
Example:
q_embed, k_embed = apply_rotary_pos_embd(q, k, cos, sin)
"""
# We need to make sure cos and sin can be properly broadcast
# to the shape of q and k by adding the heads dimension
cos = cos.unsqueeze(unsqueeze_dim) # [batch_size, 1, seq_len, head_dim]
sin = sin.unsqueeze(unsqueeze_dim) # [batch_size, 1, seq_len, head_dim]
# Apply complex multiplication:
# (q * cos) + (rotate_half(q) * sin)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L214
# https://github.com/huggingface/smollm/blob/main/vision/m4/models/vllama3/modeling_vllama3.py#L382
class LanguageModelGroupedQueryAttention(nn.Module):
"""
Implements Grouped Query Attention (GQA) as used in some transformer-based language models.
GQA reduces computation by using fewer key-value heads than query heads,
grouping multiple query heads to share the same key-value heads.
Args:
cfg: Configuration object containing:
- lm_n_heads (int): Number of query heads.
- lm_n_kv_heads (int): Number of key-value heads.
- lm_hidden_dim (int): Hidden embedding dimension.
- lm_dropout (float): Dropout rate.
"""
def __init__(self, cfg):
super().__init__()
self.n_heads = cfg.lm_n_heads
self.n_kv_heads = cfg.lm_n_kv_heads
self.embd_dim = cfg.lm_hidden_dim
self.dropout = cfg.lm_dropout
assert self.n_heads % self.n_kv_heads == 0, "n_heads must be divisible by n_kv_heads"
assert self.embd_dim % self.n_heads == 0, "embd_dim must be divisible by num_heads"
self.n_kv_groups = self.n_heads // self.n_kv_heads
self.head_dim = self.embd_dim // self.n_heads
self.q_proj = nn.Linear(self.embd_dim, self.embd_dim, bias=False)
self.k_proj = nn.Linear(self.embd_dim, self.head_dim * self.n_kv_heads, bias=False)
self.v_proj = nn.Linear(self.embd_dim, self.head_dim * self.n_kv_heads, bias=False)
self.out_proj = nn.Linear(self.embd_dim, self.embd_dim, bias=False)
self.attn_dropout = nn.Dropout(self.dropout)
self.resid_dropout = nn.Dropout(self.dropout)
# Use scaled dot product attention if available
self.sdpa = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
if not self.sdpa:
print("Warning: scaled dot product attention not available, using standard attention in LM.")
def forward(self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, attention_mask=None, block_kv_cache=None) -> tuple[torch.Tensor, dict]:
"""
Forward pass for grouped query attention.
Args:
x (Tensor): Input tensor of shape (B, T_curr, C), where
B = batch size,
T_curr = current sequence length,
C = embedding dimension.
cos (Tensor): Rotary embedding cosines, shape compatible with q and k.
sin (Tensor): Rotary embedding sines, shape compatible with q and k.
attention_mask (Tensor, optional): Attention mask tensor of shape (B, total_kv_length),
with 1 for tokens to attend to and 0 for padding.
block_kv_cache (dict, optional): Cache dict with 'key' and 'value' tensors for autoregressive decoding.
Returns:
tuple[Tensor, dict]:
- Output tensor after attention and projection, shape (B, T_curr, C).
- Updated block_kv_cache dict for caching key-value states.
"""
is_prefill = block_kv_cache is None
B, T_curr, C = x.size() # T_curr is the sequence length of the current input x
q_curr = self.q_proj(x).view(B, T_curr, self.n_heads, self.head_dim).transpose(1, 2) # (B, n_heads, T_curr, head_dim)
k_curr = self.k_proj(x).view(B, T_curr, self.n_kv_heads, self.head_dim).transpose(1, 2) # (B, n_kv_heads, T_curr, head_dim)
v_curr = self.v_proj(x).view(B, T_curr, self.n_kv_heads, self.head_dim).transpose(1, 2) # (B, n_kv_heads, T_curr, head_dim)
# Apply rotary embeddings to the current q and k
q, k_rotated = apply_rotary_pos_embd(q_curr, k_curr, cos, sin)
# Check if we can use cached keys and values
if not is_prefill and block_kv_cache['key'] is not None:
# Concatenate with cached K, V
# k_rotated and v_curr are for the new token(s)
k = block_kv_cache['key']
v = block_kv_cache['value']
k = torch.cat([k, k_rotated], dim=2)
v = torch.cat([v, v_curr], dim=2)
block_kv_cache['key'] = k
block_kv_cache['value'] = v
else:
# No cache, this is the first pass (prefill)
k = k_rotated
v = v_curr
block_kv_cache = {'key': k, 'value': v}
# Repeat K, V for Grouped Query Attention
k_exp = k.repeat_interleave(self.n_kv_groups, dim=1) # (B, n_heads, T_kv, head_dim)
v_exp = v.repeat_interleave(self.n_kv_groups, dim=1) # (B, n_heads, T_kv, head_dim)
T_kv = k_exp.size(2) # Total sequence length of keys/values
# Prepare attention mask for SDPA or manual path
# attention_mask is (B, T_kv_total_length), 1 for attend, 0 for pad
additive_attn_mask = None
if attention_mask is not None:
# The current `attention_mask` parameter is assumed to be `[B, total_sequence_length_kv]`
# Let's make it `[B, 1, 1, T_kv]` for SDPA.
mask_for_keys = attention_mask[:, :T_kv] # Ensure mask matches key length [B, T_kv]
additive_attn_mask = (1.0 - mask_for_keys.unsqueeze(1).unsqueeze(2).float()) * torch.finfo(q.dtype).min
# This additive_attn_mask shape is [B, 1, 1, T_kv]
if self.sdpa and x.device.type != 'mps':
# During decode, no additional masking needed as [1, T_kv] is naturally causal
is_causal = (T_curr == T_kv and T_curr > 1)
y = torch.nn.functional.scaled_dot_product_attention(
q, k_exp, v_exp,
attn_mask=additive_attn_mask,
dropout_p=self.dropout if self.training else 0.0,
is_causal=is_causal
)
else:
# Manual attention implementation
attn = torch.matmul(q, k_exp.transpose(2, 3)) / math.sqrt(self.head_dim) # (B, n_heads, T_curr, T_kv)
# During decode: no additional masking needed as [1, T_kv] is naturally causal
if T_curr == T_kv and T_curr > 1:
causal_mask_val = torch.tril(torch.ones(T_curr, T_curr, device=x.device, dtype=torch.bool)).view(1, 1, T_curr, T_curr)
attn = attn.masked_fill(~causal_mask_val, float('-inf'))
if additive_attn_mask is not None: # Additive padding mask
# additive_attn_mask is [B,1,1,T_kv], needs to be broadcast to [B, n_heads, T_curr, T_kv]
attn = attn + additive_attn_mask
attn = F.softmax(attn, dim=-1)
attn = self.attn_dropout(attn)
y = attn @ v_exp
y = y.transpose(1, 2).contiguous().view(B, T_curr, C)
y = self.out_proj(y)
y = self.resid_dropout(y)
return y, block_kv_cache
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L160
class LanguageModelMLP(nn.Module):
"""
Implements the feed-forward network (MLP) block used in transformer-based language models.
This MLP uses a gated activation mechanism where two separate linear projections
are applied to the input: one passed through an activation function (gate_proj),
and the other as is (up_proj). Their element-wise product is then projected back
to the embedding dimension (down_proj).
Args:
cfg: Configuration object containing:
- lm_hidden_dim (int): The embedding dimension size.
- lm_inter_dim (int): The intermediate dimension size for the MLP.
Attributes:
activation_fn (Callable): The activation function used (SiLU).
gate_proj (nn.Linear): Linear projection for gating pathway.
up_proj (nn.Linear): Linear projection for upscaling pathway.
down_proj (nn.Linear): Linear projection for downscaling back to embedding dim.
"""
def __init__(self, cfg):
super().__init__()
self.embd_dim = cfg.lm_hidden_dim
self.inter_dim = cfg.lm_inter_dim
self.activation_fn = F.silu
self.gate_proj = nn.Linear(self.embd_dim, self.inter_dim, bias=False)
self.up_proj = nn.Linear(self.embd_dim, self.inter_dim, bias=False)
self.down_proj = nn.Linear(self.inter_dim, self.embd_dim, bias=False)
def forward(self, x):
"""
Forward pass through the gated MLP block.
Args:
x (Tensor): Input tensor of shape (batch_size, seq_length, embd_dim).
Returns:
Tensor: Output tensor of shape (batch_size, seq_length, embd_dim),
after gated MLP transformation.
"""
gate = self.activation_fn(self.gate_proj(x))
x = self.up_proj(x)
x = self.down_proj(gate * x)
return x
# https://github.com/meta-llama/llama3/blob/main/llama/model.py#L222
class LanguageModelBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.mlp = LanguageModelMLP(cfg)
self.attn = LanguageModelGroupedQueryAttention(cfg)
self.norm1 = RMSNorm(cfg) # Input Norm
self.norm2 = RMSNorm(cfg) # Post Attention Norm
def forward(self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, attention_mask: torch.Tensor=None, block_kv_cache: dict=None):
"""
Forward pass of the Transformer block.
Args:
x (Tensor): Input tensor of shape (batch_size, seq_len, hidden_dim).
cos (Tensor): Cosine positional embeddings for rotary embedding, shape
matching sequence length and head dimension.
sin (Tensor): Sine positional embeddings for rotary embedding, same shape as cos.
attention_mask (Tensor, optional): Attention mask of shape (batch_size, total_kv_length),
with 1 indicating tokens to attend to and 0 for padding tokens.
block_kv_cache (dict, optional): Key-value cache dict for cached keys and values
during decoding. If None, no cache is used.
Returns:
Tuple[Tensor, dict]: Output tensor after the block (same shape as input),
and the updated key-value cache dictionary.
"""
res = x
x = self.norm1(x)
x, block_kv_cache = self.attn(x, cos, sin, attention_mask, block_kv_cache)
x = res + x
res = x
x = self.norm2(x)
x = self.mlp(x)
x = res + x
return x, block_kv_cache
# https://github.com/meta-llama/llama3/blob/main/llama/model.py#L251
class LanguageModel(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.lm_use_tokens = cfg.lm_use_tokens
self.lm_tie_weights = cfg.lm_tie_weights
self.token_embedding = nn.Embedding(cfg.lm_vocab_size, cfg.lm_hidden_dim)
self.rotary_embd = RotaryEmbedding(cfg)
self.blocks = nn.ModuleList([
LanguageModelBlock(cfg) for _ in range(cfg.lm_n_blocks)
])
self.norm = RMSNorm(cfg) # Final Norm
self.head = nn.Linear(cfg.lm_hidden_dim, cfg.lm_vocab_size, bias=False)
if self.lm_tie_weights:
self.head.weight = self.token_embedding.weight
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
elif isinstance(module, RMSNorm):
module.weight.data.fill_(1.0)
def forward(self, x: torch.Tensor, attention_mask: torch.Tensor=None, kv_cache: list[dict]=None, start_pos: int=0):
"""
Performs a forward pass through the language model.
Args:
x (Tensor): Input tensor. If `lm_use_tokens` is True, this should be
token indices with shape (batch_size, sequence_length).
If False, it should be embeddings of shape (batch_size, sequence_length, hidden_dim).
attention_mask (Tensor, optional): Mask tensor for attention to
specify which tokens to attend to, typically of shape
(batch_size, sequence_length). Default is None.
kv_cache (list[dict], optional): List of key-value caches for each transformer
block to enable efficient autoregressive decoding.
If None, no cache is used and new ones are created. Default is None.
start_pos (int, optional): The starting position index for the current input
sequence. Used to compute rotary positional embeddings correctly,
especially for cached sequences during generation. Default is 0.
Returns:
Tuple:
- Tensor: Output logits with shape (batch_size, sequence_length, vocab_size)
if `lm_use_tokens` is True, otherwise the hidden state embeddings
(batch_size, sequence_length, hidden_dim).
- list: Updated list of key-value caches, one for each transformer block,
useful for autoregressive decoding and incremental generation.
Behavior:
- If `lm_use_tokens` is True, the input token indices are first embedded.
- Rotary positional embeddings are generated for the current input positions,
which are passed along to each transformer block.
- For each transformer block, the input is processed along with
rotary embeddings, attention mask, and optional cached key-values.
- After processing all blocks, a final RMS normalization is applied.
- If tokens are used, the normalized hidden states are projected to logits
over the vocabulary.
- The method returns the logits or embeddings along with the updated
cache for efficient decoding.
"""
if self.lm_use_tokens:
x = self.token_embedding(x)
# T_curr is the length of the current input sequence
B, T_curr, _ = x.size()
# Create position_ids for the current sequence based on start_pos
current_position_ids = torch.arange(start_pos, start_pos + T_curr, device=x.device).unsqueeze(0).expand(B, -1)
cos, sin = self.rotary_embd(current_position_ids) # Get rotary position embeddings for current tokens
# Initialize new KV cache if none provided
if kv_cache is None:
kv_cache = [None] * len(self.blocks)
for i, block in enumerate(self.blocks):
x, kv_cache[i] = block(x, cos, sin, attention_mask, kv_cache[i])
x = self.norm(x)
# Compute logits if we are using tokens, otherwise stay in the embedding space
if self.lm_use_tokens:
x = self.head(x)
return x, kv_cache
@torch.inference_mode()
def generate(self, inputs: torch.Tensor, max_new_tokens: int=20):
"""
Generate tokens autoregressively from a given input sequence.
Args:
inputs (torch.Tensor): Input tensor containing token indices or embeddings.
Shape: (batch_size, sequence_length) or (sequence_length,) for a single sequence.
max_new_tokens (int): Number of new tokens to generate after the input sequence.
Returns:
torch.Tensor: The generated sequence, including the original inputs and newly generated tokens.
Shape: (batch_size, sequence_length + max_new_tokens)
"""
# Add batch dimension if needed
if inputs.dim() == 1:
inputs = inputs.unsqueeze(0)
generated_outputs = inputs.clone()
prompt_output, kv_cache_list = self.forward(
generated_outputs,
attention_mask=None,
kv_cache=None,
start_pos=0
)
last_output = prompt_output[:, -1, :]
# Decode Phase with KV cache
for i in range(max_new_tokens):
if self.lm_use_tokens:
# Now the model outputs logits
next_output = torch.argmax(last_output, dim=-1, keepdim=True)
else:
# Now the model outputs embeddings
next_output = last_output.unsqueeze(1)
generated_outputs = torch.cat((generated_outputs, next_output), dim=1)
# The token being processed is `next_token`. Its position is `generated_outputs.size(1) - 1`.
current_token_start_pos = generated_outputs.size(1) - 1
if i == max_new_tokens - 1:
break
decode_step_output, kv_cache_list = self.forward(
next_output,
attention_mask=None,
kv_cache=kv_cache_list,
start_pos=current_token_start_pos
)
last_output = decode_step_output[:, -1, :]
return generated_outputs
# Load the model from a pretrained HuggingFace model (we don't want to have to train the Language Backbone from scratch)
@classmethod
def from_pretrained(cls, cfg):
from transformers import AutoConfig
from huggingface_hub import hf_hub_download
import safetensors
import torch.nn.init as init
import json
from huggingface_hub.utils import EntryNotFoundError
# Load the HuggingFace config
hf_config = AutoConfig.from_pretrained(cfg.lm_model_type)
# Store original HF vocab size before we modify it
original_vocab_size = hf_config.vocab_size
# print(f"Original vocabulary size from pretrained model: {original_vocab_size}")
# Configure model parameters from HF config
cfg.lm_hidden_dim = hf_config.hidden_size
cfg.lm_inter_dim = hf_config.intermediate_size
cfg.lm_rms_eps = hf_config.rms_norm_eps
cfg.lm_re_base = hf_config.rope_theta
cfg.lm_max_position_embeddings = hf_config.max_position_embeddings
# We're keeping our own vocab size in cfg, but checking it's larger than original
if hasattr(cfg, 'lm_vocab_size'):
if cfg.lm_vocab_size < original_vocab_size:
raise ValueError(f"Config vocab size ({cfg.lm_vocab_size}) is smaller than pretrained model vocab size ({original_vocab_size})")
# print(f"Using vocabulary size: {cfg.lm_vocab_size}")
else:
# If not specified, use the original
cfg.lm_vocab_size = original_vocab_size
# print(f"Using original vocabulary size: {cfg.lm_vocab_size}")
cfg.lm_n_heads = hf_config.num_attention_heads
cfg.lm_n_kv_heads = hf_config.num_key_value_heads
cfg.lm_dropout = hf_config.attention_dropout
cfg.lm_n_blocks = hf_config.num_hidden_layers
# Create our model with potentially larger vocabulary
model = cls(cfg)
try:
index_path = hf_hub_download(repo_id=cfg.lm_model_type, filename="model.safetensors.index.json")
with open(index_path, 'r') as f:
index = json.load(f)
# Get unique filenames from weight map
safetensors_filenames = sorted(list(set(index['weight_map'].values())))
# Download all the sharded files
safetensors_files = [hf_hub_download(repo_id=cfg.lm_model_type, filename=fn) for fn in safetensors_filenames]
except EntryNotFoundError:
safetensors_files = [hf_hub_download(repo_id=cfg.lm_model_type, filename="model.safetensors")]
sd = model.state_dict()
mapping = {
'model.embed_tokens.weight': 'token_embedding.weight',
'model.norm.weight': 'norm.weight'
}
for i in range(cfg.lm_n_blocks):
layer_prefix = f'model.layers.{i}.'
block_prefix = f'blocks.{i}.'
mapping.update({
f"{layer_prefix}self_attn.q_proj.weight": f"{block_prefix}attn.q_proj.weight",
f"{layer_prefix}self_attn.k_proj.weight": f"{block_prefix}attn.k_proj.weight",
f"{layer_prefix}self_attn.v_proj.weight": f"{block_prefix}attn.v_proj.weight",
f"{layer_prefix}self_attn.o_proj.weight": f"{block_prefix}attn.out_proj.weight",
f"{layer_prefix}mlp.gate_proj.weight": f"{block_prefix}mlp.gate_proj.weight",
f"{layer_prefix}mlp.up_proj.weight": f"{block_prefix}mlp.up_proj.weight",
f"{layer_prefix}mlp.down_proj.weight": f"{block_prefix}mlp.down_proj.weight",
f"{layer_prefix}input_layernorm.weight": f"{block_prefix}norm1.weight",
f"{layer_prefix}post_attention_layernorm.weight": f"{block_prefix}norm2.weight"
})
# Special handling for token embeddings with extended vocabulary
has_extended_embeddings = False
loaded_keys = set()
for safetensors_file in safetensors_files:
with safetensors.safe_open(filename=safetensors_file, framework="pt", device="cpu") as f:
for hf_key, our_key in mapping.items():
if our_key in loaded_keys:
continue
if hf_key in f.keys() and our_key in sd:
tensor = f.get_tensor(hf_key)
# Special handling for token embeddings if vocab sizes differ
if hf_key == 'model.embed_tokens.weight' and tensor.shape[0] != sd[our_key].shape[0]:
has_extended_embeddings = True
print(f"Extending token embeddings from {tensor.shape} to {sd[our_key].shape}")
# Copy existing embeddings to the beginning of our larger embedding matrix
sd[our_key][:tensor.shape[0]].copy_(tensor)
# Initialize the new embeddings using the same approach as the original model
std = 0.02 # Common value, but you might want to adjust based on model
init.normal_(sd[our_key][tensor.shape[0]:], mean=0.0, std=std)
print(f"Initialized {sd[our_key].shape[0] - tensor.shape[0]} new token embeddings")
sd['head.weight'].copy_(sd[our_key]) # Update the head weights as well
elif tensor.shape == sd[our_key].shape:
sd[our_key].copy_(tensor)
else:
print(f"Shape mismatch for {hf_key} -> {our_key}: {tensor.shape} vs {sd[our_key].shape}")
loaded_keys.add(our_key)
for hf_key, our_key in mapping.items():
if our_key not in loaded_keys:
if our_key in sd:
print(f"Warning: Key {our_key} not found in any safetensors file (HF key: {hf_key})")
# Load the state dict
model.load_state_dict(sd)
# Handle output projection / language modeling head
if has_extended_embeddings and hasattr(model, 'head') and 'head.weight' in sd:
# If we have a separate output projection layer and extended the vocab
# we should handle it similarly to the input embeddings
lm_head_loaded = False
for safetensors_file in safetensors_files:
with safetensors.safe_open(filename=safetensors_file, framework="pt", device="cpu") as f:
if 'lm_head.weight' in f.keys():
lm_head = f.get_tensor('lm_head.weight')
if lm_head.shape[0] != sd['head.weight'].shape[0]:
print(f"Extending LM head from {lm_head.shape} to {sd['head.weight'].shape}")
# Copy existing weights
sd['head.weight'][:lm_head.shape[0]].copy_(lm_head)
# Initialize new weights
std = 0.02
init.normal_(sd['head.weight'][lm_head.shape[0]:], mean=0.0, std=std)
# Load updated weights
model.load_state_dict(sd)
lm_head_loaded = True
break
# Handle weight tying (if needed)
if cfg.lm_tie_weights and hasattr(model, 'head') and hasattr(model, 'token_embedding'):
model.head.weight = model.token_embedding.weight
# print("Tied token embedding and LM head weights")
print(f"Successfully loaded {cfg.lm_model_type} weights from safetensors. Model has {sum(p.numel() for p in model.parameters()):,} parameters.")
return model
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