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import torch
from torch import nn
from typing import Optional, Tuple
import math
from src.model.modules.kv_cache import KVCache
class GemmaConfig:
def __init__(
self,
vocab_size,
hidden_size,
intermediate_size,
num_hidden_layers,
num_attention_heads,
num_key_value_heads,
head_dim=256,
max_position_embeddings=8192,
rms_norm_eps=1e-6,
rope_theta=10000.0,
attention_bias=False,
attention_dropout=0.0,
pad_token_id=None,
**kwargs,
):
super().__init__()
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.head_dim = head_dim
self.num_key_value_heads = num_key_value_heads
self.rms_norm_eps = rms_norm_eps
self.rope_theta = rope_theta
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.pad_token_id = pad_token_id
class GemmaRMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.zeros(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
output = self._norm(x.float())
# Llama does x.to(float16) * w whilst Gemma is (x * w).to(float16)
# See https://github.com/huggingface/transformers/pull/29402
output = output * (1.0 + self.weight.float())
return output.type_as(x)
class GemmaRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.dim = dim # it is set to the head_dim
self.max_position_embeddings = max_position_embeddings
self.base = base
# Calculate the theta according to the formula theta_i = base^(2i/dim) where i = 0, 1, 2, ..., dim // 2
inv_freq = 1.0 / (
self.base
** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim)
)
self.register_buffer("inv_freq", tensor=inv_freq, persistent=False)
@torch.no_grad()
def forward(self, x, position_ids, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
self.inv_freq.to(x.device)
# Copy the inv_freq tensor for batch in the sequence
# inv_freq_expanded: [Batch_Size, Head_Dim // 2, 1]
inv_freq_expanded = (
self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
)
# position_ids_expanded: [Batch_Size, 1, Seq_Len]
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type
device_type = (
device_type
if isinstance(device_type, str) and device_type != "mps"
else "cpu"
)
with torch.autocast(device_type=device_type, enabled=False):
# Multiply each theta by the position (which is the argument of the sin and cos functions)
# freqs: [Batch_Size, Head_Dim // 2, 1] @ [Batch_Size, 1, Seq_Len] --> [Batch_Size, Seq_Len, Head_Dim // 2]
freqs = (
inv_freq_expanded.float() @ position_ids_expanded.float()
).transpose(1, 2)
# emb: [Batch_Size, Seq_Len, Head_Dim]
emb = torch.cat((freqs, freqs), dim=-1)
# cos, sin: [Batch_Size, Seq_Len, Head_Dim]
cos = emb.cos()
sin = emb.sin()
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
def rotate_half(x):
# Build the [-x2, x1, -x4, x3, ...] tensor for the sin part of the positional encoding.
x1 = x[..., : x.shape[-1] // 2] # Takes the first half of the last dimension
x2 = x[..., x.shape[-1] // 2 :] # Takes the second half of the last dimension
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
cos = cos.unsqueeze(unsqueeze_dim) # Add the head dimension
sin = sin.unsqueeze(unsqueeze_dim) # Add the head dimension
# Apply the formula (34) of the Rotary Positional Encoding paper.
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class GemmaMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
def forward(self, x):
# Equivalent to:
# y = self.gate_proj(x) # [Batch_Size, Seq_Len, Hidden_Size] -> [Batch_Size, Seq_Len, Intermediate_Size]
# y = torch.gelu(y, approximate="tanh") # [Batch_Size, Seq_Len, Intermediate_Size]
# j = self.up_proj(x) # [Batch_Size, Seq_Len, Hidden_Size] -> [Batch_Size, Seq_Len, Intermediate_Size]
# z = y * j # [Batch_Size, Seq_Len, Intermediate_Size]
# z = self.down_proj(z) # [Batch_Size, Seq_Len, Intermediate_Size] -> [Batch_Size, Seq_Len, Hidden_Size]
return self.down_proj(
nn.functional.gelu(self.gate_proj(x), approximate="tanh") * self.up_proj(x)
)
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(
batch, num_key_value_heads, n_rep, slen, head_dim
)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
class GemmaAttention(nn.Module):
def __init__(self, config: GemmaConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = config.head_dim
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.is_causal = True
assert self.hidden_size % self.num_heads == 0
self.q_proj = nn.Linear(
self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
self.hidden_size,
self.num_key_value_heads * self.head_dim,
bias=config.attention_bias,
)
self.v_proj = nn.Linear(
self.hidden_size,
self.num_key_value_heads * self.head_dim,
bias=config.attention_bias,
)
self.o_proj = nn.Linear(
self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias
)
self.rotary_emb = GemmaRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
kv_cache: Optional[KVCache] = None,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size() # [Batch_Size, Seq_Len, Hidden_Size]
# [Batch_Size, Seq_Len, Num_Heads_Q * Head_Dim]
query_states = self.q_proj(hidden_states)
# [Batch_Size, Seq_Len, Num_Heads_KV * Head_Dim]
key_states = self.k_proj(hidden_states)
# [Batch_Size, Seq_Len, Num_Heads_KV * Head_Dim]
value_states = self.v_proj(hidden_states)
# [Batch_Size, Num_Heads_Q, Seq_Len, Head_Dim]
query_states = query_states.view(
bsz, q_len, self.num_heads, self.head_dim
).transpose(1, 2)
# [Batch_Size, Num_Heads_KV, Seq_Len, Head_Dim]
key_states = key_states.view(
bsz, q_len, self.num_key_value_heads, self.head_dim
).transpose(1, 2)
# [Batch_Size, Num_Heads_KV, Seq_Len, Head_Dim]
value_states = value_states.view(
bsz, q_len, self.num_key_value_heads, self.head_dim
).transpose(1, 2)
# [Batch_Size, Seq_Len, Head_Dim], [Batch_Size, Seq_Len, Head_Dim]
cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
# [Batch_Size, Num_Heads_Q, Seq_Len, Head_Dim], [Batch_Size, Num_Heads_KV, Seq_Len, Head_Dim]
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin
)
if kv_cache is not None:
key_states, value_states = kv_cache.update(
key_states, value_states, self.layer_idx
)
# Repeat the key and values to match the number of heads of the query
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
# Perform the calculation as usual, Q * K^T / sqrt(head_dim). Shape: [Batch_Size, Num_Heads_Q, Seq_Len_Q, Seq_Len_KV]
attn_weights = torch.matmul(
query_states, key_states.transpose(2, 3)
) / math.sqrt(self.head_dim)
assert attention_mask is not None
attn_weights = attn_weights + attention_mask
# Apply the softmax
# [Batch_Size, Num_Heads_Q, Seq_Len_Q, Seq_Len_KV]
attn_weights = nn.functional.softmax(
attn_weights, dim=-1, dtype=torch.float32
).to(query_states.dtype)
# Apply the dropout
attn_weights = nn.functional.dropout(
attn_weights, p=self.attention_dropout, training=self.training
)
# Multiply by the values. [Batch_Size, Num_Heads_Q, Seq_Len_Q, Seq_Len_KV] x [Batch_Size, Num_Heads_KV, Seq_Len_KV, Head_Dim] -> [Batch_Size, Num_Heads_Q, Seq_Len_Q, Head_Dim]
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
# Make sure the sequence length is the second dimension. # [Batch_Size, Num_Heads_Q, Seq_Len_Q, Head_Dim] -> [Batch_Size, Seq_Len_Q, Num_Heads_Q, Head_Dim]
attn_output = attn_output.transpose(1, 2).contiguous()
# Concatenate all the heads together. [Batch_Size, Seq_Len_Q, Num_Heads_Q, Head_Dim] -> [Batch_Size, Seq_Len_Q, Num_Heads_Q * Head_Dim]
attn_output = attn_output.view(bsz, q_len, -1)
# Multiply by W_o. [Batch_Size, Seq_Len_Q, Hidden_Size]
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights
class GemmaDecoderLayer(nn.Module):
def __init__(self, config: GemmaConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = GemmaAttention(config=config, layer_idx=layer_idx)
self.mlp = GemmaMLP(config)
self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = GemmaRMSNorm(
config.hidden_size, eps=config.rms_norm_eps
)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
kv_cache: Optional[KVCache] = None,
) -> Tuple[
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
]:
residual = hidden_states
# [Batch_Size, Seq_Len, Hidden_Size]
hidden_states = self.input_layernorm(hidden_states)
# [Batch_Size, Seq_Len, Hidden_Size]
(
hidden_states,
_,
) = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
kv_cache=kv_cache,
)
# [Batch_Size, Seq_Len, Hidden_Size]
hidden_states = residual + hidden_states
# [Batch_Size, Seq_Len, Hidden_Size]
residual = hidden_states
# [Batch_Size, Seq_Len, Hidden_Size]
hidden_states = self.post_attention_layernorm(hidden_states)
# [Batch_Size, Seq_Len, Hidden_Size]
hidden_states = self.mlp(hidden_states)
# [Batch_Size, Seq_Len, Hidden_Size]
hidden_states = residual + hidden_states
return hidden_states
class GemmaModel(nn.Module):
def __init__(self, config: GemmaConfig):
super().__init__()
self.config = 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(
[
GemmaDecoderLayer(config, layer_idx)
for layer_idx in range(config.num_hidden_layers)
]
)
self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def get_input_embeddings(self):
return self.embed_tokens
# Ignore copy
def forward(
self,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
kv_cache: Optional[KVCache] = None,
) -> torch.FloatTensor:
# [Batch_Size, Seq_Len, Hidden_Size]
hidden_states = inputs_embeds
# [Batch_Size, Seq_Len, Hidden_Size]
normalizer = torch.tensor(
self.config.hidden_size**0.5, dtype=hidden_states.dtype
)
hidden_states = hidden_states * normalizer
for decoder_layer in self.layers:
# [Batch_Size, Seq_Len, Hidden_Size]
hidden_states = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
kv_cache=kv_cache,
)
# [Batch_Size, Seq_Len, Hidden_Size]
hidden_states = self.norm(hidden_states)
# [Batch_Size, Seq_Len, Hidden_Size]
return hidden_states
class GemmaForCausalLM(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.model = GemmaModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
def get_input_embeddings(self):
return self.model.embed_tokens
def tie_weights(self):
self.lm_head.weight = self.model.embed_tokens.weight
def forward(
self,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
kv_cache: Optional[KVCache] = None,
) -> Tuple:
# input_embeds: [Batch_Size, Seq_Len, Hidden_Size]
# outputs: [Batch_Size, Seq_Len, Hidden_Size]
outputs = self.model(
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
kv_cache=kv_cache,
)
hidden_states = outputs
logits = self.lm_head(hidden_states)
logits = logits.float()
return_data = {
"logits": logits,
}
if kv_cache is not None:
# Return the updated cache
return_data["kv_cache"] = kv_cache
return return_data
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