[claudesquad] update from 'add-llama-1' on 10 Jan 26 21:03 CST
This commit is contained in:
Zijie Tian
194
nanovllm/models/llama.py
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194
nanovllm/models/llama.py
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import torch
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from torch import nn
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import torch.distributed as dist
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from nanovllm.layers.activation import SiluAndMul
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from nanovllm.layers.attention import Attention
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from nanovllm.layers.layernorm import RMSNorm
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from nanovllm.layers.linear import QKVParallelLinear, MergedColumnParallelLinear, RowParallelLinear
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from nanovllm.layers.rotary_embedding import get_rope
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from nanovllm.layers.embed_head import VocabParallelEmbedding, ParallelLMHead
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from nanovllm.models.registry import register_model
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class LlamaAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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max_position: int = 4096 * 32,
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head_dim: int | None = None,
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rope_theta: float = 10000,
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rope_scaling: dict | None = None,
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) -> None:
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super().__init__()
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tp_size = dist.get_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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assert self.total_num_kv_heads % tp_size == 0
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self.num_kv_heads = self.total_num_kv_heads // tp_size
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self.head_dim = head_dim or hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim ** -0.5
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=False, # Llama has no attention bias
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position,
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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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self.num_kv_heads,
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q = q.view(-1, self.num_heads, self.head_dim)
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k = k.view(-1, self.num_kv_heads, self.head_dim)
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v = v.view(-1, self.num_kv_heads, self.head_dim)
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# Llama has no q_norm/k_norm
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q, k = self.rotary_emb(positions, q, k)
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o = self.attn(q, k, v)
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output = self.o_proj(o.flatten(1, -1))
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return output
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class LlamaMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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)
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate_up = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x = self.down_proj(x)
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return x
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class LlamaDecoderLayer(nn.Module):
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def __init__(self, config) -> None:
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super().__init__()
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self.self_attn = LlamaAttention(
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hidden_size=config.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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max_position=config.max_position_embeddings,
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head_dim=getattr(config, 'head_dim', None),
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rope_theta=getattr(config, "rope_theta", 10000),
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rope_scaling=getattr(config, "rope_scaling", None),
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)
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self.mlp = LlamaMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: torch.Tensor | None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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hidden_states, residual = self.input_layernorm(hidden_states), hidden_states
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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hidden_states = self.self_attn(positions, hidden_states)
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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class LlamaModel(nn.Module):
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def __init__(self, config) -> None:
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super().__init__()
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self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size)
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self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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) -> torch.Tensor:
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hidden_states = self.embed_tokens(input_ids)
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residual = None
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for layer in self.layers:
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hidden_states, residual = layer(positions, hidden_states, residual)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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@register_model("LlamaForCausalLM")
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class LlamaForCausalLM(nn.Module):
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packed_modules_mapping = {
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"q_proj": ("qkv_proj", "q"),
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"k_proj": ("qkv_proj", "k"),
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"v_proj": ("qkv_proj", "v"),
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"gate_proj": ("gate_up_proj", 0),
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"up_proj": ("gate_up_proj", 1),
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}
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def __init__(self, config) -> None:
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super().__init__()
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self.model = LlamaModel(config)
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self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
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if getattr(config, 'tie_word_embeddings', False):
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self.lm_head.weight.data = self.model.embed_tokens.weight.data
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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) -> torch.Tensor:
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return self.model(input_ids, positions)
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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return self.lm_head(hidden_states)
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