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# -*- coding: utf-8 -*-
from typing import Any, Dict, List, Optional, Tuple
import torch
from torch import nn
from transformers import LlamaConfig
from vllm.model_executor.input_metadata import InputMetadata
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention import PagedAttention
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (LinearMethodBase,
MergedColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
VocabParallelEmbedding, ParallelLMHead)
from vllm.model_executor.parallel_utils.parallel_state import (
get_tensor_model_parallel_world_size)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.model_executor.weight_utils import (default_weight_loader,
hf_model_weights_iterator)
from vllm.sequence import SamplerOutput
KVCache = Tuple[torch.Tensor, torch.Tensor]
class InternLMMLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(
hidden_size, [intermediate_size] * 2,
bias=False,
linear_method=linear_method)
self.down_proj = RowParallelLinear(intermediate_size,
hidden_size,
bias=False,
linear_method=linear_method)
if hidden_act != "silu":
raise ValueError(f"Unsupported activation: {hidden_act}. "
"Only silu is supported for now.")
self.act_fn = SiluAndMul()
def forward(self, x):
gate_up, _ = self.gate_up_proj(x)
x = self.act_fn(gate_up)
x, _ = self.down_proj(x)
return x
class InternLMAttention(nn.Module):
def __init__(
self,
hidden_size: int,
num_heads: int,
bias: bool,
rope_theta: float = 10000,
max_position_embeddings: int = 8192,
linear_method: Optional[LinearMethodBase] = None,
rope_scaling: Optional[Dict[str, Any]] = None,
):
super().__init__()
self.hidden_size = hidden_size
tensor_model_parallel_world_size = (
get_tensor_model_parallel_world_size())
self.total_num_heads = num_heads
assert self.total_num_heads % tensor_model_parallel_world_size == 0
self.num_heads = (self.total_num_heads //
tensor_model_parallel_world_size)
self.head_dim = hidden_size // self.total_num_heads
self.scaling = self.head_dim**-0.5
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.qkv_proj = QKVParallelLinear(
hidden_size,
self.head_dim,
self.total_num_heads,
bias=bias,
linear_method=linear_method,
)
self.o_proj = RowParallelLinear(
self.total_num_heads * self.head_dim,
hidden_size,
bias=bias,
linear_method=linear_method,
)
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=self.max_position_embeddings,
base=self.rope_theta,
rope_scaling=rope_scaling,
)
self.attn = PagedAttention(self.num_heads, self.head_dim, self.scaling)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
kv_cache: KVCache,
input_metadata: InputMetadata,
) -> torch.Tensor:
qkv, _ = self.qkv_proj(hidden_states)
q, k, v = qkv.chunk(chunks=3, dim=-1)
q, k = self.rotary_emb(positions, q, k)
k_cache, v_cache = kv_cache
attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
output, _ = self.o_proj(attn_output)
return output
class InternLMDecoderLayer(nn.Module):
def __init__(
self,
config: LlamaConfig,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
self.hidden_size = config.hidden_size
rope_theta = getattr(config, "rope_theta", 10000)
max_position_embeddings = getattr(config, "max_position_embeddings",
8192)
self.self_attn = InternLMAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
bias=config.bias,
rope_theta=rope_theta,
max_position_embeddings=max_position_embeddings,
linear_method=linear_method,
rope_scaling=getattr(config, "rope_scaling", None),
)
self.mlp = InternLMMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
linear_method=linear_method,
)
self.input_layernorm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
kv_cache: KVCache,
input_metadata: InputMetadata,
residual: Optional[torch.Tensor],
) -> Tuple[torch.Tensor, torch.Tensor]:
# Self Attention
if residual is None:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
else:
hidden_states, residual = self.input_layernorm(
hidden_states, residual)
hidden_states = self.self_attn(
positions=positions,
hidden_states=hidden_states,
kv_cache=kv_cache,
input_metadata=input_metadata,
)
# Fully Connected
hidden_states, residual = self.post_attention_layernorm(
hidden_states, residual)
hidden_states = self.mlp(hidden_states)
return hidden_states, residual
class InternLMModel(nn.Module):
def __init__(
self,
config: LlamaConfig,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
vocab_size = ((config.vocab_size + 63) // 64) * 64
self.embed_tokens = VocabParallelEmbedding(
vocab_size,
config.hidden_size,
)
self.layers = nn.ModuleList([
InternLMDecoderLayer(config, linear_method)
for _ in range(config.num_hidden_layers)
])
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[KVCache],
input_metadata: InputMetadata,
) -> torch.Tensor:
hidden_states = self.embed_tokens(input_ids)
residual = None
for i in range(len(self.layers)):
layer = self.layers[i]
hidden_states, residual = layer(
positions,
hidden_states,
kv_caches[i],
input_metadata,
residual,
)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
class InternLMForCausalLM(nn.Module):
def __init__(
self,
config,
linear_method: Optional[LinearMethodBase] = None,
):
super().__init__()
self.config = config
self.linear_method = linear_method
self.model = InternLMModel(config, linear_method)
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
self.sampler = Sampler(config.vocab_size)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[KVCache],
input_metadata: InputMetadata,
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, kv_caches,
input_metadata)
return hidden_states
def sample(
self,
hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[SamplerOutput]:
next_tokens = self.sampler(self.lm_head.weight, hidden_states,
sampling_metadata)
return next_tokens
def load_weights(self,
model_name_or_path: str,
cache_dir: Optional[str] = None,
load_format: str = "auto",
revision: Optional[str] = None):
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("qkv_proj", "q_proj", "q"),
("qkv_proj", "k_proj", "k"),
("qkv_proj", "v_proj", "v"),
("gate_up_proj", "gate_proj", 0),
("gate_up_proj", "up_proj", 1),
]
params_dict = dict(self.named_parameters())
for name, loaded_weight in hf_model_weights_iterator(
model_name_or_path, cache_dir, load_format, revision):
if "rotary_emb.inv_freq" in name:
continue
for (param_name, weight_name, shard_id) in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
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