# coding=utf-8 # Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. # All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # This code is based off the following work: # https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/modeling_stablelm_epoch.py # https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/config.json """Inference-only StabeLM (https://github.com/Stability-AI/StableLM) model compatible with HuggingFace weights.""" from typing import Iterable, List, Optional, Tuple import torch from torch import nn from transformers import PretrainedConfig from vllm.attention import Attention, AttentionMetadata from vllm.config import CacheConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.sampler import Sampler, SamplerOutput from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.sequence import IntermediateTensors class StablelmMLP(nn.Module): def __init__(self, config: PretrainedConfig, quant_config: Optional[QuantizationConfig] = None) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.gate_up_proj = MergedColumnParallelLinear( config.hidden_size, [config.intermediate_size] * 2, bias=False, quant_config=quant_config) self.down_proj = RowParallelLinear(config.intermediate_size, config.hidden_size, bias=False) self.act_fn = SiluAndMul() def forward(self, x: torch.Tensor) -> torch.Tensor: gate_up, _ = self.gate_up_proj(x) x = self.act_fn(gate_up) x, _ = self.down_proj(x) return x class StablelmAttention(nn.Module): def __init__(self, config: PretrainedConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size tp_size = get_tensor_model_parallel_world_size() self.total_num_heads = config.num_attention_heads self.num_heads = self.total_num_heads // tp_size self.total_num_key_value_heads = config.num_key_value_heads if self.total_num_key_value_heads >= tp_size: # Number of KV heads is greater than TP size, so we partition # the KV heads across multiple tensor parallel GPUs. assert self.total_num_key_value_heads % tp_size == 0 else: # Number of KV heads is less than TP size, so we replicate # the KV heads across multiple tensor parallel GPUs. assert tp_size % self.total_num_key_value_heads == 0 self.num_key_value_heads = max( 1, self.total_num_key_value_heads // tp_size) self.head_dim = self.hidden_size // self.total_num_heads self.max_position_embeddings = config.max_position_embeddings rope_pct = getattr(config, "rope_pct", getattr(config, "partial_rotary_factor", 1)) self.rotary_ndims = int(self.head_dim * rope_pct) self.scaling = self.head_dim**-0.5 self.q_size = self.num_heads * self.head_dim self.kv_size = self.num_key_value_heads * self.head_dim self.qkv_bias = getattr(config, "use_qkv_bias", False) if (self.head_dim * self.num_heads * tp_size) != self.hidden_size: raise ValueError(f"hidden_size must be divisible by num_heads " f"(got `hidden_size`: {self.hidden_size}" f" and `num_heads`: {self.num_heads}).") self.qkv_proj = QKVParallelLinear(self.hidden_size, self.head_dim, self.total_num_heads, self.total_num_key_value_heads, self.qkv_bias, quant_config=quant_config) self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim, self.hidden_size, bias=False, quant_config=quant_config) self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.rotary_ndims, max_position=self.config.max_position_embeddings, base=self.config.rope_theta, ) self.attn = Attention(self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_key_value_heads, cache_config=cache_config, quant_config=quant_config) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: AttentionMetadata, ) -> torch.Tensor: qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v, kv_cache, attn_metadata) output, _ = self.o_proj(attn_output) return output class StablelmDecoderLayer(nn.Module): def __init__( self, config: PretrainedConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ) -> None: super().__init__() self.self_attn = StablelmAttention(config, cache_config, quant_config) self.mlp = StablelmMLP(config, quant_config) norm_eps = getattr(config, "norm_eps", getattr(config, "layer_norm_eps", 1e-05)) self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=norm_eps) self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=norm_eps) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: AttentionMetadata, ) -> Tuple[torch.Tensor, torch.Tensor]: # Self Attention residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, kv_cache=kv_cache, attn_metadata=attn_metadata, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states return hidden_states, residual class StableLMEpochModel(nn.Module): def __init__(self, config: PretrainedConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None) -> None: super().__init__() self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, ) self.layers = nn.ModuleList([ StablelmDecoderLayer(config, cache_config, quant_config) for _ in range(config.num_hidden_layers) ]) norm_eps = getattr(config, "norm_eps", getattr(config, "layer_norm_eps", 1e-05)) self.norm = nn.LayerNorm(config.hidden_size, eps=norm_eps) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, ) -> torch.Tensor: hidden_states = self.embed_tokens(input_ids) for i in range(len(self.layers)): layer = self.layers[i] hidden_states, residual = layer( positions, hidden_states, kv_caches[i], attn_metadata, ) hidden_states = self.norm(hidden_states) return hidden_states class StablelmForCausalLM(nn.Module): def __init__( self, config: PretrainedConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ) -> None: super().__init__() self.config = config self.quant_config = quant_config self.model = StableLMEpochModel(config, cache_config, quant_config) self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size, quant_config=quant_config) if self.config.tie_word_embeddings: self.lm_head.weight = self.model.embed_tokens.weight self.logits_processor = LogitsProcessor(config.vocab_size) self.sampler = Sampler() def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, intermediate_tensors: Optional[IntermediateTensors] = None, ) -> torch.Tensor: hidden_states = self.model(input_ids, positions, kv_caches, attn_metadata) return hidden_states def compute_logits( self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[torch.Tensor]: logits = self.logits_processor(self.lm_head, hidden_states, sampling_metadata) return logits def sample( self, logits: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[SamplerOutput]: next_tokens = self.sampler(logits, sampling_metadata) return next_tokens def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): 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 weights: if "rotary_emb.inv_freq" in name: continue if ("rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name): # Models trained using ColossalAI may include these tensors in # the checkpoint. Skip them. 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)