# coding=utf-8 # Copyright 2024 BigCode and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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. """ PyTorch Starcoder2 model.""" from typing import Iterable, List, Optional, Tuple import torch from torch import nn from transformers import Starcoder2Config from aphrodite.attention import Attention, AttentionMetadata from aphrodite.common.config import CacheConfig from aphrodite.common.sequence import IntermediateTensors, SamplerOutput from aphrodite.distributed import get_tensor_model_parallel_world_size from aphrodite.modeling.layers.activation import get_act_fn from aphrodite.modeling.layers.linear import (ColumnParallelLinear, QKVParallelLinear, RowParallelLinear) from aphrodite.modeling.layers.logits_processor import LogitsProcessor from aphrodite.modeling.layers.rotary_embedding import get_rope from aphrodite.modeling.layers.sampler import Sampler from aphrodite.modeling.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from aphrodite.modeling.model_loader.weight_utils import default_weight_loader from aphrodite.modeling.sampling_metadata import SamplingMetadata from aphrodite.quantization.base_config import QuantizationConfig class Starcoder2Attention(nn.Module): def __init__(self, config: Starcoder2Config, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = 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 assert self.total_num_heads % tp_size == 0 self.num_heads = self.total_num_heads // tp_size self.total_num_kv_heads = config.num_key_value_heads if self.total_num_kv_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_kv_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_kv_heads == 0 self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) self.head_dim = self.hidden_size // self.total_num_heads self.q_size = self.num_heads * self.head_dim self.kv_size = self.num_kv_heads * self.head_dim self.scaling = self.head_dim**-0.5 self.rope_theta = config.rope_theta self.max_position_embeddings = config.max_position_embeddings self.use_bias = config.use_bias self.qkv_proj = QKVParallelLinear( self.hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=self.use_bias, quant_config=quant_config, ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, self.hidden_size, bias=self.use_bias, quant_config=quant_config, ) self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=self.max_position_embeddings, base=int(self.rope_theta), is_neox_style=True, ) self.attn = Attention(self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_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 Starcoder2MLP(nn.Module): def __init__(self, config: Starcoder2Config, quant_config: Optional[QuantizationConfig] = None): super().__init__() self.c_fc = ColumnParallelLinear( config.hidden_size, config.intermediate_size, bias=config.use_bias, quant_config=quant_config, ) self.c_proj = RowParallelLinear( config.intermediate_size, config.hidden_size, bias=config.use_bias, quant_config=quant_config, ) self.act = get_act_fn(config.hidden_act, quant_config, config.intermediate_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states, _ = self.c_fc(hidden_states) hidden_states = self.act(hidden_states) hidden_states, _ = self.c_proj(hidden_states) return hidden_states class Starcoder2DecoderLayer(nn.Module): def __init__(self, config: Starcoder2Config, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None): super().__init__() self.hidden_size = config.hidden_size self.self_attn = Starcoder2Attention(config, cache_config, quant_config=quant_config) self.mlp = Starcoder2MLP(config, quant_config=quant_config) self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon) self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: AttentionMetadata, ) -> 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 class Starcoder2Model(nn.Module): def __init__(self, config: Starcoder2Config, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None): super().__init__() self.config = config self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size # TODO: consider padding_idx (currently removed) self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size) self.layers = nn.ModuleList([ Starcoder2DecoderLayer(config, cache_config, quant_config=quant_config) for _ in range(config.num_hidden_layers) ]) self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon) 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 = layer(positions, hidden_states, kv_caches[i], attn_metadata) hidden_states = self.norm(hidden_states) return hidden_states class Starcoder2ForCausalLM(nn.Module): def __init__(self, config: Starcoder2Config, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None): super().__init__() self.config = config self.model = Starcoder2Model(config, cache_config, quant_config=quant_config) self.vocab_size = config.vocab_size self.unpadded_vocab_size = config.vocab_size if config.tie_word_embeddings: self.lm_head = self.model.embed_tokens else: self.unpadded_vocab_size = config.vocab_size self.lm_head = ParallelLMHead( self.unpadded_vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, padding_size=DEFAULT_VOCAB_PADDING_SIZE, quant_config=quant_config, ) self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, 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: Optional[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"), ] params_dict = dict(self.named_parameters(remove_duplicate=False)) for name, loaded_weight in weights: 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) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: if self.config.tie_word_embeddings and "lm_head.weight" in name: continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight)