# coding=utf-8 # Adapted from # https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_phi.py # Copyright 2023 The PygmalionAI team. # Copyright 2023 The vLLM team. # Copyright (c) Microsoft Corporation. # Licensed under the MIT license. # # BSD 3-Clause License # # Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """Inference-only Phi model compatible with HuggingFace weights.""" from typing import List, Optional, Tuple import torch from torch import nn from transformers import PretrainedConfig from aphrodite.modeling.metadata import InputMetadata from aphrodite.modeling.layers.activation import get_act_fn from aphrodite.modeling.layers.attention import PagedAttention from aphrodite.modeling.layers.linear import ( ColumnParallelLinear, LinearMethodBase, QKVParallelLinear, RowParallelLinear, ) from aphrodite.modeling.layers.rotary_embedding import get_rope from aphrodite.modeling.layers.sampler import Sampler, QuantSampler from aphrodite.modeling.layers.vocab_parallel_embedding import ( VocabParallelEmbedding, ParallelLMHead, ) from aphrodite.modeling.megatron.parallel_state import ( get_tensor_model_parallel_world_size, ) from aphrodite.modeling.sampling_metadata import SamplingMetadata from aphrodite.modeling.hf_downloader import ( default_weight_loader, hf_model_weights_iterator, ) from aphrodite.common.sequence import SamplerOutput KVCache = Tuple[torch.Tensor, torch.Tensor] class PhiAttention(nn.Module): def __init__( self, config: PretrainedConfig, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() self.total_num_heads = config.num_attention_heads self.hidden_size = config.hidden_size self.head_size = self.hidden_size // self.total_num_heads tensor_model_parallel_world_size = ( get_tensor_model_parallel_world_size()) assert self.total_num_heads % tensor_model_parallel_world_size == 0 self.num_heads = (self.total_num_heads // tensor_model_parallel_world_size) # pylint: disable=C0103 if (linear_method is not None and not linear_method.quant_config.merge_weight()): self.merge_weight = False self.q_proj = ColumnParallelLinear( self.hidden_size, self.hidden_size, bias=True, linear_method=linear_method, ) self.k_proj = ColumnParallelLinear( self.hidden_size, self.hidden_size, bias=True, linear_method=linear_method, ) self.v_proj = ColumnParallelLinear( self.hidden_size, self.hidden_size, bias=True, linear_method=linear_method, ) else: self.merge_weight = True self.qkv_proj = QKVParallelLinear( self.hidden_size, self.head_size, self.total_num_heads, bias=True, linear_method=linear_method, ) self.dense = RowParallelLinear( self.hidden_size, self.hidden_size, linear_method=linear_method, ) scaling = self.head_size**-0.5 rotary_dim = int(config.partial_rotary_factor * (config.hidden_size // config.num_attention_heads)) assert rotary_dim % 2 == 0 # pylint: disable=C0301 # Refer to: # https://huggingface.co/microsoft/phi-1_5/blob/d212a789620c380ff32ca1d1ee9943a777360987/modeling_phi.py#L518 rope_theta = 10000 max_position_embeddings = getattr(config, "n_positions", 2048) is_neox_style = (True if linear_method is None or linear_method.quant_config.rope_style() is None else linear_method.quant_config.rope_style()) self.rotary_emb = get_rope( self.head_size, rotary_dim=rotary_dim, max_position=max_position_embeddings, base=rope_theta, is_neox_style=is_neox_style, ) self.attn = PagedAttention(self.num_heads, self.head_size, scaling) def forward( self, position_ids: torch.Tensor, hidden_states: torch.Tensor, kv_cache: KVCache, input_metadata: InputMetadata, ) -> torch.Tensor: if self.merge_weight: qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.chunk(chunks=3, dim=-1) else: q, _ = self.q_proj(hidden_states) k, _ = self.k_proj(hidden_states) v, _ = self.v_proj(hidden_states) q, k = self.rotary_emb(position_ids, q, k) k_cache, v_cache = kv_cache attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata) output, _ = self.dense(attn_output) return output class PhiMLP(nn.Module): def __init__( self, config: PretrainedConfig, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() n_inner = getattr(config, "n_inner", None) n_inner = n_inner if n_inner is not None else 4 * config.hidden_size self.fc1 = ColumnParallelLinear( config.hidden_size, n_inner, linear_method=linear_method, ) self.fc2 = RowParallelLinear( n_inner, config.hidden_size, linear_method=linear_method, ) quant_config = getattr(linear_method, "quant_config", None) self.act = get_act_fn(config.hidden_act, quant_config, n_inner) def forward(self, hidden_states): hidden_states, _ = self.fc1(hidden_states) hidden_states = self.act(hidden_states) hidden_states, _ = self.fc2(hidden_states) return hidden_states class PhiLayer(nn.Module): def __init__( self, config: PretrainedConfig, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.self_attn = PhiAttention(config, linear_method) self.mlp = PhiMLP(config, linear_method) def forward( self, position_ids: torch.Tensor, hidden_states: torch.Tensor, kv_cache: KVCache, input_metadata: InputMetadata, ) -> torch.Tensor: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) attn_outputs = self.self_attn( position_ids=position_ids, hidden_states=hidden_states, kv_cache=kv_cache, input_metadata=input_metadata, ) feed_forward_hidden_states = self.mlp(hidden_states) hidden_states = attn_outputs + feed_forward_hidden_states + residual return hidden_states class PhiModel(nn.Module): def __init__( self, config: PretrainedConfig, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() self.config = config self.linear_method = linear_method self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size, linear_method=linear_method) self.layers = nn.ModuleList([ PhiLayer(config, linear_method) for _ in range(config.num_hidden_layers) ]) self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_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) for i in range(self.config.num_hidden_layers): layer = self.layers[i] hidden_states = layer( positions, hidden_states, kv_caches[i], input_metadata, ) hidden_states = self.final_layernorm(hidden_states) return hidden_states class PhiForCausalLM(nn.Module): def __init__( self, config: PretrainedConfig, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() self.config = config self.linear_method = linear_method self.model = PhiModel(config, linear_method) self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, bias=True, linear_method=linear_method, ) self.sampler = Sampler(config.vocab_size) self.quant_sampler = QuantSampler(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]: if (self.linear_method is not None and not self.linear_method.quant_config.merge_weight()): next_tokens = self.quant_sampler(self.lm_head(hidden_states), sampling_metadata) else: 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"), ] if (self.linear_method is not None and not self.linear_method.quant_config.merge_weight()): stacked_params_mapping = [] params_dict = dict(self.named_parameters()) for name, loaded_weight in hf_model_weights_iterator( model_name_or_path, cache_dir, load_format, revision, self.config): 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 # pylint: disable=E1136 param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight)