# coding=utf-8 # Adapted from # https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_phi.py # 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. 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"""Inference-only Phi-1.5 model compatible with HuggingFace weights.""" from typing import Iterable, List, Optional, Tuple import torch from torch import nn from transformers import PhiConfig from vllm.attention import Attention, AttentionMetadata from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import get_act_fn from vllm.model_executor.layers.linear import (ColumnParallelLinear, 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 from .interfaces import SupportsLoRA class PhiAttention(nn.Module): def __init__(self, config: PhiConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = 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 self.qkv_proj = QKVParallelLinear( self.hidden_size, self.head_size, self.total_num_heads, bias=True, quant_config=quant_config, ) self.dense = RowParallelLinear( self.hidden_size, self.hidden_size, quant_config=quant_config, ) 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) self.rotary_emb = get_rope( self.head_size, rotary_dim=rotary_dim, max_position=max_position_embeddings, base=rope_theta, ) self.attn = Attention(self.num_heads, self.head_size, scaling, cache_config=cache_config, quant_config=quant_config) def forward( self, position_ids: 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.chunk(chunks=3, dim=-1) q, k = self.rotary_emb(position_ids, q, k) attn_output = self.attn(q, k, v, kv_cache, attn_metadata) output, _ = self.dense(attn_output) return output class PhiMLP(nn.Module): def __init__(self, config: PhiConfig, quant_config: Optional[QuantizationConfig] = 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, quant_config=quant_config, ) self.fc2 = RowParallelLinear( n_inner, config.hidden_size, quant_config=quant_config, ) 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: PhiConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None): super().__init__() self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.self_attn = PhiAttention(config, cache_config, quant_config) self.mlp = PhiMLP(config, quant_config) def forward( self, position_ids: torch.Tensor, hidden_states: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: AttentionMetadata, ) -> 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, attn_metadata=attn_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: PhiConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None): super().__init__() self.config = config self.quant_config = quant_config self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size) self.layers = nn.ModuleList([ PhiLayer(config, cache_config, quant_config) 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[torch.Tensor], attn_metadata: AttentionMetadata, ) -> 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], attn_metadata, ) hidden_states = self.final_layernorm(hidden_states) return hidden_states class PhiForCausalLM(nn.Module, SupportsLoRA): packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ] } # LoRA specific attributes supported_lora_modules = [ "qkv_proj", "dense", "fc1", "fc2", ] embedding_modules = {} embedding_padding_modules = [] def __init__( self, config: PhiConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, lora_config: Optional[LoRAConfig] = None, ): super().__init__() self.config = config # lm_head use bias, cannot share word embeddings assert not config.tie_word_embeddings self.lora_config = lora_config self.quant_config = quant_config self.model = PhiModel(config, cache_config, quant_config) self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size, bias=True, quant_config=quant_config) 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, self.lm_head.bias) 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") ] params_dict = dict(self.named_parameters()) 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) # 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)