# coding=utf-8 # Copyright 2024 Cohere 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. # This file is based on the LLama model definition file in transformers """PyTorch Cohere model.""" from typing import List, Optional, Tuple import torch import torch.utils.checkpoint from torch import nn from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS from transformers import CohereConfig from aphrodite.modeling.metadata import InputMetadata from aphrodite.modeling.layers.activation import SiluAndMul from aphrodite.modeling.layers.attention import PagedAttention as Attention from aphrodite.modeling.layers.linear import ( LinearMethodBase, MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear, ) from aphrodite.modeling.layers.rotary_embedding import get_rope from aphrodite.modeling.layers.sampler import Sampler from aphrodite.modeling.layers.vocab_parallel_embedding import ( VocabParallelEmbedding) 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 # limitations under the License. """ Cohere model configuration""" KVCache = Tuple[torch.Tensor, torch.Tensor] class LayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-5, bias=False): super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.bias = nn.Parameter(torch.zeros(hidden_size)) if bias else None self.variance_epsilon = eps def forward(self, hidden_states, residuals=None): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) mean = hidden_states.mean(-1, keepdim=True) variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True) hidden_states = (hidden_states - mean) * torch.rsqrt(variance + self.variance_epsilon) hidden_states = self.weight.to(torch.float32) * hidden_states if self.bias is not None: hidden_states = hidden_states + self.bias.to(torch.float32) return hidden_states.to(input_dtype), residuals ALL_LAYERNORM_LAYERS.append(LayerNorm) # Copied from transformers.models.llama.modeling_llama.LlamaMLP Llama->Cohere class CohereMLP(nn.Module): def __init__( self, config, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.gate_up_proj = MergedColumnParallelLinear( self.hidden_size, [self.intermediate_size] * 2, bias=False, linear_method=linear_method, ) self.down_proj = RowParallelLinear( self.intermediate_size, self.hidden_size, bias=False, linear_method=linear_method, ) 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 CohereAttention(nn.Module): def __init__( self, config: CohereConfig, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() tp_size = get_tensor_model_parallel_world_size() self.config = config self.attention_dropout = config.attention_dropout self.hidden_size = config.hidden_size self.total_num_heads = config.num_attention_heads self.num_heads = self.total_num_heads // tp_size self.head_dim = self.hidden_size // self.total_num_heads 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.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.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.rope_scaling = getattr(config, "rope_scaling", None) self.is_causal = True self.qkv_proj = QKVParallelLinear( self.hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=False, linear_method=linear_method, ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, self.hidden_size, bias=False, 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=self.rope_scaling, is_neox_style=False, ) self.attn = Attention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, ) 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.split([self.q_size, self.kv_size, self.kv_size], 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 CohereDecoderLayer(nn.Module): def __init__(self, config: CohereConfig, linear_method: Optional[LinearMethodBase] = None): super().__init__() self.hidden_size = config.hidden_size self.self_attn = CohereAttention(config, linear_method=linear_method) self.mlp = CohereMLP(config, linear_method=linear_method) self.input_layernorm = LayerNorm(config.hidden_size, eps=config.layer_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 residual = hidden_states hidden_states, residual = self.input_layernorm(hidden_states, residual) hidden_states_attention = self.self_attn( positions=positions, hidden_states=hidden_states, kv_cache=kv_cache, input_metadata=input_metadata, ) hidden_states_mlp = self.mlp(hidden_states) # Add everything together hidden_states = residual + hidden_states_attention + hidden_states_mlp return hidden_states, residual class CohereModel(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`CohereDecoderLayer`] Args: config: CohereConfig """ def __init__( self, config: CohereConfig, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() self.config = config self.vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size) self.layers = nn.ModuleList([ CohereDecoderLayer(config, linear_method=linear_method) for _ in range(config.num_hidden_layers) ]) self.norm = 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) 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 CohereForCausalLM(nn.Module): def __init__( self, config: CohereConfig, linear_method: Optional[LinearMethodBase] = None, ) -> None: super().__init__() self.config = config self.unpadded_vocab_size = config.vocab_size self.linear_method = linear_method self.model = CohereModel(config, linear_method) self.sampler = Sampler(config.vocab_size) @torch.no_grad() 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.model.embed_tokens.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()) loaded_params = set() for name, loaded_weight in hf_model_weights_iterator( model_name_or_path, cache_dir, load_format, revision): for param_name, shard_name, shard_id in stacked_params_mapping: if shard_name not in name: continue name = name.replace(shard_name, param_name) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name)