# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # 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 collections.abc import Iterable from itertools import islice import torch from torch import nn from transformers import Cohere2Config, CohereConfig from vllm.attention.layer import Attention from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig from vllm.distributed import get_pp_group, 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 import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name, row_parallel_weight_loader, ) from vllm.model_executor.utils import set_weight_attrs from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant from .utils import ( AutoWeightsLoader, extract_layer_index, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix, ) @torch.compile(backend=current_platform.simple_compile_backend) def layer_norm_func(hidden_states, weight, variance_epsilon): 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 + variance_epsilon) hidden_states = weight.to(torch.float32) * hidden_states return hidden_states.to(input_dtype) class LayerNorm(nn.Module): def __init__(self, param_shape=None, eps=1e-5): super().__init__() self.weight = nn.Parameter(torch.ones(param_shape)) self.variance_epsilon = eps set_weight_attrs(self.weight, {"weight_loader": row_parallel_weight_loader}) def forward(self, hidden_states, residuals=None): hidden_states = layer_norm_func( hidden_states, self.weight, self.variance_epsilon ) return hidden_states, residuals # Copied from transformers.models.llama.modeling_llama.LlamaMLP Llama->Cohere class CohereMLP(nn.Module): def __init__( self, config: CohereConfig | Cohere2Config, quant_config: QuantizationConfig | None = None, prefix: str = "", ): 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, quant_config=quant_config, prefix=f"{prefix}.gate_up_proj", ) self.down_proj = RowParallelLinear( self.intermediate_size, self.hidden_size, bias=False, quant_config=quant_config, prefix=f"{prefix}.down_proj", ) 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 | Cohere2Config, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", ): 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 = getattr( config, "model_max_length", None ) or getattr(config, "max_position_embeddings", 8192) self.use_qk_norm = getattr(config, "use_qk_norm", False) self.qkv_proj = QKVParallelLinear( self.hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=False, quant_config=quant_config, prefix=f"{prefix}.qkv_proj", ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, self.hidden_size, bias=False, quant_config=quant_config, prefix=f"{prefix}.o_proj", ) self.rotary_emb = get_rope( self.head_dim, max_position=self.max_position_embeddings, rope_parameters=config.rope_parameters, is_neox_style=False, ) # Model v2 has interleaved sliding windows, v1 does not self.v1 = isinstance(config, CohereConfig) self.sliding_window = None if not self.v1: layer_idx = extract_layer_index(prefix) if config.layer_types[layer_idx] == "sliding_attention": self.sliding_window = config.sliding_window 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, per_layer_sliding_window=self.sliding_window, prefix=f"{prefix}.attn", ) if self.use_qk_norm: self.q_norm = LayerNorm( param_shape=(self.num_heads, self.head_dim), eps=config.layer_norm_eps ) self.k_norm = LayerNorm( param_shape=(self.num_kv_heads, self.head_dim), eps=config.layer_norm_eps, ) def _apply_qk_norm(self, q, k): q = q.view(*q.shape[:-1], -1, self.head_dim) k = k.view(*k.shape[:-1], -1, self.head_dim) q, _ = self.q_norm(q) k, _ = self.k_norm(k) q = q.view(*q.shape[:-2], -1) k = k.view(*k.shape[:-2], -1) return q, k def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> torch.Tensor: qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) if self.use_qk_norm: q, k = self._apply_qk_norm(q, k) if self.v1 or self.sliding_window: q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v) output, _ = self.o_proj(attn_output) return output class CohereDecoderLayer(nn.Module): def __init__( self, config: CohereConfig | Cohere2Config, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() self.hidden_size = config.hidden_size self.self_attn = CohereAttention( config, cache_config, quant_config=quant_config, prefix=f"{prefix}.self_attn", ) self.mlp = CohereMLP(config, quant_config=quant_config, prefix=f"{prefix}.mlp") self.input_layernorm = LayerNorm( param_shape=(config.hidden_size), eps=config.layer_norm_eps ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, residual: torch.Tensor | None, ) -> 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, ) hidden_states_mlp = self.mlp(hidden_states) # Add everything together hidden_states = residual + hidden_states_attention + hidden_states_mlp return hidden_states, residual @support_torch_compile class CohereModel(nn.Module): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config self.quant_config = quant_config self.config = config self.vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size ) self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: CohereDecoderLayer( config, cache_config, quant_config, prefix=prefix ), prefix=f"{prefix}.layers", ) self.norm = LayerNorm( param_shape=(config.hidden_size), eps=config.layer_norm_eps ) self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], config.hidden_size ) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor | IntermediateTensors: if get_pp_group().is_first_rank: if inputs_embeds is not None: hidden_states = inputs_embeds else: hidden_states = self.embed_input_ids(input_ids) residual = None else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] for layer in islice(self.layers, self.start_layer, self.end_layer): hidden_states, residual = layer( positions, hidden_states, residual, ) if not get_pp_group().is_last_rank: return IntermediateTensors( {"hidden_states": hidden_states, "residual": residual} ) hidden_states, _ = self.norm(hidden_states, residual) return hidden_states def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: 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[str] = set() for name, loaded_weight in weights: if self.quant_config is not None and ( scale_name := self.quant_config.get_cache_scale(name) ): # Loading kv cache quantization scales param = params_dict[scale_name] weight_loader = getattr(param, "weight_loader", default_weight_loader) loaded_weight = ( loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0] ) weight_loader(param, loaded_weight) loaded_params.add(scale_name) continue 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) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if is_pp_missing_parameter(name, self): 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 # Remapping the name of FP8 kv-scale. name = maybe_remap_kv_scale_name(name, params_dict) if name is None: continue if is_pp_missing_parameter(name, self): continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) return loaded_params class CohereForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsQuant): packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], "gate_up_proj": [ "gate_proj", "up_proj", ], } # LoRA specific attributes embedding_modules = {"embed_tokens": "input_embeddings"} def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config quant_config = vllm_config.quant_config self.config = config # currently all existing command R models have `tie_word_embeddings` # enabled assert config.tie_word_embeddings self.quant_config = quant_config self.logits_processor = LogitsProcessor( config.vocab_size, scale=config.logit_scale ) self.model = CohereModel( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model") ) self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors ) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.embed_input_ids(input_ids) @torch.no_grad() def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None = None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor | IntermediateTensors: hidden_states = self.model( input_ids, positions, intermediate_tensors, inputs_embeds ) return hidden_states def compute_logits( self, hidden_states: torch.Tensor, ) -> torch.Tensor | None: is_not_lora = hasattr(self.model.embed_tokens, "weight") if is_not_lora: logits = self.logits_processor(self.model.embed_tokens, hidden_states) else: logits = self.logits_processor( self.model.embed_tokens.base_layer, hidden_states ) return logits def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader( self, skip_prefixes=["lm_head", "rotary_emb.inv_freq"] ) return loader.load_weights(weights)