# SPDX-License-Identifier: Apache-2.0 # Adapted from # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py # Copyright 2023 The vLLM team. # Copyright 2022 EleutherAI 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. """Inference-only FM9G model compatible with HuggingFace weights.""" import math from typing import Any, Dict, Iterable, Optional, Set, Tuple, Union, List import torch from torch import nn from vllm.transformers_utils.configs import FM9GConfig from vllm.attention 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_rank, get_tensor_model_parallel_world_size, tensor_model_parallel_all_reduce) from vllm.model_executor.layers.activation import FatreluAndMul, SiluAndMul from vllm.model_executor.layers.fused_moe import fused_moe from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, QKVParallelLinear, ReplicatedLinear, 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 ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.sampling_metadata import SamplingMetadata 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 from .utils import (AutoWeightsLoader, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix) class FM9GMoE(nn.Module): """A tensor-parallel MoE implementation that shards each expert across all ranks. Each expert's weights are sharded across all ranks and a fused MoE kernel is used for the forward pass, and finally we reduce the outputs across ranks. """ def __init__( self, num_experts: int, top_k: int, hidden_size: int, intermediate_size: int, params_dtype: Optional[torch.dtype] = None, tp_size: Optional[int] = None, ): super().__init__() self.tp_size = tp_size or get_tensor_model_parallel_world_size() self.num_total_experts = num_experts self.top_k = top_k self.hidden_size = hidden_size self.intermediate_size = intermediate_size // self.tp_size if params_dtype is None: params_dtype = torch.get_default_dtype() self.params_dtype = params_dtype self.gate = ReplicatedLinear(self.hidden_size, self.num_total_experts, bias=False, params_dtype=self.params_dtype, quant_config=None) self.ws = nn.Parameter( torch.empty(self.num_total_experts, 2 * self.intermediate_size, self.hidden_size, device=current_platform.device_type, dtype=self.params_dtype)) self.w2s = nn.Parameter( torch.empty(self.num_total_experts, self.hidden_size, self.intermediate_size, device=current_platform.device_type, dtype=self.params_dtype)) set_weight_attrs(self.ws, { "weight_loader": self.weight_loader, }) set_weight_attrs(self.w2s, { "weight_loader": self.weight_loader, }) def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor, weight_name: str, expert_id: int): tp_rank = get_tensor_model_parallel_rank() param_data = param.data shard_size = self.intermediate_size shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size) if weight_name.endswith("w1.weight"): param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :] if weight_name.endswith("w3.weight"): param_data[expert_id, shard_size:2 * shard_size, :] = loaded_weight[shard, :] if weight_name.endswith("w2.weight"): param_data[expert_id, :, :] = loaded_weight[:, shard] def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: num_tokens, hidden_size = hidden_states.shape hidden_states = hidden_states.view(-1, self.hidden_size) # router_logits: (num_tokens, n_experts) router_logits, _ = self.gate(hidden_states) final_hidden_states = fused_moe(hidden_states, self.ws, self.w2s, router_logits, self.top_k, renormalize=True, inplace=True) if self.tp_size > 1: final_hidden_states = tensor_model_parallel_all_reduce( final_hidden_states) return final_hidden_states.view(num_tokens, hidden_size) class FM9GMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, hidden_act_param: float, quant_config: Optional[QuantizationConfig] = None, ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config) self.down_proj = RowParallelLinear(intermediate_size, hidden_size, bias=False, quant_config=quant_config) if hidden_act == "silu": self.act_fn = SiluAndMul() elif hidden_act == "fatrelu": self.act_fn = FatreluAndMul(threshold=hidden_act_param) else: raise ValueError(f"Unsupported activation: {hidden_act}. " "Only silu and fatrelu are supported for now.") 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 FM9GAttention(nn.Module): def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, rope_theta: float = 10000, rope_scaling: Optional[Dict[str, Any]] = None, max_position_embeddings: int = 8192, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.hidden_size = hidden_size tp_size = get_tensor_model_parallel_world_size() self.total_num_heads = num_heads assert self.total_num_heads % tp_size == 0 self.num_heads = self.total_num_heads // tp_size self.total_num_kv_heads = num_kv_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 = 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 = rope_theta self.max_position_embeddings = max_position_embeddings self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=False, quant_config=quant_config, ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, quant_config=quant_config, ) self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=max_position_embeddings, base=rope_theta, rope_scaling=rope_scaling, ) # set rope as fp32 instead of bf16 self.rotary_emb.cos_sin_cache = self.rotary_emb._compute_cos_sin_cache( ) 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, prefix=f"{prefix}.attn") 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) orig_dtype = q.dtype q, k = q.float(), k.float() q, k = self.rotary_emb(positions, q, k) q, k = q.to(orig_dtype), k.to(orig_dtype) attn_output = self.attn(q, k, v) output, _ = self.o_proj(attn_output) return output class FM9GDecoderLayer(nn.Module): def __init__( self, config: FM9GConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.config = config self.cache_config = cache_config self.quant_config = quant_config self.hidden_size = config.hidden_size self.rope_theta = getattr(config, "rope_theta", 10000) self.rope_scaling = getattr(config, "rope_scaling", None) self.max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.prefix = prefix self._init_attn_block() self._init_ffn_block() def _init_attn_block(self): self.input_layernorm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps) self.self_attn = FM9GAttention( hidden_size=self.hidden_size, num_heads=self.config.num_attention_heads, num_kv_heads=self.config.num_key_value_heads, rope_theta=self.rope_theta, rope_scaling=self.rope_scaling, max_position_embeddings=self.max_position_embeddings, cache_config=self.cache_config, quant_config=self.quant_config, prefix=f"{self.prefix}.self_attn", ) def _init_ffn_block(self): self.post_attention_layernorm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps) self.num_experts = getattr(self.config, "num_experts", 0) if self.num_experts == 0: self.mlp = FM9GMLP( hidden_size=self.hidden_size, intermediate_size=self.config.intermediate_size, hidden_act=self.config.hidden_act, hidden_act_param=getattr(self.config, "hidden_act_param", 0.), quant_config=self.quant_config, ) else: self.mlp = FM9GMoE( num_experts=self.config.num_experts, top_k=self.config.num_experts_per_tok, hidden_size=self.config.hidden_size, intermediate_size=self.config.intermediate_size) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, residual: Optional[torch.Tensor], ) -> Tuple[torch.Tensor, 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, ) hidden_states = residual + hidden_states * \ (self.config.scale_depth / math.sqrt(self.config.num_hidden_layers)) # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states * \ (self.config.scale_depth / math.sqrt(self.config.num_hidden_layers)) return hidden_states, None @support_torch_compile class FM9GModel(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 lora_config = vllm_config.lora_config self.config = config self.cache_config = cache_config self.quant_config = quant_config lora_vocab = (lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1)) if lora_config else 0 self.vocab_size = config.vocab_size + lora_vocab self.org_vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( self.vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, ) self.num_experts = getattr(self.config, "num_experts", 0) self._init_layers(prefix, config, cache_config, quant_config) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.make_empty_intermediate_tensors = ( make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], self.config.hidden_size)) def _init_layers( self, prefix: str, config: FM9GConfig, cache_config: Optional[CacheConfig], quant_config: Optional[QuantizationConfig], ): self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: FM9GDecoderLayer( config, cache_config, quant_config, prefix=prefix), prefix=f"{prefix}.layers") def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: embedding = self.embed_tokens(input_ids) return embedding * self.config.scale_emb def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, IntermediateTensors]: if get_pp_group().is_first_rank: if inputs_embeds is not None: hidden_states = inputs_embeds else: hidden_states = self.get_input_embeddings(input_ids) residual = None else: hidden_states = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] for layer in 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) 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), ] expert_params_mapping = [ # (param_name, weight_name, expert_id) ("ws" if weight_name in ["w1", "w3"] else "w2s", f"experts.{expert_id}.{weight_name}.weight", expert_id) for expert_id in range(self.num_experts) for weight_name in ["w1", "w2", "w3"] ] params_dict = dict(self.named_parameters()) loaded_params: Set[str] = set() for name, loaded_weight in weights: if "rotary_emb.inv_freq" in name: continue if ("rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name): # Models trained using ColossalAI may include these tensors in # the checkpoint. Skip them. 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 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: for param_name, weight_name, expert_id in expert_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) if is_pp_missing_parameter(name, self): continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, weight_name, expert_id=expert_id) break else: # 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 = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) return loaded_params class FM9GForCausalLM(nn.Module, SupportsLoRA, SupportsPP): 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", "lm_head": "output_embeddings", } embedding_padding_modules = ["lm_head"] 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 lora_config = vllm_config.lora_config self.prefix = prefix self.vllm_config = vllm_config self.config = config self.lora_config = lora_config self.cache_config = cache_config self.quant_config = quant_config self.model = self._init_model(vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")) unpadded_vocab_size = config.vocab_size if lora_config: unpadded_vocab_size += lora_config.lora_extra_vocab_size self.lm_head = ParallelLMHead( unpadded_vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, padding_size=DEFAULT_VOCAB_PADDING_SIZE # We need bigger padding if using lora for kernel # compatibility if not lora_config else lora_config.lora_vocab_padding_size, quant_config=quant_config, ) if config.tie_word_embeddings: self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens) self.scale_width = self.config.hidden_size / self.config.dim_model_base self.logits_processor = LogitsProcessor(unpadded_vocab_size, config.vocab_size) self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors) def _init_model(self, *, vllm_config: VllmConfig, prefix: str = ""): return FM9GModel(vllm_config=vllm_config, prefix=prefix) def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.get_input_embeddings(input_ids) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> Union[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, sampling_metadata: SamplingMetadata, ) -> Optional[torch.Tensor]: hidden_states = hidden_states / self.scale_width logits = self.logits_processor(self.lm_head, hidden_states, sampling_metadata) return logits def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]: loader = AutoWeightsLoader( self, skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None), ) return loader.load_weights(weights)