# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Adapted from https://huggingface.co/mosaicml/mpt-7b/tree/main import math from collections.abc import Iterable from itertools import islice import torch import torch.nn as nn from transformers import MptConfig 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_rank, 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 import QuantizationConfig from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.sequence import IntermediateTensors from .interfaces import SupportsPP from .utils import ( AutoWeightsLoader, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix, ) def _get_alibi_slopes( total_num_heads: int, alibi_bias_max: int, ) -> torch.Tensor: next_power_of_2 = 2 ** math.ceil(math.log2(total_num_heads)) m = torch.arange(1, next_power_of_2 + 1, dtype=torch.float32) m = m.mul(alibi_bias_max / next_power_of_2) slopes = 1.0 / torch.pow(2, m) if next_power_of_2 != total_num_heads: slopes = torch.concat([slopes[1::2], slopes[::2]])[:total_num_heads] return slopes class MPTAttention(nn.Module): def __init__( self, config: MptConfig, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() self.d_model = config.d_model self.total_num_heads = config.n_heads self.head_dim = self.d_model // self.total_num_heads self.clip_qkv = config.attn_config.clip_qkv self.qk_ln = config.attn_config.qk_ln self.alibi_bias_max = config.attn_config.alibi_bias_max if "kv_n_heads" in config.attn_config: self.total_num_kv_heads = config.attn_config.kv_n_heads else: self.total_num_kv_heads = self.total_num_heads assert not config.attn_config.prefix_lm assert config.attn_config.alibi # pylint: disable=invalid-name self.Wqkv = QKVParallelLinear( self.d_model, self.d_model // self.total_num_heads, self.total_num_heads, self.total_num_kv_heads, bias=not config.no_bias, quant_config=quant_config, prefix=f"{prefix}.Wqkv", ) if self.qk_ln: self.q_ln = nn.LayerNorm(self.d_model) self.k_ln = nn.LayerNorm(self.d_model) self.out_proj = RowParallelLinear( self.d_model, self.d_model, bias=not config.no_bias, quant_config=quant_config, prefix=f"{prefix}.out_proj", ) tp_world_size = get_tensor_model_parallel_world_size() assert self.total_num_heads % tp_world_size == 0 self.num_heads = self.total_num_heads // tp_world_size if self.total_num_kv_heads >= tp_world_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_world_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_world_size % self.total_num_kv_heads == 0 self.num_kv_heads = max(1, self.total_num_kv_heads // tp_world_size) self.q_size = self.num_heads * self.head_dim self.kv_size = self.num_kv_heads * self.head_dim # Create the alibi slopes and slice them. tp_rank = get_tensor_model_parallel_rank() head_start = tp_rank * self.num_heads head_end = (tp_rank + 1) * self.num_heads alibi_slopes = _get_alibi_slopes(self.total_num_heads, self.alibi_bias_max) alibi_slopes = alibi_slopes[head_start:head_end].tolist() self.head_dim = self.d_model // self.total_num_heads scaling = self.head_dim**-0.5 self.attn = Attention( self.num_heads, self.head_dim, scaling, alibi_slopes=alibi_slopes, num_kv_heads=self.num_kv_heads, cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.attn", ) def forward( self, position_ids: torch.Tensor, hidden_states: torch.Tensor, ) -> torch.Tensor: del position_ids # unused. qkv, _ = self.Wqkv(hidden_states) if self.clip_qkv is not None: qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) if self.qk_ln: q = self.q_ln(q) k = self.k_ln(k) attn_output = self.attn(q, k, v) output, _ = self.out_proj(attn_output) return output class MPTMLP(nn.Module): def __init__( self, config: MptConfig, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() hidden_size = config.d_model expansion_ratio = config.expansion_ratio intermediate_size = expansion_ratio * hidden_size self.up_proj = ColumnParallelLinear( hidden_size, intermediate_size, bias=not config.no_bias, quant_config=quant_config, prefix=f"{prefix}.up_proj", ) self.act = get_act_fn("gelu") self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=not config.no_bias, quant_config=quant_config, prefix=f"{prefix}.down_proj", ) def forward(self, x: torch.Tensor) -> torch.Tensor: x, _ = self.up_proj(x) x = self.act(x) x, _ = self.down_proj(x) return x class MPTBlock(nn.Module): def __init__( self, config: MptConfig, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() hidden_size = config.d_model self.norm_1 = nn.LayerNorm(hidden_size) self.attn = MPTAttention( config, cache_config, quant_config, prefix=f"{prefix}.attn" ) self.norm_2 = nn.LayerNorm(hidden_size) self.ffn = MPTMLP(config, quant_config, prefix=f"{prefix}.ffn") def forward( self, position_ids: torch.Tensor, hidden_states: torch.Tensor, ) -> torch.Tensor: x = self.norm_1(hidden_states) x = self.attn( position_ids=position_ids, hidden_states=x, ) hidden_states = hidden_states + x x = self.norm_2(hidden_states) x = self.ffn(x) hidden_states = hidden_states + x return hidden_states @support_torch_compile class MPTModel(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 assert config.embedding_fraction == 1.0 assert config.norm_type == "low_precision_layernorm" self.wte = VocabParallelEmbedding( config.vocab_size, config.d_model, ) self.start_layer, self.end_layer, self.blocks = make_layers( config.n_layers, lambda prefix: MPTBlock(config, cache_config, quant_config, prefix=prefix), prefix=f"{prefix}.blocks", ) self.norm_f = nn.LayerNorm(config.d_model) if config.no_bias: for module in self.modules(): if hasattr(module, "bias") and isinstance(module.bias, nn.Parameter): # Remove the bias term in Linear and LayerNorm. module.register_parameter("bias", None) self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( ["hidden_states"], config.d_model ) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.wte(input_ids) def forward( self, input_ids: torch.Tensor, position_ids: 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) else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] for block in islice(self.blocks, self.start_layer, self.end_layer): hidden_states = block(position_ids, hidden_states) if not get_pp_group().is_last_rank: return IntermediateTensors({"hidden_states": hidden_states}) hidden_states = self.norm_f(hidden_states) return hidden_states def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: params_dict = dict(self.named_parameters(remove_duplicate=False)) loaded_params: set[str] = set() for name, loaded_weight in weights: # 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 MPTForCausalLM(nn.Module, SupportsPP): 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 assert config.tie_word_embeddings self.quant_config = quant_config self.transformer = MPTModel( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer") ) self.lm_head = self.transformer.wte self.logits_processor = LogitsProcessor(config.vocab_size) self.make_empty_intermediate_tensors = ( self.transformer.make_empty_intermediate_tensors ) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.transformer.embed_input_ids(input_ids) 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.transformer( input_ids, positions, intermediate_tensors, inputs_embeds ) return hidden_states def compute_logits( self, hidden_states: torch.Tensor, ) -> torch.Tensor | None: logits = self.logits_processor(self.lm_head, hidden_states) return logits def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader(self) return loader.load_weights(weights)