# coding=utf-8 # Adapted from https://huggingface.co/mosaicml/mpt-7b/tree/main import math from typing import List, Optional, Tuple import torch import torch.nn as nn from transformers import MptConfig from vllm.model_executor.input_metadata import InputMetadata from vllm.model_executor.layers.activation import get_act_fn from vllm.model_executor.layers.attention import PagedAttentionWithALiBi from vllm.model_executor.layers.sampler import Sampler from vllm.model_executor.weight_utils import (convert_pyslice_to_tensor, hf_model_weights_iterator, load_tensor_parallel_weights) from vllm.model_executor.parallel_utils.parallel_state import ( get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) from vllm.model_executor.parallel_utils.layers import (VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear) from vllm.sequence import SamplerOutput KVCache = Tuple[torch.Tensor, torch.Tensor] 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): super().__init__() self.d_model = config.d_model self.total_num_heads = config.n_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 assert not config.attn_config.prefix_lm assert config.attn_config.alibi self.qkv_proj = ColumnParallelLinear( self.d_model, 3 * self.d_model, bias=not config.no_bias, gather_output=False, ) 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, input_is_parallel=True, ) 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 # 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 = PagedAttentionWithALiBi(self.num_heads, self.head_dim, scaling, alibi_slopes) def forward( self, position_ids: torch.Tensor, hidden_states: torch.Tensor, kv_cache: KVCache, input_metadata: InputMetadata, cache_event: Optional[torch.cuda.Event], ) -> torch.Tensor: del position_ids # unused. qkv, _ = self.qkv_proj(hidden_states) if self.clip_qkv is not None: qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv) q, k, v = qkv.chunk(chunks=3, dim=-1) if self.qk_ln: q = self.q_ln(q) k = self.k_ln(k) k_cache, v_cache = kv_cache attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata, cache_event) output, _ = self.out_proj(attn_output) return output class MptMLP(nn.Module): def __init__(self, config: MptConfig): 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, gather_output=False, ) self.act = get_act_fn("gelu") self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=not config.no_bias, input_is_parallel=True, ) 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): super().__init__() hidden_size = config.d_model self.norm_1 = nn.LayerNorm(hidden_size) self.attn = MptAttention(config) self.norm_2 = nn.LayerNorm(hidden_size) self.ffn = MptMLP(config) def forward( self, position_ids: torch.Tensor, hidden_states: torch.Tensor, kv_cache: KVCache, input_metadata: InputMetadata, cache_event: Optional[torch.cuda.Event], ) -> torch.Tensor: x = self.norm_1(hidden_states) x = self.attn( position_ids=position_ids, hidden_states=x, kv_cache=kv_cache, input_metadata=input_metadata, cache_event=cache_event, ) hidden_states = hidden_states + x x = self.norm_2(hidden_states) x = self.ffn(x) hidden_states = hidden_states + x return hidden_states class MptModel(nn.Module): def __init__(self, config: MptConfig): super().__init__() assert config.embedding_fraction == 1.0 assert config.norm_type == "low_precision_layernorm" self.wte = VocabParallelEmbedding( config.vocab_size, config.d_model, ) self.blocks = nn.ModuleList( [MptBlock(config) for _ in range(config.n_layers)]) self.norm_f = nn.LayerNorm(config.d_model) if config.no_bias: for module in self.modules(): if hasattr(module, "bias"): if isinstance(module.bias, nn.Parameter): # Remove the bias term in Linear and LayerNorm. module.register_parameter("bias", None) def forward( self, input_ids: torch.Tensor, position_ids: torch.Tensor, kv_caches: List[KVCache], input_metadata: InputMetadata, cache_events: Optional[List[torch.cuda.Event]], ) -> torch.Tensor: hidden_states = self.wte(input_ids) for i in range(len(self.blocks)): if cache_events is None: cache_event = None else: cache_event = cache_events[i] block = self.blocks[i] hidden_states = block( position_ids, hidden_states, kv_caches[i], input_metadata, cache_event, ) hidden_states = self.norm_f(hidden_states) return hidden_states class MptForCausalLM(nn.Module): def __init__(self, config: MptConfig): super().__init__() self.config = config assert config.tie_word_embeddings self.transformer = MptModel(config) # TODO(zhuohan): create a new weight after implementing pipeline # parallelism self.lm_head_weight = self.transformer.wte.weight self.sampler = Sampler(config.vocab_size) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[KVCache], input_metadata: InputMetadata, cache_events: Optional[List[torch.cuda.Event]], ) -> SamplerOutput: hidden_states = self.transformer(input_ids, positions, kv_caches, input_metadata, cache_events) next_tokens = self.sampler(self.lm_head_weight, hidden_states, input_metadata) return next_tokens _column_parallel_weights = ["wte.weight", "up_proj.weight", "up_proj.bias"] _row_parallel_weights = ["out_proj.weight", "down_proj.weight"] def load_weights(self, model_name_or_path: str, cache_dir: Optional[str] = None, load_format: str = "auto", revision: Optional[str] = None): tp_world_size = get_tensor_model_parallel_world_size() tp_rank = get_tensor_model_parallel_rank() state_dict = self.state_dict() for name, loaded_weight in hf_model_weights_iterator( model_name_or_path, cache_dir, load_format, revision): if "Wqkv" in name: # NOTE(woosuk): MPT's fused QKV has the shape of # [3 * num_heads * head_size, hidden_size]. # When tensor model parallelism is used, we need to shard # the weight along the hidden dimension. total_num_heads = self.config.num_attention_heads hidden_size = self.config.hidden_size head_size = hidden_size // total_num_heads num_heads = total_num_heads // tp_world_size head_start = tp_rank * num_heads head_end = (tp_rank + 1) * num_heads loaded_weight = convert_pyslice_to_tensor(loaded_weight) if name.endswith(".weight"): loaded_weight = loaded_weight.view(3, total_num_heads, head_size, hidden_size) loaded_weight = loaded_weight[:, head_start:head_end, :, :] loaded_weight = loaded_weight.reshape(-1, hidden_size) elif name.endswith(".bias"): loaded_weight = loaded_weight.view(3, total_num_heads, head_size) loaded_weight = loaded_weight[:, head_start:head_end, :] loaded_weight = loaded_weight.reshape(-1) else: raise ValueError(f"Unexpected parameter name {name}") name = name.replace("Wqkv", "qkv_proj") param = state_dict[name] load_tensor_parallel_weights(param, loaded_weight, name, self._column_parallel_weights, self._row_parallel_weights, tp_rank)