# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Adapted from # https://github.com/huggingface/transformers/blob/a5cc30d72ae2dc19af534e4b35c986cc28db1275/src/transformers/models/falcon/modeling_falcon.py # Copyright 2023 The vLLM team. # Copyright 2023 the Falcon authors and HuggingFace Inc. team. All rights # reserved. # # 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. """PyTorch Falcon model.""" import math from collections.abc import Iterable from itertools import islice from typing import TypeAlias import torch from torch import nn from torch.nn import LayerNorm from transformers import FalconConfig as HF_FalconConfig 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, tensor_model_parallel_all_reduce, ) 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.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding, ) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.sequence import IntermediateTensors from vllm.transformers_utils.configs import RWConfig from .interfaces import SupportsPP from .utils import ( AutoWeightsLoader, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix, ) FalconConfig: TypeAlias = HF_FalconConfig | RWConfig def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor: closest_power_of_2 = 2 ** math.floor(math.log2(total_num_heads)) base = torch.tensor( 2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), dtype=torch.float32 ) powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32) slopes = torch.pow(base, powers) if closest_power_of_2 != total_num_heads: extra_base = torch.tensor( 2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), dtype=torch.float32 ) num_remaining_heads = min( closest_power_of_2, total_num_heads - closest_power_of_2 ) extra_powers = torch.arange( 1, 1 + 2 * num_remaining_heads, 2, dtype=torch.int32 ) slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0) return slopes class FalconAttention(nn.Module): def __init__( self, config: FalconConfig, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() self.hidden_size = config.hidden_size tp_size = get_tensor_model_parallel_world_size() self.total_num_heads = config.num_attention_heads assert self.total_num_heads % tp_size == 0 self.num_heads = self.total_num_heads // tp_size self.head_dim = self.hidden_size // self.total_num_heads assert self.head_dim * self.total_num_heads == self.hidden_size self.new_decoder_architecture = config.new_decoder_architecture self.multi_query = config.multi_query if self.new_decoder_architecture: self.total_num_kv_heads = config.num_kv_heads elif self.multi_query: self.total_num_kv_heads = 1 else: self.total_num_kv_heads = self.total_num_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.query_key_value = QKVParallelLinear( self.hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=config.bias, skip_bias_add=True, quant_config=quant_config, prefix=f"{prefix}.query_key_value", ) self.q_size = self.num_heads * self.head_dim self.kv_size = self.num_kv_heads * self.head_dim # Layer-wise attention scaling self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim) self.reduce_row_parallel_results = not ( config.new_decoder_architecture or config.parallel_attn ) self.dense = RowParallelLinear( self.hidden_size, self.hidden_size, bias=config.bias, skip_bias_add=True, quant_config=quant_config, reduce_results=self.reduce_row_parallel_results, prefix=f"{prefix}.dense", ) self.use_rotary = config.rotary self.use_alibi = config.alibi assert not (self.use_rotary and self.use_alibi), ( "Rotary and alibi are mutually exclusive." ) if self.use_rotary: max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.rotary_emb = get_rope( self.head_dim, max_position=max_position_embeddings, rope_parameters=config.rope_parameters, ) self.attn = Attention( self.num_heads, self.head_dim, self.inv_norm_factor, num_kv_heads=self.num_kv_heads, quant_config=quant_config, prefix=f"{prefix}.attn", ) elif self.use_alibi: 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.inv_norm_factor ) alibi_slopes = alibi_slopes[head_start:head_end].tolist() self.attn = Attention( self.num_heads, self.head_dim, self.inv_norm_factor, num_kv_heads=self.num_kv_heads, alibi_slopes=alibi_slopes, quant_config=quant_config, prefix=f"{prefix}.attn", ) else: self.attn = Attention( self.num_heads, self.head_dim, scale=self.inv_norm_factor, 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, bias = self.query_key_value(hidden_states) if bias is not None: qkv += bias q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) if self.use_rotary: q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v) attn_output, bias = self.dense(attn_output) return attn_output, bias class FalconMLP(nn.Module): def __init__( self, config: FalconConfig, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() hidden_size = config.hidden_size self.dense_h_to_4h = ColumnParallelLinear( hidden_size, 4 * hidden_size, bias=config.bias, skip_bias_add=True, quant_config=quant_config, prefix=f"{prefix}.dense_h_to_4h", ) self.act = get_act_fn("gelu") self.reduce_row_parallel_results = not ( config.new_decoder_architecture or config.parallel_attn ) self.dense_4h_to_h = RowParallelLinear( 4 * hidden_size, hidden_size, bias=config.bias, skip_bias_add=True, reduce_results=self.reduce_row_parallel_results, quant_config=quant_config, prefix=f"{prefix}.dense_4h_to_h", ) def forward(self, x: torch.Tensor) -> torch.Tensor: # NOTE(zhuohan): Following huggingface, we do not fuse bias add here. x, bias = self.dense_h_to_4h(x) if bias is not None: x += bias x = self.act(x) x, bias = self.dense_4h_to_h(x) return x, bias class FalconDecoderLayer(nn.Module): def __init__( self, config: FalconConfig, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.self_attention = FalconAttention( config, cache_config, quant_config, prefix=f"{prefix}.self_attention" ) self.mlp = FalconMLP(config, quant_config, prefix=f"{prefix}.mlp") self.config = config if not hasattr(config, "num_ln_in_parallel_attn"): config.num_ln_in_parallel_attn = None if config.num_ln_in_parallel_attn is None and config.new_decoder_architecture: config.num_ln_in_parallel_attn = 2 if not config.parallel_attn: self.post_attention_layernorm = LayerNorm( hidden_size, eps=config.layer_norm_epsilon ) self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) else: if config.num_ln_in_parallel_attn == 2: # The layer norm before self-attention self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) # The layer norm before the MLP self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) else: self.input_layernorm = LayerNorm( hidden_size, eps=config.layer_norm_epsilon ) self.reduce_row_parallel_results = not ( config.new_decoder_architecture or config.parallel_attn ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> torch.Tensor: residual = hidden_states if self.config.num_ln_in_parallel_attn == 2: attention_layernorm_out = self.ln_attn(hidden_states) mlp_layernorm_out = self.ln_mlp(hidden_states) else: attention_layernorm_out = self.input_layernorm(hidden_states) # Self attention. attention_output, attention_bias = self.self_attention( positions=positions, hidden_states=attention_layernorm_out, ) if self.reduce_row_parallel_results and attention_bias is not None: attention_output += attention_bias if not self.config.new_decoder_architecture: if self.config.parallel_attn: mlp_layernorm_out = attention_layernorm_out else: residual += attention_output mlp_layernorm_out = self.post_attention_layernorm(residual) if ( self.config.new_decoder_architecture and self.config.parallel_attn and self.config.num_ln_in_parallel_attn == 1 ): mlp_layernorm_out = attention_layernorm_out # MLP. mlp_output, mlp_bias = self.mlp(mlp_layernorm_out) if self.reduce_row_parallel_results and mlp_bias is not None: mlp_output += mlp_bias if not self.reduce_row_parallel_results: # When MLP and Attention layers are parallel, we can use # only one all-reduce operator to reduce the results from # both MLP and Attention layers. mlp_output += attention_output mlp_output = tensor_model_parallel_all_reduce(mlp_output) if attention_bias is not None: mlp_output += attention_bias if mlp_bias is not None: mlp_output += mlp_bias output = mlp_output + residual return output @support_torch_compile class FalconModel(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.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.use_alibi = config.alibi # Embedding + LN Embedding self.word_embeddings = VocabParallelEmbedding( config.vocab_size, self.embed_dim, ) # Transformer blocks self.start_layer, self.end_layer, self.h = make_layers( config.num_hidden_layers, lambda prefix: FalconDecoderLayer( config, cache_config, quant_config, prefix=prefix ), prefix=f"{prefix}.h", ) # Final Layer Norm self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( ["hidden_states"], config.hidden_size ) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.word_embeddings(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) else: hidden_states = intermediate_tensors["hidden_states"] for layer in islice(self.h, self.start_layer, self.end_layer): hidden_states = layer(positions, hidden_states) if not get_pp_group().is_last_rank: return IntermediateTensors({"hidden_states": hidden_states}) hidden_states = self.ln_f(hidden_states) return hidden_states def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: total_num_heads = self.config.num_attention_heads if self.config.new_decoder_architecture: total_num_kv_heads = self.config.num_kv_heads elif self.config.multi_query: total_num_kv_heads = 1 else: total_num_kv_heads = total_num_heads num_query_heads_per_kv_head = total_num_heads // total_num_kv_heads 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] if "query_key_value" in name: output_dim = getattr(param, "output_dim", None) loaded_weight_shape = loaded_weight.shape if output_dim is not None: loaded_weight = loaded_weight.view( loaded_weight_shape[:output_dim] + (total_num_kv_heads, num_query_heads_per_kv_head + 2, -1) + loaded_weight_shape[output_dim + 1 :] ) wq = loaded_weight.narrow( output_dim + 1, 0, num_query_heads_per_kv_head ).reshape( *loaded_weight_shape[:output_dim], -1, *loaded_weight_shape[output_dim + 1 :], ) wk = loaded_weight.narrow( output_dim + 1, num_query_heads_per_kv_head, 1 ).reshape( *loaded_weight_shape[:output_dim], -1, *loaded_weight_shape[output_dim + 1 :], ) wv = loaded_weight.narrow( output_dim + 1, num_query_heads_per_kv_head + 1, 1 ).reshape( *loaded_weight_shape[:output_dim], -1, *loaded_weight_shape[output_dim + 1 :], ) loaded_weight = torch.cat([wq, wk, wv], dim=output_dim) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) return loaded_params class FalconForCausalLM(nn.Module, SupportsPP): packed_modules_mapping = { "query_key_value": ["query_key_value"], } 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 self.quant_config = quant_config self.transformer = FalconModel( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer") ) # only Falcon-11B doesn't share lm_head weight with word embeddings # and previous Falcon model doesn't have tie_word_embeddings config # so we set tie_word_embeddings to True by default self.tie_word_embeddings = ( config.tie_word_embeddings if config.tie_word_embeddings is not None else True ) if self.tie_word_embeddings: self.lm_head = self.transformer.word_embeddings else: self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=maybe_prefix(prefix, "lm_head"), ) 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.LongTensor, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None = None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor: 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, skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None), ) return loader.load_weights(weights)