# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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. from ..._utils import pad_vocab_size from ...functional import Tensor, allreduce, recv, send from ...layers import (MLP, Attention, AttentionMaskType, ColumnLinear, Embedding, LayerNorm) from ...module import Module from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM, PretrainedConfig) class FalconDecoderLayer(Module): def __init__(self, config: PretrainedConfig, layer_idx: int): super().__init__() self.layer_idx = layer_idx self.config = config hidden_size = config.hidden_size dtype = config.dtype tp_group = config.mapping.tp_group tp_size = config.mapping.tp_size tp_rank = config.mapping.tp_rank layernorm_epsilon = config.norm_epsilon self.input_layernorm = LayerNorm(normalized_shape=hidden_size, eps=layernorm_epsilon, dtype=dtype) self.new_decoder_architecture = config.new_decoder_architecture self.parallel_attn = config.parallel_attention if self.is_parallel_attention: # Not to apply allreduce inside the Attention/MLP layers. # allreduce applies after those layer. tp_group = None layers_range = config.mapping.pp_layers(config.num_hidden_layers) local_layer_idx = layer_idx - layers_range[0] self.attention = Attention( local_layer_idx=local_layer_idx, hidden_size=hidden_size, num_attention_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, max_position_embeddings=config.max_position_embeddings, attention_mask_type=AttentionMaskType.causal, dtype=dtype, tp_group=tp_group, tp_size=tp_size, tp_rank=tp_rank, bias=config.bias, position_embedding_type=config.position_embedding_type, quant_mode=config.quant_mode, ) mlp_hidden_size = hidden_size * 4 if config.intermediate_size is None else config.intermediate_size if self.new_decoder_architecture: # Layernorm before MLP. self.mlp_layernorm = LayerNorm(normalized_shape=hidden_size, eps=layernorm_epsilon, dtype=dtype) else: self.mlp_layernorm = None self.mlp = MLP( hidden_size=hidden_size, ffn_hidden_size=mlp_hidden_size, hidden_act=config.hidden_act, dtype=dtype, bias=config.bias, tp_group=tp_group, tp_size=tp_size, quant_mode=config.quant_mode, ) if self.is_parallel_attention: self.post_layernorm = None else: self.post_layernorm = LayerNorm(normalized_shape=hidden_size, dtype=dtype) @property def is_parallel_attention(self): return self.new_decoder_architecture or self.parallel_attn def forward(self, hidden_states: Tensor, attention_mask=None, use_cache=False, kv_cache_params=None, attention_params=None): assert isinstance(hidden_states, Tensor) residual = hidden_states if self.new_decoder_architecture: mlp_ln_output = self.mlp_layernorm(hidden_states) hidden_states = self.input_layernorm(hidden_states) input_ln_output = hidden_states attention_output = self.attention(hidden_states, attention_mask=attention_mask, use_cache=use_cache, kv_cache_params=kv_cache_params, attention_params=attention_params) if use_cache: attention_output, presents = attention_output if not self.new_decoder_architecture: if self.parallel_attn: hidden_states = input_ln_output else: hidden_states = residual + attention_output residual = hidden_states hidden_states = self.post_layernorm(hidden_states) else: hidden_states = mlp_ln_output hidden_states = self.mlp(hidden_states) if self.is_parallel_attention: hidden_states = hidden_states + attention_output if self.config.mapping.tp_size > 1: hidden_states = allreduce(hidden_states, self.config.mapping.tp_group) hidden_states = residual + hidden_states if use_cache: return hidden_states, presents return hidden_states class FalconModel(Module): def __init__(self, config: PretrainedConfig): super().__init__() self.config = config if config.mapping.is_first_pp_rank(): self.vocab_embedding = Embedding(config.vocab_size, config.hidden_size, dtype=config.dtype) self.layers = DecoderLayerList(FalconDecoderLayer, config) if config.mapping.is_last_pp_rank(): self.ln_f = LayerNorm(normalized_shape=config.hidden_size, dtype=config.dtype) def forward(self, input_ids: Tensor, position_ids=None, use_cache=False, attention_mask=None, kv_cache_params=None, attention_params=None, hidden_states=None): if self.config.mapping.is_first_pp_rank(): hidden_states = self.vocab_embedding(input_ids) else: hidden_states = recv(hidden_states, self.config.mapping.prev_pp_rank()) hidden_states = self.layers(hidden_states, use_cache=use_cache, attention_mask=attention_mask, kv_cache_params=kv_cache_params, attention_params=attention_params) if use_cache: hidden_states, presents = hidden_states if self.config.mapping.is_last_pp_rank(): hidden_states = self.ln_f(hidden_states) else: hidden_states = send(hidden_states, self.config.mapping.next_pp_rank()) if use_cache: return (hidden_states, tuple(presents)) return hidden_states class FalconForCausalLM(DecoderModelForCausalLM): def __init__(self, config: PretrainedConfig): self.check_config(config) transformer = FalconModel(config) if config.mapping.is_last_pp_rank(): vocab_size_padded = pad_vocab_size(config.vocab_size, config.mapping.tp_size) lm_head = ColumnLinear(config.hidden_size, vocab_size_padded, bias=False, dtype=config.dtype, tp_group=config.mapping.tp_group, tp_size=config.mapping.tp_size, gather_output=True) else: lm_head = None super().__init__(config, transformer, lm_head) def check_config(self, config): config.set_if_not_exist('bias', True) config.set_if_not_exist('new_decoder_architecture', False) config.set_if_not_exist('parallel_attention', False)