# 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 typing import Optional, Union from ..._utils import pad_vocab_size from ...functional import PositionEmbeddingType, Tensor, allreduce from ...layers import (MLP, Attention, AttentionMaskType, ColumnLinear, Embedding, LayerNorm) from ...mapping import Mapping from ...module import Module from ..modeling_utils import DecoderLayerList, DecoderModelForCausalLM from .config import GPTJConfig from .convert import load_weights_from_hf_model class GPTJDecoderLayer(Module): def __init__(self, config: GPTJConfig, layer_idx: int): super().__init__() self.layer_idx = layer_idx self.config = config hidden_size = config.hidden_size num_attention_heads = config.num_attention_heads rotary_dim = config.rotary_dim dtype = config.dtype 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) 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=num_attention_heads, rotary_embedding_percentage=rotary_dim / (hidden_size // num_attention_heads), max_position_embeddings=config.max_position_embeddings, attention_mask_type=AttentionMaskType.causal, dtype=dtype, tp_group=None, tp_size=tp_size, tp_rank=tp_rank, bias=False, position_embedding_type=PositionEmbeddingType.rope_gptj, quant_mode=config.quant_mode) self.mlp = MLP(hidden_size=hidden_size, ffn_hidden_size=hidden_size * 4, hidden_act=config.hidden_act, dtype=dtype, bias=True, tp_group=None, tp_size=tp_size, quant_mode=config.quant_mode) 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 hidden_states = self.input_layernorm(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 attention_output = attention_output feed_forward_hidden_states = self.mlp(hidden_states) hidden_states = attention_output + feed_forward_hidden_states if self.config.mapping.tp_size > 1: hidden_states = allreduce(hidden_states, self.config.mapping.tp_group) hidden_states = hidden_states + residual if use_cache: return (hidden_states, presents) return hidden_states class GPTJModel(Module): def __init__(self, config: GPTJConfig): 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(GPTJDecoderLayer, 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 = self.vocab_embedding(input_ids) 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 hidden_states = self.ln_f(hidden_states) if use_cache: return (hidden_states, tuple(presents)) return hidden_states class GPTJForCausalLM(DecoderModelForCausalLM): config_class = GPTJConfig def __init__(self, config: GPTJConfig): transformer = GPTJModel(config) vocab_size_padded = pad_vocab_size(config.vocab_size, config.mapping.tp_size) if config.mapping.is_last_pp_rank(): lm_head = ColumnLinear(config.hidden_size, vocab_size_padded, bias=True, 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) @classmethod def from_hugging_face( cls, hf_model_or_dir: Union[str, 'transformers.PreTrainedModel'], dtype: str = 'auto', mapping: Optional[Mapping] = None, quant_config=None, **kwargs): import transformers use_preloading = isinstance(hf_model_or_dir, transformers.PreTrainedModel) if use_preloading: hf_model = hf_model_or_dir hf_config_or_dir = hf_model.config else: hf_model_dir = hf_model_or_dir hf_config_or_dir = hf_model_or_dir config = GPTJConfig.from_hugging_face(hf_config_or_dir, dtype=dtype, mapping=mapping, quant_config=quant_config, **kwargs) if not use_preloading: trust_remote_code = kwargs.pop('trust_remote_code', True) hf_model = transformers.AutoModelForCausalLM.from_pretrained( hf_model_dir, torch_dtype='auto', trust_remote_code=trust_remote_code) weights = load_weights_from_hf_model(hf_model, config) model = GPTJForCausalLM(config) model.load(weights) return model