# 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 PositionEmbeddingType, Tensor, allreduce from ...layers import (MLP, Attention, AttentionMaskType, ColumnLinear, Embedding, KeyValueCacheParams, LayerNorm) from ...module import Module from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM, PretrainedConfig) class GPTJDecoderLayer(Module): def __init__(self, config: PretrainedConfig, 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) self.attention = Attention( layer_idx=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: PretrainedConfig): super().__init__() self.config = config if config.mapping.is_first_pp_rank(): if config.use_parallel_embedding: self.vocab_embedding = Embedding( config.vocab_size, config.hidden_size, dtype=config.dtype, tp_group=config.mapping.tp_group, tp_size=config.mapping.tp_size, sharding_dim=config.embedding_sharding_dim, tp_rank=config.mapping.tp_rank) else: 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) kv_cache_params.fill_none_tensor_list(len(self.layers)) if use_cache: presents = [] for layer, past in zip(self.layers, kv_cache_params.past_key_value): hidden_states = layer( hidden_states, use_cache=use_cache, kv_cache_params=KeyValueCacheParams( past_key_value=[past], host_past_key_value_lengths=kv_cache_params. host_past_key_value_lengths, host_max_attention_window_sizes=kv_cache_params. host_max_attention_window_sizes, host_sink_token_length=kv_cache_params. host_sink_token_length, kv_cache_block_pointers=kv_cache_params. kv_cache_block_pointers, host_kv_cache_block_pointers=kv_cache_params. host_kv_cache_block_pointers, cache_indirection=kv_cache_params.cache_indirection), attention_params=attention_params) if use_cache: presents.append(hidden_states[1]) hidden_states = hidden_states[0] hidden_states = self.ln_f(hidden_states) if use_cache: return (hidden_states, tuple(presents)) return hidden_states class GPTJForCausalLM(DecoderModelForCausalLM): def __init__(self, config: PretrainedConfig): self.check_config(config) 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) def check_config(self, config): config.set_if_not_exist('rotary_dim', 64)