# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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 from ...layers import (MLP, Attention, AttentionMaskType, ColumnLinear, LayerNorm, PositionEmbeddingType) from ...module import Module from ..gpt.model import GPTEmbedding from ..modeling_utils import DecoderLayerList, DecoderModelForCausalLM class OPTDecoderLayer(Module): def __init__(self, config, layer_idx): super().__init__() self.layer_idx = layer_idx self.config = config self.do_layer_norm_before = self.config.do_layer_norm_before hidden_size = config.hidden_size dtype = config.dtype tp_group = config.mapping.tp_group tp_size = config.mapping.tp_size self.input_layernorm = LayerNorm(normalized_shape=hidden_size, dtype=dtype) self.attention = Attention( hidden_size, config.num_attention_heads, max_position_embeddings=config.max_position_embeddings, attention_mask_type=AttentionMaskType.causal, dtype=dtype, tp_group=tp_group, tp_size=tp_size) mlp_hidden_size = hidden_size * 4 if config.intermediate_size is None else config.intermediate_size self.mlp = MLP(hidden_size=hidden_size, ffn_hidden_size=mlp_hidden_size, hidden_act=config.hidden_act, dtype=dtype, tp_group=tp_group, tp_size=tp_size) self.post_layernorm = LayerNorm(normalized_shape=hidden_size, dtype=dtype) def forward(self, hidden_states: Tensor, attention_mask=None, use_cache=False, kv_cache_params=None, attention_params=None): residual = hidden_states attention_input = hidden_states if self.do_layer_norm_before: attention_input = self.input_layernorm(hidden_states) # At this point the hidden_states object must be a Tensor. assert isinstance(attention_input, Tensor) attention_output = self.attention(attention_input, 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 hidden_states = residual + attention_output if not self.do_layer_norm_before: hidden_states = self.input_layernorm(hidden_states) residual = hidden_states if self.do_layer_norm_before: hidden_states = self.post_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states if not self.do_layer_norm_before: hidden_states = self.post_layernorm(hidden_states) if use_cache: return (hidden_states, presents) return hidden_states class OPTModel(Module): def __init__(self, config): super().__init__() self.do_layer_norm_before = config.do_layer_norm_before use_parallel_embedding = config.use_parallel_embedding embedding_sharding_dim = config.embedding_sharding_dim use_prompt_tuning = config.use_prompt_tuning mapping = config.mapping self.embedding = GPTEmbedding( config.vocab_size, config.hidden_size, config.max_position_embeddings, position_embedding_type=PositionEmbeddingType.learned_absolute, dtype=config.dtype, use_prompt_tuning=use_prompt_tuning, tensor_parallel=mapping.tp_size if use_parallel_embedding else 1, tensor_parallel_group=mapping.tp_group if use_parallel_embedding else None, sharding_dim=embedding_sharding_dim, tp_rank=mapping.tp_rank) self.layers = DecoderLayerList(OPTDecoderLayer, config) if self.do_layer_norm_before: 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, prompt_embedding_table=None, prompt_tasks=None, prompt_vocab_size=None): hidden_states = self.embedding(input_ids, position_ids, prompt_embedding_table, prompt_tasks, prompt_vocab_size) 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.do_layer_norm_before: hidden_states = self.ln_f(hidden_states) if use_cache: return (hidden_states, tuple(presents)) return hidden_states class OPTForCausalLM(DecoderModelForCausalLM): def __init__(self, config): use_parallel_embedding = config.use_parallel_embedding embedding_sharding_dim = config.embedding_sharding_dim share_embedding_table = config.share_embedding_table mapping = config.mapping if share_embedding_table and mapping.tp_size > 1: if (not use_parallel_embedding) or (use_parallel_embedding and embedding_sharding_dim == 1): raise NotImplementedError( 'For multiple-processes cases, sharing the embedding table must set use_parallel_embedding=True and embedding_sharding_dim = 0' ) transformer = OPTModel(config) vocab_size_padded = pad_vocab_size(config.vocab_size, config.mapping.tp_size) share_weight = None if share_embedding_table: share_weight = transformer.embedding.vocab_embedding.weight lm_head = ColumnLinear(config.hidden_size, vocab_size_padded, bias=False, dtype=config.dtype, tp_group=mapping.tp_group, tp_size=mapping.tp_size, gather_output=True, share_weight=share_weight) super().__init__(config, transformer, lm_head)