# 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
[docs]
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)
[docs]
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
[docs]
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)
[docs]
def check_config(self):
self.config.set_if_not_exist('use_parallel_embedding', False)
self.config.set_if_not_exist('embedding_sharding_dim', 0)
self.config.set_if_not_exist('share_embedding_table', False)
self.config.set_if_not_exist('do_layer_norm_before', False)