# 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,
check_share_embedding)
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
[docs]
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)
[docs]
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
[docs]
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)
[docs]
@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:
hf_model = transformers.AutoModelForCausalLM.from_pretrained(
hf_model_dir, torch_dtype='auto', trust_remote_code=True)
weights = load_weights_from_hf_model(hf_model, config)
check_share_embedding(weights, config)
model = GPTJForCausalLM(config)
model.load(weights)
return model