# 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 Tensor, allreduce, recv, send
from ...layers import (MLP, Attention, AttentionMaskType, ColumnLinear,
Embedding, LayerNorm)
from ...mapping import Mapping
from ...module import Module
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
QuantConfig, check_share_embedding)
from .config import FalconConfig
from .convert import load_weights_from_hf_by_shard, load_weights_from_hf_model
class FalconDecoderLayer(Module):
def __init__(self, config: FalconConfig, layer_idx: int):
super().__init__()
self.layer_idx = layer_idx
self.config = config
hidden_size = config.hidden_size
dtype = config.dtype
tp_group = config.mapping.tp_group
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.new_decoder_architecture = config.new_decoder_architecture
self.parallel_attn = config.parallel_attention
self.num_ln_in_parallel_attn = config.num_ln_in_parallel_attn
if self.num_ln_in_parallel_attn is None and self.new_decoder_architecture:
self.num_ln_in_parallel_attn = 2
if self.is_parallel_attention:
# Not to apply allreduce inside the Attention/MLP layers.
# allreduce applies after those layer.
tp_group = None
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=config.num_attention_heads,
num_kv_heads=config.num_key_value_heads,
max_position_embeddings=config.max_position_embeddings,
attention_mask_type=AttentionMaskType.causal,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
tp_rank=tp_rank,
bias=config.bias,
position_embedding_type=config.position_embedding_type,
rotary_embedding_base=config.rotary_base,
quant_mode=config.quantization.quant_mode,
)
mlp_hidden_size = hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
if self.new_decoder_architecture and self.num_ln_in_parallel_attn == 2:
# Layernorm before MLP.
self.mlp_layernorm = LayerNorm(normalized_shape=hidden_size,
eps=layernorm_epsilon,
dtype=dtype)
else:
self.mlp_layernorm = None
self.mlp = MLP(
hidden_size=hidden_size,
ffn_hidden_size=mlp_hidden_size,
hidden_act=config.hidden_act,
dtype=dtype,
bias=config.bias,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=config.quantization.quant_mode,
)
if self.is_parallel_attention:
self.post_layernorm = None
else:
self.post_layernorm = LayerNorm(normalized_shape=hidden_size,
dtype=dtype)
@property
def is_parallel_attention(self):
return self.new_decoder_architecture or self.parallel_attn
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
if self.new_decoder_architecture and self.num_ln_in_parallel_attn == 2:
mlp_ln_output = self.mlp_layernorm(hidden_states)
hidden_states = self.input_layernorm(hidden_states)
input_ln_output = 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
if not self.new_decoder_architecture:
if self.parallel_attn:
hidden_states = input_ln_output
else:
hidden_states = residual + attention_output
residual = hidden_states
hidden_states = self.post_layernorm(hidden_states)
elif self.num_ln_in_parallel_attn == 2:
hidden_states = mlp_ln_output
if (self.new_decoder_architecture and self.parallel_attn
and self.num_ln_in_parallel_attn == 1):
hidden_states = input_ln_output
hidden_states = self.mlp(hidden_states)
if self.is_parallel_attention:
hidden_states = hidden_states + attention_output
if self.config.mapping.tp_size > 1:
hidden_states = allreduce(hidden_states,
self.config.mapping.tp_group)
hidden_states = residual + hidden_states
if use_cache:
return hidden_states, presents
return hidden_states
[docs]
class FalconModel(Module):
def __init__(self, config: FalconConfig):
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(FalconDecoderLayer, 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=None):
if self.config.mapping.is_first_pp_rank():
hidden_states = self.vocab_embedding(input_ids)
else:
hidden_states = recv(hidden_states,
self.config.mapping.prev_pp_rank())
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.config.mapping.is_last_pp_rank():
hidden_states = self.ln_f(hidden_states)
else:
hidden_states = send(hidden_states,
self.config.mapping.next_pp_rank())
if use_cache:
return (hidden_states, tuple(presents))
return hidden_states
[docs]
class FalconForCausalLM(DecoderModelForCausalLM):
config_class = FalconConfig
def __init__(self, config: FalconConfig):
self.check_config(config)
transformer = FalconModel(config)
if config.mapping.is_last_pp_rank():
vocab_size_padded = pad_vocab_size(config.vocab_size,
config.mapping.tp_size)
lm_head = ColumnLinear(config.hidden_size,
vocab_size_padded,
bias=False,
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]
def check_config(self, config):
config.set_if_not_exist('bias', True)
config.set_if_not_exist('new_decoder_architecture', False)
config.set_if_not_exist('parallel_attention', False)
[docs]
@classmethod
def from_hugging_face(
cls,
hf_model_or_dir: Union[str, 'transformers.PreTrainedModel'],
dtype: str = 'auto',
mapping: Optional[Mapping] = None,
quant_config: Optional[QuantConfig] = None,
**kwargs):
''' Create a FalconForCausalLM object from give parameters
'''
import transformers
load_by_shard = kwargs.pop('load_by_shard', False)
# load_model_on_cpu is ignored here, since specify target device_map will fail when workers > 1.
assert hf_model_or_dir is not None
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 = FalconConfig.from_hugging_face(hf_config_or_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
**kwargs)
if use_preloading:
assert not load_by_shard
weights = load_weights_from_hf_model(hf_model, config)
elif load_by_shard:
weights = load_weights_from_hf_by_shard(hf_model_dir, config)
else:
hf_model = transformers.AutoModelForCausalLM.from_pretrained(
hf_model_dir, torch_dtype='auto')
weights = load_weights_from_hf_model(hf_model, config)
check_share_embedding(weights, config)
model = cls(config)
model.load(weights)
return model