File size: 7,350 Bytes
5000658 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 |
# 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 Tensor, recv, send
from ...layers import (MOE, Attention, AttentionMaskType, ColumnLinear,
Embedding, GatedMLP, LayerNorm)
from ...module import Module
from ..modeling_utils import DecoderLayerList, DecoderModelForCausalLM
from .config import DbrxConfig
class DbrxDecoderLayer(Module):
def __init__(self, config: DbrxConfig, layer_idx: int):
super().__init__()
self.layer_idx = layer_idx
self.config = config
self.input_layernorm = LayerNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
bias=False,
dtype=config.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=config.hidden_size,
num_attention_heads=config.num_attention_heads,
num_kv_heads=config.num_key_value_heads,
max_position_embeddings=config.max_position_embeddings,
dtype=config.dtype,
attention_mask_type=AttentionMaskType.causal,
bias=config.bias,
position_embedding_type=config.position_embedding_type,
rotary_embedding_base=config.rotary_base,
rotary_embedding_scaling=config.rotary_scaling,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
quant_mode=config.quant_mode,
clip_qkv=config.clip_qkv)
ClsMLP = GatedMLP
mlp_kwargs = {}
if config.moe.has_moe():
ClsMLP = MOE
mlp_kwargs = {
"moe_config": config.moe,
"mapping": config.mapping,
}
self.mlp = ClsMLP(hidden_size=config.hidden_size,
ffn_hidden_size=config.intermediate_size,
hidden_act=config.hidden_act,
dtype=config.dtype,
bias=config.bias,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
quant_mode=config.quant_mode,
**mlp_kwargs)
self.post_layernorm = LayerNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
bias=False,
dtype=config.dtype)
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
hidden_states = residual + attention_output
residual = hidden_states
hidden_states = self.post_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
if use_cache:
return (hidden_states, presents)
return hidden_states
class DbrxModel(Module):
def __init__(self, config: DbrxConfig):
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(DbrxDecoderLayer, config)
if config.mapping.is_last_pp_rank():
self.ln_f = LayerNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
bias=False,
dtype=config.dtype)
def forward(self,
input_ids,
position_ids,
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
class DbrxForCausalLM(DecoderModelForCausalLM):
config_class = DbrxConfig
def __init__(self, config: DbrxConfig):
transformer = DbrxModel(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=config.bias,
dtype=config.dtype,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
gather_output=True)
else:
lm_head = None
self.quant_mode = config.quant_mode
self.mapping = config.mapping
super().__init__(config, transformer, lm_head)
|