File size: 6,902 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 |
# 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
from transformers import AutoModelForCausalLM
from ..._utils import pad_vocab_size
from ...functional import PositionEmbeddingType, Tensor
from ...layers import (MLP, Attention, AttentionMaskType, Embedding, LayerNorm,
ParallelLMHead)
from ...module import Module
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
PretrainedConfig, save_checkpoint)
from .convert import convert_hf_config, convert_hf_weights
class PhiDecoderLayer(Module):
def __init__(self, config: PretrainedConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
tp_group = config.mapping.tp_group
tp_size = config.mapping.tp_size
self.input_layernorm = LayerNorm(normalized_shape=config.hidden_size,
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,
rotary_embedding_percentage=config.partial_rotary_factor,
position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
rotary_embedding_base=config.rotary_base,
max_position_embeddings=config.max_position_embeddings,
dtype=config.dtype,
attention_mask_type=AttentionMaskType.causal,
bias=True,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=config.quant_mode)
self.mlp = MLP(hidden_size=config.hidden_size,
ffn_hidden_size=config.intermediate_size,
hidden_act=config.hidden_act,
dtype=config.dtype,
tp_group=tp_group,
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,
):
residual = hidden_states
input_layernorm_output = self.input_layernorm(hidden_states)
attention_output = self.attention(
input_layernorm_output,
attention_mask=attention_mask,
use_cache=use_cache,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
norm_before_bmm1=True,
)
if use_cache:
attention_output, presents = attention_output
feed_forward_hidden_states = self.mlp(input_layernorm_output, )
hidden_states = attention_output + feed_forward_hidden_states + residual
if use_cache:
return (hidden_states, presents)
return hidden_states
class PhiModel(Module):
def __init__(self, config: PretrainedConfig):
super().__init__()
self.vocab_embedding = Embedding(num_embeddings=config.vocab_size,
embedding_dim=config.hidden_size,
dtype=config.dtype)
self.layers = DecoderLayerList(PhiDecoderLayer, config)
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,
):
args = [prompt_embedding_table, prompt_tasks, prompt_vocab_size
] if prompt_embedding_table is not None else []
hidden_states = self.vocab_embedding(input_ids, *args)
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
class PhiForCausalLM(DecoderModelForCausalLM):
def __init__(self, config: PretrainedConfig):
self.check_config(config)
transformer = PhiModel(config)
vocab_size_padded = pad_vocab_size(config.vocab_size,
config.mapping.tp_size)
lm_head = ParallelLMHead(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)
super().__init__(config, transformer, lm_head)
def check_config(self, config):
config.set_if_not_exist('partial_rotary_factor', 0.4)
config.set_if_not_exist('rotary_base', 10000.0)
@classmethod
def convert_hf_checkpoint(cls,
hf_model_dir: str,
dtype: Optional[str] = "float16",
output_dir: Optional[str] = None,
args=None):
'''
Convert Huggingface checkpoint to TRT-LLM checkpoint
'''
hf_model = AutoModelForCausalLM.from_pretrained(hf_model_dir,
torch_dtype="auto",
trust_remote_code=True)
config = convert_hf_config(hf_model.config, dtype, args)
weights = convert_hf_weights(hf_model, dtype, args)
if output_dir:
save_checkpoint(output_dir, config=config, weights=weights)
return {"weights": weights, "config": config}
|