# 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, is_gated_activation, non_gated_version, recv,
send)
from ...layers import (MLP, MOE, Attention, AttentionMaskType, ColumnLinear,
Embedding, GatedMLP, LayerNorm, MoeConfig,
PositionEmbeddingType)
from ...lora_manager import LoraConfig, use_lora
from ...mapping import Mapping
from ...module import Module
from ...quantization import QuantMode
from ...quantization.functional import quantize_fp8_per_token
from ...quantization.layers import Fp8RowwiseMLP
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
QuantConfig, check_share_embedding)
from .config import GPTConfig
from .convert import (load_hf_gpt, load_weights_from_hf_model,
load_weights_from_nemo)
def MLPFactory(hidden_size,
ffn_hidden_size,
hidden_act,
bias=True,
dtype=None,
moe_config: MoeConfig = MoeConfig(),
tp_group=None,
tp_size=1,
mapping=Mapping(),
quant_mode=QuantMode(0),
inner_layernorm=False,
eps=1e-05):
if moe_config.has_moe():
return MOE(moe_config,
hidden_size,
ffn_hidden_size,
hidden_act,
mapping=mapping,
bias=bias,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=quant_mode)
MLPClass = GatedMLP if is_gated_activation(hidden_act) else MLP
hidden_act = non_gated_version(hidden_act)
return MLPClass(
hidden_size,
ffn_hidden_size,
hidden_act,
bias,
dtype,
tp_group,
tp_size,
quant_mode,
inner_layernorm=inner_layernorm,
eps=eps,
)
class GPTDecoderLayer(Module):
def __init__(self, config: GPTConfig, layer_idx: int):
super().__init__()
self.layer_idx = layer_idx
self.config = config
tp_group = config.mapping.tp_group
tp_size = config.mapping.tp_size
tp_rank = config.mapping.tp_rank
self.input_layernorm = LayerNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
layers_range = config.mapping.pp_layers(config.num_hidden_layers)
local_layer_idx = layer_idx - layers_range[0]
inner_layernorm = config.inner_layernorm if hasattr(
config, "inner_layernorm") else False
attention_head_size = config.head_size if hasattr(config,
"head_size") else None
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,
num_layers=config.num_hidden_layers,
q_scaling=config.q_scaling,
apply_query_key_layer_scaling=config.apply_query_key_layer_scaling,
dtype=config.dtype,
attention_mask_type=AttentionMaskType.causal,
attention_head_size=attention_head_size,
position_embedding_type=config.position_embedding_type,
rotary_embedding_percentage=config.rotary_pct,
rotary_embedding_base=config.rotary_base,
rotary_embedding_scaling=config.rotary_scaling,
bias=config.bias,
tp_group=tp_group,
tp_size=tp_size,
tp_rank=tp_rank,
quant_mode=config.quant_mode,
qk_layernorm=config.qk_layernorm,
inner_layernorm=inner_layernorm,
eps=config.norm_epsilon)
mlp_hidden_size = config.hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
self.norm_before_bmm1 = config.norm_before_bmm1 if hasattr(
config, "norm_before_bmm1") else False
self.mlp = MLPFactory(hidden_size=config.hidden_size,
ffn_hidden_size=mlp_hidden_size,
hidden_act=config.hidden_act,
dtype=config.dtype,
bias=config.bias,
moe_config=config.moe,
tp_group=tp_group,
tp_size=tp_size,
mapping=config.mapping,
quant_mode=config.quant_mode,
inner_layernorm=inner_layernorm,
eps=config.norm_epsilon)
self.post_layernorm = LayerNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
def forward(self,
hidden_states: Tensor,
attention_mask=None,
use_cache=False,
kv_cache_params=None,
attention_params=None,
lora_layer_params=None,
spec_decoding_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,
spec_decoding_params=spec_decoding_params,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
lora_layer_params=lora_layer_params,
norm_before_bmm1=self.norm_before_bmm1)
if use_cache:
attention_output, presents = attention_output
hidden_states = residual + attention_output
residual = hidden_states
hidden_states = self.post_layernorm(hidden_states)
# Quantize per-token for fp8
if isinstance(self.mlp, Fp8RowwiseMLP):
hidden_states = quantize_fp8_per_token(hidden_states)
hidden_states = self.mlp(hidden_states,
lora_layer_params=lora_layer_params)
hidden_states = residual + hidden_states
if use_cache:
return (hidden_states, presents)
return hidden_states
[docs]
class GPTModel(Module):
def __init__(self, config: GPTConfig):
super().__init__()
self.mapping = config.mapping
self.position_embedding_type = config.position_embedding_type
if config.mapping.is_first_pp_rank():
self.vocab_embedding = Embedding(config.vocab_size,
config.hidden_size,
dtype=config.dtype)
self.embedding_scale = config.embedding_scale
if config.position_embedding_type == PositionEmbeddingType.learned_absolute:
self.position_embedding = Embedding(
num_embeddings=config.max_position_embeddings,
embedding_dim=config.hidden_size,
dtype=config.dtype)
self.layers = DecoderLayerList(GPTDecoderLayer, config)
if config.mapping.is_last_pp_rank():
self.ln_f = LayerNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
[docs]
def forward(self,
input_ids,
position_ids,
use_cache=False,
attention_mask=None,
kv_cache_params=None,
attention_params=None,
hidden_states=None,
prompt_embedding_table=None,
prompt_tasks=None,
prompt_vocab_size=None,
lora_params=None,
spec_decoding_params=None):
if self.mapping.is_first_pp_rank():
ptuning_args = [
prompt_embedding_table, prompt_tasks, prompt_vocab_size
] if prompt_embedding_table is not None else []
hidden_states = self.vocab_embedding(input_ids, *ptuning_args)
if self.embedding_scale is not None:
hidden_states *= self.embedding_scale
if self.position_embedding_type == PositionEmbeddingType.learned_absolute:
hidden_states = hidden_states + self.position_embedding(
position_ids)
else:
hidden_states = recv(hidden_states, self.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,
lora_params=lora_params,
spec_decoding_params=spec_decoding_params)
if use_cache:
hidden_states, presents = hidden_states
if self.mapping.is_last_pp_rank():
hidden_states = self.ln_f(hidden_states)
else:
hidden_states = send(hidden_states, self.mapping.next_pp_rank())
if use_cache:
return (hidden_states, tuple(presents))
return hidden_states
[docs]
class GPTForCausalLM(DecoderModelForCausalLM):
config_class = GPTConfig
def __init__(self, config: GPTConfig):
transformer = GPTModel(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
self.trtllm_modules_to_hf_modules = {
"attn_q": "q_proj",
"attn_k": "k_proj",
"attn_v": "v_proj",
"attn_dense": "o_proj",
"mlp_h_to_4h": "c_fc",
"mlp_4h_to_h": "c_proj",
}
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: Optional[QuantConfig] = None,
**kwargs):
''' Create a LLaMAForCausalLM object from give parameters
'''
import transformers
load_model_on_cpu = kwargs.pop('load_model_on_cpu', False)
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 = GPTConfig.from_hugging_face(hf_config_or_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
**kwargs)
if not use_preloading:
hf_model = load_hf_gpt(hf_model_dir, load_model_on_cpu)
weights = load_weights_from_hf_model(hf_model, config)
check_share_embedding(weights, config)
model = cls(config)
model.load(weights)
return model
[docs]
@classmethod
def quantize(
cls,
hf_model_dir: str,
output_dir: str,
dtype: str = 'auto',
mapping: Optional[Mapping] = None,
quant_config: Optional[QuantConfig] = None,
*,
device: str = 'cuda',
calib_dataset: str = 'cnn_dailymail',
calib_batches: int = 512,
calib_batch_size: int = 1,
calib_max_seq_length: int = 512,
random_seed: int = 1234,
tokenizer_max_seq_length: int = 2048,
**kwargs,
):
if quant_config.requires_modelopt_quantization:
# modelopt quantization flow
super().quantize(hf_model_dir,
output_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
device=device,
calib_dataset=calib_dataset,
calib_batches=calib_batches,
calib_batch_size=calib_batch_size,
calib_max_seq_length=calib_max_seq_length,
random_seed=random_seed,
tokenizer_max_seq_length=tokenizer_max_seq_length)
elif quant_config.requires_calibration:
# non-modelopt quantization flow
from . import convert
config = GPTConfig.from_hugging_face(hf_model_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
**kwargs)
convert.quantize(hf_model_dir,
output_dir,
config=config,
device=device,
calib_dataset=calib_dataset)
else:
raise ValueError(
f"The quant_config ({quant_config}) does not require calibration, try {cls.__name__}.from_hugging_face instead."
)
[docs]
@classmethod
def from_nemo(cls,
nemo_ckpt_dir: str,
dtype: str = 'auto',
mapping: Optional[Mapping] = None,
quant_config: Optional[QuantConfig] = None,
**kwargs):
config = GPTConfig.from_nemo(nemo_ckpt_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
**kwargs)
weights = load_weights_from_nemo(nemo_ckpt_dir, config, **kwargs)
check_share_embedding(weights, config)
model = cls(config)
model.load(weights)
return model
[docs]
def use_lora(self, lora_config: LoraConfig):
use_lora(self, lora_config, self.trtllm_modules_to_hf_modules)