# 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
import torch
from transformers import AutoModel
from ..._common import default_net
from ..._utils import pad_vocab_size
from ...functional import Tensor, concat, shape
from ...layers import (MLP, Attention, AttentionMaskType, AttentionParams,
ColumnLinear, Embedding, KeyValueCacheParams, LayerNorm,
RmsNorm)
from ...mapping import Mapping
from ...module import Module
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
QuantConfig, check_share_embedding)
from .config import GLM_ARCH1_VERSIONS, GLM_ARCH2_VERSIONS, ChatGLMConfig
from .convert import load_weights_from_hf_model
class ChatGLMDecoderLayer(Module):
def __init__(self, config: ChatGLMConfig, layer_idx: int):
super().__init__()
self.layer_idx = layer_idx
self.config = config
self.chatglm_version = config.chatglm_version
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.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
self.alpha = (2 * config.num_hidden_layers)**0.5
norm_cls = RmsNorm if config.rmsnorm else LayerNorm
if config.chatglm_version == 'glm':
attention_mask_type = AttentionMaskType.bidirectionalglm
elif config.chatglm_version == 'chatglm':
attention_mask_type = AttentionMaskType.bidirectional
elif config.chatglm_version in GLM_ARCH2_VERSIONS:
attention_mask_type = AttentionMaskType.causal
self.input_layernorm = norm_cls(
normalized_shape=hidden_size,
eps=layernorm_epsilon,
elementwise_affine=True,
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=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,
apply_query_key_layer_scaling=config.apply_query_key_layer_scaling,
attention_mask_type=attention_mask_type,
bias=config.add_qkv_bias,
dense_bias=config.add_bias_linear,
dtype=config.dtype,
position_embedding_type=config.position_embedding_type,
rotary_embedding_base=config.rotary_base,
rotary_embedding_scaling=config.rotary_scaling,
rotary_embedding_percentage=config.rotary_pct,
tp_group=tp_group,
tp_size=tp_size,
tp_rank=tp_rank,
quant_mode=config.quant_mode,
q_scaling=1.0,
cross_attention=False,
relative_attention=False,
max_distance=0,
num_buckets=0,
)
mlp_hidden_size = hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
self.mlp = MLP(
hidden_size=hidden_size,
ffn_hidden_size=mlp_hidden_size,
hidden_act=config.hidden_act,
bias=config.add_bias_linear,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=config.quant_mode,
)
self.post_layernorm = norm_cls(
normalized_shape=hidden_size,
eps=layernorm_epsilon,
elementwise_affine=True,
dtype=dtype,
)
def forward(
self,
hidden_states: Tensor,
attention_mask: Tensor = None,
position_ids: Tensor = None, # only used in ChatGLM-6B
use_cache: bool = False,
kv_cache_params: KeyValueCacheParams = None,
attention_params: AttentionParams = None,
):
norm_output = self.input_layernorm(hidden_states)
attention_output = self.attention(
hidden_states=norm_output,
attention_mask=attention_mask,
use_cache=use_cache,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
encoder_output=None,
position_embedding=position_ids,
)
if use_cache:
attention_output, presents = attention_output
if self.chatglm_version == 'chatglm':
residual = norm_output
norm_input = residual * self.alpha + attention_output
norm_output = self.post_layernorm(norm_input)
mlp_output = self.mlp(norm_output)
residual = norm_output
output = residual * self.alpha + mlp_output
else:
residual = norm_output if self.apply_residual_connection_post_layernorm else hidden_states
norm_input = residual + attention_output
norm_output = self.post_layernorm(norm_input)
mlp_output = self.mlp(norm_output)
residual = norm_output if self.apply_residual_connection_post_layernorm else norm_input
output = residual + mlp_output
if use_cache:
return (output, presents)
return output
[docs]
class ChatGLMModel(Module):
def __init__(self, config: ChatGLMConfig):
super().__init__()
self.chatglm_version = config.chatglm_version
norm_cls = RmsNorm if config.rmsnorm else LayerNorm
self.vocab_embedding = Embedding(config.vocab_size,
config.hidden_size,
dtype=config.dtype)
if config.chatglm_version == 'glm':
self.position_embedding = Embedding(
config.max_position_embeddings + 1,
config.hidden_size,
dtype=config.dtype,
)
self.block_embedding = Embedding(
config.max_position_embeddings + 1,
config.hidden_size,
dtype=config.dtype,
)
self.layers = DecoderLayerList(ChatGLMDecoderLayer, config)
self.ln_f = norm_cls(
normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
elementwise_affine=True,
dtype=config.dtype,
)
[docs]
def forward(
self,
input_ids: Tensor = None,
position_ids: Tensor = None, # only used in ChatGLM-6B
use_cache: bool = False,
attention_mask: Tensor = None,
kv_cache_params: KeyValueCacheParams = None,
attention_params: AttentionParams = None,
):
hidden_states = self.vocab_embedding(input_ids)
if self.chatglm_version == 'glm':
if default_net().plugin_config.remove_input_padding:
position_ids_list = position_ids.split(1, dim=0)
else:
position_ids_list = position_ids.split(1, dim=1)
position_embedding = self.position_embedding(position_ids_list[0])
block_embedding = self.block_embedding(position_ids_list[1])
position_embedding = position_embedding + block_embedding
if default_net().plugin_config.remove_input_padding:
position_embedding = position_embedding.view(
concat([
shape(position_embedding, 1),
shape(position_embedding, 2)
]))
else:
position_embedding = position_embedding.view(
concat([
shape(position_embedding, 0),
shape(position_embedding, 2),
shape(position_embedding, 3),
]))
hidden_states = hidden_states + position_embedding
hidden_states = self.layers(hidden_states,
use_cache=use_cache,
attention_mask=attention_mask,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
position_ids=position_ids)
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 ChatGLMForCausalLM(DecoderModelForCausalLM):
config_class = ChatGLMConfig
def __init__(self, config: ChatGLMConfig):
transformer = ChatGLMModel(config)
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)
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
'''
load_model_on_cpu = kwargs.pop('load_model_on_cpu', False)
trust_remote_code = kwargs.pop('trust_remote_code', True)
config = ChatGLMConfig.from_hugging_face(hf_model_or_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
**kwargs)
if config.chatglm_version == 'glm':
device_map = 'cuda' if not load_model_on_cpu else 'cpu'
else:
device_map = 'auto' if not load_model_on_cpu else 'cpu'
hf_model = AutoModel.from_pretrained(
hf_model_or_dir,
trust_remote_code=trust_remote_code,
torch_dtype='auto' if config.chatglm_version != 'glm' else getattr(
torch, config.dtype),
device_map=device_map)
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 = ChatGLMConfig.from_hugging_face(hf_model_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
**kwargs)
convert.quantize(hf_model_dir,
output_dir,
config=config,
calib_dataset=calib_dataset,
device=device)
else:
raise ValueError(
f"The quant_config ({quant_config}) does not require calibration, try {cls.__name__}.from_hugging_face instead."
)