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[to #42322933] add plug doc string 

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10337105
master
suluyan.sly yingda.chen 3 years ago
parent
4c99363804
commit
02c913a0fe
3 changed files with 240 additions and 212 deletions
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  1. +94
    -71
      modelscope/models/nlp/plug/configuration_plug.py
  2. +43
    -1
      modelscope/models/nlp/plug/distributed_plug.py
  3. +103
    -140
      modelscope/models/nlp/plug/modeling_plug.py

+ 94
- 71
modelscope/models/nlp/plug/configuration_plug.py View File

@@ -40,8 +40,6 @@ class PlugNLUConfig(PretrainedConfig):
max_position_embeddings=2048,
type_vocab_size=3,
initializer_range=0.00707,
deep_init=False,
deepspeed=False,
lr_decay_style='linear',
weight_decay=1e-2,
clip_grad=1.0,
@@ -53,20 +51,7 @@ class PlugNLUConfig(PretrainedConfig):
fp32_tokentypes=False,
layernorm_epsilon=1e-5,
dec_hidden_layers=6,
pruning_method=None,
pruning_mask_init='constant',
pruning_mask_scale=0.0,
pruning_initial_threshold=1.0,
pruning_final_threshold=0.01,
pruning_initial_warmup=1,
pruning_final_warmup=20,
pruning_module='decoder',
pruning_decay_step=50,
pruning_decay_type='exp',
ft_module=None,
attn_separate=False,
LR_weight_rank=8,
LR_mask_rank=8,
**kwargs):
super().__init__(layer_norm_eps=layernorm_epsilon, **kwargs)

@@ -82,8 +67,6 @@ class PlugNLUConfig(PretrainedConfig):
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.deep_init = deep_init
self.deepspeed = deepspeed
self.lr_decay_style = lr_decay_style
self.weight_decay = weight_decay
self.clip_grad = clip_grad
@@ -95,20 +78,7 @@ class PlugNLUConfig(PretrainedConfig):
self.layernorm_epsilon = layernorm_epsilon
self.fp32_tokentypes = fp32_tokentypes
self.dec_hidden_layers = dec_hidden_layers
self.pruning_method = pruning_method
self.pruning_mask_init = pruning_mask_init
self.pruning_mask_scale = pruning_mask_scale
self.pruning_module = pruning_module
self.pruning_initial_threshold = pruning_initial_threshold
self.pruning_final_threshold = pruning_final_threshold
self.pruning_initial_warmup = pruning_initial_warmup
self.pruning_final_warmup = pruning_final_warmup
self.pruning_decay_step = pruning_decay_step
self.pruning_decay_type = pruning_decay_type
self.ft_module = ft_module
self.attn_separate = attn_separate
self.LR_weight_rank = LR_weight_rank
self.LR_mask_rank = LR_mask_rank

@classmethod
def from_dict(cls, json_object):
@@ -148,47 +118,115 @@ class PlugNLUConfig(PretrainedConfig):


class PlugNLGConfig(PlugNLUConfig):
"""
This is the configuration class to store the configuration of a [`PlugModel`]. It is used to instantiate a
PLUG understanding model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the PLUG
[PLUG](https://modelscope.cn/models/damo/nlp_plug_text-generation_27B/summary) architecture.

Configuration objects inherit from [`PlugNLUConfig`] and can be used to control the model outputs. Read the
documentation from [`PlugNLUConfig`] for more information.

Args:
vocab_size (`int`, *optional*, defaults to 21504):
Padded vocabulary size of the PLUG model for vocab tensor parallel. Defines the number of different tokens
that can be represented by the `inputs_ids` passed when calling [`PlugModel`].
original_vocab_size (`int`, *optional*, defaults to 21128):
True vocabulary size of the PLUG model. Defines the number of different tokens that can be represented.
hidden_size (`int`, *optional*, defaults to 8192):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
dec_hidden_layers (`int`, *optional*, defaults to 6):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 128):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 32768):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the Transformer Attention.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 3):
The vocabulary size of the `token_type_ids` passed when calling [`PlugModel`].
initializer_range (`float`, *optional*, defaults to 0.00707):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
lr_decay_style (`str`, *optional*, defaults to 'linear'):
The decay style of learning rate during fine-tunining. If string, `"linear"`, `"cosine"`, `"exponential"`,
`"constant"`, `"None"` are supported.
weight_decay (`float`, *optional*, defaults to 1e-2):
Decoupled weight decay to apply.
clip_grad (`float`, *optional*, defaults to 1.0):
Maximum gradient norm for gradient clipping.
warmup (`float`, *optional*, defaults to 0.01):
Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
pre_ln (`boolean`, *optional*, defaults to `True`):
Whether or not to apply LayerNorm to the input instead of the output in the blocks.
fp16 (`boolean`, *optional*, defaults to `True`):
Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.
fp32_layernorm (`boolean`, *optional*, defaults to `True`):
Whether to use fp32 32-bit precision LayerNorm training while the argument `fp16` set to `True`.
fp32_embedding (`boolean`, *optional*, defaults to `False`):
Whether to use fp32 32-bit precision Embedding training while the argument `fp16` set to `True`.
fp32_tokentypes (`boolean`, *optional*, defaults to `False`):
Whether to use fp32 32-bit precision token types training while the argument `fp16` set to `True`.
layernorm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
attn_separate (`boolean`, *optional*, defaults to `False`):
Whether or not to separate query-key-value to query, key, value in the Attention.

Example:

```python
>>> # The PLUG model has 27B parameters and usually need to run on multiple GPUs. The example given
>>> # here only initializes a slice of the model on a single GPU.
>>> # Check out the [`~DistributedPipeline.__init__`] method to initialize entire PLUG model.
>>> from modelscope.models.nlp.plug import PlugNLGConfig, PlugModel

>>> # Initializing a Plug configuration
>>> configuration = PlugNLGConfig()

>>> # Initializing a model from the configuration
>>> model = PlugModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```
"""

model_type = 'plugNLG'

def __init__(self,
vocab_size=21504,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
original_vocab_size=21128,
hidden_size=8192,
num_hidden_layers=24,
dec_hidden_layers=6,
num_attention_heads=128,
intermediate_size=32768,
hidden_act='gelu',
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
max_position_embeddings=2048,
type_vocab_size=3,
initializer_range=0.00707,
deep_init=False,
deepspeed=False,
lr_decay_style='linear',
weight_decay=1e-2,
clip_grad=1.0,
warmup=0.01,
pre_ln=False,
fp16=False,
fp32_layernorm=False,
pre_ln=True,
fp16=True,
fp32_layernorm=True,
fp32_embedding=False,
fp32_tokentypes=False,
layernorm_epsilon=1e-12,
dec_hidden_layers=6,
pruning_method=None,
pruning_mask_init='constant',
pruning_mask_scale=0.0,
pruning_initial_threshold=1.0,
pruning_final_threshold=0.01,
pruning_initial_warmup=1,
pruning_final_warmup=20,
pruning_module='decoder',
pruning_decay_step=50,
pruning_decay_type='exp',
ft_module=None,
layernorm_epsilon=1e-5,
attn_separate=False,
LR_weight_rank=8,
LR_mask_rank=8,
**kwargs):
super().__init__(layer_norm_eps=layernorm_epsilon, **kwargs)

@@ -203,8 +241,6 @@ class PlugNLGConfig(PlugNLUConfig):
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.deep_init = deep_init
self.deepspeed = deepspeed
self.lr_decay_style = lr_decay_style
self.weight_decay = weight_decay
self.clip_grad = clip_grad
@@ -216,17 +252,4 @@ class PlugNLGConfig(PlugNLUConfig):
self.layernorm_epsilon = layernorm_epsilon
self.fp32_tokentypes = fp32_tokentypes
self.dec_hidden_layers = dec_hidden_layers
self.pruning_method = pruning_method
self.pruning_mask_init = pruning_mask_init
self.pruning_mask_scale = pruning_mask_scale
self.pruning_module = pruning_module
self.pruning_initial_threshold = pruning_initial_threshold
self.pruning_final_threshold = pruning_final_threshold
self.pruning_initial_warmup = pruning_initial_warmup
self.pruning_final_warmup = pruning_final_warmup
self.pruning_decay_step = pruning_decay_step
self.pruning_decay_type = pruning_decay_type
self.ft_module = ft_module
self.attn_separate = attn_separate
self.LR_weight_rank = LR_weight_rank
self.LR_mask_rank = LR_mask_rank

+ 43
- 1
modelscope/models/nlp/plug/distributed_plug.py View File

@@ -20,6 +20,48 @@ logger = get_logger(__name__)


class DistributedPlug(TorchModel):
"""
The wapper class of PLUG Model to initialize parallel environment, load model weights, generate sentences.
Parameters:
model_dir (`str`, *required*):
Path to model damo/nlp_plug_text-generation_27B.
The model structure in model_dir should be like this:
model_dir
|_ config.json
|_ configuration.json
|_ ds_zero-offload_10B_config.json
|_ vocab.txt
|_ model <-- an empty directory

Model binaries shall be downloaded separately to populate the model directory, so that
the model directory would contain the following binaries:
|_ model
|_ mp_rank_00_model_states.pt
|_ mp_rank_01_model_states.pt
|_ mp_rank_02_model_states.pt
|_ mp_rank_03_model_states.pt
|_ mp_rank_04_model_states.pt
|_ mp_rank_05_model_states.pt
|_ mp_rank_06_model_states.pt
|_ mp_rank_07_model_states.pt
rank (`int`, *required*):
Used to identify different GPUs in a tensor parallel environment. eg. The rank of GPU #0 is 0, and the
model file `mp_rank_00_model_states.pt` will be loaded on this GPU.
world_size (`int`, *required*, defaults to 8):
The parallel size in total.
model_parallel_size (`int`, *required*, defaults to 8):
The parallel size of model(tensor parallel).
master_ip (`str`, *required*):
The master IP, can usually be set to `"127.0.0.1"`, used as part of
[`~torch.distributed.init_process_group`] method parameter `init_method`.
`init_method` = `"tcp://{master_ip}:{master_port}"`
master_port (`str`, *required*):
The master port, can usually be set to `"29500"`, used as part of
[`~torch.distributed.init_process_group`] method parameter `init_method`.
`init_method` = `"tcp://{master_ip}:{master_port}"`
seed (`int`, *optional*, defaults to 42):
Random seed to control sampling.
"""

def __init__(self, model_dir, rank, **kwargs):
super().__init__(model_dir, **kwargs)
@@ -29,7 +71,7 @@ class DistributedPlug(TorchModel):
initialize_distributed(rank, mpu, kwargs['world_size'],
kwargs['model_parallel_size'],
kwargs['master_ip'], kwargs['master_port'])
seed = 0 if 'seed' not in kwargs else kwargs['seed']
seed = 42 if 'seed' not in kwargs else kwargs['seed']
set_random_seed_mpu(seed)
self.iteration = 0
self.dist_model = self.initialize_model(path_load_tag='model')


+ 103
- 140
modelscope/models/nlp/plug/modeling_plug.py View File

@@ -152,15 +152,7 @@ class BertSelfOutput(nn.Module):
bias=True,
input_is_parallel=True,
stride=1,
init_method=init_method,
pruning_method=config.pruning_method if config.pruning_module in [
'all', 'encoder', 'encoder_self', 'encoder_selfvo',
'encoder_selfo'
] else None,
pruning_mask_init=config.pruning_mask_init,
pruning_mask_scale=config.pruning_mask_scale,
LR_weight_rank=config.LR_weight_rank,
LR_mask_rank=config.LR_mask_rank)
init_method=init_method)
self.fp32_layernorm = config.fp32_layernorm
if not config.pre_ln:
self.LayerNorm = BertLayerNorm(
@@ -173,12 +165,8 @@ class BertSelfOutput(nn.Module):
self,
hidden_states,
input_tensor,
pruning_threshold=None,
):
hidden_states = self.dense(
hidden_states,
pruning_threshold=pruning_threshold,
)
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
ln_input = hidden_states + input_tensor
if self.LayerNorm is not None:
@@ -210,20 +198,13 @@ class BertAttention(nn.Module):
output_parallel=True,
init_method=normal_init_method(
mean=0.0, std=config.initializer_range),
separate=config.attn_separate,
pruning_method=config.pruning_method,
pruning_mask_init=config.pruning_mask_init,
pruning_mask_scale=config.pruning_mask_scale,
pruning_module=config.pruning_module,
LR_weight_rank=config.LR_weight_rank,
LR_mask_rank=config.LR_mask_rank)
separate=config.attn_separate)
self.output = BertSelfOutput(config)

def forward(
self,
input_tensor,
attention_mask,
pruning_threshold=None,
):
if self.LayerNorm is not None:
ln_input = input_tensor
@@ -236,20 +217,16 @@ class BertAttention(nn.Module):
self_output = self.self(
ln_output,
attention_mask,
pruning_threshold=pruning_threshold,
)
else:
self_output = self.self(
input_tensor,
attention_mask,
pruning_threshold=pruning_threshold,
)
output_pruning_threshold = pruning_threshold

attention_output = self.output(
self_output,
input_tensor,
pruning_threshold=output_pruning_threshold,
)
return attention_output

@@ -265,25 +242,15 @@ class BertIntermediate(nn.Module):
gather_output=False,
stride=1,
init_method=normal_init_method(
mean=0.0, std=config.initializer_range),
pruning_method=config.pruning_method if config.pruning_module
in ['all', 'encoder', 'encoder_ffn'] else None,
pruning_mask_init=config.pruning_mask_init,
pruning_mask_scale=config.pruning_mask_scale,
LR_weight_rank=config.LR_weight_rank,
LR_mask_rank=config.LR_mask_rank)
mean=0.0, std=config.initializer_range))
self.intermediate_act_fn = ACT2FN[config.hidden_act] \
if isinstance(config.hidden_act, str) else config.hidden_act

def forward(
self,
hidden_states,
pruning_threshold=None,
):
hidden_states = self.dense(
hidden_states,
pruning_threshold=pruning_threshold,
)
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states

@@ -306,13 +273,7 @@ class BertOutput(nn.Module):
bias=True,
input_is_parallel=True,
stride=1,
init_method=init_method,
pruning_method=config.pruning_method if config.pruning_module
in ['all', 'encoder', 'encoder_ffn'] else None,
pruning_mask_init=config.pruning_mask_init,
pruning_mask_scale=config.pruning_mask_scale,
LR_weight_rank=config.LR_weight_rank,
LR_mask_rank=config.LR_mask_rank)
init_method=init_method)
self.fp32_layernorm = config.fp32_layernorm
if not config.pre_ln:
self.LayerNorm = BertLayerNorm(
@@ -325,12 +286,8 @@ class BertOutput(nn.Module):
self,
hidden_states,
input_tensor,
pruning_threshold=None,
):
hidden_states = self.dense(
hidden_states,
pruning_threshold=pruning_threshold,
)
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
ln_input = hidden_states + input_tensor
if self.LayerNorm is not None:
@@ -359,14 +316,8 @@ class BertLayer(nn.Module):
else:
self.LayerNorm = None

def forward(
self,
hidden_states,
attention_mask,
pruning_threshold=None,
):
attention_output = self.attention(
hidden_states, attention_mask, pruning_threshold=pruning_threshold)
def forward(self, hidden_states, attention_mask):
attention_output = self.attention(hidden_states, attention_mask)
if self.LayerNorm is not None:
ln_input = attention_output
previous_type = attention_output.type()
@@ -375,15 +326,10 @@ class BertLayer(nn.Module):
ln_output = self.LayerNorm(ln_input)
if self.fp32_layernorm:
ln_output = ln_output.type(previous_type)
intermediate_output = self.intermediate(
ln_output, pruning_threshold=pruning_threshold)
intermediate_output = self.intermediate(ln_output)
else:
intermediate_output = self.intermediate(
attention_output, pruning_threshold=pruning_threshold)
layer_output = self.output(
intermediate_output,
attention_output,
pruning_threshold=pruning_threshold)
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output


@@ -407,7 +353,6 @@ class BertEncoder(nn.Module):
output_all_encoded_layers=True,
checkpoint_activations=False,
detach_index=-1,
pruning_threshold=None,
):
all_encoder_layers = []

@@ -417,8 +362,7 @@ class BertEncoder(nn.Module):
layers = self.layer[start:end]
x_ = inputs[0]
for layer in layers:
x_ = layer(
x_, inputs[1], pruning_threshold=pruning_threshold)
x_ = layer(x_, inputs[1])
return x_

return custom_forward
@@ -654,7 +598,6 @@ class BertModel(PreTrainedBertModel):
output_all_encoded_layers=True,
checkpoint_activations=False,
detach_index=-1,
pruning_threshold=None,
):
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
@@ -683,8 +626,7 @@ class BertModel(PreTrainedBertModel):
extended_attention_mask,
output_all_encoded_layers=output_all_encoded_layers,
checkpoint_activations=checkpoint_activations,
detach_index=detach_index,
pruning_threshold=pruning_threshold)
detach_index=detach_index)
sequence_output = encoded_layers[-1]
for p in self.pooler.parameters():
if p is None:
@@ -709,18 +651,6 @@ class DecodeLayer(nn.Module):
std=config.initializer_range,
num_layers=config.num_hidden_layers)

self_pruning_method = config.pruning_method
cross_pruning_method = config.pruning_method
ffn_pruning_method = config.pruning_method

if config.ft_module is not None:
if 'decoder_self' in config.ft_module:
self_pruning_method = 'finetune'
if 'decoder_cross' in config.ft_module:
cross_pruning_method = 'finetune'
if 'decoder_ffn' in config.ft_module:
ffn_pruning_method = 'finetune'

self.attention = mpu.GPT2ParallelSelfAttention(
hidden_size=config.hidden_size,
num_attention_heads=config.num_attention_heads,
@@ -728,13 +658,6 @@ class DecodeLayer(nn.Module):
output_dropout_prob=config.hidden_dropout_prob,
init_method=init_method,
output_layer_init_method=output_layer_init_method,
pruning_method=self_pruning_method if config.pruning_module in [
'all', 'decoder', 'decoder_self', 'decoder_self+ffn'
] else None,
pruning_mask_init=config.pruning_mask_init,
pruning_mask_scale=config.pruning_mask_scale,
LR_weight_rank=config.LR_weight_rank,
LR_mask_rank=config.LR_mask_rank,
)

self.cross_attention = mpu.PalmParallelCrossAttention(
@@ -745,12 +668,6 @@ class DecodeLayer(nn.Module):
init_method=init_method,
attn_separate=False,
output_layer_init_method=output_layer_init_method,
pruning_method=cross_pruning_method,
pruning_mask_init=config.pruning_mask_init,
pruning_mask_scale=config.pruning_mask_scale,
pruning_module=config.pruning_module,
LR_weight_rank=config.LR_weight_rank,
LR_mask_rank=config.LR_mask_rank,
)

self.input_layernorm = BertLayerNorm(
@@ -765,12 +682,6 @@ class DecodeLayer(nn.Module):
config.intermediate_size,
gather_output=False,
init_method=init_method,
pruning_method=ffn_pruning_method if config.pruning_module
in ['all', 'decoder', 'decoder_ffn', 'decoder_self+ffn'] else None,
pruning_mask_init=config.pruning_mask_init,
pruning_mask_scale=config.pruning_mask_scale,
LR_weight_rank=config.LR_weight_rank,
LR_mask_rank=config.LR_mask_rank,
)
self.intermediate_act_fn = ACT2FN[config.hidden_act] \
if isinstance(config.hidden_act, str) else config.hidden_act
@@ -779,12 +690,6 @@ class DecodeLayer(nn.Module):
config.hidden_size,
input_is_parallel=True,
init_method=output_layer_init_method,
pruning_method=ffn_pruning_method if config.pruning_module
in ['all', 'decoder', 'decoder_ffn', 'decoder_self+ffn'] else None,
pruning_mask_init=config.pruning_mask_init,
pruning_mask_scale=config.pruning_mask_scale,
LR_weight_rank=config.LR_weight_rank,
LR_mask_rank=config.LR_mask_rank,
)

self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)
@@ -804,8 +709,7 @@ class DecodeLayer(nn.Module):
enc_hidden_states,
enc_attn_mask,
dec_attn_mask,
is_infer=False,
pruning_threshold=None):
is_infer=False):
residual = hidden_states
previous_type = hidden_states.type()
hidden_states = self.input_layernorm(
@@ -813,10 +717,7 @@ class DecodeLayer(nn.Module):
if self.fp32_layernorm:
hidden_states = hidden_states.type(previous_type)
hidden_states = self.attention(
hidden_states,
dec_attn_mask,
is_infer=is_infer,
pruning_threshold=pruning_threshold)
hidden_states, dec_attn_mask, is_infer=is_infer)

hidden_states = residual + hidden_states

@@ -825,23 +726,18 @@ class DecodeLayer(nn.Module):
self.type_converter(hidden_states))
if self.fp32_layernorm:
hidden_states = hidden_states.type(previous_type)
hidden_states = self.cross_attention(
hidden_states,
enc_hidden_states,
enc_attn_mask,
pruning_threshold=pruning_threshold)
hidden_states = self.cross_attention(hidden_states, enc_hidden_states,
enc_attn_mask)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.post_cross_attention_layernorm(
self.type_converter(hidden_states))
if self.fp32_layernorm:
hidden_states = hidden_states.type(previous_type)
hidden_states = self.intermediate(
hidden_states, pruning_threshold=pruning_threshold)
hidden_states = self.intermediate(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)

hidden_states = self.output(
hidden_states, pruning_threshold=pruning_threshold)
hidden_states = self.output(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = residual + hidden_states

@@ -866,8 +762,7 @@ class BertDecoder(nn.Module):
dec_attn_mask,
checkpoint_activations=False,
output_all_encoded_layers=False,
is_infer=False,
pruning_threshold=None):
is_infer=False):

def custom(start, end):

@@ -880,8 +775,7 @@ class BertDecoder(nn.Module):
inputs[1],
inputs[2],
dec_attn_mask * 1,
is_infer=is_infer,
pruning_threshold=pruning_threshold)
is_infer=is_infer)
return x_

return custom_forward
@@ -904,8 +798,7 @@ class BertDecoder(nn.Module):
enc_hidden_states,
enc_attn_mask,
dec_attn_mask,
is_infer=is_infer,
pruning_threshold=pruning_threshold)
is_infer=is_infer)

previous_type = hidden_states.type()
if self.fp32_layernorm:
@@ -932,8 +825,7 @@ class DecodeModel(PreTrainedBertModel):
enc_attn_mask=None,
dec_attn_mask=None,
checkpoint_activations=False,
is_infer=False,
pruning_threshold=None):
is_infer=False):
extended_attention_mask = enc_attn_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(
dtype=next(self.decoder.parameters()).dtype) # fp16 compatibility
@@ -946,8 +838,7 @@ class DecodeModel(PreTrainedBertModel):
extended_attention_mask,
dec_attn_mask,
checkpoint_activations=False,
is_infer=is_infer,
pruning_threshold=pruning_threshold)
is_infer=is_infer)
return sequence_output[-1]


@@ -972,16 +863,14 @@ class PalmForPreTraining(PreTrainedBertModel):
checkpoint_activations=False,
is_infer=False,
sequence_output=None,
parallel_output=True,
pruning_threshold=None):
parallel_output=True):
if sequence_output is None:
sequence_output, pooled_output = self.bert(
input_ids,
token_type_ids,
attention_mask,
output_all_encoded_layers=False,
checkpoint_activations=checkpoint_activations,
pruning_threshold=pruning_threshold)
checkpoint_activations=checkpoint_activations)
prediction_scores, seq_relationship_score = self.cls(
sequence_output, pooled_output)
else:
@@ -998,8 +887,7 @@ class PalmForPreTraining(PreTrainedBertModel):
attention_mask,
decode_attention_mask,
checkpoint_activations=checkpoint_activations,
is_infer=is_infer,
pruning_threshold=pruning_threshold)
is_infer=is_infer)

transformer_output_parallel = mpu.copy_to_model_parallel_region(
decode_output)
@@ -1017,6 +905,29 @@ class PalmForPreTraining(PreTrainedBertModel):


class PlugModel(torch.nn.Module):
"""
The bare Plug Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`PlugNLGConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~DistributedPlug.initialize_model`] method to load the model weights.
Example:

```python
>>> # The PLUG model has 27B parameters and usually need to run on multiple GPUs. The example given
>>> # here only initializes a slice of the model on a single GPU.
>>> # Check out the [`~DistributedPipeline.__init__`] method to initialize entire PLUG model.
>>> from modelscope.models.nlp.plug import PlugNLGConfig, PlugModel

>>> # Initializing a Plug configuration
>>> configuration = PlugNLGConfig()

>>> # Initializing a model from the configuration
>>> model = PlugModel(configuration)
"""

def __init__(self, config):
super(PlugModel, self).__init__()
@@ -1034,6 +945,58 @@ class PlugModel(torch.nn.Module):
is_infer=False,
sequence_output=None,
parallel_output=True):
"""
Parameters:
input_tokens (`torch.LongTensor` of shape `(batch_size, input_tokens_length)`):
`input_tokens_length` = `sequence_length`. Indices of input sequence tokens in the vocabulary.
Indices can be obtained using transformers [`BertTokenizer`]. See
[`TextGenerationPreprocessor.__call__`] for details.
token_type_ids (`torch.LongTensor` of shape `(batch_size, input_tokens_length)`, *optional*, defaults to
None):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:

- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.

attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*, defaults to None):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.

target_tokens (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*, defaults to None):
Target token ids(labels) for language modeling. Note that the labels **are shifted** inside the model,
i.e. you can set `target_tokens = input_tokens` Indices are selected in
`[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only
computed for labels in `[0, ..., config.vocab_size]`

position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*, defaults to None):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
`[0, config.max_position_embeddings - 1]`.

decode_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*, defaults
to None):
Mask to avoid performing attention on padding token indices of target tokens. Mask values selected in
`[0, 1]`:

- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.

checkpoint_activations (`boolean`, *optional*, defaults to `False`):
Whether gradient checkpointing is activated for this model or not.
is_infer (`boolean`, *optional*, defaults to `False`):
Whether or not to perform single inference.
sequence_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*,
defaults to None):
Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the
model. A single forward() call can produce one single token. To generate the current token, the
sequence_output generated by the `forward()` of the previous token is required.
parallel_output (`boolean`, *optional*, defaults to `True`):
To parallel return output, or gather it before return.


"""
return self.model(
input_tokens,
token_type_ids,


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