cpm-bee-1b / modeling_cpmbee.py
Gong Baitao
fix repetition penalty
190abe7
# coding=utf-8
# Copyright 2022 The OpenBMB Team The HuggingFace Inc. team. All rights reserved.
#
# 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.
""" PyTorch CpmBee model."""
import copy
import math
from collections import UserDict
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from transformers.generation.beam_search import BeamHypotheses, BeamSearchScorer
from transformers.generation.streamers import BaseStreamer
from transformers.generation.utils import (
GenerationConfig,
LogitsProcessorList,
StoppingCriteriaList,
dist,
inspect,
is_deepspeed_zero3_enabled,
warnings,
)
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, ModelOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_cpmbee import CpmBeeConfig
from .tokenization_cpmbee import CpmBeeTokenizer
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "openbmb/cpm-bee-10b"
_CONFIG_FOR_DOC = "CpmBeeConfig"
CPMBEE_PRETRAINED_MODEL_ARCHIVE_LIST = [
"openbmb/cpm-bee-10b",
"openbmb/cpm-bee-5b",
"openbmb/cpm-bee-2b",
"openbmb/cpm-bee-1b",
# See all CPMBee models at https://huggingface.co/models?filter=cpmbee
]
class CpmBeeLinear(nn.Linear):
def __init__(self, dim_in, dim_out, dtype):
"""
Construct a linear for CPMBee. It contains a scale operation.
"""
super().__init__(dim_in, dim_out, bias=False)
self.dim_in = self.in_features = dim_in
self.dim_out = self.out_features = dim_out
self.weight = torch.nn.parameter.Parameter(torch.empty((dim_out, dim_in), dtype=dtype))
def forward(self, x: torch.Tensor):
"""
Args:
x (`torch.Tensor` of shape `(batch, seq_len, dim_in)`): The input of linear layer
Returns:
`torch.Tensor` of shape `(batch, seq_len, dim_out)`: The output of the linear transform y.
"""
x = nn.functional.linear(x, self.weight)
x = x / math.sqrt(self.dim_in)
return x
class CpmBeeLayerNorm(nn.Module):
"""
We use Root Mean Square (RMS) Layer Normalization, please see https://arxiv.org/abs/1910.07467 for details."
"""
def __init__(self, config: CpmBeeConfig):
super().__init__()
self.eps = config.eps
self.dim_norm = config.hidden_size
self.weight = nn.Parameter(torch.empty(config.hidden_size, dtype=config.torch_dtype))
def forward(self, hidden_states: torch.Tensor):
"""
Args:
hidden_states (`torch.Tensor` of shape `(batch, seq_len, dim_in)`)
"""
if hidden_states.size(-1) != self.dim_norm:
raise AssertionError("hidden_states.size(-1) != self.dim_norm")
old_dtype = hidden_states.dtype
variance = hidden_states.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)
hidden_states = (hidden_states * torch.rsqrt(variance + self.eps)).to(old_dtype) * self.weight
return hidden_states
class CpmBeeAttention(nn.Module):
def __init__(self, config: CpmBeeConfig):
super().__init__()
self.dim_model = config.hidden_size
self.num_heads = config.num_attention_heads
self.dim_head = config.dim_head
self.project_q = CpmBeeLinear(self.dim_model, self.num_heads * self.dim_head, dtype=config.torch_dtype)
self.project_k = CpmBeeLinear(self.dim_model, self.num_heads * self.dim_head, dtype=config.torch_dtype)
self.project_v = CpmBeeLinear(self.dim_model, self.num_heads * self.dim_head, dtype=config.torch_dtype)
self.attention_out = CpmBeeLinear(self.num_heads * self.dim_head, self.dim_model, dtype=config.torch_dtype)
self.softmax = torch.nn.Softmax(dim=-1)
if config.dropout_p is not None:
self.dropout = torch.nn.Dropout(p=config.dropout_p)
else:
self.dropout = None
def forward(
self,
hidden_q: torch.Tensor,
hidden_kv: torch.Tensor,
attention_mask: torch.BoolTensor,
position_bias: torch.Tensor,
output_attentions: Optional[bool] = False,
past_key_values: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
use_cache: Optional[bool] = None,
):
"""
Args:
hidden_q (`torch.Tensor`):
Input of transformer block(self-attention block). It can be the raw embedding of a batch of sequences.
hidden_kv (`torch.Tensor` of shape `(batch, len_k, dim_model)`)):
Tensor *key_value* and *query* of shape `(batch, len_k, dim_model)`
attention_mask (`torch.Tensor` of shape `(batch, len_seq, len_seq)`):
Avoid invalid areas to participate in the calculation of self-attention.
position_bias (`torch.Tensor` of shape `(batch, len_seq, len_seq)`):
Provide positional information to self-attention block.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers.
past_key_values (`Tuple[torch.Tensor, torch.Tensor]`, *optional*):
Cached past key and value projection states.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
"""
batch_size = hidden_q.size(0)
len_q = hidden_q.size(1)
len_k = hidden_kv.size(1)
query = self.project_q(hidden_q)
key = self.project_k(hidden_kv)
value = self.project_v(hidden_kv)
query = query.view(batch_size, len_q, self.num_heads, self.dim_head).permute(0, 2, 1, 3)
key = key.view(batch_size, len_k, self.num_heads, self.dim_head).permute(0, 2, 1, 3)
value = value.view(batch_size, len_k, self.num_heads, self.dim_head).permute(0, 2, 1, 3)
if past_key_values is not None:
key = torch.cat([past_key_values[0], key], dim=-2)
value = torch.cat([past_key_values[1], value], dim=-2)
len_k = key.size(-2)
# (batch_size, num_heads, len_q, dim_head) @ (batch_size, num_heads, dim_head, len_k) -> (batch_size, num_heads, len_q, len_k)
score = torch.matmul(query, key.transpose(-1, -2)) / math.sqrt(self.dim_head)
score = score + position_bias
score = torch.masked_fill(
score,
attention_mask.view(batch_size, 1, len_q, len_k) == torch.tensor(False),
torch.scalar_tensor(float("-inf"), device=score.device, dtype=score.dtype),
)
score = self.softmax(score)
score = torch.masked_fill(
score,
attention_mask.view(batch_size, 1, len_q, len_k) == torch.tensor(False),
torch.scalar_tensor(0, device=score.device, dtype=score.dtype),
)
if output_attentions:
attn_weights = score
else:
attn_weights = None
if self.dropout is not None:
score = self.dropout(score)
# (batch_size, num_heads, len_q, len_k) @ (batch_size, num_heads, len_k, dim_head) -> (batch_size, num_heads, len_q, dim_head)
score = torch.matmul(score, value)
score = score.view(batch_size, self.num_heads, len_q, self.dim_head).permute(0, 2, 1, 3)
score = score.contiguous().view(batch_size, len_q, self.num_heads * self.dim_head)
score = self.attention_out(score)
past_key_values = None
if use_cache:
past_key_values = (key, value)
return score, attn_weights, past_key_values
class CpmBeeSelfAttentionBlock(nn.Module):
def __init__(self, config: CpmBeeConfig):
super().__init__()
self.layernorm_before_attention = CpmBeeLayerNorm(config)
self.self_attention = CpmBeeAttention(config)
if config.dropout_p:
self.dropout = torch.nn.Dropout(config.dropout_p)
else:
self.dropout = None
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
position_bias: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
past_key_values: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
use_cache: Optional[bool] = None,
):
"""
Args:
hidden_states (`torch.Tensor` of shape `(batch, len_seq, dim_model)`):
Input of transformer block(self-attention block). It can be the raw embedding of a batch of sequences.
attention_mask (`torch.Tensor` of shape `(batch, len_seq, len_seq)`):
Avoid invalid areas to participate in the calculation of self-attention.
position_bias (`torch.Tensor` of shape `(batch, len_seq, len_seq)`):
Provide positional information to self-attention block.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers.
past_key_values (`Tuple(torch.FloatTensor)`, *optional*):
Cached past key and value projection states.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
"""
outputs = self.layernorm_before_attention(hidden_states)
outputs = self.self_attention(
outputs, outputs, attention_mask, position_bias, output_attentions, past_key_values, use_cache
)
outputs, attn_weights, current_key_value = outputs
if self.dropout is not None:
outputs = self.dropout(outputs)
hidden_states = (hidden_states + outputs) / 1.05
return hidden_states, attn_weights, current_key_value
class CpmBeeDenseGatedACT(nn.Module):
def __init__(self, config: CpmBeeConfig):
super().__init__()
self.w_0 = CpmBeeLinear(config.hidden_size, config.dim_ff, dtype=config.torch_dtype)
self.w_1 = CpmBeeLinear(config.hidden_size, config.dim_ff, dtype=config.torch_dtype)
self.act = torch.nn.GELU()
def forward(self, hidden_states: torch.Tensor):
"""Transform an input tensor from one feature space to another via a nonlinear operation
Args:
hidden_states (`torch.Tensor` of shape `(batch, seq_len, dim_in)`)
"""
gate_score = self.act(self.w_0(hidden_states))
hidden_states = self.w_1(hidden_states)
hidden_states = gate_score * hidden_states
return hidden_states
class CpmBeeFeedForward(nn.Module):
def __init__(self, config: CpmBeeConfig):
super().__init__()
self.w_in = CpmBeeDenseGatedACT(config)
if config.dropout_p is not None:
self.dropout = torch.nn.Dropout(config.dropout_p)
else:
self.dropout = None
self.w_out = CpmBeeLinear(config.dim_ff, config.hidden_size, dtype=config.torch_dtype)
def forward(self, hidden_states: torch.Tensor):
"""
Args:
hidden_states (`torch.Tensor` of shape `(batch, seq_len, dim_in)`)
"""
hidden_states = self.w_in(hidden_states)
if self.dropout is not None:
hidden_states = self.dropout(hidden_states)
hidden_states = self.w_out(hidden_states)
return hidden_states
class CpmBeeFFNBlock(nn.Module):
def __init__(self, config: CpmBeeConfig):
super().__init__()
self.layernorm_before_ffn = CpmBeeLayerNorm(config)
self.ffn = CpmBeeFeedForward(config)
if config.dropout_p:
self.dropout = torch.nn.Dropout(config.dropout_p)
else:
self.dropout = None
def forward(
self,
hidden_states: torch.Tensor,
):
"""
Args:
hidden_states (`torch.Tensor` of shape `(batch, len_seq, dim_model)`):
Hidden states before feed forward layer.
"""
ln_outputs = self.layernorm_before_ffn(hidden_states)
outputs = self.ffn(ln_outputs)
if self.dropout is not None:
outputs = self.dropout(outputs)
hidden_states = (hidden_states + outputs) / 1.05
return hidden_states
class CpmBeeTransformerBlock(nn.Module):
def __init__(self, config: CpmBeeConfig, mask_att: bool = False, mask_ffn: bool = False):
super().__init__()
self.mask_att = mask_att
self.mask_ffn = mask_ffn
if not self.mask_att:
self.self_att = CpmBeeSelfAttentionBlock(config)
if not self.mask_ffn:
self.ffn = CpmBeeFFNBlock(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
position_bias: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
past_key_values: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
use_cache: Optional[bool] = None,
):
"""
Args:
hidden_states (`torch.Tensor`):
Input to the layer of shape `(batch, seq_len, dim_model)`
attention_mask (`torch.Tensor`):
Avoid invalid areas to participate in the calculation of shape `(batch, seq_len, seq_len)`
position_bias (`torch.Tensor`):
Provides position information to attention mechanism of shape `(num_heads, seq_len, seq_len)`
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers.
past_key_values (`Tuple[torch.Tensor, torch.Tensor])`, *optional*):
Cached past key and value projection states
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
"""
if not self.mask_att:
hidden_states = self.self_att(
hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
output_attentions=output_attentions,
past_key_values=past_key_values,
use_cache=use_cache,
)
hidden_states, attn_weights, current_key_value = hidden_states
else:
attn_weights, current_key_value = None, (None, None)
if not self.mask_ffn:
hidden_states = self.ffn(hidden_states)
return hidden_states, attn_weights, current_key_value
class CpmBeeEncoder(nn.Module):
def __init__(self, config: CpmBeeConfig):
super().__init__()
self.num_layers = config.num_hidden_layers
if config.mask_modules is not None:
assert len(config.mask_modules) == self.num_layers, "The total number of masks should equal to num_layers"
for mask_module in config.mask_modules:
assert len(mask_module) == 2, "For encoder, each mask should be (mask_att, mask_ffn)"
else:
config.mask_modules = [(False, False)] * self.num_layers
self.layers = nn.ModuleList(
[
CpmBeeTransformerBlock(
config, mask_att=config.mask_modules[ith][0], mask_ffn=config.mask_modules[ith][1]
)
for ith in range(self.num_layers)
]
)
self.output_layernorm = CpmBeeLayerNorm(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
position_bias: torch.Tensor,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
past_key_values: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
use_cache: Optional[bool] = None,
):
"""
Args:
hidden_states (`torch.Tensor`):
Input to the layer of shape `(batch, seq_len, dim_model)`
attention_mask (`torch.Tensor`):
Avoid invalid areas to participate in the calculation of shape `(batch, seq_len, seq_len)`
position_bias (`torch.Tensor`):
Provides position information to attention mechanism of shape `(num_heads, seq_len, seq_len)`
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers.
past_key_values (`Tuple[torch.Tensor, torch.Tensor])`, *optional*):
Cached past key and value projection states
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
"""
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
current_key_values = () if use_cache else None
for i, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = layer(
hidden_states,
attention_mask,
position_bias,
output_attentions=output_attentions,
past_key_values=past_key_values[i] if past_key_values else None,
use_cache=use_cache,
)
hidden_states, attn_weights, current_key_value = layer_outputs
if output_attentions:
all_self_attns += (attn_weights,)
if current_key_values is not None:
current_key_values = current_key_values + (current_key_value,)
hidden_states = self.output_layernorm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
return hidden_states, current_key_values, all_hidden_states, all_self_attns
class CpmBeeBucketPositionBias(nn.Module):
def __init__(self, config: CpmBeeConfig) -> None:
super().__init__()
self.num_heads = config.num_attention_heads
self.num_buckets = config.position_bias_num_buckets
self.num_segment_bucket = config.position_bias_num_segment_buckets
self.max_distance = config.position_bias_max_distance
self.relative_attention_bias = nn.Parameter(
torch.empty(
config.position_bias_num_buckets + config.position_bias_num_segment_buckets,
config.num_attention_heads,
dtype=config.torch_dtype,
),
)
def forward(self, query_pos: torch.Tensor, key_pos: torch.Tensor, rel_buckets: torch.Tensor):
with torch.no_grad():
batch = key_pos.size(0)
keylen = key_pos.size(1)
querylen = query_pos.size(1)
if key_pos.size(0) != query_pos.size(0):
raise AssertionError(
f"key_pos.size(0) should be equal to query_pos.size(0), but got {key_pos.size(0)} and {query_pos.size(0)}!"
)
if rel_buckets.size(0) != batch:
raise AssertionError(
f"rel_buckets.size(0) should be equal to batch, but got {rel_buckets.size(0)} and {batch}!"
)
if rel_buckets.size(1) != querylen:
raise AssertionError(
f"rel_buckets.size(1) should be equal to querylen, but got {rel_buckets.size(1)} and {querylen}!"
)
if rel_buckets.size(2) != keylen:
raise AssertionError(
f"rel_buckets.size(2) should be equal to keylen, but got {rel_buckets.size(2)} and {keylen}!"
)
relative_position_bucket = rel_buckets - 1 + self.num_buckets
inner_segment_bucket = self._position_bucket(
key_pos[..., None, :] - query_pos[..., :, None],
num_buckets=self.num_buckets,
max_distance=self.max_distance,
)
relative_position_bucket = torch.where(
rel_buckets == 0,
inner_segment_bucket,
relative_position_bucket,
)
embeds = nn.functional.embedding(relative_position_bucket, self.relative_attention_bias)
embeds = embeds.permute(0, 3, 1, 2).contiguous()
return embeds
def _position_bucket(self, relative_position, num_buckets=32, max_distance=128):
relative_buckets = 0
num_buckets //= 2
relative_buckets = (relative_position > 0).to(torch.int32) * num_buckets
relative_position = torch.abs(relative_position)
max_exact = num_buckets // 2
is_small = relative_position < max_exact
relative_postion_if_large = max_exact + (
torch.log(relative_position.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.int32)
relative_postion_if_large = torch.min(
relative_postion_if_large,
torch.full_like(relative_postion_if_large, num_buckets - 1),
)
relative_buckets += torch.where(is_small, relative_position.to(torch.int32), relative_postion_if_large)
return relative_buckets
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->CPMBee
class CpmBeeOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class CpmBeeRotaryEmbedding(nn.Module):
"""
RotaryEmbedding embeds the unk token and special token. It will embeds the "...<mask>...<mask>...<unk>...<unk>..."
to "...<mask_0>...<mask_1>...<unk_0>...<unk_1>..."" to help model to specify different special tokens and unk
tokens.
"""
def __init__(self, config: CpmBeeConfig):
super().__init__()
inv_freq = 1.0 / (10000 ** (torch.arange(0, config.hidden_size, 2, dtype=torch.float32) / config.hidden_size))
self.distance_scale = config.distance_scale
self.dtype = config.torch_dtype
self.inv_freq = inv_freq.to(config.torch_dtype)
def forward(self, x: torch.Tensor, x_pos: torch.Tensor):
inv_freq = self.inv_freq.to(device=x.device, dtype=x.dtype)
x_pos = x_pos * self.distance_scale
freqs = x_pos[..., None] * inv_freq[None, :] # (..., dim/2)
emb = torch.cat((freqs, freqs), dim=-1) # (..., dim)
emb_cos = emb.cos() # (..., dim)
emb_sin = emb.sin() # (..., dim)
rotate_x = torch.cat([-x[..., x.size(-1) // 2 :], x[..., : x.size(-1) // 2]], dim=-1) # (..., dim)
return x * emb_cos + rotate_x * emb_sin
class CpmBeeEmbeddingExt(nn.Embedding):
"""
Contains a RotaryEmbedding.
"""
def __init__(self, config: CpmBeeConfig):
super().__init__(config.vocab_size, config.hidden_size, dtype=config.torch_dtype)
self.dim_model = config.hidden_size
self.rotary_emb = CpmBeeRotaryEmbedding(config)
def forward(self, ids: torch.Tensor, ids_sub: torch.Tensor):
embeds = super().forward(ids) / math.sqrt(self.dim_model)
return self.rotary_emb(embeds, ids_sub)
def projection(self, x: torch.Tensor, ext_table: Optional[torch.Tensor] = None):
logits = nn.functional.linear(x / math.sqrt(self.dim_model), self.weight)
if ext_table is not None:
logits_ext = nn.functional.linear(x, ext_table)
logits = torch.cat([logits, logits_ext], dim=-1)
return logits
class CpmBeePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = CpmBeeConfig
base_model_prefix = "cpmbee"
supports_gradient_checkpointing = True
_keys_to_ignore_on_load_missing = [r"position_ids"]
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.init_std)
if module.bias is not None:
module.bias.data.zero_()
# still needed
elif isinstance(module, CpmBeeEmbeddingExt):
module.weight.data.normal_(mean=0.0, std=self.config.init_std)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, CpmBeeLayerNorm):
module.weight.data.fill_(1.0)
elif isinstance(module, CpmBeeBucketPositionBias):
module.relative_attention_bias.data.normal_(mean=0.0, std=self.config.init_std)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, CpmBeeEncoder):
module.gradient_checkpointing = value
CPMBEE_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters
config ([`~CpmBeeConfig`]): Model configuration class with all the parameters of the
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
CPMBEE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`CPMBeeTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
input_id_sub (`torch.Tensor` of shape `(batch_size, seq_len)`):
Subscription of input sequence tokens in the vocabulary.
Subscription of normal text will be zero while the special tokens of each group will be the 0, 1, 2, ...
<ans_0>, <ans_1>, <ans_2> ... belongs to group <ans>. <mask_0>, <mask_1>, <mask_2> ... belongs to group
<mask>.
position (`torch.Tensor` of shape `(batch_size, seq_len)`):
The position of input sequence tokens in the vocabulary for each segment. if segment1 is 0, 1, 2 and
segment2 is 0, 1, 2, 3, the position will be 0, 1, 2, 0, 1, 2, 3
context (`torch.Tensor` of shape `(batch_size, seq_len)`):
Whether this token id is context or not. If is context, the value is 1. If not, the value is 0. If a token
id is context, it does not need to be predicted.
sample_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
Give a sample id to every token id. The token ids with same sample ids belongs to the same sample.
num_segments (`torch.Tensor` of shape `(batch_size, seq_len)`):
Total number of segments in the current input.
segment (`torch.Tensor` of shape `(batch_size, seq_len)`):
Give a segment id to every token id. The token ids with same segment ids belongs to the same sample.
Generally, a string key or value in input data will be a segment. For example, input {"input": "hello, ",
"<ans>": ""}, the segments includes: "input", "hello, ", "<ans>" and "".
segment_rel_offset (`torch.Tensor` of shape `(batch_size, seq_len)`):
The offset of segment rel.
segment_rel (`torch.Tensor` of shape `(batch_size, seq_len)`):
The segment relevance. A relative implementation of measuring the importance of segments.
past_states (`Dict[str, Union[torch.Tensor, List]]`):
Store the history information including position, context, sample_ids, num_segments, segment and
past_key_values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
A dummy arguments for CPMBee. The `past_states` contains pre-computed hidden-states (key and values in the
self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) and
other history arguments to speed up sequential decoding.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare CPMBee Model outputting raw hidden-states without any specific head on top.",
CPMBEE_START_DOCSTRING,
)
class CpmBeeModel(CpmBeePreTrainedModel):
def __init__(self, config: CpmBeeConfig):
super().__init__(config)
if config.half:
config.torch_dtype = torch.half
else:
config.torch_dtype = torch.float
self.encoder = CpmBeeEncoder(config)
self.input_embedding = CpmBeeEmbeddingExt(config)
self.position_bias = CpmBeeBucketPositionBias(config)
self.vocab_size = config.vocab_size
self.post_init()
def get_input_embeddings(self):
return self.input_embedding
def set_input_embeddings(self, embeddings, **kwargs):
self.input_embedding = embeddings
@add_start_docstrings_to_model_forward(CPMBEE_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: torch.Tensor,
input_id_sub: Optional[torch.Tensor] = None,
length: Optional[torch.Tensor] = None,
context: Optional[torch.Tensor] = None,
sample_ids: Optional[torch.Tensor] = None,
num_segments: Optional[torch.Tensor] = None,
segment: Optional[torch.Tensor] = None,
segment_rel_offset: Optional[torch.Tensor] = None,
segment_rel: Optional[torch.Tensor] = None,
span: Optional[Dict] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
past_key_values: Optional[List] = None,
use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
use_cache = use_cache if use_cache is not None else self.config.use_cache
# dummy setting for common tests
if input_id_sub is None:
dtype, device = input_ids.dtype, input_ids.device
batch, seq_length = input_ids.size()
segment = torch.where(input_ids != 0, 2, 0).to(dtype=dtype, device=device)
context = torch.full((batch, seq_length), 1, dtype=dtype, device=device)
position = torch.arange(seq_length, dtype=dtype, device=device).repeat(batch, 1)
input_id_sub = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
segment_rel_offset = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
segment_rel = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
num_segments = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
sample_ids = torch.zeros_like(input_ids)
with torch.no_grad():
batch = input_ids.size(0)
seqlen = input_ids.size(1)
device = input_ids.device
# calc segment bucket
segment_rel_2d = torch.masked_fill(
segment[:, :, None] * num_segments[:, :, None]
+ segment[:, None, :]
+ segment_rel_offset[:, :, None],
~(
(sample_ids[:, :, None] == sample_ids[:, None, :])
& (span[:, None, :] == span[:, :, None])
), # not in the same span or sample
0, # avoid torch.gather overflow
).view(batch, seqlen * seqlen)
segment_bucket = torch.gather(
input=segment_rel,
dim=1,
index=segment_rel_2d.long(),
).view(batch, seqlen, seqlen)
segment_bucket.masked_fill_(
~(
(sample_ids[:, :, None] == sample_ids[:, None, :])
& (span[:, None, :] == span[:, :, None])
), # not in the same span or sample
1, # bucket is used for in-context samples
)
# directional mask
directional_mask_2d = torch.arange(seqlen, device=device) <= torch.arange(
seqlen, device=device
).view(-1, 1)
# sample mask
sample_mask_2d = (sample_ids[:, :, None] == 0) | (
sample_ids[:, :, None] == sample_ids[:, None, :]
)
# context mask
attention_mask = context[:, None, :] | (
context[:, :, None].logical_not() & directional_mask_2d.view(1, seqlen, seqlen)
)
# span mask
attention_mask = (
attention_mask & sample_mask_2d & (span[:, None, :] == span[:, :, None])
)
# length mask
mask_1d = (
torch.arange(seqlen, device=device)[None, :].repeat(batch, 1) < length[:, None]
)
attention_mask = (
mask_1d.view(batch, seqlen, 1) & mask_1d.view(batch, 1, seqlen) & attention_mask
)
position = torch.arange(seqlen, device=device).expand(batch, seqlen)
hidden_states = self.input_embedding(input_ids, input_id_sub)
position_bias = self.position_bias(position, position, segment_bucket)
hidden_states, present_key_values, all_hidden_states, all_attentions = self.encoder(
hidden_states,
attention_mask,
position_bias,
output_attentions,
output_hidden_states,
past_key_values=None,
use_cache=False
)
if not return_dict:
return tuple(
v for v in [hidden_states, present_key_values, all_hidden_states, all_attentions] if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=present_key_values,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
def inference(
self,
input_ids: torch.Tensor,
input_id_sub: Optional[torch.Tensor] = None,
position: Optional[torch.Tensor] = None,
context: Optional[torch.Tensor] = None,
sample_ids: Optional[torch.Tensor] = None,
num_segments: Optional[torch.Tensor] = None,
segment: Optional[torch.Tensor] = None,
segment_rel_offset: Optional[torch.Tensor] = None,
segment_rel: Optional[torch.Tensor] = None,
past_states: Optional[Dict] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
past_key_values: Optional[List] = None,
use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
use_cache = use_cache if use_cache is not None else self.config.use_cache
# dummy setting for common tests
if input_id_sub is None:
dtype, device = input_ids.dtype, input_ids.device
batch, seq_length = input_ids.size()
segment = torch.where(input_ids != 0, 2, 0).to(dtype=dtype, device=device)
context = torch.full((batch, seq_length), 1, dtype=dtype, device=device)
position = torch.arange(seq_length, dtype=dtype, device=device).repeat(batch, 1)
input_id_sub = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
segment_rel_offset = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
segment_rel = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
num_segments = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
sample_ids = torch.zeros_like(input_ids)
with torch.no_grad():
if past_states is None:
present_position = position
present_context = context
present_sample_ids = sample_ids
present_num_segments = num_segments
present_segments = segment
present_buffer = None
else:
present_position = torch.cat([past_states["buffer_position"], position], dim=-1)
present_context = torch.cat([past_states["buffer_context"], context], dim=-1)
present_sample_ids = torch.cat([past_states["buffer_sample_ids"], sample_ids], dim=-1)
present_num_segments = torch.cat([past_states["buffer_num_segments"], num_segments], dim=-1)
present_segments = torch.cat([past_states["buffer_segments"], segment], dim=-1)
present_buffer = past_states["buffer"]
batch = input_ids.size(0)
len_q = input_ids.size(1)
len_buffer = present_position.size(1)
segment_rel_2d = torch.masked_fill(
segment[:, :, None] * num_segments[:, :, None]
+ present_segments[:, None, :]
+ segment_rel_offset[:, :, None],
~((sample_ids[:, :, None] == present_sample_ids[:, None, :])), # not in the same sample
0, # avoid torch.gather overflow
).view(batch, len_q * len_buffer)
segment_bucket = torch.gather(
input=segment_rel,
dim=1,
index=segment_rel_2d.long(),
).view(batch, len_q, len_buffer)
segment_bucket.masked_fill_(
~((sample_ids[:, :, None] == present_sample_ids[:, None, :])), # not in the same span or sample
1, # bucket is used for in-context samples
)
# directional mask
directional_mask_2d = present_position[:, None, :] <= position[:, :, None]
# sample mask
sample_mask_2d = (sample_ids[:, :, None] == 0) | (sample_ids[:, :, None] == present_sample_ids[:, None, :])
# context mask
attention_mask = present_context[:, None, :] | (
context[:, :, None].logical_not() & directional_mask_2d.view(batch, len_q, len_buffer)
)
# span mask
attention_mask = attention_mask & sample_mask_2d
# length mask
mask_1d = present_num_segments != 0
attention_mask = mask_1d.view(batch, 1, len_buffer) & attention_mask
hidden_states = self.input_embedding(input_ids, input_id_sub)
position_bias = self.position_bias(position, present_position, segment_bucket)
hidden_states, present_key_values, all_hidden_states, all_attentions = self.encoder(
hidden_states,
attention_mask,
position_bias,
output_attentions,
output_hidden_states,
present_buffer,
use_cache,
)
if not return_dict:
return tuple(
v for v in [hidden_states, present_key_values, all_hidden_states, all_attentions] if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=present_key_values,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
class CpmBeeBeamHypotheses(BeamHypotheses):
def __init__(self, num_beams: int, length_penalty: float, early_stopping: bool, max_length: Optional[int] = None):
"""
Override BeamHypotheses for CpmBee. The hyp to add is list but not tensor.
"""
super().__init__(num_beams, length_penalty, early_stopping, max_length)
def add(self, hyp: List, sum_logprobs: float, beam_indices: Optional[torch.LongTensor] = None):
"""
Add a new hypothesis to the list.
"""
score = sum_logprobs / (len(hyp) ** self.length_penalty)
if len(self) < self.num_beams or score > self.worst_score:
self.beams.append((score, hyp, beam_indices))
if len(self) > self.num_beams:
sorted_next_scores = sorted([(s, idx) for idx, (s, _, _) in enumerate(self.beams)])
del self.beams[sorted_next_scores[0][1]]
self.worst_score = sorted_next_scores[1][0]
else:
self.worst_score = min(score, self.worst_score)
class CpmBeeBeamSearchScorer(BeamSearchScorer):
"""
Override BeamSearchScorer for CPMBee to support:
1. Replace beam_tokens by beam_states, containing `idx`, `ans`, `nx_token_id`...
2. The `process` will update the beam_states
3. The `finalize` will just return the best hypotheses as a list.
"""
def __init__(
self,
batch_size: int,
num_beams: int,
device: torch.device,
length_penalty: Optional[float] = 1.0,
do_early_stopping: Optional[Union[bool, str]] = False,
num_beam_hyps_to_keep: Optional[int] = 1,
num_beam_groups: Optional[int] = 1,
max_length: Optional[int] = None,
**model_kwargs,
):
self.num_beams = num_beams
self.device = device
self.length_penalty = length_penalty
self.do_early_stopping = do_early_stopping
self.num_beam_hyps_to_keep = num_beam_hyps_to_keep
self.num_beam_groups = num_beam_groups
self.group_size = self.num_beams // self.num_beam_groups
self._is_init = False
self._beam_hyps = [
CpmBeeBeamHypotheses(
num_beams=self.num_beams,
length_penalty=self.length_penalty,
early_stopping=self.do_early_stopping,
max_length=max_length,
)
for _ in range(batch_size)
]
self._done = torch.tensor([False for _ in range(batch_size)], dtype=torch.bool, device=self.device)
self.beam_states = []
for sent_id in range(batch_size):
instance_beam_states = []
for _ in range(self.num_beams):
instance_beam_states.append(
{
"idx": 0,
"ans": [],
"nx_token_id": 6,
"nx_token_sub": 0,
"nx_segment_id": model_kwargs["other_info"][sent_id]["predict_segments"][0][0],
"nx_position": 0,
}
)
self.beam_states.append(instance_beam_states)
def process(
self,
batch_size: int,
cur_len: int,
_next_scores: torch.FloatTensor,
next_scores: torch.FloatTensor,
next_tokens: torch.LongTensor,
vocab_size: Optional[int] = None,
pad_token_id: Optional[int] = None,
bos_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
max_length: Optional[int] = None,
ext_table_sub_cpu: Optional[torch.Tensor] = None,
ext_table_ids_cpu: Optional[torch.Tensor] = None,
**model_kwargs,
) -> Tuple[torch.Tensor]:
next_beam_state = []
for sent_id in range(batch_size):
self._done[sent_id] = self._done[sent_id] or self._beam_hyps[sent_id].is_done(
next_scores[sent_id].max().item(), cur_len
)
if self._done[sent_id]:
next_beam_state.append(
[
(
{
"idx": 0,
"ans": [],
"nx_token_id": pad_token_id,
"nx_token_sub": 0,
"nx_segment_id": 0,
"nx_position": 0,
},
0,
0,
)
]
* self.num_beams
)
continue
next_instance_beam_states = []
for idx, value in zip(next_tokens[sent_id], next_scores[sent_id]):
beam_id = torch.div(idx, _next_scores.size(-1), rounding_mode="floor").item()
word_id = (idx % _next_scores.size(-1)).item()
curr_info = self.beam_states[sent_id][beam_id]
if (
word_id == eos_token_id
and (curr_info["idx"] + 1 == len(model_kwargs["other_info"][sent_id]["predict_segments"]))
) or cur_len == max_length:
self._beam_hyps[sent_id].add(
self.beam_states[sent_id][beam_id]["ans"]
+ [
(
word_id,
model_kwargs["other_info"][sent_id]["predict_segments"][curr_info["idx"]][1],
)
],
value.item(),
)
elif word_id == eos_token_id:
next_instance_beam_states.append(
(
{
"idx": curr_info["idx"] + 1,
"ans": curr_info["ans"]
+ [
(
word_id,
model_kwargs["other_info"][sent_id]["predict_segments"][curr_info["idx"]][1],
)
],
"nx_token_id": bos_token_id,
"nx_token_sub": 0,
"nx_segment_id": model_kwargs["other_info"][sent_id]["predict_segments"][
curr_info["idx"] + 1
][0],
"nx_position": 0,
},
value.item(),
sent_id * self.num_beams + beam_id,
)
)
else:
raw_word_id = word_id
word_id_sub = 0
if word_id >= vocab_size:
word_id -= vocab_size
word_id_sub = int(ext_table_sub_cpu[word_id].item())
word_id = int(ext_table_ids_cpu[word_id].item())
next_instance_beam_states.append(
(
{
"idx": curr_info["idx"],
"ans": curr_info["ans"]
+ [
(
raw_word_id,
model_kwargs["other_info"][sent_id]["predict_segments"][curr_info["idx"]][1],
)
],
"nx_token_id": word_id,
"nx_token_sub": word_id_sub,
"nx_segment_id": curr_info["nx_segment_id"],
"nx_position": curr_info["nx_position"] + 1,
},
value.item(),
sent_id * self.num_beams + beam_id,
)
)
if len(next_instance_beam_states) == self.num_beams:
break
assert len(next_instance_beam_states) == 0 if cur_len == max_length else self.num_beams
next_beam_state.append(next_instance_beam_states)
if cur_len == max_length:
return None
beam_reorder_idx = []
beam_new_scores = []
beam_states = []
for sent_id in range(batch_size):
instance_beam_states = []
for beam_id in range(self.num_beams):
state, value, beam_idx = next_beam_state[sent_id][beam_id]
beam_reorder_idx.append(beam_idx)
beam_new_scores.append(value)
instance_beam_states.append(state)
beam_states.append(instance_beam_states)
self.beam_states = beam_states
return UserDict(
{
"next_beam_scores": torch.tensor(beam_new_scores, device=self.device).view(-1),
"next_beam_states": beam_states,
"next_beam_indices": torch.tensor(beam_reorder_idx, dtype=torch.int32, device=self.device).view(-1),
}
)
def finalize(self) -> Tuple[torch.LongTensor]:
results = []
for _, hypotheses in enumerate(self._beam_hyps):
best_hyp = max(hypotheses.beams, key=lambda x: x[0])[1]
results.append(best_hyp)
return results
@staticmethod
def apply_repetition_penalty(
logits,
batch_size,
num_beams,
prev_output_tokens,
repetition_penalty,
start_idx=None,
end_idx=None,
window_size=None,
):
# only conduct repetition penalty for the output
assert repetition_penalty >= 1, "repetition penalty coefficient should >= 1"
# repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
for i in range(batch_size * num_beams):
if start_idx is None or end_idx is None:
output_tokens = prev_output_tokens[i].tolist()
else:
if end_idx >= start_idx:
if window_size:
output_tokens = prev_output_tokens[i][
max(start_idx, end_idx + 1 - window_size) : end_idx + 1
].tolist()
else:
output_tokens = prev_output_tokens[i][start_idx : end_idx + 1].tolist()
else:
output_tokens = []
for previous_token in set(output_tokens):
# if score < 0 then repetition penalty has to
# multiplied to reduce the previous token probability
if logits[i, previous_token] < 0:
logits[i, previous_token] *= repetition_penalty
else:
logits[i, previous_token] /= repetition_penalty
@add_start_docstrings(
"""
The CPMBee Model with a language modeling head on top (linear layer with weights tied to the input embeddings).
""",
CPMBEE_START_DOCSTRING,
)
class CpmBeeForCausalLM(CpmBeePreTrainedModel):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
def __init__(self, config: CpmBeeConfig):
super().__init__(config)
self.cpmbee = CpmBeeModel(config)
# lm_head.weight is tied to cpmbee.input_embedding.weight
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.post_init()
@add_start_docstrings_to_model_forward(CPMBEE_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
input_id_sub: Optional[torch.Tensor] = None,
length: Optional[torch.Tensor] = None,
context: Optional[torch.Tensor] = None,
sample_ids: Optional[torch.Tensor] = None,
num_segments: Optional[torch.Tensor] = None,
segment: Optional[torch.Tensor] = None,
segment_rel_offset: Optional[torch.Tensor] = None,
segment_rel: Optional[torch.Tensor] = None,
span: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
past_key_values: Optional[List] = None,
use_cache: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
ext_table_ids: Optional[torch.Tensor] = None, # (ext_table_size) int32
ext_table_sub: Optional[torch.Tensor] = None, # (ext_table_size) int32
**kwargs,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
input_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`CPMBeeTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
input_id_sub (`torch.Tensor` of shape `(batch_size, seq_len)`):
Subscription of input sequence tokens in the vocabulary.
Subscription of normal text will be zero while the special tokens of each group will be the 0, 1, 2,
... <ans_0>, <ans_1>, <ans_2> ... belongs to group <ans>. <mask_0>, <mask_1>, <mask_2> ... belongs to
group <mask>.
length (`torch.Tensor` of shape `(batch_size)`):
The length of sequences in batch.
context (`torch.Tensor` of shape `(batch_size, seq_len)`):
Whether this token id is context or not. If is context, the value is 1. If not, the value is 0. If a
token id is context, it does not need to be predicted.
sample_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
Give a sample id to every token id. The token ids with same sample ids belongs to the same sample.
num_segments (`torch.Tensor` of shape `(batch_size, seq_len)`):
Total number of segments in the current input.
segment (`torch.Tensor` of shape `(batch_size, seq_len)`):
Give a segment id to every token id. The token ids with same segment ids belongs to the same sample.
Generally, a string key or value in input data will be a segment. For example, input {"input": "hello,
", "<ans>": ""}, the segments includes: "input", "hello, ", "<ans>" and "".
segment_rel_offset (`torch.Tensor` of shape `(batch_size, seq_len)`):
The offset of segment rel.
segment_rel (`torch.Tensor` of shape `(batch_size, seq_len)`):
The segment relevance. A relative implementation of measuring the importance of segments.
span (`Dict[str, Union[torch.Tensor, List]]`):
Span will record every input_ids shape.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
A dummy arguments for CPMBee. The `past_states` contains pre-computed hidden-states (key and values in
the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values`
input) and other history arguments to speed up sequential decoding.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
ext_table_ids (`torch.Tensor`, *optional*):
ext_table ids for embedding projection.
ext_table_sub (`torch.Tensor`, *optional*):
ext_table subscriptions for embedding projection.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
model_output = self.cpmbee(
input_ids,
input_id_sub,
length,
context,
sample_ids,
num_segments,
segment,
segment_rel_offset,
segment_rel,
span,
output_attentions,
output_hidden_states,
past_key_values,
use_cache,
return_dict,
)
hidden_states = model_output.last_hidden_state if return_dict else model_output[0]
if ext_table_ids is not None:
ext_table = self.cpmbee.input_embedding(ext_table_ids, ext_table_sub)
else:
ext_table = None
logits = self.cpmbee.input_embedding.projection(hidden_states, ext_table)
loss = None
if labels is not None:
loss_func = nn.CrossEntropyLoss()
loss = loss_func(logits.view(-1, logits.size(-1)), labels.long().view(-1))
if not return_dict:
output = (logits,) + model_output[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=model_output.past_key_values,
hidden_states=model_output.hidden_states,
attentions=model_output.attentions,
)
def inference(
self,
input_ids: Optional[torch.Tensor] = None,
input_id_sub: Optional[torch.Tensor] = None,
position: Optional[torch.Tensor] = None,
context: Optional[torch.Tensor] = None,
sample_ids: Optional[torch.Tensor] = None,
num_segments: Optional[torch.Tensor] = None,
segment: Optional[torch.Tensor] = None,
segment_rel_offset: Optional[torch.Tensor] = None,
segment_rel: Optional[torch.Tensor] = None,
past_states: Optional[Dict] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
past_key_values: Optional[List] = None,
use_cache: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
ext_table_ids: Optional[torch.Tensor] = None, # (ext_table_size) int32
ext_table_sub: Optional[torch.Tensor] = None, # (ext_table_size) int32
**kwargs,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
input_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`CPMBeeTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
input_id_sub (`torch.Tensor` of shape `(batch_size, seq_len)`):
Subscription of input sequence tokens in the vocabulary.
Subscription of normal text will be zero while the special tokens of each group will be the 0, 1, 2,
... <ans_0>, <ans_1>, <ans_2> ... belongs to group <ans>. <mask_0>, <mask_1>, <mask_2> ... belongs to
group <mask>.
position (`torch.Tensor` of shape `(batch_size, seq_len)`):
The position of input sequence tokens in the vocabulary for each segment. if segment1 is 0, 1, 2 and
segment2 is 0, 1, 2, 3, the position will be 0, 1, 2, 0, 1, 2, 3
context (`torch.Tensor` of shape `(batch_size, seq_len)`):
Whether this token id is context or not. If is context, the value is 1. If not, the value is 0. If a
token id is context, it does not need to be predicted.
sample_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
Give a sample id to every token id. The token ids with same sample ids belongs to the same sample.
num_segments (`torch.Tensor` of shape `(batch_size, seq_len)`):
Total number of segments in the current input.
segment (`torch.Tensor` of shape `(batch_size, seq_len)`):
Give a segment id to every token id. The token ids with same segment ids belongs to the same sample.
Generally, a string key or value in input data will be a segment. For example, input {"input": "hello,
", "<ans>": ""}, the segments includes: "input", "hello, ", "<ans>" and "".
segment_rel_offset (`torch.Tensor` of shape `(batch_size, seq_len)`):
The offset of segment rel.
segment_rel (`torch.Tensor` of shape `(batch_size, seq_len)`):
The segment relevance. A relative implementation of measuring the importance of segments.
past_states (`Dict[str, Union[torch.Tensor, List]]`):
Store the history information including position, context, sample_ids, num_segments, segment and
past_key_values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
A dummy arguments for CPMBee. The `past_states` contains pre-computed hidden-states (key and values in
the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values`
input) and other history arguments to speed up sequential decoding.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
ext_table_ids (`torch.Tensor`, *optional*):
ext_table ids for embedding projection.
ext_table_sub (`torch.Tensor`, *optional*):
ext_table subscriptions for embedding projection.
Example:
Text Generation with CpmBeeForCausalLM.
```python
>>> from transformers import CpmBeeTokenizer, CpmBeeForCausalLM
>>> texts = {"input": "今天天气不错,", "<ans>": ""}
>>> model = CpmBeeForCausalLM.from_pretrained("openbmb/cpm-bee-10b")
>>> tokenizer = CPMBeeTokenizer.from_pretrained("openbmb/cpm-bee-10b")
>>> output_texts = model.generate({"input": "今天天气不错,", "<ans>": ""}, tokenizer)
>>> print(output_texts)
{'input': '今天天气不错,', '<ans>': '适合睡觉。'}
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
model_output = self.cpmbee.inference(
input_ids,
input_id_sub,
position,
context,
sample_ids,
num_segments,
segment,
segment_rel_offset,
segment_rel,
past_states,
output_attentions,
output_hidden_states,
past_key_values,
use_cache,
return_dict,
)
hidden_states = model_output.last_hidden_state if return_dict else model_output[0]
if ext_table_ids is not None:
ext_table = self.cpmbee.input_embedding(ext_table_ids, ext_table_sub)
else:
ext_table = None
logits = self.cpmbee.input_embedding.projection(hidden_states, ext_table)
loss = None
if labels is not None:
loss_func = nn.CrossEntropyLoss()
loss = loss_func(logits.view(-1, logits.size(-1)), labels.view(-1))
if not return_dict:
output = (logits,) + model_output[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=model_output.past_key_values,
hidden_states=model_output.hidden_states,
attentions=model_output.attentions,
)
def get_input_embeddings(self):
return self.cpmbee.input_embedding
def set_input_embeddings(self, embeddings):
self.cpmbee.input_embedding = embeddings
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def prepare_inputs_for_generation(
self,
input_ids: torch.Tensor,
batch_size: int,
beam_scorer: CpmBeeBeamSearchScorer = None,
input_id_subs: Optional[torch.Tensor] = None,
input_pos: Optional[torch.Tensor] = None,
segment_ids: Optional[torch.Tensor] = None,
batch_ext_table_ids: Optional[torch.Tensor] = None,
batch_ext_table_sub: Optional[torch.Tensor] = None,
other_info: Optional[Dict] = None,
**model_kwargs,
):
"""
Choose the current input according to beam states.
"""
# init preparation
context = model_kwargs.get("context")
sample_ids = model_kwargs.get("sample_ids")
segment_rel_offset = model_kwargs.get("segment_rel_offset")
num_segments = model_kwargs.get("num_segments")
segment_rel = model_kwargs.get("segment_rel")
past_states = model_kwargs.get("past_states", None)
past_key_values = model_kwargs.get("past_key_values", None)
_input_ids = input_ids
# update input in generation
if beam_scorer is not None:
tmp_input = []
tmp_input_sub = []
tmp_position = []
tmp_segment = []
for sent_id in range(batch_size):
for beam_id in range(beam_scorer.num_beams):
tmp_input.append(beam_scorer.beam_states[sent_id][beam_id]["nx_token_id"])
tmp_input_sub.append(beam_scorer.beam_states[sent_id][beam_id]["nx_token_sub"])
tmp_position.append(beam_scorer.beam_states[sent_id][beam_id]["nx_position"])
tmp_segment.append(beam_scorer.beam_states[sent_id][beam_id]["nx_segment_id"])
model_kwargs["input_id_subs"] = input_id_subs = torch.tensor(
tmp_input_sub, dtype=torch.int32, device=self.device
).view(batch_size * beam_scorer.num_beams, 1)
model_kwargs["input_pos"] = input_pos = torch.tensor(
tmp_position, dtype=torch.int32, device=self.device
).view(batch_size * beam_scorer.num_beams, 1)
model_kwargs["segment_ids"] = segment_ids = torch.tensor(
tmp_segment, dtype=torch.int32, device=self.device
).view(batch_size * beam_scorer.num_beams, 1)
input_ids = torch.cat(
[
input_ids,
torch.tensor(tmp_input, dtype=torch.int32, device=self.device).view(
batch_size * beam_scorer.num_beams, 1
),
],
dim=-1,
)
_input_ids = input_ids[:, -1:]
return {
"input_ids": _input_ids,
"input_id_sub": input_id_subs,
"position": input_pos,
"context": context,
"sample_ids": sample_ids,
"segment_rel_offset": segment_rel_offset,
"segment": segment_ids,
"num_segments": num_segments,
"segment_rel": segment_rel,
"use_cache": True,
"past_key_values": past_key_values,
"ext_table_ids": batch_ext_table_ids,
"ext_table_sub": batch_ext_table_sub,
"past_states": past_states,
}, input_ids
def _update_model_kwargs_for_generation(
self,
outputs: ModelOutput,
model_inputs=None,
**model_kwargs,
) -> Dict[str, Any]:
"""
Concatenate the history input and current input.
"""
old_past_states = model_kwargs["past_states"]
model_kwargs["past_states"] = {
"buffer_position": torch.cat([old_past_states["buffer_position"], model_inputs["position"]], dim=-1),
"buffer_context": torch.cat([old_past_states["buffer_context"], model_inputs["context"]], dim=-1),
"buffer_sample_ids": torch.cat([old_past_states["buffer_sample_ids"], model_inputs["sample_ids"]], dim=-1),
"buffer_num_segments": torch.cat(
[old_past_states["buffer_num_segments"], model_inputs["num_segments"]], dim=-1
),
"buffer_segments": torch.cat([old_past_states["buffer_segments"], model_inputs["segment"]], dim=-1),
"buffer": outputs.past_key_values,
}
return model_kwargs
def _reorder_cache(self, past_key_values: Dict, beam_idx: torch.Tensor):
beam_idx = beam_idx.tolist()
for kw in past_key_values.keys():
if kw == "buffer":
buf_list = past_key_values[kw]
nw_buf_list = []
for buf in buf_list:
if buf == (None, None):
nw_buf_list.append((None, None))
else:
k_buf, v_buf = buf
nw_buf_list.append((k_buf[beam_idx, :], v_buf[beam_idx, :]))
past_key_values[kw] = nw_buf_list
else:
past_key_values[kw] = past_key_values[kw][beam_idx, :]
return past_key_values
@staticmethod
def _expand_inputs_for_generation(
expand_size: int = 1,
is_encoder_decoder: bool = False,
input_ids: Optional[torch.LongTensor] = None,
**model_kwargs,
) -> Tuple[torch.LongTensor, Dict[str, Any]]:
"""Expands tensors from [batch_size, ...] to [batch_size * expand_size, ...]"""
# do not expand ext_table_ids and ext_table_sub
def _expand_dict_for_generation(dict_to_expand):
for key in dict_to_expand:
if (
dict_to_expand[key] is not None
and isinstance(dict_to_expand[key], torch.Tensor)
and "ext_table" not in key
):
dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0)
return dict_to_expand
if input_ids is not None:
input_ids = input_ids.repeat_interleave(expand_size, dim=0)
model_kwargs = _expand_dict_for_generation(model_kwargs)
if is_encoder_decoder:
if model_kwargs.get("encoder_outputs") is None:
raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"])
return input_ids, model_kwargs
def adjust_logits_during_generation(
self,
logits: torch.FloatTensor,
batch_size: int,
beam_size: int,
vocab_size: int,
ext_table_ids: torch.Tensor,
**model_kwargs,
) -> torch.FloatTensor:
"""
Implement in subclasses of [`PreTrainedModel`] for custom behavior to adjust the logits in the generate method.
"""
for sent_id in range(batch_size):
if 1 not in model_kwargs["other_info"][sent_id]["ext_table"]:
# unk is not allowed, mask unk
logits[sent_id * beam_size : (sent_id + 1) * beam_size, 1] = -10000
ext_ids = set()
for v in model_kwargs["other_info"][sent_id]["ext_table"].keys():
ext_ids.add(v)
for ext_id in range(vocab_size, vocab_size + ext_table_ids.size(0)):
if ext_id not in ext_ids:
logits[sent_id * beam_size : (sent_id + 1) * beam_size, ext_id] = -10000
return logits
def beam_search(
self,
input_ids: torch.LongTensor,
beam_scorer: CpmBeeBeamSearchScorer,
repetition_penalty: Optional[float] = 1.0,
logits_processor: Optional[LogitsProcessorList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
bos_token_id: Optional[Union[int, List[int]]] = None,
vocab_size: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: bool = False,
**model_kwargs,
) -> List:
"""
Override the beam_search for CPMBee.
"""
# init values
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
bos_token_id = bos_token_id if bos_token_id is not None else self.generation_config.bos_token_id
vocab_size = vocab_size if vocab_size is not None else self.generation_config.vocab_size
max_length = max_length if max_length is not None else self.generation_config.max_new_tokens
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
batch_size = len(beam_scorer._beam_hyps)
num_beams = beam_scorer.num_beams
batch_beam_size, cur_len = input_ids.shape
if num_beams * batch_size != batch_beam_size:
raise ValueError(
f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
)
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and output_scores) else None
beam_indices = (
tuple(() for _ in range(batch_beam_size)) if (return_dict_in_generate and output_scores) else None
)
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attentions = () if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# initialise score of first beam with 0 and the rest with -1e9. This makes sure that only tokens
# of the first beam are considered to avoid sampling the exact same tokens across all beams.
beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=self.device)
beam_scores[:, 1:] = -1e9
beam_scores = beam_scores.view((batch_size * num_beams,))
this_peer_finished = False # used by synced_gpus only
# init inference
model_inputs, input_ids = self.prepare_inputs_for_generation(input_ids, batch_size, **model_kwargs)
pred_start_index = input_ids.size(-1)
outputs = self.inference(
**model_inputs,
return_dict=True,