bgi-promptcblue-baichuan-13b / modeling_baichuan.py
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# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.utils import logging
from transformers.generation.utils import GenerationConfig
from .configuration_baichuan import BaichuanConfig
logger = logging.get_logger(__name__)
def _get_interleave(n):
def _get_interleave_power_of_2(n):
start = (2 ** (-2 ** -(math.log2(n) - 3)))
ratio = start
return [start * ratio ** i for i in range(n)]
if math.log2(n).is_integer():
return _get_interleave_power_of_2(n)
else:
closest_power_of_2 = 2 ** math.floor(math.log2(n))
return _get_interleave_power_of_2(closest_power_of_2) + \
_get_interleave(2 * closest_power_of_2)[0::2][:n - closest_power_of_2]
def _fill_with_neg_inf(t):
"""FP16-compatible function that fills a tensor with -inf."""
return t.float().fill_(float("-inf")).type_as(t)
def _gen_alibi_mask(n_head, max_pos):
slopes = torch.Tensor(_get_interleave(n_head))
alibi = slopes.unsqueeze(1).unsqueeze(1) * torch.arange(max_pos).unsqueeze(0).unsqueeze(0).expand(
n_head, -1, -1)
alibi = alibi.view(n_head, 1, max_pos)
alibi_mask = torch.triu(
_fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1
)
alibi_mask = alibi_mask.unsqueeze(0) + alibi
return alibi_mask
class RMSNorm(torch.nn.Module):
def __init__(self, hidden_size, epsilon=1e-6):
super().__init__()
self.weight = torch.nn.Parameter(torch.empty(hidden_size))
self.epsilon = epsilon
def forward(self, hidden_states):
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.epsilon)
# convert into half-precision
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states
class MLP(torch.nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
):
super().__init__()
self.gate_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
self.down_proj = torch.nn.Linear(intermediate_size, hidden_size, bias=False)
self.up_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
self.act_fn = ACT2FN[hidden_act]
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
class BaichuanAttention(torch.nn.Module):
def __init__(self, config: BaichuanConfig):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.max_position_embeddings = config.model_max_length
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size {self.hidden_size} is not divisible by num_heads {self.num_heads}"
)
self.W_pack = torch.nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
self.o_proj = torch.nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
proj = self.W_pack(hidden_states)
proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None:
if attn_weights.size(-2) == 1:
attention_mask = attention_mask[:, -1:, :]
attn_weights = attn_weights + attention_mask.unsqueeze(0)
attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class BaichuanLayer(torch.nn.Module):
def __init__(self, config: BaichuanConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = BaichuanAttention(config=config)
self.mlp = MLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
)
self.input_layernorm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if use_cache:
outputs += (present_key_value,)
return outputs
class BaichuanPreTrainedModel(PreTrainedModel):
config_class = BaichuanConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["BaichuanLayer"]
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, torch.nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, torch.nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, BaichuanModel):
module.gradient_checkpointing = value
class BaichuanModel(BaichuanPreTrainedModel):
def __init__(self, config: BaichuanConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.n_head = config.num_attention_heads
self.embed_tokens = torch.nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = torch.nn.ModuleList([BaichuanLayer(config) for _ in range(config.num_hidden_layers)])
self.norm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
self.gradient_checkpointing = config.gradient_checkpointing
self.post_init()
self.max_cache_pos = config.model_max_length
self.first_run = True
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def get_alibi_mask(self, tensor, seq_length_with_past):
if self.first_run:
self.first_run = False
self.register_buffer("future_mask", _gen_alibi_mask(self.n_head, self.max_cache_pos).to(tensor), persistent=False)
if seq_length_with_past > self.max_cache_pos:
self.max_cache_pos = seq_length_with_past
self.register_buffer("future_mask", _gen_alibi_mask(self.n_head, self.max_cache_pos).to(tensor), persistent=False)
mask = self.future_mask[:self.n_head, :seq_length_with_past, :seq_length_with_past]
return mask
def forward(
self,
input_ids: torch.LongTensor = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, BaseModelOutputWithPast]:
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot provide both input_ids and inputs_embeds simultaneously")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You need to provide input_ids or inputs_embeds")
seq_length_with_past = seq_length
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
attention_mask = self.get_alibi_mask(inputs_embeds, seq_length_with_past)
hidden_states = inputs_embeds
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, output_attentions, None)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
attention_mask,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class BaichuanForCausalLM(BaichuanPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.model = BaichuanModel(config)
self.lm_head = torch.nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: torch.LongTensor = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
**kwargs
) -> Union[Tuple, CausalLMOutputWithPast]:
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
if past_key_values:
input_ids = input_ids[:, -1:]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
}
)
return model_inputs
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
return tuple(
tuple(past_state.index_select(0, beam_idx) for past_state in layer_past)
for layer_past in past_key_values
)
def quantize(self, bits: int):
try:
from .quantizer import QLinear
except ImportError:
raise ImportError(
f"Needs QLinear to run quantize."
)
for layer in self.model.layers:
layer.self_attn.W_pack = QLinear(
bits=bits,
weight=layer.self_attn.W_pack.weight,
bias = None,
)
layer.self_attn.o_proj = QLinear(
bits=bits,
weight=layer.self_attn.o_proj.weight,
bias = None,
)
layer.mlp.gate_proj = QLinear(
bits=bits,
weight=layer.mlp.gate_proj.weight,
bias = None,
)
layer.mlp.down_proj = QLinear(
bits=bits,
weight=layer.mlp.down_proj.weight,
bias = None,
)
layer.mlp.up_proj = QLinear(
bits=bits,
weight=layer.mlp.up_proj.weight,
bias = None,
)
return self
def _build_chat_input(self, tokenizer, messages: List[dict], max_new_tokens: int=0):
max_new_tokens = max_new_tokens or self.generation_config.max_new_tokens
max_input_tokens = self.config.model_max_length - max_new_tokens
max_input_tokens = max(self.config.model_max_length // 2, max_input_tokens)
total_input, round_input = [], []
for i, message in enumerate(messages[::-1]):
content_tokens = tokenizer.encode(message['content'])
if message['role'] == 'user':
round_input = [self.generation_config.user_token_id] + content_tokens + round_input
if total_input and len(total_input) + len(round_input) > max_input_tokens:
break
else:
total_input = round_input + total_input
if len(total_input) >= max_input_tokens:
break
else:
round_input = []
elif message['role'] == 'assistant':
round_input = [
self.generation_config.assistant_token_id
] + content_tokens + [
self.generation_config.eos_token_id
] + round_input
else:
raise ValueError(f"message role not supported yet: {message['role']}")
total_input = total_input[-max_input_tokens:] # truncate left
total_input.append(self.generation_config.assistant_token_id)
total_input = torch.LongTensor([total_input]).to(self.device)
return total_input
@torch.no_grad()
def chat(self, tokenizer, messages: List[dict], stream=False,
generation_config: Optional[GenerationConfig]=None):
generation_config = generation_config or self.generation_config
input_ids = self._build_chat_input(tokenizer, messages, generation_config.max_new_tokens)
if stream:
from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig
self.__class__.generate = NewGenerationMixin.generate
self.__class__.sample_stream = NewGenerationMixin.sample_stream
stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True)
def stream_generator():
outputs = []
for token in self.generate(input_ids, generation_config=stream_config):
outputs.append(token.item())
yield tokenizer.decode(outputs, skip_special_tokens=True)
return stream_generator()
else:
self.__class__.generate = PreTrainedModel.generate # disable stream
outputs = self.generate(input_ids, generation_config=generation_config)
response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
return response