Baichuan-13B-Chat / modeling_baichuan.py
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import math
from typing import List, Optional, Tuple, Union
import torch
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_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
past_key_values_length = 0
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,
) -> 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