mirror of https://github.com/InternLM/InternLM
feat(tools): support dynamic ntk rope in transformers (#470)
* support dynamic ntk in transformers * support dynamic ntk in transformers * support dynamic ntk in transformers * add rope doc * add rotary config in configuration_internlm.py --------- Co-authored-by: YWMditto <862779238@qq.com>pull/475/head
YWMditto
GitHub
parent
42ad9cc786
commit
095ebfff9d
|
|
@ -19,9 +19,8 @@
|
|||
# limitations under the License.
|
||||
""" InternLM model configuration"""
|
||||
|
||||
from transformers.utils import logging
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
|
||||
from transformers.utils import logging
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
|
@ -30,9 +29,9 @@ INTERNLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
|
|||
|
||||
class InternLMConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate an InternLM
|
||||
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
||||
defaults will yield a similar configuration to that of the InternLM-7B.
|
||||
This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate
|
||||
an InternLM model according to the specified arguments, defining the model architecture. Instantiating a
|
||||
configuration with the defaults will yield a similar configuration to that of the InternLM-7B.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
|
@ -81,7 +80,7 @@ class InternLMConfig(PretrainedConfig):
|
|||
model_type = "internlm"
|
||||
_auto_class = "AutoConfig"
|
||||
|
||||
def __init__(
|
||||
def __init__( # pylint: disable=W0102
|
||||
self,
|
||||
vocab_size=103168,
|
||||
hidden_size=4096,
|
||||
|
|
@ -98,6 +97,7 @@ class InternLMConfig(PretrainedConfig):
|
|||
eos_token_id=2,
|
||||
tie_word_embeddings=False,
|
||||
bias=True,
|
||||
rotary={"base": 10000, "type": "dynamic"}, # pylint: disable=W0102
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
|
|
@ -111,6 +111,7 @@ class InternLMConfig(PretrainedConfig):
|
|||
self.rms_norm_eps = rms_norm_eps
|
||||
self.use_cache = use_cache
|
||||
self.bias = bias
|
||||
self.rotary = rotary
|
||||
super().__init__(
|
||||
pad_token_id=pad_token_id,
|
||||
bos_token_id=bos_token_id,
|
||||
|
|
|
|||
|
|
@ -19,26 +19,36 @@
|
|||
# limitations under the License.
|
||||
""" PyTorch InternLM model."""
|
||||
import math
|
||||
import queue
|
||||
import threading
|
||||
from typing import List, Optional, Tuple, Union
|
||||
import threading, queue
|
||||
|
||||
import torch
|
||||
import torch.utils.checkpoint
|
||||
from torch import nn
|
||||
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
||||
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
|
||||
from transformers.modeling_utils import PreTrainedModel
|
||||
from transformers.generation.streamers import BaseStreamer
|
||||
from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
|
||||
from .configuration_internlm import InternLMConfig
|
||||
from transformers.modeling_outputs import (
|
||||
BaseModelOutputWithPast,
|
||||
CausalLMOutputWithPast,
|
||||
SequenceClassifierOutputWithPast,
|
||||
)
|
||||
from transformers.modeling_utils import PreTrainedModel
|
||||
from transformers.utils import (
|
||||
add_start_docstrings,
|
||||
add_start_docstrings_to_model_forward,
|
||||
logging,
|
||||
replace_return_docstrings,
|
||||
)
|
||||
|
||||
from .configuration_internlm import InternLMConfig
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
_CONFIG_FOR_DOC = "InternLMConfig"
|
||||
|
||||
|
||||
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
|
||||
def _make_causal_mask(
|
||||
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
||||
|
|
@ -73,6 +83,8 @@ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int]
|
|||
|
||||
|
||||
class InternLMRMSNorm(nn.Module):
|
||||
"""RMSNorm implemention."""
|
||||
|
||||
def __init__(self, hidden_size, eps=1e-6):
|
||||
"""
|
||||
InternLMRMSNorm is equivalent to T5LayerNorm
|
||||
|
|
@ -93,6 +105,14 @@ class InternLMRMSNorm(nn.Module):
|
|||
|
||||
|
||||
class InternLMRotaryEmbedding(torch.nn.Module):
|
||||
"""Implement InternLM's rotary embedding.
|
||||
|
||||
Args:
|
||||
dim (int): Characteristic dimension of each self-attentional head.
|
||||
max_position_embeddings (int, optional): Model's training length. Defaults to 2048.
|
||||
base (int, optional): The rotation position encodes the rotation Angle base number. Defaults to 10000.
|
||||
device (Any, optional): Running device. Defaults to None.
|
||||
"""
|
||||
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
||||
super().__init__()
|
||||
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
|
||||
|
|
@ -124,6 +144,66 @@ class InternLMRotaryEmbedding(torch.nn.Module):
|
|||
)
|
||||
|
||||
|
||||
class InternLMDynamicNTKScalingRotaryEmbedding(torch.nn.Module):
|
||||
"""Implement InternLM's DyanmicNTK extrapolation method, thereby broadening the model support context to 16K.
|
||||
|
||||
Args:
|
||||
dim (int): Characteristic dimension of each self-attentional head.
|
||||
max_position_embeddings (int, optional): Model's training length. Defaults to 2048.
|
||||
base (int, optional): The rotation position encodes the rotation Angle base number. Defaults to 10000.
|
||||
device (Any, optional): Running device. Defaults to None.
|
||||
scaling_factor (float, optional): NTK method extrapolation coefficient. Defaults to 1.0.
|
||||
"""
|
||||
|
||||
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
||||
super().__init__()
|
||||
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
|
||||
self.register_buffer("inv_freq", inv_freq)
|
||||
self.dim = dim
|
||||
self.base = base
|
||||
self.scaling_factor = scaling_factor
|
||||
|
||||
# Build here to make `torch.jit.trace` work.
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.max_seq_len_cached = max_position_embeddings
|
||||
t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
|
||||
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
||||
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
|
||||
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
|
||||
|
||||
def _update_cached(self, x, seq_len=None):
|
||||
self.max_seq_len_cached = max(seq_len, self.max_position_embeddings)
|
||||
if seq_len > self.max_position_embeddings:
|
||||
base = self.base * (
|
||||
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
|
||||
) ** (self.dim / (self.dim - 2))
|
||||
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(x.device) / self.dim))
|
||||
else:
|
||||
inv_freq = self.inv_freq
|
||||
t = torch.arange(self.max_seq_len_cached, device=inv_freq.device, dtype=inv_freq.dtype)
|
||||
freqs = torch.einsum("i,j->ij", t, inv_freq)
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
|
||||
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
|
||||
|
||||
def forward(self, x, seq_len=None):
|
||||
# x: [bs, num_attention_heads, seq_len, head_size]
|
||||
# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
|
||||
if seq_len <= self.max_position_embeddings:
|
||||
# Reset the tables if the sequence length has changed,
|
||||
if self.max_seq_len_cached > self.max_position_embeddings:
|
||||
self._update_cached(x, seq_len)
|
||||
else:
|
||||
self._update_cached(x, seq_len)
|
||||
|
||||
return (
|
||||
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
|
||||
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
|
||||
)
|
||||
|
||||
|
||||
def rotate_half(x):
|
||||
"""Rotates half the hidden dims of the input."""
|
||||
x1 = x[..., : x.shape[-1] // 2]
|
||||
|
|
@ -135,10 +215,18 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
|
|||
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
|
||||
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
|
||||
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
|
||||
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
|
||||
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
|
||||
q_embed = (q * cos) + (rotate_half(q) * sin)
|
||||
k_embed = (k * cos) + (rotate_half(k) * sin)
|
||||
cos = cos.unsqueeze(0).unsqueeze(0).expand(len(position_ids), -1, -1, -1)
|
||||
sin = sin.unsqueeze(0).unsqueeze(0).expand(len(position_ids), -1, -1, -1)
|
||||
if q.size(2) == 1:
|
||||
q_embed = (q * cos[:, :, -1, :]) + (rotate_half(q) * sin[:, :, -1, :])
|
||||
else:
|
||||
q_embed = (q * cos) + (rotate_half(q) * sin)
|
||||
|
||||
if k.size(2) == 1:
|
||||
k_embed = (k * cos[:, :, -1, :]) + (rotate_half(k) * sin[:, :, -1, :])
|
||||
else:
|
||||
k_embed = (k * cos) + (rotate_half(k) * sin)
|
||||
|
||||
return q_embed, k_embed
|
||||
|
||||
|
||||
|
|
@ -179,7 +267,25 @@ class InternLMAttention(nn.Module):
|
|||
self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias)
|
||||
self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias)
|
||||
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
|
||||
self.rotary_emb = InternLMRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
|
||||
self.rotary_emb = self._init_rope()
|
||||
|
||||
def _init_rope(self):
|
||||
if self.config.rotary["type"] == "origin":
|
||||
self.rotary_emb = InternLMRotaryEmbedding(
|
||||
self.head_dim,
|
||||
max_position_embeddings=self.max_position_embeddings,
|
||||
base=self.config.rotary["base"],
|
||||
)
|
||||
elif self.config.rotary["type"] == "dynamic":
|
||||
self.rotary_emb = InternLMDynamicNTKScalingRotaryEmbedding(
|
||||
self.head_dim,
|
||||
max_position_embeddings=self.max_position_embeddings,
|
||||
base=self.config.rotary["base"],
|
||||
scaling_factor=self.config.rotary.get("scaling_factor", 1.0),
|
||||
)
|
||||
else:
|
||||
raise ValueError("Currently we only support rotary embedding's type being one of ('origin', 'dynamic').")
|
||||
return self.rotary_emb
|
||||
|
||||
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()
|
||||
|
|
@ -199,20 +305,18 @@ class InternLMAttention(nn.Module):
|
|||
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
value_states = self.v_proj(hidden_states).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]
|
||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||||
# [bsz, nh, t, hd]
|
||||
|
||||
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)
|
||||
|
||||
# print(use_cache)
|
||||
past_key_value = (key_states, value_states) if use_cache else None
|
||||
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||||
|
||||
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
|
||||
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
||||
|
|
@ -386,7 +490,8 @@ INTERNLM_INPUTS_DOCSTRING = r"""
|
|||
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
||||
config.n_positions - 1]`.
|
||||
[What are position IDs?](../glossary#position-ids)
|
||||
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
||||
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
|
||||
when `config.use_cache=True`):
|
||||
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
||||
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
||||
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
||||
|
|
@ -423,6 +528,7 @@ class InternLMModel(InternLMPreTrainedModel):
|
|||
Args:
|
||||
config: InternLMConfig
|
||||
"""
|
||||
|
||||
_auto_class = "AutoModel"
|
||||
|
||||
def __init__(self, config: InternLMConfig):
|
||||
|
|
@ -745,50 +851,56 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
|
|||
for layer_past in past_key_values:
|
||||
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
|
||||
return reordered_past
|
||||
|
||||
|
||||
def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = []):
|
||||
prompt = ""
|
||||
for record in history:
|
||||
prompt += f"""<|User|>:{record[0]}<eoh>\n<|Bot|>:{record[1]}<eoa>\n"""
|
||||
prompt += f"""<|User|>:{query}<eoh>\n<|Bot|>:"""
|
||||
return tokenizer([prompt], return_tensors="pt")
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def chat(self,
|
||||
tokenizer,
|
||||
query: str,
|
||||
history: List[Tuple[str, str]] = [],
|
||||
streamer: Optional[BaseStreamer] = None,
|
||||
max_new_tokens: int = 1024,
|
||||
do_sample: bool = True,
|
||||
temperature: float = 0.8,
|
||||
top_p: float = 0.8,
|
||||
**kwargs):
|
||||
def chat(
|
||||
self,
|
||||
tokenizer,
|
||||
query: str,
|
||||
history: List[Tuple[str, str]] = [],
|
||||
streamer: Optional[BaseStreamer] = None,
|
||||
max_new_tokens: int = 1024,
|
||||
do_sample: bool = True,
|
||||
temperature: float = 0.8,
|
||||
top_p: float = 0.8,
|
||||
**kwargs,
|
||||
):
|
||||
inputs = self.build_inputs(tokenizer, query, history)
|
||||
inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
|
||||
outputs = self.generate(**inputs,
|
||||
streamer=streamer,
|
||||
max_new_tokens=max_new_tokens,
|
||||
do_sample=do_sample,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
**kwargs)
|
||||
outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]):]
|
||||
outputs = self.generate(
|
||||
**inputs,
|
||||
streamer=streamer,
|
||||
max_new_tokens=max_new_tokens,
|
||||
do_sample=do_sample,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
**kwargs,
|
||||
)
|
||||
outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
|
||||
response = tokenizer.decode(outputs, skip_special_tokens=True)
|
||||
response = response.split("<eoa>")[0]
|
||||
history = history + [(query, response)]
|
||||
return response, history
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def stream_chat(self,
|
||||
tokenizer,
|
||||
query: str,
|
||||
history: List[Tuple[str, str]] = [],
|
||||
max_new_tokens: int = 1024,
|
||||
do_sample: bool = True,
|
||||
temperature: float = 0.8,
|
||||
top_p: float = 0.8,
|
||||
**kwargs):
|
||||
def stream_chat(
|
||||
self,
|
||||
tokenizer,
|
||||
query: str,
|
||||
history: List[Tuple[str, str]] = [],
|
||||
max_new_tokens: int = 1024,
|
||||
do_sample: bool = True,
|
||||
temperature: float = 0.8,
|
||||
top_p: float = 0.8,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
Return a generator in format: (response, history)
|
||||
Eg.
|
||||
|
|
@ -834,12 +946,12 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
|
|||
tokenizer=tokenizer,
|
||||
query=query,
|
||||
streamer=ChatStreamer(tokenizer=tokenizer),
|
||||
history=history,
|
||||
history=history,
|
||||
max_new_tokens=max_new_tokens,
|
||||
do_sample=do_sample,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
**kwargs
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def consumer():
|
||||
|
|
|
|||
Loading…
Reference in New Issue