From 496bb2e845b3d82877e45050781ba2a73412c745 Mon Sep 17 00:00:00 2001 From: "Wang, Jian4" <61138589+hzjane@users.noreply.github.com> Date: Fri, 15 Dec 2023 13:34:33 +0800 Subject: [PATCH] LLM: Support load BaiChuan model family gguf model (#9685) * support baichuan model family gguf model * update gguf generate.py * add verify models * add support model_family * update * update style * update type * update readme * update * remove support model_family --- .../Advanced-Quantizations/GGUF/README.md | 8 +- .../Advanced-Quantizations/GGUF/generate.py | 5 +- .../Advanced-Quantizations/GGUF/README.md | 8 +- .../src/bigdl/llm/transformers/gguf/api.py | 5 + .../llm/transformers/gguf/models/baichuan.py | 123 +++ .../model_implement/baichuan/__init__.py | 15 + .../baichuan/configuration_baichuan.py | 66 ++ .../baichuan/modeling_baichuan.py | 715 ++++++++++++++++++ .../baichuan/tokenization_baichuan.py | 262 +++++++ 9 files changed, 1200 insertions(+), 7 deletions(-) create mode 100644 python/llm/src/bigdl/llm/transformers/gguf/models/baichuan.py create mode 100644 python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/__init__.py create mode 100644 python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/configuration_baichuan.py create mode 100644 python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/modeling_baichuan.py create mode 100644 python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/tokenization_baichuan.py diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md b/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md index 5f160576..3603d086 100644 --- a/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md +++ b/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md @@ -1,6 +1,10 @@ # Loading GGUF models -In this directory, you will find examples on how to load GGUF model into `bigdl-llm`. For illustration purposes, we utilize the [llama-2-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/main) as a reference LLaMA2 GGUF model. ->Note: Only LLaMA2 family models are currently supported +In this directory, you will find examples on how to load GGUF model into `bigdl-llm`. + +## Verified Models(Q4_0) +- [Llama-2-7B-Chat-GGUF](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/main) +- [Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF) +- [Baichuan2-7B-Chat-GGUF](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/tree/main) ## Requirements To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../../../README.md#system-support) for more information. diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/generate.py b/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/generate.py index a1f19354..78e106e8 100644 --- a/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/generate.py +++ b/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/generate.py @@ -18,12 +18,11 @@ import torch import time import argparse -from transformers import LlamaTokenizer from bigdl.llm.transformers import AutoModelForCausalLM # you could tune the prompt based on your own model, # here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style -LLAMA2_PROMPT_FORMAT = """### HUMAN: +PROMPT_FORMAT = """### HUMAN: {prompt} ### RESPONSE: @@ -47,7 +46,7 @@ if __name__ == '__main__': # Generate predicted tokens with torch.inference_mode(): - prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt) + prompt = PROMPT_FORMAT.format(prompt=args.prompt) input_ids = tokenizer.encode(prompt, return_tensors="pt") st = time.time() output = model.generate(input_ids, diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md b/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md index 8d1d2782..3d148ff6 100644 --- a/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md +++ b/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md @@ -1,6 +1,10 @@ # Loading GGUF models -In this directory, you will find examples on how to load GGUF model into `bigdl-llm`. For illustration purposes, we utilize the [llama-2-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/main) as a reference LLaMA2 GGUF model. ->Note: Only LLaMA2 family models are currently supported +In this directory, you will find examples on how to load GGUF model into `bigdl-llm`. + +## Verified Models(Q4_0) +- [Llama-2-7B-Chat-GGUF](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/main) +- [Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF) +- [Baichuan2-7B-Chat-GGUF](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/tree/main) ## Requirements To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../../../README.md#system-support) for more information. diff --git a/python/llm/src/bigdl/llm/transformers/gguf/api.py b/python/llm/src/bigdl/llm/transformers/gguf/api.py index d52a1e49..d791542b 100644 --- a/python/llm/src/bigdl/llm/transformers/gguf/api.py +++ b/python/llm/src/bigdl/llm/transformers/gguf/api.py @@ -32,6 +32,7 @@ def load_gguf_model(fpath: str, dtype: torch.dtype = torch.float): loader = GGUFFileLoader(fpath) model_family = loader.config["general.architecture"] + print("model_family:" + model_family) qtype = loader.config["general.file_type"] invalidInputError(qtype in qtype_map, f"Unsupported gguf quantize type: {qtype}") @@ -42,6 +43,10 @@ def load_gguf_model(fpath: str, dtype: torch.dtype = torch.float): from .models.llama import load_gguf_llama model, tokenizer = load_gguf_llama(loader, dtype) + elif model_family == "baichuan": + from .models.baichuan import load_gguf_baichuan + + model, tokenizer = load_gguf_baichuan(loader, dtype) else: invalidInputError(False, f"Unsupported model family: {model_family}") diff --git a/python/llm/src/bigdl/llm/transformers/gguf/models/baichuan.py b/python/llm/src/bigdl/llm/transformers/gguf/models/baichuan.py new file mode 100644 index 00000000..52441b01 --- /dev/null +++ b/python/llm/src/bigdl/llm/transformers/gguf/models/baichuan.py @@ -0,0 +1,123 @@ +# +# Copyright 2016 The BigDL Authors. +# +# 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. +# + +import os +import torch +from accelerate import init_empty_weights +from accelerate.utils import set_module_tensor_to_device +from tempfile import NamedTemporaryFile +from .model_implement.baichuan.configuration_baichuan import BaiChuanConfig +from .model_implement.baichuan.modeling_baichuan import BaiChuanForCausalLM +from .model_implement.baichuan.tokenization_baichuan import BaiChuanTokenizer + +from ..gguf import GGUFFileLoader + + +def load_gguf_baichuan(loader: GGUFFileLoader, dtype: torch.dtype = torch.float): + config = loader.config + + baichuan_config = BaiChuanConfig( + vocab_size=len(config['tokenizer.ggml.tokens']), + hidden_size=config['baichuan.embedding_length'], + intermediate_size=config['baichuan.feed_forward_length'], + num_hidden_layers=config['baichuan.block_count'], + num_attention_heads=config['baichuan.attention.head_count'], + num_key_value_heads=config['baichuan.attention.head_count_kv'], + hidden_act="silu", + max_position_embeddings=config['baichuan.context_length'], + rms_norm_eps=config['baichuan.attention.layer_norm_rms_epsilon'], + use_cache=True, + pad_token_id=None, + bos_token_id=config['tokenizer.ggml.bos_token_id'], + eos_token_id=config['tokenizer.ggml.eos_token_id'], + pretraining_tp=1, + ) + + ckpt = loader.tensors(dtype) + n_head = config['baichuan.attention.head_count'] + n_head_kv = config['baichuan.attention.head_count_kv'] + ckpt = restore_baichuan_weight(ckpt, n_head, n_head_kv) + + state_dict = {} + state_dict['model.embed_tokens.weight'] = ckpt['token_embd.weight'] + state_dict['model.norm.weight'] = ckpt['output_norm.weight'] + state_dict['lm_head.weight'] = ckpt['output.weight'] + for i in range(config['baichuan.block_count']): + # rebuild W_pack + a = ckpt[f'blk.{i}.attn_q.weight'] + b = ckpt[f'blk.{i}.attn_k.weight'] + c = ckpt[f'blk.{i}.attn_v.weight'] + d = torch.cat([a, b, c], dim=0) + state_dict[f'model.layers.{i}.self_attn.W_pack.weight'] = d + + state_dict[f'model.layers.{i}.self_attn.o_proj.weight'] = \ + ckpt[f'blk.{i}.attn_output.weight'] + state_dict[f'model.layers.{i}.mlp.gate_proj.weight'] = \ + ckpt[f'blk.{i}.ffn_gate.weight'] + state_dict[f'model.layers.{i}.mlp.up_proj.weight'] = \ + ckpt[f'blk.{i}.ffn_up.weight'] + state_dict[f'model.layers.{i}.mlp.down_proj.weight'] = \ + ckpt[f'blk.{i}.ffn_down.weight'] + state_dict[f'model.layers.{i}.input_layernorm.weight'] = \ + ckpt[f'blk.{i}.attn_norm.weight'] + state_dict[f'model.layers.{i}.post_attention_layernorm.weight'] = \ + ckpt[f'blk.{i}.ffn_norm.weight'] + + with init_empty_weights(): + model = BaiChuanForCausalLM(baichuan_config) + + for name, weight in state_dict.items(): + set_module_tensor_to_device(model, name, "cpu", weight) + + model = model.cpu() + + # see https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto + from transformers.convert_slow_tokenizer import import_protobuf + spm_pb2 = import_protobuf("Failed to import protobuf") + + pieces = loader.tokenizer_pieces() + trainer_spec = spm_pb2.TrainerSpec(byte_fallback=True, + model_type=spm_pb2.TrainerSpec.ModelType.BPE) + proto = spm_pb2.ModelProto(pieces=pieces, trainer_spec=trainer_spec) + proto = proto.SerializeToString() + + with NamedTemporaryFile(delete=False) as f: + f.write(proto) + f.close() + tokenizer = BaiChuanTokenizer(f.name) + os.remove(f.name) + + return model, tokenizer + + +def restore_baichuan_weight(ckpt: dict, n_head: int, n_head_kv: int): + # see https://github.com/ggerganov/llama.cpp/blob/master/convert-hf-to-gguf.py#L535 + + for name, weight in ckpt.items(): + head, hd_size = weight.shape[0], weight.shape[1:] + if n_head != n_head_kv: + new_n_head = n_head // n_head_kv + else: + new_n_head = n_head + if name.endswith("attn_q.weight"): + ckpt[name] = (weight.reshape(new_n_head, head // new_n_head // 2, 2, *hd_size) + .swapaxes(1, 2) + .reshape(weight.shape)) + elif name.endswith("attn_k.weight"): + ckpt[name] = (weight.reshape(new_n_head, head // new_n_head // 2, 2, *hd_size) + .swapaxes(1, 2) + .reshape(weight.shape)) + return ckpt diff --git a/python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/__init__.py b/python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/__init__.py new file mode 100644 index 00000000..2151a805 --- /dev/null +++ b/python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright 2016 The BigDL Authors. +# +# 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. +# diff --git a/python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/configuration_baichuan.py b/python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/configuration_baichuan.py new file mode 100644 index 00000000..3d091418 --- /dev/null +++ b/python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/configuration_baichuan.py @@ -0,0 +1,66 @@ +# +# Copyright 2016 The BigDL Authors. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# 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. + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + + +class BaiChuanConfig(PretrainedConfig): + model_type = "baichuan" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=64000, + hidden_size=4096, + intermediate_size=11008, + num_hidden_layers=32, + num_attention_heads=32, + hidden_act="silu", + max_position_embeddings=4096, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + pad_token_id=0, + bos_token_id=1, + eos_token_id=2, + tie_word_embeddings=False, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/modeling_baichuan.py b/python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/modeling_baichuan.py new file mode 100644 index 00000000..b061554c --- /dev/null +++ b/python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/modeling_baichuan.py @@ -0,0 +1,715 @@ +# +# Copyright 2016 The BigDL Authors. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# 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. +from .configuration_baichuan import BaiChuanConfig +from transformers import PreTrainedModel, add_start_docstrings +from transformers.activations import ACT2FN +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, \ + SequenceClassifierOutputWithPast +from transformers.utils import logging, add_start_docstrings_to_model_forward, \ + replace_return_docstrings + +import math +from typing import List, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +logger = logging.get_logger(__name__) + + +# 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 +): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz, tgt_len = input_ids_shape + mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, + device=device), device=device) + mask_cond = torch.arange(mask.size(-1), device=device) + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) + mask = mask.to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, + device=device), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + + +# Copied from transformers.models.bart.modeling_bart._expand_mask +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + +class RMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + 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.variance_epsilon) + + # convert into half-precision if necessary + if self.weight.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.to(self.weight.dtype) + + return self.weight * hidden_states + + +class RotaryEmbedding(torch.nn.Module): + 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)) + self.register_buffer("inv_freq", inv_freq) + + # Build here to make `torch.jit.trace` work. + 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 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_seq_len_cached: + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=x.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).to(x.device) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) + 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] + x2 = x[..., x.shape[-1] // 2:] + return torch.cat((-x2, x1), dim=-1) + + +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) + return q_embed, k_embed + + +class MLP(nn.Module): + def __init__( + self, + hidden_size: int, + intermediate_size: int, + hidden_act: str, + ): + super().__init__() + self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False) + self.up_proj = 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 Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + 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.max_position_embeddings + + if (self.head_dim * self.num_heads) != self.hidden_size: + logger.error( + f"hidden_size must be divisible by num_heads (got `hidden_size`:{self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self.rotary_emb = RotaryEmbedding(self.head_dim, + max_position_embeddings=self.max_position_embeddings) + + 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, + position_ids: Optional[torch.LongTensor] = 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)) # batch_size x source_len x hidden_size + key_states = (proj[1].view(bsz, q_len, self.num_heads, self.head_dim) + .transpose(1, 2)) # batch_size x target_len x head_size + value_states = (proj[2].view(bsz, q_len, self.num_heads, self.head_dim) + .transpose(1, 2)) # batch_size x source_len x hidden_size + + 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) + + 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 attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + logger.error( + f"Attention weights should be of size " + f"{(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + logger.error( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}," + f" but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + attn_weights = torch.max(attn_weights, + torch.tensor(torch.finfo(attn_weights.dtype).min)) + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to( + query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + logger.error( + f"`attn_output` should be of " + f"size {(bsz, self.num_heads, q_len, self.head_dim)}, " + f"but is" + f" {attn_output.size()}" + ) + + 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 DecoderLayer(nn.Module): + def __init__(self, config: BaiChuanConfig): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = Attention(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, eps=config.rms_norm_eps) + self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = 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]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape + `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are + indicated by very large negative values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all + attention layers. See `attentions` under + returned tensors for more detail. + 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`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): + cached past key and value projection states + """ + + 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, + position_ids=position_ids, + 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 output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +class PreTrainedModel(PreTrainedModel): + config_class = BaiChuanConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["DecoderLayer"] + _keys_to_ignore_on_load_unexpected = [r"decoder\.version"] + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, 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, Model): + module.gradient_checkpointing = value + + +class Model(PreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. + Each layer is a [`DecoderLayer`] + + Args: + config: BaiChuanConfig + """ + + def __init__(self, config: BaiChuanConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, + past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, + inputs_embeds.dtype, + device=inputs_embeds.device, + past_key_values_length=past_key_values_length, + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, + tgt_len=input_shape[-1]).to( + inputs_embeds.device + ) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None + else expanded_attn_mask + combined_attention_mask + ) + + return combined_attention_mask + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + 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 + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + logger.error( + "You cannot specify both decoder_input_ids " + "and decoder_inputs_embeds at the same time") + 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: + logger.error( + "You have to specify either decoder_input_ids or decoder_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 position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, + device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + # embed positions + if attention_mask is None: + attention_mask = torch.ones( + (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device + ) + attention_mask = self._prepare_decoder_attention_mask( + attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length + ) + + 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, + position_ids, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + 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(PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.model = Model(config) + + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[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] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. + Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). + Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens + with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, ModelForCausalLM + + >>> model = ModelForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you consciours? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, + clean_up_tokenization_spaces=False)[0] + "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." + ```""" + + 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 + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_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:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -1].unsqueeze(-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( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + 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 diff --git a/python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/tokenization_baichuan.py b/python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/tokenization_baichuan.py new file mode 100644 index 00000000..d4d68995 --- /dev/null +++ b/python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/baichuan/tokenization_baichuan.py @@ -0,0 +1,262 @@ +# +# Copyright 2016 The BigDL Authors. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# 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. + +import os +from shutil import copyfile +from typing import Any, Dict, List, Optional, Tuple + +import sentencepiece as spm + +from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} + +PRETRAINED_VOCAB_FILES_MAP = { + "vocab_file": {}, + "tokenizer_file": {}, +} +PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {} + + +class BaiChuanTokenizer(PreTrainedTokenizer): + """ + Construct a BaiChuan tokenizer. Based on byte-level Byte-Pair-Encoding. + + Args: + vocab_file (`str`): + Path to the vocabulary file. + """ + + vocab_files_names = VOCAB_FILES_NAMES + pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP + max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file, + unk_token="", + bos_token="", + eos_token="", + pad_token=None, + sp_model_kwargs: Optional[Dict[str, Any]]=None, + add_bos_token=True, + add_eos_token=False, + clean_up_tokenization_spaces=False, + **kwargs, + ): + self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs + bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) \ + if isinstance(bos_token, str) else bos_token + eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) \ + if isinstance(eos_token, str) else eos_token + unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) \ + if isinstance(unk_token, str) else unk_token + pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) \ + if isinstance(pad_token, str) else pad_token + self.vocab_file = vocab_file + self.add_bos_token = add_bos_token + self.add_eos_token = add_eos_token + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(vocab_file) + super().__init__( + bos_token=bos_token, + eos_token=eos_token, + unk_token=unk_token, + pad_token=pad_token, + add_bos_token=add_bos_token, + add_eos_token=add_eos_token, + sp_model_kwargs=self.sp_model_kwargs, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + def __getstate__(self): + state = self.__dict__.copy() + state["sp_model"] = None + return state + + def __setstate__(self, d): + self.__dict__ = d + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(self.vocab_file) + + @property + def vocab_size(self): + """Returns vocab size""" + return self.sp_model.get_piece_size() + + def get_vocab(self): + """Returns vocab as a dict""" + vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + def _tokenize(self, text): + """Returns a tokenized string.""" + return self.sp_model.encode(text, out_type=str) + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.sp_model.piece_to_id(token) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + token = self.sp_model.IdToPiece(index) + return token + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + current_sub_tokens = [] + out_string = "" + prev_is_special = False + for i, token in enumerate(tokens): + # make sure that special tokens are not decoded using sentencepiece model + if token in self.all_special_tokens: + if not prev_is_special and i != 0: + out_string += " " + out_string += self.sp_model.decode(current_sub_tokens) + token + prev_is_special = True + current_sub_tokens = [] + else: + current_sub_tokens.append(token) + prev_is_special = False + out_string += self.sp_model.decode(current_sub_tokens) + return out_string + + def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]: + """ + Save the vocabulary and special tokens file to a directory. + + Args: + save_directory (`str`): + The directory in which to save the vocabulary. + + Returns: + `Tuple(str)`: Paths to the files saved. + """ + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + out_vocab_file = os.path.join( + save_directory, + (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + + if (os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) + and os.path.isfile(self.vocab_file)): + copyfile(self.vocab_file, out_vocab_file) + elif not os.path.isfile(self.vocab_file): + with open(out_vocab_file, "wb") as fi: + content_spiece_model = self.sp_model.serialized_model_proto() + fi.write(content_spiece_model) + + return (out_vocab_file,) + + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): + bos_token_id = [self.bos_token_id] if self.add_bos_token else [] + eos_token_id = [self.eos_token_id] if self.add_eos_token else [] + + output = bos_token_id + token_ids_0 + eos_token_id + + if token_ids_1 is not None: + output = output + bos_token_id + token_ids_1 + eos_token_id + + return output + + def get_special_tokens_mask( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, + already_has_special_tokens: bool = False + ) -> List[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. + This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted + with special tokens for the model. + + Returns: + `List[int]`: A list of integers in the range [0, 1]: + 1 for a special token, 0 for a sequence token. + """ + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + bos_token_id = [1] if self.add_bos_token else [] + eos_token_id = [1] if self.add_eos_token else [] + + if token_ids_1 is None: + return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id + return ( + bos_token_id + + ([0] * len(token_ids_0)) + + eos_token_id + + bos_token_id + + ([0] * len(token_ids_1)) + + eos_token_id + ) + + def create_token_type_ids_from_sequences( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Creates a mask from the two sequences passed to + be used in a sequence-pair classification task. An ALBERT + sequence pair mask has the following format: + + ``` + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + ``` + + if token_ids_1 is None, only returns the first portion of the mask (0s). + + Args: + token_ids_0 (`List[int]`): + List of ids. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [token type IDs](../glossary#token-type-ids) + according to the given sequence(s). + """ + bos_token_id = [self.bos_token_id] if self.add_bos_token else [] + eos_token_id = [self.eos_token_id] if self.add_eos_token else [] + + output = [0] * len(bos_token_id + token_ids_0 + eos_token_id) + + if token_ids_1 is not None: + output += [1] * len(bos_token_id + token_ids_1 + eos_token_id) + + return output