Upload 10 files
Browse files- config.json +29 -0
- configuration_baichuan.py +46 -0
- generation_config.json +7 -0
- modeling_baichuan.py +607 -0
- pytorch_model.bin.index.json +290 -0
- quantizer.py +123 -0
- special_tokens_map.json +30 -0
- tokenization_baichuan.py +232 -0
- tokenizer.model +3 -0
- tokenizer_config.json +47 -0
config.json
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{
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"_from_model_config": true,
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"_name_or_path": "/home/incoming/zhengyw/baichuan-13b-base",
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"architectures": [
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"BaichuanForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_baichuan.BaichuanConfig",
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"AutoModel": "modeling_baichuan.BaichuanForCausalLM",
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"AutoModelForCausalLM": "modeling_baichuan.BaichuanForCausalLM"
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},
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 13696,
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"model_max_length": 4096,
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"model_type": "baichuan",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-06,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.31.0.dev0",
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"use_cache": true,
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"vocab_size": 64000
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}
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configuration_baichuan.py
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# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
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from transformers.configuration_utils import PretrainedConfig
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class BaichuanConfig(PretrainedConfig):
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model_type = "baichuan"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=64000,
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hidden_size=5120,
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intermediate_size=13696,
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num_hidden_layers=40,
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num_attention_heads=40,
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hidden_act="silu",
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model_max_length=4096,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=False,
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gradient_checkpointing=False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.model_max_length = model_max_length
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.gradient_checkpointing = gradient_checkpointing,
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"transformers_version": "4.31.0.dev0"
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}
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modeling_baichuan.py
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# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
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import math
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch.nn import CrossEntropyLoss
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from transformers import PreTrainedModel
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from transformers.activations import ACT2FN
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from transformers.utils import logging
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from transformers.generation.utils import GenerationConfig
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from .configuration_baichuan import BaichuanConfig
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logger = logging.get_logger(__name__)
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def _get_interleave(n):
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def _get_interleave_power_of_2(n):
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start = (2 ** (-2 ** -(math.log2(n) - 3)))
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ratio = start
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return [start * ratio ** i for i in range(n)]
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if math.log2(n).is_integer():
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return _get_interleave_power_of_2(n)
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else:
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closest_power_of_2 = 2 ** math.floor(math.log2(n))
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return _get_interleave_power_of_2(closest_power_of_2) + \
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_get_interleave(2 * closest_power_of_2)[0::2][:n - closest_power_of_2]
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def _fill_with_neg_inf(t):
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"""FP16-compatible function that fills a tensor with -inf."""
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return t.float().fill_(float("-inf")).type_as(t)
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def _gen_alibi_mask(n_head, max_pos):
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"""used in inference only"""
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slopes = torch.Tensor(_get_interleave(n_head))
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alibi = slopes.unsqueeze(1).unsqueeze(1) * torch.arange(max_pos).unsqueeze(0).unsqueeze(0).expand(
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n_head, -1, -1)
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alibi = alibi.view(n_head, 1, max_pos)
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alibi_mask = torch.triu(
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_fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1
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)
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alibi_mask = alibi_mask.unsqueeze(0) + alibi
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return alibi_mask
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def _buffered_future_mask(tensor, maxpos, alibi, attn_heads):
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"""used in training only"""
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dim = tensor.size(1)
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_future_mask = torch.triu(
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_fill_with_neg_inf(torch.zeros([maxpos, maxpos])), 1
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)
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_future_mask = _future_mask.unsqueeze(0) + alibi
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_future_mask = _future_mask.to(tensor)
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return _future_mask[:tensor.shape[0] * attn_heads, :maxpos, :maxpos]
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class RMSNorm(torch.nn.Module):
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def __init__(self, hidden_size, epsilon=1e-6):
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super().__init__()
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self.weight = torch.nn.Parameter(torch.empty(hidden_size))
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self.epsilon = epsilon
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def forward(self, hidden_states):
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variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
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68 |
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hidden_states = hidden_states * torch.rsqrt(variance + self.epsilon)
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69 |
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70 |
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# convert into half-precision
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71 |
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if self.weight.dtype in [torch.float16, torch.bfloat16]:
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72 |
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hidden_states = hidden_states.to(self.weight.dtype)
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73 |
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return self.weight * hidden_states
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class MLP(torch.nn.Module):
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def __init__(
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self,
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hidden_size: int,
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81 |
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intermediate_size: int,
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hidden_act: str,
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):
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super().__init__()
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self.gate_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
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86 |
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self.down_proj = torch.nn.Linear(intermediate_size, hidden_size, bias=False)
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87 |
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self.up_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
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88 |
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self.act_fn = ACT2FN[hidden_act]
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89 |
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90 |
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def forward(self, x):
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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92 |
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93 |
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+
class BaichuanAttention(torch.nn.Module):
|
95 |
+
def __init__(self, config: BaichuanConfig):
|
96 |
+
super().__init__()
|
97 |
+
self.config = config
|
98 |
+
self.hidden_size = config.hidden_size
|
99 |
+
self.num_heads = config.num_attention_heads
|
100 |
+
self.head_dim = self.hidden_size // self.num_heads
|
101 |
+
self.max_position_embeddings = config.model_max_length
|
102 |
+
|
103 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
104 |
+
raise ValueError(
|
105 |
+
f"hidden_size {self.hidden_size} is not divisible by num_heads {self.num_heads}"
|
106 |
+
)
|
107 |
+
self.W_pack = torch.nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
|
108 |
+
self.o_proj = torch.nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
|
109 |
+
|
110 |
+
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
111 |
+
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
112 |
+
|
113 |
+
def forward(
|
114 |
+
self,
|
115 |
+
hidden_states: torch.Tensor,
|
116 |
+
attention_mask: Optional[torch.Tensor] = None,
|
117 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
118 |
+
output_attentions: bool = False,
|
119 |
+
use_cache: bool = False,
|
120 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
121 |
+
|
122 |
+
bsz, q_len, _ = hidden_states.size()
|
123 |
+
|
124 |
+
proj = self.W_pack(hidden_states)
|
125 |
+
proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
|
126 |
+
query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
127 |
+
key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
128 |
+
value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
129 |
+
|
130 |
+
kv_seq_len = key_states.shape[-2]
|
131 |
+
if past_key_value is not None:
|
132 |
+
kv_seq_len += past_key_value[0].shape[-2]
|
133 |
+
|
134 |
+
if past_key_value is not None:
|
135 |
+
# reuse k, v, self_attention
|
136 |
+
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
137 |
+
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
138 |
+
|
139 |
+
past_key_value = (key_states, value_states) if use_cache else None
|
140 |
+
|
141 |
+
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
142 |
+
|
143 |
+
if attention_mask is not None:
|
144 |
+
if q_len == 1: # inference with cache
|
145 |
+
if len(attention_mask.size()) == 4:
|
146 |
+
attention_mask = attention_mask[:, :, -1:, :]
|
147 |
+
else:
|
148 |
+
attention_mask = attention_mask[:, -1:, :]
|
149 |
+
attn_weights = attn_weights + attention_mask
|
150 |
+
attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
|
151 |
+
|
152 |
+
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
|
153 |
+
|
154 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
155 |
+
|
156 |
+
attn_output = attn_output.transpose(1, 2)
|
157 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
158 |
+
attn_output = self.o_proj(attn_output)
|
159 |
+
|
160 |
+
if not output_attentions:
|
161 |
+
attn_weights = None
|
162 |
+
|
163 |
+
return attn_output, attn_weights, past_key_value
|
164 |
+
|
165 |
+
|
166 |
+
class BaichuanLayer(torch.nn.Module):
|
167 |
+
def __init__(self, config: BaichuanConfig):
|
168 |
+
super().__init__()
|
169 |
+
self.hidden_size = config.hidden_size
|
170 |
+
self.self_attn = BaichuanAttention(config=config)
|
171 |
+
self.mlp = MLP(
|
172 |
+
hidden_size=self.hidden_size,
|
173 |
+
intermediate_size=config.intermediate_size,
|
174 |
+
hidden_act=config.hidden_act,
|
175 |
+
)
|
176 |
+
self.input_layernorm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
|
177 |
+
self.post_attention_layernorm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
|
178 |
+
|
179 |
+
def forward(
|
180 |
+
self,
|
181 |
+
hidden_states: torch.Tensor,
|
182 |
+
attention_mask: Optional[torch.Tensor] = None,
|
183 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
184 |
+
output_attentions: Optional[bool] = False,
|
185 |
+
use_cache: Optional[bool] = False,
|
186 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
187 |
+
|
188 |
+
residual = hidden_states
|
189 |
+
|
190 |
+
hidden_states = self.input_layernorm(hidden_states)
|
191 |
+
|
192 |
+
# Self Attention
|
193 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
194 |
+
hidden_states=hidden_states,
|
195 |
+
attention_mask=attention_mask,
|
196 |
+
past_key_value=past_key_value,
|
197 |
+
output_attentions=output_attentions,
|
198 |
+
use_cache=use_cache,
|
199 |
+
)
|
200 |
+
hidden_states = residual + hidden_states
|
201 |
+
|
202 |
+
# Fully Connected
|
203 |
+
residual = hidden_states
|
204 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
205 |
+
hidden_states = self.mlp(hidden_states)
|
206 |
+
hidden_states = residual + hidden_states
|
207 |
+
|
208 |
+
outputs = (hidden_states,)
|
209 |
+
|
210 |
+
if use_cache:
|
211 |
+
outputs += (present_key_value,)
|
212 |
+
|
213 |
+
return outputs
|
214 |
+
|
215 |
+
|
216 |
+
class BaichuanPreTrainedModel(PreTrainedModel):
|
217 |
+
config_class = BaichuanConfig
|
218 |
+
base_model_prefix = "model"
|
219 |
+
supports_gradient_checkpointing = True
|
220 |
+
_no_split_modules = ["BaichuanLayer"]
|
221 |
+
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
|
222 |
+
|
223 |
+
def _init_weights(self, module):
|
224 |
+
std = self.config.initializer_range
|
225 |
+
if isinstance(module, torch.nn.Linear):
|
226 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
227 |
+
if module.bias is not None:
|
228 |
+
module.bias.data.zero_()
|
229 |
+
elif isinstance(module, torch.nn.Embedding):
|
230 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
231 |
+
if module.padding_idx is not None:
|
232 |
+
module.weight.data[module.padding_idx].zero_()
|
233 |
+
|
234 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
235 |
+
if isinstance(module, BaichuanModel):
|
236 |
+
module.gradient_checkpointing = value
|
237 |
+
|
238 |
+
|
239 |
+
class BaichuanModel(BaichuanPreTrainedModel):
|
240 |
+
def __init__(self, config: BaichuanConfig):
|
241 |
+
super().__init__(config)
|
242 |
+
self.padding_idx = config.pad_token_id
|
243 |
+
self.vocab_size = config.vocab_size
|
244 |
+
self.n_head = config.num_attention_heads
|
245 |
+
self.embed_tokens = torch.nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
246 |
+
self.layers = torch.nn.ModuleList([BaichuanLayer(config) for _ in range(config.num_hidden_layers)])
|
247 |
+
self.norm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
|
248 |
+
|
249 |
+
self.gradient_checkpointing = config.gradient_checkpointing
|
250 |
+
self.post_init()
|
251 |
+
self.max_cache_pos = config.model_max_length
|
252 |
+
self.first_run = True
|
253 |
+
self.alibi_mask = None
|
254 |
+
|
255 |
+
def get_input_embeddings(self):
|
256 |
+
return self.embed_tokens
|
257 |
+
|
258 |
+
def set_input_embeddings(self, value):
|
259 |
+
self.embed_tokens = value
|
260 |
+
|
261 |
+
def get_alibi_mask(self, tensor, seq_length_with_past):
|
262 |
+
if self.training:
|
263 |
+
slopes = torch.Tensor(_get_interleave(self.n_head))
|
264 |
+
alibi = slopes.unsqueeze(1).unsqueeze(1) * torch.arange(seq_length_with_past).unsqueeze(0).unsqueeze(0).expand(
|
265 |
+
self.n_head,
|
266 |
+
-1, -1)
|
267 |
+
alibi = alibi.view(self.n_head, 1, seq_length_with_past)
|
268 |
+
mask = _buffered_future_mask(tensor, seq_length_with_past, alibi, self.n_head)
|
269 |
+
else:
|
270 |
+
if self.first_run:
|
271 |
+
self.first_run = False
|
272 |
+
self.register_buffer("future_mask", _gen_alibi_mask(self.n_head, self.max_cache_pos).to(tensor), persistent=False)
|
273 |
+
if seq_length_with_past > self.max_cache_pos:
|
274 |
+
self.max_cache_pos = seq_length_with_past
|
275 |
+
self.register_buffer("future_mask", _gen_alibi_mask(self.n_head, self.max_cache_pos).to(tensor), persistent=False)
|
276 |
+
mask = self.future_mask[:self.n_head, :seq_length_with_past, :seq_length_with_past]
|
277 |
+
return mask
|
278 |
+
|
279 |
+
def forward(
|
280 |
+
self,
|
281 |
+
input_ids: torch.LongTensor = None,
|
282 |
+
attention_mask: Optional[torch.Tensor] = None,
|
283 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
284 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
285 |
+
use_cache: Optional[bool] = False,
|
286 |
+
output_attentions: Optional[bool] = False,
|
287 |
+
output_hidden_states: Optional[bool] = False,
|
288 |
+
return_dict: Optional[bool] = True,
|
289 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
290 |
+
|
291 |
+
if input_ids is not None and inputs_embeds is not None:
|
292 |
+
raise ValueError("You cannot provide both input_ids and inputs_embeds simultaneously")
|
293 |
+
elif input_ids is not None:
|
294 |
+
batch_size, seq_length = input_ids.shape
|
295 |
+
elif inputs_embeds is not None:
|
296 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
297 |
+
else:
|
298 |
+
raise ValueError("You need to provide input_ids or inputs_embeds")
|
299 |
+
|
300 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
301 |
+
|
302 |
+
seq_length_with_past = seq_length
|
303 |
+
|
304 |
+
if past_key_values is not None:
|
305 |
+
past_key_values_length = past_key_values[0][0].shape[2]
|
306 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
307 |
+
|
308 |
+
if inputs_embeds is None:
|
309 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
310 |
+
|
311 |
+
if self.training:
|
312 |
+
if self.alibi_mask is None or self.alibi_mask.shape[-1] != seq_length_with_past:
|
313 |
+
self.alibi_mask = self.get_alibi_mask(inputs_embeds, seq_length_with_past)
|
314 |
+
alibi_mask = self.alibi_mask
|
315 |
+
else:
|
316 |
+
alibi_mask = self.get_alibi_mask(inputs_embeds, seq_length_with_past)
|
317 |
+
|
318 |
+
if attention_mask is not None:
|
319 |
+
if len(attention_mask.shape) == 2:
|
320 |
+
expanded_mask = attention_mask.to(alibi_mask.dtype)
|
321 |
+
expanded_mask = torch.tril(torch.gt(expanded_mask[:, :, None] * expanded_mask[:, None, :], 0)
|
322 |
+
) * torch.eq(expanded_mask[:, :, None] - expanded_mask[:, None, :], 0)
|
323 |
+
else:
|
324 |
+
expanded_mask = attention_mask
|
325 |
+
bsz = inputs_embeds.size(0)
|
326 |
+
src_len, tgt_len = alibi_mask.size()[-2:]
|
327 |
+
expanded_mask = expanded_mask.unsqueeze(1).expand(bsz, 1, src_len, tgt_len).to(alibi_mask.dtype)
|
328 |
+
inverted_mask = 1.0 - expanded_mask
|
329 |
+
inverted_mask = inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(alibi_mask.dtype).min)
|
330 |
+
attention_mask = inverted_mask + alibi_mask.unsqueeze(0)
|
331 |
+
else:
|
332 |
+
attention_mask = alibi_mask
|
333 |
+
|
334 |
+
hidden_states = inputs_embeds
|
335 |
+
|
336 |
+
if self.gradient_checkpointing and self.training:
|
337 |
+
if use_cache:
|
338 |
+
logger.warning_once(
|
339 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
340 |
+
)
|
341 |
+
use_cache = False
|
342 |
+
|
343 |
+
# decoder layers
|
344 |
+
all_hidden_states = () if output_hidden_states else None
|
345 |
+
all_self_attns = () if output_attentions else None
|
346 |
+
next_decoder_cache = () if use_cache else None
|
347 |
+
|
348 |
+
for idx, decoder_layer in enumerate(self.layers):
|
349 |
+
if output_hidden_states:
|
350 |
+
all_hidden_states += (hidden_states,)
|
351 |
+
|
352 |
+
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
353 |
+
|
354 |
+
if self.gradient_checkpointing and self.training:
|
355 |
+
|
356 |
+
def create_custom_forward(module):
|
357 |
+
def custom_forward(*inputs):
|
358 |
+
# None for past_key_value
|
359 |
+
return module(*inputs, output_attentions, None)
|
360 |
+
|
361 |
+
return custom_forward
|
362 |
+
|
363 |
+
layer_outputs = torch.utils.checkpoint.checkpoint(
|
364 |
+
create_custom_forward(decoder_layer),
|
365 |
+
hidden_states,
|
366 |
+
attention_mask,
|
367 |
+
None,
|
368 |
+
)
|
369 |
+
else:
|
370 |
+
layer_outputs = decoder_layer(
|
371 |
+
hidden_states,
|
372 |
+
attention_mask=attention_mask,
|
373 |
+
past_key_value=past_key_value,
|
374 |
+
output_attentions=output_attentions,
|
375 |
+
use_cache=use_cache,
|
376 |
+
)
|
377 |
+
|
378 |
+
hidden_states = layer_outputs[0]
|
379 |
+
|
380 |
+
if use_cache:
|
381 |
+
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
382 |
+
|
383 |
+
if output_attentions:
|
384 |
+
all_self_attns += (layer_outputs[1],)
|
385 |
+
|
386 |
+
hidden_states = self.norm(hidden_states)
|
387 |
+
|
388 |
+
# add hidden states from the last decoder layer
|
389 |
+
if output_hidden_states:
|
390 |
+
all_hidden_states += (hidden_states,)
|
391 |
+
|
392 |
+
next_cache = next_decoder_cache if use_cache else None
|
393 |
+
if not return_dict:
|
394 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
395 |
+
return BaseModelOutputWithPast(
|
396 |
+
last_hidden_state=hidden_states,
|
397 |
+
past_key_values=next_cache,
|
398 |
+
hidden_states=all_hidden_states,
|
399 |
+
attentions=all_self_attns,
|
400 |
+
)
|
401 |
+
|
402 |
+
|
403 |
+
class BaichuanForCausalLM(BaichuanPreTrainedModel):
|
404 |
+
def __init__(self, config):
|
405 |
+
super().__init__(config)
|
406 |
+
self.model = BaichuanModel(config)
|
407 |
+
self.lm_head = torch.nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
408 |
+
|
409 |
+
# Initialize weights and apply final processing
|
410 |
+
self.post_init()
|
411 |
+
|
412 |
+
def get_input_embeddings(self):
|
413 |
+
return self.model.embed_tokens
|
414 |
+
|
415 |
+
def set_input_embeddings(self, value):
|
416 |
+
self.model.embed_tokens = value
|
417 |
+
|
418 |
+
def get_output_embeddings(self):
|
419 |
+
return self.lm_head
|
420 |
+
|
421 |
+
def set_output_embeddings(self, new_embeddings):
|
422 |
+
self.lm_head = new_embeddings
|
423 |
+
|
424 |
+
def set_decoder(self, decoder):
|
425 |
+
self.model = decoder
|
426 |
+
|
427 |
+
def get_decoder(self):
|
428 |
+
return self.model
|
429 |
+
|
430 |
+
def forward(
|
431 |
+
self,
|
432 |
+
input_ids: torch.LongTensor = None,
|
433 |
+
attention_mask: Optional[torch.Tensor] = None,
|
434 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
435 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
436 |
+
labels: Optional[torch.LongTensor] = None,
|
437 |
+
use_cache: Optional[bool] = None,
|
438 |
+
output_attentions: Optional[bool] = False,
|
439 |
+
output_hidden_states: Optional[bool] = False,
|
440 |
+
return_dict: Optional[bool] = True,
|
441 |
+
**kwargs
|
442 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
443 |
+
|
444 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
445 |
+
|
446 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
447 |
+
outputs = self.model(
|
448 |
+
input_ids=input_ids,
|
449 |
+
attention_mask=attention_mask,
|
450 |
+
past_key_values=past_key_values,
|
451 |
+
inputs_embeds=inputs_embeds,
|
452 |
+
use_cache=use_cache,
|
453 |
+
output_attentions=output_attentions,
|
454 |
+
output_hidden_states=output_hidden_states,
|
455 |
+
return_dict=return_dict,
|
456 |
+
)
|
457 |
+
|
458 |
+
hidden_states = outputs[0]
|
459 |
+
logits = self.lm_head(hidden_states)
|
460 |
+
|
461 |
+
loss = None
|
462 |
+
if labels is not None:
|
463 |
+
# Shift so that tokens < n predict n
|
464 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
465 |
+
shift_labels = labels[..., 1:].contiguous()
|
466 |
+
# Flatten the tokens
|
467 |
+
loss_fct = CrossEntropyLoss()
|
468 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
469 |
+
shift_labels = shift_labels.view(-1)
|
470 |
+
# Enable model parallelism
|
471 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
472 |
+
loss = loss_fct(shift_logits, shift_labels)
|
473 |
+
|
474 |
+
if not return_dict:
|
475 |
+
output = (logits,) + outputs[1:]
|
476 |
+
return (loss,) + output if loss is not None else output
|
477 |
+
|
478 |
+
return CausalLMOutputWithPast(
|
479 |
+
loss=loss,
|
480 |
+
logits=logits,
|
481 |
+
past_key_values=outputs.past_key_values,
|
482 |
+
hidden_states=outputs.hidden_states,
|
483 |
+
attentions=outputs.attentions,
|
484 |
+
)
|
485 |
+
|
486 |
+
def prepare_inputs_for_generation(
|
487 |
+
self,
|
488 |
+
input_ids: torch.LongTensor,
|
489 |
+
past_key_values: Optional[torch.Tensor] = None,
|
490 |
+
attention_mask: Optional[torch.Tensor] = None,
|
491 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
492 |
+
**kwargs
|
493 |
+
):
|
494 |
+
if past_key_values:
|
495 |
+
input_ids = input_ids[:, -1:]
|
496 |
+
|
497 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
498 |
+
if inputs_embeds is not None and past_key_values is None:
|
499 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
500 |
+
else:
|
501 |
+
model_inputs = {"input_ids": input_ids}
|
502 |
+
|
503 |
+
model_inputs.update(
|
504 |
+
{
|
505 |
+
"past_key_values": past_key_values,
|
506 |
+
"use_cache": kwargs.get("use_cache"),
|
507 |
+
"attention_mask": attention_mask
|
508 |
+
}
|
509 |
+
)
|
510 |
+
return model_inputs
|
511 |
+
|
512 |
+
@staticmethod
|
513 |
+
def _reorder_cache(past_key_values, beam_idx):
|
514 |
+
return tuple(
|
515 |
+
tuple(past_state.index_select(0, beam_idx) for past_state in layer_past)
|
516 |
+
for layer_past in past_key_values
|
517 |
+
)
|
518 |
+
|
519 |
+
def quantize(self, bits: int):
|
520 |
+
try:
|
521 |
+
from .quantizer import QLinear
|
522 |
+
except ImportError:
|
523 |
+
raise ImportError(
|
524 |
+
f"Needs QLinear to run quantize."
|
525 |
+
)
|
526 |
+
|
527 |
+
for layer in self.model.layers:
|
528 |
+
layer.self_attn.W_pack = QLinear(
|
529 |
+
bits=bits,
|
530 |
+
weight=layer.self_attn.W_pack.weight,
|
531 |
+
bias = None,
|
532 |
+
)
|
533 |
+
layer.self_attn.o_proj = QLinear(
|
534 |
+
bits=bits,
|
535 |
+
weight=layer.self_attn.o_proj.weight,
|
536 |
+
bias = None,
|
537 |
+
)
|
538 |
+
layer.mlp.gate_proj = QLinear(
|
539 |
+
bits=bits,
|
540 |
+
weight=layer.mlp.gate_proj.weight,
|
541 |
+
bias = None,
|
542 |
+
)
|
543 |
+
layer.mlp.down_proj = QLinear(
|
544 |
+
bits=bits,
|
545 |
+
weight=layer.mlp.down_proj.weight,
|
546 |
+
bias = None,
|
547 |
+
)
|
548 |
+
layer.mlp.up_proj = QLinear(
|
549 |
+
bits=bits,
|
550 |
+
weight=layer.mlp.up_proj.weight,
|
551 |
+
bias = None,
|
552 |
+
)
|
553 |
+
return self
|
554 |
+
|
555 |
+
def _build_chat_input(self, tokenizer, messages: List[dict], max_new_tokens: int=0):
|
556 |
+
max_new_tokens = max_new_tokens or self.generation_config.max_new_tokens
|
557 |
+
max_input_tokens = self.config.model_max_length - max_new_tokens
|
558 |
+
max_input_tokens = max(self.config.model_max_length // 2, max_input_tokens)
|
559 |
+
total_input, round_input = [], []
|
560 |
+
for i, message in enumerate(messages[::-1]):
|
561 |
+
content_tokens = tokenizer.encode(message['content'])
|
562 |
+
if message['role'] == 'user':
|
563 |
+
round_input = [self.generation_config.user_token_id] + content_tokens + round_input
|
564 |
+
if total_input and len(total_input) + len(round_input) > max_input_tokens:
|
565 |
+
break
|
566 |
+
else:
|
567 |
+
total_input = round_input + total_input
|
568 |
+
if len(total_input) >= max_input_tokens:
|
569 |
+
break
|
570 |
+
else:
|
571 |
+
round_input = []
|
572 |
+
elif message['role'] == 'assistant':
|
573 |
+
round_input = [
|
574 |
+
self.generation_config.assistant_token_id
|
575 |
+
] + content_tokens + [
|
576 |
+
self.generation_config.eos_token_id
|
577 |
+
] + round_input
|
578 |
+
else:
|
579 |
+
raise ValueError(f"message role not supported yet: {message['role']}")
|
580 |
+
total_input = total_input[-max_input_tokens:] # truncate left
|
581 |
+
total_input.append(self.generation_config.assistant_token_id)
|
582 |
+
total_input = torch.LongTensor([total_input]).to(self.device)
|
583 |
+
return total_input
|
584 |
+
|
585 |
+
@torch.no_grad()
|
586 |
+
def chat(self, tokenizer, messages: List[dict], stream=False,
|
587 |
+
generation_config: Optional[GenerationConfig]=None):
|
588 |
+
generation_config = generation_config or self.generation_config
|
589 |
+
input_ids = self._build_chat_input(tokenizer, messages, generation_config.max_new_tokens)
|
590 |
+
if stream:
|
591 |
+
from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig
|
592 |
+
self.__class__.generate = NewGenerationMixin.generate
|
593 |
+
self.__class__.sample_stream = NewGenerationMixin.sample_stream
|
594 |
+
stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True)
|
595 |
+
|
596 |
+
def stream_generator():
|
597 |
+
outputs = []
|
598 |
+
for token in self.generate(input_ids, generation_config=stream_config):
|
599 |
+
outputs.append(token.item())
|
600 |
+
yield tokenizer.decode(outputs, skip_special_tokens=True)
|
601 |
+
|
602 |
+
return stream_generator()
|
603 |
+
else:
|
604 |
+
self.__class__.generate = PreTrainedModel.generate # disable stream
|
605 |
+
outputs = self.generate(input_ids, generation_config=generation_config)
|
606 |
+
response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
|
607 |
+
return response
|
pytorch_model.bin.index.json
ADDED
@@ -0,0 +1,290 @@
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|
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|
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|
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|
|
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|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"metadata": {
|
3 |
+
"total_size": 26529802240
|
4 |
+
},
|
5 |
+
"weight_map": {
|
6 |
+
"lm_head.weight": "pytorch_model-00003-of-00003.bin",
|
7 |
+
"model.embed_tokens.weight": "pytorch_model-00001-of-00003.bin",
|
8 |
+
"model.layers.0.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
9 |
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"model.layers.0.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
|
10 |
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"model.layers.0.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
|
11 |
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"model.layers.0.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
|
12 |
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"model.layers.0.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
13 |
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"model.layers.0.self_attn.W_pack.weight": "pytorch_model-00001-of-00003.bin",
|
14 |
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"model.layers.0.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
|
15 |
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"model.layers.1.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
16 |
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|
17 |
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|
18 |
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|
19 |
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"model.layers.1.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
20 |
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|
21 |
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|
22 |
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|
23 |
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|
24 |
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|
25 |
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|
26 |
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|
27 |
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|
28 |
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|
29 |
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|
30 |
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|
31 |
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|
32 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
42 |
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|
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|
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|
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|
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|
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|
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|
49 |
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|
50 |
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|
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|
52 |
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|
53 |
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|
54 |
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|
55 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
75 |
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|
76 |
+
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|
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"model.layers.5.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
254 |
+
"model.layers.5.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
|
255 |
+
"model.layers.5.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
|
256 |
+
"model.layers.5.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
|
257 |
+
"model.layers.5.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
258 |
+
"model.layers.5.self_attn.W_pack.weight": "pytorch_model-00001-of-00003.bin",
|
259 |
+
"model.layers.5.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
|
260 |
+
"model.layers.6.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
261 |
+
"model.layers.6.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
|
262 |
+
"model.layers.6.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
|
263 |
+
"model.layers.6.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
|
264 |
+
"model.layers.6.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
265 |
+
"model.layers.6.self_attn.W_pack.weight": "pytorch_model-00001-of-00003.bin",
|
266 |
+
"model.layers.6.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
|
267 |
+
"model.layers.7.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
268 |
+
"model.layers.7.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
|
269 |
+
"model.layers.7.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
|
270 |
+
"model.layers.7.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
|
271 |
+
"model.layers.7.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
272 |
+
"model.layers.7.self_attn.W_pack.weight": "pytorch_model-00001-of-00003.bin",
|
273 |
+
"model.layers.7.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
|
274 |
+
"model.layers.8.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
275 |
+
"model.layers.8.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
|
276 |
+
"model.layers.8.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
|
277 |
+
"model.layers.8.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
|
278 |
+
"model.layers.8.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
279 |
+
"model.layers.8.self_attn.W_pack.weight": "pytorch_model-00001-of-00003.bin",
|
280 |
+
"model.layers.8.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
|
281 |
+
"model.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
282 |
+
"model.layers.9.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
|
283 |
+
"model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
|
284 |
+
"model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
|
285 |
+
"model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
286 |
+
"model.layers.9.self_attn.W_pack.weight": "pytorch_model-00001-of-00003.bin",
|
287 |
+
"model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
|
288 |
+
"model.norm.weight": "pytorch_model-00003-of-00003.bin"
|
289 |
+
}
|
290 |
+
}
|
quantizer.py
ADDED
@@ -0,0 +1,123 @@
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|
|
1 |
+
# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
|
2 |
+
|
3 |
+
import torch
|
4 |
+
from typing import List
|
5 |
+
import bz2
|
6 |
+
import base64
|
7 |
+
import ctypes
|
8 |
+
from transformers.utils import logging
|
9 |
+
logger = logging.get_logger(__name__)
|
10 |
+
|
11 |
+
try:
|
12 |
+
from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up
|
13 |
+
|
14 |
+
class Kernel:
|
15 |
+
def __init__(self, code: bytes, function_names: List[str]):
|
16 |
+
self.code = code
|
17 |
+
self._function_names = function_names
|
18 |
+
self._cmodule = LazyKernelCModule(self.code)
|
19 |
+
|
20 |
+
for name in self._function_names:
|
21 |
+
setattr(self, name, KernelFunction(self._cmodule, name))
|
22 |
+
quantization_code = "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"
|
23 |
+
kernels = Kernel(
|
24 |
+
bz2.decompress(base64.b64decode(quantization_code)),
|
25 |
+
[
|
26 |
+
"int4_to_fp16",
|
27 |
+
"fp16_to_int4",
|
28 |
+
"int8_to_fp16",
|
29 |
+
"fp16_to_int8",
|
30 |
+
"int4_to_bf16",
|
31 |
+
"bf16_to_int4",
|
32 |
+
"int8_to_bf16",
|
33 |
+
"bf16_to_int8",
|
34 |
+
],
|
35 |
+
)
|
36 |
+
except Exception as exception:
|
37 |
+
kernels = None
|
38 |
+
logger.warning("Failed to load kernels:" + str(exception))
|
39 |
+
|
40 |
+
def quant4(weight: torch.Tensor, scale: torch.Tensor):
|
41 |
+
stream = torch.cuda.current_stream()
|
42 |
+
num_row = weight.size(0)
|
43 |
+
num_chan_fp16 = weight.size(1)
|
44 |
+
# 4bit
|
45 |
+
num_chan_int = num_chan_fp16 // 8
|
46 |
+
qweight = torch.zeros((num_row, num_chan_int), dtype=torch.int32, device=weight.device)
|
47 |
+
intweight = torch.empty(num_row, num_chan_fp16, dtype = torch.int32)
|
48 |
+
intweight = torch.clip(torch.round(weight.to(scale.dtype) / scale[:, None]),-16, 15).to(dtype=torch.int32)
|
49 |
+
|
50 |
+
for j in range(num_chan_int):
|
51 |
+
qweight[:, j] = ((intweight[:, j*8+7] & 0x0f) << 28) \
|
52 |
+
| ((intweight[:, j*8+6] & 0x0f) << 24) \
|
53 |
+
| ((intweight[:, j*8+5] & 0x0f) << 20) \
|
54 |
+
| ((intweight[:, j*8+4] & 0x0f) << 16) \
|
55 |
+
| ((intweight[:, j*8+3] & 0x0f) << 12) \
|
56 |
+
| ((intweight[:, j*8+2] & 0x0f) << 8) \
|
57 |
+
| ((intweight[:, j*8+1] & 0x0f) << 4) \
|
58 |
+
| ((intweight[:, j*8] & 0x0f))
|
59 |
+
return qweight
|
60 |
+
|
61 |
+
def dequant4(qweight: torch.Tensor, scale: torch.Tensor, input: torch.Tensor):
|
62 |
+
stream = torch.cuda.current_stream()
|
63 |
+
num_row = qweight.size(0)
|
64 |
+
num_chan_int = qweight.size(1)
|
65 |
+
# 4bit
|
66 |
+
num_chan_fp16 = num_chan_int * 8
|
67 |
+
|
68 |
+
out = torch.empty((num_row, num_chan_fp16), dtype=input.dtype, device=qweight.device)
|
69 |
+
|
70 |
+
blockDim = (128, 1, 1)
|
71 |
+
gridDim = ((num_chan_int + blockDim[0] - 1) // blockDim[0], num_row, 1)
|
72 |
+
if input.dtype == torch.bfloat16:
|
73 |
+
kernels.int4_to_bf16(
|
74 |
+
gridDim,
|
75 |
+
blockDim,
|
76 |
+
0,
|
77 |
+
stream,
|
78 |
+
[ctypes.c_void_p(out.data_ptr()), ctypes.c_void_p(qweight.data_ptr()),
|
79 |
+
ctypes.c_void_p(scale.data_ptr()), ctypes.c_int32(num_row), ctypes.c_int32(num_chan_int), ctypes.c_int32(num_chan_fp16)],
|
80 |
+
)
|
81 |
+
elif input.dtype == torch.float16:
|
82 |
+
kernels.int4_to_fp16(
|
83 |
+
gridDim,
|
84 |
+
blockDim,
|
85 |
+
0,
|
86 |
+
stream,
|
87 |
+
[ctypes.c_void_p(out.data_ptr()), ctypes.c_void_p(qweight.data_ptr()),
|
88 |
+
ctypes.c_void_p(scale.data_ptr()), ctypes.c_int32(num_row), ctypes.c_int32(num_chan_int), ctypes.c_int32(num_chan_fp16)],
|
89 |
+
)
|
90 |
+
return out
|
91 |
+
|
92 |
+
class QLinear(torch.nn.Module):
|
93 |
+
def __init__(self, bits: int, weight: torch.Tensor, bias=None):
|
94 |
+
super().__init__()
|
95 |
+
self.quant_bits = bits
|
96 |
+
self.scale = weight.abs().max(dim=-1).values / ((2 ** (bits - 1)) - 1)
|
97 |
+
self.scale = self.scale.to(torch.float32)
|
98 |
+
if self.quant_bits == 4:
|
99 |
+
self.weight = quant4(weight, self.scale)
|
100 |
+
elif self.quant_bits == 8:
|
101 |
+
self.weight = torch.round(weight.to(self.scale.dtype) / self.scale[:, None]).to(torch.int8)
|
102 |
+
if self.quant_bits == 8:
|
103 |
+
self.weight = self.weight.T
|
104 |
+
self.bias = None
|
105 |
+
|
106 |
+
def forward(self, input):
|
107 |
+
if self.quant_bits == 4:
|
108 |
+
assert(input.dtype == torch.bfloat16 or input.dtype == torch.float16)
|
109 |
+
|
110 |
+
if self.weight.device != input.device:
|
111 |
+
self.weight = self.weight.to(input.device)
|
112 |
+
self.scale = self.scale.to(input.device)
|
113 |
+
|
114 |
+
if self.quant_bits == 4:
|
115 |
+
self.scale = self.scale.to(input.dtype)
|
116 |
+
rweight = dequant4(self.weight, self.scale, input).T
|
117 |
+
output = torch.matmul(input, rweight)
|
118 |
+
elif self.quant_bits == 8:
|
119 |
+
rweight = self.weight.to(input.dtype) * self.scale.to(input.dtype)
|
120 |
+
output = torch.matmul(input, rweight)
|
121 |
+
if self.bias is not None:
|
122 |
+
output = output + self.bias
|
123 |
+
return output
|
special_tokens_map.json
ADDED
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"bos_token": {
|
3 |
+
"content": "<s>",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": true,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": true
|
8 |
+
},
|
9 |
+
"eos_token": {
|
10 |
+
"content": "</s>",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": true,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": true
|
15 |
+
},
|
16 |
+
"pad_token": {
|
17 |
+
"content": "<unk>",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": true,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": true
|
22 |
+
},
|
23 |
+
"unk_token": {
|
24 |
+
"content": "<unk>",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": true,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": true
|
29 |
+
}
|
30 |
+
}
|
tokenization_baichuan.py
ADDED
@@ -0,0 +1,232 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
1 |
+
# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
|
2 |
+
|
3 |
+
import os
|
4 |
+
from shutil import copyfile
|
5 |
+
from typing import Any, Dict, List, Optional, Tuple
|
6 |
+
|
7 |
+
import sentencepiece as spm
|
8 |
+
from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
|
9 |
+
from transformers.utils import logging
|
10 |
+
|
11 |
+
|
12 |
+
logger = logging.get_logger(__name__)
|
13 |
+
|
14 |
+
VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
|
15 |
+
|
16 |
+
PRETRAINED_VOCAB_FILES_MAP = {
|
17 |
+
"vocab_file": {},
|
18 |
+
"tokenizer_file": {},
|
19 |
+
}
|
20 |
+
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
|
21 |
+
|
22 |
+
|
23 |
+
class BaichuanTokenizer(PreTrainedTokenizer):
|
24 |
+
"""
|
25 |
+
Construct a Baichuan tokenizer. Based on byte-level Byte-Pair-Encoding.
|
26 |
+
|
27 |
+
Args:
|
28 |
+
vocab_file (`str`):
|
29 |
+
Path to the vocabulary file.
|
30 |
+
"""
|
31 |
+
|
32 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
33 |
+
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
34 |
+
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
35 |
+
model_input_names = ["input_ids", "attention_mask"]
|
36 |
+
|
37 |
+
def __init__(
|
38 |
+
self,
|
39 |
+
vocab_file,
|
40 |
+
unk_token="<unk>",
|
41 |
+
bos_token="<s>",
|
42 |
+
eos_token="</s>",
|
43 |
+
pad_token=None,
|
44 |
+
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
45 |
+
add_bos_token=True,
|
46 |
+
add_eos_token=False,
|
47 |
+
clean_up_tokenization_spaces=False,
|
48 |
+
**kwargs,
|
49 |
+
):
|
50 |
+
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
51 |
+
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
|
52 |
+
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
|
53 |
+
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
|
54 |
+
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
|
55 |
+
super().__init__(
|
56 |
+
bos_token=bos_token,
|
57 |
+
eos_token=eos_token,
|
58 |
+
unk_token=unk_token,
|
59 |
+
pad_token=pad_token,
|
60 |
+
add_bos_token=add_bos_token,
|
61 |
+
add_eos_token=add_eos_token,
|
62 |
+
sp_model_kwargs=self.sp_model_kwargs,
|
63 |
+
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
64 |
+
**kwargs,
|
65 |
+
)
|
66 |
+
self.vocab_file = vocab_file
|
67 |
+
self.add_bos_token = add_bos_token
|
68 |
+
self.add_eos_token = add_eos_token
|
69 |
+
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
70 |
+
self.sp_model.Load(vocab_file)
|
71 |
+
|
72 |
+
def __getstate__(self):
|
73 |
+
state = self.__dict__.copy()
|
74 |
+
state["sp_model"] = None
|
75 |
+
return state
|
76 |
+
|
77 |
+
def __setstate__(self, d):
|
78 |
+
self.__dict__ = d
|
79 |
+
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
80 |
+
self.sp_model.Load(self.vocab_file)
|
81 |
+
|
82 |
+
@property
|
83 |
+
def vocab_size(self):
|
84 |
+
"""Returns vocab size"""
|
85 |
+
return self.sp_model.get_piece_size()
|
86 |
+
|
87 |
+
def get_vocab(self):
|
88 |
+
"""Returns vocab as a dict"""
|
89 |
+
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
90 |
+
vocab.update(self.added_tokens_encoder)
|
91 |
+
return vocab
|
92 |
+
|
93 |
+
def _tokenize(self, text):
|
94 |
+
"""Returns a tokenized string."""
|
95 |
+
return self.sp_model.encode(text, out_type=str)
|
96 |
+
|
97 |
+
def _convert_token_to_id(self, token):
|
98 |
+
"""Converts a token (str) in an id using the vocab."""
|
99 |
+
return self.sp_model.piece_to_id(token)
|
100 |
+
|
101 |
+
def _convert_id_to_token(self, index):
|
102 |
+
"""Converts an index (integer) in a token (str) using the vocab."""
|
103 |
+
token = self.sp_model.IdToPiece(index)
|
104 |
+
return token
|
105 |
+
|
106 |
+
def convert_tokens_to_string(self, tokens):
|
107 |
+
"""Converts a sequence of tokens (string) in a single string."""
|
108 |
+
current_sub_tokens = []
|
109 |
+
out_string = ""
|
110 |
+
prev_is_special = False
|
111 |
+
for i, token in enumerate(tokens):
|
112 |
+
# make sure that special tokens are not decoded using sentencepiece model
|
113 |
+
if token in self.all_special_tokens:
|
114 |
+
if not prev_is_special and i != 0:
|
115 |
+
out_string += " "
|
116 |
+
out_string += self.sp_model.decode(current_sub_tokens) + token
|
117 |
+
prev_is_special = True
|
118 |
+
current_sub_tokens = []
|
119 |
+
else:
|
120 |
+
current_sub_tokens.append(token)
|
121 |
+
prev_is_special = False
|
122 |
+
out_string += self.sp_model.decode(current_sub_tokens)
|
123 |
+
return out_string
|
124 |
+
|
125 |
+
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
126 |
+
"""
|
127 |
+
Save the vocabulary and special tokens file to a directory.
|
128 |
+
|
129 |
+
Args:
|
130 |
+
save_directory (`str`):
|
131 |
+
The directory in which to save the vocabulary.
|
132 |
+
|
133 |
+
Returns:
|
134 |
+
`Tuple(str)`: Paths to the files saved.
|
135 |
+
"""
|
136 |
+
if not os.path.isdir(save_directory):
|
137 |
+
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
138 |
+
return
|
139 |
+
out_vocab_file = os.path.join(
|
140 |
+
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
141 |
+
)
|
142 |
+
|
143 |
+
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
144 |
+
copyfile(self.vocab_file, out_vocab_file)
|
145 |
+
elif not os.path.isfile(self.vocab_file):
|
146 |
+
with open(out_vocab_file, "wb") as fi:
|
147 |
+
content_spiece_model = self.sp_model.serialized_model_proto()
|
148 |
+
fi.write(content_spiece_model)
|
149 |
+
|
150 |
+
return (out_vocab_file,)
|
151 |
+
|
152 |
+
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
153 |
+
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
154 |
+
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
155 |
+
|
156 |
+
output = bos_token_id + token_ids_0 + eos_token_id
|
157 |
+
|
158 |
+
if token_ids_1 is not None:
|
159 |
+
output = output + bos_token_id + token_ids_1 + eos_token_id
|
160 |
+
|
161 |
+
return output
|
162 |
+
|
163 |
+
def get_special_tokens_mask(
|
164 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
165 |
+
) -> List[int]:
|
166 |
+
"""
|
167 |
+
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
168 |
+
special tokens using the tokenizer `prepare_for_model` method.
|
169 |
+
|
170 |
+
Args:
|
171 |
+
token_ids_0 (`List[int]`):
|
172 |
+
List of IDs.
|
173 |
+
token_ids_1 (`List[int]`, *optional*):
|
174 |
+
Optional second list of IDs for sequence pairs.
|
175 |
+
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
176 |
+
Whether or not the token list is already formatted with special tokens for the model.
|
177 |
+
|
178 |
+
Returns:
|
179 |
+
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
180 |
+
"""
|
181 |
+
if already_has_special_tokens:
|
182 |
+
return super().get_special_tokens_mask(
|
183 |
+
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
184 |
+
)
|
185 |
+
|
186 |
+
bos_token_id = [1] if self.add_bos_token else []
|
187 |
+
eos_token_id = [1] if self.add_eos_token else []
|
188 |
+
|
189 |
+
if token_ids_1 is None:
|
190 |
+
return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
|
191 |
+
return (
|
192 |
+
bos_token_id
|
193 |
+
+ ([0] * len(token_ids_0))
|
194 |
+
+ eos_token_id
|
195 |
+
+ bos_token_id
|
196 |
+
+ ([0] * len(token_ids_1))
|
197 |
+
+ eos_token_id
|
198 |
+
)
|
199 |
+
|
200 |
+
def create_token_type_ids_from_sequences(
|
201 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
202 |
+
) -> List[int]:
|
203 |
+
"""
|
204 |
+
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
|
205 |
+
sequence pair mask has the following format:
|
206 |
+
|
207 |
+
```
|
208 |
+
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
|
209 |
+
| first sequence | second sequence |
|
210 |
+
```
|
211 |
+
|
212 |
+
if token_ids_1 is None, only returns the first portion of the mask (0s).
|
213 |
+
|
214 |
+
Args:
|
215 |
+
token_ids_0 (`List[int]`):
|
216 |
+
List of ids.
|
217 |
+
token_ids_1 (`List[int]`, *optional*):
|
218 |
+
Optional second list of IDs for sequence pairs.
|
219 |
+
|
220 |
+
Returns:
|
221 |
+
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
|
222 |
+
"""
|
223 |
+
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
224 |
+
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
225 |
+
|
226 |
+
output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
|
227 |
+
|
228 |
+
if token_ids_1 is not None:
|
229 |
+
output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
|
230 |
+
|
231 |
+
return output
|
232 |
+
|
tokenizer.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f7d1ab69d25c74644af5c5e4dcd1cc6e96d33783dbd257b6bdea55b643c72813
|
3 |
+
size 1136765
|
tokenizer_config.json
ADDED
@@ -0,0 +1,47 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"add_bos_token": false,
|
3 |
+
"add_eos_token": false,
|
4 |
+
"auto_map": {
|
5 |
+
"AutoTokenizer": [
|
6 |
+
"tokenization_baichuan.BaichuanTokenizer",
|
7 |
+
null
|
8 |
+
]
|
9 |
+
},
|
10 |
+
"bos_token": {
|
11 |
+
"__type": "AddedToken",
|
12 |
+
"content": "<s>",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": true,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": true
|
17 |
+
},
|
18 |
+
"clean_up_tokenization_spaces": false,
|
19 |
+
"eos_token": {
|
20 |
+
"__type": "AddedToken",
|
21 |
+
"content": "</s>",
|
22 |
+
"lstrip": false,
|
23 |
+
"normalized": true,
|
24 |
+
"rstrip": false,
|
25 |
+
"single_word": true
|
26 |
+
},
|
27 |
+
"model_max_length": 4096,
|
28 |
+
"pad_token": {
|
29 |
+
"__type": "AddedToken",
|
30 |
+
"content": "<unk>",
|
31 |
+
"lstrip": false,
|
32 |
+
"normalized": true,
|
33 |
+
"rstrip": false,
|
34 |
+
"single_word": true
|
35 |
+
},
|
36 |
+
"padding_side": "left",
|
37 |
+
"sp_model_kwargs": {},
|
38 |
+
"tokenizer_class": "BaichuanTokenizer",
|
39 |
+
"unk_token": {
|
40 |
+
"__type": "AddedToken",
|
41 |
+
"content": "<unk>",
|
42 |
+
"lstrip": false,
|
43 |
+
"normalized": true,
|
44 |
+
"rstrip": false,
|
45 |
+
"single_word": true
|
46 |
+
}
|
47 |
+
}
|