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Upload internlm-20b-chat

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.gitattributes CHANGED
@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00002-of-00005.bin filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00003-of-00005.bin filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00004-of-00005.bin filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00005-of-00005.bin filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00001-of-00005.bin filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.model filter=lfs diff=lfs merge=lfs -text
config.json ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "InternLMForCausalLM"
4
+ ],
5
+ "auto_map": {
6
+ "AutoConfig": "configuration_internlm.InternLMConfig",
7
+ "AutoModel": "modeling_internlm.InternLMForCausalLM",
8
+ "AutoModelForCausalLM": "modeling_internlm.InternLMForCausalLM"
9
+ },
10
+ "bias": false,
11
+ "bos_token_id": 1,
12
+ "eos_token_id": 2,
13
+ "hidden_act": "silu",
14
+ "hidden_size": 5120,
15
+ "initializer_range": 0.02,
16
+ "intermediate_size": 13824,
17
+ "max_position_embeddings": 2048,
18
+ "model_type": "internlm",
19
+ "num_attention_heads": 40,
20
+ "num_hidden_layers": 60,
21
+ "num_key_value_heads": 40,
22
+ "pad_token_id": 0,
23
+ "pretraining_tp": 1,
24
+ "rms_norm_eps": 1e-06,
25
+ "rope_scaling": null,
26
+ "rope_theta": 10000.0,
27
+ "tie_word_embeddings": false,
28
+ "torch_dtype": "bfloat16",
29
+ "transformers_version": "4.33.1",
30
+ "use_cache": true,
31
+ "vocab_size": 103168
32
+ }
configuration_internlm.py ADDED
@@ -0,0 +1,120 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ InternLM model configuration"""
21
+
22
+ from transformers.utils import logging
23
+ from transformers.configuration_utils import PretrainedConfig
24
+
25
+
26
+ logger = logging.get_logger(__name__)
27
+
28
+ INTERNLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
29
+
30
+
31
+ class InternLMConfig(PretrainedConfig):
32
+ r"""
33
+ This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate an InternLM
34
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
35
+ defaults will yield a similar configuration to that of the InternLM-7B.
36
+
37
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
38
+ documentation from [`PretrainedConfig`] for more information.
39
+
40
+
41
+ Args:
42
+ vocab_size (`int`, *optional*, defaults to 32000):
43
+ Vocabulary size of the InternLM model. Defines the number of different tokens that can be represented by the
44
+ `inputs_ids` passed when calling [`InternLMModel`]
45
+ hidden_size (`int`, *optional*, defaults to 4096):
46
+ Dimension of the hidden representations.
47
+ intermediate_size (`int`, *optional*, defaults to 11008):
48
+ Dimension of the MLP representations.
49
+ num_hidden_layers (`int`, *optional*, defaults to 32):
50
+ Number of hidden layers in the Transformer encoder.
51
+ num_attention_heads (`int`, *optional*, defaults to 32):
52
+ Number of attention heads for each attention layer in the Transformer encoder.
53
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
54
+ The non-linear activation function (function or string) in the decoder.
55
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
56
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
57
+ just in case (e.g., 512 or 1024 or 2048).
58
+ initializer_range (`float`, *optional*, defaults to 0.02):
59
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
60
+ rms_norm_eps (`float`, *optional*, defaults to 1e-12):
61
+ The epsilon used by the rms normalization layers.
62
+ use_cache (`bool`, *optional*, defaults to `True`):
63
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
64
+ relevant if `config.is_decoder=True`.
65
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
66
+ Whether to tie weight embeddings
67
+ Example:
68
+
69
+ ```python
70
+ >>> from transformers import InternLMModel, InternLMConfig
71
+
72
+ >>> # Initializing a InternLM internlm-7b style configuration
73
+ >>> configuration = InternLMConfig()
74
+
75
+ >>> # Initializing a model from the internlm-7b style configuration
76
+ >>> model = InternLMModel(configuration)
77
+
78
+ >>> # Accessing the model configuration
79
+ >>> configuration = model.config
80
+ ```"""
81
+ model_type = "internlm"
82
+ _auto_class = "AutoConfig"
83
+
84
+ def __init__(
85
+ self,
86
+ vocab_size=103168,
87
+ hidden_size=4096,
88
+ intermediate_size=11008,
89
+ num_hidden_layers=32,
90
+ num_attention_heads=32,
91
+ hidden_act="silu",
92
+ max_position_embeddings=2048,
93
+ initializer_range=0.02,
94
+ rms_norm_eps=1e-6,
95
+ use_cache=True,
96
+ pad_token_id=0,
97
+ bos_token_id=1,
98
+ eos_token_id=2,
99
+ tie_word_embeddings=False,
100
+ bias=True,
101
+ **kwargs,
102
+ ):
103
+ self.vocab_size = vocab_size
104
+ self.max_position_embeddings = max_position_embeddings
105
+ self.hidden_size = hidden_size
106
+ self.intermediate_size = intermediate_size
107
+ self.num_hidden_layers = num_hidden_layers
108
+ self.num_attention_heads = num_attention_heads
109
+ self.hidden_act = hidden_act
110
+ self.initializer_range = initializer_range
111
+ self.rms_norm_eps = rms_norm_eps
112
+ self.use_cache = use_cache
113
+ self.bias = bias
114
+ super().__init__(
115
+ pad_token_id=pad_token_id,
116
+ bos_token_id=bos_token_id,
117
+ eos_token_id=eos_token_id,
118
+ tie_word_embeddings=tie_word_embeddings,
119
+ **kwargs,
120
+ )
generation_config.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 1,
4
+ "eos_token_id": 2,
5
+ "transformers_version": "4.33.1"
6
+ }
modeling_internlm.py ADDED
@@ -0,0 +1,998 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ PyTorch InternLM model."""
21
+ import math
22
+ from typing import List, Optional, Tuple, Union
23
+ import threading, queue
24
+
25
+ import torch
26
+ import torch.utils.checkpoint
27
+ from torch import nn
28
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
29
+
30
+ from transformers.activations import ACT2FN
31
+ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
32
+ from transformers.modeling_utils import PreTrainedModel
33
+ from transformers.generation.streamers import BaseStreamer
34
+ from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
35
+ from .configuration_internlm import InternLMConfig
36
+
37
+
38
+ logger = logging.get_logger(__name__)
39
+
40
+ _CONFIG_FOR_DOC = "InternLMConfig"
41
+
42
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
43
+ def _make_causal_mask(
44
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
45
+ ):
46
+ """
47
+ Make causal mask used for bi-directional self-attention.
48
+ """
49
+ bsz, tgt_len = input_ids_shape
50
+ mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
51
+ mask_cond = torch.arange(mask.size(-1), device=device)
52
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
53
+ mask = mask.to(dtype)
54
+
55
+ if past_key_values_length > 0:
56
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
57
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
58
+
59
+
60
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
61
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
62
+ """
63
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
64
+ """
65
+ bsz, src_len = mask.size()
66
+ tgt_len = tgt_len if tgt_len is not None else src_len
67
+
68
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
69
+
70
+ inverted_mask = 1.0 - expanded_mask
71
+
72
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
73
+
74
+
75
+ class InternLMRMSNorm(nn.Module):
76
+ def __init__(self, hidden_size, eps=1e-6):
77
+ """
78
+ InternLMRMSNorm is equivalent to T5LayerNorm
79
+ """
80
+ super().__init__()
81
+ self.weight = nn.Parameter(torch.ones(hidden_size))
82
+ self.variance_epsilon = eps
83
+
84
+ def forward(self, hidden_states):
85
+ variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
86
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
87
+
88
+ # convert into half-precision if necessary
89
+ if self.weight.dtype in [torch.float16, torch.bfloat16]:
90
+ hidden_states = hidden_states.to(self.weight.dtype)
91
+
92
+ return self.weight * hidden_states
93
+
94
+
95
+ class InternLMRotaryEmbedding(torch.nn.Module):
96
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
97
+ super().__init__()
98
+ inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
99
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
100
+
101
+ # Build here to make `torch.jit.trace` work.
102
+ self.max_seq_len_cached = max_position_embeddings
103
+ t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
104
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
105
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
106
+ emb = torch.cat((freqs, freqs), dim=-1)
107
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
108
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
109
+
110
+ def forward(self, x, seq_len=None):
111
+ # x: [bs, num_attention_heads, seq_len, head_size]
112
+ # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
113
+ if seq_len > self.max_seq_len_cached:
114
+ self.max_seq_len_cached = seq_len
115
+ t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
116
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
117
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
118
+ emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
119
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
120
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
121
+ return (
122
+ self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
123
+ self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
124
+ )
125
+
126
+
127
+ def rotate_half(x):
128
+ """Rotates half the hidden dims of the input."""
129
+ x1 = x[..., : x.shape[-1] // 2]
130
+ x2 = x[..., x.shape[-1] // 2 :]
131
+ return torch.cat((-x2, x1), dim=-1)
132
+
133
+
134
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
135
+ # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
136
+ cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
137
+ sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
138
+ cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
139
+ sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
140
+ q_embed = (q * cos) + (rotate_half(q) * sin)
141
+ k_embed = (k * cos) + (rotate_half(k) * sin)
142
+ return q_embed, k_embed
143
+
144
+
145
+ class InternLMMLP(nn.Module):
146
+ def __init__(
147
+ self,
148
+ hidden_size: int,
149
+ intermediate_size: int,
150
+ hidden_act: str,
151
+ ):
152
+ super().__init__()
153
+ self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
154
+ self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
155
+ self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
156
+ self.act_fn = ACT2FN[hidden_act]
157
+
158
+ def forward(self, x):
159
+ return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
160
+
161
+
162
+ class InternLMAttention(nn.Module):
163
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
164
+
165
+ def __init__(self, config: InternLMConfig):
166
+ super().__init__()
167
+ self.config = config
168
+ self.hidden_size = config.hidden_size
169
+ self.num_heads = config.num_attention_heads
170
+ self.head_dim = self.hidden_size // self.num_heads
171
+ self.max_position_embeddings = config.max_position_embeddings
172
+
173
+ if (self.head_dim * self.num_heads) != self.hidden_size:
174
+ raise ValueError(
175
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
176
+ f" and `num_heads`: {self.num_heads})."
177
+ )
178
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias)
179
+ self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias)
180
+ self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias)
181
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
182
+ self.rotary_emb = InternLMRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
183
+
184
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
185
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
186
+
187
+ def forward(
188
+ self,
189
+ hidden_states: torch.Tensor,
190
+ attention_mask: Optional[torch.Tensor] = None,
191
+ position_ids: Optional[torch.LongTensor] = None,
192
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
193
+ output_attentions: bool = False,
194
+ use_cache: bool = False,
195
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
196
+ bsz, q_len, _ = hidden_states.size()
197
+
198
+ query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
199
+ key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
200
+ value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
201
+
202
+ kv_seq_len = key_states.shape[-2]
203
+ if past_key_value is not None:
204
+ kv_seq_len += past_key_value[0].shape[-2]
205
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
206
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
207
+ # [bsz, nh, t, hd]
208
+
209
+ if past_key_value is not None:
210
+ # reuse k, v, self_attention
211
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
212
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
213
+
214
+ past_key_value = (key_states, value_states) if use_cache else None
215
+
216
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
217
+
218
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
219
+ raise ValueError(
220
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
221
+ f" {attn_weights.size()}"
222
+ )
223
+
224
+ if attention_mask is not None:
225
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
226
+ raise ValueError(
227
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
228
+ )
229
+ attn_weights = attn_weights + attention_mask
230
+ attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
231
+
232
+ # upcast attention to fp32
233
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
234
+ attn_output = torch.matmul(attn_weights, value_states)
235
+
236
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
237
+ raise ValueError(
238
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
239
+ f" {attn_output.size()}"
240
+ )
241
+
242
+ attn_output = attn_output.transpose(1, 2)
243
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
244
+
245
+ attn_output = self.o_proj(attn_output)
246
+
247
+ if not output_attentions:
248
+ attn_weights = None
249
+
250
+ return attn_output, attn_weights, past_key_value
251
+
252
+
253
+ class InternLMDecoderLayer(nn.Module):
254
+ def __init__(self, config: InternLMConfig):
255
+ super().__init__()
256
+ self.hidden_size = config.hidden_size
257
+ self.self_attn = InternLMAttention(config=config)
258
+ self.mlp = InternLMMLP(
259
+ hidden_size=self.hidden_size,
260
+ intermediate_size=config.intermediate_size,
261
+ hidden_act=config.hidden_act,
262
+ )
263
+ self.input_layernorm = InternLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
264
+ self.post_attention_layernorm = InternLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
265
+
266
+ def forward(
267
+ self,
268
+ hidden_states: torch.Tensor,
269
+ attention_mask: Optional[torch.Tensor] = None,
270
+ position_ids: Optional[torch.LongTensor] = None,
271
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
272
+ output_attentions: Optional[bool] = False,
273
+ use_cache: Optional[bool] = False,
274
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
275
+ """
276
+ Args:
277
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
278
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
279
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
280
+ output_attentions (`bool`, *optional*):
281
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
282
+ returned tensors for more detail.
283
+ use_cache (`bool`, *optional*):
284
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
285
+ (see `past_key_values`).
286
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
287
+ """
288
+
289
+ residual = hidden_states
290
+
291
+ hidden_states = self.input_layernorm(hidden_states)
292
+
293
+ # Self Attention
294
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
295
+ hidden_states=hidden_states,
296
+ attention_mask=attention_mask,
297
+ position_ids=position_ids,
298
+ past_key_value=past_key_value,
299
+ output_attentions=output_attentions,
300
+ use_cache=use_cache,
301
+ )
302
+ hidden_states = residual + hidden_states
303
+
304
+ # Fully Connected
305
+ residual = hidden_states
306
+ hidden_states = self.post_attention_layernorm(hidden_states)
307
+ hidden_states = self.mlp(hidden_states)
308
+ hidden_states = residual + hidden_states
309
+
310
+ outputs = (hidden_states,)
311
+
312
+ if output_attentions:
313
+ outputs += (self_attn_weights,)
314
+
315
+ if use_cache:
316
+ outputs += (present_key_value,)
317
+
318
+ return outputs
319
+
320
+
321
+ INTERNLM_START_DOCSTRING = r"""
322
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
323
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
324
+ etc.)
325
+
326
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
327
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
328
+ and behavior.
329
+
330
+ Parameters:
331
+ config ([`InternLMConfig`]):
332
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
333
+ load the weights associated with the model, only the configuration. Check out the
334
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
335
+ """
336
+
337
+
338
+ @add_start_docstrings(
339
+ "The bare InternLM Model outputting raw hidden-states without any specific head on top.",
340
+ INTERNLM_START_DOCSTRING,
341
+ )
342
+ class InternLMPreTrainedModel(PreTrainedModel):
343
+ config_class = InternLMConfig
344
+ base_model_prefix = "model"
345
+ supports_gradient_checkpointing = True
346
+ _no_split_modules = ["InternLMDecoderLayer"]
347
+ _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
348
+
349
+ def _init_weights(self, module):
350
+ std = self.config.initializer_range
351
+ if isinstance(module, nn.Linear):
352
+ module.weight.data.normal_(mean=0.0, std=std)
353
+ if module.bias is not None:
354
+ module.bias.data.zero_()
355
+ elif isinstance(module, nn.Embedding):
356
+ module.weight.data.normal_(mean=0.0, std=std)
357
+ if module.padding_idx is not None:
358
+ module.weight.data[module.padding_idx].zero_()
359
+
360
+ def _set_gradient_checkpointing(self, module, value=False):
361
+ if isinstance(module, InternLMModel):
362
+ module.gradient_checkpointing = value
363
+
364
+
365
+ INTERNLM_INPUTS_DOCSTRING = r"""
366
+ Args:
367
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
368
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
369
+ it.
370
+
371
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
372
+ [`PreTrainedTokenizer.__call__`] for details.
373
+
374
+ [What are input IDs?](../glossary#input-ids)
375
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
376
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
377
+
378
+ - 1 for tokens that are **not masked**,
379
+ - 0 for tokens that are **masked**.
380
+
381
+ [What are attention masks?](../glossary#attention-mask)
382
+
383
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
384
+ [`PreTrainedTokenizer.__call__`] for details.
385
+
386
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
387
+ `past_key_values`).
388
+
389
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
390
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
391
+ information on the default strategy.
392
+
393
+ - 1 indicates the head is **not masked**,
394
+ - 0 indicates the head is **masked**.
395
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
396
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
397
+ config.n_positions - 1]`.
398
+
399
+ [What are position IDs?](../glossary#position-ids)
400
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
401
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
402
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
403
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
404
+
405
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
406
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
407
+
408
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
409
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
410
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
411
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
412
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
413
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
414
+ model's internal embedding lookup matrix.
415
+ use_cache (`bool`, *optional*):
416
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
417
+ `past_key_values`).
418
+ output_attentions (`bool`, *optional*):
419
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
420
+ tensors for more detail.
421
+ output_hidden_states (`bool`, *optional*):
422
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
423
+ more detail.
424
+ return_dict (`bool`, *optional*):
425
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
426
+ """
427
+
428
+
429
+ @add_start_docstrings(
430
+ "The bare InternLM Model outputting raw hidden-states without any specific head on top.",
431
+ INTERNLM_START_DOCSTRING,
432
+ )
433
+ class InternLMModel(InternLMPreTrainedModel):
434
+ """
435
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLMDecoderLayer`]
436
+
437
+ Args:
438
+ config: InternLMConfig
439
+ """
440
+ _auto_class = "AutoModel"
441
+
442
+ def __init__(self, config: InternLMConfig):
443
+ super().__init__(config)
444
+ self.padding_idx = config.pad_token_id
445
+ self.vocab_size = config.vocab_size
446
+
447
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
448
+ self.layers = nn.ModuleList([InternLMDecoderLayer(config) for _ in range(config.num_hidden_layers)])
449
+ self.norm = InternLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
450
+
451
+ self.gradient_checkpointing = False
452
+ # Initialize weights and apply final processing
453
+ self.post_init()
454
+
455
+ def get_input_embeddings(self):
456
+ return self.embed_tokens
457
+
458
+ def set_input_embeddings(self, value):
459
+ self.embed_tokens = value
460
+
461
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
462
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
463
+ # create causal mask
464
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
465
+ combined_attention_mask = None
466
+ if input_shape[-1] > 1:
467
+ combined_attention_mask = _make_causal_mask(
468
+ input_shape,
469
+ inputs_embeds.dtype,
470
+ device=inputs_embeds.device,
471
+ past_key_values_length=past_key_values_length,
472
+ )
473
+
474
+ if attention_mask is not None:
475
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
476
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
477
+ inputs_embeds.device
478
+ )
479
+ combined_attention_mask = (
480
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
481
+ )
482
+
483
+ return combined_attention_mask
484
+
485
+ @add_start_docstrings_to_model_forward(INTERNLM_INPUTS_DOCSTRING)
486
+ def forward(
487
+ self,
488
+ input_ids: torch.LongTensor = None,
489
+ attention_mask: Optional[torch.Tensor] = None,
490
+ position_ids: Optional[torch.LongTensor] = None,
491
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
492
+ inputs_embeds: Optional[torch.FloatTensor] = None,
493
+ use_cache: Optional[bool] = None,
494
+ output_attentions: Optional[bool] = None,
495
+ output_hidden_states: Optional[bool] = None,
496
+ return_dict: Optional[bool] = None,
497
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
498
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
499
+ output_hidden_states = (
500
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
501
+ )
502
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
503
+
504
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
505
+
506
+ # retrieve input_ids and inputs_embeds
507
+ if input_ids is not None and inputs_embeds is not None:
508
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
509
+ elif input_ids is not None:
510
+ batch_size, seq_length = input_ids.shape
511
+ elif inputs_embeds is not None:
512
+ batch_size, seq_length, _ = inputs_embeds.shape
513
+ else:
514
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
515
+
516
+ seq_length_with_past = seq_length
517
+ past_key_values_length = 0
518
+
519
+ if past_key_values is not None:
520
+ past_key_values_length = past_key_values[0][0].shape[2]
521
+ seq_length_with_past = seq_length_with_past + past_key_values_length
522
+
523
+ if position_ids is None:
524
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
525
+ position_ids = torch.arange(
526
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
527
+ )
528
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
529
+ else:
530
+ position_ids = position_ids.view(-1, seq_length).long()
531
+
532
+ if inputs_embeds is None:
533
+ inputs_embeds = self.embed_tokens(input_ids)
534
+ # embed positions
535
+ if attention_mask is None:
536
+ attention_mask = torch.ones(
537
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
538
+ )
539
+ attention_mask = self._prepare_decoder_attention_mask(
540
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
541
+ )
542
+
543
+ hidden_states = inputs_embeds
544
+
545
+ if self.gradient_checkpointing and self.training:
546
+ if use_cache:
547
+ logger.warning_once(
548
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
549
+ )
550
+ use_cache = False
551
+
552
+ # decoder layers
553
+ all_hidden_states = () if output_hidden_states else None
554
+ all_self_attns = () if output_attentions else None
555
+ next_decoder_cache = () if use_cache else None
556
+
557
+ for idx, decoder_layer in enumerate(self.layers):
558
+ if output_hidden_states:
559
+ all_hidden_states += (hidden_states,)
560
+
561
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
562
+
563
+ if self.gradient_checkpointing and self.training:
564
+
565
+ def create_custom_forward(module):
566
+ def custom_forward(*inputs):
567
+ # None for past_key_value
568
+ return module(*inputs, output_attentions, None)
569
+
570
+ return custom_forward
571
+
572
+ layer_outputs = torch.utils.checkpoint.checkpoint(
573
+ create_custom_forward(decoder_layer),
574
+ hidden_states,
575
+ attention_mask,
576
+ position_ids,
577
+ None,
578
+ )
579
+ else:
580
+ layer_outputs = decoder_layer(
581
+ hidden_states,
582
+ attention_mask=attention_mask,
583
+ position_ids=position_ids,
584
+ past_key_value=past_key_value,
585
+ output_attentions=output_attentions,
586
+ use_cache=use_cache,
587
+ )
588
+
589
+ hidden_states = layer_outputs[0]
590
+
591
+ if use_cache:
592
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
593
+
594
+ if output_attentions:
595
+ all_self_attns += (layer_outputs[1],)
596
+
597
+ hidden_states = self.norm(hidden_states)
598
+
599
+ # add hidden states from the last decoder layer
600
+ if output_hidden_states:
601
+ all_hidden_states += (hidden_states,)
602
+
603
+ next_cache = next_decoder_cache if use_cache else None
604
+ if not return_dict:
605
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
606
+ return BaseModelOutputWithPast(
607
+ last_hidden_state=hidden_states,
608
+ past_key_values=next_cache,
609
+ hidden_states=all_hidden_states,
610
+ attentions=all_self_attns,
611
+ )
612
+
613
+
614
+ class InternLMForCausalLM(InternLMPreTrainedModel):
615
+ _auto_class = "AutoModelForCausalLM"
616
+
617
+ def __init__(self, config):
618
+ super().__init__(config)
619
+ self.model = InternLMModel(config)
620
+
621
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
622
+
623
+ # Initialize weights and apply final processing
624
+ self.post_init()
625
+
626
+ def get_input_embeddings(self):
627
+ return self.model.embed_tokens
628
+
629
+ def set_input_embeddings(self, value):
630
+ self.model.embed_tokens = value
631
+
632
+ def get_output_embeddings(self):
633
+ return self.lm_head
634
+
635
+ def set_output_embeddings(self, new_embeddings):
636
+ self.lm_head = new_embeddings
637
+
638
+ def set_decoder(self, decoder):
639
+ self.model = decoder
640
+
641
+ def get_decoder(self):
642
+ return self.model
643
+
644
+ @add_start_docstrings_to_model_forward(INTERNLM_INPUTS_DOCSTRING)
645
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
646
+ def forward(
647
+ self,
648
+ input_ids: torch.LongTensor = None,
649
+ attention_mask: Optional[torch.Tensor] = None,
650
+ position_ids: Optional[torch.LongTensor] = None,
651
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
652
+ inputs_embeds: Optional[torch.FloatTensor] = None,
653
+ labels: Optional[torch.LongTensor] = None,
654
+ use_cache: Optional[bool] = None,
655
+ output_attentions: Optional[bool] = None,
656
+ output_hidden_states: Optional[bool] = None,
657
+ return_dict: Optional[bool] = None,
658
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
659
+ r"""
660
+ Args:
661
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
662
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
663
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
664
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
665
+
666
+ Returns:
667
+
668
+ Example:
669
+
670
+ ```python
671
+ >>> from transformers import AutoTokenizer, InternLMForCausalLM
672
+
673
+ >>> model = InternLMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
674
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
675
+
676
+ >>> prompt = "Hey, are you consciours? Can you talk to me?"
677
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
678
+
679
+ >>> # Generate
680
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
681
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
682
+ "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
683
+ ```"""
684
+
685
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
686
+ output_hidden_states = (
687
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
688
+ )
689
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
690
+
691
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
692
+ outputs = self.model(
693
+ input_ids=input_ids,
694
+ attention_mask=attention_mask,
695
+ position_ids=position_ids,
696
+ past_key_values=past_key_values,
697
+ inputs_embeds=inputs_embeds,
698
+ use_cache=use_cache,
699
+ output_attentions=output_attentions,
700
+ output_hidden_states=output_hidden_states,
701
+ return_dict=return_dict,
702
+ )
703
+
704
+ hidden_states = outputs[0]
705
+ logits = self.lm_head(hidden_states)
706
+
707
+ loss = None
708
+ if labels is not None:
709
+ # Shift so that tokens < n predict n
710
+ shift_logits = logits[..., :-1, :].contiguous()
711
+ shift_labels = labels[..., 1:].contiguous()
712
+ # Flatten the tokens
713
+ loss_fct = CrossEntropyLoss()
714
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
715
+ shift_labels = shift_labels.view(-1)
716
+ # Enable model parallelism
717
+ shift_labels = shift_labels.to(shift_logits.device)
718
+ loss = loss_fct(shift_logits, shift_labels)
719
+
720
+ if not return_dict:
721
+ output = (logits,) + outputs[1:]
722
+ return (loss,) + output if loss is not None else output
723
+
724
+ return CausalLMOutputWithPast(
725
+ loss=loss,
726
+ logits=logits,
727
+ past_key_values=outputs.past_key_values,
728
+ hidden_states=outputs.hidden_states,
729
+ attentions=outputs.attentions,
730
+ )
731
+
732
+ def prepare_inputs_for_generation(
733
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
734
+ ):
735
+ if past_key_values:
736
+ input_ids = input_ids[:, -1:]
737
+
738
+ position_ids = kwargs.get("position_ids", None)
739
+ if attention_mask is not None and position_ids is None:
740
+ # create position_ids on the fly for batch generation
741
+ position_ids = attention_mask.long().cumsum(-1) - 1
742
+ position_ids.masked_fill_(attention_mask == 0, 1)
743
+ if past_key_values:
744
+ position_ids = position_ids[:, -1].unsqueeze(-1)
745
+
746
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
747
+ if inputs_embeds is not None and past_key_values is None:
748
+ model_inputs = {"inputs_embeds": inputs_embeds}
749
+ else:
750
+ model_inputs = {"input_ids": input_ids}
751
+
752
+ model_inputs.update(
753
+ {
754
+ "position_ids": position_ids,
755
+ "past_key_values": past_key_values,
756
+ "use_cache": kwargs.get("use_cache"),
757
+ "attention_mask": attention_mask,
758
+ }
759
+ )
760
+ return model_inputs
761
+
762
+ @staticmethod
763
+ def _reorder_cache(past_key_values, beam_idx):
764
+ reordered_past = ()
765
+ for layer_past in past_key_values:
766
+ reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
767
+ return reordered_past
768
+
769
+ def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = []):
770
+ prompt = ""
771
+ for record in history:
772
+ prompt += f"""<s><|User|>:{record[0]}<eoh>\n<|Bot|>:{record[1]}<eoa>\n"""
773
+ if len(prompt) == 0:
774
+ prompt += "<s>"
775
+ prompt += f"""<|User|>:{query}<eoh>\n<|Bot|>:"""
776
+ return tokenizer([prompt], return_tensors="pt")
777
+
778
+ @torch.no_grad()
779
+ def chat(self,
780
+ tokenizer,
781
+ query: str,
782
+ history: List[Tuple[str, str]] = [],
783
+ streamer: Optional[BaseStreamer] = None,
784
+ max_new_tokens: int = 1024,
785
+ do_sample: bool = True,
786
+ temperature: float = 0.8,
787
+ top_p: float = 0.8,
788
+ **kwargs):
789
+ inputs = self.build_inputs(tokenizer, query, history)
790
+ inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
791
+ outputs = self.generate(**inputs,
792
+ streamer=streamer,
793
+ max_new_tokens=max_new_tokens,
794
+ do_sample=do_sample,
795
+ temperature=temperature,
796
+ top_p=top_p,
797
+ **kwargs)
798
+ outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]):]
799
+ response = tokenizer.decode(outputs, skip_special_tokens=True)
800
+ response = response.split("<eoa>")[0]
801
+ history = history + [(query, response)]
802
+ return response, history
803
+
804
+ @torch.no_grad()
805
+ def stream_chat(self,
806
+ tokenizer,
807
+ query: str,
808
+ history: List[Tuple[str, str]] = [],
809
+ max_new_tokens: int = 1024,
810
+ do_sample: bool = True,
811
+ temperature: float = 0.8,
812
+ top_p: float = 0.8,
813
+ **kwargs):
814
+ """
815
+ Return a generator in format: (response, history)
816
+ Eg.
817
+ ('你好,有什么可以帮助您的吗', [('你好', '你好,有什么可以帮助您的吗')])
818
+ ('你好,有什么可以帮助您的吗?', [('你好', '你好,有什么可以帮助您的吗?')])
819
+ """
820
+
821
+ response_queue = queue.Queue(maxsize=20)
822
+
823
+ class ChatStreamer(BaseStreamer):
824
+ def __init__(self, tokenizer) -> None:
825
+ super().__init__()
826
+ self.tokenizer = tokenizer
827
+ self.queue = response_queue
828
+ self.query = query
829
+ self.history = history
830
+ self.response = ""
831
+ self.received_inputs = False
832
+ self.queue.put((self.response, history + [(self.query, self.response)]))
833
+
834
+ def put(self, value):
835
+ if len(value.shape) > 1 and value.shape[0] > 1:
836
+ raise ValueError("ChatStreamer only supports batch size 1")
837
+ elif len(value.shape) > 1:
838
+ value = value[0]
839
+
840
+ if not self.received_inputs:
841
+ # The first received value is input_ids, ignore here
842
+ self.received_inputs = True
843
+ return
844
+
845
+ token = self.tokenizer.decode([value[-1]], skip_special_tokens=True)
846
+ if token.strip() != "<eoa>":
847
+ self.response = self.response + token
848
+ history = self.history + [(self.query, self.response)]
849
+ self.queue.put((self.response, history))
850
+
851
+ def end(self):
852
+ self.queue.put(None)
853
+
854
+ def stream_producer():
855
+ return self.chat(
856
+ tokenizer=tokenizer,
857
+ query=query,
858
+ streamer=ChatStreamer(tokenizer=tokenizer),
859
+ history=history,
860
+ max_new_tokens=max_new_tokens,
861
+ do_sample=do_sample,
862
+ temperature=temperature,
863
+ top_p=top_p,
864
+ **kwargs
865
+ )
866
+
867
+ def consumer():
868
+ producer = threading.Thread(target=stream_producer)
869
+ producer.start()
870
+ while True:
871
+ res = response_queue.get()
872
+ if res is None:
873
+ return
874
+ yield res
875
+
876
+ return consumer()
877
+
878
+
879
+ @add_start_docstrings(
880
+ """
881
+ The InternLM Model transformer with a sequence classification head on top (linear layer).
882
+
883
+ [`InternLMForSequenceClassification`] uses the last token in order to do the classification, as other causal models
884
+ (e.g. GPT-2) do.
885
+
886
+ Since it does classification on the last token, it requires to know the position of the last token. If a
887
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
888
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
889
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
890
+ each row of the batch).
891
+ """,
892
+ INTERNLM_START_DOCSTRING,
893
+ )
894
+ class InternLMForSequenceClassification(InternLMPreTrainedModel):
895
+ _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
896
+
897
+ def __init__(self, config):
898
+ super().__init__(config)
899
+ self.num_labels = config.num_labels
900
+ self.model = InternLMModel(config)
901
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
902
+
903
+ # Initialize weights and apply final processing
904
+ self.post_init()
905
+
906
+ def get_input_embeddings(self):
907
+ return self.model.embed_tokens
908
+
909
+ def set_input_embeddings(self, value):
910
+ self.model.embed_tokens = value
911
+
912
+ @add_start_docstrings_to_model_forward(INTERNLM_INPUTS_DOCSTRING)
913
+ def forward(
914
+ self,
915
+ input_ids: torch.LongTensor = None,
916
+ attention_mask: Optional[torch.Tensor] = None,
917
+ position_ids: Optional[torch.LongTensor] = None,
918
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
919
+ inputs_embeds: Optional[torch.FloatTensor] = None,
920
+ labels: Optional[torch.LongTensor] = None,
921
+ use_cache: Optional[bool] = None,
922
+ output_attentions: Optional[bool] = None,
923
+ output_hidden_states: Optional[bool] = None,
924
+ return_dict: Optional[bool] = None,
925
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
926
+ r"""
927
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
928
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
929
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
930
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
931
+ """
932
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
933
+
934
+ transformer_outputs = self.model(
935
+ input_ids,
936
+ attention_mask=attention_mask,
937
+ position_ids=position_ids,
938
+ past_key_values=past_key_values,
939
+ inputs_embeds=inputs_embeds,
940
+ use_cache=use_cache,
941
+ output_attentions=output_attentions,
942
+ output_hidden_states=output_hidden_states,
943
+ return_dict=return_dict,
944
+ )
945
+ hidden_states = transformer_outputs[0]
946
+ logits = self.score(hidden_states)
947
+
948
+ if input_ids is not None:
949
+ batch_size = input_ids.shape[0]
950
+ else:
951
+ batch_size = inputs_embeds.shape[0]
952
+
953
+ if self.config.pad_token_id is None and batch_size != 1:
954
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
955
+ if self.config.pad_token_id is None:
956
+ sequence_lengths = -1
957
+ else:
958
+ if input_ids is not None:
959
+ sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
960
+ else:
961
+ sequence_lengths = -1
962
+
963
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
964
+
965
+ loss = None
966
+ if labels is not None:
967
+ labels = labels.to(logits.device)
968
+ if self.config.problem_type is None:
969
+ if self.num_labels == 1:
970
+ self.config.problem_type = "regression"
971
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
972
+ self.config.problem_type = "single_label_classification"
973
+ else:
974
+ self.config.problem_type = "multi_label_classification"
975
+
976
+ if self.config.problem_type == "regression":
977
+ loss_fct = MSELoss()
978
+ if self.num_labels == 1:
979
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
980
+ else:
981
+ loss = loss_fct(pooled_logits, labels)
982
+ elif self.config.problem_type == "single_label_classification":
983
+ loss_fct = CrossEntropyLoss()
984
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
985
+ elif self.config.problem_type == "multi_label_classification":
986
+ loss_fct = BCEWithLogitsLoss()
987
+ loss = loss_fct(pooled_logits, labels)
988
+ if not return_dict:
989
+ output = (pooled_logits,) + transformer_outputs[1:]
990
+ return ((loss,) + output) if loss is not None else output
991
+
992
+ return SequenceClassifierOutputWithPast(
993
+ loss=loss,
994
+ logits=pooled_logits,
995
+ past_key_values=transformer_outputs.past_key_values,
996
+ hidden_states=transformer_outputs.hidden_states,
997
+ attentions=transformer_outputs.attentions,
998
+ )
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