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README.md ADDED
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config.json ADDED
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+ {
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+ "_name_or_path": "OEvortex/HelpingAI-3B-v2.2",
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+ "architectures": [
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+ "HelpingAIForCausalLM"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "auto_map": {
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+ "AutoConfig": "configuration_HelpingAI.HelpingAIConfig",
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+ "AutoModelForCausalLM": "modeling_HelpingAI.HelpingAIForCausalLM"
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+ },
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+ "bos_token_id": 50278,
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+ "eos_token_id": 50279,
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+ "head_dim": 256,
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+ "hidden_act": "silu",
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+ "hidden_activation": null,
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+ "hidden_dropout": 0.0,
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+ "hidden_size": 2560,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 6912,
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 4096,
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+ "model_type": "HelpingAI",
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+ "norm_eps": 1e-05,
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+ "num_attention_heads": 32,
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+ "num_experts_per_tok": 2,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 32,
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+ "num_local_experts": 8,
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+ "output_router_logits": false,
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+ "pad_token_id": 50279,
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+ "partial_rotary_factor": 0.25,
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+ "rms_norm_eps": 1e-06,
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+ "rope_pct": 0.25,
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+ "rope_scaling": null,
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+ "rope_theta": 10000,
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+ "router_aux_loss_coef": 0.02,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.41.2",
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+ "use_cache": false,
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+ "use_qkv_bias": false,
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+ "vocab_size": 50281
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+ }
configuration_HelpingAI.py ADDED
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+ """ HelpingAI model configuration"""
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+
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+ from transformers import PretrainedConfig
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+ from transformers.utils import logging
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+
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+
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+ logger = logging.get_logger(__name__)
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+
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+ class HelpingAIConfig(PretrainedConfig):
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+ keys_to_ignore_at_inference = ["past_key_values"]
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+ model_type = "HelpingAI"
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+ def __init__(
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+ self,
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+ vocab_size=50304,
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+ hidden_size=2560,
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+ intermediate_size=6912,
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+ num_hidden_layers=32,
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+ num_attention_heads=32,
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+ num_key_value_heads=32,
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+ head_dim=256,
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+ hidden_act="silu",
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+ max_position_embeddings=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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+ hidden_activation=None,
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+ rope_theta=10000,
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+ rope_pct=0.25,
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+ attention_bias=False,
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+ attention_dropout=0.0,
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+ num_experts_per_tok=2,
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+ num_local_experts=8,
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+ router_aux_loss_coef=0.02,
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+ output_router_logits=False,
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+ norm_eps=1.0e-5,
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+ **kwargs,
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+ ):
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+ self.vocab_size = vocab_size
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+ self.max_position_embeddings = max_position_embeddings
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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.head_dim = head_dim
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+ self.hidden_act = hidden_act
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+ self.hidden_activation = hidden_activation
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+ self.num_key_value_heads = num_key_value_heads
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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.rope_theta = rope_theta
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+ self.attention_bias = attention_bias
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+ self.attention_dropout = attention_dropout
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+ self.num_experts_per_tok = num_experts_per_tok
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+ self.num_local_experts = num_local_experts
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+ self.router_aux_loss_coef = router_aux_loss_coef
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+ self.output_router_logits = output_router_logits
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+ self.rope_pct = rope_pct
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+ self.norm_eps = norm_eps
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+ super().__init__(**kwargs)
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+ }
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+ }
modeling_HelpingAI.py ADDED
@@ -0,0 +1,670 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """ HelpingAI model . """
2
+ from typing import Optional, Tuple, Union
3
+ import math
4
+
5
+ import torch
6
+ import torch.utils.checkpoint
7
+ from transformers import AutoModel, AutoModelForCausalLM
8
+ from torch import nn
9
+ from torch.nn import CrossEntropyLoss
10
+ from transformers.modeling_outputs import (
11
+ BaseModelOutputWithPast,
12
+ CausalLMOutputWithPast,
13
+ )
14
+ from transformers.modeling_utils import PreTrainedModel
15
+ from transformers.utils import logging
16
+ from .configuration_HelpingAI import HelpingAIConfig
17
+
18
+
19
+ logger = logging.get_logger(__name__)
20
+
21
+
22
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
23
+ def _make_causal_mask(
24
+ input_ids_shape: torch.Size,
25
+ dtype: torch.dtype,
26
+ device: torch.device,
27
+ past_key_values_length: int = 0,
28
+ ):
29
+ """Make causal mask used for bi-directional self-attention."""
30
+ batch_size, tgt_len = input_ids_shape
31
+ mask = torch.full((tgt_len, tgt_len), torch.finfo(torch.float16).min, device=device)
32
+ mask_cond = torch.arange(mask.size(-1), device=device)
33
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
34
+ mask = mask.to(dtype)
35
+ if past_key_values_length > 0:
36
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
37
+ return mask[None, None, :, :].expand(batch_size, 1, tgt_len, tgt_len + past_key_values_length)
38
+
39
+
40
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
41
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
42
+ """Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, tgt_seq_len, src_seq_len]`."""
43
+ batch_size, src_len = mask.size()
44
+ tgt_len = tgt_len if tgt_len is not None else src_len
45
+
46
+ expanded_mask = mask[:, None, None, :].expand(batch_size, 1, tgt_len, src_len).to(dtype)
47
+ inverted_mask = 1.0 - expanded_mask
48
+
49
+ return inverted_mask.masked_fill(
50
+ inverted_mask.to(torch.bool), torch.finfo(dtype).min
51
+ )
52
+
53
+
54
+ class RotaryEmbedding(nn.Module):
55
+ def __init__(
56
+ self,
57
+ dim: int,
58
+ max_position_embeddings: int,
59
+ base: int = 10_000,
60
+ device: Optional[torch.device] = None,
61
+ ):
62
+ super().__init__()
63
+
64
+ self.dim = dim
65
+ self.max_position_embeddings = max_position_embeddings
66
+ self.base = base
67
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
68
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
69
+
70
+ # Build here to make `torch.jit.trace` work.
71
+ self._set_cos_sin_cache(
72
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype(),
73
+ )
74
+
75
+ def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
76
+ self.max_seq_len_cached = seq_len
77
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
78
+
79
+ # Don't do einsum, it converts fp32 to fp16 under AMP
80
+ # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
81
+ freqs = torch.outer(t, self.inv_freq)
82
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
83
+ emb = torch.cat((freqs, freqs), dim=-1)
84
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
85
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
86
+
87
+ def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
88
+ # x: [batch_size, num_heads, seq_len, head_size]
89
+ if seq_len > self.max_seq_len_cached:
90
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.get_default_dtype())
91
+ return (
92
+ self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
93
+ self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
94
+ )
95
+
96
+
97
+ def rotate_half(x: torch.Tensor):
98
+ """Rotates half the hidden dims of the input."""
99
+ x1, x2 = torch.chunk(x, 2, dim=-1)
100
+ return torch.cat((-x2, x1), dim=-1)
101
+
102
+
103
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
104
+ # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
105
+ cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
106
+ sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
107
+ cos = cos[position_ids].unsqueeze(1) # [batch_size, 1, seq_len, dim]
108
+ sin = sin[position_ids].unsqueeze(1) # [batch_size, 1, seq_len, dim]
109
+ q_embed = (q * cos) + (rotate_half(q) * sin)
110
+ k_embed = (k * cos) + (rotate_half(k) * sin)
111
+ return q_embed, k_embed
112
+
113
+
114
+ class MLP(nn.Module):
115
+ def __init__(self, config: HelpingAIConfig):
116
+ super().__init__()
117
+ self.config = config
118
+ self.hidden_size = config.hidden_size
119
+ self.intermediate_size = config.intermediate_size
120
+ self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
121
+ self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
122
+ self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
123
+ self.act_fn = nn.SiLU()
124
+
125
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
126
+ return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
127
+
128
+
129
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
130
+ """
131
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
132
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
133
+ """
134
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
135
+ if n_rep == 1:
136
+ return hidden_states
137
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
138
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
139
+
140
+
141
+ class Attention(nn.Module):
142
+ def __init__(self, config: HelpingAIConfig):
143
+ super().__init__()
144
+ self.config = config
145
+ self.hidden_size = config.hidden_size
146
+ self.num_heads = config.num_attention_heads
147
+ self.head_dim = self.hidden_size // self.num_heads
148
+ self.num_key_value_heads = config.num_key_value_heads
149
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
150
+ self.max_position_embeddings = config.max_position_embeddings
151
+
152
+ if (self.head_dim * self.num_heads) != self.hidden_size:
153
+ raise ValueError(
154
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
155
+ f" and `num_heads`: {self.num_heads})."
156
+ )
157
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
158
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
159
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
160
+ self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
161
+
162
+ self._init_rope()
163
+
164
+ def _init_rope(self):
165
+ self.rotary_ndims = int(self.head_dim * self.config.rope_pct)
166
+ self.rotary_emb = RotaryEmbedding(
167
+ self.rotary_ndims,
168
+ max_position_embeddings=self.config.max_position_embeddings,
169
+ base=self.config.rope_theta,
170
+ )
171
+
172
+ def forward(
173
+ self,
174
+ hidden_states: torch.FloatTensor,
175
+ attention_mask: torch.FloatTensor,
176
+ position_ids: torch.LongTensor,
177
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
178
+ output_attentions: Optional[bool] = False,
179
+ use_cache: Optional[bool] = False,
180
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
181
+ bsz, q_len, _ = hidden_states.size()
182
+
183
+ query_states = self.q_proj(hidden_states)
184
+ key_states = self.k_proj(hidden_states)
185
+ value_states = self.v_proj(hidden_states)
186
+
187
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
188
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
189
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
190
+
191
+ query_rot = query_states[..., : self.rotary_ndims]
192
+ query_pass = query_states[..., self.rotary_ndims :]
193
+ key_rot = key_states[..., : self.rotary_ndims]
194
+ key_pass = key_states[..., self.rotary_ndims :]
195
+
196
+ kv_seq_len = key_states.shape[-2]
197
+ if past_key_value is not None:
198
+ kv_seq_len += past_key_value[0].shape[-2]
199
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
200
+ query_states, key_states = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
201
+
202
+ # [batch_size, num_heads, seq_len, head_dim]
203
+ query_states = torch.cat((query_states, query_pass), dim=-1)
204
+ key_states = torch.cat((key_states, key_pass), dim=-1)
205
+
206
+ if past_key_value is not None:
207
+ # Reuse k, v, self_attention
208
+ key_states = torch.cat((past_key_value[0], key_states), dim=2)
209
+ value_states = torch.cat((past_key_value[1], value_states), dim=2)
210
+
211
+ past_key_value = (key_states, value_states) if use_cache else None
212
+
213
+ # Repeat k/v heads if n_kv_heads < n_heads
214
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
215
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
216
+
217
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
218
+
219
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
220
+ raise ValueError(
221
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
222
+ f" {attn_weights.size()}"
223
+ )
224
+
225
+ if attention_mask is not None:
226
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
227
+ raise ValueError(
228
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
229
+ )
230
+ attn_weights = attn_weights + attention_mask
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
+ # Merge heads
243
+ attn_output = attn_output.transpose(1, 2).contiguous()
244
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
245
+
246
+ # Final linear projection
247
+ attn_output = self.o_proj(attn_output)
248
+
249
+ if not output_attentions:
250
+ attn_weights = None
251
+
252
+ return attn_output, attn_weights, past_key_value
253
+
254
+
255
+ class DecoderLayer(nn.Module):
256
+ def __init__(self, config: HelpingAIConfig):
257
+ super().__init__()
258
+ self.self_attn = Attention(config)
259
+ self.mlp = MLP(config)
260
+ self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
261
+ self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
262
+
263
+ def forward(
264
+ self,
265
+ hidden_states: Optional[torch.FloatTensor],
266
+ attention_mask: Optional[torch.FloatTensor] = None,
267
+ position_ids: Optional[torch.LongTensor] = None,
268
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
269
+ output_attentions: Optional[bool] = False,
270
+ use_cache: Optional[bool] = False,
271
+ ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
272
+ residual = hidden_states
273
+
274
+ hidden_states = self.input_layernorm(hidden_states)
275
+
276
+ # Self Attention
277
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
278
+ hidden_states=hidden_states,
279
+ attention_mask=attention_mask,
280
+ position_ids=position_ids,
281
+ past_key_value=past_key_value,
282
+ output_attentions=output_attentions,
283
+ use_cache=use_cache,
284
+ )
285
+ hidden_states = residual + hidden_states
286
+
287
+ # Fully Connected
288
+ residual = hidden_states
289
+ hidden_states = self.post_attention_layernorm(hidden_states)
290
+ hidden_states = self.mlp(hidden_states)
291
+ hidden_states = residual + hidden_states
292
+
293
+ outputs = (hidden_states,)
294
+
295
+ if output_attentions:
296
+ outputs += (self_attn_weights,)
297
+
298
+ if use_cache:
299
+ outputs += (present_key_value,)
300
+
301
+ return outputs
302
+
303
+
304
+ class HelpingAIPreTrainedModel(PreTrainedModel):
305
+ """An abstract class to handle weights initialization and a simple interface
306
+ for downloading and loading pretrained models.
307
+ """
308
+
309
+ config_class = HelpingAIConfig
310
+ base_model_prefix = "transformer"
311
+ supports_gradient_checkpointing = True
312
+ _no_split_modules = ["DecoderLayer"]
313
+ _skip_keys_device_placement = "past_key_values"
314
+
315
+ def _init_weights(self, module: nn.Module):
316
+ """Initialize the weights"""
317
+ if isinstance(module, nn.Linear):
318
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
319
+ if module.bias is not None:
320
+ module.bias.data.zero_()
321
+ elif isinstance(module, nn.Embedding):
322
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
323
+ if module.padding_idx is not None:
324
+ module.weight.data[module.padding_idx].zero_()
325
+ elif isinstance(module, nn.LayerNorm):
326
+ module.bias.data.zero_()
327
+ module.weight.data.fill_(1.0)
328
+
329
+ def _set_gradient_checkpointing(self, module: nn.Module, value=False):
330
+ if isinstance(module, HelpingAIModel):
331
+ module.gradient_checkpointing = value
332
+
333
+
334
+ class HelpingAIModel(HelpingAIPreTrainedModel):
335
+ def __init__(self, config: HelpingAIConfig):
336
+ super().__init__(config)
337
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
338
+ self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
339
+ self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
340
+
341
+ self.gradient_checkpointing = False
342
+ # Initialize weights and apply final processing
343
+ self.post_init()
344
+
345
+ def get_input_embeddings(self):
346
+ return self.embed_tokens
347
+
348
+ def set_input_embeddings(self, value: nn.Module):
349
+ self.embed_tokens = value
350
+
351
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
352
+ def _prepare_decoder_attention_mask(
353
+ self,
354
+ attention_mask: torch.Tensor,
355
+ input_shape: torch.Size,
356
+ inputs_embeds: torch.Tensor,
357
+ past_key_values_length: int,
358
+ ):
359
+ # Create causal mask
360
+ # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
361
+ combined_attention_mask = None
362
+ if input_shape[-1] > 1:
363
+ combined_attention_mask = _make_causal_mask(
364
+ input_shape,
365
+ inputs_embeds.dtype,
366
+ device=inputs_embeds.device,
367
+ past_key_values_length=past_key_values_length,
368
+ )
369
+
370
+ if attention_mask is not None:
371
+ # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
372
+ expanded_attn_mask = _expand_mask(
373
+ attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
374
+ ).to(inputs_embeds.device)
375
+ combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
376
+
377
+ return combined_attention_mask
378
+
379
+ def forward(
380
+ self,
381
+ input_ids: Optional[torch.LongTensor] = None,
382
+ attention_mask: Optional[torch.FloatTensor] = None,
383
+ position_ids: Optional[torch.LongTensor] = None,
384
+ past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
385
+ inputs_embeds: Optional[torch.FloatTensor] = None,
386
+ use_cache: Optional[bool] = None,
387
+ output_attentions: Optional[bool] = None,
388
+ output_hidden_states: Optional[bool] = None,
389
+ return_dict: Optional[bool] = None,
390
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
391
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
392
+ output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
393
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
394
+
395
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
396
+
397
+ # Retrieve input_ids and inputs_embeds
398
+ if input_ids is not None and inputs_embeds is not None:
399
+ raise ValueError(
400
+ "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
401
+ )
402
+ elif input_ids is not None:
403
+ batch_size, seq_length = input_ids.shape
404
+ elif inputs_embeds is not None:
405
+ batch_size, seq_length, _ = inputs_embeds.shape
406
+ else:
407
+ raise ValueError(
408
+ "You have to specify either decoder_input_ids or decoder_inputs_embeds"
409
+ )
410
+
411
+ seq_length_with_past = seq_length
412
+ past_key_values_length = 0
413
+
414
+ if past_key_values is not None:
415
+ past_key_values_length = past_key_values[0][0].shape[2]
416
+ seq_length_with_past = seq_length_with_past + past_key_values_length
417
+
418
+ if position_ids is None:
419
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
420
+ position_ids = torch.arange(
421
+ past_key_values_length,
422
+ seq_length + past_key_values_length,
423
+ dtype=torch.long,
424
+ device=device,
425
+ )
426
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
427
+ else:
428
+ position_ids = position_ids.view(-1, seq_length).long()
429
+
430
+ if inputs_embeds is None:
431
+ inputs_embeds = self.embed_tokens(input_ids)
432
+ # Embed positions
433
+ if attention_mask is None:
434
+ attention_mask = torch.ones(
435
+ (batch_size, seq_length_with_past),
436
+ dtype=torch.bool,
437
+ device=inputs_embeds.device,
438
+ )
439
+ attention_mask = self._prepare_decoder_attention_mask(
440
+ attention_mask,
441
+ (batch_size, seq_length),
442
+ inputs_embeds,
443
+ past_key_values_length,
444
+ )
445
+
446
+ hidden_states = inputs_embeds
447
+
448
+ if self.gradient_checkpointing and self.training:
449
+ if use_cache:
450
+ logger.warning(
451
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
452
+ )
453
+ use_cache = False
454
+
455
+ # Decoder layers
456
+ all_hidden_states = () if output_hidden_states else None
457
+ all_self_attns = () if output_attentions else None
458
+ next_decoder_cache = () if use_cache else None
459
+
460
+ for idx, decoder_layer in enumerate(self.layers):
461
+ if output_hidden_states:
462
+ all_hidden_states += (hidden_states,)
463
+
464
+ past_key_value = (
465
+ past_key_values[idx] if past_key_values is not None else None
466
+ )
467
+
468
+ if self.gradient_checkpointing and self.training:
469
+
470
+ def create_custom_forward(module):
471
+ def custom_forward(*inputs):
472
+ # None for past_key_value
473
+ return module(*inputs, past_key_value, output_attentions)
474
+
475
+ return custom_forward
476
+
477
+ layer_outputs = torch.utils.checkpoint.checkpoint(
478
+ create_custom_forward(decoder_layer),
479
+ hidden_states,
480
+ attention_mask,
481
+ position_ids,
482
+ )
483
+ else:
484
+ layer_outputs = decoder_layer(
485
+ hidden_states,
486
+ attention_mask=attention_mask,
487
+ position_ids=position_ids,
488
+ past_key_value=past_key_value,
489
+ output_attentions=output_attentions,
490
+ use_cache=use_cache,
491
+ )
492
+
493
+ hidden_states = layer_outputs[0]
494
+
495
+ if use_cache:
496
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
497
+
498
+ if output_attentions:
499
+ all_self_attns += (layer_outputs[1],)
500
+
501
+ hidden_states = self.norm(hidden_states)
502
+
503
+ # Add hidden states from the last decoder layer
504
+ if output_hidden_states:
505
+ all_hidden_states += (hidden_states,)
506
+
507
+ next_cache = next_decoder_cache if use_cache else None
508
+ if not return_dict:
509
+ return tuple(
510
+ v
511
+ for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
512
+ if v is not None
513
+ )
514
+ return BaseModelOutputWithPast(
515
+ last_hidden_state=hidden_states,
516
+ past_key_values=next_cache,
517
+ hidden_states=all_hidden_states,
518
+ attentions=all_self_attns,
519
+ )
520
+
521
+
522
+ class HelpingAIForCausalLM(HelpingAIPreTrainedModel):
523
+ _tied_weights_keys = ["lm_head.weight"]
524
+
525
+ def __init__(self, config: HelpingAIConfig):
526
+ super().__init__(config)
527
+
528
+ self.model = HelpingAIModel(config)
529
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
530
+
531
+ # Initialize weights and apply final processing
532
+ self.post_init()
533
+
534
+ def get_input_embeddings(self):
535
+ return self.model.embed_tokens
536
+
537
+ def set_input_embeddings(self, value):
538
+ self.model.embed_tokens = value
539
+
540
+ def get_output_embeddings(self):
541
+ return self.lm_head
542
+
543
+ def set_output_embeddings(self, new_embeddings: nn.Module):
544
+ self.lm_head = new_embeddings
545
+
546
+ def get_decoder(self):
547
+ return self.transformer
548
+
549
+ def set_decoder(self, decoder):
550
+ self.transformer = decoder
551
+
552
+ def forward(
553
+ self,
554
+ input_ids: Optional[torch.LongTensor] = None,
555
+ attention_mask: Optional[torch.FloatTensor] = None,
556
+ position_ids: Optional[torch.LongTensor] = None,
557
+ past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
558
+ inputs_embeds: Optional[torch.FloatTensor] = None,
559
+ labels: Optional[torch.LongTensor] = None,
560
+ use_cache: Optional[bool] = None,
561
+ output_attentions: Optional[bool] = None,
562
+ output_hidden_states: Optional[bool] = None,
563
+ return_dict: Optional[bool] = None,
564
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
565
+ output_attentions = (
566
+ output_attentions
567
+ if output_attentions is not None
568
+ else self.config.output_attentions
569
+ )
570
+ output_hidden_states = (
571
+ output_hidden_states
572
+ if output_hidden_states is not None
573
+ else self.config.output_hidden_states
574
+ )
575
+ return_dict = (
576
+ return_dict if return_dict is not None else self.config.use_return_dict
577
+ )
578
+
579
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
580
+ outputs = self.model(
581
+ input_ids,
582
+ attention_mask=attention_mask,
583
+ position_ids=position_ids,
584
+ past_key_values=past_key_values,
585
+ inputs_embeds=inputs_embeds,
586
+ use_cache=use_cache,
587
+ output_attentions=output_attentions,
588
+ output_hidden_states=output_hidden_states,
589
+ return_dict=return_dict,
590
+ )
591
+
592
+ hidden_states = outputs[0]
593
+ logits = self.lm_head(hidden_states).float()
594
+
595
+ loss = None
596
+ if labels is not None:
597
+ # Shift so that tokens < n predict n
598
+ shift_logits = logits[..., :-1, :].contiguous()
599
+ shift_labels = labels[..., 1:].contiguous()
600
+ # Flatten the tokens
601
+ loss_fct = CrossEntropyLoss()
602
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
603
+ shift_labels = shift_labels.view(-1)
604
+ # Enable model parallelism
605
+ shift_labels = shift_labels.to(shift_logits.device)
606
+ loss = loss_fct(shift_logits, shift_labels)
607
+
608
+ if not return_dict:
609
+ output = (logits,) + outputs[1:]
610
+ return (loss,) + output if loss is not None else output
611
+
612
+ return CausalLMOutputWithPast(
613
+ loss=loss,
614
+ logits=logits,
615
+ past_key_values=outputs.past_key_values,
616
+ hidden_states=outputs.hidden_states,
617
+ attentions=outputs.attentions,
618
+ )
619
+
620
+ def prepare_inputs_for_generation(
621
+ self,
622
+ input_ids,
623
+ past_key_values: Optional[torch.Tensor] = None,
624
+ attention_mask: Optional[torch.Tensor] = None,
625
+ inputs_embeds: Optional[torch.Tensor] = None,
626
+ **kwargs,
627
+ ):
628
+ # Trim decoder_input_ids if past is used
629
+ if past_key_values and past_key_values[0] is not None:
630
+ input_ids = input_ids[:, -1:]
631
+
632
+ position_ids = kwargs.get("position_ids", None)
633
+ if attention_mask is not None and position_ids is None:
634
+ # Create position_ids on the fly for batch generation
635
+ position_ids = attention_mask.long().cumsum(-1) - 1
636
+ position_ids.masked_fill_(attention_mask == 0, 1)
637
+ if past_key_values:
638
+ position_ids = position_ids[:, -1].unsqueeze(-1)
639
+
640
+ # If `inputs_embeds` are passed, we only want to use them in the 1st generation step
641
+ if inputs_embeds is not None and past_key_values is None:
642
+ model_inputs = {"inputs_embeds": inputs_embeds}
643
+ else:
644
+ model_inputs = {"input_ids": input_ids}
645
+
646
+ model_inputs.update(
647
+ {
648
+ "attention_mask": attention_mask,
649
+ "past_key_values": past_key_values,
650
+ "use_cache": kwargs.get("use_cache"),
651
+ "position_ids": position_ids,
652
+ }
653
+ )
654
+ return model_inputs
655
+
656
+ @staticmethod
657
+ def _reorder_cache(past_key_values, beam_idx):
658
+ reordered_past = ()
659
+ for layer_past in past_key_values:
660
+ reordered_past += (
661
+ tuple(
662
+ past_state.index_select(0, beam_idx.to(past_state.device))
663
+ for past_state in layer_past
664
+ ),
665
+ )
666
+ return reordered_past
667
+
668
+
669
+ HelpingAIConfig.register_for_auto_class()
670
+ HelpingAIForCausalLM.register_for_auto_class("AutoModelForCausalLM")