init
Browse files- config.json +40 -0
- configuration_cogvlm.py +45 -0
- generation_config.json +7 -0
- model-00001-of-00008.safetensors +3 -0
- model-00002-of-00008.safetensors +3 -0
- model-00003-of-00008.safetensors +3 -0
- model-00004-of-00008.safetensors +3 -0
- model-00005-of-00008.safetensors +3 -0
- model-00006-of-00008.safetensors +3 -0
- model-00007-of-00008.safetensors +3 -0
- model-00008-of-00008.safetensors +3 -0
- model.safetensors.index.json +0 -0
- modeling_cogvlm.py +785 -0
- util.py +483 -0
- visual.py +135 -0
config.json
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{
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"_name_or_path": "cogvlm-grounding-generalist",
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"architectures": [
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"CogVLMForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_cogvlm.CogVLMConfig",
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"AutoModelForCausalLM": "modeling_cogvlm.CogVLMForCausalLM"
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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": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 2048,
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-05,
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"template_version": "base",
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.35.0",
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"use_cache": true,
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"vision_config": {
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"dropout_prob": 0.0,
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"hidden_act": "gelu",
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"hidden_size": 1792,
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"image_size": 490,
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"in_channels": 3,
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"intermediate_size": 15360,
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"layer_norm_eps": 1e-06,
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"num_heads": 16,
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"num_hidden_layers": 63,
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"num_positions": 1226,
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"patch_size": 14
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},
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"vocab_size": 32000
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}
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configuration_cogvlm.py
ADDED
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from typing import Literal
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from transformers import PretrainedConfig
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class CogVLMConfig(PretrainedConfig):
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_auto_class = "AutoConfig"
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def __init__(
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self,
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vocab_size=32000,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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hidden_act='silu',
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max_position_embeddings=2048,
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initializer_range=0.02,
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rms_norm_eps=1e-06,
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template_version: Literal["base", "chat"] = "chat",
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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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use_cache=True,
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**kwargs,
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):
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_attention_heads = num_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.rms_norm_eps = rms_norm_eps
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self.initializer_range = initializer_range
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self.vocab_size = vocab_size
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self.num_hidden_layers = num_hidden_layers
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self.hidden_act = hidden_act
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self.template_version = template_version
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self.use_cache = use_cache
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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": "4.35.0"
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}
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model-00001-of-00008.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:ab245c9b171545099d652eefcd863aa20f95b7ec2c18dea754ffa661ddcdeebd
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size 4938885184
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model-00002-of-00008.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c4c34bd99e8317404ef58c253a01648c120b356b3b8f95933da46dd02fbb73ba
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size 4947290688
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model-00003-of-00008.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0affb830c00b94b9fc5ecfa41d7ae0d62fa42c73300df2537cd7c4496f947014
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size 4947307592
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model-00004-of-00008.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:ed1d44e236c4af9263f8a32406769d4e8dcf2fefad205a17c2c2191d948c0e07
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size 4991331080
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model-00005-of-00008.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9dc0499401747dd885bd9e212cec57296fad9b0c2d59c8c1984063f9c9d22eeb
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size 4991331088
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model-00006-of-00008.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7196fa96d96992a65ae041f2a2921afef149003e059bec884520d386c814ce0a
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size 4970162920
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model-00007-of-00008.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:340afeec8059f2d6fb2b890041611a448bb54440ae185d8a5433bf39e2711d9b
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size 4960543792
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model-00008-of-00008.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9122674b65c1b3c9c8d498ff684591c106081a906f0c85e9fcb8e1a2cc0a270e
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size 532677104
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model.safetensors.index.json
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modeling_cogvlm.py
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|
1 |
+
"""largely copy from llama and adapt for cogvlm"""
|
2 |
+
import warnings
|
3 |
+
from typing import TYPE_CHECKING, Optional, Tuple, List, Union, Literal, Dict, Any
|
4 |
+
|
5 |
+
import math
|
6 |
+
import torch
|
7 |
+
from torch import nn
|
8 |
+
from torch.nn import CrossEntropyLoss
|
9 |
+
from torchvision import transforms
|
10 |
+
from einops import rearrange
|
11 |
+
|
12 |
+
from transformers import PreTrainedModel, PreTrainedTokenizer
|
13 |
+
from transformers.utils.logging import get_logger
|
14 |
+
from transformers.activations import ACT2FN
|
15 |
+
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
|
16 |
+
|
17 |
+
from .configuration_cogvlm import CogVLMConfig
|
18 |
+
from .util import FastRotaryEmbedding
|
19 |
+
from .visual import EVA2CLIPModel
|
20 |
+
|
21 |
+
if TYPE_CHECKING:
|
22 |
+
from transformers.utils import ModelOutput
|
23 |
+
|
24 |
+
logger = get_logger(__name__)
|
25 |
+
|
26 |
+
LANGUAGE_TOKEN_TYPE = 0
|
27 |
+
VISION_TOKEN_TYPE = 1
|
28 |
+
|
29 |
+
|
30 |
+
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
|
31 |
+
def _make_causal_mask(
|
32 |
+
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
33 |
+
):
|
34 |
+
"""
|
35 |
+
Make causal mask used for bi-directional self-attention.
|
36 |
+
"""
|
37 |
+
bsz, tgt_len = input_ids_shape
|
38 |
+
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
|
39 |
+
mask_cond = torch.arange(mask.size(-1), device=device)
|
40 |
+
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
|
41 |
+
mask = mask.to(dtype)
|
42 |
+
|
43 |
+
if past_key_values_length > 0:
|
44 |
+
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
|
45 |
+
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
|
46 |
+
|
47 |
+
|
48 |
+
# Copied from transformers.models.bart.modeling_bart._expand_mask
|
49 |
+
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
50 |
+
"""
|
51 |
+
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
|
52 |
+
"""
|
53 |
+
bsz, src_len = mask.size()
|
54 |
+
tgt_len = tgt_len if tgt_len is not None else src_len
|
55 |
+
|
56 |
+
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
57 |
+
|
58 |
+
inverted_mask = 1.0 - expanded_mask
|
59 |
+
|
60 |
+
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
|
61 |
+
|
62 |
+
|
63 |
+
class RMSNorm(nn.Module):
|
64 |
+
def __init__(self, hidden_size, eps=1e-6):
|
65 |
+
super().__init__()
|
66 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
67 |
+
self.variance_epsilon = eps
|
68 |
+
|
69 |
+
def forward(self, hidden_states):
|
70 |
+
input_dtype = hidden_states.dtype
|
71 |
+
hidden_states = hidden_states.to(torch.float32)
|
72 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
73 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
74 |
+
return (self.weight * hidden_states).to(input_dtype)
|
75 |
+
|
76 |
+
|
77 |
+
class MLP(nn.Module):
|
78 |
+
def __init__(self, config):
|
79 |
+
super().__init__()
|
80 |
+
self.hidden_size = config.hidden_size
|
81 |
+
self.intermediate_size = config.intermediate_size
|
82 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
83 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
84 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
85 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
86 |
+
|
87 |
+
def forward(self, x):
|
88 |
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
89 |
+
return down_proj
|
90 |
+
|
91 |
+
|
92 |
+
def get_expert_mask(token_type_ids: "torch.LongTensor(B, L)") -> "[torch.BoolTensor(B, L), torch.BoolTensor(B, L)]":
|
93 |
+
vision_token_mask = torch.zeros_like(token_type_ids, dtype=torch.bool)
|
94 |
+
vision_token_mask[:, :-1] = (token_type_ids[:, :-1] == VISION_TOKEN_TYPE) & (token_type_ids[:, 1:] == VISION_TOKEN_TYPE)
|
95 |
+
language_token_mask = ~vision_token_mask
|
96 |
+
return vision_token_mask, language_token_mask
|
97 |
+
|
98 |
+
|
99 |
+
class VisionExpertMLP(nn.Module):
|
100 |
+
def __init__(self, config):
|
101 |
+
super().__init__()
|
102 |
+
self.language_mlp = MLP(config)
|
103 |
+
self.vision_mlp = MLP(config)
|
104 |
+
|
105 |
+
def forward(self, hidden_states: "torch.Tensor(B, L, D)", token_type_ids: "torch.LongTensor(B, L)"):
|
106 |
+
output = torch.empty(hidden_states.shape, dtype=hidden_states.dtype, device=hidden_states.device)
|
107 |
+
vision_token_mask, language_token_mask = get_expert_mask(token_type_ids)
|
108 |
+
output[vision_token_mask] = self.vision_mlp(hidden_states[vision_token_mask])
|
109 |
+
output[language_token_mask] = self.language_mlp(hidden_states[language_token_mask])
|
110 |
+
return output
|
111 |
+
|
112 |
+
|
113 |
+
def attention_fn(
|
114 |
+
query_layer: "torch.tensor(B, H, L, HD)",
|
115 |
+
key_layer: "torch.tensor(B, H, L, HD)",
|
116 |
+
value_layer: "torch.tensor(B, H, L, HD)",
|
117 |
+
attention_mask: "torch.tensor(B, H, L, HD)",
|
118 |
+
*,
|
119 |
+
scaling_attention_score: bool = True,
|
120 |
+
attention_dropout: nn.Module = None
|
121 |
+
):
|
122 |
+
attention_mask_bool = (attention_mask == 0)
|
123 |
+
is_low_triangle = (attention_mask_bool == torch.ones_like(attention_mask_bool, dtype=torch.float).tril()).all()
|
124 |
+
is_full = (attention_mask_bool > 0).all()
|
125 |
+
if not (int(torch.__version__.split('.')[0]) >= 2):
|
126 |
+
warnings.warn("It's recommended to use torch2.0 or higher.")
|
127 |
+
if int(torch.__version__.split('.')[0]) >= 2 and scaling_attention_score and (is_full or is_low_triangle):
|
128 |
+
dropout_p = 0. if attention_dropout is None or not attention_dropout.training else attention_dropout.p
|
129 |
+
return torch.nn.functional.scaled_dot_product_attention(
|
130 |
+
query_layer, key_layer, value_layer,
|
131 |
+
attn_mask=None,
|
132 |
+
dropout_p=dropout_p,
|
133 |
+
is_causal=not is_full
|
134 |
+
)
|
135 |
+
else:
|
136 |
+
if scaling_attention_score:
|
137 |
+
query_layer = query_layer / math.sqrt(query_layer.shape[-1])
|
138 |
+
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
139 |
+
attention_scores = attention_scores + attention_mask
|
140 |
+
attention_scores = nn.functional.softmax(attention_scores, dim=-1, dtype=torch.float32).to(query_layer.dtype)
|
141 |
+
if attention_dropout is not None:
|
142 |
+
attention_scores = attention_dropout(attention_scores)
|
143 |
+
context_layer = torch.matmul(attention_scores, value_layer)
|
144 |
+
return context_layer
|
145 |
+
|
146 |
+
|
147 |
+
class VisionExpertAttention(nn.Module):
|
148 |
+
def __init__(self, config):
|
149 |
+
super().__init__()
|
150 |
+
self.config = config
|
151 |
+
self.hidden_size = config.hidden_size
|
152 |
+
self.num_heads = config.num_attention_heads
|
153 |
+
self.head_dim = self.hidden_size // self.num_heads
|
154 |
+
self.max_position_embeddings = config.max_position_embeddings
|
155 |
+
|
156 |
+
# self.rotary_emb = RotaryEmbedding(self.hidden_size // self.num_heads)
|
157 |
+
self.rotary_emb = FastRotaryEmbedding(dim=self.head_dim, pos_idx_in_fp32=False)
|
158 |
+
self.vision_expert_query_key_value = nn.Linear(self.hidden_size, self.hidden_size * 3, bias=False)
|
159 |
+
self.vision_expert_dense = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
|
160 |
+
self.language_expert_query_key_value = nn.Linear(self.hidden_size, self.hidden_size * 3, bias=False)
|
161 |
+
self.language_expert_dense = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
|
162 |
+
|
163 |
+
def _transpose_for_scores(self, tensor):
|
164 |
+
"""Transpose a 3D tensor [B, L, H*HD] into a 4D tensor with size [B H L HD]."""
|
165 |
+
new_tensor_shape = tensor.size()[:-1] + (self.num_heads, self.head_dim)
|
166 |
+
tensor = tensor.view(*new_tensor_shape)
|
167 |
+
return tensor.permute(0, 2, 1, 3)
|
168 |
+
|
169 |
+
def forward(
|
170 |
+
self,
|
171 |
+
hidden_states: torch.Tensor,
|
172 |
+
token_type_ids: torch.LongTensor,
|
173 |
+
position_ids: torch.LongTensor,
|
174 |
+
attention_mask: Optional[torch.Tensor] = None,
|
175 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
176 |
+
output_attentions: bool = False,
|
177 |
+
use_cache: bool = False,
|
178 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
179 |
+
bsz, q_len, _ = hidden_states.size()
|
180 |
+
vision_token_mask, language_token_mask = get_expert_mask(token_type_ids)
|
181 |
+
|
182 |
+
shape = list(hidden_states.shape)
|
183 |
+
shape[-1] = shape[-1] * 3
|
184 |
+
mixed_raw_layer = torch.empty(shape, dtype=hidden_states.dtype, device=hidden_states.device)
|
185 |
+
mixed_raw_layer[vision_token_mask] = self.vision_expert_query_key_value(hidden_states[vision_token_mask])
|
186 |
+
mixed_raw_layer[language_token_mask] = self.language_expert_query_key_value(hidden_states[language_token_mask])
|
187 |
+
|
188 |
+
query_states, key_states, value_states = torch.split(mixed_raw_layer, self.hidden_size, dim=-1)
|
189 |
+
query_states = self._transpose_for_scores(query_states) # B, H, L, HD
|
190 |
+
key_states = self._transpose_for_scores(key_states) # B, H, L, HD
|
191 |
+
value_states = self._transpose_for_scores(value_states) # B, H, L, HD
|
192 |
+
|
193 |
+
kv_seq_len = key_states.shape[-2]
|
194 |
+
if past_key_value is not None:
|
195 |
+
kv_seq_len += past_key_value[0].shape[-2]
|
196 |
+
|
197 |
+
query_states, key_states = self.rotary_emb(query_states, key_states, position_ids=position_ids, max_seqlen=position_ids.max() + 1)
|
198 |
+
|
199 |
+
if past_key_value is not None:
|
200 |
+
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
201 |
+
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
202 |
+
|
203 |
+
past_key_value = (key_states, value_states) if use_cache else None
|
204 |
+
|
205 |
+
context_layer = attention_fn(
|
206 |
+
query_layer=query_states, key_layer=key_states, value_layer=value_states, attention_mask=attention_mask,
|
207 |
+
scaling_attention_score=True, attention_dropout=None)
|
208 |
+
if context_layer.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
209 |
+
raise ValueError(
|
210 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
211 |
+
f" {context_layer.size()}"
|
212 |
+
)
|
213 |
+
context_layer = context_layer.transpose(1, 2).contiguous().reshape(bsz, q_len, self.hidden_size)
|
214 |
+
|
215 |
+
attn_output = torch.empty(context_layer.shape, dtype=hidden_states.dtype, device=hidden_states.device)
|
216 |
+
attn_output[vision_token_mask] = self.vision_expert_dense(context_layer[vision_token_mask])
|
217 |
+
attn_output[language_token_mask] = self.language_expert_dense(context_layer[language_token_mask])
|
218 |
+
|
219 |
+
if output_attentions:
|
220 |
+
warnings.warn("output_attentions is not implemented.")
|
221 |
+
|
222 |
+
return attn_output, None, past_key_value
|
223 |
+
|
224 |
+
|
225 |
+
class CogVLMDecoderLayer(nn.Module):
|
226 |
+
def __init__(self, config):
|
227 |
+
super().__init__()
|
228 |
+
self.hidden_size = config.hidden_size
|
229 |
+
self.self_attn = VisionExpertAttention(config=config)
|
230 |
+
self.mlp = VisionExpertMLP(config)
|
231 |
+
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
232 |
+
self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
233 |
+
|
234 |
+
def forward(
|
235 |
+
self,
|
236 |
+
hidden_states: torch.Tensor,
|
237 |
+
token_type_ids: torch.LongTensor,
|
238 |
+
position_ids: torch.LongTensor,
|
239 |
+
attention_mask: Optional[torch.Tensor] = None,
|
240 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
241 |
+
output_attentions: Optional[bool] = False,
|
242 |
+
use_cache: Optional[bool] = False,
|
243 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
244 |
+
residual = hidden_states
|
245 |
+
|
246 |
+
hidden_states = self.input_layernorm(hidden_states)
|
247 |
+
|
248 |
+
# Self Attention
|
249 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
250 |
+
hidden_states=hidden_states,
|
251 |
+
token_type_ids=token_type_ids,
|
252 |
+
position_ids=position_ids,
|
253 |
+
attention_mask=attention_mask,
|
254 |
+
past_key_value=past_key_value,
|
255 |
+
output_attentions=output_attentions,
|
256 |
+
use_cache=use_cache,
|
257 |
+
)
|
258 |
+
hidden_states = residual + hidden_states
|
259 |
+
|
260 |
+
# Fully Connected
|
261 |
+
residual = hidden_states
|
262 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
263 |
+
hidden_states = self.mlp(hidden_states, token_type_ids=token_type_ids)
|
264 |
+
hidden_states = residual + hidden_states
|
265 |
+
|
266 |
+
outputs = (hidden_states,)
|
267 |
+
|
268 |
+
if output_attentions:
|
269 |
+
outputs += (self_attn_weights,)
|
270 |
+
|
271 |
+
if use_cache:
|
272 |
+
outputs += (present_key_value,)
|
273 |
+
|
274 |
+
return outputs # type: ignore
|
275 |
+
|
276 |
+
|
277 |
+
class CogVLMPreTrainedModel(PreTrainedModel):
|
278 |
+
config_class = CogVLMConfig
|
279 |
+
base_model_prefix = "model"
|
280 |
+
supports_gradient_checkpointing = False
|
281 |
+
_no_split_modules = ["CogVLMDecoderLayer"]
|
282 |
+
_skip_keys_device_placement = "past_key_values"
|
283 |
+
|
284 |
+
def _init_weights(self, module):
|
285 |
+
std = self.config.initializer_range
|
286 |
+
if isinstance(module, nn.Linear):
|
287 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
288 |
+
if module.bias is not None:
|
289 |
+
module.bias.data.zero_()
|
290 |
+
elif isinstance(module, nn.Embedding):
|
291 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
292 |
+
if module.padding_idx is not None:
|
293 |
+
module.weight.data[module.padding_idx].zero_()
|
294 |
+
|
295 |
+
|
296 |
+
def is_empty(images_list: Optional[List[List[torch.Tensor]]]):
|
297 |
+
if images_list is None or len(images_list) == 0:
|
298 |
+
return True
|
299 |
+
for image_list in images_list:
|
300 |
+
if len(image_list):
|
301 |
+
return False
|
302 |
+
return True
|
303 |
+
|
304 |
+
|
305 |
+
def build_position_ids(x: "torch.BoolTensor(B, L)", attention_mask: Optional["torch.BoolTensor(B, L)"] = None) -> "torch.LongTensor(B, L)":
|
306 |
+
if attention_mask is not None:
|
307 |
+
tmp = x.clone()
|
308 |
+
tmp[~(attention_mask.bool())] = -1
|
309 |
+
else:
|
310 |
+
tmp = x.clone()
|
311 |
+
# image boi eoi token as LANGUAGE_TOKEN_TYPE
|
312 |
+
is_boi_eoi = torch.zeros_like(x, dtype=torch.bool)
|
313 |
+
is_boi_eoi[:, 1:] |= (tmp[:, 1:] == VISION_TOKEN_TYPE) & (tmp[:, :-1] == LANGUAGE_TOKEN_TYPE)
|
314 |
+
is_boi_eoi[:, 0] |= (tmp[:, 0] == VISION_TOKEN_TYPE)
|
315 |
+
is_boi_eoi[:, :-1] |= (tmp[:, :-1] == VISION_TOKEN_TYPE) & (tmp[:, 1:] == LANGUAGE_TOKEN_TYPE)
|
316 |
+
is_boi_eoi[:, -1] |= (tmp[:, -1] == VISION_TOKEN_TYPE)
|
317 |
+
tmp[is_boi_eoi] = LANGUAGE_TOKEN_TYPE
|
318 |
+
# final position ids
|
319 |
+
y = torch.zeros_like(x, dtype=torch.long)
|
320 |
+
y[:, 1:] = (tmp[:, 1:] == LANGUAGE_TOKEN_TYPE) | ((tmp[:, 1:] == VISION_TOKEN_TYPE) & (tmp[:, :-1] == LANGUAGE_TOKEN_TYPE))
|
321 |
+
y = y.cumsum(dim=-1)
|
322 |
+
return y
|
323 |
+
|
324 |
+
|
325 |
+
class CogVLMModel(CogVLMPreTrainedModel):
|
326 |
+
def __init__(self, config):
|
327 |
+
super().__init__(config)
|
328 |
+
self.padding_idx = config.pad_token_id
|
329 |
+
self.vocab_size = config.vocab_size
|
330 |
+
|
331 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
332 |
+
self.layers = nn.ModuleList([CogVLMDecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
333 |
+
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
334 |
+
|
335 |
+
self.vision = EVA2CLIPModel(config)
|
336 |
+
|
337 |
+
self.gradient_checkpointing = False
|
338 |
+
# Initialize weights and apply final processing
|
339 |
+
self.post_init()
|
340 |
+
|
341 |
+
def encode_images(self, images: List[List[torch.Tensor]]) -> torch.Tensor:
|
342 |
+
images_list, images = images, []
|
343 |
+
|
344 |
+
images = []
|
345 |
+
for image_list in images_list:
|
346 |
+
for image in image_list:
|
347 |
+
images.append(image)
|
348 |
+
|
349 |
+
images = torch.stack(images)
|
350 |
+
images_features = self.vision(images)
|
351 |
+
return images_features
|
352 |
+
|
353 |
+
def forward(
|
354 |
+
self,
|
355 |
+
input_ids: torch.LongTensor = None,
|
356 |
+
images: List[List[torch.Tensor]] = None,
|
357 |
+
token_type_ids: Optional[torch.LongTensor] = None,
|
358 |
+
attention_mask: Optional[torch.Tensor] = None,
|
359 |
+
position_ids: Optional[torch.LongTensor] = None,
|
360 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
361 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
362 |
+
use_cache: Optional[bool] = None,
|
363 |
+
output_attentions: Optional[bool] = None,
|
364 |
+
output_hidden_states: Optional[bool] = None,
|
365 |
+
return_dict: Optional[bool] = None,
|
366 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
367 |
+
"""take care of image_encode, token_type_ids, position_ids and (attention_mask = None is fine)"""
|
368 |
+
|
369 |
+
if past_key_values is not None:
|
370 |
+
pass # generate mode with past_key_values. the image features are already mapped
|
371 |
+
else:
|
372 |
+
# not allow for inputs_embeds, because we want to process image feature
|
373 |
+
assert input_ids is not None and inputs_embeds is None, f"{input_ids} {inputs_embeds}"
|
374 |
+
if not is_empty(images): # multi-modality
|
375 |
+
assert token_type_ids is not None, f"multi-modality requires `token_type_ids`!"
|
376 |
+
assert len(input_ids) == len(images), f"{len(input_ids)} {len(images)}"
|
377 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
378 |
+
images_features = self.encode_images(images)
|
379 |
+
images_features = rearrange(images_features, 'b n d -> (b n) d')
|
380 |
+
images_features = images_features.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)
|
381 |
+
inputs_embeds = inputs_embeds.index_put([token_type_ids == VISION_TOKEN_TYPE], images_features)
|
382 |
+
else: # single-modality
|
383 |
+
if token_type_ids is None:
|
384 |
+
token_type_ids = torch.ones_like(input_ids, dtype=torch.long, device=input_ids.device) * LANGUAGE_TOKEN_TYPE
|
385 |
+
assert not (token_type_ids == VISION_TOKEN_TYPE).any(), f"{(token_type_ids == VISION_TOKEN_TYPE).sum()}"
|
386 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
387 |
+
|
388 |
+
if position_ids is None:
|
389 |
+
position_ids = build_position_ids(token_type_ids, attention_mask)
|
390 |
+
input_ids = None
|
391 |
+
|
392 |
+
return self.llm_forward(
|
393 |
+
input_ids=input_ids,
|
394 |
+
token_type_ids=token_type_ids,
|
395 |
+
attention_mask=attention_mask,
|
396 |
+
position_ids=position_ids,
|
397 |
+
past_key_values=past_key_values,
|
398 |
+
inputs_embeds=inputs_embeds,
|
399 |
+
use_cache=use_cache,
|
400 |
+
output_attentions=output_attentions,
|
401 |
+
output_hidden_states=output_hidden_states,
|
402 |
+
return_dict=return_dict,
|
403 |
+
)
|
404 |
+
|
405 |
+
def llm_forward(
|
406 |
+
self,
|
407 |
+
input_ids: torch.LongTensor = None,
|
408 |
+
token_type_ids: torch.LongTensor = None,
|
409 |
+
attention_mask: Optional[torch.Tensor] = None,
|
410 |
+
position_ids: Optional[torch.LongTensor] = None,
|
411 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
412 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
413 |
+
use_cache: Optional[bool] = None,
|
414 |
+
output_attentions: Optional[bool] = None,
|
415 |
+
output_hidden_states: Optional[bool] = None,
|
416 |
+
return_dict: Optional[bool] = None,
|
417 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
418 |
+
"""largely copy from llama forward and adapt for cogvlm with `token_type_ids`"""
|
419 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
420 |
+
output_hidden_states = (
|
421 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
422 |
+
)
|
423 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
424 |
+
|
425 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
426 |
+
|
427 |
+
# retrieve input_ids and inputs_embeds
|
428 |
+
if input_ids is not None and inputs_embeds is not None:
|
429 |
+
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
430 |
+
elif input_ids is not None:
|
431 |
+
batch_size, seq_length = input_ids.shape
|
432 |
+
elif inputs_embeds is not None:
|
433 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
434 |
+
else:
|
435 |
+
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
436 |
+
|
437 |
+
seq_length_with_past = seq_length
|
438 |
+
past_key_values_length = 0
|
439 |
+
|
440 |
+
if past_key_values is not None:
|
441 |
+
past_key_values_length = past_key_values[0][0].shape[2]
|
442 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
443 |
+
|
444 |
+
if position_ids is None:
|
445 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
446 |
+
position_ids = torch.arange(
|
447 |
+
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
448 |
+
)
|
449 |
+
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
450 |
+
else:
|
451 |
+
position_ids = position_ids.view(-1, seq_length).long()
|
452 |
+
|
453 |
+
if inputs_embeds is None:
|
454 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
455 |
+
# embed positions
|
456 |
+
if attention_mask is None:
|
457 |
+
attention_mask = torch.ones(
|
458 |
+
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
|
459 |
+
)
|
460 |
+
attention_mask = self._prepare_decoder_attention_mask(
|
461 |
+
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
462 |
+
)
|
463 |
+
|
464 |
+
hidden_states = inputs_embeds
|
465 |
+
|
466 |
+
# decoder layers
|
467 |
+
all_hidden_states = () if output_hidden_states else None
|
468 |
+
all_self_attns = () if output_attentions else None
|
469 |
+
next_decoder_cache = () if use_cache else None
|
470 |
+
|
471 |
+
for idx, decoder_layer in enumerate(self.layers):
|
472 |
+
if output_hidden_states:
|
473 |
+
all_hidden_states += (hidden_states,)
|
474 |
+
|
475 |
+
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
476 |
+
layer_outputs = decoder_layer(
|
477 |
+
hidden_states,
|
478 |
+
token_type_ids=token_type_ids,
|
479 |
+
attention_mask=attention_mask,
|
480 |
+
position_ids=position_ids,
|
481 |
+
past_key_value=past_key_value,
|
482 |
+
output_attentions=output_attentions,
|
483 |
+
use_cache=use_cache,
|
484 |
+
)
|
485 |
+
hidden_states = layer_outputs[0]
|
486 |
+
|
487 |
+
if use_cache:
|
488 |
+
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
489 |
+
|
490 |
+
if output_attentions:
|
491 |
+
all_self_attns += (layer_outputs[1],)
|
492 |
+
|
493 |
+
hidden_states = self.norm(hidden_states)
|
494 |
+
|
495 |
+
# add hidden states from the last decoder layer
|
496 |
+
if output_hidden_states:
|
497 |
+
all_hidden_states += (hidden_states,)
|
498 |
+
|
499 |
+
next_cache = next_decoder_cache if use_cache else None
|
500 |
+
if not return_dict:
|
501 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
502 |
+
return BaseModelOutputWithPast(
|
503 |
+
last_hidden_state=hidden_states,
|
504 |
+
past_key_values=next_cache,
|
505 |
+
hidden_states=all_hidden_states,
|
506 |
+
attentions=all_self_attns,
|
507 |
+
)
|
508 |
+
|
509 |
+
def get_input_embeddings(self):
|
510 |
+
return self.embed_tokens
|
511 |
+
|
512 |
+
def set_input_embeddings(self, value):
|
513 |
+
self.embed_tokens = value
|
514 |
+
|
515 |
+
# noinspection PyMethodMayBeStatic
|
516 |
+
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
517 |
+
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
518 |
+
# create causal mask
|
519 |
+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
520 |
+
combined_attention_mask = None
|
521 |
+
if input_shape[-1] > 1:
|
522 |
+
combined_attention_mask = _make_causal_mask(
|
523 |
+
input_shape,
|
524 |
+
inputs_embeds.dtype,
|
525 |
+
device=inputs_embeds.device,
|
526 |
+
past_key_values_length=past_key_values_length,
|
527 |
+
)
|
528 |
+
|
529 |
+
if attention_mask is not None:
|
530 |
+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
531 |
+
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
|
532 |
+
inputs_embeds.device
|
533 |
+
)
|
534 |
+
combined_attention_mask = (
|
535 |
+
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
536 |
+
)
|
537 |
+
|
538 |
+
return combined_attention_mask
|
539 |
+
|
540 |
+
|
541 |
+
def chat_history_to_prompt(history, query):
|
542 |
+
prompt = " [INST] "
|
543 |
+
for i, (old_query, response) in enumerate(history):
|
544 |
+
prompt += old_query + " [/INST] " + response + " [INST] "
|
545 |
+
prompt += query + " [/INST] "
|
546 |
+
return prompt
|
547 |
+
|
548 |
+
|
549 |
+
def base_history_to_prompt(history, query):
|
550 |
+
prompt = query
|
551 |
+
return prompt
|
552 |
+
|
553 |
+
|
554 |
+
_history_to_prompt = {
|
555 |
+
"base": base_history_to_prompt,
|
556 |
+
"chat": chat_history_to_prompt
|
557 |
+
}
|
558 |
+
|
559 |
+
|
560 |
+
class CogVLMForCausalLM(CogVLMPreTrainedModel):
|
561 |
+
_auto_class = "AutoModelForCausalLM"
|
562 |
+
|
563 |
+
def __init__(self, config):
|
564 |
+
super().__init__(config)
|
565 |
+
self.model = CogVLMModel(config)
|
566 |
+
self.vocab_size = config.vocab_size
|
567 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
568 |
+
|
569 |
+
# Initialize weights and apply final processing
|
570 |
+
self.post_init()
|
571 |
+
|
572 |
+
def get_input_embeddings(self):
|
573 |
+
return self.model.embed_tokens
|
574 |
+
|
575 |
+
def set_input_embeddings(self, value):
|
576 |
+
self.model.embed_tokens = value
|
577 |
+
|
578 |
+
def get_output_embeddings(self):
|
579 |
+
return self.lm_head
|
580 |
+
|
581 |
+
def set_output_embeddings(self, new_embeddings):
|
582 |
+
self.lm_head = new_embeddings
|
583 |
+
|
584 |
+
def set_decoder(self, decoder):
|
585 |
+
self.model = decoder
|
586 |
+
|
587 |
+
def get_decoder(self):
|
588 |
+
return self.model
|
589 |
+
|
590 |
+
def forward(
|
591 |
+
self,
|
592 |
+
input_ids: torch.LongTensor = None,
|
593 |
+
images: List[List[torch.Tensor]] = None,
|
594 |
+
token_type_ids: Optional[torch.LongTensor] = None,
|
595 |
+
attention_mask: Optional[torch.Tensor] = None,
|
596 |
+
position_ids: Optional[torch.LongTensor] = None,
|
597 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
598 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
599 |
+
use_cache: Optional[bool] = None,
|
600 |
+
output_attentions: Optional[bool] = None,
|
601 |
+
output_hidden_states: Optional[bool] = None,
|
602 |
+
return_dict: Optional[bool] = None,
|
603 |
+
labels: Optional[torch.LongTensor] = None,
|
604 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
605 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
606 |
+
output_hidden_states = (
|
607 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
608 |
+
)
|
609 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
610 |
+
|
611 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
612 |
+
outputs = self.model(
|
613 |
+
input_ids=input_ids,
|
614 |
+
images=images,
|
615 |
+
token_type_ids=token_type_ids,
|
616 |
+
attention_mask=attention_mask,
|
617 |
+
position_ids=position_ids,
|
618 |
+
past_key_values=past_key_values,
|
619 |
+
inputs_embeds=inputs_embeds,
|
620 |
+
use_cache=use_cache,
|
621 |
+
output_attentions=output_attentions,
|
622 |
+
output_hidden_states=output_hidden_states,
|
623 |
+
return_dict=return_dict,
|
624 |
+
)
|
625 |
+
|
626 |
+
hidden_states = outputs[0]
|
627 |
+
logits = self.lm_head(hidden_states)
|
628 |
+
logits = logits.float()
|
629 |
+
|
630 |
+
loss = None
|
631 |
+
if labels is not None:
|
632 |
+
# Shift so that tokens < n predict n
|
633 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
634 |
+
shift_labels = labels[..., 1:].contiguous()
|
635 |
+
# Flatten the tokens
|
636 |
+
loss_fct = CrossEntropyLoss()
|
637 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
638 |
+
shift_labels = shift_labels.view(-1)
|
639 |
+
# Enable model parallelism
|
640 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
641 |
+
loss = loss_fct(shift_logits, shift_labels)
|
642 |
+
|
643 |
+
if not return_dict:
|
644 |
+
output = (logits,) + outputs[1:]
|
645 |
+
return (loss,) + output if loss is not None else output
|
646 |
+
|
647 |
+
return CausalLMOutputWithPast(
|
648 |
+
loss=loss,
|
649 |
+
logits=logits,
|
650 |
+
past_key_values=outputs.past_key_values,
|
651 |
+
hidden_states=outputs.hidden_states,
|
652 |
+
attentions=outputs.attentions,
|
653 |
+
)
|
654 |
+
|
655 |
+
def _prepare_attention_mask_for_generation(
|
656 |
+
self,
|
657 |
+
inputs: torch.Tensor,
|
658 |
+
pad_token_id: Optional[int],
|
659 |
+
eos_token_id: Optional[Union[int, List[int]]],
|
660 |
+
) -> torch.LongTensor:
|
661 |
+
return torch.ones(inputs.shape[:2], dtype=torch.long, device=inputs.device) # type: ignore
|
662 |
+
|
663 |
+
def prepare_inputs_for_generation(
|
664 |
+
self, input_ids, token_type_ids, images=None, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
665 |
+
):
|
666 |
+
# build position_ids if needed
|
667 |
+
position_ids = kwargs.get("position_ids", None)
|
668 |
+
if position_ids is None:
|
669 |
+
position_ids = build_position_ids(token_type_ids, attention_mask)
|
670 |
+
|
671 |
+
if past_key_values:
|
672 |
+
input_ids = input_ids[:, -1:]
|
673 |
+
token_type_ids = token_type_ids[:, -1:]
|
674 |
+
position_ids = position_ids[:, -1:]
|
675 |
+
|
676 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
677 |
+
if inputs_embeds is not None and past_key_values is None:
|
678 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
679 |
+
else:
|
680 |
+
model_inputs = {"input_ids": input_ids}
|
681 |
+
|
682 |
+
model_inputs.update(
|
683 |
+
{
|
684 |
+
"token_type_ids": token_type_ids,
|
685 |
+
"images": images,
|
686 |
+
"position_ids": position_ids,
|
687 |
+
"past_key_values": past_key_values,
|
688 |
+
"use_cache": kwargs.get("use_cache"),
|
689 |
+
"attention_mask": attention_mask,
|
690 |
+
}
|
691 |
+
)
|
692 |
+
return model_inputs
|
693 |
+
|
694 |
+
def _update_model_kwargs_for_generation(
|
695 |
+
self,
|
696 |
+
outputs: "ModelOutput",
|
697 |
+
model_kwargs: Dict[str, Any],
|
698 |
+
is_encoder_decoder: bool = False,
|
699 |
+
standardize_cache_format: bool = False,
|
700 |
+
) -> Dict[str, Any]:
|
701 |
+
# update past_key_values
|
702 |
+
model_kwargs["past_key_values"] = self._extract_past_from_model_output(
|
703 |
+
outputs, standardize_cache_format=standardize_cache_format
|
704 |
+
)
|
705 |
+
if getattr(outputs, "state", None) is not None:
|
706 |
+
model_kwargs["state"] = outputs.state
|
707 |
+
|
708 |
+
# update token_type_ids with last value
|
709 |
+
if "token_type_ids" in model_kwargs:
|
710 |
+
token_type_ids = model_kwargs["token_type_ids"]
|
711 |
+
new_token_type_ids = torch.ones(size=(token_type_ids.shape[0], 1), dtype=token_type_ids.dtype, device=token_type_ids.device) * LANGUAGE_TOKEN_TYPE
|
712 |
+
model_kwargs["token_type_ids"] = torch.cat([token_type_ids, new_token_type_ids], dim=-1)
|
713 |
+
|
714 |
+
if not is_encoder_decoder:
|
715 |
+
# update attention mask
|
716 |
+
if "attention_mask" in model_kwargs:
|
717 |
+
attention_mask = model_kwargs["attention_mask"]
|
718 |
+
model_kwargs["attention_mask"] = torch.cat(
|
719 |
+
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
|
720 |
+
)
|
721 |
+
else:
|
722 |
+
# update decoder attention mask
|
723 |
+
if "decoder_attention_mask" in model_kwargs:
|
724 |
+
decoder_attention_mask = model_kwargs["decoder_attention_mask"]
|
725 |
+
model_kwargs["decoder_attention_mask"] = torch.cat(
|
726 |
+
[decoder_attention_mask, decoder_attention_mask.new_ones((decoder_attention_mask.shape[0], 1))],
|
727 |
+
dim=-1,
|
728 |
+
)
|
729 |
+
|
730 |
+
return model_kwargs
|
731 |
+
|
732 |
+
def _reorder_cache(self, past_key_values, beam_idx):
|
733 |
+
reordered_past = ()
|
734 |
+
for layer_past in past_key_values:
|
735 |
+
reordered_past += (
|
736 |
+
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
737 |
+
)
|
738 |
+
return reordered_past
|
739 |
+
|
740 |
+
def build_conversation_input_ids(
|
741 |
+
self,
|
742 |
+
tokenizer: "PreTrainedTokenizer",
|
743 |
+
*,
|
744 |
+
query: str,
|
745 |
+
history: Optional[List[Tuple[str, str]]] = None,
|
746 |
+
images: Optional[List["PIL.Image"]] = None,
|
747 |
+
template_version: Optional[Literal["base", "chat"]] = None,
|
748 |
+
):
|
749 |
+
image_size: int = self.config.vision_config['image_size']
|
750 |
+
patch_size: int = self.config.vision_config['patch_size']
|
751 |
+
template_version = template_version or self.config.template_version
|
752 |
+
assert images is None or len(images) <= 1, f"not support multi images by now."
|
753 |
+
history = history or []
|
754 |
+
text = _history_to_prompt[template_version](history, query)
|
755 |
+
|
756 |
+
input_ids = [tokenizer.bos_token_id]
|
757 |
+
token_type_ids = [LANGUAGE_TOKEN_TYPE]
|
758 |
+
if images is not None and len(images) == 1:
|
759 |
+
# vision
|
760 |
+
transform = transforms.Compose(
|
761 |
+
[
|
762 |
+
transforms.Resize(
|
763 |
+
(image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC
|
764 |
+
),
|
765 |
+
transforms.ToTensor(),
|
766 |
+
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
|
767 |
+
]
|
768 |
+
)
|
769 |
+
images = [transform(images[0])]
|
770 |
+
# language
|
771 |
+
vision_token_num = (image_size // patch_size) * (image_size // patch_size) + 2
|
772 |
+
input_ids += [tokenizer.pad_token_id] * vision_token_num
|
773 |
+
token_type_ids += [VISION_TOKEN_TYPE] * vision_token_num
|
774 |
+
text_ids = tokenizer.encode(text, add_special_tokens=False)
|
775 |
+
|
776 |
+
input_ids += text_ids
|
777 |
+
token_type_ids += [LANGUAGE_TOKEN_TYPE] * len(text_ids)
|
778 |
+
attention_mask = [1] * len(input_ids)
|
779 |
+
|
780 |
+
return {
|
781 |
+
'input_ids': torch.tensor(input_ids, dtype=torch.long),
|
782 |
+
'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),
|
783 |
+
'attention_mask': torch.tensor(attention_mask, dtype=torch.long),
|
784 |
+
'images': images,
|
785 |
+
}
|
util.py
ADDED
@@ -0,0 +1,483 @@
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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 |
+
from typing import Optional, Tuple, Union
|
2 |
+
|
3 |
+
import torch
|
4 |
+
from einops import rearrange, repeat
|
5 |
+
import torch.nn.functional as F
|
6 |
+
|
7 |
+
import triton
|
8 |
+
import triton.language as tl
|
9 |
+
|
10 |
+
|
11 |
+
# @triton.autotune(
|
12 |
+
# configs=[
|
13 |
+
# triton.Config({"BLOCK_M": 2}),
|
14 |
+
# triton.Config({"BLOCK_M": 4}),
|
15 |
+
# triton.Config({"BLOCK_M": 8}),
|
16 |
+
# triton.Config({"BLOCK_M": 16}),
|
17 |
+
# ],
|
18 |
+
# key=["CACHE_KEY_SEQLEN", "BLOCK_K", "INTERLEAVED"],
|
19 |
+
# )
|
20 |
+
@triton.jit
|
21 |
+
def rotary_kernel(
|
22 |
+
OUT, # Pointers to matrices
|
23 |
+
X,
|
24 |
+
COS,
|
25 |
+
SIN,
|
26 |
+
CU_SEQLENS,
|
27 |
+
SEQLEN_OFFSETS, # this could be int or a pointer
|
28 |
+
# Matrix dimensions
|
29 |
+
seqlen,
|
30 |
+
nheads,
|
31 |
+
rotary_dim,
|
32 |
+
seqlen_ro,
|
33 |
+
CACHE_KEY_SEQLEN,
|
34 |
+
# strides
|
35 |
+
stride_out_batch,
|
36 |
+
stride_out_nheads,
|
37 |
+
stride_out_seqlen,
|
38 |
+
stride_out_headdim,
|
39 |
+
stride_x_batch,
|
40 |
+
stride_x_nheads,
|
41 |
+
stride_x_seqlen,
|
42 |
+
stride_x_headdim,
|
43 |
+
# Meta-parameters
|
44 |
+
BLOCK_K: tl.constexpr,
|
45 |
+
IS_SEQLEN_OFFSETS_TENSOR: tl.constexpr,
|
46 |
+
IS_VARLEN: tl.constexpr,
|
47 |
+
INTERLEAVED: tl.constexpr,
|
48 |
+
CONJUGATE: tl.constexpr,
|
49 |
+
BLOCK_M: tl.constexpr,
|
50 |
+
):
|
51 |
+
pid_m = tl.program_id(axis=0)
|
52 |
+
pid_batch = tl.program_id(axis=1)
|
53 |
+
pid_head = tl.program_id(axis=2)
|
54 |
+
rotary_dim_half = rotary_dim // 2
|
55 |
+
|
56 |
+
if not IS_VARLEN:
|
57 |
+
X = X + pid_batch * stride_x_batch + pid_head * stride_x_nheads
|
58 |
+
OUT = OUT + pid_batch * stride_out_batch + pid_head * stride_out_nheads
|
59 |
+
COS = COS + pid_batch * seqlen_ro * rotary_dim_half
|
60 |
+
SIN = SIN + pid_batch * seqlen_ro * rotary_dim_half
|
61 |
+
else:
|
62 |
+
start_idx = tl.load(CU_SEQLENS + pid_batch)
|
63 |
+
seqlen = tl.load(CU_SEQLENS + pid_batch + 1) - start_idx
|
64 |
+
X = X + start_idx * stride_x_seqlen + pid_head * stride_x_nheads
|
65 |
+
OUT = OUT + start_idx * stride_out_seqlen + pid_head * stride_out_nheads
|
66 |
+
|
67 |
+
if pid_m * BLOCK_M >= seqlen:
|
68 |
+
return
|
69 |
+
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
|
70 |
+
if not IS_SEQLEN_OFFSETS_TENSOR:
|
71 |
+
rm_cs = rm + SEQLEN_OFFSETS
|
72 |
+
else:
|
73 |
+
rm_cs = rm + tl.load(SEQLEN_OFFSETS + pid_batch)
|
74 |
+
rk = tl.arange(0, BLOCK_K)
|
75 |
+
rk_half = tl.arange(0, BLOCK_K // 2)
|
76 |
+
|
77 |
+
if not INTERLEAVED:
|
78 |
+
# Load the 1st and 2nd halves of X, do calculation, then store to 1st and 2nd halves of OUT
|
79 |
+
X = X + (rm[:, None] * stride_x_seqlen + rk_half[None, :] * stride_x_headdim)
|
80 |
+
COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :])
|
81 |
+
SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :])
|
82 |
+
cos = tl.load(
|
83 |
+
COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=1.0
|
84 |
+
)
|
85 |
+
sin = tl.load(
|
86 |
+
SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=0.0
|
87 |
+
)
|
88 |
+
x0 = tl.load(
|
89 |
+
X, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0
|
90 |
+
)
|
91 |
+
x1 = tl.load(
|
92 |
+
X + rotary_dim_half * stride_x_headdim,
|
93 |
+
mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half),
|
94 |
+
other=0.0,
|
95 |
+
)
|
96 |
+
if CONJUGATE:
|
97 |
+
sin = -sin
|
98 |
+
o0 = x0 * cos - x1 * sin
|
99 |
+
o1 = x0 * sin + x1 * cos
|
100 |
+
# write back result
|
101 |
+
OUT = OUT + (rm[:, None] * stride_out_seqlen + rk_half[None, :] * stride_out_headdim)
|
102 |
+
tl.store(OUT, o0, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half))
|
103 |
+
tl.store(
|
104 |
+
OUT + rotary_dim_half * stride_out_headdim,
|
105 |
+
o1,
|
106 |
+
mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half),
|
107 |
+
)
|
108 |
+
else:
|
109 |
+
# We don't want to load X[0, 2, 4, ...] and X[1, 3, 5, ...] separately since both are slow.
|
110 |
+
# Instead, we load x0 = X[0, 1, 2, 3, ...] and x1 = X[1, 0, 3, 2, ...].
|
111 |
+
# Loading x0 will be fast but x1 will be slow.
|
112 |
+
# Then we load cos = COS[0, 0, 1, 1, ...] and sin = SIN[0, 0, 1, 1, ...].
|
113 |
+
# Then we do the calculation and use tl.where to pick put the right outputs for the even
|
114 |
+
# and for the odd indices.
|
115 |
+
rk_swap = rk + ((rk + 1) % 2) * 2 - 1 # 1, 0, 3, 2, 5, 4, ...
|
116 |
+
rk_repeat = tl.arange(0, BLOCK_K) // 2
|
117 |
+
X0 = X + (rm[:, None] * stride_x_seqlen + rk[None, :] * stride_x_headdim)
|
118 |
+
X1 = X + (rm[:, None] * stride_x_seqlen + rk_swap[None, :] * stride_x_headdim)
|
119 |
+
COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :])
|
120 |
+
SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :])
|
121 |
+
cos = tl.load(
|
122 |
+
COS,
|
123 |
+
mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half),
|
124 |
+
other=1.0,
|
125 |
+
).to(tl.float32)
|
126 |
+
sin = tl.load(
|
127 |
+
SIN,
|
128 |
+
mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half),
|
129 |
+
other=0.0,
|
130 |
+
).to(tl.float32)
|
131 |
+
x0 = tl.load(X0, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim), other=0.0).to(
|
132 |
+
tl.float32
|
133 |
+
)
|
134 |
+
x1 = tl.load(
|
135 |
+
X1, mask=(rm[:, None] < seqlen) & (rk_swap[None, :] < rotary_dim), other=0.0
|
136 |
+
).to(tl.float32)
|
137 |
+
if CONJUGATE:
|
138 |
+
sin = -sin
|
139 |
+
x0_cos = x0 * cos
|
140 |
+
x1_sin = x1 * sin
|
141 |
+
out = tl.where(rk[None, :] % 2 == 0, x0_cos - x1_sin, x0_cos + x1_sin)
|
142 |
+
OUT = OUT + (rm[:, None] * stride_out_seqlen + rk[None, :] * stride_out_headdim)
|
143 |
+
tl.store(OUT, out, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim))
|
144 |
+
|
145 |
+
|
146 |
+
def apply_rotary(
|
147 |
+
x: torch.Tensor,
|
148 |
+
cos: torch.Tensor,
|
149 |
+
sin: torch.Tensor,
|
150 |
+
seqlen_offsets: Union[int, torch.Tensor] = 0,
|
151 |
+
cu_seqlens: Optional[torch.Tensor] = None,
|
152 |
+
max_seqlen: Optional[int] = None,
|
153 |
+
interleaved=False,
|
154 |
+
inplace=False,
|
155 |
+
conjugate=False,
|
156 |
+
) -> torch.Tensor:
|
157 |
+
"""
|
158 |
+
Arguments:
|
159 |
+
x: (batch, seqlen, nheads, headdim) if cu_seqlens is None
|
160 |
+
else (total_seqlen, nheads, headdim).
|
161 |
+
cos: (seqlen_ro, rotary_dim / 2)
|
162 |
+
sin: (seqlen_ro, rotary_dim / 2)
|
163 |
+
seqlen_offsets: integer or integer tensor of size (batch,)
|
164 |
+
cu_seqlens: (batch + 1,) or None
|
165 |
+
max_seqlen: int
|
166 |
+
Returns:
|
167 |
+
y: (batch, seqlen, nheads, headdim)
|
168 |
+
"""
|
169 |
+
|
170 |
+
batch, nheads, seqlen, headdim = x.shape
|
171 |
+
|
172 |
+
batch_ro, seqlen_ro, rotary_dim = cos.shape
|
173 |
+
|
174 |
+
assert batch == batch_ro
|
175 |
+
assert sin.shape == cos.shape
|
176 |
+
rotary_dim *= 2
|
177 |
+
assert rotary_dim <= headdim, "rotary_dim must be <= headdim"
|
178 |
+
assert headdim <= 256, "Only support headdim <= 256"
|
179 |
+
|
180 |
+
assert seqlen_ro >= seqlen, "seqlen_ro must be >= seqlen"
|
181 |
+
|
182 |
+
assert (
|
183 |
+
cos.dtype == sin.dtype
|
184 |
+
), f"cos and sin must have the same dtype, got {cos.dtype} and {sin.dtype}"
|
185 |
+
assert (
|
186 |
+
x.dtype == cos.dtype
|
187 |
+
), f"Input and cos/sin must have the same dtype, got {x.dtype} and {cos.dtype}"
|
188 |
+
|
189 |
+
cos, sin = cos.contiguous(), sin.contiguous()
|
190 |
+
if isinstance(seqlen_offsets, torch.Tensor):
|
191 |
+
assert seqlen_offsets.shape == (batch,)
|
192 |
+
assert seqlen_offsets.dtype in [torch.int32, torch.int64]
|
193 |
+
seqlen_offsets = seqlen_offsets.contiguous()
|
194 |
+
else:
|
195 |
+
assert seqlen_offsets + seqlen <= seqlen_ro
|
196 |
+
|
197 |
+
output = torch.empty_like(x) if not inplace else x
|
198 |
+
if rotary_dim < headdim and not inplace:
|
199 |
+
output[..., rotary_dim:].copy_(x[..., rotary_dim:])
|
200 |
+
|
201 |
+
BLOCK_K = (
|
202 |
+
32
|
203 |
+
if rotary_dim <= 32
|
204 |
+
else (64 if rotary_dim <= 64 else (128 if rotary_dim <= 128 else 256))
|
205 |
+
)
|
206 |
+
grid = lambda META: (triton.cdiv(seqlen, META["BLOCK_M"]), batch, nheads) # noqa
|
207 |
+
BLOCK_M = 4 if interleaved else (8 if rotary_dim <= 64 else 4)
|
208 |
+
|
209 |
+
# Need this, otherwise Triton tries to launch from cuda:0 and we get
|
210 |
+
# ValueError: Pointer argument (at 0) cannot be accessed from Triton (cpu tensor?)
|
211 |
+
with torch.cuda.device(x.device.index):
|
212 |
+
rotary_kernel[grid](
|
213 |
+
output, # data ptrs
|
214 |
+
x,
|
215 |
+
cos,
|
216 |
+
sin,
|
217 |
+
cu_seqlens,
|
218 |
+
seqlen_offsets,
|
219 |
+
seqlen, # shapes
|
220 |
+
nheads,
|
221 |
+
rotary_dim,
|
222 |
+
seqlen_ro,
|
223 |
+
seqlen // 128, # key for triton cache (limit number of compilations)
|
224 |
+
output.stride(0), # batch_strides
|
225 |
+
output.stride(-3), # nheads_stride
|
226 |
+
output.stride(-2), # seqlen_stride
|
227 |
+
output.stride(-1), # headdim_stride
|
228 |
+
x.stride(0), # batch_strides
|
229 |
+
x.stride(-3), # nheads stride
|
230 |
+
x.stride(-2), # seqlen stride
|
231 |
+
x.stride(-1), # headdim stride
|
232 |
+
BLOCK_K,
|
233 |
+
isinstance(seqlen_offsets, torch.Tensor),
|
234 |
+
False,
|
235 |
+
interleaved,
|
236 |
+
conjugate,
|
237 |
+
BLOCK_M,
|
238 |
+
)
|
239 |
+
return output
|
240 |
+
|
241 |
+
|
242 |
+
class ApplyRotaryEmb(torch.autograd.Function):
|
243 |
+
@staticmethod
|
244 |
+
def forward(
|
245 |
+
ctx,
|
246 |
+
x,
|
247 |
+
cos,
|
248 |
+
sin,
|
249 |
+
interleaved=False,
|
250 |
+
inplace=False,
|
251 |
+
seqlen_offsets: Union[int, torch.Tensor] = 0,
|
252 |
+
cu_seqlens: Optional[torch.Tensor] = None,
|
253 |
+
max_seqlen: Optional[int] = None,
|
254 |
+
):
|
255 |
+
out = apply_rotary(
|
256 |
+
x,
|
257 |
+
cos,
|
258 |
+
sin,
|
259 |
+
seqlen_offsets=seqlen_offsets,
|
260 |
+
cu_seqlens=cu_seqlens,
|
261 |
+
max_seqlen=max_seqlen,
|
262 |
+
interleaved=interleaved,
|
263 |
+
inplace=inplace,
|
264 |
+
)
|
265 |
+
if isinstance(seqlen_offsets, int):
|
266 |
+
ctx.save_for_backward(cos, sin, cu_seqlens) # Can't save int with save_for_backward
|
267 |
+
ctx.seqlen_offsets = seqlen_offsets
|
268 |
+
else:
|
269 |
+
ctx.save_for_backward(cos, sin, cu_seqlens, seqlen_offsets)
|
270 |
+
ctx.seqlen_offsets = None
|
271 |
+
ctx.interleaved = interleaved
|
272 |
+
ctx.inplace = inplace
|
273 |
+
ctx.max_seqlen = max_seqlen
|
274 |
+
return out if not inplace else x
|
275 |
+
|
276 |
+
@staticmethod
|
277 |
+
def backward(ctx, do):
|
278 |
+
seqlen_offsets = ctx.seqlen_offsets
|
279 |
+
if seqlen_offsets is None:
|
280 |
+
cos, sin, cu_seqlens, seqlen_offsets = ctx.saved_tensors
|
281 |
+
else:
|
282 |
+
cos, sin, cu_seqlens = ctx.saved_tensors
|
283 |
+
# TD [2023-09-02]: For some reason Triton (2.0.0.post1) errors with
|
284 |
+
# "[CUDA]: invalid device context", and cloning makes it work. Idk why. Triton 2.1.0 works.
|
285 |
+
if not ctx.interleaved and not ctx.inplace:
|
286 |
+
do = do.clone()
|
287 |
+
dx = apply_rotary(
|
288 |
+
do,
|
289 |
+
cos,
|
290 |
+
sin,
|
291 |
+
seqlen_offsets=seqlen_offsets,
|
292 |
+
cu_seqlens=cu_seqlens,
|
293 |
+
max_seqlen=ctx.max_seqlen,
|
294 |
+
interleaved=ctx.interleaved,
|
295 |
+
inplace=ctx.inplace,
|
296 |
+
conjugate=True,
|
297 |
+
)
|
298 |
+
return dx, None, None, None, None, None, None, None
|
299 |
+
|
300 |
+
|
301 |
+
def apply_rotary_emb(
|
302 |
+
x,
|
303 |
+
cos,
|
304 |
+
sin,
|
305 |
+
interleaved=False,
|
306 |
+
inplace=False,
|
307 |
+
seqlen_offsets: Union[int, torch.Tensor] = 0,
|
308 |
+
cu_seqlens: Optional[torch.Tensor] = None,
|
309 |
+
max_seqlen: Optional[int] = None,
|
310 |
+
):
|
311 |
+
"""
|
312 |
+
Arguments:
|
313 |
+
x: (batch_size, seqlen, nheads, headdim) if cu_seqlens is None
|
314 |
+
else (total_seqlen, nheads, headdim)
|
315 |
+
cos, sin: (seqlen_rotary, rotary_dim / 2)
|
316 |
+
interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
|
317 |
+
of 1st half and 2nd half (GPT-NeoX style).
|
318 |
+
inplace: if True, apply rotary embedding in-place.
|
319 |
+
seqlen_offsets: (batch_size,) or int. Each sequence in x is shifted by this amount.
|
320 |
+
Most commonly used in inference when we have KV cache.
|
321 |
+
cu_seqlens: (batch + 1,) or None
|
322 |
+
max_seqlen: int
|
323 |
+
Return:
|
324 |
+
out: (batch_size, seqlen, nheads, headdim) if cu_seqlens is None
|
325 |
+
else (total_seqlen, nheads, headdim)
|
326 |
+
rotary_dim must be <= headdim
|
327 |
+
Apply rotary embedding to the first rotary_dim of x.
|
328 |
+
"""
|
329 |
+
return ApplyRotaryEmb.apply(
|
330 |
+
x, cos, sin, interleaved, inplace, seqlen_offsets, cu_seqlens, max_seqlen
|
331 |
+
)
|
332 |
+
|
333 |
+
|
334 |
+
# For backward compatibility
|
335 |
+
apply_rotary_emb_func = apply_rotary_emb
|
336 |
+
|
337 |
+
|
338 |
+
class FastRotaryEmbedding(torch.nn.Module):
|
339 |
+
"""
|
340 |
+
The rotary position embeddings from RoFormer_ (Su et. al).
|
341 |
+
A crucial insight from the method is that the query and keys are
|
342 |
+
transformed by rotation matrices which depend on the relative positions.
|
343 |
+
|
344 |
+
Other implementations are available in the Rotary Transformer repo_ and in
|
345 |
+
GPT-NeoX_, GPT-NeoX was an inspiration
|
346 |
+
|
347 |
+
.. _RoFormer: https://arxiv.org/abs/2104.09864
|
348 |
+
.. _repo: https://github.com/ZhuiyiTechnology/roformer
|
349 |
+
.. _GPT-NeoX: https://github.com/EleutherAI/gpt-neox
|
350 |
+
|
351 |
+
If scale_base is not None, this implements XPos (Sun et al., https://arxiv.org/abs/2212.10554).
|
352 |
+
A recommended value for scale_base is 512: https://github.com/HazyResearch/flash-attention/issues/96
|
353 |
+
Reference: https://github.com/sunyt32/torchscale/blob/main/torchscale/component/xpos_relative_position.py
|
354 |
+
"""
|
355 |
+
|
356 |
+
def __init__(
|
357 |
+
self,
|
358 |
+
dim: int,
|
359 |
+
base=10000,
|
360 |
+
interleaved=False,
|
361 |
+
scale_base=None,
|
362 |
+
pos_idx_in_fp32=True,
|
363 |
+
device=None,
|
364 |
+
):
|
365 |
+
"""
|
366 |
+
interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
|
367 |
+
of 1st half and 2nd half (GPT-NeoX style).
|
368 |
+
pos_idx_in_fp32: if True, the position indices [0.0, ..., seqlen - 1] are in fp32,
|
369 |
+
otherwise they might be in lower precision.
|
370 |
+
This option was added because previously (before 2023-07-02), when we construct
|
371 |
+
the position indices, we use the dtype of self.inv_freq. In most cases this would
|
372 |
+
be fp32, but if the model is trained in pure bf16 (not mixed precision), then
|
373 |
+
self.inv_freq would be bf16, and the position indices are also in bf16.
|
374 |
+
Because of the limited precision of bf16 (e.g. 1995.0 is rounded to 2000.0), the
|
375 |
+
embeddings for some positions will coincide.
|
376 |
+
To maintain compatibility with models previously trained in pure bf16,
|
377 |
+
we add this option.
|
378 |
+
"""
|
379 |
+
super().__init__()
|
380 |
+
self.dim = dim
|
381 |
+
self.base = base
|
382 |
+
self.pos_idx_in_fp32 = pos_idx_in_fp32
|
383 |
+
# Generate and save the inverse frequency buffer (non trainable)
|
384 |
+
inv_freq = self._compute_inv_freq(device)
|
385 |
+
self.register_buffer("inv_freq", inv_freq)
|
386 |
+
self.interleaved = interleaved
|
387 |
+
self.scale_base = scale_base
|
388 |
+
scale = (
|
389 |
+
(torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
|
390 |
+
if scale_base is not None
|
391 |
+
else None
|
392 |
+
)
|
393 |
+
self.register_buffer("scale", scale, persistent=False)
|
394 |
+
|
395 |
+
self._seq_len_cached = 0
|
396 |
+
self._cos_cached = None
|
397 |
+
self._sin_cached = None
|
398 |
+
self._cos_k_cached = None
|
399 |
+
self._sin_k_cached = None
|
400 |
+
self.cos = None
|
401 |
+
self.sin = None
|
402 |
+
|
403 |
+
def _compute_inv_freq(self, device=None):
|
404 |
+
return 1.0 / (
|
405 |
+
self.base
|
406 |
+
** (torch.arange(0, self.dim, 2, device=device) / self.dim)
|
407 |
+
# ** (torch.arange(0, self.dim, 2, device=device).float() / self.dim)
|
408 |
+
)
|
409 |
+
|
410 |
+
def _update_cos_sin_cache(self, seqlen, position_id, device=None, dtype=None):
|
411 |
+
|
412 |
+
if (
|
413 |
+
seqlen > self._seq_len_cached
|
414 |
+
):
|
415 |
+
self._seq_len_cached = seqlen
|
416 |
+
# We want fp32 here, not self.inv_freq.dtype, since the model could be loaded in bf16
|
417 |
+
# And the output of arange can be quite large, so bf16 would lose a lot of precision.
|
418 |
+
# However, for compatibility reason, we add an option to use the dtype of self.inv_freq.
|
419 |
+
if self.pos_idx_in_fp32:
|
420 |
+
t = torch.arange(seqlen, device=device, dtype=torch.float32)
|
421 |
+
# We want fp32 here as well since inv_freq will be multiplied with t, and the output
|
422 |
+
# will be large. Having it in bf16 will lose a lot of precision and cause the
|
423 |
+
# cos & sin output to change significantly.
|
424 |
+
# We want to recompute self.inv_freq if it was not loaded in fp32
|
425 |
+
if self.inv_freq.dtype != torch.float32:
|
426 |
+
inv_freq = self._compute_inv_freq(device=device)
|
427 |
+
else:
|
428 |
+
inv_freq = self.inv_freq
|
429 |
+
else:
|
430 |
+
t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
|
431 |
+
inv_freq = self.inv_freq
|
432 |
+
freqs = torch.einsum("i,j->ij", t, inv_freq)
|
433 |
+
if self.scale is None:
|
434 |
+
self._cos_cached = torch.cos(freqs).to(dtype)
|
435 |
+
self._sin_cached = torch.sin(freqs).to(dtype)
|
436 |
+
|
437 |
+
else:
|
438 |
+
power = (
|
439 |
+
torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device)
|
440 |
+
- seqlen // 2
|
441 |
+
) / self.scale_base
|
442 |
+
scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1")
|
443 |
+
# We want the multiplication by scale to happen in fp32
|
444 |
+
self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
|
445 |
+
self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
|
446 |
+
self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
|
447 |
+
self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
|
448 |
+
|
449 |
+
def forward(
|
450 |
+
self,
|
451 |
+
q: torch.Tensor,
|
452 |
+
k: torch.Tensor,
|
453 |
+
position_ids: torch.Tensor,
|
454 |
+
max_seqlen,
|
455 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
456 |
+
"""
|
457 |
+
q: (batch, nheads, seqlen, headdim)
|
458 |
+
k: (batch, nheads, seqlen, headdim)
|
459 |
+
position_id: (batch, seqlen)
|
460 |
+
max_seqlen: int
|
461 |
+
layer_id: int
|
462 |
+
only if layer_id == 0, then update cons and sin
|
463 |
+
Apply rotary embedding *inplace* to q k.
|
464 |
+
"""
|
465 |
+
|
466 |
+
self._update_cos_sin_cache(max_seqlen, position_ids, device=q.device, dtype=q.dtype)
|
467 |
+
cos, sin = F.embedding(position_ids, self._cos_cached), F.embedding(position_ids, self._sin_cached)
|
468 |
+
|
469 |
+
q = apply_rotary_emb_func(
|
470 |
+
q,
|
471 |
+
cos,
|
472 |
+
sin,
|
473 |
+
interleaved=self.interleaved,
|
474 |
+
inplace=True
|
475 |
+
)
|
476 |
+
k = apply_rotary_emb_func(
|
477 |
+
k,
|
478 |
+
cos,
|
479 |
+
sin,
|
480 |
+
interleaved=self.interleaved,
|
481 |
+
inplace=True
|
482 |
+
)
|
483 |
+
return q, k
|
visual.py
ADDED
@@ -0,0 +1,135 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
import torch
|
2 |
+
from torch import nn
|
3 |
+
from argparse import Namespace
|
4 |
+
import xformers.ops as xops
|
5 |
+
from transformers.activations import ACT2FN
|
6 |
+
|
7 |
+
|
8 |
+
class PatchEmbedding(nn.Module):
|
9 |
+
def __init__(self, config):
|
10 |
+
super().__init__()
|
11 |
+
self.proj = nn.Conv2d(config.in_channels, config.hidden_size, kernel_size=config.patch_size, stride=config.patch_size)
|
12 |
+
self.cls_embedding = nn.Parameter(torch.zeros(1, config.hidden_size))
|
13 |
+
self.position_embedding = nn.Embedding(config.num_positions, config.hidden_size)
|
14 |
+
|
15 |
+
def forward(self, images: "tensor(B, C, H, W)") -> "tensor(B, L, D)":
|
16 |
+
x = self.proj(images)
|
17 |
+
x = x.flatten(2).transpose(1, 2)
|
18 |
+
cls_token = self.cls_embedding.expand(x.shape[0], -1, -1)
|
19 |
+
x = torch.cat((cls_token, x), dim=1)
|
20 |
+
x += self.position_embedding.weight.unsqueeze(0)
|
21 |
+
return x
|
22 |
+
|
23 |
+
|
24 |
+
class Attention(nn.Module):
|
25 |
+
def __init__(self, config):
|
26 |
+
super().__init__()
|
27 |
+
self.num_heads = config.num_heads
|
28 |
+
head_dim = config.hidden_size // config.num_heads
|
29 |
+
self.scale = head_dim ** -0.5
|
30 |
+
self.query_key_value = nn.Linear(config.hidden_size, config.hidden_size * 3)
|
31 |
+
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
32 |
+
self.output_dropout = torch.nn.Dropout(config.dropout_prob)
|
33 |
+
|
34 |
+
def forward(self, x: "tensor(B, L, D)") -> "tensor(B, L, D)":
|
35 |
+
B, L, _ = x.shape
|
36 |
+
qkv = self.query_key_value(x)
|
37 |
+
qkv = qkv.reshape(B, L, 3, self.num_heads, -1).permute(2, 0, 1, 3, 4) # 3, B, L, H, D
|
38 |
+
q, k, v = qkv[0], qkv[1], qkv[2]
|
39 |
+
|
40 |
+
out = xops.memory_efficient_attention(
|
41 |
+
q, k, v, scale=self.scale,
|
42 |
+
)
|
43 |
+
output = self.dense(out.view(B, L, -1))
|
44 |
+
output = self.output_dropout(output)
|
45 |
+
return output
|
46 |
+
|
47 |
+
def attention(self, q, k, v):
|
48 |
+
attn_weights = torch.matmul(q * self.scale, k.transpose(-2, -1))
|
49 |
+
attn_weights = attn_weights.softmax(dim=-1)
|
50 |
+
output = torch.matmul(attn_weights, v)
|
51 |
+
return output
|
52 |
+
|
53 |
+
|
54 |
+
class MLP(nn.Module):
|
55 |
+
def __init__(self, config):
|
56 |
+
super().__init__()
|
57 |
+
self.config = config
|
58 |
+
self.activation_fn = ACT2FN[config.hidden_act]
|
59 |
+
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
|
60 |
+
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
|
61 |
+
|
62 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
63 |
+
x = self.fc1(x)
|
64 |
+
x = self.activation_fn(x)
|
65 |
+
x = self.fc2(x)
|
66 |
+
return x
|
67 |
+
|
68 |
+
|
69 |
+
class TransformerLayer(nn.Module):
|
70 |
+
def __init__(self, config):
|
71 |
+
super().__init__()
|
72 |
+
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
73 |
+
self.attention = Attention(config)
|
74 |
+
self.mlp = MLP(config)
|
75 |
+
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
76 |
+
|
77 |
+
def forward(self, hidden_states):
|
78 |
+
attention_input = hidden_states
|
79 |
+
attention_output = self.input_layernorm(self.attention(attention_input))
|
80 |
+
hidden_states = attention_input + attention_output
|
81 |
+
mlp_input = hidden_states
|
82 |
+
mlp_output = self.post_attention_layernorm(self.mlp(mlp_input))
|
83 |
+
output = mlp_input + mlp_output
|
84 |
+
return output
|
85 |
+
|
86 |
+
|
87 |
+
class Transformer(nn.Module):
|
88 |
+
def __init__(self, config):
|
89 |
+
super().__init__()
|
90 |
+
self.layers = nn.ModuleList([TransformerLayer(config) for _ in range(config.num_hidden_layers)])
|
91 |
+
|
92 |
+
def forward(self, hidden_states):
|
93 |
+
for layer_module in self.layers:
|
94 |
+
hidden_states = layer_module(hidden_states)
|
95 |
+
return hidden_states
|
96 |
+
|
97 |
+
|
98 |
+
class GLU(nn.Module):
|
99 |
+
def __init__(self, config, in_features):
|
100 |
+
super().__init__()
|
101 |
+
self.linear_proj = nn.Linear(in_features, config.hidden_size, bias=False)
|
102 |
+
self.norm1 = nn.LayerNorm(config.hidden_size)
|
103 |
+
self.act1 = nn.GELU()
|
104 |
+
self.act2 = nn.functional.silu
|
105 |
+
self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
|
106 |
+
self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
|
107 |
+
self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
|
108 |
+
|
109 |
+
def forward(self, x):
|
110 |
+
x = self.linear_proj(x)
|
111 |
+
x = self.act1(self.norm1(x))
|
112 |
+
x = self.act2(self.gate_proj(x)) * self.dense_h_to_4h(x)
|
113 |
+
x = self.dense_4h_to_h(x)
|
114 |
+
return x
|
115 |
+
|
116 |
+
|
117 |
+
class EVA2CLIPModel(nn.Module):
|
118 |
+
def __init__(self, config):
|
119 |
+
super().__init__()
|
120 |
+
vision_config = Namespace(**config.vision_config)
|
121 |
+
self.patch_embedding = PatchEmbedding(vision_config)
|
122 |
+
self.transformer = Transformer(vision_config)
|
123 |
+
self.linear_proj = GLU(config, in_features=vision_config.hidden_size)
|
124 |
+
self.boi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
|
125 |
+
self.eoi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
|
126 |
+
|
127 |
+
def forward(self, images: "tensor(B, C, H, W)") -> "tensor(B, L, D)":
|
128 |
+
x = self.patch_embedding(images)
|
129 |
+
x = self.transformer(x)
|
130 |
+
x = x[:, 1:]
|
131 |
+
x = self.linear_proj(x)
|
132 |
+
boi = self.boi.expand(x.shape[0], -1, -1)
|
133 |
+
eoi = self.eoi.expand(x.shape[0], -1, -1)
|
134 |
+
x = torch.cat((boi, x, eoi), dim=1)
|
135 |
+
return x
|