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Airbnb-CBv0.1-M-3epoch

Safetensors
molmo
custom_code
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  1. .gitattributes +1 -0
  2. added_tokens.json +428 -0
  3. config.json +33 -0
  4. config_molmo.py +60 -0
  5. generation_config.json +6 -0
  6. global_step412/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt +3 -0
  7. global_step412/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt +3 -0
  8. global_step412/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt +3 -0
  9. global_step412/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt +3 -0
  10. global_step412/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt +3 -0
  11. global_step412/bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt +3 -0
  12. global_step412/bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt +3 -0
  13. global_step412/bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt +3 -0
  14. global_step412/zero_pp_rank_0_mp_rank_00_model_states.pt +3 -0
  15. global_step412/zero_pp_rank_1_mp_rank_00_model_states.pt +3 -0
  16. global_step412/zero_pp_rank_2_mp_rank_00_model_states.pt +3 -0
  17. global_step412/zero_pp_rank_3_mp_rank_00_model_states.pt +3 -0
  18. global_step412/zero_pp_rank_4_mp_rank_00_model_states.pt +3 -0
  19. global_step412/zero_pp_rank_5_mp_rank_00_model_states.pt +3 -0
  20. global_step412/zero_pp_rank_6_mp_rank_00_model_states.pt +3 -0
  21. global_step412/zero_pp_rank_7_mp_rank_00_model_states.pt +3 -0
  22. image_preprocessing_molmo.py +546 -0
  23. latest +1 -0
  24. merges.txt +0 -0
  25. model-00001-of-00004.safetensors +3 -0
  26. model-00002-of-00004.safetensors +3 -0
  27. model-00003-of-00004.safetensors +3 -0
  28. model-00004-of-00004.safetensors +3 -0
  29. model.safetensors.index.json +592 -0
  30. modeling_molmo.py +2367 -0
  31. preprocessing_molmo.py +192 -0
  32. preprocessor_config.json +32 -0
  33. processor_config.json +6 -0
  34. rng_state_0.pth +3 -0
  35. rng_state_1.pth +3 -0
  36. rng_state_2.pth +3 -0
  37. rng_state_3.pth +3 -0
  38. rng_state_4.pth +3 -0
  39. rng_state_5.pth +3 -0
  40. rng_state_6.pth +3 -0
  41. rng_state_7.pth +3 -0
  42. sft_args.json +302 -0
  43. special_tokens_map.json +435 -0
  44. tokenizer.json +3 -0
  45. tokenizer_config.json +3853 -0
  46. trainer_state.json +890 -0
  47. training_args.bin +3 -0
  48. vocab.json +0 -0
  49. zero_to_fp32.py +760 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
added_tokens.json ADDED
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+ }
config.json ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "/root/.cache/huggingface/hub/models--pbarker--ComputerBase-v0.1-M-3epoch/snapshots/915dc4d8809028264506a79c6e71387c8a26aa35",
3
+ "architectures": [
4
+ "MolmoForCausalLM"
5
+ ],
6
+ "attention_layer_norm": false,
7
+ "auto_map": {
8
+ "AutoConfig": "config_molmo.MolmoConfig",
9
+ "AutoModelForCausalLM": "modeling_molmo.MolmoForCausalLM"
10
+ },
11
+ "clip_qkv": null,
12
+ "embedding_size": 152064,
13
+ "hidden_size": 3584,
14
+ "initializer_range": 0.02,
15
+ "intermediate_size": 37888,
16
+ "layer_norm_eps": 1e-06,
17
+ "layer_norm_type": "rms",
18
+ "max_position_embeddings": 4096,
19
+ "model_type": "molmo",
20
+ "norm_after": false,
21
+ "num_attention_heads": 28,
22
+ "num_hidden_layers": 28,
23
+ "num_key_value_heads": 4,
24
+ "qkv_bias": true,
25
+ "rope_theta": 1000000.0,
26
+ "tie_word_embeddings": false,
27
+ "torch_dtype": "bfloat16",
28
+ "transformers_version": "4.47.1",
29
+ "use_cache": true,
30
+ "use_position_ids": true,
31
+ "vocab_size": 152064,
32
+ "weight_tying": false
33
+ }
config_molmo.py ADDED
@@ -0,0 +1,60 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List
2
+
3
+ from transformers import PretrainedConfig, AutoTokenizer
4
+
5
+
6
+ class MolmoConfig(PretrainedConfig):
7
+ model_type = "molmo"
8
+ keys_to_ignore_at_inference = ["past_key_values"]
9
+
10
+ def __init__(
11
+ self,
12
+ vocab_size=50304,
13
+ embedding_size=50304,
14
+ hidden_size=4096,
15
+ intermediate_size=11008,
16
+ num_hidden_layers=32,
17
+ num_attention_heads=32,
18
+ num_key_value_heads=None,
19
+ max_position_embeddings=2048,
20
+ initializer_range=0.02,
21
+ use_cache=True,
22
+ layer_norm_eps: float = 1e-5,
23
+ rope_theta=10000.0,
24
+ clip_qkv=None,
25
+ qkv_bias: bool = False,
26
+ weight_tying: bool = False,
27
+ use_position_ids: bool=True,
28
+ tie_word_embeddings: bool=True,
29
+ attention_layer_norm: bool=False,
30
+ norm_after: bool = False,
31
+ layer_norm_type: str="rms",
32
+ **kwargs,
33
+ ):
34
+ self.vocab_size = vocab_size
35
+ self.embedding_size = embedding_size
36
+ self.max_position_embeddings = max_position_embeddings
37
+ self.hidden_size = hidden_size
38
+ self.intermediate_size = intermediate_size
39
+ self.num_hidden_layers = num_hidden_layers
40
+ self.num_attention_heads = num_attention_heads
41
+ self.layer_norm_eps = layer_norm_eps
42
+ self.weight_tying = weight_tying
43
+ self.use_position_ids = use_position_ids
44
+ self.attention_layer_norm = attention_layer_norm
45
+ self.num_key_value_heads = num_key_value_heads
46
+ self.initializer_range = initializer_range
47
+ self.use_cache = use_cache
48
+ self.rope_theta = rope_theta
49
+ self.clip_qkv = clip_qkv
50
+ self.qkv_bias = qkv_bias
51
+ self.norm_after = norm_after
52
+ self.tie_word_embeddings = tie_word_embeddings
53
+ self.layer_norm_type = layer_norm_type
54
+
55
+ super().__init__(
56
+ tie_word_embeddings=tie_word_embeddings,
57
+ **kwargs,
58
+ )
59
+
60
+ MolmoConfig.register_for_auto_class()
generation_config.json ADDED
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+ {
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+ "eos_token_id": 151643,
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+ "max_new_tokens": 2048,
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+ "pad_token_id": 151643,
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+ "transformers_version": "4.47.1"
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+ }
global_step412/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt ADDED
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global_step412/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt ADDED
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global_step412/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt ADDED
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global_step412/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt ADDED
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global_step412/bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt ADDED
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global_step412/bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt ADDED
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global_step412/bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt ADDED
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global_step412/zero_pp_rank_0_mp_rank_00_model_states.pt ADDED
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global_step412/zero_pp_rank_1_mp_rank_00_model_states.pt ADDED
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+ oid sha256:8354ea1e5ccefb48cdf18bd9006343f1cdcfb637ba5368efc1041079517464d2
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global_step412/zero_pp_rank_2_mp_rank_00_model_states.pt ADDED
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+ oid sha256:50627b8a21f8fbd0072a80f576737eace787095e07ac906ef57e33ba15a675d5
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global_step412/zero_pp_rank_3_mp_rank_00_model_states.pt ADDED
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global_step412/zero_pp_rank_4_mp_rank_00_model_states.pt ADDED
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+ oid sha256:61cadc80a73bb54a82b2d907853ec419a9565dbea3cdbe439d866d62a3827f6b
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global_step412/zero_pp_rank_5_mp_rank_00_model_states.pt ADDED
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+ oid sha256:9b8117239a0ed06722fc8df0ce89498835f1cffd9f08cb634b5cf58f6dfcbd9b
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+ size 328563
global_step412/zero_pp_rank_6_mp_rank_00_model_states.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:49bb6fa62d1627bd34748f03249b227ac633ce21cffa4b024d7f100b1f1a3a50
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+ size 328563
global_step412/zero_pp_rank_7_mp_rank_00_model_states.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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image_preprocessing_molmo.py ADDED
@@ -0,0 +1,546 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Image processor class for Molmo"""
2
+ from typing import List, Optional, Union, Mapping
3
+
4
+ import numpy as np
5
+ import einops
6
+ import torch
7
+ import torchvision.transforms
8
+ from torchvision.transforms import InterpolationMode
9
+ from torchvision.transforms.functional import convert_image_dtype
10
+
11
+ from transformers.image_utils import (
12
+ OPENAI_CLIP_MEAN,
13
+ OPENAI_CLIP_STD,
14
+ ImageInput,
15
+ is_valid_image,
16
+ )
17
+ from transformers.processing_utils import ImagesKwargs
18
+ from transformers.image_processing_utils import BaseImageProcessor
19
+ from transformers.utils import logging
20
+
21
+
22
+ logger = logging.get_logger(__name__)
23
+
24
+
25
+ def pad_to_bounding_box(
26
+ image, offset_height, offset_width, target_height,
27
+ target_width, value=0
28
+ ):
29
+ height, width = image.shape[:2]
30
+ after_padding_width = target_width - offset_width - width
31
+ after_padding_height = target_height - offset_height - height
32
+ return np.pad(image, [
33
+ [offset_height, after_padding_height],
34
+ [offset_width, after_padding_width],
35
+ [0, 0]
36
+ ], constant_values=value)
37
+
38
+
39
+ def normalize_image(image, offset, scale):
40
+ image -= np.array(offset, dtype=np.float32)[None, None, :]
41
+ image /= np.array(scale, dtype=np.float32)[None, None, :]
42
+ return image
43
+
44
+
45
+ def resize_and_pad(
46
+ image,
47
+ desired_output_size,
48
+ resize_method="torch-bilinear",
49
+ pad_value=0,
50
+ normalize=True,
51
+ image_mean=OPENAI_CLIP_MEAN,
52
+ image_std=OPENAI_CLIP_STD,
53
+ ):
54
+ desired_height, desired_width = desired_output_size
55
+ height, width = image.shape[:2]
56
+
57
+ # Cast into float32 since the training code did this in float32 and it (very rarely) effects
58
+ # the results after rounding.
59
+ image_scale_y = np.array(desired_height, np.float32) / np.array(height, np.float32)
60
+ image_scale_x = np.array(desired_width, np.float32) / np.array(width, np.float32)
61
+ image_scale = min(image_scale_x, image_scale_y)
62
+ scaled_height = int(np.array(height, np.float32) * image_scale)
63
+ scaled_width = int(np.array(width, np.float32) * image_scale)
64
+
65
+ if resize_method == "tensorflow":
66
+ # This how the original training code did resizing, it can produce slightly different
67
+ # results then using torch resize so we keep it just in case
68
+ import tensorflow as tf
69
+ image = tf.image.convert_image_dtype(tf.constant(image), dtype=tf.float32)
70
+ image = tf.image.resize(
71
+ image,
72
+ [scaled_height, scaled_width],
73
+ method=tf.image.ResizeMethod.BILINEAR,
74
+ antialias=True,
75
+ )
76
+ image = tf.clip_by_value(image, 0.0, 1.0)
77
+ image = image.numpy()
78
+ elif resize_method == "torch-bilinear":
79
+ image = torch.permute(torch.from_numpy(image), [2, 0, 1])
80
+ image = convert_image_dtype(image) # resize in float32 to match the training code
81
+ image = torchvision.transforms.Resize(
82
+ [scaled_height, scaled_width], InterpolationMode.BILINEAR, antialias=True
83
+ )(image)
84
+ image = torch.clip(image, 0.0, 1.0)
85
+ image = torch.permute(image, [1, 2, 0]).numpy()
86
+ else:
87
+ raise NotImplementedError(resize_method)
88
+
89
+ top_pad = (desired_height - scaled_height) // 2
90
+ left_pad = (desired_width - scaled_width) // 2
91
+ padding = [
92
+ [top_pad, desired_height - scaled_height - top_pad],
93
+ [left_pad, desired_width - scaled_width - left_pad],
94
+ [0, 0]
95
+ ]
96
+ image_mask = np.pad(np.ones_like(image[:, :, 0], dtype=bool), padding[:2])
97
+ image = np.pad(image, padding, constant_values=pad_value)
98
+ if normalize:
99
+ image = normalize_image(image, offset=image_mean, scale=image_std)
100
+ return image, image_mask
101
+
102
+
103
+ def select_tiling(h, w, patch_size, max_num_patches):
104
+ """Decide how best to divide in image of size [w, h] in up to max_num_patches of size patch_size"""
105
+ original_size = np.stack([h, w]) # [1, 2]
106
+ original_res = h * w
107
+ tilings = []
108
+ for i in range(1, max_num_patches+1):
109
+ for j in range(1, max_num_patches+1):
110
+ if i*j <= max_num_patches:
111
+ tilings.append((i, j))
112
+ # sort so argmin and argmax favour smaller tilings in the event of a tie
113
+ tilings.sort(key=lambda x: (x[0]*x[1], x[0]))
114
+ candidate_tilings = np.array(tilings, dtype=np.int32) # [n_resolutions, 2]
115
+ candidate_resolutions = candidate_tilings * patch_size # [n_resolutions, 2]
116
+
117
+ # How much we would need to scale the image to fit exactly in each tiling
118
+ original_size = np.stack([h, w], dtype=np.float32) # [1, 2]
119
+ required_scale_d = candidate_resolutions.astype(np.float32) / original_size
120
+ required_scale = np.min(required_scale_d, axis=-1, keepdims=True) # [n_resolutions, 1]
121
+ if np.all(required_scale < 1):
122
+ # We are forced to downscale, so try to minimize the amount of downscaling
123
+ ix = np.argmax(required_scale)
124
+ else:
125
+ # Pick the resolution that required the least upscaling so that it most closely fits the image
126
+ required_scale = np.where(required_scale < 1.0, 10e9, required_scale)
127
+ ix = np.argmin(required_scale)
128
+ return candidate_tilings[ix]
129
+
130
+
131
+ class MolmoImagesKwargs(ImagesKwargs, total=False):
132
+ max_crops: Optional[int]
133
+ overlap_margins: Optional[List[int]]
134
+ base_image_input_size: Optional[List[int]]
135
+ image_token_length_w: Optional[int]
136
+ image_token_length_h: Optional[int]
137
+ image_patch_size: Optional[int]
138
+ image_padding_mask: Optional[bool]
139
+
140
+
141
+ class MolmoImageProcessor(BaseImageProcessor):
142
+ """Preprocess images and multi-model inputs"""
143
+
144
+ def __init__(
145
+ self,
146
+ max_crops: int = 12,
147
+ overlap_margins: List[int] = (4, 4),
148
+ base_image_input_size: List[int] = (336, 336),
149
+ image_token_length_w: int = 12,
150
+ image_token_length_h: int = 12,
151
+ image_patch_size: int = 14,
152
+ image_padding_mask: bool = True,
153
+ do_normalize: bool = True,
154
+ image_mean: Optional[Union[float, List[float]]] = None,
155
+ image_std: Optional[Union[float, List[float]]] = None,
156
+ **kwargs,
157
+ ):
158
+ super().__init__(**kwargs)
159
+ self.max_crops = max_crops
160
+ self.overlap_margins = overlap_margins
161
+ self.base_image_input_size = base_image_input_size
162
+ self.image_token_length_w = image_token_length_w
163
+ self.image_token_length_h = image_token_length_h
164
+ self.image_patch_size = image_patch_size
165
+ self.image_padding_mask = image_padding_mask
166
+ self.do_normalize = do_normalize
167
+ self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
168
+ self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
169
+
170
+ def image_to_patches_and_tokens(
171
+ self,
172
+ image: ImageInput,
173
+ image_patch_token_id: int,
174
+ image_col_token_id: int,
175
+ image_start_token_id: int,
176
+ image_end_token_id: int,
177
+ max_crops: Optional[int] = None,
178
+ overlap_margins: Optional[List[int]] = None,
179
+ base_image_input_size: Optional[Union[int, List[int]]] = None,
180
+ image_token_length_w: Optional[int] = None,
181
+ image_token_length_h: Optional[int] = None,
182
+ image_patch_size: Optional[int] = None,
183
+ ):
184
+ if isinstance(base_image_input_size, int):
185
+ base_image_input_size = (base_image_input_size, base_image_input_size)
186
+
187
+ base_image_input_d = image_patch_size
188
+ tokens_per_image = image_token_length_w * image_token_length_h
189
+ image_base_patch_w = base_image_input_size[1] // base_image_input_d
190
+ image_base_patch_h = base_image_input_size[0] // base_image_input_d
191
+
192
+ original_image_h, original_image_w = image.shape[:2]
193
+ crop_size = base_image_input_size[0]
194
+
195
+ # Discard this many patches from the (left/top, right/bottom) of crops
196
+ left_margin, right_margin = overlap_margins
197
+ # left_margin, right_margin = 2, 2
198
+ assert left_margin % 2 == 0 # Required for compatibility with 2x2 pooling
199
+ total_margin_pixels = base_image_input_d*(right_margin + left_margin) # pixels removed per dim
200
+ crop_patches = base_image_input_size[0] // base_image_input_d # patches per crop dim
201
+ crop_window_patches = crop_patches - (right_margin + left_margin) # usable patches
202
+ crop_window_size = crop_window_patches * base_image_input_d
203
+ tiling = select_tiling(
204
+ original_image_h - total_margin_pixels,
205
+ original_image_w - total_margin_pixels,
206
+ crop_window_size,
207
+ max_crops
208
+ )
209
+ src, img_mask = resize_and_pad(
210
+ image,
211
+ [tiling[0]*crop_window_size+total_margin_pixels, tiling[1]*crop_window_size+total_margin_pixels]
212
+ )
213
+
214
+ # Now we have to split the image into crops, while keeping track of how each patch in the
215
+ # each crop should be ordered in the global image, this require a lot of tricky booking
216
+ n_crops = tiling[0] * tiling[1]
217
+ patches_arr = []
218
+ mask_arr = []
219
+ patch_ordering_arr = []
220
+
221
+ # We assume 2x2 pooling, but can allow padding the right/bottom with extra
222
+ # patches if the number of patches per side is not even
223
+ assert (crop_patches+1)//2 == image_token_length_h
224
+ assert (crop_patches+1)//2 == image_token_length_w
225
+ on = 0
226
+ on_patch = 0
227
+ for i in range(tiling[0]):
228
+ y0 = i*crop_window_size
229
+ if i == 0:
230
+ crop_y0 = 0
231
+ else:
232
+ crop_y0 = left_margin // 2
233
+
234
+ crop_h = image_base_patch_h - (right_margin + left_margin)
235
+ if i == 0:
236
+ crop_h += left_margin
237
+ if i == (tiling[0]-1):
238
+ crop_h += right_margin
239
+ for j in range(tiling[1]):
240
+ x0 = j*crop_window_size
241
+ if j == 0:
242
+ crop_x0 = 0
243
+ else:
244
+ crop_x0 = left_margin // 2
245
+
246
+ crop_w = image_base_patch_w - (right_margin + left_margin)
247
+ if j == 0:
248
+ crop_w += left_margin
249
+ if j == (tiling[1]-1):
250
+ crop_w += right_margin
251
+
252
+ pooled_w = (crop_w + 1) // 2
253
+ pooled_h = (crop_h + 1) // 2
254
+ patch_ordering_arr.append(
255
+ pad_to_bounding_box(
256
+ np.reshape(np.arange(on, on+pooled_h*pooled_w, dtype=np.int32), (pooled_h, pooled_w, 1)),
257
+ crop_y0, crop_x0, image_token_length_h, image_token_length_w, value=-1
258
+ )[:, :, 0]
259
+ )
260
+ patches_arr.append(src[y0:y0+crop_size, x0:x0+crop_size])
261
+ mask_arr.append(img_mask[y0:y0+crop_size, x0:x0+crop_size])
262
+
263
+ on += pooled_h*pooled_w
264
+ on_patch += 1
265
+ patches = np.stack(patches_arr)
266
+ patch_ordering = np.stack(patch_ordering_arr)
267
+ img_mask = np.stack(mask_arr)
268
+
269
+ # Switch to [n_crops, n_patches, pixels_per_patch] format
270
+ image_layout_impatch_w, image_layout_impatch_h = tiling[0], tiling[1]
271
+ patches = einops.rearrange(
272
+ patches, 'p (h dh) (w dw) c -> p (h w) (dh dw c)',
273
+ dh=base_image_input_d,
274
+ dw=base_image_input_d,
275
+ h=image_base_patch_h,
276
+ w=image_base_patch_w
277
+ )
278
+ img_mask = einops.rearrange(
279
+ img_mask, 'p (h dh) (w dw) -> p (h w) (dh dw)',
280
+ dh=base_image_input_d,
281
+ dw=base_image_input_d,
282
+ h=image_base_patch_h,
283
+ w=image_base_patch_w
284
+ )
285
+
286
+ img_mask = img_mask.astype(np.float32).mean(axis=-1)
287
+ patch_ordering = np.reshape(patch_ordering, [-1])
288
+ valid = patch_ordering >= 0
289
+
290
+ # Transpose order, to get left-to-right order instead of crop-by-crop order
291
+ patch_ordering_rh = np.reshape(
292
+ patch_ordering,
293
+ [tiling[0], tiling[1], image_token_length_h, image_token_length_w]
294
+ )
295
+ patch_ordering_rh = np.transpose(patch_ordering_rh, [0, 2, 1, 3])
296
+ patch_ordering_rh = np.reshape(patch_ordering_rh, [-1])
297
+
298
+ # The transpose will screw up which patches are masked, project the
299
+ # new order into sparse structure of `patch_ordering` to fix this
300
+ patch_ordering[valid] = patch_ordering_rh[patch_ordering_rh >= 0]
301
+
302
+ # Now build the output tokens
303
+ h = tiling[0] * crop_window_patches + (right_margin+left_margin)
304
+ w = tiling[1] * crop_window_patches + (right_margin+left_margin)
305
+ per_row = np.full(
306
+ ((w+1)//2,),
307
+ image_patch_token_id,
308
+ )
309
+ per_row = np.concatenate([per_row, [image_col_token_id]], 0)
310
+
311
+ joint = np.tile(per_row, [(h+1)//2])
312
+ joint = [
313
+ [image_start_token_id],
314
+ joint,
315
+ [image_end_token_id]
316
+ ]
317
+
318
+ # Finally do the same for the global image
319
+ resized, _ = resize_and_pad(image, base_image_input_size)
320
+ resized = einops.rearrange(
321
+ resized, '(h dh) (w dw) c -> (h w) (dh dw c)',
322
+ dh=base_image_input_d,
323
+ dw=base_image_input_d,
324
+ h=image_base_patch_h,
325
+ w=image_base_patch_w
326
+ )
327
+ patches = np.concatenate([np.expand_dims(resized, 0), patches], 0)
328
+
329
+ # Global image goes first, so the order of patches in previous crops gets increased
330
+ patch_ordering = np.where(
331
+ patch_ordering >= 0,
332
+ patch_ordering + tokens_per_image,
333
+ -1
334
+ )
335
+ patch_ordering = np.concatenate([np.arange(0, tokens_per_image), patch_ordering], 0)
336
+ per_row = np.full(
337
+ (image_token_length_w,),
338
+ image_patch_token_id,
339
+ )
340
+ per_row = np.concatenate([per_row, [image_col_token_id]], 0)
341
+ extra_tokens = np.tile(per_row, [image_token_length_h])
342
+ joint = [
343
+ [image_start_token_id],
344
+ extra_tokens,
345
+ [image_end_token_id],
346
+ ] + joint
347
+
348
+ joint = np.concatenate(joint, 0)
349
+ img_mask = np.pad(img_mask, [[0, 1], [0, 0]], constant_values=-1)
350
+ return patches, joint, patch_ordering, img_mask
351
+
352
+ def build_image_input_idx(
353
+ self,
354
+ image_tokens: np.ndarray,
355
+ patch_order: np.ndarray,
356
+ image_patch_token_id: int,
357
+ no_image: Optional[bool] = None,
358
+ image_token_length_w: Optional[int] = None,
359
+ image_token_length_h: Optional[int] = None,
360
+ ):
361
+ """Converts `patch_order` into a mapping of token_id -> patch_id"""
362
+
363
+ tokens_per_image = image_token_length_w * image_token_length_h
364
+ if no_image is not None and no_image:
365
+ return np.zeros((0, tokens_per_image), np.int32)
366
+
367
+ # Indices to insert the patches
368
+ image_input_idx = image_tokens == image_patch_token_id
369
+ image_input_idx = np.nonzero(image_input_idx)[0].astype(np.int32)
370
+
371
+ if patch_order is not None:
372
+ n_tokens = image_input_idx.shape[0]
373
+ patch_order = np.reshape(patch_order, [-1])
374
+ n_patches = patch_order.shape[0]
375
+
376
+ valid = patch_order >= 0
377
+ n_valid_patches = valid.sum()
378
+ assert len(image_input_idx) == n_valid_patches
379
+
380
+ sorted_patch_ixs = np.zeros([n_tokens], np.int32)
381
+ sorted_patch_ixs[patch_order[valid]] = np.arange(n_valid_patches, dtype=np.int32)
382
+
383
+ # Project the inverted mapping into same sparse structure
384
+ sorted_patch_ixs_ex = np.full(np.shape(patch_order), -1)
385
+ sorted_patch_ixs_ex[valid] = sorted_patch_ixs
386
+
387
+ # Do the gather and then re-masked outputs that were masked in `sorted_patch_ixs`
388
+ valid = (sorted_patch_ixs_ex >= 0).astype(np.int32)
389
+ image_input_idx = image_input_idx[sorted_patch_ixs_ex*valid]
390
+ image_input_idx = image_input_idx*valid - 100*(1 - valid)
391
+ image_input_idx = np.reshape(image_input_idx, [-1, tokens_per_image])
392
+ return image_input_idx
393
+
394
+ def preprocess(
395
+ self,
396
+ image: np.ndarray,
397
+ image_patch_token_id: int,
398
+ image_col_token_id: int,
399
+ image_start_token_id: int,
400
+ image_end_token_id: int,
401
+ max_crops: Optional[int] = None,
402
+ overlap_margins: Optional[List[int]] = None,
403
+ base_image_input_size: Optional[Union[int, List[int]]] = None,
404
+ image_token_length_w: Optional[int] = None,
405
+ image_token_length_h: Optional[int] = None,
406
+ image_patch_size: Optional[int] = None,
407
+ **kwargs,
408
+ ):
409
+ """Preprocesses an image
410
+
411
+ Returns:
412
+ crops: (n_crops, n_patches, patch_dim) individual crops, `n_crops` might
413
+ change between images but the other dimension are fixed
414
+ tokens: (n_tokens,) int32 tokens, pad tokens indicate where to insert the
415
+ patch features, might include other special tokens as well
416
+ image_idx: (n_crops, n_patches) index in `tokens` to put the patch features from the
417
+ crops after pooling, negative values indicates patches features to exclude
418
+ padding_mask: (n_crops, n_patches) what percent of each crop is padding, can be None
419
+ if the image mask is not being used.
420
+ """
421
+
422
+ max_crops = max_crops or self.max_crops
423
+ overlap_margins = overlap_margins or self.overlap_margins
424
+ base_image_input_size = base_image_input_size or self.base_image_input_size
425
+ image_token_length_w = image_token_length_w or self.image_token_length_w
426
+ image_token_length_h = image_token_length_h or self.image_token_length_h
427
+ image_patch_size = image_patch_size or self.image_patch_size
428
+
429
+ crops, image_tokens, patch_ordering, img_mask = self.image_to_patches_and_tokens(
430
+ image,
431
+ image_patch_token_id,
432
+ image_col_token_id,
433
+ image_start_token_id,
434
+ image_end_token_id,
435
+ max_crops,
436
+ overlap_margins,
437
+ base_image_input_size,
438
+ image_token_length_w,
439
+ image_token_length_h,
440
+ image_patch_size,
441
+ )
442
+ patch_idx = self.build_image_input_idx(
443
+ image_tokens,
444
+ patch_ordering,
445
+ image_patch_token_id,
446
+ image_token_length_w=image_token_length_w,
447
+ image_token_length_h=image_token_length_h,
448
+ )
449
+ return crops, image_tokens, patch_idx, img_mask
450
+
451
+ def multimodal_preprocess(
452
+ self,
453
+ images: np.ndarray,
454
+ tokens: List[int],
455
+ image_idx: np.ndarray,
456
+ sequence_length: int,
457
+ image_patch_token_id: int,
458
+ image_col_token_id: int,
459
+ image_start_token_id: int,
460
+ image_end_token_id: int,
461
+ **kwargs,
462
+ ):
463
+ """Merge images and text tokens into multi-modal features for the model
464
+
465
+ :param images: images to use as input
466
+ :param tokens: input text tokens
467
+ :param image_idx: where to insert the images into `tokens`
468
+ :params image_patch_token_id: id to use of tokens that will contain image features
469
+ :params image_col_token_id: token id for image column special tokens
470
+ :params image_start_token_id: token id for image start special tokens
471
+ :params image_end_token_id: token id for image end special tokens
472
+ :params kwargs: override preprocessor default args
473
+ """
474
+ max_total_crops = kwargs.get("max_crops") or self.max_crops
475
+ image_token_length_w = kwargs.get("image_token_length_w") or self.image_token_length_w
476
+ image_token_length_h = kwargs.get("image_token_length_h") or self.image_token_length_h
477
+ image_patch_size = kwargs.get("image_patch_size") or self.image_patch_size
478
+ base_image_input_size = kwargs.get("base_image_input_size") or self.base_image_input_size
479
+ image_num_patch = (
480
+ base_image_input_size[0] // image_patch_size,
481
+ base_image_input_size[1] // image_patch_size,
482
+ )
483
+ image_padding_mask = kwargs.get("image_padding_mask") or self.image_padding_mask
484
+
485
+ tokens_per_image = image_token_length_w * image_token_length_h
486
+ n_pixels = image_patch_size * image_patch_size * 3
487
+ n_patches = image_num_patch[0] * image_num_patch[1]
488
+
489
+ if images is None:
490
+ return {
491
+ "input_ids": tokens,
492
+ }
493
+ else:
494
+ n = len(images)
495
+ all_crops = []
496
+ all_image_idx = []
497
+ out_tokens = []
498
+ all_crop_masks = []
499
+
500
+ for ix in range(n):
501
+ token_ix = image_idx[ix]
502
+ crops, image_tokens, patch_idx, img_mask = self.preprocess(
503
+ images[ix],
504
+ image_patch_token_id,
505
+ image_col_token_id,
506
+ image_start_token_id,
507
+ image_end_token_id,
508
+ **kwargs,
509
+ )
510
+
511
+ if token_ix == -1: # -1 is an image inserted at the very start
512
+ start = 0
513
+ token_ix = 0
514
+ end = 0
515
+ else:
516
+ start = 0 if ix == 0 else image_idx[ix-1] + 1
517
+ end = token_ix + 1
518
+
519
+ all_image_idx.append(patch_idx + token_ix)
520
+ all_crops.append(crops)
521
+ out_tokens.append(tokens[start:token_ix])
522
+ out_tokens.append(image_tokens)
523
+ if ix == (n - 1):
524
+ out_tokens.append(tokens[end:])
525
+ if image_padding_mask:
526
+ all_crop_masks.append(img_mask)
527
+
528
+ input_ids = np.concatenate(out_tokens, 0)
529
+ images = np.concatenate(all_crops, 0)
530
+ image_input_idx = np.concatenate(all_image_idx, 0)
531
+ if image_padding_mask:
532
+ image_masks = np.concatenate(all_crop_masks, 0)
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+ else:
534
+ image_masks = None
535
+
536
+ out = {
537
+ "input_ids": input_ids,
538
+ "images": images,
539
+ "image_input_idx": image_input_idx
540
+ }
541
+ if image_masks is not None:
542
+ out["image_masks"] = image_masks
543
+ return out
544
+
545
+
546
+ MolmoImageProcessor.register_for_auto_class()
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+ }
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+ }
modeling_molmo.py ADDED
@@ -0,0 +1,2367 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import logging
2
+ import math
3
+ from copy import deepcopy
4
+ from dataclasses import fields, dataclass, replace
5
+ from enum import Enum
6
+ from typing import List, Optional, Tuple, Union, Dict, Any, Sequence, Callable, cast, MutableMapping
7
+
8
+ import torch
9
+ from einops import einsum, einops
10
+ from transformers import PreTrainedModel, GenerationConfig
11
+ from transformers.cache_utils import Cache
12
+ from transformers.modeling_outputs import CausalLMOutputWithPast, ModelOutput
13
+ from transformers.models.auto import AutoModelForCausalLM
14
+ from torch import nn
15
+
16
+ from .config_molmo import MolmoConfig
17
+ from torch.nn import functional as F
18
+
19
+
20
+ log = logging.getLogger(__name__)
21
+
22
+
23
+ class BufferCache(dict, MutableMapping[str, torch.Tensor]):
24
+ """
25
+ Cache for attention biases and other things that would normally be stored as buffers.
26
+ We avoid using buffers because we've run into various issues doing so with FSDP.
27
+ In general it appears the way FSDP handles buffers is not well-defined.
28
+ It doesn't shard them but apparently it does synchronize them across processes, which we want to avoid
29
+ since (A) it isn't necessary, and (B) we sometimes have `-inf` in these biases which might get turned into
30
+ NaNs when they're synchronized due to casting or some other issue.
31
+ """
32
+
33
+
34
+ class StrEnum(str, Enum):
35
+ def __str__(self) -> str:
36
+ return self.value
37
+
38
+ def __repr__(self) -> str:
39
+ return f"'{str(self)}'"
40
+
41
+
42
+ class ImageProjectType(StrEnum):
43
+ mlp = "mlp"
44
+ mlpx2 = "2mlp"
45
+ linear = "linear"
46
+
47
+
48
+ class ImagePooling2DType(StrEnum):
49
+ attention = "attention"
50
+ attention_meanq = "attention-meanq"
51
+ attention_2wide = "attention_2wide"
52
+ attention_v2 = "attention-v2"
53
+ none = "none"
54
+ stack = "stack"
55
+
56
+
57
+ class ActivationType(StrEnum):
58
+ quick_gelu = "quick_gelu"
59
+ gelu = "gelu"
60
+ gelu_tanh = "gelu_tanh"
61
+ relu = "relu"
62
+ silu = "silu"
63
+ llama_geglu = "llama_geglu"
64
+ llama_geglu_tanh = "llama_geglu_tanh"
65
+ llama_swiglu = "llama_swiglu"
66
+ swiglu = "swiglu"
67
+
68
+
69
+ def ensure_finite_(x: torch.Tensor, check_neg_inf: bool = True, check_pos_inf: bool = False):
70
+ """
71
+ Modify ``x`` in place to replace ``float("-inf")`` with the minimum value of the dtype when ``check_neg_inf``
72
+ is ``True`` and to replace ``float("inf")`` with the maximum value of the dtype when ``check_pos_inf`` is ``True``.
73
+ """
74
+ if check_neg_inf:
75
+ x.masked_fill_(x == float("-inf"), torch.finfo(x.dtype).min)
76
+ if check_pos_inf:
77
+ x.masked_fill_(x == float("inf"), torch.finfo(x.dtype).max)
78
+
79
+
80
+ class MolmoConfigurationError(Exception):
81
+ pass
82
+
83
+
84
+ def _non_meta_init_device(config) -> torch.device:
85
+ if config.init_device is not None and config.init_device != "meta":
86
+ return torch.device(config.init_device)
87
+ else:
88
+ return torch.device("cuda" if torch.cuda.is_available() else "cpu")
89
+
90
+
91
+ class RotaryEmbedding(nn.Module):
92
+ """
93
+ [Rotary positional embeddings (RoPE)](https://arxiv.org/abs/2104.09864).
94
+ """
95
+
96
+ def __init__(self, config: MolmoConfig, cache: BufferCache):
97
+ super().__init__()
98
+ self.config = config
99
+ self.__cache = cache
100
+ # Warm up cache.
101
+ self.get_rotary_embedding(
102
+ config.max_position_embeddings or config.max_sequence_length,
103
+ _non_meta_init_device(config)
104
+ )
105
+
106
+ def get_rotary_embedding(self, seq_len: int, device: torch.device) -> Tuple[torch.Tensor, torch.Tensor]:
107
+ if (
108
+ (pos_sin := self.__cache.get("rope_pos_sin")) is not None
109
+ and (pos_cos := self.__cache.get("rope_pos_cos")) is not None
110
+ and pos_sin.shape[-2] >= seq_len
111
+ and pos_cos.shape[-2] >= seq_len
112
+ ):
113
+ if pos_sin.device != device:
114
+ pos_sin = pos_sin.to(device)
115
+ self.__cache["rope_pos_sin"] = pos_sin
116
+ if pos_cos.device != device:
117
+ pos_cos = pos_cos.to(device)
118
+ self.__cache["rope_pos_cos"] = pos_cos
119
+ return pos_sin[:, :, :seq_len, :], pos_cos[:, :, :seq_len, :]
120
+
121
+ with torch.autocast(device.type, enabled=False):
122
+ dim = self.config.d_model // self.config.n_heads
123
+ inv_freq = 1.0 / (self.config.rope_theta ** (torch.arange(0, dim, 2, device=device, dtype=torch.float) / dim))
124
+ seq = torch.arange(seq_len, device=device, dtype=torch.float)
125
+ freqs = torch.einsum("i , j -> i j", seq, inv_freq)
126
+ if self.config.rope_impl == "interleave":
127
+ positions = freqs.repeat_interleave(2, dim=-1)
128
+ else:
129
+ positions = torch.cat((freqs, freqs), dim=-1)
130
+ pos_sin, pos_cos = positions.sin()[None, None, :, :], positions.cos()[None, None, :, :]
131
+ self.__cache["rope_pos_sin"] = pos_sin
132
+ self.__cache["rope_pos_cos"] = pos_cos
133
+ return pos_sin, pos_cos
134
+
135
+ def rotate_half(self, x: torch.Tensor) -> torch.Tensor:
136
+ B, nh, T, hs = x.size()
137
+ x = x.view(B, nh, T, 2, hs // 2)
138
+ x1, x2 = x.unbind(dim=-2)
139
+ return torch.cat((-x2, x1), dim=-1)
140
+
141
+ def rotate_every_two(self, x: torch.Tensor) -> torch.Tensor:
142
+ B, nh, T, hs = x.size()
143
+ x = x.view(B, nh, T, hs // 2, 2)
144
+ x1, x2 = x.unbind(dim=-1)
145
+ x = torch.stack((-x2, x1), dim=-1)
146
+ return x.view(B, nh, T, hs)
147
+
148
+ def apply_rotary_pos_emb(self, pos_sin: torch.Tensor, pos_cos: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
149
+ if self.config.rope_impl == "interleave":
150
+ return ((t * pos_cos) + (self.rotate_every_two(t) * pos_sin)).to(t.dtype)
151
+ else:
152
+ return ((t * pos_cos) + (self.rotate_half(t) * pos_sin)).to(t.dtype)
153
+
154
+ def forward(
155
+ self,
156
+ q: torch.Tensor,
157
+ k: torch.Tensor,
158
+ position_ids: Optional[torch.Tensor] = None
159
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
160
+ if self.config.rope_full_precision:
161
+ q_, k_ = q.float(), k.float()
162
+ else:
163
+ q_, k_ = q, k
164
+
165
+ with torch.autocast(q.device.type, enabled=False):
166
+ batch_size = q_.shape[0]
167
+ query_len, key_len = q_.shape[-2], k_.shape[-2] # could be different if layer_past not None
168
+ if position_ids is not None:
169
+ freqs_cis_len = (self.config.max_position_embeddings or self.config.max_sequence_length)
170
+ else:
171
+ freqs_cis_len = key_len
172
+ pos_sin, pos_cos = self.get_rotary_embedding(freqs_cis_len, q_.device)
173
+ pos_sin = pos_sin.type_as(q_)
174
+ pos_cos = pos_cos.type_as(q_)
175
+ if position_ids is not None:
176
+ assert query_len == key_len, "Query and key lengths must be equal when using position IDs."
177
+ pos_sin = pos_sin[0, 0][position_ids].view(
178
+ (batch_size, 1, key_len, pos_sin.shape[-1])
179
+ )
180
+ pos_cos = pos_cos[0, 0][position_ids].view(
181
+ (batch_size, 1, key_len, pos_cos.shape[-1])
182
+ )
183
+ q_ = self.apply_rotary_pos_emb(
184
+ pos_sin[:, :, key_len - query_len : key_len, :],
185
+ pos_cos[:, :, key_len - query_len : key_len, :],
186
+ q_,
187
+ )
188
+ k_ = self.apply_rotary_pos_emb(pos_sin, pos_cos, k_)
189
+ return q_.type_as(q), k_.type_as(k)
190
+
191
+
192
+ class MolmoBlock(nn.Module):
193
+ """
194
+ A base class for transformer block implementations.
195
+ """
196
+
197
+ def __init__(self, layer_id: int, config: MolmoConfig, cache: BufferCache):
198
+ super().__init__()
199
+ self.layer_id = layer_id
200
+ self.config = config
201
+ self.hidden_size = (
202
+ config.mlp_hidden_size if config.mlp_hidden_size is not None else config.mlp_ratio * config.d_model
203
+ )
204
+ self.__cache = cache
205
+ self._activation_checkpoint_fn = None
206
+
207
+ # Dropout.
208
+ self.dropout = Dropout(config.residual_dropout)
209
+
210
+ # Layer norms.
211
+ self.k_norm: Optional[LayerNormBase] = None
212
+ self.q_norm: Optional[LayerNormBase] = None
213
+ if config.attention_layer_norm:
214
+ assert config.effective_n_kv_heads is not None
215
+ self.k_norm = LayerNormBase.build(
216
+ config,
217
+ size=(config.d_model // config.n_heads) * config.effective_n_kv_heads,
218
+ elementwise_affine=config.attention_layer_norm_with_affine,
219
+ )
220
+ self.q_norm = LayerNormBase.build(config, elementwise_affine=config.attention_layer_norm_with_affine)
221
+
222
+ # Make sure QKV clip coefficient is positive, otherwise it's not well-defined.
223
+ if config.clip_qkv is not None:
224
+ assert config.clip_qkv > 0
225
+
226
+ # Activation function.
227
+ self.act = Activation.build(config)
228
+ assert (self.act.output_multiplier * self.hidden_size) % 1 == 0
229
+
230
+ # Attention output projection.
231
+ input_dim = config.d_model
232
+ self.attn_out = nn.Linear(
233
+ input_dim, config.d_model,
234
+ bias=config.include_bias,
235
+ device=config.init_device
236
+ )
237
+
238
+ # Feed-forward output projection.
239
+ self.ff_out = nn.Linear(
240
+ int(self.act.output_multiplier * self.hidden_size),
241
+ config.d_model,
242
+ bias=config.include_bias,
243
+ device=config.init_device,
244
+ )
245
+ self.ff_out._is_residual = True # type: ignore
246
+
247
+ # Rotary embeddings.
248
+ if self.config.rope:
249
+ self.rotary_emb = RotaryEmbedding(config, self.__cache)
250
+
251
+ self.flash_attn_func = None
252
+ if config.attention_type == "flash":
253
+ try:
254
+ from flash_attn import flash_attn_func # type: ignore
255
+
256
+ self.flash_attn_func = flash_attn_func
257
+ except ModuleNotFoundError:
258
+ pass
259
+
260
+ def reset_parameters(self):
261
+ if self.k_norm is not None:
262
+ self.k_norm.reset_parameters()
263
+ if self.q_norm is not None:
264
+ self.q_norm.reset_parameters()
265
+ init_weights(
266
+ self.config,
267
+ self.attn_out,
268
+ d=self.config.d_model,
269
+ layer_id=self.layer_id,
270
+ type_of_module=ModuleType.out_module,
271
+ )
272
+ init_weights(
273
+ self.config,
274
+ self.ff_out,
275
+ d=self.ff_out.in_features,
276
+ layer_id=self.layer_id,
277
+ type_of_module=ModuleType.out_module,
278
+ )
279
+
280
+ @classmethod
281
+ def _cast_attn_bias(cls, bias: torch.Tensor, input_dtype: torch.dtype) -> torch.Tensor:
282
+ target_dtype = input_dtype
283
+ # NOTE: `is_autocast_enabled()` only checks for CUDA autocast, so we use the separate function
284
+ # `is_autocast_cpu_enabled()` for CPU autocast.
285
+ # See https://github.com/pytorch/pytorch/issues/110966.
286
+ if bias.device.type == "cuda" and torch.is_autocast_enabled():
287
+ target_dtype = torch.get_autocast_gpu_dtype()
288
+ elif bias.device.type == "cpu" and torch.is_autocast_cpu_enabled():
289
+ target_dtype = torch.get_autocast_cpu_dtype()
290
+ if bias.dtype != target_dtype:
291
+ bias = bias.to(target_dtype)
292
+ ensure_finite_(bias, check_neg_inf=True, check_pos_inf=False)
293
+ return bias
294
+
295
+ def _scaled_dot_product_attention(
296
+ self,
297
+ q: torch.Tensor,
298
+ k: torch.Tensor,
299
+ v: torch.Tensor,
300
+ attn_mask: Optional[torch.Tensor] = None,
301
+ dropout_p: float = 0.0,
302
+ response_dropout_p: float = 0.0,
303
+ is_causal: bool = False,
304
+ ) -> torch.Tensor:
305
+ """
306
+ Computes scaled dot product attention on query, key and value tensors, using an optional
307
+ attention mask if passed, and applying dropout if a probability greater than 0.0 is specified.
308
+ """
309
+ if attn_mask is not None:
310
+ attn_mask = attn_mask.to(q.device)
311
+
312
+ if self.flash_attn_func is not None and attn_mask is None:
313
+ r = self.flash_attn_func(
314
+ q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), dropout_p=dropout_p, causal=is_causal
315
+ )
316
+ return r.transpose(1, 2)
317
+ else:
318
+ # torch's sdpa doesn't support GQA, so we're doing this
319
+ assert k.size(1) == v.size(1)
320
+ num_kv_heads = k.size(1)
321
+ num_q_heads = q.size(1)
322
+ if num_q_heads != num_kv_heads:
323
+ assert num_q_heads % num_kv_heads == 0
324
+ k = k.repeat_interleave(num_q_heads // num_kv_heads, dim=1, output_size=num_q_heads)
325
+ v = v.repeat_interleave(num_q_heads // num_kv_heads, dim=1, output_size=num_q_heads)
326
+
327
+ return F.scaled_dot_product_attention(
328
+ q,
329
+ k,
330
+ v,
331
+ attn_mask=attn_mask,
332
+ dropout_p=dropout_p,
333
+ is_causal=is_causal,
334
+ )
335
+
336
+ def attention(
337
+ self,
338
+ q: torch.Tensor,
339
+ k: torch.Tensor,
340
+ v: torch.Tensor,
341
+ attention_bias: Optional[torch.Tensor] = None,
342
+ position_ids: Optional[torch.Tensor] = None,
343
+ layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
344
+ use_cache: bool = False,
345
+ ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
346
+ B, T, C = q.size() # batch size, sequence length, d_model
347
+ dtype = k.dtype
348
+
349
+ # Optionally apply layer norm to keys and queries.
350
+ if self.q_norm is not None and self.k_norm is not None:
351
+ q = self.q_norm(q).to(dtype=dtype)
352
+ k = self.k_norm(k).to(dtype=dtype)
353
+
354
+ # Move head forward to be next to the batch dim.
355
+ # shape: (B, nh, T, hs)
356
+ q = q.view(B, T, self.config.n_heads, C // self.config.n_heads).transpose(1, 2)
357
+ # shape: (B, n_kv_h, T, hs)
358
+ k = k.view(B, T, self.config.effective_n_kv_heads, C // self.config.n_heads).transpose(1, 2)
359
+ # shape: (B, n_kv_h, T, hs)
360
+ v = v.view(B, T, self.config.effective_n_kv_heads, C // self.config.n_heads).transpose(1, 2)
361
+
362
+ if self.config.use_position_ids and self.config.rope:
363
+ # Apply rotary embeddings
364
+ q, k = self.rotary_emb(q, k, position_ids=position_ids)
365
+
366
+ if layer_past is not None:
367
+ past_key, past_value = layer_past
368
+ k = torch.cat((past_key.to(k.device), k), dim=-2)
369
+ v = torch.cat((past_value.to(v.device), v), dim=-2)
370
+
371
+ present = (k, v) if use_cache else None
372
+ query_len, key_len = q.shape[-2], k.shape[-2] # could be different if layer_past not None
373
+
374
+ if not self.config.use_position_ids and self.config.rope:
375
+ # Apply rotary embeddings
376
+ q, k = self.rotary_emb(q, k)
377
+
378
+ if attention_bias is not None:
379
+ # Resize and cast attention bias.
380
+ # The current dtype of the attention bias might not match the dtype that the SDP attn function will
381
+ # run in if AMP is enabled, and this can be a problem if some tokens are masked out due to padding
382
+ # as down-casting the attention bias to the autocast precision will result in -infs, which will
383
+ # cause the SDP attn function to produce NaNs.
384
+ attention_bias = self._cast_attn_bias(
385
+ attention_bias[:, :, key_len - query_len : key_len, :key_len], dtype
386
+ )
387
+
388
+ # Get the attention scores.
389
+ # shape: (B, nh, T, hs)
390
+ att = self._scaled_dot_product_attention(
391
+ q,
392
+ k,
393
+ v,
394
+ attn_mask=attention_bias,
395
+ dropout_p=0.0 if not self.training else self.config.attention_dropout,
396
+ response_dropout_p=0.0 if not self.training else self.config.response_attention_dropout,
397
+ is_causal=attention_bias is None,
398
+ )
399
+
400
+ # Re-assemble all head outputs side-by-side.
401
+ att = att.transpose(1, 2).contiguous().view(B, T, C)
402
+
403
+ # Apply output projection.
404
+ return self.attn_out(att), present
405
+
406
+ def forward(
407
+ self,
408
+ x: torch.Tensor,
409
+ attention_bias: Optional[torch.FloatTensor] = None,
410
+ position_ids: Optional[torch.Tensor] = None,
411
+ layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
412
+ use_cache: bool = False,
413
+ ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
414
+ raise NotImplementedError
415
+
416
+ @classmethod
417
+ def build(cls, layer_id: int, config: MolmoConfig, cache: BufferCache):
418
+ return MolmoSequentialBlock(layer_id, config, cache)
419
+
420
+
421
+ class MolmoSequentialBlock(MolmoBlock):
422
+ """
423
+ This is a typical transformer block where the output is computed as ``MLP(LN(x + Attention(LN(x))))``
424
+ (plus another skip connection).
425
+ """
426
+
427
+ def __init__(self, layer_id: int, config: MolmoConfig, cache: BufferCache):
428
+ super().__init__(layer_id, config, cache)
429
+ # Layer norms.
430
+ self.attn_norm = LayerNorm.build(config)
431
+ self.ff_norm = LayerNorm.build(config)
432
+ # Attention input projection. Projects x -> (q, k, v)
433
+
434
+ head_dim = config.d_model // config.n_heads
435
+ self.fused_dims = (
436
+ config.d_model,
437
+ config.effective_n_kv_heads * head_dim,
438
+ config.effective_n_kv_heads * head_dim,
439
+ )
440
+ self.att_proj = nn.Linear(
441
+ config.d_model, sum(self.fused_dims),
442
+ bias=config.include_bias or config.qkv_bias,
443
+ device=config.init_device
444
+ )
445
+ # Feed-forward input projection.
446
+ self.ff_proj = nn.Linear(
447
+ config.d_model, self.hidden_size, bias=config.include_bias, device=config.init_device
448
+ )
449
+
450
+ def reset_parameters(self):
451
+ super().reset_parameters()
452
+ self.attn_norm.reset_parameters()
453
+ self.ff_norm.reset_parameters()
454
+ # NOTE: the standard deviation for these weights does not depend on the layer.
455
+ init_weights(
456
+ self.config, self.att_proj, d=self.config.d_model, layer_id=None, type_of_module=ModuleType.in_module
457
+ )
458
+ init_weights(
459
+ self.config, self.ff_proj, d=self.config.d_model, layer_id=None, type_of_module=ModuleType.in_module
460
+ )
461
+
462
+ def forward(
463
+ self,
464
+ x: torch.Tensor,
465
+ attention_bias: Optional[torch.Tensor] = None,
466
+ position_ids: Optional[torch.Tensor] = None,
467
+ layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
468
+ use_cache: bool = False,
469
+ ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
470
+ # Get query, key, value projections.
471
+ # shape:
472
+ # - for regular attn q, k, v: (batch_size, seq_len, d_model)
473
+ # - for multi-query attn q: (batch_size, seq_len, d_model)
474
+ # k, v: (batch_size, seq_len, d_model // n_heads)
475
+ # - for group query attn q: (batch_size, seq_len, d_model)
476
+ # k, v: (batch_size, seq_len, d_model // n_kv_heads)
477
+
478
+ if not self.config.norm_after:
479
+ if self._activation_checkpoint_fn is not None:
480
+ atten_in = self._activation_checkpoint_fn(self.attn_norm, x)
481
+ else:
482
+ atten_in = self.attn_norm(x)
483
+ else:
484
+ atten_in = x
485
+ qkv = self.att_proj(atten_in)
486
+
487
+ if self.config.clip_qkv is not None:
488
+ qkv.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
489
+
490
+ q, k, v = qkv.split(self.fused_dims, dim=-1)
491
+
492
+ # Get attention scores.
493
+ if self._activation_checkpoint_fn is not None:
494
+ att, cache = self._activation_checkpoint_fn( # type: ignore
495
+ self.attention, q, k, v, attention_bias, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache
496
+ )
497
+ else:
498
+ att, cache = self.attention(q, k, v, attention_bias, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache)
499
+
500
+ if self.config.norm_after:
501
+ if self._activation_checkpoint_fn is not None:
502
+ att = self._activation_checkpoint_fn(self.attn_norm, att)
503
+ else:
504
+ att = self.attn_norm(att)
505
+
506
+ # Add attention scores.
507
+ # shape: (B, T, C)
508
+ x = x + self.dropout(att)
509
+
510
+ # Add feed-forward projection.
511
+ # shape: (batch_size, seq_len, d_model)
512
+ og_x = x
513
+
514
+ if not self.config.norm_after:
515
+ if self._activation_checkpoint_fn is not None:
516
+ x = self._activation_checkpoint_fn(self.ff_norm, x) # type: ignore
517
+ else:
518
+ x = self.ff_norm(x)
519
+
520
+ x = self.ff_proj(x)
521
+ if self._activation_checkpoint_fn is not None:
522
+ x = self._activation_checkpoint_fn(self.act, x) # type: ignore
523
+ else:
524
+ x = self.act(x)
525
+ x = self.ff_out(x)
526
+
527
+ if self.config.norm_after:
528
+ if self._activation_checkpoint_fn is not None:
529
+ x = self._activation_checkpoint_fn(self.ff_norm, x) # type: ignore
530
+ else:
531
+ x = self.ff_norm(x)
532
+
533
+ x = self.dropout(x)
534
+ x = og_x + x
535
+
536
+ return x, cache
537
+
538
+
539
+ class Embedding(nn.Module):
540
+ def __init__(
541
+ self,
542
+ num_embeddings: int,
543
+ num_new_embeddings: int,
544
+ features: int,
545
+ device: Union[str, torch.device],
546
+ initializer_range: float = 0.02,
547
+ new_embed_initializer_range: float = 0.02,
548
+ ):
549
+ super().__init__()
550
+ self.initializer_range = initializer_range
551
+ self.new_embed_initializer_range = new_embed_initializer_range
552
+ self.embedding = nn.Parameter(
553
+ torch.zeros(num_embeddings, features, device=device),
554
+ )
555
+ self.new_embedding = nn.Parameter(
556
+ torch.zeros(num_new_embeddings, features, device=device),
557
+ )
558
+
559
+ def reset_parameters(self):
560
+ nn.init.normal_(self.embedding, std=self.initializer_range)
561
+ nn.init.normal_(self.new_embedding, std=self.new_embed_initializer_range)
562
+
563
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
564
+ return F.embedding(x, torch.cat([self.embedding, self.new_embedding], dim=0))
565
+
566
+
567
+ class Dropout(nn.Dropout):
568
+ def __init__(
569
+ self,
570
+ p: float = 0.5,
571
+ inplace: bool = False,
572
+ mask_p: float = 0,
573
+ broadcast_dims: Sequence[int] = (),
574
+ ):
575
+ super().__init__(p, inplace)
576
+ self.mask_p = mask_p
577
+ self.broadcast_dims = broadcast_dims
578
+
579
+ def forward(self, input: torch.Tensor) -> torch.Tensor:
580
+ """
581
+ :param input: A tensor of shape `(batch_size, seq_len, embed_dim)`
582
+ """
583
+ if self.p == 0.0 and (self.mask_p is None or self.mask_p == 0.0):
584
+ return input
585
+ else:
586
+ if self.p > 0. and len(self.broadcast_dims) > 0 and self.training:
587
+ keep_prob = 1.0 - self.p
588
+ dropout_shape = list(input.shape)
589
+ for dim in self.broadcast_dims:
590
+ dropout_shape[dim] = 1
591
+ keep = input.new_empty(dropout_shape).bernoulli_(keep_prob)
592
+ multiplier = keep.broadcast_to(input.shape)
593
+ multiplier.div_(keep_prob)
594
+ input = input * multiplier
595
+ else:
596
+ return F.dropout(input, self.p, self.training, self.inplace)
597
+
598
+
599
+ @dataclass
600
+ class VisionBackboneConfig:
601
+ image_default_input_size: Tuple[int, int] = (336, 336)
602
+ image_patch_size: int = 14
603
+ image_pos_patch_size: int = 14
604
+ image_emb_dim: int = 1024
605
+ image_num_heads: int = 16
606
+ image_num_key_value_heads: int = 16
607
+ image_num_layers: int = 24
608
+ image_head_dim: int = 64
609
+ image_mlp_dim: int = 4096
610
+ image_mlp_activations: str = "gelu"
611
+ image_dropout_rate: float = 0.0
612
+ image_num_pos: int = 577
613
+ image_norm_eps: float = 1e-5
614
+ attention_dropout: float = 0.0
615
+ residual_dropout: float = 0.0
616
+ initializer_range: float = 0.02
617
+ fsdp_wrap: bool = False
618
+ resize_mode: str = "default"
619
+
620
+ def __post_init__(self):
621
+ self.image_default_input_size = tuple(self.image_default_input_size) # type: ignore[assignment]
622
+
623
+ @property
624
+ def image_num_patch(self):
625
+ h, w = self.image_default_input_size
626
+ return h // self.image_patch_size, w // self.image_patch_size
627
+
628
+
629
+ @dataclass
630
+ class FullMolmoConfig:
631
+ d_model: int = 768
632
+ n_heads: int = 12
633
+ n_kv_heads: Optional[int] = None
634
+ qkv_bias: bool = False
635
+ clip_qkv: Optional[float] = None
636
+ n_layers: int = 12
637
+ mlp_ratio: int = 4
638
+ mlp_hidden_size: Optional[int] = None
639
+ activation_type: str = "swiglu"
640
+ block_group_size: int = 1
641
+ rope: bool = True
642
+ rope_full_precision: bool = True
643
+ rope_theta: float = 10000.
644
+ rope_impl: str = "interleave"
645
+ vision_backbone: Optional[VisionBackboneConfig] = None
646
+ attention_type: str = "sdpa"
647
+ float32_attention: bool = True
648
+ attention_dropout: float = 0.1
649
+ response_attention_dropout: float = 0.0
650
+ multi_query_attention: Optional[bool] = None
651
+ attention_layer_norm: bool = False
652
+ residual_dropout: float = 0.1
653
+ embedding_dropout: float = 0.1
654
+ layer_norm_type: str = "default"
655
+ layer_norm_with_affine: bool = True
656
+ layer_norm_eps: Optional[float] = None
657
+ attention_layer_norm_with_affine: bool = True
658
+ max_sequence_length: int = 1024
659
+ max_position_embeddings: Optional[int] = None
660
+ include_bias: bool = True
661
+ bias_for_layer_norm: Optional[bool] = None
662
+ scale_logits: bool = False
663
+ vocab_size: int = 50257
664
+ embedding_size: Optional[int] = 50304
665
+ additional_vocab_size: Optional[int] = None
666
+ new_embedding_init_range: float = 0.02
667
+ weight_tying: bool = True
668
+ pad_token_id: int = -1
669
+ init_device: Optional[str] = None
670
+ init_std: float = 0.02
671
+ init_cutoff_factor: Optional[float] = None
672
+ norm_after: bool = False
673
+ precision: Optional[str] = None
674
+ image_padding_embed: Optional[str] = None
675
+ vit_layers: Tuple = (-1,)
676
+ image_pooling_h: int = 2
677
+ image_pooling_w: int = 2
678
+ image_pooling_2d: str = "attention"
679
+ image_projector: str = "mlp"
680
+ image_feature_dropout: float = 0.0
681
+ initializer_range: float = 0.02
682
+ normalize_input_embeds: bool = False
683
+ use_position_ids: bool = True
684
+
685
+ @property
686
+ def effective_n_kv_heads(self) -> int:
687
+ if self.n_kv_heads is None:
688
+ if self.multi_query_attention is True:
689
+ return 1
690
+ else:
691
+ return self.n_heads
692
+ else:
693
+ if self.multi_query_attention is None:
694
+ return self.n_kv_heads
695
+ if self.multi_query_attention:
696
+ n_kv_heads_should_be = 1
697
+ else:
698
+ n_kv_heads_should_be = self.n_heads
699
+ if self.n_kv_heads == n_kv_heads_should_be:
700
+ return n_kv_heads_should_be
701
+ else:
702
+ raise MolmoConfigurationError(
703
+ "You can't set `multi_query_attention` and `n_kv_heads` at the same time."
704
+ )
705
+
706
+ @property
707
+ def image_num_patch(self):
708
+ assert self.vision_backbone is not None
709
+ return self.vision_backbone.image_num_patch
710
+
711
+ @property
712
+ def image_patch_size(self):
713
+ assert self.vision_backbone is not None
714
+ return self.visoin_backbone.image_patch_size
715
+
716
+ def llm_patches_per_crop(self):
717
+ h, w = self.image_num_patch
718
+ # Round up in case we need to pad the image features for pooling
719
+ h = (h + self.image_pooling_h - 1) // self.image_pooling_h
720
+ w = (w + self.image_pooling_w - 1) // self.image_pooling_w
721
+ return h, w
722
+
723
+
724
+ def _expand_token(token, batch_size: int):
725
+ return token.view(1, 1, -1).expand(batch_size, -1, -1)
726
+
727
+
728
+ class ViTMLP(nn.Module):
729
+ def __init__(self, config: FullMolmoConfig):
730
+ super().__init__()
731
+ self.config = config
732
+ v_cfg = config.vision_backbone
733
+
734
+ self.w1 = nn.Linear(
735
+ v_cfg.image_emb_dim,
736
+ v_cfg.image_mlp_dim,
737
+ bias=True,
738
+ device=config.init_device,
739
+ )
740
+ # Activation function.
741
+ cfg = deepcopy(config)
742
+ cfg.activation_type = v_cfg.image_mlp_activations
743
+ self.act = Activation.build(cfg)
744
+ self.w2 = nn.Linear(
745
+ v_cfg.image_mlp_dim,
746
+ v_cfg.image_emb_dim,
747
+ bias=True,
748
+ device=config.init_device,
749
+ )
750
+
751
+ def reset_parameters(self):
752
+ v_cfg = self.config.vision_backbone
753
+ nn.init.trunc_normal_(self.w1.weight, std=math.sqrt(1 / v_cfg.image_emb_dim), a=-2.0, b=2.0)
754
+ nn.init.trunc_normal_(self.w2.weight, std=math.sqrt(1 / v_cfg.image_mlp_dim), a=-2.0, b=2.0)
755
+ nn.init.zeros_(self.w1.bias)
756
+ nn.init.zeros_(self.w2.bias)
757
+
758
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
759
+ x = self.w1(x)
760
+ x = self.act(x)
761
+ x = self.w2(x)
762
+ return x
763
+
764
+
765
+ class ResidualAttentionBlock(nn.Module):
766
+
767
+ def __init__(self, config: FullMolmoConfig):
768
+ super().__init__()
769
+ self.config = config
770
+
771
+ v_cfg = config.vision_backbone
772
+ self.attention = MultiHeadDotProductAttention(config)
773
+ self.feed_forward = ViTMLP(config)
774
+ self.attention_norm = nn.LayerNorm(
775
+ v_cfg.image_emb_dim,
776
+ eps=v_cfg.image_norm_eps,
777
+ device=config.init_device,
778
+ )
779
+ self.ffn_norm = nn.LayerNorm(
780
+ v_cfg.image_emb_dim,
781
+ eps=v_cfg.image_norm_eps,
782
+ device=config.init_device,
783
+ )
784
+
785
+ def reset_parameters(self):
786
+ self.attention.reset_parameters()
787
+ self.feed_forward.reset_parameters()
788
+ self.attention_norm.reset_parameters()
789
+ self.ffn_norm.reset_parameters()
790
+
791
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
792
+ x = x + self.attention(self.attention_norm(x))
793
+ x = x + self.feed_forward(self.ffn_norm(x))
794
+ return x
795
+
796
+
797
+ class BlockCollection(nn.Module):
798
+
799
+ def __init__(self, config: FullMolmoConfig):
800
+ super().__init__()
801
+ self.config = config
802
+ self.grad_checkpointing: bool = False
803
+
804
+ v_cfg = config.vision_backbone
805
+ self.resblocks = nn.ModuleList([
806
+ ResidualAttentionBlock(config) for _ in range(v_cfg.image_num_layers)
807
+ ])
808
+
809
+ def reset_parameters(self):
810
+ for r in self.resblocks:
811
+ r.reset_parameters()
812
+
813
+ def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
814
+ hidden_states = []
815
+ for r in self.resblocks:
816
+ x = r(x)
817
+ hidden_states.append(x)
818
+ return hidden_states
819
+
820
+
821
+ class LayerNormFp32(nn.LayerNorm):
822
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
823
+ orig_type = x.dtype
824
+ x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight.to(torch.float32),
825
+ self.bias.to(torch.float32), self.eps)
826
+ return x.to(orig_type)
827
+
828
+
829
+ class VisionTransformer(nn.Module):
830
+
831
+ def __init__(self, config: FullMolmoConfig):
832
+ super().__init__()
833
+ self.config = config
834
+
835
+ v_cfg = config.vision_backbone
836
+ # class embeddings and positional embeddings
837
+ self.scale = v_cfg.image_emb_dim ** -0.5
838
+ self.class_embedding = nn.Parameter(
839
+ torch.zeros(v_cfg.image_emb_dim, device=config.init_device),
840
+ )
841
+ self.num_prefix_tokens: int = 1
842
+ self.positional_embedding = nn.Parameter(
843
+ torch.zeros(v_cfg.image_num_pos, v_cfg.image_emb_dim, device=config.init_device),
844
+ )
845
+
846
+ image_patch_size = v_cfg.image_patch_size
847
+ self.patch_embedding = nn.Linear(
848
+ image_patch_size * image_patch_size * 3,
849
+ v_cfg.image_emb_dim,
850
+ bias=False,
851
+ device=config.init_device,
852
+ )
853
+
854
+ self.pre_ln = LayerNormFp32(
855
+ v_cfg.image_emb_dim,
856
+ eps=v_cfg.image_norm_eps,
857
+ )
858
+
859
+ self.transformer = BlockCollection(config)
860
+
861
+ @torch.jit.ignore
862
+ def set_grad_checkpointing(self, enable=True):
863
+ self.transformer.grad_checkpointing = enable
864
+
865
+ def reset_parameters(self):
866
+ nn.init.normal_(self.class_embedding, std=self.scale)
867
+ nn.init.normal_(self.positional_embedding, std=self.scale)
868
+ nn.init.normal_(self.patch_embedding.weight, std=0.02)
869
+ self.pre_ln.reset_parameters()
870
+ self.transformer.reset_parameters()
871
+
872
+ def add_pos_emb(self, x: torch.Tensor, patch_num: int) -> torch.Tensor:
873
+ cls_emb = self.positional_embedding[0:1]
874
+ pos_emb = self.positional_embedding[1:]
875
+
876
+ pos_emb = pos_emb.reshape(
877
+ (int(math.sqrt(pos_emb.shape[0])), int(math.sqrt(pos_emb.shape[0])), pos_emb.shape[1])
878
+ )
879
+
880
+ (patch_num_0, patch_num_1) = patch_num
881
+
882
+ if pos_emb.shape[0] != patch_num_0 or pos_emb.shape[1] != patch_num_1:
883
+ # Dervied from https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
884
+ # antialias: default True in jax.image.resize
885
+ pos_emb = pos_emb.unsqueeze(0).permute(0, 3, 1, 2)
886
+ pos_emb = F.interpolate(
887
+ pos_emb, size=(patch_num_0, patch_num_1), mode="bicubic", align_corners=False, antialias=True,
888
+ )
889
+ pos_emb = pos_emb.permute(0, 2, 3, 1).squeeze(0)
890
+
891
+ pos_emb = pos_emb.reshape(-1, pos_emb.shape[-1])
892
+ x = x + torch.cat([cls_emb[None, :, :], pos_emb[None, :, :]], dim=1).to(x.dtype)
893
+ return x
894
+
895
+ def forward(self, x: torch.Tensor, patch_num: int = None) -> List[torch.Tensor]:
896
+ """
897
+ : param x: (batch_size, num_patch, n_pixels)
898
+ """
899
+ if patch_num is None:
900
+ patch_num = self.config.vision_backbone.image_num_patch
901
+ B, N, D = x.shape
902
+
903
+ x = self.patch_embedding(x)
904
+
905
+ # class embeddings and positional embeddings
906
+ x = torch.cat([_expand_token(self.class_embedding, x.shape[0]).to(x.dtype), x], dim=1)
907
+ x = self.add_pos_emb(x, patch_num)
908
+
909
+ x = self.pre_ln(x)
910
+
911
+ hidden_states = self.transformer(x)
912
+ return hidden_states
913
+
914
+
915
+ class MultiHeadDotProductAttention(nn.Module):
916
+ def __init__(self, config: FullMolmoConfig, use_bias: bool = True, is_vit_layer: Optional[bool] = True):
917
+ super().__init__()
918
+ self.config = config
919
+ self.use_bias = use_bias
920
+
921
+ v_cfg = config.vision_backbone
922
+ self.embed_dim = v_cfg.image_emb_dim
923
+ self.num_heads = v_cfg.image_num_heads
924
+ self.head_dim = v_cfg.image_head_dim
925
+ self.num_key_value_heads = v_cfg.image_num_key_value_heads
926
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
927
+ self.initializer_range = v_cfg.initializer_range
928
+ self.is_vit_layer = is_vit_layer
929
+
930
+ nlayers = 1 if (is_vit_layer or config.vit_layers is None) else len(config.vit_layers)
931
+
932
+ self.wq = nn.Linear(
933
+ nlayers * self.embed_dim,
934
+ self.num_heads * self.head_dim,
935
+ bias=use_bias,
936
+ device=config.init_device,
937
+ )
938
+ self.wk = nn.Linear(
939
+ nlayers * self.embed_dim,
940
+ self.num_key_value_heads * self.head_dim,
941
+ bias=use_bias,
942
+ device=config.init_device,
943
+ )
944
+ self.wv = nn.Linear(
945
+ nlayers * self.embed_dim,
946
+ self.num_key_value_heads * self.head_dim,
947
+ bias=use_bias,
948
+ device=config.init_device,
949
+ )
950
+ self.wo = nn.Linear(
951
+ self.num_heads * self.head_dim,
952
+ self.embed_dim,
953
+ bias=use_bias,
954
+ device=config.init_device,
955
+ )
956
+ self.attention_dropout: Optional[Dropout] = None
957
+ if v_cfg.attention_dropout > 0:
958
+ self.attention_dropout = Dropout(v_cfg.attention_dropout, broadcast_dims=(0, 1))
959
+ self.residual_dropout = Dropout(v_cfg.residual_dropout)
960
+
961
+ def reset_parameters(self):
962
+ nn.init.normal_(self.wq.weight, std=self.initializer_range)
963
+ nn.init.normal_(self.wk.weight, std=self.initializer_range)
964
+ nn.init.normal_(self.wv.weight, std=self.initializer_range)
965
+ nn.init.normal_(self.wo.weight, std=self.initializer_range)
966
+ if self.use_bias:
967
+ nn.init.constant_(self.wq.bias, 0)
968
+ nn.init.constant_(self.wk.bias, 0)
969
+ nn.init.constant_(self.wv.bias, 0)
970
+ nn.init.constant_(self.wo.bias, 0)
971
+
972
+ def _split_heads(self, hidden_states, num_heads) -> torch.Tensor:
973
+ return hidden_states.reshape(hidden_states.shape[:2] + (num_heads, self.head_dim))
974
+
975
+ def _merge_heads(self, hidden_states) -> torch.Tensor:
976
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
977
+
978
+ def forward(self, inputs_q: torch.Tensor, inputs_kv: Optional[torch.Tensor] = None) -> torch.Tensor:
979
+
980
+ if inputs_kv is not None:
981
+ inputs_k = inputs_kv
982
+ inputs_v = inputs_kv
983
+ else:
984
+ inputs_k = inputs_q
985
+ inputs_v = inputs_q
986
+
987
+ xq, xk, xv = self.wq(inputs_q), self.wk(inputs_k), self.wv(inputs_v)
988
+
989
+ xq = self._split_heads(xq, self.num_heads)
990
+ xk = self._split_heads(xk, self.num_key_value_heads)
991
+ xv = self._split_heads(xv, self.num_key_value_heads)
992
+
993
+ if self.num_heads != self.num_key_value_heads:
994
+ xk = xk.repeat_interleave(self.num_key_value_groups, dim=2, output_size=self.num_heads)
995
+ xv = xv.repeat_interleave(self.num_key_value_groups, dim=2, output_size=self.num_heads)
996
+
997
+ og_dtype = xq.dtype
998
+
999
+ if self.config.float32_attention:
1000
+ xq = xq.to(torch.float)
1001
+ xk = xk.to(torch.float)
1002
+
1003
+ if self.config.attention_type == "direct":
1004
+ attn_weights = torch.einsum("...qhd,...khd->...hqk", xq / math.sqrt(xq.size(-1)), xk)
1005
+ attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(xq.dtype)
1006
+ if self.attention_dropout is not None:
1007
+ attn_weights = self.attention_dropout(attn_weights)
1008
+ attn_output = torch.einsum("...hqk,...khd->...qhd", attn_weights.to(xv.dtype), xv)
1009
+
1010
+ elif self.config.attention_type == "sdpa":
1011
+ if self.config.float32_attention and not torch.is_autocast_enabled():
1012
+ xv = xv.to(torch.float32)
1013
+ attn_output = F.scaled_dot_product_attention(
1014
+ xq.transpose(1, 2).contiguous(),
1015
+ xk.transpose(1, 2).contiguous(),
1016
+ xv.transpose(1, 2).contiguous(),
1017
+ is_causal=False,
1018
+ dropout_p=self.config.vision_backbone.attention_dropout
1019
+ ).transpose(1, 2)
1020
+ else:
1021
+ raise NotImplementedError(self.config.attention_type)
1022
+ attn_output = attn_output.to(og_dtype)
1023
+ attn_output = self._merge_heads(attn_output)
1024
+ attn_output = self.wo(attn_output)
1025
+ attn_output = self.residual_dropout(attn_output)
1026
+
1027
+ return attn_output
1028
+
1029
+
1030
+ class MultiHeadAttentionPool(nn.Module):
1031
+ def __init__(
1032
+ self,
1033
+ config: FullMolmoConfig,
1034
+ factor: int = 1,
1035
+ use_bias: bool = True,
1036
+ dropout: bool = True,
1037
+ output_layer: bool = True,
1038
+ mean_residual: bool = False,
1039
+ query: str = "mean",
1040
+ is_vit_layer: Optional[bool] = True
1041
+ ):
1042
+ super().__init__()
1043
+ self.config = config
1044
+ self.factor = factor
1045
+ self.use_bias = use_bias
1046
+ self.dropout = dropout
1047
+ self.output_layer = output_layer
1048
+ self.mean_residual = mean_residual
1049
+ self.query = query
1050
+
1051
+ v_cfg = config.vision_backbone
1052
+ input_dim = v_cfg.image_emb_dim
1053
+ self.embed_dim = v_cfg.image_emb_dim * factor
1054
+ self.num_heads = v_cfg.image_num_heads
1055
+ self.head_dim = v_cfg.image_head_dim * factor
1056
+ self.num_key_value_heads = v_cfg.image_num_key_value_heads
1057
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
1058
+ self.initializer_range = v_cfg.initializer_range
1059
+
1060
+ nlayers = 1 if (is_vit_layer or config.vit_layers is None) else len(config.vit_layers)
1061
+
1062
+ if query != "vector":
1063
+ self.wq = nn.Linear(
1064
+ nlayers * input_dim,
1065
+ self.num_heads * self.head_dim,
1066
+ bias=use_bias,
1067
+ device=config.init_device,
1068
+ )
1069
+ self.wk = nn.Linear(
1070
+ nlayers * input_dim,
1071
+ self.num_key_value_heads * self.head_dim,
1072
+ bias=use_bias,
1073
+ device=config.init_device,
1074
+ )
1075
+ self.wv = nn.Linear(
1076
+ nlayers * input_dim,
1077
+ self.num_key_value_heads * self.head_dim,
1078
+ bias=use_bias,
1079
+ device=config.init_device,
1080
+ )
1081
+
1082
+ if query == "vector":
1083
+ self.attention_query = nn.Parameter(
1084
+ torch.zeros(
1085
+ 1, self.num_key_value_heads * self.head_dim, device=config.init_device,
1086
+ ),
1087
+ )
1088
+
1089
+ if output_layer:
1090
+ self.wo = nn.Linear(
1091
+ self.num_heads * self.head_dim,
1092
+ self.embed_dim,
1093
+ bias=use_bias,
1094
+ device=config.init_device,
1095
+ )
1096
+ self.attention_dropout = Dropout(v_cfg.attention_dropout, broadcast_dims=(0, 1))
1097
+ if dropout:
1098
+ self.residual_dropout = Dropout(v_cfg.residual_dropout)
1099
+
1100
+ def reset_parameters(self):
1101
+ if self.query != "vector":
1102
+ nn.init.normal_(self.wq.weight, std=self.initializer_range)
1103
+ nn.init.normal_(self.wk.weight, std=self.initializer_range)
1104
+ nn.init.normal_(self.wv.weight, std=self.initializer_range)
1105
+ if self.output_layer:
1106
+ nn.init.normal_(self.wo.weight, std=self.initializer_range)
1107
+ if self.use_bias:
1108
+ if self.query != "vector":
1109
+ nn.init.constant_(self.wq.bias, 0)
1110
+ nn.init.constant_(self.wk.bias, 0)
1111
+ nn.init.constant_(self.wv.bias, 0)
1112
+ if self.output_layer:
1113
+ nn.init.constant_(self.wo.bias, 0)
1114
+ if self.query == "vector":
1115
+ nn.init.normal_(self.attention_query, std=self.initializer_range)
1116
+
1117
+ def _split_heads(self, hidden_states, num_heads):
1118
+ return hidden_states.reshape(hidden_states.shape[:2] + (num_heads, self.head_dim))
1119
+
1120
+ def _merge_heads(self, hidden_states):
1121
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
1122
+
1123
+ def forward(self, inputs_kv: torch.Tensor) -> torch.Tensor:
1124
+
1125
+ xk, xv = self.wk(inputs_kv), self.wv(inputs_kv)
1126
+
1127
+ if self.query == "mean":
1128
+ inputs_q = inputs_kv.mean(dim=1, keepdim=True)
1129
+ xq = self.wq(inputs_q)
1130
+ elif self.query == "first":
1131
+ inputs_q = inputs_kv[:, :1]
1132
+ xq = self.wq(inputs_q)
1133
+ elif self.query == "vector":
1134
+ xq = self.attention_query.expand(inputs_kv.size(0), -1, -1)
1135
+ elif self.query == "constant":
1136
+ inputs_q = torch.ones_like(inputs_kv[:, :1]) / math.sqrt(inputs_kv.shape[-1])
1137
+ xq = self.wq(inputs_q)
1138
+ else:
1139
+ raise ValueError(f"Unknown query type: {self.query}")
1140
+
1141
+ xq = self._split_heads(xq, self.num_heads)
1142
+ xk = self._split_heads(xk, self.num_key_value_heads)
1143
+ xv = self._split_heads(xv, self.num_key_value_heads)
1144
+
1145
+ if self.num_heads != self.num_key_value_heads:
1146
+ xk = xk.repeat_interleave(self.num_key_value_groups, dim=2, output_size=self.num_heads)
1147
+ xv = xv.repeat_interleave(self.num_key_value_groups, dim=2, output_size=self.num_heads)
1148
+
1149
+ xq = xq.to(torch.float)
1150
+ xk = xk.to(torch.float)
1151
+
1152
+ xq = xq / math.sqrt(xq.size(-1))
1153
+ attn_weights = torch.einsum("...qhd,...khd->...hqk", xq, xk)
1154
+
1155
+ attn_weights = F.softmax(attn_weights, dim=-1).to(xq.dtype)
1156
+
1157
+ attn_weights = self.attention_dropout(attn_weights).to(xv.dtype)
1158
+
1159
+ attn_output = torch.einsum("...hqk,...khd->...qhd", attn_weights, xv)
1160
+ attn_output = self._merge_heads(attn_output)
1161
+ if self.output_layer:
1162
+ attn_output = self.wo(attn_output)
1163
+ if self.dropout:
1164
+ attn_output = self.residual_dropout(attn_output)
1165
+ if self.mean_residual:
1166
+ attn_output += inputs_kv.mean(dim=1, keepdim=True)
1167
+
1168
+ return attn_output
1169
+
1170
+
1171
+ class MLP(nn.Module):
1172
+ def __init__(self, config: FullMolmoConfig, input_dim: int, dropout: float = 0.0):
1173
+ super().__init__()
1174
+ self.config = config
1175
+ self.hidden_size = (
1176
+ config.mlp_hidden_size if config.mlp_hidden_size is not None else config.mlp_ratio * config.d_model
1177
+ )
1178
+ self.initializer_range = config.initializer_range
1179
+
1180
+ self.w1 = nn.Linear(
1181
+ input_dim,
1182
+ self.hidden_size // 2,
1183
+ bias=False,
1184
+ device=config.init_device,
1185
+ )
1186
+ self.w2 = nn.Linear(
1187
+ self.hidden_size // 2,
1188
+ config.d_model,
1189
+ bias=False,
1190
+ device=config.init_device,
1191
+ )
1192
+ self.w3 = nn.Linear(
1193
+ input_dim,
1194
+ self.hidden_size // 2,
1195
+ bias=False,
1196
+ device=config.init_device,
1197
+ )
1198
+ # Activation function.
1199
+ self.act = Activation.build(config)
1200
+ self.dropout = Dropout(dropout)
1201
+
1202
+ def reset_parameters(self):
1203
+ nn.init.normal_(self.w1.weight, std=self.initializer_range)
1204
+ nn.init.normal_(self.w2.weight, std=self.initializer_range)
1205
+ nn.init.normal_(self.w3.weight, std=self.initializer_range)
1206
+
1207
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
1208
+ x = self.w2(self.act(self.w1(x), self.w3(x)))
1209
+ x = self.dropout(x)
1210
+ return x
1211
+
1212
+
1213
+ class Residual(nn.Module):
1214
+ def __init__(self, submodule: nn.Module):
1215
+ super().__init__()
1216
+ self.submodule = submodule
1217
+
1218
+ def reset_parameters(self):
1219
+ self.submodule.reset_parameters()
1220
+
1221
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
1222
+ return x + self.submodule(x)
1223
+
1224
+
1225
+ class OLMoVisionBackbone(nn.Module):
1226
+ def __init__(self, config: FullMolmoConfig):
1227
+ super().__init__()
1228
+ self.config = config
1229
+ self.image_vit = VisionTransformer(config)
1230
+
1231
+ input_dim: int = None
1232
+ self.image_pooling_2d: nn.Module = None
1233
+ if config.image_pooling_2d in {ImagePooling2DType.attention, ImagePooling2DType.attention_meanq}:
1234
+ self.image_pooling_2d = MultiHeadDotProductAttention(config, is_vit_layer=False)
1235
+ input_dim = config.vision_backbone.image_emb_dim
1236
+ elif config.image_pooling_2d == ImagePooling2DType.attention_2wide:
1237
+ cfg = deepcopy(config)
1238
+ cfg.vision_backbone.image_emb_dim *= 2
1239
+ cfg.vision_backbone.image_head_dim *= 2
1240
+ self.image_pooling_2d = MultiHeadDotProductAttention(cfg, is_vit_layer=False)
1241
+ input_dim = cfg.vision_backbone.image_emb_dim
1242
+ elif config.image_pooling_2d == ImagePooling2DType.attention_v2:
1243
+ assert config.vit_layers is not None
1244
+ use_bias = True
1245
+ dropout = True
1246
+ output_layer = True
1247
+ query = "mean"
1248
+ mean_residual = False
1249
+ factor = len(config.vit_layers)
1250
+ self.image_pooling_2d = MultiHeadAttentionPool(
1251
+ config,
1252
+ factor=factor,
1253
+ use_bias=use_bias,
1254
+ dropout=dropout,
1255
+ output_layer=output_layer,
1256
+ mean_residual=mean_residual,
1257
+ query=query,
1258
+ is_vit_layer=False,
1259
+ )
1260
+ input_dim = config.vision_backbone.image_emb_dim * factor
1261
+ elif config.image_pooling_2d in [ImagePooling2DType.none, ImagePooling2DType.stack]:
1262
+ self.image_pooling_2d = None
1263
+ nlayers = 1 if config.vit_layers is None else len(config.vit_layers)
1264
+ input_dim = nlayers * config.vision_backbone.image_emb_dim
1265
+ else:
1266
+ raise NotImplementedError(f"Unknown image pooling 2D method: {config.image_pooling_2d}")
1267
+
1268
+ self.input_dim = input_dim
1269
+
1270
+ # `MLP` assume the activation takes two inputs, so it must be a 'llama' version
1271
+ if config.activation_type == ActivationType.swiglu:
1272
+ mlp_config = replace(config, activation_type=ActivationType.llama_swiglu)
1273
+ elif config.activation_type == ActivationType.gelu:
1274
+ mlp_config = replace(config, activation_type=ActivationType.llama_geglu)
1275
+ else:
1276
+ mlp_config = config
1277
+ if config.image_projector == ImageProjectType.mlpx2:
1278
+ self.image_projector = nn.ModuleList(
1279
+ [MLP(mlp_config, input_dim), Residual(MLP(config, input_dim))]
1280
+ )
1281
+ elif config.image_projector == ImageProjectType.mlp:
1282
+ self.image_projector = MLP(mlp_config, input_dim)
1283
+ elif config.image_projector == ImageProjectType.linear:
1284
+ self.image_projector = nn.Linear(
1285
+ input_dim,
1286
+ config.d_model,
1287
+ bias=False,
1288
+ device=config.init_device,
1289
+ )
1290
+ else:
1291
+ raise NotImplementedError(f"Unknown image projector: {config.image_projector}")
1292
+
1293
+ self.image_feature_dropout = Dropout(config.image_feature_dropout)
1294
+
1295
+ def reset_parameters(self):
1296
+ if self.image_pooling_2d is not None:
1297
+ self.image_pooling_2d.reset_parameters()
1298
+ if self.config.image_projector == "2mlp":
1299
+ for module in self.image_projector:
1300
+ module.reset_parameters()
1301
+ elif self.config.image_projector == "linear":
1302
+ nn.init.xavier_uniform_(self.image_projector.weight)
1303
+ else:
1304
+ self.image_projector.reset_parameters()
1305
+
1306
+ def forward(self, images: torch.Tensor, image_masks: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
1307
+ raise NotImplementedError
1308
+
1309
+
1310
+ class OLMoPretrainedVisionBackbone(OLMoVisionBackbone):
1311
+ def __init__(self, config: FullMolmoConfig):
1312
+ super().__init__(config)
1313
+ v_cfg = self.config.vision_backbone
1314
+ self.grad_checkpointing = False
1315
+
1316
+ self.num_prefix_tokens = self.image_vit.num_prefix_tokens
1317
+ assert self.num_prefix_tokens in {0, 1}, "Only 0 or 1 prefix tokens are supported"
1318
+
1319
+ self.pad_embed = None
1320
+ if config.image_padding_embed:
1321
+ image_dim = v_cfg.image_emb_dim*len(self.config.vit_layers)
1322
+ if config.image_padding_embed in ["pad_embed", "regress"]:
1323
+ self.pad_embed = nn.Parameter(
1324
+ torch.zeros((image_dim,), device=config.init_device))
1325
+ elif config.image_padding_embed == "pad_and_partial_pad":
1326
+ self.pad_embed = nn.Parameter(
1327
+ torch.zeros((2, image_dim), device=config.init_device))
1328
+ else:
1329
+ raise ValueError(config.image_padding_embed)
1330
+
1331
+ def reset_parameters(self):
1332
+ super().reset_parameters()
1333
+ self.image_vit.reset_parameters()
1334
+
1335
+ def encode_image(self, images: torch.Tensor) -> torch.Tensor:
1336
+ """
1337
+ : param images: (batch_size, num_crops, num_patch, n_pixels)
1338
+ """
1339
+ cfg = self.config
1340
+ v_cfg = self.config.vision_backbone
1341
+ B, T, N, D = images.shape
1342
+
1343
+ mask = ~torch.all(images.view(B * T, N, D) == -1, dim=(1, 2), keepdim=True)
1344
+
1345
+ # Output all hidden states
1346
+ # n_layers x (batch_num_crops, (1+)n_tokens, image_emb_dim)
1347
+ images = images.view(B * T, N, D)
1348
+ image_features = self.image_vit(images)
1349
+
1350
+ if cfg.vit_layers is not None:
1351
+ features = []
1352
+ for layer in cfg.vit_layers:
1353
+ features.append(image_features[layer])
1354
+ image_features = torch.cat(features, dim=-1)
1355
+ else:
1356
+ image_features = image_features[-1]
1357
+
1358
+ cls_embed: torch.Tensor = None
1359
+ if self.num_prefix_tokens > 0:
1360
+ cls_embed = image_features[:, 0]
1361
+ image_features = image_features[:, 1:]
1362
+
1363
+ image_features = image_features * mask
1364
+ image_features = image_features.view(B, T, N, -1)
1365
+
1366
+ cls_embed = cls_embed.view(B, T, -1) if cls_embed is not None else None
1367
+
1368
+ return image_features, cls_embed
1369
+
1370
+ def forward(self, images: torch.Tensor, image_masks: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
1371
+ cfg = self.config
1372
+
1373
+ # image_features: (batch_size, num_crops(=num_image), num_patch, nximage_emb_dim)
1374
+ batch_size, num_image = images.shape[:2]
1375
+ image_features, cls_embed = self.encode_image(images)
1376
+
1377
+ if cfg.image_padding_embed:
1378
+ assert image_masks is not None
1379
+ if cfg.image_padding_embed == "pad_embed":
1380
+ all_pad = (image_masks == 0).to(dtype=torch.float32)
1381
+ pad_embed = self.pad_embed[None, None, None, :]
1382
+ image_features = image_features + pad_embed * torch.unsqueeze(all_pad, -1)
1383
+ elif cfg.image_padding_embed == "regress":
1384
+ pad_embed = self.pad_embed[None, None, None, :]
1385
+ image_features = image_features + pad_embed * torch.unsqueeze(torch.maximum(image_masks, torch.zeros_like(image_masks)), -1)
1386
+ elif cfg.image_padding_embed == "pad_and_partial_pad":
1387
+ pad_embed = self.pad_embed[:, None, None, None, :]
1388
+ all_pad = image_masks == 0
1389
+ partial_pad = torch.logical_and(image_masks < 1, torch.logical_not(all_pad)).to(dtype=image_features.dtype)
1390
+ all_pad = all_pad.to(dtype=image_features.dtype)
1391
+ image_features = image_features + pad_embed[0] * torch.unsqueeze(all_pad, -1)
1392
+ image_features = image_features + pad_embed[1] * torch.unsqueeze(partial_pad, -1)
1393
+ else:
1394
+ raise ValueError(cfg.image_padding_embed)
1395
+
1396
+ image_features = self.image_feature_dropout(image_features)
1397
+ if cls_embed is not None:
1398
+ cls_embed = self.image_feature_dropout(cls_embed)
1399
+
1400
+ image_features = image_features.reshape(
1401
+ (batch_size, num_image) + cfg.image_num_patch + (-1,),
1402
+ )
1403
+
1404
+ if cfg.image_num_patch[0] % cfg.image_pooling_h == 1:
1405
+ # Pad so we can still pool 2x2 patches
1406
+ image_features = F.pad(
1407
+ image_features,
1408
+ (0, 0, 0, 1, 0, 1, 0, 0, 0, 0),
1409
+ )
1410
+
1411
+ # image pooling
1412
+ image_features = einops.rearrange(
1413
+ image_features,
1414
+ 'b n (h dh) (w dw) c -> (b n h w) (dh dw) c',
1415
+ dh=cfg.image_pooling_h,
1416
+ dw=cfg.image_pooling_w,
1417
+ )
1418
+
1419
+ if cfg.image_pooling_2d == ImagePooling2DType.attention_meanq:
1420
+ query = image_features.mean(-2, keepdim=True)
1421
+ image_features = self.image_pooling_2d(query, image_features)
1422
+ elif cfg.image_pooling_2d not in {ImagePooling2DType.none, ImagePooling2DType.stack}:
1423
+ if self.grad_checkpointing:
1424
+ from torch.utils.checkpoint import checkpoint
1425
+ image_features = checkpoint(self.image_pooling_2d, image_features[:, :1, :], image_features, use_reentrant=False)
1426
+ else:
1427
+ image_features = self.image_pooling_2d(image_features[:, :1, :], image_features)
1428
+
1429
+ h, w = cfg.llm_patches_per_crop()
1430
+ image_features = image_features.reshape(batch_size, num_image, h * w, -1)
1431
+
1432
+ # MLP layer to map the feature.
1433
+ if self.grad_checkpointing:
1434
+ from torch.utils.checkpoint import checkpoint
1435
+ image_features = checkpoint(self.image_projector, image_features, use_reentrant=False)
1436
+ else:
1437
+ image_features = self.image_projector(image_features)
1438
+
1439
+ # image_features: (batch_size, num_image, num_patch, d_model)
1440
+ # cls_embed: (batch_size, num_image, d_model)
1441
+ return image_features, cls_embed
1442
+
1443
+
1444
+ class ModuleType(str, Enum):
1445
+ in_module = "in"
1446
+ out_module = "out"
1447
+ emb = "emb"
1448
+ final_out = "final_out"
1449
+
1450
+
1451
+ def init_weights(
1452
+ config: FullMolmoConfig,
1453
+ module: Union[nn.Linear, nn.Embedding],
1454
+ d: Optional[int] = None,
1455
+ layer_id: Optional[int] = None,
1456
+ std_factor: float = 1.0,
1457
+ type_of_module: Optional[ModuleType] = None,
1458
+ ) -> None:
1459
+ d = d if d is not None else config.d_model
1460
+ std = config.init_std * std_factor
1461
+ if config.init_cutoff_factor is not None:
1462
+ cutoff_value = config.init_cutoff_factor * std
1463
+ nn.init.trunc_normal_(module.weight, mean=0.0, std=std, a=-cutoff_value, b=cutoff_value)
1464
+ else:
1465
+ nn.init.normal_(module.weight, mean=0.0, std=std)
1466
+
1467
+
1468
+ class LlamaSwiGLU(nn.Module):
1469
+ def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
1470
+ return F.silu(x1) * x2
1471
+
1472
+ @property
1473
+ def output_multiplier(self) -> float:
1474
+ return 0.5
1475
+
1476
+
1477
+ class SwiGLU(nn.Module):
1478
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
1479
+ x, gate = x.chunk(2, dim=-1)
1480
+ return F.silu(gate) * x
1481
+
1482
+ @property
1483
+ def output_multiplier(self) -> float:
1484
+ return 0.5
1485
+
1486
+
1487
+ class Activation(nn.Module):
1488
+ def __init__(self, config: FullMolmoConfig):
1489
+ super().__init__()
1490
+ self.config = config
1491
+
1492
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
1493
+ raise NotImplementedError
1494
+
1495
+ @property
1496
+ def output_multiplier(self) -> float:
1497
+ raise NotImplementedError
1498
+
1499
+ @classmethod
1500
+ def build(cls, config: FullMolmoConfig) -> 'Activation':
1501
+ if config.activation_type == "quick_gelu":
1502
+ return QuickGELU(config)
1503
+ elif config.activation_type == "gelu":
1504
+ return cast(Activation, GELU(approximate="none"))
1505
+ elif config.activation_type == "gelu_tanh":
1506
+ return cast(Activation, GELU(approximate="tanh"))
1507
+ elif config.activation_type == "relu":
1508
+ return cast(Activation, ReLU(inplace=False))
1509
+ elif config.activation_type == "silu":
1510
+ return cast(Activation, SiLU(inplace=False))
1511
+ # elif config.activation_type == "llama_geglu":
1512
+ # return LlamaGEGLU(config)
1513
+ # elif config.activation_type == "llama_geglu_tanh":
1514
+ # return LlamaGEGLUTanh(config)
1515
+ elif config.activation_type == "llama_swiglu":
1516
+ return LlamaSwiGLU()
1517
+ elif config.activation_type == "swiglu":
1518
+ return SwiGLU()
1519
+ else:
1520
+ raise NotImplementedError(f"Unknown activation: '{config.activation_type}'")
1521
+
1522
+
1523
+ class QuickGELU(Activation):
1524
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
1525
+ return x * torch.sigmoid(1.702 * x)
1526
+
1527
+ @property
1528
+ def output_multiplier(self) -> float:
1529
+ return 1.0
1530
+
1531
+
1532
+ class GELU(nn.GELU):
1533
+ @property
1534
+ def output_multiplier(self) -> float:
1535
+ return 1.0
1536
+
1537
+
1538
+ class ReLU(nn.ReLU):
1539
+ @property
1540
+ def output_multiplier(self) -> float:
1541
+ return 1.0
1542
+
1543
+
1544
+ class SiLU(nn.SiLU):
1545
+ @property
1546
+ def output_multiplier(self) -> float:
1547
+ return 1.0
1548
+
1549
+
1550
+ def causal_attention_bias(seq_len: int, device: torch.device) -> torch.FloatTensor:
1551
+ att_bias = torch.triu(
1552
+ torch.ones(seq_len, seq_len, device=device, dtype=torch.float),
1553
+ diagonal=1,
1554
+ )
1555
+ att_bias.masked_fill_(att_bias == 1, torch.finfo(att_bias.dtype).min)
1556
+ return att_bias.view(1, 1, seq_len, seq_len) # type: ignore
1557
+
1558
+
1559
+ def get_causal_attention_bias(cache: BufferCache, seq_len: int, device: torch.device) -> torch.Tensor:
1560
+ if (causal_bias := cache.get("causal_attention_bias")) is not None and causal_bias.shape[-1] >= seq_len:
1561
+ if causal_bias.device != device:
1562
+ causal_bias = causal_bias.to(device)
1563
+ cache["causal_attention_bias"] = causal_bias
1564
+ return causal_bias
1565
+ with torch.autocast(device.type, enabled=False):
1566
+ causal_bias = causal_attention_bias(seq_len, device)
1567
+ cache["causal_attention_bias"] = causal_bias
1568
+ return causal_bias
1569
+
1570
+
1571
+ class LayerNormBase(nn.Module):
1572
+ def __init__(
1573
+ self,
1574
+ config: MolmoConfig,
1575
+ *,
1576
+ size: Optional[int] = None,
1577
+ elementwise_affine: Optional[bool] = True,
1578
+ eps: float = 1e-05,
1579
+ weight_initializer: Optional[Callable] = torch.ones,
1580
+ bias_initializer: Optional[Callable] = torch.zeros,
1581
+ ):
1582
+ super().__init__()
1583
+ self.config = config
1584
+ self.eps = self.config.layer_norm_eps or eps
1585
+ self.normalized_shape = (size or config.d_model,)
1586
+ if elementwise_affine or (elementwise_affine is None and self.config.layer_norm_with_affine):
1587
+ self.weight = nn.Parameter(weight_initializer(self.normalized_shape, device=config.init_device))
1588
+ use_bias = self.config.bias_for_layer_norm
1589
+ if use_bias is None:
1590
+ use_bias = self.config.include_bias
1591
+ if use_bias:
1592
+ self.bias = nn.Parameter(bias_initializer(self.normalized_shape, device=config.init_device))
1593
+ else:
1594
+ self.register_parameter("bias", None)
1595
+ else:
1596
+ self.register_parameter("bias", None)
1597
+ self.register_parameter("weight", None)
1598
+
1599
+ @classmethod
1600
+ def build(cls, config: FullMolmoConfig, size: Optional[int] = None, **kwargs):
1601
+ if config.layer_norm_type == "default":
1602
+ return LayerNorm(config, size=size, low_precision=False, **kwargs)
1603
+ elif config.layer_norm_type == "low_precision":
1604
+ return LayerNorm(config, size=size, low_precision=True, **kwargs)
1605
+ elif config.layer_norm_type == "rms":
1606
+ return RMSLayerNorm(config, size=size, **kwargs)
1607
+ else:
1608
+ raise NotImplementedError(f"Unknown LayerNorm type: '{config.layer_norm_type}'")
1609
+
1610
+
1611
+ class RMSLayerNorm(LayerNormBase):
1612
+ """
1613
+ RMS layer norm, a simplified :class:`LayerNorm` implementation
1614
+ """
1615
+
1616
+ def __init__(
1617
+ self,
1618
+ config: FullMolmoConfig,
1619
+ size: Optional[int] = None,
1620
+ elementwise_affine: Optional[bool] = None,
1621
+ eps: float = 1e-5,
1622
+ ):
1623
+ super().__init__(config, size=size, elementwise_affine=elementwise_affine, eps=eps)
1624
+
1625
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
1626
+ with torch.autocast(enabled=False, device_type=x.device.type):
1627
+ og_dtype = x.dtype
1628
+ x = x.to(torch.float32)
1629
+ variance = x.pow(2).mean(-1, keepdim=True)
1630
+ x = x * torch.rsqrt(variance + self.eps)
1631
+ x = x.to(og_dtype)
1632
+
1633
+ if self.weight is not None:
1634
+ if self.bias is not None:
1635
+ return self.weight * x + self.bias
1636
+ else:
1637
+ return self.weight * x
1638
+ else:
1639
+ return x
1640
+
1641
+
1642
+ class LayerNorm(LayerNormBase):
1643
+ """
1644
+ The default :class:`LayerNorm` implementation which can optionally run in low precision.
1645
+ """
1646
+
1647
+ def __init__(
1648
+ self,
1649
+ config: FullMolmoConfig,
1650
+ size: Optional[int] = None,
1651
+ low_precision: bool = False,
1652
+ elementwise_affine: Optional[bool] = None,
1653
+ eps: float = 1e-05,
1654
+ ):
1655
+ super().__init__(config, size=size, elementwise_affine=elementwise_affine, eps=eps)
1656
+ self.low_precision = low_precision
1657
+
1658
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
1659
+ if self.low_precision:
1660
+ module_device = x.device
1661
+ downcast_x = self._cast_if_autocast_enabled(x)
1662
+ downcast_weight = (
1663
+ self._cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight
1664
+ )
1665
+ downcast_bias = self._cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias
1666
+ with torch.autocast(enabled=False, device_type=module_device.type):
1667
+ return F.layer_norm(
1668
+ downcast_x, self.normalized_shape, weight=downcast_weight, bias=downcast_bias, eps=self.eps
1669
+ )
1670
+ else:
1671
+ return F.layer_norm(x, self.normalized_shape, weight=self.weight, bias=self.bias, eps=self.eps)
1672
+
1673
+
1674
+ class Molmo(nn.Module):
1675
+ def __init__(self, config: FullMolmoConfig, init_params: bool = True):
1676
+ super().__init__()
1677
+ self.config = config
1678
+ self.__cache = BufferCache()
1679
+
1680
+ # Validate config.
1681
+ if self.config.embedding_size is not None and self.config.embedding_size != self.config.vocab_size:
1682
+ if self.config.embedding_size < self.config.vocab_size:
1683
+ raise MolmoConfigurationError("embedding size should be at least as big as vocab size")
1684
+ elif self.config.embedding_size % 128 != 0:
1685
+ import warnings
1686
+
1687
+ warnings.warn(
1688
+ "Embedding size is not a multiple of 128! This could hurt throughput performance.", UserWarning
1689
+ )
1690
+ torch.backends.cuda.enable_flash_sdp(True)
1691
+ torch.backends.cuda.enable_mem_efficient_sdp(False) # this is super slow so make sure torch won't use it
1692
+
1693
+ wte = None
1694
+ if self.config.additional_vocab_size is not None:
1695
+ wte = Embedding(
1696
+ config.embedding_size or config.vocab_size,
1697
+ config.additional_vocab_size,
1698
+ config.d_model,
1699
+ device=config.init_device,
1700
+ initializer_range=config.initializer_range,
1701
+ new_embed_initializer_range=config.new_embedding_init_range
1702
+ )
1703
+ else:
1704
+ wte=nn.Embedding(
1705
+ config.embedding_size or config.vocab_size, config.d_model, device=config.init_device
1706
+ )
1707
+
1708
+ self.transformer = nn.ModuleDict(
1709
+ dict(
1710
+ wte=wte,
1711
+ emb_drop=Dropout(config.embedding_dropout),
1712
+ ln_f=LayerNorm.build(config),
1713
+ )
1714
+ )
1715
+
1716
+ blocks = [MolmoBlock.build(i, config, self.__cache) for i in range(config.n_layers)]
1717
+ if self.config.block_group_size > 1:
1718
+ raise NotImplementedError()
1719
+ else:
1720
+ self.transformer.update({"blocks": nn.ModuleList(blocks)})
1721
+
1722
+ if not self.config.rope:
1723
+ self.transformer.update(
1724
+ {"wpe": nn.Embedding(config.max_sequence_length, config.d_model, device=config.init_device)}
1725
+ )
1726
+ if not config.weight_tying:
1727
+ self.transformer.update(
1728
+ {
1729
+ "ff_out": nn.Linear(
1730
+ config.d_model,
1731
+ config.embedding_size or config.vocab_size,
1732
+ bias=config.include_bias,
1733
+ device=config.init_device,
1734
+ )
1735
+ }
1736
+ )
1737
+
1738
+ self.vision_backbone: Optional[OLMoVisionBackbone] = None
1739
+ if config.vision_backbone is not None:
1740
+ self.vision_backbone = OLMoPretrainedVisionBackbone(config)
1741
+
1742
+ self.__num_fwd_flops: Optional[int] = None
1743
+
1744
+ def reset_parameters(self):
1745
+ if self.vision_backbone is not None:
1746
+ self.vision_backbone.reset_parameters()
1747
+ self.reset_non_vision_parameters()
1748
+
1749
+ def reset_non_vision_parameters(self):
1750
+ self.transformer.wte.reset_parameters()
1751
+ if hasattr(self.transformer.wte, "new_embedding"):
1752
+ nn.init.normal_(self.transformer.wte.new_embedding, std=self.config.new_embedding_init_range)
1753
+
1754
+ if hasattr(self.transformer, "wpe"):
1755
+ nn.init.normal_(self.transformer.wpe, mean=0.0, std=1.0)
1756
+
1757
+ self.transformer.ln_f.reset_parameters() # type: ignore
1758
+
1759
+ if hasattr(self.transformer, "ff_out"):
1760
+ nn.init.normal_(self.transformer.ff_out, mean=0.0, std=0.02)
1761
+
1762
+ if self.config.block_group_size == 1:
1763
+ for block in self.transformer.blocks:
1764
+ block.reset_parameters()
1765
+ else:
1766
+ for block_group in self.transformer.block_groups:
1767
+ block_group.reset_parameters()
1768
+
1769
+
1770
+ def forward(
1771
+ self,
1772
+ input_ids: torch.LongTensor,
1773
+ input_embeddings: Optional[torch.FloatTensor] = None,
1774
+ attention_mask: Optional[torch.Tensor] = None,
1775
+ attention_bias: Optional[torch.Tensor] = None,
1776
+ response_mask: Optional[torch.Tensor] = None,
1777
+ images: Optional[torch.Tensor] = None,
1778
+ image_masks: Optional[torch.Tensor] = None,
1779
+ image_input_idx: Optional[torch.Tensor] = None,
1780
+ subsegment_ids: Optional[torch.Tensor] = None,
1781
+ position_ids: Optional[torch.Tensor] = None,
1782
+ past_key_values: Optional[Sequence[Tuple[torch.Tensor, torch.Tensor]]] = None,
1783
+ use_cache: bool = False,
1784
+ last_logits_only: bool = False,
1785
+ output_hidden_states: Optional[bool] = None,
1786
+ append_last_valid_logits: Optional[torch.Tensor] = None,
1787
+ ) -> ModelOutput:
1788
+ """
1789
+ :param input_ids: A tensor of shape `(batch_size, seq_len)`.
1790
+ :param input_embeddings: A tensor of shape `(batch_size, seq_len, d_model)` with input
1791
+ embeddings. When provided, it is treated as the output of the input embedding layer.
1792
+ :param attention_mask: A tensor of shape `(batch_size, seq_len)` that indicates
1793
+ which input IDs are masked. A `1` value in the mask means that
1794
+ the corresponding input ID should *not* be ignored. A `0` means
1795
+ that the corresponding input ID is masked.
1796
+
1797
+ This has the same meaning as the `attention_mask` in HuggingFace's `transformers`
1798
+ library.
1799
+ :param attention_bias: A tensor of shape `(batch_size, 1, seq_len, seq_len)`,
1800
+ `(1, 1, seq_len, seq_len)`, or `(seq_len, seq_len)`. This is used
1801
+ to introduce causal or other biases.
1802
+
1803
+ If the tensor is a bool or byte tensor, a `True` or `1` at `attention_bias[:, :, i, j]`
1804
+ indicates that the i-th element in the sequence is allowed to attend to the j-th
1805
+ element in the sequence.
1806
+
1807
+ If the tensor is a float tensor, it will just be added to the attention
1808
+ scores before the softmax.
1809
+
1810
+ The default is causal, which corresponds to a lower-diagonal byte matrix of ones.
1811
+ :param response_mask: A tensor of shape `(batch_size, seq_len)` that indicates
1812
+ the response mask. A `1` value in the mask means that the corresponding token
1813
+ is a response token. A `0` means that the corresponding token is not
1814
+ a response token.
1815
+ :param past_key_values: Pre-computed keys and values for each attention block.
1816
+ Can be used to speed up sequential decoding. The `input_ids` which have
1817
+ their past given to this model should not be passed as `input_ids` as they have already been computed.
1818
+ :param use_cache: If `True`, return key and value tensors for each block.
1819
+ :param last_logits_only: If `True`, only compute the logits for the last token of each sequence.
1820
+ This can speed up decoding when you only care about the next token.
1821
+ """
1822
+ output_hidden_states = output_hidden_states if output_hidden_states is not None else False
1823
+
1824
+ if past_key_values:
1825
+ assert len(past_key_values) == self.config.n_layers
1826
+
1827
+ has_image = images is not None
1828
+
1829
+ assert not (has_image and input_embeddings is not None), "Cannot provide both images and input embeddings."
1830
+ assert not (has_image and past_key_values is not None), "Cached key and values should not be used with images."
1831
+
1832
+ batch_size, seq_len = input_ids.size() if input_embeddings is None else input_embeddings.size()[:2]
1833
+ if past_key_values is None:
1834
+ past_length = 0
1835
+ else:
1836
+ past_length = past_key_values[0][0].size(-2)
1837
+
1838
+ if self.config.use_position_ids and attention_mask is None:
1839
+ attention_mask = input_ids != -1
1840
+
1841
+ if subsegment_ids is not None:
1842
+ assert not use_cache, "Subsegment_ids cannot be used with cache."
1843
+ subsegment_mask = subsegment_ids.unsqueeze(2) <= subsegment_ids.unsqueeze(1)
1844
+ attention_mask = (
1845
+ subsegment_mask.to(attention_mask.dtype) *
1846
+ attention_mask.unsqueeze(2) *
1847
+ attention_mask.unsqueeze(1))
1848
+ if position_ids is None:
1849
+ raise ValueError(f"Positioned ids must be given if using subsegment_ids")
1850
+ else:
1851
+ if self.config.use_position_ids and position_ids is None:
1852
+ position_ids = torch.clamp(
1853
+ torch.cumsum(attention_mask.to(torch.int32), dim=-1) - 1,
1854
+ min=0,
1855
+ ).broadcast_to((batch_size, attention_mask.shape[-1]))
1856
+
1857
+ # Get embeddings of input.
1858
+ # shape: (batch_size, seq_len, d_model)
1859
+ if input_ids is not None:
1860
+ input_ids = input_ids * (input_ids != -1).to(input_ids.dtype)
1861
+ x = self.transformer.wte(input_ids) if input_embeddings is None else input_embeddings # type: ignore
1862
+
1863
+ num_image: Optional[int] = None
1864
+ if images is not None:
1865
+ # shape: (batch_size, num_image, num_patch, d_model)
1866
+ # cls_embed: (batch_size, num_image, d_model)
1867
+ image_features, cls_embed = self.vision_backbone(images, image_masks)
1868
+ num_image, num_patch = image_features.shape[1:3]
1869
+ assert image_input_idx.shape == (batch_size, num_image, num_patch)
1870
+
1871
+ # inster the image feature into the embedding.
1872
+ image_features = image_features.view(batch_size, num_image * num_patch, -1)
1873
+ image_input_idx = image_input_idx.view(batch_size, num_image * num_patch)
1874
+
1875
+ valid = image_input_idx >= 0
1876
+ batch_idx = torch.arange(batch_size, device=x.device)
1877
+ batch_idx = torch.tile(batch_idx[:, None], [1, image_features.shape[1]])
1878
+
1879
+ # For hf demo/endpoint
1880
+ image_features = image_features.to(x.device)
1881
+
1882
+ x[batch_idx[valid], image_input_idx[valid]] += image_features[valid]
1883
+
1884
+ if not self.config.rope:
1885
+ # Get positional embeddings.
1886
+ # shape: (1, seq_len)
1887
+ pos = torch.arange(past_length, past_length + seq_len, dtype=torch.long, device=x.device).unsqueeze(0)
1888
+ # shape: (1, seq_len, d_model)
1889
+ pos_emb = self.transformer.wpe(pos) # type: ignore
1890
+ x = pos_emb + x
1891
+
1892
+ # Add input + positional embeddings and apply dropout.
1893
+ # shape: (batch_size, seq_len, d_model)
1894
+ x = self.transformer.emb_drop(x) # type: ignore
1895
+
1896
+ # normalized
1897
+ if self.config.normalize_input_embeds:
1898
+ x = x * (self.config.d_model ** 0.5)
1899
+
1900
+ # Transform the attention mask into what the blocks expect.
1901
+ if attention_mask is not None:
1902
+ # shape: (batch_size, 1, 1, seq_len)
1903
+ if len(attention_mask.shape) == 2:
1904
+ attention_mask = attention_mask[:, :past_length + seq_len]
1905
+ attention_mask = attention_mask.to(dtype=torch.float).view(batch_size, -1)[:, None, None, :]
1906
+ else:
1907
+ attention_mask = attention_mask.unsqueeze(1).to(dtype=torch.float)
1908
+ attention_mask = (1.0 - attention_mask) * torch.finfo(attention_mask.dtype).min
1909
+
1910
+ # Merge attention mask with attention bias.
1911
+ if (
1912
+ attention_bias is not None
1913
+ or attention_mask is not None
1914
+ # NOTE (epwalsh): we need to initialize the attn bias in order for attn to work properly
1915
+ # with key+value cache. Otherwise `F.scaled_dot_product_attention()` doesn't seem to compute
1916
+ # scores correctly.
1917
+ or past_key_values is not None
1918
+ ):
1919
+ if attention_bias is None:
1920
+ attention_bias = get_causal_attention_bias(self.__cache, past_length + seq_len, x.device)
1921
+ elif attention_bias.dtype in (torch.int8, torch.bool):
1922
+ attention_bias = attention_bias.to(dtype=torch.float)
1923
+ attention_bias.masked_fill_(attention_bias == 0.0, torch.finfo(attention_bias.dtype).min)
1924
+
1925
+ # Transform to the right shape and data type.
1926
+ mask_len = seq_len
1927
+ if attention_mask is not None:
1928
+ mask_len = attention_mask.shape[-1]
1929
+ elif past_key_values is not None:
1930
+ mask_len = past_key_values[0][0].shape[-2] + seq_len
1931
+ attention_bias = attention_bias[:, :, :mask_len, :mask_len].to(dtype=torch.float)
1932
+
1933
+ # Add in the masking bias.
1934
+ if attention_mask is not None:
1935
+ attention_bias = attention_bias + attention_mask
1936
+ # Might get -infs after adding attention mask, since dtype.min + dtype.min = -inf.
1937
+ # `F.scaled_dot_product_attention()` doesn't handle -inf like you'd expect, instead
1938
+ # it can produce NaNs.
1939
+ ensure_finite_(attention_bias, check_neg_inf=True, check_pos_inf=False)
1940
+
1941
+ attn_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = [] if use_cache else None
1942
+
1943
+ # decoder layers
1944
+ all_hidden_states = []
1945
+
1946
+ # Apply blocks one-by-one.
1947
+ if self.config.block_group_size == 1:
1948
+ for block_idx, block in enumerate(self.transformer.blocks):
1949
+ if output_hidden_states:
1950
+ # add hidden states
1951
+ all_hidden_states.append(x)
1952
+
1953
+ layer_past = None if past_key_values is None else past_key_values[block_idx]
1954
+ x, cache = block(x, attention_bias=attention_bias, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache)
1955
+
1956
+ if attn_key_values is not None:
1957
+ assert cache is not None
1958
+ attn_key_values.append(cache)
1959
+ else:
1960
+ for group_idx, block_group in enumerate(self.transformer.block_groups):
1961
+ if output_hidden_states:
1962
+ # add hidden states
1963
+ all_hidden_states.append(x)
1964
+
1965
+ layers_past = (
1966
+ None
1967
+ if past_key_values is None
1968
+ else past_key_values[
1969
+ group_idx * self.config.block_group_size : (group_idx + 1) * self.config.block_group_size
1970
+ ]
1971
+ )
1972
+ x, cache = block_group(
1973
+ x, attention_bias=attention_bias, position_ids=position_ids, layers_past=layers_past, use_cache=use_cache
1974
+ )
1975
+ if attn_key_values is not None:
1976
+ assert cache is not None
1977
+ attn_key_values.extend(cache)
1978
+
1979
+ if last_logits_only:
1980
+ # shape: (batch_size, 1, d_model)
1981
+ if append_last_valid_logits is not None:
1982
+ last_valid_output = x[
1983
+ torch.arange(x.shape[0], device=x.device), append_last_valid_logits.to(x.device)]
1984
+ x = last_valid_output.unsqueeze(1)
1985
+ else:
1986
+ x = x[:, -1, :].unsqueeze(1)
1987
+
1988
+ # Apply final layer norm.
1989
+ # shape: (batch_size, seq_len or 1, d_model)
1990
+ x = self.transformer.ln_f(x) # type: ignore
1991
+ if output_hidden_states:
1992
+ # add final hidden state post-final-layernorm, following HuggingFace's convention
1993
+ all_hidden_states.append(x)
1994
+
1995
+ # Get logits.
1996
+ # shape: (batch_size, seq_len or 1, vocab_size)
1997
+ if self.config.weight_tying:
1998
+ logits = F.linear(x, self.transformer.wte.weight, None) # type: ignore
1999
+ else:
2000
+ logits = self.transformer.ff_out(x) # type: ignore
2001
+ if self.config.scale_logits:
2002
+ logits.mul_(1 / math.sqrt(self.config.d_model))
2003
+
2004
+ if not last_logits_only and append_last_valid_logits is not None:
2005
+ last_valid_logit = logits[
2006
+ torch.arange(logits.shape[0], device=logits.device), append_last_valid_logits]
2007
+ logits = torch.cat([logits[:, :-1], last_valid_logit[:, None]], dim=1)
2008
+
2009
+ return ModelOutput(logits=logits, attn_key_values=attn_key_values, hidden_states=tuple(all_hidden_states) if output_hidden_states else None) # type: ignore[arg-type]
2010
+
2011
+
2012
+ class MolmoForCausalLM(PreTrainedModel):
2013
+ config_class = MolmoConfig
2014
+ base_model_prefix = "model"
2015
+ _no_split_modules = ["MolmoBlock"]
2016
+
2017
+ def __init__(self, config: MolmoConfig, model: Optional[Molmo] = None, init_params: bool = False):
2018
+ super().__init__(config)
2019
+
2020
+ if not model:
2021
+ full_config = FullMolmoConfig(
2022
+ image_padding_embed="pad_and_partial_pad",
2023
+ image_pooling_2d="attention-meanq",
2024
+ attention_layer_norm=config.attention_layer_norm,
2025
+ rope_impl="llama",
2026
+ vocab_size=config.vocab_size,
2027
+ max_sequence_length=config.max_position_embeddings,
2028
+ qkv_bias=config.qkv_bias,
2029
+ norm_after=config.norm_after,
2030
+ embedding_size=config.embedding_size,
2031
+ attention_type="sdpa",
2032
+ embedding_dropout=0,
2033
+ attention_dropout=0,
2034
+ residual_dropout=0,
2035
+ rope=True,
2036
+ weight_tying=False,
2037
+ include_bias=False,
2038
+ d_model=config.hidden_size,
2039
+ mlp_hidden_size=config.intermediate_size,
2040
+ n_layers=config.num_hidden_layers,
2041
+ additional_vocab_size=128,
2042
+ n_heads=config.num_attention_heads,
2043
+ n_kv_heads=config.num_key_value_heads,
2044
+ rope_theta=config.rope_theta,
2045
+ layer_norm_eps=config.layer_norm_eps,
2046
+ layer_norm_type=config.layer_norm_type,
2047
+ vit_layers=[-2, -9],
2048
+ vision_backbone=VisionBackboneConfig(
2049
+ image_default_input_size=(336, 336),
2050
+ image_patch_size=14,
2051
+ image_pos_patch_size=14,
2052
+ image_emb_dim=1024,
2053
+ image_num_heads=16,
2054
+ image_num_key_value_heads=16,
2055
+ image_num_layers=23,
2056
+ image_head_dim=64,
2057
+ image_mlp_dim=4096,
2058
+ image_mlp_activations="quick_gelu",
2059
+ image_dropout_rate=0.0,
2060
+ image_num_pos=577,
2061
+ image_norm_eps=1e-5,
2062
+ attention_dropout=0.0,
2063
+ residual_dropout=0.0,
2064
+ initializer_range=0.02,
2065
+ )
2066
+ )
2067
+ self.model = Molmo(full_config, init_params=init_params)
2068
+ else:
2069
+ self.model = model
2070
+
2071
+
2072
+ def forward(
2073
+ self,
2074
+ input_ids: torch.LongTensor = None,
2075
+ inputs_embeds: Optional[torch.FloatTensor] = None,
2076
+ attention_mask: Optional[torch.Tensor] = None,
2077
+ attention_bias: Optional[torch.Tensor] = None,
2078
+ response_mask: Optional[torch.Tensor] = None,
2079
+ images: Optional[torch.Tensor] = None,
2080
+ image_masks: Optional[torch.Tensor] = None,
2081
+ image_input_idx: Optional[torch.Tensor] = None,
2082
+ subsegment_ids: Optional[torch.Tensor] = None,
2083
+ position_ids: Optional[torch.Tensor] = None,
2084
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
2085
+ labels: Optional[torch.LongTensor] = None,
2086
+ loss_masks: Optional[torch.Tensor] = None,
2087
+ use_cache: Optional[bool] = None,
2088
+ last_logits_only: Optional[bool] = None,
2089
+ output_attentions: Optional[bool] = None,
2090
+ output_hidden_states: Optional[bool] = None,
2091
+ append_last_valid_logits: Optional[torch.Tensor] = None,
2092
+ return_dict: Optional[bool] = None,
2093
+ cache_position: Optional[
2094
+ Cache
2095
+ ] = None, # This is a hack mitigation of an issue in transformers `4.39.x` https://github.com/huggingface/transformers/issues/29426
2096
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
2097
+ if use_cache is None:
2098
+ use_cache = self.config.use_cache
2099
+
2100
+ if output_attentions:
2101
+ raise ValueError("output_attentions is not yet supported in Molmo")
2102
+
2103
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
2104
+
2105
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
2106
+ outputs = self.model.forward(
2107
+ input_ids=input_ids,
2108
+ input_embeddings=inputs_embeds,
2109
+ attention_mask=attention_mask,
2110
+ attention_bias=attention_bias,
2111
+ response_mask=response_mask,
2112
+ images=images,
2113
+ image_masks=image_masks,
2114
+ image_input_idx=image_input_idx,
2115
+ subsegment_ids=subsegment_ids,
2116
+ position_ids=position_ids,
2117
+ past_key_values=past_key_values,
2118
+ use_cache=use_cache,
2119
+ last_logits_only=last_logits_only,
2120
+ output_hidden_states=output_hidden_states,
2121
+ append_last_valid_logits=append_last_valid_logits,
2122
+ )
2123
+
2124
+ logits = outputs.logits
2125
+ hidden_states = outputs.hidden_states
2126
+
2127
+ loss = None
2128
+ if labels is not None:
2129
+ if loss_masks is not None:
2130
+ loss_masks = loss_masks * (loss_masks > 0)
2131
+ batch_size_in_tokens = max(loss_masks.sum().item(), 1)
2132
+ labels = labels.long()
2133
+ labels.masked_fill_(~(loss_masks > 0), -100)
2134
+ labels = labels.view(-1)
2135
+ logits_for_loss = logits.to(torch.float32).view(-1, logits.size(-1))
2136
+ loss_fct = torch.nn.CrossEntropyLoss(ignore_index=-100, reduction='none')
2137
+ loss = loss_fct(logits_for_loss, labels)
2138
+ loss = loss.view(input_ids.shape[0], -1)
2139
+ loss = loss * loss_masks
2140
+ loss = loss.sum() / batch_size_in_tokens
2141
+ use_zloss = getattr(self.config, "softmax_auxiliary_loss", False)
2142
+ if use_zloss:
2143
+ z_squared = logits_for_loss.logsumexp(-1).pow(2)
2144
+ z_loss = self.config.softmax_auxiliary_loss_scale * z_squared
2145
+ z_loss = z_loss.view(input_ids.shape[0], -1)
2146
+ z_loss = z_loss * loss_masks
2147
+ z_loss = z_loss.sum() / batch_size_in_tokens
2148
+ loss += z_loss
2149
+ else:
2150
+ # Shift so that tokens < n predict n
2151
+ shift_logits = logits[..., :-1, :].contiguous()
2152
+ shift_labels = labels[..., 1:].contiguous()
2153
+ # Flatten the tokens
2154
+ loss_fct = torch.nn.CrossEntropyLoss()
2155
+ shift_logits = shift_logits.view(-1, self.config.embedding_size)
2156
+ shift_labels = shift_labels.view(-1)
2157
+ # Enable model parallelism
2158
+ shift_labels = shift_labels.to(shift_logits.device)
2159
+ loss = loss_fct(shift_logits, shift_labels)
2160
+
2161
+ if not return_dict:
2162
+ output = (logits,) + outputs[1:]
2163
+ return (loss,) + output if loss is not None else output
2164
+
2165
+ return CausalLMOutputWithPast(
2166
+ loss=loss,
2167
+ logits=logits,
2168
+ past_key_values=outputs.attn_key_values,
2169
+ hidden_states=hidden_states,
2170
+ )
2171
+
2172
+ def can_generate(self) -> bool:
2173
+ return True
2174
+
2175
+ @torch.no_grad()
2176
+ def generate_from_batch(
2177
+ self,
2178
+ batch: Dict[str, Any],
2179
+ generation_config: Optional[GenerationConfig] = None,
2180
+ **kwargs,
2181
+ ):
2182
+ if generation_config is not None:
2183
+ assert generation_config.use_cache
2184
+
2185
+ images = batch.get("images")
2186
+ image_masks = batch.get("image_masks")
2187
+ image_input_idx = batch.get("image_input_idx")
2188
+
2189
+ # Validate inputs.
2190
+ input_ids = batch["input_ids"]
2191
+ batch_size, seq_len = input_ids.shape
2192
+ attention_mask = batch.get("attention_mask", None)
2193
+ max_new_tokens = generation_config.max_new_tokens
2194
+ assert max_new_tokens is not None
2195
+ mask_len = seq_len + max_new_tokens if self.config.use_position_ids else seq_len
2196
+ position_ids: Optional[torch.Tensor] = None
2197
+ append_last_valid_logits: Optional[torch.Tensor] = None
2198
+ if self.config.use_position_ids and attention_mask is None:
2199
+ attention_mask = input_ids != -1
2200
+ position_ids = torch.clamp(
2201
+ torch.cumsum(attention_mask.to(torch.int32), dim=-1) - 1,
2202
+ min=0
2203
+ )
2204
+ append_last_valid_logits = attention_mask.long().sum(dim=-1) - 1
2205
+ attention_mask = torch.cat(
2206
+ [attention_mask, attention_mask.new_ones((batch_size, max_new_tokens))],
2207
+ dim=1,
2208
+ )
2209
+ if attention_mask is not None:
2210
+ assert attention_mask.shape == (batch_size, mask_len)
2211
+
2212
+ out = super().generate(
2213
+ batch["input_ids"],
2214
+ generation_config,
2215
+ attention_mask=attention_mask,
2216
+ images=images,
2217
+ image_masks=image_masks,
2218
+ image_input_idx=image_input_idx,
2219
+ position_ids=position_ids,
2220
+ append_last_valid_logits=append_last_valid_logits,
2221
+ **kwargs,
2222
+ )
2223
+
2224
+ return out
2225
+
2226
+ def prepare_inputs_for_generation(
2227
+ self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple]] = None, **kwargs
2228
+ ):
2229
+ if past_key_values:
2230
+ # This is because we want the model to only process the last generated token.
2231
+ input_ids = input_ids[:, -1:]
2232
+
2233
+ if self.config.use_position_ids:
2234
+ attention_mask = kwargs.get("attention_mask")
2235
+ images = kwargs.get("images")
2236
+ image_masks = kwargs.get("image_masks")
2237
+ image_input_idx = kwargs.get("image_input_idx")
2238
+ position_ids = kwargs.get("position_ids")
2239
+ append_last_valid_logits = kwargs.get("append_last_valid_logits")
2240
+ model_inputs = {
2241
+ "input_ids": input_ids,
2242
+ "attention_mask": attention_mask,
2243
+ "position_ids": position_ids,
2244
+ "past_key_values": past_key_values,
2245
+ "use_cache": True,
2246
+ "last_logits_only": True,
2247
+ }
2248
+ if past_key_values is None:
2249
+ model_inputs["images"] = images
2250
+ model_inputs["image_masks"] = image_masks
2251
+ model_inputs["image_input_idx"] = image_input_idx
2252
+ model_inputs["append_last_valid_logits"] = append_last_valid_logits
2253
+ else:
2254
+ model_inputs = {"input_ids": input_ids, "past_key_values": past_key_values}
2255
+
2256
+ model_inputs.update(kwargs)
2257
+ model_inputs["use_cache"] = kwargs.pop("use_cache", self.config.use_cache)
2258
+ return model_inputs
2259
+
2260
+ def _update_model_kwargs_for_generation(
2261
+ self,
2262
+ outputs: ModelOutput,
2263
+ model_kwargs: Dict[str, Any],
2264
+ is_encoder_decoder: bool = False,
2265
+ num_new_tokens: int = 1,
2266
+ ) -> Dict[str, Any]:
2267
+ if self.config.use_position_ids:
2268
+ model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
2269
+ if "append_last_valid_logits" in model_kwargs:
2270
+ del model_kwargs["append_last_valid_logits"]
2271
+ if "images" in model_kwargs:
2272
+ del model_kwargs["images"]
2273
+ del model_kwargs["image_masks"]
2274
+ del model_kwargs["image_input_idx"]
2275
+ cache_name, cache = super()._extract_past_from_model_output(outputs)
2276
+ model_kwargs[cache_name] = cache
2277
+ model_kwargs["cache_position"] = model_kwargs["cache_position"][-1:] + num_new_tokens
2278
+ return model_kwargs
2279
+
2280
+ def get_input_embeddings(self) -> torch.nn.Module:
2281
+ return self.model.transformer.wte
2282
+
2283
+ def set_input_embeddings(self, value: torch.nn.Module):
2284
+ self.model.transformer.wte = value
2285
+
2286
+ def get_output_embeddings(self):
2287
+ if self.config.weight_tying:
2288
+ return self.model.transformer.wte
2289
+ else:
2290
+ return self.model.transformer.ff_out
2291
+
2292
+ def set_output_embeddings(self, value: torch.nn.Module):
2293
+ if self.config.weight_tying:
2294
+ self.model.transformer.wte = value
2295
+ else:
2296
+ self.model.transformer.ff_out = value
2297
+
2298
+ def tie_weights(self):
2299
+ """
2300
+ This function is intentionally left as a no-op.
2301
+
2302
+ Weight tying is handled as follows:
2303
+ - When the model is initialized, the `ff_out` layer is conditionally defined based on the `weight_tying` configuration.
2304
+ See: `if not config.weight_tying: self.transformer.update(...)` in `olmo/model.py`.
2305
+ - When computing logits, the `wte` weights are used directly if `weight_tying` is enabled.
2306
+ See: `if self.config.weight_tying: logits = F.linear(x, self.transformer.wte.weight, None)` in the `forward` method.
2307
+
2308
+ Therefore, there is no need to explicitly tie the weights in this function.
2309
+ """
2310
+ pass
2311
+
2312
+ def resize_token_embeddings(
2313
+ self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
2314
+ ) -> torch.nn.Embedding:
2315
+ """
2316
+ Resizes input token embeddings matrix of the model if `new_num_tokens != config.embedding_size`.
2317
+
2318
+ Takes care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.
2319
+
2320
+ Arguments:
2321
+ new_num_tokens (`int`, *optional*):
2322
+ The new number of tokens in the embedding matrix. Increasing the size will add newly initialized
2323
+ vectors at the end. Reducing the size will remove vectors from the end. If not provided or `None`, just
2324
+ returns a pointer to the input tokens `torch.nn.Embedding` module of the model without doing anything.
2325
+ pad_to_multiple_of (`int`, *optional*):
2326
+ If set will pad the embedding matrix to a multiple of the provided value. If `new_num_tokens` is set to
2327
+ `None` will just pad the embedding to a multiple of `pad_to_multiple_of`.
2328
+
2329
+ This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
2330
+ `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. For more
2331
+ details about this, or help on choosing the correct value for resizing, refer to this guide:
2332
+ https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc
2333
+
2334
+ Return:
2335
+ `torch.nn.Embedding`: Pointer to the input tokens Embeddings Module of the model.
2336
+
2337
+ Note:
2338
+ This method differs from the base class implementation by resizing the `embedding_size` attribute of the
2339
+ model configuration instead of the `vocab_size`. It also includes a warning if the resized `embedding_size`
2340
+ is less than the `vocab_size`. In OLMo, `embedding_size` refers to the dimensionality of the model's token
2341
+ embeddings, while `vocab_size` refers to the number of unique tokens in the vocabulary.
2342
+ """
2343
+ model_embeds = self._resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
2344
+ if new_num_tokens is None and pad_to_multiple_of is None:
2345
+ return model_embeds
2346
+
2347
+ # Update base model and current model config
2348
+ self.config.embedding_size = model_embeds.weight.shape[0]
2349
+ self.model.config.embedding_size = model_embeds.weight.shape[0]
2350
+
2351
+ # Check if the embedding size is less than the vocab size
2352
+ if self.config.embedding_size < self.config.vocab_size:
2353
+ warning_message = (
2354
+ f"Resizing token embeddings to size {self.config.embedding_size}, which is less than the vocab size "
2355
+ f"{self.config.vocab_size} defined in the model configuration. Make sure your tokenizer's vocabulary "
2356
+ "size is less than or equal to the new token embedding size."
2357
+ )
2358
+ log.warning(warning_message)
2359
+
2360
+ # Tie weights again if needed
2361
+ self.tie_weights()
2362
+
2363
+ return model_embeds
2364
+
2365
+
2366
+ # Always register for multi-modal features
2367
+ AutoModelForCausalLM.register(MolmoConfig, MolmoForCausalLM)
preprocessing_molmo.py ADDED
@@ -0,0 +1,192 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Processor class for Molmo.
3
+ """
4
+
5
+ from typing import Optional
6
+
7
+ import PIL
8
+ from PIL import ImageOps
9
+ from PIL.Image import Image
10
+
11
+ try:
12
+ from typing import Unpack
13
+ except ImportError:
14
+ from typing_extensions import Unpack
15
+
16
+ import numpy as np
17
+ import torch
18
+
19
+ from transformers.image_utils import ImageInput
20
+ from transformers.processing_utils import (
21
+ TextKwargs,
22
+ ProcessingKwargs,
23
+ ProcessorMixin,
24
+ )
25
+
26
+ from transformers.tokenization_utils_base import TextInput, PreTokenizedInput
27
+ from transformers.utils import logging
28
+
29
+ from transformers import AutoTokenizer
30
+ from .image_preprocessing_molmo import MolmoImagesKwargs, MolmoImageProcessor
31
+
32
+
33
+ logger = logging.get_logger(__name__)
34
+
35
+
36
+ DEFAULT_IMAGE_PATCH_TOKEN = f"<im_patch>"
37
+ DEFAULT_IM_START_TOKEN = f"<im_start>"
38
+ DEFAULT_IM_END_TOKEN = f"<im_end>"
39
+ DEFAULT_IM_COL_TOKEN = f"<im_col>"
40
+ IMAGE_PROMPT = "<|image|>"
41
+
42
+ EXTRA_TOKENS = (DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_COL_TOKEN, IMAGE_PROMPT)
43
+
44
+
45
+ def get_special_token_ids(tokenizer):
46
+ ids = tokenizer.encode("".join(EXTRA_TOKENS), add_special_tokens=False)
47
+ assert len(ids) == len(EXTRA_TOKENS)
48
+ return {k: i for k, i in zip(EXTRA_TOKENS, ids)}
49
+
50
+
51
+ class MolmoTextKwargs(TextKwargs, total=False):
52
+ style: Optional[str]
53
+ system_prompt: Optional[str]
54
+ message_format: Optional[str]
55
+ always_start_with_space: Optional[bool]
56
+ sequence_length: Optional[int]
57
+
58
+
59
+ class MolmoProcessorKwargs(ProcessingKwargs, total=False):
60
+ text_kwargs: MolmoTextKwargs
61
+ images_kwargs: MolmoImagesKwargs
62
+ _defaults = {
63
+ "images_kwargs": {
64
+ "max_crops": 12,
65
+ "overlap_margins": [4, 4],
66
+ "base_image_input_size": [336, 336],
67
+ "image_token_length_w": 12,
68
+ "image_token_length_h": 12,
69
+ "image_patch_size": 14,
70
+ "image_padding_mask": True,
71
+ },
72
+ "text_kwargs": {
73
+ "style": "long_caption",
74
+ "system_prompt": "none",
75
+ "message_format": "role",
76
+ "always_start_with_space": True,
77
+ "sequence_length": 1536,
78
+ "padding": False,
79
+ },
80
+ }
81
+
82
+
83
+ class MolmoProcessor(ProcessorMixin):
84
+ attributes = ["image_processor", "tokenizer"]
85
+ image_processor_class = "AutoImageProcessor"
86
+ tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
87
+
88
+ def __init__(self, image_processor: MolmoImageProcessor = None, tokenizer : AutoTokenizer = None, **kwargs):
89
+ # self.image_processor = image_processor
90
+ # self.tokenizer = tokenizer
91
+ super().__init__(image_processor, tokenizer)
92
+ self._special_tokens = None
93
+
94
+ @property
95
+ def special_token_ids(self):
96
+ if self._special_tokens is None:
97
+ self._special_tokens = get_special_token_ids(self.tokenizer)
98
+ return self._special_tokens
99
+
100
+ def get_tokens_input(self, prompt, message_format, always_start_with_space):
101
+ if message_format == "none" or message_format is None:
102
+ pass
103
+ elif message_format == "role":
104
+ prompt = "User: " + prompt + " Assistant:"
105
+ else:
106
+ raise NotImplementedError(f"Message format {message_format} not implemented")
107
+
108
+ if always_start_with_space:
109
+ prompt = " " + prompt
110
+
111
+ tokens = self.tokenizer.encode(prompt, add_special_tokens=False)
112
+
113
+ return tokens
114
+
115
+ def process(
116
+ self,
117
+ text: TextInput = None,
118
+ images: ImageInput = None,
119
+ *,
120
+ tokens: Optional[PreTokenizedInput] = None,
121
+ **kwargs: Unpack[MolmoProcessorKwargs],
122
+ ):
123
+ output_kwargs = self._merge_kwargs(
124
+ MolmoProcessorKwargs,
125
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
126
+ **kwargs,
127
+ )
128
+
129
+ if tokens is None:
130
+ tokens = self.get_tokens_input(
131
+ text,
132
+ output_kwargs["text_kwargs"]["message_format"],
133
+ output_kwargs["text_kwargs"]["always_start_with_space"],
134
+ )
135
+
136
+ image_token_id = self.special_token_ids[IMAGE_PROMPT]
137
+
138
+ if images is not None:
139
+ if not isinstance(images, (list, tuple)):
140
+ images = [images]
141
+ image_arrays = []
142
+ for image in images:
143
+ if isinstance(image, Image):
144
+ image = image.convert("RGB")
145
+ # Handle images with EXIF orientation tags, which PIL will ignore by default
146
+ # https://github.com/python-pillow/Pillow/issues/4703
147
+ img = ImageOps.exif_transpose(image)
148
+ image_arrays.append(np.array(image))
149
+ else:
150
+ assert len(image.shape) == 3 and image.shape[-1] == 3
151
+ image_arrays.append(image.astype(np.uint8))
152
+ images = image_arrays
153
+ # For now only support inserting images at the start
154
+ image_idx = [-1]*len(images)
155
+ else:
156
+ image_idx = None
157
+
158
+ sequence_length = output_kwargs["text_kwargs"]["sequence_length"]
159
+
160
+ image_patch_token_id = self.special_token_ids[DEFAULT_IMAGE_PATCH_TOKEN]
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+ image_col_token_id = self.special_token_ids[DEFAULT_IM_COL_TOKEN]
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+ image_start_token_id = self.special_token_ids[DEFAULT_IM_START_TOKEN]
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+ image_end_token_id = self.special_token_ids[DEFAULT_IM_END_TOKEN]
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+ out = self.image_processor.multimodal_preprocess(
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+ images=images,
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+ image_idx=image_idx,
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+ tokens=np.asarray(tokens).astype(np.int32),
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+ sequence_length=sequence_length,
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+ image_end_token_id=image_end_token_id,
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+ **output_kwargs["images_kwargs"]
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+ )
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+
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+ # Prepend BOS
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+ # qwen2 and olmo do not have a BOS, and instead use EOS as a generic seperator token.
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+ bos = self.tokenizer.bos_token_id or self.tokenizer.eos_token_id
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+ decoder_input_tokens = np.pad(out["input_ids"], [[1, 0]], constant_values=bos)
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+ out["input_ids"] = decoder_input_tokens
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+ if "image_input_idx" in out:
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+ # Shift patch mapping up by one since we added BOS
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+ image_input_idx = out["image_input_idx"]
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+ out["image_input_idx"] = np.where(image_input_idx < 0, image_input_idx, image_input_idx + 1)
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+
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+ for k, v in out.items():
187
+ out[k] = torch.from_numpy(v)
188
+
189
+ return out
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+
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+
192
+ MolmoProcessor.register_for_auto_class()
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'stage3_prefetch_bucket_size': 'auto', 'stage3_param_persistence_threshold': 'auto', 'stage3_max_live_parameters': 1000000000.0, 'stage3_max_reuse_distance': 1000000000.0, 'stage3_gather_16bit_weights_on_model_save': True}, 'gradient_accumulation_steps': 'auto', 'gradient_clipping': 'auto', 'steps_per_print': 2000, 'train_batch_size': 'auto', 'train_micro_batch_size_per_gpu': 'auto', 'wall_clock_breakdown': False}, label_smoothing_factor=0.0, optim=<OptimizerNames.ADAMW_TORCH: 'adamw_torch'>, optim_args=None, adafactor=False, group_by_length=False, length_column_name='length', report_to=['wandb'], ddp_find_unused_parameters=True, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=True, dataloader_pin_memory=True, dataloader_persistent_workers=False, skip_memory_metrics=True, use_legacy_prediction_loop=False, push_to_hub=False, resume_from_checkpoint=None, hub_model_id=None, hub_strategy=<HubStrategy.EVERY_SAVE: 'every_save'>, hub_token=None, hub_private_repo=False, hub_always_push=False, gradient_checkpointing=False, gradient_checkpointing_kwargs=None, include_inputs_for_metrics=False, include_for_metrics=[], eval_do_concat_batches=True, fp16_backend='auto', evaluation_strategy=None, push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=None, mp_parameters='', auto_find_batch_size=False, full_determinism=False, torchdynamo=None, ray_scope='last', ddp_timeout=1800, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, dispatch_batches=None, split_batches=None, include_tokens_per_second=False, include_num_input_tokens_seen=False, neftune_noise_alpha=None, optim_target_modules=None, batch_eval_metrics=False, eval_on_start=False, use_liger_kernel=False, eval_use_gather_object=False, average_tokens_across_devices=False, sortish_sampler=False, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=GenerationConfig {\n \"eos_token_id\": 151643,\n \"max_new_tokens\": 2048,\n \"pad_token_id\": 151643\n}\n, acc_strategy='token', loss_name=None, additional_saved_files=[], train_sampler_random=True, metric_warmup_step=0, train_dataset_sample=-1)"
302
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,435 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "additional_special_tokens": [
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+ "|<EXTRA_TOKENS_0>|",
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+ "|<EXTRA_TOKENS_1>|",
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+ "|<EXTRA_TOKENS_2>|",
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+ "|<EXTRA_TOKENS_46>|",
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+ "grad_norm": 2.331626905910975,
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+ "learning_rate": 2.0132112671405244e-06,
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+ "loss": 0.14904225,
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+ "memory(GiB)": 100.58,
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+ "step": 395,
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+ "train_speed(iter/s)": 0.331532
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+ "acc": 0.93090382,
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+ "learning_rate": 1.8934938478853108e-06,
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+ "loss": 0.17768097,
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+ "memory(GiB)": 100.58,
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+ "step": 400,
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+ "train_speed(iter/s)": 0.331963
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+ },
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+ {
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+ "acc": 0.93722563,
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+ "epoch": 2.9381818181818184,
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+ "grad_norm": 2.7247775486261734,
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+ "learning_rate": 1.7766113574255145e-06,
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+ "memory(GiB)": 100.58,
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+ "step": 405,
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+ "train_speed(iter/s)": 0.332266
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+ },
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+ "acc": 0.94374504,
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+ "epoch": 2.9745454545454546,
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+ "grad_norm": 2.9951618135706055,
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+ "learning_rate": 1.6626704429712411e-06,
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+ "loss": 0.14953468,
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+ "memory(GiB)": 100.58,
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+ "step": 410,
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+ "train_speed(iter/s)": 0.332599
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+ },
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+ {
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+ "epoch": 3.0,
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+ "eval_acc": 0.9192953707496927,
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+ "eval_loss": 0.21225064992904663,
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+ "eval_runtime": 9.5239,
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+ "eval_samples_per_second": 12.18,
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+ "eval_steps_per_second": 1.575,
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+ "step": 414
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+ }
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+ ],
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+ "logging_steps": 5,
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+ "max_steps": 548,
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+ "num_input_tokens_seen": 0,
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+ "num_train_epochs": 4,
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+ "save_steps": 200,
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+ "stateful_callbacks": {
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+ "TrainerControl": {
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+ "should_epoch_stop": false,
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+ "should_evaluate": false,
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+ "should_log": false,
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+ "should_save": true,
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+ "attributes": {}
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+ "trial_name": null,
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+ "trial_params": null
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+ }
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vocab.json ADDED
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zero_to_fp32.py ADDED
@@ -0,0 +1,760 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python
2
+
3
+ # Copyright (c) Microsoft Corporation.
4
+ # SPDX-License-Identifier: Apache-2.0
5
+
6
+ # DeepSpeed Team
7
+
8
+ # This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
9
+ # copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
10
+ # the future. Once extracted, the weights don't require DeepSpeed and can be used in any
11
+ # application.
12
+ #
13
+ # example:
14
+ # python zero_to_fp32.py . output_dir/
15
+ # or
16
+ # python zero_to_fp32.py . output_dir/ --safe_serialization
17
+
18
+ import argparse
19
+ import torch
20
+ import glob
21
+ import math
22
+ import os
23
+ import re
24
+ import gc
25
+ import json
26
+ import numpy as np
27
+ from tqdm import tqdm
28
+ from collections import OrderedDict
29
+ from dataclasses import dataclass
30
+
31
+ # while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
32
+ # DeepSpeed data structures it has to be available in the current python environment.
33
+ from deepspeed.utils import logger
34
+ from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
35
+ FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
36
+ FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
37
+
38
+
39
+ @dataclass
40
+ class zero_model_state:
41
+ buffers: dict()
42
+ param_shapes: dict()
43
+ shared_params: list
44
+ ds_version: int
45
+ frozen_param_shapes: dict()
46
+ frozen_param_fragments: dict()
47
+
48
+
49
+ debug = 0
50
+
51
+ # load to cpu
52
+ device = torch.device('cpu')
53
+
54
+
55
+ def atoi(text):
56
+ return int(text) if text.isdigit() else text
57
+
58
+
59
+ def natural_keys(text):
60
+ '''
61
+ alist.sort(key=natural_keys) sorts in human order
62
+ http://nedbatchelder.com/blog/200712/human_sorting.html
63
+ (See Toothy's implementation in the comments)
64
+ '''
65
+ return [atoi(c) for c in re.split(r'(\d+)', text)]
66
+
67
+
68
+ def get_model_state_file(checkpoint_dir, zero_stage):
69
+ if not os.path.isdir(checkpoint_dir):
70
+ raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
71
+
72
+ # there should be only one file
73
+ if zero_stage <= 2:
74
+ file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
75
+ elif zero_stage == 3:
76
+ file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
77
+
78
+ if not os.path.exists(file):
79
+ raise FileNotFoundError(f"can't find model states file at '{file}'")
80
+
81
+ return file
82
+
83
+
84
+ def get_checkpoint_files(checkpoint_dir, glob_pattern):
85
+ # XXX: need to test that this simple glob rule works for multi-node setup too
86
+ ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
87
+
88
+ if len(ckpt_files) == 0:
89
+ raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
90
+
91
+ return ckpt_files
92
+
93
+
94
+ def get_optim_files(checkpoint_dir):
95
+ return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
96
+
97
+
98
+ def get_model_state_files(checkpoint_dir):
99
+ return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
100
+
101
+
102
+ def parse_model_states(files):
103
+ zero_model_states = []
104
+ for file in files:
105
+ state_dict = torch.load(file, map_location=device, weights_only=False)
106
+
107
+ if BUFFER_NAMES not in state_dict:
108
+ raise ValueError(f"{file} is not a model state checkpoint")
109
+ buffer_names = state_dict[BUFFER_NAMES]
110
+ if debug:
111
+ print("Found buffers:", buffer_names)
112
+
113
+ # recover just the buffers while restoring them to fp32 if they were saved in fp16
114
+ buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
115
+ param_shapes = state_dict[PARAM_SHAPES]
116
+
117
+ # collect parameters that are included in param_shapes
118
+ param_names = []
119
+ for s in param_shapes:
120
+ for name in s.keys():
121
+ param_names.append(name)
122
+
123
+ # update with frozen parameters
124
+ frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
125
+ if frozen_param_shapes is not None:
126
+ if debug:
127
+ print(f"Found frozen_param_shapes: {frozen_param_shapes}")
128
+ param_names += list(frozen_param_shapes.keys())
129
+
130
+ # handle shared params
131
+ shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
132
+
133
+ ds_version = state_dict.get(DS_VERSION, None)
134
+
135
+ frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
136
+
137
+ z_model_state = zero_model_state(buffers=buffers,
138
+ param_shapes=param_shapes,
139
+ shared_params=shared_params,
140
+ ds_version=ds_version,
141
+ frozen_param_shapes=frozen_param_shapes,
142
+ frozen_param_fragments=frozen_param_fragments)
143
+ zero_model_states.append(z_model_state)
144
+
145
+ return zero_model_states
146
+
147
+
148
+ def parse_optim_states(files, ds_checkpoint_dir):
149
+ total_files = len(files)
150
+ state_dicts = []
151
+ for f in tqdm(files, desc='Loading checkpoint shards'):
152
+ state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
153
+ # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
154
+ # and also handle the case where it was already removed by another helper script
155
+ state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
156
+ state_dicts.append(state_dict)
157
+
158
+ if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
159
+ raise ValueError(f"{files[0]} is not a zero checkpoint")
160
+ zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
161
+ world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
162
+
163
+ # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
164
+ # parameters can be different from data parallelism for non-expert parameters. So we can just
165
+ # use the max of the partition_count to get the dp world_size.
166
+
167
+ if type(world_size) is list:
168
+ world_size = max(world_size)
169
+
170
+ if world_size != total_files:
171
+ raise ValueError(
172
+ f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
173
+ "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
174
+ )
175
+
176
+ # the groups are named differently in each stage
177
+ if zero_stage <= 2:
178
+ fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
179
+ elif zero_stage == 3:
180
+ fp32_groups_key = FP32_FLAT_GROUPS
181
+ else:
182
+ raise ValueError(f"unknown zero stage {zero_stage}")
183
+
184
+ fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
185
+ return zero_stage, world_size, fp32_flat_groups
186
+
187
+
188
+ def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
189
+ """
190
+ Returns fp32 state_dict reconstructed from ds checkpoint
191
+
192
+ Args:
193
+ - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
194
+
195
+ """
196
+ print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
197
+
198
+ optim_files = get_optim_files(ds_checkpoint_dir)
199
+ zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
200
+ print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
201
+
202
+ model_files = get_model_state_files(ds_checkpoint_dir)
203
+
204
+ zero_model_states = parse_model_states(model_files)
205
+ print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
206
+
207
+ if zero_stage <= 2:
208
+ return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
209
+ exclude_frozen_parameters)
210
+ elif zero_stage == 3:
211
+ return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
212
+ exclude_frozen_parameters)
213
+
214
+
215
+ def _zero2_merge_frozen_params(state_dict, zero_model_states):
216
+ if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
217
+ return
218
+
219
+ frozen_param_shapes = zero_model_states[0].frozen_param_shapes
220
+ frozen_param_fragments = zero_model_states[0].frozen_param_fragments
221
+
222
+ if debug:
223
+ num_elem = sum(s.numel() for s in frozen_param_shapes.values())
224
+ print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
225
+
226
+ wanted_params = len(frozen_param_shapes)
227
+ wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
228
+ avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
229
+ print(f'Frozen params: Have {avail_numel} numels to process.')
230
+ print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
231
+
232
+ total_params = 0
233
+ total_numel = 0
234
+ for name, shape in frozen_param_shapes.items():
235
+ total_params += 1
236
+ unpartitioned_numel = shape.numel()
237
+ total_numel += unpartitioned_numel
238
+
239
+ state_dict[name] = frozen_param_fragments[name]
240
+
241
+ if debug:
242
+ print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
243
+
244
+ print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
245
+
246
+
247
+ def _has_callable(obj, fn):
248
+ attr = getattr(obj, fn, None)
249
+ return callable(attr)
250
+
251
+
252
+ def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
253
+ param_shapes = zero_model_states[0].param_shapes
254
+
255
+ # Reconstruction protocol:
256
+ #
257
+ # XXX: document this
258
+
259
+ if debug:
260
+ for i in range(world_size):
261
+ for j in range(len(fp32_flat_groups[0])):
262
+ print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
263
+
264
+ # XXX: memory usage doubles here (zero2)
265
+ num_param_groups = len(fp32_flat_groups[0])
266
+ merged_single_partition_of_fp32_groups = []
267
+ for i in range(num_param_groups):
268
+ merged_partitions = [sd[i] for sd in fp32_flat_groups]
269
+ full_single_fp32_vector = torch.cat(merged_partitions, 0)
270
+ merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
271
+ avail_numel = sum(
272
+ [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
273
+
274
+ if debug:
275
+ wanted_params = sum([len(shapes) for shapes in param_shapes])
276
+ wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
277
+ # not asserting if there is a mismatch due to possible padding
278
+ print(f"Have {avail_numel} numels to process.")
279
+ print(f"Need {wanted_numel} numels in {wanted_params} params.")
280
+
281
+ # params
282
+ # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
283
+ # out-of-core computing solution
284
+ total_numel = 0
285
+ total_params = 0
286
+ for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
287
+ offset = 0
288
+ avail_numel = full_single_fp32_vector.numel()
289
+ for name, shape in shapes.items():
290
+
291
+ unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
292
+ total_numel += unpartitioned_numel
293
+ total_params += 1
294
+
295
+ if debug:
296
+ print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
297
+ state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
298
+ offset += unpartitioned_numel
299
+
300
+ # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
301
+ # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
302
+ # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
303
+ # live optimizer object, so we are checking that the numbers are within the right range
304
+ align_to = 2 * world_size
305
+
306
+ def zero2_align(x):
307
+ return align_to * math.ceil(x / align_to)
308
+
309
+ if debug:
310
+ print(f"original offset={offset}, avail_numel={avail_numel}")
311
+
312
+ offset = zero2_align(offset)
313
+ avail_numel = zero2_align(avail_numel)
314
+
315
+ if debug:
316
+ print(f"aligned offset={offset}, avail_numel={avail_numel}")
317
+
318
+ # Sanity check
319
+ if offset != avail_numel:
320
+ raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
321
+
322
+ print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
323
+
324
+
325
+ def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
326
+ exclude_frozen_parameters):
327
+ state_dict = OrderedDict()
328
+
329
+ # buffers
330
+ buffers = zero_model_states[0].buffers
331
+ state_dict.update(buffers)
332
+ if debug:
333
+ print(f"added {len(buffers)} buffers")
334
+
335
+ if not exclude_frozen_parameters:
336
+ _zero2_merge_frozen_params(state_dict, zero_model_states)
337
+
338
+ _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
339
+
340
+ # recover shared parameters
341
+ for pair in zero_model_states[0].shared_params:
342
+ if pair[1] in state_dict:
343
+ state_dict[pair[0]] = state_dict[pair[1]]
344
+
345
+ return state_dict
346
+
347
+
348
+ def zero3_partitioned_param_info(unpartitioned_numel, world_size):
349
+ remainder = unpartitioned_numel % world_size
350
+ padding_numel = (world_size - remainder) if remainder else 0
351
+ partitioned_numel = math.ceil(unpartitioned_numel / world_size)
352
+ return partitioned_numel, padding_numel
353
+
354
+
355
+ def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
356
+ if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
357
+ return
358
+
359
+ if debug:
360
+ for i in range(world_size):
361
+ num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
362
+ print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
363
+
364
+ frozen_param_shapes = zero_model_states[0].frozen_param_shapes
365
+ wanted_params = len(frozen_param_shapes)
366
+ wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
367
+ avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
368
+ print(f'Frozen params: Have {avail_numel} numels to process.')
369
+ print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
370
+
371
+ total_params = 0
372
+ total_numel = 0
373
+ for name, shape in zero_model_states[0].frozen_param_shapes.items():
374
+ total_params += 1
375
+ unpartitioned_numel = shape.numel()
376
+ total_numel += unpartitioned_numel
377
+
378
+ param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
379
+ state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
380
+
381
+ partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
382
+
383
+ if debug:
384
+ print(
385
+ f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
386
+ )
387
+
388
+ print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
389
+
390
+
391
+ class GatheredTensor:
392
+ """
393
+ A pseudo tensor that collects partitioned weights.
394
+ It is more memory efficient when there are multiple groups.
395
+ """
396
+
397
+ def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
398
+ self.flat_groups = flat_groups
399
+ self.flat_groups_offset = flat_groups_offset
400
+ self.offset = offset
401
+ self.partitioned_numel = partitioned_numel
402
+ self.shape = shape
403
+ self.dtype = self.flat_groups[0][0].dtype
404
+
405
+ def contiguous(self):
406
+ """
407
+ Merge partitioned weights from flat_groups into a single tensor.
408
+ """
409
+ end_idx = self.offset + self.partitioned_numel
410
+ world_size = len(self.flat_groups)
411
+ pad_flat_param_chunks = []
412
+
413
+ for rank_i in range(world_size):
414
+ # for each rank, we need to collect weights from related group/groups
415
+ flat_groups_at_rank_i = self.flat_groups[rank_i]
416
+ start_group_id = None
417
+ end_group_id = None
418
+ for group_id in range(len(self.flat_groups_offset)):
419
+ if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
420
+ start_group_id = group_id
421
+ if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
422
+ end_group_id = group_id
423
+ break
424
+ # collect weights from related group/groups
425
+ for group_id in range(start_group_id, end_group_id + 1):
426
+ flat_tensor = flat_groups_at_rank_i[group_id]
427
+ start_offset = self.offset - self.flat_groups_offset[group_id]
428
+ end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
429
+ pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
430
+
431
+ # collect weights from all ranks
432
+ pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
433
+ param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
434
+ return param
435
+
436
+
437
+ def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
438
+ param_shapes = zero_model_states[0].param_shapes
439
+ avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
440
+
441
+ # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
442
+ # param, re-consolidating each param, while dealing with padding if any
443
+
444
+ # merge list of dicts, preserving order
445
+ param_shapes = {k: v for d in param_shapes for k, v in d.items()}
446
+
447
+ if debug:
448
+ for i in range(world_size):
449
+ print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
450
+
451
+ wanted_params = len(param_shapes)
452
+ wanted_numel = sum(shape.numel() for shape in param_shapes.values())
453
+ # not asserting if there is a mismatch due to possible padding
454
+ avail_numel = fp32_flat_groups[0].numel() * world_size
455
+ print(f"Trainable params: Have {avail_numel} numels to process.")
456
+ print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
457
+
458
+ # params
459
+ # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
460
+ # out-of-core computing solution
461
+ offset = 0
462
+ total_numel = 0
463
+ total_params = 0
464
+ flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
465
+ for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
466
+ unpartitioned_numel = shape.numel()
467
+ total_numel += unpartitioned_numel
468
+ total_params += 1
469
+ partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
470
+
471
+ if debug:
472
+ print(
473
+ f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
474
+ )
475
+
476
+ # memory efficient tensor
477
+ tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
478
+ state_dict[name] = tensor
479
+ offset += partitioned_numel
480
+
481
+ offset *= world_size
482
+
483
+ # Sanity check
484
+ if offset != avail_numel:
485
+ raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
486
+
487
+ print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
488
+
489
+
490
+ def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
491
+ exclude_frozen_parameters):
492
+ state_dict = OrderedDict()
493
+
494
+ # buffers
495
+ buffers = zero_model_states[0].buffers
496
+ state_dict.update(buffers)
497
+ if debug:
498
+ print(f"added {len(buffers)} buffers")
499
+
500
+ if not exclude_frozen_parameters:
501
+ _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
502
+
503
+ _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
504
+
505
+ # recover shared parameters
506
+ for pair in zero_model_states[0].shared_params:
507
+ if pair[1] in state_dict:
508
+ state_dict[pair[0]] = state_dict[pair[1]]
509
+
510
+ return state_dict
511
+
512
+
513
+ def to_torch_tensor(state_dict, return_empty_tensor=False):
514
+ """
515
+ Convert state_dict of GatheredTensor to torch tensor
516
+ """
517
+ torch_state_dict = {}
518
+ converted_tensors = {}
519
+ for name, tensor in state_dict.items():
520
+ tensor_id = id(tensor)
521
+ if tensor_id in converted_tensors: # shared tensors
522
+ shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
523
+ torch_state_dict[name] = shared_tensor
524
+ else:
525
+ converted_tensors[tensor_id] = name
526
+ if return_empty_tensor:
527
+ torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
528
+ else:
529
+ torch_state_dict[name] = tensor.contiguous()
530
+ return torch_state_dict
531
+
532
+
533
+ def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
534
+ tag=None,
535
+ exclude_frozen_parameters=False,
536
+ lazy_mode=False):
537
+ """
538
+ Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
539
+ ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
540
+ via a model hub.
541
+
542
+ Args:
543
+ - ``checkpoint_dir``: path to the desired checkpoint folder
544
+ - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
545
+ - ``exclude_frozen_parameters``: exclude frozen parameters
546
+ - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
547
+ Convert the pesduo tensor to torch tensor by ``.contiguous()``
548
+
549
+ Returns:
550
+ - pytorch ``state_dict``
551
+
552
+ A typical usage might be ::
553
+
554
+ from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
555
+ # do the training and checkpoint saving
556
+ state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
557
+ model = model.cpu() # move to cpu
558
+ model.load_state_dict(state_dict)
559
+ # submit to model hub or save the model to share with others
560
+
561
+ In this example the ``model`` will no longer be usable in the deepspeed context of the same
562
+ application. i.e. you will need to re-initialize the deepspeed engine, since
563
+ ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
564
+
565
+ If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
566
+
567
+ Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
568
+ You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
569
+ the checkpoint. Or you can load state_dict in lazy mode ::
570
+
571
+ from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
572
+ state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
573
+ for name, lazy_tensor in state_dict.item():
574
+ tensor = lazy_tensor.contiguous() # to cpu
575
+ print(name, tensor)
576
+ # del tensor to release memory if it no longer in use
577
+ """
578
+ if tag is None:
579
+ latest_path = os.path.join(checkpoint_dir, 'latest')
580
+ if os.path.isfile(latest_path):
581
+ with open(latest_path, 'r') as fd:
582
+ tag = fd.read().strip()
583
+ else:
584
+ raise ValueError(f"Unable to find 'latest' file at {latest_path}")
585
+
586
+ ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
587
+
588
+ if not os.path.isdir(ds_checkpoint_dir):
589
+ raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
590
+
591
+ state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
592
+ if lazy_mode:
593
+ return state_dict
594
+ else:
595
+ return to_torch_tensor(state_dict)
596
+
597
+
598
+ def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
599
+ output_dir,
600
+ max_shard_size="5GB",
601
+ safe_serialization=False,
602
+ tag=None,
603
+ exclude_frozen_parameters=False):
604
+ """
605
+ Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
606
+ loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
607
+
608
+ Args:
609
+ - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
610
+ - ``output_dir``: directory to the pytorch fp32 state_dict output files
611
+ - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
612
+ - ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
613
+ - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
614
+ - ``exclude_frozen_parameters``: exclude frozen parameters
615
+ """
616
+
617
+ # Dependency pre-check
618
+ if safe_serialization:
619
+ try:
620
+ from safetensors.torch import save_file
621
+ except ImportError:
622
+ print('If you want to use `safe_serialization`, please `pip install safetensors`')
623
+ raise
624
+ if max_shard_size is not None:
625
+ try:
626
+ from huggingface_hub import split_torch_state_dict_into_shards
627
+ except ImportError:
628
+ print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
629
+ raise
630
+
631
+ # Convert zero checkpoint to state_dict
632
+ state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
633
+ tag,
634
+ exclude_frozen_parameters,
635
+ lazy_mode=True)
636
+
637
+ # Shard the model if it is too big.
638
+ weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
639
+ if max_shard_size is not None:
640
+ filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
641
+ # an memory-efficient approach for sharding
642
+ empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
643
+ state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
644
+ filename_pattern=filename_pattern,
645
+ max_shard_size=max_shard_size)
646
+ else:
647
+ from collections import namedtuple
648
+ StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
649
+ state_dict_split = StateDictSplit(is_sharded=False,
650
+ filename_to_tensors={weights_name: list(state_dict.keys())})
651
+
652
+ # Save the model by shard
653
+ os.makedirs(output_dir, exist_ok=True)
654
+ filename_to_tensors = state_dict_split.filename_to_tensors.items()
655
+ for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
656
+ shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
657
+ shard_state_dict = to_torch_tensor(shard_state_dict)
658
+ output_path = os.path.join(output_dir, shard_file)
659
+ if safe_serialization:
660
+ save_file(shard_state_dict, output_path, metadata={"format": "pt"})
661
+ else:
662
+ torch.save(shard_state_dict, output_path)
663
+ # release the memory of current shard
664
+ for tensor_name in list(shard_state_dict.keys()):
665
+ del state_dict[tensor_name]
666
+ del shard_state_dict[tensor_name]
667
+ del shard_state_dict
668
+ gc.collect()
669
+
670
+ # Save index if sharded
671
+ if state_dict_split.is_sharded:
672
+ index = {
673
+ "metadata": state_dict_split.metadata,
674
+ "weight_map": state_dict_split.tensor_to_filename,
675
+ }
676
+ save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
677
+ save_index_file = os.path.join(output_dir, save_index_file)
678
+ with open(save_index_file, "w", encoding="utf-8") as f:
679
+ content = json.dumps(index, indent=2, sort_keys=True) + "\n"
680
+ f.write(content)
681
+
682
+
683
+ def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
684
+ """
685
+ 1. Put the provided model to cpu
686
+ 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
687
+ 3. Load it into the provided model
688
+
689
+ Args:
690
+ - ``model``: the model object to update
691
+ - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
692
+ - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
693
+
694
+ Returns:
695
+ - ``model`: modified model
696
+
697
+ Make sure you have plenty of CPU memory available before you call this function. If you don't
698
+ have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
699
+ conveniently placed for you in the checkpoint folder.
700
+
701
+ A typical usage might be ::
702
+
703
+ from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
704
+ model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
705
+ # submit to model hub or save the model to share with others
706
+
707
+ Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
708
+ of the same application. i.e. you will need to re-initialize the deepspeed engine, since
709
+ ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
710
+
711
+ """
712
+ logger.info(f"Extracting fp32 weights")
713
+ state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
714
+
715
+ logger.info(f"Overwriting model with fp32 weights")
716
+ model = model.cpu()
717
+ model.load_state_dict(state_dict, strict=False)
718
+
719
+ return model
720
+
721
+
722
+ if __name__ == "__main__":
723
+ parser = argparse.ArgumentParser()
724
+ parser.add_argument("checkpoint_dir",
725
+ type=str,
726
+ help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
727
+ parser.add_argument("output_dir",
728
+ type=str,
729
+ help="directory to the pytorch fp32 state_dict output files"
730
+ "(e.g. path/checkpoint-12-output/)")
731
+ parser.add_argument(
732
+ "--max_shard_size",
733
+ type=str,
734
+ default="5GB",
735
+ help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
736
+ "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
737
+ "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
738
+ "without CPU OOM issues.")
739
+ parser.add_argument(
740
+ "--safe_serialization",
741
+ default=False,
742
+ action='store_true',
743
+ help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
744
+ parser.add_argument("-t",
745
+ "--tag",
746
+ type=str,
747
+ default=None,
748
+ help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
749
+ parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
750
+ parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
751
+ args = parser.parse_args()
752
+
753
+ debug = args.debug
754
+
755
+ convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
756
+ args.output_dir,
757
+ max_shard_size=args.max_shard_size,
758
+ safe_serialization=args.safe_serialization,
759
+ tag=args.tag,
760
+ exclude_frozen_parameters=args.exclude_frozen_parameters)