File size: 20,640 Bytes
e7d3e35
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
import dataclasses
import gc
import json
import logging
from contextlib import contextmanager
from enum import Enum

import accelerate
import psutil
import pynvml
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from accelerate.state import AcceleratorState
from PIL import Image
from transformers import (  # AddedToken is needed for the eval of the tokenizer params # noqa: F401
    AddedToken,
    AutoTokenizer,
)


IMAGE_TOKEN = "<image>"
FAKE_TOKEN_AROUND_IMAGE_V2 = "<fake_token_around_image>"
FAKE_TOKEN_AROUND_IMAGE_V1 = "\n\n"
# Originally taken from the values used in OpenCLIP
IMAGE_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
IMAGE_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
logger = logging.getLogger(__name__)


class LoggingTypes(Enum):
    """Types of logging to use for the gradient and parameter statistics"""

    JSONL = "jsonl"
    WANDB = "wandb"
    PRINT = "print"


class JSONEncoderForDataclasses(json.JSONEncoder):
    """
    Use to serialize dataclass object, like so:
    json.dump(data, fp, indent=2, cls=JSONEncoderForDataclasses)
    """

    def default(self, obj):
        if dataclasses.is_dataclass(obj):
            return dataclasses.asdict(obj)
        return super().default(obj)


def freeze_model(model, module_exceptions=[]):
    mapping = {
        "LayerNorm": nn.LayerNorm,
        "Linear": nn.Linear,
        "Embedding": nn.Embedding,
    }
    module_exceptions_mapped = [mapping[m] for m in module_exceptions]
    for module in model.modules():
        if module_exceptions and any([isinstance(module, t) for t in module_exceptions_mapped]):
            module.requires_grad_(True)  # Explicitly setting it to true to avoid any mistakes
        else:
            module.requires_grad_(False)
    return model


def _convert_to_rgb(image):
    # `image.convert("RGB")` would only work for .jpg images, as it creates
    # a wrong background for transparent images. The call to `alpha_composite`
    # handles this case
    if image.mode == "RGB":
        return image
    image_rgba = image.convert("RGBA")
    background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
    alpha_composite = Image.alpha_composite(background, image_rgba)
    alpha_composite = alpha_composite.convert("RGB")
    return alpha_composite


# TODO(aps): Take parameters from config
def build_image_transform(image_size=224, eval=False):
    return transforms.Compose(
        [
            _convert_to_rgb,
            (
                transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC)
                if eval
                else transforms.RandomResizedCrop(
                    (image_size, image_size), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
                )
            ),
            transforms.ToTensor(),
            transforms.Normalize(mean=IMAGE_DATASET_MEAN, std=IMAGE_DATASET_STD),
        ]
    )


def get_tokenizer(
    tokenizer_name: str,
    tokenizer_add_tokens,
    tokenizer_add_special_tokens,
    tokenizer_params,
    additional_vocab_size,
    model_vocab_size=None,
):
    """
    We artificially separate `tokenizer_add_tokens` and `tokenizer_add_special_tokens` is a dictionary whose keys only takes into account special tokens (eos, pad, cls, etc.).
    On the contrary, `tokenizer_add_tokens` is a list of string of `AddedToken`.
    In practise, we use `tokenizer.add_special_tokens` to add all of these new special tokens or update the existing ones.

    NB: we constraint to tokenizer to be a fast tokenizer because with the slow tokenizer, we can't set the arguments of the added tokens (cf `.add_tokens`) and by default, the separators are stripped.
    """
    tokenizer_params = eval(tokenizer_params)
    assert isinstance(tokenizer_params, dict)

    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, **tokenizer_params)

    if model_vocab_size is not None:
        if model_vocab_size > len(tokenizer):
            logger.warning(
                f"The model vocabulary size ({model_vocab_size}) is larger than the tokenizer vocabulary size "
                f"({len(tokenizer)}). Updating the tokenizer to match."
            )
            if "additional_special_tokens" in tokenizer_params:
                raise ValueError(
                    "You can't use `additional_special_tokens` in `tokenizer_params` with a model vocab "
                    "size > tokenizer vocab size. We need to adjust tokenizer before adding special "
                    "tokens. Please use `tokenizer_add_tokens` instead."
                )
            # We need to pad the tokenizer vocab with fake tokens
            tokenizer.add_tokens(["<fake_token_{}>".format(i) for i in range(model_vocab_size - len(tokenizer))])

    assert str(eval(tokenizer_add_tokens)[-1]) == IMAGE_TOKEN
    assert str(eval(tokenizer_add_tokens)[-2]) == FAKE_TOKEN_AROUND_IMAGE_V2
    # This check ensures that the image token and the fake token around it will be in the `DecoupledEmbedding.additional_weight`.
    existing_special_tokens = (
        [*tokenizer.special_tokens_map_extended["additional_special_tokens"]]
        if "additional_special_tokens" in tokenizer.special_tokens_map_extended
        else []
    )
    add_special_tokens_dict = {"additional_special_tokens": existing_special_tokens + eval(tokenizer_add_tokens)}
    if tokenizer_add_special_tokens is not None:
        add_special_tokens_dict.update(eval(tokenizer_add_special_tokens))

    tokenizer.add_special_tokens(add_special_tokens_dict)

    assert IMAGE_TOKEN in tokenizer.convert_ids_to_tokens(
        [idx for idx in range(len(tokenizer) - additional_vocab_size, len(tokenizer))]
    )
    assert FAKE_TOKEN_AROUND_IMAGE_V2 in tokenizer.convert_ids_to_tokens(
        [idx for idx in range(len(tokenizer) - additional_vocab_size, len(tokenizer))]
    )
    # This verifies that `<image>` was correctly added to the tokenizer vocabulary
    # XXX: opt-1.3b fails here
    # assert tokenizer.is_fast == tokenizer_params.get("use_fast", True)

    return tokenizer


def pynmvl_handle(accelerator):
    if not torch.cuda.is_available():
        return None

    pynvml.nvmlInit()
    return pynvml.nvmlDeviceGetHandleByIndex(accelerator.local_process_index)


def pynvml_get_total_energy_in_joules(handle):
    if not torch.cuda.is_available():
        return 0
    return pynvml.nvmlDeviceGetTotalEnergyConsumption(handle) / 1000


def compute_tflops_per_batch_per_gpu(
    num_layers,
    batch_size,
    q_seq_len,
    k_seq_len,
    hidden_size,
    kv_in_dim,
    ff_exp_factor=None,
    grad_acc_size=1,
    swiglu=False,
    vocab_size=None,
    count_backward=False,
    use_grad_checkpointing=False,
):
    multiply_add_factor = torch.tensor(2)
    query_transformation = multiply_add_factor * batch_size * q_seq_len * hidden_size**2
    # k_seq_len == v_seq_len
    key_value_transformation = multiply_add_factor * batch_size * k_seq_len * (2 * hidden_size * kv_in_dim)
    attention_matrix_computation = multiply_add_factor * batch_size * q_seq_len * k_seq_len * hidden_size
    attention_softmax = multiply_add_factor * q_seq_len * k_seq_len
    att_over_values_computation = multiply_add_factor * batch_size * q_seq_len * k_seq_len * hidden_size
    post_attention_linear_proj = multiply_add_factor * batch_size * q_seq_len * hidden_size**2

    # There are usually 2 expansion_linear_layers because first one expands, and second one retracts back to hidden_size
    # When using a classic decoder, some blocks don't have those feed-forward layers
    # Swiglu duplicates the first linear layer, so we have to account for 3 of them instead of 2
    if ff_exp_factor and swiglu:
        expansion_linear_layers = 3 * (
            multiply_add_factor * batch_size * q_seq_len * (hidden_size * ff_exp_factor) * hidden_size
        )
    elif ff_exp_factor:
        expansion_linear_layers = 2 * (
            multiply_add_factor * batch_size * q_seq_len * (hidden_size * ff_exp_factor) * hidden_size
        )
    else:
        expansion_linear_layers = torch.tensor(0)

    transformer_block_flops = (
        query_transformation
        + key_value_transformation
        + attention_matrix_computation
        + attention_softmax
        + att_over_values_computation
        + post_attention_linear_proj
        + expansion_linear_layers
    )

    # This computation should only be added if the model has a language head
    if vocab_size:
        language_head_computation = multiply_add_factor * batch_size * q_seq_len * hidden_size * vocab_size
    else:
        language_head_computation = torch.tensor(0)

    forward_fact = 1
    backward_factor = 2 if count_backward else 0
    grad_checkpointing_factor = 1 if use_grad_checkpointing else 0
    model_flops = (forward_fact + backward_factor + grad_checkpointing_factor) * (
        num_layers * transformer_block_flops + language_head_computation
    )
    model_tflops = model_flops / (10**12)

    return model_tflops


def compute_perceiver_tflops_per_batch_per_gpu(
    num_layers,
    batch_size,
    q_seq_len,
    vision_embed_seq_len,
    q_k_v_input_dim,
    attention_hidden_size,
    ff_exp_factor=None,
    count_backward=False,
    use_grad_checkpointing=False,
):
    multiply_add_factor = torch.tensor(2)
    query_transformation = multiply_add_factor * batch_size * q_seq_len * q_k_v_input_dim * attention_hidden_size
    # k_seq_len == v_seq_len
    key_value_transformation = (
        multiply_add_factor * batch_size * vision_embed_seq_len * (2 * attention_hidden_size * q_k_v_input_dim)
    )

    k_seq_len = vision_embed_seq_len + q_seq_len
    attention_matrix_computation = multiply_add_factor * batch_size * q_seq_len * k_seq_len * attention_hidden_size
    attention_softmax = multiply_add_factor * q_seq_len * k_seq_len
    att_over_values_computation = multiply_add_factor * batch_size * q_seq_len * k_seq_len * attention_hidden_size
    post_attention_linear_proj = multiply_add_factor * batch_size * q_seq_len * attention_hidden_size * q_k_v_input_dim

    # There are usually 2 expansion_linear_layers because first one expands, and second one retracts back to hidden_size
    # When using a classic decoder, some blocks don't have those feed-forward layers
    if ff_exp_factor:
        expansion_linear_layers = 2 * (
            multiply_add_factor * batch_size * q_seq_len * (q_k_v_input_dim * ff_exp_factor) * q_k_v_input_dim
        )
    else:
        expansion_linear_layers = torch.tensor(0)

    transformer_block_flops = (
        query_transformation
        + key_value_transformation
        + attention_matrix_computation
        + attention_softmax
        + att_over_values_computation
        + post_attention_linear_proj
        + expansion_linear_layers
    )

    forward_fact = 1
    backward_factor = 2 if count_backward else 0
    grad_checkpointing_factor = 1 if use_grad_checkpointing else 0
    model_flops = (forward_fact + backward_factor + grad_checkpointing_factor) * (num_layers * transformer_block_flops)
    model_tflops = model_flops / (10**12)

    return model_tflops


def mem_usage_formatted(logging_type=LoggingTypes.PRINT):
    # adapted from deepspeed's see_memory_usage

    torch.cuda.empty_cache()

    # python doesn't do real-time garbage collection so do it explicitly to get the correct usage reports
    gc.collect()
    vm_stats = psutil.virtual_memory()

    mem = {
        "gpu mem alloc": f"{torch.cuda.memory_allocated()/2**30:0.2f}GB",
        "max alloc": f"{torch.cuda.max_memory_allocated()/2**30:0.2f}GB",
        "reserv": f"{torch.cuda.memory_reserved()/2**30:0.2f}GB",
        "max reserv": f"{torch.cuda.max_memory_reserved()/2**30:0.2f}GB",
        "cpu vm used": f"{(vm_stats.total-vm_stats.available)/2**30:0.2f}GB {vm_stats.percent}%",
    }

    if logging_type == LoggingTypes.PRINT:
        mem = " | ".join([f"{k}: {v}" for k, v in mem.items()]) + " | "

    # get the peak memory to report correct data, so reset the max_memory_allocated counter for the next call
    torch.cuda.reset_peak_memory_stats()

    return mem


def is_deepspeed_used():
    deepspeed_plugin = get_deepspeed_plugin()
    return deepspeed_plugin is not None


def get_deepspeed_stage():
    deepspeed_plugin = get_deepspeed_plugin()
    if deepspeed_plugin is None:
        return 0
    ds_config = deepspeed_plugin.deepspeed_config
    stage = ds_config.get("zero_optimization", {}).get("stage", 0)
    # from accelerate>=0.17.1 can do instead:
    # stage = deepspeed_plugin.zero_stage
    return stage


def is_deepspeed_zero3_used():
    return get_deepspeed_stage() == 3


def accelerate_torch_dtype():
    """
    derive and return `torch_dtype` to be used in `from_pretrained` from either Deepspeed config or if
    Deepspeed isn't used than accelerator state
    """
    if not is_accelerate_initialized():
        return None

    accelerator_state = AcceleratorState()

    if is_deepspeed_used():
        deepspeed_plugin = accelerator_state.deepspeed_plugin
        ds_config = deepspeed_plugin.deepspeed_config
        if ds_config.get("fp16", {}).get("enabled", False):
            torch_dtype = torch.float16
        elif ds_config.get("bf16", {}).get("enabled", False):
            torch_dtype = torch.bfloat16
        else:
            torch_dtype = None
    else:  # no Deepspeed
        if accelerator_state.mixed_precision == "fp16":
            torch_dtype = torch.float16
        elif accelerator_state.mixed_precision == "bf16":
            torch_dtype = torch.bfloat16
        else:
            torch_dtype = None

    return torch_dtype


def is_accelerate_initialized():
    return accelerate.state.is_initialized()


def get_deepspeed_plugin():
    if is_accelerate_initialized():
        return AcceleratorState().deepspeed_plugin
    else:
        return None


def get_deepspeed_engine(accelerator):
    return accelerator.deepspeed_engine_wrapped.engine


def is_deepspeed_zero_init_enabled():
    deepspeed_plugin = get_deepspeed_plugin()
    if deepspeed_plugin is not None:
        return deepspeed_plugin.is_zero3_init_enabled()
    else:
        return False


@contextmanager
def hf_trainer_disable_zero3_init_context_manager():
    # monkey patch hack to emulate a context that has zero_init disabled as it's used in
    # modeling_utils.py in transformers for from_config and from_pretrained.
    import transformers.modeling_utils  # noqa

    orig = transformers.modeling_utils.is_deepspeed_zero3_enabled
    transformers.modeling_utils.is_deepspeed_zero3_enabled = lambda: False
    yield
    transformers.modeling_utils.is_deepspeed_zero3_enabled = orig


def deepspeed_zero_init_disabled_context_manager():
    """
    returns either a context list that includes one that will disable zero.Init or an empty context list
    """
    deepspeed_plugin = get_deepspeed_plugin()
    if deepspeed_plugin is not None:
        return [deepspeed_plugin.zero3_init_context_manager(enable=False)]
    else:
        return [hf_trainer_disable_zero3_init_context_manager()]


def deepspeed_gathered_parameters_context_manager(params, modify=True):
    """
    Under zero.Init returns a context manager that will gather the sharded param, otherwise returns an empty list

    If `modify` is `True`, gather the shards and once the context exits update the shards with the
    modified data - one wants that when modifying the gathered param. If one wants to just gather
    the shards in order to read the param and no modifications are done to it, use `modify=False` as
    it's more efficient.

    `params` - can be a single parameter, a list, or a tuple of parameters to collect.

    Example:

    from transformers.utils import ContextManagers
    from m4.training.utils import deepspeed_gathered_parameters_context_manager
    with ContextManagers(deepspeed_gathered_parameters_context_manager(module.weight, modify=True)):
        module.weight.data.normal_(mean=0.0, std=std)
        if module.padding_idx is not None:
            module.weight.data[module.padding_idx].zero_()


    """
    if is_deepspeed_zero_init_enabled():
        import deepspeed

        # 0 is for updating `params` shards after modifying it, `None` is for read-only (only gather)
        modifier_rank = 0 if modify else None
        return [deepspeed.zero.GatheredParameters(params, modifier_rank=modifier_rank)]
    else:
        return []


# adapted from https://github.com/huggingface/transformers/blob/a081f292ca8479eaf66d7396186021268f128829/src/transformers/modeling_utils.py#L438-L496
# as it appears to be a private function
def load_state_dict_into_model(model_to_load, state_dict, start_prefix):
    # Convert old format to new format if needed from a PyTorch state_dict
    old_keys = []
    new_keys = []
    for key in state_dict.keys():
        new_key = None
        if "gamma" in key:
            new_key = key.replace("gamma", "weight")
        if "beta" in key:
            new_key = key.replace("beta", "bias")
        if new_key:
            old_keys.append(key)
            new_keys.append(new_key)
    for old_key, new_key in zip(old_keys, new_keys):
        state_dict[new_key] = state_dict.pop(old_key)

    # copy state_dict so _load_from_state_dict can modify it
    metadata = getattr(state_dict, "_metadata", None)
    state_dict = state_dict.copy()
    if metadata is not None:
        state_dict._metadata = metadata

    error_msgs = []

    # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
    # so we need to apply the function recursively.
    def load(module: torch.nn.Module, state_dict, prefix=""):
        local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
        args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
        # Parameters of module and children will start with prefix. We can exit early if there are none in this
        # state_dict
        if len([key for key in state_dict if key.startswith(prefix)]) > 0:
            if is_deepspeed_zero_init_enabled():
                import deepspeed

                # In sharded models, each shard has only part of the full state_dict, so only gather
                # parameters that are in the current state_dict.
                named_parameters = dict(module.named_parameters(prefix=prefix[:-1], recurse=False))
                params_to_gather = [named_parameters[k] for k in state_dict.keys() if k in named_parameters]
                if len(params_to_gather) > 0:
                    # because zero3 puts placeholders in model params, this context
                    # manager gathers (unpartitions) the params of the current layer, then loads from
                    # the state dict and then re-partitions them again
                    with deepspeed.zero.GatheredParameters(params_to_gather, modifier_rank=0):
                        if torch.distributed.get_rank() == 0:
                            module._load_from_state_dict(*args)
            else:
                module._load_from_state_dict(*args)

        for name, child in module._modules.items():
            if child is not None:
                load(child, state_dict, prefix + name + ".")

    load(model_to_load, state_dict, prefix=start_prefix)
    # Delete `state_dict` so it could be collected by GC earlier. Note that `state_dict` is a copy of the argument, so
    # it's safe to delete it.
    del state_dict

    return error_msgs


def get_stats(var, ctx):
    if var is None:
        return {}
    var = var.float()
    abs_var = var.abs()
    return {
        f"{ctx}_var_min": var.min().item(),
        f"{ctx}_var_max": var.max().item(),
        f"{ctx}_var_mean": var.mean().item(),
        f"{ctx}_var_std": var.std().item(),
        f"{ctx}_abs_var_min": abs_var.min().item(),
        f"{ctx}_abs_var_max": abs_var.max().item(),
        f"{ctx}_abs_var_mean": abs_var.mean().item(),
        f"{ctx}_abs_var_std": abs_var.std().item(),
        f"{ctx}_var_norm_2": (var.norm(p=2) / var.numel()).item(),
        f"{ctx}_var_norm_1": (var.norm(p=1) / var.numel()).item(),
        f"{ctx}_nonzero": (var != 0).sum().item(),
    }


def get_stats_format(ctx):
    return {
        f"{ctx}_var_min": "e",
        f"{ctx}_var_max": "e",
        f"{ctx}_var_mean": "e",
        f"{ctx}_var_std": "e",
        f"{ctx}_abs_var_min": "e",
        f"{ctx}_abs_var_max": "e",
        f"{ctx}_abs_var_mean": "e",
        f"{ctx}_abs_var_std": "e",
        f"{ctx}_var_norm_2": "e",
        f"{ctx}_var_norm_1": "e",
        f"{ctx}_nonzero": "",
    }