File size: 15,056 Bytes
60616b8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
"""Utility functions for training and inference."""
import math
import pickle
import sys
from contextlib import nullcontext
from io import BytesIO
from pathlib import Path
from typing import TYPE_CHECKING, ContextManager, Dict, List, Mapping, Optional, TypeVar, Union

import lightning as L
import torch
import torch.nn as nn
import torch.utils._device
from lightning.fabric.strategies import FSDPStrategy
from lightning.fabric.utilities.load import _lazy_load as lazy_load
from torch.serialization import normalize_storage_type

if TYPE_CHECKING:
    from model import GPT


def find_multiple(n: int, k: int) -> int:
    assert k > 0
    if n % k == 0:
        return n
    return n + k - (n % k)


def num_parameters(module: nn.Module, requires_grad: Optional[bool] = None) -> int:
    total = 0
    for p in module.parameters():
        if requires_grad is None or p.requires_grad == requires_grad:
            if hasattr(p, "quant_state"):
                # bitsandbytes 4bit layer support
                total += math.prod(p.quant_state[1])
            else:
                total += p.numel()
    return total


def gptq_quantization(enabled: bool = False) -> ContextManager:
    if not enabled:
        return nullcontext()

    from lightning.fabric.plugins.precision.utils import _ClassReplacementContextManager

    from quantize.gptq import ColBlockQuantizedLinear

    class QuantizedLinear(ColBlockQuantizedLinear):
        def __init__(self, *args, **kwargs):
            super().__init__(*args, bits=4, tile_cols=-1, **kwargs)

    return _ClassReplacementContextManager({"torch.nn.Linear": QuantizedLinear})


def check_valid_checkpoint_dir(checkpoint_dir: Path) -> None:
    files = {
        "lit_model.pth": (checkpoint_dir / "lit_model.pth").is_file(),
        "lit_config.json": (checkpoint_dir / "lit_config.json").is_file(),
        "tokenizer.json OR tokenizer.model": (checkpoint_dir / "tokenizer.json").is_file() or (
            checkpoint_dir / "tokenizer.model"
        ).is_file(),
        "tokenizer_config.json": (checkpoint_dir / "tokenizer_config.json").is_file(),
    }
    if checkpoint_dir.is_dir():
        if all(files.values()):
            # we're good
            return
        problem = f" is missing the files: {[f for f, exists in files.items() if not exists]!r}"
    else:
        problem = " is not a checkpoint directory"

    # list locally available checkpoints
    available = list(Path("checkpoints").glob("*/*"))
    if available:
        options = "\n --checkpoint_dir ".join([""] + [repr(str(p.resolve())) for p in available])
        extra = f"\nYou have downloaded locally:{options}\n"
    else:
        extra = ""

    error_message = (
        f"--checkpoint_dir {str(checkpoint_dir.absolute())!r}{problem}."
        "\nFind download instructions at https://github.com/Lightning-AI/lit-gpt/blob/main/tutorials\n"
        f"{extra}\nSee all download options by running:\n python scripts/download.py"
    )
    print(error_message, file=sys.stderr)
    raise SystemExit(1)


class SavingProxyForStorage:
    def __init__(self, obj, saver, protocol_version=5):
        self.protocol_version = protocol_version
        self.saver = saver
        if not (isinstance(obj, torch.storage.TypedStorage) or torch.is_storage(obj)):
            raise TypeError(f"expected storage, not {type(obj)}")

        # this logic is taken from PyTorch 2.0+ torch/serialization.py
        if isinstance(obj, torch.storage.TypedStorage):
            # PT upstream wants to deprecate this eventually...
            storage = obj._untyped_storage
            storage_type_str = obj._pickle_storage_type()
            storage_type = getattr(torch, storage_type_str)
            storage_numel = obj._size()
        else:
            storage = obj
            storage_type = normalize_storage_type(type(obj))
            storage_numel = storage.nbytes()

        storage_key = saver._write_storage_and_return_key(storage)
        location = torch.serialization.location_tag(storage)

        self.storage_info = ("storage", storage_type, storage_key, location, storage_numel)

    def __reduce_ex__(self, protocol_version):
        assert False, "this should be handled with out of band"


class SavingProxyForTensor:
    def __init__(self, tensor, saver, protocol_version=5):
        self.protocol_version = protocol_version
        self.reduce_ret_fn, reduce_args = tensor.__reduce_ex__(protocol_version)
        if reduce_args[0] == torch._utils._rebuild_tensor_v2:
            # for Tensors with Python attributes
            (a0, a1, (storage, *a2_other), *other_reduce_args) = reduce_args
            assert isinstance(storage, torch.storage.TypedStorage), "Please check for updates"
            storage_proxy = SavingProxyForStorage(storage, saver, protocol_version=protocol_version)
            self.reduce_args = (a0, a1, (storage_proxy, *a2_other), *other_reduce_args)
        else:
            (storage, *other_reduce_args) = reduce_args
            assert isinstance(storage, torch.storage.TypedStorage), "Please check for updates"
            storage_proxy = SavingProxyForStorage(storage, saver, protocol_version=protocol_version)
            self.reduce_args = (storage_proxy, *other_reduce_args)

    def __reduce_ex__(self, protocol_version):
        if protocol_version != self.protocol_version:
            raise RuntimeError(f"Unexpected protocol version: expected {self.protocol_version}, got {protocol_version}")
        return self.reduce_ret_fn, self.reduce_args


class IncrementalPyTorchPickler(pickle.Pickler):
    def __init__(self, saver, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.storage_dtypes = {}
        self.saver = saver
        self.id_map = {}

    # this logic is taken from PyTorch 2.0+ torch/serialization.py
    def persistent_id(self, obj):
        # FIXME: the docs say that persistent_id should only return a string
        # but torch store returns tuples. This works only in the binary protocol
        # see
        # https://docs.python.org/2/library/pickle.html#pickling-and-unpickling-external-objects
        # https://github.com/python/cpython/blob/master/Lib/pickle.py#L527-L537
        if isinstance(obj, SavingProxyForStorage):
            return obj.storage_info

        if isinstance(obj, torch.storage.TypedStorage) or torch.is_storage(obj):
            if isinstance(obj, torch.storage.TypedStorage):
                # TODO: Once we decide to break serialization FC, this case
                # can be deleted
                storage = obj._untyped_storage
                storage_dtype = obj.dtype
                storage_type_str = obj._pickle_storage_type()
                storage_type = getattr(torch, storage_type_str)
                storage_numel = obj._size()

            else:
                storage = obj
                storage_dtype = torch.uint8
                storage_type = normalize_storage_type(type(obj))
                storage_numel = storage.nbytes()

            # If storage is allocated, ensure that any other saved storages
            # pointing to the same data all have the same dtype. If storage is
            # not allocated, don't perform this check
            if storage.data_ptr() != 0:
                if storage.data_ptr() in self.storage_dtypes:
                    if storage_dtype != self.storage_dtypes[storage.data_ptr()]:
                        raise RuntimeError(
                            "Cannot save multiple tensors or storages that view the same data as different types"
                        )
                else:
                    self.storage_dtypes[storage.data_ptr()] = storage_dtype

            storage_key = self.id_map.get(storage._cdata)
            if storage_key is None:
                storage_key = self.saver._write_storage_and_return_key(storage)
                self.id_map[storage._cdata] = storage_key
            location = torch.serialization.location_tag(storage)

            return ("storage", storage_type, storage_key, location, storage_numel)

        return None


class incremental_save:
    def __init__(self, name):
        self.name = name
        self.zipfile = torch._C.PyTorchFileWriter(str(name))
        self.has_saved = False
        self.next_key = 0

    def __enter__(self):
        return self

    def store_early(self, tensor):
        if isinstance(tensor, torch.Tensor):
            return SavingProxyForTensor(tensor, self)
        raise TypeError(f"can only store tensors early, not {type(tensor)}")

    def save(self, obj):
        if self.has_saved:
            raise RuntimeError("have already saved")
        # Write the pickle data for `obj`
        data_buf = BytesIO()
        pickler = IncrementalPyTorchPickler(self, data_buf, protocol=5)
        pickler.dump(obj)
        data_value = data_buf.getvalue()
        self.zipfile.write_record("data.pkl", data_value, len(data_value))
        self.has_saved = True

    def _write_storage_and_return_key(self, storage):
        if self.has_saved:
            raise RuntimeError("have already saved")
        key = self.next_key
        self.next_key += 1
        name = f"data/{key}"
        if storage.device.type != "cpu":
            storage = storage.cpu()
        num_bytes = storage.nbytes()
        self.zipfile.write_record(name, storage.data_ptr(), num_bytes)
        return key

    def __exit__(self, type, value, traceback):
        self.zipfile.write_end_of_file()


T = TypeVar("T")


def chunked_cross_entropy(

    logits: Union[torch.Tensor, List[torch.Tensor]], targets: torch.Tensor, chunk_size: int = 128

) -> torch.Tensor:
    # with large max_sequence_lengths, the beginning of `backward` allocates a large memory chunk which can dominate
    # the memory usage in fine-tuning settings with low number of parameters.
    # as a workaround hack, the cross entropy computation is chunked to force it to deallocate on the go, reducing
    # the memory spike's magnitude

    # lm_head was chunked (we are fine-tuning)
    if isinstance(logits, list):
        # don't want to chunk cross entropy
        if chunk_size == 0:
            logits = torch.cat(logits, dim=1)
            logits = logits.reshape(-1, logits.size(-1))
            targets = targets.reshape(-1)
            return torch.nn.functional.cross_entropy(logits, targets, ignore_index=-1)

        # chunk cross entropy
        logit_chunks = [logit_chunk.reshape(-1, logit_chunk.size(-1)) for logit_chunk in logits]
        target_chunks = [target_chunk.reshape(-1) for target_chunk in targets.split(logits[0].size(1), dim=1)]
        loss_chunks = [
            torch.nn.functional.cross_entropy(logit_chunk, target_chunk, ignore_index=-1, reduction="none")
            for logit_chunk, target_chunk in zip(logit_chunks, target_chunks)
        ]
        return torch.cat(loss_chunks).mean()

    # no chunking at all
    logits = logits.reshape(-1, logits.size(-1))
    targets = targets.reshape(-1)
    if chunk_size == 0:
        return torch.nn.functional.cross_entropy(logits, targets, ignore_index=-1)

    # lm_head wasn't chunked, chunk cross entropy
    logit_chunks = logits.split(chunk_size)
    target_chunks = targets.split(chunk_size)
    loss_chunks = [
        torch.nn.functional.cross_entropy(logit_chunk, target_chunk, ignore_index=-1, reduction="none")
        for logit_chunk, target_chunk in zip(logit_chunks, target_chunks)
    ]
    return torch.cat(loss_chunks).mean()


def map_old_state_dict_weights(state_dict: Dict, mapping: Mapping, prefix: str) -> Dict:
    for checkpoint_name, attribute_name in mapping.items():
        full_checkpoint_name = prefix + checkpoint_name
        if full_checkpoint_name in state_dict:
            full_attribute_name = prefix + attribute_name
            state_dict[full_attribute_name] = state_dict.pop(full_checkpoint_name)
    return state_dict


def get_default_supported_precision(training: bool) -> str:
    """Return default precision that is supported by the hardware: either `bf16` or `16`.



    Args:

        training: `-mixed` or `-true` version of the precision to use



    Returns:

        default precision that is suitable for the task and is supported by the hardware

    """
    from lightning.fabric.accelerators import MPSAccelerator

    if MPSAccelerator.is_available() or (torch.cuda.is_available() and not torch.cuda.is_bf16_supported()):
        return "16-mixed" if training else "16-true"
    return "bf16-mixed" if training else "bf16-true"


def load_checkpoint(fabric: L.Fabric, model: nn.Module, checkpoint_path: Path, strict: bool = True) -> None:
    if isinstance(fabric.strategy, FSDPStrategy):
        fabric.load_raw(checkpoint_path, model, strict=strict)
    else:
        state_dict = lazy_load(checkpoint_path)
        state_dict = state_dict.get("model", state_dict)
        model.load_state_dict(state_dict, strict=strict)


def flops_per_param(max_seq_length: int, n_layer: int, n_embd: int, n_params: int) -> int:
    flops_per_token = 2 * n_params  # each parameter is used for a MAC (2 FLOPS) per network operation
    # this assumes that all samples have a fixed length equal to the block size
    # which is most likely false during finetuning
    flops_per_seq = flops_per_token * max_seq_length
    attn_flops_per_seq = n_layer * 2 * 2 * (n_embd * (max_seq_length**2))
    return flops_per_seq + attn_flops_per_seq


def estimate_flops(model: "GPT", training: bool) -> int:
    """Measures estimated FLOPs for MFU.



    Refs:

        * https://ar5iv.labs.arxiv.org/html/2205.05198#A1

        * https://ar5iv.labs.arxiv.org/html/2204.02311#A2

    """
    # using all parameters for this is a naive over estimation because not all model parameters actually contribute to
    # this FLOP computation (e.g. embedding, norm). For this reason, the result will be higher by a fixed percentage
    # (~10%) compared to the measured FLOPs, making those lower but more realistic.
    # For a proper estimate, this needs a more fine-grained calculation as in Appendix A of the paper.
    n_trainable_params = num_parameters(model, requires_grad=True)
    trainable_flops = flops_per_param(
        model.max_seq_length, model.config.n_layer, model.config.n_embd, n_trainable_params
    )
    # forward + backward + gradients (assumes no gradient accumulation)
    ops_per_step = 3 if training else 1
    n_frozen_params = num_parameters(model, requires_grad=False)
    frozen_flops = flops_per_param(model.max_seq_length, model.config.n_layer, model.config.n_embd, n_frozen_params)
    # forward + backward
    frozen_ops_per_step = 2 if training else 1
    return ops_per_step * trainable_flops + frozen_ops_per_step * frozen_flops