m4/training/utils.py

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": "",
    }