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Running
on
Zero
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# Adopted from https://github.com/ddlBoJack/SLAM-LLM/blob/main/src/slam_llm/models/encoder.py
import types
import deepspeed
import torch
import torch.nn as nn
import torch.nn.functional as F
from egogpt.utils import rank0_print
from .model import ModelDimensions, Whisper
def load_zero_partitions(
model,
state_dict,
is_deepspeed_zero3_enabled,
pretrained_model_path,
ignore_mismatched_sizes=False,
):
"""
adept from pytorch lightning and transformers
with deepspeed.zero.Init():
model = MyModel()
state_dict = torch.load(model_path, map_location="cpu")
load_zero_partitions(model, prefix="")
"""
# 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
model_state_dict = model.state_dict()
expected_keys = list(model_state_dict.keys())
loaded_keys = list(state_dict.keys())
missing_keys = list(set(expected_keys) - set(loaded_keys))
unexpected_keys = list(set(loaded_keys) - set(expected_keys))
# Mistmatched keys contains tuples key/shape1/shape2 of weights in the checkpoint that have a shape not
# matching the weights in the model.
mismatched_keys = []
if ignore_mismatched_sizes:
for checkpoint_key in loaded_keys:
model_key = checkpoint_key
if (
model_key in model_state_dict
and state_dict[checkpoint_key].shape
!= model_state_dict[model_key].shape
):
mismatched_keys.append(
(
checkpoint_key,
state_dict[checkpoint_key].shape,
model_state_dict[model_key].shape,
)
)
del state_dict[checkpoint_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, prefix=""):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
if is_deepspeed_zero3_enabled:
# 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(
list(module.parameters(recurse=False)), 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, prefix + name + ".")
# Make sure we are able to load base models as well as derived models (with heads)
start_prefix = ""
model_to_load = model
load(model_to_load, prefix=start_prefix)
del state_dict
if len(error_msgs) > 0:
error_msg = "\n\t".join(error_msgs)
if "size mismatch" in error_msg:
error_msg += "\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method."
raise RuntimeError(
f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}"
)
if len(unexpected_keys) > 0:
rank0_print(
f"Some weights of the model checkpoint at {pretrained_model_path} were not used when"
f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task or"
" with another architecture (e.g. initializing a BertForSequenceClassification model from a"
" BertForPreTraining model).\n- This IS NOT expected if you are initializing"
f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly identical"
" (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)."
)
else:
rank0_print(
f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n"
)
if len(missing_keys) > 0:
rank0_print(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
f" {pretrained_model_path} and are newly initialized: {missing_keys}\nYou should probably"
" TRAIN this model on a down-stream task to be able to use it for predictions and inference."
)
elif len(mismatched_keys) == 0:
rank0_print(
f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at"
f" {pretrained_model_path}.\nIf your task is similar to the task the model of the checkpoint"
f" was trained on, you can already use {model.__class__.__name__} for predictions without further"
" training."
)
if len(mismatched_keys) > 0:
mismatched_warning = "\n".join(
[
f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
for key, shape1, shape2 in mismatched_keys
]
)
rank0_print(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
f" {pretrained_model_path} and are newly initialized because the shapes did not"
f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be able"
" to use it for predictions and inference."
)
class WhisperWrappedEncoder(nn.Module):
def __init__(self, config, delay_load=False):
super().__init__()
self.is_loaded = False
self.speech_encoder_name = config.speech_encoder
if not delay_load:
rank0_print(f"Loading speech encoder: {self.speech_encoder_name}")
self.load_model(config)
def load_model(self, model_config):
if self.is_loaded:
print(
"{} is already loaded, `load_model` called again, skipping.".format(
self.speech_encoder_name
)
)
return
def replace_layer_norm(module):
from whisper.model import LayerNorm
for name, child in module.named_children():
if isinstance(child, LayerNorm):
old_params = child.state_dict()
new_layer_norm = nn.LayerNorm(
child.normalized_shape,
eps=child.eps,
elementwise_affine=child.elementwise_affine,
)
new_layer_norm.load_state_dict(old_params)
setattr(module, name, new_layer_norm)
else:
replace_layer_norm(child)
# import whisper
# self.encoder = whisper.load_model(name=model_config.speech_encoder, device='cpu').encoder
checkpoint = torch.load(self.speech_encoder_name, map_location="cpu")
dims = ModelDimensions(**checkpoint["dims"])
model = Whisper(dims)
deepspeed3_enabled = True
# print(deepspeed3_enabled)
load_zero_partitions(
model,
checkpoint["model_state_dict"],
deepspeed3_enabled,
self.speech_encoder_name,
)
self.encoder = model.encoder
replace_layer_norm(self.encoder)
self.encoder.requires_grad_(False)
self.is_loaded = True
def forward(self, audio):
return self.encoder(audio)
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