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Feat: implement working prediction

This commit is contained in:
Richard Wong 2024-09-12 22:57:19 +09:00
parent f523560141
commit edd9c3551f
4 changed files with 189 additions and 333 deletions
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1
.gitignore vendored
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@ -1,3 +1,4 @@
*.ipynb *.ipynb
t5_*/ t5_*/
exports/ exports/
modified_t5_model/

27
check_time.py Normal file
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@ -0,0 +1,27 @@
from pathlib import Path
# Define the folder to check
folder = Path(".")
# Get all .py and .ipynb files in the folder
py_files = {file.stem: file for file in folder.glob("*.py")}
ipynb_files = {file.stem: file for file in folder.glob("*.ipynb")}
# Check for linked .py and .ipynb files
all_newer = True
for stem, py_file in py_files.items():
if stem in ipynb_files:
ipynb_file = ipynb_files[stem]
# Compare the modification times
if py_file.stat().st_mtime > ipynb_file.stat().st_mtime:
print(f"{py_file} is newer than {ipynb_file}.")
else:
print(f"{py_file} is not newer than {ipynb_file}.")
all_newer = False
if all_newer:
print("All linked .py files are newer than their corresponding .ipynb files.")
else:
print("Some .py files are not newer than their corresponding .ipynb files.")

293
t5_jax.py
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@ -16,67 +16,6 @@
# %% [markdown] # %% [markdown]
# # T5 implementation using jax # # T5 implementation using jax
# %% [markdown]
# ## import
# %% [raw]
# import json
# import logging
# import math
# import os
# import sys
# import time
# from dataclasses import asdict, dataclass, field
# from enum import Enum
# from functools import partial
# from pathlib import Path
# from typing import Callable, Optional
#
# import datasets
# import evaluate
# import jax
# import jax.numpy as jnp
# import nltk # Here to have a nice missing dependency error message early on
# import numpy as np
# import optax
# from datasets import Dataset, load_dataset
# from filelock import FileLock
# from flax import jax_utils, traverse_util
# from flax.jax_utils import pad_shard_unpad, unreplicate
# from flax.training import train_state
# from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
# from tqdm import tqdm
#
# import transformers
# from transformers import (
# CONFIG_MAPPING,
# FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
# AutoConfig,
# AutoTokenizer,
# FlaxAutoModelForSeq2SeqLM,
# HfArgumentParser,
# is_tensorboard_available,
# )
# from transformers.utils import is_offline_mode, send_example_telemetry
#
#
# logger = logging.getLogger(__name__)
#
# try:
# nltk.data.find("tokenizers/punkt")
# except (LookupError, OSError):
# if is_offline_mode():
# raise LookupError(
# "Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
# )
# with FileLock(".lock") as lock:
# nltk.download("punkt", quiet=True)
#
#
# MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys())
# MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
# %% # %%
import jax import jax
import jax.numpy as jnp import jax.numpy as jnp
@ -88,8 +27,11 @@ import math
# jax.config.update("jax_default_matmul_precision", "tensorfloat32") # jax.config.update("jax_default_matmul_precision", "tensorfloat32")
jax.config.update("jax_default_matmul_precision", "high") jax.config.update("jax_default_matmul_precision", "high")
jax.config.update("jax_enable_x64", False) jax.config.update("jax_enable_x64", False)
# enable cache
jax.config.update("jax_compilation_cache_dir", "/tmp/jax_cache")
jax.config.update("jax_persistent_cache_min_entry_size_bytes", -1)
jax.config.update("jax_persistent_cache_min_compile_time_secs", 0)
from transformers import FlaxAutoModelForSeq2SeqLM, AutoConfig from transformers import FlaxAutoModelForSeq2SeqLM, AutoConfig
@ -108,6 +50,7 @@ from flax import jax_utils, traverse_util
from flax.jax_utils import pad_shard_unpad, unreplicate from flax.jax_utils import pad_shard_unpad, unreplicate
from flax.training import train_state from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
import flax.core
import time import time
@ -116,14 +59,15 @@ import time
# %% # %%
import os import os
os.environ['XLA_FLAGS'] = ( os.environ['XLA_FLAGS'] = (
'--xla_gpu_enable_triton_softmax_fusion=True ' '--xla_gpu_triton_gemm_any=true --xla_gpu_enable_custom_fusions=true --xla_gpu_enable_address_computation_fusion=true'
'--xla_gpu_triton_gemm_any=True '
) )
os.environ.update({ os.environ.update({
"CUDA_DEVICE_MAX_CONNECTIONS" : "1",
"NCCL_LL128_BUFFSIZE": "-2", "NCCL_LL128_BUFFSIZE": "-2",
"NCCL_LL_BUFFSIZE": "-2", "NCCL_LL_BUFFSIZE": "-2",
"NCCL_PROTO": "SIMPLE,LL,LL128", "NCCL_PROTO": "SIMPLE,LL,LL128",
"XLA_PYTHON_CLIENT_MEM_FRACTION" : ".95"
}) })
# %% # %%
@ -132,17 +76,10 @@ print(xla_bridge.get_backend().platform)
# %% # %%
# nltk.download('punkt')
try: try:
nltk.data.find("tokenizers/punkt") nltk.data.find("tokenizers/punkt")
except (LookupError, OSError): except (LookupError, OSError):
if is_offline_mode(): print("error")
raise LookupError(
"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
)
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
# %% [markdown] # %% [markdown]
@ -153,17 +90,8 @@ except (LookupError, OSError):
model_name_or_path = "t5-small" # Replace with your specific model name model_name_or_path = "t5-small" # Replace with your specific model name
# Load configuration # Load configuration
config = AutoConfig.from_pretrained(model_name_or_path) config = AutoConfig.from_pretrained(model_name_or_path,
force_download=False)
# Load model
model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
model_name_or_path
)
# %%
model_module = __import__(model.__module__, fromlist=["shift_tokens_tight"])
shift_tokens_right_fn = getattr(model_module, "shift_tokens_right")
# %% # %%
@ -173,7 +101,11 @@ save_path = 't5_80_1'
# file_path = 'combined_data' # file_path = 'combined_data'
split_datasets = load_from_disk(file_path) split_datasets = load_from_disk(file_path)
# prepare tokenizer # %%
split_datasets['train'][0]
# %%
from transformers import T5TokenizerFast from transformers import T5TokenizerFast
tokenizer = T5TokenizerFast.from_pretrained("t5-base", return_tensors="np", clean_up_tokenization_spaces=True) tokenizer = T5TokenizerFast.from_pretrained("t5-base", return_tensors="np", clean_up_tokenization_spaces=True)
# Define additional special tokens # Define additional special tokens
@ -183,6 +115,43 @@ tokenizer.add_special_tokens({"additional_special_tokens": additional_special_to
max_length = 86 max_length = 86
# %%
len(tokenizer)
# %%
# load pytorch model first
# from transformers import AutoModelForSeq2SeqLM
# model_checkpoint = "t5-base"
# model_pt = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
# # important! after extending tokens vocab
# model_pt.resize_token_embeddings(len(tokenizer))
# model_pt.save_pretrained('./modified_t5_model')
# model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
# pretrained_model_name_or_path="modified_t5_model",
# dtype=jax.numpy.bfloat16,
# from_pt=True
# )
# %%
model_path = './t5_80_1'
# model_path = 't5=base'
model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
pretrained_model_name_or_path=model_path,
dtype=jax.numpy.float32
)
# %%
model.params_shape_tree['shared']
# %%
model_module = __import__(model.__module__, fromlist=["shift_tokens_tight"])
shift_tokens_right_fn = getattr(model_module, "shift_tokens_right")
# %%
# In Flax, for seq2seq models we need to pass `decoder_input_ids` # In Flax, for seq2seq models we need to pass `decoder_input_ids`
# as the Flax models don't accept `labels`, we need to prepare the decoder_input_ids here # as the Flax models don't accept `labels`, we need to prepare the decoder_input_ids here
# for that dynamically import the `shift_tokens_right` function from the model file # for that dynamically import the `shift_tokens_right` function from the model file
@ -191,21 +160,19 @@ max_length = 86
# given a dataset entry, run it through the tokenizer # given a dataset entry, run it through the tokenizer
# Setting padding="max_length" as we need fixed length inputs for jitted functions # Setting padding="max_length" as we need fixed length inputs for jitted functions
def preprocess_function(example): def preprocess_function(example):
input = example['input'] inputs = example['input']
target = example['output'] targets = example['output']
# text_target sets the corresponding label to inputs # text_target sets the corresponding label to inputs
# there is no need to create a separate 'labels' # there is no need to create a separate 'labels'
model_inputs = tokenizer( model_inputs = tokenizer(
input, inputs,
text_target=target,
max_length=max_length, max_length=max_length,
padding="max_length", padding="max_length",
truncation=True, truncation=True,
return_tensors="np" return_tensors="np"
) )
labels = tokenizer( labels = tokenizer(
input, text_target=targets,
text_target=target,
max_length=max_length, max_length=max_length,
padding="max_length", padding="max_length",
truncation=True, truncation=True,
@ -233,15 +200,18 @@ tokenized_datasets = split_datasets.map(
) )
tokenized_datasets.set_format(type='numpy',
columns=['input_ids', 'attention_mask',
# %% 'labels', 'decoder_input_ids',
tokenized_datasets 'decoder_attention_mask'])
# %% # %%
train_dataset = tokenized_datasets["train"] train_dataset = tokenized_datasets["train"]
eval_dataset = tokenized_datasets["validation"] eval_dataset = tokenized_datasets["validation"]
# %%
train_dataset[0]
# %% # %%
def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False, drop_last=True): def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False, drop_last=True):
@ -270,65 +240,14 @@ def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuf
yield batch yield batch
# %% [markdown]
# Now we have model inputs in terms of the variable tokenized_datasets
# %%
# metric
metric = evaluate.load("sacrebleu")
def postprocess_text(preds, labels):
preds = [pred.strip() for pred in preds]
labels = [label.strip() for label in labels]
# rougeLSum expects newline after each sentence
preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
return preds, labels
# def compute_metrics(preds, labels):
# decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
# decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
#
# # Some simple post-processing
# decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
#
# result = metric.compute(predictions=decoded_preds, references=decoded_labels)
# result = {k: round(v * 100, 4) for k, v in result.items()}
# prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
# result["gen_len"] = np.mean(prediction_lens)
# return result
def compute_metrics(preds, labels):
# In case the model returns more than the prediction logits
if isinstance(preds, tuple):
preds = preds[0]
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
# Replace -100s in the labels as we can't decode them
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
# Some simple post-processing
decoded_preds = [pred.strip() for pred in decoded_preds]
decoded_labels = [[label.strip()] for label in decoded_labels]
result = metric.compute(predictions=decoded_preds, references=decoded_labels)
return {"bleu": result["score"]}
# %% [markdown] # %% [markdown]
# # Model # # Model
# %% # %%
# Store some constant # Store some constant
seed = 117 seed = 117
num_epochs = 80 num_epochs = 40
batch_size = 96 batch_size = 32
num_train_epochs = num_epochs num_train_epochs = num_epochs
per_device_train_batch_size = batch_size per_device_train_batch_size = batch_size
train_batch_size = per_device_train_batch_size * jax.device_count() train_batch_size = per_device_train_batch_size * jax.device_count()
@ -338,16 +257,16 @@ steps_per_epoch = len(train_dataset) // train_batch_size
total_train_steps = steps_per_epoch * num_epochs total_train_steps = steps_per_epoch * num_epochs
warmup_steps = 0 warmup_steps = 0
learning_rate = 5e-5 learning_rate = 2e-5
weight_decay = 0.0 weight_decay = 0.01
adam_beta1 = 0.9 adam_beta1 = 0.9
adam_beta2 = 0.999 adam_beta2 = 0.999
adam_epsilon = 1e-8 adam_epsilon = 1e-8
label_smoothing_factor = 0.0 label_smoothing_factor = 0.0
num_beams = 1 num_beams = 1
val_max_target_length = None val_max_target_length = 128
predict_with_generate = True predict_with_generate = True
@ -421,15 +340,14 @@ class TrainState(train_state.TrainState):
def replicate(self): def replicate(self):
return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng)) return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
# Ensure model.params is properly initialized (this is just an example) # set bf16 for model params
# Normally you would get this from a model initialization call with dummy input # model.params = model.to_bf16(model.params)
params = model.params params = model.params
# Cast parameters to bfloat16 if desired # Cast parameters to bfloat16 if desired
params_bf16 = jax.tree_util.tree_map(lambda x: x.astype(jnp.bfloat16), params) # params = jax.tree_util.tree_map(lambda x: x.astype(jnp.bfloat16), params)
# Setup train state # Setup train state
state = TrainState.create(apply_fn=model.__call__, params=params_bf16, tx=adamw, dropout_rng=dropout_rng) state = TrainState.create(apply_fn=model.__call__, params=params, tx=adamw, dropout_rng=dropout_rng)
# label smoothed cross entropy # label smoothed cross entropy
def loss_fn(logits, labels, padding_mask, label_smoothing_factor=0.0): def loss_fn(logits, labels, padding_mask, label_smoothing_factor=0.0):
@ -481,21 +399,6 @@ def train_step(state, batch, label_smoothing_factor=0.0):
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)} metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
return new_state, metrics return new_state, metrics
# Define eval fn
def eval_step(params, batch, label_smoothing_factor=0.0):
labels = batch.pop("labels")
logits = model(**batch, params=params, train=False)[0]
loss, num_labels = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
num_labels = jax.lax.psum(num_labels, "batch")
# true loss = total loss / total samples
loss = jax.lax.psum(loss, "batch")
loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
metrics = {"loss": loss}
return metrics
# Define generation function # Define generation function
max_length = ( max_length = (
val_max_target_length if val_max_target_length is not None else model.config.max_length val_max_target_length if val_max_target_length is not None else model.config.max_length
@ -512,7 +415,7 @@ def generate_step(params, batch):
p_train_step = jax.pmap( p_train_step = jax.pmap(
partial(train_step, label_smoothing_factor=label_smoothing_factor), "batch", donate_argnums=(0,) partial(train_step, label_smoothing_factor=label_smoothing_factor), "batch", donate_argnums=(0,)
) )
p_eval_step = jax.pmap(partial(eval_step, label_smoothing_factor=label_smoothing_factor), "batch") # p_eval_step = jax.pmap(partial(eval_step, label_smoothing_factor=label_smoothing_factor), "batch")
p_generate_step = jax.pmap(generate_step, "batch") p_generate_step = jax.pmap(generate_step, "batch")
# Replicate the train state on each device # Replicate the train state on each device
@ -563,50 +466,6 @@ for epoch in epochs:
f" {train_metric['learning_rate']})" f" {train_metric['learning_rate']})"
) )
# ======================== Evaluating ==============================
eval_metrics = []
eval_preds = []
eval_labels = []
eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size, drop_last=False)
eval_steps = math.ceil(len(eval_dataset) / eval_batch_size)
for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
# Model forward
batch = next(eval_loader)
labels = batch["labels"]
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
state.params, batch, min_device_batch=per_device_eval_batch_size
)
eval_metrics.append(metrics)
# generation
if predict_with_generate:
generated_ids = pad_shard_unpad(p_generate_step)(state.params, batch)
eval_preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
eval_labels.extend(labels)
# normalize eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_util.tree_map(jnp.mean, eval_metrics)
# compute metrics
rouge_desc = ""
if predict_with_generate:
rouge_metrics = compute_metrics(eval_preds, eval_labels)
eval_metrics.update(rouge_metrics)
rouge_desc = " ".join([f"Eval {key}: {value} |" for key, value in rouge_metrics.items()])
# Print metrics and update progress bar
desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']} | {rouge_desc})"
epochs.write(desc)
epochs.desc = desc
# Save metrics
# if has_tensorboard and jax.process_index() == 0:
# cur_step = epoch * (len(train_dataset) // train_batch_size)
# write_metric(summary_writer, train_metrics, eval_metrics, train_time, cur_step)
output_dir = save_path output_dir = save_path
# save checkpoint after each epoch and push checkpoint to the hub # save checkpoint after each epoch and push checkpoint to the hub
if jax.process_index() == 0: if jax.process_index() == 0:
@ -614,7 +473,3 @@ for epoch in epochs:
model.save_pretrained(output_dir, params=params) model.save_pretrained(output_dir, params=params)
tokenizer.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir)
# %% [markdown]
# #

201
t5_jax_prediction.py
View File

@ -39,10 +39,9 @@ from transformers import FlaxAutoModelForSeq2SeqLM, AutoConfig
import datasets import datasets
from datasets import Dataset, load_dataset from datasets import Dataset
import evaluate import evaluate
from tqdm import tqdm from tqdm import tqdm
from datasets import load_from_disk
import nltk # Here to have a nice missing dependency error message early on import nltk # Here to have a nice missing dependency error message early on
@ -76,9 +75,9 @@ def process_df(df):
# 'input': f"<NAME>{row['tag_name']}<NAME><DESC>{row['tag_description']}<DESC><UNIT>{row['unit']}<UNIT>", # 'input': f"<NAME>{row['tag_name']}<NAME><DESC>{row['tag_description']}<DESC><UNIT>{row['unit']}<UNIT>",
# 'input': f"<DESC>{row['tag_description']}<DESC><UNIT>{row['unit']}<UNIT>", # 'input': f"<DESC>{row['tag_description']}<DESC><UNIT>{row['unit']}<UNIT>",
'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>", 'output': f"<THING_START>{row['thing']}<THING_END><PROPERTY_START>{row['property']}<PROPERTY_END>",
'answer': f"{row['thing']} {row['property']}", # 'answer': f"{row['thing']} {row['property']}",
'answer_thing': row['thing'], # 'answer_thing': row['thing'],
'answer_property': row['property'], # 'answer_property': row['property'],
} for _, row in df.iterrows()] } for _, row in df.iterrows()]
return output_list return output_list
@ -93,14 +92,14 @@ test_dataset = Dataset.from_list(process_df(df))
# %% # %%
# load model # load model
model_name_or_path = "t5_80_1" # Replace with your specific model name model_name_or_path = "./t5_80_1" # Replace with your specific model name
# Load configuration # Load configuration
config = AutoConfig.from_pretrained(model_name_or_path) config = AutoConfig.from_pretrained(model_name_or_path)
# Load model # Load model
model = FlaxAutoModelForSeq2SeqLM.from_pretrained( model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
model_name_or_path pretrained_model_name_or_path=model_name_or_path
) )
@ -124,21 +123,19 @@ shift_tokens_right_fn = getattr(model_module, "shift_tokens_right")
# given a dataset entry, run it through the tokenizer # given a dataset entry, run it through the tokenizer
# Setting padding="max_length" as we need fixed length inputs for jitted functions # Setting padding="max_length" as we need fixed length inputs for jitted functions
def preprocess_function(example): def preprocess_function(example):
input = example['input'] inputs = example['input']
target = example['output'] targets = example['output']
# text_target sets the corresponding label to inputs # text_target sets the corresponding label to inputs
# there is no need to create a separate 'labels' # there is no need to create a separate 'labels'
model_inputs = tokenizer( model_inputs = tokenizer(
input, inputs,
text_target=target,
max_length=max_length, max_length=max_length,
padding="max_length", padding="max_length",
truncation=True, truncation=True,
return_tensors="np" return_tensors="np"
) )
labels = tokenizer( labels = tokenizer(
input, text_target=targets,
text_target=target,
max_length=max_length, max_length=max_length,
padding="max_length", padding="max_length",
truncation=True, truncation=True,
@ -191,7 +188,7 @@ def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuf
yield batch yield batch
# %% [markdown] # %% [markdown]
# # Model Training # # model generation
# %% # %%
seed = 117 seed = 117
@ -205,17 +202,8 @@ eval_batch_size = per_device_eval_batch_size * jax.device_count()
steps_per_epoch = len(test_dataset) // train_batch_size steps_per_epoch = len(test_dataset) // train_batch_size
total_train_steps = steps_per_epoch * num_epochs total_train_steps = steps_per_epoch * num_epochs
warmup_steps = 0
learning_rate = 5e-5
weight_decay = 0.0
adam_beta1 = 0.9
adam_beta2 = 0.999
adam_epsilon = 1e-8
label_smoothing_factor = 0.0
num_beams = 1 num_beams = 1
val_max_target_length = None val_max_target_length = 128
predict_with_generate = True predict_with_generate = True
@ -224,55 +212,6 @@ predict_with_generate = True
rng = jax.random.PRNGKey(seed) rng = jax.random.PRNGKey(seed)
rng, dropout_rng = jax.random.split(rng) rng, dropout_rng = jax.random.split(rng)
def create_learning_rate_fn(
train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
) -> Callable[[int], jnp.ndarray]:
"""Returns a linear warmup, linear_decay learning rate function."""
steps_per_epoch = train_ds_size // train_batch_size
num_train_steps = steps_per_epoch * num_train_epochs
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
decay_fn = optax.linear_schedule(
init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
)
schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
return schedule_fn
# Create learning rate schedule
linear_decay_lr_schedule_fn = create_learning_rate_fn(
len(test_dataset),
train_batch_size,
num_train_epochs,
warmup_steps,
learning_rate,
)
# We use Optax's "masking" functionality to not apply weight decay
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
# mask boolean with the same structure as the parameters.
# The mask is True for parameters that should be decayed.
def decay_mask_fn(params):
flat_params = traverse_util.flatten_dict(params)
# find out all LayerNorm parameters
layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
layer_norm_named_params = {
layer[-2:]
for layer_norm_name in layer_norm_candidates
for layer in flat_params.keys()
if layer_norm_name in "".join(layer).lower()
}
flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
return traverse_util.unflatten_dict(flat_mask)
# create adam optimizer
adamw = optax.adamw(
learning_rate=linear_decay_lr_schedule_fn,
b1=adam_beta1,
b2=adam_beta2,
eps=adam_epsilon,
weight_decay=weight_decay,
mask=decay_mask_fn,
)
# %% # %%
@ -288,23 +227,14 @@ model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
model_name_or_path model_name_or_path
) )
# Training functions
class TrainState(train_state.TrainState):
dropout_rng: jnp.ndarray
def replicate(self):
return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
# Ensure model.params is properly initialized (this is just an example) # Ensure model.params is properly initialized (this is just an example)
# Normally you would get this from a model initialization call with dummy input # Normally you would get this from a model initialization call with dummy input
params = model.params params = model.params
# Cast parameters to bfloat16 if desired # ensure full size floats
params_bf16 = jax.tree_util.tree_map(lambda x: x.astype(jnp.bfloat16), params) params_f16 = jax.tree_util.tree_map(lambda x: x.astype(jnp.float32), params)
# we need to replicate model over devices
replicated_params = jax.device_put_replicated(params_f16, jax.devices())
# Setup train state
state = TrainState.create(apply_fn=model.__call__, params=params_bf16, tx=adamw, dropout_rng=dropout_rng)
# Define generation function # Define generation function
@ -315,18 +245,14 @@ num_beams = num_beams if num_beams is not None else model.config.num_beams
gen_kwargs = {"max_length": max_length, "num_beams": num_beams} gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def generate_step(params, batch): def generate_step(params, batch):
model.params = params output_ids = model.generate(batch["input_ids"], attention_mask=batch["attention_mask"], params=params, **gen_kwargs)
output_ids = model.generate(batch["input_ids"], attention_mask=batch["attention_mask"], **gen_kwargs)
return output_ids.sequences return output_ids.sequences
# Create parallel version of the train and eval step # Create parallel version of the train and eval step
p_generate_step = jax.pmap(generate_step, "batch") p_generate_step = jax.pmap(generate_step, "batch")
# Replicate the train state on each device
state = state.replicate()
pred_metrics = []
pred_generations = [] pred_generations = []
pred_labels = [] pred_labels = []
@ -342,45 +268,92 @@ print(f" Instantaneous batch size per device = {per_device_train_batch_size}")
print(f" Total test batch size (w. parallel & distributed) = {train_batch_size}") print(f" Total test batch size (w. parallel & distributed) = {train_batch_size}")
for _ in tqdm(range(pred_steps), desc="Predicting...", position=0, leave=False): for _ in tqdm(range(pred_steps), desc="Predicting..."):
# Model forward # Model forward
batch = next(pred_loader) batch = next(pred_loader)
labels = batch["labels"] labels = batch["labels"]
# generation # generation
if predict_with_generate: generated_ids = pad_shard_unpad(p_generate_step)(replicated_params, batch)
generated_ids = pad_shard_unpad(p_generate_step)(state.params, batch) pred_generations.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
pred_generations.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"]))) pred_labels.extend(labels)
pred_labels.extend(labels)
# Print metrics # %% [markdown]
# desc = f"Predict Loss: {pred_metrics['loss']})" # # process predictions
# print(desc)
# %% # %%
# save predictions to parquet # code to get special token ids
# sentence = "<THING_START><THING_END><PROPERTY_START><PROPERTY_END><NAME><DESC><DESC><UNIT>"
# tokens = tokenizer.tokenize(sentence)
# print("Tokens:", tokens)
# # Get the IDs (integer indices) of specific tokens
# token_ids = [tokenizer.convert_tokens_to_ids(token) for token in tokens]
# print("Token IDs:", token_ids)
# %%
# extract sequence and decode
def extract_seq(tokens, start_value, end_value):
if start_value not in tokens or end_value not in tokens:
return None # Or handle this case according to your requirements
start_id = np.where(tokens == start_value)[0][0]
end_id = np.where(tokens == end_value)[0][0]
return tokens[start_id+1:end_id]
def process_tensor_output(tokens):
thing_seq = extract_seq(tokens, 32100, 32101) # 32100 = <THING_START>, 32101 = <THING_END>
property_seq = extract_seq(tokens, 32102, 32103) # 32102 = <PROPERTY_START>, 32103 = <PROPERTY_END>
p_thing = None
p_property = None
if (thing_seq is not None):
p_thing = tokenizer.decode(thing_seq, skip_special_tokens=False) # retain <COLLIDE>
if (property_seq is not None):
p_property = tokenizer.decode(property_seq, skip_special_tokens=False) # retain <COLLIDE>
return p_thing, p_property
# %%
# decode prediction labels # decode prediction labels
def decode_preds(preds): def decode_preds(tokens_list):
# In case the model returns more than the prediction logits thing_prediction_list = []
if isinstance(preds, tuple): property_prediction_list = []
preds = preds[0] for tokens in tokens_list:
p_thing, p_property = process_tensor_output(tokens)
thing_prediction_list.append(p_thing)
property_prediction_list.append(p_property)
return thing_prediction_list, property_prediction_list
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) thing_prediction_list, property_prediction_list = decode_preds(pred_generations)
decoded_preds = [pred for pred in decoded_preds]
return decoded_preds
# %%
# add labels too
thing_actual_list, property_actual_list = decode_preds(pred_labels)
# Convert the list to a Pandas DataFrame # Convert the list to a Pandas DataFrame
df = pd.DataFrame(decode_preds(pred_labels)) df = pd.DataFrame({'p_thing': thing_prediction_list,
'p_property': property_prediction_list,
# Save the DataFrame as a Parquet file (using pyarrow or fastparquet) 'thing': thing_actual_list,
df.to_parquet("exports/output_file.parquet", engine="pyarrow") # or use engine="fastparquet" 'property' : property_actual_list})
df['p_thing_correct'] = df['p_thing'] == df['thing']
df['p_property_correct'] = df['p_property'] == df['property']
# %% # %%
print("thing accuracy", sum(df['p_thing_correct'])/len(df))
print("property accuracy", sum(df['p_property_correct'])/len(df))
print("total accuracy", sum(df['p_property_correct'] & df['p_thing_correct'])/len(df))
# %%
df[~df["p_property_correct"]]
# %%
df['p_thing']
# %%
# Save the DataFrame as a Parquet file (using pyarrow or fastparquet)
# df.to_parquet("exports/output_file.parquet", engine="pyarrow") # or use engine="fastparquet"