407 lines
12 KiB
Python
407 lines
12 KiB
Python
# %% [markdown]
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# # T5 implementation using jax with pjit
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# MARK: START
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# %%
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# let's make 8-device simulator
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import os
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# Set this to True to run the model on CPU only.
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USE_CPU_ONLY = False
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flags = os.environ.get("XLA_FLAGS", "")
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if USE_CPU_ONLY:
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flags += " --xla_force_host_platform_device_count=8" # Simulate 8 devices
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# Enforce CPU-only execution
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os.environ["CUDA_VISIBLE_DEVICES"] = ""
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os.environ["JAX_PLATFORMS"] = "cpu"
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else:
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# GPU flags
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flags += (
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"--xla_gpu_enable_triton_softmax_fusion=true "
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# "--xla_gpu_triton_gemm_any=false "
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# "--xla_gpu_enable_async_collectives=true "
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# "--xla_gpu_enable_latency_hiding_scheduler=true "
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# "--xla_gpu_enable_highest_priority_async_stream=true "
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)
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os.environ["XLA_FLAGS"] = flags
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import functools
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from functools import partial
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from pprint import pprint
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from typing import Any, Dict, Tuple, Callable, Sequence
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import flax.linen as nn
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import jax
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import jax.numpy as jnp
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import numpy as np
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from jax.experimental.shard_map import shard_map
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from jax.sharding import Mesh
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from jax.experimental.pjit import pjit
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from jax.sharding import PartitionSpec as P
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from ml_collections import ConfigDict
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import optax
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import logging
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import time
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from datasets import Dataset, load_from_disk
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from flax import jax_utils, traverse_util
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from flax.jax_utils import pad_shard_unpad, unreplicate
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from flax.training import train_state
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from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
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import flax.core
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from tqdm import tqdm
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from dataload import DataPrepare
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PyTree = Any
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Metrics = Dict[str, Tuple[jax.Array, ...]]
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if USE_CPU_ONLY:
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jax.config.update('jax_platform_name', 'cpu')
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else:
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jax.config.update("jax_default_matmul_precision", "bfloat16")
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# # %%
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# import jax
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# import jax.numpy as jnp
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# import optax
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# import numpy as np
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# from functools import partial
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# from typing import Callable, Optional
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# import math
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#
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# # jax.config.update("jax_default_matmul_precision", "tensorfloat32")
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# jax.config.update("jax_default_matmul_precision", "bfloat16")
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# # jax.config.update("jax_enable_x64", False)
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# # enable cache
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# jax.config.update("jax_compilation_cache_dir", "/tmp/jax_cache")
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# jax.config.update("jax_persistent_cache_min_entry_size_bytes", -1)
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# jax.config.update("jax_persistent_cache_min_compile_time_secs", 0)
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#
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#
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# # from transformers import FlaxAutoModelForSeq2SeqLM, AutoConfig
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#
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# from flax import jax_utils, traverse_util
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# from flax.jax_utils import pad_shard_unpad, unreplicate
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# from flax.training import train_state
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# from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
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# import flax.core
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# %%
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# get platform type
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from jax.lib import xla_bridge
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print(xla_bridge.get_backend().platform)
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# %%
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# config options
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file_path = '/home/richard/Projects/learn_t5/simple_model/combined_data_t5_retrieval'
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save_path = 't5_80_1_bf16'
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# file_path = 'combined_data'
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split_datasets = load_from_disk(file_path)
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training_size = len(split_datasets['train'])
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# Store some constant
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seed = 117
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num_epochs = 5
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batch_size = 384 # 384 is the best
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num_train_epochs = num_epochs
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per_device_train_batch_size = batch_size
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train_batch_size = per_device_train_batch_size * jax.device_count()
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per_device_eval_batch_size = batch_size
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eval_batch_size = per_device_eval_batch_size * jax.device_count()
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steps_per_epoch = training_size // train_batch_size
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total_train_steps = steps_per_epoch * num_epochs
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warmup_steps = 0
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learning_rate = 2e-5
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weight_decay = 0.01
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adam_beta1 = 0.9
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adam_beta2 = 0.999
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adam_epsilon = 1e-8
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label_smoothing_factor = 0.0
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num_beams = 1
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val_max_target_length = 128
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predict_with_generate = True
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# %%
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# prepare data
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# init object
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# e.g. Config
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data_config = ConfigDict(
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dict(
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max_length=86,
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pad_token_id=0,
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decoder_start_token_id=0
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)
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)
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dataprep = DataPrepare(split_datasets['train'], data_config)
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# # example usage
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# # %%
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# seed = 117
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# rng = jax.random.PRNGKey(seed)
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# train_loader = dataprep.data_loader(rng, batch_size=1)
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# batch = next(iter(train_loader))
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# %%
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# model
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from transformers import FlaxT5ForConditionalGeneration
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from transformers import T5Config
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config = T5Config()
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# If you want don't want to cast certain parameters (for example layer norm bias and scale)
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# then pass the mask as follows
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from flax import traverse_util
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model = FlaxT5ForConditionalGeneration.from_pretrained("t5-base")
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# useful for transformer model
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model.enable_gradient_checkpointing()
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# enable bf16 except for layer_norm
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flat_params = traverse_util.flatten_dict(model.params)
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mask = {
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path: not (path[-2] == "layer_norm" and path[-1] == "weight") for path in flat_params
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}
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mask = traverse_util.unflatten_dict(mask)
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model.params = model.to_bf16(model.params, mask)
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# %%
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# %%
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from jax.sharding import Mesh, NamedSharding
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from jax.experimental import mesh_utils
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from jax.sharding import PartitionSpec as P
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from pjit_partition import set_partitions
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devices = np.asarray(jax.devices())
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mesh_axis_names = ('data')
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mesh = Mesh(devices, 'batch')
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sharding = NamedSharding(mesh, P(mesh_axis_names))
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replicated_sharding = NamedSharding(mesh, P())
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# %% [markdown]
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# # Model
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#
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#
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#
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# %%
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# Initialize our training
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rng = jax.random.PRNGKey(seed)
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rng, dropout_rng = jax.random.split(rng)
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# %%
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# optimization functions
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def create_learning_rate_fn(
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train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
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) -> Callable[[int], jnp.ndarray]:
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"""Returns a linear warmup, linear_decay learning rate function."""
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steps_per_epoch = train_ds_size // train_batch_size
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num_train_steps = steps_per_epoch * num_train_epochs
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warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
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decay_fn = optax.linear_schedule(
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init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
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)
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schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
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return schedule_fn
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# Create learning rate schedule
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linear_decay_lr_schedule_fn = create_learning_rate_fn(
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training_size,
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train_batch_size,
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num_train_epochs,
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warmup_steps,
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learning_rate,
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)
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# We use Optax's "masking" functionality to not apply weight decay
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# to bias and LayerNorm scale parameters. decay_mask_fn returns a
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# mask boolean with the same structure as the parameters.
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# The mask is True for parameters that should be decayed.
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def decay_mask_fn(params):
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flat_params = traverse_util.flatten_dict(params)
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# find out all LayerNorm parameters
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layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
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layer_norm_named_params = {
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layer[-2:]
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for layer_norm_name in layer_norm_candidates
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for layer in flat_params.keys()
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if layer_norm_name in "".join(layer).lower()
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}
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flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
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return traverse_util.unflatten_dict(flat_mask)
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# create adam optimizer
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adamw = optax.adamw(
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learning_rate=linear_decay_lr_schedule_fn,
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b1=adam_beta1,
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b2=adam_beta2,
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eps=adam_epsilon,
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weight_decay=weight_decay,
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mask=decay_mask_fn,
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)
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# %%
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# state will serve as our "params"
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state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw, dropout_rng=dropout_rng)
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# label smoothed cross entropy
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def loss_fn(logits, labels, padding_mask, label_smoothing_factor=0.0):
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"""
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The label smoothing implementation is adapted from Flax's official example:
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https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
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"""
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vocab_size = logits.shape[-1]
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confidence = 1.0 - label_smoothing_factor
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low_confidence = (1.0 - confidence) / (vocab_size - 1)
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normalizing_constant = -(
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confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
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)
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soft_labels = onehot(labels, vocab_size, on_value=confidence, off_value=low_confidence)
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loss = optax.softmax_cross_entropy(logits, soft_labels)
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loss = loss - normalizing_constant
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# ignore padded tokens from loss
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loss = loss * padding_mask
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loss = loss.sum()
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num_labels = padding_mask.sum()
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return loss, num_labels
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# MARK: train_step
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# Define gradient update step fn
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def train_step(state, batch):
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label_smoothing_factor=0.0
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dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
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def compute_loss(params):
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labels = batch.pop("labels")
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logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
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loss, num_labels = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
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return loss, num_labels
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# compute gradients through computational graph
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grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
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(loss, num_labels), grad = grad_fn(state.params)
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num_labels = jax.lax.psum(num_labels, "batch")
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# true loss = total loss / total samples
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# loss = jax.lax.psum(loss, "batch")
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# loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
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# true grad = total grad / total samples
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grad = jax.lax.psum(grad, "batch")
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grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
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new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
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metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
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return new_state, metrics
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# max_length = (
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# val_max_target_length if val_max_target_length is not None else model.config.max_length
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# )
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# num_beams = num_beams if num_beams is not None else model.config.num_beams
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# gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
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# Create parallel version of the train and eval step
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# only state and batch
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p_train_step = jax.jit(
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train_step,
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# state for first, batch for second
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in_shardings=(P("data"), P("data")),
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out_shardings=(P("data"), P("data")),
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donate_argnames=("state"),
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)
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# %%
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print("***** Running training *****")
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print(f" Num examples = {training_size}")
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print(f" Num Epochs = {num_epochs}")
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print(f" Instantaneous batch size per device = {per_device_train_batch_size}")
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print(f" Total train batch size (w. parallel & distributed) = {train_batch_size}")
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print(f" Total optimization steps = {total_train_steps}")
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# %%
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# jax.profiler.start_trace("./traces")
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# Example batch (sharded across devices)
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sharded_batch = {
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'input_ids': jax.device_put_sharded(batch['input_ids'], devices),
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'attention_mask': jax.device_put_sharded(batch['attention_mask'], devices),
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'labels': jax.device_put_sharded(batch['labels'], devices),
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'decoder_input_ids': jax.device_put_sharded(batch['decoder_input_ids'], devices),
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'decoder_attention_mask': jax.device_put_sharded(batch['decoder_attention_mask'], devices),
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}
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# Initial TrainState (pjit-ted TrainState)
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sharded_state = jax.device_put_replicated(train_state, devices)
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# %%
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rng, input_rng = jax.random.split(rng)
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train_time = 0
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epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
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for epoch in epochs:
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train_start = time.time()
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# Create sampling rng
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train_metrics = []
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rng, data_rng = jax.random.split(rng)
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train_loader = dataprep.data_loader(data_rng, batch_size=batch_size)
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steps_per_epoch = training_size // train_batch_size
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# Generate an epoch by shuffling sampling indices from the train dataset
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for _ in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
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batch = next(train_loader)
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# batch = shard(batch)
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state, train_metric = p_train_step(state, batch)
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train_metrics.append(train_metric)
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train_time = time.time() - train_start
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train_metric = unreplicate(train_metric)
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train_metric['loss'].block_until_ready()
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epochs.write(
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# f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, "
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f"Epoch... ({epoch + 1}/{num_epochs} | "
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# f"Learning Rate:{train_metric['learning_rate']}, "
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f"Last train time: {train_time})"
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)
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# jax.profiler.stop_trace()
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# %%
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# output_dir = save_path
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# # save checkpoint after each epoch and push checkpoint to the hub
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# if jax.process_index() == 0:
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# params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params))
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# params = jax.tree_util.tree_map(lambda x: x.astype(jnp.float32), params)
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# model.save_pretrained(output_dir, params=params)
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# tokenizer.save_pretrained(output_dir)
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