test_model_parallel_encoder.py

# Copyright (c) 2022-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
"""Encoder training on multi-GPU with tesnor parallelism"""
import argparse
import unittest
from functools import partial

import flax
import jax
import jax.numpy as jnp
import nltk
import numpy as np
import optax
from datasets import load_dataset
from flax import linen as nn
from flax.linen import partitioning as nn_partitioning
from flax.training import train_state
from jax.experimental import mesh_utils
from jax.experimental.pjit import pjit

import transformer_engine.jax as te
import transformer_engine.jax.flax as te_flax

DEVICE_DP_AXIS = 'data'
DEVICE_TP_AXIS = 'model'
NAMED_BROADCAST_AXIS = 'my_broadcast_axis'
NAMED_TP_AXIS = 'my_tp_axis'
PARAMS_KEY = 'params'
PARAMS_AXES_KEY = PARAMS_KEY + '_axes'
DROPOUT_KEY = 'dropout'
INPUT_KEY = 'input_rng'


class Net(nn.Module):
    """NLP Encoder"""
    num_embed: int

    @nn.compact
    def __call__(self, x, mask, disable_dropout=False):
        x = nn.Embed(num_embeddings=self.num_embed, features=256, dtype=jnp.bfloat16)(x)

        te_Encoder = partial(te_flax.TransformerLayer,
                             hidden_size=256,
                             mlp_hidden_size=1024,
                             num_attention_heads=8,
                             hidden_dropout=0.1,
                             attention_dropout=0.1,
                             dropout_rng_name=DROPOUT_KEY,
                             layer_type=te_flax.TransformerLayerType.ENCODER,
                             self_attn_mask_type='padding',
                             enable_relative_embedding=False,
                             dtype=jnp.bfloat16)
        x = te_Encoder()(x, attention_mask=mask, deterministic=disable_dropout)

        x = x.reshape(x.shape[0], -1)

        x = te_flax.DenseGeneral(features=256,
                                 kernel_axes=(NAMED_BROADCAST_AXIS, NAMED_TP_AXIS),
                                 bias_axes=(NAMED_TP_AXIS,),
                                 dtype=jnp.bfloat16)(x)

        x = te_flax.DenseGeneral(features=256,
                                 kernel_axes=(NAMED_TP_AXIS, NAMED_BROADCAST_AXIS),
                                 bias_axes=(NAMED_BROADCAST_AXIS,),
                                 dtype=jnp.bfloat16)(x)

        x = nn.Dense(features=2, dtype=jnp.bfloat16)(x)
        return x


def train_step(state, inputs, masks, labels, var_collect, rngs):
    """Computes gradients, loss and accuracy for a single batch."""

    def loss_fn(var_collect, disable_dropout=False):
        logits = state.apply_fn(var_collect, inputs, masks, disable_dropout, rngs=rngs)
        one_hot = jax.nn.one_hot(labels, 2)
        loss = jnp.mean(optax.softmax_cross_entropy(logits=logits, labels=one_hot))
        return loss, logits

    var_collect = {**var_collect, PARAMS_KEY: state.params}
    grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
    (loss, logits), grads = grad_fn(var_collect)
    accuracy = jnp.mean(jnp.argmax(logits, -1) == labels)

    var_collect, grads = flax.core.pop(grads, PARAMS_KEY)
    state = state.apply_gradients(grads=grads)

    return state, loss, accuracy, var_collect


def train_epoch(state, train_ds, batch_size, rngs, var_collect, train_fn):
    """Train for a single epoch."""
    train_ds_size = len(train_ds['sentence'])
    steps_per_epoch = train_ds_size // batch_size
    perms = jax.random.permutation(rngs[INPUT_KEY], train_ds_size)
    perms = perms[:steps_per_epoch * batch_size]    # skip incomplete batch
    perms = perms.reshape((steps_per_epoch, batch_size))
    epoch_loss = []
    epoch_accuracy = []

    for perm in perms:
        batch_inputs = train_ds['sentence'][perm, ...]
        batch_masks = train_ds['mask'][perm, ...]
        batch_labels = train_ds['label'][perm, ...]
        state, loss, accuracy, var_collect = train_fn(state, batch_inputs, batch_masks,
                                                      batch_labels, var_collect, rngs)
        epoch_loss.append(loss)
        epoch_accuracy.append(accuracy)

    avg_loss = np.mean(epoch_loss)
    avg_accuracy = np.mean(epoch_accuracy)
    return state, avg_loss, avg_accuracy, var_collect


def eval_step(state, inputs, masks, labels, var_collect):
    """Computes loss and accuracy for a single batch."""

    def loss_fn(var_collect, disable_dropout=False):
        logits = state.apply_fn(var_collect, inputs, masks, disable_dropout)
        one_hot = jax.nn.one_hot(labels, 2)
        loss = jnp.mean(optax.softmax_cross_entropy(logits=logits, labels=one_hot))
        return loss, logits

    var_collect = {**var_collect, PARAMS_KEY: state.params}
    loss, logits = loss_fn(var_collect, disable_dropout=True)
    accuracy = jnp.mean(jnp.argmax(logits, -1) == labels)
    return loss, accuracy


def eval_model(state, test_ds, batch_size, var_collect, eval_fn):
    """Evaluation loop."""
    test_ds_size = len(test_ds['sentence'])
    num_steps = test_ds_size // batch_size
    valid_size = num_steps * batch_size
    all_loss = []
    all_accuracy = []

    for batch_start in range(0, valid_size, batch_size):
        batch_end = batch_start + batch_size
        batch_inputs = test_ds['sentence'][batch_start:batch_end]
        batch_masks = test_ds['mask'][batch_start:batch_end]
        batch_labels = test_ds['label'][batch_start:batch_end]
        loss, accuracy = eval_fn(state, batch_inputs, batch_masks, batch_labels, var_collect)
        all_loss.append(loss)
        all_accuracy.append(accuracy)

    avg_loss = np.mean(all_loss)
    avg_accuracy = np.mean(all_accuracy)
    return avg_loss, avg_accuracy


def data_preprocess(dataset, vocab, word_id, max_seq_len):
    """Convert tokens to numbers."""
    nltk.download('punkt')
    dataset_size = len(dataset['sentence'])
    output = np.zeros((dataset_size, max_seq_len), dtype=np.int32)
    mask_3d = np.ones((dataset_size, max_seq_len, max_seq_len), dtype=np.uint8)

    for j, sentence in enumerate(dataset['sentence']):
        tokens = nltk.word_tokenize(sentence)
        tensor = output[j]

        for i, word in enumerate(tokens):
            if i >= max_seq_len:
                break

            if word not in vocab:
                vocab[word] = word_id
                tensor[i] = word_id
                word_id = word_id + 1
            else:
                tensor[i] = vocab[word]

        seq_len = min(len(tokens), max_seq_len)
        mask_2d = mask_3d[j]
        mask_2d[:seq_len, :seq_len] = 0

    new_dataset = {
        'sentence': output,
        'label': dataset['label'].astype(np.float32),
        'mask': mask_3d.reshape((dataset_size, 1, max_seq_len, max_seq_len))
    }
    return new_dataset, vocab, word_id


def get_datasets(max_seq_len):
    """Load GLUE train and test datasets into memory."""
    vocab = {}
    word_id = 0

    train_ds = load_dataset('glue', 'cola', split='train')
    train_ds.set_format(type='np')
    train_ds, vocab, word_id = data_preprocess(train_ds, vocab, word_id, max_seq_len)

    test_ds = load_dataset('glue', 'cola', split='validation')
    test_ds.set_format(type='np')
    test_ds, vocab, word_id = data_preprocess(test_ds, vocab, word_id, max_seq_len)
    return train_ds, test_ds, word_id


def check_fp8(state, var_collect, inputs, masks, labels):
    "Check if model includes FP8."
    rngs = {DROPOUT_KEY: jax.random.PRNGKey(0)}
    assert "fp8_" in str(
        jax.make_jaxpr(train_step)(state, inputs, masks, labels, var_collect, rngs))


def get_params_pspec(sharding_rules, abs_var_collect):
    """Refer params to create params partition spec"""
    rules_dict = {}
    for key, value in sharding_rules:
        rules_dict[key] = value

    def to_device_axis(logical_axis):
        partitions = [rules_dict[key] for key in logical_axis]
        return jax.sharding.PartitionSpec(*partitions)

    params_axes = abs_var_collect.get(PARAMS_AXES_KEY, {})
    params_axes_pspec = jax.tree_map(to_device_axis, nn_partitioning.get_axis_names(params_axes))
    params_axes_pspec = flax.core.unfreeze(params_axes_pspec)
    params_pspec = jax.tree_map(lambda x: jax.sharding.PartitionSpec(), abs_var_collect[PARAMS_KEY])
    params_pspec = {**params_pspec, **params_axes_pspec}
    return params_pspec


def get_state_pspec(state, params_pspec):
    """Refer params_pspec to create state partition spec"""

    def replace_params(x):
        return params_pspec if isinstance(x, dict) else None

    state_pspec = jax.tree_map(replace_params, state, is_leaf=lambda x: isinstance(x, dict))
    return state_pspec


def train_and_evaluate(args):
    """Execute model training and evaluation loop."""
    print(args)
    train_ds, test_ds, num_embed = get_datasets(args.max_seq_len)

    num_gpu = jax.local_device_count()
    num_gpu_tp = 2
    if num_gpu % num_gpu_tp == 0:
        num_gpu_dp = num_gpu // num_gpu_tp
    else:
        num_gpu_dp = 1
        num_gpu_tp = 1

    assert args.batch_size % num_gpu_dp == 0, f"Batch size needs to be multiple of {num_gpu_dp}"
    assert args.test_batch_size % num_gpu_dp == 0, \
        f"Test batch size needs to be multiple of {num_gpu_dp}"

    device_mesh = mesh_utils.create_device_mesh((num_gpu_dp, num_gpu_tp))
    with jax.sharding.Mesh(devices=device_mesh, axis_names=(DEVICE_DP_AXIS, DEVICE_TP_AXIS)):

        rng = jax.random.PRNGKey(args.seed)
        rng, params_rng = jax.random.split(rng)
        rng, dropout_rng = jax.random.split(rng)
        init_rngs = {PARAMS_KEY: params_rng, DROPOUT_KEY: dropout_rng}

        input_shape = [args.batch_size, args.max_seq_len]
        mask_shape = [args.batch_size, 1, args.max_seq_len, args.max_seq_len]
        label_shape = [args.batch_size]

        with te.fp8_autocast(args.use_fp8,
                             mesh_resource=te.MeshResource(DEVICE_DP_AXIS, DEVICE_TP_AXIS, None,
                                                           None)):
            encoder = Net(num_embed)
            inputs = jnp.zeros(input_shape, dtype=jnp.int32)
            masks = jnp.zeros(mask_shape, dtype=jnp.uint8)
            abs_var_collect = jax.eval_shape(encoder.init, init_rngs, inputs, masks)

            customized_rules = ((NAMED_BROADCAST_AXIS, None), (NAMED_TP_AXIS, DEVICE_TP_AXIS))
            sharding_rules = te_flax.extend_logical_axis_rules(tuple()) + customized_rules
            params_pspec = get_params_pspec(sharding_rules, abs_var_collect)
            inputs_pspec = jax.sharding.PartitionSpec(DEVICE_DP_AXIS, None)
            masks_pspec = jax.sharding.PartitionSpec(DEVICE_DP_AXIS, None, None, None)

            in_shardings = (None, inputs_pspec, masks_pspec)
            out_shardings = {key: params_pspec if key is PARAMS_KEY else None \
                                    for key in abs_var_collect}
            pjit_encoder_init = pjit(encoder.init, in_shardings, out_shardings)
            var_collect = pjit_encoder_init(init_rngs, inputs, masks)

            optimizer = optax.adamw(args.lr)
            var_collect, params = flax.core.pop(var_collect, PARAMS_KEY)
            state = train_state.TrainState.create(apply_fn=encoder.apply,
                                                  params=params,
                                                  tx=optimizer)
            state_pspec = get_state_pspec(state, params_pspec)
            labels_pspec = jax.sharding.PartitionSpec(DEVICE_DP_AXIS,)

            in_shardings = (state_pspec, inputs_pspec, masks_pspec, labels_pspec, None, None)
            out_shardings = (state_pspec, None, None, None)
            pjit_train_step = pjit(train_step, in_shardings, out_shardings)

            in_shardings = (state_pspec, inputs_pspec, masks_pspec, labels_pspec, None)
            out_shardings = (None, None)
            pjit_eval_step = pjit(eval_step, in_shardings, out_shardings)

            if args.use_fp8:
                labels = jnp.zeros(label_shape, dtype=jnp.bfloat16)
                check_fp8(state, var_collect, inputs, masks, labels)

            if args.dry_run:
                labels = jnp.zeros(label_shape, dtype=jnp.bfloat16)
                rngs = {DROPOUT_KEY: dropout_rng}
                pjit_train_step(state, inputs, masks, labels, var_collect, rngs)
                print("PASSED")
                return None

            for epoch in range(1, args.epochs + 1):
                rng, input_rng = jax.random.split(rng)
                rng, dropout_rng = jax.random.split(rng)
                rngs = {INPUT_KEY: input_rng, DROPOUT_KEY: dropout_rng}

                state, train_loss, train_accuracy, var_collect = train_epoch(
                    state, train_ds, args.batch_size, rngs, var_collect, pjit_train_step)

                test_loss, test_accuracy = eval_model(state, test_ds, args.test_batch_size,
                                                      var_collect, pjit_eval_step)

                print(f"Epoch: {epoch:>2} "
                      f"Train Loss: {train_loss:.6f} "
                      f"Train Accuracy: {train_accuracy:.6f} "
                      f"Test Loss: {test_loss:.6f} "
                      f"Test Accuracy: {test_accuracy:.6f} ")

            return [train_loss, train_accuracy, test_loss, test_accuracy]


def encoder_parser(args):
    """Training settings."""
    parser = argparse.ArgumentParser(description="JAX Encoder Example")
    parser.add_argument(
        "--batch-size",
        type=int,
        default=64,
        metavar="N",
        help="input batch size for training (default: 64)",
    )
    parser.add_argument(
        "--test-batch-size",
        type=int,
        default=64,
        metavar="N",
        help="input batch size for testing (default: 64)",
    )
    parser.add_argument(
        "--max-seq-len",
        type=int,
        default=32,
        metavar="N",
        help="maximum sequence length (default: 32)",
    )
    parser.add_argument(
        "--epochs",
        type=int,
        default=3,
        metavar="N",
        help="number of epochs to train (default: 3)",
    )
    parser.add_argument(
        "--lr",
        type=float,
        default=0.0001,
        metavar="LR",
        help="learning rate (default: 0.0001)",
    )
    parser.add_argument(
        "--dry-run",
        action="store_true",
        default=False,
        help="quickly check a single pass",
    )
    parser.add_argument("--seed", type=int, default=0, metavar="S", help="random seed (default: 0)")
    parser.add_argument("--use-fp8",
                        action="store_true",
                        default=False,
                        help="Use FP8 for inference and training without recalibration")

    return parser.parse_args(args)


class TestEncoder(unittest.TestCase):
    """Encoder unittests"""

    gpu_has_fp8, reason = te.fp8.is_fp8_available()

    @classmethod
    def setUpClass(cls):
        """Run 3 epochs for testing"""
        cls.args = encoder_parser(["--epochs", "3"])

    def test_te_bf16(self):
        """Test Transformer Engine with BF16"""
        actual = train_and_evaluate(self.args)
        assert actual[0] < 0.45 and actual[1] > 0.79

    @unittest.skipIf(not gpu_has_fp8, reason)
    def test_te_fp8(self):
        """Test Transformer Engine with FP8"""
        self.args.use_fp8 = True
        actual = train_and_evaluate(self.args)
        assert actual[0] < 0.45 and actual[1] > 0.79


if __name__ == "__main__":
    train_and_evaluate(encoder_parser(None))